A SOCIAL NETWORK ANALYSIS OF TEXAS ALLIANCE FOR …

A SOCIAL NETWORK ANALYSIS OF

TEXAS ALLIANCE FOR WATER CONSERVATION PRODUCERS

By

NELLIE HILL, B.S.

A THESIS

IN

AGRICULTURAL COMMUNICATIONS

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

David Doerfert

Chairperson of Committee

Courtney Meyers

Cindy Akers

Dominick Casadonte

Interim Dean of the Graduate School

December, 2013

© Copyright 2013, Nellie Hill

Texas Tech University, Nellie Hill, December 2013

ii

ACKNOWLEDGEMENTS

When I decided to pursue a master’s degree, my intentions were to challenge

myself to risk more boldly and learn more deeply. Moving away from the beloved

sunflowers, Kansas State Wildcats, and trees of Kansas for life in West Texas has caused

me to grow in ways I never imagined. Completing this thesis has been a challenge that

made me more thankful for education and the amazing people who helped make mine

possible.

To my family: Your encouragement and support has never wavered; even when

you weren’t sure of what I was getting myself into. Whenever I am in need of an attitude

adjustment or pep talk, Dad is my very first phone call. If I need help with the particulars

of living away from home, Mom always knows the answer. I continue to be amazed by

the perseverance and achievements of Heath and Cassidy, my younger siblings.

To my friends, near and far: Thank you for allowing me to escape from my reality

when it became too much to keep writing. Road trips, concerts, long chats, and notes of

love helped more than each of you will ever know.

To my committee: I appreciate the trust and time you invested in me as I explored

this research with a methodology that we each equally knew little about. Thank you to

Dr. Doerfert for well-timed words of encouragement and challenges to my love of

learning. When I was at my wits end, I turned to Dr. Meyers, knowing we would hash it

out with sarcasm and a few laughs. Thank you, Dr. Akers for always sharing a kind

smile and an excitement for this research.


iii

Finally, to the Texas Alliance for Water Conservation and the involved producers

of West Texas: Without the support of the TAWC and the willingness of the producers to

be interviewed, this research would not have been possible.


iv

TABLE OF CONTENTS

ACKNOWLEDGEMENTS…………...…………………………………………………..ii

ABSTRACT…………………………………………………..…………………………vii

LIST OF TABLES…….……………………………...………………………………...viii

LIST OF FIGURES………………………………………………………………………ix

I. INTRODUCTION…………….………....……………………………………………...1

Overview…………………………...……………………………………………...1

Interpersonal Communication……………...……………………...………………2

Texas Alliance for Water Conservation…………...………………………..……..4

Social Network Analysis…………...……………………………………….……..7

Statement of the Problem…………...……………………………………………10

Purpose and Objectives…………...……………………..……………………….11

Definition of Terms…………...……………...…………………………………..12

Limitations………….……………………………………………………………15

Basic Assumptions…………...………..…………………………………………15

II. LITERATURE REVIEW…….…………………………………………………….16

Overview………….……………………………………………………………...16

Theoretical Framework…...………………………………………………….......16

Diffusion of Innovations…......…………………...………………….......16

Relation to Study……..…………………………...………………….......26

Conceptual Framework…...………………………………………………….......27

Producer as an Individual Entrepreneur..……..…………………….........27

Interpersonal Communication……………………....……............27

Social Exchange Theory……………………………...…….........35

Social Comparison Theory………………………...…..…...........38

Producer as an Agricultural Professional……….……..………...……….44

Communities of Practice……………………………………...….44

Uncertainty Reduction Theory…………………………...………46


v

Relation to Study…...…………………….....………………………........51

Operational Framework…...………………………..…………….………….......51

Social Network Analysis…...………………...…..……….………….......51

Relation to Study…...………………………..……………………….......58

Summary…...………...………..…………….……………………………….......58

III. METHODOLOGY…..…………….………………….……………...………….......60

Overview……………………..…………………………………...………….......60

Research Design……….…………………...……………………………….........61

Population……...…………………...………………………………....................63

Data Collection……………………...………………………………...................64

Data Analysis..…………….....………………………………..............................65

Variable Analysis…………………....………………...............................65

Network Analysis………………...……..…………..................................65

Typological Analysis…………...………………………..........................69

IV. RESULTS………..……...……………………..…….................................................72

Overview………..……...……………………………….......................................72

Research Objective One…………….....………………........................................72

Research Objective Two…………….………………….......................................76

Cohesion………….………………….......................................................77

Structural Equivalence………………………….......................................80

Prominence………...……………….........................................................88

Range………...……….…….....................................................................88

Brokerage…...….……………...................................................................89

Research Objective Three……………..…………………....................................90

V. IMPLICATIONS…………....…................................................................................101

Overview…………... ….….................................................................................101

Conclusions…………... ….….............................................................................102

Research Objective One...........................................................................102

Research Objective Two……..................................................................104


vi

Cohesion......................................................................................104

Structural Equivalence.................................................................105

Prominence..................................................................................107

Range...........................................................................................108

Brokerage.....................................................................................109

Summary..................................................................................................110

Research Objective Three........................................................................111

Discussion............................................................................................................113

Recommendations................................................................................................120

Practitioners.............................................................................................120

Researchers..............................................................................................122

REFERENCES................................................................................................................125

A. HUMAN RESEARCH PROTECTION PROGRAM APPROVAL LETTER………145

B. TAWC PRODUCER TELEPHONE SCRIPT……………..………………………...146

C. TAWC PRODUCER INFORMATION SHEET……………...……………………..147

D. TAWC PRODUCER INTERVIEW INSTRUMENT……..………………………...148


vii

ABSTRACT

Networks of relationships form the foundation of our social lives. Understanding

and utilizing these connections can help practitioners and researchers more effectively

and efficiently disseminate information and innovations within a group. The Texas

Alliance for Water Conservation is concerned with identifying the best practices and new

technologies for water management in West Texas. The project also desires to share

knowledge beyond the currently involved members to other producers in the region. This

study sought to describe the interpersonal relations of the TAWC Demonstration Project

producers through social network analysis.

Semi-structured interviews were conducted with TAWC producers in order to

describe producers and their interpersonal connections in terms of relations and typology.

NodeXL for Microsoft Excel, QDA Miner, and WordStat software tools were used for

data analysis. Results indicated TAWC producers are diverse in their attributes, both

personally and in their farming operations. Analysis revealed a change agent and several

opinion leaders within the TAWC producer network. Furthermore, the knowledge

developed through the TAWC has reach beyond the TAWC producers. The study results

will facilitate further social network analysis of the population and guide further

information and innovation dissemination to the TAWC producer network.


viii

LIST OF TABLES

4.1 Summary of TAWC Demonstration Project Producers………………………….75

4.2 Individual Network Measures for TAWC Producers……………………………78

4.3 Word Frequency and Phrase Frequency of Cluster One…………………………91

4.4 Word Frequency and Phrase Frequency of Cluster Two………………………...92

4.5 Word Frequency and Phrase Frequency of Cluster Three……………………….93

4.6 Word Frequency and Phrase Frequency of Cluster Four………...………………94

4.7 Word Frequency and Phrase Frequency of Cluster Five………...………………95

4.8 Word Frequency and Phrase Frequency of Cluster Six………...………………..95

4.9 Word Frequency and Phrase Frequency of Cluster Seven………...……………..96

4.10 Word Frequency of Change Agent and Cluster Opinion Leaders………….……97

4.11 Phrase Frequency of Change Agent and Cluster Opinion Leaders………….…...99


ix

LIST OF FIGURES

1.1 Mathematical Model of Communication……………..…………………………...3

1.2 Example of a Sociogram……..……………………………………...………….....9

2.1 Model of Five Stages in the Innovation-Decision Process………………..…......18

2.2 Adopter Categorization on the Basis of Innovativeness…………...……….........23

2.3 Methods of Gathering Data…………………………………..…..........................25

2.4 Mathematical Model of Communication……………………………...................29

2.5 Convergence Model of Communication……………………………....................31

2.6 Basic Components of the Convergence Model…………………..........................33

2.7 Model of Uncertainty Reduction………………………………………………...47

2.8 Directed Sociogram and the Affiliated Adjacency Matrix………………………53

2.9 Types of Social Network Data and Analysis……...………..................................57

3.1 Node Classifications…....………………………..................................................68

4.1 Sociogram of TAWC Producer Network……..…...…..........................................79

4.2 Cluster One within TAWC Producer Network……………..................................81

4.3 Cluster Two within TAWC Producer Network…….............................................82

4.4 Cluster Three within TAWC Producer Network……...........................................83

4.5 Cluster Four within TAWC Producer Network...……..........................................84

4.6 Cluster Five within TAWC Producer Network…………….................................85

4.7 Cluster Six within TAWC Producer Network……………...................................86

4.8 Cluster Seven within TAWC Producer Network……………...............................87


1

CHAPTER I

INTRODUCTION

Overview

Social networks are the core of human society (Kadushin, 2012). We share a vast

array of relationships with people, ranging from acquaintances to close family bonds.

What is exchanged through these relationships is as diverse as the type of connections.

Friendship, ideas, goods, power, and information are just a few examples of what is given

and taken in these relationships. Communities are formed through the connections of

relationships. These communities are complex, dynamic, and influence the attitudes and

beliefs of those within the network (Giuffre, 2013).

For universities and organizations involved with projects and programs that seek

to address the challenges facing society and a growing global community, there is a

growing need to share information, best practices, and lessons learned efficiently and

effectively. To that end, social network analysis has emerged as a research methodology

and data analysis technique that increases understanding of the vast and complex

relationships among people (Scott, 2013).

The Texas Alliance for Water Conservation Demonstration Project (hereafter

referred to as TAWC) personnel are interested in understanding how producers interact

with other producers. Agricultural communicators and conservationists are challenged to

understand how the community of producers involved in the TAWC share and receive

information related to irrigation water management. Social network analysis can give

researchers and practitioners a new perspective and a deeper understanding of the


2

characteristics, relationships, and beliefs within the social structure composed of TAWC

producers.

Interpersonal Communication

The TAWC is primarily concerned with how to best to share information with the

involved producers and encourage the information to spread beyond the boundaries of the

project. Diffusion of information requires interpersonal communication between

producers to complete the social process (Rogers, 2003). Ryan and Gross (1943) found

the primary influence on hybrid corn seed adoption by producers was interpersonal

communication. More recent studies support Ryan and Gross’ findings that producers

principally prefer interpersonal communication methods (Gamon, Bounaga, & Miller,

1992; Lasley, Padgitt, & Hanson, 2001; Licht & Martin, 2007a; Richardson & Mustian,

1994; Riesenberg & Gor, 1989; Suvedi, Lapinski, & Campo, 2000; Vergot III, Israel, &

Mayo, 2005).

Shannon and Weaver (1949) developed the mathematical model of

communication (Figure 1.1). This linear model describes the process and units necessary

for communication. The information source sends message(s) of various types intended

for the destination. The transmitter influences the message in some way so that it is

appropriate for the channel used for communication. The channel is the platform used to

get the message(s) from the information source to the destination. The receiver does the

opposite of the transmitter, reconstructing the message sent by the information source to a

suitable medium to be understood by the destination. The destination is the intended

recipient of the message(s). Noise is any interruption to the communicated message(s).


3

Noise may cause the message received by the destination to be different from the

message originally sent by the information source (Shannon & Weaver, 1949).

Figure 1.1. Mathematical Model of Communication (Shannon & Weaver, 1949)

Interpersonal communication can be mediated or unmediated. Mediated

interpersonal communication includes the intervention of an electronic or mechanical

medium through which messages are transmitted from an information source to a

destination (Burgoon et al., 2002). No electronic or mechanical medium is used to

transmit a message in unmediated communication. Unmediated communication is face-

to-face exchanging of messages (Flanagin & Metzger, 2001).

All forms of communication are motivated by goals (Westmyer, DiCioccio &

Rubin, 1998). To fulfill needs or wants, we select a decidedly appropriate channel for

communication (Schutz, 1966). Interpersonal communication, Schutz (1966) concluded,

is motivated by a need for inclusion, control, and affection. Inclusion, behaviorally, is

the need to execute successful exchanges with others. In the emotional sense, inclusion is

the need to develop a reciprocated interest with others. Behaviorally, control is the need

to obtain and hold influence over others. Emotionally, control is the need to gain and

Message

Received

Signal Signal Message

Information

Source

Transmitter Receiver Destination

Noise

Source


4

maintain mutual respect with others. Affection, behaviorally, is the need to establish and

preserve relationships based on appreciation, loyalty, and admiration (Schutz, 1966).

Interpersonal communication is the primary way producers within the TAWC

communicate. Given the desire of TAWC stakeholders to effectively communicate and

share information with producers internal and external of the demonstration project, there

is a need to understand the relationships and communication channels within the network

of producers.

Texas Alliance for Water Conservation

The High Plains region, located in the northern Panhandle of Texas, northeastern

New Mexico, eastern Colorado, and western Kansas (Encyclopedia Britannica, 2013) is

economically dependent on the exhaustible water source, the Ogallala Aquifer (TAWC,

n.d.b). Water determines success for producers in the region (TAWC, n.d.b). Certain

counties use more water for crop irrigation purposes than others. Wheeler (2005)

identified nine counties expected, over the next 60 years, to drawdown the aquifer to less

than 30 feet of saturated thickness. Two of these high-use counties, Hale and Floyd, are a

part of the TAWC’s effort to identify practices and technologies that will conserve water

for many years to come.

The TAWC was established in 2005 with the mission to “conserve water for

future generations by collaborating to identify those agricultural production practices and

technologies that, when integrated across farms and landscapes, will reduce the depletion

of ground water while maintaining or improving agricultural production and economic

opportunities” (TAWC, 2011, para. 1). A grant from the Texas Water Development


5

Board made the project possible and funded through 2019. One of the major goals of the

TAWC is to extend the life of the Ogallala Aquifer with respect for upholding the

viability of local farms and communities. The collaboration of area producers, data

collection technologies, as well as industry, university, and government agency partners

makes the TAWC unique.

The National Research Council (1996) stated irrigation must evolve to continue to

be an asset to the country. In order to maintain farm operation profitability while

improving water use, and irrigation efficiency, the TAWC uses on-farm demonstrations

of cropping and livestock systems. The demonstration project is overseen by a Water

Conservation Demonstration Producer Board made up of Hale and Floyd county

producers in cooperation with personnel from Texas Tech University College of

Agricultural Sciences and Natural Resources, Texas A&M AgriLife Research and

Extension, USDA Agricultural Research Service and Natural Resources Conservation

Service, and the High Plains Underground Water District No. 1 (TAWC, 2013b).

Across 4,300 acres in 29 TAWC field sites owned by producers in Hale and Floyd

counties, practices, technologies, and systems are compared (TAWC, 2013b). The field

sites represent a range of agricultural practices. Practices include fully integrated crop

and livestock systems, monoculture cropping systems, no-till and conventional tillage

practices, crop rotations and a variety of irrigation practices. These practices are applied

to a variety of crops including cotton, corn, sorghum, wheat, and specialty crops (TAWC,

n.d.b).


6

To monitor the water use, soil moisture depletion, crop productivity and economic

return, each site is equipped with an instrument. The instrument calculates total water

applied from the Ogallala Aquifer, solar radiation, temperature, rainfall, timing, irrigation

events, and soil moisture. A single database stores this data, transmitted by an integrated

central processing controller (TAWC, n.d.a).

The unique data set spanning all eight years of the project thus far is just one of

the major accomplishments of the TAWC. In addition, the use of irrigation management

tools (Resource Allocation Analyzer and Irrigation Scheduling Tool) have aided

producers in maximizing profitability and making irrigation scheduling decisions to use

water more efficiently. Producers within the TAWC have elected to adopt more efficient

irrigation equipment, schedule irrigation based on evapotranspiration, and diversify the

crops they plant. These changes combine to allow more water to reach the root zone,

decreased evaporation and increased crop yields. Based upon these successes, the

TAWC and the producers involved have developed new best management practices to

implement. Producers in the project also test emerging technologies on their field sites.

TAWC has studied the effectiveness of these new technologies to aid producers in

making the best purchasing decisions for their operations. Each producer in the project

has also benefited from field site-specific and whole-farm financial analyses (TAWC,

2013a).

In order to share these major accomplishments and unique findings, the TAWC

holds field days and workshops to showcase producers’ experiences with new water

conservation technologies. Results of the project have been presented in journals and


7

conferences, attracting interest from producers and agricultural water-related stakeholders

from across the nation. The project has been awarded grants in order to continue to

expand the influence of the TAWC demonstrations and test sites beyond Hale and Floyd

counties (TAWC, 2013). Little is known about how best to share the successes and

findings of the project within and beyond those involved in the TAWC. It is critical to

the continuance and effectiveness of the project to better understand and utilize the

established network of producers within the TAWC and beyond.

Social Network Analysis

“In short, who you know has a significant impact on what you come to know”

(Cross, Parker, & Borgatti, 2002, p. 3). Human society is based on social networks. In

the language of social network analysis, individuals, also known as nodes, are connected

by one or more relationships, or ties (Marin & Wellman, 2011; Scott, 2013; Wasserman

& Faust, 1994). People have always lived within social and professional networks. We

are tied together by relationships and are dependent on each other (Kadushin, 2012).

Mentally, people keep network maps of people they know. There are people we see or

talk to daily. There are people who we count on for different tasks in a variety of

situations. There are people we know, who know each other. Some of the people we

know get along with each other, while others do not connect (Chua, Madej, & Wellman,

2011).

New technology has changed the way we think about networks or communities.

According to Chua et al. (2011), there are three perspectives of communities. First,

communities can be bound by geography. Connected people can take a walk or short


8

drive to visit each other. Second, communities can be bound by shared interests. For

example, there are people who connect to create a community because of their affinity for

sports. Finally, communities can consist of all connections from any level of bond; from

acquaintances to close family members. These communities are composed of local and

distant ties. Connections are often bound to each other. Communities have shifted from

being spatially defined by geography to being relationally defined by connections (Chua

et al.).

No longer are communities strictly based on geography. The physical connection

of households being connected by telephone lines or the need to see a person face-to-face

to communicate is not the determining factor in creating a connection, then developing

into a community. Online and offline networks now exist. Physical linkages have given

way to direct personal linkages through the use of mobile phones, email, and social media

(Chua et al., 2011). These individualized and specialized interactions between people are

referred to as networked individualism, the contemporary form of community (Wellman,

2001).

As humans are social beings, we strive to build relationships, creating communities, or

networks, online and offline. Therefore, understanding networks of connections is

essential to understanding how we share information, ideas, and other resources. Human

networks are complicated by conflict and cooperation, belonging and alienation, or

distance and closeness. Social network analysis allows researchers to untangle networks

by examining social relationships to see a new perspective on connections (Giuffre,

2013). The resulting data analysis creates a map, referred to as a sociogram, illustrating


9

the social network. Groups of individuals and the relations between them compose a

social network, represented by networks of nodes (individuals, groups, or

organizations) and the connections (shown as lines representing the relationships) as

seen in Figure 1.2. (Kadushin, 2012).

Figure 1.2. Example of a Sociogram

Social network analysis has emerged as a research methodology and data analysis

technique that increases understanding of the vast and complex relationships among

people. It has gained a significant following in anthropology, biology, communication

studies, economics, geography, information science, organizational studies, social

psychology, and sociolinguistics, and has become a popular topic of speculation and

study (Scott, 2013).

A challenge for all researchers and a variety of practitioners is the sharing and

application/adoption of knowledge and best practices gained through study. To

encourage, enhance, and support organizational learning and sharing of information,

programs, and methods can be designed. For example, the TAWC holds fields days and

shares information via a website, radio spots, and written materials. However, the impact

and flow of these techniques is often difficult to understand (Cross et al., 2002).

A

C

B

D


10

Social network analysis allows stakeholders to understand the complex

relationships among a group that can either help or hinder diffusion of information and

innovations. To find out, researchers can seek to answer some simple questions. How

does information flow within an organization? To whom do people go to for advice or

information? What subgroups have emerged? Are they sharing information effectively?

When the answers to these questions are mapped into a social network sociogram, new

insight is gained about patterns in relationships and individual relativity (Cross et al.,

2002).

Statement of the Problem

The networks humans are engaged in have become increasingly vast and complex

(Chua et al., 2011). As the basis for human life, networks are an essential area of study to

understand how people communicate (Giuffre, 2013). What is the purpose of sharing

information, ideas, and other resources with a network if it is not being shared

effectively? Social network analysis gives researchers a new perspective that might not

otherwise be realized about networks of relationships.

Water conservation continues to be a pressing issue for everyone, but especially

agriculturalists in areas with quickly depleting resources that their life’s work depends

upon. It is essential that researchers and producers collaborate to implement new

technologies and methods. Producers do not necessarily have to sacrifice profitability for

water conservation, as the TAWC is working to prove.

This study examines the relationships of producers in the TAWC in order to better

understand how to more effectively and efficiently share water management information


11

with the network. Producers have many different resources they can use to seek

information and advice about their operation. Interpersonal communication has been

proven to be their primary source of information gathering.

Purpose and Objectives

The National Research Agenda: American Association for Agricultural

Education’s Research Priority Areas 2011-2015 (Doerfert, 2011) places an emphasis on

understanding adoption decision processes regarding new technologies, practices, and

products.

The findings of this study provide stakeholders of the TAWC insight into how

information is diffused among the network of producers and beyond. In addition, this

study provides an improved understanding of how to apply social network analysis to

networks within the agricultural industry.

The purpose of this research was to describe the TAWC producers and analyze their

interpersonal network in terms of attributes, ideations, and relationships with others as it

relates to sharing farming and water management information.

