loomis y finn. development and validation of a specialization

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Development andValidation of aSpecialization Index andTesting of SpecializationTheoryRonald J. Salz, David K. Loomis & Kelly L.Finn

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To cite this article: Ronald J. Salz, David K. Loomis & Kelly L. Finn (2001):Development and Validation of a Specialization Index and Testing ofSpecialization Theory, Human Dimensions of Wildlife: An International Journal,6:4, 239-258

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Human Dimensions of Wildlife, 6:239258, 2001Copyright 2001 Taylor & Francis1087-1209 /01 $12.00 + .00

Partial funding for this project was provided by the Cooperative State Research, Extension,Education Service, U.S. Department of Agriculture, Massachusetts Agricultural Experiment StationProject Number 782.

Address correspondence to David K. Loomis, Department of Natural Resources Conservation,Human Dimensions Research Unit, Holdsworth National Resources Center, University of Massa-chusetts, Amherst, MA 01003-4210. E-mail: [email protected]

Development and Validation of a SpecializationIndex and Testing of Specialization Theory

RONALD J. SALZDAVID K. LOOMISHuman Dimensions Research UnitUniversity of Massachusetts-AmherstAmherst, Massachusetts, USA

KELLY L. FINNCalifornia Department of TransportationSan Diego, California, USA

Recreation specialization can be viewed as a continuum of behavior fromthe general to the particular. Along this continuum, participants can be lo-cated into meaningful subgroups based on specific criteria. Previous stud-ies have defined, measured, and segmented specialization groups in a vari-ety of ways. The research reported here builds on the Ditton, Loomis, andChoi reconceptualization of recreation specialization. A specialization in-dex was developed to segment anglers into four groups based on their ori-entation, experiences, relationships, and commitment. Internal validationanalysis supported the use of this specialization index as a tool for anglersegmentation. Subsequent hypotheses tested for differences among special-ization groups in frequency of participation, importance of activity andnonactivity-specific elements, support for management regulations, and side-bets. Results provide strong support for the conceptual framework developedby Ditton et al. These findings indicate a multidimensional index can beused to segment anglers into discreet, meaningful specialization categories.

Keywords Recreation specialization, segmentation, specialization index,anglers

Peer-Reviewed Articles

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240 R. J. Salz et al.

Introduction

Outdoor recreation participants generally display wide variation in their experi-ences, avidity, expertise, commitment, economic expenditures, and social inter-actions related to a particular activity. Connected to this variation are importantsociological and psychological differences affecting motivations, expectations,desired outcomes, satisfaction levels, perceptions, and social norms. Outdoor rec-reation managers must recognize and accommodate these differences to providesatisfactory experiences to a widely diverse clientele. Recreation specialization isan area of study that attempts to describe this variation through segmentation ofparticipants into meaningful and identifiable subgroups. Bryan (1977) was thefirst to conceptualize recreational specialization as a continuum of behavior fromthe general to the particular, reflected by equipment and skills used and activitysetting preferences. The four levels of specialization he identified in a populationof trout anglers were occasional anglers, generalists, technique specialists, andtechnique-setting specialists. Bryan (1977) suggested that more highly special-ized anglers are part of a leisure social world with a shared sense of group identi-fication derived from similar attitudes, beliefs, and experiences.

Recreation specialization studies following Bryan used a variety of classifi-cation techniques and variables to segment participants into specialization levels.Some studies found that a single-item measure of specialization could be used tosegment participants. For example, Graefe (1980) noted that frequency of par-ticipation (i.e., avidity) was a useful surrogate for measuring angler specializa-tion. He found that anglers who fished more frequently (i.e., were more special-ized) had higher self-reported skill levels, participated in more diverse fishingsettings, and had a greater dependency on the resource. Ditton, Loomis, and Choi(1992) also used avidity to segment recreational anglers into four specializationlevels. Similarly, Schreyer, Lime, and Williams (1984) used total number of riverruns as a means of classifying river users into six groups and found differencesbetween the groups in the type of prior river experience, motives for participation,perceptions of conflict, and support for managerial regulations.

Other studies took a multidimensional approach to recreation specializationby incorporating several variables into a specialization index. Chipman and Helfrich(1988) concluded that investment, consumptive habits, and frequency of partici-pation were important characteristics for determining specialization among an-glers. Kauffman and Graefe (1984) used preferences for river characteristics tosegment canoeists into more-specialized and less-specialized groups. Fedler andDitton (1986) segmented anglers into levels of consumptive orientation based onresponses to statements regarding the importance of catching fish. Wellman,Roggenbuck, and Smith (1982) used a specialization index based on equipmentinvestment, past experience, and centrality to lifestyle to segment anglers intogroups that reflect respondents attitudes toward depreciative behavior. Virdenand Schreyer (1988) constructed a specialization index to segment hikers basedon equipment and economic commitment, centrality to lifestyle, general experi-ence, and past experience variables.

