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Effects of the aesthetic design of icons on app downloads: evidence from an android market

Abstract

With the rapid development of the mobile app market, understanding the determinants of mobile app success has become vital to researchers and mobile app developers. Extant research on mobile applications primarily focused on the numerical and textual attributes of apps. Minimal attention has been provided to how the visual attributes of apps affect the download behavior of users. Among the features of app “appearance”, this study focuses on the effects of app icon on demand. With aesthetic product and interface design theories, we analyze icons from three aspects, namely, color, complexity, and symmetry, through image processing. Using a dataset collected from one of the largest Chinese Android websites, we find that icon appearance influences the download behavior of users. Particularly, apps with icons featuring higher colorfulness, proper complexity, and slight asymmetry lead to more downloads. These findings can help developers design their apps.

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References

  1. Altaboli, A., & Lin, Y. (2011). Objective and subjective measures of visual aesthetics of website interface design: The two sides of the coin, Human-computer interaction. Design and development approaches. Berlin: Springer.

    Google Scholar 

  2. Areni, C. S., & Kim, D. (1994). The influence of in-store lighting on consumers’ examination of merchandise in a wine store. International Journal of Research in Marketing, 11(2), 117–125.

    Article  Google Scholar 

  3. Bauerly, M., & Liu, Y. (2008). Effects of symmetry and number of compositional elements on interface and design aesthetics. International journal of human-computer interaction, 24(3), 275–287.

    Article  Google Scholar 

  4. Böhmer, M., & Krüger, A., (2013). A study on icon arrangement by smartphone users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. April 27–May 2, Paris, France.

  5. Brunel, F. F., & Kumar, R. (2007). Design and the big five: Linking visual product aesthetics to product personality. Advances in Consumer Research, 34, 238–239.

    Google Scholar 

  6. Byrne, M.D., (1993). Using icons to find documents: Simplicity is critical. In Proceedings of the INTERACT’93 and CHI’93 conference on Human factors in computing systems. April 24–29, Amsterdam, Netherlands.

  7. Carare, O. (2012). The impact of bestseller rank on demand: Evidence from the app market. International Economic Review, 53(3), 717–742.

    Article  Google Scholar 

  8. Chen, C.-C. (2015). User recognition and preference of app icon stylization design on the smartphone, Hci international 2015-posters’ extended abstracts. Berlin: Springer.

    Google Scholar 

  9. Choi, J. H., & Lee, H. J. (2012). Facets of simplicity for the smartphone interface: A structural model. International Journal of Human–Computer Studies, 70(2), 129–142.

    Article  Google Scholar 

  10. Creusen, M. E., Veryzer, R. W., & Schoormans, J. P. (2010). Product value importance and consumer preference for visual complexity and symmetry. European Journal of Marketing, 44(9/10), 1437–1452.

    Article  Google Scholar 

  11. Degeratu, A. M., Rangaswamy, A., & Wu, J. (2000). Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of Research in Marketing, 17(1), 55–78.

    Article  Google Scholar 

  12. Deng, L., & Poole, M. S. (2010). Affect in web interfaces: A study of the impacts of web page visual complexity and order. MIS Quarterly, 34(4), 711–730.

    Google Scholar 

  13. eMarketer. (2015). 2 billion consumers worldwide to get smart(phones) by 2016. Retrieved December 5, 2015, from http://www.emarketer.com/Article/2-Billion-Consumers-Worldwide-Smartphones-by-2016/1011694

  14. Fedorovskaya, E. A., de Ridder, H., & Blommaert, F. J. (1997). Chroma variations and perceived quality of color images of natural scenes. Color Research & Application, 22(2), 96–110.

    Article  Google Scholar 

  15. Fenk, A. (1998). Symbols and icons in diagrammatic representation. Pragmatics & Cognition, 6(1–2), 301–334.

    Google Scholar 

  16. Forsythe, A., Sheehy, N., & Sawey, M. (2003). Measuring icon complexity: An automated analysis. Behavior Research Methods, Instruments, & Computers, 35(2), 334–342.

