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The Theory of planned behavior in predicting attendance at environmental horticulture extension programs

University of Florida Institutional Repository

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THE THEORY OF PLANNED BEHAVIOR IN PREDICTING ATTENDANCE AT ENVIRONMENTAL HORTICULTURE EXTENSION PROGRAMS By ALEXIS A. CLARK-RICHARDSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMEN T OF THE REQUIREMNTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2003

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For my family

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iii ACKNOWLEDGEMENTS Conducting this research has been a great learning experience for me. I would like to thank the members of my committee, Dr. Ri ck Schoellhorn, Dr. Ji m Barrett, Dr. Tracy Irani and Elizabeth Felter, fo r giving me the opportunity to work with them during this time. Their patience and assistance, as well as much needed advice, have been greatly appreciated. I would also like to thank my parents and sisters, and my husband for their neverending encouragement of my educational jo urney. The support and blessings of my family and friends will always be remembered.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTS ..............................................................................................iii LIST OF TABLES ..........................................................................................................vi ABSTRACT ..................................................................................................................viii CHAPTER 1 INTRODUCTION .....................................................................................................1 Cooperative State Research, Education and Extension Service ................................2 Environmental Horticulture in Florida .......................................................................5 Purpose and Objective ...............................................................................................6 Theoretical Framework ..............................................................................................7 2 LITERATURE REVIEW ........................................................................................13 3 METHODOLOGY ..................................................................................................20 Subjects ....................................................................................................................20 Research Design .......................................................................................................20 Pilot Study ................................................................................................................21 Procedure .................................................................................................................21 Instrumentation ........................................................................................................23 Data Interpretation ...................................................................................................27 Reliability .................................................................................................................27 Hypotheses ...............................................................................................................31 Data Analysis ...........................................................................................................31 4 RESULTS ................................................................................................................33 Descriptive Information ...........................................................................................33 Testing the Hypotheses ............................................................................................39 Summary ..................................................................................................................44 5 DISCUSSION ..........................................................................................................45

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v Key Findings and Implications ................................................................................45 Limitations ...............................................................................................................48 Conclusions and Directions for Future Research .....................................................48 Recommendations ....................................................................................................49 APPENDIX A COVER LETTER AND QUESTIONNAIRE .........................................................51 B THEORY OF PLANNED BEHAVIOR (figure) .....................................................58 LITERATURE CITED ...................................................................................................60 BIOGRAPHICAL SKETCH ..........................................................................................63

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vi LIST OF TABLES Table page 3-1 Independent Samples Test for Ea rly Respondents vs. Late Respondents ...............23 3-2 Attitude Scale Item (Direct Measure) ......................................................................24 3-3 Behavioral Belief Scale Items .................................................................................24 3-4 Subjective Norm Scale Items ..................................................................................25 3-5 PBC Scale Items ......................................................................................................26 3-6 Motivation ...............................................................................................................26 3-7 Perceived Level of Knowledge ................................................................................26 3-8 Behavioral Intent Scale Items ..................................................................................27 3-9 Cronbach Alpha Reliability Co efficients: Behavioral Beliefs ................................28 3-10 Cronbach Alpha Reliability Coefficients: Outcome Evaluation ...........................28 3-11 Cronbach Alpha Reliability Coeffi cients: Attitude (direct measure) ....................28 3-12 Cronbach Alpha Reliability Coefficients: Normative Beliefs ...............................28 3-13 Cronbach Alpha Reliability Co efficients: Motivation to Comply ........................29 3-14 Cronbach Alpha Reliability Coeffici ents: Subjective Norm (direct measure) ......29 3-15 Cronbach Alpha Reliability Coe fficients: Control Belief Strength ......................29 3-16 Cronbach Alpha Reliability Co efficients: Control Belief Power .........................29 3-17 Cronbach Alpha Reliability Co efficients: PBC (direct measure) ..........................29 3-18 Cronbach Alpha Reliabi lity Coefficients: Intent ...................................................29 3-19 Descriptive Statistics: TPB Model Constructs ......................................................30

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vii 3-20 Descriptive Statistics: TPB M odel Constructs: Attendees/Non-attendees ............30 3-21 Pearson Correlations betw een the TPB Model Constructs ....................................31 4-1 Cross-tabulation: Attendance/Sales .........................................................................35 4-2 Cross-tabulation: Attendance/Production System ...................................................35 4-3 Cross-tabulation: Motivati on to Attend Extension Programs ..................................36 4-4 Descriptive Statistics: Direct/Belief-based Attitude Measures ...............................37 4-5 Descriptive Statistics: Belief-Based Attitude Measures: Attendee/Non-attendee ...38 4-6 Independent Samples Test: Attendee/Non-attendee ................................................40 4-7 Independent Samples Test: Knowledge/Attendance ...............................................42 4-8 Multiple Regression Coefficients: Entire Sample ...................................................42 4-9 Multiple Regression Coefficients: Attendees ..........................................................43 4-10 Multiple Regression Coefficients: Non-attendees .................................................44

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viii Abstract of Thesis Presented to the Graduate School of th e University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE THEORY OF PLANNE D BEHAVIOR IN PRED ICTING ATTENDANCE AT ENVIRONMENTAL HORTICULTURE EXTENSION PROGRAMS By Alexis A. Clark-Richardson August 2003 Chair: Dr. Rick Schoellhorn Major Department: Environmental Horticulture The Florida Cooperative Extension Serv ice has a long trad ition of serving clientele via many different channels. One pr imary technique used by many agents is hosting workshops or demonstrations. Hortic ulture extension ag ents have a large audience and target this clientele for thei r major programs by using flyers, newsletter announcements, email, and phone calls. These ag ents have expressed a need to discover why a larger percentage of this audience is not participating. Ther efore, the Theory of Planned Behavior was utilized to determ ine how attitudes, subjective norms and perceived behavioral control predict the inte nt of horticulture pr ofessionals to attend horticulture-based Extension programs. A purposive sample of 3000 professionals was surveyed. Overall, results showed that the TPB model explained 53% of the variation in behavioral intent, and all three constructs were significant predictors of intent. However, significant differences existed among attend ees and non-attendees with regard to the model. Attitude was the only significant pred ictor of intent for non-attendees. It was

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ix concluded that in order to boost participation of horticulture professionals at Extension programs, a specific need exists for unders tanding and, possibly, ch anging the attitudes and beliefs of non-attendees.

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1 CHAPTER 1 INTRODUCTION Interviews with various extension agents and specialists reveal that horticulture industry professionals in Flor ida are targeted for extensi on programs, but attendance at programs does not seem to represent this e ffort (L. Felter, T. Hurt, R. Schoellhorn, personal communication, 2002). Ag ents are interested in le arning what would motivate more people to attend their programs. Theref ore, the purpose of the current study was to determine why horticulture indus try professionals participat e in Extension programs and what would possibly motivate those who do not attend to become more active in these programs. Client satisfaction and program accountab ility is a driving force behind the Extension service (Habeeb, Birkenholz & Weston, 1987; Martin & Omer, 1987; UF/IFAS Fact Digest, 2003). Therefore, a c onstant need for unde rstanding the program environment and target audience exists fo r Extension Agents (Martin & Omer, 1987). Literature suggests that quality programming is important to maintaining and promoting new audiences (Bowling, 2001; Israel, 2001; Norland, 1992; Summerhill & Taylor, 1992). Suggestions for improving program planning include gathering valuable information about the target audiences and thei r needs, having the cl ientele participate in the planning process, unders tanding the program life cycl e and knowing when to end a program, and properly evaluating the progr ams (Bowling, 2001; Israel, 2001; Norland, 1992; Summerhill & Taylor, 1992).

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2 Anecdotal information reveals that agen ts are targeting larg e groups of growers and nursery owners, but attendance at progr ams does not seem representative of this effort (L. Felter, T. Hurt, R. Schoellh orn, personal communication, 2002). Primary marketing tactics used to disseminate info rmation about programs are flyers, newsletter announcements, emails and phone calls. Agen ts have expressed an interest in understanding the basic question of what factors would help increase the number of people at their programs. Even though th ese agents do many evaluations of their programs, they indicate that the data collected from the evaluations fails to answer that question. One reason may be instrument de sign (Jacob & Ferrer, 2000)). Many program evaluations indicate lik es and dislikes of attendees, such as the delivery method, presenter, or location, but fail to discover a deeper understanding of what motivated the grower to actually attend (Jacob & Ferrer, 2000). Cooperative State Research, Education, and Extension Service The three main objectives of the U. S. Cooperative State Research, Education and Extension Service are to offer the information gathered at the land-grant universities; encourage the adoption of new techniques and ideas; and use the educational process to improve lives of clientele. In essence, the motto encompasses all that the Extension service does: “Help people help themse lves” (Habeeb, Birkenholz, & Weston, 1987; N. Place, personal communication, 2001). The Florida Cooperative Extension Service (F CES) is one of three branches in the University of Florida’s Institute of Food a nd Agricultural Sciences (UF/IFAS), which was established in April 1964 when The Univer sity of Florida’s Co llege of Agriculture, School of Forestry, Agriculture programs Experiment Stations and the Cooperative

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3 Extension Service were combined. FCES is a partnership between UF/IFAS, the United States Department of Agricultu re, and county governments in Florida. Each of Florida’s 67 counties is home to an Extension office and many agents. In addition, IFAS incorporates 17 on-campus academic departments, 14 Research and Education Centers (REC), 7 research and dem onstration sites and 5 loca tions with Degree Program Partnerships. The Extension service utilizes three conceptual models when delivering educational information. Agents attempt to balance technology transfer, problem solving, and knowledge change when developi ng and delivering educational programs. The goal of these programs is to elicit a be havioral change in th e target group (Habeeb, Birkenholz, & Weston, 1987). Therefore, Extens ion agents are continually searching for the most effective way to m eet the needs of their audience (Martin & Omer, 1987). Many different types and sizes of Ex tension programs exist in the va rious areas of agriculture, such as pest management, water conservati on, horticulture, forestry, child development, business, marketing and many more. Delivery methods range from workshops and demonstrations to one-on-one sessions and web-based activities. Program development is defined as the activities involved in building, creating, planning or developing an educational program (Taylor, 1994). Furthermor e, the Extension service has a variety of categories for their programs, including rout ine program, maintenance program, impact program, and major program designations. Extension program development is challenging to the agent and specialists involved, requiring large amounts of time and personal commitment that directly affects the success or failure of their program s (Israel, 2001; Place, 2001). Research has

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4 indicated that the extension service is a major supplier of farmer education about new technology and farming practices (Ford, 1995). Many studies have been conducted that explain the importance of the Extension service to its clientele. The audience of each of these programs range from the general public to specialized industr y professionals such as teachers, farmers, and business owners. Mo st of the respondents in these studies are satisfied with the services provided and st ate that the knowledge gained from meetings, workshops, phone calls, etc., are important to the success of their businesses (Alston & Reding, 1998; Ford, 1995; Habeeb, Birkenhol z, & Weston, 1987; Martin & Omer, 1987). Many dollars are spent each year on pr oducing extension programs. The total national CREES budget for 2003 is over $1 billion (USDA, 2003). In 2002, local finances to fund Extension in Florida amounted to $29.2 million. Therefore, suggestions have been made to the Extension service re garding better planning techniques that could increase participation (Als ton & Reding, 1998; Bruening, Radhakrislma, & Rollins, 1992; Martin & Omer, 1987). Identifying the ta rget audience is a common theme throughout the literature (Alston & Reding, 1998; Br uening, Radhakrislma, & Rollins, 1992; Habeeb, Birkenholz, & Weston, 1987; Mar tin & Omer, 1987; Schmitt, Durgan, & Iverson, 2000). Agents should understand the ch aracteristics of their audience and focus on specific needs and expectations as they rela te to the real problems of the participants (Alston & Reding, 1998; Place, 2001; Schmitt, Durgan, & Iverson, 2000). Therefore, understanding who participates a nd why are major factors that need to be addressed when planning educational programs (Alston & Reding, 1998; Bruening, Radhakrislma, & Rollins, 1992; Martin & Omer, 1987).

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5 Environmental Horticulture in Florida The Horticulture Industry in Florida is growing. The entire nursery and landscape industry was worth about $8.5 billion in 2001. This figure has almost doubled since 1997 (DeSousa, 2002). The 2000 figures provided by FN GA indicate that the value added to the economy was $4.38 billion. Also, the industry provided employment for approximately 170,000 people, and paid total wa ges and salaries of $2.91 billion. Information provided by the Florida Ag riculture Statisti cal Service (2002) suggests that ornamental production, which incl udes cut flowers, potted plants, hanging baskets, potted foliage, cut foliage, bedding and garden plants, and woody ornamentals is a large business in Florida. The state is ranke d second to California. However, Florida is leading the country in wholesale sales of potted foliage for use indoors and in hanging baskets. Sales for this particular industry were $361.2 million in 2001. Lake, Orange and Seminole counties alone accounted for 35% of these sales (FASS, 2002). According to this information, the industry is economically importa nt to Florida. Of all the agriculture commodities in the state, the nursery industry is the "single largest dollar producer" (DeSousa, 2002). Over $1.5 billion is contributed to Hillsborough County alone, which is equivalent to the revenues of the Port of Tampa or hosting a Super Bowl every weekend (DeSousa, 2002). The industry is highly aware of issues concerning pest management, labor relations, technology advances and various other business related items. The people involved in this industry are a major contri bution to its success. Therefore, it can be argued that the extension se rvice, through its commitment to sharing resources and knowledge, should be a common link betw een the issues and the people.

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6 Purpose and Objectives The main goal of the Extension servi ce is to generate information through research and education, and ultimately pass this information on to the public. Agriculture Extension programs have been developed to supply hands-on knowledge that consumers can use immediately (Habeeb, Birkenholz, & Weston, 1987). Developing and delivering these Extension programs is challenging for agents and usually requires immense amounts of time and resources (Place, 2001). It has been established that effective program planning in the Extension service begi ns and ends with c lientele satisfaction. Therefore, identifying target audiences a nd understanding their needs are essential to planning and maintaining a successful program. Therefore, the purpose of the current rese arch was to determine why horticulture industry professionals particip ate in Extension programs a nd what would motivate those who do not attend to become more active Based on the above, the objectives of the study are as follows. To describe Florida commerci al nursery professionals in terms of demographics and perceptions toward the Florida Cooperative Extension Service and its programming. Utilizing the Theory of Planned Behavior framework, determine how differences in attitudes, subjective norms and perceived behavioral controls toward extension programming affect inte nt to participate. Past research studies utilizing the Th eory of Planned Behavior model have concluded that attitude and PBC correlate most strongly with behavioral intent, and subjective norm was the weak est predictor of intent (A jzen, 1988; Beedell & Rehman, 2000; Eagly & Chaiken, 1993; Pouta & Rekola, 2001). Therefore, the study was designed to test the follo wing null hypotheses.

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7 H1: No significant difference exists between attendees and non-attendees regarding possible motivational factors. H2: No significant difference exists for atte ndees and non-attendees regarding perceived level of knowledge about the Florid a Cooperative Extension Service. H3: No significant difference exists for atte ndees and non-attendees regarding perceived level of knowledge abou t Institute of Food and Agricultural Sciences. H4a: No relationship exists between behavior al intention of horticu lture professionals to attend Extension programs and the three determin ant variables: attitude, subjective norm and perceived behavioral control. H4b: No relationship exists between behavior al intention of horticu lture professionals to attend Extension programs and the three determin ant variables: attitude, subjective norm and perceived behavioral, controlling for attendees and non-attendees. Theoretical Framework One theoretical framework that has been us ed to look at the cons tructs of attitude, subjective norms and perceived behavioral cont rols is Icek Ajzen’s Theory of Planned Behavior (TPB, see figure B-1). Developed in the late 1980s, the theo ry is an extension of Ajzen’s Theory of Reasoned Action (Fishbe in and Ajzen, 1975). Intention to perform a particular behavior is the central factor of the theory (Ajzen, 1988; Eagly & Chaiken, 1993). The three independent determinants of intentions developed by Ajzen are attitude toward the behavior, subjective norms, a nd perceived behavioral control (PBC). According to Ajzen (2001), three sets of salient beliefs guide human behavior and create the determinants mentioned above. In th e model, attitude refe rs to the individual’s positive or negative evaluation of performing a behavior, and is determined by beliefs

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8 relating to the behavior (behavioral beliefs) and the evaluation of performing the behavior (outcome evaluations). Subjective norms are the individual’s per ceptions of social pr essures that exist pertaining to performance of the behavior (Ajzen, 1988; Eagly & Chaiken, 1993). This concept is comprised of beliefs about social expectations (normative beliefs) and the need to adhere to those expectati ons (motivation to comply). Perceived behavioral cont rol is related to an indi vidual's perception of how difficult the task will be to perform. A ccording to Ajzen, PBC includes past experience and anticipated obstacles. PBC is based on be liefs about factors that are for or against performing the behavior and the perceived power of those factors (control belief strength and control belief power). Generally, the intention to perform a be havior is strong when performance of a particular behavior elicits a favorable attitude from the i ndividual, the surrounding social environment is conducive to the behavior, a nd the individual feels confident of their ability to perform the behavior (Ajzen, 1988; Eagly & Chaiken, 1993). Another theory pertaining to adult part icipation in extension programs is the theory of adult learning or andragogy (Knowles, 1990). This theory is has six main assumptions regarding adult educ ation. Knowles (1990) states that adults must have an understanding of why the new information is important and how it w ill affect them. Selfconcept is also a major factor for adults wh en they are approached with possible learning situations. Past learning experiences such as school create anxiety in the adult and may directly affect their desire to continue with the educational process (Knowles, 1990; Rogers, 2001). The level of experience an adult has pertaining to the educational

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9 program also influences the success of the adult. This allows for adult educational sessions to be enriched with a more dive rse group of people with different backgrounds and experience levels (Knowles, 1990). This fa ctor must be taken into consideration because if past experience of the learner is not given due justice, then the educator risks insulting the self-identity of th e adult learner (Knowles, 1990). The adult must also be ready to learn, mean ing they are in need of the information at that point in time (Knowles, 1990). For exampl e, no need exists for adults to attend an information session on greenhouse irrigation if they have no intention of building a greenhouse. When they make the decisions to build, then irriga tion will become more important to them. This factor is similar to Knowles' (1990) orient ation to learn, which states that adults need learning situations to be related to realistic situations. Adults want to be able to apply what they learn to something tangible in their lives. Finally, the last assumption of Knowles' andragogy theory is motivation. Both extrinsic and intrinsic motiva tion exists within adults, a nd Knowles (1990) states that intrinsic is the most important. Intrinsic mo tivation centers on the internal well-being of the individual and can serve to influence the pa rticipation in adult learning activities more than extrinsic motivational factors such as increased salary or bonus points (Knowles, 1990; Rogers, 2001). Increasing evidence exists that the theo ry of adult learni ng is serving as a foundation for adult educators when pr oducing programs and is changing the organization of these pr ograms (Knowles, 1990). An extensive amount of liter ature is available regardi ng the Theory of Planned Behavior (TPB) and its use in predicting be havior. Studies have been conducted using

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10 the TPB in areas such as health (Spark s & Sheperd, 1992), leisur e activities (Ajzen & Driver, 1992), education (Ingram, Cope, Harju, & Wuensch, 2000), and agriculture (Beedell & Rehman, 2000). As a consequence of the theory's extensive use, several metaanalyses have been performed to determine the validity of the theory and its constructs. For example, a 1998 study by Sutton sought to evaluate the ef fectiveness of the TRA/TPB models. He uses a series of other meta-analyse s to gather data about the predictive power of the models regarding intention and beha vior. In the study, he also made a distinction between prediction and e xplanation. Explanation is the process of identifying and specifying intention or be havior determinants. Models regarding explanation are causal in nature and can be represente d graphically. For this reason, Sutton states that both TRA and TP B models are causal in nature. However, prediction does not require expl anations. This means that if the exact reason for a behavior or process is not co mpletely understood, a prediction can still be made. According to Sutton, targeted interventi ons are easer to make if a prediction is available. However, he stressed that unde rstanding the reasoning behind an action is much more useful. Sutton’s conclusions, based on the findings of the research, indicated that the models explained between 40% and 50% of the variance in intention, and between 19% and 38% of the variance in behavior in the studies he analyzed. He concluded that the models’ performance depended on the compar ison standard and he suggested nine reasons for poor predictions. These may be re garded as limitations in some research studies. These possible limitations were: (1) in tentions may change, (2) intentions ma be provisional, (3) violation of the principal of compatibility, (4) violation of scale

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11 correspondence, (5) unequal number of respons e categories for intentions and behavior, (6) random measurement error, (7) Restricti ons of range or variance, (8) marginal distributions do not match, (9) intention not su fficient causes of be havior. Finally, Sutton recommended some strategies for further re search using the models based on the nine reasons. Some suggestions were to include th e role of memory, situ ational factors, and past behavior. A 2001 study of the efficacy of the TPB by Armitage and Conner used a “quantitative integration and review” of 161 published journal articles and book chapters utilizing the theory. Major findings incl ude support for PBC as a determinant for intention. This analysis concluded that the correlation of PBC and intention accounted for 27% of the variance in predicting behavior. PBC was added to the original model and many studies have been conducted regarding its usefulness. Not only is PBC used to predict intention, but it also has a direct link with pred iction of behavior (Ajzen, 1988; Eagly & Chaiken, 1993). It is important to remember that PBC refers to perceived control, not actual control. Actual control takes into acc ount actual factors of available resources and opportunity, whereas perceived co ntrol is only the perception of ability to perform a behavior (Ajzen, 1988; Eagly & Chaiken, 1993). The analysis found supporting evidence for the us e of attitude and subjective norms in the models as well. However, subjective norm was determined to be the weakest predictor of intention. Other literature suggests the same finding (Pout a & Rekola, 2001; Sparks & Shepherd, 1992). Armitage and Connor offer the suggestion that measurement error was the cause of the weak predictive power of subjective norms. Use of "multi-item" scales verses "single-item" scales could be more reli able for measuring this construct. Overall,

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12 the model was successful for predicting intention and behavior. The analysis also supported Ajzen’s theory that PBC independe ntly contributes to the prediction of intention and behavior.

