Next Article in Journal
Application of Three-Dimensional Vision Technology in Dance
Previous Article in Journal
AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
ASI
Volume 7
Issue 5
10.3390/asi7050079
asi-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact?

by
Giuseppina Tovillo
,
Mariachiara Rapuano
,
Alessandro Milite
and
Gennaro Ruggiero
*
Laboratory of Cognitive Science and Immersive Virtual Reality, CS-IVR, Department of Psychology, University of Campania “Luigi Vanvitelli”, Viale Ellittico, 31, 81100 Caserta, Italy
*
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2024, 7(5), 79; https://doi.org/10.3390/asi7050079
Submission received: 4 June 2024 / Revised: 2 August 2024 / Accepted: 23 August 2024 / Published: 30 August 2024
Browse Figures
Versions Notes

Abstract

:
Since in the automotive field colors play an important role, the present study tried to answer the following questions: is the perceived quality (PQ) of the vehicle interior color different after visually exploring the car body color? If so, how? Here, exploiting immersive virtual reality simulations and eye-tracking technology, participants were asked to visually explore an unbranded car in different exterior/interior color combinations and rate its PQ. Fixation duration (time eyes are fixed on a target) was considered as an implicit measure of visual attention allocation while PQ evaluations were considered as explicit measures of individual preferences for car colors. As for eye-tracking data, the results showed that white and red car exteriors affected the attention to interiors with the fixation duration being longer for gray than black interiors. Moreover, the subjective evaluations of car PQ predicted eye-tracking patterns: as the negative evaluation increased, the fixation duration on car interiors also increased. Overall, these preliminary results suggested the need to further explore the relationship between PQ and attentional/motivational processing as well as the role of subjective aesthetic preferences for color combinations in the automotive field.

1. Introduction

Research has shown that the choice to buy a new vehicle is based on several factors such as brand value and the visual perception of exterior and interior features [1,2]. Among visual characteristics, the color of the vehicle seems to play an important role in customers’ buying decisions [3,4,5,6,7]. From a psychological and applied point of view, the literature on color perception has a long tradition and a broad field of approaches (e.g., [8,9,10,11,12,13,14,15,16,17,18]). Research has demonstrated that human color preferences are classifiable into three types: phenomenological (i.e., experience-based), biological (i.e., neural activity to distinct colors), and ecological (i.e., affective responses to colors) [19,20]. Furthermore, according to the harmony color perspective (e.g., [21]), individual aesthetic preferences for color combinations rely on different attributes such as the color arrangement, proximity, similarity, and familiarity that, altogether, produce a pleasing and satisfying effect [21,22]. This is of crucial importance considering that the aesthetic quality of a product influences consumer attitudes and is a determinant factor of a product’s marketplace success [23,24]. Color favors processes of consolidation of objects’ identity [25] and plays a role in decision-making processes on what customers like and dislike (e.g., [5]). Consistently, the association of color with an object changes its preference by people [26]. Color is also a useful cue to distinguish between objects of similar shape and aids the visual segmentation of objects from their backgrounds and the recognition of visual scenes (e.g., [27]).
In the automotive field, colors drive the positive experience of a vehicle and increase its perceived quality (PQ) [28,29]. PQ does not only refer to the customer’s subjective evaluation of a vehicle or brand value [30,31,32]; it rather embraces aspects such as the aesthetic and functional attributes in satisfying customers’ needs [33,34], the quality and reputation of the product [35,36] related to the purpose of the purchase, the alternatives in the market, users’ expectations, and previous experience [30]. Evidence has shown positive and negative associations of colors, e.g., it has been shown that the most preferred colors are white, gray, black, and red as compared to others [6,28,37] and that color repetition and harmony principles can foster the perception of a pleasant interior car ambience [2,29,38]. In this perspective, Wagner and colleagues [2] aimed at identifying patterns in color choice of potential car customers by asking the participants to create different vehicle interior designs, after which they compared the most and least preferred color compositions. The results showed that the most preferred colors were brown and beige, while the least preferred was gray.
Therefore, although the literature has addressed color perception in the automotive field, to our knowledge, no study has investigated whether the perception of the car’s exterior color could influence the PQ of the interior colors [1,6,18,28,29].
Here we aimed at understanding whether and to what extent different color combinations of cars’ exterior/interior sections affect users’ PQ. Specifically, we wondered whether the PQ of a car interior is affected after visually exploring its exterior. Throughout immersive virtual reality (IVR) simulations, participants were asked to visually explore an unbranded car presented in different exterior/interior color combinations (e.g., same color: white exterior–white interior; different colors: white exterior–black interior, etc.) and rate its PQ with an ad hoc 6-item questionnaire recording the following aspects of the interior car ambience: (a) comfort; (b) pleasantness; (c) quality; (d) originality; and (e) efficacy.
During the visual exploration, eye-tracking technology was used. Several psychology and neuroscience studies have recorded eye movement to understand the visual, cognitive, attentional, and motivational aspects of human performance [39,40]. Evidence has been reported that the vehicle purchase decision can be attributed to color after a short visual exploration (up to about 90 s) [41]. This implies that eye-tracking can be a useful tool to assess individuals’ behavior and to interpret their responses to different visual stimuli [42].
Here, the eye-tracking patterns, indexed by fixation duration (i.e., the amount of time spent looking at a visual target), represent a reliable measure of individual differences in attentional and motivational processes [43,44]. Therefore, we expected the visual exploration to be different depending on the color combinations of the car’s exterior and interior parts. Specifically, based on color harmony principles and attentional/motivational mechanisms, we put forward the following hypotheses:
(1)
A shorter fixation duration when exterior and interior color combinations are perceived as familiar (e.g., black interiors) or similar (e.g., same color combinations).
(2)
A longer fixation duration for unfamiliar or unexpected color combinations (e.g., color combinations with red). We supposed that new or unfamiliar stimuli may draw more attention than familiar or expected ones [45,46,47].
(3)
Subjective PQ evaluations have a role in individuals’ attentional behavior (e.g., eye-tracking patterns).
Moreover, due to male/female differences in automotive color preference, gender differences were also investigated [16].
Finally, the discussion of the main findings, practical implications tied to the automotive field, limitations, and future studies are presented.

