2.1. Participants

Ethical clearance for this study was obtained from the Research Ethics Committee at the Directorate of Research and Innovation, Ho Technical University, Ghana (HTU/DRI/EC/VOL.I/24/022) to conduct the study. The study objective was explained to participants, and their consent was obtained verbally to voluntarily participate in study under the witness of their colleagues as the participation of individuals in the research poses no risk of physical, psychological, legal, or social harm. The recruitment of the participants commenced on 15^th February 2024 and ended on 29^th March 2024. All methods of the study were carried out in accordance with relevant guidelines outlined in research ethics policy.

The study was carried out among drivers across the sixteen (16) regions of Ghana. Participants were purposely selected based on their driving experience, as the focus of the study is on individuals who actively interact with dashboard indicator lights. Since these lights provide information and guidance specifically to drivers, individuals without driving experience would not be able to provide appropriate information. Therefore, purposive sampling was employed to ensure that only experienced drivers, who regularly encounter dashboard indicators were included in the study.

Two thousand five hundred and thirty (2530) drivers participated in the study. The participants consist of (i) private vehicle drivers, (ii) commercial vehicle drivers and (iii) both private and commercial vehicle drivers. The study shows that 79% of the participants were males while 21% females as shown in Table 1. Majority of the participants (35.8%) were in the 36–45 years age bracket and had their education up to primary/middle/ junior high school (63.8%). This clearly shows that most of the participants qualify to drive since they are within the required minimum age of 18 years. This is the required age that qualifies a Ghanaian citizen to drive. In addition, most of the participants (57.9%) have a Ghanaian driving licence class B which qualifies them to drive light vehicles, cross country vehicles, etc. Most participants (70.1%) were private car drivers only and (32.9%) had 5–10 years driving experience and drove between 1–4 hours daily (60.5%).

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Table 1. Demographic characteristics of participants.

https://doi.org/10.1371/journal.pone.0323386.t001

2.2. Data collection instrument

The study employed a self-reported questionnaire as the primary data collection instrument. A pilot study was conducted, involving 80 private and commercial drivers to test the clarity and relevance of the questions. Feedback from the pilot participants highlighted the need for rewording to ensure better comprehension and appropriate response. Based on the feedback, adjustments were made to the final survey instrument by rewording the instructions for the response variable (driver compliance to maintenance practices) and suggested improvements to the Likert-scale response options of the question.

The final instrument was divided into two (2) sections. The first section focused on collecting demographic information as shown in Table 1. Besides, the section included a question assessing the extent of action taken by the participants to comply with preventive maintenance practices when the dashboard indicator light symbols illuminate. This response variable was measured using 3-point Likert-scale, ranging from 1 representing ‘No action taken’, 2 denoting ‘Some action taken’, and 3 representing ‘Prompt action taken’. In the second section, participants were presented with 25 coloured images of dashboard indicator light symbols to solicit information on correct identification and understanding of the indicator light symbols. The participants were asked to identify the symbols (see S1 Appendix in the online supporting information) and write down the meaning of each symbol in their own words to assess their understanding levels. Additionally, participants were asked to indicate the appropriateness of indicator light symbols and measured on 3-point Likert-scale, ranging from 1 representing ‘Not appropriate’, 2 denoting ‘Appropriate’, and 3 representing ‘Most appropriate’. The instrument was administered to participants through a face-to-face approach.

Out of the 25 selected indicator light symbols, 10 were classified as warning lights, while engine and emission light and safety light consisted of 5 symbols each. Drivetrain light included 3 symbols and lighting and comfort light each consisted of 1 symbol. These symbols were selected because they are common on vehicles used in Ghana. Table 2 provides a detailed overview of the various indicator light symbols presented to the participant for identification and interpretation. The questions related to these symbols were open-ended to allow for an assessment of participants’ comprehension. After receiving the responses, we categorised the responses and coded as 1 = don’t know, 2 = wrong response, and 3 = correct response. The responses coded 1 were based on the responses where participants explicitly stated that they did not know the meaning of the symbol or failed to provide any response. This Categorization distinguishes between a complete lack of recognition and misinterpretation. It also allowed for an accurate measure of the proportion of participants who had no familiarity with the symbols. The responses coded 2 (wrong response) indicate that the participant attempted to interpret the symbol but provided an incorrect meaning. This category reflects cases where the symbol was recognized but misunderstood. Responses coded 3 (correct responses) indicate that the participant accurately understood and explained the meaning of the symbol as intended. This category reflects a successful comprehension of the indicator lights. This classification method was adapted from the five SAE and ISO categorizations, although categories such as “Partially correct” and “No response given” were excluded due to a lack of responses in these areas [16,17]. The common method bias (potential distortion that may arise in the findings) was controlled by collecting responses from a diverse and representative sample, including private drivers from various workplaces and commercial drivers from different terminals. The approaches encompassed various demographics, experience levels, and license type to minimize selection bias. Additionally, the survey instrument underwent review by experts in human factors, automotive engineering, and traffic psychology to refine questions, eliminate ambiguity, and ensure neutrality, thereby reducing measurement bias. Furthermore, response bias was mitigated by assuring participants anonymity which resolved the assessment worry [18]. Incorporating these measures, enhances the validity and ensures that conclusions drawn are reliable. A total of two thousand five hundred and thirty (2530) participants fully responded to the symbols presented.

