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Recreation Specialization Among NewZealand River Recreation Users: AMultiactivity Study of Motivation and SitePreferenceShayne Galloway aa School of Physical Education University of Otago , Dunedin , NewZealandPublished online: 11 May 2012.

To cite this article: Shayne Galloway (2012) Recreation Specialization Among New Zealand RiverRecreation Users: A Multiactivity Study of Motivation and Site Preference, Leisure Sciences: AnInterdisciplinary Journal, 34:3, 256-271, DOI: 10.1080/01490400.2012.669690

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Leisure Sciences, 34: 256–271, 2012Copyright C© Taylor & Francis Group, LLCISSN: 0149-0400 print / 1521-0588 onlineDOI: 10.1080/01490400.2012.669690

Recreation Specialization Among New ZealandRiver Recreation Users: A Multiactivity Study

of Motivation and Site Preference

SHAYNE GALLOWAY

School of Physical EducationUniversity of OtagoDunedin, New Zealand

This study explored the influence of specialization and activity on motivation to partic-ipate and site preference among three activities: whitewater kayaking, multisport rac-ing, and fishing. Subjects consisted of 270 river-based recreationists in New Zealand.MANOVA results indicate a moderate effect for activity on motives to participate andsite preference and a weak effect for specialization. Inter-activity motivation and sitepreference differences reveal similarity between whitewater kayaking and multisportand distinct differences between these and fishing. In these data, the distinctive natureof the activity provides greater explanatory power for motivation to participate and sitepreference than does the specialization construct.

Keywords motivation, recreation specialization, site preferences

Most recreation specialization research analyses participation within single recreation ac-tivities and setting. However, specialization research thus far has not compared patterns ofspecialization across activities. Do participants, for example, in similar activities share mo-tivations and site preferences significantly different from those of participants in distinctlydifferent activities? How do kayakers and anglers relate to one another in their motivationsfor participation and site preferences? Does specialization account for individual differ-ences in motivation and site preference, or is the activity itself a sufficient explanation?Greater empirical understanding of the influence of activity participation versus activityspecialization on selected variables in a single environmental context may yield new insightfor resource management and decision making in multiple-use settings. This study exploreshow activity and specialization may influence motivation to participate and site preferencesamong participants in different activities at river settings.

Riverine environments are multiple-use resources in New Zealand, as in many otherplaces, and feature competing interests such as boating, fishing, hydroelectric power gen-eration, and irrigation. The multiple-use nature of New Zealand rivers provides a varietyof recreation opportunities to both residents and tourists, but managing these resourcesfor each use presents a challenge to resource management. For example, a given river

Received 9 May 2010; accepted 13 November 2011.This study was conducted with funding from Sport and Recreation New Zealand (SPARC) as part of a

national survey of river-based recreation. A summary report on the project can be obtained from: http://www.sparc.org.nz/research-policy/research-/research-grants/awarded-grants#april07

Address correspondence to Shayne Galloway, School of Physical Education, University of Otago, PO Box56, Dunedin, 9054, New Zealand. E-mail: shayne.galloway@otago.ac.nz

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may be renowned for its specialized fishing and its Grade III kayaking and also may beunder consideration for development for power generation and irrigation. Rarely are riversmanaged for single recreational activities, and resource managers must balance the expec-tations of multiple users with the nature of the river itself. Hammett, Backlund, and Bixler(2004) suggest that recreation resource managers use specialization information to aid inpolicy and planning decisions, and these might include facets such as resource allocation,permitting, facilities, and so forth.

Numerous specialization studies have examined river users’ motivation to participate(e.g., Williams, Schreyer, & Knopf, 1990), crowding and conflict (e.g., Kuentzel & McDon-ald, 1992), place attachment, lifestyle, and place dependence (e.g., Bricker & Kerstetter,2000), site preference and gender (e.g. Lee, Graefe, & Li, 2007), and management consid-erations (e.g., Siderelis & Moore, 2006). These studies, however, have focused on a single,usually boat-related, activity (e.g., canoeing, kayaking). Scott and Shafer (2001) call forresearch that “compare[s] the dynamics of progression across different leisure activities,”pointing to Bryan’s (1979) position that activities would vary in terms of complexity andopportunities for progression. This study will explore how specialization among multipleactivities in river-based settings in New Zealand predicts motives to participate and sitepreference. Activities selected for this research include whitewater kayaking, multisportracing, and fishing. The activity of multisport racing includes several outdoor pursuits(i.e., trail running, mountain biking, kayaking), and in New Zealand this usually includespaddling a five-meter boat on a river. This research focuses only on the river paddlingcomponent of multisport racing.