2. Materials and Methods

2.1. Participants

The sample size was determined with G*power 3.1.9.2 software [48]. With α = 0.05, power (1-β) = 0.90, one group of participants and four repeated measures (average correlations among measures = 0.50), the minimum required sample size was 30 participants to detect an effect size (Cohen’s f) = 0.25. Thirty-four students (18 females) aged 18–35 (M = 24.71; SD = 4.64) participated in the experiment in exchange for course credits. They had normal or corrected-to-normal vision. To exclude deficits in color perception, participants underwent the Ishihara test for color blindness [49]. Furthermore, participants were presented with a semi-structured clinical interview to exclude visuo-spatial deficits [50]. Nobody claimed discomfort or vertigo during the IVR experience and reported being aware of the experimental purpose. The study complied with the requirements of the local Ethics Committee of the Department of Psychology of the University of Campania L. Vanvitelli (Italy) (prot. #7, 8/2023) and the 2013 Helsinki Declaration [51].

2.2. Setting, Virtual Reality, and Eye-Tracking Apparatus

The experiment took place in a sound-proofed room of the Laboratory of Cognitive Science and Immersive Virtual Reality (CS-IVR; Dept. of Psychology, University of Campania L. Vanvitelli Caserta, Italy). The workstation comprised a computer with a large screen (23.8″), a comfortable chair to simulate the driver’s seat and the IVR equipment. This last included the HTC Vive (HTC, Corporation, Taipei, Taiwan) head-mounted display (HMD) having two OLED display panels with a resolution of 1080 × 1200 per eye, with a refresh rate of 120 Hz and a 110-degree field of view and integrated with the Tobii Eye-tracking Retrofit system (https://www.tobii.com/, Tobii Technology, Stockholm, Sweden, accessed on 3 January 2023). The IVR system continuously tracked participants’ head orientation through two cameras connected to the HMD. Visual information was updated in real-time.

2.3. Virtual Stimuli

All the virtual stimuli were modeled using Blender 3.1.2 software (https://www.blender.org, Blender Foundation, Amsterdam, The Netherlands, accessed on 2 December 2022) on a 1:1 scale and consisted of three-dimensional (3D) pictures of exterior/interior parts of an unbranded car placed in an empty parking area. According to the literature, the car doors represent the most observed area of interest (AOI) before entering the car [52,53]. Therefore, the 3D pictures of the exterior part depicted the car door in four different colors: white, black, gray, and red, respectively (Figure 1a). The 3D pictures of the interior depicted the following AOIs from the driver’s perspective: the steering wheel, the dashboard, and the gearbox [54] in four different colors: white, black, gray, and red, respectively. The combination of the different colors for the exterior/interior car parts resulted in 16 scenarios. Therefore, with respect to the exterior, the interior scenarios could have the same color (e.g., white exterior–white interior; black exterior–black interior, etc.) or different colors (e.g., white exterior–black interior; black exterior–red interior, etc.) (Figure 1b). Colors were selected based on data from the automotive industry concerning the best-selling car colors (from the year 2000 to 2022) and customer preferences [55].

2.4. Color Perceived Quality Questionnaire

Based on the literature review of previous studies about PQ in the automotive industry [28,56,57], we selected the most commonly used items to measure perceived quality and devised the Color Perceived Quality Questionnaire (CPQQ), an ad hoc 6-item questionnaire to evaluate five different aspects of PQ: comfort (How uncomfortable are the interiors?); pleasantness (How unpleasant is the color of the interiors?); quality (How poor is the quality of the vehicle?); originality (How banal is the color of the interiors?); and efficacy (How relaxing is the color of the interiors? how disturbing is the color of the interiors?) (Cronbach’s alpha = 0.80). Each item was rated on a 9-point Likert scale (1 = not at all; 9 = very much).

