Bait Shop Owners as Opinion Leaders

Abstract

Boaters and anglers who move between bodies of water are a primary cause of the spread of aquatic invasive species (AIS), which are non-native plants or animals that pose a threat to water quality, disrupt ecosystems, reduce biodiversity, and cause economic harm. Research suggests that engagement with these individuals through opinion leaders within their social networks has the potential to encourage attitude and behavior change. Using the theory of planned behavior as a framework, this article explores factors that may enhance AIS outreach behaviors among opinion leaders, namely, bait shop owners and their employees, to communicate with their customers. The results of this study suggest that perceptions about normative social pressures are a strong predictor of intentions to engage in outreach activities, but perceived behavioral control is a stronger predictor of actual engagement with their customers.

Keywords

environmental communication aquatic invasive species social marketing pro-environmental behavior opinion leaders

Introduction

Aquatic invasive species (AIS) are non-native plants or animals that pose a significant threat to water quality and the health of fisheries in large areas of the United States and around the world. Recreational boaters and anglers who move between bodies of water are one of the primary causes of the spread of AIS as aquatic plants and animals can attach themselves to their equipment. Researchers have suggested that engaging with lake users to encourage pro-environmental behaviors is crucial for preventing the spread of AIS (Kolar & Lodge, 2000; Shaw, Dalrymple, & Brossard, 2012). Additional research suggests that engaging opinion leaders, or individuals deemed highly influential within a social network, can encourage attitude and behavior change (Nisbet & Kotcher, 2009). However, this assumes that opinion leaders feel capable of sharing related information with other people in their extended social networks, perceive social norms that encourage them to promote AIS prevention behavior, and believe that performing prevention steps will make a difference in stopping or slowing the spread of AIS. In the context of AIS prevention, bait shop owners and their employees are in a unique position to communicate with transient anglers information relevant to AIS prevention, given their frequent interactions with this audience (Dalrymple, Shaw, & Brossard, 2013). Therefore, as a first step in this endeavor, this study explores potential factors for increasing AIS-related outreach activities among Wisconsin bait shop owners.

Background

Although mass media campaigns can be successful, there are often more effective ways to encourage behavior change. This is especially true for issues that are complex and have broad ecological and societal implications, such as AIS (Prior, 2005). While broad mass media campaigns have been shown to increase levels of objective knowledge and awareness about certain issues, these factors can be poor predictors of changes in both attitude and behavior (Noar, 2006). In the context of social marketing campaigns targeted at behavior change, information mediated through interpersonal networks may be more likely to influence attitudes and related behaviors (Abroms & Maibach, 2008; McKenzie-Mohr & Smith, 1999). In fact, research has indicated that people often look toward others for evidence of how to act, particularly in instances surrounded by uncertainty about acceptable behaviors (Cialdini & Goldstein, 2004; Shaw, 2009).

Of importance to this study is the idea of injunctive norms, which is the degree to which people feel their behavior would produce approval or disapproval among relevant reference groups (Cialdini, Reno, & Kallgren, 1990). Research indicates that high injunctive normative beliefs strengthen pro-environmental behaviors (Göckeritz et al., 2010). Injunctive norms can improve the efficacy of messages using descriptive norms and diminish possibly negative influence of descriptive norms among less compliant individuals (Schultz, Nolan, Cialdini, Goldstein, & Griskevicius, 2007). Furthermore, there are times in which injunctive norms may be more likely to be effective to the extent that there is consistency in expectations among relevant reference groups (McDonald, Fielding, & Louis, 2013), though injunctive norms work better when paired with supportive descriptive norms (Smith et al., 2012).

