Annals of Tourism Research, Vol. 33, No. 4, pp. 918–938, 20060160-7383/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

Printed in Great Britain

doi:10.1016/j.annals.2006.02.002www.elsevier.com/locate/atoures

CAUSAL NETWORK METHODOLOGYTourism Research Applications

Robert NashThe Robert Gordon University, UK

Abstract: Causal network methodology is evaluated as to its effectiveness as a means of iden-tifying tourism issues in peripheral locations and is specifically applied to North Scotland.The aim of this applied research was to build models or diagrams for each respondent inter-view and to aggregate these to highlight the variables that are influential in three case studyregions. This iterative process starts with the initial variables being drawn up as a result of theliterature review, key informant feedback and prior knowledge, and the development of cau-sal streams representing respondent feedback. The process concludes with the final modelrepresenting the views of all respondents from each case study region. Keywords: causal net-works, applications, peripherality, Scotland. � 2006 Elsevier Ltd. All rights reserved.

Resume: Methodologie des reseaux causaux: applications a la recherche en tourisme. Lamethodologie des reseaux causaux est evaluee quant a son efficacite pour identifier des ques-tions de tourisme dans des endroits peripheriques, et appliquee specialement au nord del’Ecosse. Le propos de cette recherche appliquee etait de construire des modeles ou dia-grammes pour chaque interview, puis d’agreger les interviews pour souligner les variablesinfluentes dans les trois regions des etudes de cas. Ce processus iteratif commence par laredaction d’une liste de variables initiales suivant un compte-rendu de la documentation,des reactions des informateurs cle et des connaissances prealables; ensuite le developpementdes courants causaux representant les reactions des interviewes. Le processus finit par lemodele final, representant les opinions des interviewes de chaque region d’etude de cas.Mots-cles: reseaux causaux, applications, peripheralite, Ecosse. � 2006 Elsevier Ltd. All rightsreserved.

INTRODUCTION

Contemporary studies have acknowledged the opportunities for eco-nomic regeneration presented by tourism in peripheral areas. Therehas also been an increased interest in research that takes a holisticand comparative view of issues in these regions. Baum suggests certainpractical benefits are associated with using a comparative approach toresearch. These include gauging performance on a longitudinal basis,assessing the relative performances of similar cases, identifying alterna-tive development strategies, benchmarking, learning from the experi-ence of others, assisting in the understanding of specific events, and

Robert Nash is Course Leader for the MSc International Tourism Management atAberdeen Business School, Robert Gordon University (Garthdee, Aberdeen AB10 7QG,United Kingdom. Email <R.Nash@rgu.ac.uk>). His main research interests involve tourismdelivery in peripheral areas, particularly in Scotland, as well as policy issues in a broadercontext. Also interested in qualitative research techniques, he has published mainly in arange of tourism journals.

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being able to replicate previous studies. However, he also points tosome of the problems and limitations associated with comparative stud-ies. He describes these as ‘‘the definitional ambiguity associated withboth the supply and demand side of the tourism industry, the variabledata quality, the diverse and interdisciplinary character of tourism andthe structure and organization of the industry’’ (1999:5–7).

The aim in this research was to apply and evaluate causal networkmethodology by identifying issues of concern to tourism providers inScotland, the United Kingdom. They include VisitScotland area touristboards (formerly Scottish Tourist Board and now VisitScotland Net-work Offices), enterprise companies, local authorities, and the privatesector in three peripheral case study regions of Scotland (Grampian,Inverness and Nairn, and Ross and Cromarty). The research was itera-tive in nature; Figure 1 depicts this iterative process. The initial fieldresearch was conducted in the peripheral region of Grampian (North-east Scotland) and the process was then repeated in the two subse-quent peripheral case study regions. The process in each regioninvolved the construction of model for each respondent, an aggrega-tion of the individual responses into a cognate group (such as Com-mercial, Non-Commercial and Regional respondents) and a finalaggregation into a regional diagram.

The original case study region of Grampian was the methodologicalexploratory case study. Once the issues involved in policy delivery with-in Grampian were ascertained, the information was used to construct a

Initial variables drawn up as a result of the review of literature, key informant feedback, and researcher’s previous knowledge

First/initial models drawn up after pilot interviews

Model developed in Grampian case study region

Refinement

Models tested in Inverness and Nairn case study region

Moderate refinement

Models tested in Ross and Cromarty case study region

Final model and review/evaluation of Causal Network methodology

Figure 1. The Iterative Process

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causal network model based on the issues and factors highlighted bythe respondents. This was repeated in the other two case study regions,and the process allowed for cross-site comparisons that highlightedmany variables common to the three case study regions, as well as toother peripheral areas. It showed different emphasis and different lev-els of importance associated with each variable. It was hoped, whereverpossible, to provide a ‘‘clear explanation of how and why specific rela-tionships lead to specific events’’ (Wacker 1998:5) across all three re-gions. The Causal network methodology (CNM) used in this studyaddresses some of the problems/limitations associated with compara-tive studies. Namely, it is an applied approach that highlights the eco-nomic factors impinging on tourism in three peripheral regions inScotland and further provides a deeper and richer insight into the dif-ferences and similarities associated with each region.

