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Assessing cultural values: developing an attitudinal scale

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Abstract

Effective measurement of cultural value is often elusive because of its multidimensional nature. It is also influenced by sociodemographic characteristics (manifest variables) and attitudinal characteristics (latent variables) of populations. While the former is easily available to researchers, the latter has not been fully studied. This paper suggests the use of a cultural worldview scale that was developed to measure cultural attitudes of people, using factor and cluster analysis. Four factors comprise the scale: cultural linkages, recognition of cultural values, cultural loss and preservation of traditions and customs. Some advantages of using this scale are demonstrated, and relationships with sociodemographic variables are investigated. Managerial and policy implications are discussed.

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Notes

  1. Cultural ‘value’ here refers to the ‘economic value’ of cultural goods and services. Because this term is also used in reference to social cognitions, which ‘serve as prototypes from which attitudes and behaviour are manufactured’ (Vaske and Donnelly 1999, p. 524), readers should exercise some care to interpret the correct meaning of the term from the context in which it is used. It is worth stressing that ‘social cognition values’ are generally referred to when discussing behavioural attributes in the development of the cultural worldview scale and that ‘economic value’ is generally referred to in relation to the determination of estimates of cultural value. The authors appreciate an anonymous referee for this clarification.

  2. Multidimensional nature of cultural value(s) is also supported by Mazzanti (2002). Scholars of crosscultural psychology such as Geert Hofstede and Robert R. McCrae proved the multidimensionality (5 dimensions) of cultural characteristics in the crosscultural and crossnational context (Hofstede and McCrae 2004). However, the ‘crosscultural multidimensions’ of Hofstede and McCrae (2004) and other sociopsychologists are different from ‘multidimensions’ of Mazzanti (2002) and Throsby (2001). The goal of the first group is to define and compare national characters, and that of the second group is to examine the economic value of cultural goods—cultural dimensions need to represent different degrees of preferences.

  3. Attitudes are ‘mental state’ and must refer to some object to which individuals respond favourably or unfavourably (Eagly and Chaiken 1993; Vaske and Donnelly 1999, pp. 526–527).

  4. In this sense, the CW scale is comprised of multiple subscales or factors.

  5. Beliefs are the prevailing determinants of a person’s intentions and actions, which are antecedent to behaviour-specific attitudes (Ajzen 1991, p. 189).

  6. The influence of habits or routines won’t be considered in detail, as the focus of this research is cognitive behaviours that might be influenced by LVs and SDs.

  7. Facets are aspects or expected dimensions of a scale. The five facets of the NEP scale are limits to growth, anthropocentrism, the fragility of nature’s balance, rejection of exemptionalism and the possibility of an ecocrisis.

  8. DeVellis (1991, p. 75) suggests that experts should be drawn from ‘colleagues who have worked extensively with the construct in question or related phenomena’.

  9. The development of the CW scale would not have been possible without the contributions from these experts.

  10. The final pool of 48 items for a test survey is available by contacting the corresponding author.

  11. Respondents were asked to express agreement/disagreement with each statement, using a five-point Likert scale: (1) Strongly agree, (2) Mildly agree, (3) Unsure, (4) Mildly disagree and (5) Strongly disagree.

  12. For example, Americans whose cultural heterogeneity is stronger than Australians and Koreans (Alesina et al. 2003) might interpret and answer the scale items differently, thus some item scores might not be subject to international comparisons.

  13. This aspect is about whether or not the country as a whole has a high degree of cultural homogeneity.

  14. A level of heterogeneity of a country can be determined differently by the intensity of the demand measured by the scale, which is what cluster analysis does.

  15. It is acknowledged that one examiner suggested to clarify this point.

  16. Russell (2002, p. 1632) and Landau and Everitt (2003, p. 296) clarify that principal axis analysis and principal component analysis are different, and the former is the recommended factor analysis. Principal component analysis extracts factors based on the total variance of a variable, while principal axis factoring based on its common variance (communality). In terms of results, although similar results have been reported using the two methods when communalities were relatively very high, principal axis factoring is recommended as the factor analysis.

  17. Cronbach’s alpha is the most popular measure for the factor reliability, and shows how much of the variance in the factor score is due to the true variance (not error) (DeVellis 2003, p. 29). Another possible measure for reliability is to consider the item-to-total correlation and the inter-item correlation (Hair et al. 2005, p. 137).

  18. Eigenvalue is a sum of squared factor loadings of items that are explained by a particular factor, and represents the size of variance explained by this factor. The eigenvalue plot shows eigenvalues of corresponding factors. As the first factor is the one with the highest eigenvalue, the second with the second highest, and so on, factors whose eigenvalues belong to the vertical line of the ‘L’ curve explain most of the variance in the item scores. The eigenvalue close to one is normally the lowest accepted.

