Mapping Visual Affordance Frameworks for Designing Inclusive Smartphone Interfaces for Less-Literate Artisans

Mapping Visual Affordance Frameworks for Designing Inclusive Smartphone Interfaces for Less-Literate Artisans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Visual Affordance Frameworks for Designing Inclusive Smartphone Interfaces for Less-Literate Artisans Rashmi Thakur, Prof. Dr. Bhawana Chanana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558157/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : Low-literate users face challenges with smartphone interfaces due to ambiguous icons, sparse navigation cues, and complex layouts. Although prior HCI research has examined icons, metaphors, and multimodal strategies, few studies have systematically applied a framework or analysed how cue effectiveness varies across task complexity levels. Methods : A mixed-methods approach was employed. Study 1 conducted a systematic review of 20 peer-reviewed articles (2000–2022), mapping visual cues to a structured Visual Affordance Framework across three dimensions: Task/Purpose, Markedness, and Trigger. Study 2 involved field-based observations of 10 rural artisans from Maharashtra, India using WhatsApp for vocational communication. Tasks were categorised into three complexity levels (L1–L3) to assess how cue effectiveness shifted from simple recognition to multi-step tasks. Results : The review showed that most studies offered limited exploration of visual affordances in relation to task complexity and rarely reported performance outcomes. Field observations revealed that WhatsApp’s familiar iconography and spatial layouts supported recognition-level tasks (L1) but provided little scaffolding for navigation (L2) or multi-step tasks (L3), resulting in hesitation, misinterpretation, and abandonment. Conclusion : This study advances HCI by integrating a Visual Affordance Framework with task complexity analysis to identify design gaps in mobile interfaces. It demonstrates that recognition cues alone are insufficient and recommends applying structured affordance-based cueing strategies to guide inclusive smartphone application design. Structured cueing strategies can enhance navigation, enable progression across task complexity levels, and foster digital inclusion for less-literate users. Low-literate users Visual affordances Smartphone user interfaces HCI4D Dillman Framework Task complexity Figures Figure 1 Figure 2 1. Introduction Smartphone access has expanded rapidly across rural India, enabling new forms of digital participation among less-literate populations. Among these, artisans defined by UNESCO as individuals engaged in culturally rooted, skill-based handicrafts represent a critical group whose vocational practices increasingly rely on mobile communication tools[ 1 ]​. Prior research in Human-Computer Interaction (HCI) has shown that less-literate users benefit from visual cues such as icons, spatial grouping, and color-coded elements[ 2 ],[ 3 ]. These strategies reduce cognitive load and support recognition-based interaction. However, most studies examine these elements in isolation, focusing primarily on recognition-level interactions (e.g., tapping an icon) without systematically analysing how such cues support users across different levels of task complexity, low (L1), moderate (L2), and high (L3). To address these gaps, this study adopts Dillman et al.’s Visual Interaction Cues Framework[ 4 ], originally developed for gaming contexts, as a structured lens to examine how visual cues function in mobile UIs for low-literate users. The framework classifies cues along three dimensions: Task/Purpose, Markedness, and Trigger. By combining this framework with an analysis of task complexity levels (L1–L3), the research investigates not only whether users recognise visual cues, but also whether such cues enable them to perform tasks of varying complexity in real-world digital environments. This was followed by a field study of WhatsApp use among rural artisans, providing a real-world context to evaluate these interactions. This paper makes three contributions: Framework extension : Systematically mapping visual affordance elements (icons, metaphors, overlays, colours) to Dillman et al.’s Visual Interaction Cues Framework, thereby extending its application beyond gaming into mobile UI design for low-literate users. Task complexity lens : Introducing task complexity levels (L1–L3) as an analytical dimension to show how different cue support or fail to support simple task recognition to moderate navigation and complex multi-step tasks. Design guidelines for digital inclusion : Integrating evidence from a systematic review (20 studies) and field-based observations with rural artisans using WhatsApp and proposing a set of layered cueing strategies (emphasised, integrated, persistent, agent-triggered) that combine visual affordances with task complexity for inclusive mobile interfaces. Accordingly, this study is guided by the following research questions : RQ1 : What types of visual affordance elements have been studied and implemented in digital interfaces for less-literate users? RQ2 : How can these visual affordance elements be systematically mapped to the three dimensions of Dillman’s Visual Interaction Cues Framework: Task/Purpose, Markedness, and Trigger? RQ3 : How do visual cues influence task complexity and align with the mental models of less-literate users in vocational digital environments? The remainder of this paper is organised as follows: Section 2 reviews related work, Section 3 outlines the methodology, Section 4 presents the results, Section 5 discusses the findings, and Section 6 concludes with directions for future research. 2. Related Work This section reviews key concepts pertinent to less-literate individuals and artisans, including mental models, task complexity, visual affordances in interface design, and the Visual Interaction Cues Framework. 2.1 Understanding Less-Literate Users and Artisan Contexts Low-literate individuals are defined as those whose education levels fall between Grade IV and Grade VIII in the Indian education system[ 5 ]. They may recognize basic numerals (e.g., 0–3) but face challenges in reading and understanding written material[ 6 ]. However, such challenges are not unique to the Indian context. In South Africa, Emmanuel and Muyingi [ 7 ] encountered similar difficulties while designing mobile UIs tailored for low-literate users. Similarly, Matyila [ 8 ] argued that text-heavy mobile services are inaccessible to low-literacy groups in South Africa. Medhi et al. [ 2 ] and Thies et al.[ 9 ] also recommended the use of graphics over text-based designs. Chauhan et al. [ 10 ] reinforced these findings, demonstrating a clear preference for multimedia and graphical interfaces among low-literate users. Taken together, these global studies indicate that literacy-related barriers to mobile interaction are widespread, underscoring the need for visual and non-textual design strategies that extend beyond local contexts. For the purposes of this study, the term less-literate users specifically refer to two groups: Low-literate individuals : People who have difficulty reading, writing, and understanding short, simple messages or digital content. Artisans : Skilled craftspeople engaged in traditional arts, trades, or heritage-based manual techniques. Although artisans possess deep domain expertise, many face literacy-related challenges in navigating digital content, following instructions, or using smartphone interfaces[ 11 ]. This research also explores the mental models of these user groups. Mental models refer to users’ internal representations of how a system functions[ 12 ]. These models shape expectations, guide interactions, and influence their ability to perform tasks effectively. Understanding such models is critical for designing interfaces that align with users’ cognitive processes and literacy levels, particularly in domains such as adult learning, fashion technology, and digital skill development for artisans. 2.2 Visual Affordances in Interface Design Visual affordances refer to the perceivable properties of an interface that suggest how elements are meant to be used [ 13 ]. These affordances are often operationalized as visual cues such as icons, colours, animations, spatial layouts, and shapes that guide user behaviour [ 14 ]. Several studies have recommended strategies such as replacing written labels with pictorial icons, maintaining consistent placement of action buttons, and incorporating visual cues or animated feedback[ 15 ]–[ 17 ]. For instance, Islam, Rahman, and Hossain [ 18 ] synthesized sixteen design recommendations for low-literacy contexts, including minimizing text, simplifying interaction steps, and using familiar visuals as icons. However, many of these strategies have been applied inconsistently and often lack alignment with the specific tasks they are intended to support. This mismatch has contributed to ongoing usability gaps for less-literate users. 2.3 Visual Interaction Cues Framework (Adapted from Dillman’s Framework [ 4 ]) This framework was originally developed by Dillman, Mok, Tang, Oehlberg, and Mitchell [ 4 ] in the context of video game environments and augmented reality. It provides a structured model for classifying visual cues that guide user interaction. Originally applied in gaming environments, the framework is highly relevant to mobile UI design particularly for users with low literacy because it systematically categorizes cue function, appearance, and activation. The framework comprises three core dimensions: Dimension 1: Task/Purpose Discover cues help users locate interactive elements within the interface. Look cues direct immediate visual attention to specific features or actions. Go cues support navigation or task progression. Together, these cues guide users from initial recognition to task completion an essential process for less-literate users. Dimension 2: Markedness Subtle cues blend into the background with minimal contrast. Emphasized cues use bold colours, outlines, or size contrasts to denote actionability. Integrated cues are embedded in the interface while maintaining distinctiveness. Overlaid cues (e.g., floating indicators or badges) are superimposed onto interface elements. Markedness is particularly important for less-literate users, as clearly distinguishable cues improve recognition and action. Dimension 3: Trigger User-triggered cues are activated by deliberate user actions (e.g., tapping a button). Context-triggered cues respond to passive user behavior or changes in interface state. Agent-triggered cues originate from system processes or external actors (e.g., other users, backend updates). Persistent cues remain constantly visible, offering continuous visual support. By adapting this framework from gaming to mobile UI contexts, this study evaluates how different cue types can enhance usability for less-literate users, particularly in vocational and educational applications where intuitive engagement is critical. 