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The Market for Hydrogen Cars: Non-Expert and Expert Consumers’

Product Images and Determinants of Purchase Intention

Gianluigi Guido, Alessandro M. Peluso, M. Irene Prete and Juri Quarchioni∗

Automotive companies are increasingly focusing their attention on hydrogen

cars, as they could alleviate both pollution and the international energy crisis. This

article examines the potential market of this new product by implementing a

recent model developed for assessing the impact of latent dimensions of a product

image on consumers’ purchase intention (the so-called Prospect Model, by

Caprara, Barbaranelli and Guido, 2000). More specifically, the article

investigates: (1) the determinants of purchase intention for both expert and non-

expert consumers; and (2) how their different perceived images of hydrogen cars

can influence their intention to buy. Implications for automotive marketers are

discussed.

Introduction

Pollution and the international energy issue encourage the development of

new technologies based on alternative and renewable energy sources (Baykara,

2005). Pollution, mainly due to the emissions of an increasing number of vehicles

(Burns, McCormick and Borroni-Bird, 2002), is the main cause of climatic

changes, which lead to difficult political decisions. With reference to the

international energy issue, the progressive decrease of the world’s oil reserves

∗ Gianluigi Guido (Ph.D. University of Cambridge, UK) is Full Professor of Marketing at the University of Salento, Lecce, and at the LUISS University of Rome, Italy. Alessandro M. Peluso and M. Irene Prete are Ph.D. Candidates in Economic and Quantitative Methods for the Analysis of Markets at the University of Salento, Lecce. Juri Quarchioni holds a Bachelor’s degree in Economics and Commerce at the LUISS University of Rome. Address correspondence to: Prof. Gianluigi Guido, Facoltà di Economia, Università del Salento, Palazzo Ecotekne, Via per Monteroni, 73100 Lecce (Italy); E-mail: g.guido@economia.unile.it.

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causes an obvious augmentation of the prices of traditional fuels, as well as a shift

in contractual power towards those nations belonging to the Organization of the

Petroleum Exporting Countries (OPEC) and this, in turn, supports a rise in oil

prices. Therefore, for both ecological and economic reasons, a strategic goal of

Western industrialized economies has become the reduction of chronic

dependence on oil through investments in alternative renewable energy sources

(Johnston, Mayo and Khare, 2005). From the perspective of the launch of new car

engines – which maintain their original design and utilize hydrogen propellants –

it is useful to understand potential customers’ degree of knowledge and

predisposition towards these new products, as their judgments and perceptions are

relevant considerations affecting the probable introduction of such vehicles onto

the market.

2. Research Objectives

Central objectives of this research can be summarized as follows:

• Identifying antecedents of non-expert and expert consumers’ purchase

intentions, as these two categories of consumers usually have different beliefs

influencing the purchase of innovative products;

• Analyzing how non-expert and expert consumers perceive hydrogen cars, as

they may have different perceptions of new technologies.

The identification of antecedents of non-expert and expert consumers’

intention to buy hydrogen cars and the analysis of their perceived product images

are aimed at highlighting both analogies and differences and to understand, on the

basis of obtained results, whether automotive companies can take advantage of

the implementation of specific marketing and communication strategies in this

field.

The Prospect Model (Caprara, Barbaranelli and Guido, 2000) seems

particularly appropriate to achieve the above-mentioned research objectives,

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because purchase behaviour towards the hydrogen cars is likely to be of cognitive

origin. Therefore, this model has been chosen to investigate non-expert and expert

consumers’ purchase intention and perceived images and understand analogies

and differences among them.