The following research objectives were used to guide this study:

1. Describe TAWC producers in terms of age, years in the project, acres in the

project, board member status, who initiated their involvement in the project, type

of irrigation used on their acres in the project, crops grown on their acres in the

project, and if livestock are raised on their acres in the project

2. Describe the interpersonal connections of the producers in the TAWC in terms of

relations


12


typology

Definition of Terms

Actor – Social unit of the network which can represent one individual, group or

organization. Also called a node (Carolan, 2014).

Betweenness centrality – Measures the extent to which a node lies between various other

nodes in a network. A high betweenness centrality value indicates a node plays an

important intermediary role in sharing information throughout a network (Scott, 2013).

Brokerage – Principle of social network analysis fulfilled by identifying relations that

serve important intermediary roles within a network (Haythornthwaite, 1996; Scott,

2013).

Carrier – A node with an equal number of relations directed towards (indegrees) and

away (outdegrees) from it. This node shares and receives information with an equal

number of other nodes (Wasserman, 1994).

Centralization – Measure which identifies the node(s) the sociogram is focused around

(Scott, 2013).

Cohesion – Principle of social network analysis concerned with the likelihood that actors

with present relations have equal access to information (Haythornwaite, 1996).

Density – Ratio of number of present, reported links in a network to the maximum

number of potential links in a network. Low-density networks are less interconnected

than high-density networks (Haythornwaite, 1996; Scott, 2013).


13

Eigenvector centrality – Determines the influence or power of a node by measuring the

node’s total degrees and the total degrees of the node’s relations. An actor with a high

Eigenvector centrality value would have relatively few relations with other actors, but

these relations have strong, powerful, or otherwise strategic relations to other actors

throughout the rest of the network (Hansen, Schneiderman, & Smith, 2011).

Indegree – Total number of other social relations that terminate at, or are directed

towards, a node (Scott, 2013). The total number of people who go to the actor for

information or advice.

Isolate – A node that has no relations with any other nodes in the network (Wasserman,

1994).

Prominence – Identify actors in a network who have influence or power over other actors

in the network to fulfill this principal of social network analysis (Haythornthwaite, 1996).

Ordinary - A node with relations directed towards (indegrees) and away (outdegrees)

from it. This node has a greater number of indegrees than outdegrees or vice versa

(Wasserman, 1994).

Outdegree – Total number of social relations that originate at, or are directed away from,

a node (Scott, 2013). The total number of people who the actor goes to for information

or advice.

Range – Principle of social network analysis which states that the more relations an actor

possesses, the more information they will have access to, and the more diverse the

information (Haythornthwaite, 1996).


14

Receiver – A node that only collects relations, possesses only indegrees, only obtains

information from other nodes in the network (Wasserman, 1994).

Relation – The relationship between two actors within a network. Also called a link

(Carolan, 2014).

Social network – A group of individuals and the relations between them (Wasserman &

Faust, 1994).

Social network analysis – The exploration of patterns of relationships through mapping

an illustration of all relations among actors in a given social network (Marin & Wellman,

2011).

Sociogram – A diagram or graph used to visualize social networks in which actors are

represented by points and lines represent relations. The graph can be directed or

undirected. These graphs can also be valued, if the appropriate data is collected (Scott,

2013).

Structural equivalence – Principle of social network analysis, which requires

identification of actors that hold similar roles within a social network (Haythornthwaite,

1996; Wasserman & Faust, 1994).

Transmitter – A node that only has relations originating from it, possesses only

outdegrees, or only shares information with other nodes in the network (Wasserman,

1994).

Typology – Grouping items according to how they are similar. Analysis based on types

or classification (Merriam-Webster, 2013).


15

Limitations

The following limitations of this study should be considered:

1. The study is limited to the social network of producers who are members of the

TAWC.

2. Three members of the population are missing from the analysis due to their

unwillingness to participate.

3. The generalizability of the findings is limited to the producers of the TAWC.

Basic Assumptions

The following basic assumptions were made about this study:

1. All interview answers and data reported were given honestly.

2. The respondents fully understood each question being asked.

3. Individuals being interviewed had direct and continuing experience with the

TAWC.


16

CHAPTER II

LITERATURE REVIEW

Overview

This review of literature sought to identify and explain research studies and

theories with relevance to describing the TAWC producers and examining their

interpersonal relationships through social network analysis. To expand the current body

of knowledge, it was important to examine previous studies and theories. The theoretical

framework of this study focused on the diffusion of innovations. The conceptual

framework describes producers as an individual entrepreneur and as an agricultural

professional. Interpersonal communication, Social Exchange Theory, and Social

Comparison Theory are discussed with relation to the producer as an individual

entrepreneur. With regards to the producer as an agricultural professional, communities

of practice and Uncertainty Reduction Theory are examined. Social network analysis is

explored in the operational framework of the study.

Theoretical Framework

Diffusion of Innovations

The adoption of new ideas, technologies, and practices takes time. Many

organizations want to know how to accelerate the adoption process. To do so, it is

important to understand how innovations diffuse through social systems. Rogers (2003)

defined diffusion as a process characterized by communicating an innovation through

various channels over time to a group of people.


17

In the 1930s, the seminal study described diffusion and created the foundation for

future studies of new technology adoption in rural, farming communities involved in the

agricultural industry. Ryan and Gross (1943), rural sociologists, explored the rapid

diffusion of hybrid seed corn in two Iowa farming communities. They investigated the

differences between impulsive decisions and those made based on a process. Over a six-

year period, hybrid seed corn acreage increased around these communities from 40,000 to

24 million acres. Five factors were found to impact the diffusion: (a) the quality of the

seed, (b) the economy, (c) weather conditions, (d) information shared by the Extension

Service, and (e) ease of adoption (Ryan & Gross, 1943).

Ryan and Gross (1943) determined that a typical farmer would use different

channels to gather information. The information influenced the decision of whether or

not to adopt a new hybrid seed corn. Most of the farmers had knowledge of the

innovation prior to adoption of the hybrid seed by a small number of their peers. Early

adopters most often chose not to exclusively plant the hybrid seed. By comparison, late

adopters took less time to decide to plant only the hybrid seed, once they made the

decision to adopt. Ryan and Gross (1943) also found late adopters relied more on the

experience and knowledge of their peers to reach a decision to adopt. This reliance did

not elevate peer influence to cause earlier adoption. Therefore, the researchers suggested

farmers do not completely trust opinions or information from peers (Ryan & Gross,

1943).


18

Rogers (2003) further developed the decision making process conceptualized by

Ryan and Gross (1943) into five stages, known as the innovation-decision process

(Figure 2.1.). This process is the linear progression a decision-making individual goes

through when considering the adoption of an innovation. The progression includes

becoming aware of an innovation, formulating an opinion of the innovation, choosing to

adopt or reject the innovation, implementing the decision, and seeking affirmation of

their decision. Adopters’ perceptions of the newness of an innovation, and any

associated uncertainty, is a distinct characteristic of this process when compared to other

types of decision making (Rogers, 2003).

In order for an innovation to diffuse through the innovation-decision process,

Figure 2.1. Model of Five Stages in the Innovation-Decision Process (Rogers, 2003)


19

Rogers (2003) stated four elements must be present. The process requires an

innovation, communication channels, time, and a social system. An innovation is any

new concept, process, or material object that an individual perceives as new.

Communication channels are the mediums through which the message is spread. Time is

a factor in three different aspects of diffusion: “(a) the innovation-decision process, (b)

communication channels, (c) and an innovations rate of adoption” (Rogers, 2003, p.37).

The final element, the social system, is a body of affiliated units with the common

purpose of accomplishing a specific goal.

Rogers (2003) defined each of the five sequential stages in the innovation-

decision process:

1. Knowledge occurs when an individual (or other decision-making unit) is

exposed to an innovation’s existence and gains and understanding of how it

functions.

2. Persuasion occurs when an individual (or other decision-making unit) forms a

favorable or unfavorable attitude toward the innovation.

3. Decision takes place when an individual (or other decision-making unit)

engages in activities that lead to a choice to adopt or reject the innovation.

4. Implementation occurs when an individual (or other decision-making unit)

puts a new idea into use.

5. Confirmation takes place when an individual seeks reinforcement of an

innovation-decision already made, but he or she may reverse this previous

decision if exposed to conflicting messages about the innovation (p. 169).


20

When an innovation, communicational channel, time, and a social system are

present, the information-decision process begins. During the knowledge stage, three

types of knowledge can be gained. Awareness-knowledge is present first, when an

individual has information that an innovation is available. The rate of this knowledge for

an innovation is more rapid that its rate of adoption. Rogers (2003) recommended

change agents focus on creating awareness-knowledge through mass media

communication channels to maximize their influence on the innovation-decision process.

For some individuals, this level of knowledge is enough to move them to the next stages

of the process. For others, awareness-knowledge motivates them to seek how-to

knowledge and, perhaps, principles knowledge to facilitate their decision-making process

(Rogers, 2003).

How-to knowledge is information regarding the proper uses of an innovation.

Rogers (2003) stated this type of knowledge is essential to individuals who are trialing

the innovation during the decision stage. Innovations that are more complex, or difficult

to understand, require more how-to knowledge than innovations that are less complex.

Therefore, the perceived complexity of an innovation by an individual negatively affects

the rate of adoption. If an individual is seeking how-to information and understanding,

an adequate level of knowledge must be reached prior to advancing in the innovation-

decision process (Rogers, 2003).

The foundational functioning principles of an innovation characterize the final

type of knowledge about an innovation, principles-knowledge. These principles are the

theories and fundamental elements that the innovation is built upon. Possessing


21

principles-knowledge is not essential for all innovations. Change agents often regard

creating this knowledge to be outside the scope of their responsibilities, and instead is the

obligation of the formally educated. There is a greater risk of a potential adopter

misunderstanding a new idea, practice or object, which could result in rejection or

discontinuance (Rogers, 2003).

No matter when an individual enters into the innovation-decision process, they

can achieve each type of knowledge. However, there are differences between what

Rogers (2003) referred to as earlier knowers and later knowers. Rogers described seven

generalizations about early knowing of an innovation. Early knowers of an innovation

have: (a) more education, (b) higher social status, (c) more exposure to mass media

channels, (d) more exposure to interpersonal channels, (e) more contact with change

agents, (f) more social participation, and (g) are more cosmopolite, all than later knowers.

Early knowers are aware of innovations, but do not always decide to adopt them (Rogers,

2003).

The persuasion stage of the innovation-decision process only occurs if an

individual does not gain adequate knowledge or decides the innovation would not aide in

their situation. When a person reaches the persuasion stage, they consider five perceived

characteristics of the innovation to decide their attitude toward the innovation. Those

characteristics are (a) relative advantage, (b) compatibility, (c) complexity, (d)

trialability, and (e) observability (Rogers, 2003).

During the persuasion stage, individuals seek to reduce their uncertainty about the

advantages and disadvantages of the innovation as a part of their situation. Interpersonal


22

communication channels are more important during this stage for all adopters, except

innovators, who are the first to adopt. Most individuals will turn to their peers for this

information. Peers offer subjective opinions, developed from personal experience with

the innovation, that are more accessible and convincing. The individual is unsure of his

or her attitude and therefore seeks social support of their thinking from peers. This type

of reinforcement cannot be provided by mass media messages, which are too general

(Rogers, 2003). Diffusion of innovations continues to be dependent on informal personal

networks (Allen, 1977; Cross, Laseter, Parker & Velasquez, 2006; Rogers, 2003).

Communication channels get messages from sources to receivers during each

stage of the innovation-decision process, but may have the greatest influence during the

knowledge and persuasion stages. Mass media channels are commonly the quickest,

most far reaching, and efficient ways to share information. However, interpersonal

channels are more effective in decision making due to face-to-face, two-way information

exchange between individuals. In addition, this channel has the greatest ability to help an

individual persuade another person to change a strongly held attitude. Interpersonal

channels become more effective when individuals have common characteristics, such as

socioeconomic status, interpersonal connections, or education. Later adopters rely even

more heavily on localite, interpersonal channels because by the time they are deciding

whether or not to adopt, local experience has accumulated in the social system (Rogers,

2003).

Rogers (2003) defines the time it takes an individual to complete the innovation-

decision process as the innovation-decision period. This period encompasses the time


23

Figure 2.2. Adopter Categorization on the Basis of Innovativeness (Rogers, 2003)

during which an innovation is considered for adoption, taking days, months, or years.

The measure is taken from first awareness of the innovation to the decision to adopt or

reject.

Rogers (2003) developed a model to categorize adopters based on their

innovativeness (Figure 2.2.). Adopters are divided into five categories along a normal

frequency distribution: (a) innovators, (b) early adopters, (c) early majority, (d) late

majority, and (e) laggards. The innovation-decision period is shorter for earlier adopters

when compared to late adopters. Earlier adopters also possess more innovativeness, the

likelihood of adopting a new innovation. Innovativeness is influenced by socioeconomic

status, personality values, and communication behavior (Rogers, 2003). With varying

levels of innovativeness among individuals comes a variance in the length of the

innovation-decision period.

Members of an adoption category usually have similar traits. The ideal innovator

seeks ideas and possesses relationships outside the local network, therefore able to be a

gatekeeper and introduce innovations to the social system. Early adopters are well

respected and have the most opinion leadership of any other type of adopter. They make


24

judicious decisions to adopt an innovation and then share a subjective evaluation through

interpersonal networks. The early majority is an important link in the diffusion process

and bridge connections in the social system’s interpersonal networks. Adopters in the

late majority category are cautious and skeptical of innovations. For these individuals,

adoption must be motivated by peer pressure. Laggards are the last in a social system to

adopt an innovation or may never adopt. They are usually very traditional and rely

heavily on past experiences to make future decisions (Rogers, 2003).

The rate of adoption is the speed with which an innovation is adopted in a social

system. Rogers (2003) recognized that interpersonal networks within a social system

increase the rate of adoption for innovations. The first diffusion network study,

conducted by Coleman, Katz, and Menzel (1966), explored the spread of a new drug

among medical doctors. When the doctors became aware of the new drug, they asked

their peers for information to help make the adoption decision. An early adopter shared

his or her opinion and personal experience with two or more doctors, who might adopt

and then interpersonally share their opinion and personal experience with more doctors,

and so on. The chain-reaction contagion process caused an S-shaped rate of adoption

curve (Figure 2.3.). This study established network links as important predictors of

innovation adoption (Rogers, 2003).


25

Figure 2.3. Methods of Gathering Data (Rogers, 2003)

Change agents, individuals who influence the innovation-decisions of others, are

the links between a resource system and a social system. They bridge the gap between

the two systems to facilitate the flow of innovations from the resource system to the

members of the social system. Change agents are well educated in general and especially

regarding the diffused innovations. They also understand the needs of the members of

the social system and communicate feedback to the resource system on their behalf.

Change agents usually differ from members of the social system, but have the most

contact with those who are the similar to them (Rogers, 2003).

When change agents engage in the process of introducing an innovation to a

system of clients, there are seven ideal, sequential steps in roles the change agent

assumes:


26

1. To develop a need for change.

2. To establish an information exchange relationship.

3. To diagnose problems.

4. To create an intent to change in the client.

5. To translate an intent into action.

6. To stabilize adoption and prevent discontinuance.

7. To achieve a terminal relationship (Rogers, 2003, pp. 369-370).

When change agents identify and mobilize opinion leaders, the adoption of an

innovation is likely to be higher within a social system. Opinion leaders influence other

individuals’ attitudes or behaviors. Opinion leaders are innovators who have high

exposure to mass media, are cosmopolite, participate socially, and have elevated

socioeconomic status. Change agents should use opinion leaders for communication

activities to more effectively use resources of time and energy. Messages to the network

from near peers, including opinion leaders, are credible in convincing an individual to

adopt an innovation (Rogers & Kincaid, 1981; Rogers, 2003).

Relation to Study

The researcher is concerned with the diffusion of innovations through the network

of TAWC producers under study. The innovations are best practices, new technologies,

and information from project coordinators. The relationships between producers are the

interpersonal communication channels that the innovations pass through. These

relationships will be mapped using social network analysis. Diffusions through networks


27

take varying amounts of time, depending on each TAWC producer’s adopter

categorization. The TAWC producers serve as the social system for this study.

Information is sought from a variety of sources in order to gain knowledge about

an innovation. Change agents and opinion leaders play a key role in the diffusion of

innovations. Therefore, the researcher is interested in identifying who those people are in

the studied social system. The interpersonal relationships of producers with each other,

change agents, and opinion leaders will have great impact, according to theory, on

producer’s decision to adopt or reject an innovation. The Diffusion of Innovations

framework will be an important guide to the execution of this research.

Conceptual Framework

Producers of agricultural products, farmers and ranchers, operate as both an

individual and a professional. In order to describe the communications of producers as a

single unit and as an organization, the conceptual framework is divided into two parts,

discussing the producer as an individual entrepreneur and the producer as an agricultural

professional.

Producer as an Individual Entrepreneur

Interpersonal communication.

Interpersonal communication occurs when two connected people exchange

informational messages that are sent, received, encoded, and decoded (Berko, Aitkey, &

Wolvin, 2010; DeVito, 2007). Diffusion of information is dependent on interpersonal

communication to complete the social process (Rogers, 2003). Interpersonal

communication is critical in the process of change, especially changes in strongly held


28

attitudes (Rogers, 2003; Rogers & Kincaid, 1981). Interpersonal communication

channels can be used as a decision-making aid to facilitate two-way exchange of

information, with the opportunity to clarify or request further details. These channels can

either be localized, within a network, or cosmopolite, connecting individuals to others

who are outside the defined network. Localite, interpersonal channels involve face-to-

face communication between peers. Cosmopolite, interpersonal channels include change

agents, tours outside the local community, and visitors from outside the community

(Rogers, 2003).

The three primary models of communication are the linear, interactional, and

transactional models. Each model represents a different perspective on the process of

sharing information to reach shared meaning (Fujishin, 2012). Most communication

research has been conducted within the context of these models (Rogers & Kincaid,

1981).

The linear model of communication represents left-to-right, one-way

communication (Rogers & Kincaid, 1981). A source encodes and sends a message

through sensory channels to a receiver who decodes the message (Berko et al., 2010).

Shannon and Weaver (1949) developed the mathematical model of communication, a

linear model (Figure 2.4.).


29

Figure 2.4. Mathematical Model of Communication (Shannon & Weaver, 1949)

The information source sends message(s) of various types intended for the

destination. The transmitter influences the message in some way so that it is appropriate

for the channel used for communication. The channel is the platform used to get the

message(s) from the information source to the destination. The receiver does the

opposite of the transmitter, reconstructing the message sent by the information source to a

suitable medium to be understood by the destination. The destination is the intended

recipient of the message(s). Noise is any interruption to the communicated message(s).

Noise may cause the message received by the destination to be different from the

message originally sent by the information source (Shannon & Weaver, 1949).

Interpersonal communication can be mediated or unmediated. Mediated

interpersonal communication includes the intervention of an electronic or mechanical

medium through which messages are transmitted from an information source to a

destination (Burgoon et al., 2002). No electronic or mechanical medium is used to

transmit a message in unmediated communication, relying on the face-to-face exchange

of messages (Flanagin & Metzger, 2001).

Message

Received

Signal Signal Message

Information

Source

Transmitter Receiver Destination

Noise Source


30

In the interactional model of communications, a source uses channels to send a

message to be decoded by a receiver. The receiver then sends feedback to the original

source to be decoded. The original source reacts in a manner to ensure correct

interpretation, also called adaption. This model builds on the linear model by adding the

components of feedback and adaption (Berko et al., 2010).

Some researchers argue that the linear and interactional models oversimplify

communication. To address this notion, in the transactional model, messages are

processed simultaneously by the participants. The source encodes and sends a message

to the receiver who responds with feedback. These actions can occur at the same time

(Berko et al., 2010).

Rogers and Kincaid (1981) stated linear communication models do not fully

encapsulate the natural flow of conversation. The lack of appropriate language to capture

the dynamic, cyclical nature of communication has been a challenge to improving these

models. Communication is better understood when examined under the lens of complete

cycles in which two or more people share information back and forth between each other

for a common purpose (Rogers & Kincaid, 1981).

There are two obstacles to the adoption of a systems approach to human

communication. These obstacles are the lack of an adequate model to represent

interdependent relationships of parts and the lack of appropriate research methods to

study the relationships through which communication flows. Therefore, Rogers and

Kincaid (1981) proposed shifting human communication study to focus on information-


31

exchange relationships through the intellectual paradigm of network analysis and the

convergence model of communication (Figure 2.5.).

Figure 2.5. Convergence Model of Communication (Kincaid, 1979; Kincaid & Schramm,

1975)

The model shows the cyclical process of communication where participants

exchange information to converge upon mutual understanding. Convergence implies

movement and encapsulates the dynamic nature of communication. This model takes a

relational perspective on human communication, examining mutual causation and the

interdependent relationships between participants (Rogers & Kincaid, 1981).

Convergence toward mutual understanding beings with “and then…”, which

implies that something has occurred before the process began to be observed. Each

participant has a history that influences the information shared by that individual, which

the other communication process participant(s) may or may not consider. Participant A


32

shares a thought to express themselves to Participant B (I1). Participant B perceives and

interprets the information and then may respond with new information (I2). Participant A

perceives and interprets the information and may respond (I3). This process continues

until one or both participants decide there is adequate mutual understanding about the

specific topic for the purpose at hand. The process returns to I1 when a new topic is

established (Rogers & Kincaid, 1981).

To further explain the convergence model of communication, Figure 2.6. shows

the basic components. The model operates on the assumptions that there is innate

uncertainty in information processing and the basic purpose of communication is mutual

understanding. There is no beginning or end to the model or communication. The model

unifies information and action, showing information causes action and, sometimes, vice

versa. The model is organized within three levels of reality, or abstraction: physical,

psychological, and social realities. Information and mutual understanding are the

foundations on which the model is based. Information processing occurs individually for

two or more people involved in communication exchange, as shown in the figure as

psychological reality A and B. Each person perceives, interprets, understands and

believes shared information. Individual processing of shared information becomes

human communication when two or more people have the same common purpose of

understanding one another (Rogers & Kincaid, 1981).