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241Development and Validation of a Specialization Index

Using a variety of segmentation methods, recreation specialization studiesshowed that more specialized users differed from less-specialized users on nu-merous attributes. These included motives for participation (Kauffman & Graefe,1984; Schreyer et al., 1984), importance of nonactivity-specific elements (Fedler& Ditton, 1986), preferences for management strategies (Chipman & Helfrich,1988; Hammitt & McDonald, 1983), perceptions about crowding (Vaske, Donnelly,& Heberlein, 1978), environmental preferences (Kauffman & Graefe, 1984;Schreyer et al. 1984; Virden & Schreyer, 1988), equipment ownership and use(Chipman & Helfrich, 1988; Wellman et al., 1982), and centrality to lifestyle(Virden & Schreyer, 1988; Wellman et al., 1982). In general, these studies pro-vided support for Bryans specialization concept, and greatly advanced the gen-eral understanding of diversity among outdoor recreation participants.

However, the lack of any empirical testing of recreation specialization re-mained an issue. As pointed out by Ditton et al. (1992), any attempt to test Bryansframework for specialization was problematic because it was tautological (circu-lar) in its reasoning; specialization level, defined in terms of behaviors and prefer-ences, was then used to predict specialized behaviors and experiential preferences.As a result, recreation specialization as a concept could never be empirically testedbecause specialization and its subsequent propositions were both defined andmeasured in the same terms (Ditton et al., 1992).

Ditton et al. (1992) initiated development of a testable theory that links rec-reation specialization with elements of social worlds as described by Unruh (1979).Unruh (1979) defined a social world as an internally recognizable constellationof actors, organizations, events and practices which have coalesced into a per-ceived sphere of interest and involvement for participants. According to this per-spective, members of the same social world hold similar attitudes, beliefs, andmotivations that create a sense of group identity. Unruh (1979) further suggestedthat members within a social world could be ordered along a theoretical dimen-sion of involvement level based on four key characteristics: orientation, experi-ences, relationships, and commitment. For each characteristic, Unruh (1979) de-scribes four involvement levels that correspond to four trans-situational socialtypes: strangers, tourists, regulars, and insiders (Table 1).

Ditton et al. (1992) reconceptualized and redefined recreation specialization

TABLE 1 Characteristics and Types of Social World Participation (from Unruh,1979)

Social types or subworlds

Characteristics Strangers Tourists Regulars Insiders

Orientation naivete curiosity habituation identityExperiences disorientation orientation integration creationRelationships superficiality transiency familiarity intimacyCommitment detachment entertainment attachment recruitment

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as a process by which recreation social worlds and subworlds segment and inter-sect into new recreation subworlds, and the subsequent ordered arrangement ofthese subworlds and their members along a continuum. Subworld types are arrangedby Ditton et al. (1992) on a continuum from least specialized to most specialized.

Ditton et al. (1992) developed eight recreation specialization propositions.They tested three of these, using frequency of participation to segment anglersinto four specialization levels. Their results provided empirical support for spe-cialization by showing that the four groups differed as predicted in their resourcedependency, level of mediated interaction, and the importance they attach to ac-tivity-specific and nonactivity-specific elements within a recreational activity.Highly specialized anglers were found to have a higher resource dependency thandid less specialized anglers. The highly specialized groups placed more impor-tance on catching big, distinctive, or trophy fish, whereas the less specializedanglers appear to be less interested in the rare event aspect of the fishing expe-rience. They found that anglers who were more specialized had a greater involve-ment in various types of mediated means of communication than did less special-ized anglers. Finally, Ditton et al. (1992) found that as level of specializationincreased, the importance attached to catch-related angling motivations (e.g., catch-ing fish of preferred size, number, or species) decreased relative to noncatch-relatedangling motivations (e.g., to be outdoors, to relax, to be with friends, etc.).

Although their single dimension (i.e., frequency of participation) approachto angler segmentation proved successful, Ditton et al. (1992) recognized thatother variables can and should be used as a means of classifying individuals intospecialization subgroups. A single variable (such as avidity) cannot adequatelymeasure these distinct dimensions of specialization and may result in highmisclassification rates. In this paper, we suggest that the testing of recreation special-ization theory, and its application, is advanced when using a multivariable ap-proach to segmentation that incorporates orientation, experiences, relationships,and commitment.

