literature review example consumer behaviour

• DOI: 10.1362/1469347012569896
• Corpus ID: 8289381

Consumer Behaviour: a Literature Review Consumer Behaviour: a Literature Review Consumer Behaviour: a Literature Review Consumer Behaviour: a Literature Review

• Moneesha Pachau
• Published 1 September 2001

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A Brief Literature Review on Consumer Buying Behaviour

Introduction

It is worth noting that consumer buying behaviour is studied as a part of the marketing and its main objective it to learn the way how the individuals, groups or organizations choose, buy use and dispose the goods and the factors such as their previous experience, taste, price and branding on which the consumers base their purchasing decisions (Kotler and Keller, 2012).

One of such studies of consumer buying behaviour has been conducted by Acebron et al (2000). The aim of the study was to analyze the impact of previous experience on buying behaviour of fresh foods, particularly mussels. In their studies the authors used structural equation model in order to identify the relationship between the habits and previous experience on the consumer buying decision. Their findings show that personal habits and previous experience on of the consumers have a direct impact on the consumers’ purchase decision in the example of purchasing fresh mussels. They also found that the image of the product has a crucial impact on the purchasing decision of the consumer and further recommended that the product image should continuously be improved in order to encourage the consumers towards purchasing.

Another study conducted by Variawa (2010) analyzed the influence of packaging on consumer decision making process for Fast Moving Consumer Goods. The aim of the research was to analyze the impact of packaging for decision making processes of low-income consumers in retail shopping. A survey method has been used in order to reach the research objectives. In a survey conducted in Star Hyper in the town of Canterville 250 respondents participated. The findings of the research indicate that low-income consumers have more preferences towards premium packaging as this can also be re-used after the product has been consumed. Although the findings indicate that there is a weak relationship between the product packaging and brand experience. However, it has been proven by the findings of the research that low-income consumers have greater brand experience from the purchase of ‘premium’ products when compared to their experience from purchasing ‘cheap’ brand products.

Lee (2005) carried out study to learn the five stages of consumer decision making process in the example of China. The researcher focuses on the facts that affect the consumer decision making process on purchasing imported health food products, in particular demographic effects such as gender, education, income and marital status. The author employed questionnaire method in order to reach the objectives of the research. Analysis of five stages of consumer decision making process indicate that impact of family members on the consumer decision making process of purchasing imported health food products was significant.

The author further explains this by the fact Chinese tradition of taking care of young and old family members have long been developed and marriage is considered to be extremely important in Chinese tradition. This reflects in the findings of the study that the purchase of imported health food products made by a person for the people outside the family is declined significantly by both male and female Chinese after they get married.

Five Stages Model of consumer decision making process has also been studied by a number of other researchers. Although different researchers offer various tendencies towards the definitions of five stages, all of them have common views as they describe the stages in similar ways. One of the common models of consumer decision making process has been offered by Blackwell et al (2006). According to him, the five stages of consumer decision making process are followings: problem/need recognition, information search, evaluation of alternatives, purchase decision made and post-purchase evaluation.

Each stage is then defined by a number of researchers varying slightly but leading to a common view about what each stage involves. For example, according to Bruner (1993) first stage, need recognition occurs when an individual recognizes the difference between what they have and what they want/need to have. This view is also supported by Neal and Questel (2006) stating that need recognition occurs due to several factors and circumstances such as personal, professional and lifestyle which in turn lead to formation of idea of purchasing.

In the next stage, consumer searches information related to desired product or service (Schiffman and Kanuk, 2007). Information search process can be internal and external. While internal search refers to the process where consumers rely on their personal experiences and believes, external search involves wide search of information which includes addressing the media and advertising or feedbacks from other people (Rose and Samouel, 2009).

Once the relevant information about the product or service is obtained the next stage involves analyzing the alternatives. Kotler and Keller (2005) consider this stage as one of the important stages as the consumer considers all the types and alternatives taking into account the factors such as size, quality and also price.

Backhaus et al (2007) suggested that purchase decision is one of the important stages as this stage refers to occurrence of transaction. In other words, once the consumer recognized the need, searched for relevant information and considered the alternatives he/she makes decision whether or not to make the decision. Purchasing decision can further be divided into planned purchase, partially purchase or impulse purchase as stated by Kacen (2002) which will be discussed further in detail in the next chapters.

Finally, post-purchase decision involves experience of the consumer about their purchase. Although the importance of this stage is not highlighted by many authors Neal et al (2004) argues that this is perhaps one of the most important stages in the consumer decision making process as it directly affects the consumers’ purchases of the same product or service from the same supplier in the future.

The most noteworthy writers that serve as academic advocates of The Five Stage Model of consumer decision making include Tyagi (2004), Kahle and Close (2006) Blackwell et al. (2006), and others.

It is important to note that The Five Stage Model is not the only model related to consumer decision-making, and there are also a range of competing models that include Stimulus-Organism-Response Model of Decision Making developed by Hebb in 1950’s, Prescriptive Cognitive Models, The Theory of Trying (Bagozzi and Warsaw, 1990), Model of Goal Directed Behaviour (Perugini and Bagozzi, 2001) and others. All of these models are analysed in great detail in Literature Review chapter of this work.

Factors Impacting Consumer Buyer Behaviour

It has been established that the consumer buying behaviour is the outcome of the needs and wants of the consumer and they purchase to satisfy these needs and wants. Although it sounds simple and clear, these needs can be various depending on the personal factors such as age, psychology and personality. Also there are some other external factors which are broad and beyond the control of the consumer.

A number of researches have been carried out by academics and scholars on identifying and analyzing those factors affecting the consumers’ buying behaviour and as a result, various types of factors have been identified. These factors have been classified into different types and categories in different ways by different authors. For instance, Wiedermann et al (2007) classified them into internal and external factor. On the other hand, Winer (2009) divided them into social, personal and psychological factors. Despite the fact that they have been classified into different groups by different authors they are similar in scope and purpose (Rao, 2007).

There is a wide range of factors that can affect consumer behaviour in different ways. These factors are divided by Hoyer et al. (2012) into four broad categories: situational, personal, social and cultural factors.

Situational factors impacting consumer behaviour may include location, environment, timing and even weather conditions (Hoyer et al., 2012). In order to benefit from situational factors major retailers attempt to construct environment and situations in stores that motivate perspective customers to make purchase decision. Range of available tools to achieve such an outcome include playing relaxing music in stores, producing refreshing smells in stores and placing bread and milk products in supermarkets towards the opposite end of stores to facilitate movement of customers throughout the store to make additional purchases etc.

The temporary nature of situational factors is rightly stressed by Batra and Kazmi (2008).

Personal factors, on the other hand, include taste preferences, personal financial circumstances and related factors. The impact of personal factors on consumer decision-making is usually addressed by businesses during market segmentation, targeting and positioning practices by grouping individuals on the basis of their personal circumstances along with other criteria, and developing products and services that accommodate these circumstances in the most effective manner.

According to Hoyer et al. (2012) social factors impacting consumer behaviour arise as a result of interactions of perspective consumers with others in various levels and circumstances. Targeting members of society perceived as opinion leaders usually proves effective strategy when marketing products and services due to the potential of opinion leaders to influence behaviour of other members of society as consumers.

Lastly, cultural factors affecting consumer behaviour are related to cross-cultural differences amongst consumers on local and global scales. Culture can be defined as “the ideas, customs, and social behaviour of a particular people or society” (Oxford Dictionaries, 2015) and the tendency of globalisation has made it compulsory for cross-cultural differences amongst consumers to be taken into account when formulating and communicating marketing messages.

Marketing mix and consumer behaviour

Marketing mix or 4Ps of marketing is one of the major concepts in the field of marketing and each individual element of marketing mix can be adopted as an instrument in order to affect consumer behaviour.

Importance of the marketing mix can be explained in a way that “successful marketing depends on customers being aware of the products or services on offer, finding them available in favourably judging that practitioners of the offering in terms of both price and performance” (Meldrum and McDonald, 2007, p.4).

Core elements of marketing mix consist of product, price, place and promotion. Marketing mix has been expanded to comprise additional 3Ps as processes, people and physical evidence.

Product element of marketing mix relates to products and services that are offered to customers to be purchased. Products can have three levels: core, actual and supporting products. For example, core product in relation to mobile phones can be explained as the possibility to communicate with other people in distance.  Actual product, on the other hand, relates to specific brand and model of a mobile phone, whereas augmented product may relate to product insurance and one-year warranty associated with the purchase of a mobile phone.

Price represents another critically important element of marketing and four major types of pricing strategies consist of economy, penetration, skimming, and premium pricing strategies (East et al., 2013).

Place element of marketing mix relates to point of distribution and sales of products and services. Advent of online sales channel has changed the role of place element of marketing mix to a considerable extent.

Promotion element of marketing mix refers to any combination of promotion mix integrating various elements of advertising, public relations, personal selling and sales promotions to varying extents (Kotler, 2012).

Processes, on the other hand, refer to business procedures and policies related to products and services. For example, integration of a greater range of payment systems such as PayPal, SAGE Pay and Visa in online sales procedures may have positive implications on the volume of sales by creating payment convenience to customers.

People element of marketing mix is primarily related to skills and competencies of the workforce responsible for customer service aspect of the business. Importance of people element of marketing mix in general, and providing personalised customer services in particular is greater today than ever before.

Physical evidence relates to visual tangible aspects of a brand and its products. For instance, for a large supermarket chain such as Sainsbury’s physical evidence is associated with design and layout of a store, quality of baskets and trolleys, layout of shelves within the store etc.

It can be forecasted that further intensification of competition in global markets and more intensive search of businesses for additional bases for competitive advantage may result in emergence of additional ‘P’s to compliment the framework of marketing mix in the future.

Bagozzi, R. & Warsaw, L. (1990) “Trying to Consumer” Journal of Consumer Research 17, (2) pp. 127 – 140.

Backhaus, K. Hillig, T. and Wilken, R. (2007) “Predicting purchase decision with different conjoint analysis methods”, International Journal of Market Research . 49(3). Pp. 341-364.

Batra, S.K. & Kazmi, S. (2008) “Consumer Behaviour” 2 nd edition, EXCEL Books

Blackwell, R., Miniard, P. and Engel, J. (2006) “Consumer behavior”, Mason: Thompson

Culture (2015) Oxford Dictionaries, Available at: http://www.oxforddictionaries.com/definition/english/culture

East, R., Wright, M. & Vanhuele, M. (2013) “Consumer Behaviour: Applications in Marketing” 2 nd edition, SAGE

Hoyer, W.D. & Macinnis, D.J. (2008) “Consumer Behaviour”, 5 th edition, Cengage Learning

Hoyer, W.D., Macinnis, D.J. & Pieters, R. (2012) “Consumer Behaviour” 6 th edition

Kacen. J. J. and Lee. J. A., (2002) “The influence of culture on consumer impulsive buying behaviour”, Journal of consumer psychology. 12(2), pp. 163-174.

Kahle L.R. and Close, A. (2006) “Consumer Behaviour Knowledge for Effective Sports and Event Marketing”, Taylor & Francis, New York, USA

Kotler, P.  (2012) “Kotler on Marketing” The Free Press

Meldrum, M. & McDonald, M. (2007) “Marketing in a Nutshell: Key Concepts for Non-Specialists” Butterworth-Heinemann

Neal, C., Quester, P. and Pettigrew, S. (2006) “Consumer Behaviour: Implications for Marketing Strategy” (5 th edition) Berkshire: McGraw-Hill

Perugini, M. & Bagozzi, R. (2001) “The role of desires and anticipated emotions in goal-directed behaviours: Broadening and deepening the theory of planned behaviour” British Journal of Social Psychology , 40, pp. 79-98.

Rao, K. (2007) “Services Marketing”, New Delhi: Pearson Education

Rose, S. and Samouel, P., (2009) “Internal psychological versus external market-driven determinants of the amount of consumer information search amongst online shopper”, Journal of Marketing Management . 25(1/2), pp. 171-190

Schiffman, L., Hansen H. and Kanuk L. (2007) “Consumer Behaviour: A European Outlook”, London: Pearson Education

Stallworth, P. (2008) “Consumer behaviour and marketing strategic”, online, pp.9.

Tyagi, C. and Kumar, A. (2004) “Consumer Behaviour”, Atlantic Publishers, US

Wiedmann, K., Hennigs, N. and Siebels, A. (2007) “Measuring Luxury consumer perception: A cross-culture framework”, Academy of Marketing Science review , 2007(7)

Winer, R. (2009), “New Communications Approaches in Marketing: Issues and Research Directions,” Journal of Interactive Marketing , 23 (2), 108–17

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Consumer Buying Behaviour – A Literature Review

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Realizing, and as well, analyzing the purchasing behaviour of consumer is the core constituent to provide efficient consumer satisfaction. A consumer is not only purchasing a produce, but he alone determines the victory of a firm. Hence for every successful firm, there exists a consumer support behind it. That support is technically called behavioural support and behind the support there is lot of theories to analyze and discuss the various concerns involving to consumer behaviour. Since World War II, taking into account the dire need of the public, the marketers started to market and encourage the produce what the consumers needed, instead of producing what the companies prefer. The concept of understanding the behaviour of consumer emerged in late 1940’s from which it has taken into so many dimensions. This is now known as “modern concepts of marketing”. At present, Consumer behaviour is commonly influenced by social, psychoanalytic and economical approaches. Each factor openly or not directly accounts to the characteristics of a buyer. Hence it is vital to be aware of the role of factors influencing the buying nature of consumer. The main iota of this research paper is to analyze the theoretical underpinnings and factors involved in consumer behaviour and its implications, in the light of developments crop upped in the recent past.

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Consumer behaviour can be defined as the decisions and actions taken by the consumers which influence their purchasing behaviour. Consumers' response to external stimulus either in form of marketing strategies or personal, economic and social attributes and their decision and buying behaviour is largely affected by this stimulus. It is thus, an inter-disciplinary social science that draws upon the disciplines of anthropology, psychology, sociology and marketing apart from economics. Therefore, many marketers often believe that a clear understanding of the buying behaviour of the consumers helps to analyse both past, present and future market scenario. The examination of the economic theories is helpful in identifying the consumer behaviour from the perspective of utility, prices and other economic aspects. But they do not reflect the perceptions or attitude of a consumer towards a product. So, to understand the consumer behaviour, a more holistic approach is required, that involves economic, non-economic theories and the decision making models. This paper is an attempt to understand the economic and psychological theories that influences the consumer behaviour. Further, an attempt has been made to correlate the consumer behaviour theories and consumer decision making models to explain the factors affecting the buying decisions of the consumers.

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Psychological factors impacts on carsharing use

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• Published: 21 August 2024

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• Mohamed Abouelela 1 ,
• Christelle Al Haddad 1 &
• Constantinos Antoniou 1  

Carsharing services have a significant potential for improving urban mobility by increasing the independence and freedom of travel and reducing traffic externalities. Although carsharing has been used for over a decade, several aspects need further investigation, such as the impact of user’s psychological factors on service use, as well as the factors impacting users’ choices between different carsharing operators, in particular their preferences for different payment schemes, and their perceptions of the operators’ application rating. Accordingly, four hybrid choice models (HCM) were estimated to investigate factors impacting (i) the knowledge about carsharing services, (ii) carsharing adoption, (iii) the shift from other modes to carsharing, (iv) the choice between carsharing operators with different payment schemes, using a large survey sample (N = 1044 responses 9469 SP observation) from Munich, Germany. The models showed the significance of sociodemographics, such as income level, education level, household size, employment status, ownership of a bike, access to a car, the availability of a driving license, and public transport subscription-based tickets on the carsharing use directly and indirectly, and four psychological factors encompassing different personality traits (i.e., adventurous), travel behavior, and attitudes were found to be significant in the various models; the latter covered service-related attitudes (perceived carsharing app importance) and travel behavior attitudes or profiles (frequent public transport user and frequent shared micromobility user). This research raises questions regarding the inequitable use of carsharing, the impacts of mobile applications on using the service, and the potential of integrating carsharing in mobility as a Service platforms to increase the potential for multimodality.

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Introduction

In the past ten years, there has been a significant increase in the acceptance, utilization, development, and improvement of app-based shared mobility services. This growth has been made possible by revolutionary advancements in information and communication technologies (ICT). Shared mobility services encompass various options, including schemes, services, vehicles, and business models. Examples of these services include ridesharing and carpooling, in which people share or split a ride, as well as carsharing and shared micromobility options such as bikesharing and e-scooter-sharing, in which vehicles can be rented based on time or distance (Narayanan and Antoniou 2022 ; Gilibert and Ribas 2019 ). These new mobility services have changed the landscape of urban mobility by introducing the concepts of on-demand services and pay-per-use, which increases the attractiveness of such services for users due to their ease of use, ease of payment, convenience, but also as they are perceived to be convenient, safe, and environmentally friendly (Arteaga-Sánchez et al. 2020 ; Tirachini and del Río 2019 ; Watanabe et al. 2017 ; Rayle et al. 2016 ).

Shared mobility services benefits are not limited to the individual level but it could extended to the city scale and could be an attractive solution for various transportation problems as they do not need large infrastructure investments and are quick to implement in most of the cases (Abouelela et al. 2022 ). Maintaining, upgrading, and constructing transportation infrastructure generally needs significant investments and a long time to materialize, which is not always a viable solution; one example is extending the transportation system’s accessibility to suburban areas with inefficient public transportation’s access (Burghard and Dütschke 2019 ; Abouelela et al. 2022 ). Shared mobility services could reduce the demand and congestion on roads, as well as the vehicle kilometer traveled (VKT), under the conditions of not replacing public transportation trips and replacing low occupancy vehicles (Tirachini et al. 2020 ). Alonso-Mora et al. ( 2017 ) concluded that shared rides could as well reduce the number of cars on city roads. The same promises of reducing the number of vehicles on the streets could be achieved using carsharing services, as private cars are idly parked for around 90% of the time (Zhang et al. 2015 ). Transport for London (TfL) sees carsharing services as complementary to public transportation services (Akyelken et al. 2018 ). Carsharing use could even be correlated with the increase in public transportation use (Aguilera-García et al. 2022 ).

