COMPETENCIES, IMPORTANCE, AND MOTIVATIONS FOR AGRICULTURAL
PRODUCERS’ USE OF ONLINE COMMUNICATIONS
By
KELSEY FLETCHER SHAW, 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
Courtney Meyers
Chairperson of the Committee
David Doerfert
Erica Irlbeck
Dominick J. Casadonte, Jr.
Interim Dean of The Graduate School
May, 2013
Copyright 2013, Kelsey Fletcher Shaw
Texas Tech University, Kelsey F. Shaw, May 2013
ACKNOWLEDGMENTS
This thesis-writing process has been one of the hardest things I have ever done. It
would be an understatement to say I am glad to be on the final lap of this race. Though
my final writing semester has shaken out much differently than I had originally
anticipated, I’m pleased with the final product and hope it might serve some use to
whoever is reading this in the future.
My time in a master’s program at Texas Tech has reminded me to live for the
moment. I feel like many times I am always looking ahead to the next big thing,
sometimes not appreciating a phase in my life until it is over. As I start this new phase in
my life, I’m learning and trying to appreciate the here and now, trusting that God will
lead my life in the best direction.
There are several people that have been so important to my success in graduate
school, college, and ultimately in life. First is my adviser, my committee chair, and my
friend, Dr. Courtney Meyers. You certainly helped to cultivate and develop my passion
for agricultural communications. I depended a lot on your love and guidance as an
undergraduate and graduate student, and am so blessed to have you and your family play
such a large role in my life. Dr. Doerfert served as a member of my graduate committee,
but also as an integral piece of my growth as a communicator and professional in the
industry. Starting in magazine, you constantly challenged my way of thinking and
pushed me to do bigger and better things. And Dr. Irlbeck, you truly planted my interest
in agricultural communications as a freshman, and continued to serve as a friend and role
model both personally and professionally.
ii
Texas Tech University, Kelsey F. Shaw, May 2013
Finally, in addition to my Lord and Savior, I depended so much on three essential
groups in my life to celebrate, grieve, and help me successfully reach this point in my
education. First, I am thankful for what I think of as three phases of friends in my
office/building. I depended on you girls (and one boy) to give me advice, listen, and help
me through this process. Second, my family has been very supportive of my journey,
always encouraging me to accomplish and be successful in whatever path I choose. And
finally, my newly-minted husband, Martin. In addition to the typical duties of supportive
husband, you have also had to help me with schoolwork as a fellow graduate student. I
am excited to graduate together and know we are stronger because of this journey.
Though this light at the end of the tunnel has seemed so far away for a long time,
it is bittersweet to come to the end of this journey. Thank you to everyone who has had a
hand in getting me this far and helping me grow into the woman I’ve become.
iii
Texas Tech University, Kelsey F. Shaw, May 2013
TABLE OF CONTENTS
ACKNOWLEDGMENTS……………………………………………………………...…ii
ABSTRACT…………………………………………………………………………….viii
LIST OF TABLES………………………………………………………………………..ix
LIST OF FIGURES……………………………………………………………………..xiii
I. INTRODUCTION ........................................................................................................... 1
Background and Setting .................................................................................................. 1
Farming in the United States ........................................................................................... 3
The Internet and Communication .................................................................................... 4
Social Media.................................................................................................................... 6
Agricultural Communications Preferences ..................................................................... 9
Need for Study/Significance ......................................................................................... 13
Theoretical Framework ................................................................................................. 14
Problem Statement ........................................................................................................ 15
Purpose and Research Objectives ................................................................................. 16
Limitations of the Study ................................................................................................ 16
Basic Assumptions ........................................................................................................ 17
II. REVIEW OF LITERATURE....................................................................................... 19
Conceptual Framework ................................................................................................. 19
Theoretical Framework ................................................................................................. 21
Diffusion of Innovations ............................................................................................ 21
Fundamental Concepts. .......................................................................................... 22
iv
Texas Tech University, Kelsey F. Shaw, May 2013
Realities of Diffusion in Social Systems. .............................................................. 27
Diffusion & Communications. ............................................................................... 29
Technology Acceptance Model ................................................................................. 31
Theory Tenets. ....................................................................................................... 32
Relevant Use of the Theory. .................................................................................. 34
Uses and Gratifications Theory ................................................................................. 35
Fundamental Aspects of the Uses and Gratifications Theory. ............................... 36
Applications to the Internet. ................................................................................... 38
III. METHODOLOGY ..................................................................................................... 43
Purpose and Research Objectives ................................................................................. 43
Research Design ............................................................................................................ 43
Population & Sample .................................................................................................... 46
Instrument...................................................................................................................... 48
Agriculturists’ Current Use of Social Media Tools ................................................... 49
Importance and Competence Using Online Communication Tools .......................... 50
Motivations and Barriers Toward Social Media Training ......................................... 50
Demographic Characteristics ..................................................................................... 51
Data Collection .............................................................................................................. 51
Data Analysis ................................................................................................................ 52
IV. RESULTS ................................................................................................................... 54
Introduction ................................................................................................................... 54
Comparing Respondents from Each State ..................................................................... 54
v
Texas Tech University, Kelsey F. Shaw, May 2013
Combined Demographics .............................................................................................. 60
Research Objective One ................................................................................................ 63
Research Objective Two ............................................................................................... 66
Facebook .................................................................................................................... 66
Twitter ....................................................................................................................... 74
Blogs .......................................................................................................................... 80
Websites..................................................................................................................... 87
Other Online Communication Tasks ......................................................................... 94
Computer-Based Communication Technology ....................................................... 101
Research Objective Three ........................................................................................... 108
V. CONCLUSIONS, DISCUSSION, AND RECOMMENDATIONS .......................... 111
Introduction ................................................................................................................. 111
Conclusions and Discussion ........................................................................................ 111
Research Objective One .......................................................................................... 115
Research Objective Two .......................................................................................... 118
Facebook. ............................................................................................................. 119
Twitter. ................................................................................................................. 121
Blogging, Websites and Other Online Communications Tools. .......................... 123
Research Question Three ......................................................................................... 127
Recommendations ....................................................................................................... 129
Practitioners ............................................................................................................. 129
Research................................................................................................................... 133
vi
Texas Tech University, Kelsey F. Shaw, May 2013
REFERENCES ............................................................................................................... 136
A. INSTRUMENT .......................................................................................................... 149
B. INSTITUTIONAL REVIEW BOARD APPROVAL ................................................ 161
C. PRELIMINARY EMAIL TO PARTICIPANTS ....................................................... 163
D. REMINDER EMAIL TO PARTICIPANTS.............................................................. 165
vii
Texas Tech University, Kelsey F. Shaw, May 2013
ABSTRACT
Farmer demographics are drastically changing and it is essential that farmers and
ranchers are taking the story of agriculture directly to the consumer. Online
communication tools may serve as a tool for this farmer to consumer communication.
The purpose of this study was to determine agriculturists’ use of online communication
tools. The target population for this study was members of organizations targeting
beginning farmers and ranchers in Texas, Illinois, and Georgia. An online survey was
administered electronically to members of seven organizations, and 185 completed
questionnaires were analyzed for this study. It was determined that agriculturists of all
levels of experience are not currently utilizing online communication tools to their full
potential, for either business or personal reasons. Additionally, several specific training
needs were identified regarding these tools. A wide variety of motivations and barriers
were identified that might encourage or discourage agriculturists from attending future
training sessions.
viii
Texas Tech University, Kelsey F. Shaw, May 2013
LIST OF TABLES
4.1
Crosstab of Respondents’ Gender by State of Residence.…………
55
4.2
Crosstab of Industry Segments by State……………………………
56
4.3
Crosstab of Respondents’ Years Owning an Operation…………...
57
4.4
Crosstab of Respondents’ Beginning Farmer or Rancher Status….
57
4.5
Crosstab of Respondents’ Alternative Operation Status…………...
58
4.6
Crosstab of Respondents’ Direct-to-Consumer Marketing Status...
59
4.7
ANOVA for Respondents’ Mean Age……………………………..
59
4.8
Years Spent Owning an Operation…………………………………
61
4.9
Electronic Devices Owned with Internet Access..…………………
63
4.10
Relationships Between Online Communication Tool Frequency of Use
for Personal or Agriculture Use………………………………
65
Respondents’ Perceptions of the Importance of Facebook Tasks and
Their Competence at Performing the Tasks…..……….............
68
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Facebook Importance……………………………
70
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Facebook Importance ……………………………………………...
71
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Facebook Competence…………………………...
72
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Facebook Competence …………………………………….............
73
Respondents’ Perceptions of the Importance of Twitter Tasks and Their
Competence at Performing the Tasks…..……………………
75
4.11
4.12
4.13
4.14
4.15
4.16
ix
Texas Tech University, Kelsey F. Shaw, May 2013
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
4.26
4.27
4.28
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Twitter Importance………………………………
76
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Twitter Importance ………………………………………………..
77
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Twitter Competence……………………………..
78
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Twitter Competence ……………………………………………….
79
Respondents’ Perceptions of the Importance of Blogging Tasks and
Their Competence at Performing the Tasks…………………...
81
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Blogging Importance…………………………….
82
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Blogging Importance……………………………………………..
83
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Blogging Competence…………………………
84
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Blogging Competence……………………………………………
85
Respondents’ Perceptions of the Importance of Website Tasks and Their
Competence at Performing the Tasks………………………..
87
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Website Importance……………………………
88
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Website Importance…………………………………………….......
89
x
Texas Tech University, Kelsey F. Shaw, May 2013
4.29
4.30
4.31
4.32
4.33
4.34
4.35
4.36
4.37
4.38
4.39
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Website Competence…………………………….
90
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Website Competence……………………………………………….
91
Respondents’ Perceptions of the Importance of Other Online
Communication Tool-Related Tasks and Their Competence at
Performing the Tasks………………………………………………
93
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Other Online Communication Tool Importance…
95
Independent Samples T-test for Significant Difference Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Other Online Communication Tool Importance…………………
96
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Other Online Communication Tool Competence..
97
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Other Online Communication Tool Competence…………………..
98
Respondents’ Perceptions of the Importance of Computer-Based
Communication Technology Tasks and Their Competence at Performing
the Tasks..…………………………………………….
99
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Computer-Based Communication Technology
Importance………………………………………………………….
101
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Computer-Based Communication Technology Importance
101
Independent Samples T-test for Significant Differences Between
Beginning Farmers and Ranchers and More Experienced Farmers and
Ranchers on Computer-Based Communication Technology
Competence………………………………………………………...
102
xi
Texas Tech University, Kelsey F. Shaw, May 2013
4.40
4.41
Independent Samples T-test for Significant Differences Between Those
Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Computer-Based Communication Technology Competence………
102
Online Communication Tool Training Needs of Agriculturists Using the
Borich Needs Assessment Model……………………….
103
xii
Texas Tech University, Kelsey F. Shaw, May 2013
LIST OF FIGURES
2.1
2.2
2.3
Rogers’ (2003) A Model of Stages in the Innovation-Decision
Process………….……………………………………………..………..
23
Adopter Categories Based on Roger’s (2003) Degrees of
Innovativeness………………………………………………………….
25
The Technology Acceptance Model…………………..…….................
33
xiii
Texas Tech University, Kelsey F. Shaw, May 2013
CHAPTER I
INTRODUCTION
Background and Setting
For the past century, populations have slowly shifted from more rural areas to
fast-growing urban centers. In 1900, only 40% of the nation’s population resided in
urbanized areas, while the remaining 60% lived in rural America (Gibson & Lennon,
1999). In 2000, these numbers had more than reversed, with 79% of all Americans living
in urban areas (U.S. Census Bureau, 2011).
Fewer and fewer young people are choosing to return to the farm (Davis &
Marema, 2007); since 1930, the number of farmers in America has been declining by an
average of 88,000 each year. Much of this is due to innovations in technology and in the
nation’s economy. Now, less than 2% of Americans are engaged in farming as their
primary profession (Davis & Marema, 2007). Farmer demographics are also drastically
changing; numbers of women and minority farmers are on the rise (American Farm
Bureau Federation, 2011). In 1997, women represented a mere 9% of farmers, while
minorities represented only 2% of all farmers (National Ag Statistics Service, 1997).
Today, the percentage of women farmers has grown to 14% and the percentage of
minority farmers has doubled in size, now representing 4% of the nation’s farmers
(National Ag Statistics Service, 2007).
Because of these shifts in population and demographics, more farms became big
business while other farms were forced to sell (Bullock, Lockaby, & Akers, 2002).
According to the American Farm Bureau Federation (2011), only 17% of farms complete
1
Texas Tech University, Kelsey F. Shaw, May 2013
more than $100,000 in sales each year, but those 17% account for more than 80% of total
sales.
Due to changes in the economy and a decreasing brand connection, consumers are
more attracted to a lower price than specific product, so companies that can mass produce
goods at a lower price have an advantage (Bohlen, Carlotti, & Mihas, 2009). However,
there is also a trend for locally-grown products, in comparison to the more expensive and
federally-regulated organic products (Adams & Salois, 2010). Recent legislation and
consumer desire to track their agricultural products all have an impact traditional
agriculture’s bottom line (Greene et al., 2009). These trends require that producers
examine their practices and production to ensure future appeal to the United States
consumer.
These producers must continue to explore innovative technologies and new ways
to interact with potential consumers. “With less than two percent of the U.S. population
involved in farming, we have to take our stories directly to the consumer” (Lohr, 2011,
para.10). Today, communicating with consumers is considered a farmer’s responsibility,
according to young farmers and ranchers surveyed by the American Farm Bureau
Federation (2012). Because of the new opportunities available through social media and
online commerce, greater opportunities for jobs, socialization, education, and cultural
activities may also be as readily available for those who reside in rural areas (Bullock et
al., 2002).
2
Texas Tech University, Kelsey F. Shaw, May 2013
Farming in the United States
A farm is defined as “any place where $1,000 or more of agricultural products
were produced and sold, or normally would have been sold, during the [census year],”
(Ahearn & Newton, 2009, p. 3). The National Ag Statistics Service (2007) reported there
are currently more than 3 million U.S. farm operators. A farm operator is the more
formal term for the person who runs the day-to-day operations of a farm, not necessarily
the financier (Weber & Ahearn, 2012). “The operator may be the owner, a member of
the owner’s household, a hired manager, a tenant, a renter or a sharecropper” (AFBF,
2011, para. 1). The terms “farmer” and “farmer operator” are frequently used
interchangeably, by both the USDA (Ahearn & Newton, 2009) and United States
Department of Labor (2012).
Currently, there are more than 2 million farms in the United States with a wide
variety of industries represented (National Ag Statistics Service, 2007). The top four
industries — cattle, hay, grains, and aquaculture — remained in these leading slots in
both the 2002 and 2007 Censuses of Agriculture. The average farm size is 418 acres,
with annual sales of about $135,000. Also, the average age of an American farm operator
is 57 years; and 45% of the farm operators reported farming as their primary occupation
(National Ag Statistics Service, 2007). Currently, only 14% of women serve as principal
farm operators.
The USDA also tracks and offers exclusive programs to those designated as
beginning farmers or ranchers in order to help encourage the growth of American
agriculture (Ahearn & Newton, 2009). According to the USDA definition, “beginning
3
Texas Tech University, Kelsey F. Shaw, May 2013
farmers or ranchers” must have less than 10 years farming experience. In 2007,
beginning farmers and ranchers made up about a quarter of all farm operators (Ahearn &
Newton, 2009). The American Farm Bureau Federation (2012) said these beginning
farmers and ranchers grow a wide variety of products and commodities and span the
entire nation. According to the Census of Agriculture (2007), new farms tend to be
smaller and have younger operators, as well as lower sales in relation to the national
average.
Many smaller-scale or alternative farmers tend to utilize direct marketing
techniques to promote their business or products (National Ag Statistics Service, 2007).
Direct marketing includes social networking, email newsletters, direct mail pieces, online
commercials, and a website or blog. These pieces are ideal for smaller businesses who
are aiming to speak directly to the client or potential customer and eliminate the
middleman. It also is cheaper overall and allows business owners to have more control
over their message (Bullock, 2011). Although there is no USDA standard, the term
“alternative” refers to operations that produce some sort of nontraditional crop, livestock,
or other farm product; service, recreation, tourism, food processing, forest/woodlot, or
other enterprise based on farm and natural resources; or unconventional production
system such as organic farming or aquaculture using direct marketing or other
entrepreneurial marketing strategy (Gold, 2007).
The Internet and Communication
Throughout history, communication has been an integral tool to facilitate
relationships between groups and individuals, even though the methods have drastically
4
Texas Tech University, Kelsey F. Shaw, May 2013
changed (Anderson-Wilk, 2009). While newspaper, magazine, and broadcast television
viewership has declined, radio listenership remains stable at 90%. Internet is the only
mass medium that has actually increased in audience use (Brody, 2004). Though the
term “Internet” refers to a large collection of networked computers and the term “world
wide web” more specifically references a way of accessing information on these devices,
now the two terms are utilized interchangeably to describe this electronic web of
information (Knight & Burn, 2005). According to Bullock et al. (2002), the Internet is
evolving to include a number of utilities such as the exchanging and delivery of
information, a gathering place for both professional and personal groups, and establishing
a sense of belonging among users that may even enhance job satisfaction and
performance.
Today, 78.3% of all Americans are accessing the Internet (Miniwatts Marketing
Group, 2012). Comparatively, 52% of those in rural areas have access to the Internet
(Pew Research Center, 2004), while 67% of those living in urban areas are online. Also,
rural users are shown to be participating in many of the same online activities as their
urban counterparts – including using email, search engines, and pursing hobbies. Most
Americans (56%) are accessing the Internet via a wireless device, and the most popular
tool is a laptop computer, which 39% of adults have used (Horrigan, 2009). Age is not
proving to be a factor inhibiting online activity. Zickuhr and Madden (2012) found that
53% of seniors over the age of 65 are utilizing the Internet or email in some capacity.
5
Texas Tech University, Kelsey F. Shaw, May 2013
Social Media
Duggan and Brenner (2013) reported that 67% of Internet users use some sort of
social media tool. Social media sites are “web-based services that allow individuals to
(1) construct a public or semi-public profile within a bounded system, (2) articulate a list
of other users with whom they share a connection, and (3) view and traverse their list of
connections and those made by others within the system” (Boyd & Ellison, 2007, p.1).
Social media technologies allow user-generated content to not only be shared
boundlessly, but also allow for multiple influences on a community of learning (Pfeil,
Arjan, & Zaphiris, 2009). When users are able to read, respond, and interact with
comments posted by other users, these tools usually result in greater engagement,
improved retention, and a higher likelihood of behavior change. Social networking is the
act of engaging using social media or other tools (Hartshorn, 2010). Typically social
networking involves groups of people discussing a common topic or area of interest.
Since the first social media site was made available in 1997 (Boyd & Ellison,
2007), the number of social media sites has increased along with the number of
individuals who use these online communication tools. Social media have a greater
appeal to younger audiences (Telg & Barnes, 2012), with 75% of 18-24-year-olds and
57% of 25-34-year-olds currently utilizing some sort of social media outlet (Lenhart,
2009).
Some of the most popular social media tools currently include Facebook, Twitter,
blogs, podcasting, and RSS feeds. Facebook is one of the most popular social media
tools, and has grown from 50 million users in 2007 to 1 billion users in September 2012
6
Texas Tech University, Kelsey F. Shaw, May 2013
(Facebook, 2012), with more than 600 million mobile users. The median age of
Facebook user is 22 and each user has an average of 305 friends.
Twitter is a form of microblogging, or sending short messages with 140
characters, which allows for faster communication (Smith, 2012). Twitter now has 127
million “active” users who log in at least once a month (Evans, 2011). Of these active
users, 54% are accessing the site via mobile devices (Skelton, 2012). In contrast to other
social media sites, older adult generations are driving Twitter’s popularity, with 58% of
users over the age of 35 (Skelton, 2012).
Blogs are websites maintained by a web user where posts may contain
commentary, news, photos, videos, or other content (Kaye, 2010). These sites are
interactive and typically encourage reader comment, response, and sharing. The Nielsen
Company (2012) reported that there are more than 181 million blogs posted online, which
is an increase from the 36 million blogs identified in 2006. Podcasts are a combination of
broadcast and blogging that allow Internet users to record audio or video files and then
post them online to be viewed or downloaded (Watson, 2013). According to Edison
Research (2012), 29% of Americans have listened to a podcast. Many Internet patrons
utilize RSS, or Rich Site Summary, as a method to deliver new blog or podcast content
(USA.gov, 2013). A user can sign up for a service that will either deliver selected web
content updates to a program dedicated to displaying this new Internet content.
The rapid adoption of social media tools like these can be attributed to several
reasons including peer pressure and the desire to develop and maintain friendships (Pfeil
et al., 2009). Adoption of social media has also been accelerated due to smartphones,
7
Texas Tech University, Kelsey F. Shaw, May 2013
which facilitate fast and reliable access to networking sites, without being tied to a wired
Internet connection (Smith, 2009). Pew Research Center (2012) found that 88% of all
Americans currently own a cell phone and that more than half of these adults (55%) use
their phones to access the Internet.
Typically, social media tools are useful and rewarding for those who use them
wisely (Paulson, 2009). Social media allow companies of all sizes and structures to
engage in timely and direct end-consumer contact at relatively low cost and higher levels
of efficiency than can be achieved with more traditional communication tools (Kaplan &
Haenlein, 2010). Unfortunately, this can be very time consuming for the expert who is
facilitating these interactions (Anderson-Wilk, 2009). In order to maintain a successful
social media presence, an organization or group must have an active and committed
group of supporters (Rigby, 2008).
Social media sites allow for communication and relationship building as part of an
advocacy or public relations campaign or effort (Grunig, 2009). Advocacy is publicly
taking a stand for an individual, organization, or idea with the intentions of persuading
target audiences to favor or at least accept the chosen point of view (Edgett, 2002).
When this term is used in reference to agriculture, sometimes the term “agvocacy” or
“agvocate” is used instead (Black, 2011). Public relations is used to connect an
organization with the public to raise awareness for an issue, organization, product, or
service (Cutlip, Center, & Broom, 1985). Social media facilitate community building and
foster interaction between people around central ideas, beliefs, or topics, which can
extend the communications arm of a message or campaign (Edman, 2010). By building
8
Texas Tech University, Kelsey F. Shaw, May 2013
relationships using social media tools, organizations or entities can affect how audiences
adopt messages marketed through these channels (Rajagopalan & Subramani, 2003).
Agricultural Communications Preferences
Farmers’ preferences for receiving information can vary due to a number of
demographic characteristics including age, income, education level, and farm size
(Mishra & Park, 2005). Doerfert, Graber, Meyers, and Irlbeck (2012) found that an older
demographic of farmers preferred magazines or television for various types of
agricultural information, but overall showed preference for all traditional media channels
(print and broadcast) over online or emerging media.
While people do tend to prefer a particular source for their agricultural
information (Ruth-McSwain, 2008), because of differing backgrounds and personal
preferences, it is likely that no single delivery method is suitable for everyone. There is a
tendency of agricultural professionals to focus their media relations efforts on the
agricultural print media, even though they realize print media is diminishing and perceive
television as having the most impact on their audiences (Ruth-McSwain, 2008).
Focusing solely on print media outlets could be preventing agricultural messages from
reaching non-agricultural audiences. In the future, the utilization of all media outlets—
broadcast, print, and Internet—will increase the efficiency and value of media relations
efforts to the organization, and those who ignore these trends will do so at their own peril
(Ruth-McSwain, 2008).
More recently, the Internet has provided unprecedented opportunities for those in
agriculture to communicate with many different publics in new ways (Irani, 2000).
9
Texas Tech University, Kelsey F. Shaw, May 2013
Grassroots environmental organizations have reported being better able to spread
information to voters and supporters through Internet channels (Kutner, 2000), while
home horticulturists have indicated they utilize online resources to learn gardening tips
and information (Ellis et al., 2012). Social media sites in particular have the ability to
serve as a forum for personalized and targeted communications, as well as for reaching a
large audience quickly (Anderson-Wilk, 2009).
Although prior research has found farmers prefer to receive printed information,
their acceptance of information passed through channels on the Internet is steadily
increasing (McCarthy, Beede, & Edgecomb, 2008). Prior experience and degree of
innovativeness have also been shown to affect someone’s acceptance of Internet
technologies (Irani, 2000). Additional research indicates that younger farmers and
ranchers may have a higher likelihood of utilizing online technologies in their businesses
than older members of this population (American Farm Bureau Federation, 2012).
According to a survey conducted by the American Farm Bureau Federation
(2012), 93% of all young farmers and ranchers report utilizing a computer for their
operation, while 99% had some sort of access to the Internet, with the majority (79%)
having access to a high speed connection. The most popular use of the Internet by young
farmers and ranchers is to gather news and agricultural information (American Farm
Bureau Federation, 2012).
The American Farm Bureau Federation (2012) determined that 79% of all young
farmers and ranchers use Facebook, with the most popular use being to gather news and
agricultural information. A reporter for Southeast Farm Press (Lohr, 2011) interviewed
10
Texas Tech University, Kelsey F. Shaw, May 2013
several farmers about their effective use of social media, particularly Facebook. One
farmer reported using his Facebook page to increase business for his seasonal vegetable
and agritourism operation by posting daily updates on farm activities and marketing what
products are available each day. Another farmer reported utilizing Facebook in his
wholesale business to notify chefs, clubs, and caterers of their available produce before
hand-delivering orders each week (Lohr, 2011).
Twitter users reported several important applications for this particular social
media tool to agricultural business owners, including putting a face with the farmer,
dialogue between agriculturists and those unfamiliar with agriculture, and connecting
members of the agricultural industry (Payn-Knoper, 2009). Allen, Abrams, Meyers, and
Schultz 0(2010) suggested many additional agricultural uses for Twitter, including
posting updates on daily activities completed on the farm, responding to follower
questions and comments regarding various agricultural practices and issues, or speaking
on behalf of the agricultural industry to issues currently affecting agriculture. Messages
sent from the producer level could help the public better connect their food and fiber to
their origins and address agricultural myths or any negative publicity the industry may be
weathering. By posting to a social media site such as Facebook or Twitter, agriculturists
are immediately able to share a realistic picture of agriculture with the public and create
awareness for important issues (Meyers, Irlbeck, Graybill-Leonard, & Doerfert, 2011).
