1 Introduction

Language complexity is an often-underestimated factor in Web Accessibility. Early research and development within Web Accessibility mainly focused on perceptibility and operability. In recent years, aspects that support understandability, such as content design, structure and wording, have gained increasing importance [1, 2]. While understandability is often discussed in the context of cognitive and learning disabilities, benefits for users with a variety of abilities have been reported [3,4,5,6].

The Web Content Accessibility Guidelines 2.0 address language complexity in a rather broad sense: Unusual words (criterion 3.1.3) as well as abbreviations (criterion 3.1.4) should be explained, while the overall reading ability required to understand the text should not exceed lower secondary education (criterion 3.1.5). More specific recommendations to reduce language complexity are proposed with the concepts of Plain Language and Easy-to-Read Language. Both approaches differ regarding their formalisation and their intended audience. Plain Language has its roots in efforts to improve government information and focuses on clear and precise writing [7]. It is centred on the user’s goals and tries to make the content easily scannable and understandable by avoiding long, convoluted sentences and jargon. There is no clearly defined target group, as writings in Plain Language aim at being understandable for as broad an audience as possible. Easy-to-Read Language, in contrast, was specifically designed to meet the needs of people with cognitive and learning disabilities [8]. However, Easy-to-Read Language also benefits a potentially larger audience, such as people with low language skills or auditory disabilities [1]. Texts in Easy-to-Read Language attempt to be as simple as possible. Guidelines include the use of very clear sentence structures, making only one statement per sentence and avoiding difficult words. Additional recommendations for optimal readability exist. Easy-to-Read Language is characterised by the rule to present one sentence per line, turning the text presentation into a list-form [9].

Research generally reports that Easy-to-Read Language benefits users with cognitive and learning disabilities [8]. However, studies have found unintended side-effects on non-disabled users [5, 10]. While non-disabled users also seem to benefit from better text understanding, they prefer conventional language with regards to text liking. Further, their intention to revisit a website was reduced when Easy-to-Read Language was applied [10]. Importantly, these findings contrast recent studies that showed no drawbacks of implementing other Web Accessibility criteria [11]. Because non-disabled users arguably represent the main user base of most websites, practitioners are very sensitive to potential trade-offs and will not implement controversial recommendations [12]. Hence, further research is necessary to find solutions suitable for all user groups [1, 5].

The present work addresses potential unintended side-effects by proposing a dynamic and a static approach for combining conventional with Easy-to-Read Language (see Sect. 2.2). The contribution is three-fold: (1) Different approaches for countering potential negative side-effects of reducing language complexity are tested in an experimental design. (2) Thanks to a collaboration with the Swiss Federal Railways, a real-world example of practical relevance is studied. (3) The theoretical and practical discourse about the application of Plain and Easy-to-Read Language on websites is advanced.

2 Method

2.1 Participants and Design

A total of 336 participants completed an online study. A priori power analysis suggested a minimum sample size of 280 participants. Recruitment was conducted by the recruiting-service TestingTime, targeting a balanced sample in terms of age and gender. Participants received a payment of 10 Swiss Francs (about 8.50 Euros) for completing the study. Twenty-eight participants were subsequently excluded from the sample: Seven participants indicated that they did not answer the questionnaire seriously, 14 participants completed the questionnaire in less than 10 min and one participant’s response time exceeded one hour. Finally, 6 participants were excluded because they did not fully answer the cloze test to estimate their level of literacy.

In total, 308 non-disabled participants (age M = 41.8, SD = 16.0, range 18–79; 165 women, 140 men, 3 non-binary or not specified) were included in the analysis. On average, the study took 21 min (SD = 8.5 min) to complete. The study consisted of a one-factorial between-subjects design with four conditions. Experimental groups did not significantly differ with regards to age, gender distribution or literacy.