Current understanding of the specialization construct varies somewhat from Bryan’s(1977) original conceptualization of a linear “continuum of behavior from the generalto the particular, reflected by equipment and skills used in the sport and activity settingpreferences” (p. 175). Instead, the development of an individual’s participation in an activityappears to be multifaceted and multidimensional and as much reflects the characteristics ofindividual circumstances (Kuentzel & Heberlein, 2008) and involvement in social worlds(Ditton, Loomis, & Choi, 1992) as it does the nature of the recreation activity itself. Scottand Shafer (2001b) suggest that progression towards higher levels of specialization maybe the least common path for recreationists within an activity. Many recreation resourcessupport multiple activities, and both managers and recreationists stand to benefit frombetter understanding of shared or contrasting motivations or site preferences. Findingsfrom this research may guide managers in supporting a broad array of experiences that canaccommodate users in multiple activities with different specialization levels, motivations,and site preferences.

Literature Review

Some participants in outdoor recreation activities develop along complex pathways of spe-cialization (Bryan, 1977, 2000, 2001) that affects their use patterns and decision makingregarding participation. A great deal of research has used Bryan’s framework as a segmen-tation tool to help understand individual differences within a given activity. The sectionsthat follow review research that has focused on the relationship between specialization andindividual motivation and between specialization and site preference.

Motivation

Motivation to participate in a given activity may be defined as the individual’s intrinsicor extrinsic incentive to engage with that activity (Manfredo, Driver, & Tarrant, 1996).

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Following the work of Manfredo et al., individual motivation to participate in recreationactivities remains a central concern of recreation and leisure studies. Specialization researchhas frequently examined motivational differences across specialization levels in a givenrecreational activity.

Ewert (1994) developed a model of motivational matching in mountaineers wheremotivation to participate differed as individuals increased in specialization. Less special-ized mountaineers tended to focus on technical and skill-related motivations while moreexperienced mountaineers developed an aesthetic motivational set. Kuentzel and McDon-ald (1992) identified differential relationships among past experience, commitment, andlifestyle on a range of participant motivations including proximity to home, to be in a nat-ural environment, excitement, and reputation of the river. Hvenegaard (2002) found partialsupport for specialization-related differences in bird watcher motivations. More specializedbirders were less motivated by nonbirding-focused activities such as scenery and photogra-phy; less advanced birders were motivated by these related aspects. Lee et al. (2007) founddifferences in the importance of relaxation, social contact, challenge, and competition forpaddlers. High and very high specialists were more motivated by relaxation, social contact,and for challenge than those at low to medium levels. While very high specialists weremotivated by competition, paddlers as a whole were not motivated by competition. Theyalso found specialization-related differences in facility preferences, social skill, new sites,and wilderness. Specialized paddlers placed less importance on facilities and greater im-portance on new sites and wilderness than those at lower levels of specialization. Oh andDitton (2008) examined causal relationships between the conservation-related attitudesand behaviors of anglers and recreation specialization, identifying a mediating effect ofparticipants’ motivations.

This brief review shows that variation in participants’ motives to participate as a productof specialization has been addressed, but variation due to the effect of participation in anactivity net of the effects of specialization has not. For example, do anglers, kayakers, andmultisport racers share an appreciation for challenge, solitude, or wilderness values? Or dothese motives vary among the activities, leading participants in each activity to the river fora distinct set of reasons?

Preference for Site Characteristics

Site preference was an original component in Bryan’s (1977, 1979) specialization frame-work. At a given point in an individual’s involvement with an activity, there would con-ceivably be a corresponding set of site characteristics that attract their participation andengagement in a given place. For example, a beginning kayaker may seek out sectionsof Class II rapids with easy access for practice, and an expert kayaker might shun thesesections for more challenging Class IV–V rapids in remote locations. An angler wouldview these same sections of river differently, preferring other features such as the depth ofthe pools or underwater features that provide habitat for fish and the solitude or wildernessvalues it offers. The end result, however, might be that both the angler and the kayaker mayprefer similar site characteristics for different reasons.