2.4 Task Complexity and Levels of Interaction Task complexity plays a central role in interface design, particularly for less-literate users who rely more on visual cues than on symbolic language. Drawing from prior HCI4D research, user tasks can be categorized into three levels of interaction complexity [ 19 ], [ 20 ]: Level 1 (Low) : Single-step actions such as tapping icons, identifying call buttons, or viewing notifications. Level 2 (Moderate) : Multi-step navigation such as searching, switching tabs, or recognizing cue transitions (e.g., mic → waveform). Level 3 (High) : Complex Task involving coordination or data entry, such as sharing location, deleting a user from a group chat, adding a person to a video call, or switching between apps to share a document. These levels provide a structured lens to evaluate how cue effectiveness varies with task demands, informing interface design for inclusive digital participation. Previous studies have shown a strong preference for graphic interfaces and layouts over text-heavy designs among less-literate groups [ 21 ],[ 16 ],[ 22 ]. The following subsections examine how visual affordances support such interaction (Section 2.2 ) and present the Visual Interaction Cues Framework as a structured analytic model (Section 2.3 ). 3. Methodology This study employed a mixed-methods design combining (a) a systematic literature review of prior research on visual cues for low-literacy interface design, and (b) a field-based user study with 10 rural artisans in Maharashtra, India. Both phases were guided by Dillman et al.’s Visual Interaction Cues Framework, which classifies cues by Task/Purpose, Markedness, and Trigger . 3.1 Study I: Systematic Literature Review 3.1.1 Search Strategy A systematic literature review (SLR) was conducted to identify studies that implemented graphic or visual elements in digital interfaces designed for people with low literacy skills and evaluated these elements through real-world tasks. The review followed a PRISMA-aligned protocol across four databases: ProQuest, IEEE Xplore, ScienceDirect, and ACM Digital Library . Two coders independently screened studies, achieving strong inter-rater reliability (κ = 0.82). Search strings combined the following terms: “illiterate” OR “low-literate” OR “semi-literate” “user interface” OR “UI design” OR “human-computer interaction” “visual cues” OR “icons” OR “affordance” 3.1.2 Screening Process The screening process was conducted in two stages: 3.1.2.1 Title and Abstract Screening The initial database search yielded 235 records . After removing 42 duplicates , titles and abstracts were screened against predefined inclusion criteria: (a) Peer-reviewed empirical studies published between 2000–2022 (b) Written in English (c) Focused on digital interfaces for low- or semi-literate populations (d) Included graphic or visual elements tested in real-world or applied settings 3.1.2.2 Full-Text Screening Full-text evaluation was then performed on the remaining studies. Exclusion criteria removed review papers, dissertations, incomplete articles, and studies focusing on unrelated user groups (e.g., literate professionals, students) or peripheral technologies (e.g., haptics, AR/VR, text input systems). 3.1.3 Exclusion Breakdown The following categories of articles were excluded: Disability-focused interfaces (n = 11) Literate or unrelated user groups (n = 39) Haptics and immersive AR (n = 11) Program evaluations (n = 3) Dissertations (n = 2) Incomplete/preliminary studies (n = 15) Text/SMS interfaces (n = 16) Voice-only or speech interaction (n = 41) Theoretical or discussion-based papers (n = 57) Literature reviews (n = 10) Narrowly scoped design studies (n = 10) After exclusions, the final sample comprised 20 peer-reviewed articles (see Fig. 1 ). This appendix lists the research papers that were qualitatively reviewed to support the coding and analysis described in Appendix B: Selected Research Papers for Review . 3.1.4 Coding and Analysis Each article was qualitatively coded to extract: (a) Types of visual or graphic elements used, (b) Rationale for their inclusion in interface design (c) Context and nature of real-world task evaluation. To enhance validity, coding was reviewed independently by three postgraduate designers and one UI design expert . Their feedback confirmed the relevance of selected studies, highlighted recurring design strengths, and identified gaps in how visual affordances are applied for people with low literacy skills in practical settings. 3.2 Study II: Field-Based User Research The second phase involved field-based research with 10 participants with low literacy skills and a comparison group of 4 literate participants (3 postgraduate design students, 1 UI expert). 3.2.1 Participants Artisan participants (8 women, 2 men; aged 24–48) were enrolled in a certified vocational training program in tailoring, embroidery, and handloom. Education levels ranged from Grade IV–VIII . All were first-generation smartphone users, with 6–18 months’ experience and no formal digital training. Participants were purposively recruited through local cooperatives. The comparison group of literate participants provided a baseline for interpreting usability patterns. 3.2.2 Data Collection Two complementary methods were employed: (a) Semi-structured interviews Collected information on participants’ education, smartphone use, and perceptions of interface elements. Conducted in Hindi or Marathi, audio-recorded with consent, and transcribed for analysis. (b) Task-based observations Participants performed standardized tasks in WhatsApp , designed to represent low, moderate, and high-complexity interactions. Tasks were counterbalanced across participants to reduce order effects. Each task had a 3-minute time limit, as shown in Table 1 . Sessions were video-recorded, and outcomes coded as correct completion, hesitation, error , or task abandonment . Table 1 Task Set and Complexity Levels: Task Description Complexity Level (a) Sending a voice note Level 1 (Low) (b) Placing and disconnecting a call Level 1 (Low) (c) Locating unread messages Level 2 (Moderate) (d) Identifying group administrators Level 2 (Moderate) (e) Sharing a photo Level 3 (High) (f) Sharing location Level 3 (High) 3.2.3 Data Analysis Data were analysed using a framework-driven coding approach guided by Dillman et al.’s Visual Interaction Cues Framework. Interactions were classified along two analytical frames: Task complexity (low, moderate, high) Visual cue dimensions (Task/Purpose, Markedness, Trigger) Two researchers independently coded the interview transcripts and task observations. Disagreements were discussed until consensus was reached, ensuring analytic reliability. 3.3 Summary of Methodology This research combined a systematic literature review (Study I) with a field study (Study II). Study I mapped visual cues from 20 prior works onto Dillman’s framework to identify design strengths and gaps. Study II observed 10 artisans and 4 literate comparators performing WhatsApp tasks of varying complexity, supported by interviews. Together, these methods link evidence from existing research with real-world user behaviour, enabling a triangulated analysis of how visual cues support—or hinder—people with low literacy skills. 4. Results And Analysis Findings from both studies were analysed using Dillman et al.’s Visual Interaction Cues Framework, which classifies interface elements across three dimensions: Task/Purpose, Markedness, and Trigger. Study I synthesized evidence from 20 peer-reviewed articles, while Study II applied the framework to field observations with 10 artisans using WhatsApp. 4.1 Study I: Systematic Literature Review The review categorized visual affordances from 20 studies, mapping them to Dillman’s framework and aligning them with task complexity levels (L1 = low, L2 = moderate, L3 = high). Coding was independently validated by three postgraduate design students and one UI expert. 4.1.1 Task/Purpose Discover cues were the most prevalent, appearing in 18 studies. Examples included highlighted sections[ 18 ], breadcrumb trails[ 23 ], and red–green arrows[ 24 ]. Look cues were found in 12 studies, such as celebrity photographs [ 16 ], animated overlays, and weather icons[ 25 ]. Go cues were identified in only 7 studies, including directional arrows, ticks/crosses[ 24 ], and “Return Home” icons [ 26 ]. 4.1.2 Markedness Subtle cues (n = 3) included glowing icons and color-coded tabs [ 17 ]. Emphasized cues (10 + studies) included red–green arrows [ 16 ] and highlighted icons[ 27 ], [ 28 ]. Integrated cues (n = 4) used contextual cues (e.g., tractor icons[ 15 ]) or animations of familiar activities[ 29 ]. Overlaid cues (n = 3) like breadcrumb trails [ 16 ] and map overlays [ 23 ],[ 30 ]. 4.1.3 Trigger User-triggered cues (n = 6) involved tapping or selecting icons [ 31 ], [ 32 ]. Context-triggered cues (n = 4) included animated metaphors[ 29 ] and progress bars[ 16 ], though consistency varied. Agent-triggered cues (n = 2) involved facilitators initiating prompts [ 33 ],[ 34 ]. Persistent cues (n = 1) such as breadcrumb trails[ 23 ]. 4.1.4 Cue Distribution by Task Complexity Table 2 summarizes the distribution of visual cues across the 20 reviewed studies, mapped to Dillman’s dimensions and task complexity levels. Most studies focused on cues supporting low-complexity (L1) recognition tasks. Fewer studies addressed cues for moderate (L2) navigation or high-complexity (L3) Task. Table 2 The distribution of visual cues across the 20 reviewed studies, mapped to Dillman’s dimensions and task complexity levels. Cue Dimension Low Complexity (L1) – Recognition Medium Complexity (L2) – Multi-step Navigation High Complexity (L3) – Vocational Task Total Studies Using Cue Task/Purpose Discover Highlighted sections [ 14 ], red–green arrows [ 20 ], breadcrumb trails [ 19 ] Breadcrumb trails [ 19 ] Mockups only (no task execution) [ 14 ] 18 Look Celebrity photographs [ 12 ], animated overlays [ 21 ] Weather icons [ 21 ], alerts [ 21 ] None 12 Go Directional arrows [ 20 ], ticks/crosses [ 20 ], “Return Home” icons [ 22 ] Breadcrumb trails [ 19 ] None 7 Markedness Subtle Glowing icons, color-coded tabs [ 13 ] Not suitable for L2/L3 None 3 Emphasized Red–green arrows [ 12 ], highlighted icons [ 23 ], [ 24 ] Same cues reused Risk of overload in L3 10+ Integrated Contextual icons (e.g., tractor icons) [ 11 ], animations [ 25 ] Suitable with training Risk of confusion in L3 4 Overlaid Breadcrumb trails [ 12 ], map overlays [ 19 ], [ 26 ] Persistent guidance Underutilized in L3 3 Trigger User Tap/select icons [ 27 ], [ 28 ] Assumes prior familiarity Not scalable to L3 6+ Agent Facilitator-initiated prompts [ 29 ], [ 30 ] Contextual guidance Rare in L3 2 Context Animated metaphors [ 25 ], progress bars [ 12 ] Medium-complexity monitoring Inconsistent across sessions 4 Persistent Breadcrumb trails [ 19 ] -- Lacked layered feedback 1 Figure 25 further illustrates this distribution, showing that more than half of the reviewed studies did not explicitly define task complexity, while only a small fraction reported cues supporting medium (L2) or high (L3) tasks. 