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background: none; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0.layout-1 { grid-template-columns: 60px minmax(0,1fr); grid-column-gap: 8px; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0.layout-2 { grid-template-columns: minmax(0,1fr) 60px; grid-column-gap: 8px; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0.layout-2 .text-icon { margin-left: 0; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0.layout-3 { grid-template-columns: 60px minmax(0,1fr); } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0.layout-3 .dt-text-title { margin-left: 0px; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0 .dt-text-title, .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0 .dt-text-title a { color: #1f365c; background: none; font-size: 16px; line-height: 27px; font-weight: bold; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0 .dt-text-title { margin-bottom: 0px; } .icon-with-text-shortcode.icon-text-id-e0dec62241846163792d7e530eb2d1d0 .dt-text-desc { color: #1f365c; background: none; margin-bottom: 0px; font-size: 17px; line-height: 23px; font-weight: bold; } Riedl, J., Wengler, S., Czaban, M., & Steudtel, S. (2023). Sexism in advertisements – a cross-cultural analysis. Marketing Science & Inspirations, 18(3), 2–16. https://doi.org/10.46286/msi.2023.18.3.1 This paper examines the evaluation of advertising with particular reference to possible sexism and the differences in response among individuals of different religious affiliation, religiosity, and origin. Religion, religiosity and migration background make small explanatory contributions to the evaluation of advertising in four relevant dimensions, but in the overall picture prove to be less significant than sociodemographic and psychographic criteria beyond religion and origin. 1 Introduction In 2016, Germany experienced a hefty public discussion on sexism in advertisements. Despite an enormous public outcry and the federal governments’ strong determination, the political parties were unable to pass a new federal law due to rather controversial positions. Instead, selected regulations were enacted at the state and local level to prevent any display of content deemed sexually discriminatory on public advertising spaces. Along the discussion, all stakeholders argued rather emotionally, largely based on their political values and believes. Real data on the population’s perceptions, experiences and expectations on sexism in advertisement were missing. Accordingly, we decided to bring more light into the emotional debate and provide empirical data on which contents are really perceived sexually discriminatory by the German population and which not. 2 Conceptual framework Discrimination occurs almost everywhere and can refer to a wide variety of personal characteristics like sex, age, origin etc. Sexism includes all gender-related forms of discrimination. For reasons of simplification, in the present studies sexism was examined exclusively in connection with contents perceived as misogynistic. Besides analyzing the interviewees’ general attitude concerning advertisements, the authors also checked for the big five personality traits (Costa and McCrae 1985; McCrae and John 1992; Rammstedt et al. 2004) to get more comprehensive insights on the participants’ media consumption as well as their assessment of specific advertisements. 3 Empirical studies 3.1 Research approach and design A partially standardized approach was used to collect the relevant constructs. Open-ended questions were used to ascertain whether and, if so, what the respondents had personally been pleased or annoyed about in relation to advertising in the past two years (using the Critical Incident Technique based on Flanagan (1954)). These questions were asked before the issue of sexist or discriminatory advertising had even been raised, so it is possible to read from the sum and distribution of responses the extent to which the population itself has the issue of sexism on its mind. The other questions were quantitative in nature. In the form of rating scales, respondents had to answer the following blocks of items on the following sets of questions: • extent of perceived annoyance by advertising in the various media (TV, print, brochures, posters, radio, email, internet advertising), • self-rating on personality type (Big Five in the version of Rammstedt et al. (2004)), • 11-item battery for the assessment of advertising, which was compiled on the basis of explorative preliminary research, • media consumption (hours/minutes daily) in relation to TV, radio and digital media, and • 5 items for assessment (aesthetics, fit to advertised product, degree of erotic charge, degree of associated sexism, desire to ban such advertising). With the exception of the recording of temporal media consumption, a scale with expressions from 0 (not at all, does not apply, etc.) to 10 (completely, completely applies, etc.) was given for all ratings. The authors decided for this scaling to overcome the arbitrariness of a scale selection, as it is the case with the widely used 5-7 or 9-point Likert scales. Since