Also relevant to this study, people are more likely to pay attention to trusted sources who are in a position of authority in their community (J. A. Kelly et al., 1992). It should be noted that authority in this context does not imply that opinion leaders are heavy-handed with those they communicate with in advocating particular behaviors. Individuals can possess expertise on a topic or be considered highly influential within a certain context without holding a formal position of power. According to research on opinion leaders, these individuals tend to serve as the “connective communication tissue” that alerts peers to what matters when it comes to political events, consumer choices, and social and environmental issues (Nisbet & Kotcher, 2009). The use of opinion leaders in communication was developed from the theory of two-step flow of information developed by Katz and Lazarsfeld (1955). This and related research found that opinion leaders tend to have more influence on people’s opinions, actions, and behaviors than traditional forms of media (Katz, 1957). Opinion leaders are deemed especially influential in part because they act as an intermediary between media and the less-engaged public, but they also tend to be similar in demographics, interests, and personality to those they interact with (Katz & Lazarsfeld, 1955). They are also considered to be highly competent in a certain field and strategically located within a social network (Katz, 1957).

Further considering the potential of opinion leaders in behavior change campaigns, we may consider their potential role from the lens of the Diffusions of Innovation model (Rogers, 2003). The model suggests individuals fall into one of five categories when faced with changing their behavior: innovators, early adopters, early majority adopters, late majority adopters, and “laggards.” Opinion leaders can be helpful in a given behavioral domain to communicate the importance of an issue to “early adopters” who in turn influence transient anglers farther back in the curve to adopt a new behavior and build momentum toward establishing positive injunctive and descriptive social norms, which in many domains is associated with behavioral compliance. In addition, some research on guided group discussions has shown that receiving information via discussion is more influential on attitude change than through lectures or more formal media (Lewin, 1952; Werner & Stanley, 2011). A potential advantage is that a discussion-like format could also allow for positive feedback from customers, which can, in turn, reinforce acceptable normative behaviors.

In light of this, this study focuses on engaging opinion leaders who are in a position to influence transient boaters and anglers on AIS prevention–related behaviors. “Opinion leaders not only help draw the attention of others to a particular issue, product or behavior but also, perhaps most importantly, signal how others should in turn respond or act” (Nisbet & Kotcher, 2009, p. 332). Bait shop owners have the potential to act as a natural agent for change, because they represent an obligatory passage point for transient anglers who use bait and tackle. In addition, bait shop owners and employees often serve as hubs of information about local fishing conditions and fishing-related rules and regulations. Based on previous research, most bait shop owners in Wisconsin also report carrying some AIS prevention–related informational materials in their stores (Dalrymple et al., 2013).

Identifying and targeting opinion leaders represent just one part of the process. An arguably more important and possibly more challenging endeavor involves the engagement and empowerment of these individuals. Therefore, our work focuses on encouraging bait shop owners to leverage their positions within their social networks to engage customers in conversations related to AIS prevention. It should be noted that while the concept of social network often refers to more persistent relationships, we include any customers who visit bait shops as a part of our conceptualization.

Theoretical Framework

As the focus of this study is to encourage behavior change among bait shop owners as opinion leaders in their communities, we look to behavioral theories to provide a framework for this engagement. A number of theories exist to predict behaviors, but one of the most prominent is the theory of planned behavior (Ajzen, 1985). The theory of planned behavior is an extension of an earlier theory called the theory of reasoned action, which attempted to predict behavior but focused on behaviors in which people lack volitional control (Ajzen, 1991). Both the theory of planned behavior and the theory of reasoned action focused on the importance of intention of performing a specific behavior. The addition of a variable concerned with perceptions of control over behaviors, also called perceived behavioral control, served to extend the theory of reasoned action into the theory of planned behavior (Ajzen, 1991).