CAUSAL NETWORKS AND PERIPHERALITY

The use of networking and causality in social science research hasbeen mainly undertaken in areas such as philosophy, business, educa-tion, and medicine (Friederichsen and Neef 2002; Kemerling 2002;Patterson, Dahle, Nix, Collins and Abbott 2002; Trochim 2003), andthere has been only limited application of the methodology in the fieldof tourism research. This study provides an analysis and evaluation ofthe causal network methodology (CNM) in a tourism context, withinperipheral areas. According to Miles and Huberman (1991), this ap-proach involves data collection, suggested themes, and groupings.The models are both textual and diagrammatic representations thathighlight both independent and dependent variables involved in thefield study. Further, they describe and highlight causation, links, andoutcomes that are involved in the particular case study. Because theCNM in this study was conducted in areas of Scotland that are periph-eral in nature, it would be appropriate to identify certain issues that im-pact on peripheral economies, in general, and on the three Scottishcase studies, in particular.

Tourism in Scotland is of great importance. As its largest employer,this industry provides 200,000 jobs in 20,000 businesses and generatesannual revenue of $7.48 billion or 5% of its GDP (Telfer 2005). In aScottish context, many rural areas are peripheral and are characterizedby inequalities of wealth, status, and power (Scottish Office 1999:1).Tourism in these peripheral areas, such as the North of Scotland(including the three case study regions involved in this research), isseen as a major factor in improving the prosperity of the regions andthe continued viability of remote communities. In most cases, theperiphery and peripheral zones tend to be regions that have sufferedeconomically in their relationships with the core. Jenkins, Hall andTroughton (1999:49) suggest that in peripheral areas the traditionalrural economy has failed and restructuring in economically viableterms is difficult. They point out that ‘‘in many cases peripheral re-gions continue to be zones which demand transfers of public funds’’.

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According to Nash and Martin, regions exhibit ‘‘a core-periphery rela-tionship with each other and the degree of peripherality is relative.From a tourism perspective the core would refer to the main generat-ing region, which in tourist terms would be the United States or Eur-ope’’(2003:164). In a Scottish context, the core would be the centralbelt cities of Edinburgh and Glasgow, and examples of the peripherywould be the three case study regions in this research.

The changes that have occurred in the economic and political struc-tures of Europe have not been equally beneficial to all areas. Some,such as those situated at the periphery, have faced very difficult timesin adjusting to economic policies directed from the core (This is cer-tainly the situation for the three case studies in this study). Theseperipheral areas are often rural in nature and are almost always remotein location. Richardson points out that as far as the European Union isconcerned peripheral areas ‘‘are equated with rural and agriculturalareas’’ (2000:59). Because of the relatively uneven development associ-ated with peripheral regions in the European Union (EU), there hasbeen increased interest in addressing the regional disadvantages asso-ciated with them. The EU has become a major player in the regionaldevelopment of these areas (including the case study regions here),mainly through its structural fund assistance. The challenge for alldevelopment agencies is to design and implement policies that im-prove the balance of economic opportunities available to such regions.It is partly as a result of the increased focus on tourism in peripheralareas that the three case study regions of Grampian, Inverness andNairn, and Ross and Cromarty were chosen for this research.

Causal Network Development

This exploratory methodological study was designed to identify prob-lems associated with peripheral areas and to evaluate the contributionof CNM in assessing the issues involved in tourism. All the three re-gions (Figure 2) are located in North Scotland and each has been cat-egorized as peripheral—some more so than others—by the EU and theScottish Office (1994).

Common characteristics associated with peripheral areas includeremoteness from markets, declining employment opportunities, andseasonality. The selection of cases that share similarities aids in the pro-cess of comparing one case with another. This makes the identificationof variables and streams more meaningful. All three regions were cho-sen because of similarities in terms of peripherality, location, economicfactors, and political and institutional arrangements. However, despitethe obvious similarities, there are always going to be different stake-holders, agendas, and political priorities operating in each region.For example, each could exhibit dissimilar levels of cooperation andleadership, local political make-ups (depending on which local partyis in control of the local government at the time), and marketing strat-egies to achieve their somewhat common goals. Each case is also un-ique in terms of individual physical attributes and natural

Figure 2. Grampian, Ross and Cromarty, and Inverness and Nairn

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environments. For example, Grampian shares many similarities withInverness and Nairn, but its natural environment, although similar,does not have the same image and consequent tourism potential ofthe Inverness region (in the Highlands) or the West Coast regions.

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Sample Population, Data Collection and Analysis. In Grampian, 32respondents were interviewed (19 in Ross and Cromarty and 17 inInverness and Nairn), with the main focus being on the public sector(although there were also representatives of the private and voluntarysectors). The main public sector organizations included the ScottishTourist Board (VisitScotland), local authorities (Aberdeen City andAberdeenshire Councils), the local enterprise company (ScottishEnterprise Grampian), and the area tourist board (Aberdeen andGrampian Tourist Board) which has subsequently been integrated intothe VisitScotland network. These were chosen because they tend to bethe main focus for tourism developments in the region. They organizeforums, develop regional plans for tourism, and hold the purse stringsfor funding developments. The focus of the research was on experts intourism policy development, so random or quota samples were notappropriate. Consequently, purposive sampling was used and the ini-tial respondents (key informants) were selected based on the research-er’s judgement as to their typicality, interest, experience, orknowledge. Further respondents were chosen using the snowball tech-nique, asking those interviewed to suggest other names. This processwas repeated in the remaining two study regions, with the focus stillmainly (although not totally) on the public sector.