4.1.5 Summary Across the 20 reviewed studies, visual affordances were most frequently designed to support recognition-level tasks (L1). Discover cues appeared in 18 studies, Look cues in 12, and Go cues in 7. Emphasized cues such as icons and colours were commonly used, whereas persistent or continuity cues were rare. More than half of the studies did not specify task complexity levels in their analysis or design approach. Representative figures are included below, and detailed mappings for all reviewed studies are provided in Appendix A . 4.2 Study II: Field-Based Research Ten artisans were recruited through purposive sampling. All participants had prior exposure to smartphones but limited formal education, aligning with the study’s focus on less-literate user groups. Each participant engaged in a 60–90-minute session combining semi-structured interviews with task-based observations on WhatsApp. Observed interactions were mapped to Dillman’s cue dimensions and categorized according to task complexity levels (L1–L3). Table 3 Analysis of Visual Cue Types in WhatsApp, Mapped to Dillman’s Dimensions and Task Complexity (N = 10 artisans) Cue Type (Dillman Dimension) Examples Task Complexity Supported Observed Outcomes Emphasized Markedness (Color-coded) Green call button (6/10), Red disconnect (5/10), Blue double tick (6/10), Grey tick (5/10), Green unread badge (5/10) L1 – Recognition tasks Frequently identified; enabled recognition of calls and message status. Integrated Cues (Real-world metaphors) Phone (6/10), Video camera (5/10), Camera (5/10), Mute icons (4/10) L1–L2 – Initiating tasks Supported recognition of functions such as calling or taking photos; execution often incomplete. Overlaid + Emphasized Cues Floating action button, Call notification bar, Red missed call icon L1 – Simple action tasks Directed attention toward actions; enabled quick responses. Subtle Markedness + Contextual Triggers Grey ticks, Chat headers, Group admin prompts L2–L3 – Group management, tracking Frequently overlooked or misread; led to hesitation or task abandonment in several cases. Trigger Dimension (User/Agent) Tapping camera/status icons, Error message “Message could not be sent” L1–L2 – Corrective tasks Linked actions to system responses; supported immediate correction in simple cases. 4.2.1 Observed Task Outcomes Low-complexity tasks (L1): Most artisans successfully completed actions such as sending a voice note or placing a call. Recognition was primarily supported by emphasized and integrated cues. Moderate-complexity tasks (L2): Tasks such as locating unread messages or identifying group administrators showed mixed outcomes. Some participants hesitated or misread cues, particularly subtle indicators. High-complexity tasks (L3): Tasks such as sharing a photo or location were less frequently completed. Several participants abandoned these tasks, often after repeated errors or confusion with contextual cues. 5. Discussion The findings from the literature review and the field study converge on a common theme: visual cues in mobile interfaces are effective in drawing the attention of less-literate users, but they rarely provide the support required to complete complex, multi-step tasks. The discussion below synthesises these insights, interprets them through Dillman et al.’s Visual Interaction Cues Framework, and introduces a structured design framework for cueing strategies across different levels of task complexity. 5.1 Patterns in Visual Affordance Design Analysis of prior research and field observations indicates that Discover cues—including bold colours, familiar icons, and highlighted buttons—were the most widely used. These cues reliably enabled recognition of functions such as calls or message status, but their effectiveness generally ended at initial identification. By contrast, Go cues, which guide task progression, and Look cues, which sustain visual attention, appeared far less frequently. A second pattern relates to task complexity. More than half of the reviewed studies did not explicitly define whether their interventions addressed low (L1), moderate (L2), or high (L3) levels of complexity. In the field study, artisans completed most L1 tasks successfully, yet their performance dropped sharply for L2 and L3 tasks. This gap illustrates a mismatch between the design of cues and the cognitive demands of real-world digital activities. Finally, evidence on performance outcomes was limited. Very few studies reported completion times, error rates, or hesitation, and cue types such as agent-triggered or overlaid indicators were especially underexplored. Within WhatsApp, for example, participants frequently misinterpreted contextual elements such as grey ticks or group admin labels. These shortcomings highlight the need for more deliberate, structured approaches to cue design, which are addressed in Section 5.4. 5.2 Mapping to Dillman’s Framework When the findings were mapped onto Dillman et al.’s Visual Interaction Cues Framework, a clear concentration of Discover cues emerged, while Go cues were relatively absent. Emphasised icons and colours succeeded in attracting attention but, when not linked to follow-up actions, sometimes added to cognitive load. The Trigger dimension revealed further gaps. Contextual cues, though present, were often misinterpreted, and persistent cues such as breadcrumb trails or anchored navigation bars were rarely implemented. Taken together, these patterns suggest that most interface designs provide initial entry points into interaction but fail to support continuity or completion. 5.3 Task Complexity and Digital Inclusion The analysis also underscores a structural limitation in current interface design. Most cues are oriented toward recognition-level tasks (L1), enabling users to initiate simple actions. However, tasks requiring navigation (L2) or multi-step Task (L3) frequently resulted in hesitation, misinterpretation, or abandonment. For artisans, higher-level tasks—such as sharing media, locating unread messages, or managing groups—are integral to professional communication and livelihood activities. Without visual cues that scaffold progression across levels of complexity, participation in digital environments remains superficial. True inclusion requires not only access to devices but also interfaces that enable sustained, independent engagement. 5.4 Framework for Visual Cue Design Across Task Complexity Based on the synthesis of findings from both studies, a layered framework was developed to align visual cue types with task complexity levels (L1–L3). The framework draws on established principles of interface design while emphasising visual elements such as colour, shape, overlays, animations, and feedback. Its aim is to ensure that cues do not end at recognition but also support interpretation, navigation, and task completion. Representative illustrations are provided in Appendix A . 5.4.1 Level 1: Low-Complexity Tasks (Recognition / Recall) Task type: Single-step actions such as tapping an icon. Design rules: Subtle visual emphasis to avoid overstimulation. Light, non-urgent highlights (e.g., soft glow or border contrast). Familiar geometric shapes such as circles or crosses. Context-aware prompts that appear at initial load to guide attention. 5.4.2 Level 2: Moderate-Complexity Tasks (Interpretation + Sub-Tasks) Task type: Decision-making with one or two sub-steps. Design rules: Strongly emphasised elements to direct focus toward the next required action. High-contrast colours for critical selections. Directional indicators such as arrows or numbered steps to sequence actions. Immediate visual feedback, for example, colour change or enlargement after selection. User-driven highlighting to confirm choices. 5.4.3 Level 3: High-Complexity Tasks (Multi-Step Task) Task type: Sequential or layered interactions such as media sharing or group management. Design rules: Semi-transparent overlays (30–50% opacity) that guide progression without obscuring the interface. Fixed-position overlaid aids such as progress trackers or step indicators. Combination of contextual cues (appearing alongside relevant elements) and persistent cues (remaining visible throughout the task) to maintain orientation and continuity. 5.4 Limitations This study has several limitations. First, the systematic review was restricted to peer-reviewed studies in English, which may exclude relevant work in other languages or informal design contexts. Second, the field study focused on WhatsApp, a widely used but platform-specific application. Its interface conventions may not generalize to vocational or government apps. Third, the artisan sample was small and context-specific, limiting transferability. Finally, the absence of quantitative performance metrics (e.g., completion times, error rates) restricts claims about the causal impact of specific cue types on performance. 6. Conclusion and Future Work This study examined how visual affordances can support the use of smartphone interfaces by less-literate users, with a particular focus on rural artisans in India. By integrating Dillman et al.’s Visual Interaction Cues Framework with an analysis of task complexity levels (L1–L3), the research offered two key contributions. First, it showed that current interface designs predominantly employ cues that support recognition-level tasks, while providing limited scaffolding for navigation or multi-step task. Second, it introduced a layered framework that aligns cue types with task complexity, providing structured guidance for designing inclusive mobile interfaces. The findings highlight both progress and limitations in the design of digital systems for low-literate users. Recognition-based cues such as bold icons and colour-coded buttons help users initiate interactions, but more advanced tasks remain challenging without cues that provide persistence, clarity, and continuity. For artisans, these gaps restrict the potential of mobile technologies to fully support vocational communication and participation in digital economies. Several directions for future research emerge from this work. Larger and more diverse participant samples would enable the validation of visual cue effectiveness across different user groups. Quantitative metrics such as task completion time, error rates, and hesitation should be systematically captured to complement observational findings. Further testing is also required in contexts beyond WhatsApp—such as government services, adult skilling initiatives like Digital Skill India , or e-commerce platforms—where higher levels of task complexity are common and where inclusive design has direct social and economic implications. In sum, visual affordances remain a promising strategy for enabling digital participation among less-literate populations. Their full potential, however, will only be realised if visual cues are designed not merely to capture attention but to support sustained, independent engagement across tasks of varying complexity. Declarations Funding No funding was received to support this research. Competing Interests The authors declare that they have no competing interests. Ethics Approval This study was conducted in accordance with ethical research practices. As