Study Objectives

The first purpose of this research was to develop and validate a multivariablespecialization index based on a social world view of recreation specialization.The second purpose of this research was to use this index to test recreation special-ization theory by re-examining one of the propositions tested by Ditton et al.(1992), examining two other propositions that have not yet been tested, and devel-oping and testing a new proposition. The proposition to be retested states: as levelof specialization in a given recreation activity increases, the importance of activity-specific elements of the experience will decrease relative to nonactivity-specificelements of the experience (Proposition Eight in Ditton et al., 1992). Ditton et al.(1992) found that more-specialized anglers placed less importance on activity-specific elements, such as catching fish, and more importance on the nonactivity-

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sheila.romeroResaltado




specific elements of the fishing experience, such as enjoying nature, relaxing,being with friends or family, and so forth.

The second proposition states that participants who are more specialized wouldindicate greater support for management rules and regulatory procedures, as wellas for social norms that identify and often dictate acceptable behavior, than wouldless-specialized participants (Proposition Four in Ditton et al., 1992). Temporaryor seasonal closures due to overfishing, for example, would have a greater impactfor more-specialized individuals than for less-specialized individuals. Therefore,by voluntarily accepting rules and social norms associated with the activity, par-ticipants help to ensure its continuation (Ditton et al., 1992). The third proposi-tion states that more-specialized anglers have higher levels of side-bets than doless-specialized anglers. Side-bets denote when something of value (time, money,social relations) is invested in the activity with the condition that to discontinuethe activity could result in a loss of the investment (Alluto, Hrebiniak, & Alonso,1973; Becker, 1960). More-specialized individuals are proposed to have a greaterfinancial and emotional investment in a given activity than less-specialized indi-viduals (Proposition Two in Ditton et al., 1992).

The new proposition that we propose here states that as level of specializa-tion in a given recreation activity increases, frequency of participation in thatactivity will increase. We base this proposition on the results of previous research.Graefe (1980) found avidity to be a surrogate measure for specialization level.Schreyer et al. (1984) similarly used number of river runs to segment river usersinto subgroups and found significant differences between these subgroups .Chipman and Helfrich (1988) successfully used frequency of participation as oneelement of determining specialization level. Finally, Ditton et al. (1992) used avidityto segment a population of anglers into specialization subgroups, and found sig-nificant differences between the subgroups. We would view this as PropositionNine as added to the eight previously stated by Ditton et al. (1992).

HypothesesBased on the previous propositions, the following hypotheses were generated.

Ha1(a): High-specialization anglers will attach less importance to activity-spe-cific elements of the fishing experience than will low-specializationanglers.

Ha1(b): High-specialization anglers will attach more importance to nonactivity-specific elements of the fishing experience than will low-specializationanglers.

Ha2: High-specialization anglers will have a greater support for various man-agement tools and regulations than will low-specialization anglers.

Ha3: High-specialization anglers will have generated a greater value of side-bets than will low-specialization anglers.

Ha4: High-specialization anglers will have a greater frequency of participa-tion than will low-specialization anglers.

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Methods

Specialization Index

In developing our specialization index, we chose to pursue an a priori approachthat builds on theory, and that uses theory to generate the index items. Our spe-cialization index items, therefore, were derived from the four characteristics (ori-entation, experiences, relationships, and commitment) used by Unruh (1979) toplace participants in a particular subworld (or in our case a particular specializa-tion level). For each characteristic, Unruh described four subworld types of par-ticipants: strangers, tourists, regulars, and insiders (Table 1). Based on these de-scriptions, we developed four survey questions (i.e., corresponding to the fourcharacteristics), each containing four possible response options (i.e., correspond-ing to four specialization levels). Question response options, consisting of state-ments describing a participants connection to an activity relative to that particu-lar characteristic, were ordered from least specialized (response option = 1) tomost specialized (response option = 4) along a 4-point scale (Table 2). It wasexpected that for each item, the least-specialized participants would select re-sponse option 1, and the most-specialized participants would select response op-tion 4.

The sum of the four responses (e.g., least specialized: 1 + 1 + 1 + 1 = 4,highly specialized: 4 + 4 + 4 + 4 = 16) was then used to locate anglers along therecreation specialization continuum. The actual process of developing and testingthe specialization index used for segmentation of anglers into specialization lev-els is described in the Results section.

