A Multi-Process System for Investigating Inclusive Design in User Interfaces for Low-Income Countries
Abstract
:1. Introduction
2. Engineering Design Meta-Model for Interface Design
2.1. Engineering Design Domains
2.2. Engineering Design Models
2.3. Engineering Design Meta-Model Network for Methodology Representation
3. ERSA (Engineering Design Research Meta-Model Based Systematic Analysis) Process
3.1. Phase 1–Selection of the Baseline Articles
3.2. Phase 2–Collection of Related Articles Using Snowballing
3.3. Phase 3–Completion of the Collection
3.4. Phase 4–Representation of Methodologies Using Engineering Design Multilayer Attributed Network for Computation
3.5. Phase 5–Discovering Similarities of the Methodologies
3.6. Phase 6–Construction of the Learnings
4. Application and Results
4.1. Phase 1–Selection of the Baseline Articles
4.2. Phase 2–Collection of Related Articles Using Snowballing
4.3. Phase 3–Completion of the Collection
4.4. Phase 4–Representation of Methodologies Using Engineering Design Multilayer Attributed Network for Computation
4.5. Phase 5–Discovering Similarities of the Methodologies
- Cluster 1: The 1st cluster is the most populated cluster, with 95 methodologies entering it. It is also one of the simplest ones, since almost all the methodologies have only one step, except for one. Most of the articles also studied the conceptual model, except for two that are interested in the experimental model of the physical domain (ExpePhys).
- Cluster 2: The 2nd cluster is formed of 2 methodologies that both have around four or five steps, start from the conceptual model of the user domain (ConcUser), and finish on ExpePhys. During the process, both consider aspects of the conceptual models and the user domain, each in a different way.
- Cluster 3: The 3rd cluster is composed of 15 methodologies that, except for two, all have one step only, on the experimental model of the user domain (ExpeUser). The two exceptions both end on this step while starting on the physical domain.
- Cluster 4: The 4th cluster is made of 5 clusters that all have around four to six steps. The difference between this and the 2nd cluster is that while both start on conceptual models and end on experiments, the 4th cluster is considering the computational model of the physical domain (CompPhys).
- Cluster 5: The 5th cluster is a simple cluster formed by 21 methodologies, 19 being simple step methodologies over the conceptual model of the functional domain (ConcFunc). The two exceptions are no less than three steps and also take into account ConcFunc.
- Cluster 6: The 6th cluster is a cluster of 14 methodologies that all have two or three steps and end on ExpePhys, while starting on different models but mainly on models of the conceptual model. Most of the methodologies also consider CompPhys.
- Cluster 7: Cluster 7 is only formed of one six-step methodology that starts on ConcUser, ends on ExpePhys, and iterates over this same ExpePhys step.
- Cluster 8: Cluster 8 is formed by two methodologies that start by ConcUser and the conceptual model of the physical domain (ConcPhys). One stops here, while the other ends with ExpePhys.
- Cluster 9: Cluster 9 has 5 one-step methodologies that have interest in the mathematical model of the use domain (MathUser). It is the simplest cluster, the only one having all its methodologies being one step.
- Cluster 10: Cluster 10 has 2 three-step methodologies that both consider CompPhys and ConcFunc, while adding a step in the conceptual model before or after those two steps.
- Cluster 11: Cluster 11 is only formed of one eight-step methodology that iterates three times over ConcPhys and CompPhys before ending on the mathematical model of the physical domain (MathPhys) and ExpePhys.
- Cluster 12: Cluster 12 is composed of 3 methodologies that consider three to four steps, all ending on ExpUse. Two have just before considered MathPhy and ConcPhy.
- Cluster 13: Cluster 13 has 3 two-step methodologies that all end on MathUser. Two of the three start on ConcUser, while the last one starts on the conceptual model of the process domain (ConcProc)
- Cluster 14: Cluster 14 is composed of only one methodology. It is the longest with 17 steps and highly iterative, and it has already been identified in the previous phase.
- Cluster 15: The last cluster is formed of 3 methodologies that all consider ConcPhys while being no more than two steps long.
- Clusters with one-step methodologies: The clusters n°1, 3, 5, and 9 are part of this first family. The research articles present in those methodologies do not propose a true methodology, since they focus only on one part of the design work. The high number of methodologies could be explained by the fact that the algorithm has tried to group together methodologies that were heavily focused on one model of the engineering design meta-model: the 1st cluster revolves around the conceptual domain of the user model (ConcUser), the 3rd cluster is focused on the experimental model of the user domain (ExpeUser), the 5th cluster is interested in the conceptual model of the functional domain (ConcFunc), and the 9th cluster is composed only of one-step methodologies that consider the mathematical model of the physical domain (MathPhys).