the institution does not have a formal ethics committee, the study followed general ethical guidelines for human-centered research, including voluntary participation, privacy protection, and transparency of purpose. Consent to Participate Verbal informed consent was obtained from all participants. The purpose of the study, procedures, and their right to withdraw at any time were explained in their local language before participation. Consent for Publication Not applicable, as no identifiable personal data or images are published in this article. Data Availability De-identified interview excerpts and coded task logs are available from the corresponding author upon reasonable request. Audio and video recordings cannot be shared to protect participant privacy. Code or Materials Availability Not applicable. AI/LLM use statement During manuscript preparation, OpenAI’s ChatGPT was used to assist with initial drafting of select sections. All AI-generated content was critically reviewed and edited by the authors to ensure accuracy and integrity. Author Contribution Rashmi Thakur conceptualized the study, designed the Visual Affordance Framework, and drafted the main manuscript. She also conducted the field observations. Both authors collaborated on interpreting the findings, analyzing task complexity, and refining the manuscript. All authors reviewed and approved the final version Data Availability De-identified interview excerpts and coded task logs are available from the corresponding author upon reasonable request. Audio and video recordings cannot be shared to protect participant privacy. References Gossling, Handicrafts and Employment Generation for the Poorest Youth and Women:, United Nations Educ. Sci. Cult. Organ., vol. 17, p. 65, 2007, [Online]. 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Contin. 62 (3), 1473–1486 (2020). 10.32604/cmc.2020.08917 Medhi-thies, I., Ferreira, P., Gupta, N., Neill, J.O., Cutrell, E.: KrishiPustak: A Social Networking System for Low-Literate Farmers, CSCW March 14–18, 2015, Vancouver, BC, Canada, pp. 1670–1681, 2015, (2015). 10.1145/2675133.2675224 Nagesh, P.C., Kathpal, A.: Rangoli: A Visual Phonebook for Low-literate Users, in Proceedings of the 6th International Systems and Storage Conference on - SYSTOR ’13, New York, New York, USA: ACM Press, p. 1. (2013). 10.1145/2485732.2485744 Additional Declarations No competing interests reported. Supplementary Files Appendixs.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":180822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRISMA Flow Diagram of Study Selection Process.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7558157/v1/30efaaa0c39be3a59749a666.png"},{"id":94906601,"identity":"375e3bf6-75e6-469d-bc04-e4ee55de560f","added_by":"auto","created_at":"2025-11-01 07:32:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of task complexity levels reported across reviewed studies (N = 20)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7558157/v1/6e92f7c4680df3b7c4c8fe64.png"},{"id":109762040,"identity":"35d216e4-b8c2-4f3f-b559-5914deb31525","added_by":"auto","created_at":"2026-05-22 07:30:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":507333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7558157/v1/b17f4956-39a6-4cf4-ba33-5e80a48dd207.pdf"},{"id":94987570,"identity":"5d58a61c-9fa4-44c9-b76a-172dbda70daa","added_by":"auto","created_at":"2025-11-03 07:02:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3295761,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixs.docx","url":"https://assets-eu.researchsquare.com/files/rs-7558157/v1/24b0b17929a49645a44365ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping Visual Affordance Frameworks for Designing Inclusive Smartphone Interfaces for Less-Literate Artisans\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmartphone access has expanded rapidly across rural India, enabling new forms of digital participation among less-literate populations. Among these, artisans defined by UNESCO as individuals engaged in culturally rooted, skill-based handicrafts represent a critical group whose vocational practices increasingly rely on mobile communication tools[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]​.\u003c/p\u003e\u003cp\u003ePrior research in Human-Computer Interaction (HCI) has shown that less-literate users benefit from visual cues such as icons, spatial grouping, and color-coded elements[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These strategies reduce cognitive load and support recognition-based interaction. However, most studies examine these elements in isolation, focusing primarily on recognition-level interactions (e.g., tapping an icon) without systematically analysing how such cues support users across different levels of task complexity, low (L1), moderate (L2), and high (L3).\u003c/p\u003e\u003cp\u003eTo address these gaps, this study adopts Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], originally developed for gaming contexts, as a structured lens to examine how visual cues function in mobile UIs for low-literate users. The framework classifies cues along three dimensions: Task/Purpose, Markedness, and Trigger. By combining this framework with an analysis of task complexity levels (L1\u0026ndash;L3), the research investigates not only whether users recognise visual cues, but also whether such cues enable them to perform tasks of varying complexity in real-world digital environments. This was followed by a field study of WhatsApp use among rural artisans, providing a real-world context to evaluate these interactions.\u003c/p\u003e\u003cp\u003eThis paper makes three contributions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFramework extension\u003c/b\u003e: Systematically mapping visual affordance elements (icons, metaphors, overlays, colours) to Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework, thereby extending its application beyond gaming into mobile UI design for low-literate users.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTask complexity lens\u003c/b\u003e: Introducing task complexity levels (L1\u0026ndash;L3) as an analytical dimension to show how different cue support or fail to support simple task recognition to moderate navigation and complex multi-step tasks.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDesign guidelines for digital inclusion\u003c/b\u003e: Integrating evidence from a systematic review (20 studies) and field-based observations with rural artisans using WhatsApp and proposing a set of layered cueing strategies (emphasised, integrated, persistent, agent-triggered) that combine visual affordances with task complexity for inclusive mobile interfaces.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccordingly, this study is guided by the following research questions\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ1\u003c/b\u003e: What types of visual affordance elements have been studied and implemented in digital interfaces for less-literate users?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ2\u003c/b\u003e: How can these visual affordance elements be systematically mapped to the three dimensions of Dillman\u0026rsquo;s Visual Interaction Cues Framework: Task/Purpose, Markedness, and Trigger?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ3\u003c/b\u003e: How do visual cues influence task complexity and align with the mental models of less-literate users in vocational digital environments?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe remainder of this paper is organised as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews related work, Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the methodology, Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results, Section \u003cspan refid=\"Sec29\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the findings, and Section \u003cspan refid=\"Sec38\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes with directions for future research.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eThis section reviews key concepts pertinent to less-literate individuals and artisans, including mental models, task complexity, visual affordances in interface design, and the Visual Interaction Cues Framework.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Understanding Less-Literate Users and Artisan Contexts\u003c/h2\u003e\u003cp\u003eLow-literate individuals are defined as those whose education levels fall between Grade IV and Grade VIII in the Indian education system[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They may recognize basic numerals (e.g., 0\u0026ndash;3) but face challenges in reading and understanding written material[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, such challenges are not unique to the Indian context. In South Africa, Emmanuel and Muyingi [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] encountered similar difficulties while designing mobile UIs tailored for low-literate users. Similarly, Matyila [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] argued that text-heavy mobile services are inaccessible to low-literacy groups in South Africa. Medhi et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and Thies et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] also recommended the use of graphics over text-based designs. Chauhan et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reinforced these findings, demonstrating a clear preference for multimedia and graphical interfaces among low-literate users. Taken together, these global studies indicate that literacy-related barriers to mobile interaction are widespread, underscoring the need for visual and non-textual design strategies that extend beyond local contexts.\u003c/p\u003e\u003cp\u003eFor the purposes of this study, the term \u003cem\u003eless-literate users\u003c/em\u003e specifically refer to two groups:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLow-literate individuals\u003c/b\u003e: People who have difficulty reading, writing, and understanding short, simple messages or digital content.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eArtisans\u003c/b\u003e: Skilled craftspeople engaged in traditional arts, trades, or heritage-based manual techniques. Although artisans possess deep domain expertise, many face literacy-related challenges in navigating digital content, following instructions, or using smartphone interfaces[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis research also explores the mental models of these user groups. Mental models refer to users\u0026rsquo; internal representations of how a system functions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These models shape expectations, guide interactions, and influence their ability to perform tasks effectively. Understanding such models is critical for designing interfaces that align with users\u0026rsquo; cognitive processes and literacy levels, particularly in domains such as adult learning, fashion technology, and digital skill development for artisans.