Data Collection

Data were collected by way of a mail survey administered to a random sample oflicensed Massachusetts anglers. The basic survey design and implementation fol-lowed accepted principles based on Salant and Dillman (1994). A personalizedadvance-notice letter was sent to all members of the sample announcing they hadbeen selected to participate in the survey and that they would be receiving thequestionnaire in the mail within the following week. One week later a set of sur-vey materials was mailed to all members of the sample. These materials includedthe questionnaire, a cover letter describing the intent of the survey, and a self-addressed stamped envelope for returning the completed survey. Two weeks aftermailing the advance notice letter, a thank you/reminder postcard was mailed to allmembers of the sample. This follow-up served to thank those who had alreadycompleted and returned their questionnaire, and to request a response from thosewho had not. Five weeks after mailing the advance notice letter, a second set ofsurvey materials was sent to those who had not yet responded. This second surveypackage was identical to the first, except that the personalized cover letter wasrevised to further encourage the subject to complete and return their survey.

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TABLE 2 Recreation Specialization Index Survey Questions and ResponseOptions

Q. Please indicate your general orientation to the sport of fishing.1) I am an outsider. I am uncomfortable when I go fishing, and dont really feel

like I am part of the fishing scene.2) I am an observer or irregular participant. Sometimes it is fun, entertaining, or

rewarding to go fishing.3) I am a habitual and regular participant in the sport of fishing.4) I am an insider to the sport. Fishing is an important part of who I am.

Q. Please indicate how you would best describe yourself during a fishing experi-ence.

1) I am often uncertain. I am unsure about what I can or cannot do while fishing,or how to do it.

2) I have some understanding of fishing, but I am still in the process of learningmore about fishing. I am becoming more familiar and comfortable with fish-ing.

3) I have become comfortable with the sport. I have regular, routine and predict-able experiences. I have a good understanding of what I can do while fishing,and how to do it.

4) I am a facilitator in the sport. I encourage, teach and enhance opportunities forothers who are interested in fishing.

Q. Please indicate how you would best describe your relationships with otheranglers.

1) Superficial. I really dont know any other anglers.2) Very limited. I know some other anglers by sight and sometimes talk with

them, but I dont know their names.3) One of familiarity. I know the names of other anglers, and often speak with

them.4) Close. I have personal and close relationships with other anglers. These friend-

ships often revolve around fishing.

Q. Please indicate how you would best describe your commitment to fishing.1) Almost nonexistent. I am basically indifferent about going fishing.2) Moderate commitment. I will continue to go fishing as long as it is entertain-

ing and provides the benefits I want.3) Fairly strong commitment. I have a sense of being a member of the activity,

and it is likely that I will continue to fish for a long time.4) Very strong commitment. I am totally committed to fishing. I encourage oth-

ers to go fishing and seek to ensure the activity continues into the future.

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Testing Specialization Theory

One-way ANOVA tests were used to test for mean differences between special-ization groups. A significance level of .10 was used to test the null hypotheses.This level of confidence reflects a balance between a higher probability of com-mitting a Type I error (rejecting a true null hypothesis) and consequently decreas-ing the probability of committing a Type II error (failure to reject the null when itis false). Gregorie and Driver (1979) suggest this as being a more appropriatelevel (than 0.01 or 0.05), so later studies would not mistakenly consider some ofthe insignificant differences as being unimportant, when in fact they might havebeen due to the commission of a Type II error.

Results

Response Rate

A total of 1,411 questionnaires (54.6%) were returned in usable form (Table 3).There were 312 questionnaires returned as undeliverable by the U.S. Postal Ser-vice, 3 were returned because the addressee was deceased, and 29 returned byrespondents were unusable. The remainder were nonresponses.

Index Development and Internal Validation

Frequency distributions were calculated for each of the four index items (Figure1). On a scale of responses from 1 (least specialized) to 4 (highly special-ized), the modal response for all four items was 3. The proportion of responsesin the least-specialized category (i.e., response = 1) was 2% or less for orienta-tion, experience, and commitment. The proportion of least-specialized re-sponses (response = 1) was considerably greater for relationships (7.3%), al-though this was still small compared to the proportion for the other three response

TABLE 3 Status of Sport Angler Questionnaire Response

Type of response N %

Initial sample 2,930 Mortality 344

Deceased (3)Nondeliverable (312)Not-usable upon return (29)

Effective sample 2,586 100.0Nonresponse 1,175 45.4

Usable returned surveys 1,411 54.6

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options. Nearly 60% of respondents chose 3 for the question regarding experi-ence. The other variables were more evenly distributed across responses, exceptfor the previously noted lack of 1 responses (Figure 1).