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Table 2. Interpretation of dashboard indicator light symbols.

https://doi.org/10.1371/journal.pone.0323386.t002

2.3. Data analysis

The data collected were entered in excel and imported into STATA version 15 for analysis. Descriptive statistics were computed to assess the frequency pattern of the symbols and appropriateness ranking technique used to determine the suitability of the various symbols. Cross tabulation was employed to assess the relationship between familiarity as well as comprehension of the symbols and Chi-square test further conducted to examine the existence of significant differences between the familiarity and comprehension levels of the symbols. Finally, generalised ordered logistic regression analysis was explored to assess the impact of demographic factors and comprehensibility of the indicator light symbols on drivers’ preventive maintenance compliance.

2.4. Generalized ordered logistic regression

Generalised Ordered Logistic Regression (GOLR) is a statistical model used when the response variable has more than two ordered categories. This model is an extension of the traditional ordered logistic regression model. In the current study, one of the objectives is to evaluate the impact of demographic factors and comprehension of the indicator light symbols on drivers’ preventive maintenance compliance. The response variable, which is the preventive maintenance compliance, has ordered categories such as ‘No action taken’. ‘Some action taken’, and ‘Prompt action taken’. The predictors include both demographic factors and comprehension of dashboard indicator symbols. The GOLR model estimates the probability of observing each category of the ordinal outcome. The cumulative probabilities for the ordered categories can be modelled using eq. 1:

(1)

Let be an ordinal response variable with ordered categories, and let be a vector of predictor variables. , is the threshold parameter for the category and are the coefficients associated with the predictor variables for the category.

To define the cumulative odds for each category, the model is express as indicated in eq. 2:

(2)

The parameters were estimated using maximum likelihood estimation (MLE). The likelihood function was constructed based on the observed outcomes and was maximised to obtain the estimates of the parameters . represent the threshold between the categories of the outcome variable. is for each coefficient indicating the change in the log odds of being in a category or lower versus being in a higher category for a one unit increase in the predictor variable.

3.1. Monitoring and familiarity of indicator light symbols

According to the results, the majority of the participants 1308 (51.7%) always looked at their instrument cluster when driving (see Fig 1). This is one of the best practices and principles when driving. This serves as an alert guide to drivers, providing warnings and directives on precautionary measures to prevent accidents and enhance vehicle safety [19].

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Fig 1. Rates of monitoring panel indicator light symbols.

https://doi.org/10.1371/journal.pone.0323386.g001

Generally, the result in Fig 2 indicates that a higher percentage of participants were familiar with most of the dashboard indicator light symbols. Out of the 25 symbols presented, most participants recognized 16 (64%) of the symbols. Seat Belt indicator symbols have the highest percentage of familiarity (94.0%). This is not surprising because all vehicles are equipped with seatbelts which is one of the passive safety devices. The seatbelts are worn to minimise damages and reduce the risk of injuries to vehicle occupants (i.e., driver and passengers) during the time of impact or crash [20]. The battery charging warning indicator symbol light was the second highest to be recognized (92%). This could be attributed to the fact that the battery is the main source of power for the vehicle with any associated fault being signalled onto the dashboard for the driver to detect. Most of the drivers might have witnessed any battery related challenge accounting for the second highest identification and recognition. On the contrary, the automatic shift lock symbol received the least recognition (26%) by participants. This could be due to the fact that it is only associated with automatic transmitted vehicles which some of participants might have not used.