2.5. Procedure

After giving their written consent, participants were asked to fill out a questionnaire concerning their interest in cars and their frequency of driving. Overall, participants declared themselves to be car users (91.18% of the sample had a driving license) and drive frequently. Subsequently, the experimenter gave instructions about the experimental procedure and asked participants to stand on a pre-marked position (i.e., a comfortable chair), which corresponded to the driver’s seat in the IVR scenario. The subject’s position remained the same throughout the entire experimental session. After that, participants were asked to wear the HMD and to freely explore the virtual environment by moving their heads. The experimental session was divided into 2 blocks, with a 5 min break in between. In each block, the 16 scenarios, corresponding to the experimental trials, were presented in randomized order (total trials = 32). The first block started with a training session with 3D pictures not included in the experiment to let the participants become familiar with the virtual scenarios and procedure. Each trial consisted of a fixation cross appearing for 500 ms, followed by the car exterior (5 s) and interior (5 s) scenarios (see Figure 2). Once the car interior scenario disappeared, participants were again in the empty parking area while the experimenter administered the 6-item CPQQ. Participants provided verbal answers recorded by the experimenter. The entire experimental session lasted about 40 min. Finally, at the end of the experiment, participants evaluated their color preferences (Table 1) and their experience with the virtual stimuli.

2.6. Data Treatment and Statistical Analysis

2.6.1. Data Treatment

As for the eye-tracking data, in each trial the gaze duration (i.e., the total amount of time spent looking at the whole IVR scenario) was recorded. Thereafter, the fixation duration (in seconds) was computed for specific AOIs.
Specifically, for the exterior parts, we considered the time participants spent looking at the entire car door over a 5 s time window; for the interior parts, we considered the time participants spent looking at the steering wheel, the dashboard, and the gearbox, respectively, over a 5 s time window.
As for the subjective PQ evaluation, in each trial, the mean scores (range 1–9) for each PQ aspect (i.e., comfort, pleasantness, originality, and efficacy; henceforth dimensions) were computed.

2.6.2. Data Analysis

First, a Chi-square analysis was performed to explore whether the color preferences for a car did or did not depend on its exterior/interior sections.
Then, to account for potential confounding factors and individual differences, we performed two mixed model analyses considering the exterior car colors per se and then their combinations for interiors. For both analyses, the GAMLj module of the Jamovi statistical platform [58,59] was used; all the models were built with “subjects” as the cluster variable and “condition” (exterior color vs. interior colors) and “participants’ gender” (to test for possible gender differences) as the factors. The effects of condition, gender, and their interaction were estimated as fixed effects; a random intercept across subjects was included.
Finally, the role of the subjective PQ evaluations on the participants’ attentional behavior toward the vehicle was tested with a multiple regression analysis (forward method) considering the mean scores of each dimension of the CPQQ as predictors and the total fixation duration (in each exterior/interior color combination condition) as the criterion.

3. Results

3.1. Chi-Square Analysis

The analysis was significant: x2 = 57.99, df(9), p < 0.00001. The results showed that car color and exterior/interior car section preferences were associated. Specifically, participants preferred the color black for car exterior/interior sections, while they did not appreciate the color red for the exterior/interior sections (p < 0.05) (see Table 1).

3.2. Eye-Tracking Data

3.2.1. Mixed Model Analysis for Exterior Car Colors

As shown in Table 2, the model was not significant (all Fs < 1).

3.2.2. Mixed Model Analysis for Car Exterior/Interior Color Combinations

Only significant results were reported.

White Exterior and Its Interior Color Combinations

As shown in Table 3, the main effect of condition (COND) was significant (F(4) = 2.503, p < 0.05). The Bonferroni post hoc test showed that, after exploring the white exterior, the fixation duration was longer for the gray interior than the black one (difference = 0.50; SE = 0.17; t = 2.93; df = 128; p = 0.04).

Red Exterior and Its Interior Color Combinations

As shown in Table 4, the main effect of the condition (COND) was significant (F(4) = 2.514, p < 0.05). The Bonferroni post hoc test showed that, after exploring the red exterior, fixation duration was longer for the gray interior than the black one (difference = 0.51; SE = 0.18; t = 2.83; df = 128; p = 0.05).

3.3. Regression Analysis

Only significant results were reported.
White exterior/black interior color combinations. The results showed that the model with the dimension “poor quality” was significant (F(1,32) = 6.94, p < 0.05, R = 0.42, R2 = 0.18). As shown in Table 5, as the negative PQ evaluation increased, the fixation duration for the black interior also increased.
Black exterior/black interior color combinations. The model with four predictors was significant (F(4,29) = 3.49, p < 0.05, R = 0.57, R2 = 0.32); however, only the dimension “disturbing” significantly contributed to the model. As shown in Table 6, as the negative PQ evaluation increased, the fixation duration for the black interior also increased.
Black exterior/gray interior color combinations. The model with three predictors was significant (F(3,30) = 3.31, p < 0.05, R = 0.50, R2 = 0.25); however, only the dimensions “disturbing” and “uncomfortable” significantly contributed to the model. As shown in Table 7, as both the negative PQ evaluations increased, the fixation duration for the gray interior also increased.
Gray exterior/gray interior color combinations. The model with three predictors was significant (F(3,30) = 3.27, p < 0.05, R = 0.50, R2 = 0.25); however, only the dimension “poor quality” significantly contributed to the model. As shown in Table 8, as the negative PQ evaluation increased, the fixation duration for the gray interior also increased.
Gray exterior/black interior color combinations. The model with four predictors is significant (F(4,29) = 5.99, p < 0.01, R = 0.67, R2 = 0.45); however, only the dimensions “disturbing” and “banal” significantly contributed to the model. As shown in Table 9, as the negative PQ evaluations increased, the fixation duration for the black interior also increased.
Red exterior/red interior color combinations. The model with three predictors was significant (F(3,30) = 1.92, p < 0.05, R = 0.40, R2 = 0.16); however, only the dimension “uncomfortable” significantly contributed to the model. As shown in Table 10, as the negative PQ evaluation increased, the fixation duration for the red interior also increased.
Red exterior/gray interior color combinations. The model with three predictors was significant (F(3,30) = 1.92, p < 0.05, R = 0.40, R2 = 0.16); however, only the dimension “uncomfortable” significantly contributed to the model. As shown in Table 11, as the negative PQ evaluation increased, the fixation duration for the gray interior also increased.