On the other hand, challenges arise with the introduction of shared mobility should not be overlooked. Several problems have been raised since the introduction, such as increased safety concerns, and the rapid increase of numbers of reported severe and fatal accidents related to shared scooter use (Yang et al. 2020 ). Other problems related to operational-aspects and governance were also noted, including but not limited to, fleet-size control capping and organization, and permit cost (Gössling 2020 ). In most of the cases the evaluation of shared mobility impacts on \(\text {CO}_2\) emissions did not consider the whole life cycle assessment, and when the whole life cycle was assessed for the cases of shared e-scooter, the results showed that scooters are producing \(\text {CO}_2\) -equivalent per passenger-kilometer more than the modes they replace (Moreau et al. 2020 ), in addition to the other negative impacts that could result from shared services, such as attracting commuters from other sustainable modes, and generating extra kilometers traveled (VKT) during redistribution and maintenance processes (Møller and Simlett 2020 ). Also, the use of shared mobility require access to the Internet, smart–mobile–phone, and digital banking systems, which is not always available for all the population groups, and thus, it creates equitable use problem by being mostly accessible by the high income groups in comparison to the rest of the population (Abouelela et al. 2024 ).

Overall, the system is attractive to implement as establishing its infrastructure is considered relatively quick, its market is growing and it has the potential to continue growing in the future (Morwinsky 2023 ; Shaheen and Cohen 2020 ); however, it is not always an easy process as these services could face several challenges, such as economic viability (Golalikhani et al. 2021 ; Poltimäe et al. 2022 ), in addition to other obstacles, such as use complexity, limited availability leading to unreliability of the service, responsibility for the usage, long access time, low public awareness, and the required higher population density for economically feasible operation (Nansubuga and Kowalkowski 2021 ). An example of the struggle is the ShareNow Footnote 1 cease of operations in North America and several European cities, such as London, Brussels, and Florence in 2020 (Miljure and Ben 2019 ). ShareNow attributed ending the service to the rapid changes in the urban mobility landscape, the intense competition with different providers of different services, the unavailability of adequate infrastructure (e.g., charging infrastructure for electric vehicles), and the increased operation cost raising concerns regarding the long term economic model feasibility (Pyzyk 2019 ; Miljure and Ben 2019 ). On the other hand, there is a recent increase in the service’s popularity and adoption in Western European cities and Russia. This increase is conditional based on the city’s characteristics, such as the population’s educational level, university presence, and the number of Green party voters (Kireeva et al. 2021 ; Münzel et al. 2020 ). Therefore, it is essential to note that the implementation of the service and its economic viability is not always granted, and careful consideration, especially in the planning phase, should be attained.

Carsharing is a form of shared mobility that provides easy access to on-demand car use without the burden of car ownership responsibilities, the need to process paperwork such as for car rental services, or even the need to return the vehicle to the pickup points in most of the cases (Liao and Correia 2022 ). Carsharing services operate on different schemes when it comes to pickup and drop-off arrangements, with three main schemes: round-trip systems, where users pick up and return vehicles to the same locations, and more flexible options like one-way trips or free-floating systems, where vehicles can be dropped off at any designated point within a specified area (Amirnazmiafshar and Diana 2022 ; Jorge and Correia 2013 ).

Carsharing services and other shared mobility services are not only changing the landscape of urban mobility, but also the traditional idea of a car manufacturer producing, buying, and selling vehicles. Currently, some leading car manufacturers are promoting themselves as mobility providers (Akyelken et al. 2018 ), including Mercedes–Benz Group, BMW, Volkswagen, Toyota, and General Motors. Mercedes-Benz Group has two carsharing services (ShareNow, and Croove), acquired two taxi services (myTaxi, Footnote 2 and Hailo), is investing in two ride-hailing services (Via, Footnote 3 and Blacklane Footnote 4 ), and starting its own mobility platform moovel Footnote 5 (Akyelken et al. 2018 ). Therefore, there is an essential need to understand in–depth the different aspects of these services for better operation and integration within the urban environment.

Some of the main aspects of shared mobility that are important for the different stakeholders are the socio-demographic characteristics of the users and their general travel behavior, as well as their impacts in deriving the demand and identifying user target groups (Jochem et al. 2020 ). Psychological factors such as attitudes, perceptions, and personality traits play a significant role in individual travel behavior and mode choices (Kroesen and Chorus 2020 ). The importance of understanding the impact of psychological factors on travel behavior and mode choice lies in their ability to facilitate encouraging the use of the modes of interest, as they could be described as the underlying motivation for specific mode use (Bhagat-Conway et al. 2024 ). Previous research has shown that attitudes were found to have a significantly higher impact on the use of shared mobility as compared to sociodemographics, such as in the case of pooled rides (Abouelela et al. 2022 ). Still, there is a gap in terms of existing research on attitudes and personality traits in the scope of carsharing and shared mobility in general, mostly when comparing it to studies focusing on sociodemographics, which have been well examined and explored in the literature (Monteiro et al. 2023 ; Efthymiou and Antoniou 2016 ; Efthymiou et al. 2013 ). Several of these psychological factors are still under exploration and their role in the mode choice travel decision (Rahimi et al. 2020a ) in general, and shared mobility use in particular, is not well understood.To the best of the authors’ knowledge, many aspects of carsharing services have not yet been studied, such as the perceived service and feature offerings by different carsharing operators, including digital operator aspects (often reflected in the operator rating on the app store), as well as their impact on service adoption and use frequency (Monteiro et al. 2022 ).

The digital dimension of the carsharing services has also not been investigated in-depth, and includes the mobile application friendliness and ease of use, the service provider’s website landing page, the digital marketing of the service, the online marketing campaigns, and the business-to-business offers (Janasz and Schneidewind 2017 ). Another service feature to consider is the operator payment schemes (per minute or kilometer as recently introduced by some operators). The impact of the above-mentioned features on the operator choice still needs to be investigated. Finally, carsharing research on adoption and use has not yet been totally understood due to the novelty of the services; a large number of the carsharing studies have been completed before the services were even launched or during the early operational and adoption stages, during which users might have a different use behavior as they are getting familiar with the service. Another motivation of this paper is therefore to contribute to the existing body of research with more timely study in which the operation of carsharing services is ongoing at the time the research is done (Le Vine and Polak 2019 ; Hjorteset and Böcker 2020 ).

We therefore contribute to the current literature by updating the knowledge regarding carsharing use, using user-level information through a large online survey, and answering the following two research questions (RQ) investigating the roles of users’ psychological factors: personal attitudes, travel behaviour, and carsharing-related features on the different aspects of carsharing services.

RQ1) How do users’ psychological factors impact carsharing adoption and use?

RQ2) What factors impact the choice between different carsharing operators?

The rest of the article is organized as follows; “ Literature review ” section summarizes some of the selected studies related to user factors and attitudes impacting carsharing use and the different service-related characteristics that impact user’s choices. In “ Methods and study set-up ” section explains the methods used in the research and the case study setup used for the analysis and modeling. In “ Analysis results ” section spans across two parts that answer the research questions (RQ1 and RQ2); first, we analyze the collected data, second, we model the different factors that impact carsharing adoption and use, with a special focus on personality traits and attitudes. We also model and extract the factors impacting users’ choices between different carsharing operators. Finally, “ Discussion, limitations, and conclusions ” section discusses the study findings, highlights the policy implications, and summarizes the conclusion.

Literature review

The benefits of carsharing.

Sustainability is one of the many benefits associated with carsharing; it is considered a sustainable mode of transportation that has a wide array of positive impacts on the urban environment, such as the reduction in household car ownership and, subsequently a reduction in Greenhouse Gas (GHG) emissions that could reach up to 30–54% as a consequence of reduced Vehicle Miles/Kilometers Travelled (VMT/VKT) (Shaheen et al. 2019 ; Nijland and van Meerkerk 2017 ; Martin and Shaheen 2011a ). Also, electrification of the carsharing fleet was proven to be environmentally advantageous (Luna et al. 2020 ) and was able to yield more than 30% reduction on carsharing users’ GHG even if there was no change in VKT (Namazu and Dowlatabadi 2015 ). Several examples of the previous positive potentials of carsharing use were observed; in Germany, evidence associated with the reduction of car ownership and the number of station-based carsharing in the same area were present (Kolleck 2021 ); in China, in 2017, carsharing has caused a significant reduction of energy used, and \(CO_2\) emissions, with the expectation of higher savings by 2025 (Te and Lianghua 2020 ).

Shaheen et al. ( 2019 ) and Martin and Shaheen ( 2016 ) observed a decline in the average VKT of carsharing users ranging from 6 to 63% in North America, considering several conditions such as giving up car ownership, and the type of the service one-way or round trip. These tendencies were further corroborated by studies in Palermo, Italy (Migliore et al. 2020 ), the Netherlands (Nijland and van Meerkerk 2017 ), and London, UK (Wu et al. 2020 ). Also, Wu et al. ( 2020 ) noted that in London, higher satisfaction with the proximity to carsharing vehicles contributed to a larger reduction in VKT. Interestingly, carsharing users who live in suburban areas tend to drive fewer kilometers than their counterparts in dense urban areas (Clewlow 2016 ), which could be attributed to the lower density of available vehicles in the suburbs, resulting in carsharing users canceling the non-essential trips. Other environmental-related positive impacts, such as saving materials and reducing wastes, were observed (Harris et al. 2021 ). The environmental impacts of carsharing and their total magnitudes are heavily dependent on the occupancy rate, the used vehicle and fuel type of the fleet, the modal share of carsharing, the modes replaced by carsharing, and the vehicle’s lifespan (Poltimäe et al. 2022 ; Harris et al. 2021 ; Jung and Koo 2018 ). Also, adverse environmental impacts of carsharing were observed; in Palermo, Italy, the fleet only contains diesel and natural gas vehicles, and it was found to increase the CH \(_4\) and NO \(_x\) emissions of the city (Migliore et al. 2020 ).

Impacts related to infrastructure and built-environment were also observed, as carsharing use can help in reducing car ownership rates, which promotes more positive impacts on curb-side management, minimizing the space uptake for car parking (Golalikhani et al. 2021 ). A study among students in Ithaca, New York by Stasko et al. ( 2013 ) found that since the introduction of carsharing in the area, student parking permit sales had declined despite a continuous increase in enrollment. The causality of these occurrences was not investigated or verified. Another survey in France investigated carsharing use impact on parking derived that for every carsharing vehicle on the street, between 1.6 and 4.2 on-street parking spaces, 0.3–0.6 public parking spaces, and 2.1–4.2 private parking spaces could be eliminated (6t-Bureau de recherche and ADEME 2016 ). Diana and Chicco ( 2022 ) analyzed the spatial distribution of changes in parking demand related to carsharing and found that more relieved parking spaces could be anticipated for central areas, while more negative impacts might be imposed on the parking in peripheral areas.

Integrating carsharing with public transportation would yield more benefits by extending the spatiotemporal accessibility of public transportation. Some examples of this integration are the decentralized mobility hub (Czarnetzki and Siek 2022 ), implementation of dedicated carsharing facilities (Engel-Yan and Passmore 2013 ) and unbundled parking (Schure et al. 2012 ) in residential buildings, and appropriate financial and policy backing from the authorities in forms of aids to the low income-groups (Rabbitt and Ghosh 2013 ; Bocken et al. 2020 ). Note that the extent of carsharing impacts could highly vary depending on the region, built environment (Clewlow 2016 ; Jain et al. 2022 ), accessibility of public transportation, and the carsharing replaced modes (Shaheen et al. 2019 ; Jain et al. 2022 ; Kolleck 2021 ; Duncan 2011 ). Other positive potentials for carsharing use were observed, but they were less explored, such as benefits associated with the B2B carsharing model capabilities of reducing work trip cost as the car can be used without bearing ownership-related costs and duties, increasing thereby not only trip sustainability, but also the workplace attractiveness, which could now subsidize carsharing trips for their employees (von Wieding et al. 2022 ). Carsharing trips were also found to encourage multimodality, physical activities, and a healthier lifestyle (Kent 2014 ; Shaheen et al. 2019 ; Harris et al. 2021 ). Also, carsharing was found to increase access to cars for car-less households (who do not own private vehicles), providing them thereby with more independence and equitable access to opportunities (Stasko et al. 2013 ; Kent 2014 ; Shaheen et al. 2019 ). This in turn improves the mobility of lower-income groups by increasing the number of available travel options (Kumar Mitra 2021 ), and strengthening the sense of community among users (Hartl and Hofmann 2022 ; Harris et al. 2021 ).

Factors impacting the adoption and use of carsharing

Several factors impact the adoption and use of carsharing services; these factors could be categorized into three main groups; (i) service-related factors, (ii) exogenous factors, and (iii) user-related factors. The first group of factors included the number of available vehicles in the stations, and vehicle age; in a study by De Lorimier and El-Geneidy ( 2013 ), this encouraged carsharing use in Quebec, Canada. In metropolitan Vancouver, lowering the membership fees was found to attract more users (Namazu et al. 2018 ). The difference between the trip cost, and the mode carsharing replaced was found to be the most significant factor impacting carsharing use in Beijing, China (Yoon et al. 2017 ). Personalized use incentives were also found to attract more users (Feng et al. 2023 ). In Shanghai city, electrical vehicle battery charging level and the number of available vehicles in stations impact the user choice for the vehicles (Hu et al. 2018b ). Secondly, exogenous factors are also key such as adverse weather conditions (Yoon et al. 2017 ), availability, accessibility of public transportation station (Balac et al. 2015 ; Hu et al. 2018a ; Khan and Machemehl 2017 ), land-use (Kim et al. 2012 ; Stillwater et al. 2009 ), intersection and road density, and the availability of parking (Chen wt al. 2018 ; Yoon et al. 2017 ; Hu et al. 2018a ).

Thirdly, several studies focused on investigating the sociodemographic characteristics influencing carsharing use. The findings of these studies have identified users as young, male, well-educated, with high-income, and full-time employment (Le Vine and Polak 2019 ; Martin and Shaheen 2011a ; Alemi et al. 2018 ; Ahmed et al. 2021 ; Luo et al. 2019 ). The role of other important personal drivers to the service is less known, and here we mean the personal attitudes and personality traits, despite the fact that there is evidence suggesting the significance of attitudes on the use and adoption of carsharing services, noting that understanding personal attitudes is claimed to enhance the models predictability (Pronello and Gaborieau 2018 ). For instance, carsharing users are more likely to own “greener” vehicles (Clewlow 2016 ) and exhibit more eco-friendly behavior (Jung and Koo 2018 ), hinting at higher concerns towards environmental issues, and carsharing advocacy attitude increased the adoption of carsharing compared to other modes (Li and Kamargianni 2020 ). In the realm of carsharing, research on the role of personal attitudes has yielded mixed conclusions.

Zhang and Li ( 2020 ) and Li and Zhang ( 2021 ) discovered that subjective/social norms had the biggest influence on the intention to use carsharing, and attitudinal variables, including environmental concerns, imposed a much more limited impact, while a study in Taiwan (Buschmann et al. 2020 ) reported the complete opposite. Varieties also exist within the range of behavioral constructs that were found to be significant in carsharing familiarity and adoption. Aguilera-García et al. ( 2022 ) found that high sharing propensity, variety-seeking lifestyle, and preference for driving positively impacted familiarity, and that pro-environmental behaviors reduced carsharing usage. On the other hand, Thurner et al. ( 2022 ) concluded that people who were believers of science and technology, who were generally early adopters of novel technology, and those with self-expressive social values tended to be carsharing adopters. The previous discrepancies are unsurprising, considering the virtually unlimited spectrum of attitudes that humans might have. Yet, researchers are constrained to investigate only a select few, along with behavioral indicators which vary across the board.

Cultural context also moderates the effects of other sociodemographic variables. For instance, society could be more concerned about conforming to the norms than their expressions, leading to subjective norms being more influential in their decisions. Also, there is a complex interrelation between these attitudinal constructs, which is hard to interpret. This is well demonstrated by Zhang and Li ( 2020 ), Burghard and Scherrer ( 2022 ), Li and Zhang ( 2021 ), Acheampong and Siiba ( 2020 ) in which environmental attitudes imposed no direct impact or even negative impact on carsharing intentions, while simultaneously being positively correlated with another construct which in turn positively impacted the carsharing intention (i.e., positive indirect impact). This shows how the role of attitudes in human decision is a complex topic and requires further research with a possibly wider range of attitude constructs. For example, Hjorteset and Böcker ( 2020 ) further differentiated the resulting attitude towards carsharing into general interest, anticipated intention, and actual decision to utilize the service. Another part of human attitudes is personality traits, which are the main drivers of travel demand (Mokhtarian et al. 2001 ). Different personality traits are hypothesized to impact travel behavior differently; while the adventure-seeker personality was found to be likely to travel and drive faster than other personalities, are prone to have and create more elements of danger (Furnham and Saipe 1993 ). Redmond ( 2000 ) concluded that people with adventure-seeking personalities are more likely to enjoy leisure trips over work trips and may also prioritize them. Another personality associated with the preference for using private cars over public transport is the organizer personality (Redmond 2000 ). A summary of the factors impacting carsharing use is presented in Fig.  1 below.