For example, the study of the relationship between recreation specialization and in-dividual preferences for site attributes begins with Bryan (1977) and includes Virden andSchreyer’s (1988) study of backcountry hikers, Hopkins and Moore’s (1995) study ofmountain bikers, Merrill and Graefe’s (1998) consideration of rock climbers, and Scottand Thigpen’s (2003) research into birdwatchers. In each case, it has been shown thatparticipants’ preferences for the practice of their particular recreation activity were relatedto their level of specialization in that activity. McFarlane (2004) found that vehicle-based

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campers’ level and type of involvement interacts with the resource. Lee et al. (2007) foundrelationships among paddling specialization, importance on facilities and new sites, andwilderness locations. Hunt (2008) found a relationship between place dependence and an-glers in Canada. Site preferences extend beyond the particular features of a place (e.g.,a boat launch, rapids) to include intangible concepts related to individuals’ relationshipswith that place and the activity (i.e., it’s a novel site or the difficulty is appropriate for theperson’s skill level).

Study Purpose

Research in the area of specialization has focused exclusively on single activities, andno studies have looked at motivation and site preference from the perspective of multipleactivities in a single environmental context. Given the prevailing single-activity approachto specialization, the literature offers no insight as to the relative influence of specializationon motivation and site preference among activities. Furthermore, specialization may differbetween activities, and a multiactivity comparison may enhance management ability toprovide experiences and services to multiple user groups. Therefore, the purpose of thisstudy is to examine the relationship of activity and specialization on participant motivationand site preference. As such, the following hypotheses were proposed:

• Hypothesis 1: Motivation and site preferences for similar activities (e.g., whitewaterkayaking and multisport racing) will be more closely aligned than for different activities(e.g., fishing) occurring at the same recreation resource.

• Hypothesis 2: Participants in an activity will display homogeneity in their motivationsand site preferences regardless of degree of specialization.

Research Method

Sampling and Data Collection

This study utilized survey methodology and online data collection. Subjects were recruitedfrom July–December in 2008 through announcements to a variety of media and web-basedsources. These included activity relevant clubs, national associations, regulatory bodies,leading instruction centers, and major retailers (e.g., Whitewater New Zealand and Fish& Game New Zealand), activity-related websites, magazines, online forums, and mediaannouncements (e.g., Radio New Zealand and Fish & Game newsletters to all licenseholders). In addition, onsite contacts were made at specific activity-related events known toattract a wide range of participants (e.g., Speights Coast to Coast, Taupo Adventure Raceand Blissfest). Individuals indicating interest were asked for their email addresses and latercontacted and provided an email link to the survey site hosted by the University of Otago.

As this survey relies on a volunteer sample, it is assumed that sampling bias exists.As such, the results cannot be generalized to all river users. Volunteer samples run the riskof oversampling people who participate more frequently, which can skew specializationindicators toward the higher levels, as well as undersampling those with less frequentparticipation. However, this study focuses on a comparison across activities, and it can beassumed that sampling bias is normally distributed across all three activities. Therefore,volunteer sampling is a valid approach.

Participants were asked to respond based on their participation in a primary river-based recreation activity (i.e., whitewater kayaking, multisport, and fishing). Initially, 1,600individuals participating in 23 primary activities supplied an email address. A tailoreddesign method (Dillman, 2007) produced 1,312 responses. From this group, 499 indicated

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a primary activity other than multisport, kayaking, or fishing. As a result, 813 individualsprovided complete or partial data for this analysis. Of those, 358 indicated whitewaterkayaking, 170 indicated multisport racing, and 285 indicated fishing as their primary river-related recreational activity.

The sample included 80.4% male and 19.6% female respondents ranging in age from16 to 61 with a mean age of 38. They resided for the most part on the South Island (59.3%)with 38.5% living on the North Island. Two percent of the sample claimed internationalresidency. All regions of the country were represented with participants identifying 74 dif-ferent towns and cities as home. There are no reliable population estimates for participationin river-based recreation in New Zealand. The most recent river recreation data were foundin the unpublished river recreation study by Egarr and Egarr (1981) which focused only onkayaking. Sport and Recreation New Zealand’s SPARC Active NZ Survey (2008) foundnational participation rates for canoe/kayaking at 6.4%, freshwater fishing at 5.7%, andmultisport at 1.5%. However individuals in each of these groups participated in activitiesbeyond the scope of the study (e.g., canoeing, land-based multisport races), so direct com-parison remains problematic. Due to the lack of comparable participation and demographicdata, generalizations beyond the activity comparisons cannot be supported from these data.

