Volume 11, Issue 3 (12-2024)
Human Information Interaction 2024, 11(3): 40-70 |
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Tavosi M, Naghshineh N, Zerehsaz M, Mahboub S. Proposing a conceptual framework for the aesthetics of images on the web: Meta-synthesis and ranking components identified. Human Information Interaction 2024; 11 (3)
URL: http://hii.khu.ac.ir/article-1-3147-en.html
URL: http://hii.khu.ac.ir/article-1-3147-en.html
University of Tehran
Abstract: (6610 Views)
Philosophical inquiry into art and beauty within the Western tradition can be traced back to ancient Greece. However, the concept of aesthetic experience gained prominence in the eighteenth century (Stanford Encyclopedia of Philosophy, entry on aesthetic experience, January 20, 2023). According to the Macmillan Dictionary, the term "aesthetics" was coined in Germany during this period and did not achieve acceptance in the English language until the nineteenth century (Macmillan Dictionary). Furthermore, as noted by Boo et al. (2018), the term is derived from the Latin phrase "aisthitiki," which translates to "perception through sensation." The Merriam-Webster Dictionary defines aesthetics as "pleasing appearance." The fundamental meaning of beauty is encapsulated in the notion of "maintaining unity amidst diversity" (Moshagen & Tilsch, 2010, as cited in Venture, 1876).
While beauty is a widely discussed concept in the field of art, it assumes a different significance within human-computer interaction, where it is referred to as "computational aesthetics." In 1994, Jakob Nielsen proposed a set of ten influential factors designed to enhance user interaction systems. Among these factors is the principle of "aesthetic and minimalist design," which highlights the importance of reducing clutter in user interfaces. Understanding the dimensions of aesthetics can assist web designers in creating improved user interfaces. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web. The rapid expansion of web-based technologies has led to an increasing volume of data and information production. Concurrently, the understanding of aesthetics—previously discussed in non-web or offline contexts—has now emerged in online environments utilizing digital tools. Moreover, cognitive sciences have gained particular significance in contemporary research priorities. According to Wong and Borman (2014), websites must not only be usable but also visually appealing. Despite extensive research conducted in usability, psychological aspects related to aesthetics within web environments have received considerably less attention (Wong & Borman, 2014). This study aims to address this gap by focusing on identifying the characteristics of images in web environments from an aesthetic perspective.
Methods and Materials
The present research was conducted using a meta-synthesis method. Documents were retrieved from six databases: IRANDOC, ISC, SID, Google Scholar, Emerald, and Web of Science, utilizing a targeted keyword search and systematic approach that included 1,278 documents. Out of these, 54 documents were selected for inclusion in the study following the PRISMA approach. The importance coefficient of the identified codes was calculated using Shannon's qualitative content analysis method. EndNote software was employed for careful document storage and review. Initially, a foundational conceptual framework comprising 22 aesthetic characteristics for web images was developed based on insights from scholars and established sources. Subsequently, through meta-analysis, this framework was expanded to include 32 aesthetic codes applicable to images in web environments.
Results and Discussion
The basic conceptual framework was developed based on aesthetic theories from Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, incorporating insights from 15 English-language documents that contained two categories, four concepts, and 22 aesthetic codes. Through meta-synthesis, this framework was enhanced to include two categories, four concepts, and 32 codes. In order of priority, the codes "symmetry or proportion" and "lack of complexity" exhibited the highest Shannon importance coefficient within the category of objective aesthetics and classical aesthetic concepts. Additionally, the codes "appealing color combination" and "moderate complexity—not too low and not too high (similar to Berlyne's theory of stimulus complexity)" were identified as having significant relevance within subjective aesthetics and classical notions of beauty. The category of subjective aesthetics pertains to users' perceptions as subjects interpreting images within web environments; conversely, objective aesthetics relates to the design of uploaded images themselves as objects within this interaction. Classical aesthetic concepts address elements that are independent of meaning and appearance; in contrast, semantic aesthetics focuses on aspects related to meaning and associations rather than mere appearances.
