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CONSERVATION BEHAVIOR OF BOATERS IN THE GREATER CHARLOTTE HARBOR REGION
By
CLAIRE SUNQUIST
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
2010
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© 2010 Claire Sunquist
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To my mother and father who have helped me every step of the way
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ACKNOWLEDGMENTS
I would like to thank my committee members for their help in the completion of this
project. I would like to thank them for their patience, for their comments, and for all their
help. I thank Dr. Robert Swett for giving me the opportunity to study a topic in which I
am truly interested and for providing me with the financial support to complete my
research and obtain my degree. I thank Dr. Mickie Swisher for giving me a strong
background in research methods and data analysis techniques for a new and exciting
field. I thank Dr. Paul Monaghan for providing me with new insights into social
marketing.
I would like to thank Lee County boaters for letting me interview them. I would
especially like to thank Joy Hazell and Justin McBride for their commitment to helping
me and all of their insights into the boating community and all of the issues. I thank
them for all of their edits and comments and I especially thank them for taking me under
their wings. I would like to thank all of my expert panels and reviewers for hours of
phone time and all the participants for their patience.
I would like to thank the marinas and mangers that helped in my research
including Salty Sams Marina, Snook Bight Marina, Eldred’s Marina, Carefree Boat Club,
and Cape Harbour Marina. They were all instrumental in providing me with critical
information and insight.
I would also like to thank my parents and wonderful fiancé for all their support and
assistance throughout the research and writing process. I would like to thank Dr.
Stephen Humphrey, Meisha Wade, and Cathy Richie for giving me this opportunity and
for supporting me in my quest for an advanced degree.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION .................................................................................................... 12
Background ............................................................................................................. 12 Management of Sea Grass ..................................................................................... 14 Research Objective ................................................................................................ 16
2 LITERATURE REVIEW AND RESEARCH DESIGN .............................................. 17
Theories .................................................................................................................. 17 Theory of Planned Behavior ............................................................................. 17 Social Cognitive Theory.................................................................................... 19 Research Variables .......................................................................................... 22
Hypotheses ............................................................................................................. 24
3 METHODS .............................................................................................................. 29
Key Informant Interviews ........................................................................................ 29 Intercept Survey Procedures and the Questionnaire .............................................. 32
Self-Efficacy ..................................................................................................... 34 Control Beliefs .................................................................................................. 36 Reported Behavior ........................................................................................... 37 Demographic Information ................................................................................. 38
Interviews ................................................................................................................ 39 Social Reinforcement ....................................................................................... 41 Observational Learning .................................................................................... 41 Current Management ....................................................................................... 42
Data Collection ....................................................................................................... 42
4 RESULTS ............................................................................................................... 47
Descriptive Statistics ............................................................................................... 47 Factor Analysis ....................................................................................................... 49
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5 DISCUSSION AND CONCLUSIONS ...................................................................... 59
Hypotheses ............................................................................................................. 59 Correlations ............................................................................................................ 59 Self-Efficacy and Control Belief Scores .................................................................. 63
Actual Skills versus Reported Skills ................................................................. 65 Issues with the Theories ................................................................................... 68
Interviews ................................................................................................................ 69 Possible Biases ...................................................................................................... 76 Conclusions and Possible Interventions ................................................................. 78 Further Research .................................................................................................... 81
APPENDIX
A BOATING SURVEY QUESTIONNAIRE ................................................................. 85
B BOATER INTERVIEW GUIDE ................................................................................ 89
C ADDITIONAL RESULTS AND GRAPHS ................................................................ 95
LIST OF REFERENCES ............................................................................................. 114
BIOGRAPHICAL SKETCH .......................................................................................... 121
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LIST OF TABLES
Table page 2-1 Theory of Planned Behavior Constructs and Definitions ................................... 26
2-2 The Social Cognitive Theory Constructs and Definitions ................................... 27
3-1 Self-Efficacy Index Item-total correlations and Cronbach’s alpha values for reliability testing, n=36, 2009 .............................................................................. 44
3-2 Self-efficacy test item-total correlations and Cronbach’s alpha values for reliability testing, n=36, 2009 .............................................................................. 44
3-3 Control Belief index item-total correlations and Cronbach’s alpha reliability values, n=36, 2009 ............................................................................................. 45
3-4 Reported Behavior Index Item-total correlations and Cronbach’s alpha reliability testing, n=36, 2009. ............................................................................. 46
3-5 Overall scalar responses for the Self-efficacy index, self-efficacy test, control belief index and the reported behavior index descriptive statistics and reliability data, n=252, 2009 ................................................................................ 46
4-1 Factor Loadings (Varimax Raw) for each Independent Variable related to Demographic information ................................................................................... 51
4-3 Regression Estimates for the Self-Efficacy Index, Self-Efficacy Test, Control Belief Index, and Reported Behavior by Factor, n=252, 2009 ............................ 52
4-4 Descriptive Statistics for the Self-efficacy index, Self-efficacy test, Control belief index, and Reported Behavior index, n=252, 2009 ................................... 53
4-5 Regression Estimates for the Self-Efficacy Index, Self-Efficacy Test, Control Belief Index, and Reported Behavior by Independent Variable n=252, 2009 ..... 55
4-6 Ad Hoc and Emergent Themes from the Interviews, listed by frequency of response, n=18, 2009 ......................................................................................... 58
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LIST OF FIGURES
Figure page 2-1 Model of the Theory of Planned Behavior derived from Ajzen’s Theory of
Planned Behavior (1991). ................................................................................... 28
2-2 Model for the Social Cognitive Theory derived from Bandura’s Social Cognitive Theory (1977) ..................................................................................... 28
3-1 The ten sampling locations for the questionnaires (n=252) in Lee and Charlotte County, selected based on usage and proximity to sea grass beds. .. 43
4-1 Histogram of Self-Efficacy Index Scores by Frequency, n=252, 2009 ............... 53
4-2 Histogram of Self-Efficacy Test Scores by Frequency n=252, 2009 .................. 54
4-3 Histogram of Control Belief Index Scores by Frequency, n=252, 2009 ............. 54
4-4 Histogram of Reported Behavior Index Scores by Frequency n=252, 2009 ...... 55
4-5 Boxplot of the number of times on the water by resident group, n=252, 2009 ... 57
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LIST OF ABBREVIATIONS
CHNEP Charlotte Harbor National Estuary Program
FDEP Florida Department of Environmental Protection
FWC Florida Fish and Wildlife Conservation Commission
ICW Intracoastal Waterway. This is a shortened version of what is referred to as the Florida Gulf Intracoastal Waterway which extends from Tarpon Springs Florida south to Fort Myers Florida.
NGP Noticed General Permit
NICMZ No Internal Combustion Motor Zones, also known as Poll and Troll zones where no combustion engines can be used.
RWMS Regional Waterway Management System
SAV Submerged Aquatic Vegetation. Vegetation that lives at or below the surface of the water. These grasses are an important habitat for young fish and other aquatic organisms.
SCT Social Cognitive Theory
TPB Theory of Planned Behavior
WCIND West Coast Inland Navigational District. This is multicounty taxing district made up of Manatee, Sarasota, Charlotte, and Lee counties.
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Masters of Science
CONSERVATION BEHAVIOR OF BOATERS IN THE GREATER CHARLOTTE
HARBOR REGION
By
Claire Sunquist
May 2010
Chair: Robert Swett Major: Interdisciplinary Ecology
The increasing pressure of higher population and higher levels of development
along the coastal regions of Florida has put immense pressure on natural resources.
Increasing number of boaters have come in contact with new environments and the
shallow waters of the Greater Charlotte Harbor have suffered. The sea grasses in this
region are under threat from prop scarring by boats, decreased water quality, and
increased fishing pressures. There has been an increasing focus on how to protect
vulnerable sea grass beds and an effort to find out who is causing that damage.
Using the Theory of Planned Behavior and the Social Cognitive Theory, I
compared boaters to see if there were differences among them that could inform an
educational or marketing campaign. After consulting with an expert panel, I developed
an intercept survey and an interview guide to gain insight into boater behaviors and to
see how the concepts of self-efficacy, control beliefs, social reinforcement and
observational learning were incorporated into shallow water navigation and
conservation behavior on the water. I collected 252 intercept surveys at 10 different
boat ramps and marinas and also contacted 18 interview participants based on contacts
made at the ramps and marinas.
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My results showed that reported behavior was not correlated with self-efficacy or
control beliefs and that social reinforcement was not as important an element as
experiential learning, I did discover differences in the population and was able to
distinguish between resident groups based on self-efficacy and control belief scores.
Experience in years and time on the water were also significantly correlated to
increased self-efficacy and control belief scores. Overall, this would suggest the need
for more education for those visitor and part time residents and a social marketing
campaign to increase social reinforcement for full time residents.
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CHAPTER 1 INTRODUCTION
Background
Florida’s burgeoning population has a direct impact on the use and condition of
natural resources in coastal areas such as Greater Charlotte Harbor. For example, as
population increases so does the number of registered boats. Between 1980 and 2000,
the number of boats that were registered in two of the three coastal counties that border
Greater Charlotte Harbor more than doubled (Madley and others 2004). Importantly, the
registration numbers cited by Madley (2004) did not take into account the significant
number of boaters who travel from other Florida counties, states, and counties to use
the waterways of Greater Charlotte Harbor. The waterway traffic that has resulted from
more boats has put pressure on local natural resources, including submerged aquatic
vegetation.
Submerged aquatic vegetation (SAV) is a vital part of the Greater Charlotte Harbor
estuarine system and an important natural resource. Significant ecosystem functions
that sea grasses perform include helping to maintain water clarity, stabilizing bottom
sediments, and providing habitat for many fish, crustaceans, and shellfish (Madley and
others 2004). Sea grass communities also provide nursery grounds for many of Greater
Charlotte Harbor’s recreationally and commercially important fisheries.
The Florida Fish and Wildlife Conservation Commission (FWC) conducts surveys
of fish catches and fishing activity to quantify the economic value of sea grass habitat to
the fishing industry. For example, FWC (2002) estimated that sea grass communities in
Monroe County supported a 2002 harvest of shrimp, stone crab, yellowtail snapper,
gray snapper, and blue crab that was valued at $32.8 million. These same species
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inhabit the Greater Charlotte Harbor and are of economic importance to that region’s
fishermen and local communities.
Realizing the vital role that sea grasses play within aquatic ecosystems, scientists
have studied various sea grass species throughout the world. For example, researchers
demonstrated a strong association between eelgrass (Zostera marina) habitat
fragmentation and a drastic reduction in species richness and density (Reed and Hovel
2006, Uhrin and Holmquist 2003). In Florida, researchers often have focused on
physical damage to sea grass communities due to prop scarring. The term prop scar
refers to channels and trenches that, as the term suggests, are created when a boat
propeller disturbs sea grass rhizomes and sediment surrounding them. Prop scars can
also be created by any protruding object, including the lower unit of a motor or a vessel
keel. Most prop scars can be found in shallow inland waters less than two meters deep
(Madley and others 2004).
Researchers have determined that, though the time required for sea grass
meadows to recover from prop scar damage does vary depending on such factors as
species and water quality, recovery usually takes from one to five years (Zieman 1976,
Rasheed 1999). Full recovery of turtle grass (Thalassia testudinum) in Florida takes an
average of 3.5 to 4.1 years and up to 7.6 years in some cases (Dawes 1998).
Researchers also have determined that biological impacts from sea grass scarring vary
among fish species and may depend on the severity of scarring. For example, in
Florida’s Tampa Bay, Bell (2002) showed that while moderate scarring may not have
negative impacts on bait-fish or shrimp, the effects on sport fish are unknown (Bell and
others 2002).
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Though numerous external factors are related to sea grass damage or impairment,
prop scarring continues to have major impacts on many acres of submerged lands.
Within Greater Charlotte Harbor, the areal extent of sea grass habitat affected by prop
scarring increased by 38% from 1993 to 2003 (Madley and others 2004). The amount of
severely scarred sea grass habitat increased more than sevenfold, while lightly scarred
sea grass declined by 50 %. The report’s authors mentioned two possible
interpretations for the change that occurred during the 10 year interval: (a) meadows
that were lightly scarred in 1993 had become heavily scarred by 2003 and/or (b) lightly
scarred areas regenerated (recovered) over the ten-year period. Regardless of which
interpretation is correct, the total scarred areas increased from 21,816 acres in 1993 to
30,064 in 2003 (Madley and others 2004).
Management of Sea Grass
An important outcome of studies such as those described above, is that managers
now understand that grass meadows will sustain long-term, debilitating damage from
propeller scars if they are not protected in some way. One attempt to address the issue
of increasing vessel traffic in Southwest Florida, and to provide for better resource
(habitat) protection, is the Regional Waterway Management System created by the
Florida Department of Environmental Protection (FDEP), the West Coast Inland
Navigation District (WCIND), and the Florida Sea Grant College Program in
collaboration with county governments (Manatee, Sarasota, Charlotte, and Lee) (Swett
and others 2009). An objective of the RWMS is to balance resource use and protection.
A RWMS outcome was a Noticed General Permit (NGP) for Lee County that provides
for dredging of navigable channels and the establishment of “No Internal Combustion
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Motor Zones” (NICMZs) over areas of scarred sea grass as mitigation and for the public
interest benefit within the harbor.
The process to establish the Lee County NGP required that the FDEP hold public
meetings to receive comment on the proposed locations of the NICMZs. Two meetings
held in January 2009 were attended by many angry residents, local anglers, and other
stakeholders who felt that, rather than being asked for their input, they were being told
what was going to happen (L. Amos, personal communication, August 27th, 2009). The
public reaction at the meetings demonstrates that, although the FDEP objective was to
include the public in the planning process, their methods likely need improvement. Local
agencies and governmental entities need more information about how best to protect
fragile areas from further destruction and how to garner public support for such efforts.
The increase in the areal extent of heavily scarred sea grass beds has led local
and state agencies to examine its causes and to seek ways to ameliorate the damage.
Protection measures implemented by management normally include some mix of new
regulations, increased enforcement, and education. Zones of restricted use (NICMZ’s)
are a regulatory technique to reduce sea grass scarring, but another option is to change
the behaviors that boaters exhibit when they operate vessels within sea grass
meadows. For example, research concerning boater behaviors in manatee speed zones
indicates that the presence of law enforcement improves compliance with posted
speeds (Gorzelany 1996, Tyson and Coombs 1999). This conclusion was supported by
a survey of Florida boaters in the Tampa Bay region that asked respondents about
variables influencing their compliance with on-water regulations (Aipanjiguly and others
2003). While increased law enforcement presence is an effective way to protect sea
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grass, it has become increasingly hard to patrol all parts of Greater Charlotte harbor on
a consistent basis. Therefore, as the ratio of law enforcement officers to boaters
decreases, managers will have to discover other methods for improving compliance and
promoting natural resource conservation behaviors.
The implementation of a widespread education plan or social marketing campaign
for a geographically and culturally diverse area like Charlotte Harbor is challenging. The
difficulty of the challenge was highlighted by Duarte and others (2008), when they
observed that sea grass ecosystems receive the lowest level of coverage in the media
as compared to mangroves and coral reefs. Nonetheless, successful efforts will require
a good understanding of the boating population in order to determine which users are
responsible for scarring and what techniques will resolve the problem.