    Article  Google Scholar 

  17. Fortmann-Roe, S. (2013). Effects of hue, saturation, and brightness on color preference in social networks: Gender-based color preference on the social networking site twitter. Color research & application, 38(3), 196–202.

    Article  Google Scholar 

  18. Gatsou, C., Politis, A., & Zevgolis, D. (2012). The importance of mobile interface icons on user interaction. International Journal of Computer Science and Applications, 9(3), 92–107.

    Google Scholar 

  19. Ghose, A., & Han, S. P. (2014). Estimating demand for mobile applications in the new economy. Management Science, 60(6), 1470–1488.

    Article  Google Scholar 

  20. Gittins, D. (1986). Icon-based human-computer interaction. International Journal of Man–Machine Studies, 24(6), 519–543.

    Article  Google Scholar 

  21. Gorn, G. J., Chattopadhyay, A., Yi, T., & Dahl, D. W. (1997). Effects of color as an executional cue in advertising: They’re in the shade. Management Science, 43(10), 1387–1400.

    Article  Google Scholar 

  22. Granger, G. (1955). An experimental study of colour preferences. The Journal of General Psychology, 52(1), 3–20.

    Article  Google Scholar 

  23. Guzmán, F., Iglesias, O., César Machado, J., Vacas-de-Carvalho, L., Costa, P., & Lencastre, P. (2012). Brand mergers: Examining consumers’ responses to name and logo design. Journal of Product & Brand Management, 21(6), 418–427.

    Article  Google Scholar 

  24. Hanushek, E. A., & Jackson, J. E. (2013). Statistical methods for social scientists. New York: Academic Press.

    Google Scholar 

  25. Hartmann, J., Sutcliffe, A., & De Angeli, A., (2007). Investigating attractiveness in web user interfaces. In Proceedings of the SIGCHI conference on Human factors in computing systems. April 30–May 3, San Jose, USA.

  26. Hasler, D., & Suesstrunk, S.E., (2003). Measuring colorfulness in natural images. In Proceedings of SPIE. January 21–24, Santa Clara, California, USA.

  27. Henderson, P. W., & Cote, J. A. (1998). Guidelines for selecting or modifying logos. Journal of marketing, 62(2), 14–30.

    Article  Google Scholar 

  28. Hou, K.-C., & Ho, C.-H., (2013). A preliminary study on aesthetic of apps icon design. In Proceedings of 5th International Congress of International Association of Societies of Design Research. August 26–30, Tokyo, Japan.

  29. Hynes, N. (2009). Colour and meaning in corporate logos: An empirical study. Journal of Brand Management, 16(8), 545–555.

    Article  Google Scholar 

  30. Kaplan, S., & Kaplan, R. (1982). Cognition and environment. New York: Praeger.

    Google Scholar 

  31. Labrecque, L. I., & Milne, G. R. (2011). Exciting red and competent blue: The importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711–727.

    Article  Google Scholar 

  32. Lai, C.-Y., Chen, P.-H., Shih, S.-W., Liu, Y., & Hong, J.-S. (2010). Computational models and experimental investigations of effects of balance and symmetry on the aesthetics of text-overlaid images. International Journal of Human–Computer Studies, 68(1), 41–56.

    Article  Google Scholar 

  33. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.

    Article  Google Scholar 

  34. Lee, G., & Raghu, T. (2014). Determinants of mobile apps’ success: Evidence from the app store market. Journal of Management Information Systems, 31(2), 133–170.

    Article  Google Scholar 

  35. Liang, T.-P., Li, X., Yang, C.-T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236–260.

    Article  Google Scholar 

  36. Lindgaard, G., Dudek, C., Sen, D., Sumegi, L., & Noonan, P. (2011). An exploration of relations between visual appeal, trustworthiness and perceived usability of homepages. ACM Transactions on Computer–Human Interaction, 18(1), 1–30.

    Article  Google Scholar 

  37. Liu, Y. (2006). Word-of-mouth for movies: Its dynamics and impact on box office revenue. Journal of marketing, 70(3), 74–89.

