Digital Technologies to Support Sustainable Consumption: An Overview of the Automotive Industry

Abstract

Having in view the current global disruptive social and economic landscape, sustainability becomes more important than ever. As producers become more concerned about adopting more sustainable practices, customer awareness towards sustainable behavior must be the focus of all stakeholders. Within this context, the SHIFT framework (proposed in 2019) highlights the manner in which consumers’ traits and attitudes influence their propensity towards sustainable consumption. It consists of five factors considered to be relevant to consumer behavior: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Different from previous studies, this research focuses on applying the SHIFT framework to the automotive industry, taking into consideration the contribution of digital technologies to fostering sustainable consumer behavior throughout the entire product lifecycle. Using a qualitative research approach, the most relevant digital technologies in the automotive industry were identified and mapped in relation to the three phases of consumption (choice, usage, and disposal). The research aimed to develop and test an original conceptual framework, starting from the SHIFT. The results of the study highlight the fact that the digital technologies, in their diversity, are integrated in different ways into each of the three phases, facilitating the adoption of sustainable consumption. To achieve sustainability, the two key stakeholders, consumers and producers, should share a common ground on capitalizing the opportunities offered by digital technologies.

1. Introduction

Based on the Brundtland Report of 1987 [1], this study focuses on the philosophy of sustainable development. Although the original approach was ambitious, aiming to promote a new era of socially and environmentally sustainable economic growth, the reality has proven that, globally, the situation remains concerning. This is because, according to predecessors in research, the new vision of economic growth, which had the undeniable merit of raising awareness of sustainability issues, failed to provide an ethical platform to support effective environmental protection [2], fueled the growth of social inequalities [3], did not provide better living conditions for workers and consumers [4], and continued to promote the capitalist economic growth model, which does not pay sufficient attention to issues of social justice and ecosystem health [5].

Analyzing the sustainability and desirability of economic growth, Kallis et al. (2025) [6] showed that post-growth research has identified new ecological macroeconomic models that have the potential to better address sustainability and welfare concerns, but only if national economies are linked to global climate economy models and all stakeholders are more responsibly involved. For example, amid the latest crises, the EU has proposed the “smart, sustainable and inclusive” growth model that is climate-resilient, based on a competitive digital economy, ensuring the just transition, promoting innovation and reducing the carbon footprint.

As regards the carbon footprint (directly targeting CO₂ emissions and raw material extractions), a notable study argues that specific environmental pressures need to be analyzed in direct relation to the consumption of goods and services [7]. More specifically, the authors pointed out that resource consumption is under the impact of two major factors: the behavior of end consumers and the scale of international trade. Having as general frameworks two of the conditions of sustainable economic growth (responsible involvement of all stakeholders and the behavior of end consumers), this study places the debate in the area of the automotive industry.

Within the automotive industry, a certain segment was considered—passenger car manufacturers. This is due to the fact that the literature indicates that strategies of car manufacturers differ from the strategies of producers of other means of transport. Even for the selected segment, it has been observed that some manufacturers adopt a transformative and change/innovation-oriented approach, while others adopt a conservative and sustainability-oriented approach [8]. Other authors have emphasized the dependence of the manufacturer’s profit on fuel efficiency and performance in use, both in the scenario of different types of vehicles [9] and in the scenario of ignoring differences in vehicles [10]. In addition, the behavior of users varies across different modes of transport, such as cars, mopeds, and minibuses [11].

Globally, but also in the European Union (EU), there is an increase in the number of registered passenger cars. In just 6 years (2018–2023), the number of passenger cars on the road increased by 15.7 million (from 240.4 million in 2018 to 256.1 million in 2023) (Figure 1). Given the growing concerns about sustainability, debates on the environmental impact of car production and use have intensified. As road transport produced a fifth of the EU’s carbon dioxide emissions, European policies focused on climate neutrality were not long in emerging. Car manufacturers, in turn, have adapted their production to meet the objective of reducing pollution. Concurrent with the concerns of increasing the share of less polluting cars in the total production volume, in the context of the unprecedented advance of digital technologies, car manufacturers have made additional investments to manage the environmental impact along the entire value chain. In this context, the integration of digital technologies has been seen both as an opportunity to better meet the expectations of digital consumers, and also as a lever to facilitate the adoption of more sustainable consumption behaviors.

With the changing economic foundations of the production, commercializing and use of cars, it is noticeable that there has been an intensification of scientific debates focused on the automotive industry.

As previously noted, scientific research analyzing data from the automotive industry has expanded and addressed new themes, directly related to sustainability issues. However, the analysis of digital technologies used in the automotive industry with a focus on their impact on sustainable consumption behaviors is in its early stage of development. Few studies have been identified that directly address these interdependencies. For example, Zhang et al. (2023) [12] provided evidence on how digital technologies can be used to analyze user behavior patterns. The authors processed data collected by electric vehicle sensors and analyzed behavior patterns from the perspective of carbon emissions related to electric vehicles’ sharing operations. Considering electric vehicles as the most advanced form of technological advancements in the automotive industry (incorporating digital technologies that enable “regenerative braking, advanced battery storage, and highly efficient electric motors”), Durmus Senyapar and Aksoz (2024) [13] showed that innovations improve not only the driving experience, but also the efficiency and performance of the vehicle and, implicitly, the environmental impact. Their study highlighted the importance of consumer education (through digital campaigns, experiential marketing and sustainability messages) to shape positive attitudes, capable of marking economic benefits, reduced emissions and technological advances.

Focusing their research on smart sustainable mobility, Ketter et al. (2023) [14], admit that information systems have an important role in creating ecosystems that prove to be beneficial for both mobility users and providers, as well as for the environment. The authors point out that the development of information systems has triggered a profound socio-technical transformation of the mobility sector by incorporating various technologies into means of transport, such as those that ensure mobile connectivity, computing hardware and information systems management powered by artificial intelligence. Also, in the area of mobility, but with a focus on urban mobility, without directly targeting sustainable behavior, Lopez-Carreiro et al. (2020) [15] were concerned with using technological innovations (such as Mobility-as-a-Service) to provide personalized mobility solutions. Capitalizing on the literature review and the results of primary research (based on focus groups), the authors identified new services that can be offered by digital technologies, both to meet the daily needs of car users and also to assess the impact on the environment and human health.

Starting from the fragmented debates on the use of digital technologies in the automotive industry, to fill the existing literature gap, this study aims to identify answers to the following research questions:

RQ1. What is the current state of knowledge regarding various applications of digital technologies that might contribute to improving the value package of automotive operators/players?

RQ2. Can the integration of digital technologies into the various functionalities of the final product contribute to the adoption/consolidation of sustainable consumption behaviors by car users?

The answer to the first research question is summarized in the Literature Review Section. In order to answer the second research question, the following research objectives were defined:

• To identify and map various applications of digital technologies used in the automotive industry;
• To test the mapping results and provide evidence about the potential of digital technologies to foster a sustainable consumer behavior in the automotive industry.

In this study, focused on assessing the potential of digital technologies used in the automotive industry to create/strengthen sustainable consumption behavior, two reference frameworks were considered: the Triple Bottom Line (TBL) approach and the SHIFT conceptual framework. The analysis focuses on assessing the extent to which passenger car manufacturers use social leverages to more effectively fulfill the objectives of the environment.

This sequential approach to sustainability (based only on “people” and “planet”), without denying the importance of the third dimension (“profit”), was imposed because the research aimed to develop and test an original conceptual framework, starting from SHIFT. Specifically, consideration was given to assessing how consumer traits and attitudes (as a reflection of the social dimension), guided by the use of digital technologies, influence their tendency towards sustainable consumption (as a reflection of the environmental dimension).

A similar approach, starting with the TBL model, was carried out by Gmelin and Seuring (2018) [16], who analyzed the social and environmental dimensions of the organizations’ activities. Using the case study as a research method and assuming that the development of new products must be aligned with the sustainability objectives, the authors found that the social aspect is rarely present in the development of new products.

Other studies have reported that (under pressure from consumers, government regulations and stakeholder requirements) automotive companies have been forced to pay more attention to social and environmental issues [17]. This is because the automotive industry outputs (represented by products that integrate associated services to provide mobility) are addressed to a very large and diverse number of users. Therefore, the supply of the automotive industry must respond to a very dynamic demand, but without losing sight of the environmental aspects.

From a more pragmatic perspective, other authors pointed out that the TBL approach forced automotive companies to make trade-offs between the three dimensions (economic, environmental and social). Even if such a decision could affect the prospects for aligning the objectives of different stakeholders, the authors argue that disruptive innovations (such as additive manufacturing) can contribute to simultaneously improving one or more dimensions without compromising the others [18].

Regarding the SHIFT conceptual framework [19], the purpose of its foundation was to provide practitioners and researchers with a tool to facilitate the analysis of the conditions under which consumers are guided to adopt sustainable consumption behavior. The research problem identified by the authors was that reducing resource consumption (as evidence of environmental concerns) can only be achieved through sustainable consumption behavior. Based on an extensive literature review (280 articles), the authors identified five channels (Social Influence, Habit Formation, Individual Self, Feelings and cognition, and Tangibility) through which consumers can be stimulated to behave in a more sustainable way.