33

Figure 2.6. Basic Components of the Convergence Model (Kincaid, 1979)

Individuals involved in the exchange of information do not always reach a mutual

understanding. Individuals will reach an appropriate level of mutual understanding and

then cease communication when their needs have been met. Convergence of

understandings between individuals is never complete (Rogers & Kincaid, 1981).

Collective action can only be arrived at through mutual understanding and

agreement. According to Rogers and Kincaid (1981), there are four combinations of

mutual understanding and agreement: “(a) mutual understanding with agreement, (b)

mutual understanding with disagreement, (c) mutual misunderstanding with agreement,

and (d) mutual misunderstanding with disagreement” (p. 56). These combinations are

indicative of the alternative outcomes of each component of the model. Although the


34

terms imply positive outcomes, the opposite may also result, such as misconception,

misunderstanding, or disbelief.

Rogers and Kincaid (1981) challenged researchers to use the convergence model

of communication as a platform to study relationships as well as the differences and

similarities between people. The smallest unit of analysis should be two connected

people and then extending analysis out into cliques, personal networks, and large, intact

networks.

Agricultural producers principally prefer interpersonal communication methods

(Gamon, Bounaga, & Miller, 1992; Lasley et al., 2001; Licht & Martin, 2007a;

Richardson & Mustian, 1994; Riesenberg & Gor, 1989; Suvedi, Lapinski, & Campo,

2000; Vergott et al., 2005). Ryan and Gross (1943) found demonstration of this notion;

concluding interpersonal communication was the primary influence on hybrid corn seed

adoption. Nearly half (45.5%) of farmers selected neighbors as the most influential. The

greatest percentage (49%) of first knowledge of the innovation was acquired from

salesmen.

In addition, Riesenberg and Gor (1989) found on-farm demonstrations and tours,

as well as field trips were the most effective means of communicating with producers in

Idaho. A study of North Carolina farmers conducted by Maddox, Mustain, and Jenkins

(2003) supported Risenberg and Gor’s (1989) findings. In addition, they found fellow

producers are a major source of information.


35

Social Exchange Theory.

Social Exchange Theory proposes that a series of interactions between people

generates transactions that are mutually contingent and rewarding to produce

interpersonal attachment (Emerson, 1976). The theory studies the relationship of

exchange between two actors in an environment. People will make utility comparisons to

decide whether or not to exchange resources with another person (Emerson, 1987).

When one person shares a useful resource with another person, an obligation is created to

return a useful resource (Blau, 1964). The ability to compare interpersonal processes is

what sets Social Exchange Theory apart from economic theory (Emerson, 1987).

The seminal studies of social exchange were conducted in the 1920s by

Malinowski (1922) and Mauss (1925). Contributions by four central figures in sociology

and social psychology laid the foundation for modern Social Exchange Theory (Emerson,

1976). George C. Homans (1950, 1958, 1974) expressed social exchange as behaviorism

through individual transactions of information or material resources. This general

exchange approach was supported by the work of Thibaut and Kelly (1959) in their

construction of the compact conceptual scheme. Blau (1964) warned of too much

attention to psychology and instead put an emphasis on technical, economic analysis of

social exchange.

These early studies sought to use an individual’s supply and demand of resources

to explain the likelihood that a dyadic relationship would form (Monge & Contractor,

2001). Emerson (1972a) mixed the styles of Homans and Blau in his development of a

formal theory of exchange behavior. In addition, he used analysis of dyadic exchange


36

relationships as a framework for analyzing exchange network structures (Emerson,

1972b; Cook & Rice, 2003). Emerson (1976) contended that Social Exchange Theory is

a frame of reference with a focus on the movement of resources through social processes.

These resources will only be transacted if there is a contingent valued return, called

reinforcement or exchange.

The interpersonal exchange of resources develops relationships that evolve into

trusting, loyal, and reciprocal commitments. To build these relationships, two people

develop and abide my guidelines of exchange processes, also known as rules or norms of

exchange (Cropanzano & Mitchell, 2005). Relationships with well-established norms

have a deep, mutual understanding gained by great investments of time and energy

(Granovetter, 1973). As relationships progress, people will prefer to work more closely

with established, strong ties. Once mutual trust and understanding is established, one

party in the relationship can request resources from strong, direct ties without

reciprocation being immediately necessary (Blau, 1964; Ekeh, 1974; Lévi-Strauss, 1969;

Kim, 2006).

Six types of resources can be exchanged across interpersonal relationships: love,

status, information, money, goods, and services (Foa & Foa, 1974, 1980). A two-

dimensional matrix organizes these types of resources. The particularism of a resource,

or variance of worth based on source, is held in one dimension. For example, money is

relatively low in particularism because monetary value is constant regardless of the

source. The second dimension holds concreteness of a resource, also referred to as

tangibility. Many types of goods are high in concreteness because they are material.


37

Less concrete resources are symbolic, holding meaning beyond objective worth or value

(Cropanzano & Mitchell, 2005).

These resources are likely to be exchanged in various ways, depending on the

nature of the resource. Foa and Foa (1974, 1980) theorized that generally, the less

particularistic and more concrete a resource is, the more likely it is to be exchanged in a

short-term, reciprocated manner. Contrastingly, highly particularistic and symbolic

resources are exchanged in a way that reciprocation is not required. For example, money

is often exchanged for a good, but investment in love or status does not require or

guarantee a return (Cropanzano & Mitchell, 2005).

Social Exchange Theory has been used to explain such diverse areas as social

power (Molm, Peterson, & Takahashim, 1999), networks (Brass, Galaskiewicz, Greve, &

Tsai, 2004; Cook, Molm, & Yamagishi, 1993), board interdependence (Westphal &

Zajac, 1997), psychological contracts (Rousseau, 1995), and leadership (Liden,

Sparrowe, & Wayne, 1997).

The investigation of Social Exchange Theory within the context of agriculture

dates back to the earliest studies of the framework. Malinowski (1932) studied the social

trade relations between farming and fishing communities. The anthropologist explored

the social and economic order of the Trobriand Islanders, located off the coast of New

Guinea. More recently, Jussila, Goel, and Tuominen (2012) employed Social Exchange

Theory as a framework to better understand the management of co-operative

organizations. The researchers found this theory explains the incentives for activity

among co-operative members. In addition, there is a connection between member


38

motivations for exchange and the sustainability and success of co-operative member

exchange relationships.

Social Comparison Theory.

People actively seek to evaluate their own opinions, abilities, and life situations.

They do so by making comparisons with other people, when objective standards are not

available. Moreover, people are more likely to make comparisons with people who are

similar to them (Festinger, 1954). Based on a range of social comparisons with others

whom are perceived to be relevantly similar, people determine a level of satisfaction with

themselves and their life (Goethals & Klein, 2000).

Although the broad concept of self-identity and comparison for self-

understanding has been present since the first social philosophers and scientists, it was

not until the beginning of the 19th

century that research greatly expanded (Suls &

Wheeler, 2000). The essential role of social comparisons for subjective well-being was

established with the studies of Sherif (1936), Asch (1956), Hyman (1942), and Merton

and Kitt (1950). These research efforts, in combination with his own earlier studies,

influenced the theorizing of Festinger (1954). He was the first to introduce the term

“social comparison” and the first to propose a systematic theory.

Festinger (1954) emphasized how individuals use social groups to get the

information they need to assess their abilities and opinions. People need to know if their

opinions are correct and what they are capable of accomplishing through the use of their

abilities. To compare, people will choose others who they perceive as having similar

attributes related to the opinion or ability at hand. Pressures of uniformity are created to


39

reduce divergence in the group if discrepancies surrounding an issue are discovered

through comparisons. These pressures vary in strength with the relevance, importance,

and attraction to the group the person feels pertaining to the opinion or ability in need of

evaluation.

Social Comparison Theory contends that through comparison, people can satisfy

an array of personal motives. These include self-evaluation, common bond, self-

improvement, self-enhancement, altruism, and self-destruction. Self-esteem, comparison

target, and the usage of the comparison are considered influences on the comparison

process (Helgeson & Mickelson, 1995). People can reduce or eliminate uncertainty by

comparing their opinions and abilities with others (Festinger, 1954). Reassurance is

granted when social comparisons are made with someone who is involved, but not

worried about a particular issue (Affleck & Tennen, 1991).

Social comparison is rooted in the assessment of peer opinions, as evidenced by

informal social communication theory, which Festinger (1950) published four years prior

to Social Comparison Theory. The major difference between the two theories is the

individual’s need to compare abilities in addition to opinions (Suls, 2000). According to

Suls (2000) model, there are three types of opinion comparisons: preference assessment,

belief assessment, and preference prediction. Preference assessments judge if something

is presently right, appropriate, or favorable for a person by asking, “Do I like X?” Belief

assessments are potentially verifiable by judging the facts or correctness of a claim by

asking, “Is X true?” Preference predictions determine a person’s likely reaction to an

anticipated object or situation by asking, “Will I like X?” This model builds on the


40

previous work of Jones and Gerard (1967), Goethals and Darley (1977), and Gerard and

Orive (1987) to understand if people always seek similar individuals to compare

opinions, no matter the type of opinion.

Suls (2000) found comparisons with others who are similar is preferred and has

the strongest impact on opinions of preference assessment and preference prediction. In

contrast, when assessing beliefs for truth or correctness, comparisons with others who are

dissimilar and experts in the field to be evaluated should be preferred. This agrees with

the exception Festinger (1954) recognized in his hypothesis, stating that people have a

tendency to compare with similar others, but comparisons with others who have

somewhat different opinions will lead to reevaluation of one’s own opinion. In addition,

objective sources of information are preferred above social comparisons (Festinger,

1954). Therefore, the higher the level of expertise available to assess an opinion, the

stronger the belief assessment (Suls, 2000).

Abilities must also be accurately assessed to satisfy motivations of self-

evaluation. When confronted with a task, a person asks, “Can I do X?” Social

comparison is used to attempt to accurately determine if a person has the necessary level

of ability before investing in a potentially costly task (Martin, 2000). Performance

similarity between individuals under comparison is a foundation for accurate self-

evaluation (Festinger, 1954). Similarity of related attributes builds upon this to

strengthen comparisons (Martin, 2000).

The proxy comparison model developed by Wheeler, Martin, and Suls (1997) was

inspired and supported by the studies of Jones and Regan (1974); Kulik, Mahler, and


41

Earnest (1994); Kulik, Mahler, and Moore (1996); and Smith and Sachs (1997). The

proxy model follows the social comparison process, but with emphasis on the necessary

combination of performance and attribute similarities to accurately compare and assess

ability (Martin, 2000).

Another person who has already completed the considered task, under certain

conditions, may serve as a substitute of self when assessing personal ability. This proxy

provides the strongest comparison when both parties have completed a similar and

relevant task (task A) and the proxy has completed the novel task under appraisal (task

B). The performance of both parties on task A is one determining factor to consider

when predicting the self’s performance on task B. If the proxy and self performed

similarly on task A and proxy performed satisfactorily on task B, it may suggest that self

would perform the same way in task B. More information about the proxy regarding

other important variables is needed to make an accurate assessment of ability (Martin,

2000).

Proxies who share more information strengthen ability comparisons. Wheeler et

al. (1997) introduced, within the proxy model, maximum potential effort as a key

indicator of performance similarity usefulness to comparison. If it is known that the

proxy put forth maximum possible effort in both tasks, the self can more accurately make

a performance prediction. Maximum possible effort is often vague and difficult to

determine. When this is the case, related attribute similarity between proxy and self, in

combination with any knowledge of the proxy’s maximum potential effort, builds a more

accurate comparison of ability. If maximum potential effort is known with certainly,


42

related attributes are irrelevant in determining accurate performance predictions. The

process encapsulated by the proxy model is a systematic explication of how a person can

predict what one has the ability to accomplish (Martin, 2000).

To explore an ideal, hypothetical example at work in the context of this study,

TAWC producer A enters into the proxy model. TAWC producer A seeks to assess if his

operation will be successful in planting a new drought tolerate corn seed variety. In the

past, TAWC producer and TAWC producer B, the proxy, have both planted a drought

tolerant cotton variety, with similar, successful results. TAWC producer B planted the

new drought tolerant corn seed variety last year with high yields. The producers have a

longstanding relationship, so TAWC producer A knows TAWC producer B takes

changes to his operation very seriously and always puts forth maximum potential effort

when trying new seed varieties. Therefore, using the proxy model, TAWC producer A

can predict that he will be successful in planting the new drought tolerant corn seed

variety.

Social Comparison Theory research historically focused on the comparison

purposes of individualistic, psychological needs, while the theory is grounded in group

processes (Forsyth, 2000). Festinger (1954) employed the theory to investigate

homogeneity in groups, opinion debates, group members with higher levels of motivation

and competition, rejection of protestors, and shifts in group member opinions.

When confronted with confounding behavior or irregular findings in the study of

groups, researchers often use explanations that refer to the principle of comparison,

making Social Comparison Theory the second most favored explanation of group


43

processes. However, few studies directly assess the theory’s assumptions (Arrowood,

1978; Forsyth, 2000). Studies of group formation, affiliation, social identity, majority

and minority influence, social loafing, and the transmission of values and beliefs from

groups to individuals have used the principle of comparison to explain the behavior of

individuals in group contexts (Forsyth, 2000).

In the process of determining the accuracy of opinions and quality of abilities,

people are shaped by the very people in the very groups with whom they are engaging in

comparison. Changes are slow and subtle as people in a group make comparisons that

lead to revision of their opinions and identification of personal strengths, weaknesses,

assets, and liabilities (Forsyth, 2000). Groups serve as standards or frames of reference

when people evaluate their abilities, attitudes, beliefs, and life situations (Hyman, 1960).

Social comparisons within groups also influence the transmission of religious, economic,

moral, political, and interpersonal beliefs in groups (Forsyth, 2000).

People need to accurately assess their opinions and abilities in order to make

informed choices about dealing with the world. Inaccurate assessments can have

negative consequences that are punishing or even fatal (Festinger, 1954).

Social Comparison Theory has been used in agricultural research to better

understand the people within the industry. Bajema, Miller, and Williams (2002) studied

the aspirations of rural youth to identify perceived opportunities and barriers to achieving

their goals. The researchers found that most of the students had set educational and

occupational goals that were supported by their school environment. Vindigni, Janssen,

and Jager (2002) used Social Comparison Theory to explore consumer behavior with


44

regards to the diffusion of organic food consumption. Janssen (2001) investigated

management of nutrient dense lakes within a theoretical framework that included Social

Comparison Theory. The researcher found that if returns are low and personal

uncertainty is high, people will adopt the behaviors of other, similar people.

Producer as an Agricultural Professional

Communities of practice.

Lave and Wenger (1991) first formed the concept of communities of practice, but

the definition of the term varies from scholar to scholar (Cox, 2005). Wenger,

McDermott, and Snyder (2002) established communities of practice as “groups of people

who share a concern, a set of problems, or a passion about a topic, and who deepen their

knowledge and expertise in this area by interacting on an ongoing basis” (p. 4). Learning

happens through informal collaborative means. The network is bound together by a

shared professional identity (Wenger, 1998). The values of the community are a

determinate for prospective members (Hara, 2009).

Wenger et al. (2002) described actions to cultivate communities of practice:

1. “Design for evolution.

2. Open a dialogue between inside and outside perspectives.

3. Invite different levels of participation.

4. Develop both public and private community spaces.

5. Focus on value.

6. Combine familiarity and excitement.

7. Create a rhythm for the community” (p. 51).


45

Cultivated communities of practice are loose-knit and value-driven (McDermott,

1999). Members fully commit to the community’s work when they believe they can

extract benefits in return for their investment (McDermott, 2004; Wenger & Snyder,

2000). These benefits are most often related to improved knowledge sharing among

members of the community as well as people outside of the community who share a

common expertise with the community (Wenger & Snyder, 2000).

Knowledge sharing develops best practices, which are focused on new skills,

products, ideas, and more efficient practices. These practices do not exist inside or

outside of the community prior to development (Probst, Raub, & Romhardt, 1999).

However, knowledge sharing and the development of best practices amongst

communities of practice is complex. Members are faced with obstacles of indecision on

what, if, when, and to what extent to share information with the community (Hara, 2009).

While communities of practice are undoubtedly valuable to organizations, the

measurement of that value is difficult to assess. There remains the question as to if

communities of practice truly cultivate meaningful collaborations and organization value.

Researchers have recommended using various methods to assess value. Suggested

nontraditional methods include individual and focus group interviews (Millen, Fontaine,

& Muller, 2002), surveys (Millen & Fontaine, 2003), and social network analysis (Cross

et al., 2006; Wasko & Faraj, 2005).

Researchers have applied Wenger’s (1998) work to agricultural contexts. Morgan

(2011) explored social learning processes within communities of practice formed by

organic farmers. The researcher found that farmers engaged in social learning more often


46

when farming operation style and knowledge align. Knowledge is generated in the

context of the community’s professional focus. Attwater and Derry (2005) employed a

mix of methods to engage several communities of practice somehow invested in the

Hawkesbury Water Recycling Scheme. Oreszczyn and Lane (2006) studied the impacts

of new technologies on farmer communities of practice. The researchers found

knowledge creation and sharing was a strong feature of the communities. Unlike

members of other industries, farmers are not as wary of sharing information because of

the importance of sharing formal and informal knowledge. Farmers are adaptive and are

open to new ideas and technologies to improve their operation. The adoption of new

technologies by some members of the community, but not others, has an affect on the

dynamics of the community of practice.

Uncertainty Reduction Theory.

Berger and Calabrese (1975) originated uncertainty reduction as a theoretical

perspective by building on the work of Heider (1958). The Uncertainty Reduction

Theory states that people seek to reduce the uncomfortable feelings of uncertainty and

unpredictability through communication when faced with the unknown. People

communicate in all contexts, interpersonal, organizational, and mediated, to gather and

share information. There is a human desire to predict outcomes, both positive and

negative, resulting from actions and judgments. People communicate to reduce

uncertainty, but they also communicate in ways that create uncertainty. They may

mislead, distort, and withhold information just as they can create, share, lead, and protect

information (Heath & Bryant, 2000).


47

The model of uncertainty reduction (Figure 2.7.) shows the progression of

uncertainty as a relationship develops between people. Uncertainty is highest during

initial contact with a stranger or person whose behavior is unexpected. The information

exchanged during this entry phase in a relationship is dominantly demographic. Societal

rules and norms of exchange are followed closely when seeking, giving, and getting

information. As uncertainty is reduced, the personal phase in relation is entered.

Information regarding the attitudes, values, and beliefs of the person of interest is gained

through more relaxed communication. Finally, the exit phase in a relation begins as

uncertainty is reduced further. Little information specific to the person of uncertainty is

exchanged, while communication focuses on either future plans or avoidance of

communication, depending on if the relationship is continuing (Heath & Bryant, 2000).

Figure 2.7. Model of Uncertainty Reduction (Heath & Bryant, 2000)

When people enter into a new situation, they are filled with uncertainty about the

people around them. Although people would like to, they cannot reduce their uncertainty

about everyone. Therefore, there are three theoretical predictions of action. If a person

thinks they will continue to have interaction with another person, think the other person is

able to give rewards or punishments, or thinks the person acts in an unusual way, then the

Entry phase in a relation Personal phase in a relation Exit phase in a relation

Information

(Demographic)

Communication

guided by rules and

norms

Information

(Attitude, values

and beliefs)

Communication

more freely and

less rules

Information

(Less to none)

Communication

(Planning future

interaction plans, mostly

avoiding communication)


48

person will be motivated to take actions to reduce uncertainty (Baldwin, Perry, & Moffitt,

2004).

Once driven to reduce uncertainty, people employ one of three basic strategies to

acquire information. The passive strategy uses observation from a distance to better be

able to predict the observed person’s behavior. Informal settings, where the observed is

interacting with others in a casual environment, are commonly the most conducive to

using this strategy. The active strategy requires the person seeking to reduce uncertainty

to purposefully manipulate the environment or seek information about the person about

whom they are uncertain. The final strategy is interactive, meaning the person engages in

direct, face-to-face interaction with the person they are uncertain of to acquire

information (Berger, 1995). Each of these strategies allows a person to better understand

another and their behaviors, developing a relationship (Berger & Calabrese, 1975).

According to Berger (2006), relationship development relies on eight variables as

axioms: (a) verbal communication, (b) nonverbal warmth, (c) information seeking, (d)

self-disclosure, (e) reciprocity, (f) similarity, (g) liking, and (h) shared networks. High

levels of uncertainty will increase a person’s use of verbal communication, nonverbal

warmth, information seeking, and reciprocity to gain knowledge and increase

understanding of another person. Low levels of uncertainty will cause a person to

disclose more about themselves and appreciate the person of interest more. Similarities

and shared networks among two people will cause uncertainty to be reduced (Berger,

2006).


49

Uncertainty causes great personal discomfort. This emotional and cognitive

unease can be remedied with information. Information allows people to gain

understanding and make decisions regarding themselves, their environment, and other

people. Information exchange is argued to be a basic communication paradigm. People

give and take information to reduce their uncertainty, which leads to the creation of

relationships between the sender and the receiver of information (Heath & Bryant, 2000).

When two people talk with one another, display nonverbal warmth, or perceive

similarities in opinions and attributes, uncertainty is reduced. As uncertainty decreases,

communication and nonverbal warmth increase between two people. The higher a

person’s uncertainty of another person’s behavior, the more inquisitive they will be

(Baldwin et al., 2004).

Interpersonal communication can be used to reduce uncertainty felt by a person.

People select communication strategies they believe will most likely allow them to

reduce their feeling of uncomfortable uncertainty. Interpersonal communication is

defined by Heath and Bryant (2000) as interactions during which people develop

relationships through differing communication styles and strategies to reduce uncertainty,

be personally effective, and maximize interaction rewards. What one person says or does

affects another person; this is interpersonal communication (Heath & Bryant, 2000).

