Several studies have explored the antecedents of automobile purchase intention (API) of customers across different geographical regions of the world. These have, however, come up with contradictory findings about the polarities of effects of some antecedents, as well as about some of their differences across countries. We were motivated by these contradictions and differences to carry out a meta-analysis on API. In the study, we considered articles from peer-reviewed databases such as Google Scholar, Web of Science, Scopus, ScienceDirect, and Wiley. Our study and analysis of the full texts of 136 articles yielded 64 antecedents. The specific polarity of effect (positive or negative) on API is established for 59 of these antecedents, whereas the same remained undetermined for the rest five. We identified these as research gaps, the areas yet to be convincingly explored. Furthermore, based on the absence of publication bias and the 95% CI of impact not crossing the line of null effect, we enlisted 20 antecedents spread across the different stages of the automobile purchase decision process, which we propose to add to the theory of consumer behavior as the automobile consumer behavior (TACB). Our study can help managers to better understand consumer behavior in terms of the discovered antecedents, especially those having a higher impact at a particular stage of the process, and can result in better business decisions.

1. Introduction

Rowlan [1] defines purchase intention as a prerequisite for stimulating and pushing consumers to actually purchase products and services [2, 3]. It is the tendency of consumers to buy products of a particular kind, or undertake actions related to such purchases [1, 4]. They highlighted that to motivate the customers to consider and/or buy their products or services, it is important to bridge the gap between features, facilities, and functionalities of the desired products as provided by the company selling the products and the expectations of the customer [5–15]. Understanding the focus of and the emotions influencing consumers’ buying behavior, companies can craft new products and/or services’ marketing campaigns connecting with their global pool of customers, resulting in improved profitability [16, 17]. Kotler [18] highlighted this shift from the marketing-inclined concept to the customer-value concept, wherein attaining customers’ faith, satisfaction, and loyalty are considered more important than self-proclaimed product excellence. Unless the customers perceive a product or service as important and sense its demand, the said product or service cannot be successful in the market. The necessity of congruence between offered features and customer expectation is even more pronounced in the case of expensive products, as those are purchased based on customers perception of quality, product design, price, uniqueness, social status, and life history [16, 17, 19]. The concept of affordability and quality, as well as social status, inspired by a purchase, is often a function of the purchasing power of the potential customer, which in turn is influenced by the economic status of the latter. They are also influenced by customers’ perceptions, and these are based on one’s personal experience, as well as those of close relatives and friends [4]. Thus, products and services designed and developed in line with the customer’s expectations tend to elicit positive responses from the latter, and this can only be performed with a proper understanding of various antecedents of purchase intentions. Automobiles are no exception to this, but given that they are expensive and thus involve more customer scrutiny, it becomes all the more important to design them based on the antecedents of customer automobile purchase intentions (APIs) [20–27].

One can understand the influencers of API using one or more of the three sources, literature review of API, meta-analysis and individual articles based on surveys of people from various countries belonging to different socioeconomic statuses, and from responses to surveys conducted to understand influencers of peoples’ purchase intention [28, 29]. While scanning through the literature, we realized that there are not enough meta-analyses and literature reviews dealing with API, and thus we undertook this analysis based on our full-text reading of 136 articles.