The theory of planned behavior has been widely applied across a range of contexts to predict how an individual will behave. In the field of public health, this framework has been used to explore activities from smoking behaviors (Borland, Owen, Hill, & Schoeld, 1991; De Vries, Backbier, Kok, & Dijkstra, 1995; Godin, Valois, Lepage, & Desharnais, 1992), to weight loss (Armitage & Conner, 2001; Schifter & Ajzen, 1985), alcohol consumption (Morojele & Stephensen, 1994; Schlegel, D’Avernas, Zanna, DeCourville, & Manske, 1992), and exercise behaviors (Biddle, Goudas, & Page, 1994; Boudreau, Godin, Pineau, & Bradet, 1995), among many others. Other applications include investment behaviors (East, 1993), civic participation (C. Kelly & Breinlinger, 1995), and education (Crawley & Black, 1992). More recently, the theory of planned behavior has expanded to explore participation in environmental behaviors such as recycling (Cheung, Chan, & Wong, 1999), water conservation (Lam, 1999; Trumbo & O’Keefe, 2001), green consumerism (Sparks & Shepherd, 1992), and storm water management (Shaw, Radler, Chenoweth, Heilberger, & Dearlove, 2011).

Intent to perform a behavior is the strongest predictor of actual behaviors, according to the theory of planned behavior. The concept of behavioral intent, however, can be broken down into several predictive factors: attitude toward the behavior, subjective norms, and perceived behavioral control. These three predictors are typically teased out further into several additional predictors. Attitude focuses on the degree to which a person has a favorable or unfavorable evaluation of the behavior. This variable includes measures of behavioral beliefs, that is, feelings toward the actual behavior, and outcome evaluations, or the belief that the behavior will make a difference. Subjective norms refers to perceptions of social pressure to perform the behavior and includes measures of normative beliefs as well as personal motivation to comply with the behavior. Finally, perceived behavioral control is concerned with perceived ability, including ease or difficulty, of performing the behavior. This variable includes self-efficacy beliefs, or belief in the ease of performing the behavior, and controllability of the behavior (Ajzen, 1991).

Recent research suggests that an individual’s sense of self-efficacy may be an important and influential factor in predicting the likelihood of opinion leaders to engage with the issue of AIS. This same line of research also suggests that individuals who experience higher levels of self-efficacy, in turn, are more likely to participate in behaviors that offer the potential to influence social networks (Dalrymple et al., 2013). In a broader sense, efficacy beliefs are foundational to action, because a behavior is unlikely to happen if an individual does not feel capable of performing the action. Regardless of other motivators, actions must be based on the belief that one has the power to produce desired changes (Bandura, 1998).

As discussed, opinion leaders have the potential to influence behaviors. In the context of this project, our intent was to empower bait shop owners to engage with customers about AIS-related issues. Previous research had found that many bait shops in our sample area already were taking some steps to share AIS-related information with customers. However, in an effort to increase levels of engagement (Dalrymple et al., 2013), bait shop owners in Wisconsin were approached by a statewide network of AIS coordinators to participate in a statewide initiative that was launched in the spring of 2012 and ran through the end of that summer’s fishing season. The coordinators provided bait shops with “toolkits’ containing items that were intended to provide bait shops with educational and promotional materials related to AIS prevention.¹ The items in the toolkit were created with established social marketing principles in mind (McKenzie-Mohr & Smith, 1999) such as providing prompts that serve as reminders of the desired behaviors and encouraging public commitment by the bait shop. Examples of prompts included in the toolkit were floating key chains, brochures, and stickers for bait bucket and boating equipment. To encourage commitment in the outreach efforts, bait shops were rewarded with free press coverage and advertising in local and statewide media outlets. In addition to items already mentioned, owners and employees were also provided with a document tailored to their anticipated needs that provided answers to frequently asked questions as well as other information in order to increase their sense of self-efficacy to interact with their customers. In terms of establishing social norms, bait shop owners were engaged not only as hubs of information but also as important opinion leaders who could establish injunctive norms about “the right thing to do” when boating and fishing with their customers and extended social networks.

In November of 2012, following the launch of the outreach toolkit, we measured outreach efforts as well as other theoretical variables using a survey instrument distributed to bait shops around the state. This survey served as a follow-up and conceptual expansion of a bait shop survey conducted in 2010 by Dalrymple et al. (2013).

