Exploring Digital-Environment Habitus in Italy—How Digital Practices Reflect Users’ Environmental Orientations?

Abstract

This study employs the Bourdieusian concept of habitus to explore how users’ mental dispositions are associated with both their eco-conscious use of digital technologies and online behaviours. The digital-environmental habitus, reflecting such a combination of digital technology use and environmental attitudes, is explored through an online survey of 1188 participants. Factorial analyses are used to measure the environmental orientation of digital users, their digital expertise, and the digital-environmental habitus, encompassing both awareness and behavioural dimensions. We then use a path structural model to investigate the relationship among these constructs. The results indicate that pro-environmental dispositions are associated with digital pro-environmental awareness and behaviours. The existence of digital-specific environmental awareness also enhances pro-environmental digital behaviours, emphasising the importance of educating users about the environmental impact of digital tools. While digital expertise alone does not significantly predict digital-environmental awareness, it does moderate the digital-environmental habitus’s behavioural aspect, promoting behaviours mutually beneficial for users and the environment. Further research is needed to understand how benefit-oriented and eco-centric environmentalism manifests in the digital arena.

1. Introduction

This article draws upon and further develops the digital-environmental habitus proposed by Ruiu et al. [1,2] to capture different uses of digital technology associated with users’ environmental predispositions and attitudes.

From a theoretical point of view, this study captures the mechanisms at play associated with the differentiation of the digital-environmental dimension of the habitus as encompassing pre-existing backgrounds, as well as the assimilated increased use of digital technologies in people’s daily lives, reflecting individual environmental attitudes.

This is particularly critical in the Italian context, given that the Mediterranean area is expected to experience higher warming due to climate change than the global average. More specifically, the Euro-Mediterranean Centre on Climate Change predicts an increase of up to 2 °C in the average surface temperature between 2021 and 2050 relative to 1981–2010 [3] in Italy, with negative impacts on the national economy being felt [4]. On the other hand, the country has developed policies after the COVID-19 pandemic that have emphasised the centrality of the country’s digital transformation. This has been implemented through public funding to increase and improve connectivity and broadband access for enterprises, digitalise public administrations and public services, and promote initiatives under the National Strategy for Digital Skills [5]. Therefore, it is worth exploring this context in relation to the potential interconnections between the digital behaviours and the environmental dispositions of existing and new users. It is also relevant to note that the data from [6] indicate that Italy lags behind other European countries with similar levels of economic development regarding basic digital competencies. Specifically, only 46% of the population aged 16–74 possesses at least a basic level of digital skills. This figure falls short of the target outlined in the Digital Decade commitment, which aims to achieve a minimum of 80% proficiency in basic digital skills among the same age group by 2030. This disparity poses challenges not only in everyday life, considering the Italian government’s efforts to digitalise public services, but also in achieving environmental sustainability.

The originality of this work lies in expanding this conceptual toolkit to explore the interaction between digital techno-use and environmental dispositions in Italy. The study focuses on people’s online and digital technology usage habits, and it is based on an online survey of 1188 adult digital users. This work represents the first empirical attempt to investigate the digital-environmental habitus in Italy.

This study is valuable empirically, considering that digital advancements have been emphasised as pivotal in changing how people understand and interact with nature [7].

The paper is organised as follows: in the second section, we present the theoretical framework regarding the digital-environmental habitus. In the third section, we outline the research hypotheses guiding this study and the methods and analysis strategies to investigate them, the fourth section reports the results, the final section discusses the findings, and then we draw some conclusions.

2. Theoretical Background

The scientific debate acknowledges that digital technologies can contribute to the reconciliation between economic development and carbon emission reductions [8,9]. However, digital technologies also require energy consumption, which, in turn, increases carbon emissions [10,11]. In ecological economics [12], this is known as the Jevon Paradox, which highlights how technological progress can improve efficiency but simultaneously increase demand. This is also valid for digital technologies based on the Internet, which, despite improving the efficiency of data centres, exacerbate negative environmental impact due to the increasing number of users and intensity/quality of Internet use [13].

Consequently, reducing the environmental impact of digital technology depends on both the individual and collective capacity to reflect on behaviours that are linked to purchasing and consuming digital tools and adopt “digital sobriety” as a principle of action [14].

To explore how users’ environmental dispositions are associated with both their eco-conscious use of digital technologies and online behaviours, this study employs the Bourdieusian concept of habitus too. In Bourdieu’s and further developments, habitus is characterised by its stratified nature due to the involuntary and unconscious acquisition of cultural, economic, and social capitals, representing structural constraints and opportunities that determine social actors’ positions in their fields of action [15]. Habitus represents the individual unconscious capacity [16,17] to make sense of social reality [17,18], which is influenced by both structural determinants and individual agency [16,19]. It goes hand in hand with the individual experience of the context of action and the collective understanding/predispositions acquired in social groups [20,21,22].

In this sense, habitus should be seen as flexible rather than a static outcome. It is formed through the unconscious absorption of cultural, economic, and social influences from early life [15], which persist and become stratified over time [23] based on one’s affiliation with specific social groups. Simultaneously, habitus continually evolves through experiences in new fields [24,25,26,27]. The concept of field refers to the social arena in which individuals interact and seek to establish their positions [19]. Habitus bridges the gap between individual actions and the field in which they occur [1,28]. Its adaptive nature is influenced by its interaction with social transformation, resulting in various outcomes ranging from adaptation to rejecting innovation [29].

In this regard, the Internet has been identified as one of the key institutions contributing to the rapid transformation of society and, consequently, habitus [30]. Nonetheless, the accelerated digitalisation prompted by the COVID-19 pandemic has forced individuals to navigate an increasingly digitalised daily routine, creating a disjunction between their established habitus and the emerging field of digital action [19]. This disjunction arises because the new digital context requires the adoption of new habits and practices [31]. In this sense, some studies have characterised the digital arena as a field where users are positioned based on their possession of economic, cultural, and social capital, which in turn affects their Internet usage [32]. The scientific debate also refers to additional forms of capital that contribute towards positioning digital users, such as digital capital and techno-cultural capital [33,34]. These concepts combine the material resources and skills necessary to navigate a technology-driven, digitally mediated social environment [35,36]. Directly connected to the notion of digital/techno capital, the concept of technological habitus has emerged as a bridge between the collective technology practices embedded in the habitus and individual actions [37,38]. Specifically, some scholars have used the concept of digital habitus to describe the continuous use and engagement with digital technologies [39], contributing to the layering of habitus across generations [40]. At the same time, while habitus shapes perceptions within a specific field, the acquired dispositions can be applied in fields unrelated to their original development [16].