the respondents are used to thinking in the decimal system from everyday life, no cognitive effort is required to transfer a personal assessment into a numerical response value. This eliminates a possible source of error in the survey, and at the same time there are no refusals to answer due to „cumbersome“ scale specifications. Scaling in the decimal system, in which only the two extremes are labeled and which at the same time dispenses with any further verbalization of gradations, is becoming more and more widespread in empirical research in many disciplines (Osman et al. 1994; Miles et al. 2011), whereby it seems to be irrelevant for the respondents ability to provide information whether a scaling from 0 to 10 or 0 to 100 is used (e.g., Lorig et al. 1989), since the subjects mostly answer in increments of ten on the 100 scale. Four further questions served as a summary assessment by the survey participants. The survey concluded with 8 questions about the person (age, gender, school-leaving qualification, location of residence, satisfaction with current living situation, migration background, self-assessment of religiosity, religious affiliation). 3.2 Method and sampling The authors conducted an empirical study in two waves, the first one ending in Q1 2017 and the second one five years later in Q1 2022. In both waves the same questionnaire was used, but the empirical methodologies were different due to the corona pandemic. While the first wave was conducted as a face-to-face study with 1460 cases, the second wave was an online survey resulting in 900 cases. In both cases, the survey was conducted as a convenience sample, leaving it up to the interviewers to select the respondents, but with quota specifications, thus ensuring that the resulting sample corresponds to the average of the German population in terms of the three characteristics age, gender and level of education. Further quota characteristics were not specified due to the associated increasingly complex search for participants, but it was determined that a maximum of one person per family could be interviewed. 3.3 Comparison of the two waves of the study Comparing the two survey dates reveals similarities, but also significant differences in the sample: #supsystic-table-148_wrapper table { border-collapse: collapse; }#supsystic-table-148_wrapper table.stripe tbody tr.even { }#supsystic-table-148_wrapper table.stripe.order-column tbody tr > .sorting_1 { }#supsystic-table-148_wrapper table.hover tbody tr:hover { }#supsystic-table-148_wrapper table.stripe.order-column tbody tr.even > .sorting_1 { }#supsystic-table-148_wrapper table.order-column tbody tr > .sorting_1 { }#supsystic-table-148_wrapper table.hover.order-column tbody tr:hover > .sorting_1 { }#supsystic-table-148_wrapper tbody td { background-color: inherit; } year (wave) of survey 2017 (n= 1449) 2022 (n= 900) crosstabs, percentage contingency coefficient p Sex female 52.0% 49.8% .062 .011 male 48.0% 49.7% diverse .0% .6% Migration background yes 13.4% 9.9% .052 .011 no 86.6% 90.1% mean, variance analysis F (Anova) p Age (Years) 39.0 42.6 22.459 .001 Index Education (1 = secondary school - 5 = university) 2.7 2.6 4.477 0.034 Satisfaction with the situation in life (0=min - 10=max.) 7.8 7.4 33.355 0.001 Table 1: Comparison of the 2017 and 2022 samples Source: Authors As Table 1 shows, the proportion of genders is largely identical in both studies, and the same applies to the index of educational qualifications surveyed. In contrast, there are slightly fewer respondents with an immigrant background in the 2022 study; at the same time, the average age is a considerable 3.5 years higher. Life satisfaction is somewhat lower in 2022 than in 2017. The first reason for the differences found here is the change in method. Persons with a migration background have a somewhat lower propensity to participate in surveys. While this can be partially compensated for in the face-to-face survey by friendly follow-up from the personal interviewers, this possibility is missing in the online survey, so that the resulting proportion of people in the sample is lower. Another influencing factor is the covid pandemic existing in 2022. This may partly explain the overall decline in life satisfaction. Parallel unaided surveys also show that younger individuals, often still in education, devoted so much time to online media during the pandemic that any further survey focused on the online sector met with a reduced willingness to participate. In contrast, the willingness of older target groups to participate remained unchanged, so that the result is an increased average age of the sample, even though the quota characteristics specified for contacting potential respondents were identical to those of 2017. In summary, in terms of individual time spent on the survey, gender distribution, and education level, there are good matches between the samples from the two survey dates. There are significant, but nevertheless explainable, differences in the proportion of people with an immigrant background, the average age and satisfaction with the living situation. Overall, under these conditions, the summary and comparative evaluation of the collected data with respect to the central constructs such as attitudes toward advertising, sexist advertising, the Big Five, and others appear justifiable. It will be necessary to clarify in a follow-up survey planned as another online study whether the findings presented below prove to be stable. 