Part of the focus of this research was to highlight obstacles to effec-tive tourism policy delivery in peripheral areas. This involved qualita-tive in-depth interviews (to collect the respondent data), withsecondary data coming from published empirical sources. The inter-view used was semistructured and face-to-face (which allowed fornon-verbal cues to be picked up). This involved an open questionnairewhere respondents were asked the same set of questions but also hadthe opportunity to develop their responses. This process allowed forthe interviewer to pursue a particular line of questioning if it appearedto be informative or relevant. Qualitative data (such as interviews) areacknowledged as being subjective in nature, a situation which is oftenseen as an issue. The use of the open questionnaires allowed for anexamination of the subjective values, attitudes, beliefs, and motivationsof the tourism providers, which were essential in this research. Despitethe rich indepth nature of the responses generated as a result of thisapproach, difficulties associated with vested interests were anticipated.These issues revolve around the self interest of each participant, interms of their individual job or organizational roles. This is likely tolead to different agendas and perceptions for each individual respon-dent, depending on which organization they represent. To combatthese potential problems, multiple sources of evidence (such as, min-utes, archive files, and printed sources) were sought from each to allowfor the cross-checking of data. Quantitative analysis was not consideredsuitable because it was felt that the nature and depth of response re-quired a more personal, interactive approach.

Participants were asked to grade their responses using an ordinalscale or ranking. The grading or ordinal scale used was based on Milesand Huberman (1991) and consisted of the categories of low, moder-ate, and high (Later the categories of low/moderate and moderate/

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high were introduced). The emphasis was to encourage them to answerin as full a way as possible and to grade their responses wherever pos-sible. All visits took place face-to face and replies were transcribed dur-ing the interview and soon after recorded onto a matrix, whichinvolved inputting the data that listed the respondents, their affilia-tions, and the identified variables. To identify key themes and issues,this study used ‘‘pattern matching’’ which ‘‘involves comparing, ormatching, several pieces of information from the case study in an at-tempt to either import or export lessons. . . [including] matching theemerging patterns associated with the individual respondent’s feed-back’’ (Nash and Martin 2003:168). Yin suggests that case study re-search is well suited to ‘‘take advantage of pattern matchingtechniques’’ (1993:19).

There are a number of limitations associated with the methodologyas outlined. First, case study regions are not sealed units. The problemwith most boundaries is that they are often arbitrary and, in manycases, inflexible, and it is often difficult to decide on recognizedboundaries. The boundaries used in this study are those of the localenterprise companies, and they do not represent the same areas aseither the area tourist board or the local authorities. Second, the studycould also have included a larger sample and in so doing would bemore representative of tourism providers in the three case study re-gions. That said, this investigation was based on a purposive sampleand included all the public sector organizations (as well as some pri-vate and voluntary representatives) involved in tourism in the region(in peripheral areas the public sector tends to dominate). Third, thesnowball sampling technique had limitations in that respondents arelikely to suggest other respondents they know or whom they have deal-ings with. This means that in many cases the selection may be biasedtowards those who share views similar to the original respondent’s.

Development of Models. During the data collection, a list was drawn upwhich identified the issues involved in tourism in peripheral areas ingeneral, and in the three case study regions in particular. Wacker sug-gests that ‘‘generally the literature provides a base for defining vari-ables’’(1998:9). Such was the case in this research where theliterature, knowledge of the region, and feedback from key informantsand pilot study respondents provided the basis for the initial variables.These were used as the starting points for the models; and further vari-ables, or outcomes, connected to each, were included later. All inter-views were interpreted, condensed, and inputted onto a matrixwhere the respondents were categorized according to cognate groupson the horizontal axis (local authorities, area tourist boards, or privatesector, etc.), and the issues and responses were grouped according tothemes, on the vertical axis. The latter was based on the queries fromthe open questionnaires that had been identified from the literatureon peripheral areas and included items on issues associated withperipherality itself, such as rural economy, economic decline, publicsector support, infrastructure, and seasonality.

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This list developed as the data collection progressed, to include moreof the variables suggested by the respondents. Every effort was madeduring the interviews to encourage them to respond using variables re-lated to the headings used in the questionnaire; however, in some cases,this did not occur. For example, a question about economic activity mayhave been answered in terms of high unemployment. Another on sea-sonality may have elicited a response about local business closures, orone about marketing may have referred to tourist perceptions. In suchcases, the respondent data had to be interpreted, to compartmentalizethe information into usable chunks, variables or ratings, so that it couldbe incorporated into the matrix (and ultimately into the models). Thisinvolved a process of reducing, aggregating, or compartmentalizing therespondent’s data. This process was relatively subjective in that it in-volved interpreting or categorizing the data into variables that maynot have been directly mentioned by the respondents. Invariably thismeant a process of aggregation and a loss of some of the richnessand depth of the original data collected, but this is an important stepin the production and development of the causal network models.