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13 CHAPTER 2 LITERATURE REVIEW A broad base of literature is available regarding the Cooperative Extension Service, the Theory of Planned Behavior, and adult participation in educational programs. This review is organized conceptu ally based on thes e factors. First, literature pertaining to Extensi on participation studies will be presented. These are articles that attempt to explain why adults may or may not participate in educational programs. They offer suggestions to professionals in the industry about successful marketing and retention of clientel e. This information also suggests reasons for effective or non-effective Extension progr ams and indicates clie ntele perceptions of the extension service. Last, a review of agriculturally-based items that specifically utilize the TPB. This is important to understand the success of the theory when predicting farmers’ behavior. Norland (1992) synthesized informati on from various sources and a 1987 study of Ohio Cooperative Extension Service client ele. She sought to answer some of the questions that plague Extension personnel on a daily basis. Why do adults participate? What barriers exist to participating? Why do some adults drop out of programs or stop attending? She cited Johnstone and Riveria (1965) when refe rring to situational barriers, institutional barriers, sociodemographic barrie rs, and dispositional f actors that describe adult participation. Norland cited a 1987 Ohio study as her main source of information and made conclusions based on the results. Th e survey studied Extension clientele who

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14 had previously been involved in Extension programs. Questionnaires were sent to 599 individuals with a final respons e number of 276. They did a principal-component factor analysis of the results and discovered five main factors related to participation: low anticipated difficulties with arrangements, high commitment to Extension organization, anticipated positive social i nvolvement, anticipated high quality of information, and possession of high internal motivation to learn. The implications of the study were that people participate in Extension programs based on what they know about extension and what learning opportunity is available for them from the program. Therefore, the image of Extension as percei ved by potential or existing clients is important and can be used as a marketing tool for recruitment. Opportunity for social intera ction among clientele and conve nience of the programs were also major factors of participation. Dollisso and Martin (1999) determined that young farmers are both intrinsically and extrinsically motivated to participate in educational programs. They mailed a questionnaire to 148 members of the Iowa Young Farmers Educational Association (IYFEA) to determine their perceptions toward learning, preferred learning methods, participation motivators, and barriers of pa rticipation. Major findings focused on the idea that adults desire a sense of choice. The young farmers pr eferred hands-on activities and individual projects. Economic sustainabil ity was a motivator for most farmers to participate. The study indicated that farmers’ participation might in crease as a result of their inclusion in the planning process. The au thors inferred that researchers and teachers could use this information to better prepare programs for their audiences. The authors recommended that program planners focus on profitability and new technology when

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15 targeting this audience. Current informati on and practicality of the subject matter were also important tips for planners. They also recommended that larger populations, including non-farmers and ag ribusinesses, be studied using various methods for comparability and reliability purposes. A previous study of IYFEA by Martin and Omer (1987) sought to determine their use of various agriculture agencies, especia lly the agriculture extension service. Their main purpose was to discover awareness and pa rticipation factors. They mailed surveys to approximately 75 people, and had a final response rate of 68% (51 respondents). The extension service awareness a nd satisfaction levels were high among the young farmers. The indicated an interest in programs that focused on marketing, record keeping, and management techniques. The authors determined that understanding th e characteristics of participation and profiles of the audience were important fact ors in program planning. They also concluded that involvement of the young farmers in th e planning process was needed. The process would begin with the clientele input, guiding th e direction of the pr ogram to meet their needs. Alson and Reding (1998) conducted a study to determine what factors were associated with adoption and e ducational techniques of the integrated pest management program in Utah. Two hundred sixty tw o fruit tree growers and 1,700 field crop producers in Utah received que stionnaires. Results indicate d that both groups preferred the Extension service (agent and/or office) for information regarding pest management practices. Other growers and trained empl oyees were also important sources of information. The publications and workshops provided by the extensi on service were the

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16 preferred information sources. Computer acce ss was on the list of l east preferred sources for pest management facts. Growers whose ma jor source of income was their farm placed more emphasis on the use of Extension serv ices and recommendations than those whose farm was not their primary employment. The conclusion was that in order to reach these grower audiences with information a bout IPM programs, grower backgrounds, perceptions, practices and pr eferences should be given extreme consideration. Ford (1995) assessed the educational pr iorities of small farmers in West Tennessee. Specifically, the study was desi gned to determine th e preferred delivery methods, programs, and program activities of their Extension service. Descriptive research methods were used to survey a sample of 150 small farmers who made less than $20,000 in gross income from farm sales. Farm ers rated their feelings on a one-99 scale, with individual values given to no importance, little importance, etc. Farm visits were used to gather data because ex tension agents in the area indi cated that response rates with mailed questionnaires were historically very lo w with the small farmers. A final response rate of 72% was achieved with this method. The author discovered that crop marketi ng, soil conservation, and pesticide use were areas that needed more emphasis from ed ucational programs. The small farmer also expressed an interest in the use of extension agents for one-on-one help with solving various problems. Recommendations were made regarding the development of programs that would focus on technical and business related skills, esp ecially marketing. A 1987 study by Habeeb, Birkenholz, a nd Weston sought to determine the perceptions of county extension officers a nd extension clientele toward the Missouri Extension service. Four hundred farmer s with some extension background and

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17 prominence in the community, and 150 extensio n officers were stratified by counties and then selected randomly. A 43-item questionnaire was used to determine their level of extension knowledge and opinions. Significan t differences were found between officers and clientele perceptions of extension information and ex tension specialists. Amount of extension contact, attendance of extension meetings, and innovativeness level of the respondent explained some of the variability associated w ith the differing opinions. Overall, extension information, speci alists, methods, and programs were considered satisfactory. The higher the level of contact with the extension service and agents, the higher the satisfaction ratings of the extension service tended to be. The recommendations of the authors included pl anning and conducting meetings for a larger target audience, and increasing the am ount of clientele/ag ent contact. With respect to adoption behavior of ex tension clientele and the general public, Pouta and Rekola (2001) tested the TPB mode l for predicting the “willingness to pay [WTP] for abatement of forest regeneration”. They used survey research methods to gather data for the continge nt valuation (CV) study of 600 people in Loppi, Finland. Two rounds of surveys were administer ed—one concerned forest recreation and respondent background, and the other focu sed on WTP measures and regeneration attitudes. One important aspect of the study wa s that it focused on predicting WTP responses using the attitudes, subjective norms and perceived behavioral controls of the respondents. Two attitudes were used—attitude toward forest regeneration and attitude toward supporting the abatement policy. Th e results indicated that the use of both attitude variables explained WTP significantl y. PBC contributed significantly to the

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18 prediction of WTP, sugges ting that respondents fully understood their personal limitations. Subjective norms were not significant. Beedell and Rehman (2000) studied farmer s’ conservation behavior by using the TPB model. One hundred twenty five farmers in Bedforshire, England participated in the study and were divided into three groups: fa rmer, FWAG farmers, and conservationist (FWAG: Farming and Wildlife Awareness Gr oup). The authors a dded moral obligation to the model because respondents indicated an obligation to the land and this obligation affects business decisions. Six behaviors were studied: hedge management, field margin management, tree planting management, hedge removal, hedge planting, and pesticide use. FWAG farmers viewed these behaviors more importantly than farmers. Hedge removal was not regarded as good because it is an “anti-conservation” practice. FWAG farmers felt a stronger moral obligation than farmers, suggesting that farmers have an internal oblig ation to the land and the FWAG farmers feel both social pressures and internal motivation to conserve. The two groups also behaved di fferently regarding managing field margins. However, the authors explaine d that the definition of a “ good” field margin might differ among groups. They suggest further rese arch in that particular area. Results of the study showed that FWAG farmers were more aware of conservation concerns than non-member farmer s. FWAG farmers were more concerned with environment issues than business issues regarding farming behavior. From these results, the authors conclude d that the TPB model was an acceptable tool for predicting farmer behaviors.

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19 Based on the review of literature, understanding audience profiles and characteristics are an important aspect to pr ogram planning in Extension. Clientele are interested in learning about prac tical, current information that is relevant to their interests and will attend programs based on this information. Regarding the prediction of particular behaviors, the TPB model has been successful in many different fields of study, including agriculture. Therefore, utilizi ng the TPB model to predict attendance at horticulture-based Extension programs is a logical step toward improved program planning.

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20 CHAPTER 3 METHODOLOGY This study utilized the Theory of Pla nned Behavior as a model for determining the intention of nursery industry professiona ls in Florida to at tend Florida Cooperative Extension Service programs. This behavior is under investigation for several reasons. Mainly, Extension agents in Florida have e xpressed a need to understand what motivates nursery professionals to attend programs that ar e targeted specifically for them. The TPB was used because it has been widely accepted as a framework for predicting and attempting to understanding specific behaviors. Subjects The population for the current study was horticulture industry professionals in Florida, which included the wholesale, retail, landscape and allied trade industries. To conduct the study, two mailing lists were obtai ned and combined. One was from the Florida Nurserymen and Growers Associa tion (N=2700), and the other was from a Commercial Horticulture Extension agent in Central Florida (N=300). Because the entire group of professionals, (N=3000), was utiliz ed, it is known as a purposive sample. Research Design The basic design of this study is known as ex post facto research. In Latin, ex post facto means “after the fact” and is conducted once the variable of interest has already been altered or changed in some fashi on (Ary, Jacobs, and Razavieh, 2002).The purpose of this method is to determine cause and e ffect relationships among independent variable,

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21 which is why this design is sometimes referre d to as causal compara tive research. One of the main reasons this method is used is when the research does not allow for manipulation of variables, as is th e case with a true experiment. Instead of exposing a group of people to different treatments, ex post facto research begins with the group having alrea dy been exposed and attempts to determine what differences exist and why. In the pres ent study, nursery industr y professionals were examined to determine what factors strongl y influence their attendance at horticulture based Extension programs. Pilot Study The Theory of Planned Behavior model is based on beliefs about a particular behavior. Behavioral beliefs lead to the formation of attitudes. Normative beliefs lead to an understanding of the perceived level of soci al pressure that exis ts about a behavior, and control beliefs about the beha vior lead to overall perceive d behavioral control. These beliefs can be measure directly (direct measur es) and indirectly (belief-based measures). In order to identify the salient belief s of horticulture industry professionals, a series of pilot studies was conducted at vari ous Extension programs in Central Florida. Participants were asked a range of closed and open-ended questi ons that addressed various aspects of the Extension programs th ey attend or would like to attend. A list of the most common beliefs were constructed and used to create the final questionnaire. A panel of 10 experts examined and a pproved the final questionnaire. Procedure In order to attempt to achieve a good response rate with a high-quality mailed survey, Dillman (2000) suggests the Total Design Method (TDM). Basically, the TDM

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22 focuses on creating a user friendly survey envi ronment that “increases perceived rewards for responding, decreases perceived costs and pr omotes trust in beneficial outcomes from the survey (Dillman, 2000).” It is based on multiple personalized contacts with the participants, also known as waves. This met hod has been proven to increase response rates when compared to traditional ma il surveys (Dillman, 2000). The five main elements of the TDM include a respondent-friendl y questionnaire, up to five contacts with the participants, stam ped return envelopes, pers onalized correspondence and a financial incentive (Dillman, 2000). The current study involved sending a pack et containing a cover letter, a 62-item questionnaire and a business reply envelope to the nursery profe ssionals in Florida (N=3000). The second wave was a reminder pos t card sent to all participants. No financial incentive was offered. On November 8, 2002, the packets were mailed to all 3000 professionals. A reminder post-card was mailed six weeks late r. By February 12, 411 surveys had been returned for a response rate of 14% (N=411) The majority of those responses, 75% (N=308), had been returned by the end of November. Considering that the response rate wa s low, a comparison of early to late respondents was conducted for validity reasons. According to Ary, Jacobs and Razavieh (2002) nonrespondents and late respondents are usually similar. Therefore, the two respondent groups were created. The 411 respondents were divide d into four quartiles for the purpose of comparing the firs t quartile (early respondents) to the fourth quartile (late respondents). The two groups were compared vi a an independent sample t-test based on the following variables: attitude, subject ive norms, perceived behavioral control and

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23 intent. With an alpha level of .05, none of the differences were significant, and it was concluded that late respondents were simila r to the nonrespondents. Table 3-1 displays the results. Table 3-1: Independent Sa mples Test for Early Respondents vs Late Respondents Variable N Mean t Attitude Early respondents 98 4.91 1.21* Late respondents 109 4.78 Subjective Norm Early respondents 96 3.09 .910* Late respondents 107 2.10 PBC Early respondents 96 4.30 .389* Late respondents 108 4.27 Intent Early respondents 94 4.18 .951* Late respondents 103 4.18 *p > .05 Instrumentation The Theory of Planned Behavior served as the theoretical framework of this study as well as supplying the basic model for th e questionnaire and interpretation of the results. The 62-item instrument utilized in this research elicited responses, directly and indirectly, based on the constructs of the m odel, as well as severa l factors outside the model used for profiling the industry. Thir ty-five questions were directly based on the theory and were used to create indices of each construct. Answers were given using a 5point Likert scale where responses ranged from 1=Strongly Agree to 5=Strongly Disagree. Attitude was measured directly using a 7-point semantic differential scale comprised of six items. Table 3-2 provides an example. Two attitudinal variables were

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24 measured: the attitude toward attending ex tension programs related to the horticulture industry, and the attitude toward the Fl orida Cooperative Extension Service. Table 3-2: Attitude Scale Item (Direct Measure) My attitude toward attending extension programs is Favorable:____:____:____:____:___ _:____:____:Unfavorable Useful:____:____:____:____:____:____:____:Useless Good:____:____:____:____:____:____:____:Bad Pleasant:____:____:____: ____:____:____:____:Unpleasant Reliable:____:____:____:____:____:____:____:Unreliable Valuable:____:____:____: ____:____:____:____:Worthless Attitude was also measured indirectly based on the behavioral beliefs and outcome evaluations of the respondents (belie f-based measures). According to Ajzen (2001), these beliefs and evaluations impa rt important inform ation regarding an individual’s decision to behave in a particular manner. Se ven behavioral belief questions and five outcome evaluation questions were cons tructed. Table 3-3 pres ents an example. Table 3-3: Behavioral Belief Scale Items Extension programs offer up-to-date info rmation on the horticulture industry. Strongly Agree 1 2 3 4 5 Strongly Disagree Keeping up-to-date on the horticulture industry is important to me. Strongly Agree 1 2 3 4 5 Strongly Disagree Behavioral Belief Outcome Evaluation To construct the belief-based measures index for attitude, the beliefs were multiplied by the outcomes as shown in the following equation. AB bi ei

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25 Subjective norms were also measured dire ctly and indirectly. The questions were used to determine the respondent’s perception of social pressure regarding attendance at Extension programs. Two questions elicited th e direct measure for subjective norms, and eight normative beliefand motiv ation to comply-type questions were used to create an index for indirect measuring. Example ques tions for subjective norm are in Table 3-4 and, the equation for creating the index base d on multiplying normative beliefs strengths and motivation is: SN ni mi Table 3-4: Subjective Norm Scale Items It is expected of me to attend as many extension programs as I can that are about horticulture issues. Strongly Agree 1 2 3 4 5 Strongly Disagree The opinions of horticulture professionals in my industry are important to me. Strongly Agree 1 2 3 4 5 Strongly Disagree Generally speaking, I do what other horticultu re industry professionals think I should do regarding attendance at extension programs. Strongly Agree 1 2 3 4 5 Strongly Disagree Direct measure Normative belief Motivation to comply Perceived behavioral contro l was also measured direc tly and indirectly. Seven questions were designed to crea te the index for perceived be havioral control, measuring the respondent’s evaluation of how easy or di fficulty it would be to attend extension programs. Example questions for PBC are in Table 3-5, and the e quation for constructing the PBC index is: PBC ci pi

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26 Table 3-5: PBC Scale Items It is mostly up to me whether or not I attend extension programs relating to the horticulture industry. Strongly Agree 1 2 3 4 5 Strongly Disagree If I wanted to, I could attend an extension program relating to the horticulture industry. Strongly Agree 1 2 3 4 5 Strongly Disagree I feel in complete control over whether I attend an extension program relating to the horticulture industry. Strongly Agree 1 2 3 4 5 Strongly Disagree Direct measure Control Belief Strength Control Belief Power Also on the survey were several questi ons designed to determine what would motivate horticulture industry professionals to attend more Exte nsion programs. Two open-ended questions and five questions using the Likert scale were created for this purpose. An example of each of these questions is in Table 3-6. Table 3-6: Motivation If I knew that I could learn about employee management techniques, I would be more likely to attend extension programs. Strongly Agree 1 2 3 4 5 Strongly Disagree The biggest problems facing the horticulture industry are open-ended question Two questions asked the respondents perceived level of knowledge about the Florida Cooperative Extension Service and the Institute of Food and Agricultural Sciences (IFAS) and are displayed in Table 3-7. Table 3-7: Perceived Level of Knowledge My knowledge of the Florida C ooperative Extension service is: Extremely High:____:____:____:__ __:____:____:____:Extremely Low My knowledge of the Institute of Food and Agricultural Sciences (IFAS) is: Extremely High:____:____:____:__ __:____:____:____:Extremely Low

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27 Finally, behavioral intent was meas ured directly via f our questions on the instrument. Ajzen (1988) states that behavioral intention of an individual is comprised of the motivational factors involved in making th e decision to engage in the behavior. Basically, intention is an indica tor of the individuals’ willingn ess to attempt the behavior. If the individuals state their intent to perform the behavior they can be relied upon to do so (Ajzen, 1988). Therefore, we should be able to accurately predict behavior by determining intentions. Two ex amples are shown in Table 3-8. Table 3-8: Behavioral Intent Scale Items I intend to attend extension programs relating to the horticultural indus try within the next year. Strongly Agree 1 2 3 4 5 Strongly Disagree I will try to attend extension programs relati ng to the horticultural industry within the next year. Strongly Agree 1 2 3 4 5 Strongly Disagree Data Interpretation The questionnaire was initially written with higher numbers representing lower evaluations of the questions (i .e. Strongly Agree=1 to Strong ly Disagree=5). Therefore, the data was recoded in the Statistical Packag e for Social Science (SPSS) in order to have higher numbers represent higher evaluations of the items (i.e. Strongly Agree=5 to Strongly Disagree=1). Reliability To measure the internal consistency of the items prior to creating the indices for each construct, Cronbach alpha coefficients were determined. Cronbach alpha is used when items are scaled and the scores can be a range of values, as is the case with Likert scales and semantic differential scales (A ry, Jacobs and Razavieh, 2002). Alphas in the

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28 range of .50 to .60 are acceptable when making decisions regarding groups of people for research purposes (Ary, Jacobs and Razavie h, 2002). Cronbach alphas are listed in Tables 3-9 through 3-18 Table 3-9: Cronbach Alpha Reliability Coefficients: Behavioral Beliefs Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Belief 1 4.27 .71 .72 .78 Belief 2 4.04 .83 .64 .79 Belief 3 4.31 .87 .54 .81 Belief 4 4.28 .75 .65 .80 Belief 5 3.51 .90 .45 .82 Belief 6 4.43 .69 .52 .80 Belief 7 4.32 .88 .51 .81 Behavioral Belief Scale Alpha = .83 Table 3-10: Cronbach Alpha Reliability Coefficients: Outcome Evaluations Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted OE 1 4.47 .59 .65 .68 OE 2 4.54 .56 .66 .67 OE 3 4.42 .74 .65 .66 OE 4 4.27 .77 .52 .70 OE 5 3.88 1.11 .33 .83 Outcome Evaluation Scale Alpha = .75 Table 3-11: Cronbach Alpha Reliability Co efficients: Attitude (direct measure)* Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Attitude 1 6.22 1.12 .88 .95 Attitude 2 6.17 1.10 .89 .95 Attitude 3 6.24 1.00 .93 .95 Attitude 4 6.10 1.10 .82 .96 Attitude 5 6.08 1.12 .88 .96 Attitude 6 6.10 1.15 .89 .95 Attitude Scale Alpha = .96 *measured on 7-point scale Table 3-12: Cronbach Alpha Reliability Coefficients: Normative Beliefs Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Norm 1 3.45 .94 .59 .68 Norm 2 3.28 1.01 .67 .57 Norm 3 2.88 1.03 .51 .76

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29 Table 3-12. Continued Normative Belief Scale Alpha = .78 Table 3-13: Cronbach Alpha Reliability Coefficients: Motivation to Comply Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted MC 1 2.31 1.05 .73 .78 MC 2 2.40 1.02 .76 .74 MC 3 2.23 .99 .66 .84 Motivation to Comply S cale Alpha = .85 Table 3-14: Cronbach Alpha Reliability Coeffi cients: Subjective Norm (direct measure) Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted SN 1 3.84 .92 .47 SN 2 3.59 1.03 .47 Subjective Norm Scale Alpha = .64 Table 3-15: Cronbach Alpha Reliability Coefficients: Control Belief Strength Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Strength 1 4.37 .71 .57 Strength 2 4.47 .68 .57 Control Belief Strength S cale Alpha = .72 Table 3-16: Cronbach Alpha Reliability Coefficients: Control Belief Power Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Power 1 4.29 .85 .56 Power 2 4.35 .68 .56 Control Belief Power Scale Alpha = .71 Table 3-17: Cronbach Alpha Reliability Coe fficients: Perceived Behavioral Control (direct measure) Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted PBC 1 4.02 1.08 .49 PBC 2 4.31 .73 .49 PBC Scale Alpha = .62 Table 3-18: Cronbach Alpha Reli ability Coefficients: Intent Item Mean Standard Deviation Corrected itemtotal correlation Alpha if item deleted Intent 1 4.26 .87 .78 .82 Intent 2 4.21 .86 .82 .81

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30 Table 3-18. Contintued Intent 3 4.07 .96 .66 .88 Intent 4 4.27 .75 .70 .86 Behavioral Intent Scal e Alpha = .88 An overall descriptive analysis revealed the means for each of the constructs based on the averages of each of their respec tive measures. The results can be found in Table 3-19. Table 3-19: Descriptive Statistics: TPB Model Constructs Variable N Mean SD Attitude 402 4.85 .65 Subjective Norm 394 3.03 .71 PBC 395 4.30 .58 Intent 385 4.18 .72 In addition, the descriptive analysis of each of the TPB constructs was conducted on attendees and non-attendees. Table 3-20 displays the results. Table 3-20: Descriptive Statistics: TPB M odel Constructs: Attendees/Non-attendees Variable N Mean Attitude Attendee 321 4.97 Non-attendee 72 4.41 Subjective Norm Attendee 320 3.10 Non-attendee 73 2.75 PBC Attendee 320 4.37 Non-attendee 74 3.99 Intent Attendee 312 4.30 Non-attendee 72 4.18 Pearson product moment correlations betw een each of the variables for the entire sample indicated significant rela tionships with behavioral inten tion at the .05 alpha level. In addition, significant relati onships were observed among each of the variables. The results can be found in Table 3-21.