Consequently, habitus can be seen as an individual’s capacity to navigate particular circumstances. Yet, it is also susceptible to changes in attitude and behaviour influenced by the dynamicity of the fields of action [30]. This suggests that the constructs of habitus and field can be valuable to understanding habitus’ capability to transfer its accumulated capital between the digital and environmental fields. In this direction, in addition to the aforementioned digital dimension of the habitus [41,42], the scientific debate has acknowledged a dimension of the habitus associated with environmental dispositions [43,44] as seen in the context of eco-habitus [45,46]. This concept encapsulates individual tastes, practices, and orientations towards the environment [47,48], which can be transmitted intergenerationally [49,50]. Therefore, while habitus is generally recognised as being shaped by pre-existing and consolidated backgrounds, it is continually influenced by experiences within various fields. Applying these considerations to global issues arising from digital and environmental changes, these challenges have become integral to individuals’ everyday experiences, thus, rendering individuals increasingly familiar with these domains.

Moreover, the COVID crisis, coupled with increased media coverage of environmental disasters and pervasive use of digital technologies, has created opportunities for reflecting on how digital technologies may either mitigate or exacerbate individual carbon footprints. This combination of events suggests that examining users’ dispositions regarding how their digital activities (including online behaviours and how they use/dispose of digital tools) impact the environment could help understand how the digital and environmental fields contribute to shaping habitus. This is supported by a positive association identified in the literature between digital literacy and eco-friendly consumption habits [51], thanks to the development of a capacity to locate, evaluate, and use digital information related to the environment.

However, habitus’s digital and environmental dimensions have been mainly studied as separate domains. Studies on the digital economy have paid limited attention to the exploration of digital behaviours and the deliberate use of digital technologies to reduce their physical impact on the environment. At the same time, there is some evidence of the synergies between digital and environmental dimensions that contribute to sustainable digital practices, as observed in telework [52]. In a study on digital technologies in Italian Higher Education, Agasisti, Frattini, and Soncin [53] identified digital readiness, cultural openness, and strategic orientation as facilitators of digital innovation geared toward sustainability. In the consumption field, it has also been recognised that digital technology is transforming consumption by influencing consumers’ habits and increasing the number of options and channels for consumption practices [54].

Ruiu et al. [2] labelled the acquisition and transposition of dispositions between the digital and environmental fields as “digital–environmental habitus”, defined as “the use of digital technology—specifically ICTs—in a broader environmental-oriented way […]. Digital-environmental habitus includes both individual perceptions of the impacts of using digital technologies on the environment (digital carbon footprint) and behavioural responses”.

The authors define the digital-environmental dimension of the habitus as encompassing pre-existing backgrounds, as well as the assimilated increased use of digital technologies in people’s daily lives, reflecting individual environmental attitudes. This theoretical approach supported the analysis of how the rapid techno and digital changes resulting from the COVID-19 pandemic, which imposed the migration of services, resources, and opportunities online, impacted specific vulnerable groups in England. The study showed the value of the habitus concept in understanding how digital users perceive social reality and engage in the digital realm based on pre-existing patterns and expectations [55]. Interpreting habitus as embodied dispositions that inform individuals’ understanding and responses to different situations suggests that awareness and behaviours related to digital technologies and environmental sustainability can be interpreted as manifestations of individuals’ habitus, influenced by their internalised norms, values, and experiences. Therefore, following this approach, two components of the digital-environmental habitus can be identified, one connected to the development of an awareness of the environmental impacts of Information Communication Technologies (ICTs) and another tied to the behavioural patterns of ICTs’ use [2].

The present study employs this definition of digital-environmental habitus to explore how Italian digital users’ environmental orientation and digital expertise interact with their use of digital technologies and online behaviour, considering their environmental impact. The digital-environmental habitus is used here as the combination of users’ ability to adapt to the increased use of digital technologies in their daily lives and their understanding of the environmental consequences of these digital uses. This definition underscores users’ capacity to engage with, assimilate, and respond to contemporary digital and ecological stimuli.

3. Methodology

3.1. Research Hypotheses

Despite the ongoing scientific debate surrounding both the theorisation and empirical observation of the Bourdieusian concept of habitus [30], it could be argued that individuals’ dispositions, behaviours, and attitudes are generally understood to be shaped by their social backgrounds. However, as mentioned in the previous section, habitus can transpose these behaviours and dispositions when operating in different fields, such as the digital or environmental realms. Therefore, the initial step in understanding specific nuances of habitus, such as the digital-environmental habitus, involves considering individuals’ pre-existing socio-economic and cultural backgrounds. Despite different and sometimes contrasting results on the effect of sociodemographic traits on the type and quality of the digital experience, the scientific debate tends to agree on the effect of certain factors. Age, gender [56,57], income [58,59,60], and education [35,61] are widely recognised as predictors of users’ access and digital competencies. Moreover, they have also been shown to influence environmental perceptions [62,63] and green choices [64,65]. For instance, studies have pointed out that females, as well as younger and highly educated and wealthy consumers [66,67], tend to be more oriented toward green consumption.

In addition to these factors, it is reasonable to assume that offline environmental predispositions could be reflected in users’ awareness of how digital technologies can impact the environment and their online behaviours. Indeed, habitus has been described as ingrained in individual practice (and vice versa) [68]. Examining such practices can help observe the concretisation of internalised predispositions resulting from the combination of economic, social, and cultural processes [69] and field experience. Therefore, the first hypothesis of this study investigates the impact of overall environmental orientation, controlled by sociodemographic traits, on both digital-environmental awareness and behaviours. The term “overall environmental orientation” refers to a general understanding of an individual’s impacts on the environment, extending beyond their digital experience.

H1. 

Environmental dispositions (overall environmental orientation) affect both dimensions of the digital-environmental habitus: awareness (path a in Figure 1) and engagement (path b in Figure 1).