Summary of factors impacting carsharing adoption and use (own illustration)

Synergies between carsharing use and travel behavior

Carsharing might play a significant impact on travel behavior and users’ long and short-term travel decisions. It can impact the decision to give-up a car and forego/delay the decision to acquire a new one (Ko et al. 2019 ; Seo and Lee 2021 ). Although varying conclusions exist across case studies, the general consensus suggests a decline in the level of car ownership, with studies quoting four (Migliore et al. 2020 ; Shaheen et al. 2018 ) to twenty-three (Lane 2005 ) private vehicles being replaced for every carsharing vehicle in operation. This conclusion is consistent with Le Vine and Polak ( 2019 ) findings, which highlighted, based on a survey in London (N = 347 responses), that as much as 37% of respondents had their car ownership decisions impacted by using carsharing, as users opted to drop the decision of buying a car or dispose of their currently owned car. Factors affecting a user to dispose or forego buying a car include income level, age, housing type, satisfaction towards the carsharing service, access time to carsharing station, fuel type, and the price or cost of the service (Jung and Koo 2018 ; Ko et al. 2019 ). Simultaneity bias can also be a concern as Jain et al. ( 2020 ) found within their case study; carsharing mostly acted as an enabler of mobility lifestyle change but was not the primary cause of households shedding their private cars, as life events had a stronger influence.

Furthermore, carsharing’s overall impact on sustainability depends on the modes it replaces, whether they are “greener” and more active modes such as walking, cycling, and public transportation (Chicco and Diana 2021 ). The impact of carsharing on the general car use is less conclusive as some studies reported that the majority of carsharing users drove less frequently (than before carsharing adoption) (Martin and Shaheen 2011b ; Shaheen et al. 2018 ), while others studies claimed the contrary (Stasko et al. 2013 ; Martin and Shaheen 2016 ). This is due to the fact that the effect of those who dispose of private cars is counterbalanced by the impact of those who gain access to cars through carsharing (Lane 2005 ). While such studies often relied on user surveys, the latter have often been criticized as they focus on carsharing users and therefore create self-selection biases, which might impact the conclusions.

Modeling techniques

Attitudes are often treated as latent variables derived from stated behavioral statements. To capture these latent attitudes and determine the indicating constructs, several methods have been used in the past, including Structural Equation Models (SEMs) (Yazdanpanah and Hosseinlou 2016 ; Aguilera-García et al. 2022 ; Rahimi et al. 2020b ; Zhang and Li 2020 ), Principal Component Analysis (PCA) (Queiroz et al. 2020 ; Thurner et al. 2022 ), and Latent Class Analysis (LCA) (Olaru et al. 2021 ). Subsequent regression analysis [e.g., Bivariate Logit (Queiroz et al. 2020 ), and Hybrid Choice Model (HCM), or Integrated Choice and Latent Variable models (ICLV) (Sun et al. 2021 )] incorporating the latent attitudinal variables in models is frequently conducted to assess the causality between attitudes and other variables in question (e.g., acceptance of carsharing). The main objective of this integration is to enhance the model’s ability to understand the choice process by incorporating the user’s cognitive behavior, attitude, and psychological factors into the choice model. This integration also aims to improve the model’s goodness of fit where applicable (Vij and Walker 2016 ; Temme et al. 2007 ; Ben-Akiva et al. 1999 ). ICLV models were, for instance, used to quantify the factors impacting the frequency of pooled-rides uses in Mexico City, Mexico (Abouelela et al. 2022 ). Theory of Planned Behavior (Jain et al. 2021 ; Zhang and Li 2020 ; Li and Zhang 2021 ), Rogers et al. ( 2014 )’s Theory of Innovation Diffusion (Jain et al. 2021 ; Burghard and Scherrer 2022 ), and the Theory of Reasoned Action (TRA) along with its extensions (Buschmann et al. 2020 ) are often incorporated in assessing the role of personal attitudes. Further scientific frameworks that are prevalent in this research topic are the Technology Acceptance Model (TAM) (Al Haddad et al. 2020 ; Schlüter and Weyer 2019 ; Buschmann et al. 2020 ) and its modifications, such as the Unified Theory of Acceptance and Use of Technology (UTAUT) (Fleury et al. 2017 ).

Gap analysis

The review of the current research shows several gaps in the current body of literature that need to be bridged to utilize the maximum benefits of shared mobility use, specifically carsharing services. First of all, a significant portion of carsharing-related studies were developed before the implementation of the services or during the early deployment stages. Accordingly, there is a pressing need to update the current literature with more recent studies, especially for users who are already familiar with the service and have used it for an extended period (Hjorteset and Böcker 2020 ; Le Vine and Polak 2019 ; Namazu et al. 2018 ). The importance of covering this point is to test the previously anticipated or observed pre-and-early-use behavior of carsharing and decide if the current operations and policies might need modification or changes.

Secondly, the investigating of the impact of carsharing-related features, such as operator offering and the used mobile app, on service use, adoption, and choice between different operators is still scarce in the existing body of the literature and generally overlooked (Monteiro et al. 2022 ). As discussed in the following sections, such aspects significantly impact service adoption and use.

Finally, the impacts of psychological factors such as personal attitudes and personality traits on the use and adoption of carsharing services are not well established (Aguilera-García et al. 2022 ), despite their essential impacts on travel behavior and mode choice, and the use of the different shared mobility options (Abouelela et al. 2022 ). Understanding the interaction between the different psychological factors and travel behavior is essential to understanding how they could alter the current travel behavior to be more sustainable (Steg 2007 ). In other words, shared mobility, especially carsharing services, a niche service, and understanding these psychological factors impacting their use could help in driving policies that increase the sustainable adoption of these services (Burghard and Scherrer 2022 ), for example, social norms were found to be positively increasing the adoption of carsharing when it was promoted as a low-carbon mobility option in Sweden (Mundaca et al. 2022b , a ). Also, the role of psychological factors is not only limited to understanding and altering travel behavior, but it also could help in anticipating which policies, such as environmental policies, could be accepted (Ejelöv and Nilsson 2020 ). Therefore, we are trying to understand additional psychological factors that might impact carsharing use, to understand how to integrate the service with other modes of transport, and which factors could attract users to the service or be used as a pull measure.

In summary, this study addresses the gaps mentioned above by testing the impacts of different psychological factors, namely personality traits, regular travel behavior, and attitudes (importance of service-related features), on carsharing use and adoption, thereby answering the research questions.

Methods and study set-up

Survey design.

The main goal of this research is to understand the impacts of attitudes, travel behavior, and personality traits on the use of carsharing services; therefore, we designed a survey in four parts, which was implemented online using Limesurvey platform ( https://Limesurvey.com ), and disseminated to different users group in Munich, Germany, during the period of 20 of January to 25 of March 2022. We opted to deploy the survey online as it was deployed during the COVID-19 pandemic, and we wanted to eliminate the chances of infection during the data collection process. As carsharing users are likely young, we targeted them in our data collection process. Young users are commonly adopters of shared mobility in general and carsharing in particular, as highlighted in studies in different locations, such as in Munich and Madrid (Aguilera-García et al. 2022 ), in Vancouver, Canada (Namazu et al. 2018 ), in Puget Sound region in the state of Washington, USA (Dias et al. 2017 ), and all over Germany (Burghard and Dütschke 2019 ). Also, we collected data from non-users to check the different reasons for not adopting the service, as well as to evaluate the differences between the two groups. Overall, we collected 1170 completed responses, and the average survey completion time was 12 min. The survey consisted of four main parts;

In the first part, general travel behavior was investigated, where users were asked to specify their usage frequency for different urban modes of transport, whether they had a public transportation subscription ticket (such as a monthly ticket), whether they owned bikes, e-bikes, a private car, and whether or not they had a valid driver’s license in Germany. The modes that their use frequency was investigated are:

In the second part, we investigated user familiarity with and usage of carsharing services; we focused on usage frequency, willingness to walk to the vehicle pickup location, trip purpose. Respondents were also asked about the modes they would have used instead of carsharing for their last carsharing trip. Finally, respondents were asked to evaluate the importance of different aspects of carsharing services, such as mobile-application rating on the digital store, application ease of use, service availability in different cities, service availability in EV, service availability in the airport, service availability in different size vehicles (SUV, trucks, etc.), and the availability of offers bundles (discounts, e.g., for all-day rental, and long-distance rentals).

The third part of the survey was the stated preference experiment; refer to Fig.  2 . In this experiment, respondents had to choose one carsharing service to perform an 11-kilometer trip; the choice was between operator A, where the user pays a fixed cost per kilometer. The other choice was operator B, where the trip cost would vary between a minimum cost, an average cost, and a maximum cost based on congestion conditions. The latter (cost range) would vary based on speeds (maximum, average, and minimum, respectively) of previous trips (previous trip distribution).

Scenario details and one block example

Table  1 shows the attributes and their corresponding levels that were used for the experiment. We opted to use travel cost, as it is a decisive factor in travel mode choice, and we wanted to investigate two new factors that were not investigated previously, which are the access distance users needed to walk to the nearest available vehicle and the service rating on the digital application store. The attribute levels were calculated as follows:

Travel cost:

\(*\) Operator A , payment by km scheme, the average cost per km is 0.89 €/km, obtained from the operator’s online website and is similar to values used by Abouelela et al. ( 2021 ). A variation of this level ( \(-\)  0.25%, 0%, +25%) would result in a range of (0.66, 0.89, and 1.11) €/km.

\(*\) Operator B , based on actual carsharing speed distribution. Essentially, minimum, average, and maximum costs were calculated using the same carsharing trips in Munich, Germany, for 2016, as described in Abouelela et al. ( 2021 ). The trip cost was calculated based on the speed distribution and multiplied by the cost per minute. The minimum cost was calculated based on the average speed for the first speed quartile distribution. The average speed was calculated based on the average speed, and the maximum cost was calculated based on the third-speed quartile average speed. For each speed, subsequent cost ( \(-\)  0.25%, 0%, +25%) values were calculated.

\(*\) Operator B , costs per minute were obtained from operators’ online websites and similar to the values used by Abouelela et al. ( 2021 ).

\(\bullet\) The levels of access distance calculated for this experience considered that the walking speeds are around 4–6 km/h (Bohannon and Andrews 2011 ), and that more than 50% of pooled ride users opted to walk less than ten minutes for the ride pick up location (Abouelela et al. 2022 ).

\(\bullet\) Application rating on the digital application store was created specifically for this experiment, as no similar attributes were not investigated before.

\(\bullet\) Engine type was used to check the impact of the electric engine type on the user’s choice, and it was a binary attribute with two levels: yes, and no. A similar attribute was used by Monteiro et al. ( 2022 ).

The fourth part of the survey investigated the sociodemographic characteristics of the users, where we asked users to specify their age, gender, education level, occupation, number of people and children in the household, and average monthly income. Also, in this part, we asked users to specify their agreement on a five-points-scale (totally disagree, disagree, neutral, agree, totally agree) on how much they identify with each of the 18 personality traits below, as used by Queiroz et al. ( 2020 ), Mokhtarian et al. ( 2001 ), Redmond ( 2000 ):

Modeling framework

The main target of this research is to model the impact of attitudes and personality traits on carsharing use, using the collected survey data. The survey consists of answers to attitudinal and personality evaluation, revealed preference, and stated preference questions. The different parts of the survey were used to answer the research question related to investigating factors impacting adoption, the shift from other modes, the choice between operators, and finally, the knowledge or awareness level regarding carsharing service (essentially the research questions RQ1 and RQ2). In investigating the examined factors, Hybrid Choice Models (HCM) were estimated. The main purpose of estimating HCM models was to integrate and investigate the impacts of user cognitive behavior, personality, and attitudes on the service adoption (Abouelela et al. 2022 ; Bolduc and Alvarez-Daziano 2010 ; Ben-Akiva et al. 2002 ), but also to get a more realistic choice behavior, as pointed out in Raveau et al. ( 2010 ), Bolduc and Alvarez-Daziano ( 2010 ).

The first step in HCM is to estimate the latent constructs of the data (namely attitudes, travel behavior, and personality) using Exploratory Factor Analysis (EFA). We started the analysis by performing a scree test (Cattell 1966 ) to decide on the optimum number of factors. The test showed two factors as the desired number, and we kept attributes with factor loading 0.4 or larger, based on the sample size and following (Hair et al. 1998 ). Varimax rotation was applied to obtain an orthogonal structure between the different factors, and the polychoric correlation was used as it suits the ordered nature of the data better than other correlation methods (Holgado-Tello et al. 2010 ). After deciding on the estimated factors for each of the question groups, the corresponding discrete outcome model was first estimated, and the latent variable model was added afterward. Four HCM models were estimated using Apollo package (Hess and Palma 2019 ) under the statistical software R (R Core Team 2023 ).

Study setup

Munich is the third largest city in Germany, with a population of around one and a half million and six million inhabitants in the metropolitan area (Aguilera-García et al. 2022 ). The city has a strong transportation infrastructure network reflected in many aspects of the inhabitants’ daily travel behavior, where 80% of the population owns at least one bike, served by a 1,200 km long bike lanes network and 28,000 bike parking spaces. Also, the overall city modal shift reflects the solid public transportation culture, where 33% of the trips are made by cars, 23% by public transportation, and 44% of daily trips are done by active mobility, walking and biking. Footnote 6 The city-shared mobility landscape is vibrant, with different options for carsharing, bikesharing, shared e-scooters, moped scooters, and e-hailing. Munich City demonstrates an excellent example of a case study for carsharing use city, with the free-floating carsharing service starting in 2011. In 2019, there were around 2100 shared cars on the city streets. Different operators adopt different pricing schemes, such as pay per minute, hour, and day, and lately, some operators are calculating trip prices based on trip length (Aguilera-García et al. 2022 ).

Analysis results

Summary of sociodemographic and travel behavior characteristics.

The survey resulted in 1170 valid and complete responses. Table  2 shows the collected sample demographic characteristics compared to the city of Munich. The collected data is skewed compared to the city population in terms of age, education, occupation, and income, which is a direct result of the sampling strategy targeting young users. In general, the sociodemographic characteristics of the shared mobility users are different from the ones of the average population, as discussed in “ Literature review ” section.

In terms of age, 89% of the sample is younger than 36 years old, compared to 40% of the average city residents age; also, users are highly educated, with 85% of the sample having at least a bachelor’s degree compared to 26% of the city’s residents. The number of students in the sample is over-representative in comparison to the city, as 43% of the sample respondents are students compared to only 4.5% of the city population. Therefore, the age and occupation of the respondents are reflected in other aspects, such as income being lower than the city average and the low number of children in the households. As the focus target group of this research are users younger than 35 years old, we only considered them in the following analysis, excluding all the other users (N = 1044). When comparing carsharing users with non-users, using a Perason’s Chi-square test ( \(\chi _2\) ) (Pearson 1900 ), the differences were found to be significant in terms of users being males, more educated, with higher income, compared to the average population, full-time occupation, having access to a car, and owning a driving license that is valid in Germany. This profile of the carsharing user is similar to other shared mobility services in other locations, such as the United States, Canada, Great Britain, and Australia (Howe and Bock 2018 ; Degele et al. 2018 ; Raux et al. 2017 ; Shaheen and Martin 2015 ; Kim et al. 2015 )

Travel behavior is an important factor that impacts users’ adoption of shared mobility services (Abouelela et al. 2022 ); therefore, we asked respondents about the frequency of their use of twelve modes of transport. The majority of the sample can be described as active PT users, with at least 40% of the sample using PT more than once a week, which is reflected in their subscription to PT weekly and monthly tickets. The subscription to PT services reflects various aspects, such as the users’ loyalty to the service or the high quality of the PT system. Also, younger respondents more actively using PT than their older counterparts, who tend to use more private cars, was observed in other locations as well (Chaisomboon et al. 2020 ). A considerable percentage of users have access to private car use, as reflected in their car usage. Active travel is evident in the sample, mainly in the form of walking and personal bike, and not much use of shared micromobility modes. We further analyzed the modes used by users versus non-users and also made an assessment by gender; see Fig.  3 , Tables  10 and  11 . The differences in travel behavior between the genders are well established, where women generally tend to utilize slower transportation modes like public transport and walk more frequently than men. They generally travel shorter distances and have more complex trip arrangements, and are more likely to travel accompanied by children or dependents, facing more challenges related to physical accessibility, safety, and security (Pourhashem et al. 2022 ; Xu 2020 ; Tilley and Houston 2016 ). Gender is a decisive factor in shared mobility use, and specifically in the case of carsharing, as discussed in “ Literature review ” section. Therefore, we considered the travel behaviour analysis per gender to further investigate these differences and test their impacts on the carsharing use.

Figure  3 shows the frequency of using the different urban modes for users and non-users; to assess the significance of these differences, we performed a chi-square test. From the twelve compared modes, nine were found to have significant differences, and only three modes did not have significant differences, namely walking, tram, and the underground metro. Carsharing users were, on average, more frequent users of all other modes than non-users (of carsharing services), except for bus(es). In terms of gender, differences in mode frequency were limited and were significant in the case of car use as a passenger and as a driver, shared bike, and taxi; in particular, males used, on average more bikesharing systems and were more often car drivers, as compared to their female counterparts.

Urban modes use frequency for users and non-users of carsharing services

Familiarity with carsharing and carsharing use

In this section, we explore the respondent familiarity with carsharing services, and the way they use the service. We asked the users to rank their familiarity with the carsharing service on a four-point scale ranging from: “I do not know about them” to “Very familiar, I know almost everything about them.” Most users (65%) knew about the service, and around one-fifth were very familiar with the service. We asked this question as we believed carsharing use is correlated with user familiarity with them, and we wanted to test the familiarity impact on the different service use aspects as explained in detail in “ Analysis results ” section. Table  9 shows the summary statistics for the familiarity with carsharing services for each user and non-users, per gender. Results indicated that users generally had a higher level of familiarity with the service compared to non-users; 88% of users were familiar with the service as compared to 43% of non-users. It is important to highlight that the 12% of the users who were unfamiliar with the service reported that they had used carsharing mainly as passengers. When assessing by gender, there was no significant difference in terms of knowledge, except that males were very familiar with the service, as compared to females.

