User:Eurodog/sandbox227

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An annoyance factor (aka annoyance effect) in advertising and brand management is either (i) a strategic aspect of an advertisement intended to help a message stick in the minds of consumers or (ii) a measured or inferred aspect used to evaluate the effectiveness of an advertisement or (iii) both. Traditional annoyance factors typically feature repetitive phrases or an annoying communicator. Annoyance factors – whether nuanced, subtle, or overt – might involve creating an unpleasant sound, such as a bad jingle – one that consumers can't get out of their minds. Advertisers generally try to appeal to positive emotions – and, using various gradations of annoyance can achieve that. Nonetheless, the goal is to etch a message in the minds of consumers without turning them off. Capital outlay for the use of it can be relatively expensive for major consumer product companies and the research behind it, highly sophisticated.

Applications[edit]

Annoyance factors – visual or auditory or both – can be in any combination of loudness, repetition, length ... in television,[1] on the internet (e.g., pop-up,[2] floating, and banner ads – see alarm fatigue), on the radio, print media, mobile devices, packaging, product displays, mail, false ads, and even pricing (e.g., tobacco – see Laffer curve).[3]

The annoyance factors of some ad campaigns are subtle and increase over time. For instance, Folgers Coffee, on TV for years, ran high frequency ads featuring Mrs. Olson, portrayed by actress Virginia Christine (1920–1996). Some consumers initially perceived her messages as pleasant, but over time, annoying – as some research found. Yet, the annoyance technique was a successful brand-strengthening strategy. And, in this case (a high-frequency campaign), the target market could be multilayered. Consumers who infrequently watch TV will likely see the message at least once – while those who binge-watch, even if annoyed, might still choose Folgers, if for no other reason, because the name is etched in their heads.

There are, however, annoyance thresholds – thresholds that advertisers carefully monitor. Crossing them can adversely affect brands and consumer behavior.[4]

Selected analysis[edit]

Factor analysis of perceptual items and attitude measures in online advertising:

Academicians Kelli S. Burns, PhD, and Richard J. Lutz, PhD, surveyed online users in 2002. In doing so, they chose six online ad formats: (i) banners, (ii) pop-ups, (iii) floating ads, (iv) skyscrapers, (v) large rectangles, and (vi) interstitials.

To develop perceptual factors, ratings of the 15 perceptual items for all six on-line ad formats were run through principal components analysis with varimax rotation. The authors inferred – from a scree plot – a possible three-factor solution. The first three factors accounted for over 68% of the total variance. The remaining 12 reflected no more than 5% of the variance, each. The below table, produced by Burns and Lutz, shows the loadings of the factors generated through principal component extraction and varimax rotation.[5]

Table 1[edit]

Table 1
Summary of Factor Loadings for the Rotated Three-Factor Solution for Perceptual Items[5]
   Perception Factor scores
Factor I
entertainment 		Factor II
annoyance 		Factor III
information
1)      Innovative 0.81 (0.01) 0.07
2)      Different 0.75 (0.01) (0.06)
3)      Entertaining 0.75 (0.27) 0.14
4)      Sophisticated 0.72 (0.07) 0.22
5)      Amusing 0.71 (0.34) 0.11
6)      Elaborate 0.70 0.24 0.17
7)      Eye-catching 0.70 0.24 0.17
8)      Attractive 0.64 (0.37) 0.32
9)      Disruptive (0.04) 0.89 (0.21)
10)      Intrusive 0.06 0.87 (0.14)
11)      Overbearing (0.03) 0.86 (0.23)
12)      Annoying (0.12) 0.85 (0.25)
13)      Informative 0.08 (0.23) 0.84
14)      Useful 0.29 (0.37) 0.74
15)      Beneficial 0.35 (0.45) 0.65
          (2002)     Green boldface data indicate items loading on each factor

Table 2[edit]