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Fig 2. Familiarity of the dashboard indicator light symbols.

https://doi.org/10.1371/journal.pone.0323386.g002

3.2. Interpretation of dashboard indicator light symbols

Generally, out of the 25-dashboard indicator light symbols presented, participants interpreted 11 (44%) correctly and 14 (56%) wrongly. Seatbelt indicator symbol received the highest correct interpretation of 96.1% which could be attributed to it being common in all vehicles and related to safety of vehicle’s occupants. Additionally, it could also be that the majority of vehicles now used in Ghana have visual and auditory alerts for all seats. This was followed by battery charging warning light symbol (89.7%) which might also be due battery being the main power source of all vehicles and thus a very important feature drivers must know about. On the contrary, the automatic parking light and automatic shift lock dashboard indicator light symbols received (75.8%) and (70.4%) wrong responses respectively. This could probably be due to the association of these symbols to automatic transmitted vehicles only, which some of the participants might have not driven. In general, the percentages of dashboard indicator light symbols that the participants don’t know were very low with rear fog light symbols recording 11.8%, which is the highest percentage. This is because most vehicles used do not have rear fog lights, leading to lower understanding among participants.

The International Standards Organization (ISO) recommends that 85% correct response of graphical symbols indicate a well-known symbol [21,22]. Using the ISO criterion, dashboard indicator symbol lights with correct responses of 85% and above were regarded as having high understanding and well recognized, whereas correct responses with less than 85% were classified as having low understanding and poorly recognized. Out of the 25 symbols, only two symbols namely battery charge warning and seatbelt indicator symbols had correct responses greater than 85%; an indication of high understanding and well interpretation of the symbols. This is not surprising since these two dashboard indicator symbols have pictorial design and are very important and common in all vehicles. Thus, high understanding and interpretation are regarded as familiarity of the symbol which implies a complete state of knowing the symbols very well.

3.3. Comprehensibility of dashboard indicator light symbols

Table 3 was used to categorise drivers’ comprehensibility into nine comprehension scores for easy comparison among the symbols. This nine-score comprehension categorization was adopted from the study of Campbell et al. [14], which conducted comprehension testing of active safety symbols. As shown in Table 4, the categorization ranges from intended exact matching of the symbols to the meaning (1) to critical confusions or errors in the meaning leading to unsafe action (9).

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Table 3. Rating scales for categorising and scoring comprehensibility.

https://doi.org/10.1371/journal.pone.0323386.t003

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Table 4. Dashboard indicator light symbols comprehension ratings in percentage.

https://doi.org/10.1371/journal.pone.0323386.t004

The categorisation provides a structured framework for assessing how accurately participants interpret symbols, ranging from precise understanding to misinterpretation or lack of response. This categorization systematically evaluates comprehension levels, distinguishing varying degrees of accuracy and relevance in participants’ interpretations. Using this rating scale, identified areas for improving symbol clarity and effectiveness.

Based on the categorization ratings as indicated in Table 4, the first column labelled “1-2 High,” is defined as high comprehension which results from the combination of 1 and 2 scoring categories. The column labelled “3-4 Low” is defined as low comprehension was formed from the combination of scores from scoring categories 3 and 4. The column labelled “5-8 None” is defined as no comprehension and a reflection of combined scores from scoring categories 5,6, 7 and 8. The column labelled “9 Crit. Con.” Is defined as critical confusion which is scores from scoring category 9. The critical confusions or errors category represent responses by participants that do not match the exact meaning. For instance, a forward collision warning symbol being perceived by a participant to indicate a rear collision [14].

It can be observed that out of the 25 symbols presented, 6 (24%) had high comprehension, 1 (4%) were comprehended low and majority 18 (72%) had no comprehension across board. The results clearly show that participants were not able to comprehend most of the dashboard indicator light symbols. The six highly comprehended symbols were “check engine light”, “low fuel level”, “coolant temperature warning light”, “engine oil pressure light”, “seatbelt indicator” and “airbag indicator symbol lights’‘. These highly comprehended indicator light symbols are commonly seen on vehicle dashboards and drivers are likely to recognize and comprehend appropriately. Generally, none of the symbols was associated with the critical confusion category of rating. These symbols are effectively communicated to drivers and are likely to be recognized and responded to appropriately in various driving situations.

3.4. Appropriateness of the dashboard indicator light symbols

The appropriateness ranking technique [23] was employed to determine the suitability of the various symbols. Table 5 shows the appropriateness ranking of the dashboard indicator light symbols. The symbols were ranked into three categories namely ‘most appropriate’, ‘appropriate’ and ‘not appropriate’. It was observed that low fuel level symbol was ranked as the most appropriate with 82.9%, followed by engine oil pressure light symbol (66.6%) and seatbelt indicator symbol (53.3%). This could be due to the familiarity of these symbols since they are common and related to all vehicles. In addition, they are warning indicator lights which call for much concern. However, automatic parking light and 4*4 engagement gear symbols were ranked not appropriate with 99% and 87.7% respectively. In addition, the security door lock symbol was the third ranked as not appropriate with 86.7%. These dashboard indicator symbols are associated with some specific vehicles and not common to all vehicles which might account for their unfamiliarity to most drivers.