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31 Table 3-21: Pearson Correlations between the TPB Model Constructs Variable 1 2 3 4 1. Attitude --2. Subjective Norm .463* --3. PBC .393* .150* --4. Intent .686* .403* .393* --*p < .01 Hypotheses Based on the objectives of this study, the following hypotheses were developed. H1: No significant difference exists between attendees and non-attendees regarding possible motivational factors. H2: No significant difference exists for atte ndees and non-attendees regarding perceived level of knowledge about the Florid a Cooperative Extension Service. H3: No significant difference exists for atte ndees and non-attendees regarding perceived level of knowledge abou t Institute of Food and Agricultural Sciences. H4a: No relationship exists between behavior al intention of horticu lture professionals to attend Extension programs and the three determin ant variables: attitude, subjective norm and perceived behavioral control. H4b: No relationship exists between behavior al intention of horticu lture professionals to attend Extension programs and the three determin ant variables: attitude, subjective norm and perceived behavioral, controlling for attendees and non-attendees. Data Analysis The following data analyses were conducted using SPSS. Frequencies and Cross-tabul ations were used to ga in an understanding of the demographics of the respondents. Correlational analyses using the Pearson pr oduct moment correlation coefficient were conducted to determine the strengths and directions of relationships between variables.

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32 Multiple linear regression was used to examine the amount of variation in the dependent variable that was explai ned by the independent variables. Analysis of variance was used to comp are the differences in means of the independent variables on the dependent variable.

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33 CHAPTER 4 RESULTS The purpose of this study was to determin e what factors affected the behavioral intent of a sample of horticulture industry professionals to part icipate in Extension programs. The Theory of Planned Behavior was chosen as the theoretical framework and basic model for this study because it has b een shown to aid in the prediction and understanding of how people behave (Ajzen, 1988). When applying the model to this study, behavioral beliefs about Extension pr ograms relating to the horticulture industry create a particular attitude toward attendi ng these programs. Normative beliefs regarding the social pressure to attend these programs create an individual’s subjective norm. Control beliefs about the ability to a ttend these programs indicate the perceived behavioral control of the indi vidual (Ajzen, 1988). All of th ese variables combined were utilized to provide an explanation of the in tentions of a sample of horticulture industry professionals to attend Extension programs targeted for them. Descriptive Information One of the main objectives of this res earch was to gather demographic profiling information on the horticulture industry in Florida. The instrument contained 12 questions used for this purpose. As to de mographics, the majority, 76% (N=313), of the respondents were male and 19% (N= 79) we re female. Regarding position of the respondents in the business, 61% (N=254) were owners, 16% (N=66) were managers and 3% (N=11) said they were both. To assess possible differences between men and women,

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34 a cross-tabulation was created and revealed that 68% (N=209) of the male respondents were owners and 16% (N=51) were mana gers, while 50% (N=38) of the women respondents were owners and 18% (N=14) we re managers. Nineteen business positions were stated other than the fi ve offered on the survey. Answers included representatives of the education field, parks and recreations department, as well as combinations of positions such as owner/manager/sales or sales/support staff. When respondents were asked if they atte nded Extension programs relating to the horticulture industry, 78% (N= 321) answered yes and 21% (N=86) said no. Twentynine percent (N=120) stated that they attend ed the programs themselves, 3% (N=15) sent employees and 31% (N=130) stated that they attended the programs with their employees. Forty-three pe rcent (N=178) of the res pondents were in wholesale production and 25% (N=105) classified them selves in the landscape industry. Twopercent (N=10) of the respondent s said they were in allied tr ade, and 4% (N=18) stated they had a retail nursery operation. Twen ty-one other business categories were represented ranging from golf course s to municipalities. Overall, 44% (N=183) of the respo ndents had average annual sales over $500,000, and 12% (N=50) had sales in the $250,000-$499,000 range. To determine if differences existed between attendees and nonattendees regarding a nnual sales, a crosstabulation was created. Of those who atte nd, 58% (N=155) have average annual sales over $500,000, while 36% (N=27) of those who do not attend have average annual sales over $500,000. This cross-tabula tion between attendees and non-attendees regarding average annual sales also indicated that 85.3% (N=155) of the respondents indicating sales above $500,000 attend programs, while 14.8% (N=27) do not. The respondent

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35 group with the next highest level of a ttendance had average sales between $50,000 and $149,999. Of this group, 80% (N=32) attended programs and 20% (N=8) did not. Results are displayed in Table 4-1. Table 4-1: Cross-tabulat ion: Attendance/Sales $0 $19,999 $20,000 $49,999 $50,000 $149,999 $150,000 $249,999 $250-000 $499,999 $500,000 + Attend Yes 15 (62.5%) 10 (52.6%) 32 (80%) 16 (59.3%) 39 (78%) 155 (85.2%) No 9 (37.5%) 9 (47.4%) 8 (20%) 11 (40.7%) 11 (14.6%) 27 (14.8%) Total 24 19 40 27 50 182 When asked about production systems, container production was the primary answer, 58% (N=239), and field production was the least chosen system, 38% (N=156). To determine the differences among attendees and non-attendees, a cross-tabulation was conducted. It revealed that, of those w ho attend, 55% (N=140) use greenhouses, 61% (N= 159) use shadehouses, 52% (N=124) us e field production and 69% (N=188) use container production. Of those who do not attend programs, 46% (N=30) use greenhouses, 46% (N=31) use shadehouses, 49% (N=32) use field production, and 66% (N=50) use container production. Ta ble 4-2 displays the results. Table 4-2: Cross-tabulation: Attendance/Production System Production System used Attend Yes No Container 188 (69%) 50 (66%) Shadehouse 159 (61%) 31 (46%) Greenhouse 140 (55%) 30 (46%) Field 124 (52%) 32 (49%) Furthermore, cross-tabulations reveal ed that of the respondents who utilize container production (N=238), 79% (N=188) attend programs and 21% (N=50) do not.

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36 Of those respondents who stated they used greenhouses (N=170), 82% (N=140) attend programs and 18% (N=30) do not. Eighty-thre e percent (N=159) of the 190 respondents who utilize shadehouses attend programs, wh ile 16% (N=31) do not. For the respondents who use field production (N=156), 79% (N= 124) attend programs and 21% (N=32) do not. These results indicate that horticu lture professionals who utilize greenhouse production systems and container production system s might be a large target audience for the Commercial Horticulture Extension Agents. Another aspect of this study was to de termine various motivating factors that might influence the participation level of horticulture professionals at Extension programs. Five questions were designed using a 5-item Likert scale ranging from Strongly Disagree (1) to Strongly Agree (5). An example of on e of the questions was “If I could learn about business ma nagement techniques, I woul d be more likely to attend Extension programs.” Overall, of the five questions, results i ndicated that learning a bout the programs at least one month in advance would be a possi ble motivational factor (M=4.08). Another important factor to respondent s was learning about the latest pesticides, herbicides and fungicides available on the market (M=4.09). Table 4-3 displays the results. Table 4-3: Motivation to attend Extension programs Question N Mean SD Learn about latest pesticides, herbicides and fungicides 384 4.09 .85 Learn about programs at least one month in advance 389 4.08 .83 Learn about employee mana gement techniques 379 3.75 .96 Learn about business management techniques 382 3.71 1 Receive CEUs 379 3.55 1.07 In addition to the general demographic information, descriptive statistics were obtained for the direct and belief-based meas ures of attitude. These analyses were

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37 conducted on attendees and non-attendees to further understand some of the differences that exist among the two groups. Attitude toward attending Extension programs was measured directly using a 7point semantic differential scale comprised of six items, with higher values representing positive attitudes and lower values representing negative attitudes. Results indicated that attendees had a higher mean attitude (M= 6.31) than non-attendees (M=5.46). This suggests that respondents who attend horticu lture-based Extension programs had a more positive attitude toward attending those progr ams than respondents who do not attend. Results are shown in Table 4-4. In addition to the direct measure of atti tude toward attendance, the belief-based measures were also analyzed. The behavior al beliefs of the sample of horticulture professionals as well as their evaluation of those beliefs (outcome evaluations) were measured using a 5-point Likert scale ra nging from Strongly Ag ree (5) to Strongly Disagree (1). For attendees, the mean for behavioral beliefs was 4.27, and the mean for non-attendees was 3.73. The means for the outco me evaluations were also higher for attendees (M=4.39) than for non-attendees (M=4.04). These results support the conclusion that respondents who attend Extension programs have more positive beliefs about Extension than non-attendees. Resu lts are displayed in Table 4-4. Table 4-4: Descriptive Statistics: Di rect/Belief-Based Attitude Measures Measure N Mean SD Attitude (direct)* Attend 313 6.31 .83 Not attend 73 5.46 1.45 Behavioral Beliefs Attend 320 4.26 .47 Not attend 77 3.73 .74 Outcome Evaluations Attend 319 4.39 .50

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38 Table 4-4. Continued Not attend 73 4.04 .67 *measured on a 7-point scale Examples of the behavioral beliefs that were analyzed and their means for each group (attendee/non-attendee) are displayed in Table 4-5. This analysis revealed that attendees agreed w ith the following two statements more than non-attendees: (1) Extension programs offer up-to-date inform ation; (2) Extension programs offer an opportunity to increase their knowledge of ne w products on the market more than nonattendees. Attendees also str ongly agreed that Extension programs offer an opportunity to obtain CEUs. Furthermore, non-attendees agreed more than attendees with the following two statements: (1) Horticulture prof essionals do not benefit from participating in Extension programs; (2) Extension progr ams are not an effective way to spread information to the horticulture industry. Table 4-5: Descriptive Statistics: Belie f-Based Attitude Measures: Attendees/Nonattendees Belief N Mean Extension programs offer opportu nity to obtain CEUs Attendee 2954.56 Non-attendee 67 3.94 Extension programs offer up-to-date information Attendee 2934.39 Non-attendee 70 3.75 Extension programs offer an opportunity to increase knowledge of latest chemicals Attendee 2944.37 Non-attendee 67 3.83 Extension programs offer an opportunity to increase knowledge of products on the market Attendee 2944.09 Non-attendee 69 3.73 Extension programs provide information about business management techniques

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39 Table 4-5. Continued Attendee 2913.49 Non-attendee 68 3.37 Horticulture professionals do not benefit from participating Attendee 2921.53 Non-attendee 67 2.36 Extension programs are not an effective way to spread information to the horticulture industry Attendee 2941.55 Non-attendee 68 2.26 Testing the Hypotheses The current study was designed to determ ine how the attitudes, subjective norms and perceived behavioral contro l of horticulture industry prof essionals in Florida affect their intent to attend Cooperative Exte nsion Service programs. The TPB model constructs as well as motivational factor s and perceived knowledge were analyzed separately for respondents who attend pr ograms and for those who do not attend programs. Therefore, this section is organized in the following manner. To understand some of the differences between attendees a nd non-attendees, the first three hypotheses concerning motivation and knowledge were analy zed. Then, to determine the influence of attitude, subjective norm and PBC on the behavior al intent of this sample of horticulture professionals, the final tw o hypotheses were tested. H1: No significant difference exists between attendees and non-attendees regarding possible motivational factors. To determine if a difference existed between attendees a nd non-attendees, an independent samples t-test was conducted with regard the five motivational questions. At the alpha level of .05, the means for all fi ve questions differed significantly among the

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40 two groups. The null hypothesis was rejected. The means for attendees were consistently higher than the means of non-attendees. Learni ng about the latest pesticides, herbicides and fungicides (chemicals) was the most im portant factor for respondents who attend programs (Chemicals, M=4.19). The second factor that was important to attendees was learning about the programs at least one month in advance (Time, M=4.18). For respondents who do not attend programs, chemic als and time were also the factors with the highest means. However, time had a slightly higher mean (Time, M=3.70) than chemicals (Chemicals, M=3.68). For both a ttendees and non-attendees, the questing regarding CEU availability received the lo west means (attendees, M=3.68; non-attendees, M=3.00). These results indica te that chemical update pr ograms are important to horticulture professionals. Timely promoti on of programs dealing with pesticides, fungicides and herbicides might increase attenda nce levels at these programs. The results can be found in Table 4-6. Table 4-6: Independent Sample s Test: Attendees/Non-attendees Question N Mean SD t Chemicals Attend 311 4.19 .78 -4.71* Not attend 72 3.68 .97 Time Attend 314 4.18 .74 -4.51* Not attend 74 3.70 1.05 Employee Mgmt Attend 306 3.84 .90 -3.65* Not attend 72 3.39 1.10 Business Mgmt Attend 310 3.77 .96 -2.24* Not attend 71 3.48 1.13 CEU Attend 308 3.68 1.02 -4.93* Not attend 70 3.00 1.11 *Significant at the 0.05 level

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41 H2: No significant difference exists fo r attendees and non-attendees regarding perceived level of knowledge about the Florida Cooperative Extension Service. H3: No significant difference exists for attendees and non-attendees regarding perceived level of knowledge ab out the Institute of Food and Agricultural Sciences. Two questions on the survey were designe d to gather information regarding the perceived level of knowledge that respondent s believe they have about the Florida Cooperative Extension Service a nd the Institute of Food and Agricultural Sciences. The 7-item semantic differential scale ranged fr om extremely low (1) to extremely high (7). Overall, the mean knowledge level for the Extension service was 5.08 with a standard deviation of 1.5 (N=396). The mean level for IFAS was 4.40 with st andard deviation of 1.8 (N=393). To analyze these hypotheses, an examina tion of the differences between attendees and non-attendees was conducted. An independent samples t-test revealed a significant difference in means between attendees and non attendees with regard to the perceived level of knowledge about the Extension servic e (t= -8.86; p=.000) a nd perceived level of knowledge of IFAS (t= -5.63; p=.000). Th e null hypotheses were rejected. Those who attended Extension programs had higher perceived knowledge levels about both the Extension service and IFAS than those who di d not attend programs. This indicates that Extension programs might be su ccessful at relaying inform ation about other services provided by the Cooperative Extension Service, but may not be helping horticulture professionals make the connection between Extension and IFAS. The results are displayed in Table 4-7.

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42 Table 4-7: Independent Sample s Test: Knowledge/Attendance Variable N Mean SD t Knowledge of Extension Attend 316 5.40 1.2 8.86* Not attend 79 3.87 1.9 Knowledge of IFAS Attend 315 4.66 1.7 5.63* Not attend 77 3.39 1.9 *Significant at the 0.05 level H4a: No relationship exists between be havioral intention of horticulture professionals to attend Extension program s and the three det erminant variables: attitude, subjective norm and perceived behavioral control. To test the hypothesis, a multiple lin ear regression analysis using the TPB variables in the enter method was performed. The regression was si gnificant (F=145.57; p<.001), and the constructs of the TPB model accounted for 53% of the variance in the intent of horticulture profe ssionals to attend programs. For all respondents, attitude, subjective norm and PBC were significant pred ictors of behavioral intent (p < .05). Attitude toward attending programs exerted the strongest influence on intent ( = .578), followed by PBC ( = .155) and then subjective norm ( = .150). The null hypothesis was rejected, and the results are presented in Table 4-8. Table 4-8: Multiple Regression Coefficients: Entire Sample, (N=380) Variable B SE B Beta Attitude .693 .051 .578* Subjective Norm .158 .041 .150* PBC .206 .051 .155* *Significant at the .05 level H4b: No relationship exists between be havioral intention of horticulture professionals to attend Extension program s and the three det erminant variables: attitude, subjective norm and perceived behavioral, controlling for attendees and non-attendees.

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43 This data was further analyzed to de termine what differences existed among attendees and non-attendees with regard to the TPB model. First, the multiple linear regression analysis was conducted for attend ees only. Then, the analysis was run on nonattendees and comparisons were ma de between the two groups. Controlling for attendees, the regressi on was significant (F=80.43; p < .001), and the model explained 44% of the variance in beha vioral intent. All th ree constructs were significant predictors of intent at the alpha .05 level. Attitude remained the strongest predictor ( = .451), followed by PBC ( = .235) and subjective norm ( = .165). For respondents who attend programs, attitude, s ubjective norms and PBC all contribute to their intent to participate in Extension, with their attitudes influenc ing their decisions the most. The results are presented in Table 4-9. Table 4-9: Multiple Regression Coefficients: Attendees, (N=311) Variable B Std. Error B Beta Attitude .573 .065 .451* Subjective Norm .156 .044 .165* PBC .277 .055 .235* *Significant at the 0.05 level Next, the multiple regression analysis was conducted controlling for nonattendees. The regression was significant (F =32.39; p < .001), and the model explained 60% of the total variation of behavioral intent. However, the influence of the variables changed considerably. Attitude continued to be the strongest predictor of intent ( = .709), but was followed by subjective norm ( = .131). PBC exerted a negative influence on behavioral intent ( = -.029). In addition, attitude was the only significant predictor of the behavioral intent of n on-attendees to attend programs. Therefore, for respondents who do not attend programs, attitude was the primary indicator of their intent to participate in Extension. Their behavior was not affected significantly by their

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44 surrounding social environment or their perceive d levels of control regarding attendance. The negative beta on PBC indicates that more control over their a ttendance at Extension programs, may actually result in less intent to attend. Results are presented in Table 4-10. Table 4-10: Multiple Regression Coefficients: Non-attendees, (N=68) Variable B Std. Error B Beta Attitude .773 .104 .709* Subjective Norm .147 .103 .131 PBC -.044 .126 -.029 *Significant at the 0.05 level Summary Overall, the results indicated that lear ning about programs at least one month in advance and learning about the latest chemical s available in the market were two factors that might help Commercial Horticulture Extension Agents increase participation levels of horticulture professionals. In addition, pa ying close attention to the attitudes of these professionals is important to program planning and marketing.

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45 CHAPTER 5 DISCUSSION Reasons for participation and possible motiv ational factors were the goals of this research. The Theory of Planned Behavior was us ed as the theoretical framework of the study as well as the basic model for c onducting the research because it has been successful at predicting behavioral intention. It is based on three main ideas, attitude toward the behavior, subjective norms (per ceived social pressures) and perceived behavioral control. Generally, the behavioral intention of a person to perform an action is strengthened when all three constructs ar e viewed favorably. A questionnaire was created based on the construc ts of the TPB model and ad ministered to horticulture industry professionals in Florida. Demographic information and the four hypotheses were analyzed using the Statistical Package for Social Science (SPSS) The following procedures were conducted: frequencies, cross-tabulations, Pearson pr oduct moment correlations, multiple linear regression and ANOVA. All hypotheses were tested at the alpha level of .05. Key Findings and Implications The overall results of the study indica ted that attitudes, subjective norms and perceived behavioral control of horticulture industry professionals in Florida were positively related to intention to attend horticulture based Extension programs. Demographic information provided the profile of those professionals who do attend or do

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46 not attend programs. Possible motivational f actors for promoting future attendance of horticulture professionals were also determined from the results. According to the results a large portion of horticultu re industry professionals attend programs or at least send representativ es. Over half of the respondents had average annual sales over $500,000. Furthermore, of those who stated that they did not attend programs, one-third were in the $500,000 and up category. A major aspect of this research was determining possible motivation fa ctors that would help increase attendance at Extension programs. Pilot testing and anec dotal information initially revealed that horticulture professionals attend programs in order to receive CEUs for licensing and recertification purposes. Therefore, five items were tested for importance. These five questions asked respondents if they would be more likely to attend programs if they knew they would be learning about business mana gement techniques, receiving continuing education units (CEUs), learning about the pr ograms one month in advance, learning about employee management techniques, and up dates on latest pestic ides, herbicides and fungicides. The results of this study indicated that acquiring CEUs was not as likely to attract or maintain participants as chemical updates. Learning about the program at least one month in advance was also very impor tant to the respondents. These findings suggest that while CEUs are important to horticulture professionals, they may not be the main influence on their attendance at programs. Chemical updates are more likely to attract and maintain participation of this population in Extension programs. Furthermore, horticu lture professionals expressed a need to receive marketing or promotion materials well in advance of the programs.