There are additional tools within the social media landscape, including blogging,
RSS, and podcasts. Blogging is gaining popularity as a mainstream social media tool
(Fannin & Chenault, 2005) and many agriculturists are successfully using blogs to reach
11
Texas Tech University, Kelsey F. Shaw, May 2013
both traditional and new audiences (Moore, Meyers, Irlbeck, & Burris, 2013). Fannin
and Chenault (2005) found that utilizing RSS (Really Simple Syndication) feeds for an
agriculture news website led to increased awareness for the public, as well as farmrelated media outlets. Podcasting, which is an online delivery of audio feeds and content,
is a technology that allows both general consumers and various agricultural audiences to
receive a wide variety of agricultural news content (Fannin, 2006).
Baumgarten (2012) said those in agriculture are not against participating in social
media discussions, but simply may not understand the benefits of using social media tools
to promote or enhance business. Even though farmers may have access to the Internet
through smartphones and other improved technology, many are still not realizing the full
power social media can have on their business or agriculture as a whole (Telg & Barnes,
2012). Agriculturists should broaden the scope of their social media efforts and begin to
focus efforts externally to better communicate the message of agriculture to nonagriculture publics and expose them to the truth about how food is produced (Telg &
Barnes, 2012).
Social media are a farming revolution in the same way that the advent of radio
was able to disperse market prices and weather information; both aim to make farming
more profitable and accurately depict agriculture to the public (Lohr, 2011). Through
social media, farmers have the opportunity to interact, promote, and advocate for
agriculture. It has been become clear that it is beneficial for those in agriculture to utilize
various social media tools to communicate agricultural issues to the public (Graybill,
2011; Moore, 2012). This is largely due to the potential for social media to quickly and
12
Texas Tech University, Kelsey F. Shaw, May 2013
efficiently distribute information and other news. Beyond individual farmers and
ranchers, if agricultural communications units such as extension, non-profits, and other
rural businesses can learn to reap the full benefits of the Internet and social media, one’s
physical location may become irrelevant in the agricultural business community (Bullock
et al., 2002). Baumgarten (2012) said agribusinesses are utilizing social media in
revolutionary ways, and many agriculturists argue that social media will serve as
agriculture’s newest survival tool (Wisconsin State Farmer, 2011).
Need for Study/Significance
Agricultural audiences tend to consume information through a variety of mass
media channels, so it seems inevitable that these same individuals will readily consume
information from agricultural businesses and services through these same channels
(Rhoades & Aue, 2010). Typically, agricultural organizations operate with a one-person
communications staff with small budgets, so it makes it more difficult for these
organizations to explore new technological innovations (Bullock et al., 2002). Because
social media tools are so new, they have not been fully explored in the context of
agriculture or agricultural communications (Meyers et al., 2011).
Utilizing social media in agricultural endeavors has become a requirement, not an
option, and by using these tools, farmers and ranchers have the potential to impact the
public’s perception of agriculture (Hoffman, 2009). “Consumers are more willing to trust
farmers than companies” (Wisconsin State Farmer, 2011, para. 14), so farmers and
ranchers should be encouraged to connect with customers and consumers (Hoffman,
2009). However, sharing openly about daily activities is unfamiliar and uncomfortable to
13
Texas Tech University, Kelsey F. Shaw, May 2013
most farmers (Katims, 2010). Telg and Barnes (2012) said that more research needs to
be conducted to investigate barriers to farmers and ranchers in the adoption of social
media tools and how those barriers can be overcome.
Social media is not a fad, but needs to be carefully studied and considered as it
influences the future of communications (Telg & Barnes, 2012). Doerfert et al. (2012)
recommended that the Internet and social media should be closely monitored and
reevaluated as their role evolves within the agricultural industry.
Theoretical Framework
Theories utilized in this study include Rogers’ (2003) diffusion of innovations
theory, the technology acceptance model (Davis, 1989), and the uses and gratifications
theory (Katz, Blumler, & Gurevitch, 1973). Rogers’s (2003) diffusion of innovations
theory explains how new ideas, techniques or items are dispersed through a series of
channels, following a strict sequence of adopters: innovators, early adopters, early
majority, late majority, and laggards. The theory focuses on the likelihood of adoption
for an innovation, in this case, use of social media based on a number of factors including
evaluation of the innovation’s attributes: relative economic or social advantage,
compatibility with existing values, complexity of the idea, trialability, and observability.
An additional theory in this theoretical framework is the technology acceptance
model, which is based on the Theory of Reasoned Action (Fishbein & Ajzen, 1975). This
theory examines how attitudes and beliefs toward technology can influence eventual
decisions to use the particular technology (Davis, 1989). Additionally, various external
factors have the potential to influence behavior decisions (Davis, 1993). Researchers
14
Texas Tech University, Kelsey F. Shaw, May 2013
have utilized this theory to study adoption of technological advances such as email,
voicemail, word processing and the Internet (Lederer, Maupin, Sena, & Zhuang, 2000).
Irani (2000) established the theory’s usefulness in agricultural communications and
Internet applications.
The third theory in this study’s theoretical framework was uses and gratifications
theory (Katz, Blumler, & Gurevitch, 1973), which addresses how people choose
particular media to fulfill certain needs they expect to be met (Joinson, 2008). This idea
also extends to groups, businesses, and society as a whole. While typically used to
address choices in traditional media such as print, radio, and television, the theory has
been recently extended to types of electronic media, including social media. Due to this
shift, Bumgarner (2007) insisted the role this theory plays in the lives of people is even
more relevant than in previous instances. Utilized properly, this theory can help explain
why audiences are attracted to a specific media (Katz et al., 1973) and how target
audiences can be encouraged to utilize new or emerging media.
Problem Statement
It seems evident that in order for agriculturists to become better users of social
media, they must build upon their current understanding, familiarity, and bias concerning
the relevant tools. Therefore, in order to help these producers build knowledge, we must
assess their current levels of knowledge, self-efficacy, and opinion of using popular
social media tools. More simply, investigations must be made to determine how the
producers are using current online communication tools and what additional resources
could be provided to support their use.
15
Texas Tech University, Kelsey F. Shaw, May 2013
This study helped identify which tools and applications beginning farmers and
ranchers currently are using in the operations, as well as which tools these participants
would be interested in learning for future use. Results will be utilized to develop face-toface training content as well as online training modules to help agriculturists better utilize
these online communication tools to market and promote their businesses or services.
Purpose and Research Objectives
The American Association for Agricultural Education’s 2011-2015 National
Research Agenda (Doerfert, 2011) recognized the need to address the challenges and
opportunities changing technologies present. Social media and other online
communication tools are included in these changing technologies that need to be further
researched in the agriculture discipline. The purpose of this study was to determine
agriculturists’ use of online communication tools. The following research objectives
were used to achieve this purpose:
1. Determine the extent of respondents’ personal and business use of online
communication tools.
2. Determine potential needs in utilizing various forms of online communication.
3. Identify motivations and barriers to receiving additional training to utilize online
communication tools.
Limitations of the Study
While reviewing the study, the following limitations were identified:
16
Texas Tech University, Kelsey F. Shaw, May 2013
1. Sample groups for the study were selected based on accessible beginning farmer
and rancher groups in Texas, Illinois, and Georgia. These groups may not
necessarily be representative of the entire related population.
2. Participants may have integrated personal and business social media efforts, and
may not be able to easily separate the two experiences.
3. The researcher focused on the use of a few chosen social media tools, while
agricultural business may actually employ a very wide spectrum of social media
programs and platforms.
4. The researcher relied on participants to accurately evaluate their own levels of
knowledge, while being aware of the challenges of self-reporting.
5. Respondents could have been included on more than one of the organizational
member lists.
Basic Assumptions
This study is based on the following assumptions:
1. All survey respondents own and operate some sort of agricultural business or
service.
2. All survey respondents utilize at least one form of a social media tool for the
purpose of promoting or marketing their agricultural business or service.
3. Each respondent accurately answered survey questions regarding his or her levels
of knowledge and self-efficacy of use, as well as opinions and experiences
regarding the social media tools used.
17
Texas Tech University, Kelsey F. Shaw, May 2013
4. Some agricultural businesses or services may have an additional employee, intern
or spouse to focus on social media efforts, while others may be required to
complete all business operations and marketing efforts themselves. The amount
of time devoted to social media may vary greatly based on business size, stability
and structure.
5. No respondent filled out the instrument more than once.
18
Texas Tech University, Kelsey F. Shaw, May 2013
CHAPTER II
REVIEW OF LITERATURE
This review of literature focuses on the conceptual and theoretical framework for
this study. The conceptual framework presents the main aspects of emerging online
media. The theoretical framework includes diffusion of innovations theory, the
technology acceptance model, and uses and gratifications theory.
Conceptual Framework
In the past 20 years, the media landscape has changed drastically. Media users
now have many more choices as to where, when, and how to obtain information.
Emerging online media, sometimes referred to as new media, encompass information
including distributed over the internet, including websites, streaming audio and video,
and social media. Three distinct characteristics of new media make it vastly different
from traditional media – interactivity, demassification, and asynchroneity (Ruggiero,
2000). Interactivity allows users to personalize their media experience. In addition to
simply receiving information from either reliable or unreliable online sources, users can
discuss these stories with others, as well as report their own opinions or accounts of
events or issues. McMillan and Hwang (2002) sought to better define interactivity, and
through studying important aspects of others use of the term, were able to identify
direction of communication, user control, and time to load or find as vital pieces. It is
only when all three of these pieces are present that a tool or media can be deemed
interactive.
Demassification refers to the control level of a user, or the opportunity to select
from a wide variety of options (Ruggerio, 2000). Users can select what information they
19
Texas Tech University, Kelsey F. Shaw, May 2013
would like to receive, and can receive it on their phones, computers, tablets, or other
technological tools. This aspect has especially benefitted public relations professionals
(Alexander, 2002), who are now able to utilize emerging media as a tool to communicate
directly to a key public, while managing a two-way flow of communication and
developing feedback mechanisms. Heath (1998) echoed the same sentiments, stating that
in light of emerging online media, reporters, editors, and other officials no longer have as
much control in determining what issues are discussed.
Asynchroneity is the ability for messages to be posted and read at different times.
Where news used to be disseminated during a specific television or radio broadcast, or
once a day in print, news is constantly being created and reported, then consumed in a
worldwide forum. The sender and receiver do not have to be active during the same
periods of time to properly utilize the same media channel. Through interviewing
members of an e-learning classroom, Hrastinski (2008) identified that asynchronous
participation actually increased reflection and ability to process information. He argued
that tools such as email, discussion boards, and blogs be used when discussing issues that
require more consideration and reflection.
The emergence of electronic and online technologies has significantly altered the
media consumption patterns of many people (Ruggiero, 2000). Because of these
significant changes, motivation and satisfaction of the audience become major
components to completing audience analysis.
Social media users have indicated the tools are useful for developing and
maintaining relationships effectively (Urista et al., 2009). Social media tools have
proven useful for those who are looking to form connections with others who have the
20
Texas Tech University, Kelsey F. Shaw, May 2013
same interests (Chen, 2011). These connections and relationships are resulting in
satisfaction of basic needs for interpersonal communication (Urista et al., 2009).
Theoretical Framework
Diffusion of Innovations
Diffusion of innovations theory boasts strong roots in agriculture. Much diffusion
of innovation research stems from a seminal piece on the adoption of hybrid corn in Iowa
(Ryan & Gross, 1943). In the study, the Ryan and Gross conducted interviews with
farmers in two small Iowa communities to determine their dates of adoption and attitudes
toward hybrid corn seed, which had been introduced during the previous year. They
found all but two of the interviewed farmers had adopted the seed, and identified that the
pattern of adoption followed an s-shaped curve over time (Rogers, 2003). The rate of
adoption is the amount of time it takes for a social system to adopt a new innovation, and
this is typically characterized by an s-shaped curve, symbolizing slow adoption at the
beginning and end, with speedier adoption in the middle period of adoption then tapering
off near the end of adoption (Rogers, 2003).
Additionally, Ryan and Gross (1943) identified that farmers who had adopted the
seed earlier also had larger farms and higher incomes, as well as more formal education.
Typically, adopters experimented by first testing the seed on a smaller plot of land before
expanding use of the seed to their entire operation. They also identified that the farming
community served as the farmer’s social system, and this community proved to be a vital
role in the adoption of the seed by many farmers (Ryan & Gross, 1943).
Today, researchers turn to Rogers and Shoemaker (1971) or Rogers (2003) to
discuss diffusion or implementation and usage of a new “technology,” which is a term
21
Texas Tech University, Kelsey F. Shaw, May 2013
often used interchangeably with “innovation” (Rogers, 2003). However, technology is
actually the design for a process that conducts a certain action in lieu of an individual
completing the action. Typically, there is both a “hardware” and “software” component
to the new technology. The “hardware” refers to the actual product or application –
something you may have to adopt or purchase, while the “software” typically refers to an
intended use for the application or tool. An innovation could consist of a brand new
“hardware,” or simply a new “software” approach to using a particular technology
(Rogers, 2003). An agricultural example of this difference would be if a farmer received
a new piece of machinery to use in his operation. If simply the new machine is the
innovation, it would be referred to as a hardware innovation. If someone attempts to
teach that farmer to use this new machine in an innovative way, then he would be
learning a software innovation.
Fundamental Concepts.
According to Rogers (2003), “diffusion is the process in which an innovation is
communicated through certain channels over time among the members of a social
system. It is a special type of communication, in that the messages are concerned with
new ideas” (p. 5). Innovations are ideas, practices, or objects that are seen as new by an
individual (Rogers, 2003). While many incorrectly believe that useful innovations will
automatically diffuse, more often inventors and change agents must work fervently in the
background to spread an innovation, usually over a disappointingly long period of time
(Rogers, 2003). The diffusion process “involves (1) an innovation, (2) an individual or
other unit of adoption that has knowledge of, or has experienced using, the innovation,
(3) another individual or other unit that does not yet have knowledge of, or experience
22
Texas Tech University, Kelsey F. Shaw, May 2013
with, the innovation, and (4) a communication channel connecting the two units”
(Rogers, 2003, p. 18).
The innovation-decision process is comprised of five main stages that outline the
adoption of a given innovation (Figure 2.1): knowledge, persuasion, decision,
implementation, and confirmation (Rogers, 2003). Knowledge is the first stage, whereby
an individual becomes aware of an innovation and may consider adoption. Here, an
individual basically wants to know what an innovation is and how it works or would be
beneficial (Rogers, 2003). Second, the individual must be persuaded to try the innovation
for him or herself to investigate the benefits the innovation offers to his or her specific
situation (Rogers, 2003). The next stage is decision, where an individual would take
steps toward adoption or rejection, for at least a trial period of time (Rogers, 2003).
Implementation occurs when, after an adoption decision, an individual puts an innovation
into practice. This stage would also be where adaptations or reinventions of an
innovation would occur (Rogers, 2003). The final stage is confirmation, where an
individual reinforces his or her decision to adopt the innovation or can reverse the
decision. Although discontinuance of use typically occurs at this point, there are also
occasions where adoption could occur after a previous rejection. In some instances, these
stages could be re-ordered, such as if an individual was forced to adopt an innovation,
23
Texas Tech University, Kelsey F. Shaw, May 2013
their decision stage might precede their persuasion stage (Rogers, 2003).
Figure 2.1: Rogers’ (2003) A Model of Stages in the Innovation-Decision Process
There are five perceived attributes of any innovation that help determine adoption
rates and consideration of these five characteristics is vital to explaining adoption patterns
of any innovation (Rogers, 2003). The first of these is relative advantage, or the
perception of how much more useful an innovation will be in comparison to its
predecessor. It is essential that the individual sees the innovation as useful, since the
higher the relative advantage, the faster the innovation will be diffused (Rogers, 2003).
The second attribute is compatibility, which is defined as how much an innovation agrees
with current cultural and societal norms of a social system. Innovations that clash with
current norms or perception of these norms will not be adopted as quickly or easily by an
24
Texas Tech University, Kelsey F. Shaw, May 2013
individual (Rogers, 2003). Third, complexity of an innovation can affect adoption rates.
The perception of difficulty to master an innovation has the ability to hinder speed of
adoption (Rogers, 2003). Trialability refers to the ability of individuals to test-run an
innovation before committing to adoption. Ideas that can be tested by an individual tend
to be adopted more quickly (Rogers, 2003). The final perceived attribute is observability,
which references whether the innovation can be observed in use by others in a social
system, preferably by an opinion leader. Research indicates that individuals are not
frequently interested in scientific studies showing the benefits of an innovation, but
instead would rather rely on evaluations from peers who have previously tested or
adopted the innovation (Rogers, 2003).
According to Rogers (2003), “communication is a process in which participants
create and share information with one another in order to reach a mutual understanding”
(p. 5). A communication channel is a method of sending messages from one individual
to another, either through mass media channels or interpersonal channels. Mass media
includes channels such as television, radio, and print outlets that allow for a large reach
with a single placement. Interpersonal channels involve in-person communication
between two individuals or a group or individuals (Rogers, 2003).
Time is another vital piece of the innovation-diffusion process (Rogers, 2003). It
is crucial to examine how long it takes an individual to move from knowledge of an
innovation to adoption of an innovation in comparison with other individuals in a social
system, and how many individuals within that social system adopt an innovation within a
given period of time. Many innovations endure lengthy periods of diffusion (Rogers,
2003).
25
Texas Tech University, Kelsey F. Shaw, May 2013
“Innovativeness is the degree to which an individual or other unit of adoption is
relatively earlier in adopting new ideas than the other members of a system” (Rogers,
2003, p. 22). Based on the time that it takes individuals to adopt an innovation, they are
sorted into basic adopter categories, which is visually depicted in Figure 2.2 (Rogers,
2003). Each group typically has a set of distinctive characteristics. Innovators are
typically the first to adopt an innovation and cannot rely on reviews from previous
adopters. Early adopters tend to be wealthier, more educated, and more willing to take
risks. This group may also contain one or more opinion leaders within the community.
Early adopters also tend to be younger and more influential in the community. The early
majority is somewhat more conservative, but open to trying new things. The late
majority tends to be older with less education. Laggards are typically the least educated,
most conservative, and have the least financial resources.
Figure 2.2. Adopter Categories Based on Roger’s (2003) Degrees of Innovativeness
Beyond an innovation’s attributes and an individual’s classification in an adopter
category, several other variables could affect an individual’s decision to adopt. These
include access to peer reviews of the innovation, an individual’s aversion to change, and
access to financial resources (Rogers, 2003).
26
Texas Tech University, Kelsey F. Shaw, May 2013
Innovation adoption decisions can be made by an individual or entire social
system. A social system is a group of individuals interested in achieving a common goal
(Rogers, 2003). Adoption of innovations is measured within these systems to establish
boundaries of measurement. Many times, innovations can challenge norms of a social
system. “Norms are the established behavior patterns for the members of a social
system” (Rogers, 2003, p. 27). People get used to certain techniques or equipment, those
things that have been passed down from generation to generation. Additionally, people
are comfortable in these patterns and sometimes unwilling or unexcited about change.
These norms can develop barriers to adoption of an innovation. Social change is when
there is some sort of alteration within a social system. When diffusion occurs, regardless
of whether the innovation is accepted or rejected, social change also occurs (Rogers,
2003). If an innovation is adopted, a community may be changed by simply participating
in this new technique or innovation, and may be more open to changes in the future. Or,
in the case of non-adoption, a social system may develop a strong opinion set in a
specific direction, or be more close-minded to the introduction of future innovations.
When considering the adoption rate of any innovation, it is important to consider the
overall compatibility of the innovation with a social system’s values, beliefs, and
traditions (Rogers, 2003).
Realities of Diffusion in Social Systems.
It is innate that a social system will develop leaders, usually referred to as opinion
leaders, within the diffusion framework. “Opinion leadership is the degree to which an
individual is able to influence other individuals’ attitudes or overt behavior informally in
a desired way with relative frequency” (Rogers, 2003, p. 27). Opinion leaders typically
27
Texas Tech University, Kelsey F. Shaw, May 2013
have more exposure to new innovations and are somewhat innovative overall, although
the most innovative members of a social system typically are not seen as credible.
Typically opinion leaders would fall into the early adopter or early majority category
(Rogers, 2003). Additionally, opinion leaders typically have a higher or at least
moderately-high socioeconomic status relative to the social system. These combined
attributes put opinion leaders in an influential position in the middle of the interpersonal
communication networks (Rogers, 2003).
Opinion leaders have been shown to create positive buzz for innovations, and
Leonard-Barton (1985) identified that members of the public will look at an innovation
more favorably if presented by an opinion leader. Valente and Davis (1999) found it was
even possible to encourage faster diffusion or adoption of an innovation through the use
of opinion leaders, but it is vital that the leader be seen as extremely credible by members
of the community.
Although opinion leaders operate within a social system, there are those who
bring in the innovation from outside of the social system, referred to as change agents.
According to Rogers (2003), a change agent is “an individual who influences clients’
innovation-decisions in a direction deemed desirable by a change agency” (p. 27). These
change agents typically identify and utilize opinion leaders to help encourage the
adoption process (Rogers, 2003).
Telg et al. (2012) explored the idea of opinion leadership and change agents in the
agricultural industry by conducting focus groups with citrus growers in Florida to
determine their preferences for receiving agricultural information. These groups reported
preferences consistent with those Rogers (2003) had previously identified. Respondents
28
Texas Tech University, Kelsey F. Shaw, May 2013
preferred to learn about new growing techniques from activities such as face-to-face
interaction with experienced farmers at field days rather than what they viewed as
scientists who were disconnected from the industry.
When change agents introduce innovations to a social system, they plan for
consequences that are desirable, direct, and anticipated (Rogers, 2003). Desirable refers
to consequences that are positive, direct refers to changes are intended from the
innovation or whether they trickle down into secondary effects of an innovation, and
anticipated refers to those consequences that were expected or intended by the
introduction of an innovation.
Benefits of a technological adoption may not always be clear to the users or
potential users of an innovation. In fact, innovations are rarely seen as superior to the
previous tool or technique when initially presented to the potential user (Rogers, 2003).
Typically, this is overcome by allowing potential users to utilize the trialability attribute,
testing an innovation before implementing it on a larger scale.
Although previous research viewed innovations as independent from each other,
in reality, adoption of innovations are interwoven, and more investigations should be
made into clusters of innovations, specifically technology (Rogers, 2003).
Diffusion & Communications.
There have been a large number of inquiries in a wide variety of fields pertaining
to the diffusion of innovations theory (Rogers, 2003). More recently, diffusion of
innovations has been cited in numerous studies regarding the adoption of emerging online
media. The theory has been utilized to identify who is utilizing specific technologies
(Peng & Mu, 2011) as well as why some are leaving specific platforms in favor of others
29
Texas Tech University, Kelsey F. Shaw, May 2013
(Coursaris, Yun, & Sung, 2010). The adoption rates of social media is a topic of
particular interest, especially among specific groups including university communicators
(Kelleher & Sweetser, 2012), nonprofit organizations (Waters, 2010), and online election
campaigns (Gulati & Williams, 2011).
Through a mailed survey of TV, radio, and newspaper media in Florida, Bisdorf,
Irani, and Telg (2003) utilized Roger’s diffusion of innovations framework to evaluate
Internet use in Florida newsrooms. The researchers found that the Internet had diffused
quickly across these newsrooms, and was used by almost 100% of those surveyed. This
indicated that online tools were very rapidly adopted, going quickly through Rogers’
(2003) stages in a time period of only about 5 years.
Riley, Cartmell, and Naile (2012) conducted a telephone survey of Kansas beef
feedlot managers to determine their preferred sources of information in the event of
agroterrorism. In terms of Rogers’ (2003) innovation-decision process, managers were
identified as being in the persuasion stage, still working to seek information about
potential crises and response options. The researchers suggested further research in this
area to identify tools that could be used to encourage managers into more advanced
stages of adoption.
Rhoades and Aue (2010) surveyed agricultural editors and broadcasters about
their use of social media in order to ascertain if agriculture media is at a similar stage as
mainstream media in their adoption. Overall, they found that respondents were in the
early to late adopter categories, although respondents did fall into a variety of different
stages of adoption. It was determined that it was important for users to frequently update
30
Texas Tech University, Kelsey F. Shaw, May 2013
and interact using these tools or, as Rogers (2003) suggested, they may not see the full
benefit of utilizing these innovations.
Through a series of focus groups, Telg and Barnes (2012) aimed to determine the
communication preferences of members of Florida’s Young Farmers & Ranchers
program. They found that members did not implement social media tools within the
organization because they did not agree on Rogers’ (2003) perceived attributes of
innovation.. The researchers proposed that the organization develop a better social media
strategy to help successfully support the organization through the organizational change
process to more thoroughly integrate social media in their communication efforts.
These studies support the use of diffusion of innovations as a theoretical
framework and demonstrate how the theory can be applied in a variety of settings.
However, emerging media are consistently being developed and updated so additional
research needs to be conducted to further explore how individuals and groups in
agriculture are adopting these innovations.
Technology Acceptance Model
Originally, the technology acceptance model (Davis, 1989) was based on Fishbein
and Ajzen’s (1975) Theory of Reasoned Action in psychology, which explains how to
measure specific aspects of attitude, draws distinctions between “beliefs” and “attitudes,”
and discusses how outside factors influence these beliefs, attitudes, and overall behavior
decisions. The difference between the technology acceptance model and the Theory of
Reasoned Action is that the technology acceptance model intends to decipher differences
in attitude toward the new technology or object and the Theory of Reasoned Action
focuses on the behavior (Davis, 1993). Also, the technology acceptance model
31
Texas Tech University, Kelsey F. Shaw, May 2013
recognizes that there are external factors that could ultimately affect attitudes toward
adoption of a technology (Yi & Venkatesh, 1999).
After the initial development of the technology acceptance model, two main
schools of research were identified related to the theory (Lee, Kozar, & Larsen, 2003).
The first group attempted to validate the model in various scenarios and disciplines. The
second group worked to ensure the differences between the Theory of Reasoned Action
and the technology acceptance model, and to see if one model was better than the other.