Expanding on this hypothesis, despite contrasting results in the literature regarding the triggers of pro-environmental behaviours, particularly within the field of environmental psychology [70,71], there is a consensus that pro-environmental awareness and understanding of nature are linked to sustainable behaviours [72,73]. Awareness here is intended as a manifestation of the habitus reflecting the knowledge of specific issues that align with the prevailing understanding within a social context [69]. At the same time, even though it is not the only factor, awareness simultaneously shapes and is shaped by an individual’s values and practices, which reflect habitus. In this direction, we assume that the relationship between overall environmental orientation and environmentally conscious digital behaviours is, to some extent, mediated by a specific awareness of the environmental impacts of digital practices (here referred to as digital-environmental awareness). This is because, overall, pro-environmental attitudes might not automatically lead to both awareness of the environmental impacts of digital technologies and the use of digital tools that are respectful of the environment. In fact, despite an overall attitude towards environmental protection, individuals might lack knowledge of the specific impacts of their digital practice. This hypothesis is also supported by Gnanasekaran et al. [74], who observed that a lack of awareness of users’ digital carbon footprint due to its invisibility results in divergent outcomes regarding pro-environmental digital behaviours. In contrast, this effect might be mediated by the development of a specific digital-environmental awareness, which may lean toward environmental protection:

H2. 

The effect of overall environmental orientation on environmentally informed digital behaviours (digital-environmental behaviours) is partially mediated by possessing an environmentally informed digital awareness (digital-environmental awareness) (path ac in Figure 1).

Finally, it is relevant to consider the relationship between digital expertise and digital-environmental awareness and behaviours to explore the digital-environmental habitus. Digital literacy includes technical skills (the ability to use digital devices and perform online operations), cognitive skills (the ability to search for and critically assess digital information), and social–emotional competencies (the ability to use the Internet for socialising, communicating, and learning) [75]. The intersection of these dimensions results in digital expertise, which includes factors like devices used, online connectivity, length of experience, the ability to identify reliable sources and protect personal privacy, express opinions online, create content through appropriate channels, and solve technical issues. Digital expertise, in turn, may play a moderating role in the relationship between overall environmental orientation and digital-environmental awareness and behaviours. This is supported by Hu and Mengs’ [51] findings, who identified a positive relationship between digital literacy and green consumption behaviour due to users’ ability to locate, evaluate, and integrate environment-related digital information.

Therefore, H3 is split into two sub-hypotheses, one connected to the relationship between overall environmental orientation and digital-environmental awareness and the other to the relationship between overall environmental orientation and digital-environmental behaviours:

H3a. 

The effect of overall environmental orientation on digital-environmental awareness is moderated by digital expertise (Figure 1).

H3b. 

The effect of overall environmental orientation on digital-environmental behaviours is moderated by digital expertise (Figure 1).

Figure 1 helps to visualise our hypotheses.

3.2. Data and Methods

The study is based on a survey involving 1188 ICT users in Italy in September 2023. The sample panellists were recruited to complete an online survey through a national survey panel hosted by Toluna Inc. (Milan, IT, Italy), a well-known marketing company that provides market analysis data. However, their data have also been frequently used for scientific works, such as in [76].

Panellists were enlisted for the Toluna sample through various channels such as web banners, public relations efforts, website referrals, etc.

A limitation of using Internet panels is the voluntary nature of panellist recruitment, which precludes the calculation of sampling error. Therefore, all findings presented in this paper should be viewed as an initial exploratory analysis. The survey explored how the combination of environmental dispositions, existing backgrounds, and digital expertise affect the uses of digital tools. The sample was stratified by gender and age.

Table 1. Characteristics of the Sample.

		Sample		Internet Users *
		Count	%	%
Gender	Female	634	53.4	49.9
	Male	553	46.5	50.1
Age	18–24	56	4.7	8.9
	25–34	244	20.5	13.5
	35–44	235	19.8	15.5
	45–54	263	22.1	19
	55–64	275	23.1	16.8
	65+	95	9.7	12.4

* The percentages of “Internet users” were calculated based on data provided by Istat [77] (for the general population) and Istat [78] (for Internet users). The total of “Internet users” per age group is not equal to 100 because the percentages were calculated as the proportion of the population in a specific age group using the Internet, compared to the total number of users (including those aged six and above).

presents the sample characteristics compared to Internet users in Italy according to the results of a survey carried out by the Italian National Institute of Statistics (Istat).

Operationalising the concept of habitus is challenging, given its abstract nature and the sometimes-divergent interpretations offered by the scientific debate [30]. However, it entails capturing through proxies the underlying dispositions, behaviours, and attitudes that are intertwined with individuals’ social backgrounds and experiences in the social fields.

Our empirical strategy is based on two steps. In the first step, we run three factorial analyses to measure (i) the awareness component of the digital-environmental habitus; (ii) the behavioural component of the digital-environmental habitus; (iii) the overall environmental orientation of ICTs users; and (iv) the digital expertise of users.

The items used for each factorial analysis are derived from previous theoretical and empirical literature.

In the second step of our empirical strategy, we estimate path structural models using the statistical software AMOS (V. 27) to test our research hypotheses.

4. Results

This section is split into four subsections. In the first three, we focus on specific constructs: the overall environmental orientation of digital users, their digital expertise, and the digital-environmental habitus, which encompasses both awareness and behavioural dimensions. The fourth subsection presents the results of the path structural models.

4.1. Awareness and Behavioural Component of the Digital-Environmental Habitus

To explore the intersection of the habitus’ digital and environmental dimensions, we have separately examined awareness and behaviours aimed at mitigating the negative environmental impacts of digital technology use [2]. The first step was to create an index that would capture users’ awareness of both online behaviours and their uses of digital technologies, considering the impact of digital tools throughout their lifecycle, from material extraction to the end of their functionality [79].

We conducted a factorial analysis (FA) to explore respondents’ awareness of how digital technologies and online activities affect the environment (

Table 2. Digital-Environmental Awareness (DEA).

Items	DEA
I know that the consumption of digital technologies is harmful to the environment (e.g., due to high energy demand)	0.789
I know that producing technologies is harmful to the environment (e.g., due to mineral extraction)	0.761
Electronic waste has negative impacts on the environment	0.710
I find myself reflecting on how the ways I use technology can impact the environment	0.705
Internet of Things is the best way to manage my impact on the environment	0.427
Online shopping is more eco-friendly than in-store shopping	0.282
Extraction Method: Principal Component Analysis.
Kaiser–Meyer–Olkin (KMO) test =  0.739; Bartlett’s test, p < 0.000.