    Article  Google Scholar 

  38. Locher, P. J., & Nodine, C. F. (1987). Symmetry catches the eye, Eye movements: From physiology to cognition. Amsterdam: Elsevier.

    Google Scholar 

  39. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  40. Loy, G., & Eklundh, J.-O. (2006). Detecting symmetry and symmetric constellations of features, Computer vision–eccv 2006. Berlin: Springer.

    Google Scholar 

  41. Mcdougall, S. J., Curry, M. B., & de Bruijn, O. (1999). Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity, and semantic distance for 239 symbols. Behavior Research Methods, Instruments, & Computers, 31(3), 487–519.

    Article  Google Scholar 

  42. McManus, I. C. (2005). Symmetry and asymmetry in aesthetics and the arts. European Review, 13(S2), 157–180.

    Article  Google Scholar 

  43. Moshagen, M., & Thielsch, M. T. (2010). Facets of visual aesthetics. International Journal of Human–Computer Studies, 68(10), 689–709.

    Article  Google Scholar 

  44. Nadkarni, S., & Gupta, R. (2007). A task-based model of perceived website complexity. MIS Quarterly, 31(3), 501–524.

    Google Scholar 

  45. Norman, D. A. (2004). Emotional design: Why we love (or hate) everyday things. New York: Basic books.

    Google Scholar 

  46. Orth, U. R., & Malkewitz, K. (2008). Holistic package design and consumer brand impressions. Journal of marketing, 72(3), 64–81.

    Article  Google Scholar 

  47. Palmer, S. E., Schloss, K. B., & Sammartino, J. (2013). Visual aesthetics and human preference. Annual Review of Psychology, 64, 77–107.

    Article  Google Scholar 

  48. Papachristos, E., Tselios, N., & Avouris, N. (2006). Modeling perceived value of color in web sites, Advances in artificial intelligence. Berlin: Springer.

    Google Scholar 

  49. Pham, L., Pallares-Venegas, E., & Teich, J. E. (2012). Relationships between logo stories, storytelling complexity, and customer loyalty. Academy of Banking Studies Journal, 11(1), 73–92.

    Google Scholar 

  50. Reinecke, K., Yeh, T., Miratrix, L., Mardiko, R., Zhao, Y., Liu, J., & Gajos, K.Z., (2013). Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. April 27–May 2, Paris, France.

  51. Rosen, D. E., & Purinton, E. (2004). Website design: Viewing the web as a cognitive landscape. Journal of Business Research, 57(7), 787–794.

    Article  Google Scholar 

  52. Shu, W., & Lin, C.-S., (2014). Icon design and game app adoption. In Proceedings of 20th Americas Conference on Information Systems. August 7-9, Savannah, Georgia, USA.

  53. Small, J., Melewar, T., Pittard, N., Ewing, M., & Jevons, C. (2007). Aesthetic theory and logo design: Examining consumer response to proportion across cultures. International Marketing Review, 24(4), 457–473.

    Article  Google Scholar 

  54. Smith, A. R. (1978). Color gamut transform pairs. ACM Siggraph Computer Graphics, 12(3), 12–19.

    Article  Google Scholar 

  55. Statista. (2015). Number of available applications in the google play store from december 2009 to november 2015. Retrieved December 5, 2015, from http://money.cnn.com/2014/02/19/technology/social/facebook-whatsapp/

  56. Taba, S., Keivanloo, I., Zou, Y., Ng, J., & Ng, T. (2014). An exploratory study on the relation between user interface complexity and the perceived quality In: Casteleyn, S., Rossi, G., & Winckler, M. (Eds.), Web engineering: Springer International Publishing.

  57. Thorlacius, L. (2007). The role of aesthetics in web design. Nordicom Review, 28(1), 63–76.

    Google Scholar 

  58. Tuch, A. N., Bargas-Avila, J. A., & Opwis, K. (2010). Symmetry and aesthetics in website design: It’s a man’s business. Computers in Human Behavior, 26(6), 1831–1837.