During this study, we found that influencers of API differed across countries, and sometimes even across different cities of the same country. Steg et al. [30], in their study, conducted in Amsterdam (Netherlands), found the main influencers to be the locations of human activities, the needs and desires of people, and transport resistance. Becker et al. [31], in a study conducted in the neighboring city of Rotterdam, deviated in terms of their findings and found weather conditions to be the sole influencer. Similarly, Salamin et al. [32] found price discounts being important at the city of Al-Hassa in Saudi Arabia, while Sohail and Sahin [33], in nearby Riyadh, Jeddah, and Dammam in Saudi Arabia, found price, product quality, and risk to be more important. This trend is seen also in the case of three studies in 2022 from China. Hung et al. [34], in their study conducted at Chengdu, found the major influencers to be altitude, perceived behavioral control, subjective norms, perceived risk, perceived service quality of public transports, and sharing experience on API [35–44]. Conducting their study at Guangdong, Zhang et al. [45] identified the influencers as price, parking, problems, convenience, comfort, tech-savviness, environmental awareness, variety seeking, and need for privacy. At Wuhan, Yan et al. [46] came up with gender, income, residential location, vehicle availability, and epidemic severity as influencers of API. As can be seen, these three sets of influencers have very little in common. The experience is similar to studies conducted elsewhere. Fitwi [47] highlighted the role of demographic variables such as age, gender, education, and income for Hyundai vehicle customers of Addis Ababa in Ethiopia. The same has also been supported by Nerurkar [48] in the context of India, although factor loadings were not similar. Awuor [49] highlighted the contribution of strong advertisement messages for automobile customers of Nairobi. Based on the studies conducted in California, Golob et al. [50] came up with influencers to API such as income, vehicle performance (for younger customers), and safety (for older customers); Banerjee [51] came up with vehicle attributes and sociodemographic attributes; and Vigneron and Johnson [52] came up with brand awareness, brand loyalty, and brand trust. Thangam et al. [53], with survey samples from Tamil Nadu, India, found brand loyalty, safety, and design to be the main antecedents of API, while Naidu et al. [54] found those to be traffic congestion, pollution, economic benefits, and flexibility based on their sample from Karnataka, India. Continuing with this trend, Du et al. [55] and Hui et al. [56] found that sociodemographic variables have no influence on consumer behavior in the automobile industry, and authors such as Alice et al. [57]; Thomas et al. [58]; Tao, Nie, and Zhang [59]; Raghu [60]; Verma et al. [61]; Mathur et al. [62]; and Hackbarth and Reinhard [63] emphasized the importance of demographic variables in understanding the consumer behavior. Furthermore, there are geographical differences in the impacts of these attributes. Hackbarth and Reinhard [63] pointed out that graduated students of India, China, United States of America, Taiwan, and Indonesia are interested in owning four-wheelers, whereas Dutch students are inclined towards public transport due to their concern about the environment. This has also been supported by Bamberg [64]. Ikezoe et al. [65] highlighted the “attachment to private vehicles” and their survey showed that irrespective of how inexpensive the alternatives were, 74% of owners are not interested in availing alternative opportunities. Zhou et al. [66] and Kolleck [67] support this by stating “vehicle sharing does not impact purchasing of new ones.” Contradicting this, Pirzada and Singhi [68] and Naidu et al. [54] emphasized that “sharing of vehicles will reduce private ownership by more than 50%.” In the same lines, Banerjee [51] found the product features to be important, while Kolleck [67] and Alice [57] found them to be unimportant.

Summarizing the above, the literature talks of different components of API; many studies conflict with each other, thereby making it difficult to arrive at a conclusion. This calls for further probing into the matter, and thus motivated us to perform a meta-analysis on this subject.

The rest of the paper is organized as follows. Section 2 explains the research methodology applied, Section 3 deals with the categorization of factors as well as with the establishment of linkage between these factors and those that evolve from various theories of customer purchase intention leading to the development of our hypotheses. Section 4 details the meta-analysis, and finally Section 5 presents the conclusions and future research directions.

2. Research Methodology

Our intention is to analyze and synthesize the impacts of API antecedents from the research conducted across different parts of the world. We thus chose our research methodology to be a combination of literature review and meta-analysis methods [69, 70]. We used the grounded theory (noble, Mitchell [71]) for the extraction and analysis of articles. Grounded theory is a systematic methodology which involves the construction of hypotheses and theories through the collection and analysis of data and involves five steps, such as defining, searching, selecting, analyzing, and interpreting (Figure 1). These help refine the process of literature review and limit the discussion to the intended contextual domain, thereby ensuring the replicability of this rigorous and well-structured procedure of article extraction.