According to the SHIFT conceptual framework, social influence is the first channel that has the power to influence sustainable consumer behaviors. In this regard, the following tools prove their applicability: social norms, social identities and social desirability. The second channel of intervention is aimed at the formation of consumer habits. The authors point out that discontinuity in consumption and penalties can limit unhealthy habits, while repetition and recognition can strengthen positive habits. Thirdly, the authors admit that “positivity of the self-concept, self-interest, self-consistency, self-efficacy, and individual differences” can have a strong influence on consumer behaviors. Fourth, the authors take into account the concepts of feeling and cognition, which are considered the foundation of choices. Last, but not least, the authors admit that the results of environmental policies are difficult to quantify, and consumer behavior can become more sustainable if they have a clearer understanding of intangible aspects [19].

Two years after its foundation, three of the authors of the original article proposed using SHIFT to assess to what extent consumer behavior can be influenced to improve climate impact [20]. Based on a new literature review, the authors classify behavior change strategies considering the five pillars of the SHIFT conceptual framework. In addition, the authors followed the grouping of these strategies according to the consumption cycle, divided into three stages: choice, use and disposal. Finally, the authors admit that climate change is the consequence of human behavior, which justifies that “human behavior must be part of the solution”.

Noting that there are only a few studies that have investigated the role of value co-creation (through technology) in stimulating sustainable behavior, Saha et al. (2023) [21] used the SHIFT and MAPED frameworks (Market acceptance; Appreciation and rewards; Positive affirmation; Empathy in communication; Dematerialization) to assess sustainable consumption in the retail sector. The authors argue that the process of co-creation of value, based on the use of the two conceptual frameworks, can be used to stimulate sustainable consumption behavior. Moreover, the authors identify specific strategies that can be used by retailers, which have the responsibility to co-participate to achieve the same goal (minimizing environmental impact).

A recent paper highlights another direction of using the SHIFT conceptual framework. Gallen et al. (2025) [22] used it to understand adolescents’ perceptions of alternative foods. The authors noted that there is a “green gap” due to discrepancies between adolescents’ personal desires and environmental goals. The SHIFT framework has been used to identify how the green gap (in terms of food diet) can be reduced by controlling the psychological factors it incorporates. Based on the information collected from structured interviews, the authors provide evidence that SHIFT has the ability to encourage behavior change.

Following the predecessor models and capitalizing on the results of research based on the Delphi Technique, the results of this study highlight the fact that digital technologies, incorporated into the various functionalities of passenger cars, have a significant influence on the factors considered relevant for consumer behavior. Furthermore, the study reveals that digital technologies are integrated in different ways in each of the three phases of the product life cycle (choice, usage, and disposal).

The originality of this study stems from the fact that it proposes and validates a construct that has not been the subject of previous debates, but whose utility is crucial in the context of the intensification of sustainability concerns in general, and sustainable consumption behaviors in particular. The results of this study are valuable from both a scientific and a practical point of view, providing support to players from the automotive industry in their concern to assess and improve sustainability throughout the value chain. To this end, all stakeholders (especially manufacturers and consumers) must work together to capitalize on the opportunities offered by digital technologies.

The study is organized as follows: the Section 2 presents the results of the literature review, focused on how the various digital technologies—available and used in the automotive industry—can contribute to strengthening sustainable consumption behavior; the Section 3 presents the research methodology; the Section 4 presents the research results and initiates the discussions upon these; and the Section 5 highlights the conclusions of the research, specifies its limitations, and offer ideas for potential future research.

2. The Contribution of Digital Technologies to Fostering Sustainable Consumption Behavior—A Literature Review

Aminpour et al. (2020) [23] analyzed the origins of the sustainability concept and showed that, most of the time, the definition of the concept has as a benchmark the Brundtland Report: the development that “seeks to meet the needs and aspirations of the present without compromising the ability to meet those of the future” [1]. However, in order to shed more light on this concept, the authors conducted further research in the academic environment and showed that, in the relevant literature, four emerging definitions of sustainability can be identified, centered on the following key aspects: “environmentalism concerns, common understanding, neo-Malthusian environmentalism and sustainability as well-being”. The final conclusion of the authors is that the multidimensional nature of sustainability has not allowed the formulation of an unanimously accepted definition, and further research is needed to explain why sustainability is understood differently.

As a practical implementation of the sustainable macroeconomic development model established in 1987 by the Brundtland Report, seven years later, the Triple Bottom Line (TBL) was proposed as a microeconomic tool to facilitate the integration of sustainability into business strategies [24]. More specifically, TBL has been imposed due to the need for an integrated approach to the social and economic dimensions in order to make real progress in environmental matters.

According to research, TBL has evolved over time, integrating new concepts such as sustainable business models (Nogueira et al., 2023) [25] and supply chain management [26]. Moreover, in order to better capture the dimensions of sustainability, other authors proposed the introduction of a new pillar—digital technology. The argument of expanding the area of representativeness was that digitalization has boosted value creation, with companies coupling the sale of products with the provision of associated services. Thus, ‘’digitalization has reconfigured both the operational processes and the relations between companies and their stakeholders” [27].

2.1. Sustainability in Consumption Behavior

There is a growing number of researchers providing evidence that consumers engage in purchasing sustainable products. Whelan and Kronthal-Sacco [28] reported a study of purchase increase of consumer-packaged goods (CPGs) of 50%, from 2013 to 2018, based on sustainability. Focusing on whether marketing a product as sustainable would determine an increase in sales they discovered that consumers preferred sustainable brands and that companies must adjust for this trend.

Reichheld, Peto and Ritthaler (2023) [29] revealed that, based on a survey of more than 350,000 US customers aged from 18 to 98 across 500 brands and 30 sectors, a shift in consumption patterns is imminent towards truly sustainable brands and away from those that had not invested enough in sustainability. They propose three factors that drive change and explain the shift in consumption behavior: Trust (in the company), which drives consumer behavior and thus business outcomes, sustainability, which, among younger generations, promotes trust (companies conveying positive intent and demonstrating competence), and the purchasing power in the US that will soon shift towards younger generations (Gen Z and Millennial customers).

Wang and Sherry [30] analyzed whether eco-conscious customers are willing to pay premium prices for sustainability labeled products. Their dataset analyzed the comparative sales ranking of 1350 labeled products with 16,264 comparable non-labeled products over 10 months of data collected from Amazon. Sustainability labels drive consumer demand and, even though most consumers were not actively filtering for labeled items, credibility played an important role. Consumers choose products with the label when presented with a choice between labeled and unlabeled options, especially when the sustainable messages were aligned with consumer preferences across various price ranges. The investments companies made in sustainable practices positively impacted sales.

Research in consumption behavior extended beyond consumers into employees [31], as Porsche challenged its brand identity, technology and company culture towards embracing electric vehicles. Whilst some companies appeal to reason, depicting sustainability as “leaving a better world for future generations” Porsche used message framing to avoid patronizing its employees in their decision-making process of choosing their next vehicle. The research was based on a choice between battery electric vehicles (BEHs) and plug-in hybrid electric vehicles (PHEVs) for several hundred employees eligible for a new fleet car. Responses from a control group were compared with those from several hundred anonymized and randomized employees who received three different framing messages. In-depth analysis of 147 employees who first triggered an order encouraged Porsche to extend its nudge strategy for more sustainable choices on a larger scale.

In 2017, the conscious consumer market (CCM) was estimated at USD 300 billion [32]. These conscious consumers seek to “demonstrate an awareness and desire to make, in the most part, informed and considered ethical choices]” [32] (pp. 24-25). The research compares the sustainability market entry strategies of six sustainable new ventures (SNVs) and seven multinational corporations (MNCs) on CCM. Results revealed an analytical space where the authors plotted entry strategies: value alignment and scope of the target market. The customers wanted to identify with its core values, not only to shop for a brand and its products.

Engaging in sustainability implies profound transformations for companies [33], based on strong top executive commitment, collaboration with customers, nongovernmental organizations (NGOs) and other stakeholders across the value chain and business model innovation, for a new value proposition and operating model to profitably deliver the offering. To understand consumption behavior, the companies must understand how their customers think about sustainability and whether they are willing to pay premium price for a more sustainable product or service. Collaboration beyond the boundaries of the enterprise with individuals, customers, businesses and groups is a great opportunity for the company to identify meaningful sustainable objectives aligned with customer perceptions and values.

Sustainable consumption refers to consumer behaviors that minimize environmental damage and ensure that future generations can meet their needs through reducing waste, conserving resources and making environmentally responsible purchasing decisions [34]. Kostadinova (2016) [35] defines sustainable consumer behavior as the set of choices and actions individuals take to reduce environmental and social harm while satisfying their own needs, integrating practices such as green consumption, ethical consumerism and pro-environmental behavior. A clear distinction is made by the author between “green” consumer behavior, which refers to specific product choices aimed at minimizing environmental impact and “sustainable” consumer behavior, which involves a larger transformation in consumption habits, aimed at reducing overall resource use and promoting environmental and social responsibility.