2.2 Materials

The selection of texts for the study was based on the experiences of call agents working in the contact centre of the Swiss Federal Railways, covering common questions by customers. For these topics, texts with a length of approximately 300–400 words (i.e., a reading time of about 2 min) were screened. Three text samples A (excerpt of terms of conditions), B (excerpt of privacy policy) and C (advertisement letter) were selected and subsequently translated by a professional translator into a Plain Language and an Easy-to-Read variant. The Easy-to-Read variant was translated according to the ruleset of the “Forschungsstelle Leichte Sprache” of the University of Hildesheim [9]. The translation was tested by four reviewers with cognitive disabilities. Based on the reviewers’ qualitative feedback, the Easy-to-Read text was deemed adequate and accessible. Additionally, the texts were analysed with the German version of the Flesch-Reading-Ease formula [13], which provides a readability score ranging from 0 (very easy) to 100 (very difficult). This analysis showed a decrease of text difficulty for the translations compared to the original text in conventional language. Descriptives for all texts and variants are presented in Table 1.

Table 1. Descriptives for all texts and language variants including word count and the Flesch-Reading-Ease score ranging from 0 (very difficult) to 100 (very easy).

Next, four variants of a website were prepared to present the texts: (1) A control group, where the original text was presented in conventional language. (2) A condition in Plain Language text. (3) A combination of the original text with a dynamic presentation of the Easy-to-Read text (ETR Dynamic). Specifically, participants had the option to actively adapt the text complexity for each paragraph. To do so, a language toggle was presented next to each paragraph, with the original text selected as default option. To provide participants the flexibility to read only certain parts of the text in Easy-to-Read Language, changes on a per paragraph basis were favoured over adapting the entire text. (4) A combination of the original text with a static presentation of the Easy-to-Read text (ETR Static), displayed in an additional box next to the original text. Screenshots of all four conditions are presented in Fig. 1.

Fig. 1.
figure 1

Screenshot of all four conditions. Top Left: Conventional language. Bottom Left: Plain Language. Top right: ETR Dynamic. Bottom right: ETR Static.

For texts A and B, the visual design from the original website of the Swiss Federal Railways was recreated. All four conditions were identical in terms of website elements, such as pictures and the navigation bar. Text C was presented as an e-mail newsletter. It was assumed that e-mails have limitations with regards to the presentation of additional interactive elements. Therefore, only a variant in conventional and in Plain Language was created.

Finally, multiple choice and true/false statements were developed for measuring text understanding of texts A and B [10]. The study was then pre-tested with 10 participants (age M = 37.14, SD = 15.40, range 22–68; 3 women, 7 men), who were asked to provide detailed feedback regarding the presentation of the texts and the text understanding measurement.

2.3 Procedure

Participants were first asked to provide demographic information and complete a cloze test to estimate their literacy level [14]. All participants read all three texts. Each text was randomly presented in one of the four language conditions. Texts were presented in counterbalanced order. Participants could return to the questionnaire whenever they were ready to rate the text they had just read (see Sect. 2.4). Finally, participants had to indicate whether their responses were serious and had the opportunity to comment on the study.

2.4 Measures

Various performance indicators and subjective responses were measured. Performance indicators included: (1) reading time and (2) a score for text understanding. The score (maximum possible score 40 points) was calculated based on multiple choice questions and true/false statements. Additionally, in the Easy-to-Read Language conditions, the use of the language toggle was tracked.

Subjective responses included: (1) subjective comprehension (“How well did you understand the text on the website?”), (2) trust (“How trustworthy did you find the information on the website?”) and (3) two items for text liking (“I like the style in which text on the website has been written”, “The writing style of the text on the website is appealing.”) that were adopted from [10]. All questions were answered on a 7-point Likert scale. (4) Perceived aesthetics was measured with the short version of the Visual Aesthetics of Websites Inventory (VisAWI, [15]). (5) The pragmatic (PQ) and hedonic quality (HQ) of the website was assessed with the short version of the AttrakDiff [16]. In the Easy-to-Read Language conditions, participants additionally rated the helpfulness of the Easy-to-Read text (“How helpful were the additional texts for your understanding?”).

3 Results

3.1 Performance Indicators

Planned contrasts revealed that reading time differed significantly between conditions for text A. As shown in Table 2, participants spent significantly less time reading the conventional language condition compared to the experimental conditions (F(1, 262) = 5.71, \(p < .05, \eta ^2 = 0.017\)). Texts B and C did not differ significantly in terms of reading duration. Text understanding did not differ between conditions and texts.