Details are in the caption following the image — **Figure 1**
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Various stages of article extraction from the literature on API.

2.1. Defining

2.2. Searching

We explored the research databases Google Scholar, ScienceDirect, Scopus, Springer, Wiley, Web of Science, arXiv, ResearchGate, and BASE, as has been performed in the literature (Vargas and Hanandeh [72] and Kraus et al. [73]). These multiple databases fetched a lot of articles and we further applied simultaneous backward and forward snowballing (Felizardo et al. [74]) to enrich our bouquet. The total result was 87,453 articles.

2.3. Selecting

The extracted 87,453 articles had a lot of identical copies. After eliminating the instances of the same articles, we were left with 7409 unique papers. After excluding working papers and those which were unpublished, the tally went down to 1147 papers.

2.4. Analyzing

Here, we refine the list of extracted papers by eliminating those which are out of the scope of the current research. We read through the abstract and the conclusion sections of 1147 papers and found 179 articles to be relevant to our study. Subsequently, we performed a more rigorous screening and read the full texts of these 179 articles. We were finally left with 134 articles within the period 1995–2023 for synthesis and assessment. This period of approximately 26 years of study is considered sufficient for systematic literature reviews (Hiebl [75] and Niforatos et al. [76]).

2.5. Interpreting

Interpretation involves the extraction and enlistment of factors and performing the meta-analysis as detailed in Section 3. Figure 2 illustrates the stages of this research methodology. We have used R Version 4.0.3 for conducting this meta-analysis. The meta-analysis helps us to statistically judge the significance of each factor as extracted from different studies and arrive at a correlation coefficient between these factors and the API of people [77]. It yields the effect size of each study, along with I² and ρ value. I² is a heterogeneity measure which allows an estimate of the proportion of variance observed in the meta-analysis due to true effects, and not due to sampling error; values higher than 50% are acceptable [78]. ρ value is the level of significance tested at a 95% confidence level. A value of 0.05 or lower is desirable for determining significant relationships. Along with the average effect size, a range is also obtained corresponding to a 95% confidence interval (CI). Our intention is to estimate true effect sizes, that is, the effect size in a population, from effects mentioned in various articles. The true effect size can be estimated in two ways, that is, the fixed effect model and the random effect model [79–81]. We have chosen random effect model; this model is appropriate when there is heterogeneity among the studies, and it also allows unconditional references to be made, that is, the facility of generalization beyond individual studies included in a meta-analysis (Field & Gillett [82], Pearson et al., [83], and Dwivedi et al. [77]). Furthermore, in cases when the fixed effect model would have been more appropriate, selecting the random effects model does not have a substantial negative impact (Field and Gillett [82]).

To structure the information discovered from the extracted literature, we formulate the following research questions:

i.
What are the factors that influence the API of customers?
ii.
What are the magnitudes and nature of the effects of each factor on the API of a customer?
iii.
What are the possible research gaps in studies conducted on API?
iv.
What is the impact of moderators on less explored antecedents and how much heterogeneity can they explain?

3. Extraction and Categorization of Factors

We grouped the different antecedents impacting API, as discovered from the literature, into 10 categories as shown in Table 1.

Table 1. Categorization of factors of API.

Category	Antecedents belonging to this category	Explanation of independent variables in each category
Demographical	Gender, age, education, income, rise in income, fall in income, and annual household debt	In the articles studied, gender can take only two values male and female, and age ranged from 18 to 75. Income specifies earnings per annum; similarly, for rise and fall in income. Annual household debt means the summation of the equated annual installment, which all borrower members of the house need to pay to the respective lenders