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47 Overall, a strong relationship existe d between the TPB model constructs and behavioral intent to attend Extension progr ams. The attitude of the horticulture professionals was the strongest predictor of behavioral intent, followed by perceived behavioral control and subj ective norm. This suggests that the TPB model was appropriate for use in predicting the attendan ce of horticulture professionals at Extension programs. Attitudes about horticulture-ba sed Extension programs are extremely important to this group of people and, ther efore, should be closely monitored by the Extension service. The horticulture profe ssionals maintained a high level of perceived behavioral control suggesti ng that they believe barrier s do exist regarding their attendance at programs. Social pressure fr om friends, family and co-workers are not viewed as important to hor ticulture professionals. When the sample was separated into two groups, attendees and non-attendees, the results revealed that attitude was consistently the strongest predictor of behavioral intent. For those who do not attend programs, subj ective norms followed attitude, and PBC exerted a negative influence on behavioral in tent. This suggests that non-attendees have less control over their attendance at programs and, if they were to increase their perceived control, then their intent to attend programs would decrease even more. This group of professionals may be inclined to attend progr ams in the future, but only because they are required to do so for other reasons such as re-certification for a pesticide license. These results also suggest that attitude is a key f actor for non-attendees. Th erefore, it should be weighed heavily when planning future programs designed to attract this audience. The results also indicated that perc eived level of knowledge of the Florida Cooperative Extension Service and the Institu te of Food and Agricultural Sciences

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48 differed among attendees and non-attendees. Those who attend programs had higher means for both Extension and IFAS than did non-attendees. These findings suggest that by attending Extension programs, a certain amount of knowledge about the Extension Service and about IFAS is learned. Consid ering that the perceived knowledge level is lower for IFAS, Extension programs may be a setting for making the connection between the two and for passing on information about IFAS in general. Limitations The ability to generalize the findings to the entire horticulture industry in Florida is somewhat limited because of the use of a purposive sample. However, a large sample size was achieved, and testing was conducted to control for non-re sponse error. Conclusions and Directions for Future Research The results of this study indicated that, while social pressure and perceived control are important to horticu lture professionals, attitude is the key factor for predicting attendance. While few, if any, participati on studies using the TP B exist regarding the Extension service, other studi es reveal similar findings such as Norland’s 1992 study of the Ohio Cooperative Extension Service. Sh e basically concluded that attitudes drive participation levels, and the perceived imag e of Extension is important for promoting future attendance. Consideri ng that attitudes are the main issue with non-attendees, a more thorough analysis of attitudes and the be liefs that create those particular attitudes among Extension clientele is needed. Also, with regard to demographics, this study revealed that wholesale and landscape industries with aver age annual sales over $500,000 ar e major target audiences for the Extension service in Florida. These gr oups were primarily interested in chemical

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49 updates and learning about the programs in a timely manner. Furthermore, while obtaining CEUs may be important to this population, results of th is particular study revealed that this was not a driving force be hind participation. Past research indicates that successful program planning includes many variables such as timeliness and location, but equally importan t is knowing and understanding the characteristics of the audience. Therefore, a more detailed, individu al analysis of each sector in the Florida horticulture industry may useful for trul y understanding and improving the program planning process. Recommendations Commercial Horticulture Extension agen ts in Florida have the difficult job of planning, promoting and implementing edu cational programs for a large, diverse industry. They are responsible for understand ing the audience, pred icting attendance and evaluating the programs in order to create an even better program. However, many times attendance at or response to the programs may not seem representative of the effort. Several studies have shown that the highe r the level of contact with the extension service and agents, the higher th e satisfaction levels of the cl ientele. Therefore, a need may exist for the Extension service to re -determine audiences and re-evaluate the attitudes and beliefs of those audiences. Th en, the goals and objectives of particular programs can be re-assessed to determine if they are meeting the needs of those target audiences. This is very similar to the idea behind the Program Life Cycle (Bowling, 2001). It is a methodology deve loped to help agents improve program efficiency and value to the consumer as well as to the Exte nsion Service. The Li fe Cycle involves five steps: conceptualization, development, matu rity, decline, and termination. In the 1st

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50 stages, client involvement a nd understanding are very important This is where a true understanding of the target audien ce and their needs is vital. The idea of this model is not to move past the maturity stage where the program is effective and attendance is high. Once the decline and termination stages have been entered, it is very ha rd to re-organize. Therefore, in the maturity stage, it is impera tive to pay attention to signs of decline and attempt to offset them by redefining, redeveloping and revising the program. Based on this research and the literatu re involved, the Exte nsion service has a responsibility to its audience to provide educational program s that are timely and up-todate. It has the responsibility of unde rstanding the knowledge skills and, most importantly, attitudes of the clientele in or der to maintain these programs. In addition, the Extension service must do the research required for re-discove ring existing audiences and exposing new ones. Then, the many valuable Extension agents can ultimately “help people help themselves.”

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APPENDIX A COVER LETTER AND QUESTIONNAIRE

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Institute of Food and Agricultural Sciences Environmental Horticulture Department 52 Dr. Rick Schoellhorn 2523 W.M. Fifield Hall, PO Box 110670 Gainesville, FL 32611-0670 Phone (352) 392-1831 Ext. 364 Fax (352) 392-3870 Website: http://hort.ifas.ufl.edu/ Email rksch@ufl.edu Dear Nursery Industry Professional, The horticulture extension agents in your area have expressed a need to discover better ways of meeting your needs and encouraging you to participate in the many programs available. By better understanding their audience, these extensi on agents may be able to plan more effective programs for you. Therefore, we are conducting a survey to determine why horticulture industry professionals decide to attend extension programs. We hope to discover how you feel about the extension service and how that affects your a ttendance at horticulture-based programs. Your valuable answers will help to provide guidance to extension agents when they begin the program planning process. By taking time to fill out this survey, you are contributing to an extremely important project One that is based on the ultimate discovery of what you want your extension service to do for you. However, your participation is voluntary, and there is no risk or direct benefit to you as a result of completing the questionnaire. You do not have to answer any question you do no wish to answer and you may quit at any time. Also, there is no compensation for participating in this study. Please understand that the number at the t op of your questionnaire will only be used to check off your name when your survey is returned. Your identity will be kept confidential to the extent provided by law. If you have any questions about your rights concerning this study, please contact the UFIRB office, Box 112250, Univer sity of Florida, Gainesville, FL 32611-2250. Please take the time to participate in this ve ry important research. It should only take you about 10-15 minutes to complete, and we have s upplied you with everything you need to return the completed survey. You have the opportunity to provide valuable input into the design of programs developed for you by the Florida Cooperative Extension Service. If you have any questions about this research study or the survey, please contact us at 352-392-1831 ext. 364. You may also email any questions or comments to AlexisUF@ufl.edu Thank you very much for participating in this study. Sincerely, ___________________________ ____________________________ Alexis A. Richardson Dr. Rick Schoellhorn Graduate Research Assistant, UF Professor and Floriculture Specialist, UF ______________________ Elizabeth A. Felter Extension Agent, Commercial Horticulture

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Institute of Food and Agricultural Sciences Environmental Horticulture Department 53 Dr. Rick Schoellhorn 2523 W.M. Fifield Hall, PO Box 110670 Gainesville, FL 32611-0670 Phone (352) 392-1831 Ext. 364 Fax (352) 392-3870 Website: http://hort.ifas.ufl.edu/ Email rksch@ufl.edu UF/IFAS Florida Cooperative Extension Service Survey Thank you for taking time to complete this questionnaire. Our ultimate goal is to determine better ways of meeting your needs regarding the extens ion service and the programs available to you. Your valuable answers will provide guidance to the extension agents and sp ecialists when they plan programs for you. Section 1: Please answer the following questions. 1. Do you attend Florida Cooperative Extension Service programs relating to the horticulture industr y? .............................................................. 2. IF yes, please briefly explain why. 3. IF no, please briefly explain why not. 4. Do you normally send employees to the extension programs or do you attend the programs yourself? Please briefly explain. 5. How many extension programs relating to the horticulture industry do you attend each year? .... YES NO ____________extension programs per year Section 2: Please indicate how strongly you agree with the following statements by circling the number that represents your answer. Strongly Agree Agree Neutral Disagree Strongly Disagree 6. Extension programs offer up-to-date information on the hor ticulture industry...............1 2 3 4 5 7. Extension programs offer an opportunity for people in the horticulture industry to increase their knowledge of new products on the market... 1 2 3 4 5 8. Horticulture professionals do not benefit from participating in extension programs relating to their i ndustry........................................................1 2 3 4 5 9. Extension programs offer an opportunity for people in the horticulture industry to increase their knowledge of herbicides, pesticides and fungicides............................................................. 1 2 3 4 5 Please Continue

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Strongly Agree Agree Neutral Disagree Strongly Disagree 10. Extension programs provide information about business manageme nt techniques.........................1 2 3 4 5 11. Extension programs offer an opportunity for people in the horticulture industry to obtain continuing educatio n units (CEUs)...................... 1 2 3 4 5 12. Extension programs are not an effective way to spread information to the horticulture industry....1 2 3 4 5 13. My coworkers think that I should attend extension programs relating to the horticultural industry................................................................ 1 2 3 4 5 14. Other horticulture professionals in my industry encourage me to attend extension programs relating to the hor ticultural industry.....................1 2 3 4 5 15. Generally speaking, I do what other important people think I should do regarding attendance at extensio n programs.......................................... 1 2 3 4 5 16. The opinions of my coworkers are important to me.........................................................................1 2 3 4 5 17. If I wanted to, I could attend an extension program relating to the horticultural industry....... 1 2 3 4 5 18. I prefer to make the decision regarding whether or not I attend extension programs relating to the horticulture industry.......................................1 2 3 4 5 19. My friends and family encourage me to attend extension programs relating to the horticultural industry................................................................ 1 2 3 4 5 20. It is mostly up to me whether or not I attend extension programs relating to the horticulture industry................................................................1 2 3 4 5 21. Generally speaking, I do what other horticulture professionals in my industry think I should do regarding attendance at extension programs.............................................................. 1 2 3 4 5 22. It is expected of me to attend as many extension programs as I can that are about horticultura l issues...............................................1 2 3 4 5 23. I feel in complete cont rol over whether I attend an extension program relating to the horticultural industry............................................ 1 2 3 4 5 24. Generally speaking, I do what my coworkers think I should do regarding attendance at extension programs..............................................1 2 3 4 5 25. The opinions of horticulture professionals in my industry are im portant to me.......................... 1 2 3 4 5 Please Continue

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55 Strongly Agree Agree Neutral Disagree Strongly Disagree 26. If I wanted to, it would be easy for me to attend extension programs relating to the horticultural industry within the next year................................1 2 3 4 5 27. I have control over whether I attend an extension program relating to the horticultural industry................................................................ 1 2 3 4 5 28. Learning about new products on the market is important to me....................................................1 2 3 4 5 29. Keeping up-to-date on the horticulture industry is important to me................................................. 1 2 3 4 5 30. Learning about pesticides, herbicides and fungicides is im portant to me...............................1 2 3 4 5 31. Gathering new information about business management techniques is important to me......... 1 2 3 4 5 32. Obtaining continuing education units (CEUs) is important to me....................................................1 2 3 4 5 33. If I knew that I could learn about business management te chniques, I would be more likely to attend extension programs...................... 1 2 3 4 5 34. If I knew that I could receive continuing education units (CEUs), I would be more likely to attend exte nsion pr ograms................................1 2 3 4 5 35. If I knew about the extension programs at least one month in advance, I would be more likely to attend exte nsion pr ograms................................ 1 2 3 4 5 36. If I knew that I coul d learn about employee management te chniques, I would be more likely to attend extension programs......................1 2 3 4 5 37. If I knew that I could learn about the latest pesticides, herbicides and fungicides being offered in the market, I would be more likely to attend.................................................................... 1 2 3 4 5 Section 3: Please indicate how likely you would be to do the following: 38. For me to attend one extension program relating to the horticultural industry in the next year would be.......................................................1 2 3 4 5 39. For me to attend more than one extension program relating to the horticultural industry in the next year would be.........................................1 2 3 4 5 40. I intend to attend extension programs relating to the horticultural industry within the next year.......................................................................1 2 3 4 5 41. I will try to attend extension programs relating to the horticultural industry within the next year.......................................................................1 2 3 4 5 Please Continue

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56 42. I intend on becoming more aware of extension programs offered relating to my industry.......... 1 2 3 4 5 43. I will try to utilize the services of my extension office....................................................................1 2 3 4 5 How likely would you be to attend extension programs relating to horticultural issues in the: 44. SPRING................................................................1 2 3 4 5 45. SUMMER.............................................................1 2 3 4 5 46. FALL....................................................................1 2 3 4 5 47. WINTER...............................................................1 2 3 4 5 Section 4: Please indicate your attitude by marking along the range of each item. 48. My attitude toward attending extension programs relati ng to the horticulture industry is Favorable:____:____:____:____: ____:____:____:Unfavorable Useful:____:____:____:____:____:____:____:Useless Good:____:____:____:____:____:____:____:Bad Pleasant:____:____:____:____:____:____:____:Unpleasant Reliable:____:____:____:____: ____:____:____:Unreliable Valuable:____:____:____:____:____:____:____:Worthless 49. My attitude toward the Florida Cooperative Extension Service is Favorable:____:____:____:____: ____:____:____:Unfavorable Useful:____:____:____:____:____:____:____:Useless Good:____:____:____:____:____:____:____:Bad Pleasant:____:____:____:____:____:____:____:Unpleasant Reliable:____:____:____:____: ____:____:____:Unreliable Valuable:____:____:____:____:____:____:____:Worthless 50. The biggest problems facing th e horticulture industry are: ________________________________________________________________________________________ ________________________________________________________________________________________ ______________________________________ 51. What specific topics would make you more likely to attend extension programs re lating to the horticulture industry? ________________________________________________________________________________________ ________________________________________________________________________________________ ______________________________ ________

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57 52. My knowledge of the Florida Coope rative Extension service is: Extremely High:____:____:____:____: ____:____:____:Extremely Low 53. My knowledge of the Institute of Food and Agricultural Sciences (IFAS) is: Extremely High:____:____:____:____: ____:____:____:Extremely Low 54. Please list any extension programs that you have been involved with (ex: 4-H, Master Gardener, etc). Section 5: Please take a few more minutes to answ er these basic demographi c questions. Thank You. 55. Gender: Male Female 56. What is your position in the business? 1. Owner 2. Manager 3. Grower/Technician 4. Support Staff 5. Sales/Marketing Other (please specify)_________________________ 57. Age of business_________ 58. Number of employees in business_____________ 59. How many acres do you usually have in production?___________ 60. Average Annual Sales (Please choose range or provide dollar amount) 1. $0 $19,999 2. $20,000 $49,999 3. $50,000 $149,999 4. $150,000 $249,999 5. $250,000 $499,999 6. $500,000+ 61. Do you use any of the following production systems? (please circle your answer) 1. Greenhous e........................... YES NO 2. Shade house.......................... YES NO 3. Field production................... YES NO 4. Container production ........... YES NO 62. Business Category: (please circle the one that best describes your operation) 1. Wholesale production 2. Allied Trade 3. Retail Nursery 4. Landscape Industry 5. Interiorscape Industry 6. Other (please specify)____________________________________________ THANK YOU FOR TAKING TIME TO COMPLETE THIS IMPORTANT QUESTIONNAIRE. YOUR ANSWERS ARE EXTREMELY VALUABLE TO TH E SUCCESS OF THE RESEARCH STUDY.

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APPENDIX B THEORY OF PLANNED BEHAVIOR

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59 Figure B-1: Theory of Planned Behavior Model (Ajzen, 2002) Behavioral Beliefs Attitude toward the Behavior Normative Beliefs Subjective Norm Control Beliefs PBC Behavioral Beliefs Behavioral Beliefs Actual Control

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60 LITERATURE CITED Ajzen, I. (1988). Attitude s, Personality, and Behavior Chicago: The Dorsey Press. Ajzen, I. & Driver, B. L. (1992) Applicati on of the Theory of Planned Behavior to Leisure Choice. Journal of Leisure Research, 24, 207-224. Alston, D. G. & Reding, M.E. (1988) Factor s Influencing Adoption and Educational Outreach of Integrated Pest Management. Journal of Extension, 36 (3). [online], Available: http://www.joe.org/joe/1998june/a3.html February 7, 2002. Armitage, C.J. & Conner, M. (2001) Efficacy of the Theory of Planned Behavior: A Meta-Analytic Review. British Journal of Social Psychology, 40 (4) 471499. Ary, D., Jacobs, L., & Razavieh, A. (2002) Introduction to Research in Education. Belmont: Wadsworth Group. Beedell, J. & Rehman, T. (2000) Using Social-Psychology Models to Understand Farmers' Conservation Behaviour. Journal of Rural Studies, 16 117-127. Bowling, C. J. (2001) Using the Program Life Cycle Can Increase Your Return On Time Invested. Journal of Extension, 39 (3). [on-line], Available: http://www.joe.org/joe/2001june/a2.html March 11, 2002 Bruening, T., Radhakrislma, R. & Rollins, T. (1992) Environmental Issues: Farmers’ Perceptions about Usefulness of Inform ational and Organizational Sources. Journal of Agricultural Education, 33 (2). [on-line], Available: http://pubs.aged.tamu.edu /jae/pdf/Vol33/33-02-34.pdf March 11, 2002. DeSousa, J. (2002) Florida’s Nursery and Landscape Industry Soars to Record Economic Highs. Florida Nurserymen and Gr owers Association Press Release. January 7, 2002. Dillman, D. A. (2000). Mail and Internet Surveys: The Tailored Design Method 2nd ed. New York: John Wiley & Sons, Inc. Dollisso, A.D. & Martin, R.A. (1999) Pe rceptions Regarding Adult Learners

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61 Motivation to Participate in Educationa l Programs. Journal of Agricultural Education, 40 (4) 38-46. Eagly, A. & Chaiken, S. (1993) The Psychology of Attitudes Harcourt Brace Jovanovich College Publishers. Fishbein, M. & Ajzen, I. (1975) Belief, Attit ude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Florida Agriculture Statistics Service (2002) Foliage, Floriculture and Cut Greens [on line], Available: http://www.nass.usda.gov/fl March 15, 2003. Ford, C. L. (1995) Educational Priorities of Small Farmers in West Tennessee. Journal of Agricultural Education, 36 (1) 31-37. Habeeb, M., Birkenholz, R. J. & Weston, C. R. (1987) Clientele Group and Extension Council Officer Perceptions of the Cooperative Agricultural Extension Service. Journal of Agricultural Education, 28, 15-20. Ingram, K. L., Cope, J. G., Harju, B. L. & Wuensch, K. L. (2000) Applying to Graduate School: A Test of the Theory of Pl anned Behavior. Journal of Social Behavior and Personality,15, (2) 215-226. Israel, G. (2001) Using Logic Models fo r Program Development. EDIS document. [online], Available: http://edis.ifas.ufl.edu/BODY_WC041 February 19, 2002. Jacob, S. & Ferrer, M. (2000). Program Th eory for Effective Extension Program Planning. EDIS document [on-line], Available: http://edis.ifas.ufl.edu/BODY_FY031 November 11, 2001 Martin, R. & Omer, M. H. (1987) Factor s Associated with Participation of Iowa Young Farmers in Agricultural Extension Programs. Journal of Agricultural Education, 29, 45-52. Norland, E. (1992) Why Adults Par ticipate? Journal of Extension, 30 (3) [on-line], Available: http://www.joe.org/joe/1992fall/a2.html March 11, 2002. Place, N. (2001) Principals of Effectiv e Extension Educational Programs. EDIS document. [on-line], Available: http://edis.ifas.ufl.edu/BODY_WC042 March 19, 2002. Pouta, E. & Rekola, M. (2001)The Theory of Planned Behavior in Predicting Willingness to Pay for Abatement of Forest Regeneration. Society and Natural Resources, 14 ., 93-106. Rogers, J. (2001). Adults Learning: 4th Edition. Philadelphia, PA: Open University Press.

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62 Schmitt, M., Durgan, B., & Iverson, S. ( 2000) Impact Assessment and Participant Profiles of Extension’s Education Programs for Agricultural Chemical/Seed Retailers and Crop Advisors Journal of Extension, 38, (6) [on-line], Available: http://www.joe.org/joe/2000december/a2.html January 31, 2002. Sparks, P. & Shepherd, R. (1992) Self-Identity and the Theory of Planned Behavior: Assessing the Role of Identification with "Green Consumerism." Social Psychology Quarterly, 55, (4) 388-399. Summerhill, W.R & Taylor, C.L (1992) Ba sic Premises for Client Involvement in Extension Programming. EDIS document. [on-line], Available: http://edis.ifas.ufl.edu/BODY_PD013 January 22, 2002. Sutton, S. (1998) Predicting and Explaining Intentions and Behavior: How Well Are We Doing? Journal of Applied Social Psychology, 28 (15) 1313-1338. Taylor, C.L. (1994) Concept of a Major Program. EDIS document. [on-line], Available: http://edis.ifas.ufl.edu/BODY_PD034 November 11, 2001. Taylor, C. L. & Beeman, C. E. ( 1992) Ev aluation for Accountability: An Overview. EDIS document [on-line], Available: http://edis.ifas.ufl.edu/BODY_PD018 November 11, 2001. United States Department of Agricultur e. (2002) Floricultu re Crops 2001 Summary. National Agriculture Statistics Service. Sp Cr 6-1 (02)a. University of Florida Institute of Food and Agricultural Sciences Fact Digest (2003) [online], Available: http://ifas.ufl.edu May 25, 2003 Vasquez, B. C. & Nesheim, O. N. Fl orida Crop/Pest Management Profiles. EDIS document [on-line], Available: http://edis.ifas.ufl.edu/BODY_PI038 October 15, 2002.

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63 BIOGRAPHICAL SKETCH Alexis A. Clark-Richardson began her college education at Central Florida Community College where she received her A ssociate of Arts degr ee in 1998. She moved to Gainesville to begin her career at the University of Florida in 1999 and earned a Bachelor of Science degree in Agricultura l Education and Communication in 2000. She finalized her education in 2003 with a Mast er of Science degree in Environmental Horticulture. Her research was based on the evaluation and marketing of Florida Cooperative Extension Service workshops and programs. Mrs. Richardson married her husband, Steve, on May 11, 2002, and their first son is due in September 2003. They will be m oving to Crystal River, Florida and pursuing careers in the Florida agriculture industry.