Within the domain of sustainable consumption, according to Sargin and Dursun (2023) [34], consumer behavior can be split in two main categories: natural resource use and product consumption. Natural resource use involves efforts made by consumers to reduce their environmental footprint through actions such as lowering water and energy consumption, switching to sustainable transport options like electric vehicles or public transit and reducing dependance on fossil fuels through renewable energy adoption. On the other hand, product consumption refers to choices surrounding what and how individuals consume goods. This includes preferring eco-friendly and ethically produced products and reducing consumption through minimalism and voluntary simplicity. More than this, product consumption also includes actively engaging in reuse, recycling and circular economy practices such as renting or subscribing to products instead of owning them [34]. It is important to highlight that all these behaviors are increasingly supported nowadays by digital tools such as AI, blockchain and IoT, technologies that can really improve transparency and individual awareness in sustainable decision-making.

Sargin and Dursun (2023) [34] identify three main categories of factors that influence sustainable consumer behavior: individual, social, and external. Individual factors include environmental awareness, personal values, attitudes, and traits like responsibility and openness. Social and cultural factors refer to the role of peer influence, demographics, lifestyle, and the impact of media and green marketing. External factors include government policies, corporate practices, economic constraints, and technological innovations that make sustainable options more visible and accessible. Kostadinova (2016) [35] categorizes the factors influencing sustainable consumer behavior into just two main groups: individual-related factors such as environmental awareness and personal values, and contextual or situational factors, including economic constraints, social norms and the availability of sustainable options.

2.2. Sustainable Transport

Sustainable transport has gained increasing attention, particularly from stakeholders within the automotive industry. Research in this area has evolved along distinct sustainability dimensions and can be categorized into nine major thematic streams. The first emerging theme within the sustainable transport research direction is more concentrated on researching and framing specific dimensions used for its assessment and performance [36]. According to this research stream, it is necessary to understand the concept of “sustainable transport” itself. Gudmundsson (2004) presented three main approaches to be taken into consideration in addressing this particular concept: sustainable transport as a metaphor to broaden policy agenda, from the point of view of existing limitations and through the lens of a mixed approach, which takes into account sustainable dimensions and indicators in order to advance and promote sustainable transport agenda [37]. Although it is recognized that there is a necessity to incorporate economic, social and environmental dimensions and indicators, this direction remains under-explored in terms of sustainable transport operationalization, as it depends on the specific context [38]. From this point of view, researchers were rather concentrated on exploring specific methods to define specific indicators. Various methodological approaches can be observed, such as integrating life-cycle assessment [39], multi-criteria decision methods [40] or scenario building, based on the context [41]. The second research area is mostly concentrated on policy development and its impact towards increasing sustainable transport [36]. This research stream was linked further to supply chains, policy development and stakeholders’ engagement. From the perspective of policy development, an effective transition toward a sustainable behavior should be closely linked to societal values and norms such as social acceptance [42,43]. An interesting observation is that sustainable transport, from a social perspective, is influenced both by restrictive policies and by those that encourage sustainable behavior [44]. Moreover, people’s decisions and choices are dependent on the resources they possess, the opportunities they identify in terms of quality infrastructure, availability of transport choices, living or working location, and their transportation needs [36,42]. Some research directions are entirely committed to analyzing environmental metrics of sustainable transport. Considering the evolution of environmental policies within the automotive industry, it can be emphasized that there is an increasing attention toward low-emission vehicles as the main factor influencing and reducing negative environmental impact [36]. Within this research stream, the attention is rather concentrated on new technology development and usage, such as renewable energies, alternative fuels, and eco-driving [43]. From this point of view, there can be observed an increasing attention to the type of low-emission transport [45] such as sustainable buses, electrical passenger cars, and the use of bicycles [36]. Although the implementation of sustainable transport is essential in terms of promoting sustainability, it cannot be achieved without social involvement. Moreover, it requires rigorous strategic and sustainable transport planning [36,43].

2.3. Sustainable Consumption in the Automotive Industry

It is essential for the passenger car manufacturers to understand the evolving customer expectations, so that they provide the right value proposition. A key trend is the growing customer preference for sustainable mobility solutions [38,39,40]. This can be clustered together within the three pillars of the “triple bottom line” describing sustainability. In the environmental pillar, consumers desire transport solutions with reduced negative impact on climate change and ozone layer degradation [40]. They want vehicles that release fewer pollutants into the air and that consume fewer of the non-generative resources of the earth. In the social pillar, consumers expect products to ensure people’s health, safety, comfort and mobility [40]. In the economic pillar, they want reasonable operating and maintenance costs. Consumers also expect a future resale of the product at a fair price [40].

According to Accenture (2021), 64% respondents, out of a total of 8500 from seven countries, are concerned about the environmental impact of transportation [38]. Most of them (91%) are even willing to pay higher prices for sustainable vehicles. Specifically, 30% would pay 1–5% more, 44% would pay 6–15% more, 13% would pay 16–25% more, and 4% are willing to pay above 25% more [38]. However, when researching the importance of sustainability in the purchase decision of a customer, sustainability should be considered in comparison with other factors. In a study from Wellbrock et al. (2020), the participants ranked sustainability as having the lowest importance, placing more importance on quality, design, performance, price, equipment, fuel, and brand of the car [39]. Despite this ranking, the participants still agree on mobility as an important area of life where sustainability should be considered, together with food, energy supply, habitation and apparel [39].

When examining the automobile’s modules individually, consumers assess the power unit as being the most important for environmental sustainability [39]. They are shifting towards purchasing zero CO₂ emissions vehicles (ZEVs) [41]. Examples of ZEVs are Battery Electrical Vehicles (BEVs) and Fuel Cell Electrical Vehicles (FCEVs) powered by hydrogen, each type having specific advantages and disadvantages [41]. BEVs are quiet and comfortable, while having a good acceleration, but the battery is a disadvantage in terms of driving range and charging time. FCEVs have a lower refueling time, allowing for longer distances, but their price is high. Both BEVs and FCEVs have the disadvantage of a low availability of the charging infrastructure [42]. Besides the specific advantages and disadvantages of each type, there are several other financial and non-financial factors influencing the purchase decision of a customer towards a ZEV. In Slovenia, a study identified the body shape (non-financial factor) and the total vehicle price (financial factor) as being the most significant factors, according to the participants [41].

Besides ZEVs, there are other environmentally sustainable transportation alternatives that do not require owning a car. Transportation-as-a-Service or Mobility-as-a-Service allow consumers to rent a vehicle or a ride [43], using the connectivity of the car to the internet originating in Telematics [44]. Public transportation might be the preferred choice for people, if it is available, convenient, reliable and affordable. But it may not fully satisfy the need to travel without constraints of schedule or location [45]. The bicycle is also a great alternative for short-distance commutes [36]. However, these are not in the scope of the current study, since it focuses on passenger cars.

As pointed out in the previous section, this study considered the potential of digital technologies (used in the automotive industry) to create/strengthen the sustainable consumption behavior of car end-users. The assessment was carried out from the perspective of passenger car manufacturers. For these manufacturers to achieve their environmental commitments, collaboration with end-users is essential. The aim was to provide practitioners with a tool to identify the most important levers through which consumers can be stimulated to behave in a more sustainable way.

3. Materials and Methods

The methodology of this study is focused on establishing the most relevant applications of digital technologies used in the automotive industry, which have the potential to contribute to the adoption of sustainable consumption behavior by car users. The study combines qualitative and quantitative analyses, the aim being to develop and test a theoretical–practical framework, following the SHIFT model. The methodology of the study is structured in two stages, and the methods applied in each stage are summarized in

Table 1. Research methods and objectives.

Research Methods and Techniques	Objectives
Qualitative research	Identifying and mapping the digital technologies incorporated into the final product (a personal car) with the aim of facilitating sustainable end-user behavior. Developing an original construct (that will be referred to as “SHIFT for Automotive”), which incorporates the identified digital technologies.
The Delphi technique (quantitative and qualitative analyses)	Construct validation and identification of digital technologies that have the greatest potential to facilitate sustainable consumption behaviors.

.

3.1. Identifying and Mapping Digital Technologies in Accordance with the SHIFT Conceptual Framework

As presented in the Introduction Section, the SHIFT conceptual framework has been used for various purposes. For example, Saha et al. (2023) [21] used SHIFT to assess sustainable consumption in the retail sector. Gallen et al. (2025) [22] used SHIFT to explore adolescents’ attitudes towards adopting a sustainable diet and to assess the costs and benefits of adopting sustainable behaviors. Habib et al. (2021) [20] used SHIFT to analyze the extent to which consumer behavior can be appropriate for improving climate impact.