3.4 Results and discussion 3.4.1 Personality dimensions An exploratory factor analysis (principal component analysis with varimax rotation) yields the expected five personality dimensions. KMO is 0.534, and the communalities of all 10 items range from 0.572 to 0.757. #supsystic-table-149_wrapper table { border-collapse: collapse; }#supsystic-table-149_wrapper table.stripe tbody tr.even { }#supsystic-table-149_wrapper table.stripe.order-column tbody tr > .sorting_1 { }#supsystic-table-149_wrapper table.hover tbody tr:hover { }#supsystic-table-149_wrapper table.stripe.order-column tbody tr.even > .sorting_1 { }#supsystic-table-149_wrapper table.order-column tbody tr > .sorting_1 { }#supsystic-table-149_wrapper table.hover.order-column tbody tr:hover > .sorting_1 { }#supsystic-table-149_wrapper tbody td { background-color: inherit; } initial eigenvalues rotated sum of squared loadings component total % of variance cum. variance % total % of variance cum. variance % 1 1.856 18.555 18.555 1.577 15.770 15.770 2 1.351 13.511 32.066 1.362 13.623 29.393 3 1.259 12.592 44.658 1.324 13.240 42.634 4 1.187 11.871 56.529 1.303 13.035 55.668 5 1.110 11.103 67.632 1.196 11.963 67.632 6 0.894 8.941 76.572 7 0.682 6.818 83.390 8 0.589 5.891 89.282 9 0.589 5.888 95.170 10 0.483 4.830 100.000 Extraction method: principal component analysis Table 2: Variance explanation by five personality dimensions Source: Authors The five factors explain 67.6 percent of the initial variance of the items (cf. Table 2), and the loading matrix largely corresponds to the simple structure and exactly replicates the assignment of the items, as postulated by Rammstedt et al. (2004). #supsystic-table-150_wrapper table { border-collapse: collapse; }#supsystic-table-150_wrapper table.stripe tbody tr.even { }#supsystic-table-150_wrapper table.stripe.order-column tbody tr > .sorting_1 { }#supsystic-table-150_wrapper table.hover tbody tr:hover { }#supsystic-table-150_wrapper table.stripe.order-column tbody tr.even > .sorting_1 { }#supsystic-table-150_wrapper table.order-column tbody tr > .sorting_1 { }#supsystic-table-150_wrapper table.hover.order-column tbody tr:hover > .sorting_1 { }#supsystic-table-150_wrapper tbody td { background-color: inherit; } component 1 2 3 4 5 [3.01] I am rather reticent and reserved .831 [3.02] I trust others easily, believe in the good in people .771 [3.03] I am easy-going and avoid effort if possible. .800 [3.04] I am relaxed, I do not let myself be upset by stress -.840 [3.05] I have rather little interest in art -.834 [3.06] I am sociable and outgoing -.827 [3.07] I tend to criticize others -.697 [3.08] I make a point of completing tasks thoroughly -.753 [3.09] I get nervous and insecure easily .723 [3.10] I am creative and have an active imagination .809 Method: principle components analysis with varimax rotation Rotation converged in 5 iterations. Table 3: Loading structure of the five personality dimensions Source: Authors For further analysis, sum indices were calculated from the original items in the correct polarity (with two Items each representing one dimension), as they are based on the scaling of the original items and are thus easier to interpret than the standardized factor scores of SPSS. 3.4.2 Basic attitude towards advertising The eleven items assessing advertising were exploratively tested for dimensionality using factor analysis. KMO yields a value of 0.759, the eigenvalue criterion leads to the following four dimensions that together explain 66.15 percent of the baseline variance (see Table 4). initial eigenvalues rotated sum of squared loadings component total % of variance cum. variance % total % of variance cum. variance % 1 3.052 27.748 27.748 2.735 24.867 24.867 2 2.054 18.675 46.422 1.603 14.572 39.439 3 1.142 10.384 56.806 1.540 14.004 53.443 4 1.029 9.352 66.158 1.399 12.715 66.158 5 .795 7.227 73.385 6 .725 6.593 79.978 7 .615 5.595 85.574 8 .496 4.507 90.081 9 .463 4.210 94.291 10 .341 3.104 97.395 11 .287 2.605 100.000 Extraction method: principal component analysis Table 4: Variance explanation by four dimensions of attitude towards advertising Source: Authors The loadings of the items on the four factors largely correspond to the simple structure, and the assignments of the items to the four dimensions are immediately self-explanatory (Table 5). The four dimensions express: • degree of rejection of advertising due to sexist and other content, • positive view of advertising as being useful for purchase decisions, entertaining, etc., • perceived annoyance due to the volume of advertising, and • demand for government regulation of advertising. #supsystic-table-153_wrapper table { border-collapse: collapse; }#supsystic-table-153_wrapper table.stripe tbody tr.even { }#supsystic-table-153_wrapper table.stripe.order-column tbody