Venkatesh and Davis (2000) then made slight adjustments to the model at the turn of the
century in order to take into account the technological advancements at the time. They
worked to further define external variables of the model such as social influence and
pressures. During the 1990’s, many studies were conducted on the model’s validity in
reference to tools such as email, the Internet, fax, and personal computers (Lee et al.,
2003). Because most technology acceptance model studies rely on self-reporting
methods, there is some amount of bias concern for studies utilizing or testing the model
(Lee et al., 2003).
Theory Tenets.
The technology acceptance model is utilized as a theoretical framework to explain
psychological determinants of acceptance behavior and attitudes specifically toward
technology (Roberts & Henderson, 1998). The technology acceptance model helps
identify why users are accepting or rejecting a particular technology, and aids in
determining how to improve a technology to better suit a group of users (Davis, 1993).
According to the theory, two perceptions can cause people to accept or reject
information technology – perceived usefulness of technology and the perceived ease of
32
Texas Tech University, Kelsey F. Shaw, May 2013
use (Davis, 1989). Perceived usefulness is the concept that people are likely to accept or
reject a new type of technology based on whether they think it will help them perform
better or more efficiently. Perceived ease of use refers to a situation where, even if a
potential user sees the value in a new technology, they eventually reject it because they
view the technology as being hard or complicated to learn and implement (Davis, 1989).
Perceived ease of use is agreed to have a direct correlation to perceived usefulness,
because someone who finds a technology easier to use tends to also find that technology
more useful (Davis, 1993). However, the reciprocal is not true – perceived usefulness
does not positively affect perceived ease of use. Technology that a person sees as useful
and easy to use has a high likelihood of being adopted (Davis, 1989).
Additional factors may influence the likelihood of technology adoption, including
people’s attitudes toward the technology and their degree of innovativeness (Irani, 2000).
Self-efficacy also plays a large role in a users’ attitude toward adoption of technology (Yi
& Venkatesh, 1999). Although most technological advances are purely created for
improving daily processes, many are still rejected by potential users. If too many
potential users reject a new technology, typically the technology will fail overall (Davis,
1993).
It is important to note that the technology acceptance model relies solely on
people’s evaluation of their use and interest in using a particular application, so there is
an innate probability some of the evaluations will not accurately reflect an objective
reality (Davis, 1989). And even if a new technology or application would definitely aid
in efficiency or effectiveness, if a potential user does not evaluate it as being beneficial or
better than the alternative, the innovation will unlikely be implemented (Davis, 1989).
33
Texas Tech University, Kelsey F. Shaw, May 2013
Basically, the technology acceptance model attempts to explain how external variables can
influence perceived usefulness or ease of use. Perceived usefulness is the extent to which using
a particular technology will be beneficial. Perceived ease of use is how easily the user believes
he or she will be able to utilize and understand the technology (Venkatesh, 1999). These two
aspects will impact the potential user’s overall attitude toward using the technology, and then
the intent to use the technology. These factors will determine whether the user will choose to
employ a particular technology (Figure 2.3).
External
Variables
Perceived
Usefulness
Attitude
toward Use
Behavioral
Intention
to Use
System
Usage
Perceived
Ease of Use
Figure 2.3: The Technology Acceptance Model (Hubona & Geitz, 1999).
Relevant Use of the Theory.
Previously, researchers have utilized the technology acceptance model to monitor
acceptance behaviors of email, voicemail, word processing, and web usage (Lederer at
al., 2000). Although Irani (2000) identified the technology acceptance model as valid
and useful to the study of agriculture’s use of Internet communications and other
technological innovations, the model has not been utilized extensively in agriculture.
Flett et al. (2004) utilized the model to identify technology acceptance patterns in
New Zealand dairy farmers. They found the model was useful in classifying farmers as
technology users or non-users, which was important to an industry that relies heavily on
developing technology. Irani (2000) tested the validity of the model in a college-level
34
Texas Tech University, Kelsey F. Shaw, May 2013
agricultural classroom by administering a questionnaire with a battery of questions to
detail the students’ online use. Results indicated that those who had high perceived
usefulness or ease of use, typically from prior computer or technical experience, had a
more positive attitude toward use and ultimately used the technology. Irani (2000)
advocated for more application of the model in the agricultural industry as well as the
agricultural classroom. Her investigation provided support for use of the technology
acceptance model in agricultural communications research.
Lee et al. (2003) said more research is needed to test the technology acceptance
model with multi-user systems, such as social media and other online communication
tools . Additionally, as new online communication tools develop, it is vital to test the
model for these new tools to ensure vitality and usefulness of the model in the face of
innovation.
Uses and Gratifications Theory
The uses and gratifications theory is concerned with identifying how people are
utilizing methods of communication to satisfy needs and achieve goals by simply asking
them (Katz, Blumler, & Gurevitch, 1973). The theory was created to determine what
motivates and attracts audiences to a particular type of media according to what satisfies
their social and physiological needs (Katz, Haas, & Gurevitch, 1973).
While Katz, Blumler, and Gurevitch (1973) did extensive research in developing
the theory, the idea truly originated in the 1940s with Lazarsfeld and Stanton’s (1942,
1944) inquiries into the public’s selection of radio and comics. They found that people
listened to a particular radio program over another because they were motivated to guess
who would win contests and were interested in the specific content addressed in the
35
Texas Tech University, Kelsey F. Shaw, May 2013
duration (Lazarsfeld & Stanton, 1942). Uses and gratifications research began to evolve
to include television as a major media player, and researchers discovered audience
members were fulfilling needs such as escape, companionship, and emotional release
through the programming (Eighmey & McCord, 1998). Soon the research began to be
utilized to help explain why people continued to choose a specific media type (Eighmey
& McCord, 1998).
Historically, the theory has focused on satisfying cognitive and affective needs
centering on personal and entertainment values (Urista, Dong, & Day, 2009). Some
earlier research identified media selections as a reflection of various relationships the
participant had socially (Ruggiero, 2000). Also, more focus was placed on how to attract
new users to a medium, instead of focusing on how to maintain user interest in a
particular medium (Stafford, Stafford, & Schkade, 2004).
Fundamental Aspects of the Uses and Gratifications Theory.
According to Katz, Blumler, and Gurevitch (1973), there are five important
elements to the uses and gratifications model: (1) The audience is assumed to be active,
in that they are actively seeking a goal by making particular choices; (2) the audience is
assumed to have free will of choice, and can select the media which most satisfies them;
(3) any particular media is in competition with a number of others that might have the
ability to satisfy a social or psychological need; (4) people are accurately able to evaluate
their decisions and rationale behind those decisions; (5) no judgments or evaluations
should be insinuated or assumed toward a particular medium.
The purpose of uses and gratifications research is to identify that one type of
media or set of mediums that is better at satisfying a specific set of needs than another
36
Texas Tech University, Kelsey F. Shaw, May 2013
media (Katz, Blumler, & Gurevitch, 1973). Research that applies this theory typically
examines the motives for use of a particular media and rewards that are sought from use
of the media (Wimmer & Dominick, 1994). More specifically, Wimmer and Dominick
(1994) explained the theory as an investigation into “how people use the media and the
gratifications they seek and receive from their media behaviors” (p. 349).
“Media” refers to a specific type of communication, such as television, print, or
radio and “a medium or message is a source of influence with the context of other
possible influences” (Chen, 2011, p. 757). Each media type offers a unique combination
of content, attributes, and location, which in turn offers each media type an opportunity to
satisfy a different set of needs (Katz, Blumler, & Gurevitch, 1973). Non-media sources
are frequently cited as more gratifying than typical media sources (Katz, Haas, &
Gurevitch, 1973).
There are two major schools of thoughts on the uses and gratifications theory.
One group tends to view the audience as passive, with media having a strong affect on
audiences. The second group argues audiences are active and able to clearly discern
between choices in media outlets (Ruggiero, 2000). Eighmey and McCord (1998) found
that people most typically utilize a specific media because they just happen to stumble
upon it, such as a magazine at a supermarket checkout, or a website pop-up
advertisement, which is an example of a passive audience.
There are also typically two types of media gratification – content and process
(Stafford et al., 2004). Content users are interested in the actual messages presented by
or provided in the medium, while process users are interested in the experience of
utilizing the medium.
37
Texas Tech University, Kelsey F. Shaw, May 2013
Uses and gratifications theory explains that if users select a particular medium and
stick with it, the medium is filling their needs in a specific way (Chen, 2011).
However, Ruth-McSwain’s (2008) research revealed that although individuals
were seeking out media to meet their individual needs (such as familiarity or ease of use);
the selected medium may not actually be fulfilling the original creator’s communications
purpose. Basically, communicators would select methods they were more familiar or
comfortable using over a newer method that might better meet their audience members’
needs and objectives (Ruth-McSwain, 2008). She recommended further research should
be done using the theory to examine communicators’ bias as an influence on media
choice.
Typically, people are more likely to adapt a familiar media to meet their needs,
and many times base their decisions on entertainment value as much as information value
(Katz, Haas, & Gurevitch, 1973). Contemporary researchers argue that audience
members can no longer be classified as completely preferring one media over others. It is
more likely in today’s society that individuals consistently use several different types of
media to satisfy a plethora of needs (Ruggiero, 2000). According to Chen (2011), “an
active audience selecting media is still viable even though today’s media landscape offers
so many more options than it did in the past” (p. 761).
Applications to the Internet.
The Internet has changed the way we do business and communicate (Stafford et
al., 2004). It is being regarded as a revolutionary approach to marketing and the modern
business model. The Internet is the most rapidly-developing new media, and continues to
evolve and adapt to meet a wide variety of new uses (Eighmey & McCord, 1998).
38
Texas Tech University, Kelsey F. Shaw, May 2013
Although the uses and gratifications theory was somewhat ignored for several decades,
the advent of new technologies has revived its popularity (Ruggiero, 2000). It may be too
early to truly identify the social and cultural impacts of new media until we identify why
individuals are selecting these communication methods in the first place (Ruggiero,
2000). For example, Internet users are indicating they are more interested in media that
addresses them in a more casual, personalized voice. Studies that examine the theory’s
relevance to electronic or online media, and new Internet-specific gratifications, are
necessary to gain a better understanding of how and why audiences are using emerging
media (Ruggiero, 2000).
Online media is different in the fact that it is interactive, and the line between
sender and receiver is blurred (Ruggiero, 2000). One unique characteristic of the
Internet, versus other types of traditional media, is that audience members can submit
information anonymously or under false identities, so they are more likely to say things
they would be reluctant to say in person. However, because online correspondence so
directly mimics face-to-face conversation, users report they are more likely to accept
information posted on the Internet than through mass media channels (Ruggerio, 2000).
Internet is viewed as the ultimate platform for individuality. Users can customize,
what, when, and where they create or receive messages (Ruggiero, 2000). Additionally,
it is unmatched in its possibility for community and relationship building, but also
provides an opportunity for increased isolation or technological dependency such as
Internet addictions. Uses for the Internet are varied – while some may focus on visiting a
specific website to obtain desired information, others may surf or browse, even after they
have obtained the specific content. There is another group of Internet users who qualify
39
Texas Tech University, Kelsey F. Shaw, May 2013
as “lurkers” and do not participate in online content creation or discussion, but solely troll
through the content posted by others (Ruggiero, 2000).
Sampling Internet users has proven difficult for researchers, resulting in many
studies only being truly applicable to a more specific audience (Ruggiero, 2000). “Since
individual users essentially control the communicative process of the Internet by virtue of
their power to initiate access to commercial sites, or even through the choice of whether
to be online at all, [uses and gratifications] provides the theoretical framework for
understanding the specific reasons that bring consumers online” (Stafford et al., 2004, p.
267).
The uses and gratifications theory serves as a useful explanation for why people
are leaving traditional media in favor of emerging online media – these new forms of
media are filling the same social and psychological needs (Ruggiero, 2000). Many uses
and gratifications of emerging online media are very similar to those of long-standing
media types (Eighmey & McCord, 1998). Users still have a desire to learn about what is
going on around them, and in some cases simply entertain themselves using either new or
older media tools.
Because audiences are consuming so many types of different media, it is actually
better to study the uses and gratifications theory in the context of the consumer’s entire
media experience, rather than simply isolating one method or media (Ruggiero, 2000).
“Recognition of consumers’ Internet uses and sought gratifications for
Internet use in the process of designing market offerings that are more
responsive to consumer needs will lead to greater degrees of consumer
value for the Internet as a medium, and will result in greater benefits from
40
Texas Tech University, Kelsey F. Shaw, May 2013
the consumer Internet use in recognition of the vast and unique
efficiencies that this new medium provides to users, firms, and society
alike” (Stafford et al., 2004, p. 280).
More recently, the uses and gratifications theory has been tested in a variety of
applications related to new media and social media. One popular scenario is identifying
who is using a specific type of social media, such as Facebook or Twitter, and why they
are selecting that specific tool. Park, Kee, and Valezuela (2009) identified four common
gratifications for using Facebook, indicating that users are interested in socializing,
entertainment, self-status seeking, and information. However, it has been indicated in
Park et al.’s (2009) study, as well as others (Boyd & Ellison, 2007, McAndrew & Jeong,
2012), that specific gratifications for using online communication tools greatly varies
based on the user’s age, gender, and even relationship status.
Kaye (2010) explored uses and gratifications of blogs for more than 2,000 blog
users through an online survey. She determined nine prevalent motivational factors
including expression, guidance, personal debate, variety of opinion, and specific inquiry
that users reported experiencing while using the online communications tool.
Many studies focus on identifying gratifications of a specific virtual community,
or group of Internet users, such as college students (Raacke & Bonds-Raacke, 2008) or
those involved with a specific industry (Zaglia, 2013). Doerfert, Graber, Meyers, and
Irlbeck (2012) identified that older agriculturists prefer traditional media sources to social
media channels when receiving decision-making information, as they were more satisfied
with the information received through these traditional sources.
41
Texas Tech University, Kelsey F. Shaw, May 2013
Using a qualitative approach, Ruth-McSwain (2008) tested the uses and
gratifications theory through in-depth interviews and online focus groups with
agricultural communicators working with news media. She aimed to determine which
type of media outlets these professionals prefer to work with. The study demonstrated
that her audience seemed to prefer utilizing print media for reasons standard with the
theory, including familiarity, increased time in communicating to the media, and the
ability to develop deeper, more personal relationships. However, her participants did
acknowledge the need for the industry to shift more toward other forms of emerging
media, as print media continues to decline.
Meyers et al. (2011) qualitatively investigated Facebook as a public relations tool
in the context of the uses and gratifications theory. Their respondents indicated they were
satisfied with Facebook because they were physically able to measure their reach by
tracking their number of followers and the amount of information posted to the cause’s
Facebook page. Respondents were pleased with the ability to share stories, post
information, and overall interact with their supporters. Because both user and facilitator
needs were being met through this tool, the researchers recommended the tool to other
advocates and communicators.
Although general media use of agriculturalists has been researched focusing
mainly on traditional media sources (Doerfert et al., 2012; Ruth-McSwain, 2008), there is
yet to be extensive research focusing on the social media uses and gratifications of this
distinct set of users. Additional research is necessary to further explore how and why
agriculturists use emerging media and what gratifications they obtain from that use.
42
Texas Tech University, Kelsey F. Shaw, May 2013
CHAPTER III
METHODOLOGY
A literature review was conducted to establish a need for research on agricultural
producers’ use of social media tools to promote their business or operation. This chapter
explains the process used to conduct the study including research design, population and
sample, instrumentation, data collection, and data analysis..
Purpose and Research Objectives
The purpose of this study was to determine agriculturists’ use of online
communication tools. The following objectives were formed to achieve the research
purpose:
1. Determine the extent of respondents’ personal and business use of online
communication tools.
2. Determine potential needs in utilizing various forms of online communication.
3. Identify motivations and barriers to receiving additional training to utilize online
communication tools.
Research Design
To aid in the development of new farms and ranches across the nation, USDA
currently offers specific programs and funds to those who are beginning farmers and
ranchers. In addition to special loan programs, the USDA has established a grant
program to help develop “value-added market development activities” for these
beginning farmers and ranchers (Ahearn & Newton, 2009). The current study was
developed as part of such a grant awarded to researchers at Texas Tech University, the
University of Illinois, the University of Georgia, and Kansas State University. This
43
Texas Tech University, Kelsey F. Shaw, May 2013
project aims to identify online communication needs of beginning farmers and ranchers
(focusing on alternative enterprise producers) and develop training workshops and
resources to help these producers utilize online communication tools for the benefit of
their agricultural operations.
To accomplish the grant’s purpose, it was necessary to conduct a needs
assessment, which is the process of identifying an area of need or weakness, then
completing primary and secondary research to bridge gaps in areas of deficiency
(Altschuld & Kumar, 2010). A need is something that must be resolved, something that
requires action to deal with the situation; in other words, a need is “a measureable gap
between two conditions – ‘what is’ (the current status or state) and ‘what should be’ (the
desired status or state)” (Altschuld & Kumar, 2010, p. 3). Typically, these assessments
are conducted using a number of methods by a specific organization or group. The most
beneficial pieces of needs assessment can be used to quickly impact short-term needs and
those that can quickly be resolved, as well as identify high-priority needs that may be
more long-term to all those involved (Altschuld & Kumar, 2010).
A needs assessment has three major phases – pre-assessment, assessment, and
post-assessment (Altschuld & Kumar, 2010). In pre-assessment, the focus is on finding
information that is already available to determine the knowledge gap. In assessment, new
information is collected to fill the holes discovered through the pre-assessment stage.
Finally, in post-assessment, the organization works to develop solutions to the identified
issues given the data collected. Additionally, this stage would include evaluation on the
research conducted to see if any additional research should be done before finalizing the
organizational strategy (Altschuld & Kumar, 2010).
44
Texas Tech University, Kelsey F. Shaw, May 2013
To complete the needs assessment, the research in the current study utilized a
quantitative, descriptive survey research design to administer an online instrument to a
sample of farmers and ranchers in Texas, Illinois, and Georgia. While the states were
selected based on participation in a USDA grant project, specific organizations within
each state were purposively selected that represented members who could benefit most
from a direct-to-consumer marketing program to help them grow awareness of a product
or service they provide. Additionally, each of the organizations had some sort of member
database through which a program director could contact the members in order to
distribute the survey link.
Surveys are a crucial tool for social science research and are currently the most
frequently utilized method of data collection (Irani, Gregg, & Telg, 2004). Qualtrics was
used to administer the questionnaire only to those who indicated they were involved in
some sort of farming or ranching enterprise. Irani et al. (2004) stated that utilizing Webbased survey methods actually reduced time and expense, as well as possibly increasing
rate and time of response when compared to other traditional survey methods such as
mail. It is important to realize that respondents may not be extensively familiar with
techniques and processes used when completing an online survey simply because they
utilize the Internet, so surveys must be created for the lowest-end computer user in mind
(Dillman, 2007). According to Brashears, Bullock, and Akers (2003), researchers must
be cautious when using online surveys to sample large populations, because most Internet
users are male and middle- to upper-class, which may not always be representative of an
actual target population. Although validity may be a concern because not everyone is
accessible through the Internet, members of many organizations and businesses have
45
Texas Tech University, Kelsey F. Shaw, May 2013
abundant access to the Web and do not suffer from the same sample bias issues as the
general population (Irani et al., 2004).
Response to Web-based surveys is likely to be low (Dillman, 2007), but there are
ways to address this non-response error. Dillman (2007) found that non-response error
can be curbed by simply sending respondents an invitation to participate in the survey
before sending the actual link to the electronic survey. Additionally, well-timed reminder
emails assist in reminding those who may have put the survey off to complete the
questionnaire before it closes (Dillman, 2007).
Population & Sample
The target population for this study was agricultural producers in Texas, Georgia,
or Illinois. Because no universal list of the producers exists, each state utilized accessible
populations to administer the survey instrument. Groups in each state were selected that
target or are comprised of beginning farmers and ranchers engaged in some sort of directto-consumer marketing. These groups also already had an email directory of members
that would aid in sending the survey instrument and follow-up reminders to potential
survey respondents. Correspondence with members of these groups was coordinated
through a program official, allowing the researcher no access to member emails or
individual respondents. This was done because these lists were considered private and
were not able to be shared with the researcher.
In Texas, two groups were targeted for survey participation. The first group was
the Young Farmer and Rancher Program housed inside the Texas Farm Bureau
organization. The Texas Farm Bureau aims to serve as a voice for Texas agriculture, and
provides a number of services, including insurance opportunities, lobbying at both the
46
Texas Tech University, Kelsey F. Shaw, May 2013
state and national levels, and youth and leadership programs (Texas Farm Bureau,
2012a). The Young Farmer and Rancher program recruits young agriculturists ages 18 to
35 to network regarding their successes, issues, and challenges, as well as to participate
in a variety of contests (Texas Farm Bureau, 2012b). The second group surveyed in
Texas was Holistic Management International’s Beginning Farmers & Ranchers: Women
in Texas group. Holistic Management International is actually a national organization
that works with several sub-groups to improve farming and ranching practices. This
group aims to empower young women farmers with less than 10 years of agricultural
experience to create successful business by providing business, leadership, and financial
training (Holistic Management International, 2012).
In Illinois, the survey was administered to four relevant agricultural groups. The
first organization was Illinois Farm Bureau’s Young Leaders group, which is very similar
to the Texas Farm Bureau’s Young Farmer and Rancher program. This group aims to
promote learning and networking among farmers in Illinois aged 18 to 35 (Illinois Farm
Bureau Young Leaders, 2012). The second group was the Illinois Local Food Systems
and Small Farms Extension program, which is housed at the University of Illinois and
provides tools, information, workshops, and news to the state’s farmers (University of
Illinois Extension, 2012). The third group that participated in the Illinois survey was
Illinois Farm Beginnings. This organization has three distinct arms across the state that
work to help budding entrepreneurs start their own farming operation through a year-long
training and mentorship program (Farm Beginnings Collaborative, 2012). The fourth
Illinois group was the Illinois Stewardship Alliance, which is committed to promoting
local businesses and use of environmentally-friendly, socially-just growing practices in
47
Texas Tech University, Kelsey F. Shaw, May 2013
the state (Illinois Stewardship Alliance, 2012). Organization representatives serve as
advocates for these goals and aim to educate and persuade to and through their
membership.
In Georgia, the survey was administered to the Georgia Farm Bureau’s Young
Farmer program members. Like in Texas and Illinois, members of this organization aim
to contribute to their parent organization, in this case, Georgia Farm Bureau, by
developing young members through networking and educational activities across the state
(Georgia Farm Bureau, 2011).
An up-to-date list of members from each organization was compiled and the
survey link was sent to every email in each database through an organization
representative. The sample was a convenience sample, as there are young farmers and
ranchers in each state who are not members of the sampled organizations. However,
because there are not accessible lists of beginning agricultural producers, this was the
best method to research members of the target population. Shannon, Johnson, Searcy,
and Lott (2001) identified that because of the varying skill level among Internet users, it
was many times more effective to utilize listservs, professional memberships, or alumni
as sample groups for electronic surveys. The respondents were asked at the beginning of
the questionnaire if they were over the age of 18. If a respondent answered “no,” he or
she was directed to the survey completion page immediately. Those indicating they were
more than 18 years old were directed to the first set of questions.
Instrument
Questions for this instrument (Appendix A) were modified from an instrument
administered to college-age students to assess social media use and knowledge (Abrams
48
Texas Tech University, Kelsey F. Shaw, May 2013
& Baker, 2012). The updated questionnaire utilized for this evaluation was divided into
sections measuring agriculturists’ current use of social media tools; self-perceived levels
of importance and competence completing various tasks using online communication
tools; potential barriers and motivations for attending social media training; and
demographic questions. The first question asked respondents to confirm that they were
18 years of age and reminded them of their rights as a survey participant. If they marked
that they would not like to participate in the survey for any reason, they were
automatically directed away from the survey.
Agriculturists’ Current Use of Social Media Tools
The first section of the instrument asked respondents to indicate how often they
utilized a list of popular social media tools for their personal reasons. Respondents were
asked to indicate their frequency of use on a 4-point scale ranging from 0 (do not use) to
3 (use daily) for each of the 10 social media tools. The potential frequencies that could
be selected were “do not use, use monthly, use weekly, and use daily.” Then, a space
was provided for respondents to include any additional type of online communication
tool that had not been mentioned, as well as to indicate their frequency of use for this
additional tool.
The third item on the questionnaire asked respondents to again indicate their
frequency of use for 10 popular social media tools, but this time for use specifically in
their agricultural operation. The same 4-point scale was used (0 = do not use, 1 = use
monthly, 2 = use weekly, 3 = use daily) Again, a space was provided for respondents to
include any additional type of online communication tools that had not been listed and to
indicate their frequency using those tools.
49
Texas Tech University, Kelsey F. Shaw, May 2013
The final two questions in this section asked respondents to self-identify whether
or not they felt they participated in online agriculture advocacy, or if they were unaware
of what that term even meant. If respondents indicated there were engaged in online
agricultural advocacy, they were then prompted to explain how they were involved in the
practice with an open-ended question.
Importance and Competence Using Online Communication Tools
The second section of the instrument was separated by specific online
communication tool. For each tool, the respondent was presented with between 2 to 10
frequently-used tasks specific to each tool. Some of the tasks included “steps to create an
account,” “generating followers or ‘likes,’” “knowing what to post,” and “measuring
impact of tool use for my business.” Respondents were first asked to indicate their
perceived level of importance for each task in relation to his or her business or
organization. Each task’s importance could be indicated on a 5-point scale from 0
(no/none) to 4 (utmost/exceptional).
Following each importance question, respondents were asked to rank their
perceived level of competence for the same tasks on the same 5-point scale ranging from
0 (no/none) to 4 (utmost/exceptional). These pairs of questions were asked about six
social or online media tools, including Facebook, Twitter, blogs, websites, “other online
communication tasks,” and “computer-based communications technology.”