Measurement

Specialization

This research calculates specialization according to skill level, participation, andlifestyle/commitment. The skill measure utilized a self-rating of expertise (novice, interme-diate, intermediate-advanced, advanced, advanced-expert, expert) (Scott, Ditton, Stoll, &Eubanks, 2005). The participation measure included the percentage of lifetime participation(e.g., Needham, Vaske, Donnelly, & Manfredo, 2007), the number of rivers visited in therespondent’s lifetime, and the amount of participation per month. Lifestyle/commitmentincluded the number of activity-related publication subscriptions and the activity’s impor-tance to the respondent (i.e., not at all, not very, somewhat, very, and extremely). Initially,equipment was included; however, it became clear that any basis for equal comparison ofall three activities would be tenuous.

Much debate in the specialization literature has focused on the best procedure forarriving at an appropriate index (e.g., Saltz, Loomis, & Finn, 2001; Scott et al., 2005).Because the current study focused on comparing activities, an additive specialization indexwas utilized with minimal manipulation of specialization component variables. The onlyderived variable is the percentage of the respondent’s lifetime engagement with the primaryactivity (number of years of participation in the activity/age ∗ 100). Each of the special-ization items was standardized into Z-scores and summed to create a specialization indexscore that ranged widely from −9.94 to 26.34 for whitewater kayakers, −11.36 to 15.83for multisport racers, and −10.43 to 28.45 for fishing. These scores were than divided intofour equally sized groups as determined by the range within each activity.

In this case, the language of expertise contributes to a lack of clarity about how spe-cialized a person is in comparison to a) the rest of the sample, b) all river participants inthe same activity, and c) participants in other activities. Sampling bias calls into questionwhether “low specialization” actually represents naıve-novice participants and nonpartic-ipants rather than simply the lowest level of specialization likely to respond to a survey,which could in fact be an intermediate level. Terms such as novice, intermediate, and expertprovide inaccurate descriptions in this case. Shanteau (1992) defines experts as “those whohave been recognized within their profession as having the necessary skills and abilities

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to perform at the highest levels” and there are no external assessments of specializationin this study (p. 255). Similarly, as this study compares participants in three activities,the relationship between a person at a low or high level of specialization in whitewaterkayaking and an angler at a low or high level in the same setting. However, a range ofspecialization can be found in each activity and can be evenly divided subdivided into fournumerical groups (1 = lowest specialization value . . . 4 = highest specialization value) forthe purpose of comparison across activities. Thus the specialization groups will be reportedas L1, L2, L3, and L4 in this paper.

Motivation

Motivation to participate was assessed via 36 selected Recreation Experience Preference(REP) items (Driver, 1977; Manfredo et al., 1996). The items used in this study measuredthe following motive dimensions: enjoy nature, physical fitness, learning, achievement,escape, similar people, autonomy, leadership, teaching, nostalgia, introspection, creativ-ity, risk-taking, equipment, and family togetherness. The family togetherness item “to dosomething with your family and developed companionship” was adapted to accommodate aMaori context to read “to do something with your whanau/family and developed whanaun-gatanga/companionship.” An additional item, “to maintain ahi kaa (land rights),” was addedto the family togetherness dimension as these are interconnected issues in New Zealand.

Site Preferences

The site preference variables included 23 items selected from Lee et al.’s (2007) studyof motivation and site preferences among paddlers. The site preference factors includedwilderness values (five items), social/skill (five items), facilities (six items), novelty (fouritems), and challenge/skill (three items). Social/skill includes the items: having appropri-ate fitness, skilled companions, trip length, acceptable difficulty, and is not a measure ofsite features but rather the individual’s perception that the features are amenable. Partic-ipants responded to both the motivation items and site preference items with a 9-pointLikert type scale ranging from “1” meaning not at all important to “9” meaning extremelyimportant.

Motivation and Site Preference Dimensions

Because the items in the motivation and site preference measures were applied to a varietyof activities, a principal components factor analysis using Varimax rotation (eigenvalues> 1) was conducted on the motivation items to determine the underlying dimensionsshared by all activities. Items with factor loadings of less than 0.5 were dropped from themotivation scales. These included: to be creative, to take risks, to maintain ahi kaa (landrights), to experience new and different things, to experience excitement, to rely on yourwits and skills, to be in control of things that happen, and to think about the good times youhave had in the past. No items cross-loaded among the motivation items at the 0.5 level.The resulting motivation dimensions included introspection (five items), achievement (threeitems), enjoy nature (three items), similar people (three items), family togetherness (threeitems), physical fitness (three items), escape (three items), equipment (three items), andteaching others (three items). Cronbach’s alpha for the nine motivation dimensions rangedfrom 0.79 to 0.90, indicating a consistent pattern of responses.