The variables generated were then used to construct representationsor diagrams for each of the individual participants. The process in-volved identifying variables that were linked or associated with eachother. Where possible, causality was identified (where one variablecauses or leads to another), but this was not possible in all cases. Wherenot possible, the linkage between the variables suggests an association,or a connection (which was not ideal). Each variable was connected toanother with an arrow, which indicated a connection between them. Insome cases, an association or link between variables had to be intro-duced, because there may have been something missing in the respon-dent feedback that meant an association was not logical or consistentwith established knowledge or literature. As a result, some of those sug-gested by the respondents may not necessarily represent a true connec-tion or reflection of the situation. For example, if the variable offunding were removed from a stream, including area tourist board,funding, and marketing, then the link would be made directly fromthe variable area tourist board to the variable marketing. This associa-tion was part of the retroductive reasoning process that is a core featureof the causal network approach.

The relationship between any two variables must be stated; otherwisethe theory cannot be internally consistent (Wacker 1998). From eachof the variables, links or connections to others were established. Forexample, using the start variable of tourism, the next variable sug-gested by respondents (which supported the literature), was the factthat there were many diverse interests involved. This meant that diverseinterests were identified as a variable impacting on tourism. Theirnumber was rated as high (Figure 3) because of the large number ofindividuals and organizations involved in tourism in the region (thecategory of high was suggested by the respondents who were askedto use the ordinal scale of low, moderate, and high). The linking arrowbetween the variables of tourism and diverse interests indicates a con-nection between the two.

Tourism provision

Others

Public sector

Diverse interests

European Union

Private Sector

High

High

Low

Moderate

Low

Figure 3. Indication of Causality and Influence over Tourism

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Developing the variable of diverse interests further meant that theorganizations involved, or included as such, also had to be represented.These were identified (from the literature, the researchers own knowl-edge, and by the respondents), as the public sector, private sector, theEuropean Union, and others (which included voluntary organizations,pressure groups, or any organizations that did not fit into the otherthree categories). Each of these variables would also be given a ratingby the respondents, based on their influence over tourism in each re-gion, and by using the ordinal scale of low, moderate, and high (Figure3). During construction, some narrative may have been included along-side the models, in an attempt to ensure as complete a picture of therespondent’s data as possible. The idea is that this narrative would notonly inform the reader but also maintain the depth and some details,which might otherwise be compromised, while putting the informationinto a diagrammatic format.

In Figure 3, where respondents identified that there were a largenumber of diverse interests involved in the provision of tourism in thiscase study region, the public sector was considered to be most influen-tial, and consequently was rated as high (This is also reflective of theliterature on peripheral areas). The private sector was considered tobe important but was perceived by the respondents as having lower lev-els of influence and thus it was rated as moderate. Other sectors wereconsidered to have the lowest, or smallest, levels of influence andhence rated as low. The EU influence was rated as low by the respon-dents, but this rating was not necessarily reflective of the literature ortheir financial contribution to the region’s economy. The responsesmay reflect perceptions created as a result of the EU principle of sub-sidiarity, as adopted by the EU itself. The situation is a good example ofhaving to interpret or infer from the information when the respondentdata differs from the established facts, or literature. This is also a good

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example of the use of narratives to support variable inclusion or rating.In many cases, the ratings, or labels attached to the variables (low,moderate, or high) will be sufficient to relay the participant’s viewsand further development or narratives may not be required.

Each of the variables in Figure 4 represents a public sector organiza-tion involved in tourism of the region being studied. Furthermore, theinformants suggested that all had their own vested interests, and thisvariable was rated as high by them. The public sector was identifiedas having four connecting components: local authorities, local enter-prise companies, area tourist boards, and the Scottish Tourist Board.The local authority was rated as high as were the area tourist board,and the local enterprise companies. The Scottish Tourist Board wasrated as low. The literature on peripheral areas tends to emphasizethe importance of the local authorities, local enterprise companies,and the area tourist boards (Botterill, Owen, Emanuel, Foster, Gale,Nelson and Selby 1997; Burke and O’Cinneide 1995; Cavaco 1995;Keane 1992; McCleery 1991; Shucksmith, Chapman, Clark and Black1994; Wanhill 1995). However, the Scottish Tourist Board (STB), de-spite being the lead body in Scotland, was considered to have low levelsof influence. This is in contrast to what the literature suggests the levelsof influence of the STB (and other national tourism organizations)should be. The low levels attributed to the STB were, according torespondents, partly the result of poor funding, ineffective operationalprocedures, and a lack of direction associated with the board. Its influ-ence outside the central belt and the Highlands of Scotland was consid-ered to be low.

Causal Stream Construction. Each of the variables is connected orjoined with other ones to form causal streams, and these streams areintegrated and included to form the causal network diagram for eachrespondent’s interview. Once the individual, interview based models(and narratives) were constructed, they were aggregated into largercognate groups, and causal networks (and narratives) were constructed

High

High

High

High

Low

High

STB

Vested interests

Public sector

LEC

Local Authority

Area Tourist Board

Figure 4. Public Sector and Vested Interests

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for these various groups (such as commercial, non-commercial, andregional respondents). These were then aggregated to produce a finaldiagram representing the total number of respondents from the Gram-pian region (Figure 5 for model representing Grampian region). Thisprocess was repeated in the subsequent case studies in Inverness andNairn, and Ross and Cromarty. This continuing aggregation processmeant that there were three final models, each representing the totalnumber of respondents in each of the case study regions. The genera-tion of these diagrams allowed for a meaningful cross-site comparisonbetween groups and case studies, but it is acknowledged that the pro-cess of aggregation may involve the loss of some data. This approachhas been described as

a comparative analysis of all sites in a sample on variables estimated tobe the most influential in accounting for the outcome or criterionmeasures. The analyst looks at each outcome measure and examinesfor each site the stream of variables leading to or determining thatoutcome. Streams that are similar or identical across sites, and thatdiffer in some consistent way from other streams, are then extractedand interpreted (Miles and Huberman 1991:197).