When people interact through interpersonal communication, they want to reduce

their uncertainty with information about the subject as well as the person they are

communicating with. Therefore, people will assume they know who the person they are

communicating with is, which is called attribution, or the process of characterizing others


50

and self. This is the basic process of social cognition. The desire to reduce uncertainty

can cause people to falsely characterize the person they are communicating with (Heath

& Bryant, 2000).

When information exchange in organizations becomes complex, turbulent, or

organization members become overwhelmed with information, there is an increase in

uncertainty (Heath & Bryant, 2000). Complexity is measured by the number of factors

that must be considered when processing information to make a decision. Turbulence is

the degree of stability or instability in the environment. When people are overwhelmed

with information, they have a high information load, referring to the degree of difficulty

of obtaining and processing information in efficient and effective ways. When these

three elements are present, organization members are likely to modify messages they

receive (Huber & Daft, 1987).

In short, uncertainty is motivational. It causes people to seek and process

information. Whether information is gathered aggressively or passively, it can increase

or decrease certainty. Heath and Bryant (1992) summated the theory by stating,

“Uncertainty Reduction Theory is a powerful explanation for communication behavior

because it operates in all communication contexts – to help explain why people

communicate the way they do” (p. 207).

The model of uncertainty reduction is applicable to all aspects of organizations

and the systems therein. Uncertainty motivates information seeking behavior to regain

control in the contexts of interpersonal interaction, networks, public relations, marketing,

and advertising (Heath & Bryant, 2000). Fisher (1978) argued that applying Uncertainty


51

Reduction Theory to network research expanded communication studies from exploring

message-exchange channels to the broader concept of relationships.

Relation to Study

TAWC producers operate as an individual entrepreneur and as an agricultural

professional. They may engage with others to gain information and make decisions for

both roles. Interpersonal communications plays a core role in the relations of TAWC

producers. They will engage in information exchange with other producers, inside and

outside of the TAWC project, to meet their needs to assess their opinions and abilities.

The researcher was interested in who the producer exchanges information with and what

opinions or abilities might the producer be in need of comparing.

The TAWC producers also engage in a community of practice once they join the

project. The researcher was interested in describing this community as a whole and the

characteristics of the producers therein. Once in the community of practice, what do

producers do to reduce their uncertainty about the other members of the group or their

own role in the community? The researcher investigated the relationships built through

uncertainty reduction.

Operational Framework

Social Network Analysis

A social network is a set of members that are connected by one or more types of

relations (Wasserman & Faust, 1994). The social world is completely composed of

interconnected social networks. These dynamic networks develop from individuals

interacting with one another (Kadushin, 2012).


52

Everyone has their own personal network, consisting of connections to other

individuals. Members of the network have an influence on the behavior of the focal

individual. Interlocking networks consist of individuals who are all connected to one

another. Members of radical networks do not all interact with one another, making the

network less dense and more open. Therefore, members of the radical network exchange

information with a wider environment and aiding in diffusion of innovations (Rogers,

2003).

Network members, also known as actors, nodes, or points, represent individuals

or organizations. Relations, also known as ties or lines, represent relationships between

network members and connect nodes. These relationships are reciprocal, involving

exchange between two parties. The more similar two nodes are, the more information

will flow back and forth between them (Kadushin, 2012).

When nodes are tied together, they form social structures. The basic unit of

analysis is the dyad, a pair of nodes and their tie. When three nodes are tied together, a

subset called a triad is formed. Building further, subgroups are any subset of nodes and

their ties. Subgroups, also known as clusters or cliques, possess characteristics that are

different from other subgroups located in the social network (Wasserman & Faust, 1994).

The visualization of social network connections is a sociogram. This map gives

insight into the relationships between nodes in a network (Scott, 2013). Sociograms can

be directed or undirected, indicating the direction of the relationship. In addition, value

can be assigned to the tie to indicate the strength of the relationship (Scott, 2013).

Moreno (1934) stated that sociograms allow researchers to identify leaders as well as


53

isolated individuals, discover asymmetry and reciprocity, and map chains of connection

(Scott, 2013).

The larger a network, the more difficult a sociogram will be to create and read.

Data matrices have emerged as the alternative to recording connections. Large data sets

are first prepared as data matrices, which are read and mapped by computer analysis to

create a sociogram (Haythornthwaite, 1996). Adjacency matrices are the most commonly

used and are derived from an incidence matrix. Figure 2.8. shows a directed sociogram

and the affiliated adjacency matrix, where the number one indicates a relationship and the

number zero indicates no relationship.

Figure 2.8. Directed Sociogram and the Affiliated Adjacency Matrix

Social network analysis is the examination of social structures through a set of

methods that specifically explore the relational aspects of these structures (Scott, 2012).

Human relationships are complicated and intertwined, but social network analysis allows

researchers to untangle networks to see a new perspective on relationships across levels

and disciplines (Giuffre, 2013).

The earliest work in social network analysis began in the 1920s and was

conducted by anthropologist, Radcliffe-Brown, who was concerned with social structures

A B C

A 0 1 1

B 0 1 1

C 1 1 0

A

C B


54

(Radcliffe-Brown, 1940, 1957). Social anthropologists built upon his work from the

1930s through the 1970s, developing descriptors for the organization of social networks.

Contemporary social network analysis was established by Harvard University

structuralists and Manchester University anthropologists (Scott, 2013).

Freeman (2004, 2011) has extensively researched and reviewed the history of

social network analysis. Through his research, we come to understand the modern field

of social network analysis emerged in the 1930’s through the work of researchers in

psychology (Lewin & Lippit, 1938; Moreno, 1932, 1934) and business (Warner & Lunt,

1941). All of this work in the field was done independently of one another and no central

approach to structural research was accepted across the social sciences.

Between the 1940s and 1970s, 16 centers of social network research were

established at universities across the country. Each applied social network analysis

differently to a variety of fields from a diverse array of countries. While the work of

these centers certainly developed knowledge and acceptance of social network analysis,

still no accepted paradigm for the structural approach to social science research was

agreed upon (Freeman, 2011).

Then, in the 1970s, Harrison C. White of Harvard and his students established a

17th center of social network research. The broad, generalizable structural approach they

took captured social scientists across social science disciplines around the world (White,

Boorman, & Breiger, 1976). The work of the Harvard group, led by White, led to social

network analysis becoming widely recognized as a field of research (Freeman, 2011).


55

Researchers in the field of physics and biology began to publish works on social

networks in the 1990s, a revolutionary change. An influx of data from the Internet and

genome research, data from very large networks, caused researchers in the two fields to

find a way to analyze these new network data sets. Physicists and biologists joined forces

with computer scientists. They were primarily concerned with cohesive groups or

communities, for which many models have been developed, and the positions occupied

by individuals in a network (Borgatti & Everett, 1999; Everett & Borgatti, 2000). Their

work contributed to the field of social network analysis through refining existing tools

and creating their own while contributing new perspectives and new ways to analyze data

(Freeman, 2011).

Today, social network analysis has emerged as a research methodology and data

analysis technique that increases understanding of the vast and complex relationships

among people. It has gained a significant following in anthropology, biology,

communication studies, economics, geography, information science, organizational

studies, social psychology, and sociolinguistics, and has become a popular topic of

speculation and study (Scott, 2013).

All modern social network analysis follows four defining properties (Freeman,

2004):

1. The belief that relational links between social individuals are important.

2. It is based on the collection and analysis of empirical data.

3. Relies on graphic imagery to visualize patterns of those relations.


56

4. To describe and explain those patterns, it develops mathematical and

computational models.

In addition, Knoke and Yang (2008) and Carolan (2014) outline three key

assumptions of social network analysis:

1. Understanding behaviors, attitudes and beliefs is more reliant on social

relations than fixed attributes.

2. Structural mechanisms of a social system affect behaviors, attitudes and

beliefs.

3. Relationships are dynamic, which required the application of theory and

method to study.

Social scientists have formulated distinct types of data and the appropriate,

coordinating method of analysis given that all social science data involves some process

of interpretation. Figure 2.9. displays these types of data and analysis methods (Scott,

2013).


57

Figure 2.9. Types of Social Network Data and Analysis (Scott, 2013).

Social network analysis has been applied to the agricultural industry, specifically

natural resource conservation and management. Ramieriz-Sanchez (2011) used methods

of social network analysis to identify who to involve and how to improve fisheries

management and conservation. A total of 121 fishers participated in the research,

representing 75% of the households involved in fishing from seven coastal communities

in Baja California Sur, Mexico. The researcher argued that the best strategy to

implement a participatory policy for conservation is to build trusting relationships

between resource users and managers.

Tindall, Harshaw, and Taylor (2011) examined the effects of social network ties

on public satisfaction with forest management in British Columbia, Canada. A

questionnaire was mailed to the residents of three communities in the area. All three

communities were represented in the 572 responses, a response rate of 31.5%. The


58

researchers found social network ties to environmental organization members are

associated with the level of satisfaction the public has with forest management.

To examine the relations between political institutions and policy networks,

Scholz and Wang (2006) investigated the impact of local water policy networks on

political culture regarding the Clean Water Act. For one year, the researchers monitored

the enforcement and compliance of 1,648 major private National Pollutant Discharge

Elimination System permit holders. The empirical study found effective local networks

can enhance enforcement and compliance with regulations, even in conservative areas

prone to undermining such efforts.

Relation to Study

Social network analysis will be the driving force behind data collection and

analysis. This method will allow the researcher to meet the objectives of describing

TAWC project producers and analyzing their interpersonal connections in terms of

attributes, relations, and typology.

Summary

The review of literature established the need for research, through social network

analysis, into how social networks aid in the adoption of innovations. Producers were

discussed as both individual entrepreneurs and as agricultural professionals to better

reflect the reality of their connections, communications, and decision making. The linear

mathematical model and the convergence model of communication were identified as

conceptual frameworks in which individuals in networks exchange information. Social

Exchange Theory and Social Comparison Theory were argued as guides for the


59

interpersonal communications of producers. Also within the conceptual framework,

communities of practice were described and a need to further examine them through

social network analysis was established. Uncertainty reduction was posited as a

theoretical concept, which drives communications in the context of producers as

agricultural professionals. The operational framework outlined social network analysis

and further confirmed the need to examine interpersonal relationships within the purpose

and objectives of this study.

Through the process of collecting and analyzing literature, the researcher

identified a connection between communication networks and social networks. The two

types of networks are discussed separately, but often use similar language. However,

convergence of communication networks and social networks in literature was not found.


60

CHAPTER III

METHODOLOGY

Overview

The methodology for this study follows a quantitative research design in order to

collect data from TAWC producers and the TAWC project director to be analyzed. A

semi-structured questionnaire was administered through interviews with each willing

participant. The interviews were transcribed, cleaned, and analyzed for attribute data,

relational data, and typological ideology data.

The purpose of this research was to describe the TAWC producers and analyze their

interpersonal network in terms of attributes, relationships, and ideology with others as it

relates to sharing farming and water management information.



project, board member status, who imitated their involvement in the project, type




relations


typology


61

Research Design

A quantitative social network analysis research design was executed to reach the

objectives of this study. To begin collecting data for the study of a social network,

researchers must consider what kind of network will be studied. Two dimensions

encapsulate the many different kinds of network data: whole versus ego networks and

one-mode versus two-mode networks (Marin & Wellman, 2011). Whole network

analysis examines the presence or absence of a relationship between all nodes within a

network. Every actor in a network is asked about his or her relationship with every other

actor in the network. Ego network analysis examines the relations individuals maintain

with undefined others (Marin & Wellman, 2011). The focus on the network surrounding

one node, or ego, allows described relations to extend beyond the confines of the

formally defined group of TAWC producers. Therefore, the ego network analysis

approach allows the researcher to describe the TAWC producers and their interpersonal

connections with people both inclusive and exclusive to the project.

The inclusion of a node, or person, in network analysis should be decided upon by

way of the position-based approach, event-based approach, or relation-based approach to

defining the target population (Laumann, Marsden, & Prensky, 1983). The position-

based approach considers those who have a formally defined position or are a member of

an organization. The event-based approach defines a network by who participated in a

population-defining event. The relation-based approach begins with a small set of nodes

within the population of interest and then expands by including those who share a

particular type of relation with the original or previously added nodes (Marin &


62

Wellman, 2011). The researcher chose the position-based approach as the population of

interest has formally defined membership in the TAWC.

To study a network, the relation of interest between nodes must be defined.

Borgatti, Mehra, Brass, and Labianca (2009) established four broad categories of

relations: (1) similarities, (2) social relations, (3) interactions, and (4) flows. Similarities

include shared demographic characteristics, attitudes, locations, or group memberships.

Social relations refer to affective ties, cognitive awareness, and commonly defined role

relations such as friend, coworker, or parent. Interactions are behavior-based ties such as

speaking with or helping another person. Flows are the exchange or transfer of

knowledge, resources, or influence between nodes and through networks (Marin &

Wellman, 2011). For the purpose of this study, the researcher was concerned with the

similarities of the producer in terms of demographic characteristics and attitudes about

water management best practices and technologies. In addition, the study is interested in

the social relations and interactions each producer has with other producers, internal or

external of the TAWC, with regards to information pertaining to farming and water

management.

In order to gain information regarding TAWC producer relations, a semi-

structured interview format was employed. Through this format, also known as the

interview guide approach, a predetermined list of questions guides the interviewer

(Patton, 2002). This ensures the same basic questions are asked at some point during

each interview. The interviewer is free to explore these questions in conversation with

additional probing questions to gain additional information. Data collected benefits from


63

this approach in that it is somewhat systematic and possesses increased

comprehensiveness. Interviews are conversational and tailored to each situation. Given

the semi-structured format, logical gaps in data are anticipated and can be closed. There

are limitations to the interview guide approach. These limitations may include the

unintentional omission of topics. In addition, the flexibility of word choice and question

sequence may result in differing responses from differing perspectives, reducing the

comparability of answers (Patton, 2002).

The researcher-developed, semi-structured, interview questionnaire was based on

the name generator instrument recommended for egocentric networks by Marsden

(2011). This instrument includes questions that elicit a list of people with whom the

respondent is connected. Oftentimes, rosters of eligible connections for each respondent

are not available, so the answers to these questions establish the boundaries for the

studied network. Name generator questions must identify a particular type of

relationship. For this study, the researcher was concerned with the exchange of farming

operation and water management advice and information between TAWC producers and

their connections (Marsden, 2011). Dr. David Doerfert, Texas Tech University Professor

of Agricultural Communications and Graduate Studies Coordinator, reviewed the

instrument for validity. Dr. Doerfert has previous experience with the TAWC and

interacting with TAWC producers.

Population

The population for this study was all of the producers in the TAWC who reported

data for the 2012 season and the TAWC project director. The 18 producers own and


64

operate farmland in Hale or Floyd counties, located in the High Plains region of Texas.

They voluntarily joined the TAWC and enrolled some of their operations acres in the

demonstration project. Annually, the producers voluntarily report operational

information pertaining to those acres, such as crops grown, irrigation types used, yields,

and irrigation water usage to the TAWC leadership. Of the 18 TAWC producers, 15

were interviewed. The other three declined to be interviewed. In addition, a leadership

figure, the project director, within the TAWC was interviewed because the TAWC values

him as an important figure in disseminating information to the TAWC producers.

Data Collection

Texas Tech University’s Human Research Protection Program approved this

research and all corresponding communications with the TAWC producers (Appendix

A). Each TAWC producer was contacted via telephone by the researcher to ask for their

participation by using the approved telephone script (see Appendix B). The personal

interviews were conducted over a three-month period at a time and place of most

convenience to the producer. Oftentimes, the location was the producer’s home, barn, or

local cotton gin. Immediately prior to the start of the interview, producers were informed

that their names would be changed to report the results and they were provided with the

information sheet (see Appendix C) upon request. The semi-structured format instrument

guided each interview. Probing questions were used to elicit further information from

each producer. Each interview lasted between 10 to 45 minutes, depending on the

TAWC producer. The interviews were audio-recorded to be transcribed and analyzed.

Each producer was asked questions regarding demographic information, TAWC project


65

participation and outcomes, their social network, and their view for the future of the

project (see Appendix D). In addition to the interviews, the annual TAWC report is

public information and was used to gather attribute data of the producers for analysis.

Data Analysis

After all of the interviews were conducted, the audio-recordings were saved to a

computer. The audio files were sent to Verbal Ink, a U.S. based transcription service, to

be transcribed in full. From the transcriptions, the names of the relations provided by the

producers were gleaned and recorded into a data matrix in Microsoft Excel for further

analysis. All recorded names were replaced with assigned pseudonyms. The

transcriptions and the data matrix provide the data for variable, network, and typological

analysis.

Variable Analysis

In order to describe the producers in the TAWC, data were extracted from the

2012 TAWC Annual Report and the interviews conducted with the producers. This

information included age, year the producer entered the project, board member status,

who initiated their involvement with the project, how many acres they have in the project,

tillage practices, irrigation methods, planted crops, and if they raise livestock on their

acres in the project.

Network Analysis

The data matrix in Microsoft Excel, created from the producer interviews, was

imported into NodeXL 1.0.1.238 for Microsoft Excel. NodeXL was chosen from an

array of social network analysis software options because it is an inexpensive extension


66

to Microsoft Excel, a commonly used and available program, and it provides a wide

range of network analysis and visualization features. In addition, Hansen, Schneiderman,

and Smith (2011) provide step-by-step directions for using NodeXL for social network

analysis.

In NodeXL, each relationship is referred to as an edge. Vertices are each

individual person, or actor. Once the matrix was imported into the program, ‘show

graph’ was selected to begin the analysis process. The visualization created helped the

researcher to gain a better understanding of the data. Further measurements are found on

the metrics tab of the program (Hansen et al., 2011).

The analysis of an actor’s network follows five principles, defined by

Haythornthwaite (1996), Burt (1992), and Nohria (1992): (1) cohesion, (2) structural

equivalence, (3) prominence, (4) range, and (5) brokerage. Social network analysts use

these principles to study the relational and positional properties of networks (Alba, 1982;

Monge & Eisenberg, 1987). The measurement techniques of these five principles are

rooted in principles of graph theory. Sets of mathematical formulae and concepts for the

study of patterns and lines compose this theory (Alba, 1982; Scott, 2013; Wasserman &

Faust 1994).

Cohesion is the presence of social relationships among actors in a network and

their likelihood of possessing equal access to information (Haythornthwaite, 1996). To

examine cohesion with respect to the objectives of this study, density and centralization

of the network was measured. Density is the ratio of the number of present, recorded

links in a network to the maximum number of potential links in a network. It expresses


67

the general degree of linkage between all members of a network. Actors of a high-

density network are more connected to other actors of the network than are actors of a

low-density network (Haythornthwaite, 1996; Scott, 2013).

Density describes the general degree of cohesion in a network, while

centralization examines the extent to which a network is organized around a central point

(Scott, 2013). To calculate centralization, the indegrees and outdegrees of individual

actors were compared. The focus of the network is determined by defining the actors

with the highest number of degrees, or point centrality, as the center of the graph. This is

also known as nuclear centralization (Scott, 2013; Stokman, Ziegler, & Scott, 1985).

The principle of structural equivalence calls for identifying actors that hold

similar roles with the network. Structurally equivalent actors have “identical ties to and

from all other actors in the network” (Wasserman & Faust, 1994, p. 356). This principle

helps identify actors in important informational roles who shape their surrounding

network. To reach the objectives of this study, the researcher used the Girvan-Newman

algorithm built into NodeXL to conduct group clustering.

Prominence is concerned with which actors in a network have influence or power

over other actors. Certain measures of centrality assess this influence (Haythornthwaite,

1996). NodeXL calculates the Eigenvector centrality for each actor in the network. This

measure describes the importance of a node by factoring the node’s total degree and the

total degree of the nodes to which it is connected (Hansen et al., 2011).

Another measure of prominence is derived from the indegrees and outdegrees of a

node. Indegrees are denoted by dI(ni), outdegrees are denoted by dO(ni), and nodes are


68

shown as ni in the assessment of the data for the typological analysis. The number of

indegrees and outdegrees held by a node in a directed graph classifies that node into one

of four classifications (Figured 3.1):

1. Isolate if dI(ni) = dO(ni) = 0

2. Transmitter if dI(ni) = 0 and dO(ni) > 0

3. Receiver if dI(ni) > 0 and dO(ni) = 0

4. Carrier or ordinary if dI(ni) > 0 and dO(ni) > 0

Figure 3.1. Node Classifications (Wasserman & Faust, 1994)

The classification is based on the possible ways relations can interact with a given

node. An isolate node has no relations to the network. Transmitter nodes only have

relations originating from them. Nodes that only have relations directed at them are

receiver nodes. Carrier and ordinary nodes have relations directed towards and away

from them. The difference between carrier nodes and ordinary nodes is that carrier nodes

have an equal number of indegrees or outdegrees. Ordinary nodes have greater indegrees

than outdegrees or vice versa. Several researchers, including Burt (1976), Marsden

(1989), and Richards (1989) have argued that this typology is useful for describing the

roles and positions of actors within a network (Wasserman & Faust, 1994).

The principle of range refers to an actor’s access to a variety of sources. The

more ties an actor has, the more information they will have access to, and the more

Transmitter Carrier or Ordinary Receiver Isolate


69

diverse the information (Haythornthwaite, 1996). The indegrees and outdegrees of an

actor will determine their range in this research.

The final principle, brokerage, examines relations that serve intermediary roles

within a network. In order to reach the objectives of the study, betweenness centrality

was calculated to measure the extent to which a node lies between various other nodes in

a network. Actors with high betweenness centrality scores play important intermediary

roles in sharing information throughout a network. In addition, they serve as important

brokers or gatekeepers with potential to influence others in the network (Scott, 2013).