Different from previous research, the present study focuses on applying the SHIFT framework to the automotive industry in order to assess the means/instruments through which manufacturers co-participate in the adoption of sustainable consumption behaviors by end-consumers. Considering the contribution of digital technologies to stimulating sustainable consumer behavior throughout the entire product life cycle, the aim was to identify and map the most relevant digital technologies in relation to the five pillars of the SHIFT conceptual framework and the three phases of consumption (choice, use, and disposal).

To map digital technologies in accordance with the SHIFT conceptual framework, a mixed team of 4 researchers and 2 practitioners (IT experts in the automotive field) adapted the SHIFT conceptual framework, going through the following stages:

(a)

The group of practitioners (representing two PhD students in the field of “Engineering and Management”, but who are experts in the field of designing digital technologies for the automotive industry) developed a list of digital technologies currently used to equip cars; for each digital technology included in the list, the basic functionalities and the manner in which they contribute to the efficient/sustainable operation/operation of the car were highlighted.

(b)

The group of senior researchers conducted a literature review to substantiate both sustainable behavior and the pillars of the SHIFT conceptual framework: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility; according to the SHIFT conceptual framework, consumers are more inclined to engage in pro-environmental behaviors when the previously listed psychological factors are capitalized on.

(c)

Two mixed teams assessed the extent to which each digital technology serves the particular purpose of each pillar of the SHIFT conceptual framework; the results obtained by the two mixed teams were discussed and concatenated to achieve a reasonable consensus from both the practitioners’ and theorists’ points of view.

(d)

Within three rounds of debates (in mixed teams, with practitioners and theorists, and with the co-authors in this study), the digital technologies associated with the different pillars of the SHIFT conceptual framework were analyzed from the perspective of their use during the three stages of the car life cycle (choice, usage, and disposal).

(e)

By associating digital technologies with each pillar of the SHIFT conceptual framework and with each stage of the life cycle, an original construct was obtained, which was very useful, both theoretically and practically.

The adapted SHIFT conceptual framework (hereinafter referred to as “SHIFT for automotive”) included a set of 12 digital technologies—and specific applications—which, incorporated into different car functionalities, can drive end-users to adopt sustainable consumption behavior. Specifically, the technologies considered were the following: Artificial Intelligence (AI), Augmented Reality (AR), Big Data (BD), Blockchain (B), Cloud Computing (CC) and Mobile Apps (MA), Digital Twin (DT), Distributed computing (DC), In-car connectivity (IC), Internet of Things (IoT), Virtual Reality (VR), and Digital Realities (DRs). The selection of these 12 technologies was guided by their current relevance, maturity and applicability within the automotive industry, as well as their documented potential to influence sustainable consumer behavior, according to the existing literature.

Table 2. The applications of digital technologies in relation to SHIFT for the automotive industry.

Technology	Definition
Artificial Intelligence	“Systems that display intelligent behavior by analyzing their environment and taking actions—with some degree of autonomy—to achieve specific goals”. Recent innovations in the field comprise of: Generative Adversarial Networks (GANs), and General Purpose Technology (GPT) [46].
Augmented Reality	Augmented Reality is a technology that overlays digital information onto the real world, enhancing user experiences across various fields. Unlike VR, which creates entirely digital environments, AR combines reality with virtual elements [47]. Currently, it is commonly used for visual (computer-generated) information, but, technically, it could be used for all five senses [48].
Big data	A field using digital technologies for collecting, storing and analyzing the data, which is produced in huge amounts nowadays. It is characterized by 5Vs: “value, velocity, volume, veracity, and volume” [49].
Blockchain	A technology implementing a digital ledger of transactions in a distributed, decentralized and immutable way. Combined with cryptographic mechanisms and other technologies, blockchain is used in cryptocurrency, with Bitcoin being the first cryptocurrency developed [50].
Cloud Computing and Mobile Apps	A model for offering “on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [51]. A subcategory of distributed computing [52].
Digital Twin	A virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision-making [53]. A set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level [54].
Distributed Computing	A technology that solves a task by using the computation power of multiple computers connected over a network, called nodes. The nodes may be assigned a different load of the computation or even specialized functions, such as graphical processing [52].
In-car connectivity	Enables the car to transmit data in a network by using “low-energy wireless communication and sensor network technologies” [55].
Internet of Things	A system of connected devices and objects, which exchange information with each other over a network [56].
Virtual Reality	A digital environment that provides an immersive and interactive human experience, by triggering the human sensory systems by means of devices and software [57,58]. VR systems aim to engage multiple senses, including vision, sound, and touch, to create immersive and interactive experiences for users through computer-simulated environments [59,60].
Digital Realities	An integration of multiple digital technologies, creating an immersive digital environment for humans [61]. Digital realities encompass a spectrum of technologies that simulate and extend our perception of reality through digital means [62]. In this study, the term “Digital Realities” integrates Augmented Reality and Virtual Reality [61].

presents the applications of digital technologies considered in assessing SHIFT for automotive.

The Results and Discussion Section details the associations between digital technologies and the five factors underlying the formation/adaptation of sustainable consumption behaviors.

3.2. The Delphi Technique—Validation of the “SHIFT for Automotive” Construct

As a method of organizing communication in groups of experts in order to answer a clearly defined research question or a complex problem, the Delphi technique is applied to seek consensus (when opinions are divergent) or to evaluate, confirm or validate a theoretical or practical construct. For example, Dağılgan and Ercan (2025) [63] used the Delphi technique to assess the materiality and validity of sustainability themes for construction companies. Pearson et al. (2025) [64] conducted a Delphi study to obtain a consensus on the essential training needs for a specific category of educators employed at the level of the formal education system. Huang et al. (2025) [65] used the Delphi technique to assess the quality of AI-generated digital educational resources for university teaching and learning. By applying this technique, the authors identified the most important aspects of AI-generated resources: content characteristics and expressive characteristics, such as authenticity, accuracy, legitimacy, relevance, etc. Véliz et al. (2025) [66] investigated the specificities of designing and implementing circular economy strategies for construction and demolition waste management. The Delphi technique was used to identify the key factors influencing the current and future adoption of circular economy practices.

As we can see, although there are some associated limitations (such as those related to potential bias, sample size and their heterogeneity, generalizability of the results, etc.), many recent studies use the Delphi technique for various purposes. Unlike previous studies, in this study the Delphi technique was used to validate an original construct—“SHIFT for automotive”. The steps followed are detailed below:

• Defining the purpose. Evaluating the opinions of automotive industry experts on the contribution of various applications of digital technologies in shaping/shaping/stimulating the sustainable behavior of car users.
• Developing the tool for the assessment and validation of the adapted conceptual framework. Based on the “SHIFT for automotive” construct (which integrates different applications of digital technologies with the role of engaging users in pro-environmental behaviors), a set of 58 items were formulated, structured as follows:

  -

  Twelve items assessed the extent to which digital technologies (such as AI, Augmented Reality, Cloud computing, and Big Data) have a direct influence on the social factors that guide the sustainable behavior of end-users. These items assessed influences at all three stages: choice (5 items), usage (5 items) and disposal (2 items).

  -

  Eleven items assessed the extent to which digital technologies (such as AI, Big Data, Big Data Analysis, Cloud Computing, Internet of Things, and Virtual Reality) have a direct influence on the formation of sustainable consumption habits (habit formation). These items assessed influences at two of the three stages: choice (1 item), and usage (10 items).

  -

  Eight items assessed the extent to which digital technologies (such as AI, Blockchain, Digital Realities, Big Data, and Cloud Computing) have a direct influence on the lifestyle and identity of end-users (individual self), leading them to adopt a sustainable behavior. These items assessed influences at all three stages: choice (3 items), usage (4 items), and disposal (1 item).

  -

  Twelve items assessed the extent to which digital technologies (such as AI, Cloud Computing, Digital Realities, and In-car connectivity: cellular network, Augmented Reality, Mobile Apps) shape consumers’ emotions and reasoning (feelings and cognition), leading them to adopt a sustainable behavior. These items assessed influences at all three stages: choice (6 items), usage (5 items) and disposal (1 item).

  -

  Fifteen items assessed the extent to which digital technologies (such as Digital Twin, and In-car connectivity: Cellular network and Wi-Fi, AI, Internet of Things, Blockchain, Distributed computing, and Cloud Computing) influence users’ sustainable behavior by facilitating awareness of a tangible environmental impact. These items assessed influences at all three stages: choice (4 items), usage (4 items) and disposal (7 items).

Due to the large number of items, and in order to facilitate the work of the experts, the decision was made to associate the 58 items with a five-point Likert scale: 1—Strongly Disagree, 2—Disagree, 3—Neutral; 4—Agree, 5—Strongly agree. For each set of items, also taken into account was the possibility that the experts could provide their own opinions/observations/arguments by formulating an answer to an open question. According to the opinions of previous researchers [67,68], in most cases, in the application of the Delphi Technique, (mixed) standardized Likert scales combined with open questions are used.

3.