Hypotheses

Using the theory of planned behavior as a framework for our study, several hypotheses emerged related to each of the identified predictors of behavioral intentions and actual behavior. Our first hypothesis predicts that favorable attitudes among bait shop owners toward AIS prevention behaviors, that is to say appraisals that AIS are a threat to lakes in Wisconsin and the belief that AIS prevention will help to slow the spread of AIS, will be linked to increased willingness to engage in AIS outreach, which we have operationalized as behavioral intention.

Hypothesis 1: Bait shop owners’ attitudes toward AIS prevention will be positively correlated with their willingness to engage in outreach behaviors.

Second, an increasing body of research suggests that social norms can influence the behaviors with which people engage. In the case of AIS prevention, we believe that perceptions of what types of activities others expect bait shop owners to engage in will be correlated with intention to engage in AIS prevention activities. Therefore, Hypothesis 2 is as follows:

Hypothesis 2: Bait shop owners’ assessment of subjective norms about AIS prevention activities will be positively correlated with willingness to engage in outreach behaviors.

Relatedly, we are interested in exploring whether or not subjective norms have an effect on actual behavior. According to the theory of planned behavior, the effects of social norms are limited to behavioral intent and do not share a direct link with behavior. However, we will explore a potential link with the following research question:

Research Question 1: What is the relationship between subjective norms and AIS prevention behaviors?

As discussed, the theory of planned behavior places a strong emphasis on perceptions of control over performing certain behaviors, including ease or difficulty, and controllability of the behavior. We predict a positive correlation between perceived behavioral control over AIS behaviors and behavioral intention, as stated in Hypothesis 3:

Hypothesis 3: Bait shop owners’ perceived control over AIS prevention behavior will be positively correlated with willingness to engage in outreach behaviors.

In addition, previous research has suggested that perceived behavioral control is also linked with behavior, not just behavioral intent. As such, we explore a relationship between those two concepts with our fourth hypothesis:

Hypothesis 4: Bait shop owners’ perceived control of AIS prevention activities will be positively correlated with current engagement in outreach activities.

Last, one of the core tenets of the theory of planned behavior is that behavioral intention is directly correlated with actual behaviors. It is assumed that intentions capture those motivational factors that influence a behavior (Ajzen, 1991). Therefore, the higher the intentions to perform a behavior, the more likely it is the behavior will be performed. Our final hypothesis addresses this:

Hypothesis 5: Bait shop owners’ willingness to engage in AIS outreach activities will be positively correlated with current engagement.

Method

Data for this research project were collected as part of an evaluation of a statewide AIS prevention initiative in Wisconsin. Bait shop owners were identified as survey participants based on previous lists of licensed Wisconsin bait vendors. Eligible shops were selected based on cross-referencing Internet and phonebook listings. The final database included 174 bait shops. Surveys were distributed to bait shops via U.S. mail. We employed the Dillman (1991) method of survey research, which included mailing the survey with an introductory letter and a self-addressed stamped envelope. The mailed surveys were then followed up with a postcard reminder sent to nonresponders. Additional follow-up included a mailed letter reminding nonresponders of the importance of the survey, then finally with a replacement survey. The final sample included 67 bait shops, with a response rate of 38.5%.

Dependent Variables

Behavioral Intent

According to the theory of planned behavior, intent is the strongest predictor of behavior change. In our study, behavioral intent was measured by asking participants several questions about their willingness to engage in specific behaviors. Based on a five-point scale with 1 meaning “Not at all” and 5 meaning “Very,” the questions probed their willingness to remind customers of the AIS prevention steps required in Wisconsin (M = 3.03, SD = 1.1); put signs or posters in their shops (M = 4.3, SD = .88); hand out AIS publications to customers (M = 3.4, SD = 1.1); hand out stickers for bait containers, tackle boxes, boas, and trailers (M = 3.02, SD = 1.2); and be a part of community efforts to stop the spread of AIS (M = 3.72, SD = 1.1). To test the internal consistency of the index measures, item reliability was calculated with Cronbach’s alpha. An index is conventionally considered to have good reliability if the Cronbach’s alpha coefficient is .6 or higher on a range from 0.0 to 1.0 (Santos, 1999). These items were combined into a mean index with Cronbach’s alpha = .821, indicating good reliability among measures.