). The items were included according to the main findings emerging in the literature that show how the operation of the Internet network and the production of hardware produce digital pollution (especially in terms of electricity demand, extraction of material, and waste production). By contrast, the dematerialisation and the “smartness” of automated systems have been identified as beneficial to the environment (for a review, see [79]).

One component was extracted, as the second component only included two items, which also positively contributed to the first component. This component was named “digital-environmental awareness” (DEA) and accounted for 41% of the variance (Table 2).

Note that the Kaiser–Meyer–Olkin (KMO) test indicates that the partial correlations between the items belonging to the construct assume each have relatively high values (a value above 0.8 is considered good by Keiser and Rice [80]). Also, the Bartlett’s test refuses the null that the items are not correlated. Therefore, we can conclude in favour of the convergent validity of the scale. Regarding internal consistency, the Cronbach’s alpha is equal to 0.682. Generally, a value above 0.7 is considered good, however, Nunnally and Bernstein [81] suggested 0.6 as the minimum threshold for acceptability.

Behaviours were explored using a set of items, as detailed in

Table 3. Environmental orientation of digital technology behaviours.

Items	Eco-Centric-Oriented (EDEB)	Benefit-Oriented (BDEB)
I limit my online searches to limit energy waste	0.807	0.111
If I use on-demand video services or other streaming services, I make sure that videos are in low-resolution	0.780	0.105
I try to limit my social media activities online (e.g., messaging on FB, watching YouTube videos, posting on social media) because they hurt the environment	0.690	0.247
I prefer to meet people online rather than face to face (e.g., to limit my physical movements and reduce my impact on the environment)	0.671	−0.174
I check if the businesses are respectful of the environment before ordering online	0.662	0.294
I prefer to buy second-hand technology devices	0.597	0.044
I order online only if I need multiple items	0.577	0.183
I avoid express delivery (one-day delivery)	0.434	0.378
I use my technologies (e.g., mobile phones and computers) till they properly function	−0.330	0.745
I bring my old devices to specific recycling centres	0.075	0.677
I try to condensate as much information as I can in one email/message	0.339	0.542
I unsubscribe from automatically generated newsletters	0.207	0.459
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation.
Kaiser–Meyer–Olkin (KMO) test =  0.877; Bartlett’s test, p < 0.000.

, where respondents were asked to express their level of agreement on a scale from 0 (totally disagree) to 7 (totally agree) concerning the consumption and use of digital technologies and their impact on the environment. The items were selected as proxies of environmentally informed digital behaviours following the findings in the literature that identify how some individual behaviours can reduce individual environmental impact (e.g., [82,83]). An FA was conducted to combine the items. In addition to the items explored by Ruiu et al. [2] related to the online experience, we included statements concerning the tangible elements of digital technologies that contribute to the digital experience. Specifically, the digital components’ manufacturing process and the e-waste generated are recognised as integral aspects of digital pollution [82]. Two components were extracted based on an eigenvalue higher than 1, together explaining 47% of the variance. The first component reflects uses of digital technologies that are eco-centric-oriented, explaining 31% of the variance. We labelled this component “eco-centric digital-environmental behaviour” (EDEB). The second component includes pro-environmental behaviours offering additional practical benefits, explaining 16% of the variance. We labelled this variable “benefit-oriented digital-environmental behaviour” (BDEB). BDEB includes items related to both the material aspects of digital tools and online behaviours. The material components involve using digital technologies until they are no longer functional (which can offer financial benefits) and disposing of discarded devices at recycling centres (which can free up space at home). Online behaviours include unsubscribing from automatically generated newsletters (which can prevent memory overloads) and condensing content in emails/messages (which can save time). Additionally, these behaviours contribute to reducing energy demand and CO2 production [84].

4.2. Overall Environmental Orientation of Users

We conducted an FA to summarise a set of items that capture respondents’ opinions of their relationship with the environment and the consequences of certain offline behaviours on the environment. Respondents were asked to rate their level of agreement (on a scale from 1 to 7) with items describing environmental behaviours and engagement (

Table 4. Overall Environmental Orientation.

Items	Eco-Centric Oriented (EO)	Benefit Oriented (BEO)
I consider myself an activist in the environmental field (e.g., by sharing posts online, taking part in campaigns etc.)	0.791	−0.123
I use public instead of driving my car	0.708	−0.084
I check the (environmental) reputation of businesses before buying a brand	0.708	0.219
I buy organic food	0.673	0.200
I would change my car to an electric model if I could	0.605	0.194
I try to find an alternative to using the car whenever I can (e.g., walking, cycling etc.)	0.597	0.312
I make sure lights are turned off if I do not need them (e.g., at home, at work etc.)	−0.020	0.855
I buy energy-efficient light bulbs	0.067	0.839
I prefer to buy local products if I have a choice	0.315	0.672
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. Kaiser–Meyer–Olkin (KMO) test =  0.793; Bartlett’s test, p < 0.000.

). Two components were extracted with an eigenvalue higher than 1. The first component reflects an eco-centric orientation (EO) and explains 32% of the variance. It includes items related to practices such as avoiding using cars in favour of electric vehicles, purchasing organic food, and engaging in environmental activism. Even though the environmental benefits of switching to electric cars vary depending on several factors (e.g., type of electric vehicle, source of energy generation, driving conditions, charging patterns, availability of charging infrastructure, government policies, and climate of regions), studies tend to show that electric vehicles reduce greenhouse gas emissions and emissions of some pollutants (see [85,86]).

The second dimension (explaining 24% of the variance) was labelled as “benefit environmental orientation” (BEO) because it encompasses behaviours that have both environmental benefits and practical advantages. The behaviours include using energy-efficient bulbs, turning off lights when not in use, and purchasing local products. These actions might also result in cost savings and health benefits [87].

The KMO test and Bartlett’s test indicate that there are not problems with convergence validity. The Cronbach’s Alpha for EDEB and BDEB are, respectively, 0.795 and 0.773, indicating a good internal consistency.

4.3. Digital Expertise

We conducted an FA to summarise a series of items to assess respondents’ digital competencies. Following the model proposed by Ragnedda, Ruiu, and Addeo [34], this set of questions explored users’ perceptions of their digital know-how and how it enhances their daily lives. Respondents were asked to rate their level of agreement (on a scale from 0 to 10) regarding various aspects of their digital competencies. These aspects included their proficiency with digital devices, their ability to connect online, the length of their digital experience, their capacity to identify trustworthy sources and protect their privacy, their ability to express opinions and create content on appropriate digital channels, and their skill in resolving technical issues (see

Table 5. Digital Expertise (DE).

