Effects of brand and brand trust on initial trust in fully automated driving system

Zixin Cui; Nianzhi Tu; Makoto Itoh

doi:10.1371/journal.pone.0284654

. 2023 May 4;18(5):e0284654. doi: 10.1371/journal.pone.0284654

Effects of brand and brand trust on initial trust in fully automated driving system

Zixin Cui ^1,^*, Nianzhi Tu ¹, Makoto Itoh ²

Editor: Wojciech Trzebinski³

PMCID: PMC10159113 PMID: 37141217

Abstract

Before Automated Driving Systems (ADS) with full driving automation (SAE Level 5) are placed into practical use, the issue of calibrating drivers’ initial trust in Level 5 ADS to an appropriate degree to avoid inappropriate disuse or improper use should be resolved. This study aimed to identify the factors that affected drivers’ initial trust in Level 5 ADS. We conducted two online surveys. Of these, one explored the effects of automobile brands and drivers’ trust in automobile brands on drivers’ initial trust in Level 5 ADS using a Structural Equation Model (SEM). The other identified drivers’ cognitive structures regarding automobile brands using the Free Word Association Test (FWAT) and summarized the characteristics that resulted in higher initial trust among drivers in Level 5 ADS. The results showed that drivers’ trust in automobile brands positively impacted their initial trust in Level 5 ADS, which showed invariance across gender and age. In addition, the degree of drivers’ initial trust in Level 5 ADS was significantly different across different automobile brands. Furthermore, for automobile brands with higher trust in automobile brands and Level 5 ADS, drivers’ cognitive structures were richer and varied, which included particular characteristics. These findings suggest the necessity of considering the influence of automobile brands on calibrating drivers’ initial trust in driving automation.

Introduction

Following the taxonomy and definition of driving automation by the Society of Automotive Engineers [1], an Automated Driving System (ADS) with full driving automation (Level 5) can operate vehicles under all road conditions, just as a skilled human driver, and does not require any supervision. Although Level 5 ADS is a long way from reality, its practical application is always expected since it can further optimize the traffic environment and help decrease the risk rate of traffic accidents.

To realize the practical application of Level 5 ADS in society, both technical issues and social and human issues, such as drivers’ trust calibration, should be solved. Based on Lee and See’s definition of human-automation trust [2], drivers’ trust in driving automation is the attitude of the driver that driving automation will help achieve their driving targets in uncertain and vulnerable driving situations. When the drivers’ trust in driving automation is comparable to the trustworthiness of driving automation itself, it can be considered as an appropriate calibration of trust [3]. This implies that the drivers’ trust matches the actual ability of driving automation. In contrast, distrust or over-trust appear when the drivers’ trust is lower or higher than the trustworthiness of driving automation, respectively [2,4]. Appropriate trust in driving automation is essential because it has a positive relationship with usage intention and social acceptance of driving automation [5]. Instead, distrust can lead to disuse [6], and therefore, can become a great obstacle to the social realization of driving automation [7,8]. However, over-trust can also lead to misuse [6], and cause fatal accidents. Level 5 ADS can work under all road conditions, just as a skilled human driver, and can reduce risk in unmanageable conditions, such as flooded roads and glare ice [1]. However, drivers’ over-trust and misuse of Level 5 ADS are problems that are impossible to predict. Therefore, the appropriate calibration of drivers’ trust in Level 5 ADS should be considered to ensure their appropriate use in the future.

Development of human trust in automation

Trust is dynamic because it develops as the relationship between the trustor and trustee advances [2,9]. According to the dynamic trust framework [10], trust in Level 5 ADS develops successively via dispositional, initial, ongoing, and post-task trusts. Dispositional trust reflects a driver’s propensity to trust [11,12], which depends on their culture, age, gender, personality traits [12], and early trust-related experiences [13]. Since dispositional trust develops before the first interaction with driving automation or even before the recognition of driving automation, it is not influenced by the characteristics of driving automation itself and driving contexts [12]. Initial trust refers to trust that develops from dispositional trust when the driver has just known about driving automation but has not used it. Therefore, initial trust is influenced by both dispositional trust and drivers’ indirect experiences related to driving automation [10], such as information acquired from others [2]. Unlike dispositional and initial trust, ongoing and post-task trust develop by relying on direct contact with driving automation. Ongoing trust is formed during interactions based on the drivers’ initial trust and perceived real-time characteristics of driving automation. Post-task trust is formed after completing an interaction with driving automation [10,11].

We aimed to focus on initial trust as it played an important role in the connection between dispositional, ongoing, and post-task trust [10,11]. Furthermore, it could affect users’ initial decision of engaging in automation [2]. In Merritt and Ilgen’s study on operators’ trust in the Automatic Weapons Detector (AWD) [11], operators’ initial trust, formed based on their first impression of the AWD, had a positive relationship with operators’ post-task trust, which was measured immediately after the use of AWD. Hence, the higher the level of initial trust, the higher was the level of post-task trust, regardless of whether the level of trust changed with the first experience of automation. Therefore, besides increasing the experience of automation to adjust ongoing and post-task trust from the prior levels of trust to a more appropriate level [14,15], adjusting the level of initial trust before the first experience may be an effective way for adjusting ongoing and post-task trust, with lesser difficulty and cost. In addition, Merritt and Ilgen [11] found that initial trust was positively associated with operators’ usage decisions regarding automation. Therefore, excessive and inadequate initial trust before experiencing automation may lead to initial misuse and disuse, respectively. Finally, vehicles adopting Level 5 ADS have not yet entered the market. Although the public has knowledge of Level 5 ADS, they still do not have any experience or even an adequate understanding of it. Therefore, the development of the public trust in Level 5 ADS is at the stage of initial trust.

Factors that influence the formation of the trust

According to Lee and See’s conceptual model of the dynamic process of trust in automation [2], prior information regarding automation can form the basis for initial trust before interaction. Analytic information helps trustors develop trust in a cognitively demanding process with rational assessments of whether automation is trustworthy. For example, reliability information [16] and analytic information provide trustors with direct evidence of the trustworthiness automation. Conversely, analogical information helps trustors develop trust in a less cognitively demanding process of assessing category membership. Analogical information such as the observed interface features of automation, acquired gossip related to automation, and perceived brand reputation, indirectly governs the formation of trust. Many empirical studies verified the influence of analytic information on trust formation [14,17]. Compared with low-reliability information, high-reliability information led to a higher level of trust in Level 5 ADS [16]. However, empirical evidence on analogical information is lacking. Although Forster et al. [16] focused on brand reputation, they found no effect on trust.

A brand is a name, symbol, trademark, or design used as an unique identifier of an individual, organization, or company in owned commodities and services to differentiate from those of its competitors [18,19]. Just as a person has a personality, a brand has a brand personality (a set of human characteristics associated with a brand), including sincerity (down-to-earth, honest, wholesome, and cheerful), excitement (daring, spirited, imaginative, and up-to-date), competence (reliable, intelligent, and successful), sophistication (upper-class and charming), and ruggedness (outdoorsy and tough) [20]. According to Celmer et al.’s framework for trust in branded autonomous vehicles [21], drivers’ perceived brand personality will influence their trust and performance expectations. In other words, before judging the actual trustworthiness of an ADS, drivers may make a prior judgment regarding the identity and brand personality of the automobile brand that developed it. Therefore, analogous information that can influence the formation of initial trust in Level 5 ADS should not be limited to brand reputation. Instead, brands should be considered a whole that can convey information regarding each aspect of brand personality. However, empirical evidence is required to verify the effect of automobile brands or a perceived brand personality on drivers’ trust in Level 5 ADS, which was an aim of this study.

Besides the automobile brand itself, drivers’ trust in the automobile brand that developed the driving automation may be another possible influencing factor in the formation of the initial trust. Previous studies on consumer goods verified that consumers’ purchase intention largely depended on their trust in the brand [22–24]. For consumer goods in an online shopping context, website brands influenced consumers’ trust and perceived risk towards online retailers, and thus, in turn, influenced consumers’ purchase intention [25]. Likewise, equipped with driving automation, automobiles are originally a kind of consumer good, referred to as products purchased by consumers for their enjoyment and improvement of living standards, including houses, food, and clothing [26]. Thus, when consumers plan to purchase a new car equipped with ADS, they would consider whether it was developed by a trustworthy automobile brand. However, there is limited direct evidence of the relationship between trust in automobile brands and ADS products. Therefore, the existence and extent of the impact of trust in automobile brands on trust in ADS should be examined.

Cognition of automobile brand

If drivers’ trust in ADS depends on and can be influenced automobile brands that develop it, it is necessary to discuss the kinds of automobile brands that can earn more trust in themselves and an ADS. Previous studies found that consumers’ trust in a brand depended on various factors, such as consumer satisfaction [27], perceived product quality of the brand [27,28], business behavior and climate of the employees and organization [27,29–31], and corporate credibility [32]. In addition, according to the salient value similarity (SVS) model proposed by Earle and Cvetkovich [33], people trusted and entrusted a person or an organization that possessed the same or similar viewpoints, feelings, and cognition of values. Therefore, many factors that influence brand trust were related to people’s prior knowledge and information regarding their cognition of the brand. In a study on interpersonal trust among managers and professionals in organizations, McAllister [34] addressed the nature of interpersonal trust, the extent of affect-based and cognition-based trust. The level of cognition-based trust was higher than that of affect-based trust and was necessary for its development to an extent. Here, cognition-based trust refers to making a choice on whom to trust based on “good reasons” constituting evidence of trustworthiness according to prior knowledge and information [35]. Similarly, human-computer trust also includes both cognitive and affective components, although affective components are stronger indicators [36]. Accordingly, people’s cognition of a trustee plays an important role in their trust in a person, organization, or machine. Furthermore, owing to different experiences, people can have different perceptions of the same brand. To better understand the relationships among drivers’ trust in Level 5 ADS and automobile brands, clarifying their cognition bases of the automobile brands that develop Level 5 ADS is necessary.

Purpose and hypotheses

Automobile brands and drivers’ trust in automobile brands have the potential to influence drivers’ initial trust in Level 5 ADS. However, few studies have provided direct evidence to verify whether or to what degree these influences existed.