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Visual Affordances in Interface Design\u003c/h2\u003e\u003cp\u003eVisual affordances refer to the perceivable properties of an interface that suggest how elements are meant to be used [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These affordances are often operationalized as visual cues such as icons, colours, animations, spatial layouts, and shapes that guide user behaviour [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several studies have recommended strategies such as replacing written labels with pictorial icons, maintaining consistent placement of action buttons, and incorporating visual cues or animated feedback[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor instance, Islam, Rahman, and Hossain [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] synthesized sixteen design recommendations for low-literacy contexts, including minimizing text, simplifying interaction steps, and using familiar visuals as icons. However, many of these strategies have been applied inconsistently and often lack alignment with the specific tasks they are intended to support. This mismatch has contributed to ongoing usability gaps for less-literate users.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.3 Visual Interaction Cues Framework (Adapted from Dillman\u0026rsquo;s Framework\u003c/b\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e])\u003c/h2\u003e\u003cp\u003eThis framework was originally developed by Dillman, Mok, Tang, Oehlberg, and Mitchell [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] in the context of video game environments and augmented reality. It provides a structured model for classifying visual cues that guide user interaction. Originally applied in gaming environments, the framework is highly relevant to mobile UI design particularly for users with low literacy because it systematically categorizes cue function, appearance, and activation.\u003c/p\u003e\u003cp\u003eThe framework comprises three core dimensions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDimension 1: Task/Purpose\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eDiscover\u003c/em\u003e cues help users locate interactive elements within the interface.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eLook\u003c/em\u003e cues direct immediate visual attention to specific features or actions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGo\u003c/em\u003e cues support navigation or task progression.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTogether, these cues guide users from initial recognition to task completion an essential process for less-literate users.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDimension 2: Markedness\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eSubtle\u003c/em\u003e cues blend into the background with minimal contrast.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eEmphasized\u003c/em\u003e cues use bold colours, outlines, or size contrasts to denote actionability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eIntegrated\u003c/em\u003e cues are embedded in the interface while maintaining distinctiveness.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eOverlaid\u003c/em\u003e cues (e.g., floating indicators or badges) are superimposed onto interface elements.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eMarkedness is particularly important for less-literate users, as clearly distinguishable cues improve recognition and action.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDimension 3: Trigger\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eUser-triggered\u003c/em\u003e cues are activated by deliberate user actions (e.g., tapping a button).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eContext-triggered\u003c/em\u003e cues respond to passive user behavior or changes in interface state.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAgent-triggered\u003c/em\u003e cues originate from system processes or external actors (e.g., other users, backend updates).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003ePersistent\u003c/em\u003e cues remain constantly visible, offering continuous visual support.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBy adapting this framework from gaming to mobile UI contexts, this study evaluates how different cue types can enhance usability for less-literate users, particularly in vocational and educational applications where intuitive engagement is critical.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Task Complexity and Levels of Interaction\u003c/h2\u003e\u003cp\u003eTask complexity plays a central role in interface design, particularly for less-literate users who rely more on visual cues than on symbolic language. Drawing from prior HCI4D research, user tasks can be categorized into three levels of interaction complexity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLevel 1 (Low)\u003c/b\u003e: Single-step actions such as tapping icons, identifying call buttons, or viewing notifications.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLevel 2 (Moderate)\u003c/b\u003e: Multi-step navigation such as searching, switching tabs, or recognizing cue transitions (e.g., mic \u0026rarr; waveform).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLevel 3 (High)\u003c/b\u003e: Complex Task involving coordination or data entry, such as sharing location, deleting a user from a group chat, adding a person to a video call, or switching between apps to share a document.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e These levels provide a structured lens to evaluate how cue effectiveness varies with task demands, informing interface design for inclusive digital participation.\u003c/p\u003e\u003cp\u003ePrevious studies have shown a strong preference for graphic interfaces and layouts over text-heavy designs among less-literate groups [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e],[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The following subsections examine how visual affordances support such interaction (Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e) and present the Visual Interaction Cues Framework as a structured analytic model (Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employed a mixed-methods design combining (a) a systematic literature review of prior research on visual cues for low-literacy interface design, and (b) a field-based user study with 10 rural artisans in Maharashtra, India. Both phases were guided by Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework, which classifies cues by \u003cb\u003eTask/Purpose, Markedness, and Trigger\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Study I: Systematic Literature Review\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Search Strategy\u003c/h2\u003e\u003cp\u003eA systematic literature review (SLR) was conducted to identify studies that implemented graphic or visual elements in digital interfaces designed for people with low literacy skills and evaluated these elements through real-world tasks. The review followed a PRISMA-aligned protocol across four databases: \u003cb\u003eProQuest, IEEE Xplore, ScienceDirect, and ACM Digital Library\u003c/b\u003e. Two coders independently screened studies, achieving strong inter-rater reliability (κ\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e\u003cp\u003eSearch strings combined the following terms:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;illiterate\u0026rdquo; OR \u0026ldquo;low-literate\u0026rdquo; OR \u0026ldquo;semi-literate\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;user interface\u0026rdquo; OR \u0026ldquo;UI design\u0026rdquo; OR \u0026ldquo;human-computer interaction\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;visual cues\u0026rdquo; OR \u0026ldquo;icons\u0026rdquo; OR \u0026ldquo;affordance\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Screening Process\u003c/h2\u003e\u003cp\u003eThe screening process was conducted in two stages:\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section4\"\u003e\u003ch2\u003e3.1.2.1 Title and Abstract Screening\u003c/h2\u003e\u003cp\u003eThe initial database search yielded \u003cb\u003e235 records\u003c/b\u003e. After removing \u003cb\u003e42 duplicates\u003c/b\u003e, titles and abstracts were screened against predefined inclusion criteria:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e(a) Peer-reviewed empirical studies published between 2000\u0026ndash;2022\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(b) Written in English\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(c) Focused on digital interfaces for low- or semi-literate populations\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(d) Included graphic or visual elements tested in real-world or applied settings\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section4\"\u003e\u003ch2\u003e3.1.2.2 Full-Text Screening\u003c/h2\u003e\u003cp\u003eFull-text evaluation was then performed on the remaining studies. Exclusion criteria removed review papers, dissertations, incomplete articles, and studies focusing on unrelated user groups (e.g., literate professionals, students) or peripheral technologies (e.g., haptics, AR/VR, text input systems).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Exclusion Breakdown\u003c/h2\u003e\u003cp\u003eThe following categories of articles were excluded:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDisability-focused interfaces (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLiterate or unrelated user groups (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHaptics and immersive AR (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProgram evaluations (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDissertations (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncomplete/preliminary studies (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eText/SMS interfaces (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eVoice-only or speech interaction (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTheoretical or discussion-based papers (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLiterature reviews (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNarrowly scoped design studies (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAfter exclusions, the final sample comprised \u003cb\u003e20 peer-reviewed articles\u003c/b\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This appendix lists the research papers that were qualitatively reviewed to support the coding and analysis described in \u003cb\u003eAppendix B: Selected Research Papers for Review\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Coding and Analysis\u003c/h2\u003e\u003cp\u003eEach article was qualitatively coded to extract: (a) Types of visual or graphic elements used, (b) Rationale for their inclusion in interface design (c) Context and nature of real-world task evaluation.