Item	DE
I actively use a wide range of communication tools online communication	0.747
I know how to protect my personal data online (e.g., address, telephone number, passwords)	0.728
I can solve a technical problem or decide what to do when technology does not work	0.704
It does not matter where I am, I always find a way to connect to the Internet	0.584
Throughout my online experience, I learned to use different digital tools to create textual and audiovisual content online	0.643
Throughout my online experience, I have learned how to share my thoughts online using different platforms	0.628
I can select trustworthy digital media	0.614
I can support other users with their issues in using digital tools	0.587
I started using the Internet a long time ago	0.486
Extraction Method: Principal Component Analysis. Kaiser–Meyer–Olkin (KMO) test = 0.853; Bartlett’s test, p < 0.000.

) [34]. The FA resulted in extracting a single component, labelled digital expertise (DE), which explains 42% of the variance. This choice was made, as the second component only presented low score loadings. All the included items in the retained component had a positive impact, indicating that this component effectively captures the respondents’ digital expertise.

The KMO test and Bartlett’s test indicate that there are not problems with convergence validity. The Cronbach’s Alpha is 0.79, indicating good internal consistency.

As a final test for the validity of our constructs we implemented the Harman’s test for common method bias (i.e., the inflation of covariation among different constructs caused by using a set of items with similar characteristics). In particular, we ran a factorial analysis using all the 36 items, we then evaluated the variance explained by the first extracted factor. If the amount of explained variance is more than 50%, then it should be concluded that the analyses are affected by common method bias. In our case, the variance explained is 20%.

To facilitate the reading of subsequent analyses,

Table 6. Acronyms used in subsequent analyses.

Environmental Orientation
Eco-Centric-Oriented	EO
Benefit-Oriented	BEO
Digital Expertise	DE
Digital-Environmental habitus
Digital-Environmental Awareness	DEA	Digital-Environmental Behaviours
		Eco-Centric-Oriented	EDEB
		Benefit-Oriented	BDEB

provides a summary of all the acronyms used for our main variables.

4.4. Results of the Path Structural Models

To test the first hypothesis, the relationship between “eco-centric orientation” (EO) and “digital-environmental awareness” (DEA), as well as “benefit environmental orientation” (BEO) and DEA, were explored while controlling for sociodemographic characteristics. The results are reported in

Table 7. Testing Hypothesis 1 through Path Structural Model.

Relationship	Standardised Estimates	t-Values	p-Value	Conclusion
Age→DEA	0.030	1.212	0.225	Not Significant
Gender (F)→DEA	−0.17	−0.710	0.478	Not Significant
Education→DEA	0.020	0.825	0.409	Not Significant
Household size→DEA	0.039	1.710	0.087	Slightly Significant
Income→DEA	0.019	0.795	0.427	Not Significant
EO→DEA	0.394	17.341	<0.001	Significant
BEO→DEA	0.486	20.998	<0.001	Significant
Age→EDEB	0.046	1.997	0.046	Significant
Gender (F)→EDEB	−0.007	−0.304	0.761	Not Significant
Education→EDEB	−0.29	−1.293	0.196	Not Significant
Household size→EDEB	−0.021	−0.980	0.327	Not Significant
Income→EDEB	0.043	1.834	0.067	Slightly Significant
EO→EDEB	0.618	24.464	<0.001	Significant
BEO→EDEB	−0.312	−12.156	<0.001	Significant
DEA→EDEB	0.090	3.275	<0.001	Significant
Age→BDEB	0.083	3.646	<0.001	Significant
Gender (F)→BDEB	−0.003	−0.122	0.903	Not Significant
Education→BDEB	0.013	0.595	0.552	Not Significant
Household size→BDEB	−0.008	−0.380	0.704	Not Significant
Income→BDEB	−0.38	−1.673	0.094	Slightly Significant
EO→BDEB	0.136	5.732	<0.001	Significant
BEO→BDEB	0.461	18.257	<0.001	Significant
DEA→BDEB	0.272	10.054	<0.001	Significant
χ2 = 0.346, df = 1, p = 0.556, CFI = 1, IFI = 1.001, SRMR = 0.0032

, while

Table 8. Mediation effect of DEA.

Relationship	Unstandardised Estimates	CI	p-Value	Conclusion
EO→DEA→EDEB	0.037	0.015–0.062	0.003	Complementary Mediation
EO→DEA→ BDEB	0.084	0.062–0.113	0.002	Complementary Mediation
BEO→DEA→EDEB	0.056	0.023–0.098	0.003	Competitive Mediation
BEO→DEA→BDEB	0.127	0.097–0.159	0.003	Complementary Mediation

Bootstrap sample = 5000 with replacement.

reports the results for the test of the mediating effect of DEA (Hyp. 2).

Both EO (b = 0.309; t = 17.341; and p < 0.001) and BEO (b = 0.466; t = 20.998; and p ≤ 0.001) were found to have a significant effect on DEA. However, the direct effect of sociodemographics on DEA was not found to be strongly significant for any of the sociodemographic variables used, indicating that they have a minimal or inconsequential influence on the model.

Moreover, the direct effect of EO on “eco-centric digital-environmental behaviour” (EDEB), and of BEO on EDEB, was explored. In this case, EO, controlled by sociodemographic characteristics, had a positive and significant direct effect on EDEB (b = 0.588; t = 24.444; and p < 0.001), whereas BEO had a significant but negative effect on EDEB (b = −0.400; t = −12.156; and p < 0.001). DEA also had a positive and significant effect on predicting EDEB (b = 0.120; t = 3.275; and p = 0.001). The direct effect of sociodemographic variables on EDEB was found significant only for age, and with a negligible positive effect (b = 0.075; t = 1.997; and p = 0.046).

Additionally, the effects of EO and BEO on “benefit-oriented digital-environmental behaviour” (BDEB) were explored. Both EO (b = 0.107; t = 5.732; and p < 0.001) and BEO (b = 0.443; t = 18.257; and p < 0.001) had a positive and significant effect in predicting BDEB. DEA also had a significant positive effect on BDEB (b = 0.272; t = 10.054; and p < 0.001). The direct effect of sociodemographic variables on benefit-oriented digital-environmental behaviours (BDEB) was found to be positive and significant for age (b = 0.100; t = 3.6646; and p < 0.001).