Permanent Link: http://ufdc.ufl.edu/UFE0000988/00001

Material Information

Title: The Theory of planned behavior in predicting attendance at environmental horticulture extension programs
Physical Description: Mixed Material
Creator: Clark-Richardson, Alexis A. ( Author, Primary )
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Title: The Theory of planned behavior in predicting attendance at environmental horticulture extension programs
Physical Description: Mixed Material
Creator: Clark-Richardson, Alexis A. ( Author, Primary )
Publication Date: 2003
Copyright Date: 2003

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
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THE THEORY OF PLANNED BEHAVIOR IN PREDICTING
ATTENDANCE AT ENVIRONMENTAL HORTICULTURE
EXTENSION PROGRAMS
















By

ALEXIS A. CLARK-RICHARDSON


A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE


UNIVERSITY OF FLORIDA
2003















For my family















ACKNOWLEDGEMENTS


Conducting this research has been a great learning experience for me. I would like

to thank the members of my committee, Dr. Rick Schoellhom, Dr. Jim Barrett, Dr. Tracy

Irani and Elizabeth Felter, for giving me the opportunity to work with them during this

time. Their patience and assistance, as well as much needed advice, have been greatly

appreciated.

I would also like to thank my parents and sisters, and my husband for their never-

ending encouragement of my educational journey. The support and blessings of my

family and friends will always be remembered.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ......... ................................................................................ iii

L IST O F T A B L E S ........ ......................................................... ................... .. vi

A B STR A C T ................. ............................................................................................... viii

CHAPTER

1 IN TR O D U C TIO N ............................................... .. ....... .... .............. .

Cooperative State Research, Education and Extension Service ..............................2
Environmental Horticulture in Florida.............................. .....................5
Purpose and Objective ................................................. ..... ...............
T heoretical F ram ew ork .......................................... ......................... ............... 7

2 LITERA TU RE REV IEW ............................................... ............................. 13

3 M E T H O D O L O G Y ......................................................................... ...................20

Su objects ......... ......................................................................................... 2 0
R research D esig n ............. .................................................................. ........ .. ...... .. 2 0
Pilot Study ........................................... 21
P ro c e d u re ........................................................................................................... 2 1
In strum entation ................................................................2 3
D ata In terp retatio n ............................................................................................. 2 7
R e lia b ility ........................................................................................................... 2 7
H y p o th e se s ............. ................. ................. ............................................ 3 1
Data Analysis ............ ......... .. .......... ........31

4 R E SU L T S ................................................................3 3

Descriptive Information ............... ......... ........ ........33
Testing the Hypotheses ............... ......... ....... ........39
S u m m a ry ............. ..... ............ ................. .................................................4 4

5 DISCUSSION .......... .. ................ .......... 45









K ey Findings and Im plications ........................................ .......................... 45
L im stations ..............................................................................................................48
Conclusions and Directions for Future Research.........................................48
R ecom m endations .................. ........................... .. ......................... 49

APPENDIX

A COVER LETTER AND QUESTIONNAIRE ............................... ................ 51

B THEORY OF PLANNED BEHAVIOR (figure)................. ............................58

L IT E R A TU R E C ITE D ......................................................................... ....................60

BIO GRAPH ICAL SK ETCH ................................................. ............................. 63















LIST OF TABLES


Table page

3-1 Independent Samples Test for Early Respondents vs. Late Respondents ..............23

3-2 Attitude Scale Item (Direct Measure).................... .......................... ..24

3-3 B ehavioral B elief Scale Item s ........................................... .......................... 24

3-4 Subjective N orm Scale Item s ............................................................................ 25

3-5 PB C Scale Item s ...................... .................... .. .. ........... .... ....... 26

3 -6 M o tiv atio n ............................................................................................................... 2 6

3-7 Perceived Level of Knowledge....................... .... ............................. 26

3-8 Behavioral Intent Scale Items............... ............ ........._ .. ............. 27

3-9 Cronbach Alpha Reliability Coefficients: Behavioral Beliefs .............................28

3-10 Cronbach Alpha Reliability Coefficients: Outcome Evaluation ...........................28

3-11 Cronbach Alpha Reliability Coefficients: Attitude (direct measure) ....................28

3-12 Cronbach Alpha Reliability Coefficients: Normative Beliefs ..............................28

3-13 Cronbach Alpha Reliability Coefficients: Motivation to Comply......................29

3-14 Cronbach Alpha Reliability Coefficients: Subjective Norm (direct measure)......29

3-15 Cronbach Alpha Reliability Coefficients: Control Belief Strength....................29

3-16 Cronbach Alpha Reliability Coefficients: Control Belief Power .........................29

3-17 Cronbach Alpha Reliability Coefficients: PBC (direct measure)........................29

3-18 Cronbach Alpha Reliability Coefficients: Intent.............................. ............... 29

3-19 Descriptive Statistics: TPB Model Constructs ............................................... 30









3-20 Descriptive Statistics: TPB Model Constructs: Attendees/Non-attendees............30

3-21 Pearson Correlations between the TPB Model Constructs............................... 31

4-1 Cross-tabulation: Attendance/Sales...................................................................... 35

4-2 Cross-tabulation: Attendance/Production System................................................35

4-3 Cross-tabulation: Motivation to Attend Extension Programs...............................36

4-4 Descriptive Statistics: Direct/Belief-based Attitude Measures ............................37

4-5 Descriptive Statistics: Belief-Based Attitude Measures: Attendee/Non-attendee...38

4-6 Independent Samples Test: Attendee/Non-attendee................. ............... 40

4-7 Independent Samples Test: Knowledge/Attendance............................................42

4-8 Multiple Regression Coefficients: Entire Sample ................................................42

4-9 Multiple Regression Coefficients: Attendees .....................................................43

4-10 Multiple Regression Coefficients: Non-attendees..............................................44















Abstract of Thesis Presented to the Graduate School of the University of Florida in
Partial Fulfillment of the Requirements for the Degree of Master of Science

THE THEORY OF PLANNED BEHAVIOR IN PREDICTING ATTENDANCE AT
ENVIRONMENTAL HORTICULTURE EXTENSION PROGRAMS

By

Alexis A. Clark-Richardson

August 2003

Chair: Dr. Rick Schoellhorn
Major Department: Environmental Horticulture

The Florida Cooperative Extension Service has a long tradition of serving

clientele via many different channels. One primary technique used by many agents is

hosting workshops or demonstrations. Horticulture extension agents have a large

audience and target this clientele for their major programs by using flyers, newsletter

announcements, email, and phone calls. These agents have expressed a need to discover

why a larger percentage of this audience is not participating. Therefore, the Theory of

Planned Behavior was utilized to determine how attitudes, subjective norms and

perceived behavioral control predict the intent of horticulture professionals to attend

horticulture-based Extension programs. A purposive sample of 3000 professionals was

surveyed. Overall, results showed that the TPB model explained 53% of the variation in

behavioral intent, and all three constructs were significant predictors of intent. However,

significant differences existed among attendees and non-attendees with regard to the

model. Attitude was the only significant predictor of intent for non-attendees. It was









concluded that in order to boost participation of horticulture professionals at Extension

programs, a specific need exists for understanding and, possibly, changing the attitudes

and beliefs of non-attendees.















CHAPTER 1
INTRODUCTION


Interviews with various extension agents and specialists reveal that horticulture

industry professionals in Florida are targeted for extension programs, but attendance at

programs does not seem to represent this effort (L. Felter, T. Hurt, R. Schoellhorn,

personal communication, 2002). Agents are interested in learning what would motivate

more people to attend their programs. Therefore, the purpose of the current study was to

determine why horticulture industry professionals participate in Extension programs and

what would possibly motivate those who do not attend to become more active in these

programs.

Client satisfaction and program accountability is a driving force behind the

Extension service (Habeeb, Birkenholz, & Weston, 1987; Martin & Omer, 1987;

UF/IFAS Fact Digest, 2003). Therefore, a constant need for understanding the program

environment and target audience exists for Extension Agents (Martin & Omer, 1987).

Literature suggests that quality programming is important to maintaining and promoting

new audiences (Bowling, 2001; Israel, 2001; Norland, 1992; Summerhill & Taylor,

1992). Suggestions for improving program planning include gathering valuable

information about the target audiences and their needs, having the clientele participate in

the planning process, understanding the program life cycle and knowing when to end a

program, and properly evaluating the programs (Bowling, 2001; Israel, 2001; Norland,

1992; Summerhill & Taylor, 1992).









Anecdotal information reveals that agents are targeting large groups of growers

and nursery owners, but attendance at programs does not seem representative of this

effort (L. Felter, T. Hurt, R. Schoellhom, personal communication, 2002). Primary

marketing tactics used to disseminate information about programs are flyers, newsletter

announcements, emails and phone calls. Agents have expressed an interest in

understanding the basic question of what factors would help increase the number of

people at their programs. Even though these agents do many evaluations of their

programs, they indicate that the data collected from the evaluations fails to answer that

question. One reason may be instrument design (Jacob & Ferrer, 2000)). Many program

evaluations indicate likes and dislikes of attendees, such as the delivery method,

presenter, or location, but fail to discover a deeper understanding of what motivated the

grower to actually attend (Jacob & Ferrer, 2000).

Cooperative State Research, Education, and Extension Service

The three main objectives of the U. S. Cooperative State Research, Education and

Extension Service are to offer the information gathered at the land-grant universities;

encourage the adoption of new techniques and ideas; and use the educational process to

improve lives of clientele. In essence, the motto encompasses all that the Extension

service does: "Help people help themselves" (Habeeb, Birkenholz, & Weston, 1987; N.

Place, personal communication, 2001).

The Florida Cooperative Extension Service (FCES) is one of three branches in the

University of Florida's Institute of Food and Agricultural Sciences (UF/IFAS), which

was established in April 1964 when The University of Florida's College of Agriculture,

School of Forestry, Agriculture programs Experiment Stations and the Cooperative









Extension Service were combined. FCES is a partnership between UF/IFAS, the United

States Department of Agriculture, and county governments in Florida. Each of Florida's

67 counties is home to an Extension office and many agents. In addition, IFAS

incorporates 17 on-campus academic departments, 14 Research and Education Centers

(REC), 7 research and demonstration sites and 5 locations with Degree Program

Partnerships.

The Extension service utilizes three conceptual models when delivering

educational information. Agents attempt to balance technology transfer, problem

solving, and knowledge change when developing and delivering educational programs.

The goal of these programs is to elicit a behavioral change in the target group (Habeeb,

Birkenholz, & Weston, 1987). Therefore, Extension agents are continually searching for

the most effective way to meet the needs of their audience (Martin & Omer, 1987). Many

different types and sizes of Extension programs exist in the various areas of agriculture,

such as pest management, water conservation, horticulture, forestry, child development,

business, marketing and many more. Delivery methods range from workshops and

demonstrations to one-on-one sessions and web-based activities. Program development

is defined as the activities involved in building, creating, planning or developing an

educational program (Taylor, 1994). Furthermore, the Extension service has a variety of

categories for their programs, including routine program, maintenance program, impact

program, and major program designations.

Extension program development is challenging to the agent and specialists

involved, requiring large amounts of time and personal commitment that directly affects

the success or failure of their programs (Israel, 2001; Place, 2001). Research has









indicated that the extension service is a major supplier of farmer education about new

technology and farming practices (Ford, 1995). Many studies have been conducted that

explain the importance of the Extension service to its clientele. The audience of each of

these programs range from the general public to specialized industry professionals such

as teachers, farmers, and business owners. Most of the respondents in these studies are

satisfied with the services provided and state that the knowledge gained from meetings,

workshops, phone calls, etc., are important to the success of their businesses (Alston &

Reding, 1998; Ford, 1995; Habeeb, Birkenholz, & Weston, 1987; Martin & Omer, 1987).

Many dollars are spent each year on producing extension programs. The total

national CREES budget for 2003 is over $1 billion (USDA, 2003). In 2002, local

finances to fund Extension in Florida amounted to $29.2 million. Therefore, suggestions

have been made to the Extension service regarding better planning techniques that could

increase participation (Alston & Reding, 1998; Bruening, Radhakrislma, & Rollins, 1992;

Martin & Omer, 1987). Identifying the target audience is a common theme throughout

the literature (Alston & Reding, 1998; Bruening, Radhakrislma, & Rollins, 1992;

Habeeb, Birkenholz, & Weston, 1987; Martin & Omer, 1987; Schmitt, Durgan, &

Iverson, 2000). Agents should understand the characteristics of their audience and focus

on specific needs and expectations as they relate to the real problems of the participants

(Alston & Reding, 1998; Place, 2001; Schmitt, Durgan, & Iverson, 2000). Therefore,

understanding who participates and why are major factors that need to be addressed when

planning educational programs (Alston & Reding, 1998; Bruening, Radhakrislma, &

Rollins, 1992; Martin & Omer, 1987).









Environmental Horticulture in Florida

The Horticulture Industry in Florida is growing. The entire nursery and landscape

industry was worth about $8.5 billion in 2001. This figure has almost doubled since 1997

(DeSousa, 2002). The 2000 figures provided by FNGA indicate that the value added to

the economy was $4.38 billion. Also, the industry provided employment for

approximately 170,000 people, and paid total wages and salaries of $2.91 billion.

Information provided by the Florida Agriculture Statistical Service (2002)

suggests that ornamental production, which includes cut flowers, potted plants, hanging

baskets, potted foliage, cut foliage, bedding and garden plants, and woody ornamentals is

a large business in Florida. The state is ranked second to California. However, Florida is

leading the country in wholesale sales of potted foliage for use indoors and in hanging

baskets. Sales for this particular industry were $361.2 million in 2001. Lake, Orange

and Seminole counties alone accounted for 35% of these sales (FASS, 2002).

According to this information, the industry is economically important to Florida.

Of all the agriculture commodities in the state, the nursery industry is the "single largest

dollar producer" (DeSousa, 2002). Over $1.5 billion is contributed to Hillsborough

County alone, which is equivalent to the revenues of the Port of Tampa or hosting a

Super Bowl every weekend (DeSousa, 2002).

The industry is highly aware of issues concerning pest management, labor

relations, technology advances and various other business related items. The people

involved in this industry are a major contribution to its success. Therefore, it can be

argued that the extension service, through its commitment to sharing resources and

knowledge, should be a common link between the issues and the people.









Purpose and Objectives

The main goal of the Extension service is to generate information through

research and education, and ultimately pass this information on to the public. Agriculture

Extension programs have been developed to supply hands-on knowledge that consumers

can use immediately (Habeeb, Birkenholz, & Weston, 1987). Developing and delivering

these Extension programs is challenging for agents and usually requires immense

amounts of time and resources (Place, 2001). It has been established that effective

program planning in the Extension service begins and ends with clientele satisfaction.

Therefore, identifying target audiences and understanding their needs are essential to

planning and maintaining a successful program.

Therefore, the purpose of the current research was to determine why horticulture

industry professionals participate in Extension programs and what would motivate those

who do not attend to become more active

Based on the above, the objectives of the study are as follows.

* To describe Florida commercial nursery professionals in terms of demographics and
perceptions toward the Florida Cooperative Extension Service and its programming.

* Utilizing the Theory of Planned Behavior framework, determine how differences in
attitudes, subjective norms and perceived behavioral controls toward extension
programming affect intent to participate.


Past research studies utilizing the Theory of Planned Behavior model have

concluded that attitude and PBC correlate most strongly with behavioral intent, and

subjective norm was the weakest predictor of intent (Ajzen, 1988; Beedell & Rehman,

2000; Eagly & Chaiken, 1993; Pouta & Rekola, 2001). Therefore, the study was

designed to test the following null hypotheses.









HI: No significant difference exists between attendees and non-attendees regarding

possible motivational factors.

H2: No significant difference exists for attendees and non-attendees regarding perceived

level of knowledge about the Florida Cooperative Extension Service.

H3: No significant difference exists for attendees and non-attendees regarding perceived

level of knowledge about Institute of Food and Agricultural Sciences.

H4a: No relationship exists between behavioral intention of horticulture professionals to

attend Extension programs and the three determinant variables: attitude, subjective norm

and perceived behavioral control.

H4b: No relationship exists between behavioral intention of horticulture professionals to

attend Extension programs and the three determinant variables: attitude, subjective norm

and perceived behavioral, controlling for attendees and non-attendees.

Theoretical Framework

One theoretical framework that has been used to look at the constructs of attitude,

subjective norms and perceived behavioral controls is Icek Ajzen's Theory of Planned

Behavior (TPB, see figure B-l). Developed in the late 1980s, the theory is an extension

of Ajzen's Theory of Reasoned Action (Fishbein and Ajzen, 1975). Intention to perform

a particular behavior is the central factor of the theory (Ajzen, 1988; Eagly & Chaiken,

1993). The three independent determinants of intentions developed by Ajzen are attitude

toward the behavior, subjective norms, and perceived behavioral control (PBC).

According to Ajzen (2001), three sets of salient beliefs guide human behavior and

create the determinants mentioned above. In the model, attitude refers to the individual's

positive or negative evaluation of performing a behavior, and is determined by beliefs









relating to the behavior (behavioral beliefs) and the evaluation of performing the behavior

(outcome evaluations).

Subjective norms are the individual's perceptions of social pressures that exist

pertaining to performance of the behavior (Ajzen, 1988; Eagly & Chaiken, 1993). This

concept is comprised of beliefs about social expectations (normative beliefs) and the need

to adhere to those expectations (motivation to comply).

Perceived behavioral control is related to an individual's perception of how

difficult the task will be to perform. According to Ajzen, PBC includes past experience

and anticipated obstacles. PBC is based on beliefs about factors that are for or against

performing the behavior and the perceived power of those factors (control belief strength

and control belief power).

Generally, the intention to perform a behavior is strong when performance of a

particular behavior elicits a favorable attitude from the individual, the surrounding social

environment is conducive to the behavior, and the individual feels confident of their

ability to perform the behavior (Ajzen, 1988; Eagly & Chaiken, 1993).

Another theory pertaining to adult participation in extension programs is the

theory of adult learning or andragogy (Knowles, 1990). This theory is has six main

assumptions regarding adult education. Knowles (1990) states that adults must have an

understanding of why the new information is important and how it will affect them. Self-

concept is also a major factor for adults when they are approached with possible learning

situations. Past learning experiences such as school create anxiety in the adult and may

directly affect their desire to continue with the educational process (Knowles, 1990;

Rogers, 2001). The level of experience an adult has pertaining to the educational









program also influences the success of the adult. This allows for adult educational

sessions to be enriched with a more diverse group of people with different backgrounds

and experience levels (Knowles, 1990). This factor must be taken into consideration

because if past experience of the learner is not given due justice, then the educator risks

insulting the self-identity of the adult learner (Knowles, 1990).

The adult must also be ready to learn, meaning they are in need of the information

at that point in time (Knowles, 1990). For example, no need exists for adults to attend an

information session on greenhouse irrigation if they have no intention of building a

greenhouse. When they make the decisions to build, then irrigation will become more

important to them. This factor is similar to Knowles' (1990) orientation to learn, which

states that adults need learning situations to be related to realistic situations. Adults want

to be able to apply what they learn to something tangible in their lives.

Finally, the last assumption of Knowles' andragogy theory is motivation. Both

extrinsic and intrinsic motivation exists within adults, and Knowles (1990) states that

intrinsic is the most important. Intrinsic motivation centers on the internal well-being of

the individual and can serve to influence the participation in adult learning activities more

than extrinsic motivational factors such as increased salary or bonus points (Knowles,

1990; Rogers, 2001).

Increasing evidence exists that the theory of adult learning is serving as a

foundation for adult educators when producing programs and is changing the

organization of these programs (Knowles, 1990).

An extensive amount of literature is available regarding the Theory of Planned

Behavior (TPB) and its use in predicting behavior. Studies have been conducted using









the TPB in areas such as health (Sparks & Sheperd, 1992), leisure activities (Ajzen &

Driver, 1992), education (Ingram, Cope, Harju, & Wuensch, 2000), and agriculture

(Beedell & Rehman, 2000). As a consequence of the theory's extensive use, several meta-

analyses have been performed to determine the validity of the theory and its constructs.

For example, a 1998 study by Sutton sought to evaluate the effectiveness of the

TRA/TPB models. He uses a series of other meta-analyses to gather data about the

predictive power of the models regarding intention and behavior. In the study, he also

made a distinction between prediction and explanation. Explanation is the process of

identifying and specifying intention or behavior determinants. Models regarding

explanation are causal in nature and can be represented graphically. For this reason,

Sutton states that both TRA and TPB models are causal in nature.

However, prediction does not require explanations. This means that if the exact

reason for a behavior or process is not completely understood, a prediction can still be

made. According to Sutton, targeted interventions are easer to make if a prediction is

available. However, he stressed that understanding the reasoning behind an action is

much more useful.

Sutton's conclusions, based on the findings of the research, indicated that the

models explained between 40% and 50% of the variance in intention, and between 19%

and 38% of the variance in behavior in the studies he analyzed. He concluded that the

models' performance depended on the comparison standard and he suggested nine

reasons for poor predictions. These may be regarded as limitations in some research

studies. These possible limitations were: (1) intentions may change, (2) intentions ma be

provisional, (3) violation of the principal of compatibility, (4) violation of scale









correspondence, (5) unequal number of response categories for intentions and behavior,

(6) random measurement error, (7) Restrictions of range or variance, (8) marginal

distributions do not match, (9) intention not sufficient causes of behavior. Finally, Sutton

recommended some strategies for further research using the models based on the nine

reasons. Some suggestions were to include the role of memory, situational factors, and

past behavior.

A 2001 study of the efficacy of the TPB by Armitage and Conner used a

"quantitative integration and review" of 161 published journal articles and book chapters

utilizing the theory. Major findings include support for PBC as a determinant for

intention. This analysis concluded that the correlation of PBC and intention accounted for

27% of the variance in predicting behavior. PBC was added to the original model and

many studies have been conducted regarding its usefulness. Not only is PBC used to

predict intention, but it also has a direct link with prediction of behavior (Ajzen, 1988;

Eagly & Chaiken, 1993). It is important to remember that PBC refers to perceived

control, not actual control. Actual control takes into account actual factors of available

resources and opportunity, whereas perceived control is only the perception of ability to

perform a behavior (Ajzen, 1988; Eagly & Chaiken, 1993).