- Clusters with two to five steps: The 4th, 6th, 12th, 13th, and 15th clusters are part of the second family. This family of clusters shows similarities to existing methodologies; for example, the 4th cluster is similar to the UCD methodologies: the methodologies developed in this cluster start by learning about the users but also working on the specifications the end product should have. Finally, a product is developed and is tested. The 6th cluster is more likely to represent one sprint in the context of a SCRUM framework [40]; this small part of a project allows the final product to be more suited to the command instead of basing the whole process on one version of the product. The 15th cluster is similar because the methodologies found in this cluster propose a user interface without testing it. These small iterations can also be found in the 13th cluster, although it is more oriented toward MathUser. The 12th cluster, however, was more likely to experiment with the end users instead of testing a product.
- Clusters with only one methodology: These clusters form the third family. The clusters with only one methodology (the 7th, 11th, and 14th clusters detailed in Section 3.6 are populated with long methodologies that show specific approaches while retaining aspects of UCD methodologies: the users are usually a focus of interest at the start, and an experiment is conducted at the end of the methodology. These methodologies being conducted in different contexts, and requiring a different number and types of iterations, are in the end very different, which is why the algorithm has ranked them into different clusters. The 2nd cluster is of similar shape, but of medium length, which has allowed us to group together two similar methodologies.
- Exceptions or original methodologies: The 8th and 10th clusters are methodologies that are exceptions, in the sense that they are mainly focused on the conceptual model without testing the solution proposed. In this sense, they hardly follow an existing methodology.
- Identification of single-step methodologies: 75.8% (132 of 174) of the methodologies are single-step methodologies. These articles include interventions in a general topic such as a mode of interaction or a specific study of digital adoptions in certain regions.
- Identification of the most commonly utilized models as an initial step: 82.9% (34 of 41) of the multiple-step methodologies have a first step that is part of the conceptual model. Moreover, 51.2% (21 of 41) of the process methodologies have a first step that is part of the user domain. This means that overall, researchers were more likely to obtain knowledge about the end users in an abstract way before diving into more concrete aspects.
- Identification of models frequently utilized as the final step: 65.9% (27 of 41) of the process methodologies end with a step that is part of the physical domain. Moreover, 70.7% (29 of 41) of the process methodologies end with a step that is part of the experimental model. This shows that there is a will on the researchers’ part to test out the user interface (physical domain) as the final part of their methodology. All the methodologies that ended with the experimental model have put in place experiments, such as about the user interface they developed, the product they tested, or a strategy they needed to test out. In the context of user interface design, experimentation is a crucial step, since it forms the testing of the newly developed interface.
- Iterations: 22% (9 of 41) of the multiple-step methodologies presented iterations, meaning that the methodologies went back to a step that was previously developed. Of these iterations, 55% (5 of 9) happened over the computational model of the physical domain and the experimental model of the physical domain (CompPhys -> ExpePhys). All five of these iterations were conducted over different versions of a user interface, making it possible to carry out heavy testing on the interface developed, with each new version trying to improve the previous one.
- Conceptualization of design methodologies: In their article, De Silva et al. [22] adapted the Design Science Research methodology theorized by Hevner [41] for specificities of interface design for users in low-income countries. The specificities taken into account on the methodology are to enable collaborations at different times and locations. The result is a six-step methodology that is highly iterative, and where each step has their identifier, which is numbered according to the iteration number associated with each step. According to the authors, they “strongly recommend this model for any large and complex research project aiming to develop an artifact”. From the 174 research articles, it is the only one that put such a focus on the methodology.
- Coverage of the meta-model: Matrix (15) sums up the coverage of the models by the process methodologies; for example, over the 41 multiple-step methodologies, 58.5% covered the ConcUser model. The sum of percentages for the models is greater than 100% because each methodology covers more than one model. The models never encountered were MathFunc, MathProc, CompUser, CompFunc, CompProc, and ExpeProc. Considering the focus of the application on the design of interfaces adapted for users in low-income countries, it makes sense that aspects such as the knowledge of the end users (ConcUser) and aspects of the features of the app (ConcFunc) are more developed than mathematical or computational models because of the nature of the topic, which is more likely to focus on the understanding of the users or the solution proposed in a conceptual way.