The model’s fit indices, as shown in Table 7, fall within an acceptable range: CMIN/df = 0.346, goodness-of-fit (GFI) = 1 (see [88]), Tucker and Lewis index (TLI) = 1.012, confirmatory fit index (CFI) = 1 (see [87]), standardised root mean square residual (SRMR) = 0.0012, and root mean square error approximation (RMSEA) = 0.000 (see [89]).

The square multiple correlations were as follows: 0.41 for “digital-environmental awareness” (DEA), 0.47 for “eco-centric environmental behaviour” (EDEB), and 0.49 for “benefit-oriented digital behaviour” (BDEB). To assess the significance of the mediation of DEA (H2), we used a bootstrap technique with a bootstrap sample of 5000 and 95% of bias-corrected confidence intervals [90].

In summary, the results show that DEA partially mediates the relationship between overall environmental orientation (both eco-centric oriented and benefit oriented) and digital-environmental behaviour (both eco-centric-oriented and benefit-oriented) (H2). The indirect effects of both EO (b = 0.037 and p = 0.003) and BEO (b = 0.056 and p = 0.002) on EDEB and of EO (b = 0.084 and p = 0.003) and BEO (b = 0.127 and p = 0.003) on BDEB (Table 8) are statistically significant. However, DEA has a complementary mediation role in the relationship between EO and both types of digital-environmental behaviours. The effect ratio (between the indirect and total effect) suggests that DEA mediates 5% of the total effect. The proportion of the total effect mediated by DEA in the relationship between EO and BDEB is around 44%. DEA also has a complementary mediating effect between BEO and BDEB, mediating around 22% of the total effect. DEA has a competitive impact on the relationship between BEO and EDEB. This suggests that while overall benefit-oriented environmental predispositions reduce the likelihood of adopting eco-centric-oriented digital behaviours, digital-environmental awareness positively mediates this effect. The fit indices for this model (Table 8) fall within an acceptable range: CMIN/df = 0.025, GFI = 1, TLI = 1.010, CFI = 1, SRMR = 0.0004, and RMSEA = 0.000. The square multiple correlations were 0.42 for “digital-environmental awareness” (DEA), 0.47 for “eco-centric environmental behaviour” (EDEB), and 0.49 for “benefit-oriented digital behaviour” (BDEB).

Finally,

Table 9. Moderation Effect of Digital Expertise.

Direct Relationship	Unstandardised Estimates	t-Values	p-Value
EO × DE→DEA	0.056	2.860	0.004
EO × DE→EDEB	−0.085	−4.522	<0.001
EO × DE→BDEB	0.066	3.569	<0.001
BEO × DE→DEA	0.019	1.244	0.214
BEO × DE→EDEB	0.032	2.228	0.026
BEO × DE→BDEB	0.009	0.663	0.507
DE→DEA	−0.046	−1.765	0.078
DE→EDEB	−0.116	−4.674	<0.001
DE→BDEB	0.076	3.111	0.002

focuses on hypothesis 3. Hypotheses H3a and H3b were tested using a second path structural model, where we introduced the product terms for overall “eco-centric-oriented dispositions” and “digital expertise” (EO × DE) and “benefit environmental orientation” and “digital expertise” (BEO × DE). The interaction terms are the product terms of z-scores for both types of environmental orientations (EO and BEO) and “digital expertise” (DE). To simplify this second model, we excluded the sociodemographic variables that were insignificant in the first model or had a negligible effect. We retained only the variables essential for testing H3. In particular, Table 9 shows that digital expertise itself does not significantly predict digital-environmental awareness. However, DE has a negative and significant effect on EDEB (b = −0.116; t = −4.674; and p < 0.001) and a positive and significant effect on BDEB (b = 0.076; t = −3.111; and p = 0.002). This suggests that individuals with more digital expertise are likely to engage in behaviours that benefit both themselves and the environment.

Regarding the interaction terms, EO × DE is positively and significantly related to DEA (b = 0.056; t = 2.860; and p = 0.004) and BDEB (b = 0.066; t = 3.569; and p < 0.001). This suggests that digital expertise strengthens the relationship between eco-centric dispositions, digital-environmental awareness, and benefit-oriented digital-environmental behaviours. However, the interaction term is negatively related to EDEB (b = −0.085, t = −4.522; and p < 0.001), suggesting that digital expertise weakens the relationship between EO and EDEB.

The interaction term BEO × DE is not significant for predicting both DEA and BDEB, but it has a positive and significant effect on EDEB (b = 0.032; t = 2.228; and p = 0.026).

In summary, digital expertise moderates the relationship between eco-centric orientations and digital-environmental behaviours, strengthening the relationship with digital-environmental awareness and benefit-oriented behaviours while weakening the relationship with eco-centric-oriented behaviours. For benefit orientations, digital expertise strengthens the ties with eco-centric-oriented behaviours.

To better understand the effects of the moderators a “pick-a-point approach” was used [91], creating two new variables—low-level moderator (“low digital expertise”, LDE) and high-level moderator (“high digital expertise”, HDE)—by adding and subtracting the standard deviation from the standardised variable of digital expertise, respectively. The moderation analysis was conducted by introducing the product of the standardised variables for EO and BEO and the new low-level moderators (EO × LDE and BEO × LDE) and high-level moderators (EO × HDE and BEO × HDE).

The results reported in

Table 10. Probing moderation.

Moderation	Unstandardised Estimates	t-Value	p-Value
Low level of DE:
EO→DEA	−0.228	−1.055	0.291
EO→EDEB	1.503	7.318	<0.001
EO→BDEB	−0.589	−2.896	0.004
BEO→EDEB	−0.642	−4.008	<0.001
Mean level of DE:
EO→DEA	0.380	16.919	<0.001
EO→EDEB	0.586	24.634	<0.001
EO→BDEB	0.128	5.416	<0.001
BEO→EDEB	−294	−11.638	0.469
High level of DE:
EO→DEA	0.989	4.671	<0.001
EO→EDEB	−0.331	−1.630	−103
EO→BDEB	884	4.201	<0.001
BEO→EDEB	0.055	0.349	0.727

show that when users possess lower levels of digital expertise, the relationship between overall eco-centric orientations and digital-environmental awareness is not significant. In contrast, the relationship is significantly reinforced when they have higher expertise. The original test had an unstandardised regression weight of 0.380 (p < 0.001), and now, under higher levels of DE, this is 0.989 (p < 0.001). This suggests that as digital expertise increases, it also strengthens the relationship between possessing an overall eco-centric disposition and developing a digital-environmental awareness.