The analysis found supporting evidence for the use of attitude and subjective norms in the

models as well. However, subjective norm was determined to be the weakest predictor of

intention. Other literature suggests the same finding (Pouta & Rekola, 2001; Sparks &

Shepherd, 1992). Armitage and Connor offer the suggestion that measurement error was

the cause of the weak predictive power of subjective norms. Use of "multi-item" scales

verses "single-item" scales could be more reliable for measuring this construct. Overall,






12


the model was successful for predicting intention and behavior. The analysis also

supported Ajzen's theory that PBC independently contributes to the prediction of

intention and behavior.















CHAPTER 2
LITERATURE REVIEW

A broad base of literature is available regarding the Cooperative Extension

Service, the Theory of Planned Behavior, and adult participation in educational programs.

This review is organized conceptually based on these factors.

First, literature pertaining to Extension participation studies will be presented.

These are articles that attempt to explain why adults may or may not participate in

educational programs. They offer suggestions to professionals in the industry about

successful marketing and retention of clientele. This information also suggests reasons

for effective or non-effective Extension programs and indicates clientele perceptions of

the extension service.

Last, a review of agriculturally-based items that specifically utilize the TPB. This

is important to understand the success of the theory when predicting farmers' behavior.

Norland (1992) synthesized information from various sources and a 1987 study of

Ohio Cooperative Extension Service clientele. She sought to answer some of the

questions that plague Extension personnel on a daily basis. Why do adults participate?

What barriers exist to participating? Why do some adults drop out of programs or stop

attending? She cited Johnstone and Riveria (1965) when referring to situational barriers,

institutional barriers, sociodemographic barriers, and dispositional factors that describe

adult participation. Norland cited a 1987 Ohio study as her main source of information

and made conclusions based on the results. The survey studied Extension clientele who









had previously been involved in Extension programs. Questionnaires were sent to 599

individuals with a final response number of 276. They did a principal-component factor

analysis of the results and discovered five main factors related to participation: low

anticipated difficulties with arrangements, high commitment to Extension organization,

anticipated positive social involvement, anticipated high quality of information, and

possession of high internal motivation to learn.

The implications of the study were that people participate in Extension programs

based on what they know about extension and what learning opportunity is available for

them from the program. Therefore, the image of Extension as perceived by potential or

existing clients is important and can be used as a marketing tool for recruitment.

Opportunity for social interaction among clientele and convenience of the programs were

also major factors of participation.

Dollisso and Martin (1999) determined that young farmers are both intrinsically

and extrinsically motivated to participate in educational programs. They mailed a

questionnaire to 148 members of the Iowa Young Farmers Educational Association

(IYFEA) to determine their perceptions toward learning, preferred learning methods,

participation motivators, and barriers of participation. Major findings focused on the idea

that adults desire a sense of choice. The young farmers preferred hands-on activities and

individual projects. Economic sustainability was a motivator for most farmers to

participate. The study indicated that farmers' participation might increase as a result of

their inclusion in the planning process. The authors inferred that researchers and teachers

could use this information to better prepare programs for their audiences. The authors

recommended that program planners focus on profitability and new technology when









targeting this audience. Current information and practicality of the subject matter were

also important tips for planners. They also recommended that larger populations,

including non-farmers and agribusinesses, be studied using various methods for

comparability and reliability purposes.

A previous study of IYFEA by Martin and Omer (1987) sought to determine their

use of various agriculture agencies, especially the agriculture extension service. Their

main purpose was to discover awareness and participation factors. They mailed surveys

to approximately 75 people, and had a final response rate of 68% (51 respondents). The

extension service awareness and satisfaction levels were high among the young farmers.

The indicated an interest in programs that focused on marketing, record keeping, and

management techniques.

The authors determined that understanding the characteristics of participation and

profiles of the audience were important factors in program planning. They also concluded

that involvement of the young farmers in the planning process was needed. The process

would begin with the clientele input, guiding the direction of the program to meet their

needs.

Alson and Reding (1998) conducted a study to determine what factors were

associated with adoption and educational techniques of the integrated pest management

program in Utah. Two hundred sixty two fruit tree growers and 1,700 field crop

producers in Utah received questionnaires. Results indicated that both groups preferred

the Extension service (agent and/or office) for information regarding pest management

practices. Other growers and trained employees were also important sources of

information. The publications and workshops provided by the extension service were the









preferred information sources. Computer access was on the list of least preferred sources

for pest management facts. Growers whose major source of income was their farm placed

more emphasis on the use of Extension services and recommendations than those whose

farm was not their primary employment. The conclusion was that in order to reach these

grower audiences with information about IPM programs, grower backgrounds,

perceptions, practices and preferences should be given extreme consideration.

Ford (1995) assessed the educational priorities of small farmers in West

Tennessee. Specifically, the study was designed to determine the preferred delivery

methods, programs, and program activities of their Extension service. Descriptive

research methods were used to survey a sample of 150 small farmers who made less than

$20,000 in gross income from farm sales. Farmers rated their feelings on a one-99 scale,

with individual values given to no importance, little importance, etc. Farm visits were

used to gather data because extension agents in the area indicated that response rates with

mailed questionnaires were historically very low with the small farmers. A final response

rate of 72% was achieved with this method.

The author discovered that crop marketing, soil conservation, and pesticide use

were areas that needed more emphasis from educational programs. The small farmer also

expressed an interest in the use of extension agents for one-on-one help with solving

various problems. Recommendations were made regarding the development of programs

that would focus on technical and business related skills, especially marketing.

A 1987 study by Habeeb, Birkenholz, and Weston sought to determine the

perceptions of county extension officers and extension clientele toward the Missouri

Extension service. Four hundred farmers with some extension background and









prominence in the community, and 150 extension officers were stratified by counties and

then selected randomly. A 43-item questionnaire was used to determine their level of

extension knowledge and opinions. Significant differences were found between officers

and clientele perceptions of extension information and extension specialists. Amount of

extension contact, attendance of extension meetings, and innovativeness level of the

respondent explained some of the variability associated with the differing opinions.

Overall, extension information, specialists, methods, and programs were

considered satisfactory. The higher the level of contact with the extension service and

agents, the higher the satisfaction ratings of the extension service tended to be. The

recommendations of the authors included planning and conducting meetings for a larger

target audience, and increasing the amount of clientele/agent contact.

With respect to adoption behavior of extension clientele and the general public,

Pouta and Rekola (2001) tested the TPB model for predicting the "willingness to pay

[WTP] for abatement of forest regeneration". They used survey research methods to

gather data for the contingent valuation (CV) study of 600 people in Loppi, Finland.

Two rounds of surveys were administered-one concerned forest recreation and

respondent background, and the other focused on WTP measures and regeneration

attitudes.

One important aspect of the study was that it focused on predicting WTP

responses using the attitudes, subjective norms, and perceived behavioral controls of the

respondents. Two attitudes were used-attitude toward forest regeneration and attitude

toward supporting the abatement policy. The results indicated that the use of both

attitude variables explained WTP significantly. PBC contributed significantly to the









prediction of WTP, suggesting that respondents fully understood their personal

limitations. Subjective norms were not significant.

Beedell and Rehman (2000) studied farmers' conservation behavior by using the

TPB model. One hundred twenty five farmers in Bedforshire, England participated in the

study and were divided into three groups: farmer, FWAG farmers, and conservationist

(FWAG: Farming and Wildlife Awareness Group). The authors added moral obligation

to the model because respondents indicated an obligation to the land and this obligation

affects business decisions.

Six behaviors were studied: hedge management, field margin management, tree

planting management, hedge removal, hedge planting, and pesticide use. FWAG farmers

viewed these behaviors more importantly than farmers. Hedge removal was not regarded

as good because it is an "anti-conservation" practice. FWAG farmers felt a stronger

moral obligation than farmers, suggesting that farmers have an internal obligation to the

land and the FWAG farmers feel both social pressures and internal motivation to

conserve. The two groups also behaved differently regarding managing field margins.

However, the authors explained that the definition of a "good" field margin might differ

among groups. They suggest further research in that particular area.

Results of the study showed that FWAG farmers were more aware of

conservation concerns than non-member farmers. FWAG farmers were more concerned

with environment issues than business issues regarding farming behavior. From these

results, the authors concluded that the TPB model was an acceptable tool for predicting

farmer behaviors.









Based on the review of literature, understanding audience profiles and

characteristics are an important aspect to program planning in Extension. Clientele are

interested in learning about practical, current information that is relevant to their interests

and will attend programs based on this information. Regarding the prediction of

particular behaviors, the TPB model has been successful in many different fields of

study, including agriculture. Therefore, utilizing the TPB model to predict attendance at

horticulture-based Extension programs is a logical step toward improved program

planning.















CHAPTER 3
METHODOLOGY

This study utilized the Theory of Planned Behavior as a model for determining

the intention of nursery industry professionals in Florida to attend Florida Cooperative

Extension Service programs. This behavior is under investigation for several reasons.

Mainly, Extension agents in Florida have expressed a need to understand what motivates

nursery professionals to attend programs that are targeted specifically for them. The TPB

was used because it has been widely accepted as a framework for predicting and

attempting to understanding specific behaviors.

Subjects

The population for the current study was horticulture industry professionals in

Florida, which included the wholesale, retail, landscape and allied trade industries. To

conduct the study, two mailing lists were obtained and combined. One was from the

Florida Nurserymen and Growers Association (N=2700), and the other was from a

Commercial Horticulture Extension agent in Central Florida (N=300). Because the entire

group of professionals, (N=3000), was utilized, it is known as a purposive sample.

Research Design

The basic design of this study is known as ex post facto research. In Latin, ex post

facto means "after the fact" and is conducted once the variable of interest has already

been altered or changed in some fashion (Ary, Jacobs, and Razavieh, 2002).The purpose

of this method is to determine cause and effect relationships among independent variable,









which is why this design is sometimes referred to as causal comparative research. One of

the main reasons this method is used is when the research does not allow for

manipulation of variables, as is the case with a true experiment.

Instead of exposing a group of people to different treatments, ex post facto

research begins with the group having already been exposed and attempts to determine

what differences exist and why. In the present study, nursery industry professionals were

examined to determine what factors strongly influence their attendance at horticulture

based Extension programs.

Pilot Study

The Theory of Planned Behavior model is based on beliefs about a particular

behavior. Behavioral beliefs lead to the formation of attitudes. Normative beliefs lead to

an understanding of the perceived level of social pressure that exists about a behavior,

and control beliefs about the behavior lead to overall perceived behavioral control. These

beliefs can be measure directly (direct measures) and indirectly (belief-based measures).

In order to identify the salient beliefs of horticulture industry professionals, a

series of pilot studies was conducted at various Extension programs in Central Florida.

Participants were asked a range of closed- and open-ended questions that addressed

various aspects of the Extension programs they attend or would like to attend. A list of

the most common beliefs were constructed and used to create the final questionnaire. A

panel of 10 experts examined and approved the final questionnaire.

Procedure

In order to attempt to achieve a good response rate with a high-quality mailed

survey, Dillman (2000) suggests the Total Design Method (TDM). Basically, the TDM









focuses on creating a user friendly survey environment that "increases perceived rewards

for responding, decreases perceived costs and promotes trust in beneficial outcomes from

the survey (Dillman, 2000)." It is based on multiple personalized contacts with the

participants, also known as waves. This method has been proven to increase response

rates when compared to traditional mail surveys (Dillman, 2000). The five main

elements of the TDM include a respondent-friendly questionnaire, up to five contacts

with the participants, stamped return envelopes, personalized correspondence and a

financial incentive (Dillman, 2000).

The current study involved sending a packet containing a cover letter, a 62-item

questionnaire and a business reply envelope to the nursery professionals in Florida

(N=3000). The second wave was a reminder post card sent to all participants. No

financial incentive was offered.

On November 8, 2002, the packets were mailed to all 3000 professionals. A

reminder post-card was mailed six weeks later. By February 12, 411 surveys had been

returned for a response rate of 14% (N=411). The majority of those responses, 75%

(N=308), had been returned by the end of November.

Considering that the response rate was low, a comparison of early to late

respondents was conducted for validity reasons. According to Ary, Jacobs and Razavieh

(2002) nonrespondents and late respondents are usually similar. Therefore, the two

respondent groups were created. The 411 respondents were divided into four quartiles for

the purpose of comparing the first quartile (early respondents) to the fourth quartile (late

respondents). The two groups were compared via an independent sample t-test based on

the following variables: attitude, subjective norms, perceived behavioral control and









intent. With an alpha level of .05, none of the differences were significant, and it was

concluded that late respondents were similar to the nonrespondents. Table 3-1 displays

the results.

Table 3-1: Independent Samples Test for Early Respondents vs Late Respondents
Variable N Mean t
Attitude
Early respondents 98 4.91 1.21*
Late respondents 109 4.78
Subjective Norm
Early respondents 96 3.09 .910*
Late respondents 107 2.10
PBC
Early respondents 96 4.30 .389*
Late respondents 108 4.27
Intent
Early respondents 94 4.18 .951*
Late respondents 103 4.18
p > .05

Instrumentation

The Theory of Planned Behavior served as the theoretical framework of this study

as well as supplying the basic model for the questionnaire and interpretation of the

results. The 62-item instrument utilized in this research elicited responses, directly and

indirectly, based on the constructs of the model, as well as several factors outside the

model used for profiling the industry. Thirty-five questions were directly based on the

theory and were used to create indices of each construct. Answers were given using a 5-

point Likert scale where responses ranged from 1=Strongly Agree to 5=Strongly

Disagree.

Attitude was measured directly using a 7-point semantic differential scale

comprised of six items. Table 3-2 provides an example. Two attitudinal variables were









measured: the attitude toward attending extension programs related to the horticulture

industry, and the attitude toward the Florida Cooperative Extension Service.

Table 3-2: Attitude Scale Item (Direct Measure)
My attitude toward attending extension programs is

Favorable: :-Unfavorable

Useful: ::Useless

Good: :Bad

Pleasant: : : : : : : Unpleasant

Reliable: :Unreliable

Valuable: :Worthless



Attitude was also measured indirectly based on the behavioral beliefs and

outcome evaluations of the respondents (belief-based measures). According to Ajzen

(2001), these beliefs and evaluations impart important information regarding an

individual's decision to behave in a particular manner. Seven behavioral belief questions

and five outcome evaluation questions were constructed. Table 3-3 presents an example.

Table 3-3: Behavioral Belief Scale Items
Extension programs offer up-to-date information on the horticulture industry.1
Strongly Agree 1 2 3 4 5 Strongly Disagree

Keeping up-to-date on the horticulture industry is important to me.2
Strongly Agree 1 2 3 4 5 Strongly Disagree
'Behavioral Belief
20utcome Evaluation

To construct the belief-based measures index for attitude, the beliefs were

multiplied by the outcomes as shown in the following equation.

AB C b, e,









Subjective norms were also measured directly and indirectly. The questions were

used to determine the respondent's perception of social pressure regarding attendance at

Extension programs. Two questions elicited the direct measure for subjective norms, and

eight normative belief- and motivation to comply-type questions were used to create an

index for indirect measuring. Example questions for subjective norm are in Table 3-4

and, the equation for creating the index based on multiplying normative beliefs strengths

and motivation is:

SN Y n, m,

Table 3-4: Subjective Norm Scale Items
It is expected of me to attend as many extension programs as I can that are about
horticulture issues.1
Strongly Agree 1 2 3 4 5 Strongly Disagree

The opinions of horticulture professionals in my industry are important to me.2
Strongly Agree 1 2 3 4 5 Strongly Disagree

Generally speaking, I do what other horticulture industry professionals think I should do
regarding attendance at extension programs.3
Strongly Agree 1 2 3 4 5 Strongly Disagree
'Direct measure
2Normative belief
3Motivation to comply

Perceived behavioral control was also measured directly and indirectly. Seven

questions were designed to create the index for perceived behavioral control, measuring

the respondent's evaluation of how easy or difficulty it would be to attend extension

programs. Example questions for PBC are in Table 3-5, and the equation for constructing

the PBC index is:


PBC c,p,









Table 3-5: PBC Scale Items
It is mostly up to me whether or not I attend extension programs relating to the
horticulture industry.1
Strongly Agree 1 2 3 4 5 Strongly Disagree

If I wanted to, I could attend an extension program relating to the horticulture industry.2
Strongly Agree 1 2 3 4 5 Strongly Disagree

I feel in complete control over whether I attend an extension program relating to the
horticulture industry.3
Strongly Agree 1 2 3 4 5 Strongly Disagree
'Direct measure
2Control Belief Strength
3Control Belief Power

Also on the survey were several questions designed to determine what would

motivate horticulture industry professionals to attend more Extension programs. Two

open-ended questions and five questions using the Likert scale were created for this

purpose. An example of each of these questions is in Table 3-6.

Table 3-6: Motivation
If I knew that I could learn about employee management techniques, I would be more
likely to attend extension programs.
Strongly Agree 1 2 3 4 5 Strongly Disagree

The biggest problems facing the horticulture industry are...
'open-ended question

Two questions asked the respondents' perceived level of knowledge about the

Florida Cooperative Extension Service and the Institute of Food and Agricultural

Sciences (IFAS) and are displayed in Table 3-7.

Table 3-7: Perceived Level of Knowledge
My knowledge of the Florida Cooperative Extension service is:

Extremely High: __:_::Extremely Low

My knowledge of the Institute of Food and Agricultural Sciences (IFAS) is:

Extremely High: __:_::Extremely Low









Finally, behavioral intent was measured directly via four questions on the

instrument. Ajzen (1988) states that behavioral intention of an individual is comprised of

the motivational factors involved in making the decision to engage in the behavior.

Basically, intention is an indicator of the individuals' willingness to attempt the behavior.

If the individuals state their intent to perform the behavior, they can be relied upon to do

so (Ajzen, 1988). Therefore, we should be able to accurately predict behavior by

determining intentions. Two examples are shown in Table 3-8.

Table 3-8: Behavioral Intent Scale Items
I intend to attend extension programs relating to the horticultural industry within the next
year.
Strongly Agree 1 2 3 4 5 Strongly Disagree

I will try to attend extension programs relating to the horticultural industry within the
next year.
Strongly Agree 1 2 3 4 5 Strongly Disagree


Data Interpretation

The questionnaire was initially written with higher numbers representing lower

evaluations of the questions (i.e. Strongly Agree=1 to Strongly Disagree=5). Therefore,

the data was recorded in the Statistical Package for Social Science (SPSS) in order to have

higher numbers represent higher evaluations of the items (i.e. Strongly Agree=5 to

Strongly Disagree=l).

Reliability

To measure the internal consistency of the items prior to creating the indices for

each construct, Cronbach alpha coefficients were determined. Cronbach alpha is used

when items are scaled and the scores can be a range of values, as is the case with Likert

scales and semantic differential scales (Ary, Jacobs and Razavieh, 2002). Alphas in the









range of .50 to .60 are acceptable when making decisions regarding groups of people for

research purposes (Ary, Jacobs and Razavieh, 2002). Cronbach alphas are listed in

Tables 3-9 through 3-18


Table 3-9: Cronbach Alpha Reliability Coefficients: Behavioral Beliefs
Item Mean Standard Corrected item-
Deviation total correlation
Belief 1 4.27 .71 .72
Belief2 4.04 .83 .64
Belief 3 4.31 .87 .54
Belief4 4.28 .75 .65
Belief 5 3.51 .90 .45
Belief 6 4.43 .69 .52
Belief 7 4.32 .88 .51
Behavioral Belief Scale Alpha= .83


Table 3-10: Cronbach
Item

OE
OE 2
OE 3
OE 4
OE 5


Table 3-11: Cronbach
Item

Attitude 1
Attitude 2
Attitude 3
Attitude 4
Attitude 5
Attitude 6

*measured on 7-point scale

Table 3-12: Cronbach
Item

Norm 1
Norm 2
Norm 3


Alpha if item
deleted
.78
.79
.81
.80
.82
.80
.81


Alpha Reliability Coefficients: Outcome Evaluations
Mean Standard Corrected item- Alpha if item
Deviation total correlation deleted
4.47 .59 .65 .68
4.54 .56 .66 .67
4.42 .74 .65 .66
4.27 .77 .52 .70
3.88 1.11 .33 .83
Outcome Evaluation Scale Alpha = .75

Alpha Reliability Coefficients: Attitude (direct measure)*
Mean Standard Corrected item- Alpha if item
Deviation total correlation deleted
6.22 1.12 .88 .95
6.17 1.10 .89 .95
6.24 1.00 .93 .95
6.10 1.10 .82 .96
6.08 1.12 .88 .96
6.10 1.15 .89 .95
Attitude Scale Alpha = .96


Alpha Reliability Coefficients: Normative Beliefs
Mean Standard Corrected item-
Deviation total correlation
3.45 .94 .59
3.28 1.01 .67


2.88


1.03


Alpha if item
deleted
.68
.57
.76









Table 3-12. Continued


Normative Belief Scale Alpha = .78


Table 3-13:
Item

MC 1
MC 2
MC 3


Table 3-14:
Item

SN 1
SN 2


Cronbach Alpha Reliability Coefficients: Motivation to Comply
Mean Standard Corrected item- A
Deviation total correlation
2.31 1.05 .73
2.40 1.02 .76
2.23 .99 .66
Motivation to Comply Scale Alpha = .85


lpha if item
deleted
.78
.74
.84


Cronbach Alpha Reliability Coefficients: Subjective Norm (direct measure)
Mean Standard Deviation Corrected item- Alpha if item
total correlation deleted


Subjective Norm Scale Alpha = .64


Table 3-15: Cronbach
Item

Strength 1
Strength 2


Alpha Reliability Coefficients: Control Belief Strength
Mean Standard Corrected item- Alpha if item
Deviation total correlation deleted
4.37 .71 .57
4.47 .68 .57
Control Belief Strength Scale Alpha = .72


Table 3-16: Cronbach Alpha Reliability Coefficients: Control Belief Power


Standard
Deviation


Corrected item-
total correlation


Alpha if item
deleted


4.29 .85 .56
4.35 .68 .56
Control Belief Power Scale Alpha =.71


Table 3-17: Cronbach
(direct measure)
Item


PBC 1
PBC 2


Alpha Reliability Coefficients: Perceived Behavioral Control


Mean


4.02
4.31


Standard Corrected item-
Deviation total correlation
1.08 .49
.73 .49
PBC Scale Alpha = .62


Table 3-18: Cronbach Alpha Reliability Coefficients: Intent


Standard Corrected item-
Deviation total correlation
.87 .78
.86 .82


Alpha if item
deleted
.82
.81


Item


Power 1
Power 2


Mean


Alpha if item
deleted


Item


Intent 1
Intent 2


Mean

4.26
4.21









Table 3-18. Continued
Intent 3 4.07 .96 .66 .88
Intent 4 4.27 .75 .70 .86
Behavioral Intent Scale Alpha = .88


An overall descriptive analysis revealed the means for each of the constructs

based on the averages of each of their respective measures. The results can be found in

Table 3-19.