Motivations and Barriers Toward Social Media Training
Respondents were first asked to indicate their level of interest in participating in a
low cost or free social media workshop geared toward their agricultural business. On a 4point Likert-type scale, respondents were asked to rate their level of interest from 0
50
Texas Tech University, Kelsey F. Shaw, May 2013
(uninterested) to 3 (very interested). Respondents were then asked to identify their
potential motivations for participating in one of these workshops in an open-ended
question, followed by asking for any barriers to attendance in the same open-ended
format. They were also asked to indicate if they had a laptop they would be able to bring
to a potential workshop and what type of electronic devices with Internet access they
currently owned.
Demographic Characteristics
At the conclusion of the questionnaire, the researcher asked respondents to
indicate what type of agricultural operation they owned, whether the operation was
alternative, whether they utilized any direct-to-consumer marketing, how long they had
owned the farming operation, their gender, their age, and for their email, only if they
wished to receive future correspondence regarding any future social media workshops in
their area supported by the grant project.
A panel of experts was assembled consisting of agricultural education and
communications faculty from Texas Tech University, the University of Illinois, the
University of Georgia, and Kansas State University to establish face and content validity
for the survey instrument. After collecting panel feedback, the survey was revised and
submitted to the Texas Tech University Institutional Review Board (IRB), which
approved this research prior to distribution of the instrument. A copy of this approval
letter is located in Appendix B.
Data Collection
Each state had a separate time period for its survey administration, staggered
across a four-month period from July 2012 to October 2012. To disperse the surveys, a
51
Texas Tech University, Kelsey F. Shaw, May 2013
contact was established with each organization’s or program’s coordinator; the
coordinator was responsible for emailing each round of emails to individual members of
the organization. Then the researcher sent the respective coordinators each a structured
message (see Appendix C), informing the group members that they would be receiving an
email with the survey link. One week later, each group member received an email with a
link to the survey in the body of the email. Coordinators were also asked to send a
reminder email (Appendix D) one week after the initial survey launch. The follow-up
email also contained an electronic link to the survey. Qualtrics survey software stored
the survey and all the responses securely.
It is not possible to calculate a response rate for the survey because researchers
were not provided with a total number of members or list of those emailed with the
survey link for the various organizations. Because the surveys were administered
through a program coordinator, the researcher can only measure those who actually
attempted to complete the instrument. A total of 286 members from the various
organizations clicked the electronic survey link and started the Qualtrics survey.
However, many respondents did not complete the majority of the survey. If respondents
did not answer questions past the sections referring to personal and business use of online
communication tools, their results were discarded from further analysis. In total, there
were 185 useable responses from all three states.
Data Analysis
The data from each set of surveys were exported into SPSS® version 20.0 for
Windows™. Demographic questions were analyzed using descriptive statistics,
specifically means and frequencies. In order to determine similarity between the
52
Texas Tech University, Kelsey F. Shaw, May 2013
respondents across multiple states, crosstabulations, chi-square, and ANOVAs were used
to compare demographic characteristics. Overall means were reported for questions
using a 4- or 5-point scale, and open-ended questions were coded and compared using
Glaser’s (1965) constant comparative method. Study results were analyzed to identify
current use of online communication tools in agriculture for both personal and business
uses. Cramer’s V was calculated for each of these tools to provide a test of statistical
significance as well as indicating the strength of the relationship between personal and
business use (Morgan, Griego & Bloeckner, 2001).
Additionally, perceptions of importance and competency in using these tools were
reported. Gaps between importance and competency, or needs for training, were
calculated utilizing Borich’s (1980) needs assessment model. First, discrepancy scores
were calculated for each individual for each task by subtracting the competence value
from the importance value. Then, a weighted discrepancy score was calculated for each
individual by multiplying the discrepancy score by the mean importance rating. Lastly, a
mean weighted discrepancy score was calculated by using the sum of the weighted
discrepancy scores and dividing by the number of observations. These mean weighted
discrepancy scores were then ranked to determine the most important needs for training.
Barriers and motivations of the respondents were also identified for participating
in future online communication tool trainings.
53
Texas Tech University, Kelsey F. Shaw, May 2013
CHAPTER IV
RESULTS
Introduction
The purpose of this study was to determine agriculturists’ use of online
communication tools. In order to address this purpose, the researcher used a quantitative,
descriptive survey design. The researcher administered an online questionnaire to
members of several farming and ranching associations in Texas, Illinois, and Georgia to
identify their current use of online communication tools for both personal and
professional reasons; perceived competence and importance of completing predetermined online communication tasks; and motivations and barriers to attending a lowcost or free online communication training seminar; as well as basic demographic
information. Data were analyzed using SPSS® version 20.0 for Windows™ and
Microsoft Excel.
The following research objectives were used to guide the study:
1. Determine the extent of respondents’ personal and business use of online
communication tools.
2. Determine potential needs in utilizing various forms of online communication.
3. Identify motivations and barriers to receiving additional training to utilize online
communication tools.
Comparing Respondents from Each State
A total of 286 respondents started the questionnaire, but after the researcher sifted
through and removed the unfinished responses, a final set of 185 responses were
complete enough to be analyzed for this study. There were 45 responses from Texas, 111
54
Texas Tech University, Kelsey F. Shaw, May 2013
responses from Illinois, and 29 responses from Georgia. To ensure the responses were
homogenous and could be analyzed as a combined group, the researcher ran crosstabs for
each relevant demographic variable across the states.
The first variable was gender. Table 4.1 displays the number of males and
females that responded from each state. All three states were nearly equally divided
except Illinois, but there were 14 individuals who did not provide a response for gender.
A chi-square test was conducted to determine if there was a significant difference in age,
with the significance level set a priori at .05. There was not a significant difference in
this variable across the states, χ2 (2, N = 171) = 2.85, .24.
Table 4.1
Crosstab of Respondent Gender by State of Residence
Gender
Texas
Illinois
Georgia
Total
Male
21
62
17
100
Female
23
37
11
71
1
12
1
14
45
111
29
185
Non Response
Total
The questionnaire asked what type of agricultural operation the farmer or rancher
owned (Table 4.2). Respondents were given a set of predetermined industry segments,
created by the USDA (NASS, 2007), then provided with an “other” option where they
could fill in their own description if appropriate. Responses varied across the industry
segments in each state, but groups of industries were consistently popular among the
areas. For instance, cattle was one of the most frequently selected in each of the states,
while horticulture was one of the least frequent in each of the states.
55
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.2
Crosstab of Industry Segments by State
Industry Segments
Texas
Illinois
Georgia
Total
n
%
n
%
n
%
n
%
29
0.64
30
0.27
19
0.66
78
0.42
Cotton Industry
7
0.16
0
0.00
6
0.21
13
0.07
Dairy Cattle and Milk Production
4
0.09
2
0.02
2
0.07
8
0.04
Fruit, Berries, and Tree Nuts
6
0.13
22
0.20
5
0.17
33
0.18
Grain and Oilseed Farming
14
0.31
59
0.53
3
0.10
76
0.41
Hog and Pig Farming
3
0.07
14
0.13
1
0.03
18
0.10
Horticulture
1
0.02
8
0.07
1
0.03
10
0.05
Nursery, Greenhouse, and Floriculture
Operations
4
0.09
9
0.08
1
0.03
14
0.08
Poultry and Egg Production
9
0.20
14
0.13
8
0.28
31
0.17
Sheep and Goat Farming
9
0.20
8
0.07
3
0.10
20
0.11
Vegetables, Potatoes, and Melons
6
0.13
31
0.28
4
0.14
41
0.22
10
0.22
20
0.18
10
0.34
40
0.22
Cattle
Other
Note: Respondents could respond more than once. Totals do not equal 100%.
Respondents were also asked how long they had owned the operation (Table 4.3),
which aided in classifying farmers as a beginning farmer or rancher. Table 4.3 displays
the amount of years owned broken down by state.
56
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.3
Crosstab of Respondents’ Years Owning an Operation
Years Owned
Texas
Illinois
Georgia
Total
n
%
N
%
N
%
n
%
0
0
9
8
3
10
12
7
1 to 5
14
31
27
24
13
45
54
30
6 to 10
12
27
25
23
4
14
41
23
11 to 15
11
24
18
16
5
17
34
19
16 to 20
2
4
7
6
0
0
9
5
21+
3
7
12
11
2
7
17
9
Non-responses
3
7
13
12
2
7
14
8
Less than 1
A chi-square test was conducted to determine if there was a significant difference
in beginning farmer or rancher status (Table 4.4), with the significance level set a priori
at .05. All respondents who had owned their operation for 10 years or less were
classified as beginning farmers and ranchers, per the USDA definition. There was not a
significant difference between those who were classified as beginning farmers and
ranchers and those who were not across the three states, χ2 (2, N = 167) = 1.40, .50.
Table 4.4
Crosstab of Respondents’ Beginning Farmer or Rancher Status
BFR
Texas
Illinois
Georgia
Total
n
%
n
%
n
%
n
%
Yes
26
58
61
55
20
69
107
58
No
16
36
37
33
7
24
60
32
7
13
12
2
7
185
10
Non-responses
3
57
Texas Tech University, Kelsey F. Shaw, May 2013
In regard to respondents’ operations, they were also asked to indicate if all, part,
or none of their operation was classified as “alternative” by the USDA. Alternative refers
to the production of some sort of nontraditional crop, livestock, or service, including
tourism, food processing, forestry, or other enterprises (Gold, 2007). Table 4.5 displays
the alternative designation for respondents for each state, although 20 respondents
declined to designate their operation as alternative or otherwise. Primarily, alternative
status was proportionally equal between the states. Those who responded that “part” or
“all” of their operation was alternative, were classified as alternative. A chi-square test
was conducted to determine if there was a significant difference in alternative status
between the three states, with the significance level set a priori at .05. There was not a
significant difference in this set of data, χ2 (2, N = 165) = 4.32, .12.
Table 4.5
Crosstab of Respondents’ Alternative Operation Status
Alternative
Texas
Illinois
Georgia
Total
n
%
n
%
n
%
n
%
No
29
64
59
53
20
69
108
58
Yes
15
33
38
34
4
14
57
31
1
2
14
13
5
17
20
11
Non-responses
Respondents were also asked if they participated in any direct-to-consumer
marketing. Table 4.6 illustrates that respondents from all states were somewhat evenly
divided between those who did participate in direct-to-consumer marketing and those
who did not. A chi-square test was completed to determine any significant difference in
the amount of direct-to-consumer marketing for participants in the various states; the
58
Texas Tech University, Kelsey F. Shaw, May 2013
significance level set a priori at .05. There were no significant differences between the
states on this variable, χ2 (2, N = 169) = .14, .93.
Table 4.6
Crosstab of Respondents’ Direct-to-Consumer Marketing Status
Direct-to-Consumer
Texas
Illinois
Georgia
Total
n
%
n
%
n
%
n
%
No
25
56
52
47
15
52
92
50
Yes
19
42
45
41
13
45
77
42
1
2
14
13
1
3
16
9
Non-responses
The final variable analyzed to determine if the individual sets of state data could
be analyzed collectively was the ages of respondents. A one-way ANOVA was
conducted (Table 4.7) using the Bonferroni comparison to analyze the mean responses.
Respondents’ mean ages were significantly different between the three states (F2,166 =
8.20, p = .00). The mean age for Georgia respondents (M = 30.36, SD = 7.03) was
significantly less than Texas (M = 41.91, SD = 12.87) and Illinois (M = 41.13, SD =
14.56) respondents.
Table 4.7
ANOVA for Respondents’ Mean Age
Age
N
M
SD
Texas
43
41.91
12.87
Illinois
98
41.13
14.56
Georgia
28
30.36
7.03
59
F
P
8.20
.00
Texas Tech University, Kelsey F. Shaw, May 2013
Age was the only variable that resulted in a difference between the states.
Because the group surveyed in Georgia was specifically a group of self-titled Young
Farmers, it was expected that this organization housed a younger age group. Because this
was the only area of difference, it was determined that for the purpose of this study, data
from all states would be analyzed collectively. All remaining data are reported as a sum
of all available responses.
Combined Demographics
More males (n = 100, 54.1%) responded to the study than females (n = 71,
38.4%); 14 did not provide a response for gender. Respondents were also asked to
identify their year of birth. From these years, the researcher was able to garner the ages
of the respondents. Of the 169 who responded to this question, the mean age was 39
years (SD = 13.74). The median age was 33. The oldest respondent was 90 years old,
with the youngest respondent being 18 years old.
As previously indicated in Table 4.2, various industries are represented in the
group of respondents. Looking at overall operation type, the two most frequently
selected types of operations were cattle production (n = 78, 42.2%) and grain and oilseed
farming (n = 76, 41.1%). The least frequently indicated type of agricultural operations
were horticulture (n = 10, 5.4%) and dairy cattle and milk production (n = 8, 4.3%).
Some of the more frequent respondent-provided “other” operations included agritourism,
equine, bee farming, and hay production. Seventeen respondents opted not to identify
any sort of operation type and did not respond to this question.
Additionally, because respondents were able to indicate if they have more than
one of these operation types, there were a number of farmers who selected multiple
60
Texas Tech University, Kelsey F. Shaw, May 2013
operation types. In fact, only 33.3% (n = 56) identified only one of the operation types,
while the remaining 66.7% (n = 112) all selected two or more of the listed operation
types.
The next demographic question asked how long the farmer had owned his or her
operation. The majority of respondents (n = 107, 57.9%) had owned their operation for
less than 10 years, which is the USDA’s classification standard for identifying beginning
farmers and ranchers. Many (n = 54, 29.2%) indicated they had owned their agricultural
operation for between 1 to 5 years. The fewest amount of respondents had owned their
operation for between 16 to 20 years (n = 9, 4.9%). All operation ownership information
results are presented in Table 4.8.
Table 4.8
Years Spent Owning an Operation (N = 185)
Years Spent Owning an Operation
Frequency
Percent (%)
Less Than One Year
12
6.5
1 to 5 Years
54
29.2
6 to 10 Years
41
22.2
11 to 15 Years
34
18.4
16 to 20 Years
9
4.9
21 Years or more
17
9.2
No Response
18
9.6
When asked if their operation was in any part classified as alternative by the
USDA, the majority of respondents (n = 108, 58.4%) said their operation was in no part
classified as alternative, while 19.5% (n = 36) said their entire operation was alternative
61
Texas Tech University, Kelsey F. Shaw, May 2013
and 12.7% (n = 21) said only part of their operation carried alternative designation. An
additional 20 respondents opted not to report their status as alternative or otherwise.
Although 16 respondents did not respond to the question asking if they
participated in some sort of direct-to-consumer marketing, the remaining respondents
were almost equally divided. More of the respondents (n = 92, 49.7%) were not involved
in any direct-to-consumer marketing, while 41.6% (n = 77) did market to their consumers
in this way.
Additionally, respondents were asked to indicate what type of electronic devices
they owned that had some sort of Internet access (Table 4.9). Respondents could select
more than one type of device, and there was an “other” option where a respondent could
name a device that was not on the list provided. The majority of respondents (n = 145,
85.9%) did own more than one of these tools. Only 14.1% (n = 26) of those who
indicated they owned these devices only owned one.
The most common type of electronic device respondents owned with Internet
access was a laptop (n = 154, 83.3%), but more than half of respondents also owned some
brand of smartphone (n = 120, 64.9%) and a desktop computer (n = 104, 56.2%).
Fourteen respondents elected not to respond to this question.
62
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.9
Electronic Devices Owned with Internet Access (N = 185)
Device
Frequency
Laptop
154
83.2
Smartphone
120
64.9
Desktop
104
56.2
55
29.7
Tablet Computer
Percent (%)
Other
7
3.8
Note. Respondents could select multiple answers; percentages do not equal 100%
Research Objective One
Research objective one sought to determine the extent of respondents’ personal
and business use of online communication tools. Table 4.10 displays the respondents’
frequency of use for several popular online communication tools in both personal and
agricultural business capacities. Not every participant indicated a frequency for every
tool.
The most frequently utilized online communication tool for personal reasons was
websites, with 56.2% (n = 95) of respondents indicating they use the tool at least once
daily. Facebook was also indicated as a frequently-utilized tool, with 47.3% (n = 80)
reporting daily use of the social media program. More than a third of respondents (n =
65, 38.5%) also indicated using YouTube at least once each month for personal reasons.
Other online communication tools identified for personal use included AgChat, email,
Kickstarter.com, LinkedIn, and FourSquare.
For all of the other tools suggested, either the majority of respondents or close to
the majority of respondents indicated they never used the tool for personal reasons.
63
Texas Tech University, Kelsey F. Shaw, May 2013
These included Google+ (n = 75, 44.1%), Twitter (n = 128, 77%), photo sharing websites
(n = 109, 61.7%), blogging websites (n = 117, 69.6%), social bookmarking sites (n = 122,
72.2%), and social media management sites (n = 158, 94.6%).
In terms of use for their agricultural operations, websites were the only tool that at
least one third of respondents utilized every day for business purposes (n = 63, 37.3%).
For all the other tools, a majority or close to a majority indicated they did not use the
tools at all. There were a few who indicated they used “other” communication tools they
used for their agricultural business, including email, LinkedIn, cattlerange.com, and
Mailchimp.
Cramer’s V was calculated to determine statistical significance of the relationship
between personal and business use of each online communication tool. Cramer’s V
ranges from 0 to 1.0, with values closer to 1.0 indicating a stronger significance (Morgan,
et al., 2001). Cramer’s V values close to .2 indicate a small effect size, values close to .5
indicate a medium effect size, and values closer to .8 indicate a large effect size. If a
value shows a high level of significance, this means the strength of the relationship is
significant (Morgan, et al, 2001). In this case, all effect sizes would be considered a
medium effect size except for social media management tools and blogs. Social media
management tools had a small effect size, while blogs had a large effect size.
64
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.10
Relationships Between Online Communication Tool Frequency of Use for Personal or Agriculture Use
Daily
Personal
Weekly
Ag
Personal
Monthly
Ag
Personal
Never
Ag
Personal
Ag
%
n
%
n
%
n
%
n
%
Cramer’s
V
15.4 38
22.5
13
7.7
21
12.4
35
20.7
47
27.8
.50
.00
40 23.5 35
20.7 32
18.8
14
8.3
21
12.4
40
23.8
77
45.3
.44
.00
25 15.0 33
19.4 27
16.2
11
6.5
16
9.6
75
44.1
99
59.3
.52
.00
25.4 17
10.1
65
38.5
46
27.4
49
29.0
100
59.5
.50
.00
9
5.4
18
9.7
10
6.0
128
77.1
141
83.9
.50
.00
1
0.6
20
11.8
15
9.0
122
72.2
148
88.6
.46
.00
10.7 11
6.6
24
14.3
23
13.9
117
69.6
126
75.9
.70
.00
3
1.8
46
27.5
16
9.6
103
61.7
144
86.2
.50
.00
0.0
2
1.8
5
5.0
2
1.8
88
88.0
103
92.8
.58
.00
2.4
0
0.0
4
2.4
6
3.6
158
94.6
158
95.2
.33
.00
Tool
n
%
n
%
n
%
Website
95
56.2
63 37.3 26
Facebook
80
47.3
Google+
51
30.0
YouTube
12
7.1
5
3.0
43
Twitter
10
6.0
8
4.8
10
6.0
Social
10
Bookmarking
5.9
3
1.8
17
10.1
Blogging
Website
9
5.4
6
3.6
18
Photo
Sharing
Websites
8
4.8
4
2.4
10
6.0
Other
7
7.0
4
3.6
0
Social Media
Management
1
0.6
2
1.2
4
n
65
P
Texas Tech University, Kelsey F. Shaw, May 2013
Additionally, respondents were asked to indicate whether they felt they
participated in some sort of online agriculture advocacy, sometimes referred to as
“agvocacy.” Out of the 171 respondents that answered this question, almost half of the
respondents (n = 87, 47%) indicated they did not participate in any advocacy. Another
35.7% (n = 65) said they did participate in online advocacy, while the remaining 10.3%
(n = 19) indicated they were unaware of what “agvocacy” was.
If respondents indicated they did participate in some sort of online agriculture
“agvocacy,” they were provided a text box in which to describe their activities. Popular
write-in responses included variations of “Facebook posting,” “blog contributions,” and
“reading and receiving ag-related emails.”
Research Objective Two
The second research objective sought to identify potential needs in utilizing
various forms of online communication. After calculating means and standard deviations
for each of these tasks, the researcher compared responses of those who indicated they
participate in direct-to-consumer marketing to those who indicated they do not.
Responses for each task were also compared between those who would be classified by
the USDA as a beginning farmer or rancher and those who would not. Paired sample ttests were ran for each of these variables, and statistically significant differences are
reported.
Facebook
Respondents were asked to use a five-point 5-point scale ranging from 0
(no/none) to 4 (utmost/exceptional) to rank the importance of a series of tasks. Results
for attitudes toward Facebook importance are indicated in Table 4.11. The highest mean
66
Texas Tech University, Kelsey F. Shaw, May 2013
belonged to the task “Understanding the purpose for my Agricultural Business” (M =
2.48, SD = 1.28). The lowest given task was “Steps to Create a Facebook Page” (M =
1.92, SD = 1.14).
The mean scores for competency of Facebook tasks are also displayed in Table
4.11. The highest competency mean belonged to the task “Understanding the purpose for
my Agricultural Business” (M = 2.34, SD = 1.19). The lowest means for competency
were “Measuring the impact or effectiveness for my agricultural business” (M = 1.67, SD
= 1.16) and “Generating Page ‘Likes’” (M = 1.65, SD = 1.21).
Mean weighted discrepancy scores were also calculated for each Facebookrelated task utilizing Borich’s (1980) model for conducting needs assessment. Tasks with
higher scores indicate a higher need for training. Scores from competencies for all online
communication tools are compared later in the chapter.
67
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.11
Respondents’ Perceptions of the Importance of Facebook Tasks and Their Competence at
Performing the Tasks
Importance
Facebook Tasks
Competence
MWDSa
M
SD
M
SD
Engaging people/consumers
2.31
1.32
1.78
1.15
1.11
Awareness of the risk in having a
business presence and how to
mitigate them
2.25
1.22
1.72
1.19
1.06
Measuring impact or effectiveness
for my agricultural business
2.17
1.21
1.67
1.16
0.95
Knowing what I should post to
Facebook
2.33
1.28
1.90
1.22
0.85
Using one effectively for my
agricultural business
2.15
1.27
1.72
1.19
0.81
Generating Page Likes
2.05
1.25
1.65
1.21
0.72
Creating an effective Facebook Page
2.12
1.23
1.85
1.28
0.49
Understanding the purpose for my
Agricultural Business
2.48
1.28
2.34
1.19
0.42
Steps to create a Facebook Page
1.92
1.14
2.04
1.31
-0.22
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
Paired sample t-tests were conducted to compare responses to Facebook
importance and competence from beginning farmers and ranchers with the more
experienced agriculturists, as well as between those who market directly to the consumer
and those who do not. Significance was set a priori at .05. Tables 4.12 and 4.13 display
the t-test values for importance means, while tables 4.14 and 4.15 display the t-test values
for competence means for these two comparisons.
68
Texas Tech University, Kelsey F. Shaw, May 2013
The only significant difference in Facebook task importance in these sets of
pairings was between beginning farmers and ranchers (n = 104, M = 2.05, SD = 1.14) and
more experienced farmers and ranchers (n = 55, M = 1.67, SD = 1.11) on the task “Steps
to Create a Facebook Page,” t (157) = 2.00, p < .05. Beginning farmers and ranchers
were more likely to rate the importance of this task higher than their more experienced
counterparts.
The only significant difference in the competency of Facebook tasks was between
beginning farmers and ranchers (n = 104, M = 1.79, SD = 1.16) and the group of farmers
with more experience (n = 56, M = 1.36, SD = 1.21) on the task “Generating Page
‘Likes,’” t (158) = 2.21, p =.03. Beginning farmers and ranchers felt more confident in
generating page “likes” than the more experienced farmers and ranchers.