4. Discussion

The purpose of this study was to investigate whether the perceived color of a car’s exterior could influence the perceived quality (PQ) of its interior. To this end, we exploited immersive virtual reality (IVR) simulations of an unbranded car by integrating eye-tracking technology and explicit PQ subjective measures.
As regards the eye-tracking data, partially in line with our hypotheses, the results showed that attentional processes towards color aesthetic features of car interiors can be different after visually exploring its exteriors. Indeed, with respect to all other conditions, when the white and red exteriors were combined with gray and black interiors, the fixation duration was longer for the gray than the black color. As we expected, the color combinations with red received more attention presumably because they were less familiar (or unexpected). This result would seem to be consistent with attentional processing perspectives on shifting attention from a familiar (or known) to an unfamiliar (or unexpected) visual target [45,46,47]. However, participants also showed the same eye-tracking patterns with color combinations that should be considered more familiar (i.e., color combinations with white). Presumably this could depend on subjective color preferences (i.e. what people like or dislike) [5].
As regards the subjective PQ evaluations, the regression analysis showed that individual preferences for color combinations have a role in allocating attentional resources to the vehicle. Indeed, as the negative evaluations for car appearance increased, the fixation duration on car interiors also increased. In particular, when the interior sections were presented in black (after seeing the white exterior) and gray (after seeing the gray exterior), these were perceived as being of poor quality and were fixed on longer; when the interior sections were presented in black (after seeing the black and gray exteriors) and gray (after seeing the black exterior), these were perceived as disturbing and fixed on longer; when the interior sections were presented in gray (after seeing the black and red exterior) and red (after seeing the red exterior), these were perceived as uncomfortable and fixed on longer; when the interior sections were presented in black (after seeing the gray exteriors), these were perceived as banal and fixed on longer. A possible explanation for these mixed results could be given in the light of color harmony perspective [21]. As stated before, individual aesthetic preferences for color combinations rely on different attributes that, taken together, give a pleasing or satisfying effect; however, which combinations give rise to the pleasing effect is still an open question, as the perception of color harmony changes from individual to individual, from culture to culture, and even over time [60]. Thus, presumably here, some exterior/interior combinations more than others were perceived as not harmonious or pleasant.

5. Conclusions

From a practical point of view, these results seem to suggest that, in the automotive field, the aesthetics of color combinations for the exterior and interior car sections may influence the way the vehicle attracts potential customers’ attention and their further appreciation.
More importantly, the results seem to indicate that subjective preferences regarding the aesthetic qualities of car color combinations play a role in attention and motivational processes of the potential customer. This aspect is crucial considering that products perceived in a pleasant way work better and affect users’ purchase decisions [61]. However, we recognize that the present study had limitations which are discussed below (Section 5.1).

5.1. Limitations and Future Studies

We acknowledge that the results presented are preliminary and lack conclusive guidelines in the automotive field as to how exterior colors influence the car’s PQ (especially its interior) and the behavior of potential customers. Future research is needed to clarify and further explore the relationship between colors, perceived quality, and attentional processes as well buying decisions. The current study relied on a single explicit measure evaluating the PQ, tapping into a few aspects (i.e., comfort, pleasantness, efficacy, originality, and quality) of the subjective experience with the vehicle. It would be interesting to include more comprehensive measures of subjective aesthetic preferences and also to consider measures of whether and to what extent certain color combinations are (or are not) perceived as harmonious. For example, the data presented here does not allow us to discern whether certain color combinations are unpleasant per se or because they do not fulfil their purpose (e.g., to look at a high-quality, comfortable, original or reliable vehicle). Furthermore, other critical aspects could be represented by the small sample size (including only undergraduate students) and the fact that the research did not take into account the possible influence of different car brands and designs, as well as other visual characteristics (textures, color patterns, and materials) on PQ and attentional processes.