Figure 5 represents several thousand words of narrative text gatheredfrom all 32 respondents in the Grampian region. It includes all thevariables that have impact on tourism in the region and also includesthe connection, association, or causality between the variables, as wellas their relative importance. The variables are represented in num-bered boxes, to be used when describing causal streams. For example,the vested interests stream could be represented as 10–17 instead ofvested interests, cooperation, leadership, integrated strategy, market-ing, image, competition, and tourist numbers. The arrows indicate aconnection or association, but they do not represent a definitive causalassociation. They do indicate that a variable is connected to anotherone and, in most cases, causality is also implied. However, this is notalways the case. For example, a low level of cooperation may not leadto low levels of leadership, but the two are linked. The rating or grad-ing associated with each variable is on top of each individual variablebox. These represent the ratings attributed by the respondents. In Fig-ure 5, the ratings (and the variables) have been averaged and representthe views of all 32 respondents.

The process used in the generation of Figure 5 was repeated in theother case study regions of Inverness and Nairn, and Ross and Crom-arty, and resulted in two additional diagrams, each representing theviews of the respondents in the two additional regions. These werethen used to draw comparisons and to highlight differences or similar-ities associated with the three case studies. This comparison allows forthe identification of topics/issues, where one case study performs bet-ter (or worse) than the others, and will also identify areas where les-sons, or best practice, could be imported or exported between theregions. For example, the region of Inverness and Nairn could exportlessons to the Grampian region in relation to levels of cooperation andintegration or private sector involvement or image creation. This is

LOW

MOD

LOW/MOD

LOW/MOD

LOW

LOW

LOW/MOD

MOD/HIGH

LOW

17. TOURIST

NUMBERS

LOW

MOD/HIGH

LOW/MOD

HIGH

LOW

LOW

LOW

MOD/HIGH LOW

LOW

LOW/MOD

1. TOURISM

PROVISION

HIGH

24. RURAL ECONOMY

HIGH

MOD/HIGH

LOW/MOD

35. SERVICE STANDARDS

14. MARKETING

MOD/HIGH

10. VESTED INTERESTS

LOW/MOD

25. ECONOMIC ACTIVITY

LOW

26. EUROPEAN UNION

29. BUSINESS TOURISM

MOD

30. COST OF ACCOMMODATION

33. SEASONALITYLOW/MOD

32. VFR’s

ANTECEDENT VARIABLES INTERVENING VARIABLES OUTCOME VARIABLES

LOW/MOD

22. TOURISM PRODUCT

LOW/MOD

28. OIL

HIGH

2. DIVERSE INTERESTS

MOD

3. PRIVATE SECTOR

5. OTHERS: NTS. SNH.RSPB.HS.

CONSERVATION GROUPS

12. LEADERSHIP

13. INTEGRATED STRATEGY

16. COMPETITION

15. IMAGE

MOD/HIGH

9. LOCAL AUTHORITY

18. FUNDING

7. TOURIST BOARD

LOW

6. STB

8. LEC

20. TRAINING

HIGH

23. PERIPHERALITY

36. MAGNET ATTRACTION

27. INFRASTRUCTURE

11. CO-OPERATION

19. FUNDING

31. COSTS TO

TOURISTS

21. ATTRACTIONS

4. PUBLIC SECTOR

34. HOSPITALITY

Figure 5. Final Causal Network Model of the Grampian Case Study

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because each of these variables was rated higher in the region of Inver-ness and Nairn than they were in the Grampian region.

Evaluation of Causal Network Methodology

Providing an insight and evaluation into the application of CNMhighlights some of the positive and negative aspects associated with

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the methodology and its application for peripheral areas. This also pro-vides some guidance on how the methodology can be used by otherresearchers.

The Diagrammatic Model. The generation of large amounts of difficultand unwieldy data is an issue that concerned Yin. He suggested that‘‘innovative forms of presentation should be sought. The most desirableinnovations should deal with a major disadvantage of the written casestudy—its bulkiness and length’’ (1994:98). Causal networks are aneffective method of displaying data. Comparing narrative text is difficultwhen attempting to make direct comparisons between case study re-gions (as was the case in this research). This is especially the case inqualitative research where large quantities of narrative text are likelyto be generated. A diagrammatic format aids in direct comparisonsand the consequent identification of similarities and differences, inboth variables and categories, associated with these variables. Milesand Huberman suggest that the models provide a logical, transferable,flexible, and comparative diagrammatic representation, which clearlyoutlines the links between variables (1991:132). The evidence fromthe three case studies used here suggests that, to some extent, this istrue. If the empirical data were not consistent with either the literatureor other known facts, then this would suggest that there are difficultieswith the interpretation of the questions or inconsistencies between therespondents’ views and the established literature or the facts. For exam-ple, if marketing were rated as high and both the STB and the area tour-ist board were rated as low then there would be an inconsistency,because it is these two bodies that are responsible for funding the mar-keting. Each variable links to another variable in a logical way and eachshould be a progression from one to the other (Figure 5).