Typological Analysis

QDA Miner and WordStat, members of the Provalis Research suite, were chosen

as the software to conduct the typological analysis for this study. These two text analysis

software programs were chosen so as to explore other software options beyond those

typically used in the discipline. Furthermore, these programs allow for more efficient,

coding, cleaning, and analysis due to their automated nature.

The interview transcriptions were uploaded into a new project file on QDAMiner

4.0.13 for cleaning and coding. Each transcription is called a case. Each case belongs to

a producer. Variables were added to describe each case. These included the pseudonym,

age, board member status, year the producer entered the project, who got the producer

involved in the project, number of acres the producer has in the project, type of tillage

used, irrigation system, crops planted, and if the producer raises livestock on their acres

in the project.


70

To clean the transcriptions, all interviewer questions were removed. Then, the

lemmatization process commenced by substituting plurals with their single forms and all

past-tense verbs were replaced with the correlated present-tense versions (Provalis

Research, 2010). All hyphenations were also removed. The transcriptions were coded by

the corresponding interview instrument question. Once the codes were established and

assigned, all responses to questions pertaining to TAWC producer’s past experiences

with the project or recommendations for the future were removed as this analysis is only

concerned with the present state of the network.

In order to determine the sequence of analysis, node classifications recommended

by Wasserman and Faust (1994) were assigned to each producer by assessing their

indegrees and outdegrees. These assignments were made as part of the network analysis,

fulfilling research objective two, conducted in NodeXL. Several researchers, including

Burt (1976), Marsden (1989), and Richards (1989) have argued that this typology is

useful for describing the roles and positions of actors within a network (Wasserman &

Faust, 1994).

TAWC producers classified as ordinary nodes were then identified as the change

agent or an opinion leader. Identification was guided by Rogers’ (2003) definition and

explanation of change agents and opinion leaders within the context of the Diffusion of

Innovations framework. Rogers and Kincaid (1981) further describe opinion leadership

in the context of networks. Strongly considering knowledge and understanding of change

agents and opinion leaders, the researcher conducted a visual assessment of each cluster,


71

produced through network analysis, to identify the TAWC producers fulfilling the

aforementioned roles.

A word frequency and phrase frequency analysis was conducted, using WordStat

6.1.13 software, on the transcripts of the producers who were classified as either the

change agent or an opinion leader. In the past, researchers have used language analyses,

such as these, to better understand connections between people (Huffaker, 2010;

McArthur & Bruza, 2003). The top words and phrases are selected by the program and

displayed with a count. All words and phrases appear in order of number of occurrences.

For this analysis, words must have been stated at least five times, phrases at least three

times. The researcher did not predetermine words of importance. The results reflect the

nature of the TAWC producer or project director’s responses to questions. The same

analysis was done on the TAWC producers who were connected to the opinion leaders in

order to fulfill the objectives of the research. From this analysis, the researcher seeks to

learn the common themes discussed by the TAWC producers and the project director.


72

CHAPTER IV

RESULTS

Overview

The results of this research are organized by the attribute, network, and

typological analyses conducted to reach the objectives of this study. The order of

analyses is important as each analysis helped the researcher better formulate and

understand the results of the next analyses.

The purpose of this research was to describe the TAWC producers and analyze

their interpersonal network in terms of attributes, ideations, and relationships with others

as it relates to sharing farming and water management information.



project, board member status, who initiated their involvement in the project,

type of irrigation used on their acres in the project, crops grown on their acres

in the project, and if livestock are raised on their acres in the project

2. Describe the interpersonal connections of the producers in the TAWC in terms

of relations

3. Describe the interpersonal connections of the producers in the TAWC in terms

of typology

Research Objective One

Research objective one sought to describe TAWC producers in terms of age,

years in the project, acres in the project, board member status, who initiated their


73

involvement in the project, type of irrigation used on their acres in the project, crops

grown on their acres in the project, and if livestock are raised on their acres in the project.

The transcripts from 15 TAWC producer interviews and information voluntarily

shared and collected by the annual TAWC report (Texas Alliance for Water

Conservation, 2013b) were used to acquire the attribute data necessary to reach objective

one. Therefore, most attribute data was able to be collected for all TAWC producers,

while other data, including age and project involvement initiation, could not be collected

for all TAWC producers as three declined to be interviewed.

TAWC producers (n = 15) ranged in age by 36 years, from a minimum of 35 to a

maximum of 71; the mean was 53 (SD = 10.04). The ages of three TAWC producers

were not reported because they were not interviewed. Most TAWC producers (n = 14,

77%) have been involved since the establishment of the project I 2005, compared to

newer members of the TAWC who have been involved with the project between two and

four years (n = 4, 22%). Some TAWC producers are members of the TAWC

Demonstration Producer Board (n = 7, 39%). The TAWC Producer Board consists of 10

members, seven of which were 2012 TAWC producers. When asked who got the

producer involved with the TAWC, 58% (n = 11) reported TAWC project director, Rick

Kellison. Other names reported were Jeff Pate, Extension Economist – Risk

Management with the Texas A&M AgriLife Extension Service, (n = 2, 11%), Glenn

Schur, a TAWC Demonstration Producer Board member, (n = 1, 5%), and informational

meetings (n = 1, 5%).


74

The range of acres a TAWC producer had in the project was 383 acres, with a

minimum of 70 and a maximum of 453; the mean was 197.33 (SD = 109.66). A majority

of TAWC producers (n = 15, 80%) do not raise livestock on their acres in the TAWC.

Three TAWC producers (18%) use their acres in the project for raising livestock.

Producers annually report the types of irrigation they use on their acres in the

TAWC. They can report more than one type of irrigation being used on some or all of

their acres in the project. More than half of the TAWC producers strictly use pivot

irrigation (n = 10, 55%). TAWC producers also use a combination of pivot and

subsurface drip irrigation (n = 5, 28%). Individual producers have also chosen to use

dryland or no irrigation (n = 1, 5%), furrow irrigation (n = 1, 5%), or dryland, pivot, or

subsurface drip irrigation (n = 1, 5%).

In 2012, producers planted a variety of crops on their acres in the TAWC project.

Crops grown were various grasses, corn, cotton, oats, sideoats, sorghum, sunflowers, and

wheat. One producer’s acres were left to fallow. The most common crop to plant on

TAWC acres was cotton, as 12 of the 18 respondents reported (67%). The majority of

producers (n = 10, 56%) grew two or more crops. Some producers chose to grow just one

crop (n = 8, 45%). Table 4.1 summarizes the attribute data gathered for each TAWC

producer.


75

Table 4.1.

Summary of TAWC Demonstration Project Producers

Pseudo Name Age Years in

Project Involvement Initiate

Board

Member

Acres in

Project Livestock Irrigation 2012 Crop

Amancio 52 8 Rick Kellison Yes 123 No PIV Corn, wheat

Bernard 52 8 Rick Kellison Yes 225 Yes PIV, SDI Bermuda grass, corn

Channing - 4 - No 122 No PIV Cotton

David - 8 - No 284 No DRY Fallow

Dean 43 8 Rick Kellison Yes 120 No PIV Cotton

George 51 8 Rick Kellison Yes 411 Yes PIV, SDI Cotton, corn, dahl

Joshua 59 3 Rick Kellison No 192 No PIV Cotton, corn

Karl 70 8

Informational

meetings No 192 No PIV, SDI Sideoats

Kevin 51 4 Jeff Pate No 238 Yes PIV Cotton, grass mix

Larry 43 8 Rick Kellison No 93 No FUR Sorghum

Lee 54 8 Rick Kellison Yes 145 No PIV Cotton

Michael 58 8 Glenn Schur No 149 No PIV Cotton

Raymond 71 8 Rick Kellison Yes 123 No PIV Oats, cotton, sorghum

Sergey 42 8 Rick Kellison Yes 453 No

PIV, SDI,

DRY

Cotton, sorghum,

sideoats

Sheldon 60 8 Rick Kellison No 124 No PIV Wheat, cotton

Stefan 35 2 Jeff Pate No 70 No PIV Corn

Thomas 59 8 Rick Kellison No 341 No PIV, SDI Corn, cotton, wheat

Warren - 8 - No 147 No SDI, PIV Cotton, sunflowers

Note. All names are pseudonyms. All participants are Caucasian males, PIV = pivot irrigation SDI = subsurface drip irrigation FUR = furrow

irrigation DRY = dryland, no irrigation. Channing, David, and Warren declined to be interviewed. (-) indicates no data was collected.


76

Research Objective Two

Research objective two sought to describe the interpersonal connections of the

producers in the TAWC in terms of relations. One TAWC project director and 15

TAWC producer interview transcripts were used to acquire the network data necessary to

address objective two. While three TAWC producers were not interviewed, other

producers who were interviewed may have reported a connection of receiving

information from them or transmitting information to them. Therefore, they will be

included in these results. The TAWC project director is included in the results of the

network analysis because the TAWC believes he plays a key role in disseminating

information to TAWC producers. This analysis determines the extent of this role. The

number of respondents for the purpose of this analysis is 19, including all TAWC

producers and one project leader. Respondents answered two questions to define the

boundaries of the network:

1. Who do you go to for information or advice related to your farm operation?

Could you please share the names of at least three people? We will not use

their real names in our final report.

2. Who are the people that come to you for information or advice about farming?

Could you please share the names of at least three people? We will not use

their real names in our final report.

The network analysis, conducted using NodeXL for Microsoft Excel, described

the TAWC producers’ interpersonal relations in terms of five principles (1) cohesion, (2)


77

structural equivalence, (3) prominence, (4) range, and (5) brokerage (Haythornthwaite,

1996; Burt, 1992; and Nohria, 1992).

Cohesion

Cohesion is the likelihood of present social relationships among actors in a

network possessing equal access to information (Haythornthwaite, 1996). To assess the

cohesion of the population of TAWC producers, density and nuclear centralization were

measured. The graph density of the network was .035. This is a low value, meaning the

network is not highly interconnected, which is to be expected for an ego-centric network

analysis (Scott, 2013). The nuclear centralization of the network is derived from the

degrees, or unique relationships, of each actor. Degrees are denoted by ‘d’, not to be

confused with Cohen’s measure of sample effect size (Wasserman & Faust, 1994;

American Psychological Association, 2010). The range of nuclear centralization (n = 19)

was 15 degrees, with a minimum of 0 to a maximum of 15. The mean nuclear

centralization measure was 6.11 degrees (SD = 3.45). The measure of nuclear

centralization identifies Andrew (d = 15), Joshua (d = 11), Michael (d = 8), and Stefan (d

= 8) as the focus nodes of the network. The degrees for each actor are reported in Table

4. 2. The whole network, directed sociogram in Figure 4.1. further supports this finding.

TAWC producers are shown in blue, crop consultants in green, the project leader in

purple, and all other actors (connections to TAWC producers) are shown in red.


78

Table 4.2.

Individual Network Measures for TAWC Producers

Pseudo Name Eigenvector centrality Betweenness

Centrality Indegrees Outdegrees Node Classification

Amancio 0.043 96.000 3.000 4.000 Ordinary

Andrew 0.117 746.757 13.000 2.000 Ordinary

Bernard 0.034 353.667 2.000 3.000 Ordinary

Channing 0.000 0.000 0.000 0.000 Isolate

David 0.008 0.000 1.000 0.000 Receiver

Dean 0.044 55.424 3.000 4.000 Ordinary

George 0.055 133.733 2.000 5.000 Ordinary

Joshua 0.070 377.076 4.000 7.000 Ordinary

Karl 0.036 0.000 2.000 1.000 Ordinary

Kevin 0.001 222.000 3.000 4.000 Ordinary

Larry 0.044 266.310 3.000 4.000 Ordinary

Lee 0.035 216.467 2.000 3.000 Ordinary

Michael 0.023 240.000 3.000 5.000 Ordinary

Raymond 0.072 165.910 1.000 6.000 Ordinary

Sergey 0.063 170.495 5.000 2.000 Ordinary

Sheldon 0.037 113.914 1.000 3.000 Ordinary

Stefan 0.000 56.000 6.000 2.000 Ordinary

Thomas 0.022 14.333 2.000 3.000 Ordinary

Warren 0.033 0.000 1.000 1.000 Carrier


79

Figure 4.1. Sociogram of TAWC Producer Network


80

Structural Equivalence

The principle of structural equivalence calls for actors that hold similar roles

within the network to be identified. The Girvan-Newman algorithm, built into NodeXL,

was used to conduct group clustering, fulfilling this principle. The algorithm is designed

for smaller graphs (Hansen et al., 2011). Seven groups were clustered by the algorithm

and are reported in the order listed by the function in NodeXL. The order has no

meaning. Each person, or node, the in the TAWC producer network can only be a part of

one cluster. Figure 4.2. displays the largest group (Cluster One) identified in the context

of the whole network. The cluster includes Andrew, Bernard, Dean, Joshua, Karl, Kyle,

Patrick, Phil, Raymond, Sergey, Theo, and Tye.


81

Figure 4.2. Cluster One within TAWC producer network

Figure 4.2. Cluster One of TAWC Producer Network


82

Cluster Two is shown in Figure 4.3. This cluster includes Dmitry, Ernesto, Hans, Jack, Marcel, Peter, Roman, Simon, and

Stefan. These actors are not connected to the rest of the TAWC producer network.

Figure 4.3. Cluster Two of TAWC Producer Network


83

The sociogram of Cluster Three is prominently shown in Figure 4.4. Included in this cluster are Alejandro, Barry, Gerald,

Henry, James, Lee, Luis, and Michael.

Figure 4.3. Cluster Two within TAWC producer network

Figure 4.4. Cluster Three of TAWC Producer Network


84

Cluster Four includes the actors David, Donald, Jacob, Larry, Ricardo, Sheldon, and Trevor. The cluster is highlighted in

Figure 4.5.

Figure 4.5. Cluster Four of TAWC Producer Network


85

George, John, Leonardo, Samuel, Thomas, and Warren are members of Cluster Five. The connections within this cluster, with

relation to the rest of the network, are displayed in Figure 4.6.

Figure 4.6. Cluster Five of TAWC Producer Network


86

Cluster Six is connected to the whole TAWC producer network by just one connection, Ray. As shown in Figure 4.7., this

cluster includes Giorgio, Kevin, Ray, Robert, and Ronald.

Figure 4.7. Cluster Six of TAWC Producer Network


87

The last cluster (Seven) identified by using the Girvan-Newman algorithm in NodeXL, includes two actors. Amancio and

Harold are members of Cluster Seven, shown in Figure 4.8.

Figure 4.8. Cluster Seven of TAWC Producer Network


88

Prominence

To find the actors in a network who have influence or power over other actors,

satisfying the principle of prominence, Eigenvector centrality (CE) was measured and

node classification, according to the system established by Wasserman and Faust (1994),

was conducted. TAWC producers and the project leader (n = 18) range in Eigenvector

centrality by 0.117, with a minimum of 0 to a maximum of 0.117; mean value was 0.041

(SD = 0.028). No relationships, indegrees or outdegrees, were reported for one producer,

so their Eigenvector centrality was not calculated. Andrew (CE=0.117), Raymond (CE =

0.072), Joshua (CE = 0.070), and Sergey (CE = 0.063) are the most important, or

prominent, actors in the network based on individual Eigenvector centrality values.

Table 4.2. displays the node classifications of the TAWC producers. The TAWC

producers are dominantly classified as ordinary nodes (n = 16, 84%). One producer,

Channing, is classified as an isolate. David is the only producer classified as a receiver.

The only producer classified as a carrier is Warren. The most prominent ordinaries,

based on each actor’s total degrees, are Andrew (d = 15), Joshua (d = 11), Michael (d =

8), and Stefan (d = 8).

Range

An actor’s access to a variety of sources, or other actors, determines his range.

The indegrees and outdegrees of each actor are displayed in Table 4.2. These measures

satisfy the principle of range. The range of degrees for TAWC producers (n = 19) was

15, with a minimum of 0 to a maximum of 15. The mean number of degrees was 6.11


89

(SD = 3.45). Andrew (d = 15), Joshua (d = 11), Michael (d = 8), and Stefan (d = 8) have

the most overall range in the network.

The maximum number of indegrees of the TAWC producers (n = 19) was 13.

The minimum number of indegrees was 0, establishing a range of 13 degrees with a mean

of 3 (SD = 2.83). The actors with the most indegrees, or other actors using them as a

resource, are Andrew (dI = 13), Stefan (dI = 6), Sergey (dI = 5), and Joshua (dI = 4).

The number of outdegrees reported by TAWC producers (n = 19) ranges by 7,

from a maximum of 7 to a minimum of 0. The mean is 3.11 outdegrees (SD = 1.92). The

actors with the most outdegrees, or who used the greatest number of other actors as

resources, are Joshua (dO = 7), Raymond (dO = 6), Michael (dO = 5), and George (dO = 5).

Brokerage

The principle of brokerage seeks to identify relations that serve intermediary roles

within a network. In order to establish brokerage, betweenness centrality (CB) was

calculated for each actor. The results are displayed in Table 4.2. The range of

betweenness centrality for TAWC producers (n = 15) was 732.43, with a minimum of

14.33 to a maximum of 746.76. The mean betweenness centrality was 215.21 (SD =

180.86). Betweenness centrality was not calculated for four producers because the

relationships reported did not put them in any intermediary roles. The actors who play

the greatest intermediary roles in the network are Andrew (CB = 746.76), Joshua (CB =

377.08), and Bernard (CB = 353.67).


90

Research Objective Three

Research objective three sought to describe the interpersonal connections of the

TAWC producers in terms of typology. One TAWC project director and fifteen TAWC

producer interview transcripts were cleaned and analyzed to acquire the typological data

necessary to reach objective three. Three TAWC producers declined to be interviewed.

Andrew, the project director, is held in an important role in the TAWC to disseminate

information to the TAWC producers, therefore he is included in this analysis.

To begin the typological analysis, TAWC producers, previously classified as

ordinary nodes, were identified as a change agent or opinion leader based on the

explanations of Rogers (2003) and Rogers and Kincaid (1981) as well as a visual

assessment by the researcher of the seven cluster sociograms created in pursuit of

research objective two. The change agent is Andrew, the TAWC project director. All of

the opinion leaders are TAWC producers.

Table 4.3 displays the word and phrase frequencies of the Cluster One change

agent, Andrew, opinion leader, Sergey, and five TAWC producers connected to Sergey.

Those producers are Bernard, Dean, Joshua, Karl, and Raymond. The theme of water

carries through from the change agent to the opinion leader and on to the TAWC

producer connections in words and phrases. While not all parties specifically use the

word “water”, the opinion leader mentions “drip” which refers to a type of irrigation.

The TAWC producer connections use water, LEPA, pivot, and probe frequently. In

addition, the change agent frequently states the phrase “amount of water”. The TAWC

producer connections use “drip system,” “gypsum blocks,” and “water usage” frequently.


91

Therefore, the three parties expressed that water usage, management, and knowledge is

important. WordStat did not detect any phrases used frequently by the opinion leader.

The change agent uses the word “good” which could be his feeling toward the

‘project’, another frequently used word. This theme does not carry through the cluster.

This suggests that the producers and their water concerns are not as connected to the

project as the change agent. TAWC producer connections also frequently mention

commodity crops, including corn, cotton, and grass seed.

Table 4.3

Word Frequency and Phrase Frequency of Cluster One

Change Agent Opinion Leader TAWC Producer Connections

Word Phrase Word Phrase Word Phrase

Good Amount of water Asked - Call Crop consultant

Lot Bailey County Drip Corn Drip system

Producer Jeff Dunn Pretty Cotton Dry land

Project Management

team

Question

s

Crop Economic impact

Water Talk Field Farming

techniquesGood Food corn

Guess Grass seed

Half Grow grass

Learned Gypsum blocks

LEPA Have learned

Lot John Deere

Pivot Start farming

Probe Water usage

Project Year ago

Start

Usage

Water

Year


92

Stefan is the opinion leader of Cluster Two. He is not connected to any other

TAWC producers. The results of the word frequency and phrase frequency analysis of

Stefan’s interview transcript is shown in Table 4.4. Stefan frequently used the word

“people” and the phrase “lots of people”, which is reflective of his opinion leadership

role in Cluster Two. He commonly refers to acres and percentages; perhaps signally a

strong belief in maximizing the usage of land. He is also concerned with water,

frequently using the words “pivot” and “water”.

Table 4.4

Word Frequency and Phrase Frequency of Cluster Two

Opinion Leader TAWC Producer Connections

Word Phrase Word Phrase

Acre Grow corn - -

Corn Half gallons

People Lot of people

Percent Percent more money

Pivot Subpar acre

Water

Michael is the opinion leader in Cluster Three. One TAWC producer is

connected to Michael – Lee. Table 4.5 presents the most frequently used words and

phrases by Michael and Lee. Water arises in Cluster Three as an important theme as both

the opinion leader and TAWC producer connection use the word frequently. Michael

also uses the phrases “start rain” and “water management” frequently. In addition, he

uses “crop consultant,” perhaps reflecting the importance of one to his operation.


93

Table 4.5

Word Frequency and Phrase Frequency of Cluster Three

Opinion Leader TAWC Producer Connection


Buy Crop consultant Feel Real good

Call Start rain Kind

Farmers Water management Water

Guy

Information

Lot

People

Talk

Water

Year

Cluster Four includes Larry, the opinion leader, and the TAWC producers

connected to Larry – Sheldon and David. Table 4.6 exhibits the outcomes of the word

and phrase frequency analyses conducted for Larry and Sheldon. These analyses were

not executed for David because, although he is a TAWC producer, he was not

interviewed. The word frequency analysis identified water as an important theme to the

opinion leader and the TAWC producer connection. The opinion leader frequently uses

the phrases “pay attention” and “really been interested.” These phrases might suggest

Larry is producer who is interested in and curious about new farming and water

management practices and technologies. His frequent use of the word “SmartCrop”

further supports this notion as the word refers to a water management technology. The

TAWC producer connection, Sheldon, emphasizes his use of a crop consultant as


94

evidenced through the phrase frequency analysis which identifying frequency use of

“crop consultant”.