However, exploring the moderation of DE on the relationship between EO and EDEB, this relationship is significantly strengthened at lower levels of digital expertise with an unstandardised coefficient of 1.503 (p < 0.001). This suggests that individuals with lower digital expertise tend to adopt more eco-centric-oriented digital behaviours when they have overall eco-centric dispositions. At higher levels of DE, the moderation effect becomes negative and not significant. At the mean level of DE, the unstandardised coefficient was 0.586 (p < 0.001) (see Table 10). This suggests that possessing lower levels of digital expertise reinforces the relationship between overall eco-centric dispositions and adopting eco-centric-oriented digital behaviours.

Exploring the relationship between EO and BDEB through the moderation of DE, possessing higher levels of digital expertise strengthens the relationship significantly, whereas possessing lower levels of digital expertise weakens it. The original test had an unstandardised regression weight of 0.128 (p < 0.001), whereas, under higher levels of digital expertise, the coefficient is 0.844 (p < 0.001) and under lower levels is −0.589 (p = 0.004).

To summarise, users with an eco-centric disposition tend to possess higher digital-environmental awareness when they have higher levels of digital expertise. However, when their digital expertise is lower, they are more likely to adopt digital behaviours that are eco-centric-oriented. By contrast, users with higher digital expertise are more likely to adopt benefit-oriented digital behaviours.

Exploring the moderation of DE on the relationship between BEO and EDEB, at lower levels of DE this relationship is significantly weakened with an unstandardised coefficient of −0.642 (p < 0.001) whereas, at higher levels of DE, the moderator is no longer significant and negative. At the mean level, the unstandardised coefficient was negative but not significant. The analysis revealed that the interactive term BEO × DE did not significantly affect benefit-oriented digital-environmental behaviour (BDEB) and digital-environmental awareness (DEA). As a result, the moderation of different levels of digital expertise between BEO and BDEB and between BEO and DEA was not tested.

5. Discussion

Assessing H1, which postulated that overall environmental dispositions influence the digital-environmental habitus (comprising awareness and engagement), our findings suggest that individuals with different environmental orientations may still exhibit environmental awareness in the digital realm. Results suggest that the nature of one’s environmental orientation is associated with different digital behaviours. Users with an eco-centric disposition are more inclined to adopt digital behaviours that align with their environmental values. In contrast, individuals with a benefit-oriented environmental orientation may be less likely to adopt digital behaviours that are eco-centric-oriented. Additionally, our results suggest that, among sociodemographic variables, age plays a significant role, indicating that the influence of these environmental dispositions becomes more pronounced as individuals age.

This result aligns with the existing literature, particularly in the consumption field, which shows a positive association between pro-environmental orientation and sustainable behaviours [70,71]. In both cases, the original orientation captures a positive disposition towards the environment, with one that also captures more individual advantages from both adopting sustainable behaviours in general and being digitally environmentally friendly. Our results also align with previous research on the awareness of digital carbon footprints, which has indicated that digital users are willing to make compromises to align with other goals and perceived sacrifices [72,73]. Therefore, hypothesis one is supported, underscoring the interplay between overall environmental predispositions and digital-environmental orientations, encompassing both awareness and behaviours. It also supports habitus’ capacity to transfer individual tastes, practices, and orientations acquired in one field (in this case, the environmental field) to another (in this case, the digital field).

This result also has implications for policymaking by emphasising the importance of educational efforts to instil pro-environmental values among digital users, which may lead to more sustainable digital practices [92]. By fostering a culture of responsible digital consumption, policymakers and educators [93] can encourage digital practices that minimise environmental footprints [94]. These educational initiatives might, for instance, integrate environmental topics into digital literacy programs to create awareness of the environmental consequences of digital choices. In addition, as recently advanced by Cirrincione et al. [95], the use of blockchain technology in energy consumption management of buildings can also be a useful tool to promote sustainable behaviours (see also [96]).

H2 explored the relationship between users’ overall environmental orientation and their digital-environmental behaviours, focusing on whether digital-environmental awareness mediates this relationship. This hypothesis was motivated by the assumption that pro-environmental orientations might not wholly and necessarily align with environmentally respectful digital behaviours due to several factors, including values, norms, routines, dispositions, infrastructures, and social dynamics [97,98]. Previous research in consumer culture has shown that digital consumers often shift responsibility to public actors or providers regarding environmental concerns. They may continue certain digital behaviours out of necessity, even when they do not intend to harm the environment [72,99]. The analysis of indirect effects showed that digital-environmental awareness serves as a bridge linking environmental orientations to specific digital behaviours. Digital-environmental awareness reinforces the effect of an eco-centric disposition on digital behaviours, whether those behaviours are eco-centric-oriented or benefit-oriented.

While the direct effect of benefit-oriented overall dispositions suggests a reduced likelihood of engaging in eco-centric-oriented digital behaviours, possessing digital-environmental awareness mediates this relationship positively. This implies that individuals with a benefit-oriented environmental orientation may generally be less inclined to adopt eco-centric digital behaviours. Still, this tendency may change when considering digital-environmental awareness. These findings support H2, suggesting that the existence of digital-specific environmental awareness acts as a catalyst for encouraging pro-environmental digital behaviours. The new digital context demands adaptability [31] of the habitus to the digital field, coupled with the ability to leverage pre-existing orientations, knowledge, and skills to fully exploit the digital realm without compromising other beneficial conditions outside the digital experience. This contributes to shaping the digital nuance of the habitus while also presenting an opportunity to apply acquired dispositions from the environmental field to cultivate sustainable digital practices. As such, policymakers interested in promoting such digital behaviours should prioritise general environmental education and raise awareness about how digital tools and everyday digital experiences can impact the environment.

Considering H3, findings show that while digital expertise does not significantly predict digital-environmental awareness, it does play a moderating role in the behavioural dimension of the digital-environmental habitus. This is in line with previous research showing a positive relationship between the level of digital economy development, digital literacy, and green consumption behaviours [51,54]. Individuals with higher digital expertise are less likely to adopt eco-centric-oriented digital behaviours and more likely to embrace benefit-oriented digital behaviours, especially when they have eco-centric-oriented dispositions. This suggests that users with advanced digital expertise are more aware of digital-environmental issues and are inclined to engage in digital behaviours that benefit both themselves and the environment.