Author Contributions

Conceptualization, G.R. and A.M.; methodology, G.R.; software, G.T. and M.R.; validation, G.R. and A.M.; formal analysis, G.R. and M.R.; investigation, G.T.; resources, G.T.; data curation, G.T.; writing—original draft preparation, G.R., G.T. and M.R.; writing—review and editing, M.R.; visualization, M.R.; supervision, G.R.; project administration, G.R.; funding acquisition, G.R. and A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PNR 2015-2020, DEsign Solutions for Industry 4 REady processes (DESIRE) project, grant number (project ID) ARS01_01063.

Institutional Review Board Statement

The study was conducted in accordance with the 2013 Declaration of Helsinki and approved by the local Ethics Committee of the Department of Psychology of the University of Campania L. Vanvitelli (Italy), prot. #7, 8/2023.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Holder, D.; Inkermann, D.; Krasteva, P.; Vietor, T.; Maier, T. Integrated Product Gestalt Design Method for the Analysis and Definition of Interface Elements Regarding Exterior and Interior. In Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Switzerland, 2018; pp. 888–897. [Google Scholar]
  2. Wagner, A.-S.; Kilincsoy, Ü.; Vink, P. Visual customization: Diversity in color preferences in the automotive interior and implications for interior design. Color Res. Appl. 2018, 43, 471–488. [Google Scholar] [CrossRef]
  3. Koizumi, K.; Kanke, R.; Amasaka, K. Research on automobile exterior color and interior color matching. In Proceedings of the International Conference on Management and Information Systems (ICIS), Milan, Italy, 15–18 December 2013. [Google Scholar]
  4. Mandel, R.; Klarzyk, J.; Maier, T. Impact of visual preconditioning on the comfort rating of the vehicle interior. In Internationales Stuttgarter Symposium, Proceedings of the 15th Stuttgart International Symposium Automotive and Engine Technology, Stuttgart, Germany, 17–18 March 2015; Springer Fachmedien Wiesbaden: Wiesbaden, Germany, 2015; pp. 1523–1534. [Google Scholar]
  5. Ou, L.-C.; Luo, M.R.; Woodcock, A.; Wright, A. A Study of Colour Emotion and Colour Preference. Part I: Colour Emotions for Single Colours. Color Res. Appl. 2004, 29, 232–240. [Google Scholar]
  6. Satake, I.; Xin, J.H.; Tianming, T.; Hansuebsai, A.; Ando, K.; Sato, T.; Kajiwara, K.; Ohsawa, S. A Comparative Study of the Emotional Assessment of Automotive Exterior Colors in Asia. Prog. Org. Coat. 2011, 72, 528–540. [Google Scholar] [CrossRef]
  7. Tantanatewin, W.; Inkarojrit, V. The Influence of Emotional Response to Interior Color on Restaurant Entry Decision. Int. J. Hosp. Manag. 2018, 69, 124–131. [Google Scholar] [CrossRef]
  8. Sheppard, J.J.; Nimeroff, I. Human Color Perception: A Critical Study of the Experimental Foundation. Phys. Today 1969, 22, 81. [Google Scholar] [CrossRef]
  9. Verity, E. Colour Observed; Macmillan Press: London, UK, 1980. [Google Scholar]
  10. Ostwald, W. The Color Primer. In The Color Primer; De Gruyter: Berlin, Germany, 1969; pp. 17–71. [Google Scholar]
  11. Walraven, J. Discounting the Background—The Missing Link in the Explanation of Chromatic Induction. Vis. Res. 1976, 16, 289–295. [Google Scholar] [CrossRef] [PubMed]
  12. Goldstein, K. Some Experimental Observations Concerning the Influence of Colors on the Function of the Organism. Am. J. Phys. Med. Rehabil. 1942, 1, 147–151. [Google Scholar] [CrossRef]
  13. Strauss, E.D.; Schloss, K.B.; Palmer, S.E. Color Preferences Change After Experience with Liked/Disliked Colored Objects. Psychon. Bull. Rev. 2013, 20, 935–943. [Google Scholar] [CrossRef]
  14. Camgöz, N.; Yener, C.; Güvenç, D. Effects of Hue, Saturation, and Brightness on Preference. Color Res. Appl. 2002, 27, 199–207. [Google Scholar] [CrossRef]
  15. Lichtlé, M.-C. The Effect of an Advertisement’s Colour on Emotions Evoked by Attitude Towards the Ad. Int. J. Advert. 2007, 26, 37–62. [Google Scholar] [CrossRef]
  16. Barli, Ö.; Bilgili, B.; Dane, Ş. Association of Consumers’ Sex and Eyedness and Lighting and Wall Color of a Store with Price Attraction and Perceived Quality of Goods and Inside Visual Appeal. Percept. Mot. Ski. 2006, 103, 447–450. [Google Scholar] [CrossRef]
  17. Cubukcu, E.; Kahraman, I. Hue, Saturation, Lightness, and Building Exterior Preference: An Empirical Study in Turkey Comparing Architects’ and Nonarchitects’ Evaluative and Cognitive Judgments. Color Res. Appl. 2008, 33, 395–405. [Google Scholar] [CrossRef]
  18. Kato, T. Does bluish-white make a product less attractive? Empirical study on the exterior design of automobiles. Procedia Comput. Sci. 2022, 207, 701–708. [Google Scholar] [CrossRef]
  19. Palmer, S.E.; Schloss, K.B. An Ecological Valence Theory of Human Color Preference. Proc. Natl. Acad. Sci. USA 2010, 107, 8877–8882. [Google Scholar] [CrossRef]