Research Objective
The research will provide information about the skills and learning mechanisms
used by different user groups. The resulting population segmentation will suggest
appropriate mechanisms to affect or promote conservation behavior among boaters that
use Greater Charlotte Harbor. Since it is very difficult to identify specific individuals who
cause sea grass damage, this research will examine how the knowledge, skills, and
experience that boaters possess might predict their behavior on the water. If I can
distinguish user groups according to the amount of damage they cause, then I can
provide a framework for targeting an audience with marketing or education efforts.
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CHAPTER 2 LITERATURE REVIEW AND RESEARCH DESIGN
Theories
My research evaluates individual and interpersonal decisions that influence the
conservation behaviors that boaters’ exhibit when they are on the water. I chose the
Theory of Planned Behavior (TPB) to examine the individual decisions of boaters and
Social Cognitive Theory (SCT) to examine their interpersonal decisions and their
interactions with other boaters.
Theory of Planned Behavior
The TPB links the attitudes of people to their behaviors. The TPB has been used
in various fields such as advertising, public relations, and healthcare to study
relationships between beliefs, attitudes, behavioral intentions and behaviors (Table 2-1;
Figure 2-1). The majority of previous studies that applied the TPB to observed
behaviors, focused on predicting single actions, such as kidney donation (Borgida and
others 1992) or on discrete episodes of behavior, such as attending health screenings
(Armitage 2002, Conner 2000, Montano and Taplin 1991), attending exercise classes
(Blanchard and others 2002, Courneya and McAuley 1995, Estabrooks and Carron
1999), and students’ class attendance (Ajzen and Madden 1986, Prislin and Kovrlija
1992).
By definition, single-action behaviors require people to make one decision about
whether or not to perform the desired action. In contrast, discrete behaviors may require
people to make several decisions to engage in the target action. Compared with single-
action or discrete behaviors, continuous forms of behavior are more demanding
because people are required to make many decisions about whether or not to perform
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the behavior. Furthermore, these decisions may occur over the course of one day or,
perhaps, several days, months, or years into the future. Aspects of boating could be
described as continuous behavior because of the constant attention required to
navigate a boat and the multitude of navigational decisions that operators must make,
regardless of whether they are on a short or long trip.
Subjective norms, as defined by the TPB, are an individual’s perceptions about
whether most people approve or disapprove of a behavior and normative beliefs are the
individual’s beliefs about the expectations of others (See Table 2-1). In a meta-analysis
of research that used the TPB, Armitage and Conner (2001) found that the subjective
norm construct and its sub-element, normative belief, had weak predictive power
because they were often measured by a single item. While some researchers
suggested eschewing the construct entirely, others called for better measurement
techniques (Beck and Ajzen 1991, Rossi and Armstrong 1999). Other studies have
shown a strong association between self-efficacy, an element of perceived behavioral
control, and a person’s behavioral intentions (Webb and Sheeran 2006). The strength of
the association was shown to depend on the level of commitment that the individual had
toward pursing a particular behavior. For instance, studies of self-efficacy and perceived
behavioral control have shown that individuals who are most likely to commit to
voluntary actions like recycling or quitting smoking are those (a) with greater confidence
in their ability to perform the actions and (b) who feel they have more control over the
behavior. Since boating is a voluntary behavior, the same principles might be applied to
the level of effort that an individual invests in persisting in a desired behavior.
Perceived Behavioral Control is defined as the level of control that an individual
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has over performing a particular behavior. If individuals have full control, then perceived
behavioral control has no effect on their performance of the behavior in question (Ajzen
1991). In contrast, as control diminishes due to real or perceived limitations, the more
likely that the perceived behavioral construct will predict behavioral outcomes. Testing
of the TPB in relation to driving behavior established that attitude, subjective norm, and
perceived behavioral control were each statistically significant and independent
predictors of intentions, but that perceived behavioral control accounted for most of the
variance (Elliot 2007).
Social Cognitive Theory
Social Cognitive Theory (SCT) was developed by Albert Bandura and it is an
outgrowth of Social Learning Theory (Bandura 1986). According to SCT, the interaction
of personal, behavioral, and environmental determinants uniquely dictates an
individual's behavior (Table 2-2; Figure 2-2). In this process, human expectations,
beliefs, and cognitive competencies are developed and modified by social influences
and physical structures within the environment. These social influences can convey
information and activate emotional reactions through such factors as modeling,
instruction, and social persuasion (Bandura 1986). The relative influence of any of these
three factors will vary with different activities, individuals, and circumstances (Stajkovic
and Luthans 1998).
SCT proposes that our behavior is largely regulated by cognitive processes as a
part of knowledge acquisition. For example, consequences that result from performing a
behavior will help to determine one’s expectations for future behavioral outcomes.
Bandura argues that our ability to dynamically and continually form and assess these
expectations gives humans the capability to predict the outcomes of our behaviors
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before they have occurred. Thus, our future actions or behaviors may be altered as a
result of this on-going internal evaluation.
According to Bandura (1986), perceived self-efficacy often predicts one’s future
behavior better than the person’s past performance does. This is because our self-
perception, which includes perceived self-efficacy, is partly molded or influenced by the
outcomes of behaviors or actions that we have completed. Thus, perceived self-efficacy
should be able to predict performance on any given task.
An additional factor of SCT is that people are motivated to learn actions that they
value and believe will lead to rewarding consequences. People, therefore, will engage in
cognitive activities that assist learning behaviors that they value. Cognitive skill learning
may involve both enactive (reward and punishment learning) and vicarious learning
experiences (Straub 2009). Much of human learning does not involve overt behaviors or
reinforcements, but, instead, stems from watching others, reading, watching TV or
videos, or even surfing the web. Observing others’ behaviors, including media figures,
may help us to develop rules that guide our own actions and behaviors. These vicarious
learning experiences accelerate formal and informal learning and can save us from
dealing with negative consequences.
Observational learning consists of four processes, each of which is influenced by
the observer’s cognitive development and skills (Bandura 1986). First, attention to
certain models (for example, a colleague) and their behavior is affected by source and
contextual features, such as attractiveness, relevance, functional need, and affective
valence (positive or negative). Second, retention processes focus on the ability of the
observer to symbolically represent and rehearse the behavior observed and its
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consequences. Third, production focuses on translating the symbolic representations
into action, reproducing the behavior in seemingly appropriate contexts, and correcting
for any errors based on feedback. Finally, motivational processes influence whether
symbolically represented behaviors are enacted based on the nature or valence of the
reinforcement. Such reinforcement may come from feedback generated by one’s own
behavior, the observed feedback given by others, or internal incentives (Nabi and Clark
2008).
Behavior change methods derived from SCT have been widely and successfully
applied, primarily among people seeking help. One particularly interesting area of
research within SCT is how children internalize morals and values. In fact, it has been
argued that the greatest contribution of SCT is its help in understanding how children
are socialized to accept the standards and values of the society within which they live
(Schunk 1995). An SCT study of diet and exercise by Rimal (2001) is an example
research on long-term, peer reinforced behaviors that require a similar degree of
longitudinal fortitude as does acquiring boating skills. One may argue that the level of
personal gain derived from avoiding sea grass is not the same as that derived from
exercising and eating right, but all three behaviors do require similar perseverance
(repetition) in decision-making.
For my research, I chose to focus on the behavioral acquisition construct of SCT:
in particular, the self-efficacy, observational learning, and reinforcement elements of the
construct. SCT addresses the more interactive, social aspect of behavior acquisition
and I will apply this element to conservation behavior; in particular, how interactions
among boaters might influence the adoption of socially acceptable behaviors. The
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nature of the determinants of conservation behavior in SCT makes it possible for
intervention and education efforts to be directed at personal, external, or behavioral
factors.
Research Variables
The following variables were selected to represent the constructs of the TPB and SCT
for this research project.
• Personal Boating Self-Efficacy (TPB and SCT)
• Control Beliefs (TPB)
• Social Reinforcement (SCT)
• Level of Observational Learning (SCT)
Researchers who have examined the differences between the TPB and SCT
emphasize the need for a clear distinction between two aspects of self-efficacy: (a)
actual mastery of a skill versus (b) a person’s belief or confidence that they possess the
skill (whether they actually do or not) (Zimmerman 1986). The TPB defines self-efficacy
as a person’s ability to perform a particular behavior successfully, while SCT defines it
as a person’s confidence that he/she can perform the behavior successfully (Bandura
1977). Because each theory defined self-efficacy differently, I used two variables to
measure Personal Boating Self-Efficacy: (a) mastery of shallow water boating skills and
(b) confidence in personal shallow water boating skills. The TPB aspect of self-efficacy
focused on individuals’ actual ability to perform a skill, while the SCT aspect focused on
individuals’ confidence in their mastery of boating skills. Specific behaviors exhibited
included boating safety and navigation skills, such as the ability to avoid damaging sea
grasses (Chapman 1977).
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Control Beliefs were categorized as social and personal barriers that were a result
of preformed ideas. This variable measured a boater’s perceived level of control over
his boat and perceived level of control (ability) to avoid scarring sea grass.
Measurement consisted of determining the extent to which decisions made by a
respondent were influenced by factors deemed to be outside of his or her control; the
factors ranged from barriers perceived to be strong to those perceived to be low (Giles
2004). Terry and O’Leary (1995) reinforced the need for a clear distinction between the
TPB elements of self-efficacy and perceived control. They noted that we cannot assume
that an individual’s feelings about how external factors might influence a behavior
(perceived control) will be the same as their feelings about easy it might be to perform
the behavior (self-efficacy).
Social Reinforcement of boating skills is defined as positive or negative feedback
(in the form of criticism or approval of a behavior) that a boater receives from peers,
family, and/or influential groups that then increases or decreases the likelihood of the
boater to repeat the behavior in question. While other interpersonal and contextual
factors should be considered, studies have shown that by enhancing self-efficacy and
social support, you can increase participation in desired behaviors (Peterson 2008,
Heller and others 2004). By understanding the level of reinforcement (both positive and
negative) for this conservation behavior, we can collect evidence about the social
interactions that affect sea grass scarring.
As was previously stated, observational learning occurs when people watch and
assess the behaviors of others. Those who are watched often include credible role
models of the targeted behavior, including teachers and other participants. Within the
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context of my research, those who are watched might include other boaters and
anglers, law enforcement officers, and family members and friends. Examining the role
that observational learning plays in conservation behavior allows us to determine what,
if any, teaching strategies or techniques can be used to change existing behaviors or to
establish new ones. Incorporating observational learning into training can significantly
improve a participant’s performance and information retention (Yi and Davis 2003).
Hypotheses
This study sought to differentiate boater user groups according to boating skills,
learning processes, and sea grass scarring behavior in order to devise targeted sea
grass management and protection strategies. My hypotheses related to the research
variables have 7 parts.
1) Self-efficacy and control belief scores will be correlated with reported on-the-
water behavior. The lower a boater’s self-efficacy and control belief scores, the more
likely that boater will have engaged in behavior that damaged the grass flats.
2) Boaters with more years of experience on the water will have higher self-
efficacy scores and higher control belief scores. Boaters with less experience will have
lower self-efficacy scores and lower control belief scores.
3) The more times that a boater was on the water in Southwest Florida during the
past year, the higher will be that boater’s self-efficacy and control belief scores. Boaters
who have been out on the water in Southwest Florida very few times will have lower
self-efficacy scores and lower control belief scores.
4) Boaters with higher levels of boating education and those who have completed
more boating safety courses will have higher self-efficacy scores and higher control
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belief scores. Boaters with less boating education will have lower self-efficacy scores
and lower control belief scores.
5) Boaters who are full-time residents of Southwest Florida will have higher self-
efficacy scores and higher control belief scores than will boaters who are part-time
residents of Southwest Florida, and boaters who are part-time residents will have higher
self-efficacy scores and higher control belief scores than will visiting boaters and those
who do not reside in Florida.
6) Boaters who own their boat will have higher self-efficacy scores and higher
control belief scores than those who do not. Individuals who are members of a boat club
or who rent their boat will have lower self-efficacy scores and lower control belief
scores.
7) Boaters will have a high level of observational learning from watching other
boaters and anglers on the water and social reinforcement among boaters will not
encourage conservation behavior. Prop scarring will not be criticized among boaters
and their peer groups and that will translate into a lack of negative reinforcement to
impede grass damaging behavior.
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Table 2-1. Theory of Planned Behavior Constructs and Definitions Direct Constructs Construct Elements Definition Attitude Toward Behavior Overall evaluation of the behavior being
examined; Feelings about performing a behavior (positive or negative)
Behavioral Beliefs Belief that behavioral performance is associated with certain outcomes (ability to affect attributes)
Outcome Behavior Value attached to the behavioral outcome
Subjective Norms Perceptions about whether most people approve or disapprove of a behavior
Normative Beliefs Beliefs about the normative expectations of others, whether each referent approves or disapproves of the behavior
Motivation to Comply Individuals’ motivation to carry out what referent approves of or avoid what they disapprove of
Perceived Behavioral Control
Individuals perception of the degree to which a behavior is under their volitional control
Control Beliefs Beliefs about the presence of factors that may facilitate or impede performance of the behavior
Self-Efficacy Ability to perform a behavior Behavioral Intentions Perceived likelihood of performing a
behavior, process of deciding to engage in desired behavior
Behavior Desired Behavior
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Table 2-2. The Social Cognitive Theory Constructs and Definitions Overall Constructs Construct Elements Definition Environmental Determinants
Family, Friends, Peers Factors physically external to the person, providing opportunities and social support
Personal Determinants Expectations Anticipatory outcomes of a behavior; Model positive outcomes of healthful behavior
Situation Perception of the environment; correct misconceptions and promote healthful forms
Self-Control Personal regulation of goal-directed behavior or performance; Provide opportunities for self-monitoring, goal setting, problem solving, and self-reward
Behavioral Capability Knowledge and skill to perform a given behavior; promote mastery learning through skills training
Behavioral Determinants
Maintenance - Emotional Coping
Strategies or tactics that are used by a person to deal with emotional stimuli; provide training in problem solving and stress management
Maintenance - Reciprocal Interactions
The dynamic interaction of the person, the behavior, and the environment in which the behavior is performed; consider multiple avenues to behavioral change, including environmental, skill, and personal change.
Acquisition - Observational Learning
Behavioral acquisition that occurs by watching the actions and outcomes of others’ behavior; Include credible role models of the targeted behavior
Acquisition - Reinforcement
Responses to a person’s behavior that increase or decrease the likelihood of reoccurrence; Promote self-initiated rewards and incentives
Acquisition - Self Efficacy The person’s confidence in performing a particular behavior; Approach behavioral change in small steps to ensure success
Behavior Desired Behavior
28
Figure 2-1. Model of the Theory of Planned Behavior derived from Ajzen’s Theory of Planned Behavior (1991).
Figure 2-2. Model for the Social Cognitive Theory derived from Bandura’s Social Cognitive Theory (1977)
29
CHAPTER 3 METHODS
Researchers have used many different methods during the planning, development,
implementation, and evaluation phases of social marketing programs. Many of the tools
used in social marketing programs, such as focus groups, interviews, and intercept
surveys, originated in the field of commercial market research (Andreasen 1995). For
my research, I used a quasi-experimental design to sample boaters randomly and
thereby measure the range of differences in skill and learning within this population. I
also used inferential statistics and mixed methods to text my hypotheses and reach
conclusions about the population as a whole.