Bivariate relationships among the items considered for inclusion in the in-dex were then examined to determine the degree to which the items were related(Babbie, 1995). Correlation coefficients for the six pair-wise comparisons rangedfrom 0.41 to 0.60 and were all statistically significant (Table 4). This middle rangesuggests that no two items were so similar as to warrant exclusion from the indexto avoid redundancy. Therefore, although significant positive relationships werefound for all pair-wise comparisons, each item measures a somewhat differentaspect of recreation specialization. The two lowest correlation coefficients in-volved the variable relationships (0.41 and 0.43), whereas the highest correla-tion was between orientation and commitment (0.60).

Another way to analyze bivariate relationships is to examine the percent ofoccurrences when two variables differ from each other by more than a particularamount. For each of our four variables, possible responses ranged from 1 (leastspecialized) to 4 (highly specialized). For all pair-wise comparisons, less than9% of all respondents had responses for any two variables that differed by morethan one (Table 4). This further supports the strong positive relationships betweenall items. Most of the cases where an anglers responses for two variables diddiffer by more than one involved the variable relationships. Pair-wise compari-sons not involving the variable relationships differed by more than one for onlyabout 3% of respondents.

Index item reliability was tested using Cronbachs coefficient alpha(Cronbach, 1951). The reliability of the final multiple-item index was measuredwith an internal consistency coefficient (Cronbachs alpha) of 0.78. Alpha values

FIGURE 1. Distribution of angler selections of response options according to thefour index items.

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when a particular item was deleted were 0.68 for commitment, 0.74 for experi-ence, 0.70 for orientation, and 0.76 for relationships. This further supported theinclusion of all four recreation specialization social world characteristics (i.e.,orientation, commitment, experience, and relationships) in our index.

Based on our results from the bivariate comparisons and Cronbachs alpha,we decided to include all four items in creating our recreation specialization in-dex. A composite specialization rank was calculated by summing the responses tothe four items for each respondent (Figure 2). Composite scores ranged from 4through 16. Respondents were segmented into specialization groups based ontheir cumulative item score as follows:

If cumulative score = 46 Index Level = 1 (least specialized)If cumulative score = 710 Index Level = 2 (moderately specialized)If cumulative score = 1113 Index Level = 3 (very specialized)If cumulative score = 1416 Index Level = 4 (highly specialized)

TABLE 4 Bivariate Relationships Among Index Items

Correlation % of responses differingIndex item pair coefficient by more than one

Relationships and Experience 0.41 8.2%Relationships and Orientation 0.43 8.9%Relationships and Commitment 0.49 7.8%Experiences and Orientation 0.48 3.0%Experiences and Commitment 0.50 3.0%Orientation and Commitment 0.60 3.0%

FIGURE 2. Distribution of anglers according to cumulative index score.

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Again, we pursued an a priori process in developing the index. This alsoapplied to determining which item scores should correspond to which specializa-tion levels. We chose to make the score brackets as equal in size as possible (lev-els 1, 3, and 4 all had a range of 3 in their score, whereas level 2 had a range of 4).The number of anglers classified into each specialization level is the result of thisprocess, rather than the opposite in which some preconceived distribution of an-glers is forced into a manipulated set of index brackets.

This process resulted in the least specialized angler group (Level = 1) ac-counting for only 1.2% (n = 16) of all respondents (Figure 3). Moderately spe-cialized anglers (Level = 2) accounted for 32.5% (n = 440), very specializedanglers (Level = 3) accounted for 42.3% (n = 572), and highly specialized an-glers (Level = 4) accounted for 24.0% (n = 325) of all respondents.

Internal index validation is conducted to demonstrate that an index success-fully measures what it is intended to measure (Babbie, 1995). A method of inter-nal validation called item analysis was conducted to examine the extent to whichour composite index is related to (or predicts responses to) the four items (i.e.,relationships, commitment, experience, and orientation) that comprise it. Itemanalyses using direct comparisons were possible because both index scores (i.e.,index level) and item scores were based on equivalent 4-point scales ranging fromleast to highly specialized. The index score was identical to the item score fororientation 72% of the time, commitment 74% of the time, experiences66% of the time, and relationships 60% of the time. For all items, the absolutedifference between index score and item score exceeded one for less than 3% ofrespondents. These results support the internal validity of our specialization index.

As a final test, correlations were computed between specialization index leveland more traditional measures of specialization such as avidity (freshwater daysfished in past 12 months) and total years fished. The correlation between special-

FIGURE 3. Distribution of anglers according to specialization level.

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ization index level and freshwater days fished in past 12 months was 0.38, whereasthe correlation between specialization index level and years fished was 0.18. Bothwere highly significant (p < 0.0001), indicating that our specialization index cor-relates with these unidimensional specialization indicators. However, both corre-lations were also fairly low, suggesting that important differences between ourindex and these unidimensional indicators do exist.