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Table 5. Appropriateness ranking of dashboard indicator light symbols.

https://doi.org/10.1371/journal.pone.0323386.t005

Across board, the appropriateness ranking analysis was very consistent with the comprehensibility assessment on low fuel level, engine oil pressure light and seatbelt, coolant temperature and washer fluid warning light indicator symbols being highly comprehended. This result was in agreement with a similar study that evaluated in-vehicle symbols for an intersection avoidance system [24] and powertrain and Advanced Driver Assistance Systems [16]. This suggests that drivers tend to accurately recognize and interpret dashboard symbols that are critical to vehicle operation and safety. Possibly, these indicators are well-designed and possibly reinforced through driver experience and training.

3.5. Cross-tabulation of familiarity and comprehensibility of the dashboard indicator light symbols

Even though the prior result has shown that the percentage of symbol familiarity were greater than the comprehension, it is possible that drivers may be very familiar with the dashboard indicator light symbols, but may not/or may comprehend the symbols. Hence, comprehension level (i.e., whether drivers understand the message or information communicated by the symbols) of drivers who are familiar (i.e., the state of knowing the symbols very well) with the symbols was determined. The existence of significant differences between the familiarity and comprehension levels of the symbols was further examined using a Chi-square test. The Chi-square analysis was based on the following null and alternative hypothesis that:

H0: The familiarity and comprehension level of dashboard indicator light symbols are independent of each other (null hypothesis).

H1: The familiarity and comprehension level of dashboard indicator light symbols are dependent on each other (alternative hypothesis).

From Table 6, it was observed that drivers who were familiar with dashboard indicator light symbols, the majority comprehended them. This finding corresponds with previous studies on road traffic signs [13]. These could be attributed to the fact that these symbols are very common and might often be observed by most drivers. Likewise, the proportion of drivers who were not familiar with the symbols, majority did not comprehend the symbols. This finding implies that these categories of drivers may have been ignoring the warning signs and they are likely not motivated to check vehicle status when these lights signal on the vehicle dashboard.

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Table 6. Cross-tabulation analysis of familiarity and comprehension level.

https://doi.org/10.1371/journal.pone.0323386.t006

However, it is interesting to know that even though, drivers were familiar with some symbols like Rear fog light”, “4x4 gear”, “washer” and “Automatic parking light”, the majority (72%, 54.4%, 70.6% and 64% respectively) did not comprehend the meaning of these symbols. This suggests that drivers could recognize the indicator light symbols, they are likely to comprehend wrongly. In line with study on road signs the finding implies that familiarity with dashboard indicator light symbols does not necessarily mean that drivers understand [25].

Similarity, it was thought-provoking to note that although some drivers were unfamiliar with symbols such as “Battery lights warning” and “Seat belts warning” on their dashboard, the majority (61% and 80% respectively) of these drivers were able to understand the meanings conveyed by these symbols. It could be that because red lights signify urgent action, there is a level of common knowledge among drivers regarding these dashboard warning signs, even if they are not familiar with these symbols.

Taking into consideration the 5% level of significance and the critical chi-square value of 3.84, it was observed that the estimated chi-square values for all the dashboard indicator lights were higher than the critical chi-square value indicating that there is a statistical difference between familiarity and comprehension. This explains that familiarity and comprehension of these indicator symbols are significantly associated and dependent on each other. Hence, the null hypothesis is rejected. This confirmed that drivers who were familiar with these indicator light symbols were more likely to comprehend than those who were not familiar. The result supports the findings of previous studies that familiarity will improve comprehension [15]. This confirmed that drivers who were familiar with these indicator light symbols were more likely to comprehend than those who were not familiar.

3.6. Generalized ordered logistic regression model

The demographic factors such as educational background, class of driving licence, years of driving experience and attitude of regularly checking the instruments panel as well as comprehensibility of various dashboard indicator light symbols were entered as independent variables in the model. Driver’s preventive maintenance compliance was used as a response variable. This indicates whether drivers take action, such as checking their vehicle’s status or addressing any issues, when they see the indicator lights illuminate on the vehicle dashboard. Action might involve inspecting the light, or seeking assistance from a mechanic. In this study, a p-value of 0.05 suggests that there is enough evidence to conclude that there is a significant relationship observed.