Table 2
Mean Scores for Perceptual Factor Indices (with Coefficient ∝) for Each On-line Ad Format[5]
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
Attitude n = 102 n = 102 n = 97 n = 117 n = 76 n = 81
Entertainment
     M 2.87 2.94 3.20 3.19 4.01 3.51
     SD 0.59 0.81 0.60 0.68 0.56 0.72
     ∝ 0.79 0.89 0.83 0.87 0.78 0.89
​
Annoyance
     M 2.95 4.19 2.23 2.96 3.69 3.18
     SD 0.95 0.90 0.70 0.96 1.00 1.09
     ∝ 0.85 0.84 0.88 0.90 0.94 0.95
​
Information
     M 3.11 2.58 3.59 3.47 2.87 3.17
     SD 0.89 0.85 0.72 0.72 0.88 0.79
     ∝ 0.82 0.81 0.85 0.81 0.79 0.78
          (2002)      Note: 1 = strongly disagree; 5 = strongly agree

Table 3[edit]

Table 3
Mean Scores for Attitude Indices (with ∝) for Each On-line Ad Format[5]
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
Attitude n = 102 n = 102 n = 97 n = 117 n = 76 n = 81
Aad      
     M 3.42 2.86 4.05 3.90 3.40 3.53
     SD 0.88 1.19 0.87 0.83 1.35 1.19
     ∝ 0.86 0.94 0.94 0.89 0.96 0.95
​
Aformat
     M 3.25 1.85 3.83 3.36 3.07 3.29
     SD 1.00 1.07 0.82 0.92 1.41 1.06
     ∝ 0.92 0.95 0.92 0.92 0.97 0.95
          (2002)

Table 4[edit]

Table 4
Regression Results (β Weights and R2) for Predictors of Aformat for All Formats[5]
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
     Entertainment factor 0.23* 0.23* 0.42** 0.37** 0.30** 0.33**
     Annoyance factor (0.42)** (0.42)** (0.42)** (0.47)** (0.60)** (0.51)**
     Information factor 0.24* 0.24* 0.11 0.12 0.12 0.31**
     Adjusted R2 0.44** 0.44** 0.53** 0.63** 0.69** 0.80**
     *p < 0.05
     **p < 0.01
          (2002)

Table 5[edit]

Table 5
Regression Results (β Weights and R2) for Aformat for All Formats[5]
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
     Attitude towards format 0.39** 0.56** 0.68** 0.75** 0.81** 0.86**
     Adjusted R2 0.14** 0.30** 0.45** 0.56** 0.66** 0.74**
     *p < 0.05
     **p < 0.01
          (2002)

Table 6[edit]

Table 6
Regression Results (β Weights and R2) for Predictors of Aformat for All Formats[5]
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
w/o with w/o with w/o with w/o with w/o with w/o with
     Entertainment factor 0.31** 0.27* 0.53** 0.46** 0.36** 0.14 0.27** 0.04 0.28** 0.09 0.32** 0.08
     Annoyance factor (0.07) 0.00 (0.16) (0.05) (0.18) 0.05 (0.29)** (0.01) (0.39)** (0.02) (0.54)** (0.17)
     Information factor 0.25* 0.21 0.03 (0.03) 0.23* 0.18 0.25** 0.18* 0.26* 0.19* 0.18 (0.04)
     A format — 0.16 — 0.27* — 0.52* — 0.62** — 0.63** — 0.72**
     Adjusted R2 0.25 0.25 0.39 0.42 0.37 0.49 0.44 0.57 0.56 0.68 0.64 0.74
     *p < 0.05
     **p < 0.01
    ***All significant, p < .01
          (2002)

Table 7[edit]