Behavior

To measure actual outreach behaviors, bait shop owners were asked several questions related to current outreach activities. The questions asked owners how often during their busiest month they perform specific outreach activities related to AIS prevention. Responses were based on a five-point scale with 1 meaning “never” and 5 meaning “always.” Specific activities included talking one-on-one to customers (M = 2.6, SD = .99), hand out informational materials (M = 3.1, SD = 1.04), and display signs or posters (M = 4.4, SD = .86). These items were combined into a mean index with Cronbach’s alpha = .64. While this alpha is lower than optimal, it still surpasses the threshold of .60 for acceptable reliability, as outlined by Santos (1999).

Independent Variables

According to the theory of planned behavior, behavioral intent is predicted by three factors: attitude toward the behavior, subjective norms, and perceived behavioral controls. These factors served as the independent variables in this study and were composite measures of several variables.

Attitude toward the behavior is composed of two different measures, including perceptions of the severity of the threat of AIS and evaluations of the outcome of AIS prevention behaviors. Perceived threat was measured by the question “How serious a threat are AIS to fisheries in Wisconsin?” (M = 3.8, SD = 1.1). Responses were measured on a 1-to-5 Likert-type scale with 1 meaning “not at all serious” and 5 meaning “very serious.” Outcome evaluations were measured by the question “Do you agree or disagree that practicing the AIS prevention steps is unlikely to slow the spread of AIS?” (M = 3.27, SD = 1.3). The scale used for this question was a five-point scale ranging from 1 meaning “strongly agree” to 5 meaning “strongly disagree.”

Perceived behavioral control encompasses both self-efficacy and perceived knowledge about AIS prevention. Self-efficacy for engaging with customers was measured by creating a mean index of responses to questions related to the level of confidence bait shop owners felt in educating their customers about AIS-related issues. Specifically, bait shop owners were asked to rate on a five-point scale their level of agreement (1 = strongly disagree, 5 = strongly agree) with the following questions: “Bait shops can play an important role in educating customers about AIS” (M = 3.88, SD = .94), “I consider myself to be well-qualified to inform customers about AIS prevention” (M = 3.56, SD = 1.13), “I feel that I could do a good job helping my customers understand AIS prevention” (M = 3.80, SD = 1.05), and “I feel I could help my customers adopt behaviors that will help reduce the spread of AIS” (M = 3.51, SD = .94). Responses to these questions were combined and divided by four to create the independent variable self-efficacy (M = 3.65, SD = .77). The Cronbach’s alpha for the self-efficacy index was .75, indicating good reliability among measures.

Bait shop owners were asked to indicate their levels of perceived knowledge about the topic of AIS by stating their level of agreement with related statements along a five-point scale (1 = strongly disagree, 5 = strongly agree). The perceived knowledge questions included the following: “I am aware of the laws and regulations related to AIS prevention” (M = 4.3, SD = .7), “I am knowledgeable about the risks of AIS in Wisconsin” (M = 4.2, SD = .78), “I have a good understanding of the important issues related to AIS” (M = 3.9, SD = .96), and “I think I am better informed about AIS than most people” (M = 3.93, SD = .95). These four items were combined into a mean index (M = 4.13, SD = .66). Cronbach’s alpha coefficient for this index was .82.

Finally, subjective norms were measured by asking bait shop owners a series of questions related to their perceptions of how much various social networks would expect their bait business to play a role in AIS prevention. Bait shop owners were asked the question, “How much do any of the following groups listed below expect your bait business to play a role in AIS prevention?” The response set was a five-point scale with 1 meaning “not at all” and 5 meaning “very much.” Specific groups included customers (M = 2.5, SD = 1.04), anglers in their community (M = 2.4, SD = 1.02), family members (M = 2.2, SD = 1.04), friends (M = 2.3, SD = .99), lake associations (M = 3.0, SD = 1.26), other bait shops (M = 2.5, SD = 1.1), and community leaders (M = 2.3, SD = 1.2). Items were combined into a mean index with Cronbach’s alpha coefficient of .924.