Key Informant Interviews
The data collection process began with key informant (expert) interviews to help
guide development of the questionnaire that was used to survey boaters. I conducted
13 informal interviews with law enforcement officers, fishing guides, educators, and
resource managers. The topics that were discussed included pressing issues in the
area, the different boating user groups, and the experts’ sentiments surrounding sea
grass conditions in certain areas of Greater Charlotte Harbor. I also asked the experts
their opinions about factors that contribute to sea grass scarring by boaters and I
queried them about current management and education strategies to protect sea grass.
In addition, the experts provided insights regarding the sentiments of local people
toward sea grass damage and current management and education programs designed
to address the issue. The experts also offered guidance on how best to approach
certain boating user groups. I used the information the experts provided to help target
my audience and to choose the boat ramps and marinas where the boater surveys
30
would be conducted. Subsequent to the interviews, some of the experts reviewed,
critiqued, and edited the questionnaire and survey elements that I developed and
helped me to develop a comprehensive interview guide. The experts who were
interviewed included the following:
• Marine Operations Manager
• Environmental Specialist, FDEP
• FWC Law Enforcement Officer
• County Waterways Coordinator
• Manager, Fisheries Habitat Ecology Program
• Estuary Program Scientist
• Marine Patrol Officer
• Sea Grant Extension Agents (2)
• County Environmental Specialist
• Owner, Tackle business
• Fishing fleet Captain; Instructor of Boating Safety courses
• Coastal Conservation Association Coordinator
One of the first issues that came to light during the interviews was the potential
ramifications of the Lee County Noticed General Permit (NGP), a recent state
administrative rule (62-341.494). The agency experts described the reaction of Lee
County residents and boaters, expressed during public meetings, to mitigation and
zoning requirements that will affect boating patterns and institute sea grass protection
zones (NICMZs). Representatives from the National Estuary Program and FDEP
explained how the zones (NICMZ) will function and the long process that led to the
31
development of the underlying regional waterway management plan. Their hope is that
the management plan, the NGP, and the zones will serve as models for other parts of
the state. However, some local anglers, law enforcement officers, and environmental
groups disagreed with the rule measures being taken. This highly charged issue set the
stage for all other discussions about sea grass scarring and it informed me of the
attitudes and tensions in the area.
Informants in fisheries related fields provided insights into angler issues and
attitudes. They provided me with their opinions about the groups and types of boaters
that they felt were scarring sea grass and why they might be doing so. Local anglers
helped me understand the complexity of the sea grass scarring issue, areas where
scarring occurs, as well as appropriate terminology to use when discussing sea grass
scarring and associated boating behaviors. While many fishermen understood the
importance of sea grass health to fisheries, they believed that other issues such as
water quality and pollution were more important than propeller scars.
There was a diversity of opinions about how informed the public is about sea
grasses and sea grass scarring. Furthermore, it would seem that the diversity of anglers
and boaters makes it difficult to generalize levels of knowledge about sea grasses and
scarring behavior according to specific user groups. However, many of the key
informants believed that tourists and tournament fishermen were the main culprits (i.e.,
those responsible for sea grass scarring) and their sentiment was echoed during my
review of posts on fishing related websites. The experts informed me of the problems
that I might encounter trying to survey transient boaters who visit for limited amounts of
time, such as tourists and tournament fishermen.
32
With regards to potential solutions, many informants noted a decrease in general
boating and navigational knowledge among boaters and tied it back to the experiential
learning element of boating. Many law enforcement and marine patrol informants
suggested mandatory boating licenses or boating classes while others recommended a
day on a boat with a professional guide. Others suggested targeting the rental boat
community to promote safe boating practices by tourists. There was also mention of
assessing boat dealerships and stores to find channels where information and
education was not being received by boaters. Some agency experts suggested mailing
navigation maps and additional educational materials to registered boaters. While none
of these solutions were directly applicable to my project, the results of my research
could help determine if, when, and how each might be implemented.
Intercept Survey Procedures and the Questionnaire
Intercept surveys are a common method of pretesting materials such as surveys
and (Weinreich 1996). During this process, potential subjects are approached in a
public area and asked to respond to a questionnaire. For my research, I completed 252
questionnaires on 23 separate days (12 weekend days, 11 weekdays, 1 holiday)
between August 2009 and January 2010 at ten public boat ramps and public marinas in
Lee and Charlotte counties (Figure 3-1). The sampling locations were chosen because
of their accessibility to shallow water habitats, their proximity to damaged sea grass
beds, and their popularity with boaters.
Since the behavior of interest to my research was related to boating in shallow
waters, I selected boat operators above the age of 18, at random, who were launching
or retrieving shallow draft boats. Many boaters thought that I was collecting information
about fish for the Florida Fish and Wildlife Conservation Commission, so I usually would
33
introduce myself and ask what they had caught that day. I then explained who I was and
the purpose of my research, read them an informed consent that explained their rights,
and gave them my contact information. The questionnaire had three indices relating to
self-efficacy, control beliefs, and reported behavior as well as a self-efficacy test and
demographic questions. Completing the questionnaire took about ten minutes and, in
most cases, I read the questions to the respondent/boater as he or she was launching
or retrieving a boat.
Many captains and fishermen who were eager to begin fishing were unwilling to
speak with me at the start of their boating day (when launching) because of time or tide
constraints and, therefore, the majority of their responses were collected in the
afternoon when they returned to the ramp. Recreational boaters were much less
concerned about timing and were willing to answer questions whether they were
launching or retrieving their vessel. Unfortunately, much time was expended (lost) in
waiting for boat operators to finish various tasks, such as trailering their boat or cleaning
fish.
I had the approval of the marina and boat rental facility owners to approach their
customers and the support of their staff to help convince clients to answer my
questions. Generally, boaters at the marinas and boat clubs took more time to talk to
me because they did not have to load their boats onto a trailer. Approximately 50% of
rental boaters declined my survey request because they had paid their rental fee and
were leaving for their hotels or homes. Three rental boat respondents were German
tourists and the language barrier made it difficult to fully understand their responses,
even though an interpreter was present.
34
The weather was hot during the beginning part of my sampling period and people
were much less willing to stand in the sun and answer questions. However, as the
weather cooled down and the “snow birds” arrived, boaters were more willing to take the
time to talk to me. Even with improvements in the weather, less than half
(approximately 40%) of the people I approached were willing to take the ten minutes to
answer the survey. I collected these surveys over a wide variety of times and days of
the week but the refusal rate stayed fairly constant.
I anticipated some problems such as (a) difficulty in getting people to participate
at busy boat ramps and marinas, (b) overestimation by people of their confidence levels
or skills, and (c) a possible tendency to report desirable behavior. These issues and
possible inaccuracies were best addressed with the creation of multi-item measures for
each of the variables. Conner and McMillan (1999) report that the use of these
measures for sensitive subjects like drug use is a reliable way to measure intention
because the number of items in the scale make it difficult to consistently report desirable
behavior. Before combining items into a single scale, it is necessary to make sure that
there is a high degree of internal consistency among the items. Clark and others (2003)
have used these internal consistency measures to examine scales of pro-environmental
behavior. Values for an item-total correlation between 0 and 0.19 may indicate that the
question is not discriminating well, values between 0.2 and 0.39 indicate good
discrimination, and values 0.4 and above indicate very good discrimination.
Self-Efficacy
I used a self-efficacy index as the first part of my questionnaire because it
allowed me to create a multi-item measure of people’s attitudes and perceptions about
concepts (Bryman 2004). The self-efficacy index enabled me to combine multiple
35
elements and thereby better understand boater skill levels as they relate to behaviors
exhibited on the water. In addition, I created test questions to measure skills related to
shallow water boating.
Initially, thirty self-efficacy index items, in particular literature related to the use of
Likert scales to measure driving self-efficacy (Elliot 2007). The self-efficacy statements
began with the phrase “How confident do you feel that you can…?” and were followed
by a series of statements about shallow water boating skills. The expert panel reviewed
the questions for clarity, wording, and importance and, after a field test; the number of
index items was reduced to 16 that were determined to have the most discriminatory
power (See Table 3-1). Items with correlations below 0.4 were eliminated to improve
overall reliability, as discussed in Riekert and Drotar’s (2002) study that created an
index for beliefs about medication. After conducting item total correlations to determine
reliability, the index items were then cut to 11 statements regarding the level of
confidence in their boating ability (on a scale of 1 to 5). The overall reliability determined
by Cronbach’s alpha was 0.81 (Table 3-5).
Initially, fifteen self-efficacy test questions were generated based on key
informant interviews and a review of the literature. These questions were created to test
the ability of the boater to navigate in shallow water environments and examine if they
would respond appropriately in a situation involving sea grass. After a review by an
expert panel, five questions were eliminated based on clarity and applicability to
navigation. The remaining ten items were examined and item total correlations were
performed to determine the most reliable index items. Items with values above 0.4 were
kept for the final instrument (See Table 3-2). The final six test questions that were
36
scored out of a possible eight points gave them a boating ability score. Each boater will
have a total self-efficacy index score out of 55 points and a self-efficacy test score out of
eight points, generating interval data.
Control Beliefs
To collect data on the boater control beliefs, I used the literature to generate
statements that would accurately reflect different levels of perceived difficulty and level
of control on the part of the boater. I also used the information from my key informant
interviews to include items that boaters would realistically have to deal with on the
water. To create the perceived difficulty statements, I generated an index of agreement
on a scale of one to five, one being strongly disagree and five being strongly agree
based on the scales defined by Manstead and Van Eekelen (1998). Respondents were
then asked to decide how much they agreed or disagreed with the statements about
performing boating behaviors consistent with conservation of sea grasses. For example,
one statement that was used was “Avoiding running aground is easy for me”. For the
perceived control index, I used the same scaled response of agreement, but generated
statements that addressed the level of control they felt they had over conservation
behaviors. For example, one statement reads, “Avoiding running aground is a matter of
luck”. I generated ten perceived difficulty statements and 16 perceived control
statements based on the methodology found in the Sheeran paper regarding perceived
behavioral control (2003). They were then reviewed for clarity, wording, and accuracy
resulting in eight perceived difficulty and twelve perceived control statements. Those
statements were then field tested for discriminatory power (See Table 3-3), edited, and
cut to 14 total statements to create the control belief variable. Respondents generated
interval data from this scale and were scored of a possible 75 points.
37
Reported Behavior
To collect data about reported behavior, I consulted a social marketing expert at
Florida Gulf Coast University to help me establish how to get truthful answers about
boating behavior. Preliminary findings from my key informant interviews and previous
experience made me concerned that local boaters might not willing to candidly discuss
personal grass scarring behaviors. Parker (1992) reported that even with a sensitive
subject like drinking and driving, the standard regression for the perceived behavioral
control were negative, meaning as perceived behavioral control increased, behavioral
intentions decreased. With drinking and driving as well as speeding behaviors, Parker
(1992) found that the construct of Perceived Behavioral Control was the single largest
predictor of intentions. In that case study, it was also suggested that respondents could
have deliberately underreported their perceived control over the violations as a type of
ego defense. The use of self-report scales in relation to behaviors that are self-evidently
socially undesirable raises the possibility of response bias (Parker 1992). If the bias
enters into the results, it might mean a completely different strategy of enforcement for
the drivers or in our case, the boaters. Therefore, it was critical to establish a method to
accurately measure reported behavior on this subject. Based on my informant
interviews and literature I generated a series of statements that described various types
of boating behaviors that could destroy sea grasses, including running aground,
scraping bottom, or creating a propeller scar in the grass (Sargent 1995, Uhrin and
Holmquist 2003, Zieman 1976). I asked participants to describe how often they engaged
in those behaviors (e.g. Never, Once a Year, Every time - See Appendix A). I
established the categories of response based on the self-reported frequency of behavior
established by researchers looking at health behaviors (Sutton 1999). Starting with 20
38
statements, I had an expert panel review these for wording, clarity and applicability to
Charlotte Harbor. I refined the behaviors to 13 items that I pretested for discriminatory
power, resulting in eight final items with item total correlation values above 0.40 (See
Table 3-4). These responses were assigned to interval categories for comparison with
the self-efficacy and control belief scores. This part of the questionnaire was located at
the end of the survey in an attempt to gain trust from the participants so that they would
answer the behavior questions truthfully.
Demographic Information
The demographic information collected about these participants was related to
their residency, years of experience, time on the water in shallow water environments,
and participation in boating courses. These categories have been used in many other
boating surveys and were selected as the variables that might distinguish groups of
boaters in terms of skill level (Sidman and others 2005, Cottrell 2003). Since most of the
boaters in the area are usually fishing, I did not make a distinction between fishermen
and pleasure boaters. I also collected data about the type of boat that they were
operating and the draft of that boat to get more information about the types of boats that
are damaging the sea grass beds the most. This section created the independent
variables that were compared to the self-efficacy and control beliefs scores. Since this
section of the index questionnaire generated interval and nominal data, a factor analysis
was used to generate interval scores based on the independent variables.
Data generated from the questionnaire was tested for normality, and the means
were compared for each of these user groups by performing an ANOVA and
nonparametric tests. I also examined any correlations between the independent and
39
dependent variables, and regressions to evaluate the strength of the relationships
between groups for each index score (Parker 1998).
Interviews
Structured interviews are often used for exploration of topics and can also
provide data regarding the target audience, such as insights into their language, issues
and obstacles they identify, and meanings attributed to beliefs and behaviors
(Andreasen 1995). In-depth interviews are also used to help segment the target
audience and provide detailed profiles of those targets for message development and
appropriate channels for getting information to those groups. In the case of examining
behavioral reinforcement, I wanted to understand how peers and other boaters
influence behavior and if certain groups reinforce conservation behaviors relating to sea
grass. I was also very interested in understanding how the social dynamic between
boaters plays into different types of reinforcement or behavior acquisition. I chose to use
the structured interview to gain in depth information about the different groups of
boaters (Bryman 2004). I selected a heterogeneous group of boaters from all different
age and experience levels to get at the differences between boaters and how they
influence each other. Because I am targeting boat operator and passengers, I recruited
a diverse group of people based on contact information from boat club and boat rental
agencies, as well as from personally referenced members of the community.