Psychocognitive	Attachment, attitude, environmental awareness, hedonism, perceived risk, personal norms, subjective norms, lifestyle, entertainment, and important life events	Attachment is an emotion that may incline a person toward buying his/her own personal vehicle. Attitude denotes one’s own assessment of the importance of an object (private vehicle in this case) coupled with feelings and behaviors toward it. Environmental awareness indicates how much one cares about the environment. Perceived risk makes one concerned about the adverse outcome of buying a private vehicle. Personal norm defines one’s belief system, about right or wrong [84, 85]. Subjective norm is a predictor of one’s behavior; it is one’s perception of the social expectations to adopt a particular behavior. Lifestyle is one’s way of life, particularly measured by lavishness. Entertainment denotes a feeling of pleasure, recreation, or amusement related to the ownership of a personal vehicle. Important life events consist of events worth remembering such as getting a new job, marriage, and childbirth

Sociopolitical	Social influence or pressure and government policies	Social influence or pressure describes how one conforms to others, obeys rules, and interacts with those in power. It can be based on a specific action, command, or request, or it can be a response to what one perceives about others’s thoughts or actions. Government policies are guidelines which one is expected to adhere to and may influence API

Product features	Product quality, comparative product quality, design, uniqueness, safety, space, resale value, fuel efficiency, engine horsepower, product reputation in social media, electronic word of mouth, conspicuousness, availability of spare parts, comfortability, discount, facility of installment, vehicle insurance, warranty, price, and maintenance cost	Product quality specifies the perceived performance of a product, while comparative product quality involves the comparison of the performance of a product with other similar products in the market. Design refers to visual and functional appeal. Uniqueness means how well the product can be distinguished from other similar ones. Safety means the safety features included. Space in this context denotes two things, the number of people who can comfortably sit inside and the amount of storage area for luggage. Resale value is the current estimated value of the product in the market. Fuel efficiency is the average distance that the vehicle can travel with 1 L of fuel. Horsepower denotes the power of an engine and thus leads to a better driving experience mostly at the cost of fuel efficiency. Product reputation in social media refers to the way in which a product is perceived by social media users, as understood based on social media posts on the product. Electronic word of mouth refers to the reviews and comments on the product appearing on social media sites and blogs. Conspicuousness means the capability of attracting viewers’ attention based on some exclusive characteristics or features, sometimes also referred to as “road presence” in the case of vehicles. Availability of space parts is the inverse of waiting time to obtain a spare part for servicing one’s request Comfortability is the physical comfort which the particular product can provide. A discount is a reduction in the usual price. The facility of installment is a payment plan that allows a buyer to pay in smaller amounts over a set period of time; this is sometimes beneficial for buying an expensive product especially when the customer is unable to pay the price of the product in full at a time. Vehicle insurance, also known as motor insurance or auto insurance, is a contract between an insurance company and an individual that protects the policyholder and their vehicle from financial losses. It can cover a range of events, including accidents, theft, natural disasters, fire, and damage to third parties. A vehicle warranty is a written assurance from the manufacturer or a third party that promises to repair or replace a vehicle’s defective parts or issues within a specified period of time. Price is what one pays to buy the product, while maintenance cost is the estimated one-time or recurring cost of repairing to keep the product in service

Brand	Brand image, brand awareness, brand loyalty, brand association, after-sales service, availability of test driving facility, persuasion capability of salesperson, quality of advertisements, and ease of information acquisition	Brand image is the reputation of a brand, and brand awareness signifies the level of familiarity with a given brand. Brand loyalty is when one continues to purchase from the same brand over and over again, despite competitors offering similar products or services. Brand association is the emotional and/or experiential connection one makes with a brand. After-sales service is the support and assistance that a business provides to customers after one has purchased a product or service. Test driving facility is a real-time opportunity to drive the vehicle’s prepurchase to determine if a vehicle fits one’s expectation in terms of performance, space, comfort, convenience, and utility needs. Persuasion capability of a salesperson is the ability of the concerned employee to influence a potential customer to take a desired action, such as purchasing a product. Quality of advertisements refers to its relevance and capability to appeal to the audience and adherence to industry standards. Ease of information acquisition refers to the easy availability or otherwise of all relevant information about a particular product