Conclusion
It is essential to consider both subjective and objective aesthetic codes equally. This research underscores the importance of scientific collaboration between experts in computer science and humanities to enhance understanding of aesthetics and improve human-computer interactions. The proposed conceptual framework represents a pioneering effort at both national (Iran) and international levels. It is recommended that developers of the Python library "Athec" utilize this conceptual framework to more accurately define the aesthetic characteristics of digital images within web environments by incorporating a broader range of aesthetic codes into their library programming.
While beauty is a widely discussed concept in the field of art, it assumes a different significance within human-computer interaction, where it is referred to as "computational aesthetics." In 1994, Jakob Nielsen proposed a set of ten influential factors designed to enhance user interaction systems. Among these factors is the principle of "aesthetic and minimalist design," which highlights the importance of reducing clutter in user interfaces. Understanding the dimensions of aesthetics can assist web designers in creating improved user interfaces. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web. The rapid expansion of web-based technologies has led to an increasing volume of data and information production. Concurrently, the understanding of aesthetics—previously discussed in non-web or offline contexts—has now emerged in online environments utilizing digital tools. Moreover, cognitive sciences have gained particular significance in contemporary research priorities. According to Wong and Borman (2014), websites must not only be usable but also visually appealing. Despite extensive research conducted in usability, psychological aspects related to aesthetics within web environments have received considerably less attention (Wong & Borman, 2014). This study aims to address this gap by focusing on identifying the characteristics of images in web environments from an aesthetic perspective.
Methods and Materials
The present research was conducted using a meta-synthesis method. Documents were retrieved from six databases: IRANDOC, ISC, SID, Google Scholar, Emerald, and Web of Science, utilizing a targeted keyword search and systematic approach that included 1,278 documents. Out of these, 54 documents were selected for inclusion in the study following the PRISMA approach. The importance coefficient of the identified codes was calculated using Shannon's qualitative content analysis method. EndNote software was employed for careful document storage and review. Initially, a foundational conceptual framework comprising 22 aesthetic characteristics for web images was developed based on insights from scholars and established sources. Subsequently, through meta-analysis, this framework was expanded to include 32 aesthetic codes applicable to images in web environments.
Results and Discussion
The basic conceptual framework was developed based on aesthetic theories from Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, incorporating insights from 15 English-language documents that contained two categories, four concepts, and 22 aesthetic codes. Through meta-synthesis, this framework was enhanced to include two categories, four concepts, and 32 codes. In order of priority, the codes "symmetry or proportion" and "lack of complexity" exhibited the highest Shannon importance coefficient within the category of objective aesthetics and classical aesthetic concepts. Additionally, the codes "appealing color combination" and "moderate complexity—not too low and not too high (similar to Berlyne's theory of stimulus complexity)" were identified as having significant relevance within subjective aesthetics and classical notions of beauty. The category of subjective aesthetics pertains to users' perceptions as subjects interpreting images within web environments; conversely, objective aesthetics relates to the design of uploaded images themselves as objects within this interaction. Classical aesthetic concepts address elements that are independent of meaning and appearance; in contrast, semantic aesthetics focuses on aspects related to meaning and associations rather than mere appearances.
Conclusion
It is essential to consider both subjective and objective aesthetic codes equally. This research underscores the importance of scientific collaboration between experts in computer science and humanities to enhance understanding of aesthetics and improve human-computer interactions. The proposed conceptual framework represents a pioneering effort at both national (Iran) and international levels. It is recommended that developers of the Python library "Athec" utilize this conceptual framework to more accurately define the aesthetic characteristics of digital images within web environments by incorporating a broader range of aesthetic codes into their library programming.
Keywords: Computational Aesthetics, Human-Computer Interaction, Visual Complexity, Aesthetic Perception, Computer Vision
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