Among the site preference items, only “the site is far from my home/work” did notload on any factor and as such was dropped. “A friend suggested I go there” loaded on two

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factors social/skill (.423) and novelty (.444), and “the strength of the current is safe” loadedon social/skill and challenge/skill. In each case these site preference items were dropped.Retained site preference dimensions included wilderness values (five items), social/skill(five items), facilities (six items), novelty (two items), and challenge/safety (three items).Items removed as a result of the factor analysis included: a friend suggested I go there,and the site is far from my home/work. Cronbach’s alpha scores for the site preferencedimensions ranged from 0.78 to 0.90.

Results

Specialization Items

Table 1 presents the mean, standard deviation, and range of the specialization items foreach activity. The mean score for self-rated expertise was 2.96 for multisport, 3.43 forkayaking, and 3.59 for fishing. Multisport users had the smallest percentage of lifetimeparticipation (16%) followed by kayakers (33%) and anglers (58%). Whitewater kayakinghad the highest mean for total rivers visited (47) followed by fishing (38) and multisport(11). On the other hand, multisport users indicated the highest monthly participation (7.13)followed by whitewater kayaking (4.87) and fishing (3.54). Activity importance was ratedhighest by anglers (4.47), followed by whitewater kayakers (4.31), and then multisportusers (3.99). Participants in each activity indicated average publication subscriptions ofone to two with anglers reporting up to nine subscriptions, and five each for multisportusers and whitewater kayakers.

Multivariate Analysis

Initially, a 3 × 4 between-subjects multivariate analysis of variance (MANOVA) was per-formed on all 15 dependent variables (p < .005; Table 2). The data (N = 813) violate the ho-mogeneity of covariance matrices assumption (Box’s M: F = 1.447, p < .000) and the equalerror variance assumption due to significant Levene’s Test for achievement/stimulation (p <

.028), enjoy nature (p< .001), similar people (p < .003), family togetherness (p < .000),physical fitness (p < .000), wilderness values (p < .001), social/skill (p < .000), facilities

TABLE 1 Specialization Variables for Kayaking. Multisport and Fishing

Primary Activity

WhitewaterKayaking (102) Multisport (68) Fishing (100)

Mean SD Range Mean SD Range Mean SD Range

Skill LevelSelf-rated expertise 3.43 1.17 5.00 2.96 1.31 5.00 3.59 1.36 5.00

ParticipationLifetime participation (%) 31.93 16.21 67.98 16.47 12.65 50.11 57.50 28.32 90.35Rivers visited in lifetime 47.24 77.23 497.00 11.18 15.28 99.00 37.92 34.11 196.00Participation per month 4.87 5.40 37.00 7.13 6.31 29.00 3.54 3.68 20.00

Lifestyle/CommitmentActivity importance 4.31 .61 2.00 3.99 .72 3.00 4.47 .63 2.00Publication subscriptions 1.19 1.05 5.00 1.37 1.16 5.00 2.21 1.59 9.00

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TABLE 2 Multivariate Tests for Activity and Specialization—Initial Modela,c

Hypothesis Partial ObservedEffect Value F df Error df Sig. η2 Powerb

Intercept Wilks’ λ .024 2228.820 14.000 762.000 .000 .976 1.000Activity Wilks’ λ .236 57.65 28 1524 .000 .514 1.000Specialization Wilks’ λ .793 4.381 42 2261.223 .000 .074 1.000

aBox’s M: F(1.447, p = .000).bComputed using alpha = .005.cDesign: Intercept + Activity + Specialization.

(p < .000), novelty (p < .048), and challenge/safety (p < .030) (Stevens, 2009). The fourdependent variables with nonsignificant Levene tests included introspection (p < .053),escape personal/social pressures (p < .294), equipment (p < .389), and teaching others(p < .068). Post hoc analyses of dependent variables, with the homogeneity of covariancematrices assumption violated, produce unreliable estimates and were not conducted.

Because the analysis relied on multivariate statistics, the data were screened for vio-lations of the assumptions of independence, normality, and homogeneity of variance. Toassess independence of observation, the Internet Protocol (IP) address of respondents waschecked for duplicates and responses were screened for identical or similar email addresses.There were none. Cases were removed where data were missing on component variablesof the motivation, site preference, and specialization scales, as they could affect normality,though no clear pattern was detected among the missing data. Some cases were missingresponses to a scale entirely, while others were missing individual variables. Due to theeffects of outliers on Type I or Type II errors, all of the dependent variables (motivationand site preference) were screened and those with z-scores > |2| were removed (Stevens,2009).