Based on literature and information from my key informants, I developed an
interview guide with ten questions about observational learning and ten questions about
reinforcement from peers. The guide was developed using other interviews that
incorporate social cognitive elements of reinforcement and observational learning
among students and middle-aged participants (Longo and Lent 1992, Baldwin and
40
others 1996). I sent those questions to my expert panel for review. They then edited
those questions down to 12 total questions and added two additional questions
regarding management of the sea grasses. I pilot tested this interview format with a
known fishing captain and adjusted two of the questions based on his debriefing. This
was submitted to IRB for approval and once it was approved, I began conducting
interviews. Each participant was interviewed by phone and asked if they would be
willing to give any additional contact information for three other people. The informed
consent was read to them over the phone and if they agreed, I continued to interview
them. Each interview took approximately 40 minutes to complete. I interviewed two
tournament fishermen, five guided charter captains, two rental boat operators visiting
the area, one visitor operating a personally owned vessel, three boat club members,
three full time resident recreational anglers, and two part time resident recreational
anglers for a total of 18 interviews. I was limited in my ability to get information from
rental boat operators because of their transient nature and their unwillingness to give
out personal information. I contacted 24 boaters from personal contacts as well as
references from other interviewees and boaters in the area. My response rate was high
(75%) because I had personally met many of the participants to get confirmation for
their availability to participate in the interviews. I took detailed notes of each
interviewee’s responses and I then recorded personal reflections and memos about the
interview directly after it was conducted. I checked the responses with the participants
at the end of every interview by giving them a summarized version of what I heard them
say. Each interviewee was assigned a number to protect the identity of the participants
and I refer to that number when using direct quotes. After every three interviews, I
41
established what I found to be emergent themes and expected themes and that allowed
me to focus on those issues for later interviewees. I assigned themes and attitudes to
similar statements to create a thematic analysis of the responses and then developed
data regarding the frequency of their responses. As discussed by Zimmerman (1986),
participants were categorized and labeled as visitors, part time residents, full-time
resident recreational fishermen, and full-time resident guide based on the personal
information provided. The interview text was then organized to allow retrieval of key
phrases and themes during the analysis. Responses were organized by anticipated and
emergent themes and categories based on the frequency of response. I then organized
those ideas into a table to better clarify how the themes related to the emergent and ad
hoc categories. In exploring the relationships between groups of boaters, I categorized
the major differences and similarities in learning styles, peer reinforcement, and
knowledge of the issues of sea grass scarring.
Social Reinforcement
Questions in this category were related to the various types of reinforcement that
could be happening on the water. I began by listing all the types of interactions that
could happen to reinforce conservation behavior on the water. I was able to eliminate
many of the interactions based on the opinion of my expert panel and with pretesting of
the interview. I then asked the interviewees to list verbal, non-verbal, or implied
reinforcement that they might have experienced on the water and at local boat ramps
and marinas to provide insight into positive and negative reinforcement measures.
Observational Learning
Observational learning questions were asked of each interviewee to determine
how observational learning was occurring and who the models of behavior might be. I
42
asked the interviewees questions about modeling the behavior of other boaters and
anglers, law enforcement, family and friends and if they thought that they were being
watched or learned from on the water. This line of questioning also included credible
role models of the targeted behavior or other teachers and participants. They were also
asked how they learned about new regulations and what sources of information they
trusted for information.
Current Management
While this was not defined as part of my variables, it was important for me to
understand how current management actions were being received and the level of
knowledge that the user groups had about the new zones that were being put in. This
section was developed to aid managers and agencies in their effort to educate users
groups and involve them in the management of the grass beds. I also investigated the
different types of sources that the boaters and anglers used to get new information
regarding fishing and boating regulations.
Data Collection
The survey items and the interview guide were then submitted to IRB for approval
and once they were approved with IRB #U-818-2009, I began collecting data from
boaters in the Charlotte Harbor region.
43
Figure 3-1. The ten sampling locations for the questionnaires (n=252) in Lee and Charlotte County, selected based on usage and proximity to sea grass beds.
44
Table 3-1. Self-Efficacy Index Item-total correlations and Cronbach’s alpha values for reliability testing, n=36, 2009
Item – “How confident do you feel that you can…?”
Item-total Correlations (ri-r)
Cronbach’s alpha
Overall Self-efficacy Index 0.68 1. Understand navigational markers 0.26 2. Interpret navigational charts 0.47 3. Know what the tides are while boating 0.64 4. Read Chart symbols 0.33 5. Stay in marked channels 0.41 6. Navigate through channels 0.13 7. Maintain appropriate speed in channels 0.22 8. Recognize which boat has the right of way 0.40 9. Know what speed minimum wake is for your vessel 0.51 10. Remember the “rules of the road” 0.32 11. Determine if local winds will affect tides 0.29 12. Avoid running aground while boating 0.43 13. Avoid scarring grass flats while boating 0.49 14. Estimate water depth while boating 0.58 15. Navigate in an unfamiliar area 0.61 16. Recognize safe water for your boat based on draft 0.69
Table 3-2. Self-efficacy test item-total correlations and Cronbach’s alpha values for
reliability testing, n=36, 2009
Item Item-total Correlations (ri-r)
Cronbach’s alpha
Overall Self-efficacy Test 0.59 1. Navigational Test 0.11 2. Which speed draws the most water? 0.53 3. What would you do if you started to scar the sea grass?
0.69
4. What would you do if you ran hard aground? 0.36 5. What would you do if you entered an idle zone? 0.42 6. What do PVC pipes in shallow waters mean? 0.29 7. Average water depth when sea grass found? 0.18 8. Dangerous zone regulatory marker 0.56 9. Closed zone regulatory marker 0.61 10. Information regulatory maker 0.52
45
Table 3-3. Control Belief index item-total correlations and Cronbach’s alpha reliability values, n=36, 2009
Item Item-total Correlations (ri-r)
Cronbach’s alpha
Overall Control Belief Index 0.66
1. It is easy for me to gauge the depth of the water in the harbor.
0.30
2. It is easy for me to avoid damaging the grass flats. 0.55
3. It is easy for me to keep up-to-date navigational charts.
0.49
4. It is easy for me to use a GPS to avoid shallow problem spots in the harbor.
0.51
5. It would be easy for me to navigate in the marked channels.
0.42
6. It is easy for me to find a boating safety course near where I live.
0.59
7. It is easy for me to gauge the depth of the water in the shallower areas.
0.52
8. I do not have enough knowledge of the area to avoid running aground.
0.66
9. It is easy for me to find out more about sea grasses in the harbor.
0.23
10. Whether or not I stay in the channel is completely up to me.
0.27
11. It is up to me to decide when to travel into a shallow area.
0.29
12. I don’t have time to go around the grass flats. 0.61
13. Tides control my chances of avoiding prop scarring.
0.74
14. Avoiding damaging grass flats is a matter of luck. 0.68
15. Avoiding running aground is a matter of luck. 0.67
16. I do not have any impact on the grass. 0.32
17. I have full control over my boat. 0.43
18. I cannot prevent my propeller from scarring the grass.
0.53
19. If I cause prop damage to grass flats, it is usually accidental.
0.38
20. Whether or not I prop scar is completely under my control.
0.69
46
Table 3-4. Reported Behavior Index Item-total correlations and Cronbach’s alpha reliability testing, n=36, 2009.
Item – “Within the last year, how often have you…?”
Item-total Correlations (ri-r)
Cronbach’s alpha
Overall Reported Behavior Index 0.63 1. Traveled into shallow waters on a low tide 0.30 2. Felt your motor bump bottom in a shallow area 0.55 3. Cut corners on a channel and churned up grass 0.49 4. Prop scarred on accident while boating 0.51 5. Created prop wash with grass and mud 0.59 6. Run aground in a shallow area 0.68 7. Seen that your motor has uprooted sea grasses 0.14 8. Created a prop scar near a channel 0.33 9. Had to get out of your boat and push 0.66 10. Damaged your engine or propeller by running aground
0.54
11. Needed to be towed off because of grounding 0.29 12. Attempted to power off a grass flat 0.43 13. Purposely prop dredged to create a channel 0.12
Table 3-5. Overall scalar responses for the Self-efficacy index, self-efficacy test, control
belief index and the reported behavior index descriptive statistics and reliability data, n=252, 2009
Scale and Number of items Mean Range Mean Item-
total Correlations
Cronbach’s alpha
Self-Efficacy Index (n=11) 38.50 21 - 50 0.58 0.82 Self-Efficacy Test (n=6) 04.25 0 - 8 0.73 0.77 Control Belief Index (n=14) 52.06 25 - 70 0.71 0.80 Reported Behavior Index (n=8) 11.30 0 - 33 0.55 0.71
47
CHAPTER 4 RESULTS
Descriptive Statistics
The mean scores of the self-efficacy index, self-efficacy test, control belief index
and reported behavior index are displayed in Table 4-4. My hypotheses began with the
assertion that self-efficacy and control beliefs would be correlated with reported
behavior. The scores of both the self-efficacy index and reported behavior index are not
normally distributed and therefore they cannot be tested with parametric tests,
illustrated visually with Figure 4-1 and 4-4 (See Appendix C). Figure 4-2 shows that the
mean score on the Self-Efficacy Test was 4.25 out of a possible 8 points and recorded
values ranged from 0 to 8. The normality of the overall population and the user groups
by residency allowed for parametric comparisons. These tests allowed me to explore
my hypothesis that part-time residents and visitors would have lower self-efficacy and
lower control beliefs than full-time residents. The One way ANOVA Test for each of the
residency categories by Self-Efficacy Test score. Group one (full-time residents) differs
from Group four (visitors) (p<0.001) and Group three (part-time residents) differs from
group four (visitors) (p<0.001) as seen in Appendix C. There was no significant
difference between the full-time and part-time residents. These results were supported
by a non-parametric method of testing call Kruskal–Wallis one-way analysis of variance.
The test showed that for self-efficacy and control beliefs, we can reject the null
hypothesis that the values are distributed equally across all residency groups with an
alpha of 0.001. My hypotheses regarding the correlations between experience on the
water and times on the water each year proved to be accurate. The p values for the
Self-efficacy Index regressions with residency, years of boating experience, Years of
48
Southwest boating experience, and number of times on the water were all less than or
equal to 0.05 as seen in Table 4-5.The p values for the Control Belief Index regressions
with residency, years of boating experience, Years of Southwest boating experience,
and number of times on the water were all less than or equal to 0.05. The p values for
the Self-efficacy Test regressions for all variables were greater than 0.05. Reported
behavior regressions showed that only the years of boating experience in Southwest
Florida produced a p value of less than 0.05.
My hypotheses on the effects of boating education and boat ownership on self-
efficacy and control beliefs required alternative methods. The level of boating education
was collected as ordinal data and boat ownership is in the form of nominal data. These
populations were not normally distributed and had unequal variance so I performed
Kruskal–Wallis one-way analysis of variance non-parametric test to see if there were
differences based on these items within the indices. According to the test, all of the
alpha values were over 0.05, explaining that I should retain the null hypothesis that
there is no difference in median values for any of the indices across all levels of boater
safety courses. This finding was confirmed by the correlations performed. This finding
does not match up with what I originally hypothesized and it suggests that level of
boating instruction does not show a difference within these scales. Boat ownership as
an item showed a different result. The Kruskal-Wallis ANOVA showed that self-efficacy
and control beliefs both had alpha values of 0.001, allowing me to reject the null
hypothesis that the distribution of values was the same across all categories of boat
ownership. Correlations between boat ownership and self-efficacy and control beliefs
also were p> 0.001, with reported behavior at p=0.680.
49
Figure 4-5 shows the difference between the three resident groups with regards
to time on the water. Residents had an average of 25 times more trips than visitors.
Table 4-6 shows the various responses to the interview questions by frequency of
response and categorized by user group. Over 50% of the sample identified
inexperienced boaters and weekend warriors as the source of most of the sea grass
scarring.
Factor Analysis
I used factor analysis to determine which independent demographic items were
associated with each other. Tables 4-1 and 4-2 display the loading factors and
Eignenvalues of each variable. Factors with Eigenvalues over one were kept in the
analysis for further investigation, keeping in mind that there may be fewer meaningful
factors that exist within the data.
The initial results of the factor analysis showed four distinct factors with values
greater than one that were influential among the independent variables collected from
the demographic data. After a varimax rotation was used to create orthogonal factors to
use as the independent variables in the regression analysis, four factors explained
63.9% of the total variance in the data (see Appendix C). The varimax rotation assisted
in differentiating the original variables by extracted factor, showing results which make it
as easy as possible to identify each variable with a single factor. This rotation has been
used in other recreational studies and marketing analyses to simplify complex
independent variables (Spotts 1997). The loadings represent a correlation between the
item and the overall factor and these values can range from -1 to 1. In confirmatory
factor analysis, loadings higher than 0.7 can be confirmed independent variables
represented by a particular factor, on the rationale that the 0.7 level corresponds to
50
about half of the variance in the indicator being explained by the factor (Cottrell 2003).
Some researchers will use a lower level such as 0.4 for the central factor for exploratory
purposes (Jurowski 1995). I have selected the items with loadings over 0.7 for the first
three factors and I then worked with the 0.4 loading for Factor 4.
The variables that loaded highly for factor one were the type of boat and draft of
the boat in inches. This was labeled the Boat Factor. Boat type was assigned nominally
with the largest boats being categorized as higher scores and the shallower drafting
boats as lower scores (from one to eight). The Eigenvalue of this factor explained 25.6
percent of the variance of the factor analysis. Since boat type is directly related to the
draft of the boat, this factor has a clear meaning as an independent variable.
The second factor included the age of the participant and the total number of
years of boating experience. This factor was labeled as the Years Experience Factor.
Both of these measures are inter-related and so it seems clear that this is a useful
measure for the independent variables. The Eigenvalue of this factor explained 17.1
percent of the variance of the factor analysis.
The third factor examined the number of times an individual was on the water in
the past year and their level of boater education; both of the variables were negatively
loaded. Since I assigned a nominal value ranging from one to four for participant
training, and boating education and an interval value for number of times on the water,
this scoring method may partially explain the negative loading. Additionally, the captains
and guides in the sample were experienced; thus, it makes sense that the variables
would be related. This factor was labeled as the Captain Factor. The Eigenvalue of this
factor explained 11.1 % of the variance of the factor analysis.
51
Factor four included knowledge of the draft of the boat and type of boat
ownership. The knowledge of the draft of the boat was assigned a nominal score of 0 or
1, and it was negatively related to boat. The types of boat ownership included owned,
rented and borrowed and were also assigned a nominal score from one to four. This
scoring system meant that knowledge was negatively related to ownership and that
those who had higher ownership scores (borrowed or rented) had lower knowledge
scores with regards to the draft of the boat. People who owned their boat were more
likely to know the draft of the boat. This factor was called the Ownership Factor. The
Eigenvalues of this factor explained approximately 10 % of the variance, indicating it
was a less powerful explanatory variable. The significance of each of these factors on
the dependent variables can be seen in Table 4-3.