According to Miles and Huberman ‘‘humans are not very powerful asprocessors of large amounts of information; the cognitive tendency isto reduce complex information into selective and simplified Gestaltsor easily understood configurations. Better displays are a major avenueto qualitative analysis’’(1991:21). Causal networking did serve to re-duce the vast quantities of interview data (in all three case studies) intoa model format, but these diagrams themselves may prove to be diffi-cult to assimilate. They contain a large amount of data that includesnot only the relevant variables and the linkage between them, but alsotheir relative importance or categorization, as well as supportive narra-tives. However, their assimilation and understanding becomes lessproblematic (and indeed becomes relatively straight-forward) the fur-ther the researcher (or reader) goes into the research.

Allied to diagrammatic advantages, Miles and Huberman suggestthat the format also allows for some narrative associated with the dia-grams. They suggest that this extra narrative is an advantage. This iscertainly true in cases where variables are dealing with relatively com-plex issues, such as economic statistics associated with the three casestudy regions in this research. The economic-activity variable couldbe better supported by the inclusion of statistical data to accompany

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the variable that would illustrate the differences among the peripheralareas’ local economic performance. In this research, each model in-volves a narrative that provides more detail and support informationto assist in representing the respondent’s views. The depth and levelof detail will depend on the complexity (or simplicity) of the respon-dent’s comment or on the variable that needs to be explained. In somecases, the accompanying narrative would be very brief (possibly only aword or two); in others there may be more explanation required; and,in some others, the variables may be self-explanatory.

Causal Streams. Each variable is linked or associated with other vari-ables to form streams. It is the generation of these streams that demon-strates association or causality and it is these that are partly responsiblefor the unique approach used in this research. They highlight thelinks, or associations, between variables, which allow for the identifica-tion, or linking, of these streams, which can then be isolated for furtheranalysis. The streams can be used in analysis between sites and areasand, where commonalties exist, they can be used for importing lessonsbetween different cases. The individual variables and models per se donot suggest where lessons could be drawn. It is the streams that are themost appropriate means of highlighting potential lessons among thecase study regions. For example, the identification of a commonstream that is associated with the three peripheral areas used in thisstudy is the stream that includes the variables of hospitality, servicestandards, and training. This stream could serve to highlight the differ-ent levels of satisfaction associated with these variables in all three casestudy regions, and could highlight that levels of satisfaction may begreater in one region than another. The important point is that themodels themselves will not be the same because there will always be dif-ferent issues impacting on each region and on individual peripheralareas. It is the identification of shared variables among the case studyregions that is important for the transfer of lessons. These will increaseas variables link with other variables to form a stream that is consistentwith other streams involved in the other case study regions.

Analysis and Transparency. The construction of the causal networksutilizes pattern matching, which involves the coding and segmentationof data given labels, names, or codes. As a result of the generation ofthemes and configurations, there is greater validity of data generation,which allows a more inferential level of analysis. Hall and Hall suggestthat the models ‘‘help us sort out our ideas logically and aid in makingsense of data’’ (1996:147). For example, streams that emerge as a resultof the linkage between variables included themes that may be commonto other peripheral areas, such as cooperation, leadership and integra-tion, marketing, image and competition, and hospitality, training andservice standards. Miles and Huberman go further and suggest that themethod ‘‘is the analyst’s most ambitious attempt at an integratedunderstanding of a site’’ (1991:142).

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CNM does provide an effective means of establishing a level of trans-parency associated with the research protocol. This is achieved byestablishing a chain of evidence, linkage or association among vari-ables. According to Yin, the aim of such a chain of evidence is to ‘‘fol-low the derivation of any evidence from initial research questions toultimate case study conclusions’’ (1994:98). He goes on to suggest thatan external observer should be able to trace the steps in either direc-tion, either from conclusions backwards or from questions to conclu-sions. The diagrammatic and linking nature of the models allows anobserver to trace the stages in their development. For example, themarketing variable links to the image variable, which, in turn, linksto the competition variable (Figure 5). The region may have a poor,or low profile, image, which makes the marketing of the region diffi-cult. This, in turn, means that the level of competition facing the re-gion is likely to be high because their major competitors (such asthe highlands and the West Coast) have stronger images and thusare easier to market. A further example is the variable of peripherality,linking to rural economy, which links to economic activity. This streamof variables is common to many peripheral regions and is common toGrampian, Inverness and Nairn, and Ross and Cromarty. They are eachpredominantly rural in nature and, as a consequence, have a relativelyhigh degree of dependence on agriculture. This dependence on anagricultural economy means that the level of economic activity in theseregions may often be low, because of the problems associated with agri-cultural decline in many peripheral areas.