Experiential	Quality of public transport, quality of rail transit, traffic management quality, and vehicle sharing experience	Quality of public transport denotes the perceived performance and experience of people traveling in trains, buses, and various other modes of public transport. The quality of rail transit is particularly concerned about the experience of people traveling in trains. Traffic management quality refers to the administration’s management of vehicles and pedestrians to ensure their safe and efficient movement. Vehicle sharing experience is one’s perceived feeling of comfort and convenience in sharing a vehicle, mostly a car, through a carpooling service

Road and neighborhood	Congestion, highway building, parking difficulty, population density, residential arrangement in surrounding locations, road infrastructure, and pollution	Congestion occurs when there are too many vehicles and/or people on a road at a given time, causing traffic to move slower than normal. This causes problems in traveling and transportation. Highway building is the process of building and maintaining the infrastructure that allows people and goods to travel safely and efficiently. Parking difficulty is the degree of difficulty in finding a parking space. Population density in a location is a number of people residing in that location per unit area. Residential arrangement in the surrounding location determines how convenient it is to drive vehicles on roads in those locations. Road infrastructure refers to the physical assets and associated equipment that make up a road network. Pollution is the introduction of contaminants into the environment that cause adverse changes

Operational peripheral–related	Fuel cost	Fuel cost specifies the purchase price of fuel per liter

Travel characteristics–related	Distance to work and travel time	Distance to work is the distance between one’s home and workplace, while travel time refers to the time required to traverse this distance

Requirement-related (depreciation, situational, and weather-related)	Epidemic severity, weather conditions, and depreciations	Epidemic severity refers to the recent COVID-19 [86] scenario (and similar scenarios that may happen in future) which may discourage one from traveling in public vehicles or sharing vehicles with others. Harsh weather conditions may also discourage one from availing public transport, as that may entail walking out into inclement weather and may not be convenient. Depreciation is the gradual decrease in a product’s value over time due to wear and tear and aging

In the following subsections, we present the analysis of antecedents as extracted from the literature based on these 10 categories in the light of consumer purchase intention theories.

3.1. Demographical Factors

Alice et al. [57] highlighted gender as an influential demographical factor as they found that male students are more inclined to purchase private four-wheelers, while the female students prefer public transport. This is in line with Thomas et al. [58], which mentions that “women in Australia mostly consider the car as a mere status symbol and do not encourage solo driving.” Quite an opposite picture has been portrayed by Tao et al. [59] about the Nanjing city of China, where “males prefer public transport more than females.” However, all these findings indicate that gender may have an impact on the API of customers. Some studies have found education and income as two other demographic variables having a direct influence on API, while a few others found them to be mediators of other influencers of API. The craze of buying private vehicles has been noticed among the highly educated university students of India, China, United States of America, and Indonesia [63]. Therefore, education, as a demographic variable, may have a considerable influence on the API of consumers. Income, in terms of rise and fall in income, and annual household debt are also considered as important demographical factors in this context [60–62, 87]. A rise in income enables a person to purchase more comfortable and expensive vehicles, whereas a fall in income leads to a reduction in financial strength, thereby requiring one to revisit one’s prevailing taste in products. Annual household debt cuts short the financial capability of a family, thus discouraging the purchase of expensive personal vehicles (Mannering, Winston, and Starkey) [88]. Golob et al. [50] highlighted age as an API antecedent that has a significant impact on consumer’s decision-making. Older people emphasize safety, while younger ones prioritize vehicle’s performance. Income as an influencer is also supported by the Veblenian’s sociopsychological model (VSPM), which says that “mostly the people belonging to affluent classes spend a lot of income on purchasing conspicuous products such as cars and houses.” This further comes up in the literature as separate mentions such as “rise in income,” “fall in income,” and “annual debt of household.” Based on these, we develop the following hypotheses (Table 2).