Designation of a moderator and establishment of communication methods with experts. In order to optimize the management of the flow of information between the research team and the experts, a representative was designated. Since a team of internal and external experts was envisaged, communication was carried out online, with correspondence being carried out via email and communication of opinions via the Google Forms form/application).

4.

Expert recruitment. In order to have a broader perspective on the validity of the construct, 15 experts were selected. Although previous studies admit that the opinions of at least 4 experts can be considered sufficient for the application of the Delphi technique [63,69], a larger number of experts was considered, precisely to ensure the robustness of the assessment.

The criteria for selecting the experts were experience in the automotive industry and the degree of involvement in the implementation of digital technologies in the various functionalities of cars. The 15 selected experts were sent an invitation (and a request to confirm/deny their availability. The 15 experts were informed about the research objectives, the research methodology, the tools used (Google Forms), the response method, and the deadlines for each stage.

5.

Questionnaire distribution (round 1). The experts’ opinion assessment tool, to obtain consensus on the validity of the newly developed construct (“SHIFT for automotive”), was organized into 5 sections (corresponding to the original SHIFT conceptual framework): Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Experts were asked to express their opinion, voluntarily, anonymously and separately, on how each of the selected digital technologies can contribute to empowering the end-user to adopt a more sustainable consumption behavior.

6.

Collection and analysis of expert responses (round 1). For each item of the evaluation instrument, the experts’ responses were analyzed. Particular attention was paid to free responses, as they allowed the experts to freely express a point of view on the evaluated aspects. Based on these analyses, the items were grouped into two classes: items for which there is consensus among the experts and items for which consensus was not achieved. The items in the second category were inserted into a new evaluation instrument (having a similar structure to the first instrument).

7.

Retransmission of questions to experts (round 2). In this second round, experts were informed that for some items a general/reasonable consensus had not been reached, which is why they were invited to analyze the data and formulate opinions (anonymously and separately) on how each of the selected digital technologies can contribute to empowering the end-user to adopt a sustainable consumption behavior.

8.

Collection and analysis of the answers formulated by the experts in the second round. The analysis of the answers formulated by the experts (in the second round) narrowed the area of divergences of opinion and allowed the validation of the construct “SHIFT for automotive”.

9.

Building the “SHIFT for automotive” conceptual framework, taking into account the results obtained from the processing of the opinions expressed by the experts.

10.

Discussing the results and how they can be interpreted in the light of previous studies and working hypotheses.

4. Results and Discussion

The application of the Delphi technique aimed to collect the opinions of 15 experts. Out of the total number of 15 experts, 12 experts answered.

Table 3. Demographics of the experts.

Years of Experience in the Automotive Industry	What Is Your Field of Expertise in the Automotive Industry?	What Is Your Position in the Company/Project?	Are You Currently Working in the Automotive Industry?	Your Company Is Based in
>15 years	Body Controllers	Project Manager	Yes	A non-EU state
>15 years	Digital Clusters, Head-Up Display, Infotainment	Team Leader	Yes	An EU state
10–15 years	Digital Clusters, Head-Up Display, Infotainment	Project Manager	Yes	An EU state
10–15 years	Digital Clusters, Head-Up Display, Infotainment	Team Leader	Yes	An EU state
5–9 years	Digital Clusters, Head-Up Display, Infotainment	Project Manager	Yes	An EU state
5–9 years	Braking Systems	Project Manager	Yes	An EU state
5–9 years	Autonomous Mobility	System Architect	Yes	An EU state
5–9 years	Wireless Access Systems	Technical expert	Yes	A non-EU state
5–9 years	Autonomous Mobility (Assisted Driving Functions) and Process Development	Product Owner	Yes	An EU state
2–4 years	Autonomous Mobility	Software Developer	Yes	An EU state
2–4 years	Autonomous Mobility	Software Integrator	Yes	An EU state
<2 years	Digital Clusters, Head-Up Display, Infotainment	Technical expert	No	An EU state

summarizes the socio-demographic profiles of the experts.

According to the data in Table 3, more than 33% of the experts have an experience of more than 10 years in the automotive industry. A total of 42% of the experts have an experience of between 5 and 9 years. The competencies of the experts are in diverse domains of the automotive industry: Digital Clusters, Head-Up Display, Infotainment, Body Controllers, Braking Systems, Autonomous Mobility, and Wireless Access Systems. Four experts are in a project manager role, and two experts are in a team leader role. The other experts have technical roles. A total of 11 out of the 12 experts were working in the automotive industry (during the time of this study). Among the respondents, two experts are working for a company based in a non-EU state. Due to the application of the general data protection regulation, the exact residence of the experts was not made public.

Analyzed from the perspective of experience, areas of expertise, and positions held, the socio-demographic profile of experts meets the diversification requirement. Conversely, from a geographical representation perspective, the possibilities for generalizing the results remain limited, given that most of the experts are from EU countries.

Before analyzing the results of the first round of application of the Delphi Technique, the internal consistency of the items was analyzed to assess the reliability of the construct. According to the data in

Table 4. Cronbach’s Alpha Coefficients.

Information	Cronbach’sAlpha	Cronbach’s Alpha Basedon Standardized Items	No. ofItems
All components of SHIFT	0.958	0.959	58
First pillar—Social influence	0.822	0.937	12
Second pillar—Habit formation	0.767	0.781	11
Third pillar—Individual self	0.939	0.945	8
Fourth pillar—Feelings and cognition	0.887	0.897	12
Fifth pillar—Tangibility	0.898	0.906	15

, the test performed (reliability statistics) revealed that the items that formed the basis for the evaluation of the experts’ opinions (regarding the role of digital technologies in the adoption of sustainable behaviors by end-users) are correlated with the overall score of the test, the Cronbach’s Alpha coefficient registering a value of 0.959. The result indicates a high level of internal consistency for the items used within the specific sample. For greater accuracy, the Cronbach’s Alpha coefficient was calculated for each of the five sections of the construct. As can be seen in Table 4, the reliability of the construct is also proven at the level of the pillars of the SHIFT conceptual framework that formed the basis of this research.

After the first round of applying the Delphi technique, and after testing the viability of the construct, the results were analyzed to check to what extent the experts agree or disagree with regards to the potential of the applications of digital technologies to positively influence sustainable behavior consumption. The results for each of the five pillars of the SHIFT conceptual framework are presented in Figure 2.

For the 12 items evaluating the extent to which the digital technologies have a direct influence on the social factors guiding sustainable consumption, 87 (out of 144) responses were in the superior part of the Likert scale, indicating that the experts “agree” or “strongly agree”. A total of 37 responses were at the middle of the Likert scale (“neutral”), while the other 20 response were in the lower part of the Liker scale (“disagree” and “strongly disagree”). This is illustrated in Figure 2—“Social Influence”.

In terms of the extent in which digital technologies directly influence the “Habit Formation” of sustainable consumptions, the responses from the experts indicated a greater level of agreement: 85 (out of 132) responses were in the superior part of the Likert scale. This is illustrated in Figure 2—“Habit Formation”. In the analysis of the responses regarding the extent to which the application of the digital technologies directly influences the lifestyle and the identity of the users (Figure 2—“Individual Self”), 52 (out of 96) responses confirmed the potential of the digital technologies analyzed to influence the consumers to adopt a more sustainable consumption behavior.

For the 12 items evaluating the extent to which the digital technologies shape the emotions and the reasoning of consumers (Figure 2—“Feelings and Cognition”), the responses were in the superior part of the Likert scale. This validates the idea that the digital technologies analyzed have the potential to influence consumers to adopt sustainable consumption behaviors. Regarding the extent to which the applications of digital technologies influence sustainable consumer behaviors of the users of automobiles, the results for the 15 items for the pillar “Tangibility” include 127 responses in the superior part of the Likert scale. This indicates that the experts agree on the potential of the analyzed digital technologies to facilitate user awareness of the tangible impact of their behaviors on the environment.

The analysis of the responses regarding the entire construct concept indicated the following: 18 items had 100% consensus, 2 items had 83% consensus, while the results for the remaining items did not show clear agreement/disagreement. This was a strong argument to carry out a second round of the Delphi technique. In developing the evaluation instrument for the second round, significant attention was placed on the items for which the opinions of the experts were in the lower part of the Likert scale, indicating disagreement or strong disagreement. In order to simplify the evaluation process for the experts, but also to optimize the evaluation, the second round did not include the 11 items for which the first round indicated more than two responses in the lower part of the Likert scale (1—strongly disagree, 2—disagree). Thus, in the second round, the total number of items for evaluation was 27, calculated in this way: 58−20−11 = 27. After data collection, the analysis of the reliability of the partial construction (composed of the 27 items) was resumed. The results of this analysis indicated a Cronbach’s Alfa coefficient of 0.810, which confirmed that the results can be used with confidence.

The results corresponding to the two rounds of the Delphi technique are illustrated in

Table 5. Results—Social Influence.