Table 4-1. Factor Loadings (Varimax Raw) for each Independent Variable related to Demographic information
Variable Factor 1 Factor 2 Factor 3 Factor 4 Age 0.026323 0.824285 0.186859 -0.000401
Residency 0.476639 0.095229 0.408942 0.396151
Yrs Experience
-0.045708 0.895637 -0.109427 0.034419
SW Yrs Exper
-0.303619 0.440864 -0.406593 -0.161841
Times on water
-0.227113 -0.027502 -0.772846 -0.093469
Boat type 0.754312 -0.054559 0.012505 -0.193718
Boater Safety
0.042098 0.003325 -0.831200 0.059772
Boat ownership
0.458423 -0.291591 0.011324 0.520991
Draft y/n 0.090313 -0.069956 -0.007888 -0.829413
Draft inches 0.745772 -0.000461 0.162675 0.103892
Expl.Var 1.718975 1.778704 1.694437 1.204301
Prp.Totl 0.171898 0.177870 0.169444 0.120430
52
Table 4-2. Eigenvalues for each of the Factors based on Factor Loading Value Eigenvalue % Total Cumulative /10 Cumulative/100 Factor 1 2.560237 25.60237 2.560237 25.60237
Factor 2 1.718495 17.18495 4.278732 42.78732
Factor 3 1.110265 11.10265 5.388997 53.88997
Factor 4 1.007420 10.07420 6.396417 63.96417
Table 4-3. Regression Estimates for the Self-Efficacy Index, Self-Efficacy Test, Control
Belief Index, and Reported Behavior by Factor, n=252, 2009 Variable Term RSquare Estimate Std Error t Ratio Prob>|t| Self-efficacy Index
Factor 1 0.12116 -0.05106 0.01022 -5.00 0.0001*
Factor 2 0.00046 0.00314 0.01090 0.29 0.7730
Factor 3 0.06101 -0.03623 0.01056 -3.43 0.0007*
Factor 4 0.11413 -0.04955 0.01026 -4.83 0.0001*
Self-efficacy test
Factor 1 0.06300 -0.16579 0.04752 -3.49 0.0006*
Factor 2 0.00034 -0.01237 0.04908 -0.25 0.8024
Factor 3 0.00854 -0.06105 0.04888 -1.25 0.2133
Factor 4 0.00128 -0.05195 0.04894 -1.06 0.6321
Control belief index
Factor 1 0.18527 -0.05500 0.00857 -6.42 0.0001*
Factor 2 0.00012 0.00143 0.00949 0.15 0.8802
Factor 3 0.01925 -0.01773 0.00940 -1.89 0.0610
Factor 4 0.12855 -0.04582 0.00886 -5.14 0.0001*
Reported behavior index
Factor 1 0.00703 -0.01273 0.01124 -1.13 0.2590
Factor 2 0.00130 -0.00549 0.01127 -0.49 0.6268
Factor 3 0.00031 0.00267 0.01128 0.24 0.8130
Factor 4 0.03116 -0.02680 0.01110 -1.58 0.1156
53
Table 4-4. Descriptive Statistics for the Self-efficacy index, Self-efficacy test, Control belief index, and Reported Behavior index, n=252, 2009
Index Mean Median Range Variance Standard Deviation
Self-efficacy index
38.50 39.00 29 (21-50) 60.13 07.75
Self-efficacy test
04.25 04.00 8 (0-8) 02.33 01.52
Control belief index
52.06 54.00 45 (25-70) 84.67 09.20
Reported behavior index
11.30 11.00 33 (0-33) 42.54 06.52
Figure 4-1. Histogram of Self-Efficacy Index Scores by Frequency, n=252, 2009
54
Figure 4-2. Histogram of Self-Efficacy Test Scores by Frequency n=252, 2009
Figure 4-3. Histogram of Control Belief Index Scores by Frequency, n=252, 2009
55
Figure 4-4. Histogram of Reported Behavior Index Scores by Frequency n=252, 2009
Table 4-5. Regression Estimates for the Self-Efficacy Index, Self-Efficacy Test, Control Belief Index, and Reported Behavior by Independent Variable n=252, 2009
Variable Term RSquare Estimate Std Error t Ratio Prob>|t| Self-efficacy index
Residency 0.05 -1.48 0.40 -3.70 0.0003*
Age 0.01 0.03 0.03 0.96 0.3390
Years Boating
0.02 0.07 0.02 2.56 0.0110*
Years SW Boating
0.11 -0.04 0.01 -4.83 0.0001*
Times on Water
0.01 0.09 0.04 2.08 0.0380*
Boater Safety
0.01 0.01 0.01 1.57 0.1134
Ownership 0.12 -0.04 0.01 -5.97 0.0001*
Self-efficacy test
Residency 0.01 -0.46 0.08 -1.89 0.0520
Age 0.01 0.01 0.00 0.53 0.5973
Years Boating
0.01 0.01 0.01 1.69 0.0922
Years SW Boating
0.01 0.01 0.01 1.52 0.1291
56
Variable Term RSquare Estimate Std Error t Ratio Prob>|t|
Times on Water
0.01 0.01 0.00 1.47 0.1436
Boater Safety
0.01 0.04 0.02 1.71 0.0813
Ownership 0.06 -0.14 0.03 -4.32 0.0001*
Control belief index
Residency 0.14 -2.93 0.45 -6.47 0.0011*
Age 0.00 0.01 0.04 0.24 0.8098
Years Boating
0.04 0.01 0.03 3.37 0.0009*
Years SW Boating
0.04 0.17 0.05 3.26 0.0013*
Times on Water
0.05 0.03 0.01 3.71 0.0003*
Boater Safety
0.02 0.01 0.00 2.45 0.0156*
Ownership 0.13 -0.03 0.01 -6.30 0.0001*
Reported behavior index
Residency 0.00 -0.37 0.34 -1.08 0.2834
Age 0.01 -0.04 0.02 -1.52 0.1302
Years Boating
0.00 -0.00 0.02 -0.38 0.7004
Years SW Boating
0.01 0.08 0.03 2.24 0.0253*
Times on Water
0.005 0.01 0.01 1.18 0.2399
Boater Safety
0.01 0.00 0.01 0.30 0.7655
Ownership 0.00 0.00 0.01 0.41 0.6803
57
Figure 4-5. Boxplot of the number of times on the water by resident group, n=252, 2009
58
Table 4-6. Ad Hoc and Emergent Themes from the Interviews, listed by frequency of response, n=18, 2009
Theme Ad Hoc Themes Emergent Themes Observational learning
-Learning by Trial and Error -Caution on the water, slow speed -Learning from experienced boaters -Following guides to fishing spots -Watching other while in the same vessel
-Lack of knowledge derived from Coast Guard Course -Hiring out guides to train inexperienced boaters -Following Boaters in channels but not in flats -Information from rental facilities, lack of training from boat clubs
Social reinforcement
-Peer groups identified at the same level of experience or higher -Limited discussion about receiving direct advice or criticism -Hesitancy to give advice to other boaters -Expression of disapproval of destructive behavior on the flats
-Opinions of others not significant except for tournament fishermen -Limited Verbal and Nonverbal communication among boaters -Distance between boats limits comments and advice -Isolation of part time residents and visitor, no peer reinforcement -Majority of discussion about fishing techniques, spots
Management and the Environment
-Inexperienced boaters and weekend Warriors the cause of the problem -Lack of courtesy Additional Management input for boating zones -Information handed out or mailed out to boaters
-Tournament fishermen admit to being part of the problem -Mistrust of scientific data and the government -Suggestions of boating class requirements -Lack of awareness of other poll and troll zones
59
CHAPTER 5 DISCUSSION AND CONCLUSIONS
Hypotheses
I hypothesized that self-efficacy and control beliefs would be correlated with
reported behavior. I also hypothesized that boaters with more years of experience, more
time on the water and higher levels of boating education would have higher self-efficacy
and higher control belief scores. Full time residents were hypothesized to have higher
self-efficacy and control belief scores than visitors or part time residents and boaters
who owned their vessels were hypothesized to have higher self-effiacy and control
belief scores than those who were renting or borrowing boats. In order to conduct
correlation analysis, the factors were used in conjunction with alternative independent
items to prove or disprove the hypotheses.
Correlations
Correlations between dependent variables and the factors that were revealed
during the factor analysis show that some of my study hypotheses were valid. For
example, the self-efficacy index scores were significantly negatively correlated with the
Boat Factor, meaning that the larger the boat and the deeper its draft, the lower the
reported self-efficacy. It should be noted that the self-efficacy index created for this
study was designed specifically for shallow-water boaters/anglers and, therefore, it is
not surprising that operators of larger boats were not as confident about their shallow-
water skills.
The self-efficacy index scores were also significantly negatively correlated with
the Captain Factor and the Ownership Factor. The regression results showed that
boaters who spent less time on the water and who had less training and boating
60
education (Captain Factor) had lower self-efficacy scores. Regression results also
showed that boaters who did not own a boat, but instead rented or borrowed one
(Ownership Factor), would have lower self-efficacy values. The negative correlation with
the Ownership Factor indicates that a lack of familiarity with a boat also includes a lack
of knowledge about its draft. Additional regression analyses preformed on specific
dependent variables not classified as factors showed that the self-efficacy index scores
were negatively correlated with residency and positively correlated with boating
experience and time spent on the water (See Table 4-4). Non-residents had lower self-
efficacy scores than residents. The correlation of experience and time spent on the
water supports the correlations show through the factor analysis Captain Factor,
showing that boaters like fishing captains who spend more time on the water have
higher self-efficacy scores. These results support my initial hypothesis that the more
experience that boaters have and the more time spent on the water, the higher that
person’s level of self-efficacy will be.
Only one of the four factors was significantly correlated with the self-efficacy
skills test: Boat Factor had a negative correlation. This finding suggests that individuals
who operate larger and deeper draft boats know less about shallow water boating. This
was not an element of my original hypotheses but was a finding of the factor analysis.
Though none of the other three factors was significantly correlated with the skills test
scores, regression analysis using individual independent variables did show that
residency was significantly negatively correlated with the skills test scores. This
indicates that visitors had lower levels of boating knowledge than did residents. It was
surprising that no other factor or independent variable was significantly correlated with
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the skills test scores. This result could indicate an error in estimating the knowledge
level of boaters and suggests that the resulting scores may not adequately reflect
experience on the water.
Control Belief scores were significantly negatively correlated with the Boat Factor
and the Ownership Factor. These results suggest that boaters with larger boats and
boaters who do not own their boat are associated with lower levels of perceived
behavioral control. Again, since the control belief index was designed to measure
shallow-water boating and avoiding running aground, owners of larger boats would be
expected to score lower than owners of smaller boats.
Residency was negatively correlated with the control belief score, supporting the
hypothesis that residents have higher control beliefs than non-residents. As expected,
the number of times on the water and years of boating experience (both in total and in
SW Florida) were both significantly positively correlated with the control belief score.
These results suggest that with more experience and time on the water, boaters gain
more confidence in their control over their surroundings and their boat and could
change their behaviors when they encountered shallow water and sea grass beds. This
is supported by other studies of perceived behavioral control (PCB) that suggest that
when behaviors are less controllable, PBC contributed more to the variance (R^2 = .41)
(Sheeran 2003).
Only one of the four factors was significantly correlated with Reported Behavior:
The Ownership Factor was significantly negatively correlated, which means that boaters
who knew the draft of their boat were more likely to report incidents of grass damaging.
That boat owners reported this behavior may be due to the greater amount of time
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spent on the water over many years in comparison to renters and borrowers, or it could
be that the self-reported behavior among renters and borrowers was not as accurate.
Of the specific independent variables examined, only Years of Boating experience
in Southwest Florida was significantly positively correlated with reported behavior. This
finding may seem counterintuitive since it suggests that the more years of experience a
boater has, the more likely that boater will have engaged in (reported) damaging
behavior. On the other hand, as suggested previously, it could indicate that given more
time on the water a boater has more opportunity to engage (unintentionally) in
damaging behavior.
None of the other factors or independent variables including age, total years of
experience, and number of times on in the water were significantly correlated with
reported behavior. This suggests that occasional boaters, rather than experienced
boaters who spend more time on the water in the study area, may be the problem with
regards to the damaging behavior. This finding could be a consequence of the self-
reported nature of the reported behavior, but it could also mean that there are no
distinct differences between user groups when it comes to physical damage to the
grasses. It is possible that all groups of boaters are causing some kind of physical
damage and pressure and it is not feasible to assign responsibility to any one group as
a result of this assessment.
It was interesting that self-efficacy and control belief were correlated with boaters’
experience and time on the water. I had hypothesized that boaters’ experience on the
water would be correlated with self-efficacy and control beliefs because of the mastery
experience that boaters would have from years on the water. By asking them to report
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their self-efficacy, participants are recalling their ability to perform complex behaviors
under a variety of conditions. Cottrell and Graefe (1997) showed that years of boating
experience was a significant predictor of who was most likely to use pump-out stations,
suggesting that past experience can predict some environmental behaviors. While the
avoidance of sea grass scarring may not offer the same benefits as using pump-out
stations, there does seem to be some relationship between the two behaviors. Both
avoiding grass and using pump out stations require additional effort and environmental
consideration on the part of the boater. It was interesting that age was not significant in
the factor analysis, since I had assumed that older boaters might have more experience
and, therefore, more confidence than would younger boaters. However, while younger
boaters seemed to have the same level of confidence as did older boaters, this could
have been due to the high median age of the boaters in the area. My sample captured a
larger range of ages than similar boating studies of the area (Sidman 2001), but there
were not enough samples in the lower age range to provide an appropriate comparison.
Self-Efficacy and Control Belief Scores
The Self-efficacy index scores were not normally distributed, making it difficult to
create comparisons among groups or to the reported behavior. The average boater
score was 38.50 out of 50, or 77%. The scores were right skewed and participants
seem to have overestimated their confidence. Since the scores ranged from 21 to 50,
the scale may not have been accurate enough to distinguish the less skilled individuals
because of the nature of the questions and the level of personal pride associated with
confidence in boating ability. Non-parametric tests showed no significant differences
between group means. Similar studies regarding driving behavior in older drivers found
that confidence in driving ability was not significantly correlated to overall appropriate
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driving behavior (Baldock 2006). Participants with low driving test scores did not avoid
difficult driving situations because of their high confidence. With regards to boating, the
desirable behavior could be overridden by the need to get to a fishing destination or the
need to maintain their image out on the water. While boaters may have knowledge
about what to do in certain situations, that could be altered by the circumstances
leading up to the desired behavior.
The scores for the self-efficacy skill test were distributed normally. The mean
score was 4.25 out of 8 and responses ranged from 0 to 8. This test was an
independent assessment of boating skill and it was more accurate than self-assessed
efficacy. While the responses were slightly right skewed, all groups were normally
distributed with equal variance, thus allowing for parametric tests. A One-Way ANOVA
showed that a significant (p < 0.001) difference between the full-time resident scores
and the visitor scores, as well as between the part time resident scores and visitor
scores (p < 0.001). This demonstrates a significant difference in knowledge between
residents and non-residents and supports my hypothesis that visitors are less skilled
than are residents who spend significantly more time on the water in the study area.
However, despite my hypothesis that part-time residents would have lower self-efficacy
and lower control belief scores, there was no significant difference between full-time and
part-time resident scores, suggesting that “snow birds” (winter visitors) are not as
unskilled as previously anticipated.
The scores for the Control Belief index ranged from 25 to 70 and the average
score was 52.06. The sample, though right skewed, was normally distributed with equal
variance among the groups. The index proved more effective at characterizing the
65
population, which may reflect the fact that it was more about measuring environmental
conditions or constraints than skills. A One-Way ANOVA showed a significant (p <
0.001) difference between each of the three resident populations. Full-time residents
had the highest scores for perceived behavioral control over their boating and their
ability to prevent sea grass scarring. In contrast, the scores for part-time residents were
significantly lower and visitors exhibited the lowest level of perceived control. These
results support my hypothesis that the less time boaters’ reside in the study area, the
less skill and lower control they will demonstrate.
The Reported Behavior index was problematic since the scores were highly
variable and not normally distributed. The mean score was 11.3 out of 48 and
responses ranged from 0 to 33. The responses were dramatically left skewed and
scores were typically very low, especially for visitors and boat rental clients who had not
spent much time on the water. Boaters may not have accurately reported damage that
they caused because of an unintentional lack of knowledge about the boat they used or
because they intentionally wanted to report behavior that was more socially desirable. I
hypothesized that self-efficacy and control belief scores would be correlated with
reported behavior. Nonetheless, the low scores did not support my hypothesis that self-
efficacy and perceived behavioral control would predict reported behavior. The strongly
skewed scores suggest that this is an inappropriate model for this behavior or it could
mean that there is no correlation with skill and that all boaters are having some impact.