Therefore, this study aimed to examine whether drivers’ initial trust in Level 5 ADS depended on automobile brands that developed the Level 5 ADS and can be positively influenced by drivers’ trust in the automobile brands. In addition, we aimed to explore drivers’ cognition characteristics of automobile brands that could earn much more initial trust in Level 5 ADS by clarifying drivers’ cognitive structures on the automobile brands. To achieve these, we conducted two online surveys and our hypotheses are as follows:

Hypothesis 1 (H1): There would be a positive relationship between drivers’ trust in automobile brands and their initial trust in Level 5 ADS.

Hypothesis 2 (H2): The levels of drivers’ initial trust in Level 5 ADS would differ across different automobile brands.

Hypothesis 3 (H3): The cognitive structures of automobile brand drivers with higher trust and lower trust would have distinctly different characteristics.

Survey 1

Methods

Procedure and participants

An online survey in Japanese was conducted by commissioning an Internet Research Company in June 2020. In total, 206 anonymous Japanese drivers (103 men and 103 women), aged 18–69 years (M = 44.6 years, SD = 11.8), with an ordinary license participated (from June 8–9, 2020).

Participants were asked to complete nine questionnaires regarding nine randomly chosen automobile brands that developed driving automation. The questionnaires were presented to the participants in a random order to counterbalance the order of the automobile brands. In addition, due to ethical requests from the ethical review board of University of Tsukuba, the actual names of the brands were used for data collection; however, they were hidden under the code names like Brand 1 in this paper. Brands 1, 2, and 3 were famous automobile brands in Japan. Brand 4 was a famous automobile brand in the USA. Brands 5, 6, 7, and 8 were famous automobile brands from Germany. Brand 9 was a famous automobile brand in Sweden. The questionnaire consisted of two sections for each automobile brand. The first and second sections used a “brand trust scale” and “human-automation trust scale” to measure drivers’ trust in automobile brands and initial trust in the Level 5 ADS developed, respectively. Participants were instructed to imagine that the automobile brand would sell cars with Level 5 ADS. Before the participants completed the questionnaires, necessary explanations for Level 5 ADS were provided following descriptions from the SAE [37] (A Level 5 ADS is capable of all dynamic driving tasks including continuous lateral and longitudinal vehicle motion control and object and event detection and response. In addition, when continuous operation is difficult, Level 5 ADS can do sustained and unconditional responses without any expectation that a user will respond to a request to intervene.)

This survey was approved by the ethical review board of the Faculty of Engineering, Information and Systems at the University of Tsukuba (approval number 2020R369). Although informed consent could not be obtained since all participants were anonymously and randomly recruited in the sample pool of the Internet Research Company, all the participants were voluntary to finish the questionnaires without any prejudice and were paid according to the regulations of the Internet Research Company.

Measurements

The human-automation trust scale was proposed by Muir and Moray [9], which included four items: predictability, dependability, faith, and overall trust (Table 1). The brand trust scale was an eight-item measure of the first factor, “trust,” of the “Japanese consumer-brand relationship scale” developed by Hatai [38] (Table 1). Both the scales were rated on a 7-point Likert scale (for brand trust, 1: I do not think so, 7: I think so; for human-automation trust: 1: Not at all, 7: Extremely high).

Table 1. Items in the trust questionnaires [9,38].

Scale	Item
Trust in brand	TB1 I trust this brand.
	TB2 I can be in sympathy with the products and services of this brand.
	TB3 If the products come from this brand, I will be relieved.
	TB4 If someone enquired regarding this brand, I will recommend it.
	TB5 I have a good image for this brand.
	TB6 There is no failure of this brand.
	TB7 This brand will not let me down.
	TB8 The quality of this brand’s products and services is excellent.
Trust in Level 5 ADS	TA1 To what extent can the behavior of the Level 5 ADS be predicted from moment to moment? (Predictability)
	TA2 To what extent can you count on the Level 5 ADS to do its job? (Dependability)
	TA3 To what extent will the Level 5 ADS be able to cope with other system states in the future? (Faith)
	TA4 To what extent do you trust the Level 5 ADS? (Overall trust)

Open in a new tab

Data analysis

To verify H1, structural equation modeling (SEM) was conducted using Amos version 26.0. Before the structural model was analyzed to examine the impact of trust in automobile brands on trust in Level 5 ADS, it was necessary to examine the goodness-of-fit (GOF) of the measurement model. Therefore, a confirmatory factor analysis (CFA) on the two variables was performed. Subsequently, the two measurement models were modified with modification indices [39] until a moderate GOF was obtained. After the reliability and validity of the two modified measurement models was examined, a structural model analysis was conducted, during which the overall GOF was evaluated using the recommended model fit indices. Commonly used fit indices included the Chi-square/df, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). Chi-square/df and RMSEA should be less than 5 (3 if strictly) and 0.5, respectively, and the GFI, AGFI, and CFI should be greater than 0.9 [40].

Since people of different ages and gender can develop initial trust in driving automation based on different factors, such as cognitive or emotional factors towards driving automation [41], it was also necessary to verify whether the effect of trust in automobile brands on trust in Level 5 ADS was invariant across gender and age. For sex, a multigroup invariance test was performed, and the invariance was indicated by a significant difference in the chi-square value (p < .05) [40] or ΔCFI, ΔGFI, ΔAGFI, and ΔRMSEA, which were not higher than 0.01 [42]. For age, the invariance was indicated by the non-significant moderating effect of age on the relationship. Therefore, a “moderation by interaction terms” method [43] was used, in which age was first multiplied with the scores of each item of trust in the automobile brand, and the subsequent combined effect was examined against trust in Level ADS.

Finally, to verify H2, a repeated-measures analysis of variance (ANOVA) was employed via SPSS version 26.0 to analyze the main effects of automobile brands on both trust in automobile brands and ADS. All data analyses were conducted using the “General Linear Model” → “Repeated measures” module. If sphericity was violated in Mauchly’s test (p < .05), the degrees of freedom were corrected by Greenhouse-Geisser correction, if ε ≤ 0.75, or HuynhFeldt correction, if ε > 0.75.

Results

CFA and evaluation of reliability and validity

The model fit indices of both the original measurement models did not show a moderate fit to data, trust in automobile brands: χ²₍₂₀₎ = 388.80, GFI = .949, AGFI = .908, CFI = .979, RMSEA = .100; trust in Level 5 ADS: χ²₍₂₎ = 74.60, GFI = .979, AGFI = .896, CFI = .990, RMSEA = .140. Therefore, for trust in automobile brands, items TB6, TB8, TB5, and TB2 were removed during modification to achieve a moderate GOF, χ²₍₂₎ = 3.19, GFI = .999, AGFI = .996, CFI = 1.00, RMSEA = .018. For trust in the Level 5 ADS, item TA1 (predictability) was removed, and thus a moderate GOF was obtained, χ²₍₀₎ = .000, GFI = 1.000.

The modified models showed moderate internal consistency for each factor since the composite reliability (CR) values were greater than 0.6 (Table 2). Moreover, all the factor loads of the items were greater than 0.5 and significant (p < .01). Both Average of Variance Extracted (AVE) and Square Multiple Correlations (SMC) values were greater than 0.5 (Table 2). These met the evaluation criteria of Hair et al. [40], and thus reflected the good convergence validity of the modified measurement models of trust in automobile brands and Level 5 ADS. Good discriminant validity was also considered since the square root of AVE for each factor was larger than the correlation coefficient between trust in automobile brand and Level 5 ADS (Table 3) [44].

Table 2. Reliability and convergence validity.

		Parameter significance estimation				Convergence validity
		United.	S.E.	T-value	P	Std. Estimate	SMC	C.R.	AVE
Trust in automobile brand	TB1	1				.886	.785	.938	.791
	TB3	1.093	0.017	63.81	*******	.944	.891
	TB4	0.947	0.02	47.388	*******	.819	.671
	TB7	1.014	0.017	58.29	*******	.904	.817
Trust in Level 5 ADS	TA2	1				.916	.839	.943	.846
	TA3	1.01	0.016	64.094	*******	.913	.834
	TA4	1.045	0.016	66.831	*******	.930	.865

Open in a new tab

Table 3. Discriminant validity.

	AVE	Trust in Level 5 ADS	Trust in automobile brand
Trust in Level 5 ADS	.791	.889
Trust in automobile brand	.846	.652	.920

Open in a new tab

Note: The numbers in bold are the square roots of the AVE on the left. 0.652 is the correlation coefficient between trust in automobile brand and Level 5 ADS.

SEM of drivers’ trust

Fig 1 shows the final SEM of drivers’ trust with a standardized path coefficient, as well as the reported fit index values. In the measurement model, faith, dependability, and overall trust were all significant factors that positively influenced trust in the Level 5 ADS (factor load = 0.91, 0.92, and 0.93, respectively, ps < .001). In the structural model, trust in automobile brands positively influenced trust in Level 5 ADS (Std. estimate = 0.652, p < .001). For the model fit, the values of chi-square/df was approximately 5, GFI, AGFI, and CFI were greater than 0.9, and RMSEA was less than 0.05. These results indicated an acceptable model fit [40]. In addition, both the models were invariant across gender (Table 4), which showed a positive effect of trust in automobile brands on trust in Level 5 ADS for both men (Std. estimate = 0.699, p < .001) and women (Std. estimate = 0.599, p < .001). The moderating effect of age on the relationship between trust in automobile brands and Level 5 ADS was not significant (men: Std. estimate = -0.021, p = .682; women: Std. estimate = 0.025, p = .573).

Table 4. Measurement and structural invariance across sex.

	Unconstrained	Measurement weights	Structural covariances	Measurement residuals
chi-square	121.518	128.492	137.776	216.618
df	26	31	33	41
p (GOF)	< 0.001	< 0.001	< 0.001	< 0.001
chi-square/df	4.674	4.145	4.175	5.283
GFI	0.982	0.981	0.98	0.97
AGFI	0.962	0.966	0.966	0.959
CFI	0.993	0.992	0.992	0.986
RMSEA	0.045	0.041	0.041	0.048
Δchi-square	/	6.974	16.258	95.1
Δdf	/	5	7	15
p (chi-square difference)	/	0.223	0.023	<0.001
ΔGFI	/	-0.001	-0.001	-0.010
ΔAGFI	/	0.004	<0.001	-0.007
ΔCFI	/	-0.001	<0.001	-0.006
ΔRMSEA	/	-0.004	-0.004	0.003

Open in a new tab

Differences in trust among automobile brands

The average scores of trust in automobile brands and Level 5 ADS for the nine automobile brands are shown in Fig 2.