69
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.12
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Facebook Importance
BFRs
Facebook Tasks
n
Non BFRs
M
SD
n
M
SD
T
Df
p value
Understanding the purpose for my agricultural business
105
2.50
1.26
55
2.40
1.27
0.50
158
0.62
Steps to create a Facebook page
104
2.05
1.14
55
1.67
1.11
2.00
157
0.05*
Creating an effective Facebook page
103
2.19
1.25
55
1.98
1.19
1.04
156
0.30
Using one effectively for my agricultural business
104
2.25
1.28
55
1.95
1.24
1.45
157
0.15
Generating page “Likes”
104
2.14
1.28
55
1.84
1.14
1.50
157
0.14
Engaging people/consumers
104
2.39
1.33
55
2.15
1.28
1.13
157
0.26
Knowing what I should post to Facebook
103
2.42
1.24
55
2.13
1.32
1.37
156
0.17
Awareness of the risks in having a business presence and
how to mitigate them
104
2.36
1.21
55
2.02
1.19
1.69
157
0.09
Measuring impact or effectiveness for my agricultural
business
104
2.22
1.17
55
2.00
1.22
1.12
157
0.27
70
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.13
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Facebook Importance
D2Cs
Non D2Cs
Facebook Tasks
n
M
SD
Understanding the purpose for my agricultural business
73
2.49
1.24
Steps to create a Facebook page
72
2.03
Creating an effective Facebook page
72
Using one effectively for my agricultural business
M
SD
T
Df
p value
89
2.49
1.31
0.01
160
0.99
1.06
89
1.87
1.20
-0.90
159
0.37
2.18
1.18
88
2.10
1.28
-0.40
158
0.69
72
2.33
1.21
89
2.02
1.31
-1.55
159
0.12
Generating page “Likes”
72
2.14
1.18
89
2.00
1.31
-0.70
159
0.48
Engaging people/consumers
72
2.46
1.30
89
2.19
1.31
-1.29
159
0.20
Knowing what I should post to Facebook
72
2.29
1.22
88
2.38
1.33
0.41
158
0.68
Awareness of the risks in having a business presence and
how to mitigate them
72
2.26
1.15
89
2.25
1.26
-0.09
159
0.93
Measuring impact or effectiveness for my agricultural
business
72
2.18
1.16
89
2.16
1.24
-0.12
159
0.90
71
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.14
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Facebook Competence
BFRs
Facebook Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Understanding the purpose for my agricultural business
101
2.42
1.10
56
2.14
1.33
1.38
155
0.17
Steps to create a Facebook page
104
2.15
1.23
56
1.80
1.39
1.64
158
0.10
Creating an effective Facebook page
103
1.93
1.21
56
1.68
1.35
1.21
157
0.23
Using one effectively for my agricultural business
104
1.84
1.18
56
1.48
1.28
1.76
158
0.08
Generating page “Likes”
104
1.79
1.16
56
1.36
1.21
2.21
158
0.03*
Engaging people/consumers
103
1.86
1.09
55
1.60
1.21
1.39
156
0.17
Knowing what I should post to Facebook
104
1.99
1.15
55
1.71
1.30
1.35
Awareness of the risks in having a business presence and
how to mitigate them
104
1.77
1.13
55
1.60
1.23
0.87
Measuring impact or effectiveness for my agricultural
business
104
1.72
1.07
56
1.54
1.26
0.93
72
98.64
157
98.16
0.18
0.40
0.35
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.15
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Facebook Competence
D2Cs
Non D2Cs
Facebook Tasks
n
M
SD
Understanding the purpose for my agricultural business
73
2.30
1.21
Steps to create a Facebook page
74
2.03
Creating an effective Facebook page
73
Using one effectively for my agricultural business
M
SD
T
df
p value
86
2.36
1.18
0.31
157
0.76
1.33
88
2.06
1.29
0.14
160
0.89
1.88
1.26
88
1.83
1.30
-0.23
159
0.82
74
1.81
1.25
88
1.65
1.23
-0.84
160
0.41
Generating page “Likes”
74
1.76
1.21
88
1.56
1.20
-1.05
160
0.30
Engaging people/consumers
74
1.85
1.19
86
1.73
1.12
-0.65
158
0.52
Knowing what I should post to Facebook
73
1.90
1.23
88
1.90
1.22
-0.03
159
0.97
Awareness of the risks in having a business presence and
how to mitigate them
73
1.68
1.22
88
1.76
1.17
0.41
159
0.69
Measuring impact or effectiveness for my agricultural
business
74
1.69
1.19
88
1.66
1.13
-0.16
160
0.87
73
n
Texas Tech University, Kelsey F. Shaw, May 2013
Twitter
The second tool evaluated for perceived importance for an agricultural business or
organization was Twitter. Perceived importance was evaluated using a five-point scale
ranging from 0 (no/none) to 4 (utmost/exceptional). Results for attitudes toward the
importance of Twitter are indicated in Table 4.16. The highest mean response was for
“Understanding the purpose for my agricultural business” (M = 1.41, SD = 1.47), while
the lowest mean was “Steps to create a Twitter page” (M = 1.04, SD = 1.24).
Mean weighted discrepancy scores were also calculated for each Twitter-related
task utilizing Borich’s (1980) model for conducting needs assessment. Tasks with higher
scores indicate a higher need for training. Scores from competencies for all online
communication tools are compared later in the chapter.
Though well below the mid-point, the highest competency mean was for
“Understanding the purpose for my agricultural business” (M = 1.05, SD = 1.25). The
lowest mean was tied between two different tasks – “Generating followers” (M = 0.74,
SD = 1.04) and “Measuring impact or effectiveness for my agricultural business” (M =
0.74, SD = 1.09).
74
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.16
Respondents’ Perceptions of the Importance of Twitter Tasks and Their Competence at
Performing the Tasks
Importance
Twitter Tasks
Competence
MWDSa
M
SD
M
SD
Awareness of the risk in having a
Twitter presence and how to mitigate
them
1.25
1.38
0.78
1.10
0.49
Knowing what I should post to
Twitter
1.24
1.40
0.77
1.08
0.48
Generating followers
1.21
1.37
0.74
1.04
0.48
Engaging people/consumers
1.25
1.40
0.78
1.11
0.47
Measuring impact or effectiveness
for my agricultural business
1.19
1.33
0.74
1.09
0.46
Using one effectively for my
agricultural business
1.21
1.33
0.77
1.07
0.45
Understanding the purpose for my
Agricultural Business
1.41
1.47
1.05
1.25
0.42
Creating an effective Twitter page
1.16
1.34
0.78
1.11
0.38
Steps to create a Twitter page
1.04
1.24
0.84
1.19
0.18
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
Paired sample t-tests for importance and competence values were conducted to
determine if there were any statistically significant differences between the beginning or
experienced farmer and rancher groups or those who marketed directly to customers and
those who did not. Significance was set a priori at .05. Tables 4.17 and 4.18 display the
t-test values for importance means, while tables 4.19 and 4.20 display the t-test values for
competence means. No differences were found on any of the responses from this tool on
importance or competency ratings between members of these groups.
75
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.17
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Twitter Importance
BFRs
Non BFRs
Twitter Tasks
n
M
SD
Understanding the purpose for my agricultural business
97
1.41
1.50
Steps to create a Twitter page
98
1.08
Creating an effective Twitter page
98
Using it effectively for my agricultural business
M
SD
T
df
p value
55
1.33
1.39
0.35
150
0.73
1.29
55
0.95
1.15
0.65
151
0.52
1.18
1.35
55
1.09
1.32
0.41
151
0.68
98
1.26
1.35
55
1.11
1.32
0.65
151
0.52
Generating followers
98
1.24
1.36
55
1.09
1.35
0.67
151
0.50
Engaging people/consumers
97
1.27
1.41
53
1.17
1.37
0.41
148
0.68
Knowing what I should post on Twitter
98
1.30
1.42
54
1.09
1.35
0.86
150
0.39
Awareness of the risks in having a Twitter presence and
how to mitigate them
97
1.32
1.42
55
1.07
1.25
1.08
150
0.28
Measuring impact or effectiveness for my agricultural
business
97
1.26
1.39
55
1.04
1.22
0.99
150
0.33
76
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.18
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Twitter Importance
D2Cs
Non D2Cs
Twitter Tasks
n
M
SD
Understanding the purpose for my agricultural business
71
1.34
1.53
Steps to create a Twitter page
71
0.94
Creating an effective Twitter page
71
Using it effectively for my agricultural business
M
SD
T
df
p value
81
1.46
1.42
0.50
150
0.62
1.26
83
1.13
1.24
0.94
152
0.65
1.13
1.40
83
1.19
1.30
0.30
152
0.76
71
1.11
1.37
83
1.29
1.32
0.81
152
0.42
Generating followers
71
1.10
1.38
83
1.29
1.38
0.86
152
0.39
Engaging people/consumers
70
1.17
1.46
81
1.31
1.37
0.60
149
0.55
Knowing what I should post on Twitter
71
1.15
1.39
82
1.30
1.43
0.67
151
0.51
Awareness of the risks in having a Twitter presence and
how to mitigate them
70
1.21
1.42
83
1.29
1.36
0.33
151
0.74
Measuring impact or effectiveness for my agricultural
business
70
1.13
1.39
83
1.24
1.30
0.52
151
0.61
77
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.19
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Twitter Competence
BFRs
Non BFRs
Twitter Tasks
n
M
SD
Understanding the purpose for my agricultural business
97
1.04
1.25
Steps to create a Twitter page
98
0.90
Creating an effective Twitter page
98
Using it effectively for my agricultural business
M
SD
T
df
p value
55
1.05
1.24
-0.06
150
0.95
1.26
55
0.76
1.11
0.66
151
0.51
0.84
1.17
55
0.69
1.02
0.77
151
0.44
98
0.84
1.12
55
0.67
1.00
0.90
151
0.37
Generating followers
98
0.81
1.09
55
0.64
0.95
0.97
151
0.34
Engaging people/consumers
97
0.84
1.13
55
0.69
1.02
0.96
151
0.34
Knowing what I should post on Twitter
97
0.84
1.13
55
0.69
1.02
0.78
150
0.44
Awareness of the risks in having a Twitter presence and
how to mitigate them
98
0.85
1.16
55
0.67
1.00
0.94
151
0.35
Measuring impact or effectiveness for my agricultural
business
97
0.78
1.06
54
0.69
0.93
0.57
149
0.57
78
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.20
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Twitter Competence
D2Cs
Non D2Cs
Twitter Tasks
n
M
SD
Understanding the purpose for my agricultural business
70
0.99
1.28
Steps to create a Twitter page
70
0.84
Creating an effective Twitter page
70
Using it effectively for my agricultural business
M
SD
T
df
p value
83
1.07
1.23
0.43
151
0.67
1.22
84
0.83
1.18
-0.05
152
0.96
0.81
1.20
84
0.74
1.04
-0.42
152
0.67
70
0.80
1.12
84
0.73
1.03
-0.42
152
0.67
Generating followers
70
0.77
1.09
84
0.69
0.99
-0.48
152
0.63
Engaging people/consumers
70
0.83
1.15
84
0.71
1.00
-0.66
152
0.61
Knowing what I should post on Twitter
70
0.80
1.14
83
0.73
1.05
-0.37
151
0.71
Awareness of the risks in having a Twitter presence and
how to mitigate them
70
0.77
1.17
84
0.76
1.05
-0.05
152
0.96
Measuring impact or effectiveness for my agricultural
business
70
0.77
1.09
82
0.70
0.94
-0.46
150
0.64
79
n
Texas Tech University, Kelsey F. Shaw, May 2013
Blogs
Blogs were the third online communication tool evaluated for importance and
competency by survey respondents, and the responses are provided in Table 4.21. A
five-point scale was used to evaluate importance of blogging tasks, ranging from 0
(no/none) to 4 (utmost/exceptional). The blogging task with the highest mean was
“Understanding the purpose for my Agricultural Business” (M = 2.06, SD = 1.35), and
was also the only mean above the mid-point of the scale. The given tasks with the lowest
two importance means were “Steps to input multimedia into a blog post” (M = 1.72, SD =
1.29) and “Steps to create a blog” (M = 1.70, SD = 0.93).
Although all competency value means fell below the scale’s mid-point, the task
with the highest mean was “Understanding the purpose for my Agricultural Business” (M
= 1.68, SD = 1.28). The lowest given blogging task competency mean was “Generating
subscribers” (M = 1.13, SD = 1.12).
Mean weighted discrepancy scores were also calculated for each blogging-related
task utilizing Borich’s (1980) model for conducting needs assessment. Tasks with higher
scores indicate a higher need for training. Scores from competencies for all online
communication tools are compared later in the chapter.
80
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.21
Respondents’ Perceptions of the Importance of Blogging Tasks and Their Competence at
Performing the Tasks
Importance
Blogging Tasks
Competence
MWDSa
M
SD
M
SD
Engaging people/consumers
1.79
1.34
1.17
1.11
0.95
Generating subscribers
1.76
1.35
1.13
1.12
0.92
Measuring impact or effectiveness
for my agricultural business
1.76
1.31
1.16
1.11
0.88
Using it effectively for my
agricultural business
1.82
1.32
1.24
1.20
0.86
Creating an effective blog page
1.78
1.33
1.22
1.19
0.84
Awareness of the risk in having a
blog and how to mitigate it
1.76
1.36
1.23
1.20
0.79
Knowing what I should post to the
blog
1.82
1.35
1.33
1.19
0.75
Steps to input multimedia into a blog
post
1.72
1.29
1.27
1.23
0.64
Steps to create a blog
1.70
1.27
1.24
1.20
0.65
Understanding the purpose for my
Agricultural Business
2.06
1.35
1.68
1.28
0.62
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
The researcher ran paired sample t-tests on importance and competency values to
see if there were any statistically significant differences between either the beginning
farmers and ranchers and the experienced farmers and ranchers or those who utilized
some form of direct-to-consumer marketing and those who did not. Tables 4.22 and 4.23
display the t-test values for importance means, while tables 4.24 and 4.25 display the ttest values for competence means. Significance level was set a priori at .05. No
81
Texas Tech University, Kelsey F. Shaw, May 2013
significant differences were found between any of the groups on blog task importance or
competency.
82
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.22
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Blogging Importance
BFRs
Blogging Tasks
M
SD
M
SD
T
df
p value
100
2.12
1.35
53
1.92
1.39
0.84
151
0.40
Steps to create a blog
99
1.79
1.26
54
1.48
1.29
1.43
151
0.16
Steps to input multimedia into a blog post
99
1.79
1.27
54
1.54
1.30
1.16
151
0.25
Creating an effective blog page
99
1.85
1.30
54
1.57
1.35
1.23
151
0.22
Using it effectively for my agricultural business
98
1.88
1.30
54
1.65
1.35
1.03
150
0.31
Generating subscribers
98
1.80
1.29
54
1.61
1.42
0.82
150
0.42
Engaging people/consumers
99
1.85
1.30
54
1.61
1.38
1.05
151
0.29
Knowing what I should post to the blog
98
1.89
1.34
54
1.61
1.34
1.22
150
0.24
Awareness of the risks in having a blog and how to
mitigate it
99
1.83
1.34
54
1.54
1.34
1.28
151
0.20
Measuring impact or effectiveness for my agricultural
business
98
1.86
1.30
53
1.49
1.28
1.66
149
0.10
Understanding the purpose for my agricultural business
n
Non BFRs
83
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.23
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Blogging Importance
D2Cs
Non D2Cs
Blogging Tasks
n
M
SD
Understanding the purpose for my agricultural business
69
2.25
1.29
Steps to create a blog
68
1.82
Steps to input multimedia into a blog post
68
Creating an effective blog page
M
SD
T
df
p value
84
1.89
1.41
-1.61
151
0.11
1.21
86
1.62
1.33
-1.00
152
0.32
1.87
1.27
86
1.64
1.30
-1.09
152
0.28
68
1.96
1.29
86
1.65
1.35
-1.42
152
0.16
Using it effectively for my agricultural business
68
1.94
1.26
85
1.74
1.38
-0.93
151
0.36
Generating subscribers
67
1.85
1.29
86
1.71
1.41
-0.64
151
0.52
Engaging people/consumers
68
1.91
1.30
86
1.72
1.39
-0.87
152
0.39
Knowing what I should post to the blog
68
1.90
1.26
85
1.78
1.43
-0.55
151
0.59
Awareness of the risks in having a blog and how to
mitigate it
68
1.81
1.27
86
1.74
1.44
-0.29
152
0.77
Measuring impact or effectiveness for my agricultural
business
67
1.85
1.31
85
1.71
1.33
-0.67
150
0.50
84
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.24
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Blogging Competence
BFRs
Blogging Tasks
M
SD
T
df
p value
100
1.62
1.22
1.36
-0.45
151
0.65
99
1.28
1.15
1.15
0.65
150
0.52
Steps to input multimedia into a blog post
100
53
1.25
1.22
0.22
151
0.83
Creating an effective blog page
1.20
53
1.09
1.13
0.93
151
0.35
Using it effectively for my agricultural business
1.29
1.21
53
1.15
1.13
0.69
151
0.49
99
1.15
1.11
53
1.08
1.09
0.41
150
0.69
Engaging people/consumers
100
1.21
1.12
52
1.08
1.05
0.71
150
0.48
Knowing what I should post to the blog
100
1.37
1.19
51
1.22
1.17
0.76
149
0.45
Awareness of the risks in having a blog and how to
mitigate it
99
1.26
1.20
53
1.17
1.16
0.46
150
0.65
Measuring impact or effectiveness for my agricultural
business
98
1.16
1.10
52
1.13
1.09
0.15
148
0.88
Understanding the purpose for my agricultural business
Steps to create a blog
Generating subscribers
n
Non BFRs
M
SD
53
1.72
1.21
53
1.29
1.23
100
1.28
100
85
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.25
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Blogging Competence
D2Cs
Non D2Cs
Blogging Tasks
n
M
SD
Understanding the purpose for my agricultural business
68
1.85
1.34
Steps to create a blog
67
1.30
Steps to input multimedia into a blog post
68
Creating an effective blog page
M
SD
T
df
p value
87
1.54
1.24
-1.51
153
0.13
1.27
87
1.21
1.16
-0.47
152
0.64
1.38
1.31
87
1.20
1.19
-0.93
153
0.35
68
1.32
1.26
87
1.14
1.14
-0.96
153
0.34
Using it effectively for my agricultural business
68
1.32
1.22
87
1.17
1.19
-0.78
153
0.44
Generating subscribers
68
1.22
1.13
86
1.06
1.12
-0.89
152
0.38
Engaging people/consumers
67
1.24
1.14
87
1.10
1.10
-0.74
152
0.46
Knowing what I should post to the blog
67
1.34
1.19
86
1.31
1.21
-0.15
151
0.88
Awareness of the risks in having a blog and how to
mitigate it
68
1.22
1.20
86
1.23
1.21
0.06
152
0.95
Measuring impact or effectiveness for my agricultural
business
67
1.13
1.09
85
1.16
1.14
0.17
150
0.87
86
n
Texas Tech University, Kelsey F. Shaw, May 2013
Websites
Respondents were asked to identify their perceptions of importance and
compentency of several website tasks on a five-point scale, ranging from 0 (no/none) to 4
(utmost/exceptional). Results for this set of tasks are displayed in Table 4.26. The
website task with the highest importance mean was “Using a website effectively for my
agricultural business” (M = 2.59, SD = 1.32), followed closely by “Creating a website
with user-friendly templates and publishing options” (M = 2.57, SD = 1.31). The
provided website task with the lowest importance mean was “Publishing or updating your
own Web page/site” (M = 0.72, SD = 1.23).
The competency means for all website-related tasks fell below the scale’s midpoint and were very close to each other. However, the task with the highest mean was
“Using a website effectively for my agricultural business” (M = 1.56, SD = 1.22). The
given task with the lowest mean was “Publishing or updating your own Web page/site”
(M = 1.41, SD = 1.21).
Mean weighted discrepancy scores were also calculated for each website-related
task utilizing Borich’s (1980) model for conducting needs assessment. Tasks with higher
scores indicate a higher need for training. Scores from competencies for all online
communication tools are compared later in the chapter.
87
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.26
Respondents’ Perceptions of the Importance of Website Tasks and Their Competence at
Performing the Tasks
Importance
Website Tasks
Competence
MWDSa
M
SD
M
SD
Creating a website with user-friendly
templates and publishing options
2.57
1.31
1.45
1.19
2.64
Using a website effectively for my
agricultural business
2.59
1.32
1.56
1.22
2.42
Measuring impact or effectiveness of
website for business
2.48
1.30
1.43
1.19
2.35
Publishing or updating your own
Web page/site
2.45
1.32
1.41
1.21
2.35
Understanding how to manage a
website efficiently
2.47
1.33
1.45
1.24
2.30
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
Additionally, paired sample t-tests on both importance and competence values
were conducted to see if there were any statistically significant differences between
means reported by beginning farmers and ranchers and the more experienced producers,
or between those who practice direct-to-consumer marketing and those who do not. The
significance level was set a priori at .05. Tables 4.27 and 4.28 display the t-test values
for importance means, while tables 4.29 and 4.30 display the t-test values for competence
means.
The only comparison of groups that yielded a significant difference was between
those who conducted direct-to-consumer marketing (n = 73, M = 2.79, SD = 1.20) and
those who did not (n = 91, M = 2.38, SD = 1.39) on the task “Creating a website with
88
Texas Tech University, Kelsey F. Shaw, May 2013
user-friendly templates and publishing options,” t (161.05) = -2.03, p = .04. Those who
do market directly to the consumer placed more importance on this task.
89
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.27
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Website Importance
BFRs
Website Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Creating a website with user-friendly templates and
publishing options
104
2.65
1.25
58
2.36
1.44
1.35
160
0.18
Publishing or updating your own Web page/site
104
2.56
1.24
58
2.21
1.47
1.62
160
0.11
Understanding how to manage a website efficiently
104
2.58
1.24
58
2.22
1.50
1.53
100.44
0.13
Using a website effectively for my agricultural business
103
2.64
1.27
58
2.43
1.43
0.96
159
0.34
Measuring impact or effectiveness of website for
business
104
2.53
1.25
57
2.33
1.39
0.91
159
0.37
90
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.28
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Website Importance
D2Cs
Non D2Cs
Website Tasks
n
M
SD
Creating a website with user-friendly templates and
publishing options
73
2.79
1.20
Publishing or updating your own Web page/site
73
2.66
Understanding how to manage a website efficiently
73
Using a website effectively for my agricultural business
Measuring impact or effectiveness of website for
business
M
SD
91
2.38
1.25
91
2.68
1.27
72
2.78
73
2.66
91
n
T
df
p value
1.39
-2.03
161.05
0.04*
2.29
1.38
-1.79
162
0.08
91
2.30
1.38
-1.86
162
0.07
1.25
91
2.43
1.38
-1.68
161
0.10
1.26
90
2.33
1.33
-1.58
161
0.12
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.29
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Website Competence
BFRs
Website Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Creating a website with user-friendly templates and
publishing options
103
1.46
1.16
57
1.40
1.25
0.27
158
0.79
Publishing or updating your own Web page/site
101
1.40
1.14
57
1.39
1.32
0.05
156
0.96
Understanding how to manage a website efficiently
103
1.41
1.15
57
1.49
1.38
-0.39
99.41
0.70
Using a website effectively for my agricultural business
103
1.44
1.13
56
1.71
1.33
-1.32
98.13
0.19
Measuring impact or effectiveness of website for
business
103
1.34
1.12
57
1.53
1.26
-0.97
92
158
0.34
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.30
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Website Competence
D2Cs
Non D2Cs
Website Tasks
n
M
SD
Creating a website with user-friendly templates and
publishing options
73
1.63
1.29
Publishing or updating your own Web page/site
72
1.57
Understanding how to manage a website efficiently
73
Using a website effectively for my agricultural business
Measuring impact or effectiveness of website for
business
M
SD
89
1.30
1.29
88
1.62
1.32
72
1.75
73
1.56
93
n
T
df
p value
1.11
-1.71
143.31
0.09
1.28
1.14
-1.47
143.52
0.15
89
1.33
1.17
-1.47
145.08
0.14
1.28
89
1.42
1.18
-1.73
159
0.09
1.31
89
1.34
1.09
-1.19
139.62
0.24
Texas Tech University, Kelsey F. Shaw, May 2013
Other Online Communication Tasks
The next section of questions pertained to a variety of other related online
communication tasks. Respondents rated their perceptions of importance and
competency of several tasks on a five-point scale, ranging from 0 (no/none) to 4
(utmost/exceptional). As noted in Table 4.31, the task with the highest importance mean
was “Understanding how social media (in general) fits into the business strategy for my
agricultural operation” (M = 2.31, SD = 1.22). The given task with the lowest importance
mean was “Using a social media management tool (i.e., HootSuite, Tweetdeck, etc.)” (M
= 1.54, SD = 1.19).
The other online communication task with the highest competency mean was
“Understanding how social media (in general) fits into the business strategy for my
agricultural operation” (M = 1.73, SD = 1.19). The task with the lowest mean was
“Using a social media management tool (i.e., HootSuite, Tweetdeck, etc.)” (M = 1.00,
SD = 1.12). All reported means for this set of competencies were below the scale’s midpoint.
Mean weighted discrepancy scores were also calculated for each other online
communication tools-related task utilizing Borich’s (1980) model for conducting needs
assessment. Tasks with higher scores indicate a higher need for training. Scores from
competencies for all online communication tools are compared later in the chapter.
94
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.31
Respondents’ Perceptions of the Importance of Other Online Communication Tool-Related Tasks
and Their Competence at Performing the Tasks
Importance
Other Online Communication Tools
Tasks
Competence
MWDSa
M
SD
M
SD
Using social media to gather
information about
audiences/consumers as it relates to
your business
2.09
1.25
1.27
1.14
1.56
Understanding how to manage social
media effectively
2.12
1.27
1.38
1.19
1.43
Using social media to monitor
consumer trends as they relate to my
business
2.02
1.22
1.25
1.12
1.42
Understanding how social media (in
general) fits into the business
strategy for my agricultural
operation
2.31
1.22
1.73
1.19
1.30
Understanding how I can utilize
multiple people in my operation to
help with my social media presence
(family members and employees)
2.13
1.27
1.46
1.20
1.29
Using social media measurement
tools
1.93
1.23
1.19
1.16
1.24
Uploading videos to the web for the
purpose of sharing
2.10
1.22
1.48
1.20
1.16
Uploading photos to the web for the
purpose of sharing
2.19
1.23
1.66
1.23
1.00
Using a social media management
tool
1.54
1.19
1.00
1.12
0.73
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
Paired sample t-tests for both importance and competence were conducted
between beginning farmers and ranchers and their more experienced counterparts, as well
95
Texas Tech University, Kelsey F. Shaw, May 2013
as between those who market directly to their consumers and those who do not. With the
significance level set a priori at .05, there were no significant differences observed
between any of the compared means for this set of online communications tasks. Tables
4.32 and 4.33 display the t-test values for importance means, while tables 4.34 and 4.35
display the t-test values for competence means.