Analysis and Results

Prior to conducting our path analysis, we examined each of the variables included in our model. Means and correlations for each variable are shown in Table 1. All variables showed statistically significant correlations except for between the attitude and behavior variables. We then examined the ability of the theory of planned behavior to account for intentions to engage in AIS outreach and its ability to predict actual outreach behaviors. As such, all variables were entered into a path model to explore which variables serve as predictors of intention and behavior.

Table 1.

Simple Correlations Between Scales.

	Attitude	Subjective norms	Perceived behavioral control	Intention
Subjective norms	.30*
Perceived behavioral control	.26*	.56**
Intention	.41**	.63**	.39**
Behavior	.16	.64**	.67**	.65**

p ≤ .05. **p ≤ .01.

Model Assessment

Results of the path analysis, shown in Figure 1, indicate that the path model for the effects of perceived behavioral control, subjective norms, behavioral attitudes on bait shop owners’ willingness to engage in outreach behaviors, and their actual engagement in outreach activities has a good model fit. More specifically, based on the chi-square test of model fit (χ² = 1.28, df = 1, p = .258), we failed to reject the null hypothesis that the model fits the data, suggesting our model is more than just an artifact of the data. Comparative fit indices further indicate a good model fit including comparative fit index (CFI) = 0.997; non-normed fit index (NNFI) = 0.969; normed fit index (NFI) = 0.988; relative fit index (RFI) = 0.877; standardized root mean squared residual (SRMR) = 0.025; and the root mean square error of approximation (RMSEA = 0.081). Finally, the model alkaike information criterion (AIC) (29.278) was lower than the Saturated AIC (30.000), meaning the path model is more efficient than a saturated model. In other words, the path model provides a clearer picture than a regression model. In addition, the overall model accounted for a total of approximately 60% of the variance in actual engagement in outreach activities (R² = .598) and 36% of the variance in willingness to engage in outreach behaviors (R² = .203). Taken together, these indices suggest that the model is good relative to a baseline model of complete independence.

Effects of Exogenous Variables

Results of the model, indicate that subjective norms have significant effects on willingness to engage in outreach behaviors (γ = .379, p < .05) and actual engagement in outreach activities (γ = .235, p < .05), suggesting owners who perceive more pressure from their social networks are more likely to be willing to engage in outreach behavior and to actually do so. In addition, increases in perceived behavioral control have a significant effect on actual engagement in outreach activities (γ = .465, p < .05); indicating that the more owners feel they know and the more they feel capable of doing something about it, the more likely they are to engage in outreach behaviors. Finally, behavioral attitudes have a significant effect on willingness to engage in outreach behaviors (γ = .189, p < .05). This suggests that increased concerns about the consequences of AIS will increase bait shop owners’ intention to engage in outreach activities with their customers.

Effects of Endogenous Variables

The effects of endogenous variables simply depict the relationship between owners’ intentions to engage in outreach behaviors and actual engagement in outreach activities. Specifically the results demonstrate that higher levels of willingness to engage in outreach behaviors lead to actual engagement in outreach activities (β = .266, p < .05). In other words, this suggests that increases in bait shop owners’ intent to engage in outreach behavior lead to actually doing said behaviors.

Discussion

The purpose of this research was to examine what factors are likely to predict engagement in AIS prevention behaviors among bait shop owners. Because bait shop owners have the potential to serve as opinion leaders for AIS prevention among their customers and social networks, it is important to understand strategies for empowering them to do so. Using the theory of planned behavior as our theoretical framework, we identified three factors that were likely to predict behavioral intention, including attitudes toward the AIS prevention behavior, perceptions of subjective norms, and perceived control over AIS prevention behaviors. The theory of planned behavior posits not only that behavioral intention serves as a primary predictor of behavior, but that there is also a direct link between perceived behavioral control and behavior (Ajzen, 1991; Ajzen & Fishbein, 1980).