Table 2. Hypotheses generated from demographic attributes.

Hypothesis	Statement
H1:	Gender has a positive effect on API
H2:	Age has a positive effect on API
H3:	Education has a positive effect on API
H4:	Income has a positive effect on API
H5:	Rise in income has a positive effect on API
H6:	Fall in income has a negative effect on API
H7:	Annual household debt has a negative effect on API

3.2. Psychocognitive Factors

Multiple theories on consumer purchase behavior support the psychocognitive attributes. Attitude and subjective and personal norms are supported by theories of planned behavior and reasoned action [89, 90]. Ikezoe et al. [65], Steg et al. [30], and Choo and Mokhtarian [91] mention the impact of attitude, lifestyle, and personal and subjective norms on the vehicle purchase behavior of consumers. According to Ikezoe et al. [65], attachment to one’s cars is an emotion that discourages people from avoiding less-expensive alternatives. Choo and Mokhtarian [91] state that people who want to take charge of situations (leader or organizer personality) prefer large, luxury and sporty cars, whereas peaceful and not-in-charge people (calm personality) go for mid- and small-sized cars. Muromachi [92] establishes a correlation between personal vehicle purchase intention of students and the mode of transport they use to commute to their university; students who use bicycles would like to own a vehicle in the near future, whereas those who travel by train do not generally prefer to purchase a personal vehicle. Personal norms along with environmental awareness encourage people to avail themselves of public transport; they consider this as their contribution to the reduction in air pollution and cleansing of the environment, although more research is required to justify such a claim (Zhou et al. [66]). Subjective norms are based on perceived social pressure that consciously or unconsciously imbibes certain behavior on people. The need for social approval encourages one to show off one’s economic status, and this can be through the ownership of expensive luxury products. In some societies, people purchase personal vehicles during important life events such as marriage, relocation, and birth of a child. Therefore, these events may have a positive impact on API.

Perception of risk is a psychological factor that discourages one from buying expensive products, whereas hedonism is a belief that promotes the pleasure of “comfortable living” as a “way of life.” These thus affect the purchasing of private vehicles. Belief, attitude, and perception are highlighted by the stimulus response model [93, 94]. Hedonism is in line with the same model. Personality traits and experiences have been highlighted by Nicosia’s model [95], and particularly as per the third phase of this model, purchase decision is made based on the awareness about a brand and its alternatives in light of the personal experiences. This is in line with mentions of brand awareness (as a cognitive factor) and brand loyalty (as an awareness and experience-driven emotion) found in the API literature. Perception of risk has been mentioned by both Howard–Sheth’s model (HSM) and Freudian’s psychoanalytical model [96]. These help us to generate the hypotheses listed in Table 3.

Table 3. Hypotheses generated from psychocognitive attributes.

Hypothesis	Statement
H8:	Attachment has a positive effect on API
H9:	Attitude has a positive effect on API
H10:	Environmental awareness has a negative effect on API
H11:	Hedonism has a positive effect on API
H12:	Perceived risk has a negative effect on API
H13:	Personal norms have a negative effect on API
H14:	Subjective norms have a positive effect on API
H15:	Lifestyle has a positive effect on API
H16:	Entertainment has a positive effect on API
H17:	Important life events have a positive effect on API

3.3. Sociopolitical Factors

The psychological need of rising in social status and satisfying the expectations of family members and friends generates a peer pressure in the minds of youngsters to purchase one’s own personal vehicle [97, 98]. Belgiawan et al. [97] in a study involving students in Indonesia, China, Japan, Lebanon, Netherlands, and United States of America, found that “peer pressure is the highest among students of Japan and United States of America and lowest in the Netherlands because Dutch students consider personal four-wheelers as a mere status symbol.” Environmental awareness is sometimes forced politically on people by governments of various countries to discourage people from purchasing cars [99]. Other government policies also exist that may encourage or discourage a person from buying automobiles [100, 101]. Social pressure has been mentioned in the theory of consumer behavior (TCB), the unified theory of acceptance and use of technology (UTAUT), and its newer version (UTAUT2), where age, gender, and experience act as mediators. Based on these, we develop further hypotheses as seen in Table 4.