Results of the repeated-measures ANOVA (after Greenhouse-Geisser correction) showed that the main effect of automobile brand was significant on both trust in automobile brands (F _{(5.75, 1179.37)} = 53.17, p < .001, partial η² = 0.206) and Level 5 ADS (F _{(6.21, 1272.96)} = 22.74, p < .001, partial η² = 0.100).

Post-hoc pairwise comparisons corrected by the Bonferroni method showed that for both trust in automobile brand and Level 5 ADS, the average scores for Brands 1 and 2 had no significant difference (p = .075; p = .538). However, they were significantly higher than the average scores for the other seven (Brands 3–9) (ps < .05). Furthermore, the average scores of both trust in automobile brand and Level 5 ADS for Brand 4 were significantly lower than those for almost all the others (Brands 1–3, 5–9) (ps < .01), except from the comparable result with Brand 5 (p = .225) for trust in Level 5 ADS. Among the average scores of both trust in automobile brand and Level 5 ADS for the rest (Brand 3, 5–9), there was almost no significant difference (ps > .05), except from significant differences between Brands 5 and 6 (p = .000), 5 and 8 (p = .015), and 6 and 9 (p = .018) for trust in automobile brand.

Survey 2

Results of the repeated-measures ANOVA on the main effect of automobile brand on trust showed that both trust in automobile brands and Level 5 ADS differed significantly across automobile brands. Subsequently, based on the results of the pairwise comparisons, we divided Brands 1 and 2 into a high trust group, Brand 4 into a low trust group, and the remaining six (Brands 3, 5–9) into a medium trust group. However, why some brands were highly trusted than others was still unknown. Consequently, Survey 2 was conducted to obtain cues from drivers’ cognitive structures of automobile brands.

Methods

Participants

An online survey in Japanese was conducted by commissioning an Internet research company in January 2021. Anonymous responses were received (from January 8–9, 2021) from 103 Japanese drivers (58 men and 45 women), aged 25–70 years (M = 50.1 years, SD = 12.5), with a normal license. Same to Survey 1, all participants were anonymous and voluntary to finish the questionnaires without any prejudice, and were paid according to the regulations of the Internet Research Company. This survey was also approved by the ethical review board of the University of Tsukuba (approval number 2020R369-1).

Data collection

We chose Brands 1 and 2 in the high trust group, 3 and 5 in the medium trust group, and 4 in the low trust group from Survey 1 as the research objects. We used the Free Word Association Test (FWAT) to collect data, a widely used method to reveal and analyze the cognitive structure of somebody or something [45–49]. The type and number of concepts retrieved from long-term memory and the links between them could be revealed [50].

The names of the above five brands were presented individually to participants as stimulus words. According to the previous application of FWAT, participants wrote down the next word associated with the previous words that they had written down rather than the stimulus word itself [45–48,50], called the chain reaction. To avoid this risk, each stimulus word (brand name) was written five times on one page, as shown in Fig 3. Participants were asked to write down the first five words associated with a stimulus brand for an unlimited duration when each stimulant brand appeared. Similar to Survey 1, the actual name of each brand was used to collect research data; however, they were hidden under code names like Brand 1 in this paper.

Data analysis

To verify H3, we analyzed FWAT data based on the content analysis method, widely used in previous studies and showed high reliability [45–48,50]. Combined with the study purpose, there were six analytical processes. 1) The valid word number for each brand was counted, which referred to the total number of words written down by participants after invalid words, such as “none” or “unknown” were excluded. 2) The effective rates of the valid words was calculated using the formula [valid word number / planned word number (5) * participant number (103)]. 3) The different word number for each brand was calculated. Different word numbers represented the number of words that were different from the others among valid words. Table 5 illustrates the differences between the valid word numbers and different word numbers with an example. 4) The correlation between the level of trust and valid word numbers as well as different word numbers was analyzed. 5) The words with the same meaning or property were categorized after those that were stated only once and not associated with other words were excluded. This process was assessed once more, and some words were merged with other similar words or shifted to other categories. 6) The word number of each category was calculated and drivers’ cognitive structures for each brand was drawn.

Table 5. An example for the different word number and valid word number.

Category	Different word	Valid word number
Brand personality	1. First-class	3
	2. Number one in Japan	3
	3. Number one in the world	5
The sum	3	11

Open in a new tab

Note: This is an example of part of the data. For the category “Brand personality,” three different words were obtained (the medium column); thus, the different word numbers were 3. These were stated repeatedly three, three, and five times, respectively; thus, the valid word numbers for the three different words were 3, 3, and 5, respectively, and the total valid word number was 11.

To ensure reliability of the word categorization, two external researchers examined whether each word belonged to the current category. The consistency between the results for each word provided by the two researchers was marked as “Consensus” or “Divergence.” Furthermore, the reliability was calculated by the formula [Consensus / (Consensus + Divergence) X 100] [51]. Since no word was marked “consensus” that the word did not belong to the current category, no word was moved to other categories. Finally, the intercourse reliability was 92%, which was considered reliable [52].

Results

Valid word number, different word number, and trust

Since some participants were unable to come up with all five words related to automobile brands, 540 (effective rate 20.97%) valid words were obtained for all automobile brands, with 338 different words (Table 6). Both valid and different word numbers for the high trust group (M = 142.5, M = 20.5) were higher than those for the medium trust (M = 103.5, M = 13.5) and low trust groups (M = 48, M = 6).

Table 6. Valid word number, different word number, and trust.

Trust	Brand	Valid word numbers	Effective rate	Different word number	Trust in brand	Trust in Level 5 ADS
High	1	163	31.65%	106	4.75	3.91
High	2	122	23.69%	77	4.48	3.74
Medium	3	132	25.63%	76	3.72	3.36
Medium	5	75	14.56%	46	3.70	3.28
Low	4	49	9.51%	33	3.15	3.08
Sum		540	20.97%	338	/	/

Open in a new tab

A correlation analysis was conducted to confirm the relationship between valid word numbers, different word numbers, and trust in automobile brands as well as trust in Level 5 ADS. Table 7 shows a significant positive correlation between valid word numbers and different word numbers and different word numbers and both trust in automobile brands as well as trust in Level 5 ADS. Furthermore, it also shows positive non-significant correlations between valid word number and both trust in automobile brand and trust in Level 5 ADS. However, Brand 3 is an exception. Although valid word numbers and different word numbers were between the two brands in the high trust group, Brand 3 belonged to the medium trust group, in which both the levels of trust in automobile brands and Level 5 ADS were lower than brands in the high trust group.

Table 7. The correlation coefficient of the word number and trust.

	Count of category	Valid word number	Different word number	Trust in automobile brand
Count of category
Valid word number	.647 (sig = .238)
Different word number	.732 (sig = .159)	.989^**
Trust in automobile brand	.829 (sig = .082)	*.857 (sig* = .064)**	.899^*
Trust in Level 5 ADS	.825 (sig = .082)	*.874 (sig* = .053)**	.920*	.995^**

Open in a new tab

Note

** The correlation is significant at 0.01 level

* The correlation is significant at 0.05 level.

Cognitive structure by categories

For each automobile brand, words listed only once were deleted, and the remaining (338 words) were categorized according to their meaning or property. Fig 4 shows the categories and word numbers of each category for each automobile brand.

The remaining words were divided into seven categories: “brand personality,” “product,” “person,” “country/region,” “brand/logo,” “advertising” and “others.” The category of “brand personality,” contained words that described participants’ perceived brand personality, such as “reliable” and “safe.” “Product” contained words in the name of specific products. “Person” contained names of persons related to the brand, such as the CEO or advertising spokesperson. For “country/region,” names of countries or cities in which the brands were located were mentioned. “Brand/logo” was related to the word that the brand or logo itself was associated with, such as aliases or what the logo looked like. Finally, words from the advertising slogans were contained in the category “advertising,” and words that do not belong to any categories were contained “others.” To clearly understand the drivers’ cognitive structure for each brand, we drew word clouds with the remaining words (Figs 5–9). However, for ethical reasons, we hid some words, such as the name of the product, that may easily lead readers to know what these brands are with mosaic. In Figs 5–9, black words on the periphery are the names of the categories, and words in different colors belong to different categories. For example, red words are the names of specific products and green words are the names of a person. For these words in the word clouds, the more frequently the word was associated with, the larger the font size of the word.

Importantly, the count of the category was higher for brands in the high trust group (M = 6) than for brands in the medium (M = 4.5) and low trust groups (M = 4). Furthermore, it was highly positively correlated with both trust in automobile brands and trust in Level 5 ADS, although it was non-significant for both (Table 7). Moreover, there was some universality and individuality in categorizing associated words among the brands. Approximately 40% of words relevant to “product” were associated with brands in the medium and high trust groups, whereas for the brand in the low trust group (Brand 4), there was no associated word relevant to “product”. For all brands in the medium and high trust groups, “others” was only a small part (the largest proportion was 36% for Brand 2) or did not exist. However, the proportion of “others” reached up to 73% for Brand 4 in low trust group. Furthermore, for Brand 3 in the medium trust group, there was no “brand personality,” while it existed for the other four brands. Lastly, for Brand 5 in the medium trust group, there was no “person,” while it existed for the others.

Words in cognitive structures related to unethical events

After the numbers of associated words and drivers’ cognitive structures were analyzed, the content of each associated word was examined individually so that any special cognition in drivers’ cognitive structures would not be missed. Finally, for Brand 3 in the medium trust group, some associated words were relevant to unethical events that occurred recently (23.6% of the total associated words). However, there were no negative words associated with the other four brands.

Discussion

This study explored the relationship among automobile brands, drivers’ trust in automobile brands, and drivers’ initial trust in Level 5 ADS. Furthermore, we clarified the common characteristics of drivers’ cognitive structures regarding automobile brands that could earn higher trust in automobile brands and higher initial trust in Level 5 ADS. We hypothesized that drivers’ initial trust in Level 5 ADS was different across automobile brands and positively impacted by drivers’ trust in automobile brands. Moreover, drivers’ cognitive structures regarding automobile brands in the high trust group had some common characteristics. These findings were discussed and integrated with previous studies to provide implications for the practical use of Level 5 ADS.