  20. Palmer, S.; Schloss, K. An Ecological Account of Individual Differences in Color Preferences. J. Vis. 2010, 10, 412. [Google Scholar] [CrossRef]
  21. Burchett, K.E. Color harmony attributes. Color Res. Appl. 1991, 16, 275–278. [Google Scholar] [CrossRef]
  22. Bleicher, S. Contemporary Color: Theory and Use, 3rd ed.; Routledge: London, UK, 2023. [Google Scholar]
  23. Bloch, P.H. Seeking the Ideal Form: Product Design and Consumer Response. J. Mark. 1995, 59, 16. [Google Scholar] [CrossRef]
  24. Huang, S.-M. The Rating Consistency of Aesthetic Preferences for Icon-Background Color Combinations. Appl. Ergon. 2012, 43, 141–150. [Google Scholar] [CrossRef]
  25. Zelanski, P.; Fisher, M.P. Colour: For Designers and Artists. Leonardo 1989, 22, 456. [Google Scholar] [CrossRef]
  26. Schloss, K.B.; Palmer, S.E. Aesthetic Response to Color Combinations: Preference, Harmony, and Similarity. Atten. Percept. Psychophys. 2010, 73, 551–571. [Google Scholar] [CrossRef]
  27. Gegenfurtner, K.R.; Rieger, J. Sensory and Cognitive Contributions of Color to the Recognition of Natural Scenes. Curr. Biol. 2000, 10, 805–808. [Google Scholar] [CrossRef]
  28. Prasetyo, Z.; Iftadi, I.; Damayanti, R.W. Study of consumer’s preference rating on car’s exterior color design. In Proceedings of the Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering (ICEVT & IMECE), Surakarta, Indonesia, 4–5 November 2015. [Google Scholar]
  29. Tofle, R.B.; Schwarz, B.; Yoon, S.Y.; Max-Royale, A.; Des, M.E. Color in Healthcare Environments: A Critical Review of the Research Literature; The Coalition for Health Environments Research (CHER): San Fransisco, CA, USA, 2004. [Google Scholar]
  30. Castleberry, S.B.; McIntyre, F.S. Consumers Quality Evaluation Process. J. Appl. Bus. Res. 2011, 8, 74. [Google Scholar] [CrossRef]
  31. Garvin, D.A. What Does “Product Quality” Really Mean? MIT Sloan Management Review, 15 October 1984; pp. 25–43. [Google Scholar]
  32. Germann, R.; Kurth, L.; Matthiesen, S. Disruptive Factors in the Evaluation of Perceived Quality Aspects—Consideration of the Brand Influence. Int. J. Ind. Ergon. 2020, 76, 102931. [Google Scholar] [CrossRef]
  33. Parasuraman, A.; Berry, L.L.; Zeithaml, V.A. Perceived Service Quality as a Customer-Based Performance Measure: An Empirical Examination of Organizational Barriers Using an Extended Service Quality Model. Hum. Resour. Manag. 1991, 30, 335–364. [Google Scholar] [CrossRef]
  34. Schmitt, R.; Quattelbaum, B. Enthusing Perceived Product Quality-A Concept for the Distribution of Sensorial Perception Information; CORE: Los Angeles, CA, USA, 2009. [Google Scholar]
  35. Sebastianelli, R.; Swift, C.; Tamimi, N. Factors Affecting Perceived Learning, Satisfaction, and Quality in the Online MBA: A Structural Equation Modeling Approach. J. Educ. Bus. 2015, 90, 296–305. [Google Scholar] [CrossRef]
  36. Permatasari, F.F. The Role of Corporate Social Responsibility, Perceived Quality and Corporate Reputation on Purchase Intention. SSRN Electron. J. 2020. [CrossRef]
  37. Adams, R. Black, White, Silver & Grey Are Still the Most Popular Colours for New Cars. Focus Pigment. 2011, 2011, 1–2. [Google Scholar]
  38. Feisner, E.A. Color Studies; Fairchild Books: New York, NY, USA, 2014. [Google Scholar]
  39. Issa, B.; Morgan-Short, K.; Villegas, B.; Raney, G. An Eye-Tracking Study on the Role of Attention and Its Relationship with Motivation. EUROSLA Yearb. 2015, 15, 114–142. [Google Scholar] [CrossRef]
  40. Duchowski, A.T. A Breadth-First Survey of Eye-Tracking Applications. Behav. Res. Methods Instrum. Comput. 2002, 34, 455–470. [Google Scholar] [CrossRef]
  41. Powers, K.I. Color Design and Purchase Price: How Vehicle Colors Affect What Consumers Pay for New and Used Cars. In The Psychology of Design; Routledge: Oxfordshire, UK, 2015; pp. 75–90. [Google Scholar]
  42. Pinheiro, R.B.d.O.; Pradhananga, N.; Jianu, R.; Orabi, W. Eye-Tracking Technology for Construction Safety: A Feasibility Study. In Proceedings of the 33rd International Symposium on Automation and Robotics in Construction, Auburn, AL, USA, 18–21 July 2016; International Association for Automation and Robotics in Construction (IAARC): Endinburgh, UK, 2016. [Google Scholar]
  43. Castelhano, M.S.; Henderson, J.M. Stable individual differences across images in human saccadic eye movements. Can. J. Exp. Psychol./Rev. Can. Psychol. Exp. 2008, 62, 1–14. [Google Scholar] [CrossRef]
  44. Rayner, K.; Li, X.; Williams, C.C.; Cave, K.R.; Well, A.D. Eye movements during information processing tasks: Individual differences and cultural effects. Vis. Res. 2007, 47, 2714–2726. [Google Scholar] [CrossRef]
  45. Wetherford, M.J.; Cohen, L.B. Developmental Changes in Infant Visual Preferences for Novelty and Familiarity. Child Dev. 1973, 44, 416. [Google Scholar] [CrossRef] [PubMed]