  19. The number of respondents could be decided either by item-to-response ratios (4 to 10 times of the number of items) (Gorsuch 1983) or by considering inter-item communalities (Guadagnoli and Velicer 1988; MacCallum et al. 1999). Less than 100 respondents are sufficient for a high communality level (larger than 0.6), while 100–200 respondents are necessary for a lower level (about 0.5). In general, 150 to 200 respondents are recommended for a scale development in the literature (Hinkin 1995, p. 973; Russell 2002, p. 1632).

  20. More than 60% of the respondents were male, and they were either full time student (49%) or full time employed (44%), highly educated (70% with tertiary education), relatively young (44% less than 24 and 75% less than 34) and single (66%).

  21. As a result, two types of factor analysis were applied in the development process: exploratory and confirmatory (Hair et al. 2005, p. 105). They were applied for the preliminary test and the national surveys, respectively.

  22. Although, there was no such recommendation in the literature to test the influence of different directions of statements, it was necessary to do so to find a suitable formality to deliver a set of statements for a factor.

  23. Respondents showed different visiting experience for the two sites. For OPH, about 43% of the respondents have never visited it. On the other hand, the majority respondents (61%) have never visited the NMA.

  24. It should be noted that the incomplete implementation of the tailored design method (Dillman 2000) might have caused the low response rates.

  25. As the factor analysis used ‘listwise’ deletion for missing values, the number of respondents (‘count’ in Tables 4 and 5) is different between items

  26. This recommendation was made by a participant in the 14th International Conference on Cultural Economics (6–9 July 2006), Association for Cultural Economics International, Vienna, Austria. A score in a five-point scale (one to five) is not stable when its item is restated (for example, a score two of a negative item is not the same as a score four of its positive counterpart). As shown later in the case studies, a negative and a positive statement of the same item are likely to result in a different score in factor analyses. In terms of presentation of items, as respondents are given mixed statements of the factors, they are to face a positive and a negative statement in turn. This is to avoid agreement bias as recommended by DeVellis (1991). For example, see Table 9.

  27. Reliability coefficient of F3 (Cultural Loss) for the NMA version was 0.69. However, it was believed that inconsistent tones of the statements (anti vs. procultural) might have caused a lower reliability coefficient.

  28. In split-half reliability examinations, researchers divide a set of items into two, and collect data using them to examine reliability by correlating scores of the subsets. See DeVellis (2003) for more detail.

  29. The relationship between attitudinal variables and sociodemographic and economic variables has been disputed in the literature. In general, sociodemographic characteristics can be compared between different preference groups. In particular, however, people with the same characteristics in these variables might be different in their degrees of preferences for the environment or the culture.

  30. This assumption might be weak but reasonable when a large, general and randomly selected sample is considered.

  31. For example, see Morey et al. (2006), Scarpa and Menzel (2005) and Winter et al. (2003).

  32. For example, see Ben-Akiva et al. (1999), and Vredin Johansson et al. (2006).

  33. If F3 is not a valid factor for the CW scale, its inclusion in cluster analyses might bias resulting clusters because of its influence on the clustering process. Consequently, F3 was excluded from the cluster analysis.

  34. Cluster analysis is the most widely used segmentation method.

  35. Summated mean scores were used. This approach is advantageous when researchers want to compare results between different samples and to use centroids in cluster analysis (Hair et al. 2005, p. 140).

  36. The cluster analysis produced four- and seven-cluster solutions, and both of them are significant in a criterion validity test, using Statement A and F tests, at the 0.05 level (Hair et al. 2005, p. 618). However, the four-cluster solution was chosen because of a practical comparability issue between clusters.

  37. Some items changed their memberships between F1 and F2 in the OPH and NMA versions. Therefore, their ultimate memberships should be determined in more empirical studies using the final version. Although, F3 (Cultural Loss) showed a bad performance in the validity tests, they are included for further considerations in other follow-up studies.

  38. During the preliminary test, respondents were asked whether these items were confusing, to find that respondents had no difficulty in understanding and answering them.

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Acknowledgements

The empirical data used in this paper are from research funded by the Sustainable Tourism Cooperative Research Centre (CRC), established by the Australian Commonwealth Government, and by the National Capital Attractions Association. The support of Dr. Brent Ritchie, Director of the CRC at the University of Canberra, is gratefully acknowledged. Deep appreciation also goes to Professor Adam Finn and two referees for their invaluable inputs.

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  1. Environmental Management and Development Programme, Crawford School of Economics and Government, Australian National University, Canberra, ACT, 0200, Australia

    Andy S. Choi & Jeff Bennett

  2. Communication and Media Policy Institute, University of Canberra, Building 1, Canberra, ACT, 2601, Australia

    Franco Papandrea

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Correspondence to Andy S. Choi.

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Choi, A.S., Papandrea, F. & Bennett, J. Assessing cultural values: developing an attitudinal scale. J Cult Econ 31, 311–335 (2007). https://doi.org/10.1007/s10824-007-9045-8

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