Means tests were conducted on the specialization indicators to identify significantdifferences in cases included versus those excluded. The only significant difference wasfor frequency of participation per month F(1, 251.42) = 5.835, p < .016 indicating that theexclusion of 543 cases did not exacerbate the sampling bias due to the volunteer samplingmethod. As well, the mean of those excluded (M = 6.13) exceeds that of those included(M = 4.95) suggesting that, for this variable, the more specialized individuals were notoversampled. The remaining groups include kayaking (n = 102), multisport (n = 68), andfishing (n = 100); specialization: L1 (n = 62), L2 (n = 74), L3 (n = 73), and L4 (n = 61)for an overall N = 270.

Final Model

Motivation dependent variables were achievement/stimulation, enjoy nature, similar people,escape personal/social pressures, equipment, and teaching others. Site preference depen-dent variables included were wilderness values, social/skill, and challenge/safety. Severaldependent variables including introspection, family togetherness, physical fitness, facili-ties, and novelty continued to violate the assumption of multivariate normality, could not besuitably transformed, and were excluded. The independent variables included were activity(kayaking, multisport, and fishing) and specialization (L1, L2, L3, L4). The data (N =270) demonstrate homogeneity of covariance matrices (Box’s M: F = 1.032, p < .309)and equal error variances due to nonsignificant Levene’s Test for each of the dependent

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TABLE 3 Multivariate Tests for Activity and Specialization—Final Modela,c

Hypothesis Partial ObservedEffect Value F df Error df Sig. η2 Powerb

Intercept Wilks’ λ .015 1882.458 9.000 256.000 .000 .985 1.000Activity Wilks’ λ .332 20.955 18.000 512.000 .000 .424 1.000Specialization Wilks’ λ .791 2.316 27.000 748.294 .000 .075 .992

aBox’s M: F(1.032, p = .309).bComputed using alpha = .005.cDesign: Intercept + Activity + Specialization.

variables ranging from p < .955 for similar people to p < .148 for social/skill (Stevens,2009).

According to Wilk’s Lambda, the linear combination of motivation and site preferencevariables indicated significant main effects for both activity, F(18, 512) = 20.955, p <

.000, and specialization, F(27, 748.249) = 2.316, p < .000 (Table 3). The MANOVA wascalculated using p < .005 to accommodate a Bonferoni Adjustment for the nine dependentvariables, resulting in an overall α = .044. Analysis of multivariate effect size indicatesa moderate effect for activity (Partial η2 = .424) and a weak effect for specialization(Partial η2 = .075). Given that activity is a nominal variable the interaction was not tested(Tabachnick & Fidell, 2007).

Univariate Analysis

Specialization differed on only two of the nine independent variables: teaching others,F(3, 264) = 4.517, and challenge/safety, F(3, 264) = 25.163. Table 5 illustrates groupdifferences according to Tukey’s Hoc Test (p = .005). Participants at L1 (M = 3.570) wereless motivated by teaching others, F(3,264) = 4.517, than those in the L3 and L4 groups.Participants with L1 (M = 5.523) displayed much more concern with challenge/safety,F(3,264) = 25.163, than did those in the L2 (M = 4.780), L3 (M = 4.520), and L4 (M =3.997) groups. Effect size is small as indicated by Partial η2: .049 and .091, respectively,and there was adequate statistical power.

Conversely, activity differed on all nine of the dependent variables (P < .005; Table4). Participants in different activities displayed different motivations for enjoy nature,F(2,264) = 8.766, similar people, F(2,264) = 36.653, and for challenge/safety, F(2,264)= 25.163. Anglers (M = 7.212) were most motivated by enjoyment of nature followed bykayakers (M = 6.802) and multisport participants (M = 6.232). Kayakers (M = 6.577)were most interested in being around similar people followed by multisport participants (M= 5.465) and anglers (M = 4.632). Kayakers (M = 3.867) also were less concerned aboutchallenge/safety than were multisport participants (M = 4.724) and anglers (M = 5.524).