Table 7
Percentage of Respondents Reporting Ad-Related Behaviors and
Attitude-Behavior Correlations for Each On-line Ad Format
Banner Pop-up Skyscraper Large rectangle Floating Interstitial
n = 102 n = 102 n = 97 n = 117 n = 76 n = 81
Behavioral measure 75.5 37.3 55.7 40.2 21.1 24.4
     Percent clickthrough
     Percent visits later 59.8 23.5 42.3 36.8 13.2 19.5
     Clickthrough frequency
         0 35.3 75.5 60.8 67.5 80.3 80.5
         1–2 48.0 21.6 29.9 20.5 15.8 17.1
         3 or more 16.7 2.9 9.3 4.3 3.9 2.4
​
Correlation between A format and ...
     Percent clickthrough 0.46** 0.34** 0.27** 0.14 0.29* 0.27*
     Percent visits later 0.31** 0.26** 0.21* 0.08 0.21 0.33**
     Clickthrough frequency 0.38** 0.42** 0.14 0.16 0.27* 0.23*
     Behavioral index 0.46** 0.42** 0.25* 0.16 0.28* 0.33**
     *p < 0.05
     **p < 0.01
          (2002)

Annoyance factors that influence ad avoidance[edit]

Theater analogy[edit]

Using annoyance as a device in advertising to help a message sink-in can be analogous to jarring devices used in performing arts. For example, in Alvin Ailey's December 6, 2019, premier of Greenwood at City Center in New York, Donald Byrd (born 1949), the choreographer, described his work as "theater of disruption" ... "it disrupts our thinking about things, especially, in particular, things around race." The dance performance addresses a 1921 racist mob attack in Tulsa's segregated Greenwood District, which, at the time, was one of the country's most affluent African American communities, known as "America's Black Wall Street."[6][7] The New York Times, in a review of the production, posited the question, "Can Dance Make a More Just America? Donald Byrd Is Working on It."[8]

See also[edit]

References[edit]

  1. ^ "Advertising: Here's Exactly Why Watching TV Has Gotten So Annoying," by Victor Luckerson, Time, May 12, 2014
  2. ^ Digital Advertising: Theory and Research (3rd ed.), Shelly Rodgers, Esther Thorson, PhD (eds.), Routledge (1999, 2007, 2017), pps. 70 & 116; LCCN 2016-43446; OCLC 1101057869; ISBNs 978-1-138-65442-6 (hardcover), 978-1-138-65445-7 (paperback), 978-1-315-62325-2 (ebook), 978-1-317-22545-4,978-1-317-22546-1, 1-3156-2325-0, 1-3172-2545-7, 1-3172-2546-5
  3. ^ "Intrusiveness of Online Video Advertising and its Effects on Marketing Outcomes" (research-in-progress), by Kendall Goodrich, Shu Schiller, Dennis Galletta, Thirty Second International Conference on Information Systems, Shanghai 2011
  4. ^ "Got Annoyed? Examining the Advertising Effectiveness and Annoyance Dynamics," by Anindya Ghose, Param Vir Singh, Vilma Todri, Thirty-Eighth International Conference on Information Systems, South Korea (2017)
  5. ^ a b c d e f g "The Function of Format: Consumer Responses to Six On-Line Advertising Formats," by Kelli S. Burns, PhD, and Richard J. Lutz, PhD, Journal of Advertising, Vol. 35, No. 1, Spring 2006, pps. 53–63; OCLC 4646618174; ISSN 0091-3367 (accessible via JSTOR at www.jstor.org/stable/20460712)
  6. ^ "Donald Byrd's Theory of Disruption" (Richard Hake interviews Donald Byrd; audio and transcript), WNYC News (New York), December 6, 2019
  7. ^ "Donald Byrd 1949–," by Robert R. Jacobson, encyclopedia.com (retrieved December 12, 2019)
  8. ^ "Can Dance Make a More Just America? Donald Byrd Is Working on It," by Siobhan Burke, New York Times, November 28, 2019

Categories (temp)[edit]

Category:Advertising by type
Category:Business terms
Category:Consumer behaviour
Category:Consumer theory
Category:Types of marketing
Category:Advertising techniques
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