The impact of brand trust on initial trust in Level 5 ADS

Consistent with H1, we found that when drivers did not have any direct interaction experience with Level 5 ADS, their initial trust was directly affected by the level of their trust in the automobile brand that developed it. Specifically, according to the results of Survey 1, trust in the automobile brand had a positive impact on trust in Level 5 ADS, which was invariant across gender and age. Accordingly, for automobile brands, promoting drivers’ trust in automobile brands may also be a possible approach to promote drivers’ initial trust in Level 5 ADS. In addition, the positive impact of trust in automobile brands on trust in Level 5 ADS implied that over-trust in automobile brands could lead to over-trust in Level 5 ADS. Meanwhile, distrust in automobile brands could lead to distrust in Level 5 ADS. Previous studies revealed the effectiveness of elaborate practice and adequate knowledge of driving automation on the calibration of drivers’ initial trust in driving automation [15,53]. However, before calibrating drivers’ initial trust in driving automation, their prior level of trust in the automobile brand should also be considered as a precondition.

Effect of automobile brand on initial trust in Level 5 ADS

Since significant differences in the level of consumers’ brand trust existed among brands, we examined the discrimination of the chosen nine automobile brands at the level of drivers’ brand trust before we verified H2 to avoid a type I error. The results showed that drivers’ trust in automobile brands was indeed different across automobile brands, which indicated the good discrimination of the chosen automobile brands. According to the subsequent results, drivers’ initial trust in Level 5 ADS was also different across different automobile brands. Thus, H2 was verified. This implied that automobile brands influenced drivers’ initial trust in the Level 5 ADS, which provided empirical evidence for Lee and See’s conceptual model of the dynamic process of trust in automation [2] and Celmer et al.’s framework of trust in branded driving automation [21]. To adjust drivers’ initial trust in the Level 5 ADS, their prior identification and judgment of the automobile brand that developed the Level 5 ADS should be considered. Different trust calibration strategies and approaches are necessary for driving automation developed by different automobile brands.

Drivers’ cognitive structure of automobile brand

Based on the associated words analyses from automobile brands via FWAT, H3 was confirmed. Furthermore, several common characteristics of drivers’ cognitive structures of automobile brands earned higher trust in automobile brands and higher initial trust in Level 5 ADS.

Characteristic 1: Rich and varied

For automobile brands in the high trust group, both the number and category of associated words were far more than those in the medium and low trust groups. Therefore, for automobile brands that could earn higher trust in automobile brands and higher initial trust in Level 5 ADS, drivers had richer and more varied cognitive structures. This implied that to acquire higher trust in both the automobile brand itself and the Level 5 ADS, automobile brands should be well known and familiar to drivers, which was related to the accumulation of long-term brand promotion. Generally, familiarity was regarded as a precondition to trust somebody or something [54], and has been widely verified in different fields [55,56]. Ha and Perks [57] found that a high level of brand familiarity among consumers was a dimension of brand trust achievement.

Characteristic 2: Have approximately 40% of the cognition of the product

No word relevant to “product” was associated with Brand 4 in the low trust group. In contrast, for the brands in the high and medium trust groups, many automobile products were associated. Even though the word number of “product” was quite different for each brand, the proportions of “product” in the total word number was approximately 40%. Among these associated products, many were classic automobile products with decades of history and generations of innovation, and some were special products with special functions or techniques. This indicated that the quality assurance of products played an important role in brand development. Product quality was reported to influence the level of brand trust by creating consumer satisfaction [27] and influence customers’ loyalty to the brand [28].

Characteristic 3: Have some cognition more or less related to brand personality or related person

No words relevant to “person” or “brand personality” were associated with brands in the medium trust group. Thus, it could be considered that for brands with medium trust, although the part of “product” existed in drivers’ cognitive structures to almost the same degree as brands with high trust, there was always a missing piece, such as “person” or “brand personality.” Accordingly, use of the public influence of brand spokespersons to help promote brand influence and trust seemed effective. Although attractiveness of an advertisement spokesperson seemed relatively unimportant when consumers assessed a new high-technology product [58], a better fit between spokesperson and product attributes could help better shape consumers’ attitudes towards the brand [59]. In contrast, for automobile brands that could earn higher trust in the brand and higher initial trust in the products, drivers might have more cognition of brand personality, such as first-class, the best in the world, trustworthy, safe, and global. This was in accordance with a previous study that highlighted the relationship between brand personality and trust [60] and also provided empirical evidence for Celmer et al.’s framework of trust in branded driving automation [21].

Characteristic 4: Have no negative cognition about the unethical event in drivers’ cognitive structures

Focusing on Brand 3, the valid word number and different word numbers were both between the two brands in the high trust group, Brands 1 and 2, which implied that drivers’ cognitive structures for Brand 3 were almost as rich and varied as Brand 1s and 2. Hence, Brand 3 could also be highly trusted. However, it was contradictory as Brand 3 was in the medium trust group. Since Brand 3 was the only brand that has some associated words (23.6% of total associated words) relevant to unethical events, it was concluded that the negative cognition composed of these words could be invalid in drivers’ cognitive structures to help build trust or could even reduce the trust built previously. According to previous studies on business behavior and consumer trust, unethical business behavior and climate of the employees and organizations were significantly related to lower levels of trust [29,30]. In contrast, ethical business behavior positively influenced consumers’ trust [27,31]. Consequently, negative unethical events in the cognitive structure could lower drivers’ trust in automobile brands and their products. Therefore, it might be necessary for brands to avoid the formation of drivers’ negative cognition regarding such unethical events.

Practice implications

Our findings have several practical implications for calibrating initial trust in Level 5 ADS. To avoid the disuse and misuse of Level 5 ADS, the influence of automobile brands should be considered during the initial trust calibration. For automobile corporations, determining consumers’ cognition of their automobile brands and performing targeted brand management based on consumers’ cognition is required. This may help eliminate consumers’ distrust in their Level 5 ADS. In contrast, drivers’ over-trust in Level 5 ADS in some highly trusted automobile brands may exist, which can lead to misuse, and thus, cause negative consequences, such as severe accidents. Therefore, for a more appropriate initial trust of drivers in Level 5 ADS, it is also necessary to pay attention to the prevention of drivers’ over-trust in the automobile brands. Finally, considering the existing problems of distrust and over-trust in driving automation and common civil and market attributes of ADS with various SAE levels of driving automation [1], the practical implications may also be applicable to calibrating initial trust in ADS with other SAE levels of driving automation [1] besides Level 5 ADS.

Limitations

First, this study selected only nine automobile brands to verify the hypotheses in Survey 1. In addition, only five brands were further selected in Survey 2. However, besides the selected brands, many other brands or companies have been developing driving automation. Therefore, the relationships between automobile brands, trust in automobile brands, initial trust in Level 5 ADS, and drivers’ cognitive structures of automobile brands should be examined with more automobile brands. In contrast, since only one or two brands were selected for each trust group in survey 2, more conclusions regarding drivers’ cognitive structures of automobile brands could not be drawn. Moreover, all the findings should be further confirmed with experimental research that can manipulate brand characteristics to observe the differences in the levels of initial trust in driving automation. Finally, this study focused only on the brands of traditional automobile manufacturers. Currently, the social realization of driving automation is also associated with entities other than traditional automobile manufacturers, such as technology and transportation service companies (e.g., Google and Uber), which also provide technological or service support for the development of driving automation. Therefore, research can focus on trust formation when a vehicle with ADS was manufactured and promoted by multiple entities, instead of only traditional automobile manufacturers.

Conclusion

The level of drivers’ initial trust in the Level 5 ADS depended on the automobile brand that developed the Level 5 ADS and could be positively influenced by the levels of drivers’ trust in the automobile brands. For automobile brands that could earn higher levels of driver trust in the automobile brand and Level 5 ADS, drivers possessed richer and more varied cognitive structures with some particular characteristics. Consequently, the influence of automobile brands and drivers’ prior trust in automobile brands should be noted for the initial trust calibration of Level 5 ADS to avoid future disuse and misuse.

Supporting information

S1 File. Minimal underlying data set of Survey 1.

(PDF)

Click here for additional data file.^{(352KB, pdf)}

Acknowledgments

We thank our colleagues Suyang An and Fan Yang for their help with manuscript revision, and the anonymous reviewers for their comments that improved the manuscript.

Data Availability

Some data in this study cannot be shared publicly because data contain potentially identifying or sensitive brand and consumer information. Data are available from the Laboratory for Cognitive Systems Science, Faculty of Engineering, Information, and Systems at the University of Tsukuba (contact via question@css.risk.tsukuba.ac.jp) for researchers who meet the criteria for access to confidential data.