Table 4.6

Word Frequency and Phrase Frequency of Cluster Four



Putting Drill farm Water Crop Consultant

SmartCrop I’ve add

Water Pay attention

Year Really been interested

Table 4.7 shows the most frequently used words and phrases of the Cluster Five

opinion leader, George. The table also shows the same elements for Thomas, a TAWC

producer connected to George. TAWC producer, Warren, also shares a relationship with

George, but he was not interviewed. The opinion leader and TAWC producer share

“year” as a frequently used word. This is the measure of time that producers use most

often when making comparisons internal and external of their operation. The opinion

leader refers to the “project” frequently, while the TAWC producer is more concerned

with his own operation, using “farm” frequently. The TAWC producer focuses on water,

frequently stating “water” and “inches a year”.


95

Table 4.7

Word Frequency and Phrase Frequency of Cluster Five



Good Main reason Farm Inches a year

Kind Lot Size fits

Project Water

Time Year

Year

Cluster Six follows the opinion leadership of Kevin. None of his connections are

TAWC producers. The transcript of Kevin’s interview was subjected to word and phrase

frequency analyses. The results are reported in Table 4.8. The frequent use of “guess”

by the opinion leader suggests uncertainty in “crop,” “planting,” and “year.” This could

explain why he hires a “crop consultant,” whom is his only connection to the rest of the

TAWC producer network.

Table 4.8

Word Frequency and Phrase Frequency of Cluster Six



Crop Crop consultant - -

Guess Soil fertile

Planting

Talk

Year

Amancio was identified as the opinion leader of Cluster Seven. No TAWC

producers are connected to Amancio in this cluster. The results of the word and phrase

frequency analyses conducted on Amancio’s interview transcript using WordStat are


96

reported in Table 4.9. Amancio frequently uses the phrase “bad reputation”, which could

the negative perspective some producers internal and external of the TAWC have on the

project, as some of the TAWC producers expressed in their interviews. The opinion

leader also frequently refers to words and phrases associated with water and acreage

themes.

Table 4.9

Word Frequency and Phrase Frequency of Cluster Seven



Acre Acre that we plant - -

Compared Answer that question

Smaller Bad reputation

Water Crop consultant

Table 4.10 compares the most frequently used words by the change agent,

Andrew, and all seven opinion leaders, Sergey (Cluster One), Stefan (Cluster Two),

Michael (Cluster Three), Larry (Cluster Four), George (Cluster Five), Kevin (Cluster

Six), and Amancio (Cluster Seven). People are a common theme through the change

agent and some of the opinion leaders. The change agent refers to “producers,” while

Cluster Two and Cluster Three opinion leaders frequently mention “people”.

Furthermore, the opinion leader of Cluster Three often states “farmer.” The theme of

water is found throughout the group. Frequently used words in this theme are “water,”

“drip,” and “pivot.” Production is also a common theme as opinion leaders frequently

use the words “acre,” “corn,” “crop,” “planting,” and “year.” Overall, the frequently


97

used words of the change agent do not permeate throughout the entire group of opinion

leaders.


98

Table 4.10

Word Frequency of Change Agent and Cluster Opinion Leaders

Change Agent Opinion Leaders

Cluster One Cluster Two Cluster Three Cluster Four Cluster Five Cluster Six Cluster Seven

Good Asked Acre Buy Putting Good Crop Acre

Lot Drip Corn Call SmartCrop Kind Guess Compared

Producer Pretty People Farmers Water Project Planting Smaller

Project Questions Percent Guy Year Time Talk Water

Water Talk Pivot Information Year Year

Water Lot

People

Talk

Water

Year


99

Table 4.11 compares the most frequently used phrases by the change agent,

Andrew, and all seven opinion leaders, Sergey (Cluster One), Stefan (Cluster Two),

Michael (Cluster Three), Larry (Cluster Four), George (Cluster Five), Kevin (Cluster

Six), and Amancio (Cluster Seven). The analysis did not detect any phrases frequently

used by Sergey. The phrase frequency analysis demonstrates the diversity of the TAWC

producers. Each has their own take on farming, water management and the TAWC. The

lack of common themes demonstrates a need for more cohesiveness and communication

within the TAWC amongst the change agent and the opinion leaders.


100

Table 4.11

Phrase Frequency of Change Agent and Cluster Opinion Leaders

Change Agent Opinion Leaders

Cluster One Cluster Two Cluster Three Cluster Four Cluster Five Cluster Six Cluster Seven

Amount of

water

- Grow corn Crop

consultant

Drill farm Main reason Soil fertile Acre that we

plant

Bailey County Half gallons Start rain I’ve add Crop

consultant

Answer that

question

Jeff Dunn Lot of people Water

management

Pay attention Bad reputation

Management

team

Percent more

money

Really been

interested

Crop

consultant

Subpar acre


101

CHAPTER V

IMPLICATIONS

Overview

The implications of this study are first discussed by concluding the findings of

each objective. The discussion of the findings with regards to the literature review

follows. Recommendations for future application and research for practitioners and

researchers is presented in order to continue to grow the body of knowledge pertaining to

social network analysis and the social network of the TAWC.

The purpose of this research was to describe the TAWC producers and analyze

their interpersonal network in terms of attributes, ideations, and relationships with others

as it relates to sharing farming and water management information.



project, board member status, who initiated their involvement in the project, type




relations


typology


102

Conclusions

Research Objective One

The variable analysis fulfilled research objective one by describing 18 TAWC

producers in terms of age, years in the project, acres in the project, board member status,

who initiated their involvement in the project, type of irrigation used on their acres in the

project, crops grown on their acres in the project, and if livestock are raised on their acres

in the project.

The 36-year range in ages of the TAWC producers represented beginning and

experienced producers. No matter their age, the producers operations and involvement

with the TAWC are similar. Of the 15 who reported involvement information, 12

reported being involved with the project since establishment in 2005. The other four

producers had between two and four years of experience as members of the TAWC.

Therefore, the producers who participated in the study were, overall, highly qualified to

provide informed feedback about the TAWC. Rick Kellison, director of the TAWC, is

the primary person responsible for initiating the original producers into the demonstration

project. Producers who joined the TAWC later, since 2009, were more likely to report

Jeff Pate, of Texas A&M AgriLife Extension, as the person responsible for getting them

involved.

Seven of the ten TAWC Demonstration Producer Board members were 2012

TAWC producers and were interviewed for this study. Those eight producers were

involved with the TAWC since 2005, when the demonstration project began. The

operations of these producers are diverse in acres involved in the project, irrigation


103

techniques, and crops planted in 2012. Therefore, TAWC Demonstration Producer Board

members can relate to other producers within the TAWC.

The amount of acres a producer has involved in the TAWC depends largely on the

types of farmland the producer owns and what land a demonstration project leader asked

them to enroll in the project, according to the TAWC producer interviews. There appears

to be no correlations between the number of acres a producer has in the TAWC and any

other reported attribute data.

Most producers did not use their acres in the TAWC to raise livestock in 2012.

The three producers that did, Bernard, George, and Kevin, also used their acres to grow

more than one crop. This finding suggests that these three producers put even greater

value on diversifying their operations than their peers.

TAWC producers strongly favor pivot irrigation over subsurface and furrow

irrigation or dryland. If a producer uses subsurface irrigation on some of their acres in

the demonstration project, they use pivot irrigation on their other demonstration project

acres.

The TAWC producers represented several different types of agricultural

production. They produced Bermuda grass, corn, cotton, Dahl grass, oats, sorghum,

sunflowers, and wheat. Most producers grow more than one type of crop on their acres in

the TAWC, usually favoring cotton. This finding further supports the conclusion that

TAWC producers are diverse in experience and practice.


104

Research Objective Two

The interpersonal connections of the 18 TAWC producers and project leader were

described in terms of relations by the network analysis, satisfying research objective two.

To define the boundaries of the TAWC producer network, respondents answered two

questions:


Could you please share the names of at least three people? We will not use their

real names in our final report.




Five principles of network analysis were used to describe the interpersonal

relations of the TAWC producers. Those principles were: (1) cohesion, (2) structural

equivalence, (3) prominence, (4) range, and (5) brokerage (Haythornthwaite, 1996; Burt,

1992; and Nohria, 1992).

Cohesion.

Graph density and centralization were measured to assess the principle of

cohesion in the network. The graph density of a network ranges from 0 to 1, describing

the overall connection of all of the points in a network. All of the nodes within a network

with a graph density of 1, or a complete graph, are directly connected to all other nodes in

the network. This is very rare. As more nodes are connected to one another in a

network, the higher the graph density will be (Scott, 2013).


105

The graph density of the TAWC producer network was .035. Therefore, this

network is low-density, meaning the nodes are not well connected. This is common for a

directed graph in an ego-centric network in which relationships are not assumed to be

reciprocated and each individual was not questioned about their relationship with every

other individual in the network (Scott, 2013). Figure 4.1 visually displays the density of

the network, where many isolates are shown, driving down the graph density.

Centralization builds on density. Where density describes the overall cohesion

of the network, centralization describes the focal points of the cohesion. Nuclear

centralization, derived from the indegrees and outdegrees of each individual, was

measured to examine the centralized structure of the network (Scott, 2013).

The range of nuclear centralization for the TAWC producers was 15. Producers

had a minimum of zero connections to the network to a maximum of 15 connections

within the network. The average TAWC producer had 6.11 connections within the

network. The questions asked in the interview could have limited the number of

connections producers reported because respondents were asked to name three people

who the go to for information and three people who come to them for information. The

measure of nuclear centralization identified Andrew, Joshua, Michael, and Stefan as the

focal nodes of the network. With these nodes identified, the structure of the relations

between these points and all others in the network can be compared using Figure 4.1.

Structural equivalence.

The Girvan-Newman algorithm was used to conduct group clustering for

establishing structural equivalence. The seven clusters produced each included actors that


106

held similar roles within the network. Cluster One includes 12 individuals, seven of

whom are TAWC producers. The other individuals are crop consultants or undefined

connections outside of the TAWC. This is the largest of the clusters. Figure 4.2. displays

Cluster One so it can be seen that these producers are the most interconnected of the

network and share similar connections. If Andrew, the focal node, would ever leave the

TAWC producer network, one of the other nodes in this network could be a replacement

as they share similar connections with Andrew.

Cluster Two includes just one TAWC producer, Stefan. This cluster is

completely removed from the rest of the TAWC producer network. Figure 4.3. clearly

displays Stefan as the focal node of this cluster. While Stefan would be a good resource

for sharing water management knowledge gained through the TAWC with those outside

the project, his disconnection from the rest of the producers could skew the information

being shared. The TAWC needs to reconnect Stefan with the network.

Two TAWC producers, Michael and Lee, are included in Cluster Three, along

with two crop consultants, Alejandro and Luis, as well as three other undefined

connections. Michael is the focal node in this cluster. Alejandro and Luis play important

roles in the TAWC producer network. Crop consultants are often sought out for advice,

even beyond the scope of their formal job description, as explained during the TAWC

producer interviews.

Sheldon and Larry are the two TAWC producers in Cluster Four. Both producers

have been in the project since 2005 and have differing operations. Yet, Sheldon goes to

Larry for information and advice. Other connections outside the TAWC come to Larry


107

for information and advice as well. Larry has potential to be a key resource inside and

outside of the demonstration project for water management information.

Cluster Five includes three TAWC producers, George, Thomas, and Warren.

Samuel, a crop consultant, is also included. This cluster includes two other connections

outside of the TAWC. Based on their connections, these producers share the role of

information seekers. Fewer people come to them for information than they ask for

information or advice.

Ray, a crop consultant, is the only connection Cluster Six has to the rest of the

TAWC producer network. Kevin, a TAWC producer, extends the network to three other

connections outside of the project. Kevin’s disconnection from other TAWC producers

in the network could be a consequence of joining the project in 2008, whereas many of

the other producers joined in 2005. To ensure he is receiving and sharing the same water

management information as the rest of the TAWC producers, he needs to find a TAWC

producer to connect with for knowledge.

The smallest of the clusters generated by the Girvan-Newman algorithm in

NodeXL is Cluster Seven. This cluster includes one TAWC producer, Amancio, and one

connection outside of the TAWC, Harold. Amancio has connections that extend to and

from Cluster One and Cluster Five.

Prominence.

Values of Eigenvector centrality were paired with node classifications

(Wasserman & Faust, 1994) to distinguish the actors within the TAWC network who had


108

influence or power over other actors. These measures satisfy the network analysis

principle of prominence.

Eigenvector centrality gives value to nodes whose direct connections are highly

connected to other nodes (Hansen et al., 2011). The measure of Eigenvector centrality

identified Andrew, Raymond, Joshua, and Sergey as individuals with great influence over

the network, given their strategic connections with other highly connected members of

the TAWC producer network. Through these four TAWC producers, the most other

nodes, or actors, can be reached.

Most of the TAWC producers were classified as ordinary nodes by the system

developed by Wasserman and Faust (1994). This indicates that TAWC producers receive

and share information and advice, but with an unequal number of other people. They

desire to learn from others and are also willing to share what they know. The three

TAWC producers who were not interviewed for the study (Channing, David, and

Warren) were classified as an isolate, receiver, or carrier, respectively. These node

classifications are limited to the connections reported by other TAWC producers. If

Channing, David, and Warren were interviewed, they would likely report more relations

than collected and available for analysis in this study. These connections would give the

researcher a more complete picture of the connections interior and exterior of the TAWC.

Range.

Those who seek information from a TAWC producer, indegrees, and those whom

a TAWC producer goes to for information, outdegrees, together determine an


109

individual’s range. The access a TAWC producer has to a variety of sources, or other

actors within the network, is a network analysis principle.

As discussed under the principle of cohesion, TAWC producers have a maximum

number of 15 degrees, or sources. The producer sought most often for information has

the maximum number of indegrees, 13. The producer seeking the most information from

others has the maximum number of outdegrees, 7. The average TAWC producer has six

total sources, three of which are resources, and the other three are individuals who come

to them for information. These values reflect the nature of the questions asked in the

interviews with TAWC producers.

Andrew has the most sources, overall, with 15 degrees. He is the most sought

after individual in network, given his reported 13 indegrees. Stefan, Sergey, and Joshua

follow him as a resource to others. Joshua seeks the greatest number of other actors as

sources, as he reported 7 outdegrees. Raymond, Michael, and George follow him as

curious learners who want to gain as much knowledge as they can from other sources.

By seeking information and advice from a larger number of resources, TAWC producers

have greater access to diverse knowledge and experience.

Brokerage.

Measuring betweenness centrality fulfilled the final principle of brokerage. Based

on their individual betweenness centrality values, Andrew, Joshua, and Bernard have the

most pronounced intermediary roles in the TAWC producer network. These three

producers bridge or broker otherwise unconnected actors. Removing these three


110

individuals would highly disrupt the relationships between the other people in the

network.

Summary

Based on the network analysis, Andrew and Joshua are the most important actors

in the TAWC producer network. They partially centralizes the network, share similar

connections with many other producers, are highly connected beyond their direct

relationships, are willing to receive and share information, and serve prominent

intermediary roles between other actors in the network. These are the individuals with

whom to share information that needs to be disseminated throughout the network.

Michael and Stefan are important actors within the TAWC producer network as

other connections centralize around them and they are ordinary nodes, sharing and

receiving information with many other actors. These two producers give the TAWC

reach outside of the 2012 producers already knowledgeable of the project. Michael goes

to Joshua for information, but not Andrew. Stefan does not seek out Andrew or Joshua

for information. In order to better connect with the rest of the TAWC, a relationship

needs to be formed between these four actors.

Given their strategic connections with other actors who are highly connected to

the rest of the TAWC producer network, Raymond and Sergey have high influence over

the network. Raymond has a reciprocated relationship with Joshua and seeks information

from Andrew. Sergey seeks information from Andrew and shares information with

Joshua. Given their connections, Andrew and Joshua could maximize the reach of their

information through Raymond and Sergey.


111

Bernard, member of Cluster One, is an important intermediary actor who bridges

Cluster Five and Cluster Six. In this role, he may also be who the TAWC should employ

to connect with other isolates within the network, new producers as they join the TAWC,

or extend the reach of the knowledge gained through the TAWC to those outside the

current boundaries.

Research Objective Three

Word and phrase frequency analyses were conducted to describe the 16 TAWC

producers in terms of their typology. The analyses were sorted based on the role of the

TAWC producer within each cluster identified during the network analysis; change agent,

opinion leader, or connection. The change agent and all of the opinion leaders were

TAWC producers.

Comparing the frequently used words of the Andrew (change agent) to Sergey

(the opinion leader) and the five TAWC producers connected to Sergey in Cluster One,

we see some words make it through the communication flow. While both Andrew and

the TAWC producer connections frequently used good, lot, project, and water, Sergey

(the opinion leader) used none of these words frequently. This is evidence that the

message the Andrew sharing is getting through Sergey to Bernard, Dean, Joshua, Karl,

and Raymond, but Sergey is not completely buying into the message.

No frequently used phrases match between Andrew and the TAWC producer

connections. Sergey stated no phrases frequently enough for WordStat to detect. This

mismatch could be the result of the important role Andrew (the project leader) plays in


112

the network, described in the conclusions of research objective two. He thinks and

speaks differently about the TAWC than Bernard, Dean, Joshua, Karl, and Raymond.

Cluster Two is represented by Stefan (the opinion leader) who is not connected to

any other TAWC producers. Therefore, the extent of the effectiveness of his message to

his connections outside the TAWC is unknown.

Cluster Three includes Michael (the opinion leader) and Lee, Michael’s only

TAWC producer connection. Water was the only word used frequently by Michael and

Lee. This is to be expected, as the purpose of the TAWC is to develop water

management knowledge. There were no phrases identified as frequently used by both

individuals.

The word and phrase frequency analyses for the members of Cluster Four were

limited to Larry (the opinion leader) and Sheldon, as David was not interviewed for the

study, even though he is a TAWC producer. Larry and Sheldon used the word ‘water’

frequently in their individual interviews. Sheldon frequently referred to crop consultants

in his interview, while Larry did not. Therefore, Sheldon puts a larger emphasis on the

knowledge and impact of the crop consultant than Larry. Phrases frequently used by

Larry and Sheldon were not found. An interview with David would help determine if the

communications Larry is sending are getting though and resonating with his TAWC

producer connections.

The opinion leader of Cluster Five was George, who was connected to Thomas

and Warren, TAWC producers. Warren was not interviewed, so no word or phrases

frequencies are available. George and Thomas frequently stated the word ‘year’ during


113

their respective interviews. This commonly refers to what each has done in past years on

their operation or the status of a crop or technology in the current year. George and

Thomas did not share any frequently used phrases. Given the low number of matching

words used frequently and the lack of matching phrases, it is unlikely that George’s

communications are having an impact on Cluster Five. However, an interview with

Warren would provide further information to make a determination.

Cluster Six and Cluster Seven are represented by opinion leaders Kevin and

Amancio, respectively. They are not connected to any other TAWC producers.

Therefore, the extent the effectiveness of their messages to their connections outside the

TAWC is unknown.

The word and phrase frequency analyses of Andrew (the change agent) and the

opinion leaders of all seven clusters (Sergey, Stefan, Michael, Larry, George, Kevin and

Amancio) were compared. No words or phrases were used frequently by all individuals.

This may indicate that the messages being communicated by Andrew to the TAWC

producers are either not being received or not well accepted by the opinion leaders.

Discussion

This study supported the findings in the literature regarding social network

analysis as an aid for gaining insight into interpersonal relations and the adoption of

innovations. A three-part social network analysis identified the attributes of TAWC

producers, their interpersonal relations, as well as general beliefs and attitudes about the

TAWC. Producers are early knowers who have been triggered to investigate water

management practices, causing a preventative innovation adoption (Rogers, 2003).


114

TAWC producers receive and share information and advice with project peers, crop

consultants, and other producers outside of the TAWC. This study identified the change

agent and opinion leaders of the TAWC using social network analysis. The leaders of the

TAWC can use this information to more effectively and efficiently disseminate

information throughout the TAWC producer network and beyond.

The Diffusion of Innovations theoretical framework can be applied to this study to

describe the attributes and roles of TAWC producers and the relationships through which

information is transferred. TAWC producers have exposure to a variety of interpersonal

channels, contact with a change agent, engage in social participation, and are aware of

water management innovations but do not always adopt them. Therefore, TAWC

producers are early knowers (Rogers, 2003).

Based on the interviews conducted with the TAWC producers, potential water use

governance and declining water availability were driving factors in a producer’s choice to

join the TAWC. To try to avoid these undesirable, potential issues, TAWC producers

chose to join in pursuit of preventative innovation adoption. Not all TAWC producers

have adopted water management best practices or technologies suggested by the leaders

of the TAWC. This aligns with Rogers’ (2003) assertion that motivations to adopt

preventative innovations are generally weak because the unwanted event may or may not

occur and the desired innovation consequences are unknown to the producer.

A change agent and seven opinion leaders were identified by this study. Andrew

is the change agent; one who influences the innovation-decisions of the TAWC producers

in way that is intended by the leadership of the TAWC. He has completed some of the


115

seven sequential steps of change agent roles (Rogers, 2003). During the interviews,

TAWC producers often cited Andrew as someone who gave them an awareness of the

demonstration project and further amplified the need for change in water management

practices. During the knowledge stage of the innovation-decision process, Andrew

helped TAWC producers gain awareness-knowledge and how-to knowledge. Given the

number of connections Andrew has in the TAWC producer network, he has already

established information exchange relationships. Some TAWC produces have adopted

new water management best practices and technologies, while others have either adopted

them and discontinued or never adopted at all. This finding indicates Andrew needs to

work on diagnosing TAWC producer problems, creating intent in producers to adopt,

transforming intent into action, and stabilizing adoption to prevent discontinuance.