Ordinal Scale. Miles and Huberman suggest that this approach canprove to be an effective method of categorizing qualitative informationinto usable data, based on the ordinal scale suggested (1991:21). Dur-ing data collection, the informants were encouraged to respond usingthe ordinal scale of low, moderate, and high. One of the major prob-lems associated with qualitative research is how to turn the data intoa usable comparative format. The use of causal networks meant thatratings could be attributed to the respondent feedback. This rating isa valuable and important component in this methodology. The ordinalscale allowed for three categories of response and while, in many cases,the participants did provide data using these categories, there wereoccasions when they responded using different phrases. When this oc-curred, there was an element of interpreting or inferring respondent’sfeedback and recategorizing their responses. Examples of this includedlow which may represent comments such as poor, weak, few, not good,ineffective, cheap, little impact, and low. Moderate may have repre-sented comments such as average, okay, not bad, quite good, quite afew, reasonable, fine, sufficient, and generally something that fell be-tween the two extremes. High may represent good, very good, excel-lent, many, lots, expensive, costly, high impact, or high. The use ofgradings or rankings associated with the ordinal scale (low, moderate,and high) suggested by Miles and Huberman tended to limit the re-sponse opportunities and did not allow for the adequate representa-

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tion of the respondent’s views. This was addressed later on in the re-search by increasing the options for response to five with the inclusionof the two extra options (low/moderate and moderate/high).

Variable Omission, Development and Association. If a variable is missing,then progression to the outcome variables should not be possible orlogical. This is an issue of concern because the variables could linkto other ones in the streams and effectively conceal those that arenot apparent. For example, a common stream associated with the re-gions (and with other peripheral areas) would be high levels of periph-erality, high levels of seasonality, and low tourist numbers. However,the linkage between the variables in the causal stream could involveonly the variables of high levels of peripherality and low tourist num-bers. This does not mean to say that seasonality is not an issue forperipheral areas; indeed it is. What it does suggest, though, is that aperipheral area could have low tourist numbers simply because of itsperipherality (which it certainly could). The issue of interest in relationto CNM is that the variables and the streams identified can vary and dif-fer depending on the individual researcher and their interpretation ofthe same situations. This could be partially addressed by the use of nar-ratives to accompany the models and also by using a confirmatorypanel.

Miles and Huberman suggest that generating a full set of networkvariables involves an exercise in brainstorming. ‘‘The idea is to list allthe events, factors, outcomes and processes and so on that seem tobe important, then to turn them into variables. Once a full set of vari-ables is down, the list should be combed for redundancies’’(1991:136). The suggestion is that if variables appear to be redundant,or similar to other variables, then they should be combined together toform a composite variable. For example, restaurants, hotels, and barscould be combined together to form the variable of hospitality; or railand air links could be combined together to form the variable of infra-structure; or remote and isolated could be combined to form the var-iable of peripherality. This may be viewed as part of the iterativeprocess associated with the ongoing analysis associated with this re-search methodology. However, it is acknowledged that this may also in-volve a loss of rich data as a result of the aggregation process and,again, leaves the method open to criticism based on increased subjec-tivity and researcher interpretation. These issues can be partly ad-dressed using narratives and confirmatory panels.

The connections or associations suggested in the models are notdefinitive. It would be desirable for the arrows connecting the variablesto imply causation in each case, but this was not always possible. Thefact that there is no capacity to identify the differentiation between cau-sality and connection/association is acknowledged as a weakness. It isalso acknowledged that the inclusion of more information, such as averb, might aid the understanding of the relationship among the vari-ables. This association among variables was in many cases suggested bythe respondents. However, there were cases where the association or

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connection needed to be inferred retrospectively, or, as Miles andHuberman put it, making ‘‘connections that seem sensible. It helpsto make simplifying assumptions about what leads to what’’(1991:193). They suggest that these connections or associations areconsidered legitimate and are part of the ongoing process of analysis.Although Miles and Huberman support this process of inferential ret-rospective analysis, it does leave the methodology open to criticisms ofintroducing associations or connections that are not directly suggestedby the respondents. In this research, in cases where the association hadto be inferred by the researchers at a later stage, the connections werebased on secondary data such as minutes, archive files, and printedsources, as well as other respondent’s data. While this system was nottotally satisfactory, it provided a framework on which to proceed andserved to address issues associated with subjectivity.

Number of Cases and Conclusion Drawing. This methodology may be dif-ficult to use in research where there are significant numbers of casestudies. The identification of streams becomes more difficult the largerthe number of cases included in the study. In this research, the use ofthree regions meant that identifying causal streams was manageable.However, as the number of models constructed increases, the likeli-hood of comparing streams decreases. Indeed, in this research, dia-grams were constructed for each of 32 individual interviews duringthe Grampian case study. This relatively large number meant that iden-tifying streams among the cases could be complicated. On this basis,the use of CNM is more appropriate where the number of cases beingstudied is relatively limited. It could be said that this methodology pro-vides a suitable descriptive framework for the analysis of respondentdata from a discrete population (where the number may be insufficientfor quantitative statistical treatment). For example, peripheral areacase studies such as the ones used in this research, where the character-istics and commonalities tend to be similar, are highly suited to CNM.