Fishing participants differed from multisport and kayaking in their motives for achieve-ment/stimulation, F(2,264) = 5.093; escape from personal/social pressures, F(2,264) =5.163; wilderness values, F(2,264) = 21.475; and social/skill, F(2,264) = 38.743. Kayakers(M = 6.482) and multisport participants (M = 6.637) were more motivated by achieve-ment/stimulation than anglers (M = 6.047). Anglers (M = 6.267) were more motivatedby escape than kayakers (M = 5.810) and multisport participants (M = 5.417). Anglers(M = 7.372) displayed a greater preference for sites with wilderness values than kayakers(M = 6.448) or multisport participants (M = 6.238). Kayakers (M = 6.920) and multisportparticipants (M = 6.719) were more likely to prefer people present who are compatible

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Multiactivity Study of Motivation and Site Preference 267

with their skill levels than anglers (M = 5.385). The motivations and site preferences withthe largest effect among the activities in these data included being around similar people(partial η2 = .217) and social/skill (partial η2 = .227), followed by challenge/safety (partialη2 = .160), wilderness values (partial η2 = .140), and teaching others (partial η2 = .100).

Discussion

This study explored the relationship of activity and specialization with motivation to par-ticipate, as well as site preference in white water kayaking, multisport racing, and fishingwithin a river-based context. In these data, greater explanatory power for motivation andsite preference were attributable to participation in an activity than was attributable tospecialization within an activity. Multivariate results indicate that while whitewater kayak-ing, fishing, and multisport racing exhibit a few specialization-related motivation and sitepreference differences, by far the strongest indicator was the activity itself rather thanspecialization within an activity.

Specifically, findings from this study partially supported the first hypothesis; thatis, motivation and site preferences for similar activities (e.g., whitewater kayaking andmultisport racing) were more closely aligned than for different activities (e.g., fishing)occurring at the same recreation resource. The univariate analysis indicates that fishingdiffered from kayaking and multisport on four of nine dependent variables. In addition,all three activities differed from each other on three of the nine dependent variables. Thedata show that fishing is less about achievement and stimulation and more about escapefrom the pressures of life when compared with boat-related activities. Moreover, fishingis a more solitary activity not particularly dependent on others for safety (i.e., social/skill,similar people, challenge/safety), and one that celebrates wilderness values and enjoymentof nature more than kayaking or multisport racing. Fishing was not distinct from multisportbut was different from kayaking on two motivation variables—equipment and teachingothers. In addition, multisport racing and fishing tend to be more solitary, individualisticpursuits, while kayakers rely on the presence of other kayakers to provide safety andswiftwater rescue. Consequently there may be a greater motivation among kayakers toteach others the activity.

Whitewater kayaking and multisport racing appear to be distinct activities despite thefact that at least for the river-based kayaking section of a multisport race, both utilize asimilar skill set (i.e., whitewater boating, swiftwater safety) and often on the same river.Kayaking and multisport differed on five motive dimensions (enjoyment of nature, similarpeople, challenge/safety, equipment, and teaching others). This supports the notion thatwhile these activities are similar, they are not the same. Compared with whitewater kayak-ing, multisport racing is a competition against others based on time. Kayakers participateto develop their skill at, and enjoy, catching eddies and surfing holes, etc., while multisportparticipants remain intent on maneuvering down the river segment in the quickest possibletime. However, they also share more similarities than differences in terms of motivation andsite preferences. Therefore, it follows that multisport racers are less motivated by enjoyingnature and being with similar people. Whitewater kayaking, on the other hand, remainslargely focused on exploring and playing in the features of a river. While multisport races inNew Zealand occur on set courses with lower grade rapids, kayakers take advantage of thefull range of whitewater difficulty and therefore express less concern with challenge/safetythan do multisport participants. Fishing on the other hand, at least in the context of mostrivers in New Zealand, generally requires neither boat nor speed and may be focused onprocuring a meal, or not.

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268 S. Galloway

The motives to participate and preferences for sites expressed by anglers mostly differedfrom the boat-related activities in these data. However, the activities do appear to share aplace identity affinity. Bricker and Kerstetter (2000) define place identity as the collectionof values, attitudes, and behaviors surrounding an activity in a given location, rather thanthe characteristics of the place itself. The dependent variables in the present study includingenjoyment of nature, being around similar people, escaping life’s pressures; preferencesfor wilderness values, social/skill, and challenge/safety all represent indicators of values,attitudes, and behaviors that suggest a sense of place identity for recreation on New Zealandrivers. One direction for further theoretical development of participation and place identitycould be explored with a hierarchical model of the effects of participation and specializationof activities nested within different recreation settings, for example, rivers, lakes, andoceans.