Testing Recreation Specialization Theory

As mentioned before, our segmentation of respondents resulted in only 16 indi-viduals (1.2%) being classified into the least-specialized level. Because this is aninadequate sample size for our analyses, this group was subsequently dropped forhypothesis testing. Therefore, hypotheses were tested using only three specializa-tion levels: Moderately specialized (M), Very specialized (V), and Highly spe-cialized (H); levels 2, 3, and 4, respectively.

Hypothesis One

Seven items were used to measure the importance of activity-specific elements ofthe fishing experience. Results show significant differences for five of these sevenmeasures (Table 5). However, one of these items was contrary to specializationtheory because more-specialized anglers rated the item experience of the catchas more important than did less-specialized anglers. For two other items, therewas no significant difference among specialization levels. Still, four out of thefive items with significant differences were ordered as predicted by specializationtheory. Based on these results, the null hypothesis that there are no differencesaccording to level of specialization on activity-specific measures of the fishingexperience was rejected, and we accept hypothesis Ha1(a) as stated, but proposethat more investigation is needed regarding the items that did not behave as pre-dicted by specialization theory.

Ten items were used to measure the importance of nonactivity-specific ele-ments of the fishing experience. Results show significant differences for 9 out ofthe 10 items according to level of specialization (Table 6). It was predicted thatmore-specialized anglers would place greater importance on nonactivity-specificactivities than would less-specialized anglers. Because the results are as predicted,the null hypothesis is rejected and Ha1(b) is accepted as stated.

Hypothesis Two

Eleven items were used to measure support or opposition to various managementregulations. The null hypothesis, which states that there are no differences be-tween anglers in their support and opposition to management rules, was rejectedbecause significant differences were found for ten of the eleven items (Table 7).

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The prediction that more-specialized anglers would indicate a greater support formanagement rules than would less-specialized anglers was supported on 9 of the10 significant items. The mean values for one item (restricted fishing area) weredirectly opposite of that predicted. Because 9 of the 10 significant items wereordered as predicted, Ha2 is accepted as stated.

Hypothesis Three

Four items relating to the cost of replacing fishing equipment were used to mea-sure side-bets. It was predicted that more-specialized anglers would generate agreater value in side-bets than would less-specialized anglers. Significant differ-ences supporting this prediction were found according to specialization level forall four items (Table 8). Therefore, we reject the null hypothesis. Because themean differences are as predicted, we accept Ha3 as stated.

TABLE 5 One-way ANOVA Tests for Mean Differences in Importance ofActivity-specific Items According to Specialization Level

Level of specialization

Items* M V H F p

For the experience of 3.500** 3.818 4.128 30.29 0.000the catch

For the sport of fishing, 3.556 3.904 4.183 26.11 0.000not to obtain food to eat

Im just as happy if I 4.110 4.181 4.370 7.57 0.001release the fish I catch

I am just as happy if I dont 4.053 4.158 4.329 7.55 0.001keep the fish I catch

A fishing trip can be 3.792 3.834 4.031 5.90 0.003successful even if nofish are caught

When I go fishing, Im just 3.095 3.034 3.111 0.67 0.510as happy if I dont catcha fish

To obtain fish for eating, and 1.502 1.480 1.547 0.55 0.578not for sport

*For items 1, 2, and 7 mean scores were based on responses to the following categories;1 = Not at all important, 2 = Slightly important, 3 = Moderately important, 4 = Veryimportant, 5 = Extremely important. For all other items, mean scores were based onresponses to the following categories; 1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4= Agree, 5 = Strongly agree.**Means underscored by same line are not significantly different (.10) using Tukeystest.

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Hypothesis Four

Results showed significant differences on angler frequency of participation ac-cording to level of specialization (Table 9). The null hypothesis is therefore re-jected as stated. Highly-specialized anglers had significantly higher rates of par-ticipation than did moderately-specialized anglers, who in turn had significantlyhigher rates of participation than did lower-specialized anglers. Because this isconsistent with what was predicted, Ha4 is accepted as stated.