No.	Code	Items	Results	Support	Degree of Confirmation
Choice
SI.C1. Artificial Intelligence
1	SI.C1.1	AI-powered social media trends that promote sustainable vehicle choices by showing what others are purchasing	confirmed (R2)	11/12	91.67%
2	SI.C1.2	AI-driven social media campaigns that encourage sustainable vehicle choices or ridesharing platforms	confirmed (R2)	11/12	91.67%
SI.C2. Augmented Reality
3	SI.C2.1	AR Showrooms that showcase the environmental impact of different vehicle choices	infirmed (R1)	3/12	-
SI.C3. Virtual Reality
4	SI.C3.1	Interactive VR training modules for sustainable choices	confirmed (R2)	10/12	83.33%
SI.C4. Cloud computing
5	SI.C4.1	Cloud-based platforms for knowledge-sharing and best practices in sustainable automotive choices	confirmed (R2)	11/12	91.67%
Usage
SI.U1. Artificial Intelligence
6	SI.U1.1	AI-driven social comparison dashboards that rank drivers based on fuel efficiency and emissions reductions	infirmed (R2)	3/12	-
7	SI.U1.2	Eco-routing (chose an eco-route because other users in the area did so)	confirmed (R1)	12/12	100%
SI.U2. Virtual Reality
8	SI.U2.1	VR experiences that simulate sustainable driving habits (efficient acceleration, braking)	infirmed (R2)	3/12	-
SI.U3. Big Data
9	SI.U3.1	Real-time feedback providing drivers with insights into their driving patterns and how to optimize for sustainability	confirmed (R1)	11/12	91.67%
SI.C4. Cloud computing
10	SI.U4.1	Remote vehicle diagnostics to optimize energy efficiency and reduce unnecessary repairs	confirmed (R1)	12/12	100%
Disposal
SI.D1. Virtual Reality
11	SI.D1.1	VR training modules educating consumers on responsible vehicle recycling, component reuse and disposal	confirmed (R2)	11/12	91.67%
SI.D2. Blockchain
12	SI.D2.1	Transparent supply-chain tracking to verify the sustainability of vehicle components and materials	infirmed (R2)	3/12	-

,

Table 6. Results—Habit Formation.

No.	Code	Questions	Results	Support	Degree of Confirmation
Choice
HF.C1. Artificial Intelligence
1	HF.C1.1	AI can recommend new vehicle choices or features based on the users’ sustainable habits (based on distance, charging time, range, etc.)	confirmed (R1)	12/12	100%
Usage
HF.U1. Artificial Intelligence
2	HF.U1.1	Personalized eco-driving assistants that analyze driving patterns and suggest habit changes	confirmed (12)	12/12	100%
3	HF.U1.2	AI nudges that remind drivers to switch to energy-saving driving modes, reinforcing eco-driving habit	confirmed (R2)	10/12	83.33%
HF.U2. Virtual Reality
4	HF.U2.1	VR-based eco-driving training programs to help drivers optimize energy usage	confirmed (R2)	11/12	91.67%
HF.U3. Cloud Computing
5	HF.U3.1	Cloud-integrated remote diagnostics that optimize energy efficiency in EVs (electrical vehicles)	confirmed (R2)	11/12	91.67%
HF.U4. Internet of Things
6	HF.U4.1	Eco-driving	confirmed (R1)	12/12	100%
7	HF.U4.2	Eco-routing	confirmed (R1)	12/12	100%
8	HF.U4.3	Autonomous Driving Features	confirmed (R2)	12/12	100%
Disposal
HF.D1. Cloud Computing
9	HF.D1.1	Cloud platforms that send messages to users to properly dispose of parts when the components reach end-of-life	confirmed (R2)	12/12	100%
HF.D2. Artificial Intelligence
10	HF.D2.1	AI-driven waste reduction strategies for vehicle owners, suggesting part repairs instead of replacements	confirmed (R2)	11/12	91.67%
HF.D3. Big Data Analysis
11	HF.D3.1	Regulations for disposal, created based on big data analysis	confirmed (R1)	12/12	100%

,

Table 7. Results—Individual Self.

No.	Code	Questions	Results	Support	Degree of Confirmation
Choice
IS.C1. Artificial Intelligence
1	IS.C1.1	AI-powered eco-personality profiles that align green vehicle choices with a consumer’s lifestyle and identity	infirmed	3/12	-
2	IS.C1.2	AI-powered analysis of needs, for purchasing smaller vehicles, with functionalities that cover the main needs	infirmed	3/12	-
IS.C2. Blockchain
3	IS.C2.1	Decentralized vehicle history tracking, increasing trust in second-hand cars and reducing new-vehicle production demand	infirmed	5/12	-
Usage
IS.U1. Artificial Intelligence
4	IS.U1.1	Personalized energy efficiency goals that users set and track, enhancing their sense of self-efficacy	confirmed (R2)	11/12	91.67%
IS.U2. Digital Realities
5	IS.U2.1	Use of AR/VR headsets for storytelling (visualizes the direct impact of eco-friendly choices on the user’s life and value)	infirmed	4/12	-
IS.U3. Big Data
6	IS.U3.1	Personal sustainability dashboards that track fuel savings, emissions reductions, and personal impact over time	confirmed (R2)	12/12	100%
IS.U4. Cloud Computing
7	IS.U4.1	personal carbon-footprint journals that help users track their long-term sustainability journey	confirmed (R2)	11/12	91.67%
Disposal
IS.D1. Blockchain
8	IS.D1.1	Ownership and recycling certificates stored on blockchain, reinforcing responsible disposal decisions and self-commitment	infirmed	5/12	-

,

Table 8. Results—Feelings and Cognition.

No.	Code	Questions	Results	Support	Degree of Confirmation
Choice
FC.C1. Artificial Intelligence (AI)
1	FC.C1.1	AI-powered personal assistants for cost–benefit analysis of vehicles based on powertrain: BEV, FCEV, PHEV	confirmed (R1)	12/12	100%
2	FC.C1.2	AI-powered advertising campaigns for encouraging purchase of EV	confirmed (R2)	10/12	83.33%
3	FC.C1.3	Online branding campaigns for increasing brand attachment	infirmed	3/12	-
FC.C2. Cloud Computing
4	FC.C2.1	Online communities for sustainable mobility	confirmed (R2)	10/12	83.33%
5	FC.C2.2	Online customization of vehicle and comparison of pricing	infirmed	3/12	-
FC.C3. Digital Realities
6	FC.C3.1	AR and VR product visualization	infirmed	3/12	-
Usage
FC.U1. In-car connectivity: cellular network (5G, LTE), Wi-Fi
7	FC.U1.1	Over-The-Air updates: for upgrading software in vehicle	confirmed (R1)	12/12	100%
8	FC.U1.2	Car-sharing: for (un)locking the car for remote car rental	confirmed (R1)	12/12	100%
FC.U2. Augmented Reality (AR)
9	FC.U2.1	AR-enhanced repair guide	confirmed (R1)	12/12	100%
FC.U3. Cloud Computing and Mobile Apps
10	FC.U3.1	Car-ride (Mobility-as-a-Service)	confirmed (R1)	12/12	100%
11	FC.U3.2	Car-sharing (Mobility-as-a-Service)	confirmed (R1)	12/12	100%
Disposal
FC.D1. Cloud Computing and Mobile Apps
12	FC.D1.1	Digital marketplaces for resale vehicles	confirmed (R2)	12/12	100%

and

Table 9. Results—Tangibility.

No.	Code	Questions	Results	Support	Degree of Confirmation
Choice
T.C1. Digital Twin
1	T.C1.2	Simulations for carbon footprint of the vehicle (throughout its entire lifecycle)	confirmed (R1)	12/12	100%
T.C2. In-car connectivity: cellular network (5G, LTE), Wi-Fi
2	T.C2.1	Subscription-based EV ownership	infirmed	5/12	-
T.C3. Artificial Intelligence (AI)
3	T.C3.1	Digital-comparison tools for lifecycle emissions of a vehicle	confirmed (R2)	12/12	100%
4	T.C3.2	Interactive emission calculators for vehicle choices based on powertrain, driving habits and other criteria	confirmed (R2)	11/12	91.67%
Usage
T.U1. Internet of Things (IoT)
5	T.U1.1	Predictive maintenance: based on sensors in the car, driver receives recommendations for vehicle maintenance, increasing vehicle lifetime	confirmed (R1)	12/12	100%
6	T.U1.2	Vehicle-to-infrastructure (V2I) communication: reducing traffic congestion, providing accurate arrival times for public transportation	confirmed (R1)	12/12	100%
7	T.U1.3	Optimization of HVAC (Heating, Ventilation and Air Conditioning)	confirmed (R2)	11/12	91.67%
T.U2. Blockchain
8	T.U2.1	Digital-product passports providing sustainability scores	infirmed	2/12	-
Disposal
T.D1. Distributed computing
9	T.D1.1	Crowdsource databases for vehicle parts	confirmed (R2)	11/12	91.67%
10	T.D1.2	Digital marketplaces for resale of vehicle parts	confirmed (R2)	12/12	100%
T.D2. Cloud Computing
11	T.D2.1	Digital platform for upcycling recommendations for vehicle parts	confirmed (R1)	12/12	100%
12	T.D2.2	Digital education for recycling vehicle	confirmed (R1)	12/12	100%
T.D3. Artificial Intelligence (AI)
13	T.D3.1	AI-powered resale valuation for vehicles and parts	confirmed (R1)	11/12	91.67%
T.D4. Blockchain
14	T.D4.1	Blockchain-based tracking of car parts and materials	confirmed (R2)	12/12	100%
15	T.D4.2	Blockchain-based ownership history for vehicle	confirmed (R2)	12/12	100%

. The first three columns of each table present the items corresponding to the five pillars of the SHIFT conceptual framework. The “Results” column presents the decision for each item, a decision which is based on the opinions of the experts. The remaining two columns present the evidence for confirmation/rejection, based on the opinions of the twelve experts (in absolute value, indicating how many out of the twelve experts agreed with that item, but also in relative value).