Table  9 shows the summary statistics of the different aspects of use and familiarity of carsharing services for the different groups; Chi-square tests were used to test the significance of the differences between the different subgroups. The majority of users used the service as passengers, and they used it mainly less than once per week. The major trip purposes are leisure, visits, work, and shopping. Users were asked about the modes they replaced the last carsharing trip with, and the top five modes were the underground, car as a passenger, suburban train, e-hailing service, and car as a driver. These results show potential for negative impacts, as carsharing trips replace mainly PT trips which might increase the vehicle kilometer traveled (VKT) on the roads and, subsequently GHG emissions. We also asked the users to express their willingness to walk to the nearest carsharing vehicle locations, for which 75% of the users specified that they would walk up to seven minutes to the pickup location. We also tested the impact of frequency of use on the willingness to walk, for which no significant results were found. Users’ willingness to walk was uniformly distributed among the different use frequencies. Similar results were observed in fixed-route commercially organized pooled rides (Abouelela et al. 2022 ).

Modeling results

In this section, we first present the exploratory factor analysis results, after which we present the findings extracted from the four developed hybrid choice models. The aim was to first extract the latent constructs on both user and service-related aspects to carsharing, to then incorporate them and assess their impact on carsharing. In particular, the impact of personality traits and attitudes on knowledge about carsharing, carsharing adoption, and use, was assessed. The importance of service-related attributes on the choice between carsharing operators with different payment schemes was also explored.

Exploratory factor analysis

In this sub-section, the exploratory factor analysis (EFA) results are presented, based on which the latent constructs have been extracted, notably for user attitudes; the impact of the extracted factors on carsharing use was then studied. In particular, the factor analysis was conducted for three question groups relating to carsharing operator-related features (“ Carsharing operator-related features ” section), personality traits (“ Personality traits ” section), and travel behavior (“ Travel behavior ” section).

Carsharing operator-related features

For the first questions group, we asked respondents to rate how important different aspects of carsharing services were to them, on a five-point Likert (Likert 1932 ) scale that ranges from ( 1 = not important at all, 2 = not important, 3 = neutral, 4 = important, 5 = very important). Table  10 presents the summary statistics for the ratings of the seven examined aspects of the carsharing service characteristics. The rating summary shows no significant difference between gender groups in the evaluation rate; however, a slight difference in the ranking of the importance of each aspect was observed. Application ease of use was selected as the most critical aspect, while the availability of EVs in the carsharing fleet was rated as the least important factor as per the evaluation order for both genders; the latter was found to be less than neutral for male users with an average evaluation score being less than 3. The rest of the operator-related features were almost the same for both genders, with women’s evaluation scores (in terms of importance) generally consistently higher than males without any statistically significant difference.

When comparing user and non-user groups, interestingly, non-users had, on average higher evaluation scores for the different aspects, except for the availability of different size vehicles, which was the second to last least important aspect based on their rating. Also, application ease of use was the most important service aspect, with a significant difference in rating compared to the next important aspect, app rating. The differences between users and non-users were significant and evident in all aspects, except for service availability in different cities and for app ease of use.

The top part in Table  3 shows the factor analysis results with two main factors representing the main latent constructs and explaining 46% of the total data variability. Factor one can be described as the physical offers, and the second factor as the application-related factors. The results of the EFA for the carsharing operator-related features could possibly reflect on the important dimensions of the service that operators need to focus on to achieve a high level of satisfaction among users.

Personality traits

Understanding personality traits is essential for understanding human travel behavior; yet the impact of such traits on travel behavior is still not well comprehended (Jani 2014 ). Also, personality might not be a direct influence on travel behavior, but it dictates a certain pattern of behavior (Revelle 2007 ), and it is more likely to be associated with different levels of mobility; for example, having an adventurous personality might be associated with a higher level of mobility (Redmond 2000 ). The middle part in Table  10 presents the summary statistics for the answers pertaining to personality traits for different respondent groups (users, non-users, males, and females). In particular, respondents were asked to specify their agreement with different personality types on a five-point Likert scale (ranging from “Totally disagree”, “Disagree”, “Neutral”, “Agree”, “Totally agree”). After conducting Chii-square tests for assessing the statistical significance in personality traits between different respondent groups (see Table  11 middle part), we found significant differences in personality traits between users and non-users than between the different gender groups (i.e., males and females).

Our initial hypothesis for the EFA was that we would estimate five factors representing the five major personalities, namely risk-taking, loner, ambitious, organized, and lazy. The middle part in Table  3 presents the estimated EFA results for the personality-related questions, for which two main factors were extracted, interpreted as “adventurous” and “organized”. The two factors explain 39% of the data variability. The results of these factors were further used to estimate the impact of these two types of personalities on carsharing use.

Travel behavior

The final set of questions that were analyzed using EFA techniques focused on the frequency of use of the different available modes. For this question, we hypothesized three types of users: PT users, private mode users, and finally, shared mobility users. The bottom part in Table  3 bottom part presents the results of the EFA for the mode use frequency. Two factors were extracted and found to be significant, one for PT users and the other for shared micromobility users; the two factors explained 51% of the variance of the data, and the initial hypothesis was partially correct.

Factors impacting knowledge about carsharing

This model investigates the factors impacting user’s knowledge regarding carsharing. The answer to the question investigating the knowledge about carsharing was set as the dependent variable, which is ordered in nature. The answers to this question were “I do not know about them”; “I have heard about them”; “know about them, but not much details”; “Very familiar, I know almost everything about them”. Ordered HCM model was estimated, and Fig.  4 , and Table 4 show the full path diagram and the estimated model results.

Full path diagram for the ordered HCM for knowledge about carsharing

Four variables and two latent variables were significant with positive estimated coefficients ( \(+ \beta\) ), which show that these variables are associated with a higher likelihood regarding higher knowledge about carsharing services: previous use of carsharing, ownership of a driving license, full-time workers, people who live in small households, adventurous persons, and frequent PT users. The thresholds between the different knowledge levels are significant, showing that people understand the difference between the different levels.

The latent variable models can be interpreted as follows: for the measurement model adventurous personality, the positive sign for the estimated coefficient ( \(\zeta\) ) for the measurement model part shows that the more the person agrees with the statement, the more likely is this personality type, and the more likely he is to be an adventurous person. The signs of the coefficients of the Structure model part ( \(\gamma\) ) for males and bike owners show that these variables increase the probability of being an adventurous person compared to the other population group. The other latent variable is the PT frequent user, and the measurement model positive coefficient ( \(\zeta\) ) sign shows that the higher the answer the more frequently the person uses PT, and the negative sign for the high-income coefficient ( \(\gamma\) ) shows that high-income people are less likely to be frequent PT users. The estimated model partially answers the first research question (RQ1).

Factors impacting carsharing adoption

This section presents the model results for the model investigating the factors that impact the adoption of carsharing services, and partially answers RQ1. A binary choice and latent variable HCM was estimated to investigate the examined factors. For the subject model, the dependent variable was coded as a binary variable considering responses indicating that they never used carsharing as zero, with the rest of users being coded as 1.

Full path diagram for the binary HCM for carsharing adoption

Figure  5 and Table 5 present the full path diagram and the estimation results for the hybrid choice model for carsharing adoption. The estimated model shows that people familiar with carsharing services, with a driving license, who are full-time workers, owners of bikes, with a high-income level, and with a higher education level are more likely to adopt carsharing services compared to other population groups. These significant variables are aligned with the hypothesized profile of shared mobility users, who are in general, wealthier and more educated than the average population. On the other hand, people who have access to a car, live in a small household, and have a subscription to PT tickets are less likely to adopt carsharing services. The two latent variables, frequent shared micromobility users ( \(\lambda _1\) ) and adventurous personality ( \(\lambda _2\) ), were found to be significant predictors impacting the adoption of carsharing. This model shows that users with adventurous personality have a higher probability of adopting carsharing; such personality was previously (in previous studies) associated with a preference for higher levels of mobility, being outdoor, and disliking routine (Gao et al. 2017 ; Redmond 2000 ), which might be the utility provided by carsharing. The other latent variable shows that frequent micromobility users are more likely to adopt carsharing services in comparison to other population groups. This behavior was also observed in the adoption of other shared mobility services, such as in the case of pooled rides (Abouelela et al. 2022 ).

The lower part of Table 5 shows the structural equation model of the HCM. The estimation of the latent variable model for the personality part shows that the coefficients of the measurement model part ( \(\zeta\) ) is positive, which indicates that the higher the level of agreement with the personality statement questions, the more likely the person to be adventurous. Coefficients of the Structure model ( \(\gamma\) ) are positive, showing that each of males and bike owners (as opposed to females and non-bike owners) are more likely to be adventurous. The estimation of the second latent variable model shows that the coefficients of measurement models ( \(\zeta\) ) are positive, indicating that the higher the frequency of using bike-sharing and/or shared e-scooters, the higher the likelihood to be a frequent shared micromobility user. Finally, the ( \(\gamma\) ) coefficient for the Structure model part shows that users who are familiar with carsharing use are more likely to be users of shared micromobility, and car owners are more likely to use micromobility in comparison to other population groups, which matches the general profile of shared mobility users. For both latent models, we did not show the estimation results of the thresholds between the different indicators, as they have no meaning by themselves and only indicate the order of the thresholds.

Factors impacting the shift to carsharing

This model investigated factors impacting the shift from different modes to carsharing. We grouped the modes replaced by carsharing into two groups; the first one being the low-capacity vehicles groups (including cars as a driver, cars as passengers, E-hailing, and Taxis) and the second group being the PT group (with bus, tram, underground, and suburban trains). These observations amounted to 478 users who shifted from the previous specific modes, representing 93% of the total number of carsharing users (515 users). The rest of the observations (37) were removed from the sample used to estimate this model. The dependent variable of the model was coded as a binary variable with the value of one in the case of the shift taking place from a low capacity vehicle (cars as a driver, cars as passengers, E-hailing, and Taxis), the first group, and zero otherwise, similar to the approach adpoted by Abouelela et al. ( 2022 ). Table  6 shows the model estimation results, and Fig.  6 shows the model’s full path diagram.

Full path diagram for the binary HCM for shift to carsharing

The estimated model results show that high-income individuals, who are full-time employed, have access to a car, and are willing to walk less than 5 min to carsharing pick-up locations, are more likely to shift to carsharing from low-occupancy vehicles as compared to the rest of the population, which are in line with the profile of shared mobility users. Only one latent variable was significant in this model, namely the frequent PT users. The negative sign for the latent variable, LV1 ( \(\lambda\) ), showed that PT frequent users are less likely to shift from low-capacity vehicle trips to carsharing. Similar results were found in the case of pooled rides, where PT frequent users were less likely to adopt shared mobility in the form of pooled rides (Abouelela et al. 2022 ). The latent variable model shows that for the measurement model part, all the coefficients ( \(\zeta\) ) are positive, showing that the higher the use frequency, the more likely it is to be a frequent PT user, which is intuitive. The Structure model part shows that people who are familiar with carsharing services are more likely to be frequent PT users, and people who own bikes are more likely to use PT in comparison with those who do not own bikes. The estimated model answers the the remaining part of RQ1.

Factors impacting the choice between carsharing operators

This model targeted factors impacting the choice between the different operators with different payment schemes, namely payment per minute or payment per kilometer, which answers the second research question (RQ2). As shown in Fig.  2 , six options were available; certainly-A and probably-A, indifferent, probably-B, certainly-B, and “None”. Options certainly-A and probably-A were aggregated to A, the same aggregation was done for options B, the indifferent option was deleted, and option “None” was kept as the third option, following similar procedures to Abouelela et al. ( 2021 ), Fu et al. ( 2019 ), Vermeulen et al. ( 2008 ).

The indifferent options represented 9.3% of the total answers, and the choices of the remaining aggregated scenarios were distributed as 53.1% for option A, 33.6% for option B, and 4% for the none option. Our hypotheses for the model-building process were that males and people who have adventurous personalities might opt for operator B for its possibility to have cost savings; also, we believe that adventurous users would opt for option B as they were expected to drive faster for cost saving.

Full path diagram for the multinomial HCM for carsharing operator choice

Figure  7 shows the full path diagram and Tables  7 and  8 show the estimated model coefficients and parameters for the HCM of the payment schemes. The interpretation of the model results considers the “non-trip” option as the reference level for comparison with other options. The choice experiment tested the significance of four carsharing-related attributes on the choice between the payment schemes; trip cost, access distance, rating on the app, and vehicle engine type, electric or not. All the variables were significant except the access distance. The cost coefficient for option B (pay-per-minute option) was based on the average cost shown in the experiment, and the coefficient of the vehicle being electric or not was generic for both options. Interestingly, app rating was the variable with the highest absolute coefficient value for this group of variables.

The cost coefficient shows that users value the cost of paying per minute to be cheaper than paying per km; we believe that this is most likely due to the fact that there is a chance to pay a lower cost when choosing to pay per minute. Other factors show that app rating is more effective in the choice of option A, compared to option B. Six user characteristics were significant, showing that users with high-income levels, familiarity with carsharing services, valid driving licenses, and who have used carsharing before, were more likely to adopt carsharing compared to other population groups. On the other hand, people who live in small size households and who own bikes were less likely to choose car sharing in comparison to other groups. Finally, the two latent variables were only significant for option B, and they indicated that shared micromobility users were more likely to choose option B, and people who value the importance of the app were more likely to choose option B. We believe that the main reasons for this are that shared micromobility trips are paid per minute of use; besides, people who value the importance of the app in the service users are more likely to be used to the scheme of paying per minute, which was the original offer for all the shared vehicle services.

Table  8 shows the latent variable models. The first latent variable model, the importance of app-rating, can be interpreted as the coefficient ( \(\zeta\) ) for the measurement model being positive, showing that the higher the rating for the importance of app ease of use and the higher the rating on the app store, the more likely the person is to be in this user group. The structural part of the model shows that males and high-income individuals are less likely to be in this group in comparison with the rest of the population. In the second latent variable model, frequent shared micromobility users, the measurement model part coefficients ( \(\zeta\) ) shows that the more frequently shared micromobility used, the more likely to be in this group. The structural model part shows that male users are more likely to increase the use of shared micromobility in comparison to female users, which is usually observed in the case of shared mobility services.

It is important to highlight that our initial hypotheses were not significant and personality traits did not impact the choice for the payment scheme, and gender indirectly impacted the choice between payment schemes through the latent variable.

Discussion, limitations, and conclusions

In this research, we collected user and carsharing-related data to understand the impact of psychological factors including personality traits, travel behaviour, and attitudes on the knowledge about carsharing, its adoption, and use on the one hand, as well as examine the factors impacting the choice between different carsharing operators.The research was applied to a case study in Munich, Germany, focusing on young users. The collected data shows that carsharing users are young, highly educated males with high-income levels, with full-time jobs, living in small size households, and with a valid driving license, which is aligned with the general profile of shared mobility services and specifically carsharing users (Liao et al. 2020 ; Namazu et al. 2018 ). Obviously, the characteristics of carsharing users show the potential for inequitable use problems, wherein population groups, such as low-income and low-education groups, are not frequent carsharing users, which was evident in the collected sample, and revealed by the analysis process and the estimated models. Shared mobility needs a smartphone, digital banking options, and knowledge about the app use to use the service. Such conditions are not always available and add to the inequitable use situation that might result from other conditions, such as service unavailability within reach and service unaffordability (Abouelela et al. 2024 ). Digitalization therefore becomes a concern as it is often highlighted as a key enabler to sustainable development of cities (Balogun et al. 2020 ) in general, and to shared mobility in particular (Goehlich et al. 2020 ). Several strategies could help mitigate this, such as subsidizing the service and offering an alternative to digital access and digital banking options; however, these solutions do not always guarantee success. For example, in Chicago, IL, only 0.05% of shared e-scooter trips were made with non-digital banking options that were provided to help solve the inequitable use problem for shared e-scooter use (Abouelela et al. 2023 ). While providing alternatives to digital solutions might be plausible in the short-term, addressing concerns of digital literacy and access might be the only viable long-term solution, so that all population groups can have access to the service and its digital platform.

The collected data analysis showed that users and non-users have distinguished travel behavior with significant differences, which indicates the need for further investigation into how to adjust carsharing service operations to cater to the different travel behaviors and to attract non-users, if possible. Most of the users (40%) indicated that their last carsharing trip replaced PT (underground, suburban train), showing that there is a potential that carsharing might increase the VKT, as it replaces large occupancy vehicles (PT). On the other hand, 35% of users reported carsharing as a replacement for low-occupancy vehicles, including private cars as passengers or drivers and e-hailing, which may reduce the total VKT. The latter could have positive impacts such as reducing energy consumption and resulting \(CO_2\) emissions, and required parking spaces (6t-Bureau de recherche and ADEME 2016 ; Baptista et al. 2014 ). More information is required, including the access and egress modes, and the vehicle capacity and occupancy, to better quantify the impacts of carsharing on the VKT; which was not investigated, as it was not the focus of this research.

The responses to the questions regarding familiarity with carsharing services show that there is a proportional relation between carsharing use and knowledge about the service, indicating that to increase the use of such services, more marketing and reach-out plans should be conducted by providers to increase people’s knowledge and awareness regarding the service, mainly to target non-users.