    Article  Google Scholar 

  59. Tuch, A. N., Bargas-Avila, J. A., Opwis, K., & Wilhelm, F. H. (2009). Visual complexity of websites: Effects on users’ experience, physiology, performance, and memory. International Journal of Human–Computer Studies, 67(9), 703–715.

    Article  Google Scholar 

  60. Wilson, P. R. (1985). Euler formulas and geometric modeling. IEEE Computer Graphics and Applications, 5(8), 24–36.

    Article  Google Scholar 

  61. Wu, O., Chen, Y., Li, B., & Hu, W., (2011). Evaluating the visual quality of web pages using a computational aesthetic approach. In Proceedings of the fourth ACM international conference on Web search and data mining. February 9–12, Kowloon, Hong Kong.

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Acknowledgments

This work is partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant CityU 11503115) and City University of Hong Kong (Grant SRG 7004287).

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Correspondence to Mengyue Wang.

Appendix

Appendix

Colorfulness

An image is initially read by a computer as a matrix of pixels with three dimensions in the RGB color space. Supposing that (r, g, b) is the corresponding R, G, and B value matrix for photo I in RGB color space, this algorithm computes the opposite color space. The new coordinates are as follows: rg = r − g and yb = 0.5(r + g) – b.

The colorfulness of photo I can be calculated as follows:

$$Colorfulness(I)\,=\,\alpha_{rgyb}(I)\,+\,0.3\beta_{rgyb}(I)$$

where \(\alpha_{rgyb} (I) = \sqrt {\left[ {\sigma_{rg} (I)} \right]^{2} + \left[ {\sigma_{yb} (I)} \right]^{2} }\) and \(\beta_{rgyb} (I) = \sqrt {\left[ {\mu_{rg} (I)} \right]^{2} + \left[ {\mu_{yb} (I)} \right]^{2} }\).

Brightness

Lightness refers to the “L” value in the HSL color space and can be calculated as follows:

$$L(x) = \frac{{\max (r(x),g(x),b(x)) + \min (r(x),g(x),b(x))}}{2},$$

where r(x), g(x), b(x) represent the R, G, and B values for pixel x in RGB color space. In our research, we used “L” to represent the brightness of an image. We computed the average brightness for all pixels in the image as follows:

$$Btn_{1} = \frac{1}{\left| I \right|}\sum\limits_{x \in I} {L(x)}$$

Saturation

The “S” from HSL color space can be calculated as follows:

$$S(x) = \left\{ \begin{array}{ll} 0,& \quad \hbox{if\;max} (r(x),g(x),b(x)) = 0 \hfill \\ 1 - \frac{{\text{min}} (r(x),g(x),b(x))}{\hbox{max} (r(x),g(x),b(x))}, & \quad { \rm otherwise} \hfill \\ \end{array} \right.$$

Complexity

Figure 4 shows an example of complexity and symmetry. An app icon called Bad Piggies, i.e., in (a), was downloaded from Google Play. We changed the color image into a binary photo, an array of 1 s and 0 s, as shown in (b). An object refers to a set of four connected pixels in this binary photo; c shows a binary image containing only the perimeter pixel of an object. The number of object is 9, and the number of holes represented by the Euler number is −21. Perimeter refers to the sum of non-zero pixels shown in (c), and 7576 non-zero pixels exist in all. The process of computing homogeneity includes iteratively subdividing the gray scale icon into quarters, as shown in (d). A total of 764 blocks exist in all.

Fig. 4
figure 4

Example of complexity and symmetry

Symmetry

The first step is to detect the SIFT descriptor, as shown in (e). The similarity matrix is then computed to find matches, as indicated in (f). Matches at different scales are rejected, and the rotation center for satisfactory matches is determined, as indicated in (g).

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Wang, M., Li, X. Effects of the aesthetic design of icons on app downloads: evidence from an android market. Electron Commer Res 17, 83–102 (2017). https://doi.org/10.1007/s10660-016-9245-4

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Keywords

  • Mobile apps
  • Demand
  • Icon
  • Aesthetics
  • Image processing