Rogers (2003) stated that using his interpersonal network influences, especially opinion

leaders, would be the most effective in achieving these steps in the persuasion and

decision stages of the innovation-decision process. Finally, as Andrew nears the end of

his time as project director, and therefore change agent, he should seek to find another

person, perhaps an opinion leader, to fill his future absence (Rogers, 2003).

Sergey, Stefan, Michael, Larry, George, Kevin, and Amancio are opinion leaders

in the TAWC producer network; those who have earned the ability to informally

influence the behavior and attitudes of others in a desirable way. Opinion leaders are not

uniform, which is reflected in the variety of ages, acres in the TAWC, irrigation practices,

and crops produced by the seven TAWC producers identified as opinion leaders. They

each express the unique attributes of the TAWC system structure. Furthermore, they are


116

the focal point of their system’s interpersonal communication network. There is often a

breakdown, demonstrated in the typological analysis, between the opinion leader and his

followers. Rogers (2003) asserted that other members of the network should imitate the

innovative behavior and attitudes of the opinion leader. This study found this not to be

the case within the TAWC producer network. Strengthening opinion leaders’ technical

competence, social accessibility, or conformity to the norms of the network could aid in

their opinion leadership relations with other TAWC producers (Rogers, 2003). As

Sergey, Stefan, Michael, Larry, George, Kevin, and Amancio build their opinion

leadership capabilities, Andrew should be able to increasingly rely on them to secure

adoption of innovations (i.e. water management best practices and technologies) (Rogers,

2003).

The relationships between TAWC producers are the communication channels

that innovations pass through from the TAWC leadership to the change agent, the opinion

leader, and finally their other TAWC producer connections and those outside the

demonstration project. The social network analysis produced a sociogram and many

measures that described these communication channels. It is evident that the water

management knowledge developed by the TAWC has reached beyond the directly

involved producers. In addition, the analysis indicated that TAWC producers seek a

variety of sources to gain information of advice, including fellow TAWC members, crop

consultants, and people who are not members of the TAWC. These communication

channels are very important to the diffusion of an innovation, as it is a social process that

relies on interpersonal relationships for success (Rogers, 2003).


117

This study assumes producers of agricultural products (farmers and ranchers)

operate as both an individual entrepreneur and as an agricultural professional. In the

context of a producer as an individual, the concept of interpersonal communication is

useful in describing the ways TAWC producers share information. This study supports

the findings of previous research (Gamon et al., 1992; Lasley et al., 2001; Licht &

Martin, 2007; Richardson & Mustian, 1994; Riesenberg & Gor, 1989; Ryan & Gross,

1943; Suvedi et al., 2000; Vergot et al., 2005) that agricultural producers prefer

interpersonal communication methods. During the interviews, TAWC producers

commonly referred to receiving or making phone calls as well as visiting with other

producers at a coffee shop or cotton gin as the primary ways they shared and received

information.

The interpersonal relationships of TAWC producers formed as a result of

mutually contingent and rewarding transactions of information exchange in agreement

with Social Exchange Theory (Emerson, 1976). Some of these relationships within the

TAWC producer network are reciprocated. Blau (1964) stated this is due to an obligation

to return the favor, which forms when one person shares a useful resource with another

person. However, Foa and Foa (1974, 1980) explained that highly particularistic and

symbolic resources, such as knowledge, are exchanged in a way that reciprocation is not

required. This explains why other interpersonal relations are not reciprocated within the

TAWC producer network. The TAWC producer must deem the information shared by

another person useful in order to feel an obligation to reciprocate (Emerson, 1987).


118

Therefore, relationships that are not reciprocated, especially with opinion leaders, may be

due to the TAWC producer connection devaluing the information shared.

TAWC producers made comparisons with others similar to them (members of the

same cluster) in order to evaluate their own opinions, abilities, and life situations

(Festinger, 1954). Social Comparison Theory is useful in further explaining the seven

clusters identified through social network analysis. The TAWC producers chose to form

relationships with people who they perceived as having similar attributes related to water

management or agriculture. These relationships have the strongest impact on opinions of

preference assessment and preference prediction (Suls, 2000).

During the interviews, some TAWC producers stated they sought information or

advice from other producers who had tried a new practice or technology prior to their

own adoption. Social Comparison Theory and the proxy comparison model states a

producer can look to a peer, who has already completed a considered task, to predict what

they have the ability to accomplish (Martin, 2000).

In the context of the producer as a corporation, once producers become members

of the TAWC, they become a part of a community or practice. The TAWC is a

community of practice because it follows Wenger et al.’s (2002) definition: “groups of

people who share a concern, a set of problems, or a passion about a topic, and who

deepen their knowledge and expertise in this area by interacting on an ongoing basis” (p.

4). The TAWC producers share a concern for water management and issues regarding

water conservation. These producers deepen their knowledge and expertise by


119

interacting with other TAWC producers on a regular basis in order to gain information or

advice on the topics.

TAWC producers reported they seek out others for information or advice. The

Uncertainty Reduction Theory states people will seek to reduce uncomfortable feelings of

the unknown through communication. This supports a human desire to predict the

outcomes, both positive and negative, resulting from actions and judgments (Heath &

Bryant, 2000). The nature of the interview questions described TAWC producers as

engaging in interactive strategy of acquiring information to reduce uncertainty about the

topic and the person they are communicating with through interpersonal communication

(Berger, 1995; Heath & Bryant, 2000).

This study operated under the four defining principles of social network analysis

(Freeman, 2004). The researcher found value in relations between individuals, studied

those connections by gathering and analyzing relational data, and relied on sociograms of

the TAWC producer network to visualize patterns in the network. Furthermore,

mathematical and computational methods were used to describe and explain those

patterns.

To conduct the social network analysis, the researcher referred to the types of data

and appropriate means of analysis detailed by Scott (2013). Through the survey research

method, a semi-structured interview was conducted with each accessible TAWC

producer. Through these interviews, attribute, ideational, and relational data was

collected. Each of the three research objectives of this study was fulfilled by using one of

the three types of data analysis: variable, typological, or network.


120

Social network analysis was used as a research methodology and data analysis

technique to increase understanding of the vast and complex relationships among TAWC

producers. Human relationships are complicated and intertwined, but social network

analysis allowed the researcher to untangle networks to see a new perspective on

relationships.

Recommendations

Practitioners

The results of the social network analysis revealed there are smaller communities,

or clusters, that exist within the TAWC project. Based on the results, the TAWC should

utilize these communities and the individuals identified as the change agent or opinion

leaders within the project.

As the social network analysis illustrated, the project has reach beyond the 18

2012 producers. The TAWC should utilize producers connected to individuals outside of

the project to share best practices, encourage the use of irrigation monitoring and decision

tools, and expand the project beyond its current boundaries.

Based on word frequency and phrases frequency analyses, the messages shared by

the change agent with the rest of the network did not resonate with the opinion leaders

and did not flow to the subsequent TAWC producer connections. The TAWC should

ensure that important messages are being clearly and concisely shared with the change

agent. Furthermore, the change agent must share the same, uniform message equally

with all opinion. Consequently, more TAWC producers should have the same


121

information and knowledge of the TAWC and the water management knowledge it has

developed.

As the TAWC moves forward, they should consider encouraging TAWC

producers to take greater, informal leadership roles within the project. The change agent,

Andrew, can help the TAWC find producers who are interested in providing further

support and resources of information to other producers. In addition, identify and

encourage producers who naturally reach out to producers outside of the TAWC in order

to further disseminate the best practices and water management technology information

gained by the project. Finally, as Andrew nears the end of his time as project leader, the

TAWC should encourage him to work with a successor to take over for him when he

retires. This person should be someone who already has similar connections, is willing to

share and receive information, is invested in water management, and has a similar

farming operation to other TAWC producers.

Crop consultants were found, using network analysis, to be important

intermediary figures in the TAWC producer network. When a TAWC producer seeks

information or advice, they sometimes turn to a hired crop consultant. The TAWC

should share pertinent water management technology and best practices information with

crop consultants who serve producers in the project. Therefore, when a TAWC producer

seeks a crop consultant as a resource, the crop consultant is prepared to answer questions

and relay important messages shared by the TAWC.

For professionals involved in outreach efforts designed to influence the adoption

process, understanding and subsequently utilizing established networks and potential


122

opinion leaders could increase the effectiveness and efficiency of those efforts. The

results of social network analysis may allow practitioners to more effectively plan

information dissemination and education efforts towards a goal of maximizing

effectiveness while reducing the use of valuable resources (including time).

Researchers

Throughout the social network analysis, the researcher learned lessons pertaining

to the process future researchers should benefit from. Before collecting any data, define

the type of network (whole or egocentric) that will be studied. In addition, decide on the

types of ties, or relationships, to be studied. Based upon the known characteristics of the

population, determine the research method – survey, ethnographic, or documentary.

This research studied the TAWC producers as an egocentric network, interested in

producer relationships that share information regarding their operation or water

management knowledge. A survey research method was used through semi-structured

interviews. Given that no commonalities were found in this study’s typological analysis,

enhancements to the interview are recommended. The interviewer should closely follow

the same semi-structured interview guidelines for every interview, so as to get similar

types of answers from each respondent. Furthermore, the questions should be specific

and probing after each structured question is encouraged.

When asking the questions pertaining to the network analysis, do not limit the

number of people the respondent can list as resources or who comes to them as a

resource. The TAWC producer network in this study is limited due to asking the


123

respondents to list three of each type of connection. While some respondents answered

with more than three each, others limited themselves to the number requested.

NodeXL for Microsoft Excel, QDA Miner, and WordStat are recommended for

data analysis. For this study, each software tool was learned through application. It is

recommended that future researchers gather relational data from a local, easily accessed

group to create the edge list in Microsoft Excel imported into NodeXL to create a

sociogram and begin an analysis. Transcribe the interviews, clean the documents, and

make substitutions in Microsoft Word. Then, import the interview documents for coding

and analysis into QDA Miner. Once these steps are complete, a content analysis should

be conducted with WordStat. Practicing with a data set prior to a more extensive study

will help the researcher more deeply learn the process and tool.

The results of this study encourage several other research opportunities. The

TAWC producer network should be explored through a whole network, directed, and

valued analysis. This will tell researchers and TAWC leadership how strongly each

producer is connected, if at all, with every other TAWC producer and if that relationship

is reciprocated. This is the strongest social network analysis technique (Scott, 2013).

Furthermore, this study should be expanded beyond the 15 farmers interviewed to

begin creating sociograms of farmer networks within selected counties in West Texas to

determine if different network patterns exist based on the agriculture topic/issue being

discussed. Further analysis should seek to determine if network variations are present

based on individual and county-level factors. This research would build understanding of

how information flows through interpersonal networks among farmers. In addition,


124

further research could ask farmers about their communication preferences to facilitate the

study of message development and channel mix as they relate to information assimilation

and adoption of practices/technologies disseminated.

This study could serve as baseline data for annual social network analyses of the

TAWC producers. A future longitudinal study should assess how connections between

TAWC producers change over time and due to what influences.

The typological analysis revealed the interview themes of the change agent,

opinion leaders, and their TAWC producer connections did not align. The work of the

TAWC would be more effective in disseminating best practices and new technologies

throughout the network and beyond if the themes aligned more closely. Future

researchers should investigate the barriers currently hindering effective and efficient

communications between the change agent, opinion leaders, and TAWC producer

connections.

While conducting the review of literature, the researcher noticed similarities in

language between communication networks and social networks. However, no research

was found that discussed the similarities of these two types of networks. Further research

should explore the similarities between communication networks and social networks and

examine the historical timeline of both to decide if convergence of the two is appropriate.


125

REFERENCES

Alba, R. D. (1982). Taking stock of network analysis: A decade’s results. Research in

Sociology of Organizations, 1, 39-74.

Affleck, G., & Tennen, H. (1991). Social comparison and coping with major medical

problems. In J. Suls & T. A. Willis (Eds.), Social comparison: Contemporary

theory and research (pp. 369-394). Hillsdale, NJ: Lawrence Erlbaum.

Allen, T. J. (1977). Managing the flow of technology: Technology transfer and the

dissemination of technological information within the R&D organization.

Cambridge, MA: MIT Press.

American Psychological Association. (2010). Publication manual of the American

Psychological Association. (6th ed.). Washington D.C.: American Psychological

Association.

Arrowood, A. J. (1978). Social comparison theory: Revived from neglect. Contemporary

Psychology, 23, 490-491.

Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against

a unanimous majority. Psychological Monographs: General and Applied, 70(9),

1-70. doi: 10.1037/h0093718

Attwater, R., & Derry, C. (2005). Engaging communities of practice for risk

communication in the Hawkesbury Water Recycling Scheme. Action Research,

3(2), 193-209. doi: 10.1177/1476750305052144

Baldwin , J. R., Perry , S. D., & Moffitt, M. A. (2004). Communication theories for

everyday life. Boston, MA: Pearson .


126

Bajema, D. H., Miller, W. W., & Williams, D. L. (2002). Aspirations of rural youth.

Journal of Agricultural Education, 43(3), 61-71. doi: 10.5032/jae.2002.03061

Berger, C. R. (1995). A plan-based approach to strategic communication. In D. E. Hewes

(Ed.), The cognitive bases of interpersonal communication (pp. 141-179).

Hillsdale, NJ: Lawrence Erlbaum.

Berger, C. R. (2006). Uncertainty reduction theory. In E. Griffin (Ed.), A first look at

communication theory (pp. 130-141). New York, NY: McGraw-Hill.

Berger, C. R. & Calabrese, R. J. (1975). Some exploration in the initial interaction and

beyond: Toward a developmental theory of interpersonal communication. Human

Communication Research, 1(2), 99-112. doi: 10.111/j.1468-2958.1975.tb00258.x

Berko, R., Aitken, J. E., & Wolvin, A. (2010). ICOMM: Interpersonal concepts and

competencies: Foundations of interpersonal communication. Lanham, MD:

Rowman & Littlefield.

Blau, P. M. (1964). Exchange and power in social life. New York, NY: Wiley.

Borgatti, S., & Everett, M. G. (1999). Models of core/ periphery structures. Social

Networks, 21(4), 375-395. doi: 10.1016/S0378-8733(99)00019-2

Borgatti, S., Mehra, A., Brass, D., & Labianca, G. (2009). Network analysis in the social

sciences. Science, 323(5916), 892-95. doi: 10.1126/science.1165821

Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks

and organizations: A multilevel perspective. Academy of Management Journal,

47(6), 795-817. doi: 10.2307/20159624


127

Burgoon, J. K., Bonito, J. A., Ramirez, A., Jr., Dunbar, N. E., Kam, K., & Fischer, J.

(2002). Testing the interactivity principle: Effects of mediation, propinquity, and

verbal and nonverbal modalities in interpersonal interaction. Journal of

Communication, 52(3), 657-677. doi: 10.1111/j.1460-2466.2002.tb02567.x

Burt, R. S. (1976). Positions in networks. Social Forces, 55(1), 93-122. doi:

10.2307/2577097

Burt, R. S. (1992). The social structure of competition. In N. Nohria & R. G. Eccles

(Eds.), Networks and organizations: Structure, form, and action (pp. 57-91).

Boston, MA: Harvard Business School Press.

Carolan, B. V. (2014). Social network analysis and education: Theory, methods &

applications. Thousand Oaks, CA: Sage.

Chua, V., Madej, J., & Wellman, B. (2011). Personal communities: The world according

to me. In J. Scott & P. Carrington (Eds.), The Sage handbook of social network

analysis (pp. 101-115). London, England: Sage.

Coleman, J. S., Katz, E., & Menzel, H. (1966). Mediated innovation: A diffusion study.

New York, NY: Bobbs-Merrill.

Cook, K. S., Molm, L. D., & Yamagishi, T. (1993). Exchange relations and exchange

networks: Recent developments in social exchange theory. In J. Berger & M.

Zelditch (Eds.), Theoretical research programs: Studies in the growth of theory

(pp. 296-322). Stanford, CA: Stanford University Press.

Cook, K. S., & Rice, E. (2003). Social exchange theory. In J. Delamater (Ed.), Handbook

of social psychology (pp. 53-76). New York, NY: Kluwer Academic/Plenum.


128

Cox, A. (2005). What are communities of practice? A comparative review of four

seminal works. Journal of Information Science, 31(6), 527-540. doi:

10.1177/0165551505057016

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary

review. Journal of Management, 31(6), 874-900. doi:

10.1177/0149206305279602

Cross, R., Laseter, T., Parker, A., & Velasquez, G. (2006). Using social network analysis

to improve communities of practice. California Management Review, 49(1), 32-

60. doi: 10.2307/41166370

Cross, R., Parker, A., & Borgatti, S. P. (2002). A bird’s-eye view: Using social network

analysis to improve knowledge creation and sharing. Retrieved from IBM Global

Services website: http://www-

07.ibm.com/services/hk/strategy/pdf/a_birds_eye_view.pdf

DeVito, J. A. (2007). The interpersonal communication book (11th ed.). Boston, MA:

Pearson.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for

Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX:

Texas Tech University, Department of Agricultural Education and

Communications.

Ekeh, P. P. (1974). Social exchange theory: The two traditions. Cambridge, MA: Harvard

University Press.


129

Emerson, R. M. (1972a). Exchange theory, part I: A psychological basis for social

exchange. In J. Berger, M. Zelditch Jr., & B. Anderson (Eds.), Sociological

theories in progress (pp. 38-57). Boston, MA: Houghton Mifflin.

Emerson, R. M. (1972b). Exchange theory, part II: Exchange relations and networks. In J.

Berger, M. Zelditch Jr., & B. Anderson (Eds.), Sociological theories in progress

(pp. 58-87). Boston, MA: Houghton Mifflin.

Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2, 335-362.

doi: 10.1146/annurev.so.02.080176.002003

Emerson, R. M. (1987). Toward a theory of value in social exchange. In K. S. Cook

(Ed.), Social exchange theory (pp. 11-46). Newbury Park, CA: Sage.

Encyclopedia Britannica. (2013). High plains. Retrieved from

http://www.britannica.com/EBchecked/topic/265298/High-Plains

Everett, M. & Borgatti, S. R. (2000). Peripheries of cohesion subsets. Social Networks,

21(4), 397-407. doi: 10.1016/S0378-8733(99)00020-9

Festinger, L. (1950). Informal social communication. Psychological Review, 57(5), 271-

282. doi: 10.1037/h0056932

Festinger, L., (1954). A theory of social comparison processes. Human Relations, 7(2),

117-140. doi: 10.1177/001872675400700202

Fisher, B. A. (1978). Information systems theory and research: An overview. In B. D.

Ruben (Ed.), Communication yearbook 2 (pp. 263-275). New Brunswick, NJ:

Transaction Books.


130

Flanagin, A. J., & Metzger, M. J. (2001). Internet use in the contemporary media

environment. Human Communication Research, 27(1), 153-181. doi:

10.1111/j.1468-2958.2001.tb00779.x

Foa, U. G., & Foa, E. B. (1974). Societal structures of the mind. Springfield, IL: Charles

C. Thomas.

Foa, U. G., & Foa, E. B. (1980). Resource theory: Interpersonal behavior as exchange. In

K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social exchange: Advances

in theory and research (pp. 77-94). New York, NY: Plenum.

Forsyth, D. R. (2000). Social comparison and influence in groups. In J. Suls & L.

Wheeler (Eds.), Handbook of social comparison: Theory and research (pp. 81-

103). New York, NY: Kluwer Academic/ Plenum.

Freeman, L. C. (2004). The development of social network analysis: A study in the

sociology of science. Vancouver, Canada: Empirical Press.

Freeman, L.C. (2011). The development of social network analysis. In J. Scott & P.

Carrington (Eds.), The sage handbook of social network analysis (pp. 26-39).

London, England: Sage.

Fujishin, R. (2012). Natural bridges: A guide to interpersonal communication. Boston,

MA: Pearson.

Gamon, J., Bounaga, L., & Miller, W. W. (1992). Identifying information sources and

educational methods for soil conservation information used by landowners of

highly erodible fields. Journal of Applied Communications, 76(1), 1-5.


131

Gerard, H., & Orive, R. (1987). The dynamics of opinion formation. In L. Berkowitz

(Ed.), Advances in experimental social psychology, Vol. 20 (pp. 171-202). New

York, NY: Academy Press.

Giuffre, K. (2013). Communities and networks: Using social network analysis to rethink

urban and community studies. Cambridge, England: Polity Press.

Goethals, G. R., & Darley, J. (1977). Social comparison theory: An attributional

approach. In J. Suls & R. L. Miller (Eds.), Social comparison processes:

Theoretical and empirical perspectives (pp. 259-278). Washington, DC:

Hemisphere.

Goethals, G. R., & Klein, W. M. P., (2000). Interpreting and inventing social reality:

Attributional and constructive elements in social comparison. In J. Suls & L.

Wheeler (Eds.), Handbook of social comparison: Theory and research (pp. 23-

44). New York, NY: Kluwer Academic/ Plenum.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology,

78(6): 1360-1380. doi: 10.1086/225469

Hansen, D. L., Schneiderman, B., & Smith, M. A. (2011). Analyzing social media

networks with NodeXL: Insights from a connected world. Burlington, MA:

Elsevier.

Hara, N. (2009). Communities of practice: Fostering peer-to-peer learning and informal

knowledge sharing in the work place. New York, NY: Springer.


132

Haythornthwaite, C. (1996). Social network analysis: An approach and technique for the

study of information exchange. Library & Information Science Research. 18(4),

323-342. doi: S0740818896900031

Heath, R. L. & Bryant, J. (1992). Human communication theory and research: Concept,

context and challenges. Mahwah, NJ: Erlbaum.

Heath, R. L. & Bryant, J. (2000). Human communication theory and research: Concept,

context and challenges (2nd ed.). Mahwah, NJ: Erlbaum.