Overall, this study provides some useful insights into predicting intention to engage in AIS outreach activities. Our first hypothesis predicted that attitudes toward AIS prevention behavior would be positively correlated with behavioral intention. The results support this hypothesis. We also learned from the model that in terms of actual behavior, behavioral attitudes have only an indirect effect on bait shop owners’ engagement in outreach to prevent AIS. This finding is unsurprising in light of other research suggesting that attitudes are not a reliable predictor of behavior (Ajzen, 1985).

Our second hypothesis, that subjective norms (i.e., owners who perceive more pressure from their social networks) will be positively correlated with behavioral intention, was also supported. The model showed that subjective norms have a direct effect on bait shop owners’ willingness to engage in outreach behaviors. In addition, the path model suggested that subjective norms are also positively correlated with actual behavior, not just intent. These findings are interesting for a few reasons. First, prior research on the theory of planned behavior has shown that subjective norms tend to be a weak predictor of behavioral intention quite possibly due to problems with measurement, that is, using single-item measures (Armitage & Conner, 2001; Beck & Ajzen, 1991; Sheppard, Hartwick, & Warshaw, 1998; Van den Putte, 1991). Our survey measured social normative perceptions with a seven-item mean index with high inter-item reliability. Our results support Armitage and Conner’s (2001) suggestion that examinations of the role of subjective norms in the theory of planned behavior would benefit from stronger measures of perceptions of normative social pressures. This finding is also encouraging in light of more recent research by scholars on the underdetected powers of social norms (Nolan, Schultz, Cialdini, Goldstein, & Griskevicius, 2008; Schultz et al., 2007). For example, experimental studies of pro-environmental behaviors show that participants report not being consciously swayed by social norms, yet those exposed to conditions in which social norms are emphasized are much more likely to engage in the pro-environmental behavior (Griskevicius, Cialdini, & Goldstein, 2008; Nisbett & Wilson, 1977). Therefore, it is likely that increased pressure from peers and colleagues, such as friends, family, and neighboring bait shops and lake associations, can serve to empower bait shop owners in talking with customers, or at least instill a desire to behave in ways that allow them to live up to those perceived expectations. As many lake-rich communities have lake associations or local fishing clubs, future outreach efforts could target a broader fishing/boating community to leverage social norms. In addition, enhancing group discussions of AIS within these social networks may serve to create a positive feedback loop by exposing possible pluralistic ignorance and allowing productive conversations to occur (Lewin, 1952; Schroeder & Prentice, 1998; Werner, Sansone, & Brown, 2008).

Our final two hypotheses concerned the variable of perceived behavioral control. Interestingly, our third hypothesis, that perceived behavioral control would be positively correlated with behavioral intent, was not supported. However, we did find support for the fourth hypothesis that perceived behavioral control would be positively correlated with direct engagement in outreach activities. In other words, feelings of control over AIS outreach activities were not predictive of willingness to engage in the activities, but rather predicted actual engagement. A review of the literature on the theory of planned behavior highlights the idea that intentions are assumed to address the motivational factors that influence a behavior. In other words, intentions are indications of how hard people are willing to work to perform the behavior (Ajzen, 1991). The mean responses to questions related to behavioral intent suggest that bait shop owners are most likely to engage in passive behaviors, such as displaying materials like brochures and posters, than active behaviors like talking directly to customers. Therefore, bait shop owners may be most likely to participate in outreach efforts when their participation requires low levels of effort that would not take away from the other demands of running their businesses and/or are less likely to alienate customers who might disagree with them.