Actual Skills versus Reported Skills
The scores from the self-efficacy skills test were correlated with Reported
Behavior, while scores from the self-efficacy index were not. I hypothesized that self-
efficacy and control belief would be correlated with reported behavior on the water and
66
that boaters with lower self-efficacy would be more likely to engage in behavior that
damages the grass. While the correlation was not strong, it supports my hypothesis that
ability to perform a behavior is related to on-the-water behavior. These results have
implications for targeting groups of boaters and anglers, in particular since it was actual
ability that was related to behavior rather than the boaters’ confidence in their ability.
Boaters who need to increase their efficacy may believe that they already possess the
necessary skills. Increasing self-efficacy in these cases should be related to teaching
real boating skills to improve on-the-water abilities. While the strongest influence on
self-efficacy is mastery, successfully completing easy tasks does not always strengthen
efficacy and failure can harm it (Dunbar-Jacobs 2007). In the behavior-change process,
it is important to plan for success. Some theorists have suggested that goals be set at
the upper levels of efficacy because higher efficacy spurs greater effort (Strecher 1995).
This means that we should work to improve self-efficacy and mastery skills among the
part time residents and visitors through mastery experience or successful navigation
experiences. These experiences should be related to shallow water navigation and
allow boaters to personally work through problems that could be encountered on the
water with the help of an experienced guide. This could be achieved with hands-on
training or via increased signage and management.
A key problem that I encountered was related to self-reporting of skills and
behaviors. Due to the number of questions that I received from participants about the
definitions of words or other aspects of the questionnaire, I spent more time than
anticipated reading the questions to boaters and assisting them. This increased
interaction with respondents while administering the survey might have influenced their
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responses. However, Ajzen and Fishbein (2004) explained that the accuracy with which
self-reports reflect actual behavior is an empirical matter, so it cannot be assumed that
self-reports underestimate the impact of intention on behavior. Evidence within health
science regarding condom use, exercise, and smoking indicated that self-reports were
generally reliable and valid (Jaccard, McDonald, Wan, Dittus, & Quinlan, 2002; Godin,
Jobin, & Bouillon, 1986). While the accuracy of my participants reported behaviors may
be questionable, I worked with the information provided with caution. The internal
validity of the items within the reported behavior section has been documented and I
have addressed issues of overestimation of skill within the context of the study.
Studies about driving and speeding behaviors have shown that perceived
behavioral control is a robust predictor of behavioral intentions (Elliot 2007). Since I
measured behavior instead of intentions, my results may have not shown the same
strength in predictive power. Sidman and others (2005) reported that Southwest Florida
boaters were homogenous in their intended environmental behaviors and that the
majority of boaters knew correct behaviors for various on-the-water situations. This
disparity between knowledge and actions could be related to ego and the need for
respondents to prove their worth as a captain and confidence in their skills.
A study of hunting behavior showed that there was less predictive power in the
Perceived Behavioral Control variable for hunting behavior than anticipated due to the
highly complex nature of hunting behaviors and the fact that they measured intentions
rather than behavior (Rossi and Armstrong 1999). The authors claimed that the
perceived behavioral control construct might not influence behavioral intentions as
much as it might influence actual behavior. The same may be true for the complex
68
decisions and movements that boating and fishing require and the elements that are
involved with the behaviors. A review of 47 studies showed that a medium-to large-
sized change in behavioral intention engenders only a small-to-medium-sized change in
behavior (Webb and Sheeran 2005). The review findings also showed that intentions
have less impact on behavior when participants lack control over their behavior, when
there is potential for social reaction, and when circumstances of the performance are
conducive to habit formation. This could explain why there is such a discrepancy
between groups and a lack of correlation to reported behavior in this case. It is
important to mention that, with environmentally relevant behaviors, self-reported
measures of behavior and behavioral intentions can be very different from actual
behavior (Stern & Oskamp, 1987; Tarrant & Cordell, 1997). This could be another flaw
in the predictive power of the theory of planned behavior (TPB).
Issues with the Theories
In his comparison of multiple theories, Harland (1999) showed with stepwise
regressions that personal norms are important when determining intentions and
behavior. While the usual constructs of the TPB explained five specific behavioral
intentions to a considerable extent, personal norms improved their explanatory power
significantly. Another alternative to the issues that arose with inaccurate reporting could
be the need for personal norms and attitudes to be described. This may be a case
where the perceived behavioral control simply has less predictive power. If it were
possible to add those variables to a survey to get complete profiles, the predictors might
become more significant.
A limitation of my survey analysis was that my sample was not fully representative
of the various boating populations that use Lee County waterways. Lee County has over
69
50,000 registered boats and over half of them have drafts that are less than two feet
(FWC 2009). In addition to resident boaters, a significant number of visitors use Lee
County waterways, including a large contingent of German tourists. According to
equation provided in the sampling section of Sidman and others (2005), a
representative sample of a population this large would have to be approximately 1200
responses. Surveying visitors was difficult due to the number of boat rental locations
and the infrequency with which renters are encountered. In some cases, a language
barrier made the level of comprehension questionable. Part time residents also were
difficult to access because the sampling period ended as the winter season began,
when part time residents start to arrive in force. Thus, the ability to generalize the study
results to specific sub-populations of boaters is limited. While these limitations should
be recognized, the data and observations encompass a variety of user groups and
provide some insights.
Interviews
I used in-depth interviews that posed questions based on Social Cognitive theory
to examine the social interactions of boaters and the implications of these interactions
on their sea grass scarring behavior. The eighteen interviewees were screened to
ensure that they were anglers or had visited shallow areas of Greater Charlotte Harbor.
On average, the interviewees had boated for 25 years and 10 of those years occurred in
Southwest Florida. These results were consistent with the responses obtained from the
intercept surveys. Proportionally, I interviewed more licensed captains than had
responded to the intercept surveys, which raises the possibility that the interview
responses were somewhat skewed.
Two of the eighteen interviewees were not familiar with the problem of sea grass
70
scarring; these two were visitors and they were less familiar with the area and local
issues. The remaining respondents had a general awareness of the sea grass issue,
but they knew much less about the consequences of scarring and the importance of sea
grass to fisheries and for angling.
Within the construct of observational learning, I found that the majority of the
interviewees gained mastery experience through trial and error while they were boating.
This shows that with a complex behavior like boating, participants needed to get out on
the water and work through scenarios in order to become confident in their boating
abilities. I hypothesized that boaters would have a high level of observational learning
from watching the navigation of other boaters and anglers on the water. My initial
findings were not consistent with my hypothesis, but it was supported by responses
given by the key (expert) informants. Furthermore, while twelve of the eighteen
interviewees reported that they had completed at least one Coast Guard boating
course, they also noted that none of the courses included specific instructions on how to
navigate in shallow waters. This emergent theme shows that while many boaters may
take nautical/navigation courses such as those offered by the U.S. Coast Guard, the
training provided may not prepare them for shallow-water boating and angling. While it
was reported that there was one specialized course available in Cape Coral that would
teach you how to fish in shallow water, there was no hands-on training regarding
shallow water navigation.
Sixteen of the eighteen respondents reported having learned about shallow water
navigation skills from guides, boaters, or father figures who were more experienced
than they were. However, type of learning occurred primarily at the beginning of their
71
boating ‘career’ and was not repeated on a consistent basis. The predominant paradigm
seems to have been an abundance of trial and error learning coupled with a minimal
number of formal and/or non-formal opportunities. A common phrase that I heard was “If
you haven’t been aground, you haven’t been around,” which is consistent making and
correcting boating mistakes by trial and error.
While the majority of the interviewees did not believe that they learned their
boating behaviors by watching others while on the water, some did admit to following
more experienced boaters into fishing spots or through unknown navigation channels.
The more experienced interviewees felt that watching others was not a good way to
learn. In contrast, those who were less experienced felt the need to watch others in
order “to find the best places to fish and the fastest way to get there” (Interviewee #18).
If the less experienced fishermen, full time or part time residents, were to watch another
angler move through the shallow flats, the cost of damaging their propeller or damaging
the grass by imitating that behavior did not outweigh the benefits of possibly catching a
fish. Three of the guides and one of the tournament anglers complained of other boaters
who followed them into areas and/or who used their favorite fishing spots (Interviewee #
2, 10, 16). Some guides admitted that they would not sacrifice time and money to go
around shallow grassy spots; they stated that the benefits of getting to a certain fishing
spot outweighed the possibility of damaging the grass beds. I hypothesized that boaters
in the region would have a high level of observational learning from watching the
navigation of other boaters and anglers on the water. These findings were consistent
with this part of my hypothesis about the role of observational learning but I had not
considered how isolated each boater would be out on the water.
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A tendency of some to follow others whom they consider to be more experienced
may help to explain some of the sea grass scarring behaviors exhibited by new or less
experienced boaters in Greater Charlotte Harbor. In their study of sexual behavior, Nabi
and Clark (2008) found that people may be motivated to model or duplicate the
behaviors of others, even when such behaviors are negatively portrayed because of
other social pressures and expectations. When participants saw these behaviors, it was
clear that performing the risky behavior outweighed or minimized the negative outcomes
that could result, and that participants often minimized the inherent risks. Although the
study by their study (Nabi and Clark (2008) was conducted used a television media
campaign, with media in the form of television, the same issues are germane to
behavior modeling on the water.
The reported problems discussed by fifteen of the eighteen interviewees were
predominantly issues of crowded waterways and lack of courtesy on the water. Some
interviewees elaborated on discourteous behavior by explaining that people do not
respect other anglers’ space when fishing the flats. One tournament angler suggested
that this was because of differences between northern and southern fishing practices.
He explained that tactics required to catch snook (Centropomus spp.) or redfish
(Sciaenops ocellatus) on the flats in Florida are very different from those needed to
catch striped bass (Morone saxatilis) up north (Interviewee #16). I was surprised that
more people did not report discourteous behavior and that many felt that such behavior
had decreased in the past few years. This is in part due to other studies in the area that
reported “Lack of Courtesy and/or Seamanship” as the leading detractor of their
experience on the water, reported by 43% of 1,519 respondents (Sidman and others
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2005). Informal personal communication suggested that many boaters saw a decline in
courtesy in boaters in general. Since the sample size is fairly small and restricted to
anglers, this could have influenced the type of responses. When respondents did cite
problems of congestion or discourteous behavior, they associated it more with ramps
and canals than with open water boating or flats fishing.
There were interesting findings that surfaced in relation to the social
reinforcement construct. For example, every one of the professional guides and
tournament anglers who were interviewed felt that other people’s opinions of them were
important, particularly with regard to their reputation as a guide or professional angler.
They reported that, to maintain their professional image, they were careful not to run
aground or get citations when they were with clients. In contrast, recreational anglers
and the occasional boaters were significantly less concerned with others’ opinions of
their behavior on the water.
Most respondents said that their peers were other boaters with skill levels equal to
or greater than their own; they also indicated that it was from these peers that they
would take advice and/or criticism. While many anglers who were interviewed did get
information from others about fishing spots, bait or boats, almost none received advice
or feedback about environmentally conscious behaviors. The less experienced anglers
and boat club members did receive advice from guides and captains, but they received
very little communication about behaviors related to sea grass scarring. Sixteen of the
eighteen interviewees said they would offer advice to other boaters who were in distress
or whose actions affected them in some way.
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In light of their professed willingness to communicate with other boaters, I was
surprised that the interviewees reported receiving relatively little feedback from their
peers in the form of comments or behavioral reinforcement. This seemed strange that
no one was receiving the supposedly abundant advice being offered. The apparent
anomaly could be due to my interviewing a greater number of experienced boaters than
non-experienced boaters.
Nine of the eighteen interviewees claimed that they would criticize another boater
for “running over the flats all the time” (Interviewee #8). However, they also reported
that this was difficult to do when they did not know the other boater or because the other
boater often was moving too fast. Many said that they “try to talk about it but we don’t
usually get a chance” (Interviewee #11). An important emergent theme was the lack of
peer groups for part time residents and visitors that perform the same functions as
those available to residents. When asked who their peers were, the four part time
residents mentioned one or two friends to whom they referred for advice or information
relevant to Greater Charlotte Harbor, but that they had limited contact with other
boaters. The two visiting boaters said that their sources of information for local
navigation came from watching other boats or by asking at local businesses, such as
rental boat firms. These two visitors did not identify with any peer group and had limited
social support for boating activities. This contrasted with the resident recreational
anglers and local guides who were apt to network with, and receive information from,
many other anglers and guides. Both anglers and guides mentioned bait and tackle
shops as a critical source of information for fishing advice. Analyses of health behaviors
via Interviews and focus groups showed that reinforcement and self-efficacy were key
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factors contributing to adherence to diet recommendations (Bandura 2004, Beverly
2008). Without the social reinforcement and peer support, there is little evidence that
environmentally sound behaviors will be maintained. If boaters know what they should
do but are never positively reinforced, they can forget appropriate behavior and
prioritize time, money or fish over the sea grass.
Sea grass scarring was a much more contentious issue than I had originally
anticipated. In an attempt to identify a specific user group for a behavior change
program, I asked participants who they thought was responsible for sea grass scarring.
Twelve of the eighteen interviewees blamed inexperienced “weekend warriors” and
tourists, three blamed tournament fishermen, one blamed jet skis, one had no idea, and
one said that everyone was part of the problem. While those same twelve cited
inexperienced anglers were the problem, they suggested making boating safety classes
mandatory and creating new classes that would teach flats fishermen how to be
respectful and careful. Two people suggested signs as an appropriate means to warn
boaters of shallow water and three people suggested providing every registered boater
with charts, maps, or information. I was surprised that education was suggested as the
best solution to the problem since most respondents said that they learned by trial and
error. As for the management action of installing Non-combustion engine zones, sixteen
of the eighteen participants thought they would be useful or appropriate in some areas;
five qualified this by saying they needed to get input from the guides who used the area
in order to make them effective. Overall, those who had heard of such zones being used
in other areas were in favor of some kind of restriction, while those who had not heard f
poll and troll zones seemed to be against them. Ten of the eighteen reported a mistrust
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of the government and an abuse of power by officials with regards to manatee zones
and fishing regulations. This mistrust was an emergent theme and seemed to have a
significant impact on the reception to new management ideas. The visitors and part time
residents seemed less mistrustful of management solutions, while six of the seven
experienced guides were distrustful of scientific information and statistics.
This suggests that while they might care about the resources and understand
appropriate behavior, they do not agree with the process by which this permitting and
mitigation is taking place. Those concerned with new management practices claimed
they would be willing to participate if they were given a voice in the management plan.
Boaters with over 30 years of experience in Greater Charlotte Harbor wanted to be
respected for their knowledge of the water and their understanding of the ecosystem.
Two guides said that they were more experienced than the law enforcement and that
they were “out there more often than 75% of the marine units” (Interviewee#3). They
were skeptical of information that conflicted with their experiences as related to sea
grass, fish, or even manatees.