Funding Statement

This study was supported by the Japan Society for the Promotion of Science (https://www.jsps.go.jp/) KAKENHI [grant number 17KT0153] awarded to M.I. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2021; J3016_202104. Available from: https://www.sae.org/standards/content/j3016_202104/ [Google Scholar]
2.Lee JD, See KA. Trust in automation: Designing for appropriate reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2004;46(1): 50–80. doi: 10.1518/hfes.46.1.50_30392 . [DOI] [PubMed] [Google Scholar]
3.Lee JD, Moray N. Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies. 1994;40: 153–184. 10.1006/ijhc.1994.1007. [DOI] [Google Scholar]
4.de Visser EJ, Cohen M, Freedy A, Parasuraman R. A design methodology for trust cue calibration in cognitive agents. In: International Conference on Virtual, Augmented and Mixed Reality. 2014; 251–262. 10.1007/978-3-319-07458-0_24. [DOI] [Google Scholar]
5.Adnan N, Nordin SM, bin Bahruddin MA, Ali M. How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transportation Research Part A: Policy and Practice. 2018;118: 819–836. 10.1016/j.tra.2018.10.019. [DOI] [Google Scholar]
6.Parasuraman R, Riley V. Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society. 1997;39(2): 230–253. 10.1518/001872097778543886. [DOI] [Google Scholar]
7.Hutson M. A matter of trust. Science. 2017;358(6369): 1375–1377. doi: 10.1126/science.358.6369.1375 [DOI] [PubMed] [Google Scholar]
8.Nastjuk I, Herrenkind B, Marrone M, Brendel AB, Kolbe LM. What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technological Forecasting and Social Change. 2020;161: 120319. 10.1016/j.techfore.2020.120319. [DOI] [Google Scholar]
9.Muir BM, Moray N. Trust in automation: Part Ⅱ. Experimental studies of trust and human intervention in a process control simulation. Ergonomics. 1996;39(2): 429–460. doi: 10.1080/00140139608964474 . [DOI] [PubMed] [Google Scholar]
10.Gao Z, Li W, Liang J, Pan H, Xu W, Shen M. Trust in automated vehicles. Advances in Psychological Science. 2021;29(12): 2172–2183. doi: 10.3724/SP.J.1042.2021.02172 [DOI] [Google Scholar]
11.Merritt SM, Ilgen DR. Not all trust is created equal: Dispositional and history-based trust in human-automation interactions. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2008;50(2): 194–210. doi: 10.1518/001872008X288574 . [DOI] [PubMed] [Google Scholar]
12.Hoff KA, Bashir M. Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2015;57(3): 407–434. doi: 10.1177/0018720814547570 . [DOI] [PubMed] [Google Scholar]
13.Hardin R. Trustworthiness. Ethics. 1996;107: 26–42. [Google Scholar]
14.Hergeth S, Lorenz L, Krems JF. Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2017;59(3): 457–470. doi: 10.1177/0018720816678714 . [DOI] [PubMed] [Google Scholar]
15.Payre W, Cestac J, Delhomme P. Fully automated driving impact of trust and practice on manual control recovery. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2016;58(2): 229–241. doi: 10.1177/0018720815612319 . [DOI] [PubMed] [Google Scholar]
16.Forster Y, Kraus JM, Feinauer S, Baumann M. Calibration of Trust Expectancies in Conditionally Automated Driving by Brand, Reliability Information and Introductionary Videos: An online study. In: Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications—Automotive UI ‘18. 2018;118–128. 10.1145/3239060.3239070. [DOI] [Google Scholar]
17.Korber M, Baseler E, Bengler K. Introduction matters: Manipulating trust in automation and reliance in automated driving. Applied Ergonomics. 2018;66: 18–31. doi: 10.1016/j.apergo.2017.07.006 . [DOI] [PubMed] [Google Scholar]
18.Aaker DA. Managing brand equity: capitalizing on the value of a brand name. New York: The Free Press; 1991. [Google Scholar]
19.Cretu AE, Brodie RJ. The influence of brand image and company reputation where manufacturers market to small firms: A customer value perspective. Industrial Marketing Management. 2007;36(2): 230–240. 10.1016/j.indmarman.2005.08.013. [DOI] [Google Scholar]
20.Aaker JL. Dimensions of Brand Personality. Journal of Marketing Research. 1997;34(2): 347–356. 10.2307/3151897. [DOI] [Google Scholar]
21.Celmer N, Branaghan R, Chiou E. Trust in branded autonomous vehicles & performance expectations: A theoretical framework. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2018; 62(1): 1761–1765. 10.1177/154193121862139. [DOI] [Google Scholar]
22.Chinomona R, Sandada M. Customer satisfaction, trust and loyalty as predictors of customer re-purchase intention in south african retailing industry. Mediterranean Journal of Social Sciences. 2013;4(14): 437–446. doi: 10.5901/mjss.2013.v4n14p437 [DOI] [Google Scholar]
23.Hong IB, Cho H. The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management. 2011;31(5): 469–479. 10.1016/j.ijinfomgt.2011.02.001. [DOI] [Google Scholar]
24.Zhao JD, Huang JS, Su S. The effects of trust on consumers’ continuous purchase intentions in C2C social commerce: A trust transfer perspective. Journal of Retailing and Consumer Services. 2019; 50(2): 42–49. 10.1016/j.jretconser.2019.04.014. [DOI] [Google Scholar]
25.Chang HH, Su WC. The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review. 2008;32(6): 818–841. doi: 10.1108/14684520810923953 [DOI] [Google Scholar]
26.Grant M. Culture and consumption: A theoretical account of the structure and movement of the cultural meaning of consumer goods. Journal of Consumer Research, 1986;13(1): 71–84. 10.1086/209048. [DOI] [Google Scholar]
27.Kennedy MS, Ferrell LK, LeClair DT. Consumers’ trust of salesperson and manufacturer: An empirical study. Journal of Business Research. 2001;51(1): 73–86. 10.1016/S0148-2963(99)00039-9. [DOI] [Google Scholar]
28.Halim P, Swasto B, Hamid D, Firdaus MR. The influence of product quality, brand image, and quality of service to customer trust and implication on customer loyalty (Survey on customer brand sharp electronics product at the south kalimantan province). European Journal of Business and Management. 2014;6(29): 159–166. [Google Scholar]
29.Ferrell OC, Gresham LG, Fraedrich J. A synthesis of ethical decision making models for marketing. Journal of Macromark. 1989;9(2): 55–64. 10.1177/027614678900900207. [DOI] [Google Scholar]
30.Luu TT. The linkages among leadership, trust, and business ethics. Social Responsibility Journal. 2012;8(1): 133–148. [Google Scholar]
31.Lagace RR, Dahlstrom R, Gassenheimer JB. The relevance of ethical salesperson behavior on relationship quality: The pharmaceutical industry. Journal of Personal Selling and Sales Management. 1991;11(4): 39–47. [Google Scholar]
32.Goldsmith RE, Lafferty BA, Newell SJ. The impact of corporate credibility and celebrity on consumer reaction to advertisements and brands. Journal of Advertising. 2000;29(3): 43–54. 10.1080/00913367.2000.10673616. [DOI] [Google Scholar]
33.Earle TC, Cvetkovich G. Social trust: Toward a cosmopolitan Society. Westport, CT: Praeger; 1995. [Google Scholar]
34.McAllister DJ. Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. The Academy of Management Journal. 1995;38(1): 24–59. 10.2307/256727. [DOI] [Google Scholar]
35.Lewis JD, Weigert A. Trust as a social reality. Social Forces. 1985;63(4): 967–985. 10.2307/2578601. [DOI] [Google Scholar]
36.Madsen M, Gregor S. Measuring human-computer trust. In: 11th Australasian Conference on Information Systems. 2000;53: 6–8. [Google Scholar]
37.SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2018; J3016_201806. Available from: https://www.sae.org/standards/content/j3016_201806/. [Google Scholar]
38.Hatai S. The structure of the relationship between consumers and makers and the development of the measurement scale. Advances in Consumer Studies. 2004;10(1–2): 17–41. (In Japanese). [Google Scholar]
39.Kline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2010. [Google Scholar]
40.Hair JF, Black W, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. 7th ed. New York: Pearson Prentice; 2010. [Google Scholar]
41.Cui Z, Tu N, Lee J, Itoh M. Influence of Demographic Factors and Automobile Brands on the Structure of Initial Trust in Driving Automation. In: 2023 Transportation Research Board 102nd Annual Meeting. 2023. [Google Scholar]
42.Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin. 1988;103(3): 411–423. 10.1037/0033-2909.103.3.411. [DOI] [Google Scholar]
43.Wu G, Hu Z, Zheng J. Role stress, job burnout, and job performance in construction project managers: the moderating role of career calling. International journal of environmental research and public health. 2019;16(13): 2394. doi: 10.3390/ijerph16132394 . [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Chin WW. The partial least squares approach to structural equation modeling. In: Marcoulides G. A. (Ed.), Modern Methods for Business Research. 1998;295–358. [Google Scholar]
45.Dikmenli M. Biology student teachers’ conceptual frameworks regarding biodiversity. Education. 2010. a;130(3): 479–489. [Google Scholar]
46.Dikmenli M. Biology students’ conceptual structures regarding global warming. Energy Education Science and Technology Part B: Social and Educational Studies. 2010. b;2: 21–38. [Google Scholar]
47.Kurt H, Ekici G, Aktas M, Aksu O. Determining biology student teachers’ cognitive structure on the concept of "diffusion" through the free word-association test and the drawing-writing technique. International Education Studies. 2013;6(9): 187–206. doi: 10.5539/ies.v6n9p187 [DOI] [Google Scholar]
48.Türkkan E. Investigation of physics teacher candidates’ cognitive structures about "electric field": A free word association test study. Journal of Education and Training Studies. 2017;5(11): 146–156. 10.11114/jets.v5i11.2683. [DOI] [Google Scholar]
49.Erdogan A. Examining pre-service mathematics teachers’ conceptual structures about "geometry." Journal of Education and Practice. 2017;8(27): 65–74. [Google Scholar]
50.Bahar M, Johnstone AH, Sutcliffe RG. Investigation of students’ cognitive structure in elementary genetics through word association tests. Journal of Biological Education. 1999;33(3): 134–141. 10.1080/00219266.1999.9655653. [DOI] [Google Scholar]
51.Miles MB, Huberman AM. Qualitative data analysis: An expanded sourcebook. 2nd ed. Thousand Oaks, CA: SAGE Publications; 1994. [Google Scholar]
52.Aiken L. Psychological testing and assessment. 3rd ed. Boston: Allyn and Bacon; 1979. [Google Scholar]
53.Lee J, Abe G, Sato K, Itoh M. Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour. 2021;81: 384–395. 10.1016/j.trf.2021.06.013. [DOI] [Google Scholar]
54.Luhmann N, Poggi G, Burns T, Davis H, Raffan J, Rooney K. Trust and power: two works. U-M-I Out of Print Books on Demand; 1979. [Google Scholar]
55.Gefen D. E-commerce: the role of familiarity and trust. Omega. 2000;28(6): 725–737. 10.1016/S0305-0483(00)00021-9. [DOI] [Google Scholar]
56.Zablotska IB, Grulich AE, de Wit J, Prestage G. Casual sexual encounters among gay men: familiarity, trust and unprotected anal intercourse. AIDS and Behavior. 2011;15(3): 607–612. doi: 10.1007/s10461-010-9675-2 . [DOI] [PubMed] [Google Scholar]
57.Ha H, Perks H. Effects of consumer perceptions of brand experience on the web: Brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour. 2005;4(6): 438–452. doi: 10.1002/cb.29 [DOI] [Google Scholar]
58.Lafferty BA, Goldsmith RE. How influential are corporate credibility and endorser attractiveness when innovators react to advertisements for a new high-technology product? Corporate Reputation Review. 2004;7(1): 24–36. 10.1057/palgrave.crr.1540209. [DOI] [Google Scholar]
59.Bhatt N, Jayswal R, Patel JD. Impact of celebrity endorser’s source credibility on attitude towards advertisements and brands. South Asian Journal of Management. 2013;20(4): 74–95. [Google Scholar]
60.Sung Y, Kim J. Effects of brand personality on brand trust and brand affect. Psychology & Marketing. 2010; 27(7): 639–661. 10.1002/mar.20349. [DOI] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0284654.r001

Decision Letter 0

Wojciech Trzebinski

24 Jan 2023

PONE-D-22-29912Effects of brand and brand trust on initial trust in fully automated driving systemPLOS ONE

Dear Dr. Cui,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 10 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Wojciech Trzebinski, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

3. Please change "female” or "male" to "woman” or "man" as appropriate, when used as a noun (see for instance https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/gender).