Another thing to note is that the theory of planned behavior does not include perceived knowledge as a unique predictor of behavior change; however, perceived knowledge is encompassed within the broader concept of perceived behavioral control as, in many cases, knowledge and/or resources are a prerequisite for a sense of control over a particular behavior (Ajzen, 1985). A key part of feeling capable of sharing information with others is the perception that you possess enough knowledge to competently discuss the topic. Other research on the role of knowledge in behavior change as it relates to environmental issues suggests that objective knowledge alone does not consistently transfer to pro-environmental behavior (De Oliver, 1999; McKenzie-Mohr & Smith, 1999). However, increased levels of perceived knowledge may enhance a bait shop owner’s confidence in communicating about the topic with their customers. Future research could explore the variable of perceived knowledge in isolation and in relation to perceived behavioral control.

Finally, our last hypothesis was intended to close the gap between behavioral intent and behavior by predicting that intent was positively correlated with current behaviors. This hypothesis was supported by the data, suggesting that strengthening intentions via the various motivational factors, will bolster actual engagement in outreach activities. That said, the model suggests that while behavioral intent is an important factor to consider, it is not as strongly correlated with behavior as was perceived behavioral control.

Finally, it is important to address several limitations to this analysis. First of all, as this study was intended to survey bait business owners, the sample size for the study was relatively small (N = 67). Having a small sample size decreases the statistical power, which increases the chance of committing a Type II error, or failing to reject the null hypothesis when the null is false. In addition, given the applied nature of this study, surveys were returned on a voluntary basis. Therefore, it is possible that the participants in this sample used in the analysis were individuals who shared concern about the spread of AIS prior to the intervention and the survey. The self-selected nature of this study may lend a bias to the results.

Likewise, as mentioned, the behaviors included in our index for current outreach behaviors tended to be relatively passive activities, such as displaying posters or informational materials. While these activities count as outreach, they typically involve limited interactions with customers and, therefore, do not require significant changes in everyday behaviors. However, even small changes, including more passive activities, are important and should be encouraged.

In addition, the path analysis model is not intended to be comprehensive. While we feel confident that this analysis included a number of relevant concepts related to both behavioral intention and behavior, there are undoubtedly variables that we overlooked or did not account for in our survey. Additional research could also investigate more in-depth the relationship between perceived knowledge and interpersonal communication among bait shop owners and members of their social networks to explore potential interactions or mediating effects. In addition, future research could more closely examine the potential effectiveness of the statewide intervention by comparing pre- and post-test data.

In conclusion, this study focuses on predicting what factors may encourage behaviors that aid in the prevention of AIS using the framework of the theory of planned behaviors. The goal of this study is to identify potential methods to empower bait shop owners as opinion leaders on the topic of AIS prevention behaviors among their customers. The results of this study suggest that a number of factors can influence both behavior and behavioral intent, including attitudes, perceived behavioral control, and subjective norms. In terms of application of these findings, as perceived behavioral control showed a strong, significant correlation with behavior, efforts to engage bait shop owners should focus on enhancing perceived knowledge and self-efficacy. For example, it may be important for opinion leaders to feel they have enough knowledge to share with their social networks. Therefore, it is important to make sure that resources are available to targeted opinion leaders that will enhance their perceived knowledge levels. Finally, our findings suggest that communicating with opinion leaders in a way that emphasizes social expectations to participate in AIS prevention efforts could increase bait shop owners’ perceived willingness to share AIS-related information with their customers.

Footnotes

Authors’ Note

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Wisconsin Department of Natural Resources.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is based upon work supported by a grant from the Wisconsin Department of Natural Resources (Grant ACEI-074-10).

Notes

Author Biographies

Allison P. Howell is pursuing a doctoral degree in mass communications at the University of Wisconsin–Madison. Her research interests include social marketing, persuasion, behavior change, and public information campaigns.

Bret R. Shaw is an associate professor in the department of Life Sciences Communication at the University of Wisconsin and the environmental communications specialist for University of Wisconsin Extension. His work focuses on extension/outreach activities related to facilitating campaign development dealing with natural resource management.

German Alvarez is a doctoral student in mass communications at the University of Wisconsin–Madison. His research interests include political communications, international and cultural studies, inequality, and social media.

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