What drives the Pioneers? Applying lifestyle theory to early electric vehicle buyers in Canada

Axsen, Jonn; Cairns, Josh; Dusyk, Nichole; Goldberg, Suzanne B.

doi:10.1016/j.erss.2018.04.015

Cited by 77 publications

(45 citation statements)

References 52 publications

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“…3. Consumer characteristics: socio-economic (Chen et al, 2020;Mukherjee & Ryan, 2020) lifestyle (Axsen et al, 2018), social norms (Barth et al, 2016;Caperello et al, 2013;Jansson et al, 2017) and environmental beliefs (Krupa et al, 2014;Schuitema et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results may be different depending on the type of vehicle, so future studies may distinguish between them and then compare the results. In addition, future research is invited to include a number of other personal factors, including environmental beliefs highly valued in other works (Krupa et al, 2014) or socioeconomic beliefs (Mukherjee & Ryan, 2020) and lifestyle factors (Axsen et al, 2018).…”

Section: Limitations Recommendations and Avenues For Future Researchmentioning

confidence: 99%

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Adoption of electric vehicles: Which factors are really important?

Higueras-Castillo

Guillén

Herrera

et al. 2020

International Journal of Sustainable Transportation

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Electric vehicles stand out for their ability to reduce pollution. However, electric vehicle sales have not been brisk despite their positive effects. The objective of this study is twofold: first, to determine the variables that predict the purchase of an electric vehicle from the implementation of an algorithm based on computational intelligence; second, to contrast these results with two panels of experts in consumer behavior and the automobile sector. An empirical study was carried out with 404 potential consumers in Spain with regard to their beliefs, attitudes and purchase intention. The results show that range, incentives and reliability are the most reliable predictors of purchase intention. Likewise, the experts posit that the selection of these three variables would be sufficient to know the purchase intention of potential buyers of electric vehicles. In addition, it provides useful information for policy makers and private companies for decision making in the electric vehicle marketing process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Limitations Recommendations and Avenues For Future Researchmentioning

confidence: 99%

Adoption of electric vehicles: Which factors are really important?

Higueras-Castillo

Guillén

Herrera

et al. 2020

International Journal of Sustainable Transportation

View full text Add to dashboard Cite

show abstract

“…They pointed out that it is important to provide residents with information on how best to use the new devices. Thøgersen [22] and Axsen et al [23] pointed out that the difference in people's lifestyles influences their acceptance and behavior regarding energy-saving technologies. Perez-Gracia et al [24] noted that it is important to introduce technologies that match climate or building conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

In Which Time Slots Can People Save Power? An Analysis Using a Japanese Survey on Time Use

Nakano

Washizu

2019

Sustainability

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Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of DSM into homes, power usage trends according to the time of the day should be examined for individuals in relation to their lifestyles. The analyses of power usage trends can identify the types of home appliances that should be utilized differently in order to increase energy efficiency. Such analyses can also predict the individual behavioral changes that should result in home appliances being used in the time slots in which solar power is more conveniently available. The purpose of this research was to estimate and observe the amount of power saving potential for each daily time slot with respect to an individual’s particular attributes, and to derive the power saving potential of the whole country by accumulating these data on individuals. This was achieved by using the Survey on Time Use and Leisure Activities (STULA) and Energy-Saving Performance Catalog (ESPC) in Japan. According to the results of our estimation, a meaningful power saving potential is sufficient to address a power supply shortage after a disaster such as an earthquake. It is possible to save power by replacing existing home appliances with more energy efficient ones, by making environmentally conscious choices when using home appliances, and by discontinuing the use of home appliances during electricity shortages within the community as a whole. Using the estimated power saving potentials, we examined the effects of two DSMs: (1) adjusting the time for which home appliances are used; and (2) aggregating the power demand of households with different attributes. The results showed that these DSMs would contribute to a more stable power system operation. Future research might address the rapid penetration of community energy management systems and demand response systems.

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“…. ] informed and expressed by engagement in lifestyle practices" [32]. As the "energy lifestyle" of a person, we therefore highlight the activities that are part of the person's private sphere of influence and have an impact on this person's energy demand and climate impact.…”

Section: Beyond Existing Research On Energy Lifestylesmentioning

confidence: 99%

“…Others see "lifestyle" as a synonym for household-characteristics [34] or point to certain behaviours as "lifestyle elements" [35]. Some try to characterize "the western lifestyle" [29], "lower-carbon lifestyles" [36], "low carbon lifestyles" [37], or pro-environmental lifestyles [32]. Furthermore, some point towards lifestyle changes on an aggregate level [38], or "consumer lifestyle" on a national level [30], just to name a few examples of the diversity of ways in which the term has been operationalised.…”

Section: Limitation I: Limited Specificity and Comparability Of Groupmentioning

confidence: 99%

Identifying Consumer Lifestyles through Their Energy Impacts: Transforming Social Science Data into Policy-Relevant Group-Level Knowledge

2019

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The analytical framework presented herein is based on the identification of social groups with distinct patterns in their energy-relevant behaviour. This was achieved by clustering individuals according to their primary energy demands in six main areas of life. Due to the close relationship between energy-relevant behaviour and associated impacts, the suggested approach is considered better suited for the identification of groups with actual differences in their climate and energy-related behaviour than conventional approaches that cluster individuals based on their psychological or sociodemographic characteristics. Moreover, it is assumed that this focus on energy-relevant behaviour leads to a higher measuring equivalence in a country comparison or in a longitudinal setting. From an analytical point of view, the most significant benefit of the presented method over conventional lifestyle typologies is that all psychological, cultural and sociodemographic factors can be used as explanatory variables without resulting in circular reasoning. In terms of required data, the approach was designed around what could be collected by conventional survey methods. Variables such as energy use and emissions were calculated by the means of life cycle assessment (LCA) based on self-reported behaviour and equipment use.

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What drives the Pioneers? Applying lifestyle theory to early electric vehicle buyers in Canada

Cited by 77 publications

References 52 publications

Adoption of electric vehicles: Which factors are really important?

Adoption of electric vehicles: Which factors are really important?

In Which Time Slots Can People Save Power? An Analysis Using a Japanese Survey on Time Use

Identifying Consumer Lifestyles through Their Energy Impacts: Transforming Social Science Data into Policy-Relevant Group-Level Knowledge

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