3. The Prospect Model

The Prospect Model (Caprara, Barbaranelli and Guido, 2000) combines

Ajzen’s (1991) Theory of Planned Behaviour, aimed at investigating the possible

determinants of intention to behave in a certain way, with the Five-Factor Model

(cf. Digman, 1990), designed to identify the main latent dimensions of a product

image (i.e., branded product personality). Ajzen’s (1991) Theory of Planned

Behaviour (TPB) is based on the assumption that the intention to perform a

specific behaviour (for example, a purchase behaviour) is the best predictor of the

actual behaviour. Therefore, the intention becomes the key-variable in predicting

future actions. According to the TPB, the behavioural intention (INT) is usually

measured in consideration of the likelihood of engaging in that behaviour and is

affected by three main determinants. These are: (1) Attitude (ATT), that is, a

subjective positive (or negative) predisposition towards a certain behaviour that

derives from personal beliefs about performing that behaviour and the associated

evaluation of those beliefs; (2) Subjective norm (SN), that is, a consumer’s

perception of social pressures that derives from beliefs about what relevant others

think about performing that behaviour and how much one wants to comply to

them; and, finally, (3) Perceived behavioural control (PBC), that is, a perception

of how easy (or difficult) it is to engage in a certain behaviour that derives from

beliefs about the subjective probability of the occurrence of certain external

events facilitating (or hindering) that behaviour and an evaluation of their related

importance. Attitudes, subjective norm and perceived behavioural control are in

turn influenced by the so-called “salient” beliefs, namely those cognitive elements

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– such as information, convictions and thoughts – that are related to that specific

behaviour (i.e., purchase). In particular, behavioural beliefs affect attitudes

towards the behaviour, normative beliefs have an influence on subjective norm,

and control beliefs affect perceived behavioural control.

The Five-Factor Model (FFM) (cf. Digman, 1990) is among the most widely

used models for investigating human personality. The potentially infinite set of

attributes describing personality can be reduced to a limited and approachable

number of attributes (so-called markers) that are able to elicit the main traits

(a.k.a. dimensions, components, or factors) of human personality. These traits

have often been referred to as the “Big Five” and can be used, with the required

adjustments, for examining product images. More specifically, they are: (1)

Openness to experience, which refers to how disposed individuals are to modify

their notions and activities in line with new ideas or circumstances; (2)

Conscientiousness, the scrupulous, rational and persevering way by which people

seek to achieve certain objectives; (3) Introversion/Extroversion, the subjective

aversion/predisposition towards social interaction and activity; (4) Agreeableness,

the orientation towards compassion and caring about others, and away from

antagonism; and (5) Neuroticism/Emotional stability, the subjective

inability/ability to respond to external stimuli whilst keeping emotions and

impulses under control.

According to recent studies (e.g., Caprara, Barbaranelli and Guido, 2001),

human personality can be used as a “metaphor” to operationalize brand or product

images, insofar as their features may be described through personality marker

attributes. The Prospect Model integrates, in a single research tool, Ajzen’s

(1991) TPB and the FFM, with the goal to verify whether and to what extent

latent dimensions of brand and product image (operationalized through the

construct of human personality as assessed by the FFM) influence purchase

intention and its cognitive determinants as measured by the TPB (Figure 1).

Companies implementing such a research tool can improve their understanding of

how product image influences consumers’ purchase intention and its determinants

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and, therefore, can plan more effective communication strategies, capable of

influencing consumers by increasing their level of acceptance.

Figure 1: The Prospect Model

Note: ATT = Attitude towards the behaviour; SN = Subjective Norm; PBC = Perceived Behavioural Control; INT = Purchase Intention.

4. Methodology

This study consisted of two phases: (1) a pilot phase, aimed at identifying

salient beliefs at the basis of determinants of hydrogen car purchase intention and

descriptive attributes of hydrogen car image; and (2) the main phase, aimed at

assessing variables involved in the model (ATT, SN, PBC and INT) and a set of

market attributes of the product image (Caprara, Barbaranelli and Guido, 2000).

In the pilot phase, an open-ended questionnaire was created to determine

salient beliefs (behavioural, normative and control beliefs) underlying ATT, SN

and PBC, respectively, and specific attributes describing the hydrogen car image.

This questionnaire was administered to both a sample of 40 potential buyers

(average age = 40,88 years; 50% M/F), who were chosen on the basis of a

screening question concerning their intention to buy a hydrogen car in the event

of its launch on the market, and a sample of 40 experts (average age: 40,17 years;

50% M/F). The latter sample included 15 sellers of conventional cars, 10

managers of automotive companies, 8 engineers, and 7 sundry chemists and

physicians. Based on a frequency analysis, the most frequent cited responses were

used for developing items of the main questionnaire.