Qualitative data analysis is a continuous, iterative, process and thereis a concern that the drawing of conclusions on an ongoing, incremen-tal, basis, could introduce some bias into subsequent data collectionand respondent interviews. The concern is based on two possibilities:that conclusions will be formulated before the data collection is com-plete, and that respondents will be led to support the researchers evolv-ing theories and conclusions. According to Miles and Huberman‘‘from the beginning of data collection, the qualitative analyst is begin-ning to decide what things mean, is noting regularities, patterns, expla-nations, possible configurations, causal flows, and propositions’’(1991:22). They suggest that, even though final conclusions may notappear till the data collection is complete, they are often pre-figuredduring the research, and the models will allow for the continuous test-ing and, where appropriate, their modification as part of the ongoingand evolving nature of the research. In this research, patterns, associa-tions, and, in some cases, causality, began to emerge early in the datacollection (In many cases, these patterns tended to be reflective ofestablished patterns associated with other peripheral areas). The issue

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of drawing conclusions early in the research is seen by Miles andHuberman as an advantage in that conclusions can be drawn, tested,and verified on an ongoing basis as other respondents’ data is collated.

Aggregation and Subjectivity. Any form of aggregation can lead to anoversimplification and a loss of representation of the respondent’sviews, as well as a loss of the individual richness associated with quali-tative interviews. The larger the number of respondents and the great-er the number of responses aggregated, the greater the likelihood of aloss of richness and local applicability. This was an issue of concern asthe process involved in this research meant an aggregation of 32 inter-views, to form a final diagram representing all in the Grampian region.As mentioned, the use of narratives and a confirmatory panel may serveto address some of this concern. Miles and Huberman point out that‘‘the first attempt to take in and piece together local maps is usuallyjumbled and vague, and sometimes plain wrong. By the forth or fifthattempt, however, things look brighter and clearer’’ (1991:130). How-ever, because of the relatively subjective nature of researchers’ inter-pretations, the diagrams developed may appear logical and clear, butthey will still be subjective interpretations that may be interpreted inslightly different ways by other researchers. Feedback and revision willhelp, but essentially the researchers are operating at a ‘‘higher level ofinterpretative inference’’ (Miles and Huberman 1991:140). The proce-dure can be more suggestive than totally objective or definitive, andthere will be several attempts at model creation before arriving at a fi-nal one. For example, in order to achieve Figure 5, there were a min-imum of five attempts that involved repeated reference to the interviewdata and other sources of information such as minutes, reports, andother secondary data. This triangulation process goes some way incounteracting the subjectivity associated with the methodology.

CONCLUSION

This research has evaluated the effectiveness of CNM in terms oftourism research in three peripheral case study areas in Scotland.One of its most useful components is the identification of the causalstreams. It is these streams that prove to be the most useful means ofidentifying issues and variables that can be highlighted and usedamong different case study regions. Therefore, it is suggested that con-struction of complete models is a useful means of identifying issueswithin a case study, but the comparison among the regions is best doneusing the streams of associated variables.

The method does provide a useful and original way of categorizingand displaying data. It does provide a means of reducing qualitativedata into usable quantitative variables. However, there are issues asso-ciated with the aggregation of data, which is a problem for most socialscience research and was also an issue in this research. If the aggrega-tion process can be accompanied by the use of narratives, then therespondent data will provide more distinct feedback. The methodology

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would be more effective, when looking at cases where the number ofrespondents could be limited (perhaps in comparative peripheral areastudies) and where the process of aggregation is supported by includ-ing more narrative for each respondent.

Issues associated with subjectivity, identifying association, causality,or connection between variables and issues related to differing inter-pretation of data, by different researchers could be partly overcome,also by using narratives with each diagram. This would, in addition, im-prove the levels of transparency associated with the research protocol.These narratives should be supported by the use of a confirmatory pa-nel, made up of researchers involved in the project. Each would needto develop their individual causal network models and narratives thatshould then be introduced, discussed, and defended with the panel.

Because the methodology generates a full set of variables and alsoidentifies themes and configurations, there is an opportunity for whatMiles and Huberman call ‘‘higher levels of inferential analysis’’(1996:147). This methodology can provide a useful framework forthe comparative elements involved in comparative social science re-search, and it can allow for conclusions, based on experiences in differ-ent peripheral areas, to be transferred to other peripheral regions. Itcan prove to be both methodologically and pragmatically useful incases where the research requires a comparative approach. It can alsopoint to examples of best practice, that have the potential for adoptionor adaptation in other similar peripheral areas, and it can also high-light areas of deficiency within the case study regions.

Some of the tourism lessons that can be learned, or passed from oneregion to another, can be relatively simple, for example, the need toimprove service standards or levels of funding or the need for moretraining. However, the policy decisionmaking process itself may notbe so simple (as was the case in the three peripheral regions used inthis research). One of the major problems often occurs as a result ofthe large number of diverse, and often vested, interests involved. Some-times the outcome or the goals of the various projects seem relativelystraightforward, but it is the process by which these goals are arrivedat that encounters the major difficulties. CNM is more appropriatein smaller-scale research projects such as public sector arrangementswithin a given case study region or population, or peripheral area re-search, where the number of cases is limited but some commonalitiesare shared. The methodology could be applied for use within the pri-vate sector (such as hotel chains), but its application would be moreeffective in the public sector arena. The use and application of theCNM in this study has addressed process related issues in three periph-eral areas in Scotland. Some of the lessons learned from this study canbe of use for research in other peripheral areas.

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Submitted 22 January 2004. Resubmitted 25 October 2004. Resubmitted 8 July 2005.Resubmitted 5 August 2005. Accepted 26 December 2005. Refereed anonymously.Coordinating Editor: Michael J. Keane