3.6.1. Impact of demographic factors on preventive maintenance compliance.

The result in Table 7 shows that a driver with secondary and tertiary education background has a positive significant association with drivers’ preventive maintenance compliance with an odd ratio of 2.297 and 4.414 respectively, which suggests that for every one unit increase in secondary and tertiary educational background, the odds of drivers’ preventive maintenance compliance are 2.3 and 4.4 times higher respectively when the dashboard indicator light appears. This indicates that drivers with higher levels of education are more likely to initiate preventive maintenance of their vehicles, especially when they observe an indicator light appearing on the dashboard. In accordance with previous study, this could be attributed to a greater understanding of the importance of vehicle maintenance and safety [3,4] among individuals with higher educational attainment. Similarly, a previous study demonstrated a notable correlation between drivers’ educational backgrounds and their propensity to engage in vehicle maintenance behaviours [26], particularly in response to symbols.

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Table 7. Estimated model results.

https://doi.org/10.1371/journal.pone.0323386.t007

The result further indicates a negative significant relationship between drivers’ preventive maintenance compliance and drivers with licence B and C, suggesting that drivers with licence B and C are 0.003 and 0.105 times less likely, respectively, to comply with preventive maintenance practices when a dashboard indicator light illuminates. Conversely, holders of licences D and E display a markedly different pattern of action, being significantly more motivated to address maintenance issues promptly. This is evidenced by higher odds ratios of 3.665 and 3.102 respectively. This suggests that drivers with licences D and E are approximately 3.7 and 3.1 times more likely, respectively, to comply with preventive maintenance practices when a dashboard indicator light appears.

Regarding driving experience, drivers with less than 1 year of experience and those with 5–10 years of experience show negative significant coefficients indicating decreased likelihood of driver’s compliance to preventive maintenance practices when the dashboard indicator lights appear. Possibly, novice drivers with less than 1 year of experience may lack the familiarity and confidence to interpret dashboard indicators accurately or may prioritise other aspects of driving over vehicle maintenance. In line with previous study, these drivers may lack the confidence to address maintenance issues independently [3,4]. Similarly, drivers with 5–10 years of experience may have settled into driving habits and routines, potentially becoming complacent to maintenance-related signals.

Conversely, drivers with 11–14 and 15–20 years driving experience are 2.375 and 1.912 times more likely to comply with preventive maintenance when the dashboard indicator lights illuminate. These drivers likely have extensive driving experience and knowledge over the years, including the importance of proactive vehicle maintenance. Perhaps, experienced drivers may have encountered various maintenance issues and appreciate the importance of addressing dashboard indicators promptly to ensure vehicle safety and performance. The finding is contrary to the previous study, which found driving experience as not a contributing factor to correctly identifying a symbols’ meaning nor what action to take in response to the symbol [4].

Similarly, drivers who regularly and sometimes check on the dashboard while driving are 5.349 and 2.243 times respectively more likely to comply with preventive maintenance practices when the dashboard indicator lights illuminate compared to drivers who rarely or never check on the vehicle dashboard. This finding suggests that habitual dashboard monitoring heightened vigilance of the driver to detect and respond to changes in vehicle status promptly. While some drivers may not consistently monitor the dashboard, their occasional habit may still contribute to a greater awareness of vehicle status.

4.1. Limitations and future study

The study focused on a specific set of dashboard symbols, which may limit the generalizability of its findings. Future research should consider a broader range of symbols to obtain a more comprehensive understanding of driver comprehension. Another notable limitation of this study pertains to the comprehensibility of certain symbols, particularly those associated with specific vehicle types. Different vehicle types (e.g., passenger cars, trucks, motorcycles, electric vehicles) often have unique dashboard symbols tailored to their specific functionalities. Some of these symbols may not be universally recognized or understood by all participants, especially those who are not familiar with the particular makes and models. For instance, electric vehicles (EVs) include symbols related to battery charge and regenerative braking, which may not be familiar to drivers of traditional internal combustion engine vehicles. This may introduce a potential bias in the evaluation, as participants’ familiarity with vehicle type and their corresponding symbols could influence their ability to accurately interpret the symbols. Besides, a study has also found cultural differences affecting symbol comprehension, which ties into driving environments and conditions [30]. Therefore, future studies could examine the effects of vehicle type as well as regional and cultural differences, on the familiarity and comprehension of dashboard symbols. Additionally, studies could explore how to design dashboard symbols that accommodate diverse driving populations.

Moreover, the study relied on self-reported data, which introduces potential bias as drivers may overestimate their familiarity and comprehension of dashboard indicator light symbols. Future studies could address this limitation by incorporating in-vehicle monitoring systems to observe real-time driver reactions to dashboard indicators, providing more objective and accurate data on driver behaviour and comprehension.