Purchase Behaviour

Dimensions of Product Image

ATT

INTSN

PBC

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The main phase involved a sample of 275 subjects: 200 potential non-expert

consumers and 75 expert consumers (30 car sellers, 20 automotive managers, 15

engineers, and 10 sundry physics and chemists). This questionnaire consisted of

multi-item measures of the relevant constructs developed from results of the pilot

study. More specifically, it included: (1) four multi-item 7-point Likert scales

measuring ATT (a 10-item scale), SN (a 10-item scale), PBC (a 12-item scale),

and INT (a 2-item scale); (2) a multi-item semantic differential scale, including 40

bipolar marker attributes of the five main personality traits (Caprara, Barbaranelli

and Guido, 2000) plus 10 bipolar attributes resulting from the most frequently

cited responses in the pilot phase, for describing the perceived image of hydrogen

cars; and (3) two social-demographic questions regarding age and gender.

5. Results

5.1. Salient beliefs and antecedents of hydrogen car purchase intention

The average scores regarding salient beliefs considered in the main

questionnaire were computed, in order to evaluate their relative weights in the

formation of intention determinants (i.e., ATT, SN, and PBC). Results show that

both non-expert and expert consumers share the same salient beliefs, though the

potential gap in technical knowledge between the two samples. More specifically,

Environmental protection (Non-experts: M = 35.660, SD = 11.244; Experts: M =

34.187, SD = 7.830), Health care (Non-experts: M = 35.635, SD = 10.040;

Experts: M = 35.387, SD = 8.779) and High standard of life in urban centers

(Non-experts: M = 31.710, SD = 10.587; Experts: M = 31.933, SD = 8.779) are

among the main behavioural beliefs at the basis of ATT for both kinds of

consumers. Ecologists (Non-experts: M = 31.800, SD = 12.301; Experts: M =

27.440, SD = 8.777), People living in towns (Non-experts: M = 30.420, SD =

11.024; Experts: M = 27.302, SD = 8.336) and Family (Non-experts: M = 30.370,

SD = 10.008; Experts: M = 25.133, SD = 8.278) are among the main normative

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beliefs at the basis of SN for both non-expert and expert consumers. Advantages

in urban traffic (Non-experts: M = 31.875, SD = 10.307; Experts: M = 33.373,

SD = 9.572), Oil price increase (Non-experts: M =28.440, SD = 12.303; Experts:

M = 27.067, SD = 9.460) are among the main control beliefs at the base of PBC

for both kinds of consumers.

A multiple linear regression was conducted to determine which factors

(ATT, SN and BPC) have an influence on the intention to buy a hydrogen car. In

this analysis the purchase intention towards hydrogen cars was considered as the

dependent variable, whereas ATT, SN and PBC were treated as independent

variables. As regards non-expert consumers, results showed that social pressure

(SN) (β = .670, p < .001) and facilities in terms of incentives and Limited costs

(PBC) (β = .215, p < .001) greatly affect INT (see Table 1).

Table 1: Determinants of Hydrogen Car Purchase Intention for Non-Expert Consumers Variable B Std. Error β t-value p-value

(Constant) -20.113 2.739 - - 7.343 < .001 Attitude .006 .008 .042 .782 .435 Subjective Norm .116 .009 .670 1.325 < .001 Perceived Behavioural Control .033 .008 .215 4.007 < .001

Note: N = 200. Dependent Variables = Purchase intention of a hydrogen car. R = .719; R2 = .517; Adj. R2 = .510; Standard error of the estimation = 5.040; F(3,199) = 70.032, p < .001.

Conversely, ATT does not play any significant role in non-expert

consumers’ purchase intention. A similar regression model was built to test the

effectiveness of the TPB in explaining expert consumers’ purchase intention

towards hydrogen cars. Results summarized in Table 2 showed that all variables

included in Ajzen’s (1991) model significantly affect INT of expert consumers. In

particular, ATT (β = -.423, p < .001) has a negative influence on purchase

intention, while SN (β= .617, p < .001) and PBC (β= .269, p < .001) positively

affect the intention to buy the investigated product.