\u003c/p\u003e\u003cp\u003eTo enhance validity, coding was reviewed independently by \u003cb\u003ethree postgraduate designers\u003c/b\u003e and \u003cb\u003eone UI design expert\u003c/b\u003e. Their feedback confirmed the relevance of selected studies, highlighted recurring design strengths, and identified gaps in how visual affordances are applied for people with low literacy skills in practical settings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Study II: Field-Based User Research\u003c/h2\u003e\u003cp\u003eThe second phase involved field-based research with \u003cb\u003e10 participants with low literacy skills\u003c/b\u003e and a \u003cb\u003ecomparison group of 4 literate participants\u003c/b\u003e (3 postgraduate design students, 1 UI expert).\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Participants\u003c/h2\u003e\u003cp\u003eArtisan participants (8 women, 2 men; aged 24\u0026ndash;48) were enrolled in a certified vocational training program in tailoring, embroidery, and handloom. Education levels ranged from \u003cb\u003eGrade IV\u0026ndash;VIII\u003c/b\u003e. All were first-generation smartphone users, with \u003cb\u003e6\u0026ndash;18 months\u0026rsquo; experience\u003c/b\u003e and no formal digital training. Participants were purposively recruited through local cooperatives.\u003c/p\u003e\u003cp\u003eThe comparison group of literate participants provided a baseline for interpreting usability patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Data Collection\u003c/h2\u003e\u003cp\u003eTwo complementary methods were employed:\u003c/p\u003e\u003cp\u003e\u003cb\u003e(a) Semi-structured interviews\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCollected information on participants\u0026rsquo; education, smartphone use, and perceptions of interface elements.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConducted in Hindi or Marathi, audio-recorded with consent, and transcribed for analysis.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(b) Task-based observations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eParticipants performed standardized tasks in \u003cb\u003eWhatsApp\u003c/b\u003e, designed to represent low, moderate, and high-complexity interactions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTasks were counterbalanced across participants to reduce order effects. Each task had a \u003cb\u003e3-minute time limit, as shown in\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSessions were video-recorded, and outcomes coded as \u003cem\u003ecorrect completion, hesitation, error\u003c/em\u003e, or \u003cem\u003etask abandonment\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTask Set and Complexity Levels:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComplexity Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSending a voice note\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 1 (Low)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(b)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlacing and disconnecting a call\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 1 (Low)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(c)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocating unread messages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 2 (Moderate)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentifying group administrators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 2 (Moderate)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSharing a photo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 3 (High)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(f)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSharing location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 3 (High)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Data Analysis\u003c/h2\u003e\u003cp\u003eData were analysed using a framework-driven coding approach guided by Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework. Interactions were classified along two analytical frames:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTask complexity\u003c/b\u003e (low, moderate, high)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVisual cue dimensions\u003c/b\u003e (Task/Purpose, Markedness, Trigger)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTwo researchers independently coded the interview transcripts and task observations. Disagreements were discussed until consensus was reached, ensuring analytic reliability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Summary of Methodology\u003c/h2\u003e\u003cp\u003eThis research combined a \u003cb\u003esystematic literature review\u003c/b\u003e (Study I) with a \u003cb\u003efield study\u003c/b\u003e (Study II). Study I mapped visual cues from 20 prior works onto Dillman\u0026rsquo;s framework to identify design strengths and gaps. Study II observed 10 artisans and 4 literate comparators performing WhatsApp tasks of varying complexity, supported by interviews. Together, these methods link evidence from existing research with real-world user behaviour, enabling a triangulated analysis of how visual cues support\u0026mdash;or hinder\u0026mdash;people with low literacy skills.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results And Analysis","content":"\u003cp\u003eFindings from both studies were analysed using Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework, which classifies interface elements across three dimensions: Task/Purpose, Markedness, and Trigger. Study I synthesized evidence from 20 peer-reviewed articles, while Study II applied the framework to field observations with 10 artisans using WhatsApp.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Study I: Systematic Literature Review\u003c/h2\u003e\u003cp\u003eThe review categorized visual affordances from 20 studies, mapping them to Dillman\u0026rsquo;s framework and aligning them with task complexity levels (L1\u0026thinsp;=\u0026thinsp;low, L2\u0026thinsp;=\u0026thinsp;moderate, L3\u0026thinsp;=\u0026thinsp;high). Coding was independently validated by three postgraduate design students and one UI expert.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Task/Purpose\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDiscover cues\u003c/b\u003e were the most prevalent, appearing in 18 studies. Examples included highlighted sections[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], breadcrumb trails[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and red\u0026ndash;green arrows[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLook cues\u003c/b\u003e were found in 12 studies, such as celebrity photographs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], animated overlays, and weather icons[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGo cues\u003c/b\u003e were identified in only 7 studies, including directional arrows, ticks/crosses[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and \u0026ldquo;Return Home\u0026rdquo; icons [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Markedness\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSubtle cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;3) included glowing icons and color-coded tabs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEmphasized cues\u003c/b\u003e (10\u0026thinsp;+\u0026thinsp;studies) included red\u0026ndash;green arrows [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and highlighted icons[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntegrated cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;4) used contextual cues (e.g., tractor icons[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) or animations of familiar activities[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOverlaid cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;3) like breadcrumb trails [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and map overlays [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e],[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3 Trigger\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUser-triggered cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;6) involved tapping or selecting icons [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eContext-triggered cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;4) included animated metaphors[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and progress bars[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], though consistency varied.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAgent-triggered cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;2) involved facilitators initiating prompts [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e],[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePersistent cues\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;1) such as breadcrumb trails[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.1.4 Cue Distribution by Task Complexity\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the distribution of visual cues across the 20 reviewed studies, mapped to Dillman\u0026rsquo;s dimensions and task complexity levels. Most studies focused on cues supporting low-complexity (L1) recognition tasks. Fewer studies addressed cues for moderate (L2) navigation or high-complexity (L3) Task.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe distribution of visual cues across the 20 reviewed studies, mapped to Dillman\u0026rsquo;s dimensions and task complexity levels.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCue Dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow Complexity (L1) \u0026ndash; Recognition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium Complexity (L2) \u0026ndash; Multi-step Navigation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Complexity (L3) \u0026ndash; Vocational Task\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Studies Using Cue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTask/Purpose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighlighted sections [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], red\u0026ndash;green arrows [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], breadcrumb trails [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBreadcrumb trails [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMockups only (no task execution) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLook\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCelebrity photographs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], animated overlays [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeather icons [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], alerts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirectional arrows [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], ticks/crosses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], \u0026ldquo;Return Home\u0026rdquo; icons [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBreadcrumb trails [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarkedness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlowing icons, color-coded tabs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot suitable for L2/L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmphasized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRed\u0026ndash;green