The second hypothesis was only partially supported. Specialization only predictedteaching others and challenge/safety. In both cases, post hoc tests indicated differencesbetween the low level of specialization and all other levels of specialization. In bothinstances, the difference lies between the L1 specialization group and all other groups. Thiscould indicate a possible bifurcation in motivation and site preference after a relatively briefperiod of participation in an activity. Given the design focus on river-based participation, itis also possible that the narrowed context of river environments in New Zealand constrainthe range of site preferences and motivation to those associated with natural settings andvalues commonly found there, though exceptions (e.g., urban, modified, polluted rivers)certainly exist.

This study explored the relationship of activity and specialization with motivationto participate, and site preference in whitewater kayaking, multisport racing, and fishingwithin a river-based context. In these data, greater explanatory power for motivation andsite preference were attributable to participation in an activity than was attributable to spe-cialization within an activity. Multivariate results indicate that while whitewater kayaking,fishing, and multisport racing exhibit a few specialization-related motivation and site pref-erence differences, the activity itself rather than specialization within an activity providedmore predictive ability in this study. The findings of the multivariate analysis raise severalpotential questions for the consideration of specialization relative to variables of interest,such as motivation and site preference for theoretical development and recreation resourcemanagement—particularly in single settings and in comparative studies.

Kuentzel and Heberlein (2006) found that the majority of participants remained “ata low fixed level” of specialization and that more individuals regressed or maintained acasual relationship with the activity than there were individuals who specialized (p. 508).The findings from this study also suggest that resource managers account for activity as wellas specialization in multiple use contexts. Attending to within-activity specialization differ-ences remains important, however, effort should also be focused on providing opportunitiesacross the range of activity specific skill levels (i.e., challenge/safety) and opportunities formore experienced practitioners to teach those who are less skilled in an activity. Wildernessvalues and enjoyment of nature remain among the highest site preferences and motivationsacross all activities. Shared motives to participate and site preferences among recreationactivities provide distinct direction for management decisions, as well as specialization-related differences among activities.

These findings support the practical utility of activity as a basis of comparison in man-agement decision making regarding multiple use contexts. If the nature and characteristicsof whitewater kayaking versus those of fishing provide clear relationships with motivationto participate and site preference, as does an individual’s degree of specialization whenconsidering a single activity, then recreation resource managers need to investigate and

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manage the interactions among activities as well as than focusing on provision of accessand opportunity across the entire range of specialization within an activity. Certainly interms of facilities, multisport participants placed a significantly higher importance on theirpresence, but participants in all three activities rated the importance of facilities at a lowlevel. This would suggest that their importance overall, and the practical utility of focusingon them, is significantly limited.

Study Limitations

It is possible that sampling bias, introduced with volunteer sampling, reduced the predic-tive ability of the specialization measure, because it may have attracted more specializedparticipants. While every effort was made to inform the general public in New Zealand andactivity participants of all levels about this study, and solicit broad participation, it is likelythat those already active and specialized completed the study more than those who wereless active participants. As such, those who are not active participants or nonparticipantswould be underrepresented in the data. The elimination of a 513 cases to allow for soundmultivariate analysis apparently did not contribute to sampling bias. Indeed, in the onesignificant difference between those included and those excluded (participation per month)indicates that it was the more active participants who were subject to bias contrary to theusual effects of sampling bias.

This exploratory study attempted to examine specialization in similar and dissimilaractivities in a numerically comparable way. While certain dimensions of specialization lendthemselves to comparison, such as activity importance or lifetime participation, others donot. Equipment, for example, proved to be troublesome regarding the isolation of specificitems shared across the activities under study. While replacement cost may serve as inindirect estimate of the amount of equipment, it proved unreliable in this study. Thisindicates a distinct need to continue with greater consideration of the phenomenology ofparticipation.

This study was reliant on a convenience sample, self-report measures, and data col-lected via the Internet-based survey software ABBEY . Although significant effort was spentbroadcasting the availability of the survey to the widest possible range of river-based recre-ation participants in New Zealand, it is difficult to assess the location of the respondents(i.e., how they heard about the survey and what motivated them to participate) and toprecisely determine generalizability. It is also reasonable to assume that respondents to thesurvey are already engaged in a river-based activity, which may have some impact on thespecialization indices. The study relies on respondents’ memories (i.e., number of riversvisited in lifetime) and as such errors were likely introduced. The internet data collectionalso enabled participants to not respond to every question, leading to a significant loss ofdata at the level of multivariate analysis. Finally, there is no basis to generalize the findingsof this research beyond the data to other recreational activities (i.e., backpacking) or otherrecreational milieu (i.e., lakes or oceanfront).

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