Heider, F. (1958). The psychology of interpersonal relations. New York, NY: Wiley.

Helgeson, V. S., & Mickelson, K. D. (1995). Motives for social comparison. Personality

and Social Psychology Bulletin, 21(11), 1200-1209. doi:

10.1177/01461672952111008

Homans, G. C. (1950). The human group. New York, NY: Harcourt Brace.

Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology,

63(6), 597-606. doi: 10.1086/222355

Homans, G. C. (1974). Social behavior and its elementary forms. New York, NY:

Harcourt, Brace, and World.

Huber, G. P., & Daft, R. L. (1987). The information environments of organizations. In F.

M. Jablin, L. L. Putnam, K. H. Roberts, & L. W. Porter (Eds.), Handbook of

organizational communication (pp. 130-164). Newbury Park, CA: Sage.

Huffaker, D. (2010). Dimensions of leadership and social influence in online

communities. Human Communication Research, 36(4), 593-617. doi:

10.1111/j.1468-2958.2010.01390.x


133

Hyman, H. H. (1942). The psychology of status. New York, NY: Columbia University.

Hyman, H. H. (1960). Reflections on reference groups. Public Opinion Quarterly, 24(3),

383-396. doi: 10.1086/266959

Janssen, M. A., (2001). An exploratory integrated model to assess management of lake

eutrophication. Ecological Modeling, 140(1-2), 111-124. doi: 10.1016/S0304-

3800(01)00260-5

Jones, E. E., & Gerard, H. B. (1967). Foundations of social psychology. New York, NY:

John Wiley.

Jones, S. C., & Regan, D. (1974). Ability evaluation through social comparison. Journal

of Experimental Social Psychology, 10(2), 133-146. doi: 10.1016/0022-

1031(74)90062-6

Jussila, I., Goel, S., & Tuominen, P. (2012). Governance of co-operative organizations: A

social exchange perspective. Business and Management Research, 1(2), 14-25.

doi: 10.5430/bmr.v1n2p14

Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings.

New York, NY: Oxford University Press.

Kim, P. H. (2006). Organizing activities and founding processes of new ventures

(Doctoral dissertation). Retrieved from University of North Carolina Thesis and

Dissertations Collection.

Kincaid, D. L. (1979). The convergence model of communication. Paper 18. Honolulu:

East-West Communication Institute.


134

Kincaid, D. L., & Schramm, W. L. (1975). Fundamental human communication: Module

text (Vol. 1). East-West Center, East-West Communication Institute.

Knoke, D. & Yang, S. (2008). Network analysis (2nd ed.). Thousand Oaks, CA: Sage.

Kulik, J. A., Mahler, H. I. M., & Earnest, A. (1994). Social comparison and affiliation

under threat: Going beyond the affiliate-choice paradigm. Journal of Personality

and Social Psychology, 66(2), 301-309. doi: 10.1037/0022-3514.66.2.301

Kulik, J. A., Mahler, H. I. M., & Moore, P. J. (1996). Social comparison and affiliation

under threat: Effects on recovery from major surgery. Journal of Personality and

Social Psychology, 71(5), 967-979. doi: 10.1037/0022-3514.71.5.967

Lasley, P., Padgitt, S., & Hanson, M. (2001). Telecommunication technology and its

implications for producers and Extension Services. Technology in Society, 23(1),

109-120. doi: 10.1016/S0160-791X(00)00039-7

Laumann, E. O., Marsden, P. V., & Prensky, D. (1983). The boundary specification

problem in network analysis. In R. S. Burt & M. J. Minor (Eds.), Applied network

analysis: A methodological introduction (pp. 18-34). Beverly Hills, CA: Sage.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation.

Cambridge, MA: University Press.

Lévi-Strauss, C. (1969). The elementary structures of kinship. Boston, MA: Beacon

Press.

Lewin, K., & Lippit, R. (1938). An experimental approach to the study of autocracy and

democracy: A preliminary note. Sociometry, 1(3/4), 292-200. doi:

10.2307/2785585


135

Licht, M. A. R., & Martin, R. A. (2007). Agricultural issues of significance to Iowa crop

producers and their educational implications. Journal of Agricultural Education,

48(3), 57-66. doi: 10.5032/jae.2007.03057

Liden, R. C., Sparrowe, R. T., & Wayne, S. J. (1997). Leader-member exchange theory:

The past and potential for the future. In G. R. Ferris (Ed.) Research in personnel

and human resources management, Vol. 15 (pp. 47-119). Greenwich, CT: JAI.

Maddox, S. J., Mustian, R. D., & Jenkins, D. M. (2003, February). Agricultural

information preferences of North Carolina farmers. Paper presented at the

Southern Association of Agricultural Scientists Annual Meeting and Conference,

Mobile, AL. Abstract retrieved from

http://agnews.tamu.edu/saas/2003/maddox.htm

Malinowski, B. (1922). Argonauts of the Western Pacific: An account of native

enterprise and adventure in the archipelagoes of Melansian, New Guinea.

London, England: Routledge.

Malinowski, B. (1932). Crime and custom in savage society. London, England: Paul,

Trench, Trubner.

Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. In J. Scott &

P. Carrington (Eds.), The Sage handbook of social network analysis (pp. 11-25).

London, England: Sage.


136

Marsden, P. V. (1989). Methods for the characterization of role structures in network

analysis. In L. C. Freeman, D. R. White, and A. K. Romney (Eds.) Research

methods in social network analysis (pp. 489-530). Fairfax, VA: George Mason

University Press.

Marsden, P. V. (2011). Survey methods for network data. In J. Scott and P. Carrington

(Eds.) The Sage handbook of social network analysis (pp. 370-403). London,

England: Sage.

Martin, R. (2000). “Can I do X?”: Using the proxy comparison model to predict

performance. In J. Suls & L. Wheeler (Eds.), Handbook of social comparison:

Theory and research (pp. 67-80). New York, NY: Kluwer Academic/ Plenum.

Mauss, M. (1925). The gift: Forms and functions of exchange in archaic societies. New

York, NY: The Norton Library.

McArthur, R. & Bruza, P.(2003). Discover of implicit and explicit connections between

people using email utterance. In K. Kuutti, E. H. Karsten, G. Fitzpatrick, P.

Dourish, & K. Schmidt (Eds.), Proceedings of the Eighth European Conference

on Computer-Supported Cooperative Work. Retrieved from

http://www.ecscw.org/2003/002McArthur_ecscw03.pdf

McDermott, R. (1999). Learning across teams: The role of communities of practices in

team organizations. Knowledge Management Review, (8), 32-36. Retrieved from

http://www.mcdermottconsulting.com/index.php?option=com_publications&Item

id=57


137

McDermott, R. (2004). How to avoid a mid-life crisis in your CoPs: Uncovering six keys

to sustaining communities. Knowledge Management Review, 7(2), 10-13.

Merriam-Webster. (n.d.). Typology. http://www.merriam-

webster.com/dictionary/typology.

Merton, R. K., & Kitt, A. (1950). Contributions to the theory of reference group behavior.

In R. K. Merton & P. F. Lazarfield (Eds.), Continuities in social research: Studies

in the scope and method of “The American Soldier” (pp. 40-105). Glencoe, IL:

Free Press.

Millen , D. R., & Fontaine, M. A. (2003, November). Improving individual and

organizational performance through communities of practice. In Group '03:

Proceedings of the 2003 international ACM SIGGROUP conference on

supporting group work (pp. 205-211). New York, NY: ACM.

Millen, D. R., Fontaine, M. A., & Muller, M. J. (2002). Understanding the benefit and

costs of communities of practice. Communications of the ACM, 45(4), 69-73. doi:

10.1145/505248.505276

Molm, L. D., Peterson, G., & Takahashi, N. (1999). Power in negotiated and reciprocal

exchange. American Sociological Review, 64(6), 876-890. doi: 10.2307/2657408

Monge, P. R., & Contractor, N. S. (2001). Emergence of communication networks. In F.

M. Jablin & L. L. Putnam (Eds.), The new handbook of organizational

communication: Advances in theory, research, and methods (pp. 440-502).

Thousand Oaks, CA: Sage.


138

Monge, P. R., & Eisenberg, E. M. (1987). Emergent communication networks. In F. M.

Jablin, L. L. Putnam, K. H. Roberts, & L. W. Porter (Eds.), Handbook of

organizational communication: An interdisciplinary perspective (pp. 304-342).

Newbury, CA: Sage.

Moreno, J. L. (1932). Application of the group method to classification. New York, NY:

National Committee on Prisons and Prison Labor.

Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human

interactions. Washington, DC: Nervous and Mental Disease.

Morgan, S. L. (2011). Social learning among organic farmers and the application of the

communities of practice framework. Journal of Agricultural Education and

Extension, 17(1), 99-112. doi: 10.1080/1389224X.2011.536362

National Research Council (1996). A new era for irrigation. Washington, DC: National

Academy Press.

Nohria, N. (1992). Introduction: Is a network perspective a useful way of studying

organizations? In N. Nohria & R. G. Eccles (Eds.), Networks and organizations:

Structure, form, and action (pp. 1-22). Boston, MA: Harvard Business School

Press.

Oreszczyn, S., & Lane, A. (2006, April). Farmer communities of practice and high tech

futures. In Plymouth Rural Futures Conference: The rural citizen: Governance,

culture and wellbeing in the 21st century. Symposium conducted at meeting of

University of Plymouth, Plymouth, England.


139

Patton, M. Q. (2002). Qualitative research and evaluation methods. (3rd ed.) Thousand

Oaks, CA: Sage.

Probst, G., Raub, S., & Rombhardt, K. (1999). Managing knowledge: Building blocks for

success. Chichester, England: John Wiley & Sons.

Provalis Research. (2010). WordStat 6: Content analysis module for QDA Minder &

SimStat: User’s Guide. Montreal, Canada.

Radcliffe-Brown, A. R. (1940). On social structure. The Journal of the Royal

Anthrological Institute of Great Britain and Ireland, 70(1), 1-12. doi:

10.2307/2844197

Radcliffe-Brown, A. R. (1957). A natural science of society. Chicago, IL: University

Press.

Ramieriz-Sanchez, S. (2011). Who and how: Engaging well-connected fishers in social

networks to improve fisheries management and conservation. In Ö. Bodin & C.

Prell (Eds.), Social networks and natural resource management: Uncovering the

social fabric of environmental governance (pp. 119-146). New York, NY:

Cambridge University Press.

Richards, W. D. (1989). The NEGOPY analysis program. Unpublished manuscript,

Department of Communications, Simon Fraser University, Burnaby, Canada.

Richardson, J. G., & Mustian, R. D. (1994). Delivery methods preferred by targeted

Extension clientele for receiving specific information. Journal of Applied

Communications, 78(1), 22-32.


140

Riesenberg, L. E., & Gor, C. O. (1989). Farmers’ preferences for methods of receiving

information on new or innovative farming practices. Journal of Agricultural

Education, 30(3), 7-13. doi: 10.5032/jae.1989.03007

Rogers, E. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.

Rogers, E. M., & Kincaid, D. L. (1981). Communication networks: Toward a new

paradigm for research. New York, NY: Free Press.

Rousseau, D. M. (1995). Psychological contracts in organizations: Understanding

written and unwritten agreements. Thousand Oaks, CA: Sage.

Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa

communities. Rural Sociology, 8(1), 15-24.

Scholz, J. T., & Wang, C. L. (2006). Cooptation or transformation? Local policy

networks and federal regulatory enforcement. American Journal of Political

Science, 50(1), 81-97. doi: 10.1111/j.1540-5907.2006.00171.x

Schutz, W. C. (1966). The interpersonal underworld. Palo Alto, CA: Science & Behavior

Books.

Scott, J. (2013). Social network analysis (3rd ed.). London, England: Sage.

Shannon, C., & Weaver, W. (1949). Mathematical theory of communication. Champaign,

IL: University Press.

Sherif, M. (1936). The psychology of social norms. New York, NY: Harper.

Smith, W. P., & Sachs, P. R. (1997). Social comparison and task prediction: Ability and

similarity and the use of a proxy. British Journal of Social Psychology, 36(4),

587-602. doi: 10.1111/j.2044-8309.1997.tb01151.x


141

Stokman, F. N., Ziegler, R., and Scott, J. (1985). Networks of corporate power: A

comparative analysis of ten countries. Cambridge, England: Polity Press.

Suls, J. (2000). Opinion comparison: The role of the corroborator, expert, and proxy in

social influence. In J. Suls & L. Wheeler (Eds.), Handbook of social comparison:

Theory and research (pp. 105-122). New York, NY: Kluwer Academic/ Plenum.

Suls, J., & Wheeler, L. (2000). A selective history of classic and neo-social comparison

theory. In J. Suls & L. Wheeler (Eds.), Handbook of social comparison: Theory

and research, (pp. 3-19). New York, NY: Kluwer Academic/ Plenum.

Suvedi, M., Lapinski, M. K., & Campo, S. (2000). Producers’ perspectives of Michigan

State University Extension: Trends and lessons from 1996 and 1999. Journal of

Extension, 38(1). Retrieved from http://www.joe.org/index.php

Texas Alliance for Water Conservation. (n.d.a). Water is our future. Retrieved from

http://www.depts.ttu.edu/tawc/documents/Project%20Overview.pdf

Texas Alliance for Water Conservation. (n.d.b). When water determines your success:

Observed cotton, grain sorghum and grain corn fields in the Texas High Plains

2005-2011. Retrieved from

http://www.depts.ttu.edu/tawc/documents/Water_determines%20success.pdf

Texas Alliance for Water Conservation. (2011). About us. Retrieved from

http://www.depts.ttu.edu/tawc/aboutus.html

Texas Alliance for Water Conservation. (2013a). Texas Alliance for Water Conservation

project summary 2005-2012.


142

Texas Alliance for Water Conservation. (2013b). 8th

annual report to the Texas Water

Development Board.

Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups. New York, NY:

Wiley.

Tindall, D. B., Harshaw, H., & Taylor, J. M. (2011). The effects of social network ties on

the public’s satisfaction with forest management in British Columbia, Canada. In

Ö. Bodin & C. Prell (Eds.), Social networks and natural resource management:

Uncovering the social fabric of environmental governance (pp. 147-179). New

York, NY: Cambridge University Press.

Vergot III, P., Israel, G., & Mayo, D. E. (2005). Sources and channels of information

used by beef cattle producers in 12 counties of the Northwest Florida Extension

district. Journal of Extension, 43(2). Retrieved from http://www.joe.org/index.php

Vindigni, G., Janssen, M. A., & Jager, W. (2002). Organic food consumption: A multi-

theoretical framework of consumer decision making. British Food Journal,

104(8), 624-642. doi: 10.1108/00070700210425949

Warner, W. L., & Lunt, P. S. (1941). The social life of a modern community. New Haven,

CT: Yale University Press.

Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and

knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1),

35-57.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.

New York, NY: Cambridge University Press.


143

Wellman, B. (2001). The persistence and transformation of community: From

neighborhood groups to social networks. Retrieved from

http://homes.chass.utoronto.ca/~wellman/publications/lawcomm/lawcomm7.PDF

Wenger, E. (1998). Communities of practice: Learning, meaning, and identity.

Cambridge, England: University Press.

Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating communities of

practice: A guide to managing knowledge. Boston, MA: Harvard Business School

Press.

Wenger, E. C., & Snyder, W. M. (2000, January). Communities of practice: The

organizational frontier. Harvard Business Review, 78(1), 139-146.

Westmyer, S. A., DiCioccio, R. L., & Rubin, R. B. (1998). Appropriateness and

effectiveness of communication channels in competent interpersonal

communication. Journal of Communication, 48(3), 27-48. doi: 10.1111/j.1460-

2466.1998.tb02758.x

Westphal, J. D., & Zajac, E. J. (1997). Defections from the inner circle: Social exchange,

reciprocity, and the diffusion of board independence in U.S. corporations.

Administrative Science Quarterly, 42(1), 161-183. doi: 10.2307/2393812

Wheeler, A. (2005). Policy alternatives for the southern Ogallala Aquifer: Economic and

hydrologic implications (Master's thesis). Retrieved from Texas Tech University

Theses and Dissertations Collection.


144

Wheeler, L., Martin, R., & Suls, J. (1997). The proxy model of social comparison for

self-assessment of ability. Personality and Social Psychology Review, 1(1), 54-61.

doi: 10.1207/s15327957pspr0101_4

White, H. C., Boorman, S. A., and Breiger, R. L. (1976). Social structure from multiple

networks i: Blockmodels of roles and positions. American Journal of Sociology,

81(4), 730-781. doi: 10.1086/226141


145

APPENDIX A

HUMAN RESEARCH PROTECTION PROGRAM APPROVAL LETTER

Rosemary Cogan, Ph.D., ABPPProtection of Human Subjects Committee

Box 41075 | Lubbock, Texas 79409-1075 | T 806.742.3905 | F 806.742.3947 | www.vpr.ttu.edu An EEO/Affirmative Action Institution

July 10, 2013

Dr. David Doerfert Ag Ed & Communications Mail Stop: 2131

Regarding: 504025 Social Network Analysis of Texas Alliance for Water Conservation Producers

Dr. David Doerfert:

The Texas Tech University Protection of Human Subjects Committee approved your claim for anexemption for the protocol referenced above on July 10, 2013.

Exempt research is not subject to continuing review. However, any modifications that (a) changethe research in a substantial way, (b) might change the basis for exemption, or (c) might introduceany additional risk to subjects must be reported to the Human Research Protection Program (HRPP) before they are implemented.

To report such changes, you must send a new claim for exemption or a proposal for expedited or full board review to the HRPP. Extension of exempt status for exempt protocols that have not changed is automatic.

The HRPP staff will send annual reminders that ask you to update the status of your researchprotocol. Once you have completed your research, you must inform the HRPP office by responding to the annual reminder so that the protocol file can be closed.

Sincerely,


146

APPENDIX B

TAWC PRODUCER TELEPHONE SCRIPT

Hello, may I please speak with (name of TAWC producer)?

This is Nellie Hill, a master’s graduate student in the Department of Agricultural

Education and Communication at Texas Tech University. Dr. David Doerfert has asked

me to contact you about conducting an interview regarding your involvement with the

TAWC Demonstration Project. We are speaking with each producer involved in the

project for our annual TAWC report and for further research. Your name will not be

reported in the report or further research.

I would like to schedule a meeting with you at a time and place of your choosing to talk

about your experiences with the project and your operation.

Would you be willing to meet with me for this discussion?

If yes:

When is a good time within the next couple of weeks for you to meet with me?

Where would you like to meet?

Proceed with script.

If no:

Proceed with script.

Thank you for your time and consideration. Information collected will help the TAWC

better serve producers and aid in water conservation practices.

If you have questions about the interview or the TAWC Demonstration Project, you can

contact my major professor, Dr. David Doerfert at [email protected] or call (806)

742-2816. Thank you again.

Goodbye.


147

APPENDIX C

TAWC PRODUCER INFORMATION SHEET

Please share your thoughts in our research project.

What is this project studying?

The intention of this project is to describe the characteristics of TAWC producers and

gain a better understanding of the relationships among them. We want to find out how

producers share information related to water management.

What would I do if I participate?

To participate in this study, you will agree to be interviewed. During the interview, you

will be asked questions related to your participation in the TAWC Demonstration Project.

How will I benefit from participating?

You will be contributing to the study with valuable information and insights.

Furthermore, the results of this study will help TAWC members better understand how to

successfully communicate and share information with producers.

Can I quit if I become uncomfortable?

Yes, absolutely. Your participation is completely voluntary. Dr. Doerfert and the

Human Resource Protection Program have reviewed the interview questions and think

can comfortably answer them. You may also skip questions or stop answering questions

altogether at any time. You are free to stop the interview at any time. Participating is

your choice.

How long will participation take?

We are asking for 20-30 minutes of your time.

How are you protecting privacy?

You will remain anonymous. Your name will be changed for data analysis and reporting.

I have some questions about this study. Who can I ask?

This study is being conducted by Nellie Hill under the supervision of Dr. David Doerfert

from the Department of Agricultural Education & Communications at Texas Tech

University. If you have questions, you can call him at 806-742-2816 or email him at

[email protected]. TTU also has a Board that protects the rights of people who

participate. You can call to ask them questions at 806-742-2064. You can mail your

questions to the Human Research Protection Program, Office of the Vice President for

Research, Texas Tech University, Lubbock, Texas 79409, or you can email your

questions to www.hrpp.ttu.edu.


148

APPENDIX D

TAWC PRODUCER INTERVIEW INSTRUMENT

Thank you for meeting with me today. As we discussed on the phone, I’m interviewing

all of the producers in the TAWC Demonstration Project about their experiences. I am

going to record our discussion. Your name will not be associated with any information

reported in the annual report or further research. We will assign you a pseudo name, so

all of your responses will remain confidential. If there is a question you prefer not to

answer, please just say so.

1. How old are you?

2. When did you become involved in the TAWC project?

3. What interested you in becoming a part of it?

4. Who did you speak to, to get involved with the TAWC project?

5. What did they tell you about the project?

6. Why did you choose the particular fields that you have in the project?

7. What technologies have you implemented on these fields during your time with

the project?

8. Have you implemented any technologies from the project on any of your other

fields?

9. What have you learned as a result of being involved in the project, based on your

individual field site(s)?

10. How do you decide on the changes that you make within your operation?

11. Do you use the annual TAWC reports that are published each year? If yes, how

do you use them?


149

12. Do you use a crop consultant as part of your operation? If yes, what services do

they provide for you?







15. What have been the major advantages to your operation from this project? What

have you gained?

16. Have you experienced frustrations with the project? What should have been done

differently?

17. Going forward, five years from now, what should the project be doing? Is there

anything that you have heard about that we haven’t tested yet that you think the

project should try?

Documents

A SOCIAL NETWORK ANALYSIS OF TEXAS ALLIANCE FOR …