Table 5 contains the opinions of the experts with regard to the way in which consumer behavior can be influenced by social norms, opinions and expectations of the social groups the consumer is part of. This social influence can be amplified by integrating digital technologies into different functionalities of the vehicle. According to the results of this study, the digital technologies that have the potential to influence consumer behaviors towards more sustainable ones, at different stages of the product lifecycle (choice, usage, and disposal), for “Social Influence” are Artificial Intelligence, Virtual Reality, and Big Data.

Besides expressing their opinions using the 5-point Likert scale, the experts were able to provide a more detailed opinion in open text. Their comments highlight the concern of the representatives of the automotive industry regarding sustainable consumption behaviors. The following presents some of their expressed concerns (the number attributed to the expert corresponds to the order in which the response was received; this order is different between round 1 and round 2).

“I think to ‘sell’ a concept to someone, it’s useful to highlight what will bring them value, and that requires targeted discussion, not casting a broad net. While teaching consumers via training modules, knowledge sharing, etc., can be useful in educating them, sometimes sustainability will lose in favor of a smaller price if the buyer is on a budget; and showcasing the smaller environmental impact of an electric car will fail if they live in a country that lacks the infrastructure”.

Expert 2 (first round)

“I am not certain to what degree people distinguish between hype features (e.g., a feature that displays the real time energy consumption) versus features that address real problems and needs (e.g., driving patterns, eco routing). There is also the control that the manufacturers manifest when trying to control the market (e.g., in-depth vehicle diagnosis directly available for the driver) that impact how sustainability is perceived on the driver’s side, meaning that he will be influenced to still go to the dealership because they don’t want to lose business by allowing customers with too many self-diagnostic data versus a visit to the dealership service, where only a specialized mechanic can provide a diagnostic”.

Expert 10 (first round)

“Blockchain enhances transparency in electric vehicles supply chain, helping consumers make informed choices”.

Expert 2 (second round)

“I discount learning experiences (VR or otherwise, as long as they feel like classes) here based on the thought that this is a ‘show, don’t tell’ type of situation. Basically, you can only teach someone if they’re willing to learn, but peer pressure seems like a more useful tool in this scenario”.

Expert 4 (second round)

“Digital technologies can profoundly influence consumer behavior”.

Expert 5 (second round)

“There is an impact of digital technologies that I don’t know if it has been noticed: due to a lack in quality and reliability of some digital technologies, many people, especially from low–mid social class, feel that digitalization of cars has decreased the quality in comparison with classic cars (produced until 2005–2010) and are reluctant to buy a “modern” car, due to increases in complexity, lower reliability, lower repairability and lower maintainability”.

Expert 9 (second round)

According to the results summarized in Table 6, the following digital technologies have the potential to influence “Habit Formation” towards more sustainable consumption: Artificial Intelligence, Virtual Reality, Cloud Computing, Internet of Things, and Big Data.

Compared to the first pillar of the SHIFT conceptual framework (Social Influence), for the second pillar (Habit Formation), there was agreement among the experts. A total of 5 (out of 11) items were confirmed in the first round of applying the Delphi technique, and the remaining 6 were confirmed in the second round. As the table clearly shows, the applications of the selected digital technologies were perceived as having a strong potential to positively influence sustainable consumption behaviors. The comments provided by experts are also evidence for this:

“I think these are all good initiatives to aid habit formation, and I would also add some form of reinforcement of positive behavior. If AI keeps throwing “please switch to energy-saving mode!” messages at me, I may learn to switch to energy-saving mode or I may learn to turn off notifications. On the other hand, if by switching to energy-saving mode I get 20 EnergySaver points, and there’s a database showing I’m the 89th best EnergySaver in the country and the 2nd best out of all my friends, maybe I’m incentivized”.

Expert 2 (first round)

“Automotive market must be considered in the context of each society, with its rules, living habits, transportation system. If automotive is analyzed in isolation (e.g., eco-run, but the town is so crowded and there are no real alternatives like public transport or infrastructure), the influence of all the above factors under analysis will be close to minimum”.

Expert 10 (first round)

“Cloud solutions are dependent on signal availability”.

Expert 11 (first round)

Table 7 presents the results of the analyses for the third pillar—Individual Self; the purpose of the analysis was to assess the extent to which digital technologies (through specific applications) are aligned with the lifestyle and identity of consumers for the automotive industry.

According to the data in Table 7, collected during round 1 of the Delphi technique, there was no consensus for the selected 8 items. One of the experts explained this:

“… The identity of the automotive industry has changed in the last years, to become basically data mining. And this is not unexpected, it is in line with today’s massive information-based industry. However, most services that require user data are free because the user is not the client, they are the product (e.g.: social media—your data is sold to ad companies in exchange for the right to use a platform like Facebook or Instagram for free). But the automotive industry has taken this concept and removed the ability to actually GAIN something in exchange for your data—instead, you pay a lot of money for a car, and then you give the car maker your data for free, and sometimes you STILL have to pay more for subscription-based services. I do not believe this is what the identity of the automotive industry was, or should be. In a society where laws kept up with technological advance, these would be considered predatory business practices and would be regulated”.

Expert 2 (first round)

In the second round of the Delphi technique, for the pillar Individual Self, the utility of three digital technologies (and their corresponding applications) was confirmed: Artificial Intelligence, Big Data, and Cloud Computing (Table 7).

Table 8 contains the data for the fourth pillar of SHIFT conceptual framework—Feelings and cognition. Starting from a set of digital technologies selected based on the literature review and the opinions of experts in the field, the goal was to evaluate the degree in which these digital technologies (and their corresponding applications) can shape the emotions and reasoning of consumers, influencing them towards the adoption of sustainable consumer behaviors with regards to the products of the automotive industry (automobiles). The results confirm that the following digital technologies are able to influence the emotions and reasoning of users of automobiles in all of the three stages of the product lifecycle (choice, usage, and disposal): Artificial Intelligence, Cloud Computing (and Mobile Apps), Digital Realities, In-car connectivity, and Augmented Reality).

In addition to the responses using the 5-point Likert scale, some of the experts have also provided detailed comments to support their answer, as follows:

“I think most things you can throw at a consumer need to be: 1. targeted directly at that consumer’s beliefs (…); 2. targeted at their budget (…); 3. targeted at their social status (…)”.

Expert 2 (first round)

“Branding works. You’re in a hypermarket; there are 50 brands of detergent around you. If you don’t have budget constraints forcing you to limit your choice pool, you will most likely buy something you’ve heard of before. There are studies that demonstrate that brand familiarity reduces decision fatigue and increases perceived value, thereby increasing likelihood of purchase. Brand trust, loyalty and recognition all affect consumer behavior”.

Expert 4 (second round)

Table 9 presents the results for the last pillar of SHIFT: Tangibility. For this pillar, experts have evaluated the extent to which digital technologies can contribute to increasing awareness about the tangible impacts of sustainable consumption on the environment.

Out of a total of 15 items for Tangibility, 13 items were confirmed by experts (with 6 of them being confirmed in the first round). Thus, it has been proven that the following digital technologies can support users of automobiles to adopt more sustainable consumption behaviors, by increasing their awareness levels regarding the tangible impacts on the environment: Digital Twin, Artificial Intelligence, Internet of Things, Blockchain, Distributed computing, and Cloud Computing.

Regarding the last pillar of SHIFT, even though there was consensus in the responses using the 5-point Likert scale, in their comments some of the experts mentioned some particular aspects:

“If the goal is indeed increasing awareness—I think educating the people helps. A marketplace for refurbished parts will help in actually decreasing the environmental impact, but will not really increase awareness IMO (in my opinion)”.

Expert 2 (first round)

“If you want to increase awareness on something, bite-sized chunks of important, eye-catching info are the way to go. I think maybe sometimes it’s more important how the info is packaged, as not everyone will sit through a lecture on a subject, they lacked awareness in (and are therefore not already interested in). I believe most of these ideas would add value here”.