The EFA was conducted on the three main question groups (service aspect rating, personality traits, and travel behavior), and each of these groups showed two factors. The first question group related to the carsharing service’s important aspects showed two factors: (I) the app-related attributes and (II) physical offers. These estimated factors show the importance of the app-related attributes, which were not examined in previous research, up to the best of our knowledge, and which need more investigation to reach the recommended design by users, as it has a role in impacting service use, as shown in the estimated models. App-related attributes were significant in the preference of paying per minute, but physical attributes were not significant in any of the estimated models, confirming the importance of the app-related aspects of the service.

The second question group is the personality trait group, which showed two distinctive personality traits, (III) an adventurous personality and (IV) an organized personality. Our hypothesis was that an adventurous personality would be more likely to use carsharing services than other types of personality due to the higher levels of mobility and independence provided by carsharing, which fits the characteristics of the adventurous personality (Redmond 2000 ). The estimated model showed the significance of the adventurous personality in adopting carsharing services and the higher level of knowledge regarding the service.

For the last question group, travel behavior, two estimated attitudes were related to travel behavior; (V) PT frequent user and (VI) shared micromobility user. Both factors indicate a distinguished travel pattern that shapes the adoption and use of carsharing services. Shared micromobility users are likelier to adopt the service and prefer to pay per minute of use, while frequent PT users are less likely to shift from low-capacity vehicles to carsharing. The impacts of the travel behavior latent construct on the use of shared mobility use were evident in the case of pooled rides (Abouelela et al. 2022 ), showing the importance of accounting for the different travel preferences when planning new services or even integrating them with current services such as PT, and other shared services that could increase the potential of multimodality.

Frequent shared micromobility users, in this case, shared e-scooter and bikesharing, are more likely to adopt other shared mobility services, which highlights the question of the impacts of shared mobility frequent use on Mobility as a Service (MaaS) platforms adoption or would the availability of all the shared service within one platform increase the use of these services, and increase the possibilities of multimodality, which could be a sustainable outcome. Multimodality is one of the expected positive potential outcome of MaaS, and subsequently increasing the sustainability of the transport system (Ho and Tirachini 2024 ). It is also to be noticed that carsharing service plays a significant role in MaaS use and utilization, which was observed in the aces of the Augsburg, Germany MaaS trial, where customers of the Maas bundle utilized their full carsharing allowance and subsequently increase their carsharing use showing the pivotal role for carsharing in MaaS use and utilization (Reck et al. 2021 ). Also, Keller et al. ( 2018 ) observed that carsharing user have higher intention to use MaaS platforms then the rest of the population.

The estimated models showed that sociodemographics attributes, knowledge about carsharing, and personal attitudes and personality traits play significant roles in carsharing use. The estimated model showed that the attributes that increase the probability of carsharing service adoption are: high familiarity with carsharing service, having a valid driving license, full-time employment, a high education level, high-income level, owning a bike, having an adventurous personality, and being a frequent micromobility user. The results of this model are in line with the general profile of shared mobility users (Le Vine and Polak 2019 ; Martin and Shaheen 2011a ; Alemi et al. 2018 ; Ahmed et al. 2021 ; Luo et al. 2019 ). It is to be noted that the variable with the highest estimated coefficient is familiarity with carsharing services, followed by the availability of a driving license and the (high) level of education. It is clear that knowledge about the service is very important in impacting its adoption, which highlights the role of marketing in service use. Also, shared mobility users are more likely to use such services in different forms. On the other hand, users who have access to a car, users with PT subscription-based tickets, and living in small size households are more likely not to use the service, showing that there is a need to investigate the potential of integrating carsharing services in the PT subscription to increase the service use.

Again, sociodemographic characteristics and attitudes play a significant factor in the shift from different modes to carsharing, where high-income people who are full-time employed, willing to walk for a short period (less than 5 min) and have access to a car have a higher likelihood to shift from low occupancy vehicles to carsharing, while PT frequent users are less likely to do so. This model also shows the significance of sociodemographics and travel behavior in replacing different modes with carsharing services, and it is also in line with the profile of shared mobility users.

When looking at factors impacting the choice between operators with different payment schemes, trip cost, rating on the app store, and availability of electric vehicles were found to be quite significant. App rating was the coefficient with the highest reported value, showing its importance in the choice between different payment schemes. Also, people perceive the payment per minute as cheaper than the payment per km, which is an interesting result showing the preference of users for the payment scheme per minute (the oldest, more common scheme for carsharing payment) over the payment per km with all the other factors being constant. Also, sociodemographics are crucial in choosing between operators, such as high income, driving license, familiarity, and previous use of carsharing services. On the other hand, having a bike and living in a small size household reduce the likelihood of carsharing use. The highest estimated coefficient in this model related to user characteristics is the previous use of carsharing, showing that people who have experience with the service are more likely to choose to pay per minute if all other factors are kept constant. Attitudes also played a significant role, wherein respondents who valued the importance of the app and shared micromobility frequent users are more likely to use the service and choose to pay per minute of use. These findings highlight the preference for the payment per minute and could be used by operators to increase their demand by focusing on app development and rating.

The answer to the final research question regarding the knowledge about carsharing services emphasized again the importance of sociodemographics and attitudes on the level of knowledge; in particular, previous use of carsharing, availability of a driving license, living in small size households, and full-time employees were more likely to have a higher level of knowledge regarding carsharing service. Service adoption and knowledge about the service were found to be significant in increasing the probability of each other, showing the need to advertise the service to attract more users and to focus on the other social groups that do not have enough knowledge regarding the service and subsequently who do not adopt it. Also, frequent PT users and people with adventurous personalities were more likely to have a higher knowledge regarding the service. Two highlights from these findings are that frequent PT user knowledge about the service should be coupled with encouraging carsharing use as a first-last mile solution that could increase multimodality.

Study limitations and future research needs

This research tries to update the current knowledge regarding carsharing services, using a mix of revealed answer questions and a stated preference experiment. The study comes with limitations, which we believe do not impact the overall research integrity. The main objectives of appraising the limitations are to have a transparent outcome and to help similar studies avoid or consider them in the future. The collected sample was balanced in terms of users versus non-users of carsharing services and in gender, but it was unbalanced for other sociodemographic characteristics, such as income level and education level. On the other hand, shared mobility users are likely to be young and highly educated compared to the average population, which makes the sample acceptable for the purpose of the study, and the sample was not representative of the city’s population. The findings should not be directly interpolated or carried out on other social groups. Different attitudes were examined, along with their impacts on the different aspects of carsharing use, but it is important to be noted that attitude and personality traits are hard to quantify and measure. They are essential to understand user preferences for the different aspects of shared mobility use, and they might be more significant and influential in deciding travel behavior in general and shared mobility use.

The used stated preference experience examined only a number of attitudes, travel cost, app rating, electrification of the vehicle, and access distance to the nearest vehicle; other attributes could have been used as well, but this was done on purpose, not to overload the respondents with information that might distract their attention, and to have a simpler experience. The stated preference experiment assumed that the payment by KM is a fixed cost, although this can slightly change in reality, such as in the case of congestion; users could alternate from the original route, the shortest path, causing extra travel distance that would increase the trip cost. The variation of the travel cost ( \(\pm 25 \%\) ) around the average trip value would cover this possibility. The survey was deployed online, which can create a response bias, as groups with no access to the internet and older populations might not be represented in the sample, but the well established shared mobility user profile shows that they are mostly young and highly educated individuals with access to the internet. The hybrid choice models are not the only way to implement attitudes into discrete models, but we believe that in this research, they fit the required methodology to answer the main research questions. The personality traits that were estimated via EFA were what the people report, their self-perception on their own personality, but might not be how they are if they had done real psychometric tests. Finally, the assessment of the impact of modal shift (to carsharing) on VKT was not conclusive (see “ Discussion ” section), as in most cases carsharing trips replaced PT (likely increasing VKT), at the same time they also often replaced small occupancy vehicles such as cars (possibly reducing thereby VKT). To further investigate this and better quantify the impact, more information would be needed regarding the trips replaced, such as trip distance, vehicle occupancy, and the modes used to access and egress the carsharing services. To project the findings on a larger scale, additional travel behavior data would be essential, so that the modal shift analysis does not only rely on the last trip made, but rather go beyond it to take into account a longer time frame which would encompass the frequency at which such modal shift would occur. As the above was not part of this study, a further in-depth exploration for the VKT analysis is recommended for future research.

As currently carsharing only accounts for a small portion of the total modal share compared to private cars, the magnitude of its impacts is limited (Migliore et al. 2020 ). Future research could also focus on how extending the service coverage areas, fleet size, and ideally electrifying the fleet could help cities reap the optimum benefits of carsharing (Migliore et al. 2020 ; Harris et al. 2021 ; Ye et al. 2021 ).

It is important to highlight that the survey data was collected during the last waves of the COVID-19 pandemic, and it should be noted that the pandemic conditions inevitably impacted carsharing use and safety perception on different levels. Previous studies on the pandemic impact on carsharing use has been inconclusive, for instance, in Madrid, Spain, carsharing has been perceived by some users as a means to avoid public transport (and therefore as a safer mode), while for others less so, as they replaced it with walking and biking (Alonso-Almeida 2022 ). A study in Poland showed other findings, in which the pandemic was not a challenge for carsharing users, as it did not hinder their overall use (Gorzelańczyk et al. 2022 ).

Conclusions

This research investigated the impacts of personality traits and attitudes on the different aspects of carsharing use: adoption, the shift from other modes, the choice between different operators, and finally, the knowledge about the carsharing services. A large sample (N = 1044) of young user data was used in the analysis collected from Munich, Germany. The results continue to highlight the importance of the user sociodemographic characteristics in impacting service use and raise questions regarding inequitable service use and adoption. The findings of the estimated econometric models also show the significance of personality traits, travel behavior, and digital service aspects (such as app ease of use and rating on the app store) on carsharing use. These findings also stress the importance of designing user-friendly apps and maintaining good ratings, which can attract more users. Findings also showed that frequent shared mobility users adopt shared mobility in different forms of the service, showing the potential of MaaS in increasing shared mobility use and increasing the potential of multimodality. Finally, the estimated models could be used as a part of broader travel demand models that could estimate the adoption of carsharing and which might be used to quantify the share of the operators based on their payment methods.

ShareNow is a merger company between Car2Go the carsharing subsidiary of BMW, and the DriveNow, the subsidiary of Mercedes-Benz Group ( https://share-now.com ).

https://free-now.com , now the service is a joint venture between Mercedes-Benz Group and BMW.

https://info.ridewithvia.com .

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https://moovelus.com , the platform is one of the Mobility as a Service (MaaS) providers.

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The authors would like to thank Dr.-Ing. Benjamin Büttner, the head of the EIT Urban Mobility “Doctoral Training Network”, and the DTN for their support. This study was partially funded by European Union’s Horizon Europe research and innovation program under grant agreement No 101076963 [project PHOEBE (Predictive Approaches for Safer Urban Environment)].

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Exploring gen z’s identity formation and its influence on consumption of pop culture and entertainment merchandise.

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Fandoms have grown in size and influence, becoming a complex and dynamic cultural phenomenon that richly impacts the consumption of media and consumer behavior. The capitalistic United States with its diverse and media-savvy population, is a prime destination to study fan studies, particularly among young adults who are a crucial component of fandom influence.

The internet has grown to be a major component of the fan experience, an integral part of fandom culture. Online communities provide avenues for Generation Z to connect with others who share similar struggles, challenges, and interests, highlighting the importance of digital platofrms on the evolution of fandoms.

This qualitative methods study employs Interpretative Phenomenological Analysis (IPA) phenomenology to explore how Generation Z individuals are socialized through their participation in pop culture and entertainment fandoms and how this socialization influences their purchase behavior of related merchandise. Recognizing the substantial purchasing power and cultural influence of Gen Z, this research aims to fill the gap in understanding the dynamics between fandom participation and consumer behavior among this demographic. Little research has been conducted on the overlap of merchandising research and fan studies, including the relationships of variables of fandom merchandise, socialization process, identity formation, and quality online interactions.

This study consisted of six in-depth in-person interviews with Gen Z college student participants. Based on the results of the literature review, 22 questions were developed to ask participants concerning fandoms and their experiences. This study addresses which elements of socialization young Gen Z adults encourage consumption of pop culture and entertainment merchandise.

The qualitative results indicate that fandom members are socialized to purchase pop culture and entertainment merchandise due to their benefits of identity and personal growth, a strong sense of community and belonging, timely emotional engagement ties, a sense of nostalgia and continuity, the transformative and rich experiences, and the considerable budgets spent on their fandom interests. Thus, individuals sought socialization mainly from friends concerning fandom support. The findings of this study demonstrate friends similar in age tend to be the largest socialization factor. Another key finding is the influence of effective and innovative marketing strategies to target Gen Z’s demographic.

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Sustainable consumption practices among Chinese youth

• Yingxiu Hong 1 , 2 ,
• Abdullah Al Mamun   ORCID: orcid.org/0000-0002-9713-742X 3 ,
• Mohammad Masukujjaman   ORCID: orcid.org/0000-0001-9281-6530 4 &
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A global crisis caused by unsustainable practices and their detrimental effects on civilization has compelled us to rethink the adoption of green consumption practices. This study aims to determine the determinants of green consumption behavior among Chinese youth. Based on the integrated frameworks of two renowned theories, the knowledge-attitude-practice model and the theory of planned behavior, this study proposes 11 hypotheses for empirical investigation. An online survey was used for a quantitative cross-sectional study with a convenience sample of young Chinese consumers. A dataset comprising 876 observations was examined by applying partial least squares structural equation modeling using Smart-PLS 4.0. The results show that green consumption intention and perceived behavioral control significantly affect green consumption behavior. Attitudes towards eco-social benefits, attitudes towards green consumption, and subjective norms positively affect green consumption intentions. In addition, environmental concern and knowledge positively affected all three attitude dimensions. The study further identified that green self-identity moderates the connection between green consumption intention and green consumption behavior, whereas ecolabelling is not a significant moderator of the same relationship. The theoretical contribution of this study is that it employs multigroup analysis to investigate gender- and income-based variations in the relationships, offering a comprehensive framework integrating theories along with dimensions of attitude, which offers nuanced insights into green consumption behavior. Regarding its policy implications, this study highlights the necessity of promoting environmental concern and knowledge through education and awareness campaigns while supporting eco-social benefits and green consumption practices, which can be a potent way to promote positive attitudes, intentions, and sustainable behaviors among the general population.

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Introduction.

In recent years, concerns about climate change, environmental degradation, and pollution have gained global prominence. Threats like global warming, earthquakes, droughts, and storms are endangering human security. Ecological and environmental problems are now seen as security challenges, leading to a heightened public consciousness about the need to address ecological issues (Becerra et al., 2023 ). This increased awareness has coincided with the pressing challenge of finding solutions to environmental problems, including global warming, which has led to a greater focus on “unsustainable development.” The growing awareness and concern surrounding environmental issues have given rise to the concept of “green consumption” as a viable alternative approach to consumption.

One potential solution is sustainable consumption, which constitutes the twelfth goal of the UN’s 2030 Agenda (Calculli et al., 2021 ). Sustainable consumption practices have the potential to mitigate poverty, facilitate the transition towards a low-carbon green economy, and foster the advancement of sustainable development, all of which play a vital role in mitigating the effects of global warming (Suki, 2019 ). It encompasses green purchasing, the acquisition and use of commodities without adverse impact on the ecology, and responsible disposal (Wu et al., 2016 ). Eco-friendly products are intentionally designed to minimize their ecological impact by utilizing renewable and recyclable materials to reduce waste and pollution (Beatson et al., 2020 ). In addition, housing green products actively support companies committed to sustainable and environmentally friendly practices throughout their production and supply chains, helping to reduce the expanding carbon footprint and contribute to a more sustainable future (Kamalanon et al., 2022 ). Achieving sustainable development requires individuals to engage in environmentally friendly consumption practices, and many people view green consumption as an essential aspect of ethical consumption (Carrington et al., 2010 ).

Consumer interest in green products, a global trend (Kautish & Sharma, 2020 ), has been steadily increasing. Green consumerism encourages individuals to acquire and utilize cleaner and non-polluting items while considering the well-being of both human beings and the natural environment. This behavior is widely recognized as an environmentally friendly practice. Numerous companies are now producing environmentally friendly goods, and consumers are increasingly inclined to opt for eco-friendly products (Nam et al., 2017 ). Although consumers exhibit growing concerns and positive perceptions, attitudes, and intentions toward ecological preservation, the market share of green purchasing behavior and pro-environmental actions remains relatively limited (Moser, 2015 ). Despite concerns about environmental issues, customers often hesitate to make substantial changes to their regular consumption habits (Bigliardi & Filippelli, 2022 ; Perri et al., 2020 ). The younger generation has not translated into green consumption habits because of purchasing power limits, and the market share of actual purchases of eco-friendly products still hovers around 1–3% of the total market (Bray et al., 2011 ). Consequently, there is an urgent need for further research to better understand the predictors behind this limited adoption despite evident consumer interest.

Previous studies have indicated that young customers, particularly Generation Y, show a preference for purchasing environmentally friendly products (Yadav & Pathak, 2017 ). Generation Y, often referred to as those born between 1980 and 2000, aged 18 to 40, is characterized by their technological agility, self-awareness, and environmental concerns. To develop effective green marketing policies and strategies tailored to Gen Y, it is essential to understand the factors that influence this consumer segment (Hassan et al., 2019 ). Generation Y is characterized by its diverse ethnic and racial backgrounds, and its adoption of green products reflects a commitment to sustainable living. They are proactive in taking actions that positively impact the ecology of a future characterized by peace and express genuine concerns about environmental issues, actively contributing to reducing the eco-footprint (Zsoka et al., 2013 ). The younger generation demonstrates support for sustainable practices and environmental advocacy by utilizing tools, such as green budgeting, to achieve environmental goals. They also expect policymakers to foster green innovation to sustain their lifestyles and promote a consolidated future. In addition, Generation Y values companies that provide products and services in accordance with ethical and environmental guidelines. They prioritize socially responsible organizations and are inclined to support those who align their policies with green sustainability and ecological standards, ultimately improving society’s health. In the post-pandemic age, Generation Y is more concerned with preserving futurism, environmentalism, and sustainability, and they are more likely to purchase green food. Therefore, gaining insights into Generation Y’s green behavior is vital for policymakers, especially when considering factors that influence this target group’s embracing of sustainable living in emerging economies such as China. Therefore, the research questions were as follows:

RQ1. What are the predictors of green consumption behavior among Chinese Generation Y consumers?