H3a is partially supported concerning the moderating effect of digital expertise on the relationship between environmental dispositions and digital-environmental awareness. The relationship between eco-centric-oriented dispositions and digital-environmental awareness is positive and significant in the presence of the digital expertise mediator. This finding aligns with previous studies, which found that ICT competencies can mediate environmental optimism [100]. However, it is important to note that this positive association is primarily evident at higher levels of digital expertise. In contrast, the interaction between benefit-oriented environmentalism and digital expertise did not significantly impact digital-environmental awareness. This suggests that digital expertise may not moderate the effect of benefit-oriented environmentalism on environmental benefit-oriented digital behaviours.

H3b is also partially supported concerning the moderating effect of digital expertise on the relationship between environmental dispositions and digital-environmental behaviours. Findings suggest that possessing digital expertise strengthens the link between eco-centric orientations and the adoption of benefit-oriented behaviours but weakens that between eco-centric dispositions and digital behaviours that are oriented to “pure” environmentalism. Further exploration of this moderation suggests that at lower levels of digital expertise, eco-centric orientation is positively associated with eco-centric-oriented digital behaviours. In contrast, at higher levels, it loses its significance. By contrast, eco-centric-oriented dispositions at higher levels of digital expertise become a positive predictor of benefit-oriented digital behaviours. This further reinforces that the combination of an eco-centric orientation with digital expertise is associated with digital behaviours that are mutually advantageous for both users and the environment. Therefore, digital skills might have the potential to amplify behaviours driven by cognitive and technical-based mechanisms [74]. These mechanisms can empower individuals to effectively use digital tools for retrieving and critically analysing environmental information in a manner that benefits both themselves and the environment [51]. This aligns with studies emphasising how ICTs may facilitate access to up-to-date scientific knowledge and support different cognitive processes [101], including knowledge acquisition and problem-solving [102]. The introductory sections of this paper referred to additional forms of digital-related capital, as identified by some scholars [34,35], which encompass both the material resources and skills necessary to navigate a digitally mediated social environment [36]. Our study suggests that digital skills may prove valuable in facilitating the exchange between digital and environmental predispositions of the habitus by providing users with knowledge of behaviours beneficial to both individuals and the environment.

Our findings reveal the role of digital expertise in moderating the relationship between environmental dispositions and digital behaviours. Specifically, lower levels of digital expertise reinforce the connection between eco-centric dispositions and eco-centric digital behaviours, while higher levels of digital expertise significantly strengthen this relationship, enabling a broader range of environmentally beneficial digital actions. Conversely, digital expertise weakens the relationship between benefit-oriented environmental dispositions and eco-centric digital behaviours at lower expertise levels, but this effect diminishes with higher expertise. These results emphasize the complexity of digital expertise’s influence, highlighting the need for targeted educational strategies that enhance digital skills while promoting eco-centric values. Educational initiatives should focus on equipping individuals with the technical knowledge and skills necessary to navigate and utilize digital tools for environmental purposes, fostering a more informed and capable populace that effectively engages in and promotes sustainable practices. Lastly, the results suggest that improving digital competencies can have a broader impact on societal behaviours. We can create a more informed and capable individual who effectively engages in and promotes sustainable practices by fostering digital expertise. This can lead to a virtuous cycle where increased digital literacy enhances greater environmental awareness and action, further promoting the development of digital skills and innovations for sustainability.

This study serves as an initial investigation of the impacts of diverse environmental predispositions on shaping the digital-environmental habitus of digital users. It covered a limited range of items related to benefit-oriented predispositions. Therefore, further exploration is required to gain a more comprehensive understanding of how policies can facilitate the conversion of various forms of environmental dispositions into environmentally friendly digital behaviours. Nevertheless, these results suggest that improving digital competencies can benefit users’ willingness to embrace digital-environmental behaviours that are advantageous for both the environment and to themselves.

6. Conclusions

This study leverages Bourdieu’s concept of habitus to interpret the association between users’ mental dispositions and their eco-conscious use of digital technologies and online behaviours. The concept of digital-environmental habitus, as developed by Ruiu et al. [2], provided a valuable framework for understanding how individuals navigate the challenges posed by digital advancements during environmental crises. Bourdieu’s sociological theories, particularly those concerning practice, habitus, and capital, offered critical insights into the social distribution of environmentally positive practices linked to digital technologies. This approach not only highlights the role of digital technologies in shaping environmental behaviours but also extends existing theories in environmental behaviour and digital sociology, providing a holistic view of the social dynamics at play.

The results indicate that pro-environmental dispositions are strongly associated with both digital pro-environmental awareness and behaviours. Furthermore, the existence of digital-specific environmental awareness enhances pro-environmental digital behaviours, highlighting the importance of educating users about the environmental impact of digital tools. While digital expertise alone does not significantly predict digital-environmental awareness, it moderates the behavioural aspect of the digital-environmental habitus, promoting behaviours that benefit both users and the environment.

Our study further advances this concept by breaking it down into two constituent dimensions: awareness and engagement. Analysing these dimensions enabled us to study how individuals’ offline environmental values are mirrored in digital experiences.

This study highlights the importance of promoting environmental awareness among digital users. It also emphasises the role of digital expertise in shaping digital behaviours. These findings provide valuable insights for policymakers focusing on encouraging environmentally friendly digital practices and raising awareness about the environmental impact of digital choices. They highlight the necessity for targeted educational strategies that enhance digital competencies while promoting eco-centric values. For policymakers and educators, this means designing interventions that not only improve digital literacy but also integrate environmental education to empower individuals to act on their environmental values effectively in the digital realm.

The findings also underline the necessity for further research to explore how benefit-oriented and eco-centric environmentalism manifests in the digital realm, offering a comprehensive understanding of the digital-environmental habitus and its implications for fostering sustainable digital practices.

The main limitation of the analysis stems from using a non-probabilistic sampling. Even though our sample does not seem to be too far from the age and gender quotas of the Istat sample, we remain cautious about the possibility of extending our results outside the studied sample. In addition, even though our results seem to be coherent with those obtained by Ruiu et al. [2] for the UK, a cross-country study should be conducted to confirm the existence of the constructs outside the analysed contexts.