4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

"Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Both reviewers raised several concerns related to your method. Additionally, Reviewer 2 expressed some comments about the theoretical part, including how you formulated your hypotheses. Note that the comments ot Reviewer 2 are included in the attached PDF. Please address the Reviewers' comments or at least respond in to them in the rebuttal letter.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the timely contribution. I only have a couple of questions/suggestions that the authors may want to consider.

1. Although the study doesn't focus primarily on dispositional trust, it would be interesting and helpful to see results cut by age and gender, as the samples include a wide age range and has a balanced gender split. Presenting such result may also help readers to better understand the possible linkage between dispositional and initial trust.

2. Driving automation is not only offered by automobile manufacturers, but also from technology companies focused on the software development, and also distributed through transportation service companies such as Uber and Lyft. I'm wondering how trust may form when there are multiple producing/delivery parties involved, and if there's anything that can be inferred on this topic from the study conducted.

Reviewer #2: Dear author,

Thank you for submitting your article for review. I have reviewed the document and have added my feedback to the attached PDF. If there are any you do not agree with, please provide a suitable rebuttal. Finally, the manuscript requires thorough proofreading to remove grammatical errors and words used in the wrong context.

Thank you for your time and I wish you the best of luck with your future research.

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PONE-D-22-29912_reviewer.pdf

Click here for additional data file.^{(1.2MB, pdf)}

PLoS One. 2023 May 4;18(5):e0284654. doi: 10.1371/journal.pone.0284654.r002

Author response to Decision Letter 0

22 Mar 2023

Dear Reviewers,

Thank you very much for the comments and suggestions. We have further revised the manuscript for further improvement based on your comments. The major changes in the manuscript are as follows:

1.We have revised the sentences in “Introduction” to make clearer statements of the driving automation referred in the current study (Page 3, Line 30-35; Page 3-4, Line 47-52).

2.We have changed all of the “fully automated driving system (FADS)” to “Automated Driving System with full driving automation (Level 5 ADS)” based on the the taxonomy and definition of driving automation by the Society of Automotive Engineers (SAE 2021).

3.We have added data analyses against age and gender for study 1 (Page 12-13, Line 201-210 for the methods, Page 15, Line 245-249 and Page 15, Table 4 for the results).

4.We have added limitations of the current study (Page 29, Line 513-518)

5.We have addressed the language problems throughout the manuscript to make clearer statements.

All of the changes made in the revised manuscript have been highlighted in yellow in the file "Revised Manuscript with Track Changes" except for the format and language issues.

The followings are our reply to your comments one by one.

Reviewer #1

Comment 1

Although the study doesn't focus primarily on dispositional trust, it would be interesting and helpful to see results cut by age and gender, as the samples include a wide age range and has a balanced gender split. Presenting such result may also help readers to better understand the possible linkage between dispositional and initial trust.

Thank you for your suggestions.

For survey 1, we have added analyses against age and gender to test whether the effect of trust in automobile brand on trust in Level 5 ADS is invariant across gender and age. We updated the details of the methods (Page 12-13, Line 201-210). “Since people of different ages and gender can develop initial trust in driving automation based on different factors, such as cognitive or emotional factors towards driving automation [41], it was also necessary to verify whether the effect of trust in automobile brands on trust in Level 5 ADS was invariant across gender and age. For sex, a multigroup invariance test was performed, and the invariance was indicated by a significant difference in the chi-square value (p < .05) [40] or ΔCFI, ΔGFI, ΔAGFI, and ΔRMSEA, which were not higher than 0.01 [42]. For age, the invariance was indicated by the non-significant moderating effect of age on the relationship. Therefore, a “moderation by interaction terms” method [43] was used, in which age was first multiplied with the scores of each item of trust in the automobile brand, and the subsequent combined effect was examined against trust in Level ADS. ”

For gender, we added multigroup invariance test. The results showed the positive effect of trust in automobile brand on trust in Level 5 ADS was invariant for male (Std. estimate = 0.699, p < .001) and female (Std. estimate = 0.599, p < .001) (Table 4; Page 15, Line 245-247).

Then for age, we added moderating effect test by “moderation by interaction terms” based on the previous structural equation modeling. The results showed the moderating effect of age was not significant on the relationship between trust in automobile brand and trust in Level 5 ADS (male: Std. estimate = -0.021, p = .682; female: Std. estimate = 0.025, p = .573) (Page 15, Line 248-249).

However, because of the invariant results across age and gender for survey 1, we did not continue to add analyses against age and gender for survey 2.

41.Cui Z, Tu N, Lee J, Itoh M. Influence of Demographic Factors and Automobile Brands on the Structure of Initial Trust in Driving Automation. In: 2023 Transportation Research Board 102nd Annual Meeting. 2023.

42.Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin. 1988;103(3): 411-423. doi: https://doi.org/10.1037/0033-2909.103.3.411

43.Wu G, Hu Z, Zheng J. Role stress, job burnout, and job performance in construction project managers: the moderating role of career calling. International journal of environmental research and public health. 2019;16(13): 2394. doi: 10.3390/ijerph16132394 PMID: 31284496

Comment 2

Driving automation is not only offered by automobile manufacturers, but also from technology companies focused on the software development, and also distributed through transportation service companies such as Uber and Lyft. I'm wondering how trust may form when there are multiple producing/delivery parties involved, and if there's anything that can be inferred on this topic from the study conducted.

Thank you for your comments.

We agree with your opinion about discussing trust formation when there are multiple producing/delivery parties involved. This is actually a limitation of our study. We have added sentences in the section “Limitation” (Page 29, Line 513-518) to make further statements of the limitations of the current study and possible research topic in the future. “Finally, this study focused only on the brands of traditional automobile manufacturers. Currently, the social realization of driving automation is also associated with entities other than traditional automobile manufacturers, such as technology and transportation service companies (e.g., Google and Uber), which also provide technological or service support for the development of driving automation. Therefore, research can focus on trust formation when a vehicle with ADS was manufactured and promoted by multiple entities, instead of only traditional automobile manufacturers. ”

Reviewer #2

Comment 1

The manuscript requires thorough proofreading to remove grammatical errors and words used in the wrong context. Page 2, line 17；Page 8 line 130; Page 9 line 157: “win”

Thank you for your comments.

We have changed "win" to "earn" in the three places and checked the same wrongs in other places such as Page 7 line 126; Page 8 lines 151; Page 24 line 404; Page 25 line 439; Page 26 line 443; Page 27 line 470; Page 29 line 522. Besides, we checked other language-usage issues.

Comment 2

Page 2 line 19 in abstract: Please revise this sentence “For automobile brands highly trusted in, drivers possess more rich and various cognitive structures with some particular characteristics. ”

Thank you for your suggestions.

We have revised the sentence in abstract to “for automobile brands with higher trust in automobile brands and Level 5 ADS, drivers’ cognitive structures were richer and varied, which included particular characteristics.” (Page 2, Line 24-26)

Comment 3

Page 3 line 24: Should talk about SAE levels - you are referring to Level 5, which are a long way from being a reality

Thank you for your suggestions.

We have revised the the first paragraph (Page 3, Line 30-35) to make clearer statements. Since the survey object is automated driving system with full driving automation (SAE level 5), we also changed all “FADS” to “Level 5 ADS” following the descriptions in the document “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (J3016_202104)” from SAE International. “Following the taxonomy and definition of driving automation by the Society of Automotive Engineers [1], an Automated Driving System (ADS) with full driving automation (Level 5) can operate vehicles under all road conditions, just as a skilled human driver, and does not require any supervision. Although Level 5 ADS is a long way from reality, its practical application is always expected since it can further optimize the traffic environment and help decrease the risk rate of traffic accidents.”

Comment 4

Page 3 line 41: Based on your definition of FADs there are no conditions they operate as they are not a reality. I would limit the capability of what you define as a FAD to Level 4 in the introduction.

Thank you for your comments.

We have rechecked the literature we referred and the descriptions of driving automation in the document “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (J3016_202104)” from SAE International. We realized our mistakes in the understanding and presentation of FAD. Therefore, we deleted the inappropriate quotation of the previous studies, changed all “FADS” to “Level 5 ADS” and revised the sentences (Page 3-4, Line 47-52) to make clearer statements. “Level 5 ADS can work under all road conditions, just as a skilled human driver, and can reduce risk in unmanageable conditions, such as flooded roads and glare ice [1]. However, drivers’ over-trust and misuse of Level 5 ADS are problems that are impossible to predict. Therefore, the appropriate calibration of drivers’ trust in Level 5 ADS should be considered to ensure their appropriate use in the future.”

In addition, we added sentences in the section of “Practice implications” (Page 28 line 499-502) to further state that the practice implications from our findings may also be applicable for calibrating initial trust in Automated Driving System with other SAE levels of driving automation besides Level 5 ADS. “Finally, this study focused only on the brands of traditional automobile manufacturers. Currently, the social realization of driving automation is also associated with entities other than traditional automobile manufacturers, such as technology and transportation service companies (e.g., Google and Uber), which also provide technological or service support for the development of driving automation. Therefore, research can focus on trust formation when a vehicle with ADS was manufactured and promoted by multiple entities, instead of only traditional automobile manufacturers. ”

Comment 5

Page 9 line 160: You are assuming that all brands increases trust. Wouldn’t you assume that a strong respected brand would increase trust and the least respected brand would lower trust?