While the theory of planned behavior (TPB) has demonstrated its ability to predict factors influencing green consumption behavior (GCB) in several studies (Alam et al., 2023 ; Ogiemwonyi, 2022 ; Joshi, Rahman, 2015 ), it is not without limitations, despite its predictive capabilities (Joshi, Rahman, 2015 ). Some of these limitations include the lack of detailed information on how individual decisions are made and the absence of insights into how once-made decisions influence behavior (Ajzen, 1991 ). The TPB has shown improved predictability when combined with other theories, such as the theory of reasoned action, which enhances the understanding of various additional factors related to GCB (Procter et al., 2019 ). Nonetheless, specific research endeavors have encountered challenges in elucidating GCB through the TPB and the theory of reasoned action, specifically in relation to aspects such as attitudes, perceived behavioral control, and social norms. This highlights a tenuous connection within the context of GCB, as evidenced by prior studies (Jaiswal and Kant, 2018 ; Joshi, Rahman, 2015 ). In this regard, the knowledge, attitude, and practice (KAP) model holds significant relevance for assessing knowledge gaps and behavioral trends, aiding in the identification of requirements, challenges, and obstacles essential for planning and executing interventions, with knowledge as a fundamental construct (Shari et al., 2022 ). Thus, there exists a gap in the current literature, as the integration of the KAP-TPB for assessing GCB, especially from the perspective of the younger generation, remains underutilized and warrants further exploration. In addition, researchers like Cheung and To ( 2019 ) used categorized attitudes (such as attitudes towards environmental issues and attitudes toward eco-social benefits) to have a comprehensive understanding of green consumption. Categorizing attitudes, such as attitudes towards environmental issues and attitudes towards eco-social benefits, within the knowledge, attitude, and practice (KAP) theory will allow for a more nuanced understanding of how knowledge influences behavior. By breaking down attitudes into specific categories, researchers can better identify the factors driving certain behaviors, enabling targeted interventions and policies to promote environmentally friendly practices effectively.

Furthermore, in addition to the attitude-intention and intention-behavior gaps, commonly known as the green gap, these issues represent inherited challenges in the TPB model. This phenomenon suggests that individuals may express attitudes or intentions but do not always act in alignment with those intentions because of various factors, including external barriers, social influences, and situational constraints. Understanding and addressing these barriers is crucial for bridging the gap between intention and behavior. Previous research by scholars such as Park and Kwon ( 2017 ) and Wiederhold and Martinez ( 2018 ) has examined green gaps and proposed constructs like environmental concern, ethical consumption behavior, and environmental knowledge. However, they overlooked the role of green self-identity (also known as the green emotion) and eco-labeling, leaving gaps in our understanding. An individual’s overarching green identity significantly impacts their ecological actions, making it crucial to address this aspect to effectively stimulate such behavior. Studies such as Qasim et al. ( 2019 ) and Neves and Oliveira ( 2021 ) employed environmental self-identity as a moderator between various factors and behavioral intent. Cheung and To ( 2019 ) examined green product quality, and Li et al. ( 2021 ) employed green self-identity and ecological knowledge as moderating variables in the relationship between attitude and behavior. Similarly, an eco-label is a certification or logo placed on products to inform consumers about the environmental attributes of the product (Delmas & Lessem, 2015 ), which play a crucial role in promoting sustainable consumption (Fretes et al., 2021 ). Various research used eco-labeling as a direct construct influencing buying intention (Alam et al., 2023 ; Panopoulos et al., 2022 ; Chi, 2021 ) realizing the essence of green consumption. Eco-labels foster awareness, signal trust, boosting green consumption, and aligning actual buying. However, to the best of our knowledge, none of these investigations have evaluated green self-identity and eco-labeling as a moderating factor between intention and behavior to address the limitations of the TPB. Hence, there is a compelling need to investigate these factors empirically.

To bridge the existing gaps in the literature, this study examines the predictors of green consumption behavior among young adults in China. The study also assesses the moderating role of eco-labeling and green self-identity within green consumption intention and green consumption behavior. This study makes a two-fold contribution by introducing a novel framework and new variables. Specifically, it aims to identify the essential factors influencing the GCB of Chinese youth by integrating the KAP-TPB model. In addition to conventional variables, this study incorporates novel constructs, such as eco-labeling, environmental concerns, green self-identity, and environmental knowledge. eco-labeling and green self-identity were proposed as moderating variables between intention and GCB to augment our comprehension of the topic and address existing knowledge gaps. Furthermore, this study explores a three-dimensional perspective of the attitudes influencing green consumption intention (GCI) to provide a comprehensive understanding.

Literature review

Theoretical underpinning.

The TPB, initially proposed by Ajzen in 1985 (Ajzen, 2002 ), has its roots in the theory of reasoned action. The fundamental model posits that attitude, perceived behavioral control, and social norms collectively influence behavioral intentions, consequently shaping actual behavior. Ajzen, in his work on the TPB, introduced additional constructs tailored to specific research inquiries, thereby refining and expanding the TPB framework by optimizing model structures and variable pathways (Ajzen, 1991 ). The TPB has been extensively examined in multiple studies that have focused on evaluating attitudes and intentions. The TPB plays a crucial role in predicting and exerting influence on behavior (Ajzen, 1991 ; Yang et al., 2022 ).

Nevertheless, the constructs of attitude, normative beliefs, and perceived behavioral control of the TPB have been found to be insufficient for fully elucidating the factors that influence an individual’s intention to adopt. Hence, a multitude of scholarly research has advocated the integration of TPB with other theoretical frameworks or models to holistically examine the determinants of an individual’s intention (Shin et al., 2018 ; Kim and Hwang, 2020 ). Paek et al. ( 2018 ) and Liu et al. ( 2016 ) proposed a combination of the TPB and KAP frameworks.

Scholars have incorporated new elements rooted in the TPB to investigate the relationships between various variables in different research contexts. For instance, regarding the prediction of young consumers’ willingness to buy eco-friendly products in a developing economy, Yadav and Pathak ( 2017 ) identified that environmental concerns and knowledge have a significant influence. Their study emphasized the substantial influence of these facilitators in altering the behavioral intentions of young customers. Similarly, Chen and Tung ( 2014 ) commented that individuals with a heightened sense of ecological concern were inclined to select green hotels even if they paid a premium for eco-friendly practices. Based on this analysis, researchers have expanded the basic variables of the TPB to incorporate environmental concerns and knowledge. However, the applicability of these additions to diverse research subjects remains unclear. The inclusion of environmental concern and knowledge variables in investigations concerning residents’ willingness to engage in GCB may offer a more in-depth understanding of the multitude of factors affecting their intent to purchase environmentally friendly products.

The KAP model extensively examines individuals’ knowledge, attitudes, and behaviors regarding a particular subject. The KAP model, which was initially developed in the 1970s within the domain of family planning and population research (Lin & Hingson, 1974 ), has since gained substantial popularity and widespread acceptance across various research domains, such as health behavior (Schlüter et al., 2020 ), food control industries (Kwol et al., 2020 ), media literacy (Olson and Scharrer, 2018 ), disease control initiatives like COVID-19 (Reuben et al., 2021 ), the measurement of government program effectiveness (Xu et al., 2021 ), technological acceptance (Aydin, 2019 ), and environmental concerns (Pan and Pan, 2020 ). Consequently, the KAP Model is highly pertinent for evaluating knowledge gaps and behavioral trends that can help identify requirements, challenges, and obstacles and facilitate the planning and execution of interventions (Shari et al., 2022 ).

The KAP model comprises three core components: knowledge, attitude, and practice. Knowledge, the first pillar, is a fundamental tool through which individuals make sense of themselves and the world around them (Ahmad et al., 2020 ). Knowledge is a vital asset for companies, and when managers possess comprehensive knowledge of green innovation, it not only minimizes unexpected surprises but also bolsters the credibility of any proposed changes (Ahmad et al., 2020 ). The next element in the KAP model is attitude. Allport ( 1935 ) referred to attitude as “a mental and neural state of readiness, which exerts a directing influence upon the individual’s response to all objects and situations to which it is related.” Practice, the final component of the KAP model, is influenced by beliefs and attitudes as elucidated by the theory of reasoned action. Actions and behaviors are shaped by personal norms and acquired or inherited attitudes (Ahmad et al., 2020 ).

The present study defines knowledge as the comprehension of environmental information acquired through personal experiences or formal education. This study aimed to incorporate both environmental knowledge and environmental concern as integral components, replacing the sole focus on knowledge as a predictor of attitude. Attitude, in this context, pertains to an individual’s sentiments or opinions concerning ecological issues, eco-social well-being, and eco-friendly products. We expanded this concept into three dimensions: attitudes towards environmental issues, attitudes towards eco-social benefits, and attitudes towards green consumption, establishing new connections with intentions related to green consumption. In our investigation, practice signified the execution or application of labeling within an environmental context. We anticipate that knowledge of environmental facts will directly and positively influence attitudes and intentions, which will lead toward actions or behaviors.

Hypothesis development

Determinants of attitudes.

Attitudes toward environmental issues refer to an individual’s overall disposition, beliefs, and behavioral tendencies related to environmental concerns and sustainability (Cheung & To, 2019 ). Environmental concerns contribute positively to environmental preservation. There is a positive correlation between an individual’s level of environmental respect and their intention to engage in green consumption, influencing their sustainable behavior (McCright et al., 2014 ). An individual’s attitude towards environmental issues encompasses their awareness, engagement, consumer choices, eco-friendly practices, and recognition of the actions’ ecological consequences (Ritter et al., 2015 ). This reflects their perspectives and commitments to addressing environmental challenges and promoting sustainability. Environmental concern represents an individual’s awareness of the decline in ecological conditions. This reinforces their willingness to engage in behaviors that benefit society and the environment. People prioritize environmental preservation when they have heightened ecological awareness (Ritter et al., 2015 ). This heightened concern can significantly influence one’s stance on environmental matters (Cheung & To, 2019 ). Therefore, we formulate the following hypothesis:

H 1a : Environmental concern is positively associated with one’s attitude towards environmental issues .

Similarly, attitudes towards eco-social benefits refer to an individual’s overall disposition, beliefs, and perceptions of the positive social and ecological impacts associated with purchasing and using environmentally friendly or “green” products (Ritter et al., 2015 ). This attitude includes recognizing one’s role in contributing to the greater good, aligning choices with moral values, supporting sustainable businesses, considering one’s social image, and being aware of the positive environmental impact associated with green consumption (Cheung & To, 2019 ).

Moreover, environmental concerns can be powerful drivers of public support for environmental policies and initiatives. Combined with a positive attitude towards eco-social benefits, this can lead to increased demand for sustainable products and practices, influencing market trends and corporate behavior. Teng et al. ( 2014 ) proposed that people with strong environmental concerns tend to address cognitive dissonance by minimizing or resolving discrepancies in the benefits associated with social and environmental issues. Cheung and To ( 2019 ) found that ecological awareness has a notable impact on attitudes toward environmental issues and attitudes toward eco-social benefits concerning green purchasing behavior within the Chinese context. Thus, we formulate the following hypotheses:

H 1b : Environmental concern is positively associated with attitude towards eco-social benefit .

Similarly, an individual’s attitude towards green consumption encompasses their preference for green products, recognition of the environmental necessity of green consumption, favorable perception of green products, endorsement of green consumption as a good idea, and the belief that it represents a wise decision for personal and collective benefits (Alganad et al., 2023 ; Al Mamun et al., 2018 ). This attitude reflects their overall stance and mindset towards incorporating eco-friendly products into their consumption patterns. In addition, environmental concern provides the emotional and moral basis for caring about ecology, while a positive attitude towards green consumption translates these values into specific eco-conscious choices in consumption and lifestyle. These two aspects are complementary. Scholars have indicated that when individuals are equipped with environmental knowledge and awareness, they tend to exhibit favorable attitudes toward environmentally friendly products (Jaiswal and Kant, 2018 ; Yadav and Pathak, 2017 ; Suki, 2016 ). Thus, we propose:

H 1c : Environmental concern is positively associated with attitude towards green consumption .

Environmental knowledge incorporates an individual’s understanding of environmental issues, expertise, and related concerns (Li et al., 2021 ). The acquisition of environmental knowledge enhances individuals’ awareness of ecological preservation, consequently influencing their consumer attitudes and engagement in pro-environmental purchasing behaviors. The knowledge-attitude-behavior paradigm posits that environmental knowledge has the potential to elicit pro-environmental behaviors (Fabrigar et al., 2006 ). Individuals possessing substantial ecological knowledge typically exhibit favorable attitudes towards environmentally responsible behaviors and are more inclined to take proactive steps. Environmental knowledge can play a significant role in forming an individual’s attitude toward environmental issues. As people become more informed about environmental problems, their awareness of the importance of addressing them increases (Taufique et al., 2017 ). They may develop a deeper concern for the ecology and a stronger commitment to ecological protection. When people comprehend the potential negative impacts of ecological degradation on ecosystems, wildlife, human health, and the planet, they are more likely to develop serious and concerning attitudes. Previous studies (Hossian et al., 2022 ; Dhir et al., 2021 ; Cheung and To, 2019 ) have explored the positive link between environmental knowledge and attitudes toward environmental issues in different contexts. Thus, we formulate the following hypotheses:

H 2a : Environmental knowledge is positively associated with attitude towards environmental issues .

Environmental knowledge is a powerful catalyst for fostering positive attitudes towards social benefits. This provides a foundation for understanding the importance of environmentally responsible actions and their broad societal and ecological implications. As individuals become more informed, they are more likely to embrace and support initiatives that contribute to the well-being of both the ecology and society. When individuals demonstrate environmental concern, they exhibit a greater inclination towards buying environmentally friendly products, contributing not only to environmental preservation but also to the growth of the green market. Environmental concerns can motivate individuals to maximize eco-social benefits, as observed by Cheung and To ( 2019 ). For instance, an individual may choose to purchase an energy-efficient refrigerator to minimize natural resource consumption. Liu and Dong ( 2021 ) proposed that eco-friendly consumers derive greater psychological benefits from buying green products. Consequently, we formulated the following hypothesis:

H 2b : Environmental knowledge is positively associated with attitude towards eco-social benefit .

Individuals with a specific level of environmental knowledge tend to exhibit favorable attitudes toward environmental behavior and are more motivated to take proactive measures. Flamm ( 2009 ) found that households with greater environmental knowledge were more inclined to invest in energy-efficient vehicles. Several scholars have also demonstrated that environmental knowledge positively affects consumer attitudes toward eco-friendly products (Mostafa, 2009 ). Latif et al. indicated that environmental knowledge affects residents’ attitudes toward purchasing green products, subsequently affecting their willingness to buy such products (Sang and Bekhet, 2015 ). Individuals with a positive environmental knowledge base will likely develop favorable attitudes toward ecological buying and participate in responsible purchasing and consumption (Sultana et al., 2022 ; Suki, 2016 ). Consumers’ levels of environmental knowledge have been shown to significantly shape their attitudes toward green product purchases (Nguyen and Tran, 2021 ; Zhang et al., 2021 ; Nekmahmud and Fekete-Farkas, 2021 ). The proposed hypotheses are as follows.

H 2c : Environmental knowledge is positively associated with attitudes towards green consumption .

Determinants of green consumption intention

The GCI encompasses a multifaceted commitment to environmentally responsible consumption behavior. GCI includes the willingness to invest in green products, endure the inconvenience of eco-friendly choices, reduce non-green consumption, prioritize environmentally friendly products, and actively advocate green consumption practices among peers (Walton & Austin, 2011 ). Research has identified a positive association between attitude and GCI (Duong, 2023 ; Aisyah and Shihab, 2023 ). Various studies have indicated that individuals with favorable attitudes toward green products are more inclined to engage in environmentally responsible purchasing behaviors (Zhuo et al., 2022 ). People with a positive attitude towards environmental issues are more likely to develop a solid intention to engage in green consumption. Likewise, a favorable attitude towards eco-social benefits is closely linked to GCI. In addition, individuals who believe that their green consumption choices benefit society and the environment are more likely to express a solid intention to engage in such behaviors. Naturally, when people have a favorable view of green products and behaviors, they are inclined to express a solid intention to incorporate these practices into their lifestyle. Multiple studies have documented that attitudes significantly impact consumers’ purchase intentions concerning energy-saving equipment (Tan et al., 2017 ; Yadav & Pathak, 2017 ; Nguyen & Lobo et al, 2017 ). Individuals with more positive views of the environment and heightened concern for environmental issues are inclined to demonstrate a greater propensity to purchase environmentally friendly products (Kotchen and Reiling, 2000 ). (Lin and Huang 2012 ) showed that attitudes towards environmental issues significantly impact green development.