Possible Biases
These interviews are only a snapshot of the variety of opinions and strategies that
I heard over the course of the study. My interviews may be biased because those
people who were willing to give out their contact information are probably less likely to
admit their involvement in damaging the grass. The people who are willing to talk about
the issues are also those that care most about the problem. Some of my participants
were reached through scientific or agency perspectives and that could mean that they
were more informed than the average boater. I received additional contact information
for other participants but they tended to refer me to boater acquaintances who were
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more experienced than they were. The isolated nature of the part time residents and
visitors made it difficult to get new contacts for this group. This could have biased the
results and limited the application of my findings to the general population, even with the
diverse group of participants.
Additional support for the importance of the influence of personal norms on
environmentally relevant behavior comes from a review of literature on recycling
behavior. Thorgersen (1996) argued that environmentally relevant behaviors should be
classified as belonging to the domain of moral, rather than economic, behaviors. Instead
of balancing personal costs and benefits, some researchers evaluated environmentally
relevant behaviors in terms of right and wrong. The alternative theories for
environmentally responsible behavior might have more validity for this particular
conservation behavior.
Other prominent studies suggest that attitudes about environmental issues are
based on the relative importance that a person places on themselves, other people, or
plants and animals, which Stern and Dietz (1994) labeled egoistic, social-altruistic, and
biospheric. The value-basis theory is an extension of Schwartz’s (1977) norm-activation
model of altruism, and suggests that concerns about specific environmental issues are
due to an awareness of harmful consequences of environmental problems. If this theory
was applied to the issue of sea grass scarring, there would be a drastic alteration in the
methodology and marketing techniques used. If boaters are more concerned with their
own wellbeing than with sea grass or fish, then making them aware of the importance of
the ecosystem for their own wellbeing could be effective in changing this destructive
behavior. The challenge of influencing awareness comes from the problem that many
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Americans have become distant from the natural world (Hertsgaard 1999), and this may
be the case for some Florida boaters, especially those with less experience and less
time on the water. While it might seem that they are connected to their resource, it could
be that their world view does not prioritize conservation. As new boaters venture out on
the water for cruising, fishing, or recreating, they could be missing out on some of the
traditional learning methods that might be more likely to promote a conservation ethic.
Conclusions and Possible Interventions
There are multiple barriers to preventing boaters from scarring sea grass flats.
Some barriers stem from a lack of knowledge and experience, while others reflect more
concern about saving time and money (e.g., gas) than with avoiding habitat damage.
For example, boaters reported that they might navigate through a shallow area to save
time, even if the tide was too low and they perceived an increased risk of running
aground. This barrier to environmentally responsible behavior is difficult to counteract
when it is must be achieved by counting on people to sacrifice their time and money.
Furthermore, it also reflects a certain level of ignorance among boaters who do not take
into account or understand tides and wind effects.
The demographic results from my surveys were supported by other studies
conducted in Southwest Florida. Since more than half of the boats on the water in
Southwest Florida draft less than 2 feet, those boaters were the target group that might
engage in shallow water boating. The mean age reported from Sidman’s (2005) boating
characterization was 58 years old, while the mean from my surveys was 49 years old.
This age difference may be because those people using ramps were reported to be
younger than those using home docks or private marinas. The participants in my study
also averaged 25 years of boating experience, with 10 years occurring in Southwest
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Florida. Sidman and others (2001) reported an average of 8 years of boating experience
in Greater Charlotte Harbor by their survey respondents.
Boating occurs year-round in Greater Charlotte Harbor and, according to Sidman
(2001), 60% of boaters reported boating between July and September and 80%
between March and May. Sidman (2001) also reported that 54% of boating activity
occurs on 5 day weekdays and 48% on 2 day weekends. While these demographics
may have changed over the past few years, the peak seasons reported during both
studies are similar. According to my key informant interviews and observations at
boating facilities, boating activity spikes between Christmas and New Years and
continues into spring. This means that more boaters are on the water during the winter
and spring when the tides are shallower and there is a greater chance of running
aground or scarring sea grass. While the shallow tides might deter some less
experienced boaters from entering the flats, overall, the frequency of damage due to
groundings might also increase. Water clarity varies by season and summer rains make
the water more tannic, a factor that could contribute to more damage to the grass since
boaters often use water characteristics to gauge depth. The variability of tides and water
clarity can have a large impact on boating behavior, even causing less experienced
boaters to avoid fishing during certain times of the year.
The logical element of the behavior seems to be that people with a lot of time on
the water have more opportunities to damage the grass. Visitors who only get out on the
water an average of 2 times a year will be much less likely to report damage to the
grass beds than people who go out every other day, as many of the guides do. Even
with years of experience, the law of averages suggests that there may be no way
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around that difference in reported behavior. Part of this might be attributed to the fact
that many rental boats are pontoon boats and the draft of the boats combined with the
decreased knowledge of the draft means that they would have less knowledge about
how to get out of a shallow-water area.
There appear to be two possible “target audiences”; the “weekend warrior” also
known as the less experienced resident and the visitors or tourists. Marketing should
work to make the less experienced boaters aware of the damage they are doing (by
showing them a map or having a respected spokesperson explain the issue) or by
emphasizing skill and social reinforcement from more experienced or successful anglers
as an important part of fishing in the area. If experienced fishermen were able to
illustrate the proper, sea grass friendly way to catch large fish, they could provide
motivation for behavior change and alter the barrier to learning about shallow water
navigation. This could also be achieved by recruiting guides to comment on the issue
and by showcasing experienced anglers and captains who catch an abundance of fish
by using trolling motors and poles.
The second possible target audience is the visitors and tourists. These people
have very little affiliation to local peer groups and so they could be educated through
hands-on demonstrations conducted by local power squadrons or boat rental agencies.
The boat clubs should require that anglers understand the water, know how to use a
depth finder, and spend additional time with experienced anglers before fishing alone in
shallow waters. The boat clubs, who allow visitors as well as residents to take vessels
out by paying membership fees, can help to reduce boaters’ repair costs by spending
additional time with those people in order to protect their boats and propellers as well as
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the sea grass. Right now, boat club members are not encouraged to travel outside the
channel and are not told what to do in case they begin to run into sea grass. Rental
boaters who plan on fishing should receive additional information in the form of charts
and hands on instruction before they leave the dock to ensure they are aware of the
shallow water hazards. While residents may be causing more of the damage on a day-
to-day basis, visitors and part time residents are more accessible to new information
and guidance. Their lower self-efficacy scores make them targets to improve
knowledge. Part time residents would have more time to attend classes and training
sessions and are more willing to accept new regulations.
Further Research
Further research in this area could examine awareness of the newly managed
“Poll and Troll” boating zones. With the baseline knowledge assessment of a portion of
the boating and fishing community, the population could be tested again to see if
educational or marketing campaigns were effective at changing reported behavior. To
obtain a more holistic understanding of the problem, mail surveys could address norms
and attitudes to test how that might affect any new associations or correlations. Less
personal and labor intensive forms of surveying could be used to get a broader and
more anonymous assessment of who causes physical damage to the grass. The
addition of an online survey or mail survey might allow access to a higher diversity of
age groups or residency types that I was unable to reach. There could be more
possibilities for accessing online forums of discussion on the topic. With an increased
sample size, one could pinpoint types of boats that might be more likely to damage the
flats. Though I was unable to obtain a large enough sample size of pontoon, aluminum
v-hull, and Jon boats, these boats seemed to be the ones causing damage when they
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engaged in shallow water fishing activities. Some of the evidence of the damage was
from personal communication with boat operators as well as their low test scores with
regards to knowledge of how to prevent scarring the grass. Many boaters in this
category suggested backing out or powering off because of the size of the vessel and
the absence of trolling motors or poles on the vessel. With regards to smaller fishing
vessels, their ability to reach the shallow water combined with a lack of jack plate makes
them likely to cause damage with their motor. The jack plate is a new device that can be
added to any vessel that allows for the adjustment of engine height. By adjusting the
jack plate, boaters can increase fuel efficiency, increasing speed, and raising their
propeller in shallow waters, thereby preventing some damage to grasses. One study
regarding compliance with slow zones in Sarasota Bay found that Jon boat style vessels
also had relatively low level of compliance (Gorzelany 1996). This could make them
ideal as a target audience, though it might be a form of profiling the less affluent
members of the boating community. Real evidence of a change in attitude could be
measured in the recovery of the sea grass beds over the next five years.
In Monroe County, the National Park Service conducted a benefit-cost analysis
and concluded that they were going to proceed with increasing the number of signs that
inform boaters that entry into the sea grass is prohibited, a method that has been
demonstrated to reduce damage to sea grass beds (Ehringer 2000). Their analysis
showed that the damage caused by boat groundings from 1998 to 2005 over 1.05
hectares was $1,063,169.30 in 2005 dollars (Engeman 2008). Since the signs cost
approximately $4500 and one full time patrol position was equal to about 0.42 hectares
of sea grass bed damage. If no new management actions were taken, the Engeman
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(2008) estimates that continued annual loss of habitat would be valued at $1,523,819
per year. While these same costs may not be associated with the damage that is
occurring in Charlotte Harbor, even a fraction of that expensive loss to the fisheries
would be staggering. If there were more research about the effectiveness of signs in
South West Florida, this might be an even more cost effective solution than educating
individuals. If the closed areas were accessible and close together so that minimal
additional law enforcement would be needed, it could be a possible solution. Taking into
account the differences in the habitat between the two areas and the different
regulations that dictate boater access, a new plan could be formed. This would be
dependent on the establishment of restricted zones that could be easily monitored. If
the fishing guides would support non-combustion engine zones and participate in the
planning, they could assist in reinforcement and monitoring.
Having spent many hours at ramps talking to salty fishermen and local captains,
the feeling remains that almost everyone wants their story told and wants to explain
their issues and insights into the problem. Many are honored to be asked about their
opinions. Those that do not want to share are keen to keep all their current freedoms
and continue in the own way. There could be a way to harness this local knowledge and
bring it to the less experienced boaters. One trend that I observed was that many
captains took other visitors or part time residents with them on the water. It seemed
common for the visitors and part time residents to ride along with resident boaters,
especially with the price of gas being so high. Since learning by mastery seems to be
the best way to convey the complex skills of boating in shallow water, the mentoring
force of a captain to a less experienced boater could pass that knowledge on. Part time
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guides and experienced recreational boaters could be encouraged or even reimbursed
to assist others and impart their knowledge to others who might want to know more
about fishing. During the hands on education process, they would learn more about
safety and environmental responsibility and in some ways, could start discipleship.
Encouraging local fishermen and guides to be responsible and then to foster it by
training another angler could exponentially multiply the numbers of responsible boaters.
The Recreational Boating and Fishing Foundation suggests that stewardship education
should begin with appreciation and awareness and then expand to knowledge and skills
to result in responsible behavior (Felder 2001). This stewardship could be introduced in
the form of a community based education program or it could be suggested through a
marketing campaign aimed at local citizens.
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APPENDIX A BOATING SURVEY QUESTIONNAIRE
For each of the questions in the table below, please mark the box that best describes how confident you feel about performing the skill that is mentioned. Use a scale of 1 to 5 where 1 means “Not Confident at all” and 5 means “Very Confident”. Mark one box for each statement.
How confident do you feel that you can…?
1 Not
Confident at all
2 Not
Confident
3 Somewhat Confident
4 Confident
5 Very
Confident
Interpret navigational charts
Know what the tides are while boating
Stay in marked channels
Understand posted speed zones
Recognize which boat has the right of way
Know what speed minimum wake is for your vessel
Avoid running aground while boating
Avoid scarring grass flats while boating
Estimate water depth while boating
Navigate in an unfamiliar area
Recognize safe water for your boat based on draft
DIRECTIONS: Please answer the next 6 questions based on your knowledge and experience. 1) Of the four following speeds, which one has most of your boat and motor in the water (draws the most water)? Please circle one of the following: a) No wake b) Idle c) Slow d) Plane 2) Assume you find yourself in a shallow area of Southwest Florida when the engine feels bottoms and starts churning up grass and sand. What should you do under these circumstances? Check all that apply. _____ Continue at idle speed
_____ Tilt your motor up _____ Check your propeller _____ Turn the engine to neutral _____ Accelerate to get your boat on a plane
_____ Let your boat drift to deeper water _____ Pole, pull, or push your boat to a deeper spot
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_____ Call Sea Tow, US Coast Guard, or FWC 3) You are traveling in the Intracoastal Waterway at between 7 and 8 knots and you enter a speed restricted zone marked “ IDLE SPEED - NO WAKE.” What should you do? Please circle the most correct answer. a) Proceed at 5 knots b) Proceed at current speed but increase lookout c) Proceed at lowest possible speed needed for steerage d) Disregard speed and create no wake 4) Please match the following regulatory markers to their meanings for boaters. Please write the corresponding letter in the space provided. There are more choices than correct answers.
4) _______ 5) _______ 6) _______ a) Slow Speed Zone b) Information c) Dangerous Area d) Closed Area e) Open Area DIRECTIONS: For each of the statements in the table below, please indicate how strongly you agree or disagree, using a scale of 1 to 5 where 1 means “Strongly Disagree” and 5 means “Strongly Agree”. Mark one box for each statement.
Please select one response for each statement:
1 Strongly Disagree
2 Disagree
3 Neutral
4 Agree
5 Strongly Agree
Tides control my chances of avoiding prop scarring.
Avoiding damaging grass flats is a matter of luck.
Avoiding running aground is a matter of luck.
I don’t have time to go around the grass flats.
I have full control over my boat.
Whether or not I prop scar is completely within my control.
It would be easy for me to navigate in the marked channels.
It is easy for me to gauge the depth of the water in the shallower areas.
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Please select one response for each statement:
1 Strongly Disagree
2 Disagree
3 Neutral
4 Agree
5 Strongly Agree
It is easy for me to avoid damaging the grass flats.
It is easy for me to keep up-to-date navigation charts.
It is easy for me to find a boating safety course near where I live.
It is easy for me to use a GPS to avoid shallow problem spots in the harbor.
I do not have enough knowledge of the area to avoid running aground.
If I cause prop damage to grass flats, it is usually accidental.
I cannot prevent my propeller from scarring the grass.
DIRECTIONS: For each question in the table below, please mark the box that best describes how often the situation has happened to you.
How often have you…?
Every time I am boating
Once a month
Once a year
Once in the past 5 to 10 years
Once in the past 20 to 30 years
Never
Felt your motor bump bottom in a shallow area
Cut corners on a channel and churned up grass
Created prop wash with grass and mud
Prop scarred on accident while boating
Run aground (if never, skip the next 3 questions)
Had to get out of your boat and push Damaged your engine or propeller by running aground
Needed to be towed because of grounding
Attempted to “power off” a grass flat
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Finally, we have some personal questions about you as a boater. Age _______ Are you a resident of Lee or Charlotte County? Yes _____ No______ If no, are you a Florida Resident? Yes ______ No_______ If no, how many months of the year do you spend in Florida? __________ How long have you been boating? _________ In Southwest Florida?___________ How often have you been out on the water in the last year (approximately)? __________ What type of boat do you spend most of your time in? _______________________
Have you ever taken any boating courses? Check all that apply None Introductory Boating Safety and Seamanship Intermediate or advanced piloting and navigation Trained by an experienced boater Do you own a boat? Yes____ No____ If No, the boat that I am operating is (check one): Part of a Boat club Rented or Chartered Borrowed Do you know the draft of your boat? Yes____ No____ If yes, what is it? ________ (feet) _____ Thank you for taking the time to complete this questionnaire!