96
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.32
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Other Online Communication Tool Importance
BFRs
Other Online Communication Tool Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Understanding how social media fits into the business
strategy for my agricultural operation
104
2.34
1.17
58
2.22
1.33
0.56
160
0.58
Using a social media management tools
101
1.59
1.16
57
1.40
1.25
0.96
156
0.34
Using social media measurement tools
101
1.99
1.16
57
1.75
1.34
1.11
103.19
0.27
Using social media to monitor consumer trends as they
relate to my business
103
2.01
1.18
58
1.98
1.30
0.13
159
0.89
Using social media to gather information about
audiences/consumers as it relates to your business
102
2.14
1.21
58
1.95
1.32
0.92
158
0.36
Understanding how to manage social media efficiently
103
2.20
1.21
58
1.91
1.38
1.39
105.75
0.18
Uploading videos to the web for the purpose of sharing
102
2.18
1.14
57
1.91
1.37
1.24
99.54
0.22
Uploading photos to the web for the purpose of sharing
103
2.24
1.15
55
2.04
1.39
0.94
94.20
0.35
Understanding how I can utilize multiple people in my
operation to help with my social media presence
102
2.13
1.22
56
2.05
1.37
0.35
97
156
0.73
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.33
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Other Online Communication Tool Importance
D2Cs
Non D2Cs
Other Online Communication Tool Tasks
n
M
SD
Understanding how social media fits into the business
strategy for my agricultural operation
75
2.36
1.24
Using a social media management tools
72
1.63
Using social media measurement tools
72
Using social media to monitor consumer trends as they
relate to my business
M
SD
T
df
p value
89
2.27
1.22
-0.47
162
0.64
1.28
88
1.47
1.11
-0.84
158
0.40
1.97
1.22
88
1.90
1.25
-0.38
158
0.71
74
2.00
1.24
89
2.04
1.22
0.23
161
0.82
Using social media to gather information about
audiences/consumers as it relates to your business
74
2.11
1.28
88
2.07
1.24
-0.20
160
0.84
Understanding how to manage social media efficiently
74
2.15
1.33
89
2.09
1.23
-0.29
161
0.77
Uploading videos to the web for the purpose of sharing
72
2.13
1.30
89
2.08
1.17
-0.24
159
0.81
Uploading photos to the web for the purpose of sharing
72
2.33
1.28
88
2.07
1.19
-1.36
158
0.18
Understanding how I can utilize multiple people in my
operation to help with my social media presence
73
2.16
1.31
87
2.10
1.25
-0.30
158
0.76
98
n
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.34
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Other Online Communication Tool Competence
BFRs
Other Online Communication Tool Tasks
Understanding how social media fits into the business
strategy for my agricultural operation
n
102
Non BFRs
M
SD
1.73
1.12
n
M
SD
T
df
p value
56
1.70
1.32
0.15
156
0.88
Using a social media management tools
101
1.02
1.09
56
0.93
1.20
0.49
155
0.63
Using social media measurement tools
102
1.20
1.09
56
1.11
1.26
0.46
156
0.64
Using social media to monitor consumer trends as they
relate to my business
102
1.27
1.06
56
1.14
1.21
0.71
156
0.48
Using social media to gather information about
audiences/consumers as it relates to your business
102
1.26
1.08
56
1.21
1.22
0.27
156
0.79
Understanding how to manage social media efficiently
102
1.40
1.12
56
1.30
1.31
0.50
156
0.62
Uploading videos to the web for the purpose of sharing
102
1.49
1.13
56
1.46
1.33
0.13
156
0.90
Uploading photos to the web for the purpose of sharing
102
1.70
1.12
56
1.59
1.40
0.49
94.46
0.63
Understanding how I can utilize multiple people in my
operation to help with my social media presence
100
1.41
1.08
56
1.50
1.39
-0.42
92.91
0.68
99
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.35
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Other Online Communication Tool Competence
D2Cs
Non D2Cs
Other Online Communication Tool Tasks
n
M
SD
Understanding how social media fits into the business
strategy for my agricultural operation
73
1.67
1.24
Using a social media management tools
72
0.86
Using social media measurement tools
73
Using social media to monitor consumer trends as they
relate to my business
M
SD
T
df
p value
87
1.76
1.16
0.46
158
0.65
1.04
87
1.10
1.18
1.36
157
0.18
1.07
1.08
87
1.28
1.23
1.14
157.55
0.26
73
1.21
1.09
87
1.29
1.16
0.46
158
0.65
Using social media to gather information about
audiences/consumers as it relates to your business
73
1.19
1.13
87
1.32
1.16
0.72
158
0.47
Understanding how to manage social media efficiently
73
1.30
1.20
87
1.44
1.20
0.71
158
0.48
Uploading videos to the web for the purpose of sharing
73
1.42
1.25
87
1.53
1.18
0.54
158
0.59
Uploading photos to the web for the purpose of sharing
73
1.60
1.26
87
1.71
1.23
0.56
158
0.58
Understanding how I can utilize multiple people in my
operation to help with my social media presence
72
1.43
1.24
86
1.48
1.19
0.24
156
0.81
100
n
Texas Tech University, Kelsey F. Shaw, May 2013
Computer-Based Communication Technology
Finally, respondents were asked to indicate their perception of importance and
compentency for two general computer-based communication technology tasks.
Responses were placed on a five-point scale, ranging from 0 (no/none) to 4
(utmost/exceptional). The higher importance mean for the set was “Using computerbased communication technology” (M = 3.00, SD = 0.90). Of the two tasks, the task with
the higher competency mean was “Using computer-based communication technology”
(M = 2.37, SD = 0.94). Results for both of these tasks are reported in Table 4.36.
Mean weighted discrepancy scores were also calculated for each computer-based
communication-related task utilizing Borich’s (1980) model for conducting needs
assessment. Tasks with higher scores indicate a higher need for training. Scores from
competencies for all online communication tools are compared later in the chapter.
Table 4.36
Respondents’ Perceptions of the Importance of Computer-Based Communication Technology
Tasks and Their Competence at Performing the Tasks
Importance
Computer-Based Communication
Technology Tasks
Competence
MWDSa
M
SD
M
SD
Using computer-based
communication technology
3.00
0.90
2.37
0.94
1.69
Teaching myself new computerbased communications technology
2.83
1.00
2.27
0.98
0.00
Note. Attitudes were evaluated on a five-point scale where 0 = no/none and 4 =
utmost/exceptional. aMWDS: Mean Weighted Discrepancy Score
The researcher conducted paired sample t-tests for both importance and
competency means to see if there were any statistically significant differences in means
for these tasks between beginning farmers and ranchers versus the experienced farmers
101
Texas Tech University, Kelsey F. Shaw, May 2013
and ranchers, or between those who market directly to their consumers and those who do
not. The significance level was set a priori at .05. Tables 4.37 and 4.38 display the t-test
values for importance means, while tables 4.39 and 4.40 display the t-test values for
competence means. No significant differences were found between either of the groups
for any of the items in this question set.
102
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.37
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Computer-Based Communication Technology Importance
BFRs
Computer-Based Communication Technology Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Using computer-based communication technology
106
2.98
0.88
58
2.98
0.93
-0.01
162
0.99
Teaching myself new, computer-based communication
technology
104
2.83
0.98
59
2.81
1.06
0.08
161
0.94
Table 4.38
Independent Samples T-test for Significant Differences Between Those who Practice Direct-to-Consumer Marketing and Those who do not on
Computer-Based Communication Technology Importance
D2Cs
Non D2Cs
Computer-Based Communication Technology Tasks
n
M
SD
Using computer-based communication technology
75
2.99
0.89
Teaching myself new, computer-based communication
technology
73
2.78
1.06
103
n
M
SD
T
df
p value
91
3.01
0.91
0.17
164
0.86
92
2.88
0.97
0.63
163
0.53
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.39
Independent Samples T-test for Significant Differences Between Beginning Farmers and Ranchers and More Experienced Farmers and Ranchers
on Computer-Based Communication Technology Competence
BFRs
Computer-Based Communication Technology Tasks
n
Non BFRs
M
SD
n
M
SD
T
df
p value
Using computer-based communication technology
106
2.40
0.90
59
2.25
0.99
0.94
163
0.35
Teaching myself new, computer-based communication
technology
105
2.31
0.93
58
2.16
1.04
1.00
161
0.32
Table 4.40
Independent Samples T-test for Significant Differences Between Those Who Practice Direct-to-Consumer Marketing and Those Who Do Not on
Computer-Based Communication Technology Competence
D2Cs
Non D2Cs
Computer-Based Communication Technology Tasks
n
M
SD
Using computer-based communication technology
75
2.31
0.99
Teaching myself new, computer-based communication
technology
73
2.19
1.02
104
n
M
SD
T
df
p value
92
2.39
0.90
0.58
165
0.56
92
2.30
0.94
0.74
163
0.46
Texas Tech University, Kelsey F. Shaw, May 2013
After all individual competency scores had been calculated, mean weighted
discrepancy scores were calculated using Borich’s (1980) conventions. Then, final mean
weighted discrepancy scores were ranked in descending order, as shown in Table 4.41.
Out of 44 tasks compiled from all the online communications tools, 16 tasks had
mean weighted discrepancy scores more than 1.0 with the highest task having a mean
weighted discrepancy score of 2.64. The tasks with the highest mean weighted
discrepancy scores were “Website – Creating a website with user-friendly templates and
publishing options” (2.64), “Website – Using a website effectively for my agricultural
business” (2.42), “Website – Measuring impact or effectiveness of website for business”
(2.35), “Website – Publishing or updating your own Web page/site” (2.35), and “Website
– Understanding how to manage a website efficiently” (2.30).
The five tasks with the lowest mean weighted discrepancy scores were “Twitter –
Understanding the purpose for my Agricultural Business” (0.42), “Twitter – Creating an
effective Twitter page” (0.38), “Twitter – Steps to create a Twitter page” (0.18),
“Computer-Based Communication – Teaching myself new computer-based
communication technology” (0.00), and “Facebook – Steps to create a Facebook page” (0.22).
105
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.41
Online Communication Tool Training Needs of Agriculturists Using the Borich Needs
Assessment Model
MWDSa
Rank
Construct
1
Website - Creating a website with user-friendly templates and publishing
options
2.64
2
Website - Using a website effectively for my agricultural business
2.42
3
Website - Measuring impact or effectiveness of website for business
2.35
4
Website – Publishing or updating your own Web page/site
2.35
5
Website - Understanding how to manage a website efficiently
2.30
6
Computer-Based Communication - Using computer-based
communication technology
1.69
Other Online Communication Tools - Using social media to gather
information about audiences/consumers as it relates to your business
1.56
Other Online Communication Tools - Understanding how to manage
social media effectively
1.43
Other Online Communication Tools - Using social media to monitor
consumer trends as they relate to my business
1.42
Other Online Communication Tools - Understanding how social media
(in general) fits into the business strategy for my agricultural operation
1.30
Other Online Communication Tools - Understanding how I can utilize
multiple people in my operation to help with my social media presence
(family members and employees)
1.29
Other Online Communication Tools - Using social media measurement
tools (Google Analytics, Facebook Insights, etc.)
1.24
Other Online Communication Tools - Uploading videos to the web for
the purpose of sharing
1.16
14
Facebook - Engaging people/consumers
1.11
15
Facebook - Awareness of the risk in having a business presence and how
to mitigate them
1.06
Other Online Communication Tools - Uploading photos to the web for
the purpose of sharing (using Facebook, Twitter, Flickr, etc.)
1.00
7
8
9
10
11
12
13
16
106
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.41 continued
MWDSa
Rank
Construct
17
Facebook - Measuring impact or effectiveness for my agricultural
business
0.95
18
Blog - Engaging people/consumers
0.95
19
Blog - Generating subscribers
0.92
20
Blog - Measuring impact or effectiveness for my agricultural business
0.88
21
Blog - Using it effectively for my agricultural business
0.86
22
Facebook - Knowing what I should post to Facebook
0.85
23
Blog - Creating an effective blog page
0.84
24
Facebook - Using one effectively for my agricultural business
0.81
25
Blog - Awareness of the risk in having a blog and how to mitigate it
0.79
26
Blog - Knowing what I should post to the blog
0.75
27
Other Online Communication Tools - Using a social media management
tool (i.e. HootSuite, Tweetdeck, etc.)
0.73
28
Facebook - Generating Page Likes
0.72
29
Blog - Steps to create a blog
0.65
30
Blog - Steps to input multimedia into a blog post
0.64
31
Blog - Understanding the purpose for my Agricultural Business
0.62
32
Facebook - Creating an effective Facebook Page
0.49
33
Twitter - Awareness of the risk in having a Twitter presence and how to
mitigate them
0.49
34
Twitter - Knowing what I should post to Twitter
0.48
35
Twitter - Generating followers
0.48
36
Twitter - Engaging people/consumers
0.47
37
Twitter - Measuring impact or effectiveness for my agricultural business
0.46
107
Texas Tech University, Kelsey F. Shaw, May 2013
Table 4.41 continued
MWDSa
Rank
Construct
38
Twitter - Using one effectively for my agricultural business
0.45
39
Facebook - Understanding the purpose for my Agricultural Business
0.42
40
Twitter - Understanding the purpose for my Agricultural Business
0.42
41
Twitter - Creating an effective Twitter page
0.38
42
Twitter - Steps to create a Twitter page
0.18
43
Computer-Based Communication - Teaching myself new computerbased communications technology
0.00
44
a
Facebook - Steps to create a Facebook Page
-0.22
MWDS: Mean Weighted Discrepancy Score
Research Objective Three
The third research objective was to identify any motivations and barriers
participants might encounter to receiving additional online communication tool training.
Respondents were asked to rate their level of interest in participating in a free or low-cost
online communication tools training in the future using a four-point scale ranging from 0
(uninterested) to 3 (very interested). The mean response was 1.97 (n = 169, SD = 0.89),
which indicates a level of “interested” on the given scale.
Respondents were also asked what would motivate them to attend one of these
free or low-cost online communication tools trainings in an open-ended question. All
185 respondents answered this question. The researcher gathered common responses and
reported those most frequently mentioned. Some of the most prevalent motivations
included quality of the workshop content, scheduling, and distance from their homes.
One respondent commented: “I would be motivated to attend as long as the travel
distance/time was not too great and the qualifications of those teaching the workshop
108
Texas Tech University, Kelsey F. Shaw, May 2013
were sufficient.” Another echoed the sentiment, stating “if the training is offered within
60 miles of my home, I am much more likely to attend.”
Several respondents mentioned scheduling as a major motivation. One said,
“Daylong – will have to be on a weekend day. Otherwise, divide it into more than one
evening seminar.” Another said, “If it was held during a time of the year when I am not
busy, such as January or February.”
Practicality was also mentioned by several respondents. One said, “Allow us to
create our own blogs/Facebook pages/Twitter accounts/etc. while it is being taught.”
Another mentioned, “Needs to be a bona-fide expert in that technology, not somebody
who has ‘tinkered’ and thinks they are now an expert. One or more presenters sharing
their own genuine success stories.” Several also mentioned offering a free meal would
encourage them to attend. One said “a free meal and a comfortable seat” would
encourage his attendance, while another said, “a good meal and regular breaks.”
The final question asked respondents what barriers might prevent them from
attending the same hypothetical online communication tools training. All 185
respondents answered this question. The researcher gathered common responses and
reported those most frequently mentioned. Some of the most prevalent barriers included
similar responses to those factors mentioned as motivations. A frequent response was
“the distance I would have to travel” or “distance to travel and time of the year.” Again,
respondents repeated that “having it on a weekday” would be a barrier to their
attendance.
Another popular barrier was childcare. One respondent said “having a
babysitter,” while another said, “I have a small child and I would have to find childcare.”
109
Texas Tech University, Kelsey F. Shaw, May 2013
Finally, several respondents expressed concern for the content of the workshop.
One commented: “I will not attend such a workshop unless I knew my business would
benefit.” Another explained, “The content of the workshop [is a barrier], depending on
the specifics, [like] the experience level of the intended audience.”
110
Texas Tech University, Kelsey F. Shaw, May 2013
CHAPTER V
CONCLUSIONS, DISCUSSION, AND RECOMMENDATIONS
Introduction
The purpose of this study was to determine agriculturists’ use of online
communication tools. Members of farming and ranching organizations in Texas, Illinois,
and Georgia were surveyed to determine current perceptions and uses of popular online
communication tools, as well as their interest in gaining more training in the use of the
tools and programs. This chapter summarizes the results reported in Chapter IV and
provides conclusions, discussion and recommendations.
The following research objectives were used to guide the study:
1. Determine the extent of respondents’ personal and business use of online
communication tools.
2. Determine potential needs in utilizing various forms of online communication.
3. Identify motivations and barriers to receiving additional training to utilize online
communication tools.
Conclusions and Discussion
Demographics were collected to help identify the 185 respondents of the study –
45 from Texas, 111 from Illinois, and 29 from Georgia. Groups from these states were
targeted as part of USDA grant funding for beginning farmers and ranchers. Respondents
answered several demographics including age, gender, and years owning their operation.
Responses between respondents of each state were also analyzed separately to ensure all
participants were similar in demographic areas. Because respondents were similar in
almost all demographic areas, they were analyzed collectively. According to the 2007
111
Texas Tech University, Kelsey F. Shaw, May 2013
Agricultural Census (National Ag Statistics Service [NASS], 2007), there are 2.2 million
farmers in the United States, but only 14% of these farmers are women (n = 306,209).
The results of this study indicate although there was still a majority of male respondents
(n = 100, 54.1%), it is actually much more evenly split with the remaining respondents (n
= 71, 38.4%) being female (14 respondents did not answer this question). This could be a
reflection of the specific audience the researcher was trying to reach, such as
“alternative” or “beginning.” There may also be families that own a business included in
the survey, and the female is the one who is more active in the organization or handles
electronic correspondence.
The average age of respondents in this study was 39 years old. This is much
lower than the average age of U.S. farm operators, which is 57.1 years (NASS, 2007).
However, the researcher actively recruited organizations that served younger or
beginning agriculturists, so this may explain the difference in average age.
The questionnaire also aimed to identify what type of operations the respondents
owned. The two most frequently selected types of operations in this survey were “cattle
production” (n = 78, 42.2%) and “grain and oilseed farming” (n = 76, 41.1%). This is
consistent with the U.S. Census of Agriculture, in which “cattle and calves” was the most
frequent type of production operation, while “grains and oilseeds” was third, only to
“other crops and hay” (NASS, 2007). However, the U.S. Census numbers were for the
entire nation, not just for the states in this study.
It was important to identify how long survey respondents had owned their
operations in order to determine if they qualified as a beginning farmer and rancher. In
the survey, the majority of respondents had owned their operation for 10 years or fewer
112
Texas Tech University, Kelsey F. Shaw, May 2013
(n = 107, 57.9%) and thus would be classified by the USDA as a beginning farmer and
rancher. However, in the U.S. Census of Agriculture (2007), 64.3% of respondents had
been operating on the same farm for 10 or more years. This is in clear contrast to the
results of this survey, but again, the researcher sought to identify groups that would be
heavily populated with younger or less experienced farmers and ranchers.
In order to be able to compare behaviors of those who engaged in direct
marketing to those who did not, the researcher first asked whether respondents classified
their operations as “alternative” according to the USDA definition. “Alternative” is a
term used to describe an operation that focuses on some sort of nontraditional crop or
product, or service, including those that participate in what some would deem
unconventional agriculture such as organic marketing or aquaculture (Gold, 2007). The
majority of respondents indicated their operation did not have any alternative enterprises
(n = 108, 58.4%). In the 2007 Census of Agriculture (NASS, 2007), there were 2.2
million farms identified, but only 14,540 farms (6.6%) were identified as USDA-certified
“organic.” While these are not the only types of farms identified as “alternative,” the
census does not measure those farms with “alternative” enterprises (NASS, 2007).
The U. S. Census of Agriculture also measures the amount of organic products
that are sold direct to consumers. In 2008, only 6.8 % of organic sales were directly to
consumers, with only 1.9% of sales through avenues such as farmers markets (NASS,
2007). The current study found percentages much higher than those at the national level,
but it must also be taken into consideration that these statistics are only representative of
USDA-certified organic products. Producers could have considered themselves
alternative without obtaining the USDA “organic” certification. There was not a survey
113
Texas Tech University, Kelsey F. Shaw, May 2013
question that identified if they were officially classified as “organic” by USDA standards.
Instead, the respondents in the current study were contacted because they are members of
organizations that may cater to “alternative” producers.
Because many farmers who consider themselves “alternative” also participate in
some sort of direct-to-consumer marketing (NASS, 2007), respondents were also asked if
their operation participated in any sort of direct-to-consumer marketing. The respondents
were nearly evenly divided between those that did (n = 77, 41.6%) and those that did not
(n = 92, 49.7%) participate in any direct-to-consumer marketing. By specifically asking
about these two aspects of the respondents’ enterprises, the data collected could better be
analyzed for use in the USDA beginning farmers and ranchers grant project.
One of the final demographic questions asked in the survey was what type of
electronic devices the respondents owned that had Internet access. Respondents could
select more than one type of technology. In fact, a large number of respondents (n = 145,
85.9%) did own more than one of these tools. An overwhelming majority of respondents
did indicate they owned a laptop (n = 154, 83.2%), and more than half also owned a
smartphone (n = 120, 64.9%). The majority also owned a desktop computer (n = 104,
56.2%). This combats the stereotype of the farming community not having access to
technology (Bisdorf et al., 2003), but is slightly lower than statistics reported by the
American Farm Bureau Federation (2012), that indicated 93% of all young farmers and
ranchers utilize a computer for their operation, with most having access to high-speed
Internet capabilities. However, the AFBF survey was limited to those under the age of
35, while this research included agriculturists of any age, with a varying amount of onfarm experience.
114
Texas Tech University, Kelsey F. Shaw, May 2013
Research Objective One
Research objective one sought to identify which online communication tools
respondents were using in their personal and business lives, as well as how often they
were utilizing these tools. Tools listed on the instrument were websites, Facebook,
Google+, Twitter, YouTube, photo sharing websites, blogs, social bookmarking sites, and
social media management sites. Not every respondent identified a frequency for every
tool.
It was anticipated that respondents would use a wider range of these tools more
frequently for personal reasons, such as to interact with friends and family. However,
only slightly more than half of respondents indicated they use one of the most basic tools,
websites, on a daily basis (n = 95, 56.2%). In fact, about 20% of respondents (n = 35)
indicated they did not use websites whatsoever. Almost half indicated they employed
Facebook for personal reasons on a daily basis (n = 80, 47%), while nearly 40% (n = 65,
38.5%) reported visiting YouTube at least once each month. All remaining tools were
never used or were used very infrequently.
Not surprisingly, those tools with more popularity for personal use are those that
are more established and more popular in mainstream culture, such as Facebook. This
supports Rogers’ (2003) idea that people are more likely to use tools that have been
tested and approved by friends, family, and peers. Diffusion of innovations follows a
distinct curve, where more people adopt a new innovation or technology as a function of
time. Compared to many of the other online communication tools mentioned in this
study, websites and Facebook are the oldest available tools, and have the most active
users. This is consistent with research identifying a unique audience for these two popular
115
Texas Tech University, Kelsey F. Shaw, May 2013
online communication tools (Coursaris et al., 2010; Peng & Mu, 2011) and findings that
agriculturists are actively engaging in social media (Doerfert et al., 2012).
Uses and gratifications theory also helps explain why these tools may be the most
popular for personal use. Websites are a very general type of tool that can be used for a
number of reasons (American Farm Bureau Federation, 2012; Cartmell, et al., 2004).
Respondents could be using websites for ecommerce purposes or even to look up menus
for their favorite restaurants. Facebook is used as a tool to socialize and connect with
friends and family. Other tools, like Twitter, social media management tools, or blogs,
have a reputation as being more for business and may not be meeting as many needs
personally for these respondents. They are also selecting the tools that have more
potential for personal and entertainment values (Urista et al., 2009).
Respondents may be engaging in particular social media platforms as a result of a
tool being popular with customers or even competitors. If an agriculturist’s peers favor a
certain form of social media, the technology acceptance model (Davis, 1989) suggests
that same farmer or rancher may be more likely to adopt the same technology. Viewing
others using technology could suggest to a potential user that not only is the tool useful,
but it is also manageable to use, both of which have a direct correlation to the farmer or
rancher making the decision to adopt a particular technology.
Like in personal use, websites were the most frequently used tool each day for
business purposes (n = 63, 37.3%), but fewer respondents indicated using websites on a
daily basis for the business than for personal reasons. The only other tool with a
significant amount of daily use for business was Facebook (n = 40, 23.5%), but almost
half of the respondents indicated not using Facebook in any way for their business (n =
116
Texas Tech University, Kelsey F. Shaw, May 2013
77, 45.3%). In fact, all of the tools except for websites and Facebook had at least a
majority, if not almost all respondents, indicate they did not use the tool at all for
business purposes. In total, seven of the nine given online communication tools were not
being utilized by a majority of agricultural producers.
Diffusion of innovations theory (Rogers, 2003) views adoption as a function of
time and innovation attributes such as trialability, observability, and compatibility.
Currently, it seems there are not very many agricultural producers utilizing these online
communication tools for their businesses. Following Rogers’ (2003) curve of diffusion,
these tools may still be in the initial stages of adoption. Although these tools have a free,
easy availability to be experimented with, the producers in this survey may not have seen
these tools utilized in a professional, business manner by peers or competitors. Because
there is a wide range of ages in the respondents of this survey, with the mean age being
39, utilizing electronic marketing techniques may not be something that is familiar with
their views of successful business marketing techniques. If they are not comfortable
utilizing these tools for personal reasons, it is unlikely they would be interested or
committed to utilizing the same tools in a professional setting.
More than likely, these respondents might have adopted a technology because of a
recommendation from a friend or seeing a competitor’s successful use of a tool, which
supports the technology acceptance model (Irani, 2000). However, they will not begin or
continue use of a tool unless they have identified the direct benefits the online
communication tool will have for their business. An important aspect of diffusion of
innovations is relative advantage. Assuming these respondents have successful
businesses, they have been using a particular set of tools or skills to market their
117
Texas Tech University, Kelsey F. Shaw, May 2013
business. According to uses and gratifications theory (Ruggiero, 2000), if they do not see
value or gratifications in altering their marketing strategy, they have no incentive to make
these alterations. This demonstrates that for many, these online communication tools
have no relative advantage over what they have already been doing to market their
agricultural businesses.
Overall, there was a significant correlation between personal and business use of
each online communication tool at every frequency of use. This indicates that those who
are utilizing a specific tool more frequently in their personal lives are also utilizing the
same tool in their professional communications, and vice versa. In the same token, if
they are not using a tool in their personal life, they are unlikely to be exploring use of the
same tool in a business setting.
Respondents also were asked if they believed they participated in any sort of
online agricultural advocacy, or “agvocacy.” While almost half of respondents indicated
they did feel they participated in advocating for agriculture (n = 87, 47%), only 35.7% (n
= 65) indicated they did try to participate in these activities, while the rest (n = 19,
10.3%) reported they were unsure what advocating for agriculture means. If they
indicated they advocate for agriculture, respondents were asked to specify what their
actions were. Many respondents mentioned tools that had already been addressed in
previous questions. Others said that simply by being aware of agricultural issues, they
were advocating on behalf of agriculture.
Research Objective Two
The next research objective was to determine potential needs in utilizing various
forms of online communications – Facebook, Twitter, blogs, websites, other online
118
Texas Tech University, Kelsey F. Shaw, May 2013
communication tools, and computer-based communication technology. For each
communication type, respondents were given a set of typical tasks they might complete
during use of the tool. The respondents were asked to rank importance of and
competence for completing each task on a five-point scale ranging from 0 (no/none) to 4
(utmost/exceptional). Averages and standard deviations were calculated for each of the
given tasks.