Expert 4 (second round)

Using the results obtained by applying the Delphi technique and taking into consideration the five pillars of SHIFT, it was possible to define an adapted conceptual framework (named “SHIFT for automotive”).

Table 10. SHIFT for automotive.

		Digital Technologies (Applications)
		Choice	Usage	Disposal
S	Social Influence	SI.C1. Artificial Intelligence: - AI-powered social media trends that promote sustainable vehicle choices by showing what others are purchasing; - AI-driven social media campaigns that encourage sustainable vehicle choices or ridesharing platforms; SI.C3. Virtual Reality: - Interactive VR training modules for sustainable choices; SI.C4. Cloud computing: - Cloud-based platforms for knowledge-sharing and best practices in sustainable automotive choices	SI.U1. Artificial Intelligence - Eco-routing (chose an eco-route because other users in the area did so) SI.U3. Big Data - Real-time feedback providing drivers with insights into their driving patterns and how to optimize for sustainability SI.C4. Cloud computing - Remote vehicle diagnostics to optimize energy efficiency and reduce unnecessary repairs	SI.D1. Virtual Reality - VR training modules educating consumers on responsible vehicle recycling, component reuse and disposal
H	Habit Formation	HF.C1. Artificial Intelligence - AI can recommend new vehicle choices or features based on the users’ sustainable habits (based on distance, charging time, range, etc.)	HF.U1. Artificial Intelligence - Personalized eco-driving assistants that analyze driving patterns and suggest habit changes; - AI nudges that remind drivers to switch to energy-saving driving modes, reinforcing eco-driving habit; HF.U2. Virtual Reality - VR-based eco-driving training programs to help drivers optimize energy usage; HF.U3. Cloud Computing - Cloud-integrated remote diagnostics that optimize energy efficiency in EVs; HF.U4. Internet of Things - Eco-driving; - Eco-routing; - Autonomous Driving Features;	HF.D1. Cloud Computing - Cloud platforms that send messages to users to properly dispose of parts when the components reach end-of-life; - AI-driven waste reduction strategies for vehicle owners, suggesting part repairs instead of replacements; HF.D3. Big Data Analysis - Regulations for disposal, created based on big data analysis
I	Individual Self	-	IS.U1. Artificial Intelligence - Personalized energy-efficiency goals that users set and track, enhancing their sense of self-efficacy; IS.U3. Big Data - Personal sustainability dashboards that track fuel savings, emissions reductions, and personal impact over time; IS.U4. Cloud Computing - Personal carbon-footprint journals that help users track their long-term sustainability journey	-
F	Feelings and Cognition	FC.C1. Artificial Intelligence (AI) - AI-powered personal assistants for cost–benefit analysis of vehicles based on powertrain: BEV, FCEV, PHEV; - AI-powered advertising campaigns for encouraging purchase of EV; FC.C2. Cloud Computing - Online communities for sustainable mobility	FC.U1. In-car connectivity: cellular network (5G, LTE), Wi-Fi - Over-The-Air updates: for upgrading software in vehicle; - Car-sharing: for (un)locking the car for remote car rental; FC.U2. Augmented Reality (AR) - AR-enhanced repair guide; FC.U3. Cloud Computing and Mobile Apps - Car-ride (Mobility-as-a-Service); - Car-sharing (Mobility-as-a-Service)	FC.D1. Cloud Computing and Mobile Apps - Digital marketplaces for resale vehicles
T	Tangibility	T.C1. Digital Twin Simulations for carbon footprint of the vehicle (throughout its entire lifecycle); T.C3. Artificial Intelligence (AI) - Digital comparison tools for lifecycle emissions of a vehicle; - Interactive emissions calculators for vehicle choices based on powertrain, driving habits and other criteria	T.U1. Internet of Things (IoT) - Predictive maintenance: based on sensors in the car, driver receives recommendations for vehicle maintenance, increasing vehicle lifetime; - Vehicle-to-infrastructure (V2I) communication: reducing traffic congestion, providing accurate arrival times for public transportation; - Optimization of HVAC (Heating, Ventilation and Air Conditioning)	T.D1. Distributed computing - Crowdsource databases for vehicle parts; - Digital marketplaces for resale of vehicle parts; T.D2. Cloud Computing: - Digital platform for upcycling recommendations for vehicle parts; - Digital education for recycling vehicle T.D3. Artificial Intelligence - AI-powered resale valuation for vehicles and parts T.D4. Blockchain - Blockchain-based tracking of car parts and materials - Blockchain-based ownership history for vehicle

presents the digital technologies that can influence sustainable consumption behaviors of the users of automobiles, and provides details about their concrete applications.

According to the above, the Delphi technique proved useful to develop and validate the “SHIFT for automotive” construct. Different from previous studies, which focused on assessing the applicability of the SHIFT conceptual framework to other research areas [20,21,22], the present study contributes to the advancement of knowledge, providing evidence on the potential of digital technologies to contribute to the consolidation of sustainable consumption behaviors.

5. Conclusions

Different from previous studies that used the SHIFT conceptual framework for other domains [20,21,22], this study takes an original approach and transposes the construct from the perspectives of automotive industry stakeholders. The main objective was to assess the extent to which the integration of different digital technologies into the various functionalities of passenger cars can contribute to strengthening the sustainable consumption behaviors of end-users.

Using the Delphi Technique, based on the evaluation of expert opinions, it was shown that the selected and evaluated digital technologies (

Table 11. SHIFT for automotive—technologies.

		Choice	Usage	Disposal
S	Social Influence	SI.C1. Artificial Intelligence SI.C3. Virtual Reality SI.C4. Cloud computing	SI.U1. Artificial Intelligence SI.U3. Big Data SI.C4. Cloud computing	SI.D1. Virtual Reality
H	Habit Formation	HF.C1. Artificial Intelligence	HF.U1. Artificial Intelligence HF.U2. Virtual Reality HF.U3. Cloud Computing HF.U4. Internet of Things	HF.D1. Cloud Computing HF.D3. Big Data Analysis
I	Individual Self	-	IS.U1. Artificial Intelligence IS.U3. Big Data IS.U4. Cloud Computing	-
F	Feelings and Cognition	FC.C1. Artificial Intelligence (AI) FC.C2. Cloud Computing	FC.U1. In-car connectivity: cellular network (5G, LTE), Wi-Fi FC.U2. Augmented Reality (AR) FC.U3. Cloud Computing and Mobile Apps	FC.D1. Cloud Computing and Mobile Apps
T	Tangibility	T.C1. Digital Twin T.C3. Artificial Intelligence (AI)	T.U1. Internet of Things (IoT)	T.D1. Distributed computing T.D2. Cloud Computing T.D3. Artificial Intelligence T.D4. Blockchain

) can represent real levers for increasing the responsibility of end-consumers regarding their sustainable behavior.

The results of this study have profound theoretical, methodological and practical implications. From a theoretical point of view, the study proves its usefulness because it fills the literature gap in the field of research on the sustainability of consumer behavior. To the authors’ knowledge, up to the time of writing this study, no similar studies have been identified in the relevant literature.

From a methodological point of view, the study presents in a detailed and original manner a method for evaluating expert opinions. Although the purpose of the work was not the original application of the Delphi Technique, the nature of the research, the number and amplitude of the variables taken into account, and the particularities of the different stages forced a permanent adaptation to the circumstances.

The study has practical utility, serving as a guide for both automotive industry operators and car users. As experts have noted, automotive industry operators are concerned (downstream and upstream) to ensure sustainability throughout the value chain. An important link in this chain is represented by car end-users, who need to understand the facilities offered by the various digital technologies integrated into the various car functionalities, so as to be helped/motivated to develop sustainable consumption behavior.

This study has some limitations. One limitation is that only the most representative digital technologies (and related applications) were taken into account, without a prior assessment of the end-consumer’s openness to the use of these technologies. As one of the experts highlighted, these technologies may have a boomerang effect (“due to a lack in quality and reliability of some digital technologies, many people, especially from low–mid social class, feel that digitalization of cars has decreased the quality in comparison with classic cars”). To overcome this limitation, in future studies we consider evaluating the “SHIFT for automotive” conceptual framework from the perspective of end-consumers. This research direction will also consider the preliminary assessment of the nature of digital technologies and their sustainability. At the same time, the assessment of the risks associated with the digital technologies integrated into “SHIFT for automotive” will also be taken into account.

Another limitation is the poor homogeneity of the selected expert sample. Although the initial goal was to obtain the opinions of as many categories of experts as possible, it was observed that the diversity of experts may result in poor convergence of results. Future studies will consider assessing the extent to which experts’ perceptions are influenced by socio-demographic variables (number of years of experience, position in the company, position held, etc.). Another limitation is related to the representativeness of the data and the possibilities of their generalization. This limitation is produced by the fact that most experts are from EU countries. Furthermore, the present study was designed to meet the requirements of qualitative research. To expand the area of applicability of the “SHIFT for automotive” construct, in future research we consider validating the construct for other geographical areas, by inviting experts from countries/continents not covered by this study.