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APPENDIX B BOATER INTERVIEW GUIDE
Hello, May I Speak to _______________?
My name is Claire Sunquist and I am a graduate student at the University of
Florida. I am conducting research about boating and sea grass scarring in Charlotte
Harbor with the help of Lee County, Charlotte County, and Florida Sea Grant. I am
contacting you because of your willingness to talk to me about boating behaviors in your
area. I would like to learn about your boating behaviors and who influences you out on
the water. I am talking to people with a wide range of backgrounds to get a better
understanding of boating in Charlotte Harbor.
Do you have some time to answer some questions? This interview should take about 25
minutes.
Yes______
No. When would be a good time for me to call you back? ____
First, I need to ask questions about you.
How long have you been boating? (How long in Southwest Florida?)
Are you a resident of Lee or Charlotte county? How many months of the year do you
spend in Florida?
What kind of boat do you spend most of your time in?
Do you often fish in the flats and the shallower areas of Southwest Florida?
A) Observational Learning
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1) You are a fairly experienced boater (and angler). How did you learn to navigate the
shallow waters of Charlotte Harbor?
Possible probes: Who taught you what to do on a boat? Did you ever take a boating
class? Did you learn from someone or did you learn by yourself? Did you get your most
useful information from a Coast Guard course/Boating Clubs/dealerships/Trial and
error?
2) How did your friends or family learn how to boat? How do you think most people
learn how to navigate in these shallow or unfamiliar areas?
Possible Probe: Who do you think influences people’s navigation decisions when they
are out on the water?
3)Can you learn boating skills by watching other boaters? Who did you or would you
watch to learn how to be safe and responsible out on the water?
Possible Probe: Do you feel like other people watch you and use your actions as a
positive example when you are on the water? Do other boaters imitate your actions?
4) Do you feel that there are problems with lack of courtesy on the water?
If yes: Do the problems come from watching other discourteous boaters on the water?
Or because they don’t watch and imitate others? Is the number of boaters affecting your
experience? Does anyone ever say anything about the effects of the damage?
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Possible Probe: Is it just the number of boaters on the water? Lack of access?
Congestion? Do you feel that the waterways are too crowded?
B)Social Reinforcement
So we have talked a little about your own experiences and what you see out on the
water.
5) How important are other people’s opinions when it comes to responsible boating
(with regards to natural resources)?
Possible Probe: Specifically, what about sea grass scarring? Does it matter to you what
other people think your behavior on the grass flats?
6) Whose opinions do you care about the most? Do you peers opinions matter to you?
Who are your peers?
Possible Probe: Fellow anglers? Other members of a Boat Club? Fishing Captains?
Family or friends? Other people in the boat with you
Possible Probe: What about law enforcement? Would you be embarrassed if you did
something wrong? Does it matter if you get a citation or not? Are you aware of the
consequences?
7) Do you ever get comments about your boating skills or navigation abilities (positive or
negative) when you are out with other people?
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Possible Probe: Who are the people that are making these comments? Are they mostly
positive or negative comments? How does this impact the way you navigate?
8) Do you ever get the feeling that people don’t like the way you navigate/drive your
boat?
Possible Probe: Is that because of unspoken signals? How do you know?
9) Do you ever tell others what you think about their boating skills or safety?
Possible Probe: Do you ever comment or imply approval or disapproval toward other
boaters? Do you say things to their face or simply comment to others? Do you ever hear
others making comments about less experienced boaters? What do they say?
10) Are you aware of people talking about problems with sea grass scarring? Do people
criticize other people when they create prop scars?
If no, why not?
C) Environment and Conservation
11) Is there a specific user group who you feel is causing the majority of the problems
with sea grass scarring?
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Do you think they are making a choice to damage the sea grass? Or are they unaware
of the damage they are causing? Is it an accident?
12) Are there enough signs to inform less experienced about avoiding sea grass? Is it
possible to avoid running over the grass by reading the navigational charts?
Possible probe: If so, why do you think there are still problems?
What would you do to inform people about the issue? What do you wish you had
known? Would you use the same tactics to reach new boaters as you would for more
experienced boaters?
13) How do you think new boating zones impact (or could possibly impact) your
boating? (The Poll and Troll Zones) If zones were proposed, how so you feel?
Have you heard about other poll and troll zones in other parts of Florida?
Possible probe: When are these zones appropriate? Where would these zones be
appropriate?
14) What sources of information to you use now to learn more about new regulations or
fishing techniques? How do you find out about current boating laws?
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Could you suggest some other people, like yourself, that I should speak to? (Do you
know of any other boat club members, rental boaters, part-time residents you think I
might be interested in speaking to?) Would you be willing to share their contact
information?
Thank you so much for your time! We will use this information to help inform our
educational efforts in the region. Your personal information is not going to be shared
with other researchers we will maintain complete confidentiality of everything that you
shared with me. If you have any questions you can contact me personally.
95
APPENDIX C ADDITIONAL RESULTS AND GRAPHS
Figure 1. Linear Fit Regression Boat Type Factor by Self Efficacy Index, Factor 1
=2.0482491 - 0.0510598*SEI
Figure 2.Linear Fit Regression Experience Factor by Self Efficacy Index, Factor 2 = -
0.126326 + 0.0031491*SEI
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Figure 3. Linear Fit Regression Captain Factor by Self Efficacy Index, Factor 3 =
1.453474 - 0.0362329*SEI
Figure 4. Linear Fit Regression Boat Ownership Factor by Self Efficacy Index Factor 4 =
1.987963 - 0.0495569*SEI
97
Figure 5. Linear Fit Regression Boat Ownership Factor by Self-efficacy Test Factor4 = -
0.002501 - 0.0132143*SET
Figure 6. Linear Fit Regression Boat Type Factor by Control Belief Index, Factor 1 = -
0.078061 + 0.0014334*CB
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Figure 6. Linear Fit Regression Experience Factor by Control Belief Index, Factor 2 = -
0.078061 + 0.0014334*CB
Figure 7. Linear Fit Regression Captain Factor by Control Belief Index, Factor 3 =
0.9657757 - 0.017734*CB
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Figure 8. Linear Fit Regression Boat Ownership Factor by Control Belief Index, Factor4
= 1.4205428 - 0.0271571*CB
Figure 9. Linear Fit Regression Boat Ownership Factor by Reported behavior Index,
Factor4 = 1.4205428 - 0.0271571*CB
100
Figure 10. Linear Fit Regression Boat Safety Item by Self-efficacy Test, Boater Safety =
1.483118 + 0.0443702*SET
Figure 11.Linear Fit Regression Boat Safety Item by Self-efficacy Index, Boater Safety =
1.3308208 + 0.0088831*SEI
101
Figure 12. Linear Fit Regression Boat Safety Item by Control Belief Index, Boater Safety
= 1.067347 + 0.0115875*CB
Figure 13.Linear Fit Regression Boat Safety Item by Reported Behavior Index, Boater
Safety = 1.6479089 + 0.002001*RB
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Figure 14. Linear Fit Regression Boat Ownership Item by Self-efficacy Test, Owned =
2.1567503 - 0.1441325*SET
Figure 15.Linear Fit Regression Boat Ownership Item by Self-efficacy Index, Owned =
3.1570997 - 0.0420736*SEI
103
Figure 16. Linear Fit Regression Boat Ownership Item by Control Belief Index, Owned =
3.4823009 - 0.03716*CB
Figure 17. Linear Fit Regression Boat Ownership Item by Reported Behavior Index,
Owned = 1.5059172 + 0.0036719*RB
104
Table 1. Statistics for Overall Scores for the Self Efficacy Index Statistic Std. Error
Total Mean 38.50 .470 95% Confidence Interval for Mean
Lower Bound 37.57 Upper Bound 39.43
5% Trimmed Mean 38.76 Median 39.00 Variance 60.134 Std. Deviation 7.755 Minimum 21 Maximum 50 Range 29 Interquartile Range 12 Skewness -.461 .153 Kurtosis -.738 .306
Table 2. Statistics for Overall Scores for the Self Efficacy Test Statistic Std. Error
Total Mean 4.25 .096 95% Confidence Interval for Mean
Lower Bound 4.06 Upper Bound 4.44
5% Trimmed Mean 4.25 Median 4.00 Variance 2.338 Std. Deviation 1.529 Minimum 0 Maximum 8 Range 8 Interquartile Range 2 Skewness -.151 .153 Kurtosis -.407 .306
105
Table 3. Statistics for Control Belief Index Scores Statistic Std. Error
Total Mean 52.06 .580 95% Confidence Interval for Mean
Lower Bound 50.92 Upper Bound 53.21
5% Trimmed Mean 52.39 Median 54.00 Variance 84.673 Std. Deviation 9.202 Minimum 25 Maximum 70 Range 45 Interquartile Range 13 Skewness -.544 .153 Kurtosis -.122 .306
Table 4. Statistics for Overall Reported Behavior Index Scores Statistic Std. Error
Total Mean 11.30 .411 95% Confidence Interval for Mean
Lower Bound 10.49 Upper Bound 12.11
5% Trimmed Mean 11.10 Median 11.00 Variance 42.545 Std. Deviation 6.523 Minimum 0 Maximum 33 Range 33 Interquartile Range 10 Skewness .374 .153 Kurtosis .020 .306
106
Table 6.Tests of Normality of Self Efficacy Index
Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.
Total .093 252 .000 .947 252 .000 a. Lilliefors Significance Correction
Table 7.Tests of Normality for each of the Populations Self Efficacy Test Score
Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.
Total Score .140 252 .000 .958 252 .097 Visitor .133 47 .037 .954 47 .061 Part Time Resident
.153 74 .007 .960 47 .108
Full Time Resident
.155 131 .003 .952 131 .071
a. Lilliefors Significance Correction
Hypothesis #5:
Figure 18.Histogram of Full Time Resident Self Efficacy Test Scores by Frequency n=151, 2009
107
Figure 19.Histogram of Visitor Self Efficacy Test Scores by Frequency n=47, 2009
Figure 20. Histogram of Part Time Resident Self Efficacy Test Scores by Frequency
n=47, 2009
108
Table 8. One Way ANOVA – Self Efficacy Test by Residency groups
Sum of Squares df Mean Square F Sig.
Between Groups
91.015 2 45.508 22.858 .000
Within Groups
495.731 249 1.991
Total 586.746 251 Table 9. One Way ANOVA – Self Efficacy Test by Residency groups – LSD Post Hoc Test (I) Residency
(J) Residency
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval Lower Bound Upper Bound
dimension2
1 dimension3
3 .394 .205 .056 -.01 .80 4 1.622* .240 .000 1.15 2.09
3 dimension3
1 -.394 .205 .056 -.80 .01 4 1.228* .263 .000 .71 1.75
4 dimension3
1 -1.622* .240 .000 -2.09 -1.15 3 -1.228* .263 .000 -1.75 -.71
*. The mean difference is significant at the 0.05 level.
Table 10.One Way ANOVA – Control Belief Index by Residency group
Sum of Squares df Mean Square F Sig.
Between Groups
11174.324 2 5587.162 138.035 .000
Within Groups
10078.661 249 40.477
Total 21252.984 251
109
Table 11.One Way ANOVA – Control Belief Index by Residency group – LSD Post Hoc Test (I) Residency
(J) Residency Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
1 dimension3
3 5.273* .925 .000 3.45 7.09 4 17.962* 1.082 .000 15.83 20.09
3 dimension3
1 -5.273* .925 .000 -7.09 -3.45 4 12.689* 1.187 .000 10.35 15.03
4
dimension3
1 -17.962* 1.082 .000 -20.09 -15.83 3 -12.689* 1.187 .000 -15.03 -10.35
*. The mean difference is significant at the 0.05 level.
Table 12. One Way ANOVA of Control Belief Index scores by Level of Boater Education group
Sum of Squares df Mean Square F Sig.
Between Groups 569.645 4 142.411 1.701 .150 Within Groups 20683.339 247 83.738 Total 21252.984 251
110
Figure 21. Histogram of Full Time Resident Control Belief Index Scores by Frequency
n=131, 2009
111
Figure 22. Histogram of Part-Time Resident Control Belief Index Scores by Frequency,
n=74, 2009
Figure 23. Histogram of Visitor Control Belief Index Scores by Frequency, n=47, 2009
112
Hypotheses 4 and 6:
Table 13. One Way ANOVA of Self-efficacy Test scores by Level of Boater Education group
Sum of Squares df Mean Square F Sig.
Between Groups 11.074 4 2.769 .970 .425 Within Groups 705.033 247 2.854 Total 716.107 251
Table 14. One Way ANOVA of Control Belief Index scores by Boat Ownership group
Sum of Squares df Mean Square F Sig.
Between Groups 5122.122 3 1707.374 26.250 .000 Within Groups 16130.862 248 65.044 Total 21252.984 251
Table 15. One Way ANOVA of Control Belief Index scores by Boat Ownership group – LSD Post Hoc Test (I) Owned? (J)
Owned?
Mean Difference (I-J) Std. Error Sig.
95% Confidence Interval Lower Bound
Upper Bound
dimension2
1
dimension3
1.215 2.104 .564 -2.93 5.36 11.734* 1.332 .000 9.11 14.36 -.122 2.620 .963 -5.28 5.04
2
dimension3
-1.215 2.104 .564 -5.36 2.93 10.519* 2.341 .000 5.91 15.13 -1.337 3.251 .681 -7.74 5.07
3
dimension3
-11.734* 1.332 .000 -14.36 -9.11 -10.519* 2.341 .000 -15.13 -5.91 -11.857* 2.814 .000 -17.40 -6.31
4
dimension3
.122 2.620 .963 -5.04 5.28 1.337 3.251 .681 -5.07 7.74 11.857* 2.814 .000 6.31 17.40
*. The mean difference is significant at the 0.05 level.
113
Table 16. One Way ANOVA of the Self-efficacy Test by Boat Ownership group
Sum of Squares df Mean Square F Sig.
Between Groups 66.419 3 22.140 8.451 .000 Within Groups 649.688 248 2.620 Total 716.107 251
Table 18. One Way ANOVA of the Self-efficacy Test by Boat Ownership group – LSD Post Hoc Test (I) Owned? (J)
Owned? Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound
Upper Bound
dimension2
1
dimension3
-.210 .422 .620 -1.04 .62 1.282* .267 .000 .76 1.81 .778 .526 .140 -.26 1.81
2
dimension3
.210 .422 .620 -.62 1.04 1.492* .470 .002 .57 2.42 .987 .652 .131 -.30 2.27
3
dimension3
-1.282* .267 .000 -1.81 -.76 -1.492* .470 .002 -2.42 -.57 -.504 .565 .373 -1.62 .61
4
dimension3
-.778 .526 .140 -1.81 .26 -.987 .652 .131 -2.27 .30 .504 .565 .373 -.61 1.62
*. The mean difference is significant at the 0.05 level.
114
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BIOGRAPHICAL SKETCH
Claire Sunquist grew up in a small town outside of Gainesville and attended the
University of Florida during her undergraduate, receiving her bachelors in
Environmental Science in 2008. She was then accepted into a Masters program in
Interdisciplinary Ecology in Fall of 2008. Claire will graduate in May of 2010 with a
Masters of Science and a concentration in Family Youth and Community Sciences. She
enjoys fishing, boating, hiking, and photography in her spare time and looks forward to
having more time to do those things in the future.
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