Discussion

Our results provide strong support for the theory of recreation specialization asreconceptualized by Ditton et al. (1992), and for use of the specialization indexdeveloped here. Results from our hypotheses tests were as predicted for an over-whelming majority of the items we investigated. Our study also strongly supportsthe inclusion of all four characteristics of social worlds (commitment, orienta-

TABLE 6 One-way ANOVA Tests for Mean Differences in Importance ofNonactivity-specific Items According to Specialization Level


Items* M V H F p

To experience adventure 3.405** 3.732 4.009 28.77 0.000and excitement

To be close to the water 3.366 3.576 3.973 21.20 0.000For relaxation 4.218 4.345 4.559 16.48 0.000To be with friends 3.107 3.206 3.559 13.21 0.000To experience natural 4.134 4.248 4.453 12.82 0.000

surroundingsTo experience new and 2.842 2.939 3.279 12.35 0.000

different thingsTo get away from the 3.409 3.474 3.842 10.49 0.000

demands of other peopleTo be outdoors 4.177 4.236 4.450 10.44 0.000To get away from 3.800 3.912 4.159 9.78 0.000

the regular routineFor family recreation 3.279 3.136 3.256 1.72 0.179

*Mean scores were based on responses to the following categories; 1 = Not at all impor-tant, 2 = Slightly important, 3 = Moderately important, 4 = Very important, 5 = Extremelyimportant.**Means underscored by same line are not significantly different (.10) using Tukeystest.

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tions, experience, and relationships) as related and reliable measures of recreationspecialization.

Specialization Index Development

There are several possible explanations for the fact that the least specializedsubworld made up such a small proportion of our sample (only 1.2%). First, weshould not rule out the possibility that this group may, in fact, be much smaller insize than the other groups. This would be the case if the learning curve from leastspecialized to moderately specialized requires a relatively short time period.Because our survey was administered to those people who had purchased licensesduring the previous year, anglers who were least specialized at the time of li-cense purchase had at least a full fishing season to increase their specializationlevel prior to receiving our survey. Another possible explanation is that our sampledid not tap into those groups of anglers that make up the majority of the leastspecialized group. For example, children (under 17 years old), out-of-state an-glers, and 3-day license holders were not part of our survey population. One mightreasonably expect these anglers to be among the least specialized. We consider

TABLE 7 One-way ANOVA Tests for Mean Differences in Support andOpposition of Management Regulation Items According to Specialization Level


Items* M V H F p

Creel limit 4.109** 4.293 4.463 14.79 0.000No stocking allowed 3.559 3.673 3.935 13.70 0.000Maximum size 3.284 3.547 3.733 13.64 0.000Stock non-native fish 3.009 3.282 3.343 11.13 0.000Minimum size limit 4.108 4.211 4.433 8.62 0.000Restricted fishing area 3.434 3.260 3.069 7.23 0.001Mandatory catch 3.122 3.162 3.439 6.67 0.001

and releaseStock native fish 4.219 4.336 4.403 6.03 0.003Slot limit 3.117 3.190 3.388 5.86 0.003Voluntary catch and 3.874a 4.028b 4.022a,b 2.83 0.059

releaseProhibit use of certain 3.612 3.545 3.581 0.43 0.653

gear

*Mean scores were based on responses to the following categories; 1 = Strongly oppose,2 = Oppose, 3 = Neutral, 4 = Support, 5 = Strongly support.**Means underscored by same line or same superscript are not significantly different(.10) using Tukeys test.

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these explanations to be the most likely reasons for the small size of the leastspecialized group.

Nonresponse bias could also be a possible explanation if the probability ofan angler returning our survey was positively correlated to the anglers specializa-tion level. However, in a study of nonresponse bias on angler surveys, Fisher(1996) found that species preferences and scores from summated Likert scaleswere independent of response probabilities. Finally, the choice of words we usedfor the least specialized response options could explain the low percent of re-spondents selecting those options. Anglers may have felt embarrassed to identifythemselves with words such as outsider, uncomfortable, unsure or uncer-tain, all of which may have strong negative connotations. Our results suggestthat least specialized subworlds may be more difficult to sample for a variety ofreasons. A special sample design may be needed in certain situations to adequatelyaddress this group.

Our results showed that although all four social world characteristics (rela-tionships, orientation, experience, and commitment) should be included in theindex, the relationships dimension behaved somewhat differently from the otherthree. Specifically, some anglers scored least specialized for relationships butwere in the middle-to-high range of specialization for the other three dimensions.This suggests that for the activity of freshwater fishing, having personal relation-

TABLE 8 One-way ANOVA Tests for Mean Differences in the Cost of ReplacingFishing Equipment with Similar Equipment Between Specialization Level


Items* M V H F p

Replace reels $119.33* $229.49 $455.80 90.00 0.000Replace tackle 114.80 282.28 579.84 78.65 0.000Replace rods 138.31 284.52 555.28 38.18 0.000Replace electronic 262.00 436.65 580.42 6.95 0.001

equipment

*Means underscored by same line are not significantly different (.10) using Tukeys test.

TABLE 9 One-way ANOVA Tests for Mean Differences in Frequency ofParticipation According to Specialization Level


Items* M V H F p

Mean total days 15.566* 36.656 56.609 105.54 0.000fished

*Means underscored by same line are not significantly different (.10) using Tukeys test.