3.2 Subjective Responses

With regards to subjective comprehension, a Kruskal-Wallis test indicated significant differences between conditions for texts A (\(\chi ^2(3) = 19.27\), \(p < 0.001, \eta ^2 = 0.058\)), B (\(\chi ^2(3) = 14.55\), \(p < .01, \eta ^2 = 0.043\)) and C (\(\chi ^2(1) = 6.66\), \(p < .01, \eta ^2 = 0.020\)). Conover-Imans’s pairwise comparisons with a Holm correction for multiple comparisons revealed lower subjective comprehension for conventional language versus Plain Language for texts A (\(p < .001, d = 0.79\)), B (\(p < .01, d = 0.52\)), and C (\(\chi ^2 = 6.66\), \(p < .01, \eta ^2 = 0.020\)). Further, Plain Language scored better on subjective comprehension compared to ETR Dynamic for texts A (\(p < .01, d = 0.61\)) and B (\(p < .01, d = 0.64\)).

In terms of pragmatic quality, planned contrasts showed significant differences between conditions for text A and text C. Conventional language scored lower compared to all experimental conditions for text A (F(1, 262) = 5.83, \(p < .05, \eta ^2 = 0.011\)) and text C (t(270) = 2.02, \(p < .05, d = 0.25\)). Further, higher PQ ratings were found for text A (F(1, 262) = 4.68, \(p < .05, \eta ^2 = 0.006\)) for Plain Language compared to the Easy-to-Read conditions. Ratings for trust, text liking, perceived aesthetics and hedonic quality did not differ significantly between conditions and texts.

Table 2. Means and standard deviations for all dependent variables as a function of language conditions.

3.3 Helpfulness of Dynamic and Static Easy-to-Read Texts

Most participants noticed the presence of the additional Easy-to-Read texts for text A (dynamic = 54.6%, static = 85.9%) and text B (dynamic = 61.9%, static = 81.2%), which was more pronounced in the static condition. Both Easy-to-Read variants were deemed moderately helpful for text A (dynamic M = 4.66, SD = 1.70; static M = 5.00, SD = 1.82) and text B (dynamic M = 4.97, SD = 1.55; static M = 4.89, SD = 1.65). In the dynamic condition, all participants had used the language toggle at least once for text A (toggle uses M = 3.12, SD = 2.51, range 1–14) and text B (toggle uses M = 3.17, SD = 1.95, range 1–7).

4 Discussion

Results show that the proposed approaches combining Easy-to-Read Language with conventional language did not result in the unintended side-effects on text liking reported in previous studies [5, 10]. As most participants noticed the additional texts, these approaches seem to be discrete enough to prevent a negative impact on User Experience. However, whereas no negative effects on text liking or perceived aesthetics were found, no significant benefits for text understanding in the Easy-to-Read conditions were observed either. The moderate helpfulness ratings of the additional texts suggest that the information provided might not have been appropriate in the present situation or that the writing style did not appeal to users. Both factors may have reduced the active use of the additional Easy-to-Read texts. Further, as suggested by the longer reading time, too much text was perhaps presented at once, thus reducing the utility of the provided information. Nevertheless, it is important to note that the review group did rate the Easy-to-Read text as accessible. Hence, it is arguably more important to further improve the presentation of additional Easy-to-Read text for the needs of users with cognitive and learning disabilities. As long as there are no drawbacks for other users, a maximum of inclusion can be attained this way. Hence, it is essential to involve potential end users in the development process [2]. As all participants in the study used the language toggle at least once, this concept seems to work for non-disabled participants. It remains to be seen whether this also holds true for users with cognitive and learning disabilities, or if the static presentation or other solutions are preferable options.

For the Plain Language variant, multiple advantages compared to the conventional language text were found, which also applied for non-disabled users. The positive effects on subjective comprehension and pragmatic quality suggest that the Plain Language text was deemed more understandable and more suitable for users’ needs. While this effect was merely subjective and did not translate into higher text understanding scores for the present sample, this might contribute to a better overall User Experience due to a positive perception of self-efficacy [17]. Perhaps, a combination of Plain Language and Easy-to-Read texts could make full use of the potential of the approaches discussed in the present paper.

5 Conclusion

The present study demonstrated that Easy-to-Read Language may be implemented without unintended side-effects. While positive effects for people with cognitive and learning disabilities could be retained, no negative effects on other users emerged. Further work should investigate an optimal implementation of the proposed approaches and strive to extend the positive effects for as broad an audience as possible.