Table 3-19: Descriptive Statistics: TPB Model Constructs
Variable N Mean SD
Attitude 402 4.85 .65
Subjective Norm 394 3.03 .71
PBC 395 4.30 .58
Intent 385 4.18 .72

In addition, the descriptive analysis of each of the TPB constructs was conducted

on attendees and non-attendees. Table 3-20 displays the results.

Table 3-20: Descriptive Statistics: TPB Model Constructs: Attendees/Non-attendees
Variable N Mean
Attitude
Attendee 321 4.97
Non-attendee 72 4.41
Subjective Norm
Attendee 320 3.10
Non-attendee 73 2.75
PBC
Attendee 320 4.37
Non-attendee 74 3.99
Intent
Attendee 312 4.30
Non-attendee 72 4.18

Pearson product moment correlations between each of the variables for the entire

sample indicated significant relationships with behavioral intention at the .05 alpha level.

In addition, significant relationships were observed among each of the variables. The

results can be found in Table 3-21.










Table 3-21: Pearson Correlations between the TPB Model Constructs
Variable 1 2 3 4
1. Attitude ---
2. Subjective Norm .463* ---
3. PBC .393* .150* ---
4. Intent .686* .403* .393* ---
*p< .01

Hypotheses

Based on the objectives of this study, the following hypotheses were developed.

HI: No significant difference exists between attendees and non-attendees regarding

possible motivational factors.

H2: No significant difference exists for attendees and non-attendees regarding perceived

level of knowledge about the Florida Cooperative Extension Service.

H3: No significant difference exists for attendees and non-attendees regarding perceived

level of knowledge about Institute of Food and Agricultural Sciences.

H4a: No relationship exists between behavioral intention of horticulture professionals to

attend Extension programs and the three determinant variables: attitude, subjective norm

and perceived behavioral control.

H4b: No relationship exists between behavioral intention of horticulture professionals to

attend Extension programs and the three determinant variables: attitude, subjective norm

and perceived behavioral, controlling for attendees and non-attendees.

Data Analysis

The following data analyses were conducted using SPSS.

* Frequencies and Cross-tabulations were used to gain an understanding of the
demographics of the respondents.

* Correlational analyses using the Pearson product moment correlation coefficient were
conducted to determine the strengths and directions of relationships between
variables.






32


* Multiple linear regression was used to examine the amount of variation in the
dependent variable that was explained by the independent variables.

* Analysis of variance was used to compare the differences in means of the
independent variables on the dependent variable.















CHAPTER 4
RESULTS

The purpose of this study was to determine what factors affected the behavioral

intent of a sample of horticulture industry professionals to participate in Extension

programs. The Theory of Planned Behavior was chosen as the theoretical framework and

basic model for this study because it has been shown to aid in the prediction and

understanding of how people behave (Ajzen, 1988). When applying the model to this

study, behavioral beliefs about Extension programs relating to the horticulture industry

create a particular attitude toward attending these programs. Normative beliefs regarding

the social pressure to attend these programs create an individual's subjective norm.

Control beliefs about the ability to attend these programs indicate the perceived

behavioral control of the individual (Ajzen, 1988). All of these variables combined were

utilized to provide an explanation of the intentions of a sample of horticulture industry

professionals to attend Extension programs targeted for them.

Descriptive Information

One of the main objectives of this research was to gather demographic profiling

information on the horticulture industry in Florida. The instrument contained 12

questions used for this purpose. As to demographics, the majority, 76% (N=313), of the

respondents were male and 19% (N= 79) were female. Regarding position of the

respondents in the business, 61% (N=254) were owners, 16% (N=66) were managers and

3% (N=11) said they were both. To assess possible differences between men and women,









a cross-tabulation was created and revealed that 68% (N=209) of the male respondents

were owners and 16% (N=51) were managers, while 50% (N=38) of the women

respondents were owners and 18% (N=14) were managers. Nineteen business positions

were stated other than the five offered on the survey. Answers included representatives

of the education field, parks and recreations department, as well as combinations of

positions such as owner/manager/sales or sales/support staff.

When respondents were asked if they attended Extension programs relating to the

horticulture industry, 78% (N= 321) answered yes and 21% (N=86) said no. Twenty-

nine percent (N=120) stated that they attended the programs themselves, 3% (N=15) sent

employees and 31% (N=130) stated that they attended the programs with their

employees. Forty-three percent (N=178) of the respondents were in wholesale

production and 25% (N=105) classified themselves in the landscape industry. Two-

percent (N=10) of the respondents said they were in allied trade, and 4% (N=18) stated

they had a retail nursery operation. Twenty-one other business categories were

represented ranging from golf courses to municipalities.

Overall, 44% (N=183) of the respondents had average annual sales over

$500,000, and 12% (N=50) had sales in the $250,000-$499,000 range. To determine if

differences existed between attendees and non-attendees regarding annual sales, a cross-

tabulation was created. Of those who attend, 58% (N=155) have average annual sales

over $500,000, while 36% (N=27) of those who do not attend have average annual sales

over $500,000. This cross-tabulation between attendees and non-attendees regarding

average annual sales also indicated that 85.3% (N=155) of the respondents indicating

sales above $500,000 attend programs, while 14.8% (N=27) do not. The respondent









group with the next highest level of attendance had average sales between $50,000 and

$149,999. Of this group, 80% (N=32) attended programs and 20% (N=8) did not.

Results are displayed in Table 4-1.

Table 4-1: Cross-tabulation: Attendance/Sales
$0 $20,000 $50,000 $150,000 $250-000 $500,000 +
$19,999 $49,999 $149,999 $249,999 $499,999

Attend
Yes 15 10 32 16 39 155
(62.5%) (52.6%) (80%) (59.3%) (78%) (85.2%)

No 9 9 8 11 11 27
(37.5%) (47.4%) (20%) (40.7%) (14.6%) (14.8%)

Total 24 19 40 27 50 182

When asked about production systems, container production was the primary

answer, 58% (N=239), and field production was the least chosen system, 38% (N=156).

To determine the differences among attendees and non-attendees, a cross-tabulation was

conducted. It revealed that, of those who attend, 55% (N=140) use greenhouses, 61%

(N= 159) use shadehouses, 52% (N=124) use field production and 69% (N=188) use

container production. Of those who do not attend programs, 46% (N=30) use

greenhouses, 46% (N=31) use shadehouses, 49% (N=32) use field production, and 66%

(N=50) use container production. Table 4-2 displays the results.

Table 4-2: Cross-tabulation: Attendance/Production System
Production System used Attend
Yes No
Container 188 (69%) 50 (66%)
Shadehouse 159 (61%) 31(46%)
Greenhouse 140 (55%) 30 (46%)
Field 124 (52%) 32 (49%)

Furthermore, cross-tabulations revealed that of the respondents who utilize

container production (N=238), 79% (N=188) attend programs and 21% (N=50) do not.









Of those respondents who stated they used greenhouses (N=170), 82% (N=140) attend

programs and 18% (N=30) do not. Eighty-three percent (N=159) of the 190 respondents

who utilize shadehouses attend programs, while 16% (N=31) do not. For the respondents

who use field production (N=156), 79% (N=124) attend programs and 21% (N=32) do

not. These results indicate that horticulture professionals who utilize greenhouse

production systems and container production systems might be a large target audience for

the Commercial Horticulture Extension Agents.

Another aspect of this study was to determine various motivating factors that

might influence the participation level of horticulture professionals at Extension

programs. Five questions were designed using a 5-item Likert scale ranging from

Strongly Disagree (1) to Strongly Agree (5). An example of one of the questions was "If I

could learn about business management techniques, I would be more likely to attend

Extension programs."

Overall, of the five questions, results indicated that learning about the programs at

least one month in advance would be a possible motivational factor (M=4.08). Another

important factor to respondents was learning about the latest pesticides, herbicides and

fungicides available on the market (M=4.09). Table 4-3 displays the results.

Table 4-3: Motivation to attend Extension programs
Question N Mean SD
Learn about latest pesticides, herbicides and fungicides 384 4.09 .85
Learn about programs at least one month in advance 389 4.08 .83
Learn about employee management techniques 379 3.75 .96
Learn about business management techniques 382 3.71 1
Receive CEUs 379 3.55 1.07

In addition to the general demographic information, descriptive statistics were

obtained for the direct and belief-based measures of attitude. These analyses were









conducted on attendees and non-attendees to further understand some of the differences

that exist among the two groups.

Attitude toward attending Extension programs was measured directly using a 7-

point semantic differential scale comprised of six items, with higher values representing

positive attitudes and lower values representing negative attitudes. Results indicated that

attendees had a higher mean attitude (M=6.31) than non-attendees (M=5.46). This

suggests that respondents who attend horticulture-based Extension programs had a more

positive attitude toward attending those programs than respondents who do not attend.

Results are shown in Table 4-4.

In addition to the direct measure of attitude toward attendance, the belief-based

measures were also analyzed. The behavioral beliefs of the sample of horticulture

professionals as well as their evaluation of those beliefs (outcome evaluations) were

measured using a 5-point Likert scale ranging from Strongly Agree (5) to Strongly

Disagree (1). For attendees, the mean for behavioral beliefs was 4.27, and the mean for

non-attendees was 3.73. The means for the outcome evaluations were also higher for

attendees (M=4.39) than for non-attendees (M=4.04). These results support the

conclusion that respondents who attend Extension programs have more positive beliefs

about Extension than non-attendees. Results are displayed in Table 4-4.

Table 4-4: Descriptive Statistics: Direct/Belief-Based Attitude Measures
Measure N Mean SD
Attitude (direct)*
Attend 313 6.31 .83
Not attend 73 5.46 1.45
Behavioral Beliefs
Attend 320 4.26 .47
Not attend 77 3.73 .74
Outcome Evaluations
Attend 319 4.39 .50









Table 4-4. Continued
Not attend 73 4.04 .67
*measured on a 7-point scale

Examples of the behavioral beliefs that were analyzed and their means for each

group (attendee/non-attendee) are displayed in Table 4-5. This analysis revealed that

attendees agreed with the following two statements more than non-attendees: (1)

Extension programs offer up-to-date information; (2) Extension programs offer an

opportunity to increase their knowledge of new products on the market more than non-

attendees. Attendees also strongly agreed that Extension programs offer an opportunity

to obtain CEUs. Furthermore, non-attendees agreed more than attendees with the

following two statements: (1) Horticulture professionals do not benefit from participating

in Extension programs; (2) Extension programs are not an effective way to spread

information to the horticulture industry.

Table 4-5: Descriptive Statistics: Belief-Based Attitude Measures: Attendees/Non-
attendees
Belief N Mean
Extension programs offer opportunity to obtain CEUs
Attendee 295 4.56
Non-attendee 67 3.94

Extension programs offer up-to-date information
Attendee 293 4.39
Non-attendee 70 3.75

Extension programs offer an opportunity to increase knowledge of latest
chemicals
Attendee 294 4.37
Non-attendee 67 3.83

Extension programs offer an opportunity to increase knowledge of products
on the market
Attendee 294 4.09
Non-attendee 69 3.73

Extension programs provide information about business management
techniques









Table 4-5. Continued
Attendee 291 3.49
Non-attendee 68 3.37

Horticulture professionals do not benefit from participating
Attendee 292 1.53
Non-attendee 67 2.36

Extension programs are not an effective way to spread information to the
horticulture industry
Attendee 294 1.55
Non-attendee 68 2.26

Testing the Hypotheses

The current study was designed to determine how the attitudes, subjective norms

and perceived behavioral control of horticulture industry professionals in Florida affect

their intent to attend Cooperative Extension Service programs. The TPB model

constructs as well as motivational factors and perceived knowledge were analyzed

separately for respondents who attend programs and for those who do not attend

programs.

Therefore, this section is organized in the following manner. To understand some

of the differences between attendees and non-attendees, the first three hypotheses

concerning motivation and knowledge were analyzed. Then, to determine the influence of

attitude, subjective norm and PBC on the behavioral intent of this sample of horticulture

professionals, the final two hypotheses were tested.

HI: No significant difference exists between attendees and non-attendees regarding

possible motivational factors.

To determine if a difference existed between attendees and non-attendees, an

independent samples t-test was conducted with regard the five motivational questions. At

the alpha level of .05, the means for all five questions differed significantly among the









two groups. The null hypothesis was rejected. The means for attendees were consistently

higher than the means of non-attendees. Learning about the latest pesticides, herbicides

and fungicides (chemicals) was the most important factor for respondents who attend

programs (Chemicals, M=4.19). The second factor that was important to attendees was

learning about the programs at least one month in advance (Time, M=4.18). For

respondents who do not attend programs, chemicals and time were also the factors with

the highest means. However, time had a slightly higher mean (Time, M=3.70) than

chemicals (Chemicals, M=3.68). For both attendees and non-attendees, the questing

regarding CEU availability received the lowest means (attendees, M=3.68; non-attendees,

M=3.00). These results indicate that chemical update programs are important to

horticulture professionals. Timely promotion of programs dealing with pesticides,

fungicides and herbicides might increase attendance levels at these programs. The results

can be found in Table 4-6.

Table 4-6: Independent Samples Test: Attendees/Non-attendees
Question N Mean SD t
Chemicals
Attend 311 4.19 .78 -4.71*
Not attend 72 3.68 .97
Time
Attend 314 4.18 .74 -4.51*
Not attend 74 3.70 1.05
Employee Mgmt
Attend 306 3.84 .90 -3.65*
Not attend 72 3.39 1.10
Business Mgmt
Attend 310 3.77 .96 -2.24*
Not attend 71 3.48 1.13
CEU
Attend 308 3.68 1.02 -4.93*
Not attend 70 3.00 1.11
*Significant at the 0.05 level









H2: No significant difference exists for attendees and non-attendees regarding

perceived level of knowledge about the Florida Cooperative Extension Service.

H3: No significant difference exists for attendees and non-attendees regarding

perceived level of knowledge about the Institute of Food and Agricultural Sciences.

Two questions on the survey were designed to gather information regarding the

perceived level of knowledge that respondents believe they have about the Florida

Cooperative Extension Service and the Institute of Food and Agricultural Sciences. The

7-item semantic differential scale ranged from extremely low (1) to extremely high (7).

Overall, the mean knowledge level for the Extension service was 5.08 with a standard

deviation of 1.5 (N=396). The mean level for IFAS was 4.40 with standard deviation of

1.8 (N=393).

To analyze these hypotheses, an examination of the differences between attendees

and non-attendees was conducted. An independent samples t-test revealed a significant

difference in means between attendees and non attendees with regard to the perceived

level of knowledge about the Extension service (t= -8.86; p=.000) and perceived level of

knowledge of IFAS (t= -5.63; p=.000). The null hypotheses were rejected. Those who

attended Extension programs had higher perceived knowledge levels about both the

Extension service and IFAS than those who did not attend programs. This indicates that

Extension programs might be successful at relaying information about other services

provided by the Cooperative Extension Service, but may not be helping horticulture

professionals make the connection between Extension and IFAS. The results are

displayed in Table 4-7.









Table 4-7: Independent Samples Test: Knowledge/Attendance
Variable N Mean SD t
Knowledge of Extension
Attend 316 5.40 1.2 8.86*
Not attend 79 3.87 1.9
Knowledge of IFAS
Attend 315 4.66 1.7 5.63*
Not attend 77 3.39 1.9
*Significant at the 0.05 level

H4a: No relationship exists between behavioral intention of horticulture

professionals to attend Extension programs and the three determinant variables:

attitude, subjective norm and perceived behavioral control.

To test the hypothesis, a multiple linear regression analysis using the TPB

variables in the enter method was performed. The regression was significant (F=145.57;

p<.001), and the constructs of the TPB model accounted for 53% of the variance in the

intent of horticulture professionals to attend programs. For all respondents, attitude,

subjective norm and PBC were significant predictors of behavioral intent (p < .05).

Attitude toward attending programs exerted the strongest influence on intent (P = .578),

followed by PBC (P = .155) and then subjective norm (P = .150). The null hypothesis

was rejected, and the results are presented in Table 4-8.

Table 4-8: Multiple Regression Coefficients: Entire Sample, (N=380)
Variable B SE B Beta
Attitude .693 .051 .578*
Subjective Norm .158 .041 .150*
PBC .206 .051 .155*
*Significant at the .05 level

H4b: No relationship exists between behavioral intention of horticulture

professionals to attend Extension programs and the three determinant variables:

attitude, subjective norm and perceived behavioral, controlling for attendees and

non-attendees.









This data was further analyzed to determine what differences existed among

attendees and non-attendees with regard to the TPB model. First, the multiple linear

regression analysis was conducted for attendees only. Then, the analysis was run on non-

attendees and comparisons were made between the two groups.

Controlling for attendees, the regression was significant (F=80.43; p < .001), and

the model explained 44% of the variance in behavioral intent. All three constructs were

significant predictors of intent at the alpha .05 level. Attitude remained the strongest

predictor (P = .451), followed by PBC (P = .235) and subjective norm (P = .165). For

respondents who attend programs, attitude, subjective norms and PBC all contribute to

their intent to participate in Extension, with their attitudes influencing their decisions the

most. The results are presented in Table 4-9.

Table 4-9: Multiple Regression Coefficients: Attendees, (N=311)
Variable B Std. Error B Beta
Attitude .573 .065 .451*
Subjective Norm .156 .044 .165*
PBC .277 .055 .235*
*Significant at the 0.05 level

Next, the multiple regression analysis was conducted controlling for non-

attendees. The regression was significant (F=32.39; p < .001), and the model explained

60% of the total variation of behavioral intent. However, the influence of the variables

changed considerably. Attitude continued to be the strongest predictor of intent (P =

.709), but was followed by subjective norm (p = .131). PBC exerted a negative influence

on behavioral intent (P = -.029). In addition, attitude was the only significant predictor of

the behavioral intent of non-attendees to attend programs. Therefore, for respondents

who do not attend programs, attitude was the primary indicator of their intent to

participate in Extension. Their behavior was not affected significantly by their









surrounding social environment or their perceived levels of control regarding attendance.

The negative beta on PBC indicates that more control over their attendance at Extension

programs, may actually result in less intent to attend. Results are presented in Table 4-10.

Table 4-10: Multiple Regression Coefficients: Non-attendees, (N=68)
Variable B Std. Error B Beta
Attitude .773 .104 .709*
Subjective Norm .147 .103 .131
PBC -.044 .126 -.029
*Significant at the 0.05 level

Summary

Overall, the results indicated that learning about programs at least one month in

advance and learning about the latest chemicals available in the market were two factors

that might help Commercial Horticulture Extension Agents increase participation levels

of horticulture professionals. In addition, paying close attention to the attitudes of these

professionals is important to program planning and marketing.















CHAPTER 5
DISCUSSION

Reasons for participation and possible motivational factors were the goals of this

research. The Theory of Planned Behavior was used as the theoretical framework of the

study as well as the basic model for conducting the research because it has been

successful at predicting behavioral intention. It is based on three main ideas, attitude

toward the behavior, subjective norms (perceived social pressures) and perceived

behavioral control. Generally, the behavioral intention of a person to perform an action is

strengthened when all three constructs are viewed favorably. A questionnaire was

created based on the constructs of the TPB model and administered to horticulture

industry professionals in Florida.

Demographic information and the four hypotheses were analyzed using the

Statistical Package for Social Science (SPSS). The following procedures were conducted:

frequencies, cross-tabulations, Pearson product moment correlations, multiple linear

regression and ANOVA. All hypotheses were tested at the alpha level of .05.

Key Findings and Implications

The overall results of the study indicated that attitudes, subjective norms and

perceived behavioral control of horticulture industry professionals in Florida were

positively related to intention to attend horticulture based Extension programs.

Demographic information provided the profile of those professionals who do attend or do









not attend programs. Possible motivational factors for promoting future attendance of

horticulture professionals were also determined from the results.

According to the results a large portion of horticulture industry professionals

attend programs or at least send representatives. Over half of the respondents had

average annual sales over $500,000. Furthermore, of those who stated that they did not

attend programs, one-third were in the $500,000 and up category. A major aspect of this

research was determining possible motivation factors that would help increase attendance

at Extension programs. Pilot testing and anecdotal information initially revealed that

horticulture professionals attend programs in order to receive CEUs for licensing and re-

certification purposes. Therefore, five items were tested for importance. These five

questions asked respondents if they would be more likely to attend programs if they knew

they would be learning about business management techniques, receiving continuing

education units (CEUs), learning about the programs one month in advance, learning

about employee management techniques, and updates on latest pesticides, herbicides and

fungicides. The results of this study indicated that acquiring CEUs was not as likely to

attract or maintain participants as chemical updates. Learning about the program at least

one month in advance was also very important to the respondents.

These findings suggest that while CEUs are important to horticulture

professionals, they may not be the main influence on their attendance at programs.

Chemical updates are more likely to attract and maintain participation of this population

in Extension programs. Furthermore, horticulture professionals expressed a need to

receive marketing or promotion materials well in advance of the programs.