Thank you for your comments.

We have revised the sentences (Page 8, Line 153-154) to make clearer hypothesis. “Hypothesis 1 (H1): There would be a positive relationship between drivers’ trust in automobile brands and their initial trust in Level 5 ADS. ”

Comment 6

Page 9 line 164: “ higher and less initial trust ” Doesn't make sense.

Thank you for your comments.

We have revised the sentences (Page 9, Line 157-158) to make a clearer Hypothesis 3. “Hypothesis 3 (H3): The cognitive structures of automobile brand drivers with higher trust and lower trust would have distinctly different characteristics.”

Comment 7

Page 10 line 171: Was this survey in English or Japanese? Please state clearly.

Thank you for your suggestions.

We have added “in Japanese” to make a clearer state for both survey 1 (Page 9, line 162) and survey 2 (Page 17, line 285)

Comment 8

Page 10 line 179: “Brand 1 in the current paper. Brand 1, Brand 2”Please state these Brands e.g. Honda, Mercedes, etc.

Page 18 line 288: “ Brand 1 and 2 in the high trust group, Brand 3 and 5 ” Again, must make it very clear what these brands were

Page 24-25 line 383-392: Figure 5-9, Not readable

Thank you for your suggestions.

We did not revise these, because we have to comply with our ethics review results and hide the actual brands surveyed in the current study. For figure 5-9, the unreadable words with mosaics were those can easily lead readers to know what these brands are, such as the name of the product.

Comment 9

Page 10 line 187: “following”following what?

Thank you for your comments.

We have added the missed words “the descriptions of SAE” (Page 10 line 177).

Comment 10

Page 17 line 272: “The above results of repeated” Don't use the term above or below, state the section

Thank you for your comments.

We have revised the sentences to “The results of the repeated measures ANOVA on the main effect of automobile brand on trust showed both trust in automobile brand and trust in Level 5 ADS significantly differ across automobile brands.” to clearly state the section (Page 17 line 276-278）.

Comment 11

Page 17 line 283: “(58 males) ” Any females?

Thank you for your comments.

We have checked the raw data and added the data of females (Page 17 line 287).

Comment 11

Page 17 line 283: “(M = 50.7 years,” Is this (Age) right, seems very high?

Thank you for your comments.

We have checked the raw data and revised the inaccurate or wrong values of age (Page 17 line 287). The accurate age range of the collected samples in study 2 was 25-70 years. Since there were 23.3% of 40-49 years, 33.0% of 50-59 years, and 22.3% of over 60 years, the averaged age was actually very high.

Comment 12

Page 33 line 548: “SAE. Taxonomy and definitions for terms related to on-road motor vehicle automated Driving Systems. 2018;J3016_201806. Available from: https://www.sae.org/standards/content/j3016_201806/”, Updated version: https://www.sae.org/standards/content/j3016_202104/

Thank you for your comments.

We have updated the newest version in the reference list for introduction. “[1]SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2021; J3016_202104. Available from: https://www.sae.org/standards/content/j3016_202104/. ”

For the one for method, because study 1 was conducted in 2020, during which the version of J3016_201806 was used to introduce driving automation to the participants, we added another reference in the reference list. “[37] SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2018; J3016_201806. Available from: https://www.sae.org/standards/content/j3016_201806/.”

PLoS One. doi: 10.1371/journal.pone.0284654.r003

Decision Letter 1

Wojciech Trzebinski

5 Apr 2023

Effects of brand and brand trust on initial trust in fully automated driving system

PONE-D-22-29912R1

Dear Dr. Cui,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Please see the comment of Reviewer #1 (below) asking you to explain how the L5 ADS was described to the respondents. I encourage you to insert this information into the final proof. Additionally, you may wish to clarify right before listing your hypotheses that they are based on your considerations presented above.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Wojciech Trzebinski, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for addressing all previous comments. The only question I have left is how the L5 ADS was described to the respondents. Given that self-driving is a concept that is widely misunderstood by the general public, it would be helpful to see how the term was presented to participants and how a baseline understanding was established.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0284654.r004

Acceptance letter

Wojciech Trzebinski

26 Apr 2023

PONE-D-22-29912R1

Effects of brand and brand trust on initial trust in fully automated driving system

Dear Dr. Cui:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Wojciech Trzebinski

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Minimal underlying data set of Survey 1.

(PDF)

Click here for additional data file.^{(352KB, pdf)}

Attachment

Submitted filename: PONE-D-22-29912_reviewer.pdf

Click here for additional data file.^{(1.2MB, pdf)}

Data Availability Statement

[pone.0284654.ref001] 1.SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2021; J3016_202104. Available from: https://www.sae.org/standards/content/j3016_202104/ [Google Scholar]

[pone.0284654.ref002] 2.Lee JD, See KA. Trust in automation: Designing for appropriate reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2004;46(1): 50–80. doi: 10.1518/hfes.46.1.50_30392 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref003] 3.Lee JD, Moray N. Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies. 1994;40: 153–184. 10.1006/ijhc.1994.1007. [DOI] [Google Scholar]

[pone.0284654.ref004] 4.de Visser EJ, Cohen M, Freedy A, Parasuraman R. A design methodology for trust cue calibration in cognitive agents. In: International Conference on Virtual, Augmented and Mixed Reality. 2014; 251–262. 10.1007/978-3-319-07458-0_24. [DOI] [Google Scholar]

[pone.0284654.ref005] 5.Adnan N, Nordin SM, bin Bahruddin MA, Ali M. How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transportation Research Part A: Policy and Practice. 2018;118: 819–836. 10.1016/j.tra.2018.10.019. [DOI] [Google Scholar]

[pone.0284654.ref006] 6.Parasuraman R, Riley V. Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society. 1997;39(2): 230–253. 10.1518/001872097778543886. [DOI] [Google Scholar]

[pone.0284654.ref007] 7.Hutson M. A matter of trust. Science. 2017;358(6369): 1375–1377. doi: 10.1126/science.358.6369.1375 [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref008] 8.Nastjuk I, Herrenkind B, Marrone M, Brendel AB, Kolbe LM. What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technological Forecasting and Social Change. 2020;161: 120319. 10.1016/j.techfore.2020.120319. [DOI] [Google Scholar]

[pone.0284654.ref009] 9.Muir BM, Moray N. Trust in automation: Part Ⅱ. Experimental studies of trust and human intervention in a process control simulation. Ergonomics. 1996;39(2): 429–460. doi: 10.1080/00140139608964474 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref010] 10.Gao Z, Li W, Liang J, Pan H, Xu W, Shen M. Trust in automated vehicles. Advances in Psychological Science. 2021;29(12): 2172–2183. doi: 10.3724/SP.J.1042.2021.02172 [DOI] [Google Scholar]

[pone.0284654.ref011] 11.Merritt SM, Ilgen DR. Not all trust is created equal: Dispositional and history-based trust in human-automation interactions. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2008;50(2): 194–210. doi: 10.1518/001872008X288574 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref012] 12.Hoff KA, Bashir M. Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2015;57(3): 407–434. doi: 10.1177/0018720814547570 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref013] 13.Hardin R. Trustworthiness. Ethics. 1996;107: 26–42. [Google Scholar]

[pone.0284654.ref014] 14.Hergeth S, Lorenz L, Krems JF. Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2017;59(3): 457–470. doi: 10.1177/0018720816678714 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref015] 15.Payre W, Cestac J, Delhomme P. Fully automated driving impact of trust and practice on manual control recovery. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2016;58(2): 229–241. doi: 10.1177/0018720815612319 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref016] 16.Forster Y, Kraus JM, Feinauer S, Baumann M. Calibration of Trust Expectancies in Conditionally Automated Driving by Brand, Reliability Information and Introductionary Videos: An online study. In: Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications—Automotive UI ‘18. 2018;118–128. 10.1145/3239060.3239070. [DOI] [Google Scholar]

[pone.0284654.ref017] 17.Korber M, Baseler E, Bengler K. Introduction matters: Manipulating trust in automation and reliance in automated driving. Applied Ergonomics. 2018;66: 18–31. doi: 10.1016/j.apergo.2017.07.006 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref018] 18.Aaker DA. Managing brand equity: capitalizing on the value of a brand name. New York: The Free Press; 1991. [Google Scholar]

[pone.0284654.ref019] 19.Cretu AE, Brodie RJ. The influence of brand image and company reputation where manufacturers market to small firms: A customer value perspective. Industrial Marketing Management. 2007;36(2): 230–240. 10.1016/j.indmarman.2005.08.013. [DOI] [Google Scholar]

[pone.0284654.ref020] 20.Aaker JL. Dimensions of Brand Personality. Journal of Marketing Research. 1997;34(2): 347–356. 10.2307/3151897. [DOI] [Google Scholar]

[pone.0284654.ref021] 21.Celmer N, Branaghan R, Chiou E. Trust in branded autonomous vehicles & performance expectations: A theoretical framework. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2018; 62(1): 1761–1765. 10.1177/154193121862139. [DOI] [Google Scholar]

[pone.0284654.ref022] 22.Chinomona R, Sandada M. Customer satisfaction, trust and loyalty as predictors of customer re-purchase intention in south african retailing industry. Mediterranean Journal of Social Sciences. 2013;4(14): 437–446. doi: 10.5901/mjss.2013.v4n14p437 [DOI] [Google Scholar]

[pone.0284654.ref023] 23.Hong IB, Cho H. The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management. 2011;31(5): 469–479. 10.1016/j.ijinfomgt.2011.02.001. [DOI] [Google Scholar]

[pone.0284654.ref024] 24.Zhao JD, Huang JS, Su S. The effects of trust on consumers’ continuous purchase intentions in C2C social commerce: A trust transfer perspective. Journal of Retailing and Consumer Services. 2019; 50(2): 42–49. 10.1016/j.jretconser.2019.04.014. [DOI] [Google Scholar]

[pone.0284654.ref025] 25.Chang HH, Su WC. The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review. 2008;32(6): 818–841. doi: 10.1108/14684520810923953 [DOI] [Google Scholar]

[pone.0284654.ref026] 26.Grant M. Culture and consumption: A theoretical account of the structure and movement of the cultural meaning of consumer goods. Journal of Consumer Research, 1986;13(1): 71–84. 10.1086/209048. [DOI] [Google Scholar]