  46. Perone, S.; Spencer, J.P. Autonomous Visual Exploration Creates Developmental Change in Familiarity and Novelty Seeking Behaviors. Front. Psychol. 2013, 4, 648. [Google Scholar] [CrossRef] [PubMed]
  47. Laeng, B.; Alnaes, D. Pupillometry. In Eye Movement Research; Springer International Publishing: Cham, Switzerland, 2019; pp. 449–502. [Google Scholar]
  48. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.G. Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar]
  49. Shinobu Ishihara, E.J. Tests for Color Blindness. Am. J. Ophthalmol. 1918, 1, 376. [Google Scholar] [CrossRef]
  50. Ilardi, C.R.; Iavarone, A.; Villano, I.; Rapuano, M.; Ruggiero, G.; Iachini, T.; Chieffi, S. Egocentric and allocentric spatial representations in a patient with Bálint-like syndrome: A single-case study. Cortex 2021, 135, 10–16. [Google Scholar] [CrossRef]
  51. World Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. J. Am. Med. Assoc. 2013, 310, 2191. [Google Scholar] [CrossRef]
  52. Jayaram, K.; Mohanraj, J. Influence of Tolerancing Methods and Aspects of Perceived Quality on Side Closures of Luxury Cars. Procedia CIRP 2016, 43, 160–165. [Google Scholar] [CrossRef]
  53. Striegel, S.S.; Zielinski, D. Perceived quality of the split-line design and quality. In Proceedings of the 15th International Design Conference, Dubrovnik, Croatia, 21–24 May 2018; Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb: Zagreb, Croatia; The Design Society: Glasgow, UK, 2018. [Google Scholar]
  54. Shinogi, T.; Aihara, S.; Amasaka, K. Constructing an Automobile Color Matching Model. IOSR J. Bus. Manag. 2014, 16, 07–14. [Google Scholar] [CrossRef]
  55. Carvertical, Research: Car Colors and Why They Matter. Available online: https://www.carvertical.com/blog/car-colors-and-why-they-matter#body__0e61b3d99ec6 (accessed on 27 July 2024).
  56. Caberletti, L.; Elfmann, K.; Kummel, M.; Schierz, C. Influence of ambient lighting in a vehicle interior on the driver’s perceptions. Light. Res. Technol. 2010, 42, 297–311. [Google Scholar] [CrossRef]
  57. Odabaşioğlu, S.; Olguntürk, N. Effects of Coloured Lighting on the Perception of Interior Spaces. Percept. Mot. Ski. 2015, 120, 183–201. [Google Scholar] [CrossRef]
  58. Love, J.; Selker, R.; Marsman, M.; Jamil, T.; Dropmann, D.; Verhagen, J.; Ly, A.; Gronau, Q.F.; Smíra, M.; Epskamp, S.; et al. JASP: Graphical Statistical Software for Common Statistical Designs. J. Stat. Softw. 2019, 88, 1–17. [Google Scholar] [CrossRef]
  59. Selker, R.; Love, J.; Dropmann, D. The ‘jamovi’ Analyses [Package] 2019. Available online: https://cran.r-project.org/web/packages/jmv/jmv.pdf (accessed on 29 July 2024).
  60. Westland, S.; Laycock, K.; Cheung, V.; Henry, P.; Mahyar, F. Colour harmony. Colour Des. Creat. 2007, 1, 1–15. [Google Scholar]
  61. Norman, D.A. Introduction to this special section on beauty, goodness, and usability. Hum. Comput. Interact. 2004, 19, 311–318. [Google Scholar] [CrossRef]
Asi 07 00079 g001
Figure 1. The picture represents an example of stimuli: (a) 3D picture of the exterior car door colored in gray, from the driver’s perspective; (b) 3D picture of the car interior colored in gray from the driver’s perspective.
Figure 1. The picture represents an example of stimuli: (a) 3D picture of the exterior car door colored in gray, from the driver’s perspective; (b) 3D picture of the car interior colored in gray from the driver’s perspective.
Asi 07 00079 g001
Asi 07 00079 g002
Figure 2. This picture represents an example of a trial once the experimental session began: a fixation cross appeared for 500 ms; afterward, the exterior 3D picture of a (gray) car door was presented (5 s), followed by the 3D picture of a (white) car interior (5 s); once the stimuli disappeared, the empty parking area was presented and the experimenter administered the 6-item CPQQ (free time).
Figure 2. This picture represents an example of a trial once the experimental session began: a fixation cross appeared for 500 ms; afterward, the exterior 3D picture of a (gray) car door was presented (5 s), followed by the 3D picture of a (white) car interior (5 s); once the stimuli disappeared, the empty parking area was presented and the experimenter administered the 6-item CPQQ (free time).
Asi 07 00079 g002
Table 1. Contingency table with observed and expected (in parentheses) frequences for car color and car section preferences.
Table 1. Contingency table with observed and expected (in parentheses) frequences for car color and car section preferences.
Subj. Car Color PreferencesWhite Color
Observed and Expected Frequences		Black Color
Observed and Expected Frequences (p < 0.05)		Gray Color
Observed and Expected Frequences		Red Color
Observed and Expected Frequences
(p < 0.05)
Most preferred for exterior4 (6.75)17 (8.25)10 (7.75)3 (11.25)
Most preferred for interior3 (6.75)14 (8.25)11 (7.75)6 (11.25)