H 3 : Attitude towards environmental issues is positively associated with GCI

H 4 : Attitude towards eco-social benefit is positively associated with GCI

H 5 : Attitude towards green consumption is positively associated with GCI

Perceived behavioral control refers to an individual’s perception of the simplicity or complexity associated with carrying out a particular behavior, and TPB posits that perceived behavioral control is a variable that can be used to predict behavior (Ajzen, 1991 ). Perceived behavioral control encompasses the time, financial means, and abilities necessary to engage in a particular behavior (Ajzen, 1985, 1991 ). This variable positively influences environmental behavior (Wu et al., 2016 ). Research has consistently shown that perceived behavioral control significantly influences green purchasing decisions (Paul et al., 2016 ; Chen and Tung, 2014 ). Ajzen ( 1991 ) contended that perceived behavioral control is often more critical than actual behavior. However, many studies conclude that the perceived inconvenience in carrying out a particular behavior can negatively affect the intention to purchase green products (Barbarossa and De Pelsmacker, 2016 ). Wang and Zhang ( 2020 ) contended that people’s perceived behavioral control of environmental actions positively affects their ecological behavior. Therefore, we formulate the following hypotheses:

H 6 : Perceived behavioral control is positively associated with GCI .

Subjective norms are behavioral norms that people adopt because they are influenced by family members, friends, or educators. It refers to the perceived social pressure an individual feels to be involved in specific actions or behaviors. McClelland ( 1987 ) introduced the theory of needs, suggesting that people are naturally inclined to adopt behaviors endorsed by their reference groups, as seeking group affiliation and relationships is inherent in human nature—a concept known as social bonding (Ramkissoon, 2022 ). Researchers (Savari et al., 2023 ; Savari and Khaleghi, 2023 ) contended that there is a substantial positive correlation between subjective norms and buying intentions. However, it is worth noting that contrary to this view, several studies have not found supporting evidence for a positive association between subjective norms and green purchasing behavior (e.g., Paul, Modi, and Patel, 2016 ; Khare, 2015 ). In contrast, according to Wang and Zhang ( 2020 ), subjective norms concerning environmental considerations at destinations can positively impact behavioral intentions. Hence, we propose the following hypotheses:

H 7 : Subjective norms have a positive association with GCIs .

Determinants of green consumption behavior

Ajzen ( 1991 ) posited that intention can influence behavior. Sheppard et al. ( 1998 ) argued that purchasing intention benefits purchase behavior (Saba & Messina, 2003 ). GCB encompasses a range of actions and choices that demonstrate a strong commitment to environmental responsibility. It includes self-identification as an environmentally friendly individual, profound concern for ecological issues, prioritizing eco-friendly products, and the incorporation of eco-conscious values into one’s personal identity and consumption practices (Walton & Austin, 2011 ; López-Mosquera, Sánchez, 2012 ). Homburg et al. ( 2005 ) examined self-reported behaviors. However, the scarcity of behavioral data has led to only a few researchers investigating the impact of intention on real-world behavior (De Cannière et al., 2010 ). According to Alam et al. ( 2023 ), intentions are directly linked to actual behavior. Furthermore, Zeithaml ( 2000 ) observed that while the mediation of intentions has been extensively studied, its relationship with actual behavior lacks conclusive evidence. Wee et al. ( 2014 ) indicate that purchase intention significantly influences real-world behavior. Thus, we propose the following hypothesis:

H 8 : Green consumption intention is positively associated with green consumption behavior .

Perceived behavioral control plays a pivotal role in influencing purchasing behavior, as highlighted by Yang et al. ( 2018 ). Poon and Tung ( 2022 ) proposed that perceived behavioral control strengthens an individual’s inclination and intention toward specific behaviors. Fu and Juan ( 2017 ) asserted that constructs from the TPB, such as passenger attitudes and perceived behavioral control, are essential for encouraging the public to use public transportation, with perceived behavioral control being capable of directly influencing actual behaviors. Ogiemwonyi ( 2022 ) delved into the positive relationship between green behavioral control and environmentally friendly behavior. Pakpour et al. ( 2021 ) revealed a significant relationship between perceived behavioral control and green purchasing behavior among adolescents. By contrast, Sheng and Zhang ( 2022 ) reported insignificant results in their findings. Therefore, as we found mixed results, we should retest the variable of perceived behavioral control to find a relationship with GCB.

H 9 : Perceived behavioral control is positively associated with green consumption behavior .

Moderation of ecolabelling and green self-identity

Ecolabelling refers to environmental labels or certifications for products that communicate eco-friendliness or sustainability. These labels provide data on the product’s environmental attributes, such as energy efficiency, recycled content, and carbon footprint. Eco-labels serve as informative tools for guiding the utilization, disposal, consumption, and production of goods, allowing marketers to effectively convey their products’ environmental advantages through eco-labeling (Atkinson, Rosenthal, 2014 ). eco-labeling conveys functional information about environmentally sustainable items, enabling consumers to comprehend the attributes of green products and subsequently influencing their purchase intention. Ecolabelling favorably affects individuals’ pro-environmental behavior (Waris, Ahmed, 2020 ). In line with Prieto-Sandoval et al. ( 2016 ), consumers exhibit a keen interest in environmental matters, which enhances their likelihood of purchasing eco-labeled products. Furthermore, the effective communication of energy usage information through well-designed energy labels boosts customer preferences, as noted by Zhao et al. ( 2019 ) and Stadelmann and Schubert ( 2018 ). Without eco-labels, the link between GCI and behavior may weaken. Individuals may face uncertainty regarding the eco-friendliness of products, making it more challenging for them to align with their intentions. The intention-behavior connection may be less straightforward without the visual cues of eco-labels.

H 10 : eco-labeling is positively moderating with GCI and green consumption behavior .

Self-identity represents a set of roles performed by an individual, leading to consistent actions that align with their self-concept (Li et al., 2021 ). It serves as a label employed by a person to embrace or signify a specific behavior. Consequently, GS is defined as an individual’s self-perception of being environmentally conscious (Van der Werff et al., 2013 ). However, differentiating between GS and environmental identities is crucial. When an individual possesses a strong GS and sees himself or herself as a dedicated and consistent green consumer, their GCI is more likely to transform into actual behavior. Their self-identity reinforces and aligns with their intentions, making them more likely to act in ways reflecting their green values and beliefs.

H 11 : Green self-identity is positively moderating with GCI and green consumption behavior .

All associations hypothesized above are presented in Fig. 1 below:

Conceptual framework.

Methodology

Research design.

According to Neuman ( 2006 ), scientific research is governed by the paradigm chosen by researchers. Taylor et al. ( 2007 ) defined paradigm as a broad view or perspective of something. In general, three research paradigms can be identified in social research: (1) positivist, (2) constructivist, and (3) critical. Peñaloza and Venkatesh (2006) proposed that the positivist paradigm might be characterized as a scientific approach. The research has adopted a quantitative method. Quantitative methods are utilized to test hypotheses formulated for a particular study and ascertain the validity and reliability of the measured variable (Sekaran, 2005 ).

Furthermore, this research required a substantial sample size, and the formulated hypotheses were examined within the context of this study, which integrated two theoretical frameworks: the KAP model and TPB. This study proposes 11 hypothesized relationships to be tested through an empirical examination. A quantitative cross-sectional study was conducted using an online survey to collect data from young Chinese consumers. The dataset, consisting of 876 observations, was analyzed using partial least squares structural equation modeling (PLS-SEM) with Smart-PLS software (version 4.0).

Population and samples

The current research is centered on investigating GCB among the younger population in China, employing the KAP-TPB theory as its theoretical framework. Given the nature of this study, a quantitative research methodology was employed to gather data, utilizing an online cross-sectional study design to evaluate GCI and GCB within the context of the KAP-TPB framework among Chinese youth. The study’s target demographics consisted of young Chinese consumers aged 18–35.

The appropriate sample size was assessed based on statistical considerations. G-power 3.1 was used with a power level of 0.80 and an effect size ( f 2 ) of 0.15, taking into account 10 predictors. The minimum required sample size was 118 (Faul et al., 2007 ). However, a larger sample size of at least 200 participants was necessary for subsequent PLS-SEM analysis. PLS-SEM is a valuable technique for examining the intricate connections between constructs and their indicators (Hair et al., 2021 ).

Data were collected through an online questionnaire survey using convenience sampling. The surveys were published online through WJX and disseminated through social media platforms like WeChat. The survey was conducted online from April to June 2023. The final dataset has 876 valid responses out of 902 total responses. We omitted 26 responses since they gave an identical answer to every question, which was extremely implausible and thus removed from the final dataset. The top section of the survey instrument contains information about the study topic, definitions of key terms (green consumption, eco-labeling, and green products), reporting procedures (which emphasize the anonymity and confidentiality of respondents), and informed consent details. A short video link was posted at the top of the questionnaire, confirming the same details.

Survey instruments

The questionnaire comprised two main sections: Sections A and B. Section A centered on the demographic features of the participants, such as education level, gender, current location, age, and monthly income. Section B focuses on environmental concerns (Li et al., 2021 ; Durmaz and Akdoğan, 2023 ), environmental knowledge (Dhir et al., 2021 ; Li et al., 2021 ), attitude towards environmental issues and attitude towards eco-social benefits (Cheung and To, 2019 ; Ritter et al., 2015 ), attitude towards green consumption (Al Mamun et al., 2018 ; Alganad et al., 2023 ), subjective norms (Wan et al., 2017 ), perceived behavioral control (Aitken et al., 2020 ; Sultan et al., 2020 ), eco-labeling (Neves and Oliveira, 2021 ; Taufique et al., 2019 ), GS (Becerra et al., 2023 ) GCI and GCB (Walton & Austin, 2011 ; López-Mosquera, Sánchez, 2012 ) with 5 items each. To determine the measurement results, we used a 7-point Likert scale ranging from strongly disagree to strongly agree, including mostly disagree, partially disagree, neutral, partially agree, and mostly agree. All items used in this study are presented in Supporting Material S1. Survey Instrument .

Data analysis tools

The research methodology comprised a two-stage analysis. First, we quantified the validity and reliability of the measurement models. Subsequently, structural equation modeling (SEM) was used to delve into the connections between the predictor and latent variables, focusing on mediation and moderation effects. It is well-established in the field that SEM offers superior estimations compared to traditional regression methods when investigating mediation and moderation effects (Preacher & Hayes, 2004 ). Consequently, this study opted for SEM, specifically utilizing PLS-SEM via Smart-PLS 4.0, which is regarded as the preferred choice because of its usefulness in measuring intricate frameworks that include moderating variables (Hair et al., 2021 ).

Common method bias (CMB)

The impact of CMB on this study was negligible, as the single factor only accounted for 37.692% of the variance, which falls within the standard of 50.00% as established by Podsakoff et al. ( 2012 ). Additionally, we conducted a CMB test using Kock’s ( 2015 ) recommended full collinearity assessment, in which all latent constructs were regressed on a common variable. The Variance Inflation Factor (VIF) values, as presented in Table 1 ranging from 1.253 to 2.025, remained below the critical standard of 5, indicating the absence of collinearity issues in this study, as outlined by Hair et al. ( 2021 ).

Multivariate normality

The examination of multivariate normality using suitable data analysis techniques is of paramount importance. In this study, we assessed multivariate normality using an online Web Power tool (Web Power, 2018 ). The results of the multivariate normality test indicated that the p -values associated with Mardia’s multivariate skewness and kurtosis were below the significance level of 0.05, signifying the presence of non-normality in the data (Al Mamun & Fazal, 2018 ). Consequently, to address the non-normality of the dataset, we opted to utilize PLS-SEM).

Data analysis

Demographic characteristics.

Table 2 reveals a relatively balanced gender distribution, with 55.8% of the participants being female and 44.2% male. A significant proportion of respondents obtained a postgraduate degree (44.3%), followed by a bachelor’s degree (28.8%). A smaller percentage holds a diploma or advanced diploma (20.1%), and 6.8% fall into the “Others” category. The respondents’ age groups were relatively evenly distributed, with the largest group being those aged 21–25 (32.5%) and the smallest being 31–35 (19.7%). Most respondents were either single (52.1%) or married (43.6%), while divorced individuals accounted for 2.6% and widows accounted for 1.7%. A substantial proportion of the respondents were employed full-time (29.6%) or were students (26.1%). The majority of respondents had a monthly income of less than RMB 3000 (35.4%), while a significant portion fell into the RMB 3001-RMB 6000 range (17.1%). Regarding how often respondents consumed green products, the data showed that sometimes (34.6%) and rarely (27.5%) were the most common responses. A smaller proportion of the respondents always (17.5%) or often (9.1%) consumed green products, whereas 11.3% never did so. Most respondents spent less than RMB 1000 on green energy consumption per month (61.1%). A smaller but still significant portion was spent between RMB 1,001 and RMB 2000 (22.6%), and a few respondents fell into higher spending categories. Table 2 provides insights into the geographical distribution of respondents within China. South China (28.0%) and North China (18.3%) had the highest representation, whereas other regions were less prominently represented.

Measurement model

To test the internal consistency of the measurement scales, previous research recommended using Cronbach’s alpha and composite reliability (rho_a and rho_c) as reliable metrics (Dijkstra & Henseler, 2015 ; Hair et al., 2021 ). According to the guidance provided by Hair et al. ( 2021 ), this research computed and obtained the reliabilities of the latent constructs using Cronbach’s alpha and composite reliability (rho_a and rho_c) (Table 3 ). The results indicate that Cronbach’s alpha and composite reliability (rho_a and rho_c) values for all items exceeded 0.7, indicating strong internal consistency within the framework (Hair et al., 2021 ). The average variance extracted values ranged from 0.722 to 0.759, indicating that the measures accounted for a substantial proportion of the variance in each construct. The variance inflation factor values were all below the required standard of 2.5, indicating no multicollinearity issues.

To measure discriminant validity, we applied the Fornell-Larcker criterion along with loadings and cross-loadings. The detailed values for the loadings, cross-loadings, and the Fornell-Larcker criterion are presented in Table 4 . It is worth noting that all cross-loading values exceeded 0.5, which was higher than the corresponding loadings, thus providing clear evidence of discriminant validity for all items used in our study (Fornell and Larcker, 1981 ). According to Henseler et al. ( 2015 ), the benchmark for the heterotrait-monotrait (HTMT) ratio of correlations was set at 0.90, and any value surpassing this threshold indicated poor discriminant validity. In our analysis, all the values within the HTMT matrix remained below the specified threshold of 0.90, confirming a strong level of discriminant validity. However, all cross-loadings remained greater than 0.5 as well. Overall, the analysis suggests that the measures have adequate reliability and validity for the constructs studied.

Structural model

The results (as shown in Table 5 and Fig. 2 ) reveal that environmental concern ( β  = 0.263, t  = 6.531) and environmental knowledge ( β  = 0.330, t  = 8.309) significantly influenced attitude towards environmental issues. The link between environmental concern ( β  = 0.221, t  = 5.259) and environmental knowledge ( β  = 0.344, t  = 8.068) was identified as positive, signifying the positive effect of environmental concern and environmental knowledge on AC. Moreover, environmental knowledge ( β  = 0.312, t  = 7.465) and AC ( β  = 0.330, t  = 8.205) had a positive significant influence on attitude towards green consumption. All the relationships were significant at the 1% level of significance. Attitude towards green consumption ( β  = 0.097, t  = 1.959), attitude towards eco-social benefits ( β  = 0.089, t  = 1.971), and subjective norm ( β  = 0.206, t  = 4.315) positively affected GCI at 5% level of significance, whereas, attitude towards environmental issues ( β  = 0.038, t  = 0.771), and perceived behavioral control ( β  = 0.074, t  = 1.582) were found as an insignificant with the same value. Additionally, eco-labeling ( β  = 0.207, t  = 5.428), GCI ( β  = 0.090, t  = 2.848), GS ( β  = 0.272, t  = 6.487), and perceived behavioral control ( β  = 0.236, t  = 5.534) demonstrated a positive relation on GCB at 1% level of significance. Therefore, this study found that hypotheses (H 1-6 , H 8-9 , and H 11-15 ) were validated at the 1% level of significance, and hypotheses (H 7 and H 10 ) were rejected (Table 5 ).

Structural model.

The coefficient of determination ( R ²) provides a valuable understanding of the extent to which the independent variable(s) can account for variability in the dependent variable. According to Cohen ( 2013 ), an R 2 value of 0.26 or higher is considered significant, while a value of 0.13 is moderate, and a value of 0.02 or lower is weak. The R 2 value (Table 4 ) for GCB, attitude towards green consumption, and attitude towards environmental issues indicates that all the predictors explain 39.242%, 30.7%, and 26.1% of the variance in the GCB, attitude towards green consumption, and attitude towards environmental issues, respectively, which have higher explanatory power. The R 2 value for attitude towards eco-social benefits and GCI indicates that the predictors have moderate explanatory power (24% and 15.7%, respectively).

Similarly, the f 2 test measures the effect size for each independent variable in the model. These effect sizes can be categorized as small ( f 2  > 0.02), medium ( f 2  > 0.15), or large ( f 2  > 0.35) based on conventional guidelines (Cohen, 2013 ). Table 5 illustrates that the f 2 test results for the various independent variables range from 0.004 to 0.120, indicating a small effect size.

The outcome of moderation (Table 5 ) indicates that eco-labeling ( β  = −0.055, t  = 1.555, p  > 0.5) did not moderate the connection between GCI and GPB. In contrast, green self-identity significantly moderates the connection between GCI and GCB at the 5% significance level. Therefore, H11 was supported, and H10 was rejected.

Predictive accuracy

The PLS prediction program supported the evaluation of the predictive relevance of the models (Shmueli et al., 2019 ). Q ² predictions were greater than zero, and the root mean square error (RMSE) values of the PLS-SEM predictions were smaller than the linear model (LM) baseline for all indicators of GCI and GCB (Table 6 ), except for GCI2 and GCB2.

Multigroup analysis

Since assessment by PLS-SEM always uses a complete dataset, it defaults to all data from a single homogeneous population, which is usually unrealistic in practical studies. Hair et al. ( 2021 ) suggested using a multigroup analysis to address these issues. The investigation into gender and income groups specifically stems from their recognized significance in understanding consumer behavior and environmental attitudes. Gender has been identified as a variable influencing environmental concerns and behaviors, with studies highlighting differences in consumption patterns and environmental attitudes between genders. Similarly, income is a crucial factor influencing purchasing power and consumption habits, which in turn can impact environmental choices.