tr > .sorting_1 { }#supsystic-table-153_wrapper table.hover tbody tr:hover { }#supsystic-table-153_wrapper table.stripe.order-column tbody tr.even > .sorting_1 { }#supsystic-table-153_wrapper table.order-column tbody tr > .sorting_1 { }#supsystic-table-153_wrapper table.hover.order-column tbody tr:hover > .sorting_1 { }#supsystic-table-153_wrapper tbody td { background-color: inherit; } component 1 2 3 4 [4.01] There is too much advertising in the media overall. .865 [4.02] I think advertising is informative. .736 [4.03] Too much misogynistic content is shown in adverts. .871 [4.04] Most of the time I find ads just annoying. -.323 .783 [4.05] Advertising can help you make purchasing decisions. .672 [4.06] There is too much sexist content in advertising. .868 [4.07] The legislature should regulate advertising more closely. .336 .713 [4.08] I reject advertising with erotic content. .593 [4.09] When I think of advertising, I think of funny and entertaining content. .695 [4.10] The state should stay out of advertising issues. -.865 [4.11] Advertising does not sufficiently respect the dignity of women.. .858 Method: principle components analysis with varimax rotation Rotation converged in 5 iterations. Table 5: Loading structure of the four dimensions of attitude towards advertising (loadings below .3 are suppressed) Source: Authors 3.4.3 Descriptive analysis of the relationship between migration background, religiosity and religious affiliation In the descriptive evaluations for the analysis of religious affiliation the three groups „without“ (religion), „Christian“ and „Islamic“ can be included. The 32 people distributed among „other“ religions form a heterogeneous sample, with none of the named religions representing a sufficient sample to conduct sufficiently reliable evaluations in this regard. As Table 6 shows, there is a clear and highly significant (Anova, F= 594.43, p<0.001) correlation between religious affiliation and self-perceived religiosity. [22] religion mean religiosity n std.-dev. None .62 586 1.365 Christian 4.58 1667 2.706 Islamic 5.56 64 2.728 total 3.60 2317 2.999 Table 6: Self-assessment of religiosity according to religious affiliation (scale from 010) Source: Authors The variances of religious affiliation of the groups are inhomogeneous (Levenes test: p<0.001), a posthoc test for groups with inhomogeneous variance (Tamhane T-2) performed subsequently shows that all subgroups differ significantly from each other. It can be stated that members of Islam indicate a higher individual religiosity than members of the Christian religion. In the German national territory, it can be assumed that a large proportion of persons with Islamic religious affiliation have a migration background. Table 7 confirms this relationship; the corresponding contingency coefficient is 0.395 (p<0.001). [19] migration background yes no total [22] religion none n 74.0 512.0 586.0 expected 69.6 516.4 586.0 Christian n 141.0 1526.0 1667.0 expected 197.9 1469.1 1667.0 Islamic n 60.0 4.0 64.0 expected 7.6 56.4 64.0 Total n 275.0 2042.0 2317.0 expected 275.0 2042.0 2317.0 Table 7: Crosstabs: Relationship between religion belonging and migration background Source: Authors Based on the preceding analyses, it can be assumed that there is also a disproportionate correlation between migration background and self-rated religiosity. As table 8 shows, the self-rated religiosity of persons with an immigrant background (on a scale of 0 to 10) is 3.90, which is 9.2 percent higher than that of persons without an immigrant background. However, the difference is not statistically significant (Anova, F=3.077, p=0.080). [19] migration background mean n std.-dev. yes 3.90 283 3.154 no 3.57 2066 2.977 total 3.61 2349 3.000 Table 8: Self-assessment of religiosity (scale from 010) according to existence of migration background Source: Authors Thus, it can be assumed overall that religious belonging has a greater explanatory contribution to the individual´s religiosity than the presence of an immigrant background. A multiple regression analysis with the dummy variables migration background (0=no, 1= yes) and religious affiliation (0=none, 1= Christian, 2= Islamic) as independents and religious affiliation (interval-scaled 010) as a dependent supports this finding: #supsystic-table-157_wrapper table { border-collapse: collapse; }#supsystic-table-157_wrapper table.stripe tbody tr.even { }#supsystic-table-157_wrapper table.stripe.order-column tbody tr > .sorting_1 { }#supsystic-table-157_wrapper table.hover tbody tr:hover { }#supsystic-table-157_wrapper table.stripe.order-column tbody tr.even > .sorting_1 { }#supsystic-table-157_wrapper table.order-column tbody tr > .sorting_1 { }#supsystic-table-157_wrapper table.hover.order-column tbody tr:hover > .sorting_1 { }#supsystic-table-157_wrapper tbody td { background-color: inherit; } non standardized coefficients standardized coefficients regression coefficient B std.-dev. beta T Sig. 1 (constant) .120 .328 .367 .714 [19] migration background .386 .160 .042 2.406 .016 [22] religious affiliation 3.560 .108 .569 32.896
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