Least preferred for exterior12 (6.75)1 (8.25)7 (7.75)14 (11.25)
Least preferred for interior8 (6.75)1 (8.25)3 (7.73)22 (11.25)
Column tot.27333145
Table 2. Mixed model analysis for car exterior colors (white, black, gray, and red).
Table 2. Mixed model analysis for car exterior colors (white, black, gray, and red).
FNum dfDen dfpICC
COND0.778396.00.5090.82 *
GENDER0.195132.00.662
COND * GENDER0.979396.00.406
* ICC for random intercept for subjects. Satterthwaite method for degrees of freedom.
Table 3. Mixed model analysis for white exterior and interior color combinations (white, black, gray, and red).
Table 3. Mixed model analysis for white exterior and interior color combinations (white, black, gray, and red).
FNum dfDen dfpICC
COND2.5034128.00.0460.28 *
GENDER0.637132.00.431
COND * GENDER0.9084128.00.461
* ICC for random intercept for subjects. Satterthwaite method for degrees of freedom.
Table 4. Mixed model analysis for red exterior and interior color combinations (white, black, gray, and red).
Table 4. Mixed model analysis for red exterior and interior color combinations (white, black, gray, and red).
FNum dfDen dfpICC
COND2.5144128.00.0450.25 *
GENDER0.121132.00.730
COND * GENDER0.7584128.00.554
* ICC for random intercept for subjects. Satterthwaite method for degrees of freedom.
Table 5. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the white car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance level are reported.
Table 5. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the white car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance level are reported.
BSt. ErrBeta (β)tp
WB-poor quality2.410.290.422.630.01
Table 6. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the black car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 6. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the black car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
BB-disturbing0.600.870.983.720.001
BB-uncomfortable−0.220.20−0.28−1.100.28
BB-relaxing0.190.090.421.950.06
BB-poor quality−0.200.140.30−1.420.16
Table 7. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the black car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 7. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the black car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
BG-disturbing0.400.130.652.970.006
BG-uncomfortable−0.350.160.522.260.03
BG-banal0.120.090.231.340.19
Table 8. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the gray car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 8. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the gray car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
GG-poor quality0.580.190.723.010.005
GG-unpleasant−0.280.21−0.36−1.360.18
GG-relaxing0.170.150.231.100.28
Table 9. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the gray car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 9. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the black interior after seeing the gray car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
GB-disturbing0.630.150.854.080.0003
GB-banal0.200.090.312.250.03
GB-relaxing0.200.110.331.790.08
GB-poor quality−0.220.15−0.26−1.470.15
Table 10. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the red interior after seeing the red car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 10. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the red interior after seeing the red car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
RR-uncomfortable−0.260.12−0.64−2.150.04
RR-unpleasant0.110.100.361.210.24
RR-banal0.110.100.221.190.24
Table 11. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the red car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
Table 11. The table shows the effect of subjective PQ evaluations on fixation duration when participants visually explored the gray interior after seeing the red car exterior. Unstandardized (B) and standardized (Beta) coefficients, standard error, t values, and significance levels are reported.
BSt. ErrBeta (β)tp
RG-uncomfortable0.360.140.691.690.02
RG-relaxing0.240.130.401.740.09
RG-poor quality−0.210.12−0.37−1.690.10
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tovillo, G.; Rapuano, M.; Milite, A.; Ruggiero, G. Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact? Appl. Syst. Innov. 2024, 7, 79. https://doi.org/10.3390/asi7050079

AMA Style

Tovillo G, Rapuano M, Milite A, Ruggiero G. Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact? Applied System Innovation. 2024; 7(5):79. https://doi.org/10.3390/asi7050079

Chicago/Turabian Style

Tovillo, Giuseppina, Mariachiara Rapuano, Alessandro Milite, and Gennaro Ruggiero. 2024. "Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact?" Applied System Innovation 7, no. 5: 79. https://doi.org/10.3390/asi7050079

Article Metrics

Back to TopTop