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Table 2: Determinants of Hydrogen Cars Purchase Intention for Expert Consumers Variable B Std. Error β t-value p-value

(Constant) .006 2.211 - .003 < .998 Attitude -.030 .006 -.423 -4.909 < .001 Subjective Norm .058 .008 .617 7.343 < .001 Perceived Behavioural Control .022 .007 .269 3.162 .002

Note: N = 75. Dependent Variables = Purchase intention of a hydrogen car. R = .719; R2 = .517; Adj. R2 = .496; Standard error of the estimation = 2.156; F(3,74) = 25.290, p < .001.

5.2. Perceived image of hydrogen cars and its influence on purchase intention

A factor analysis was carried out on data regarding the 50 descriptive

attributes of hydrogen cars’ perceived image (using the Principal components’

method and Varimax rotation), in order to explore the dimensionality of hydrogen

cars’ perceived image. This analysis was carried out by imposing the extraction of

six factors: the Big-Five factors, according to the FFM, plus a product-specific

component (non-Big-Five). The five dimensions related personality in general

were re-interpreted and re-named, to obtain more coherent image traits for the

investigated product. In particular, Agreeableness was re-named as Familiarity;

Conscientiousness, as Accuracy; Openness to experience, as Originality;

Emotional Stability, as Reliability; and, finally, Extroversion was re-named as

Competitiveness. The non-Big-Five dimension was loaded by attributes mainly

regarding the ecological and technological characteristics of the product, such as

Ecological (factor loading = .694), Silent (factor loading = .597), Technological

(factor loading = .550), Healthy (factor loading = .476), Clean (factor loading =

.418). Therefore, this product-specific dimension of hydrogen cars was

denominated Ecological Compatibility. Table 6 (infra) contains the factor

loadings resulting from two factor analyses carried out on data on both non-expert

and expert consumers. Results summarized in this table show substantial

differences in hydrogen cars’ perceived images between non-expert and expert

consumers. More specifically, the main dimensions of the product image

perceived by non-expert consumers are Familiarity and Ecological Compatibility,

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whereas Competitiveness and Originality are the main dimensions of the product

image perceived by expert consumers.

Several multiple regression analyses were carried out to investigate how

hydrogen cars’ image affects INT and/or its cognitive determinants, by following

the Prospect Model (Caprara, Barbaranelli and Guido, 2000). The six dimensions

of hydrogen cars’ image were considered as independent variables in each

regression model and INT and its significant determinants, one at a time, as

dependent variables. These regression analyses were separately carried out on

data regarding the two investigated sub-groups of subjects (non-expert and expert

consumers).

Table 3: Dimensions of Hydrogen Cars’ Images Perceived by Non-Expert and Expert Consumers Non-Expert Consumers (N = 200) Expert Consumers (N = 75)

Dimension Attribute Factor loading Dimension Attribute Factor

loading

1st Familiarity (Agreeableness)

Content Sincere Loving Agreeable Cordial Pleasing

.633

.584

.576

.573

.553

.532

1st Competitiveness (Extroversion)

Strong Constant Energetic Fanciful Competitive

.678

.647

.627

.557

.547

2nd Ecological Compatibility (Product-Specific)

Ecological Silent Technological Healthy Clean

.694

.597

.550

.476

.418

2nd Originality (Openness to experience)

Innovative New Modern Original

.724

.702

.663

.551

3rd Accuracy (Conscientiousness)

Accurate Patient Cautious

.516

.505

.500 3rd

Ecological Compatibility (Product-Specific)

Clean Healthy Useful Respectful

.571

.532

.469

.419

4th Originality (Openness to experience)

Elegant Imaginative

.757

.493 4th Accuracy (Conscientiousness)

Patient Accurate Cautious

.670

.579

.578

5th Reliability (Emotional stability)

Stable Serene

.674

.486 5th Familiarity (Agreeableness)

Cordial Loving Sincere

.349

.326

.324

6th Competitiveness (Extroversion)

Competitive Energetic Fanciful Determined Solid

.686 628 .493 .488 .458

6th Reliability (Emotional stability)

Calm Responsible

.572

.562

Variance explained 44.89% Variance explained 50.81%

Note: Principal components’ extraction method and Varimax rotation with Kaiser Normalization.