Measurement invariance of the composite model (MICOM) of the integrated model was used to examine the measurement invariance of subgroups. The findings of the MICOM permutation p -values are greater than 0.05, except environmental knowledge → attitude towards eco-social benefits, attitude towards environmental issues → GCI, subjective norm → GCI and GCI → GCB relationships. As 22 of the 26 p -values were greater than 0.05, this study assumed equal invariance among the subgroups. The analysis highlights notable differences in the strength of relationships between certain variables and green consumption behavior across gender groups. For instance, subjective norms appear to have a stronger impact on green consumption intention among males compared to females. This suggests that social influences may play a more significant role in shaping males’ attitudes and behaviors toward environmentally friendly consumption choices. Thus, the outcome showed no significant variance between the two groups of participants regarding sex (male/female) and income (less or greater than RMB 6000) in any of the hypothesized relationships (Table 7 ). Despite the lack of significant differences in most relationships based on income levels, there are notable exceptions. Green consumption intention to green consumption behavior relationships show significant differences based on the MICOM permutation p -values. These findings imply that certain factors may have a more pronounced impact on green consumption behavior among specific income groups.

This study examines the determinants of GCB among Chinese youth. The study proposed 11 hypotheses based on the integrated KAP-TPB theory, nine of which were confirmed through empirical investigation. The exogenous constructs in the model were found to significantly influence the endogenous construct, with an explanatory power of 39.2% for GCB, indicating a good fit between the model and investigation. The following discussion provides details of the relationships identified in this study.

H 1a and H 2a both involved the influence of environmental concerns and knowledge on attitudes towards environmental issues. Both hypotheses were accepted, indicating that environmental concern and knowledge have a statistically significant positive effect on attitudes toward environmental issues. The results of this study are consistent with those of Cheung and To ( 2019 ). Environmental concerns have a significant impact on individuals’ attitudes towards the environment. Consumers with a heightened level of environmental concern engage in increased purchasing of environmentally friendly products, which aligns with the idea that people who are genuinely concerned about environmental issues may be more inclined to support and engage in environmentally friendly behaviors. These individuals may be more receptive to environmental initiatives such as conservation efforts, sustainability practices, and policies aimed at addressing environmental issues. Similarly, environmental knowledge can empower individuals to recognize the importance of environmental stewardship, make informed decisions, and advocate for sustainable practices.

Acceptance of H 1b indicates a strong and positive link between individuals’ environmental concerns and their attitudes towards eco-social benefits. People with a higher degree of environmental concern tend to be more positively disposed towards actions and practices with eco-social benefits. This means that those who care deeply about environmental issues are more prone to sustaining and engaging in activities that promote ecological sustainability and social well-being. H 2b was also accepted, suggesting a relationship between environmental knowledge and attitudes towards eco-social benefits. The relationship between environmental knowledge and attitudes towards eco-social benefits implies that individuals with a greater understanding of environmental issues are more likely to appreciate the broader societal advantages resulting from environmentally responsible actions. Environmental knowledge can have a positive impact on consumer attitudes and purchasing behaviors.

As expected, H 1c was proven to be significant, indicating a positive association between individuals’ environmental concern and their attitudes toward green consumption. This result complements the findings of previous studies (Jaiswal and Kant, 2018 ; Yadav and Pathak, 2017 ). Individuals with heightened environmental concerns are likelier to have a positive attitude toward green consumption. This finding suggests that fostering and nurturing environmental concerns among customers can play a pivotal role in marketing sustainable and eco-friendly consumption choices. According to Yadav and Pathak ( 2017 ), people who express greater environmental concern tend to be more aware of the ecological consequences of their consumption decisions. When making purchasing choices, they are more likely to consider the ecological impact of products and services, such as their carbon footprint, resource usage, and sustainability. Likewise, H 2c assumes that environmental knowledge is associated with the attitude towards green consumption. These empirical results support the findings of previous studies (Nguyen and Tran, 2021 ; Zhang et al., 2021 ; Nekmahmud and Fekete-Farkas, 2021 ). Environmental knowledge equips individuals with the information and understanding required to make informed consumer choices. When well-informed about ecological issues, people are more likely to appreciate the significance of green consumption practices and opt for products and services that align with their values and knowledge.

The empirical results reveal a notable discrepancy between Hypothesis H 3 , which posits a positive relationship between attitudes towards environmental issues and GCI, and the actual findings, which show no statistically significant links between these variables. This suggests that consumer behavior in the realm of green and sustainable consumption is influenced by a complex interplay of factors beyond environmental attitudes. While the assumption that positive environmental attitudes naturally lead to GCIs may seem intuitive, real-world consumer behavior is multifaceted and nuanced. In contrast, the results confirm H 4 , indicating a statistically significant and positive connection between attitudes toward eco-social benefits and GCI. Although the effect size is small, this signifies that individuals with more favorable attitudes towards the broader ecological and societal advantages of sustainable choices are inclined to engage in GCBs, shedding light on the multifaceted nature of sustainability motivations.

As expected, H 5 suggests a statistically significant and favorable correlation between the attitude toward green consumption and GCI in the data under examination. This outcome is based on the results of Zhuo et al. ( 2022 ), who held that more positive attitudes towards green consumption and are more likely to intend to engage in GCB. Acceptance underscores the influential role of individuals’ positive attitudes towards green consumption in shaping their intention to engage in environmentally friendly and sustainable consumption behaviors. People with favorable opinions about green consumption practices are more likely to translate these attitudes into concrete intentions to make environmentally responsible choices.

According to the outcomes, H 6 suggests a statistically significant and positive relationship between subjective norms, which are influenced by social factors, views of others, and GCI. This implies that individuals’ intentions to engage in green consumption are positively influenced by subjective norms and the influence of significant others in their social circles who support and endorse GCBs. The moderate effect size and statistical significance indicate that subjective norms play a measurable role in shaping GCI. However, the outcome was consistent with previous studies (Savri et al., 2023 , Savari and Khaleghi, 2023 , Wang and Zhang, 2020 ). In the context of green consumption, when people perceive that those around them value and endorse environmentally friendly choices, they are more likely to embrace or adopt GCI as they seek to conform to these norms.

The rejection of H 7 highlights the complexity of understanding and predicting consumer intentions, especially from the perspective of green consumption. Although it might be intuitive to assume that individuals with a higher sense of control over their actions would have stronger intentions to engage in green consumption, the results of this study indicate that the connection is not statistically significant in this dataset. The outcomes satisfy past results (Wang and Zhang, 2020 ) and contradict those of other studies (Paul, Modi, and Patel, 2016 ). Consumer intention is affected by myriad factors beyond perceived behavioral control. While control over one’s actions is undoubtedly important, GCIs can be shaped by various other factors such as attitudes, values, subjective norms, and external constraints. These factors may overshadow the influence of perceived control in this context.

Hypothesis H 8 is supported by the data, indicating a statistically significant and positive link between GCI and GCB. The past study of Alam et al. ( 2023 ) confirmed this relationship. The observed relationship between GCI and GCB aligns with well-established psychological theories such as the TPB. This theory posits that intentions are strong predictors of behavior. Thus, those who express a strong desire to make eco-friendly and sustainable choices tend to be more inclined to participate in such behaviors. The moderate effect size associated with this relationship indicates that it is not only statistically significant but also practically meaningful. Similarly, H 9 is rooted in TPB. This study aligns with the research of Sheng and Zhang ( 2022 ), who found a direct relationship between perceived behavioral control and GCB. It posits that individuals who perceive a higher degree of control over their actions regarding green consumption are more likely to engage in green behavior. This finding implies that the level of perceived control determines the actual adoption of environmentally friendly practices.

According to the moderation effect, the interaction effect suggests that eco-labeling may serve as a moderator between GCI and GCB. In other words, the impact of GCI on behavior may depend on whether eco-labeling is present. The empirical results reject this hypothesis and indicate that the effect of ecolabelling on GCB may not be uniform across all levels of GCI and vice versa. In contrast, Hypothesis (H 11 ) delves into the complexity of consumer behavior by examining the interaction between two psychological constructs: self-identity and intention. This interaction suggests that self-identity as environmentally conscious may amplify or attenuate the effects of GCI on GCB.

Implications

Theoretical implications.

This research contributes to the theory in terms of a new model, a new construct, and the production of new results. First , it integrates well-established psychological theories, such as the TPB and KAP, into the context of sustainable consumption. This study contributes to the continued relevance of explaining and predicting GCB by applying and validating these theories. Second , study-oriented attitudes are a three-dimensional construct rather than a single dimension. These dimensions are attitudes toward environmental issues, attitudes towards eco-social benefits, and attitudes towards green consumption. This detailed presentation of attitudes will help academicians understand their relationships with GCI in depth. For example, this study revealed that attitudes toward eco-social benefits and attitudes toward green consumption are significant predictors of GCI, whereas attitude towards environmental issues is not a significant factor. This provides new results and simultaneously provides a detailed relationship dimension.

Third , the research model stopped when GCB was observed instead of GCI. This means that the study worked with actual behavior (self-reported behavior), whereas many studies (Nekmahmud et al., 2022 ; Zaremohzzabieh et al., 2021 ) were limited to GCI, failing to supply comprehensive ideas about consumption behavior. This extension builds upon the foundations laid by van den Broek et al. ( 2019 ) and Bamberg, Möser ( 2007 ), who addressed sustainable consumption behavior through sustainable consumption intention. With the introduction of new constructs and theoretical model integration (TPB-KAP), distinct from those studies mentioned above, our extended conceptual framework offers unique insights into the determinants of pro-environmental behaviors. While previous extensions have predominantly focused on the overall population, our framework places greater emphasis on the younger population and their behavioral contexts.

Fourth , this research highlights the significance of external cues, such as eco-labeling, in shaping GCB. Moreover, it emphasizes the role of contextual factors in influencing the relationships between variables. This recognition of external and contextual influences enriches our understanding of the mechanisms underlying sustainable choices. Fifth , this study empirically validated several theoretical assumptions. For instance, it confirms the expected relationships between environmental concerns, knowledge, attitudes, and GCB. This empirical support strengthens the theoretical foundation in this area. This study explored the role of the GS and its interaction with GCI. This provides a nuanced perspective on how individuals’ self-concepts as environmentally conscious can amplify the influence of their intentions on their behavior. This highlights the importance of self-identity in the context of sustainable consumer behavior.

Sixth , the theoretical contribution of this study lies in its ability to provide a nuanced understanding of how gender and income interact with environmental variables to influence sustainability-related attitudes and behaviors by employing the multigroup analysis technique of PLS-SEM. The use of a multigroup analysis enhanced the generalizability of the study’s outcomes. This allowed a more comprehensive understanding of how environmental variables operate within different subgroups, making the results more applicable to diverse populations. By considering gender and income, this study contributes to the context of sustainable behavior. It recognizes that individual characteristics and socioeconomic contexts shape attitudes and intentions related to sustainability.

Practical implications

The results of this study offer valuable insights for policymakers, businesses, and researchers seeking to promote sustainable environmental behaviors. First , the study highlights the necessity of environmental knowledge and concern in influencing positive attitudes and intentions toward environmental issues. Policymakers should integrate comprehensive eco-literacy programs into school curricula to ensure that students acquire fundamental knowledge of environmental challenges and solutions. These programs should be tailored to resonate with Chinese youth, incorporating local environmental challenges and solutions. By ensuring that students acquire a solid understanding of environmental issues and their relevance to China’s ecosystem, policymakers can nurture a generation of environmentally conscious citizens. To engage Chinese youth effectively, policymakers should design awards and recognition programs that appeal to their interests and aspirations. Youth-focused categories could include innovative environmental projects, youth-led initiatives, and eco-friendly school practices. By showcasing the achievements of young environmental champions, these programs can inspire and empower the next generation of environmental leaders.

Second , to encourage more favorable attitudes towards eco-social benefits, campaigns, and initiatives should highlight the positive social outcomes of environmentally responsible behaviors. Public awareness campaigns targeted at Chinese youth should highlight the positive social outcomes of environmentally responsible behaviors. Utilizing social media platforms and youth influencers, these campaigns can showcase how green choices benefit communities, societies, and individuals in relatable and engaging ways. Policymakers can also consider leveraging popular cultural icons and trends to make environmental messages more appealing and impactful to young audiences. Policymakers can offer tax incentives and financial rewards to consumers who choose eco-friendly products and engage in sustainable behaviors, thus promoting positive attitudes towards green consumption. They can implement and enforce clear and credible eco-labeling standards, making it easier for consumers to identify and select environmentally responsible products, thereby fostering positive attitudes towards eco-social benefits. In addition, they can recognize and reward businesses that demonstrate a commitment to eco-social benefits and green consumption through sustainable practices and products.

Third , the findings underscore the role of attitudes towards green consumption in shaping eco-friendly consumption behaviors. Businesses should integrate eco-friendliness into their marketing strategies, emphasizing the ecological benefits of their products and services to resonate with Chinese youth’s values. By aligning with youth preferences for sustainable and socially responsible brands, businesses can drive more environmentally friendly purchasing decisions among this demographic. Additionally, policymakers can incentivize green consumption among youth through targeted initiatives such as discounts or rewards for purchasing eco-friendly products.

Fourth , recognizing the influence of subjective norms and perceived behavioral control on green consumption intention, policymakers should empower Chinese youth to lead sustainable lifestyles. Community initiatives and youth-led projects can nurture positive social norms around sustainability, encouraging peer support and collective action. Moreover, interventions should address practical barriers to green choices faced by youth, such as limited access to eco-friendly products or financial constraints.

Fifth , to foster green self-identity among Chinese youth, targeted interventions should emphasize personal alignment with green values and choices. Campaigns can highlight the role of youth in driving environmental change and showcase the positive impact of individual actions on the planet. Additionally, businesses and policymakers should enhance the visibility and credibility of eco-labels, making them more accessible and informative to youth consumers. By reinforcing the connection between green self-identity and eco-labeling, policymakers can encourage sustainable choices among Chinese youth.

This comprehensive study investigated the complex web of factors influencing attitudes and behaviors related to environmental issues, eco-social benefits, and green consumption among Chinese youth. Noteworthy findings from the study include the significant impact of green consumption intention and perceived behavioral control on actual green consumption behavior. Furthermore, positive associations were observed between attitudes toward eco-social benefits, attitudes towards green consumption, subjective norms, and green consumption intention. Environmental knowledge and concern emerged as influential factors positively affecting all three attitude dimensions. The study also revealed that green self-identity plays a pivotal role as a moderator between green consumption intention and behavior among young people, while ecolabelling did not show significant moderation in the same relationship. Interestingly, certain relationships, such as attitude towards environmental issues and green consumption intention, as well as perceived behavioral control and green consumption intention, were found to be insignificant. Additionally, a multigroup analysis uncovered no invariance between groups based on respondents’ gender and income level. However, this research provides valuable insights, highlighting the critical role of factors like green consumption intention, perceived behavioral control, and subjective norms while shedding light on the nuanced influence of variables such as green self-identity and ecolabelling for the Chinese youths. Certain relationships were found to be significant, underscoring the complexity of the dynamics surrounding environmental attitudes and behaviors.

While this study offers valuable insights into the links between environmental concerns, environmental knowledge, attitudes, and behaviors, it is essential to acknowledge its limitations. First, it relies on a cross-sectional design that captures a snapshot of attitudes and behaviors at a specific point in time. Sustainable behaviors often require long-term commitment and lifestyle changes. The scope of the study was limited to assessing more enduring behavioral changes, and future research should explore this aspect. Longitudinal research offers an opportunity for a deeper understanding of the evolution of these factors over an extended period. Second, this study was primarily based on self-reported data, which may have been subject to response bias, including social desirability bias. Participants may provide answers that they believe align with socially acceptable norms, potentially skewing the results, particularly for young people. Therefore, generalizing to other cohorts based on that should be done with caution. Future studies should validate the results with more investigations, considering all limitations, in the future. Third, the study identified the presence of an intention-behavior gap (e.g., H 3 and H 7 ), where intentions may not always translate into corresponding actions. This finding underscores the need to explore and address the factors contributing to this gap and provide a basis for future research to improve the alignment between intentions and sustainable behavior. Advanced research could incorporate new moderating variables (green trust, eco-literacy, etc.) to address the attitude-intention and intention-behavior gaps. Finally, while gender and income groups were selected due to their established relevance in the context of environmental research and consumption patterns, other demographic factors could certainly provide valuable insights. However, due to limitations such as the scope of the study, focusing on gender and income groups allowed for a more manageable and targeted analysis. Including additional demographic factors could certainly enhance the comprehensiveness of the study but might require a more extensive research design and data collection process.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material ( S2. Dataset ), further inquiries can be directed to the corresponding author.

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Acknowledgements

This study is supported via funding from Nanfang College Guangzhou (2022 School-level Research Project. No. 2022XK06). Fund received by: Yingxiu Hong. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Yingxiu Hong, and Qing Yang: conceptualization, investigation, methodology, writing—original draft preparation. Abdullah Al Mamun and Mohammad Masukujjaman: conceptualization, methodology, formal analysis, writing—review & editing.

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Hong, Y., Al Mamun, A., Masukujjaman, M. et al. Sustainable consumption practices among Chinese youth. Humanit Soc Sci Commun 11 , 1058 (2024). https://doi.org/10.1057/s41599-024-03582-5

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Received : 28 October 2023

Accepted : 09 August 2024

Published : 20 August 2024

DOI : https://doi.org/10.1057/s41599-024-03582-5

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