Paired sample t-tests were also conducted to identify if there were any statistically
significant differences between two distinct sets of data – beginning farmers and ranchers
versus their more experienced counterparts, and between those who participate in directto-consumer marketing tactics and those who do not. Significance level was set a priori
at .05. Any significant differences were reported.
Facebook.
Respondents indicated that “Understanding the purpose [of Facebook] for my
Agricultural Business” was the most important task within this set of questions, with a
mean score of 2.48 (SD = 1.28). All of the Facebook-related tasks had importance means
above the scale’s midpoint except for one – “Steps to Create a Facebook Page” (M =
1.92, SD = 1.14) — indicating an overall high importance for this online communication
tool.
Respondents indicated they were most competent in “Understanding the purpose
[of Facebook] for my Agricultural Business,” with a mean score of 2.34 (SD = 1.19).
The only other Facebook task that was above the scale’s midpoint was “Steps to Create a
Facebook Page” (M = 2.04, SD = 1.31). Interestingly, these two tasks were what
respondents ranked most and least important, respectively. It is clear from the means for
119
Texas Tech University, Kelsey F. Shaw, May 2013
all these Facebook tasks that respondents are not very confident in completing Facebook
tasks related to marketing for their agricultural business. The two tasks with the lowest
means were “Measuring impact or effectiveness for my agricultural business” (M = 1.67,
SD = 1.16) and “Generating Page ‘Likes’” (M = 1.65, SD = 1.21). Both of these tasks
seem to be more of the more complex actions associated with online marketing overall.
After conducting the paired sample t-tests for the importance of tasks, the only
significant difference in mean scores was between beginning farmers and ranchers (n =
104, M = 2.05, SD = 1.14) and experienced farmers and ranchers (n = 55, M = 1.67, SD =
1.11) on the task “Steps to Create a Facebook Page.” This indicates that beginning
farmers and ranchers are more likely to place more importance on the basic steps of
creating and assembling a Facebook page.
After conducting paired sample t-tests for the Facebook competence tasks
between the two sets of paired groups, the only significant difference was between
beginning farmers and ranchers (n = 104, M = 1.79, SD = 1.16) and the more experienced
group (n = 56, M = 1.36, SD = 1.21) on the task “Generating Page ‘Likes.’” This
indicates that beginning farmers and ranchers felt somewhat more confident in generating
page likes than their more experienced counterparts. It is important to consider that this
is also the task that the group as a whole ranked as “least competent.” So although there
was a statistically significant difference between the groups for this task, it is something
both groups felt least able to complete.
Overall, Facebook was ranked as one of the more important tools for use in an
agricultural business. This is probably directly connected to the fact that Facebook was
one of only two of the tools that the majority of respondents indicated they were utilizing
120
Texas Tech University, Kelsey F. Shaw, May 2013
for their agricultural operations. According to uses and gratifications theory, people only
utilize the types of media or technology they find useful or gratifying either socially or
psychologically (Katz et al., 1973), so naturally, the respondents are using this tool
because they find it beneficial to their marketing and operations.
Respondents indicated they were most competent in using Facebook in
comparison to all the other tools addressed in this survey. In fact, Facebook was the only
tool that had any tasks with mean scores above the scale’s midpoint, with the exception
of the questions asking respondents to compare overall computer-based technology tasks.
This may be due to Facebook’s immense popularity (Facebook, 2012), with a large
number of respondents indicating that they are already utilizing the tool in both personal
and professional settings.
Twitter.
In the set of Twitter tasks, respondents selected “Understanding the purpose for
my agricultural business” as the most important task (M = 1.41, SD = 1.47). However, all
the means for the Twitter tasks were significantly lower than even the lowest Facebook
task mean. The lowest mean for the list of given Twitter importance tasks was “Steps to
Create a Twitter page” (M = 1.04, SD = 1.24). Overall, it is clear the respondents do not
see Twitter as an important tool to use when marketing their business, which is
interesting, considering Twitter now has more than 100 million active users (Evans,
2011) with an older member base than other platforms, such as Facebook (Allen et al.,
2010). Because of the smaller space for updates, this tool may actually be the most
useful for agricultural businesses to connect directly to potential consumers (Allen et al.,
2010).
121
Texas Tech University, Kelsey F. Shaw, May 2013
When asked about competency completing Twitter-related tasks, respondents
reported overall low means for all tasks. The task with the highest competency mean was
“Understanding the purpose for my agricultural business” (M = 1.05, SD = 1.25).
However, the tasks with the lowest competency means, “Generating followers” (M =
0.74, SD = 1.04) and “Measuring impact or effectiveness for my agricultural business”
(M = 0.74, SD = 1.09), were almost identical to those with the lowest means for
Facebook. This could indicate respondents’ struggle with the overall concept of
generating followers or measuring impact, versus difficulty grasping a specific set of
steps within an online communication tool.
This information is consistent with the responses provided in objectives one and
two, where respondents reported not using Twitter in either personal or professional
capacities. The independent samples t-test found no differences between either set of
predetermined groups for importance or competency of Twitter tasks.
A connection can be made between the averages for Twitter tasks and the
respondents reported use of Twitter for both business and personal reasons. Twitter was
consistently not used at all for both personal (n = 128, 77.1%) and professional (n = 141,
83.9%) reasons. This is another example of an online communication tool being directly
connected to the uses and gratifications theory. If someone does not see any direct
benefits or gratifications from use of a tool, they will not take the time or effort to utilize
the tool. These producers may not be seeing any importance to utilizing Twitter
personally, so it directly impacts their use of the tool for their agricultural enterprises.
122
Texas Tech University, Kelsey F. Shaw, May 2013
Blogging, Websites and Other Online Communications Tools.
Respondents rated the importance of a number of tasks for blogging, websites,
and other online communication tasks. For each of the tools, participants selected the
task that was related to understanding or knowing how to effectively use the tool for their
agricultural business. For blogging, the highest mean response was for “Understanding
the purpose for my Agricultural Business” (M = 2.06, SD = 1.35). For the questions
related to the importance of website tasks, the task “Using a website effectively for my
agricultural business” (M = 2.59, SD = 1.32) had the highest mean. When asked about
questions related to other online communication tools, the task with the highest mean was
“Understanding how social media (in general) fits into the business strategy for my
agricultural operation” (M = 2.31, SD = 1.22).
When respondents were asked to compare the importance of using computerbased communication technology versus the ability to teach themselves new computerbased communications technology, using the technology received a higher overall
importance mean (M = 3.00, SD = 0.90). This shows respondents place more importance
on being able to utilize current technology than being able to teach themselves and
remain current with changing technology.
These sections of questions also had very similar tasks with the lowest mean for
task importance. “Steps to Create a Blog” (M = 1.70, SD = 1.27) had the lowest mean for
importance in the set of blogging tasks, and “Publishing or updating your own Web
page/site” (M = 2.45, SD = 1.32) had the lowest importance mean score for website tasks.
“Using a social media management tool” (M = 1.54, SD = 1.19) was the task with the
lowest mean in the “other online communication tools” section, while the ability to teach
123
Texas Tech University, Kelsey F. Shaw, May 2013
yourself new computer-based technology (M = 2.83, SD = 1.00) ranked below simply
using new computer-based communications technology in the final set of questions.
Next, respondents were asked about their competence completing several tasks
for blogging, websites, other online communication tools, and computer-based
communication technology. Just like in the importance rankings, the highest mean and
lowest mean responses were similar for all of these categories. For blogging, the task
with the highest mean was “Understanding the purpose for my Agricultural Business” (M
= 1.68, SD = 1.28); for websites, the task with the highest mean was “Using a website
effectively for my agricultural business” (M = 1.56, SD = 1.22). For other online
communication tools, the task with the highest mean was “Understanding how social
media (in general) fits into the business strategy for my agricultural operation” (M = 1.73,
SD = 1.19). In computer-based communication technology, the more important task was
“Using computer-based communication technology” (M = 2.37, SD = 0.94), while
“Teaching myself new computer-based communications technology” (M = 2.27, SD =
0.98) ranked slightly lower. These tasks are almost identical, indicating that respondents
feel most competent in the larger-scale planning and designing of an online
communication campaign than in tasks such as creating pieces of these online tools.
However, when considering the actual means for each of the tasks within these
categories, all values were below the midpoint on the competency scale.
The lowest mean scores for each of these tools was in the same range of the scale.
For blogging, the task with the lowest mean was “Generating subscribers” (M = 1.13, SD
= 1.12), while for websites, the task with the lowest mean was “Publishing or updating
your own Web page/site” (M = 1.41, SD = 1.21). The task with the lowest mean in the
124
Texas Tech University, Kelsey F. Shaw, May 2013
other online communication tools question block was “Using a social media management
tool (i.e., HootSuite, Tweetdeck, etc.)” (M = 1.00, SD = 1.12). Interestingly, the tasks
with the lowest means in competency are for two tools – websites and other online
communication tools – were also ranked lowest in overall importance. If the respondents
view specific tasks as unimportant, they would also rate the need of learning how to
complete them as less important.
Comparing whether respondents felt more competent simply using computerbased communication technology or actually teaching themselves new computer-based
technology, respondents indicated they feel slightly more comfortable utilizing the
technology (M = 2.37, SD = 0.94) than teaching themselves new technology(M = 2.27,
SD = 0.98). It is contrasting that respondents indicated they were more confident “using
computer-based communication technology” in this question, even though their reported
competence for most of the specific tool-related skills were ranked below the mid-point
on the scale. It is important to identify why respondents consider themselves competent
overall, but not competent in specific tool-related skills. It would also be important to
find out if there are related skills to each tool that are not being addressed with this
instrument.
It seems clear that these participants believe it is very important to know how to
properly integrate these online communication tools into their agricultural businesses,
and value these tools to help market their operations effectively. It is interesting that —
even though there are small wording differences between the blogging, website, and other
communication tool tasks — respondents rated similar tasks consistently between each
set of questions. It seems these respondents believe it is least important to understand
125
Texas Tech University, Kelsey F. Shaw, May 2013
how to create and/or maintain these online communication tools and accounts. Perhaps
this is an indication that respondents are already familiar with how to maintain these
accounts or it may be that they have another person (staff member or intern) to facilitate
these accounts. Most likely, it is a reflection of what these respondents would be
interested in learning about during potential online communication workshops.
Overall, it seems respondents do not feel they are competent in completing
various online communication tool-related tasks. However, they do feel confident using
computer-based communication technology and teaching themselves new computerbased communication technology. This may be attributed to Diffusion of Innovations
(Rogers, 2003). Respondents may be in the implementation or confirmation stages of
adoption for online communication technology in general, but may be struggling in the
persuasion or decision stages for utilizing specific social media tools. For the
communicator, the goal simply becomes promotion and awareness of these tools in order
to help audiences like these respondents adopt these newer programs and technologies.
The respondents’ evaluation of tool importance allows several connections to be
made to the diffusion of innovations theory. The theory states that there are five
attributes that help determine adoption rates – relative advantage, compatibility,
complexity, trialability, and observability (Rogers, 2003). Because the majority of
respondents indicated they are currently not using these social media tools (with the
exception of Facebook) in their personal or professional lives, they are not taking
advantage of the trialability attribute. However, respondents did indicate it is important
to see the value of utilizing the tool in their business, and Rogers (2003) said that users
are not going to adopt a new technology unless they can directly see how the benefits of
126
Texas Tech University, Kelsey F. Shaw, May 2013
using that program outweigh their current, in this case, marketing strategies. Many of
these respondents seem to be stalled in the knowledge or persuasion stages of the
innovation-decision process, and may need guidance or coaxing to fully commit to
utilizing more of these online communication tools.
Because respondents indicated they utilize Facebook more than any other online
communication tool for business purposes, it is clear they view this tool as more
beneficial than the others, and are receiving or see others receiving positive benefits from
marketing using this tool.
Utilizing Borich’s (1980) model for needs assessment, 16 of the 44 tool-related
tasks were identified by respondents as having a greater need for training. Of these 16
tasks, eight were classified under “other communication tools,” five were related to use
of a website, two involved use of Facebook, and one was about the general use of
computer-based communication. For the population of this survey, these tools and tasks
seem to be those which would be most useful in future trainings, either face-to-face or
online.
Interestingly, respondents indicated absolutely no need to learn how to train
themselves how to use computer-based communication, and all nine Twitter-related tasks
were ranked in the bottom 12 of the Borich needs ranking. This indicates the respondents
simply felt no need to receive any training for this specific tool, even though overall
competency scores for this tool were clearly the lowest.
Research Question Three
The final research question sought to determine what motivations and barriers
respondents had to participating in an online communication tool training workshop.
127
Texas Tech University, Kelsey F. Shaw, May 2013
First, respondents were asked to indicate their level of interest in participating in some
sort of workshop on a four-point scale ranging from 0 (uninterested) to 3 (very
interested). The overall mean response for this item was 1.97 (n = 169, SD = 0.89),
indicating that participants were somewhat interested in participating in future trainings.
Participants were asked an open-ended question to help identify what might
motivate them to attend future trainings. Many responded with workshop scheduling
specifics, such as close proximity to their home or work and time of year. Several
participants mentioned they wanted the training facilitator to have a deep knowledge and
experience using these tools in the real world who would be able to answer in-depth
questions. They also indicated they wanted to leave with something tangible – actually
be able to create these online tools during the workshop. Another incentive participants
mentioned frequently was that workshops should include some sort of free meal.
The final question asked participants if they could foresee any barriers that might
prevent them from attending any future training. Again, some of the logistical specifics
were mentioned such and time, date, and location. Several respondents also mentioned
the need for some sort of childcare to be provided. One thing that was very clear from
these responses is that these participants want to know in advance what will be covered
during the presentations and what benefits will come from attending the workshop.
Knowing this might help address barriers and serve as incentive for potential participants.
The motivations and barriers respondents are mentioned are consistent with
Roger’s (2003) suggestion of using change agents to help diffuse an innovation. In order
for technological advancements to infiltrate a social system, they frequently need a
champion, or an expert, to be able to explain the benefits or usefulness for a particular
128
Texas Tech University, Kelsey F. Shaw, May 2013
innovation. The respondents’ reported desire for knowledgeable, trained instructors in
the face-to-face workshops reflects this need for helpful, experienced change agents to
help guide development of these social media through the agricultural industry.
Recommendations
Practitioners
This research was conducted as part of a USDA Beginning Farmers and Ranchers
grant with the purpose of equipping beginning farmers and ranchers in Texas, Illinois,
and Georgia with the tools necessary to better market their products directly to consumers
through the use of online communication tools. This survey was conducted as a part of a
baseline needs assessment to help establish what should be covered in an 8-hour online
communication workshop presented in each state.
From the demographics, it is clear that there is a wide variety of agriculturists
who may be interested in participating in these workshops. Beginning farmers and
ranchers are a diverse group, in gender, age, experience level, and operation type.
Workshops must be developed to account for all these differences in order to best serve
the diverse audience. Although computers are necessary to access these online
communication tools, the basic computer knowledge levels of this group need to be
considered and addressed. In order to develop an understanding of each workshop’s
audience prior to the actual day, a registration form should be constructed to identify
demographic characteristics of each specific group, as well as identify what basic
computer skills each attendant possesses, to appropriately staff and pace the actual
workshop.
129
Texas Tech University, Kelsey F. Shaw, May 2013
The majority of respondents indicated they possessed more than the maximum
number of years experience to be considered a “beginning” farmer or rancher, according
to USDA standards. However, later in the survey, these more experienced respondents
expressed a need for the same types of training as those who were newer to the industry.
Although it is beneficial to market the workshops to those who do qualify as “beginning”
agriculturists, those who have more farming or ranching experience are seeking training
for online communication tools and should also be allowed to attend the workshops.
The survey results also indicated that more than three quarters of all participants
owned some sort of a laptop or other mobile device to access the Internet. From this
information, it is clear that workshops could be held at a wider variety of locations, ones
that do not necessarily provide computer labs on site. Though all computers would need
access to the Internet, this could be provided with a mobile hot spot and would allow for
these workshops to reach a much larger group of participants. Additionally, participants
may feel more comfortable navigating the web on their own computers.
As far as the content of the workshop, from the respondent data it is clear that
they see the most value and have placed the most emphasis on learning Facebook. Even
though almost half of the participants are using the tool, even for personal reasons, there
are a wide variety of skill levels reported. Facebook was also identified as the most
important tool overall, given the means reported for the Facebook-related tasks.
Facebook should be taught in reference to use in a business setting. However, the least
amount of time should be dedicated to this topic and more time should be divided among
other online tools, since respondents indicated they already felt most confident with
Facebook.
130
Texas Tech University, Kelsey F. Shaw, May 2013
Respondents indicated they viewed conceptual and planning tasks such as
“Understanding how (specific tool) can be used for my agricultural business” as more
important than more technical tasks such as “Steps to create (specific tool) account,”
“Generating subscribers,” or “Updating your own page.” This helps determine that more
time should be spent on the conceptual areas than the actual nuts and bolts of creating and
organizing online profiles for these tools.
One interesting point is that in the “other online communication tool” sections for
both importance and competence, “Using a social media management tool” received the
lowest overall mean score. Even though these respondents did not agree that social
media management tools are important to their marketing plans, this may be because so
many respondents were overall unfamiliar with many of the online communication tools.
Information about these social media management tools may be better as handouts or
brochures distributed at the end of the workshops, so users can look into them after they
have successfully started utilizing other online communication tools successfully.
According to the Borich (1980) needs assessment values, workshops should
specifically spend time focusing on tasks regarding website management and creation.
Additionally, there is a need for training regarding more abstract conventions including
the purpose of using online communication tools and how to effectively manage them.
The least amount of time should be spent training attendees on tools such as Facebook,
which was already in use by many respondents, and Twitter, which respondents indicated
was not useful for their businesses.
Finally, respondents reported interest in attending some sort of online
communication tool training and reported a number of motivations and barriers to
131
Texas Tech University, Kelsey F. Shaw, May 2013
attendance. Though several items were reported as both potential motivations and
barriers such as location, day, and time, it is clear that due to the variety of farmers that
would be served by these workshops, workshop sessions should be held in various
locations. By providing multiple workshops spanning several regions of each state, a
variety of communities could be served. Respondents mentioned that they would not be
available during times of planting or harvest. While the winter was mentioned as a
popular time period for many farmers and ranchers, prominent crops in each region
should be considered to establish when area farmers might be able to best work the
training into their schedules. If it is still hard to recruit workshop attendees, it would be
good to consider online training modules and handbooks, or even webinars, to attempt to
reach a larger audience who may be unable to travel to workshop locations. Face-to-face
workshop attendees could also benefit from having these online modules as refreshers
after they have completed the formal training.
Respondents also mentioned they would be more likely to attend a face-to-face
training if they were confident that after the workshop, they would have tangibly created
something that would be beneficial to their business. During training sessions,
participants must be able to create and experiment with accounts created for their own
businesses, versus example accounts for instruction. Although this may increase the
number of staff needed to help facilitate questions and concerns, it will better benefit the
participants in the long run. Also mentioned was that workshop content should be
prominently featured on advertising and promotion pieces so potential participants would
know exactly how they could benefit from attendance. Respondents indicated they
wanted the workshop speakers or instructors to be experienced in the programs they were
132
Texas Tech University, Kelsey F. Shaw, May 2013
teaching, that they wanted to be assured in these promotional pieces that the instructors
would really have valuable information to provide during the training. Lastly,
respondents they would be enticed if the free workshop offered some type of meal or
refreshments.
Research
This study utilized Rogers’ (2003) Diffusion of Innovations theory to study the
use of online communications tools, which are in various stages of adoption. Because
these online tools are constantly reinventing and changing, further research should be
conducted to explore their impact on the agricultural industry. As the online
communication tool landscape evolves and changes, it should also be evaluated utilizing
the uses and gratifications theory (Katz et al., 1973) to determine how farmers, ranchers,
and other agriculturists are benefitting from use of various online tools, especially in
comparison to more time-tested media such as print, radio, and television. The
technology acceptance model (Davis, 1989) must be utilized and tested much more
regarding agricultural topics, but also within social media and other online
communication tools. Very little research has been done to test the Technology
Acceptance Model within the agricultural industry previously. What has been completed
has all been regarding the Internet (Irani, 2000) and social media use (Doerfert et al.,
2012). More studies should be conducted to test the theory on a myriad of agricultural
technology, both inside and outside the online community.
This study should be replicated in order to ensure as many representatives of the
target audience are sampled as possible. Because the respondents were not a random
sample of all beginning farmers and ranchers, it would be better to somehow group
133
Texas Tech University, Kelsey F. Shaw, May 2013
together those who do have less than 10 years experience and randomly sample that data
pool. Future researchers should aim to get contact information for respondents in order
to better track response rates and send reminders to those who had not already completed
the survey.
If this instrument was adapted for future research concerning online
communication tools, respondents should only be asked to suggest additional types of
“other” online communication tools, not to indicate any frequencies for these additional
tools. Respondents did not understand how to answer these items, and some simply
skipped the question entirely. However, respondents should be asked what “other” online
communication tools they are currently utilizing, but only in short answer form.
It would also be beneficial to ask more open-ended task-related questions,
because if respondents were unfamiliar with a task, they may have marked it as
unimportant. If respondents were asked to provide several tasks they complete within
each tool, then asked to rate the importance or competency on these specific tasks, the
data may more accurately reflect their current online communication tool use. Asking
how long each respondent had used each tool would also add to the understanding of
current tool use.
This study was conducted using the members of specific groups in the three states
associated with the grant. However, from the diverse set of respondents, it can be seen
that further research should be done on broader groups of agriculturists with varying
amounts of experience. Survey results indicated that many farmers and ranchers, not
only with different amounts of experience, but also working in various agricultural
industries, could benefit and are interested in some sort of online communication tool
134
Texas Tech University, Kelsey F. Shaw, May 2013
training. Research should be conducted to determine if farmers in states or industries not
represented in this survey would benefit from the same type of online communication
tool training.
It is also important to determine what benefits, specifically, exist for agriculturists
who participate in online marketing for their agricultural operations using social media
and/or other online communication tools. If research could determine that it positively
impacts the farmer, either those who do or do not participate in direct-to-consumer
marketing, it might convince even more agriculturists to participate in online marketing.
It seems the same tasks participants viewed as important are also the ones they are
most confident completing. It is unclear whether their perception of importance caused
them to learn the specific skill or whether respondents report those skills as important
because they understand and are confident completing them. Further research should be
conducted to determine which factor influences the other and to what extent.
Finally, because these online communication tools are already being employed by
many agriculturists in many facts of the industry, it would be beneficial to complete more
qualitative research using in-depth interviews or focus groups to gain a more complete
picture of successful or unsuccessful online marketing strategy. Because the Internet
provides a time capsule of online activity, a content analysis could be conducted of
combined user accounts to help provide an accurate picture of frequency of use, type of
postings, and follower growth for each agribusiness. These reports, as well as qualitative
interview transcripts, could be used to develop case studies to better serve as examples
for future agriculturists and provide best practices and lessons learned using the tools,
specifically for the agriculture industry.
135
Texas Tech University, Kelsey F. Shaw, May 2013
REFERENCES
Abrams, K. M. & Baker, L. M. (2012, October). The effect of a new media course on
students’ thinking and behavior. Paper presented at the American Association of
Agricultural Education North Central Region Conference: Champaign, IL.
Adams, D. C. & Salois, M. J. (2010, December). Local versus organic: A turn in
consumer preferences and willingness-to-pay. Renewable Agriculture and Food
Systems, 25(4), 331-341.
Ahearn, M, & Newton, D. (2009, May). Beginning farmers and ranchers, EIB-53, U.S.
Department of Agriculture, Economic Research Service. Retrieved October 27,
2012 from http://naldc.nal.usda.gov/download/31895/PDF
Alexander, D. (2002). New information and communication technologies and the
‘demassification’ of public relations. Australia New Zealand Communication
Association Online Journal. Retrieved from
http://www.hss.bond.edu.au/ANZCA/papers/DAlexanderPaper%20.pdf
Allen, K. (2010). An analysis of U.S. consumer awareness and perceptions of beef quality
and mandatory country-of-origin labels. (Unpublished master’s thesis). Texas
Tech University, Lubbock: TX.
Allen, K., Abrams, K., Meyers C., & Schultz, A. (2010). A little birdie told me about
agriculture: Best practices and future uses of Twitter in agricultural
communications. Journal of Applied Communications, 94, 3-4.
Altschuld, J. W., & Kumar, D. D. (2010). Needs Assessment: An Overview. Thousand
Oaks: SAGE Publications, Inc.
American Farm Bureau Federation. (2011). Farm and ranch demographics are changing.
Retrieved October 19, 2012 from
http://www.fb.org/index.php?action=yourag.facts
American Farm Bureau Federation. (2012, March 8). Land availability, government regs
concern young farmers. Retrieved from
http://www.fb.org/index.php?action=newsroom.news&year=2012&file=nr0308.ht
ml
Anderson-Wilk, M. (2009). Changing the engines of change: Natural resource
conservation in the era of social media. Journal of Soil and Water Conservation,
64(4), 129A-131A. doi: 10.2489/jswc.64.4.129A
Baumgarten, C. (2012, August 31). The agriculture industry goes social. Mashable.com.
Retrieved on October 18, 2012 from http://mashable.com/2012/08/31/agricultureindustry-social-media/
136
Texas Tech University, Kelsey F. Shaw, May 2013
Bisdorf, E., Irani, T., & Telg, R. (2003). Assessing Internet Use in Florida News Rooms.
Paper presented at the Southern Association of Agricultural Scientists Conference.
Mobile, AL.
Black, W. (2011, May 25). Agvocating: Quick, short & to the point. Retrieved from
http://www.causematters.com/advocacy/agvocating-quick-short-to-the-point/
Bohlen, B., Carlotti, S., & Mihas, L. (2009). How the recession has changed US
consumer behavior. McKinsey Quarterly. Retrieved from
http://vandymkting.typepad.com/files/2009-12-mckinsey-qtrly-how-therecession-has-changed-us-consumer-behavior.pdf
Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. The
Journal of Teacher Education, 31(3), 39-42.