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ships with other anglers may not be as important of a component when advancingto higher specialization levels as previously thought. Although interaction andcommunication relate to social world boundaries (Unruh, 1980), in todays worldthese can be readily achieved through mediated channels instead of personal con-tact. Some highly specialized anglers may rely on journals, magazines, cable tele-vision, and the Internet to acquire and exchange information about fishing. If so,our question measuring relationships, which focuses only on personal contacts,may have to be expanded to include a wider range of interactive and communica-tive possibilities.

The characteristics included in our index were derived directly from the so-cial worlds literature. Still, the question of which specific measures should beused to define specific characteristics of a specialization index is open to interpre-tation (Kuentzel & McDonald, 1992). For example, commitment to an activityhas been measured as the number of related magazines one subscribes to (Bloch,Black, & Lichtenstein, 1989), the level of activity involvement (Williams &Huffman, 1986), the centrality of the activity to ones lifestyle (Chipman &Helfrich, 1988), the number of side-bets invested in, and an affective attach-ment to the activity (Buchanan, 1985). Similarly, one could come up with mul-tiple ways to define and measure orientation, experience, and relationshipsrelated to a particular activity.

Specialization dimensions can also be measured using either behavioral orcognitive measures. One of the main features of social world involvement is vol-untary identification, meaning one chooses to become a member of a social worldrather than it being a requirement (Unruh, 1980). The necessity of voluntary iden-tification suggests a strong cognitive component to entry into a social world andmovement between subworlds within that social world. This cognitive compo-nent is reflected in the questions we used in this study to measure specializationdimensions. For example, rather than measure commitment through other vari-ables as described above, anglers were asked directly to choose the statementsthat best describe their involvement in the sport.

Approaching specialization from a social worlds perspective may add sub-jectivity to the index because words like commitment, insider, and orienta-tion can mean different things to different people. However, this subjectivitydoes not necessarily bias the segmentation process, but rather, it may redefinespecialization in a new way. The assumption that a specialization index derivedfrom objective measures (i.e., gear used, days fished, magazines purchased) ispreferable to one that uses more subjective, cognitive measures should not auto-matically be made. The decision of which index to use should, perhaps, be basedon the goals of the particular study and the research purpose or management ap-plication it is intended for. The general lack of consistency in measuring special-ization in the outdoor recreation literature supports this contention. For futurestudy, it would be interesting to compare participant segmentation using our in-dex with previous specialization indices using the same survey population.

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Testing Recreation Specialization Theory

Results indicated that more-specialized anglers were more interested in a qualita-tive experience, whereas less-specialized anglers had a more simplistic view offishing that did not consider other intrinsic elements of the experience to be quiteas important. This supports specialization theory and further reconfirms the re-sults of Ditton et al. (1992).

Frequency of participation was also shown to increase as specialization lev-els increase. Our results are consistent with previous work and justify the additionof our proposed Proposition Nine. Individuals are likely to increase their fre-quency of participation when they feel some sort of attachment to an activity. Asspecialization level increases, alternative activities will be rejected as the commit-ment to participating in the primary activity increases (Buchanan, 1985; Unruh,1979).

It appears that more-specialized anglers are more receptive to managementregulations than are less-specialized anglers. The support for management regu-lations was shown to increase as specialization increases. The former group ismore likely to be impacted than the latter group if fishing activities were discon-tinued; therefore, as predicted from specialization theory, the former would bemore supportive of rules and regulations issued from fisheries managementagencies.

Finally, as predicted, side-bets anglers appropriated for fishing equipmentwere shown to increase as level of specialization increased. Because of a greaterinvolvement within the activity, more-specialized anglers will commit greater fi-nancial costs towards fishing than will less-specialized anglers.

Management Implications

There is potential here for fisheries managers to gain an understanding of groupdifferences on a variety of issues to efficiently improve services already provided.By developing and promoting services based on some aggregation of anglers, theinterests of many anglers are ignored. Managers may then be confronted with afairness issue, where some anglers perceive that resources are allocated unfairly.Segmentation by specialization recognizes that different groups have differentattributes that require different marketing schemes. Through a better understand-ing of the angling constituency, managers can avoid making resource allocationdecisions that may result in the loss of credibility for the fisheries agency (Ditton,1996; Loomis & Ditton, 1993). The results of this study provide strong supportfor the use of a multidimensional index as a means of classifying participants intohomogeneous groups, based on the recreation specialization theory developed byDitton et al. (1992). Such insight to anglers can also be used to effectively evalu-ate current management objectives and services.

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loomis y finn. development and validation of a specialization

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