arrows [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], highlighted icons [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSame cues reused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisk of overload in L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegrated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContextual icons (e.g., tractor icons) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], animations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuitable with training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisk of confusion in L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverlaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreadcrumb trails [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], map overlays [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePersistent guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnderutilized in L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrigger\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUser\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTap/select icons [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAssumes prior familiarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot scalable to L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFacilitator-initiated prompts [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContextual guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRare in L3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContext\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnimated metaphors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], progress bars [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium-complexity monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInconsistent across sessions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersistent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreadcrumb trails [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLacked layered feedback\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e25\u003c/span\u003e further illustrates this distribution, showing that more than half of the reviewed studies did not explicitly define task complexity, while only a small fraction reported cues supporting medium (L2) or high (L3) tasks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e4.1.5 Summary\u003c/h2\u003e\u003cp\u003eAcross the 20 reviewed studies, visual affordances were most frequently designed to support recognition-level tasks (L1). Discover cues appeared in 18 studies, Look cues in 12, and Go cues in 7. Emphasized cues such as icons and colours were commonly used, whereas persistent or continuity cues were rare. More than half of the studies did not specify task complexity levels in their analysis or design approach. Representative figures are included below, and detailed mappings for all reviewed studies are provided in \u003cb\u003eAppendix A\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Study II: Field-Based Research\u003c/h2\u003e\u003cp\u003eTen artisans were recruited through purposive sampling. All participants had prior exposure to smartphones but limited formal education, aligning with the study\u0026rsquo;s focus on less-literate user groups. Each participant engaged in a 60\u0026ndash;90-minute session combining semi-structured interviews with task-based observations on WhatsApp. Observed interactions were mapped to Dillman\u0026rsquo;s cue dimensions and categorized according to task complexity levels (L1\u0026ndash;L3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of Visual Cue Types in WhatsApp, Mapped to Dillman\u0026rsquo;s Dimensions and Task Complexity (N\u0026thinsp;=\u0026thinsp;10 artisans)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCue Type (Dillman Dimension)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask Complexity Supported\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eObserved Outcomes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmphasized Markedness (Color-coded)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen call button (6/10), Red disconnect (5/10), Blue double tick (6/10), Grey tick (5/10), Green unread badge (5/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL1 \u0026ndash; Recognition tasks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrequently identified; enabled recognition of calls and message status.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntegrated Cues (Real-world metaphors)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhone (6/10), Video camera (5/10), Camera (5/10), Mute icons (4/10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL1\u0026ndash;L2 \u0026ndash; Initiating tasks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSupported recognition of functions such as calling or taking photos; execution often incomplete.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverlaid\u0026thinsp;+\u0026thinsp;Emphasized Cues\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFloating action button, Call notification bar, Red missed call icon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL1 \u0026ndash; Simple action tasks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDirected attention toward actions; enabled quick responses.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtle Markedness\u0026thinsp;+\u0026thinsp;Contextual Triggers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey ticks, Chat headers, Group admin prompts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL2\u0026ndash;L3 \u0026ndash; Group management, tracking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrequently overlooked or misread; led to hesitation or task abandonment in several cases.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrigger Dimension (User/Agent)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTapping camera/status icons, Error message \u0026ldquo;Message could not be sent\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL1\u0026ndash;L2 \u0026ndash; Corrective tasks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinked actions to system responses; supported immediate correction in simple cases.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Observed Task Outcomes\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eLow-complexity tasks (L1): Most artisans successfully completed actions such as sending a voice note or placing a call. Recognition was primarily supported by emphasized and integrated cues.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eModerate-complexity tasks (L2): Tasks such as locating unread messages or identifying group administrators showed mixed outcomes. Some participants hesitated or misread cues, particularly subtle indicators.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHigh-complexity tasks (L3): Tasks such as sharing a photo or location were less frequently completed. Several participants abandoned these tasks, often after repeated errors or confusion with contextual cues.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings from the literature review and the field study converge on a common theme: visual cues in mobile interfaces are effective in drawing the attention of less-literate users, but they rarely provide the support required to complete complex, multi-step tasks. The discussion below synthesises these insights, interprets them through Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework, and introduces a structured design framework for cueing strategies across different levels of task complexity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Patterns in Visual Affordance Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of prior research and field observations indicates that Discover cues\u0026mdash;including bold colours, familiar icons, and highlighted buttons\u0026mdash;were the most widely used. These cues reliably enabled recognition of functions such as calls or message status, but their effectiveness generally ended at initial identification. By contrast, Go cues, which guide task progression, and Look cues, which sustain visual attention, appeared far less frequently.\u003c/p\u003e\n\u003cp\u003eA second pattern relates to task complexity. More than half of the reviewed studies did not explicitly define whether their interventions addressed low (L1), moderate (L2), or high (L3) levels of complexity. In the field study, artisans completed most L1 tasks successfully, yet their performance dropped sharply for L2 and L3 tasks. This gap illustrates a mismatch between the design of cues and the cognitive demands of real-world digital activities.\u003c/p\u003e\n\u003cp\u003eFinally, evidence on performance outcomes was limited. Very few studies reported completion times, error rates, or hesitation, and cue types such as agent-triggered or overlaid indicators were especially underexplored. Within WhatsApp, for example, participants frequently misinterpreted contextual elements such as grey ticks or group admin labels. These shortcomings highlight the need for more deliberate, structured approaches to cue design, which are addressed in Section 5.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Mapping to Dillman\u0026rsquo;s Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the findings were mapped onto Dillman et al.\u0026rsquo;s Visual Interaction Cues Framework, a clear concentration of Discover cues emerged, while Go cues were relatively absent. Emphasised icons and colours succeeded in attracting attention but, when not linked to follow-up actions, sometimes added to cognitive load.\u003c/p\u003e\n\u003cp\u003eThe Trigger dimension revealed further gaps. Contextual cues, though present, were often misinterpreted, and persistent cues such as breadcrumb trails or anchored navigation bars were rarely implemented. Taken together, these patterns suggest that most interface designs provide initial entry points into interaction but fail to support continuity or completion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Task Complexity and Digital Inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis also underscores a structural limitation in current interface design. Most cues are oriented toward recognition-level tasks (L1), enabling users to initiate simple actions. However, tasks requiring navigation (L2) or multi-step Task (L3) frequently resulted in hesitation, misinterpretation, or abandonment.