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Revising Our Understanding of Political Consumerism

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Varieties of Political Consumerism

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Abstract

In this chapter the author describes the context in which political consumerism has been gaining its impressive relevance as a mode of political participation. In particular, she points to the important differences in and across countries in boycotting and buycotting involvement. The author proposes that these differences are connected to individual and cross-national preferences for how socio-political and economic concerns ought to be tackled. Such preferences vary and give rise to different patterns in labelling schemes and Corporate Social Responsibility (CSR) presence. These, in turn, will affect whether citizens become boycotters and/or buycotters. Thus, to understand political consumerism, the author suggests that one has to consider and understand the ‘varieties of political consumerism’ that exist. With this ambition for the book, the author presents two original sets of quantitative data, based on which the ideas are further investigated throughout the book.

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Notes

  1. 1.

    This line of argument grounds on findings from a former study by the author. Based on survey data collected in 2012 in Germany (N = 336), the study revealed that politically consuming individuals do not necessarily have high trust in societally involved firms. However, they have more confidence in them than in firms which are not involved. Apparently, this surplus of confidence and, with this, familiarity with single firm’s social involvement suffices as an ‘anchor’ for positive political consumerism (i.e. buycotting).

  2. 2.

    The hierarchical clustering relies on ‘Ward’s Method’, as this technique centres on internal cohesion and therewith allows for systematically discerning and clustering countries according to the relative similarity of their patterns (Brosius 1998, 711; Everitt et al. 2010, 76ff.; Zaki and Meira Jr 2014, 366ff.).

  3. 3.

    The specific wording is: ‘There are different ways of trying to improve things in [country] or help prevent things from going wrong. During the last 12 months, have you done any of the following? [B21] Boycotted certain products/[B22] Deliberately bought certain products for political, ethical or environmental reasons’; see the ESS1 Source Main Questionnaire (European Social Survey 2002a) and ESS1 Appendix A3 ed. 6.4 (European Social Survey 2014, 16–17).

  4. 4.

    Unfortunately, country-wise data on retail sales is not available for the years 2002/03, i.e. when the here employed data on political consumerism was collected. Nevertheless, according to the theory it is the national concept of the state which influences political consumers in their behaviour, and thus is a concept that should be expected to remain stable over the years. As a result, the tendency of an individual to prefer labelling schemes or CSR should remain similar. Yet, to minimise the risk of a possible bias, a good alternative is to use data available for 2011. It is the most recent annual data available on Fairtrade sales that at the same time suggests a general economic state across Europe which compares very well with that in 2002/03 (in terms of real GDP growth; see Eurostat 2014). This last consideration is in fact important, seeing that labels in general and the Fairtrade label in particular come with a price premium (see Stolle and Micheletti 2013, 71 and 108ff.; see also Shreck 2002; Stokke et al. 2005), which may avert consumers from buying it in economically difficult times (see Flatters and Willmott 2009, 7). Therefore, using sales data from years in which comparable economic circumstances to the situation in 2002/03 prevailed provides a more reliable alternative for observing the relative tendencies which the different countries exhibit.

  5. 5.

    Sales data refer to the estimated retail sales values of both consumer retail sales (i.e. sales in stores and supermarkets) and the out-of-home sales (in e.g. cafés, restaurants) in sum. For more information, see Fairtrade International 2013, 13.

  6. 6.

    The index covers 51 countries worldwide and relies on multiple sources including Freedom House, Transparency International, the World Bank and the World Economic Forum. The eventual index comprises 7 components, each of which builds upon 3 to 5 different indicators (MacGillivray et al. 2003, 25–6).

  7. 7.

    The later index was published in 2005. The approximated index has been calculated as follows: Country Index 2003 = Country Index 2005 (SLO/HUN) + [sum ((Countryk Index 2003 − mean Index 2003) − (Countryk Index 2005 − mean index 2005 of countries k))/no. of countries k] + [(Country Index 2005 (SLO/HUN)) − (mean Index 2005)], where k = countries with Index for 2003. Further information can be attained in Zadek et al. 2005.

  8. 8.

    After carrying out two pre-tests, the respondents were recruited from the German online panel of the research institute Respondi. It covers 100,000 panelists who together constitute a cross-section of the citizens living in Germany as regards age, gender and education. The final sample should mirror the general distribution of the population in Germany according to three central socio-demographic parameters: age, gender and education. The German Microcensus 2012 (Statistisches Bundesamt 2013) served for establishing corresponding quotas. Maximum limits of numbers of participants per category were set and once these limits were reached, the respective groups of participants were sorted out by forwarding them directly to the questionnaire’s last page. From the panel, 8692 individuals were invited to participate, 4120 started doing so, yet 2558 fell out because of full quotas and another 65 quit before the end. After adjusting the data set for invalid responses (i.e. completing the questionnaire too fast, too little, and/or ticking ‘don’t know’ too often), the entire sample ultimately covers 1350 respondents, which equals a response rate of 15.5 per cent. The sample distributions mirror very well the German population as it was recorded in the Microcensus 2012: the distribution of gender is identical, the age groups and education levels are, overall, very well reflected, too. There are some discrepancies related to education and to age. But for descriptive analyses they could be levelled out with the help of statistical weights, without distorting the sample noticeably.

  9. 9.

    This was done using a Principal Component Analysis (PCA), which reveals one strong component with antithetic loadings for the liberal and the cooperative sides. The result is a scale spread between the two extremes.

  10. 10.

    In fact, the proceeding involves another preceding question. This first question goes along the lines of the procedure adopted in previous studies. It follows a set of four labelling schemes to which respondents are presented and asked for each whether they are familiar to them or not. Afterwards, a question asks the respondents to indicate on a scale from −3 (not at all) to +3 (fully) how much they trust such labelling schemes. Thus, the first question measures the level of trust or distrust the respondent has in a quite specific set (or idea of a set) of labelling schemes. By presenting respondents with a specific set of labelling schemes, they construct a tangible idea of labelling schemes upon which to base their answer. This furnishes results that can be put into relation with former studies and serve to check for the study’s external validity.

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  1. University of Mannheim, Mannheim, Germany

    Carolin V. Zorell

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  1. Carolin V. Zorell
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Zorell, C.V. (2019). Revising Our Understanding of Political Consumerism. In: Varieties of Political Consumerism. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-91047-5_1

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