Table 4. Hypotheses generated from sociopolitical factors.

Hypothesis	Statement
H18:	Social influences have a positive effect on API
H19:	Government policies have a negative effect on API

3.4. Product Features

Product features can further be subdivided into two groups as follows: (i) related to manufacturing technology and (ii) not related to manufacturing technology. Research on the impact of these attributes is comparatively a newer trend, and there are conflicting findings in the literature. Isa et al. [102] in their study conducted in Malaysia assigned higher weights to vehicle purchase decision criteria such as safety, comfort and fuel economy as compared to resale value and design. J.D. Powers and Opinion Research Consumer Insights (ORCIs) study conducted in the United States, states that “fuel economy, as a component of car purchase decision, was considered important in the early 1980s, dropped in 1990s, and then rose a bit in the last few years.” Kurami and Turrentine [103] interviewed prospective buyers of California and found that fuel economy had little importance to purchasers, especially those who planned to buy inexpensive cars. Gupta [104], however, found that fuel efficiency along with price and engine horsepower have strong influences on buying decisions. Kate and Handa [105] in their research on antecedents of API among people of Pune, India, found that design, quality of service, uniqueness, and word of mouth are more important as compared to safety and technological considerations. The study of Isac [106] in Romania listed the attributes as quality, safety, and price, in descending order of their importance. Manoranjitham and Singaravelu [107] found the design of a car to be one of the most crucial features for the people of Coimbatore in India. He advised the manufacturers to introduce more innovative colors and new models to increase the market share while taking initiatives for reducing maintenance costs. The study of Stella and Rajeswari [108] conducted in the Virudhunagar district of Tamil Nadu in India discovered that people assigned more importance to price, maintenance cost, and durability as compared to other features such as design and safety. Golob et al. [50], Smith and Wright [109], and Banerjee [51] emphasized the effectiveness of products as an important component of the purchase intention of customers. The criteria of comfortability and safety were raised by Steg et al. [30]. Price has a strong impact on buying of vehicles given that these are expensive products [33, 110] and has a negative effect on API [110].Along with price, discount and warranty are also highlighted [111, 112] as components of API. Saeed et al. [113] proposed the availability of spare parts as an API antecedent. The role of product reputation in social media has been advocated by Chen et al. [114], Karunanayake, and Madubashini [115]. Anandh and Shyama Sundar [116], in addition to the availability of spare parts, mention the uniqueness in design, resale value, safety, space, comfort, ease of availing automobile loans, warranty, and immediacy of delivery as important factors in Indian context. The availability of automobile loans has also been advocated by Havalder [117]. The match and contrast among the various studies mentioned above further justifiy the need for further analysis of the topic.

Among the various factors belonging to product features, perceived product quality is supported by the TCB. Product attributes, in general, are associated with the HSM, whereas performance effectiveness is associated with Marshallian’s economic theory (MET) and Fishbein’s multiattribute model (FMAM). In the limited problem-solving stage of the HSM, the customer makes himself completely aware of the various products offered by different brands, including the pros and cons of each product. MET emphasizes price, promotional expenditure, and product performance as components of API. Price as a significant player in API is also highlighted by the social cognitive theory (SCT). Comfort is a physiological need and is supported by Maslow’s hierarchy of needs theory and Pavlovian’ s learning model. Overall product performance (without mentioning any specific attribute) is strongly supported by UTAUT and UTAUT2. Table 5 shows the hypotheses we draw from the above.

Table 5. Hypotheses generated from product features.