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Results showed that hydrogen cars’ image traits affecting non-expert

consumers’ purchase intention are: Ecological-Compatibility, which affects INT

both directly (INT: β = .228, p < .001), and indirectly, through PBC (PBC: β =

.154, p < .001); Originality, which affects INT through SN (SN: β = .198, p <

.05), and Reliability, which affects INT through the PBC (PBC: β = .182, p <

.001) and the SN (SN: β = .159, p < .05). Hydrogen cars’ image traits affecting

expert consumers’ purchase intention: Originality (INT: β = -.296, p < .05);

Familiarity, both directly (INT: β = -.382, p < .05) and through the SN (SN: β = -

.348, p < .05); and Competitiveness, through the SN (SN: β = -.251, p < .05).

6. Discussion and Conclusions

Automotive companies involved in the technological and industrial

development of hydrogen cars should realize that the commercial success of new

technologies does not depend only on the adopted engineering solutions, but also

on the resources invested in increasing consumers’ level of acceptance, so that

their purchase intention be guided, not only by social pressures and other external

aspects, but also by an internal positive attitude. As regards non-expert

consumers, companies should carry out communication and marketing strategies

aimed at increasing prospective customers’ standard beliefs (for example,

advertising they fact that the purchase of a hydrogen car is an act of civility

towards the environment, the community, and one’s own family) and sense of

control (for example, through the development of traffic plans favouring

hydrogen cars and making it easier to maintain and run them). As regards expert

consumers, whose attitudes negatively influence their purchase intention,

marketers should adopt communication strategies to increase behavioural beliefs.

In particular, they should try to change the perceived disadvantages (for example,

logistic difficulties on hydrogen storage) by pointing out the advantages for health

and environment.

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This study also showed that communication strategies should include

attributes such as ecological, silent, technological, healthy and clean, in order to

influence cognitive antecedents of non-expert consumers’ purchase behaviours.

These attributes, in fact, elicit the dimension of Ecological Compatibility in

consumers’ minds. Attributes that could negatively affect expert consumers’

purchase intention relate to Originality and Familiarity. Therefore, this issue

should be carefully managed and presented as exciting challenges for the future

and not as actual achievements.

In conclusion, automotive companies that are now involved in the market of

hybrid technology cars, which is considered as a temporary solution for the

environmental crisis, should be aware of the revolution brought about by the

diffusion of hydrogen cars. This would be not only an economic and engineering

revolution, but it would also be a completely change in consumers’ habits and

perceptions. This study showed that the marketing success of hydrogen cars

depends on both technical solutions, adopted on various car models, and resources

invested by companies in encouraging consumers’ acceptance level. Future

research in this field and in product life cycle management (for example, after

product launching onto the market) could identify changes concerning

determinants of purchase intention and product image. Moreover, they could

examine whether differentials in knowledge will change and the processes

involved in forming purchase intentions in such a revolutionary market.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Baykara, S. Z. (2005). Hydrogen as fuel: A critical technology. International Journal of Hydrogen Energy, 30, 545-553.

Burns, L. D., McCormick, B. J., and Borroni-Bird, C. E. (2002). Vehicle of change: Hydrogen fuel cells could be the catalyst for a cleaner tomorrow. Scientific American, 287, 64-73.

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Caprara, G. V., Barbaranelli, C., and Guido, G. (2000). The Prospect Model: How to analyze and forecast the influence of brand and product personality on determinants of purchase intention. Testing, Psicometria, Metodologia, 7, 113-128.

Caprara, G. V., Barbaranelli, C., and Guido, G. (2001). Brand personality: How to make the metaphor fit? Journal of Economic Psychology, 22, 377-395.

Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417-440.

Johnston, B., Mayo M. C., and Khare, A. (2005). Hydrogen: The energy source for the 21st century. Technovation, 25, 569-585.