Boyd, D. M. & Ellison, N. B. (2007). Social network sites: Definition, history and
scholarship. Journal of Computer-Mediated Communication, 13(1). Retrieved
from http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html. doi: 10.1111/j.10836101.2007.00393.x
Brashears, T., Bullock, S., & Akers, C. (2003). A test of a bimodal survey model on the
Cooperative Communicators Association: A case study. Paper presented at the
Southern Association of Agricultural Scientists Conference. Mobile, AL.
Brody, E. W. (2004). Have you made the transition? Are you practicing public relations
in the 21st century rather than the 20th? Public Relations Quarterly, 49(3), 7-15.
Bullock, M. (2011, Feb). Top Direct Marketing Techniques. Retrieved from
http://voices.yahoo.com/top-direct-marketing-techniques-7932956.html
Bullock, S., Lockaby, J., & Akers, C. (2002). Virtual community: A concept ripe for
harvest. Paper presented at the Southern Association of Agricultural Scientists
Conference. Orlando, FL.
Bumgarner, B. A. (2007). You have been poked: Exploring the uses and gratifications of
Facebook among emerging adults. First Monday, 12(11), 1-17.
Cartmell II, D. D., Orr, C. L., & Kelemen, D. B. (2004). Methods of information
dissemination to limited-scale landowners. Paper presented at the Southern
Association of Agricultural Scientists Conference. Tulsa, OK.
Chen, G. M. (2011). Tweet this: A uses and gratifications perspective on how active
Twitter use gratifies the need to connect with others. Computers in Human
Behavior, 27, 755-762.
137
Texas Tech University, Kelsey F. Shaw, May 2013
Coursaris, C. D., Yun, Y., & Sung, J. (2010). Twitter users vs. quitters: A uses and
gratifications and diffusions of innovations approach in understanding the role of
mobility in microblogging. Mobile Business and 2010 Ninth Global Mobility
Roundtable Proceedings. 481-486.
Cutlip, S. M., Center, A. H., & Broom, G. M. (1985). Effective public relations.
Englewood Cliffs, New Jersey: Prentice-Hall.
Davis, D. & Marema, T. (2007). A rural perspective. Retrieved from
http://www.ruralstrategies.org/issues/perspective1.html
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D. (1993). User acceptance of information technology: System characteristics,
user perceptions, and behavioral impacts. International Journal of Man-Machine
Studies, 38, 475-487.
Dillman, D.A. (2007) Mail and internet surveys. Hoboken, NJ: John Wiley & Sons, Inc.
Doerfert, D. L., Graber, L., Meyers, C., & Irlbeck, E. (2012, May). Traditional and social
media channels used by Texas agricultural producers. Paper presented at the
American Association for Agricultural Education Research Conference.
Asheville, NC.
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.
Duggan, M., & Brenner, J. (2013, February 14). The demographics of social media users
– 2012. Retrieved from http://pewinternet.org/Reports/2013/Social-mediausers/The-State-of-Social-Media-Users.aspx
Edgett, R. (2002). Toward an ethical framework for advocacy in public relations. Journal
of Public Relations, 14(1), 1-26.
Edison Research. (2012). The Podcast Consumer 2012 [slideshow presentation].
Retrieved from http://www.slideshare.net/webby2001/the-podcast-consumer-2012
Edman, H. (2010). Twittering to the top: A content analysis of corporate tweets to
measure organization-public relationships. (Master’s thesis, Louisiana State
University).
Eighmey, J. & McCord, L. (1998). Adding value in the information age: Uses and
gratifications of sites in the world wide web. Journal of Business Research, 41,
197-194.
138
Texas Tech University, Kelsey F. Shaw, May 2013
Ellis, J. D., Gordon, B. L., & Johnson, L. (2012). Using horticulturists’ input to inform a
home horticultural website redesign process. Journal of Applied Communications,
96(2), 30-37.
Evans, S. (2011). Stats of the day: 50 new social media stats to kick-start your slide deck.
Retrieved October 26, 2012 from http://adage.com/article/adagestat/stats-day-50social-media-stats-kickstart-slide-deck/231093/
Facebook. (2012). Newsroom: Statistics. Retrieved October 26, 2012, from
http://newsroom.fb.com/imagelibrary/downloadmedia.ashx?MediaDetailsID=422
7&SizeId=-1
Fannin, B. L. & Chenault, E. A. (2005). Blogging agricultural news: A new technology to
distribute news real-time. Paper presented at the Southern Association of
Agricultural Scientists Conference. Little Rock, AR.
Fannin, B. L. (2005). Syndicating agriculture news with RSS. Paper presented at the
Southern Association of Agricultural Scientists Conference. Little Rock, AR.
Fannin, B.L. (2006). Podcasting agricultural news. Paper presented at the Southern
Association of Agricultural Scientists Conference. Orlando, FL.
Farm Beginnings Collaborative (2012). Illinois Farm Beginnings. Retrieved from
http://www.illinoisfarmbeginnings.org/
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction
to theory and research. Reading, MA: Addison-Wesley.
Flett, R., Alpass, F., Humphries, S., Massey, C., Morriss, S., & Long, N. (2004). The
technology acceptance model and use of technology in New Zealand dairy
farming. Agricultural Systems, 80(2), 199-211.
Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction.
Boston, MA: Pearson.
Georgia Farm Bureau (2011). GFB Young Farmer Program. Retrieved from
http://www.gfb.org/programs/yf/default.html
Gibson, C., & Lennon, E. (1999). Historical census statistics on the foreign-born
population of the United States: 1850-1900 (U.S. Census Population Division
Working Paper No. 29). Washington, DC: U.S. Government Printing Office.
Glaser, B. G. (1965). The constant comparative method of qualitative analysis. Social
Problems, 12(4), 436–445.
Gold, M. V. (2007). Sustainable agriculture: Definitions and terms. Retrieved from
USDA National Agricultural Libary Alternative Farming Systems Information
Center: http://www.nal.usda.gov/afsic/pubs/terms/srb9902terms.shtml#term2
139
Texas Tech University, Kelsey F. Shaw, May 2013
Graybill, M., Meyers, C., Doerfert, D., & Irlbeck, E. (2011, June). The use of Facebook
as a communication tool in agricultural-related social movements. Paper
presented at Association of Communication Excellence Research Conference,
Denver, CO.
Greene, C., Dimitri, C., Lin, B., McBride, W., Oberholtzer, L., & Smith, T. (2009).
Emerging issues in the U.S. organic industry (Economic information Bulletin No.
55). Washington DC: United States Department of Agriculture Economic
Research Service. Retrieved from
http://www.ers.usda.gov/media/155923/eib55_1_.pdf
Grunig, J. (2009). Paradigms of global public relations in an age of digitalization. Prisim
6(2), 1-19.
Gulati, J. & Williams, C. B. (2011). Diffusion of innovations and online campaigns:
Social media adoption in the 2010 U.S. congressional elections. Retrieved from
http://dx.doi.org/10.2139/ssrn.1925585
Hartshorn, S. (2010, May 4). 5 differences between social media and social networking.
Retrieved from http://socialmediatoday.com/index.php?q=SMC/194754
Heath, R. L. (1998). New communication technologies: An issues management point of
view. Public Relations Review, 24(3). 273.
Hoffman, A. (2009, July 20). Re: Social media bridges consumer-producer gap [Web log
message]. Retrieved from
http://www.fb.org/index.php?fuseaction=newsroom.focusfocus&year=2009&file
=fo0720.html
Holistic Management International (2012). Beginning Farmers & Ranchers. Retrieved
from http://holisticmanagement.org/our-work/beginning-farmers-ranchers/
Horrigan, J. (2009, July 22). Wireless internet use. Retrieved from
http://www.pewinternet.org/Reports/2009/12-Wireless-Internet-Use.aspx
Hrastinski, S. (2008). Asynchronous and synchronous e-learning. EDUCAUSE
Quarterly, 31(4), 51-55.
Hubona, G.S. & Geitz, S. (1999). External variables beliefs, attitudes and information
technology usage behavior. Retrieved from
http://www.isy.vcu.edu/~ghubona/Pub1.html
Illinois Farm Bureau Young Leaders (2012). Join Today. Retrieved from
http://youngleaders.yolasite.com/join-today.php
Illinois Stewardship Alliance (2012). About Us. Retrieved from
http://www.ilstewards.org/#
140
Texas Tech University, Kelsey F. Shaw, May 2013
Irani, T. (2000). Prior experience, perceived usefulness, and the web: Factors influencing
adoption of internet communication tools. Paper presented at the Southern
Association of Agricultural Scientists Conference. Lexington, KY.
Irani, T. A., Gregg, J. A., & Telg, R. (2004). Choosing to use the Web: Comparing early
and late respondents to an online Web-based survey designed to assess IT
computer skills perceptions of county extension agents. Journal of Southern
Agricultural Education Research, 54(1), 168-179.
Joinson, A. N. (2008, April). Looking at, looking up or keeping up with people: Motives
and use of Facebook. Paper presented at the Conference on Human Factors in
Computing Systems (CHI), Florence, Italy. Retrieved from
http://www.mendeley.com/research/looking-at-looking-up-or-keeping-up-withpeople-motives-and-use-of-facebook/
Jones, R. R. (2001). The Cooperative e-Xtension: New media, new strategies. Paper
presented at the Southern Association of Agricultural Scientists Conference. Fort
Worth, TX.
Kaplan, A. M., & Haenlein, M., (2010). Users of the world, unite! The challenges and
opportunities of Social Media. Business Horizons, 53 (1), 59-68.
Katims, L. (2010, December 3). Farmers milking social media to promote agriculture.
Government Technology. Retrieved October 19, 2012 from
http://www.govtech.com/e-government/Farmers-Social-Media-PromotesAgriculture.html
Katz, E. (1987). Communication research since Lazarsfeld. Public Opinion Quarterly,
51, 525-545.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The
Public Opinion Quarterly, 37 (4), 509-523.
Katz, E., Haas, H., & Gurevitch, M. (1973). On the use of the mass media for important
things. American Sociological Review, 38, 164-181.
Kaye, B. K. (2010). Going to the blogs: Toward the development of a uses and
gratifications measurement scale for blogs. Atlantic Journal of Communication,
18, 194-210. doi: 10.1080/15456870.2010.505904
Kelleher, T. & Sweetser, K. (2012). Social media adoption among university
communicators. Journal of Public Relations Research, 24(2), 105-122. doi:
10.1080/1062726X.2012.626130
Knight, S. & Burn, J. (2005). Developing a framework for assessing information quality
on the world wide web. Informing Science Journal, 8, 159-172.
Kogut, B., ed. (2003). The Global Internet Economy. Cambridge, MA: MIT Press.
141
Texas Tech University, Kelsey F. Shaw, May 2013
Kutner, L. A. (2000). Environmental activism and the internet. Electronic Green Journal,
1(12), 1-10.
Lazarsfeld, P. & Stanton, F. (1942). Radio research, 1941. (P. Lazarsfeld, & F. Stanton,
Eds.) New York: Duell, Sloan, Pearce.
Lazarsfeld, P. & Stanton, F. (1944). Radio research, 1942-3. New York: Duell, Sloan and
Pearce.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology
acceptance model and the world wide web. Decision Support Systems, 29, 269282.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model:
past, present, and future. Communications of the Association for Information
Systems, 12, 752-780.
Lenhart, A. (2009). Social networks grow: Friending mom and dad. Retrieved October
25, 2012, from http://pewresearch.org/pubs/1079/social-networks-grow
Leonard-Barton, D. (1985). Experts as negative opinion leaders in the diffusion of a
technological innovation. The Journal of Consumer Research, 11(4), 914-926.
Lohr, M. J. (2011, January 8). Social media: The next agricultural frontier. Southeast
Farm Press. Retrieved October 19, 2012 from
http://southeastfarmpress.com/print/management/social-media-next-agriculturalfrontier
Madden, M. & Zickuhr, K. (2011, August 26). 65% of online adults use social
networking sites. Retrieved October 27, 2012, from
http://www.pewinternet.org/Reports/2011/Social-Networking-Sites.aspx
McAndrew, F. T., & Jeong, H. S. (2012). Who does what on Facebook? Age, sex, and
relationship status as predictors of Facebook use. Computers in Human Behavior,
28(6), 2359-2365.
McCarthy, J. E., Beede, D. K., & Edgecomb, A. (2008). Digital versus printed
publication: Results from an agricultural extension readership survey. Journal of
Applied Communications, 92(1&2), 95-97.
McMillan, S. J. & Huang, J. (2002). Measures of perceived interactivity: An exploration
of the role of direction of communication, user control, and time in shaping
perceptions of interactivity. Journal of Advertising, 31(3), 29-42.
Meyers, C., Irlbeck, E., Graybill-Leonard, M., & Doerfert, D. (2011). Advocacy in
Agricultural Social Movements: Exploring Facebook as a Public Relations
Communications Tool. Journal of Applied Communications, 95(3), 68-81.
142
Texas Tech University, Kelsey F. Shaw, May 2013
Miniwatts Marketing Group (2012, Nov 24). Internet world stats: Usage and population
statistics. Retrieved from http://www.internetworldstats.com/stats2.htm
Mishra, A. K. & Park, T. A. (2005, October). An empirical analysis of internet use by
U.S. farmers. Agricultural and Resource Economics Review, 34(2). 253-264.
Moore, M. L., Meyers, C., Irlbeck, E., & Burris, S. (2013). Exploring U.S. agricultural
commodity organizations’ use of blogs as a communications tool. Paper presented
at the Southern Association of Agricultural Scientists Conference. Orlando, FL.
Morgan, G.A., Griego, O. V., & Gloeckner, G. W. (2001). Using SPSS for Windows: An
introduction to use and interpretation in research. Florence, KY: Psychology
Press.
National Agricultural Statistics Service. (1997). 1997 census of agriculture: Table 16.
Retrieved from United States Department of Agriculture:
http://www.agcensus.usda.gov/Publications/1997/Vol_1_Chapter_1_U._S._Natio
nal_Level_Data/us-51/us1_16.pdf
National Agricultural Statistics Service. (2007). 2007 census of agriculture: New farms,
new farm operators. Retrieved from United States Department of Agriculture:
http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Fact_Sheets/
Farm_Numbers/new_farms.pdf
The Nielsen Company. (2013, March 8). Buzz in the blogosphere: Millions more
bloggers and blog readers. Retrieved from
http://www.nielsen.com/us/en/newswire/2012/buzz-in-the-blogosphere-millionsmore-bloggers-and-blog-readers.html
Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking
enviornment: Facebook groups, uses and gratifications, and social outcomes.
CyberPsychology & Behavior , 12 (6), 729-733.
Paulson, H. (2009, August 25). What the heck is Twitter, and what do I do with it? Daily
Camera.
Payn-Knoper, M. (2009, June 6). Twitter’s business value to agriculture. Message posted
to http://causematters.wordpress.com/2009/06/10/twitters-business-value-toagriculture\
Peng, G. & Mu, J. (2011). Technology adoption in online social networks. Journal of
Product Innovation Management, 28(s1), p. 133-145. doi: 10.1111/j.15405885.2011.00866.x
Pew Research Center (2004, February 17). Rural Americans’ internet use has grown, but
they continue to lag behind others. Retrieved from
http://www.pewinternet.org/Press-Releases/2004/Rural-Americans-Internet-usehas-grown-but-they-continue-to-lag-behind-others.aspx
143
Texas Tech University, Kelsey F. Shaw, May 2013
Pew Research Center (2012, January 26). Cell Internet Use 2012. Retrieved from
http://www.pewinternet.org/Reports/2012/Cell-Internet-Use-2012.aspx
Pfeil, U., Arjan, R., & Zaphiris, P. (2009). Age differences in online social networking –
A study of user profiles and the social capital divide among teenagers and older
users in MySpace. Computers in Human Behavior, 25, 643-654.
Pounds, D. (1985). Putting extension information where people will find it. Journal of
Extension. [On-line]. 23(4). Available at
http://www.joe.org/joe/1985winter/a6.html
Pritchett, K. M., Naile, T. L., & Murphrey, T. P. (2012). Expressions of social presence
in agricultural conversations on Twitter: Implications for agricultural
communications. Journal of Applied Communications, 96(3), p. 51-64.
Raacke, J. & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and
gratifications theory to exploring friend-networking sites. CyberPsychology &
Behavior, 11(2), 169-174.
Rajagopalan, B. & Subramani, M. (2003). Knowledge-sharing and influence in online
social networks via viral marketing. Communications of the ACM, 46(12), 300307.
Rhoades, E. & Aue, K. (2010). Social agriculture: Adoption of social media by
agricultural editors and broadcasters. Paper presented at the Southern Association
of Agricultural Scientists Conference. Orlando, FL.
Richardson, J. G. (1995). An assessment of Clientele Preferences for Receiving
Extension Information. Raleigh, N.C.; North Carolina State University. (ERIC
Document Reproduction Service No. ED356358).
Rigby, B. (2008). Mobilizing generation 2.0: A practical guide to using Web 2.0
technologies to recruit, organize, and engage youth. San Francisco, CA: John
Wiley & Sons, Inc.
Riley, K., Cartmell, D., & Naile, T. (2012). Kansas beef feedlot managers’ trusted
sources of information concerning an agroterrorism event. Journal of Applied
Communications, 96(2), 38-49.
Roberts, P. & Henderson, R. (1998). Information Technology Acceptance in a Sample of
Government Employees: A Test of the Technology Acceptance Model.
Interacting with Computers, 12(2000), 427-443.
Rogers, E. M. & Shoemaker, F. F., (1971). Communications of Innovations. New York:
The Free Press.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: A Division of Simon
& Schuster, Inc.
144
Texas Tech University, Kelsey F. Shaw, May 2013
Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass
Communication and Society, 3(1), 3-37.
Ruth-McSwain, R. (2008). Penchant for Print: Media Strategies in Communicating
Agricultural Information. Journal of Applied Communications, 92(3-4).
Ryan, B. & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa
communities. Rural Sociology. 8. 15-24.
Shannon, D. M., Johnson, T. E., Searcy, S., & Lott, A. (2001). Using electronic surveys:
Advice from survey professionals. Practical Assessment, Research and
Evaluation, 8(2). Retrieved from http://pareonline.net/htm/v8n1.htm
Skelton, A. (2012, March). Social demographics: Who’s using today’s biggest networks.
Retrieved from http://mashable.com/2012/03/09/social-media-demographics/
Smith, A. (2009, September). Pew Internet & American Life Project: The Internet as a
diversion.
Smith, B. (2012, June). The beginner’s guide to Twitter. Retrieved from
http://mashable.com/2012/06/05/twitter-for-beginners/
Stafford, T. F., Stafford, M. R., & Schkade, L. L. (2004). Determining uses and
gratifications for the internet. Decision Sciences, 35(2), 259-288.
Telg, R. & Barnes, C. (2012). Communication preferences of Florida Farm Bureau young
farmers & ranchers. Journal of Applied Communications, 96(2), 50-65.
Telg, R., Basford, A., & Irani, T. (2005). Communication Preferences of politically active
agricultural leaders. Paper presented at the Southern Association of Agricultural
Scientists Conference. Little Rock, AR.
Telg, R., Irani, T., Monaghan, P., Chiarelli, C., Scicchitano, M. & Johns, T. (2012).
Preferred information channels and source trustworthiness: Assessing
communication methods used in Florida’s battle against citrus greening. Journal
of Applied Communications, 96(1). 42-53.
Texas Farm Bureau (2012a). About Texas Farm Bureau. Retrieved from
http://www.texasfarmbureau.org/aboutus.aspx
Texas Farm Bureau (2012b). Young Farmer and Rancher and leadership programs.
Retrieved from http://www.texasfarmbureau.org/yfr.aspx
U. S. Census Bureau (2011). 2000 Census of Population and Housing. Retrieved from
http://www.census.gov/compendia/statab/2012/tables/12s0029.xls
145
Texas Tech University, Kelsey F. Shaw, May 2013
United States Department of Labor (2012). Farmers, ranchers, and other agricultural
managers. Occupational Outlook Handbook, 2012-13 Edition. Retrieved from
http://www.bls.gov/ooh/management/farmers-ranchers-and-other-agriculturalmanagers.htm#tab-2
University of Illinois Extension (2012). Local Food Systems & Small Farms. Retrieved
from http://web.extension.illinois.edu/cfiv/localfoods.html
Urista, M. A., Dong, Q., & Day, K. D. (2009). Explaining why young adults use
MySpace and Facebook through uses and gratifications theory. Human
Communication, 12(2), p. 215-229.
USA.gov. (2013, March 8). What is RSS: Everything you need to know to subscribe to
government RSS feeds. Retrieved from http://www.usa.gov/Topics/ReferenceShelf/Libraries/RSS-Library/What-Is-RSS.shtml
Valente, T. W. & Davis, R. L. (1999). Accelerating the diffusion of innovations using
opinion leaders. Annals of the American Academy of Political and Social Science,
566(1), 55-67.
Venkatesh, V. (1999) Creation of favorable user perceptions exploring the role of
intrinsic Motivation. MIS Quarterly 23(2), 239-260.
Venkatesh, V., & Davis, F. D. (2000) A theoretical extension of the technology
acceptance model: Four longitudinal field studies. Management Science 46(2), pp.
186-204.
Watson, S. (2013). How podcasting works. Retrieved from
http://computer.howstuffworks.com/internet/basics/podcasting.htm
Waters, R. D. (2010). The use of social media by nonprofit organizations: An
examination from the diffusion of innovations perspective. In S. Dasgupta (Ed.),
Social Computing: Concepts, Methodologies, Tools, and Applications. Hershey,
PA: Information Science Reference.
Weber, J. & Ahearn, M. (2012, November). Farm household well-being glossary.
Retrieved from http://www.ers.usda.gov/topics/farm-economy/farm-householdwell-being/glossary.aspx
Wimmer, R.D., & Dominick, J.R. (1994). Mass media research: An introduction (4th ed.)
Belmont, CA: Wadsworth Publishing Company.
Wisconsin State Farmer. (2011, February 11). Social media is agriculture’s newest
survival tool. Retrieved October 19, 2012 from http://bit.ly/U2168o
Yi, M. & Venkatesh, V. (1999). Role of computer self-efficacy in predicting user
acceptance and use of information technology. Retrieved from
http://hsb.baylor.edu/ramsower/ais.ac.96/papers/mun.htm
146
Texas Tech University, Kelsey F. Shaw, May 2013
Zaglia, M. E. (2013). Brand communities embedded in social networks. Journal of
Business Research, 66(2). 216-223. doi: 10.1016/j.jbusres.2012.07.015
Zickuhr, K. & Madden, M. (2012, June 6). Older adults and internet use. Retrieved from
http://www.pewinternet.org/Reports/2012/Older-adults-and-internet-use.aspx
147
Texas Tech University, Kelsey F. Shaw, May 2013
APPENDICES
148
Texas Tech University, Kelsey F. Shaw, May 2013
APPENDIX A
INSTRUMENT
149
Texas Tech University, Kelsey F. Shaw, May 2013
150
Texas Tech University, Kelsey F. Shaw, May 2013
151
Texas Tech University, Kelsey F. Shaw, May 2013
152
Texas Tech University, Kelsey F. Shaw, May 2013
153
Texas Tech University, Kelsey F. Shaw, May 2013
154
Texas Tech University, Kelsey F. Shaw, May 2013
155
Texas Tech University, Kelsey F. Shaw, May 2013
156
Texas Tech University, Kelsey F. Shaw, May 2013
157
Texas Tech University, Kelsey F. Shaw, May 2013
158
Texas Tech University, Kelsey F. Shaw, May 2013
159
Texas Tech University, Kelsey F. Shaw, May 2013
160
Texas Tech University, Kelsey F. Shaw, May 2013
APPENDIX B
INSTITUTIONAL REVIEW BOARD APPROVAL
161
Texas Tech University, Kelsey F. Shaw, May 2013
162
Texas Tech University, Kelsey F. Shaw, May 2013
APPENDIX C
PRELIMINARY EMAIL TO PARTICIPANTS
163
Texas Tech University, Kelsey F. Shaw, May 2013
Subject line: Social Media Marking Survey
To help gather valuable information about the areas where young farmers and ranchers
need additional social media training, the Texas Young Farmers and Ranchers program is
sending you this e-mail on my behalf to request your participation in a brief online survey
conducted by the Department of Agricultural Education and Communications at Texas
Tech University. The purpose of this study is to identify the areas of social media
marketing training that would be beneficial to young agricultural producers.
You can access the online survey by clicking on this link:
http://tlpdc.qualtrics.com/SE/?SID=SV_bJgIKMQv5jMI2wI
Your time and consideration in completing the survey is greatly appreciative. By doing
so, you will help the Texas Young Farmers and Ranchers program identify areas they
could help you in social media marketing training. The results will be kept confidential
and reported in summary form only. If you have any questions or comments, please
contact myself ([email protected]) or Dr. Courtney Meyers
([email protected]) at (806) 742-2816.
Thank you in advance for your participation and contribution to this study.
Sincerely,
Kelsey Fletcher
Graduate Assistant
Texas Tech University
Department of Agricultural Education and Communications
164
Texas Tech University, Kelsey F. Shaw, May 2013
APPENDIX D
REMINDER EMAIL TO PARTICIPANTS
165
Texas Tech University, Kelsey F. Shaw, May 2013
Subject line: Reminder: Social Media Marking Survey
Last week, the Texas Young Farmers and Ranchers program sent you an e-mail with a
link to an online survey seeking to learn areas of training in social media marketing you
feel would be beneficial to young farmers and ranchers.
If you have already completed the survey, please accept my sincere thanks. If you have
not, please do so at your earliest convenience. I am especially grateful for your help
because it is only by asking people like you that we can understand the social media
marketing needs of young farmers and ranchers.
In case you have misplaced the original e-mail, a link to the survey is provided below.
http://tlpdc.qualtrics.com/SE/?SID=SV_bJgIKMQv5jMI2wI
If you have questions or comments, please contact myself ([email protected]) or
Dr. Courtney Meyers ([email protected]) at (806) 742-2816. Thank you again for
your participation.
Sincerely,
Kelsey Fletcher
Graduate Assistant
Texas Tech University
Department of Agricultural Education and Communications
166