\u003c/p\u003e\n\u003cp\u003eFor artisans, higher-level tasks\u0026mdash;such as sharing media, locating unread messages, or managing groups\u0026mdash;are integral to professional communication and livelihood activities. Without visual cues that scaffold progression across levels of complexity, participation in digital environments remains superficial. True inclusion requires not only access to devices but also interfaces that enable sustained, independent engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Framework for Visual Cue Design Across Task Complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the synthesis of findings from both studies, a layered framework was developed to align visual cue types with task complexity levels (L1\u0026ndash;L3). The framework draws on established principles of interface design while emphasising visual elements such as colour, shape, overlays, animations, and feedback. Its aim is to ensure that cues do not end at recognition but also support interpretation, navigation, and task completion. Representative illustrations are provided in \u003cstrong\u003eAppendix A\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.1 Level 1: Low-Complexity Tasks (Recognition / Recall)\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTask type:\u003c/strong\u003e Single-step actions such as tapping an icon.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDesign rules:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eSubtle visual emphasis to avoid overstimulation.\u003c/li\u003e\n \u003cli\u003eLight, non-urgent highlights (e.g., soft glow or border contrast).\u003c/li\u003e\n \u003cli\u003eFamiliar geometric shapes such as circles or crosses.\u003c/li\u003e\n \u003cli\u003eContext-aware prompts that appear at initial load to guide attention.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.2 Level 2: Moderate-Complexity Tasks (Interpretation + Sub-Tasks)\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTask type:\u003c/strong\u003e Decision-making with one or two sub-steps.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDesign rules:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eStrongly emphasised elements to direct focus toward the next required action.\u003c/li\u003e\n \u003cli\u003eHigh-contrast colours for critical selections.\u003c/li\u003e\n \u003cli\u003eDirectional indicators such as arrows or numbered steps to sequence actions.\u003c/li\u003e\n \u003cli\u003eImmediate visual feedback, for example, colour change or enlargement after selection.\u003c/li\u003e\n \u003cli\u003eUser-driven highlighting to confirm choices.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.3 Level 3: High-Complexity Tasks (Multi-Step Task)\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTask type:\u003c/strong\u003e Sequential or layered interactions such as media sharing or group management.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDesign rules:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eSemi-transparent overlays (30\u0026ndash;50% opacity) that guide progression without obscuring the interface.\u003c/li\u003e\n \u003cli\u003eFixed-position overlaid aids such as progress trackers or step indicators.\u003c/li\u003e\n \u003cli\u003eCombination of contextual cues (appearing alongside relevant elements) and persistent cues (remaining visible throughout the task) to maintain orientation and continuity.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the systematic review was restricted to peer-reviewed studies in English, which may exclude relevant work in other languages or informal design contexts. Second, the field study focused on WhatsApp, a widely used but platform-specific application. Its interface conventions may not generalize to vocational or government apps. Third, the artisan sample was small and context-specific, limiting transferability. Finally, the absence of quantitative performance metrics (e.g., completion times, error rates) restricts claims about the causal impact of specific cue types on performance.\u003c/p\u003e"},{"header":"6. Conclusion and Future Work","content":"\u003cp\u003eThis study examined how visual affordances can support the use of smartphone interfaces by less-literate users, with a particular focus on rural artisans in India. By integrating Dillman et al.’s Visual Interaction Cues Framework with an analysis of task complexity levels (L1–L3), the research offered two key contributions. First, it showed that current interface designs predominantly employ cues that support recognition-level tasks, while providing limited scaffolding for navigation or multi-step task. Second, it introduced a layered framework that aligns cue types with task complexity, providing structured guidance for designing inclusive mobile interfaces.\u003c/p\u003e\n\u003cp\u003eThe findings highlight both progress and limitations in the design of digital systems for low-literate users. Recognition-based cues such as bold icons and colour-coded buttons help users initiate interactions, but more advanced tasks remain challenging without cues that provide persistence, clarity, and continuity. For artisans, these gaps restrict the potential of mobile technologies to fully support vocational communication and participation in digital economies.\u003c/p\u003e\n\u003cp\u003eSeveral directions for future research emerge from this work. Larger and more diverse participant samples would enable the validation of visual cue effectiveness across different user groups. Quantitative metrics such as task completion time, error rates, and hesitation should be systematically captured to complement observational findings. Further testing is also required in contexts beyond WhatsApp—such as government services, adult skilling initiatives like \u003cem\u003eDigital Skill India\u003c/em\u003e, or e-commerce platforms—where higher levels of task complexity are common and where inclusive design has direct social and economic implications.\u003c/p\u003e\n\u003cp\u003eIn sum, visual affordances remain a promising strategy for enabling digital participation among less-literate populations. Their full potential, however, will only be realised if visual cues are designed not merely to capture attention but to support sustained, independent engagement across tasks of varying complexity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to support this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical research practices. As the institution does not have a formal ethics committee, the study followed general ethical guidelines for human-centered research, including voluntary participation, privacy protection, and transparency of purpose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVerbal informed consent was obtained from all participants. The purpose of the study, procedures, and their right to withdraw at any time were explained in their local language before participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as no identifiable personal data or images are published in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified interview excerpts and coded task logs are available from the corresponding author upon reasonable request. Audio and video recordings cannot be shared to protect participant privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode or Materials Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI/LLM use statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring manuscript preparation, OpenAI\u0026rsquo;s ChatGPT was used to assist with initial drafting of select sections. All AI-generated content was critically reviewed and edited by the authors to ensure accuracy and integrity.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRashmi Thakur conceptualized the study, designed the Visual Affordance Framework, and drafted the main manuscript. She also conducted the field observations. Both authors collaborated on interpreting the findings, analyzing task complexity, and refining the manuscript. All authors reviewed and approved the final version\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDe-identified interview excerpts and coded task logs are available from the corresponding author upon reasonable request. Audio and video recordings cannot be shared to protect participant privacy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGossling, Handicrafts and Employment Generation for the Poorest Youth and Women:, United Nations Educ. Sci. Cult. Organ., vol. 17, p. 65, 2007, [Online]. 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(2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1145/2485732.2485744\u003c/span\u003e\u003cspan address=\"10.1145/2485732.2485744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low-literate users, Visual affordances, Smartphone user interfaces, HCI4D, Dillman Framework, Task complexity","lastPublishedDoi":"10.21203/rs.3.rs-7558157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7558157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: Low-literate users face challenges with smartphone interfaces due to ambiguous icons, sparse navigation cues, and complex layouts. Although prior HCI research has examined icons, metaphors, and multimodal strategies, few studies have systematically applied a framework or analysed how cue effectiveness varies across task complexity levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A mixed-methods approach was employed. Study 1 conducted a systematic review of 20 peer-reviewed articles (2000–2022), mapping visual cues to a structured \u003cem\u003eVisual Affordance Framework\u003c/em\u003e across three dimensions: \u003cem\u003eTask/Purpose, Markedness,\u003c/em\u003e and \u003cem\u003eTrigger.\u003c/em\u003e Study 2 involved field-based observations of 10 rural artisans from Maharashtra, India using WhatsApp for vocational communication. Tasks were categorised into three complexity levels (L1–L3) to assess how cue effectiveness shifted from simple recognition to multi-step tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The review showed that most studies offered limited exploration of visual affordances in relation to task complexity and rarely reported performance outcomes. Field observations revealed that WhatsApp’s familiar iconography and spatial layouts supported recognition-level tasks (L1) but provided little scaffolding for navigation (L2) or multi-step tasks (L3), resulting in hesitation, misinterpretation, and abandonment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study advances HCI by integrating a \u003cem\u003eVisual Affordance Framework\u003c/em\u003e with task complexity analysis to identify design gaps in mobile interfaces. It demonstrates that recognition cues alone are insufficient and recommends applying structured affordance-based cueing strategies to guide inclusive smartphone application design. Structured cueing strategies can enhance navigation, enable progression across task complexity levels, and foster digital inclusion for less-literate users.\u003c/p\u003e","manuscriptTitle":"Mapping Visual Affordance Frameworks for Designing Inclusive Smartphone Interfaces for Less-Literate Artisans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-01 07:32:20","doi":"10.21203/rs.3.rs-7558157/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a29f0ef-57ea-4b91-a6a3-6c15f7f30520","owner":[],"postedDate":"November 1st, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-20T14:56:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T15:10:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-01 07:32:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7558157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7558157","identity":"rs-7558157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}