4.6. Phase 6–Construction of the Learnings
- Learning n°1 (Core models inclusion)–The analysis reveals that conceptual and experimental models are prominently featured across most clusters. Notably, the 9th cluster diverges by excluding both models. Additionally, despite the widespread prioritization of the conceptual and experimental models, the utilization of the mathematical model remained surprisingly low.
- Learning n°2 (Adapting iterative strategies)—The analysis demonstrates that the most common strategies include iterative processes, especially involving the physical user interface design and subsequent testing. This adaptation enables continuous user feedback when initial models are incomplete. Despite the potential benefits of iterative methodologies, the results highlight that research articles from low-income countries exhibit a low rate of iterations. This may be explained by the challenges associated with field testing and distance from end users.
- Learning n°3 (Incomplete modeling)—The analysis indicates that the methodologies examined in this study fail to fully incorporate all models of the engineering design meta-model. Indeed, none of the 15 clusters included all the models. This result underscores the incompleteness within the field and a gap in current research methodologies.
- Learning n°4 (Mathematical Modeling Gap)—The analysis exposes a reluctance in adopting mathematical modeling. Among the clusters analyzed, only clusters 2 and 13 employ the mathematical model. The results suggest that designers may be limited in their ability to take advantage of the enhanced precision, predictive capabilities, optimization potential, and validation opportunities afforded by mathematical models.
- Learning n°5 (Community-based Design)—The analysis suggests a transition from user-based design to community-based design. To better addresses local cultural needs, the results show that it is preferable to focus on, or conduct, studies on community-wide studies rather than individual users.
- Learning n°6 (Real-time dynamic solutions)—The analysis outlines the importance in incorporating cultural sensitivity across multiple cultures. The physical models should not exclude users due to socio-economic and literacy disparities. The results reveal that designs should be tested in real-time using dynamic solutions to ensure they meet the varied capacities and computer literacy levels of users.
- Learning n°7 (Versatility)—The analysis allowing for identification of 15 clusters, demonstrating the diversity of the methodologies employed. The results show that including varying levels of literacy, computer literacy, and potential future literacy is crucial to make designs of user interface versatile. More than in any other context, the results suggest that designers should correctly include users in all phases.
- Learning n°8 (Model-based Interface design)—The design of an interface can be seen as a process of building engineering models from the user domain to the process domain.
5. Discussion and Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Criteria | Low-Income Countries Versus High-Income Countries | Baseline Studies | |
---|---|---|---|
1 | Low literacy rate | In low-income countries, illiteracy is almost three times as high as in lower-middle and upper-middle income countries. The user interfaces based on text are therefore not suitable for users in low-income countries. | Medhi Thies [34] |
2 | Low level of computer literacy | Low-income countries exhibit extremely low levels of digital literacy. The user interfaces that assume users are proficient on using a computer are therefore not suitable for users in low-income countries. | Medhi Thies [34] |
3 | High rate of multilingualism with oral languages | As a whole, high-income areas have low linguistic diversity, while low-income areas have high linguistic diversity geographically. The user interfaces made for only a few major worldwide languages are therefore not suitable for users in low-income countries | Danis et al. [35] |
4 | Impossibility to access IT tools | In around 50% of the low-income countries, fewer than 50% of inhabitants report owning a smartphone. Energy access is low in poorer countries, and increases as incomes increase. A decent internet connection is out of reach for 90 per cent of people in low- and middle-income countries. The user interfaces designed on devices that are not used and/or requires frequent recharging or high connection quality is therefore not suitable for users in low-income countries | Blumenstock and Eagle [36] |
5 | User culture | As most user interfaces decisions are made in high-income countries, the need to understand the culture of the end user is utmost important. The user interfaces designed with the culture of the designers’ country in mind is therefore not suitable for users in low-income countries. | Heimgärtner [37,38,39] |
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Méhat, Y.; Sagot, S.; Ostrosi, E.; Deuff, D. A Multi-Process System for Investigating Inclusive Design in User Interfaces for Low-Income Countries. Algorithms 2024, 17, 232. https://doi.org/10.3390/a17060232
Méhat Y, Sagot S, Ostrosi E, Deuff D. A Multi-Process System for Investigating Inclusive Design in User Interfaces for Low-Income Countries. Algorithms. 2024; 17(6):232. https://doi.org/10.3390/a17060232
Chicago/Turabian StyleMéhat, Yann, Sylvain Sagot, Egon Ostrosi, and Dominique Deuff. 2024. "A Multi-Process System for Investigating Inclusive Design in User Interfaces for Low-Income Countries" Algorithms 17, no. 6: 232. https://doi.org/10.3390/a17060232