Overall, a strong relationship existed between the TPB model constructs and

behavioral intent to attend Extension programs. The attitude of the horticulture

professionals was the strongest predictor of behavioral intent, followed by perceived

behavioral control and subjective norm. This suggests that the TPB model was

appropriate for use in predicting the attendance of horticulture professionals at Extension

programs. Attitudes about horticulture-based Extension programs are extremely

important to this group of people and, therefore, should be closely monitored by the

Extension service. The horticulture professionals maintained a high level of perceived

behavioral control suggesting that they believe barriers do exist regarding their

attendance at programs. Social pressure from friends, family and co-workers are not

viewed as important to horticulture professionals.

When the sample was separated into two groups, attendees and non-attendees, the

results revealed that attitude was consistently the strongest predictor of behavioral intent.

For those who do not attend programs, subjective norms followed attitude, and PBC

exerted a negative influence on behavioral intent. This suggests that non-attendees have

less control over their attendance at programs and, if they were to increase their perceived

control, then their intent to attend programs would decrease even more. This group of

professionals may be inclined to attend programs in the future, but only because they are

required to do so for other reasons such as re-certification for a pesticide license. These

results also suggest that attitude is a key factor for non-attendees. Therefore, it should be

weighed heavily when planning future programs designed to attract this audience.

The results also indicated that perceived level of knowledge of the Florida

Cooperative Extension Service and the Institute of Food and Agricultural Sciences









differed among attendees and non-attendees. Those who attend programs had higher

means for both Extension and IFAS than did non-attendees. These findings suggest that

by attending Extension programs, a certain amount of knowledge about the Extension

Service and about IFAS is learned. Considering that the perceived knowledge level is

lower for IFAS, Extension programs may be a setting for making the connection between

the two and for passing on information about IFAS in general.

Limitations

The ability to generalize the findings to the entire horticulture industry in Florida

is somewhat limited because of the use of a purposive sample. However, a large sample

size was achieved, and testing was conducted to control for non-response error.

Conclusions and Directions for Future Research

The results of this study indicated that, while social pressure and perceived

control are important to horticulture professionals, attitude is the key factor for predicting

attendance. While few, if any, participation studies using the TPB exist regarding the

Extension service, other studies reveal similar findings such as Norland's 1992 study of

the Ohio Cooperative Extension Service. She basically concluded that attitudes drive

participation levels, and the perceived image of Extension is important for promoting

future attendance. Considering that attitudes are the main issue with non-attendees, a

more thorough analysis of attitudes and the beliefs that create those particular attitudes

among Extension clientele is needed.

Also, with regard to demographics, this study revealed that wholesale and

landscape industries with average annual sales over $500,000 are major target audiences

for the Extension service in Florida. These groups were primarily interested in chemical









updates and learning about the programs in a timely manner. Furthermore, while

obtaining CEUs may be important to this population, results of this particular study

revealed that this was not a driving force behind participation. Past research indicates

that successful program planning includes many variables such as timeliness and

location, but equally important is knowing and understanding the characteristics of the

audience. Therefore, a more detailed, individual analysis of each sector in the Florida

horticulture industry may useful for truly understanding and improving the program

planning process.

Recommendations

Commercial Horticulture Extension agents in Florida have the difficult job of

planning, promoting and implementing educational programs for a large, diverse

industry. They are responsible for understanding the audience, predicting attendance and

evaluating the programs in order to create an even better program. However, many times

attendance at or response to the programs may not seem representative of the effort.

Several studies have shown that the higher the level of contact with the extension

service and agents, the higher the satisfaction levels of the clientele. Therefore, a need

may exist for the Extension service to re-determine audiences and re-evaluate the

attitudes and beliefs of those audiences. Then, the goals and objectives of particular

programs can be re-assessed to determine if they are meeting the needs of those target

audiences. This is very similar to the idea behind the Program Life Cycle (Bowling,

2001). It is a methodology developed to help agents improve program efficiency and

value to the consumer as well as to the Extension Service. The Life Cycle involves five

steps: conceptualization, development, maturity, decline, and termination. In the 1st









stages, client involvement and understanding are very important. This is where a true

understanding of the target audience and their needs is vital. The idea of this model is not

to move past the maturity stage where the program is effective and attendance is high.

Once the decline and termination stages have been entered, it is very hard to re-organize.

Therefore, in the maturity stage, it is imperative to pay attention to signs of decline and

attempt to offset them by redefining, redeveloping and revising the program.

Based on this research and the literature involved, the Extension service has a

responsibility to its audience to provide educational programs that are timely and up-to-

date. It has the responsibility of understanding the knowledge, skills and, most

importantly, attitudes of the clientele in order to maintain these programs. In addition,

the Extension service must do the research required for re-discovering existing audiences

and exposing new ones. Then, the many valuable Extension agents can ultimately "help

people help themselves."















APPENDIX A
COVER LETTER AND QUESTIONNAIRE







,, UNIVERSITY OF 5
UNIVERSITY OF 2 Dr. Rick Schoellhom
FLORIDA 2523 W.M. Fifield Hall,
FtOP PO Box 110670
Institute of Food and Agricultural Sciences Gainesville, FL 32611-0670
Environmental Horticulture Phone (352) 392-1831 Ext. 364
Department Fax (352) 392-3870
Website: http://hort.ifas.ufl.edu/
Email rkschiufl.edu


Dear Nursery Industry Professional,

The horticulture extension agents in your area have expressed a need to discover better
ways of meeting your needs and encouraging you to participate in the many programs available.
By better understanding their audience, these extension agents may be able to plan more effective
programs for you.

Therefore, we are conducting a survey to determine why horticulture industry
professionals decide to attend extension programs. We hope to discover how you feel about the
extension service and how that affects your attendance at horticulture-based programs. Your
valuable answers will help to provide guidance to extension agents when they begin the program
planning process.

By taking time to fill out this survey, you are contributing to an extremely important
project. One that is based on the ultimate discovery of what you want your extension service to do
for you. However, your participation is voluntary, and there is no risk or direct benefit to you as a
result of completing the questionnaire. You do not have to answer any question you do no wish
to answer and you may quit at any time. Also, there is no compensation for participating in this
study. Please understand that the number at the top of your questionnaire will only be used to
check off your name when your survey is returned. Your identity will be kept confidential to the
extent provided by law. If you have any questions about your rights concerning this study, please
contact the UFIRB office, Box 112250, University of Florida, Gainesville, FL 32611-2250.

Please take the time to participate in this very important research. It should only take you
about 10-15 minutes to complete, and we have supplied you with everything you need to return
the completed survey. You have the opportunity to provide valuable input into the design of
programs developed for you by the Florida Cooperative Extension Service.

If you have any questions about this research study or the survey, please contact us at
352-392-1831 ext. 364. You may also email any questions or comments to AlexisUF@ufl.edu.

Thank you very much for participating in this study.

Sincerely,



Alexis A. Richardson Dr. Rick Schoellhom
Graduate Research Assistant, UF Professor and Floriculture Specialist, UF

Elizabeth A. Felter
Extension Agent, Commercial Horticulture







SN



hi 3


UNIVERSITY OF 53


FLORIDA


Institute of Food and Agricultural Sciences
Environmental Horticulture
Department


Dr. Rick Schoellhom
2523 W.M. Fifield Hall,
PO Box 110670
Gainesville, FL 32611-0670
Phone (352) 392-1831 Ext. 364
Fax (352) 392-3870
Website: http://hort.ifas.ufl.edu/
Email rkschiufl.edu


UF/IFAS Florida Cooperative Extension Service Survey
Thank you for taking time to complete this questionnaire. Our ultimate goal is to determine better ways of
meeting your needs regarding the extension service and the programs available to you. Your valuable answers will
provide guidance to the extension agents and specialists when they plan programs for you.

Section 1: Please answer the following
questions.


1. Do you attend Florida Cooperative Extension
Service programs relating to the horticulture
industry? ............. ..................... ...........

2. IF yes, please briefly explain why.



3. IF no, please briefly explain why not.



4. Do you normally send employees to the
extension programs or do you attend the
programs yourself? Please briefly explain.



5. How many extension programs relating to the
horticulture industry do you attend each year? ....

Section 2: Please indicate how strongly you
agree with the following statements by circling
the number that represents your answer.
6. Extension programs offer up-to-date
information on the horticulture industry...............

7. Extension programs offer an opportunity for
people in the horticulture industry to increase
their knowledge of new products on the market...
8. Horticulture professionals do not benefit from
participating in extension programs relating to
their indu stry .................... .............. ........ ...
9. Extension programs offer an opportunity for
people in the horticulture industry to increase
their knowledge of herbicides, pesticides and
fungicides ....................... ............. ...........


YES


extension programs per year


Strongly
Agree


Agree Neutral


1 2 3



1 2 3


1 2 3


Disagree


4



4


4


1 2 3


Strongly
Disagree


5



5


5


5
Please Continue












Agree Neutral


10. Extension programs provide information about
business management techniques.......................
11. Extension programs offer an opportunity for
people in the horticulture industry to obtain
continuing education units (CEUs). ...................


12. Extension programs are not an effective way to
spread information to the horticulture industry ....
13. My coworkers think that I should attend
extension programs relating to the horticultural
indu stry ...................................... .. .........


14. Other horticulture professionals in my industry
encourage me to attend extension programs
relating to the horticultural industry .................


15. Generally speaking, I do what other important
people think I should do regarding attendance
at extension program s ......................... ..............
16. The opinions of my coworkers are important to
m e .............................................. ........


17. If I wanted to, I could attend an extension
program relating to the horticultural industry.......

18. I prefer to make the decision regarding whether
or not I attend extension programs relating to
the horticulture industry .....................................
19. My friends and family encourage me to attend
extension programs relating to the horticultural
industry. ...................... ............... ............
20. It is mostly up to me whether or not I attend
extension programs relating to the horticulture
indu stry ...................................... .. .........

21. Generally speaking, I do what other
horticulture professionals in my industry think
I should do regarding attendance at extension
program s ...................... .............. .....
22. It is expected of me to attend as many
extension programs as I can that are about
horticultural issues ................ ............ ..............
23. I feel in complete control over whether I attend
an extension program relating to the
horticultural industry ................ ......... ..............
24. Generally speaking, I do what my coworkers
think I should do regarding attendance at
extension program s ................ ........... ..............

25. The opinions of horticulture professionals in
my industry are important to me ......................


1 2 3



1 2 3



1 2 3



1 2 3




1 2 3




1 2 3

1 2 3



1 2 3



1 2 3



1 2 3



1 2 3




1 2 3


1 2 3



1 2 3



1 2 3


1 2 3


5
Please Continue


Strongly
Agree


Disagree


Strongly
Disagree












Agree Neutral


26. If I wanted to, it would be easy for me to attend
extension programs relating to the horticultural
industry within the next year......................
27. I have control over whether I attend an
extension program relating to the horticultural
indu stry ...................................... .. .........

28. Learning about new products on the market is
im portant to m e ..................................... ...........

29. Keeping up-to-date on the horticulture industry
is im portant to m e................................................

30. Learning about pesticides, herbicides and
fungicides is important to me.....................


31. Gathering new information about business
management techniques is important to me .........
32. Obtaining continuing education units (CEUs) is
im portant to m e ..................................... ...........
33. If I knew that I could learn about business
management techniques, I would be more
likely to attend extension programs......................
34. If I knew that I could receive continuing
education units (CEUs), I would be more likely
to attend extension programs......................
35. If I knew about the extension programs at least
one month in advance, I would be more likely
to attend extension programs......................
36. If I knew that I could learn about employee
management techniques, I would be more
likely to attend extension programs......................

37. If I knew that I could learn about the latest
pesticides, herbicides and fungicides being
offered in the market, I would be more likely to
a tten d .................. ..... ... .............. ...........
Section 3: Please indicate how likely you
would be to do the following:
38. For me to attend one extension program
relating to the horticultural industry in the next
year w would be .......................... ...... .............
39. For me to attend more than one extension
program relating to the horticultural industry in
the next year would be ......................... ...........
40. I intend to attend extension programs relating
to the horticultural industry within the next
y e a r ............................................ ........
41. I will try to attend extension programs relating
to the horticultural industry within the next
y e a r ............................................ ........


1 2 3



1 2 3


1 2 3


1 2 3


1 2 3



1 2 3


1 2 3



1 2 3



1 2 3



1 2 3



1 2 3




1 2 3





1 2 3



1 2 3


1 2 3



1 2 3


5
Please Continue


Strongly
Agree


Disagree


Strongly
Disagree













42. I intend on becoming more aware of extension
programs offered relating to my industry ........... 1 2 3 4 5
43. I will try to utilize the services of my extension
office .............. ... ............ .... ....... ....... 1 2 3 4 5


How likely would you be to attend extension
programs relating to horticultural issues in the:
44. SPRING............................... ........... 1 2 3 4 5

45. SUMMA ER...... ........................ ........... 1 2 3 4 5

46. FALL................................. ... .............. 1 2 3 4 5

47. WINTER............................. .. ........... 1 2 3 4 5


Section 4: Please indicate your attitude by marking along the range of each item.

48. My attitude toward attending extension programs relating to the horticulture industry is

Favorable: : : : : : :Unfavorable

Useful: : : : :: :Useless

Good: : : : : : : : Bad

Pleasant: : : : : : :Unpleasant

Reliable: : : : : : : :Unreliable

Valuable: : : : : : :Worthless


49. My attitude toward the Florida Cooperative Extension Service is

Favorable: : : : : : : :Unfavorable

Useful: : : : : : : :Useless

Good: : : : : : :Bad

Pleasant: : : : : : :_ :Unpleasant

Reliable: : : : : : : :Unreliable

Valuable: : : : : : : Worthless


50. The biggest problems facing the horticulture industry are:




51. What specific topics would make you more likely to attend extension programs relating to the horticulture
industry?











52. My knowledge of the Florida Cooperative Extension service is:

Extremely High:_ :_ : : : : : Extremely Low


53. My knowledge of the Institute of Food and Agricultural Sciences (IFAS) is:

Extremely High: :_ : : : : : Extremely Low


54. Please list any extension programs that you have been involved with (ex: 4-H, Master Gardener, etc).


Section 5: Please take a few more minutes to answer these basic demographic questions. Thank You.

55. Gender: Male Female

56. What is your position in the business?
1. Owner
2. Manager
3. Grower/Technician
4. Support Staff
5. Sales/Marketing
Other (please specify)

57. Age of business

58. Number of employees in business

59. How many acres do you usually have in production?

60. Average Annual Sales (Please choose range or provide dollar amount)
1. $0 -$19,999
2. $20,000 $49,999
3. $50,000 $149,999
4. $150,000 $249,999
5. $250,000 $499,999
6. $500,000+

61. Do you use any of the following production systems? (please circle your answer)
1. Greenhouse......................... YES NO
2. Shade house........................ YES NO
3. Field production ................... YES NO
4. Container production ...........YES NO

62. Business Category: (please circle the one that best describes your operation)
1. Wholesale production
2. Allied Trade
3. Retail Nursery
4. Landscape Industry
5. Interiorscape Industry
6. Other (please specify)



THANK YOU FOR TAKING TIME TO COMPLETE THIS IMPORTANT QUESTIONNAIRE. YOUR
ANSWERS ARE EXTREMELY VALUABLE TO THE SUCCESS OF THE RESEARCH STUDY.















APPENDIX B
THEORY OF PLANNED BEHAVIOR



































Figure B-l: Theory of Planned Behavior Model (Ajzen, 2002)















LITERATURE CITED

Ajzen, I. (1988). Attitudes, Personality, and Behavior. Chicago: The Dorsey Press.

Ajzen, I. & Driver, B. L. (1992) Application of the Theory of Planned Behavior to
Leisure Choice. Journal of Leisure Research, 24, 207-224.

Alston, D. G. & Reding, M.E. (1988) Factors Influencing Adoption and Educational
Outreach of Integrated Pest Management. Journal of Extension, 36 (3). [on-
line], Available: http://www.joe.org/ioe/1998june/a3.html February 7,
2002.

Armitage, C.J. & Conner, M. (2001) Efficacy of the Theory of Planned Behavior: A
Meta-Analytic Review. British Journal of Social Psychology, 40, (4) 471-
499.

Ary, D., Jacobs, L., & Razavieh, A. (2002) Introduction to Research in Education.
Belmont: Wadsworth Group.

Beedell, J. & Rehman, T. (2000) Using Social-Psychology Models to Understand
Farmers' Conservation Behaviour. Journal of Rural Studies, 16
117-127.

Bowling, C. J. (2001) Using the Program Life Cycle Can Increase Your Return On Time
Invested. Journal of Extension, 39 (3). [on-line], Available:
http://www.joe.org/joe/2001june/a2.html March 11, 2002

Bruening, T., Radhakrislma, R. & Rollins, T. (1992) Environmental Issues: Farmers'
Perceptions about Usefulness of Informational and Organizational Sources.
Journal of Agricultural Education, 33 (2). [on-line], Available:
http://pubs.aged.tamu.edu/jae/pdf/Vol33/33-02-34.pdf March 11, 2002.

DeSousa, J. (2002) Florida's Nursery and Landscape Industry Soars to Record Economic
Highs. Florida Nurserymen and Growers Association Press Release.
January 7, 2002.

Dillman, D. A. (2000). Mail and Internet Surveys: The Tailored Design Method 2nd ed.
New York: John Wiley & Sons, Inc.

Dollisso, A.D. & Martin, R.A. (1999) Perceptions Regarding Adult Learners









Motivation to Participate in Educational Programs. Journal of Agricultural
Education, 40 (4) 38-46.

Eagly, A. & Chaiken, S. (1993). The Psychology of Attitudes. Harcourt Brace
Jovanovich College Publishers.

Fishbein, M. & Ajzen, I. (1975) Belief, Attitude, Intention and Behavior: An Introduction
to Theory and Research. Reading, MA: Addison-Wesley.

Florida Agriculture Statistics Service (2002) Foliage, Floriculture and Cut Greens
[on line], Available: http://www.nass.usda.gov/fl March 15, 2003.

Ford, C. L. (1995) Educational Priorities of Small Farmers in West Tennessee. Journal
of Agricultural Education, 36 (1) 31-37.

Habeeb, M., Birkenholz, R. J. & Weston, C. R.. (1987) Clientele Group and
Extension Council Officer Perceptions of the Cooperative Agricultural
Extension Service. Journal of Agricultural Education, 28, 15-20.

Ingram, K. L., Cope, J. G., Harju, B. L. & Wuensch, K. L. (2000) Applying to Graduate
School: A Test of the Theory of Planned Behavior. Journal of Social
Behavior and Personality,15, (2) 215-226.

Israel, G. (2001) Using Logic Models for Program Development. EDIS document. [on-
line], Available: http://edis.ifas.ufl.edu/BODY WC041 February 19, 2002.

Jacob, S. & Ferrer, M. (2000). Program Theory for Effective Extension Program
Planning. EDIS document [on-line], Available:
http://edis.ifas.ufl.edu/BODYFY031 November 11, 2001

Martin, R. & Omer, M. H. (1987) Factors Associated with Participation of
Iowa Young Farmers in Agricultural Extension Programs. Journal of
Agricultural Education, 29, 45-52.

Norland, E. (1992) Why Adults Participate? Journal of Extension, 30, (3) [on-line],
Available: http://www.joe.org/joe/1992fall/a2.html. March 11, 2002.

Place, N. (2001) Principals of Effective Extension Educational Programs. EDIS
document. [on-line], Available:
http://edis.ifas.ufl.edu/BODY WC042 March 19, 2002.

Pouta, E. & Rekola, M. (2001)The Theory of Planned Behavior in Predicting Willingness
to Pay for Abatement of Forest Regeneration. Society and Natural
Resources, 14., 93-106.

Rogers, J. (2001). Adults Learning: 4th Edition. Philadelphia, PA: Open University Press.









Schmitt, M., Durgan, B., & Iverson, S. (2000) Impact Assessment and Participant
Profiles of Extension's Education Programs for Agricultural Chemical/Seed
Retailers and Crop Advisors. Journal of Extension, 38, (6) [on-line],
Available: http://www.joe.org/joe/2000december/a2.html January 31,
2002.

Sparks, P. & Shepherd, R. (1992) Self-Identity and the Theory of Planned Behavior:
Assessing the Role of Identification with "Green Consumerism." Social
Psychology Quarterly, 55, (4) 388-399.

Summerhill, W.R & Taylor, C.L (1992) Basic Premises for Client Involvement in
Extension Programming. EDIS document. [on-line], Available:
http://edis.ifas.ufl.edu/BODY PD013 January 22, 2002.

Sutton, S. (1998) Predicting and Explaining Intentions and Behavior: How Well
Are We Doing? Journal of Applied Social Psychology, 28 (15) 1313-1338.

Taylor, C.L. (1994) Concept of a Major Program. EDIS document. [on-line],
Available: http://edis.ifas.ufl.edu/BODY PD034 November 11, 2001.

Taylor, C. L. & Beeman, C. E. ( 1992) Evaluation for Accountability: An Overview.
EDIS document [on-line], Available: http://edis.ifas.ufl.edu/BODY PD018
November 11, 2001.

United States Department of Agriculture. (2002) Floriculture Crops 2001 Summary.
National Agriculture Statistics Service. Sp Cr 6-1 (02)a.

University of Florida Institute of Food and Agricultural Sciences Fact Digest (2003) [on-
line], Available: http://ifas.ufl.edu May 25, 2003

Vasquez, B. C. & Nesheim, O. N. Florida Crop/Pest Management Profiles.
EDIS document [on-line], Available:
http://edis.ifas.ufl.edu/BODY PI038 October 15, 2002.















BIOGRAPHICAL SKETCH

Alexis A. Clark-Richardson began her college education at Central Florida

Community College where she received her Associate of Arts degree in 1998. She moved

to Gainesville to begin her career at the University of Florida in 1999 and earned a

Bachelor of Science degree in Agricultural Education and Communication in 2000. She

finalized her education in 2003 with a Master of Science degree in Environmental

Horticulture. Her research was based on the evaluation and marketing of Florida

Cooperative Extension Service workshops and programs.

Mrs. Richardson married her husband, Steve, on May 11, 2002, and their first son

is due in September 2003. They will be moving to Crystal River, Florida and pursuing

careers in the Florida agriculture industry.