[pone.0284654.ref027] 27.Kennedy MS, Ferrell LK, LeClair DT. Consumers’ trust of salesperson and manufacturer: An empirical study. Journal of Business Research. 2001;51(1): 73–86. 10.1016/S0148-2963(99)00039-9. [DOI] [Google Scholar]

[pone.0284654.ref028] 28.Halim P, Swasto B, Hamid D, Firdaus MR. The influence of product quality, brand image, and quality of service to customer trust and implication on customer loyalty (Survey on customer brand sharp electronics product at the south kalimantan province). European Journal of Business and Management. 2014;6(29): 159–166. [Google Scholar]

[pone.0284654.ref029] 29.Ferrell OC, Gresham LG, Fraedrich J. A synthesis of ethical decision making models for marketing. Journal of Macromark. 1989;9(2): 55–64. 10.1177/027614678900900207. [DOI] [Google Scholar]

[pone.0284654.ref030] 30.Luu TT. The linkages among leadership, trust, and business ethics. Social Responsibility Journal. 2012;8(1): 133–148. [Google Scholar]

[pone.0284654.ref031] 31.Lagace RR, Dahlstrom R, Gassenheimer JB. The relevance of ethical salesperson behavior on relationship quality: The pharmaceutical industry. Journal of Personal Selling and Sales Management. 1991;11(4): 39–47. [Google Scholar]

[pone.0284654.ref032] 32.Goldsmith RE, Lafferty BA, Newell SJ. The impact of corporate credibility and celebrity on consumer reaction to advertisements and brands. Journal of Advertising. 2000;29(3): 43–54. 10.1080/00913367.2000.10673616. [DOI] [Google Scholar]

[pone.0284654.ref033] 33.Earle TC, Cvetkovich G. Social trust: Toward a cosmopolitan Society. Westport, CT: Praeger; 1995. [Google Scholar]

[pone.0284654.ref034] 34.McAllister DJ. Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. The Academy of Management Journal. 1995;38(1): 24–59. 10.2307/256727. [DOI] [Google Scholar]

[pone.0284654.ref035] 35.Lewis JD, Weigert A. Trust as a social reality. Social Forces. 1985;63(4): 967–985. 10.2307/2578601. [DOI] [Google Scholar]

[pone.0284654.ref036] 36.Madsen M, Gregor S. Measuring human-computer trust. In: 11th Australasian Conference on Information Systems. 2000;53: 6–8. [Google Scholar]

[pone.0284654.ref037] 37.SAE. Taxonomy and definitions for terms related to Driving Automation Systems for On-Road Motor Vehicle. 2018; J3016_201806. Available from: https://www.sae.org/standards/content/j3016_201806/. [Google Scholar]

[pone.0284654.ref038] 38.Hatai S. The structure of the relationship between consumers and makers and the development of the measurement scale. Advances in Consumer Studies. 2004;10(1–2): 17–41. (In Japanese). [Google Scholar]

[pone.0284654.ref039] 39.Kline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Press; 2010. [Google Scholar]

[pone.0284654.ref040] 40.Hair JF, Black W, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. 7th ed. New York: Pearson Prentice; 2010. [Google Scholar]

[pone.0284654.ref041] 41.Cui Z, Tu N, Lee J, Itoh M. Influence of Demographic Factors and Automobile Brands on the Structure of Initial Trust in Driving Automation. In: 2023 Transportation Research Board 102nd Annual Meeting. 2023. [Google Scholar]

[pone.0284654.ref042] 42.Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin. 1988;103(3): 411–423. 10.1037/0033-2909.103.3.411. [DOI] [Google Scholar]

[pone.0284654.ref043] 43.Wu G, Hu Z, Zheng J. Role stress, job burnout, and job performance in construction project managers: the moderating role of career calling. International journal of environmental research and public health. 2019;16(13): 2394. doi: 10.3390/ijerph16132394 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0284654.ref044] 44.Chin WW. The partial least squares approach to structural equation modeling. In: Marcoulides G. A. (Ed.), Modern Methods for Business Research. 1998;295–358. [Google Scholar]

[pone.0284654.ref045] 45.Dikmenli M. Biology student teachers’ conceptual frameworks regarding biodiversity. Education. 2010. a;130(3): 479–489. [Google Scholar]

[pone.0284654.ref046] 46.Dikmenli M. Biology students’ conceptual structures regarding global warming. Energy Education Science and Technology Part B: Social and Educational Studies. 2010. b;2: 21–38. [Google Scholar]

[pone.0284654.ref047] 47.Kurt H, Ekici G, Aktas M, Aksu O. Determining biology student teachers’ cognitive structure on the concept of "diffusion" through the free word-association test and the drawing-writing technique. International Education Studies. 2013;6(9): 187–206. doi: 10.5539/ies.v6n9p187 [DOI] [Google Scholar]

[pone.0284654.ref048] 48.Türkkan E. Investigation of physics teacher candidates’ cognitive structures about "electric field": A free word association test study. Journal of Education and Training Studies. 2017;5(11): 146–156. 10.11114/jets.v5i11.2683. [DOI] [Google Scholar]

[pone.0284654.ref049] 49.Erdogan A. Examining pre-service mathematics teachers’ conceptual structures about "geometry." Journal of Education and Practice. 2017;8(27): 65–74. [Google Scholar]

[pone.0284654.ref050] 50.Bahar M, Johnstone AH, Sutcliffe RG. Investigation of students’ cognitive structure in elementary genetics through word association tests. Journal of Biological Education. 1999;33(3): 134–141. 10.1080/00219266.1999.9655653. [DOI] [Google Scholar]

[pone.0284654.ref051] 51.Miles MB, Huberman AM. Qualitative data analysis: An expanded sourcebook. 2nd ed. Thousand Oaks, CA: SAGE Publications; 1994. [Google Scholar]

[pone.0284654.ref052] 52.Aiken L. Psychological testing and assessment. 3rd ed. Boston: Allyn and Bacon; 1979. [Google Scholar]

[pone.0284654.ref053] 53.Lee J, Abe G, Sato K, Itoh M. Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour. 2021;81: 384–395. 10.1016/j.trf.2021.06.013. [DOI] [Google Scholar]

[pone.0284654.ref054] 54.Luhmann N, Poggi G, Burns T, Davis H, Raffan J, Rooney K. Trust and power: two works. U-M-I Out of Print Books on Demand; 1979. [Google Scholar]

[pone.0284654.ref055] 55.Gefen D. E-commerce: the role of familiarity and trust. Omega. 2000;28(6): 725–737. 10.1016/S0305-0483(00)00021-9. [DOI] [Google Scholar]

[pone.0284654.ref056] 56.Zablotska IB, Grulich AE, de Wit J, Prestage G. Casual sexual encounters among gay men: familiarity, trust and unprotected anal intercourse. AIDS and Behavior. 2011;15(3): 607–612. doi: 10.1007/s10461-010-9675-2 . [DOI] [PubMed] [Google Scholar]

[pone.0284654.ref057] 57.Ha H, Perks H. Effects of consumer perceptions of brand experience on the web: Brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour. 2005;4(6): 438–452. doi: 10.1002/cb.29 [DOI] [Google Scholar]

[pone.0284654.ref058] 58.Lafferty BA, Goldsmith RE. How influential are corporate credibility and endorser attractiveness when innovators react to advertisements for a new high-technology product? Corporate Reputation Review. 2004;7(1): 24–36. 10.1057/palgrave.crr.1540209. [DOI] [Google Scholar]

[pone.0284654.ref059] 59.Bhatt N, Jayswal R, Patel JD. Impact of celebrity endorser’s source credibility on attitude towards advertisements and brands. South Asian Journal of Management. 2013;20(4): 74–95. [Google Scholar]

[pone.0284654.ref060] 60.Sung Y, Kim J. Effects of brand personality on brand trust and brand affect. Psychology & Marketing. 2010; 27(7): 639–661. 10.1002/mar.20349. [DOI] [Google Scholar]

PERMALINK

Effects of brand and brand trust on initial trust in fully automated driving system

Zixin Cui

Nianzhi Tu

Makoto Itoh

Roles

Abstract

Introduction

Development of human trust in automation

Factors that influence the formation of the trust

Cognition of automobile brand

Purpose and hypotheses

Survey 1

Methods

Procedure and participants

Measurements

Table 1. Items in the trust questionnaires [9,38].

Data analysis

Results

CFA and evaluation of reliability and validity

Table 2. Reliability and convergence validity.

Table 3. Discriminant validity.

SEM of drivers’ trust

Fig 1. SEM of drivers’ trust.

Table 4. Measurement and structural invariance across sex.

Differences in trust among automobile brands

Fig 2. Average scores of trust in automobile brand and Level 5 ADS for the nine automobile brands.

Survey 2

Methods

Participants

Data collection

Fig 3. Example of FWAT data collecting.

Data analysis

Table 5. An example for the different word number and valid word number.

Results

Valid word number, different word number, and trust

Table 6. Valid word number, different word number, and trust.

Table 7. The correlation coefficient of the word number and trust.

Cognitive structure by categories

Fig 4. Categories and the word number of each category for each automobile brand.

Fig 5. Drivers’ cognitive structure of Brand 1 in high trust group.

Fig 9. Drivers’ cognitive structure of Brand 4 in low trust group.

Fig 6. Drivers’ cognitive structure of Brand 2 in high trust group.

Fig 7. Drivers’ cognitive structure of Brand 3 in medium trust group.

Fig 8. Drivers’ cognitive structure of Brand 5 in medium trust group.

Words in cognitive structures related to unethical events

Discussion

The impact of brand trust on initial trust in Level 5 ADS

Effect of automobile brand on initial trust in Level 5 ADS

Drivers’ cognitive structure of automobile brand

Characteristic 1: Rich and varied

Characteristic 2: Have approximately 40% of the cognition of the product

Characteristic 3: Have some cognition more or less related to brand personality or related person

Characteristic 4: Have no negative cognition about the unethical event in drivers’ cognitive structures

Practice implications

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Wojciech Trzebinski

Roles

Author response to Decision Letter 0

Decision Letter 1

Wojciech Trzebinski

Roles

Acceptance letter

Wojciech Trzebinski

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles