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Article

Consumer Intention to Buy Electric Cars: Integrating Uncertainty in the Theory of Planned Behavior

by
Roberta Riverso
,
Carmela Altamura
and
Francesco La Barbera
*
Department of Political Science, University of Naples Federico II, Via Rodinò 22/a, 80138 Naples, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(11), 8548; https://doi.org/10.3390/su15118548
Submission received: 21 April 2023 / Revised: 18 May 2023 / Accepted: 23 May 2023 / Published: 24 May 2023
(This article belongs to the Special Issue Consumer Behavior and Sustainable Marketing Development in Online and Offline Settings)
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Abstract

:
The EU’s political agenda has included among its priorities the issue of sustainable mobility, with the aim of curbing CO2 emissions and reducing air pollution. This objective implies the introduction into the EU car market of low-polluting cars, such as cars powered by an electric battery (BEV). The current research was guided by the theory of planned behavior (TPB). The intention to buy a BEV has been measured, as well as the major TPB constructs, namely attitudes, subjective norm, and perceived behavioral control. In addition, we were interested to understand the influence of individuals’ uncertainty on the intention to buy a BEV. Data collected through an online survey (N = 335) were analyzed by means of a multiple mediation model, involving the three TPB constructs as parallel mediators. Results show that (1) attitude (β = 0.67), subjective norm (β = 0.23), and perceived behavioral control (β = 0.22) significantly predict the intention to buy BEVs; (2) uncertainty has a significant negative indirect effect (b = −0.03) on intention via perceived behavioral control. Results are discussed in relation to previous research and possible practical implications.

1. Introduction

In the last decades of the 1900s, the scientific and political debate began to focus on the phenomenon of climate change, which started to elicit the first concerns [1]. At the UN Conference on Environment and Development held in Rio de Janeiro in 1992, the first United Nations Framework Convention on Climate Change [2] was drawn up, and in 1997, the first international agreement on climate change was signed [3].
Starting from the first decade of the new millennium, environmental activists introduced the expression climate emergency to emphasize that the issue of global warming does not concern a future risk, but rather a current one [4]. The United Nations Intergovernmental Panel on Climate Change [5] has stressed the need to address the issue by means of global and multilevel governance actions [6,7] because the devastating consequences of climate change are evident in every corner of the earth and occur with increasing frequency and intensity [8]. The years from 2015 to today have been the warmest ever recorded, with an increase in temperatures of 1.2 °C compared to the pre-industrial level [9]. Global temperature rise relates to other phenomena, such as changing precipitation patterns, sea level rise, and changes in jet streams; these phenomena produce damage and have negative consequences, especially on low-income countries, which have contributed less to the production of greenhouse gas [10]. In addition, food and water insecurity determined by the climate crisis contributes to generating climate migration, that is, the displacement of people due to environmental degradation [11,12,13,14]. To tackle these issues, it is necessary to reduce greenhouse gas emissions (carbon dioxide, methane, and nitrous oxide). In 2022, the highest concentration of carbon dioxide has been recorded [10]. The transport sector is one of the most polluting sources, with CO2 emissions accounting for 24% of the total, of which road transport contributes 71.7% [15]. Rapidly growing mobility needs and private vehicle ownership counteract global efforts to reduce global greenhouse gas emissions from transport [16]. To this end, the European political agenda has included among its priorities the issue of sustainable mobility, with the aim of curbing carbon dioxide emissions into the atmosphere and reducing air pollution. Achieving this goal requires ecological conversion [17], and implies the introduction into the European car market of low-polluting cars, such as cars powered by an electric battery (BEV).
BEVs are considered as a sustainable alternative to conventional vehicles and respond to the economic and environmental needs imposed by the current global issues; nevertheless, consumers have not shown enthusiasm for this new market and this type of car [18,19,20,21,22]. In 2022, in the EU, there was an increase in sales of BEVs of +29% compared to 2021. Nevertheless, there are significant differences between member states: the highest increase (+79.3%) was observed in Norway, whereas Italy showed a contraction of −0.9% despite the economic incentives provided by the Italian Government [23]. Therefore, Italy seems an optimal context to explore the persisting skepticism consumers hold toward the purchasing of BEVs.

2. Literature Review

2.1. Psychological Factors Affecting the Acceptance of BEV

Scholars have studied with increasing interest the negative attitudes and beliefs that inhibit the purchasing of electric vehicles. Several studies linked consumers’ knowledge of innovative technologies to their willingness to buy electric cars [24,25,26,27]. Studies conducted by Ansab and Kumar [28] also highlighted the importance of economic and financial incentives to improve consumer attitudes toward electric cars. Xie and colleagues [26,27] have shown that technical aspects, such as the presence of charging facilities, can improve consumer attitudes. Other scholars conceptualized the choice of buying an electric car in terms of a rational calculation made by the consumer in relation to factors such as the purchase costs, battery autonomy, and (perceived) performance gap between petrol and electric vehicles [18,22].
A relevant amount of research on this topic has been guided by the theory of planned behavior (TPB). According to the TPB [29], intention is the direct antecedent of behavior. Intention, in turn, is determined by attitude (ATT), that is, the individual’s favorable or unfavorable evaluation of a certain behavior. Attitude toward a behavior is determined by the individual’s beliefs about the positive/negative consequences of implementing that behavior (e.g., the positive and negative consequences of buying a BEV). Subjective norm (SN)—the perceived social pressure to perform or not to perform a specific behavior—is expected to be another antecedent of behavioral intention. SN is determined by the individual’s beliefs about what opinion significant others hold about performing or not performing a behavior (e.g., significant others think I should not buy a BEV), and by the expected behaviors of significant others (e.g., my friends would buy a BEV). Finally, perceived behavioral control (PBC)—the degree to which an individual perceives to be capable of implementing a behavior—is thought of as the third antecedent of behavioral intention. PBC refers to the beliefs that a subject has about factors that could facilitate or hinder a certain behavior. PBC may predict behavioral intentions; it may also predict behavior directly and moderate the effect of ATT and SN on intention, as well as the effect of intention on behavior [29,30,31]. According to the TPB’s sufficiency principle, measuring the three main constructs of the theory (ATT, SN, PBC) is sufficient to explain and predict behavioral intentions. Nevertheless, other factors such as gender, age, education, income, personality traits, and so forth—called background factors in the TPB framework—may indirectly influence intention through the mediation of ATT, SN, and PBC [32,33,34].
A number of studies have been devoted to the intention to purchase BEVs [28,35,36], sometimes applying extensions to the theory of planned behavior [37,38,39,40]. Studies using the TPB have shown that attitude is a significant predictor of intentions to adopt an electric vehicle [36,37,41]. Other research has shown significant positive associations between perceived behavioral control and the intention to buy a BEV [35]. In addition to attitudes and perceived behavioral control, subjective norm is also positively associated with intention [37,41,42]: what significant others think about electric cars is important in determining individuals’ intention to buy a BEV [36].
Research efforts have also been focused on exploring the potential of additional factors in a TPB framework. Economic considerations have been shown to be a relevant factor in the purchase choice [35,43]. The high costs of electric vehicles seem to be one of the main barriers to purchasing them [44,45]. In other research [36], environmental concern has been added to attitude, subjective norms, and perceived behavioral control. Environmental concern appears to have a significant effect on attitude and subjective norm. In addition, a positive relationship has been found between the intention to use electric cars and perceived environmental benefits [46,47,48]. Disseminating information on the positive environmental consequences of electric cars could help raise awareness and promote the adoption of electric vehicles, and environmentally friendly norms can be predictive of electric car purchases [39,49]. The TPB framework was also enriched with other factors that have been shown to be relevant, such as emotions, price value, perceived risk, environmental self-image, perceived utility and ease of use, and perceived compatibility with one’s own’s lifestyle [37,38,40,50].
In Italy, several studies have been conducted on the factors that may affect the choice of purchasing an electric car to understand why this market is still so weak in this EU member state. Research has indicated several factors that may hinder the willingness to purchase BEVs, such as the high price, low autonomy, high charging times, and lack of charging networks [21,51,52,53]. Scholars also suggest strengthening the incentive policies to make BEVs competitive in the market [54,55]. Nevertheless, there are no studies conducted in a TPB framework, and there is a lack of research on the psychological determinants of the general low intention to buy BEV.

2.2. The Role of Uncertainty

The current research was conducted in a TPB framework. The intention to buy a BEV has been studied, as well as the major TPB constructs, namely attitudes, subjective norm, and perceived behavioral control. In addition, we were interested to understand the influence of individuals’ uncertainty on the intention to buy a BEV.
Scholars have been interested in the role of uncertainty in relation to numerous and diverse topics, such as consumer choice, decision-making processes, chronic illness, or anxiety disorders [56,57,58,59]. Due to the diversity of contexts and disciplines in which uncertainty has been studied, there is no single definition of uncertainty [57,60]. Even with reference to specific psychological constructs, the meaning of the term has rarely been specified [60]. However, it is possible to distinguish the concept of state (or situational) uncertainty, linked to specific situations and characteristics of external elements, from the concept of trait (or dispositional) uncertainty, which instead refers to dispositional characteristics [61]. As regards the latter, the intolerance of uncertainty (IU) introduced by Freeston and colleagues [62] has found considerable interest [63,64]. The concept of IU represents an evolution of the intolerance of ambiguity (IA) [65]; IU is more related to concern than IA [66], and it has found applications mainly in non-clinical settings [61]. Additionally, IU also differs from the concept of uncertainty orientation (UO), which refers to the way people desire and handle situations of uncertainty [61,67,68,69].
IU is thought to influence the interpretation of the environment and to lead to cognitive, emotional, and behavioral consequences; in other terms, IU influences the way a person perceives, interprets, and responds to uncertain situations [63,70]. Subjects with a high level of IU believe that negative events—even if unlikely to happen—are unacceptable and should be avoided. The possibility of these events occurring generates anxiety and hinders action [71,72]. Therefore, individuals’ intolerance of uncertainty might influence their willingness to buy BEVs, which have been shown to be related to concerns about costs, autonomy, and so forth.
Despite the huge interest devoted by scholars to uncertainty, either of state or trait, this concept has been little explored in the context of the TPB and as regards the intention to purchase BEVs. To the best of our knowledge, no study has tried to assess the significance of dispositional constructs related to uncertainty (trait uncertainty) in a TPB framework, and few studies have evaluated factors recalling situational (state) uncertainty. In a study on decision-making processes about traveling, Quintal, Lee, and Soutar [73] integrated into a TPB-based model the concept of uncertainty, as defined in economics and marketing by Becker and Knudnes [74]. The authors understood uncertainty as “a subjectively determined expectation of ambiguity about a potential loss, in which no measure of probability can be attached to each possible outcome” [73] (p. 798), showing a significant effect on attitudes and perceived behavioral control. More recently, Li, Wen, McKeever, and Kim [75] studied perceived uncertainty arising from conflicting information on vaccinations and found that it influenced both attitudes and perceived behavioral control.
As for the intention to buy a BEV, we know that individuals are uncertain about numerous aspects related to this category of vehicles, such as battery life, selling price, and the charging process [76], and that uncertainty about electric cars negatively affects individuals’ intention to buy one [20]. In addition, some individuals are uncomfortable with technological change [77], and this has a negative impact on the intention to adopt an electric vehicle [78]. Hence, research has provided early evidence on the importance of the topic of uncertainty as regards electric cars and on electric cars, generating some uncertainty for people. Nevertheless, the influence of individuals’ dispositional uncertainty (trait uncertainty) on the intention to buy an electric car has never been addressed, neither per se, nor in a more structured TPB-based model. Individuals with high IU levels perceive ambiguous situations as threatening [79], and individuals are uncertain about numerous aspects related to electric cars [76]; thus, IU might negatively affect the intention to buy a BEV diminishing individuals’ sense of control over this novel typology of car.

3. Materials and Methods

3.1. Aims and Hypothesis

The current study aims to assess—in a TPB-based model—the significance and heuristic power of trait uncertainty (i.e., intolerance of uncertainty). The TPB maintains that three major factors—attitudes, subjective norm, and perceived behavioral control—are sufficient to predict behavioral intention; this is known as the sufficiency principle [33]. Additional factors are thus conceptualized as background factors, and they are not thought to exert any direct influence on intention; rather, they are expected to have an indirect effect on intention, mediated by attitudes, subjective norm, and perceived behavioral control [29].
Our primary aim was to study the antecedents of the intention to buy a BEV in the Italian context. In line with previous research, we hypothesized that:
H1. 
The three major TPB constructs—attitude, subjective norm, and perceived behavioral control—exert a significant effect on the intention to buy a BEV.
In addition, we sought to explore the associations of intention with intolerance of uncertainty (IU). On the basis of the theoretical considerations discussed above, we hypothesize that:
H2. 
IU negatively affects the intention to buy a BEV only indirectly through perceived behavioral control.

3.2. Participants

As a convenience sample, 350 Italian participants were administered an online survey in March 2022. Participants were recruited by sharing the link to the questionnaire on the main social networks. A total of 15 participants did not complete the questionnaire; thus, they were excluded from the analysis. The final sample consists of 335 subjects; 186 identified themselves as female, 124 as male, 1 as non-binary, and 24 participants preferred not to communicate their gender. The mean age of the sample was 34.52 (SDage = 14.58), ranging from 18 to 87. A total of 170 participants hold a university degree, and 137 completed high school; thus, highly educated people are over-represented in the sample compared to the Italian overall distribution.

3.3. Procedure

Participants volunteered for the study and received no compensation for their participation. They were assured of anonymity and informed that they were free to discontinue participation at any time without penalty. After giving their consent, they completed an online TPB questionnaire—which was built following the guidelines provided by Fishbein and Ajzen [29]—with an additional measure of uncertainty (see below). All items employed a 7-point response format conforming to and were presented in non-thematic order. The full list of items, with descriptive statistics and related constructs, is provided in Appendix A.

3.4. Measures

3.4.1. Intolerance of Uncertainty (IU)

Participants’ intolerance of uncertainty was measured by means of the scale developed by Freeston and colleagues [62]. The 12-item Italian version of the scale [80] was used in the current research. In order to keep to a minimum the total number of items in the questionnaire, and consequently the time required for participants to complete it, we used a short version of the scale, selecting the 4 items with the highest loadings. Responses were averaged, with higher values indicating higher IU (Cronbach’s α = 0.85).

3.4.2. Intention

Two items were used to measure the intention to buy a BEV. Responses were averaged, with higher values indicating a stronger intention (Spearman–Brown ϱ = 0.82).

3.4.3. Attitude (ATT)

Three items were used for measuring attitude toward BEVs. A composite measure was computed by averaging the scores (Cronbach’s α = 0.93). Higher values indicate more positive attitudes.

3.4.4. Subjective Norm (SN)

Two items were used to measure subjective norm. The answers were aggregated into a single average score (Spearman–Brown ϱ = 0.76). Higher values indicate subjective norms supportive of buying a BEV.

3.4.5. Perceived Behavioral Control (PBC)

Two items were used to measure perceived behavioral control. The items were averaged to create a single score (Spearman–Brown ϱ = 0.91). Higher values indicate higher perceived behavioral control.

3.5. Statistical Analysis

Descriptive statistics and bivariate correlations were obtained by SPSS 27 (SPSS Inc., Chicago, IL, USA). The model structure was tested through a confirmatory factor analysis (CFA) conducted by STATA 15.1. The reliability of the measures employed in the study was tested by Cronbach’s alpha; Spearman–Brown’s coefficient was used for two-item scales [81]. The mediation analysis was conducted by means of the PROCESS procedure for SPSS by Hayes [82], with 5000 bootstrap samples. The accepted level of significance of the null hypothesis test was set at p < 0.05.

4. Results

The average scores of the study variables are shown in the main diagonal of Table 1. As expected, the bivariate correlations among the TPB variables were significant and of moderate to large magnitude. Dispositional uncertainty was not correlated with intention.
A confirmatory factor analysis (CFA) was conducted to test the fit to data of the measurement model employed in the study. The CFA tested a measurement model with five correlated latent factors—intention, attitude, subjective norm, perceived behavioral control, and intolerance of uncertainty—reflected in 13 items (see Appendix A). All item loadings were >0.0.50, ps < 0.001. Fit indexes of the measurement model were satisfactory: TLI = 0.940, CFI = 0.958, and RMSEA = 0.074.
In order to test our hypotheses, we ran a multiple regression model; intention was regressed on attitude, subjective norms, perceived behavioral control, and intolerance of uncertainty. As explained, we sought to test the direct effect of ATT, SN, and PBC on intention, as well as the indirect effects of IU on intention through ATT, SN, and PBC. In order to accomplish this goal, we used the PROCESS procedure for SPSS developed by Hayes [82], a regression-based tool that provides bootstrap tests of mediation. In this procedure, among the different models of analysis available, we used the macro Model 4, which allows testing indirect effects with multiple (parallel) mediators, testing each mediation path.
The antecedent factors explained a significant and substantial proportion of variance in the intention to buy BEVs, R2 = 0.57, F (4, 330) = 109.48, p < 0.001. Attitude, subjective norm, and perceived behavioral control were significantly associated with intention, thus supporting H1. As expected, the direct effect of IU on intention was not significant: β = 0.08, p = 0.09. In line with H2, a bootstrap test (5000 bootstrap samples) found a significant and negative indirect effect of IU on intention via perceived behavioral control: b = −0.03, bias-corrected bootstrap confidence intervals [−0.066; −0.005]; the indirect effects of IU on intention via subjective norm and perceived behavioral control were not significant (in both cases, bias-corrected bootstrap CIs containing zero). Results are summarized in Figure 1.

5. Discussion and Conclusions

The current study contributes to the scientific knowledge about psychological factors which influence the intention to buy a BEV in several ways. In line with our first hypothesis (H1), the TPB-based model of the intention to buy a BEV proved significant and highly predictive. The model accounts for a significant and substantive proportion of variance in intention (57%), and the TPB factors—attitude, subjective norm, and perceived behavioral control—are all significantly associated with intention. Among the three antecedents, the influence of attitude appears prevalent, with a regression coefficient of more than double compared to SN and PBC. Attitude is determined by beliefs about the positive and negative consequences of implementing a behavior [30]; hence, the intention to buy a BEV seems mostly guided by utilitarian beliefs [83]. This is of special interest because there are no studies on the topic conducted in the Italian context within a TPB framework, and there is a lack of research on the psychological factors which might explain the very low intention to buy BEV in this country.
From a practical perspective, these findings suggest that communication about the advantages of BEV and updates about the overcoming of BEV’s major limitations—influencing utilitarian beliefs and thus, in turn, improving individuals’ attitudes—might be the best option to increase people’s intention to buy BEV [84]. This suggestion may be especially useful in contexts where this market is still relatively weak, such as the Italian context in which the study has been conducted.
The main theoretical contribution of the current research regards the role of uncertainty in the TPB framework and in relation to the intention to buy a BEV, which has never been investigated so far. Uncertainty was measured as intolerance of uncertainty (IU)—understood as a personality disposition (trait uncertainty). In particular, intolerance of uncertainty (IU) refers to the negative interpretation of future events (such as buying a new car).
In line with TPB’s sufficiency principle, we have hypothesized uncertainty to influence the intention to buy a BEV only indirectly (H2). Our hypothesis was supported by data. In addition, as hypothesized, uncertainty influences indirectly the intention to buy BEV by acting upon perceived behavioral control.
From a theoretical point of view, our results may be relevant because they suggest that uncertainty could be an under-explored influential factor in other contexts and in relation to other behaviors. Indeed, the influence of uncertainty, being indirect and mediated by PBC, may not be detected without a mediation analysis. Noticeably, the bivariate correlation between IU and intention was not significant in our dataset, whereas the indirect effect of uncertainty on intention through PBC was significant. Hence, we would have not detected the influence of uncertainty on the intention to buy a BEV without investigating the mediation process. This is in line with literature on mediation and indirect effects, which underline that a significant indirect effect may exist even in the absence of a significant total effect (i.e., bivariate correlation) between the independent and the dependent variable of interest [85]. This evidence may open a new scenario in the study of uncertainty and its influence on intentions and behaviors.
Results of our research also confirm the relationship between IU and perceived control already suggested by Buhr and Dugas [66]. Subjects who score high on the IU scale are less ready to buy an electric car because this disposition raises the perception of obstacles to purchasing BEVs. This perception of barriers may be enhanced by IU, which implies the inability of an individual to cope with adverse situations resulting from the absence of sufficiently clear information. [60,86]. In the context of the current research, IU can be also linked to the fear of adverse situations occurring in the management of BEVs. This fear could arise from the information circulating on battery-powered cars, such as the absence of facilities to recharge, long charging times, and modest autonomy. Individuals with a high score on the IU scale tend to live with frustration and stress in uncertain situations, as they need certainties, clear and predictable situations, and unambiguous settings in which they can re-propose familiar patterns [87]. These individuals feel a sense of unease in front of new situations, such as those that could arise with the purchase of new types of cars. BEVs could present unpredictable issues, which could generate anxiety.
From a practical point of view, the influence of uncertainty on intention mediated by perceived behavioral control further underlines the importance of communication, which must be clear and transparent because when information is partial or imperfect, subjects experience a situation of uncertainty that may decrease their perception of behavioral control.
To the best of our knowledge, this is the first study to address the topic of trait uncertainty in the TPB framework. As such, the suggestions deriving from our results should not be taken for granted in relation to other contexts and targets. Future research is needed to investigate this important topic toward a more complete understanding of the influence of uncertainty in the rational action approach [29] and the theory of planned behavior [88,89]. The influences and psychological mechanisms highlighted by the current study may be similar or different in relation to other targets (e.g., buying objects different from BEVs), behaviors different from purchasing, and countries different from Italy. This warrants future research on the topic.
Cross-national comparisons might be of special interest, considering that the BEV market is expanding in very different ways in the EU member states, as discussed before.
Hence, from a psychological point of view, an important research question regards what crucial beliefs make the difference between those different contexts. Recently, research conducted on BEVs in the TPB framework has been mainly devoted to the direct measure of TPB’s main constructs, without much interest in the key beliefs that determine them, and their relative power [30]. Expanding our knowledge on the most relevant behavioral, normative, and control beliefs—quantifying their influence on the intention to buy BEVs—represents an important aim for future research. Furthermore, the individuation of key beliefs is the necessary basis for programming effective communication strategies aimed at changing individuals’ intentions and behaviors [90,91,92].
Several recent studies guided by the TPB have distinguished between instrumental attitude, determined by beliefs about positive/negative consequences of a certain behavior, and experiential attitudes, determined by positive/negative expectations individuals hold about how they would feel while implementing a behavior [29,33]. Like most TPB research, the current study has explored only instrumental attitude; nevertheless, the expectations about feelings and experience connected to driving a BEV might be influential on the intention to buy it. Hence, this could be another topic worthy of investigation.
The current study presents several limitations that need to be acknowledged. The study was conducted with a convenience sample of Italian participants. As already mentioned, future research is needed before generalizing our results to the general Italian context, as well as to other contexts and behaviors. In addition, in line with the TPB, participants’ intention to buy a BEV was the study criterion variable; nevertheless, it did not include a behavioral measure. This is very common in TPB-based research, and the seriousness of this limitation is somewhat mitigated by the significant correlations generally found between intentions and behaviors [29,33]. However, future research including behavioral measures would be recommended. Finally, the online questionnaire also imposes limitations: online data collections allow researchers to optimize time and costs, yet they may determine sampling biases, generally attracting younger people and those more acquainted with new media [93].

Author Contributions

Conceptualization, R.R., C.A. and F.L.B.; Methodology, R.R.; Formal analysis, F.L.B.; Investigation, R.R. and C.A.; Data curation, C.A.; Writing—original draft, R.R. and C.A.; Writing—review & editing, R.R., C.A. and F.L.B.; Supervision, F.L.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Review Board of the Psychological and Social Research Lab R. Gentile, Federico II University of Napoli, research protocol number 01032022.

Informed Consent Statement

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

Data Availability Statement

Data will be made available upon request to the contact author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Full list of items administered in the study, with descriptive statistics and corresponding latent constructs.
Constructs and ItemsM (SD)Range
Intention
Next time I’ll buy a car, I intend to buy a BEV4.20 (2.10)1–7
I feel ready to buy a BEV3.96 (2.10)1–7
Attitude
In my opinion, buying a BEV is (negative/positive)5.18 (6.61)1–7
In my opinion, buying a BEV is (useless/useful)5.08 (1.74)1–7
In my opinion, buying a BEV is unfavorable/favorable)4.92 (1.74)1–7
Subjective Norm
Most people important to me think I should buy a BEV4.13 (1.65)1–7
People I care about would buy a BEV4.18 (1.85)1–7
Perceived Behavioral Control
In my opinion, buying a BEV is (difficult/easy)3.79 (1.81)1–7
From an economic point of view, I am able to buy a BEV3.17 (2.08)1–7
Intolerance of Uncertainty
When things happen suddenly, I get very agitated4.54 (1.83)1–7
I can’t stand it when things happen suddenly4.78 (1.74)1–7
Feeling uncertain prevent me from doing most things4.25 (1.90)1–7
Even the smallest worry can prevent me from doing things3.35 (2.03)1–7

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Sustainability 15 08548 g001 550
Figure 1. A TPB-based model of intention to buy a BEV, including intolerance of uncertainty. Note. The direct effects of variables are reported on arrows. Statistically not significant paths are represented by dashed lines. * p < 0.05. Source: Authors’ elaboration.
Figure 1. A TPB-based model of intention to buy a BEV, including intolerance of uncertainty. Note. The direct effects of variables are reported on arrows. Statistically not significant paths are represented by dashed lines. * p < 0.05. Source: Authors’ elaboration.
Sustainability 15 08548 g001
Table
Table 1. Means, standard deviations, and correlations among study variables.
Table 1. Means, standard deviations, and correlations among study variables.
INTATTSNPBCIU
INT4.08 (1.92)
ATT0.790 ***5.05 (1.59)
SN0.560 ***0.569 ***4.15 (1.57)
PBC0.349 ***0.214 ***0.284 ***3.48 (1.62)
IU0.0500.0320.0260.135 *4.32 (1.56)
Note. The table shows Pearson’s r correlation coefficients. Diagonal cells show means (SD in parentheses). INT = Intention; ATT = Attitude; SN = Subjective norm; PBC = Perceived behavioral control; IU = Intolerance of uncertainty. * p < 0.05; *** p < 0.001. Source: Authors’ elaboration.
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Riverso, R.; Altamura, C.; La Barbera, F. Consumer Intention to Buy Electric Cars: Integrating Uncertainty in the Theory of Planned Behavior. Sustainability 2023, 15, 8548. https://doi.org/10.3390/su15118548

AMA Style

Riverso R, Altamura C, La Barbera F. Consumer Intention to Buy Electric Cars: Integrating Uncertainty in the Theory of Planned Behavior. Sustainability. 2023; 15(11):8548. https://doi.org/10.3390/su15118548

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Riverso, Roberta, Carmela Altamura, and Francesco La Barbera. 2023. "Consumer Intention to Buy Electric Cars: Integrating Uncertainty in the Theory of Planned Behavior" Sustainability 15, no. 11: 8548. https://doi.org/10.3390/su15118548

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