Hypothesis	Statement
H20:	Product quality has a positive effect on API
H21:	Comparative product quality has a positive effect on API
H22:	Design has a positive effect on API
H23:	Uniqueness has a positive effect on API
H24:	Safety has a positive effect on API
H25:	Space has a positive effect on API
H26:	Resale value has a positive effect on API
H27:	Fuel efficiency has a positive effect on API
H28:	Engine horsepower has a positive effect on API
H29:	Product reputation in social media has a positive effect on API
H30:	Conspicuousness has a positive effect on API
H31:	Availability of spare parts has a positive effect on API
H32:	Comfort has a positive effect on API
H33:	Discount has a positive effect on API
H34:	Facility for payment of loans in installments has a positive effect on API
H35:	Vehicle insurance has a positive effect on API
H36:	Warranty has a positive effect on API
H37:	Price has a negative effect on API
H38:	Maintenance cost has a negative effect on API

3.5. Brand Factors

Smith and Wright [109] found, among various brand-related factors, brand image to be an important antecedent, something that conjures an overall impression of the brand in the minds of customers. A good brand image thus positively impacts the API. In addition, brand awareness, brand loyalty, and brand association have also come up in literature in this context [52, 118] as positive antecedents for API. Brand awareness specifies the familiarity of a consumer with the products or services of the brand, and brand association implies the emotional connection of a consumer with a brand. Brand loyalty encourages one to buy products of a particular brand, even when there are similar products or services offered by competitors; this is expected to strongly govern the choice of products during purchase. Good postsale service [119] and test driving facility [116] create a long-lasting impression in the minds of customers. These are categorized as brand factors as they are not dependent upon any specific product or model of the company. The persuasion capability of salespersons can significantly promote the sale of automobile products [120]. Advertisements and the ease of information acquisition are also important. Good quality advertisement content and well-structured websites and social media pages help to increase brand awareness and thus can influence API. Among these factors, brand image is supported by SCT, FMAM, and HSM [95]. This discussion generates the hypotheses of Table 6.

Table 6. Hypotheses generated from brand factors.

Hypothesis	Statement
H39:	Brand awareness has a positive effect on API
H40:	Brand loyalty has a positive effect on API
H41:	Brand association has a positive effect on API
H42:	Brand image has a positive effect on API
H43:	After-sales service has a positive effect on API
H44:	Availability of a test driving facility has a positive effect on API
H45:	Persuasion capability of a salesperson has a positive effect on API
H46:	Quality of the advertisement has a positive effect on API
H47:	Ease of information acquisition has a positive effect on API

3.6. Experiential Factors

Experiential factors include experiences of customers with respect to public transport, rail transit, and shared vehicles. Good experiences with these less-expensive alternatives refrain people from purchasing vehicles of their own [68, 116, 121]. Turoń [40] discussed about the utilities of vehicle sharing in the context of smart cities. Challenges faced by the sharing economy have been pointed out by Finger et al. [26]. These are applicable for the sharing of vehicles as well. Personal vehicles save effort and time as compared to public and shared transport and therefore are perceived to be more useful by affluent customers [23]. Various theories of consumer purchase intention have mentioned about perceived usefulness, including the TCB, the technology acceptance model (TAM), and its newer version TAM2. This discussion generates the hypotheses of Table 7.

Table 7. Hypotheses generated from experiential factors.

Hypothesis	Statement
H48:	Quality of public transport has a negative effect on API
H49:	Quality of rail transport has a negative effect on API
H50:	Traffic management quality has a positive effect on API
H51:	Vehicle sharing experience has a negative effect on API

3.7. Road and Neighborhood Factors

Good road conditions, parking facility, lesser congestion on road, as well as neighborhood are factors that promote the purchase of personal vehicles [99, 121–124]. These are associated with the criterion of ease-of-use of products. Perceived ease-of-use has been supported by the TAM and its newer version TAM2 [95]. Table 8 shows the resultant hypotheses.

Table 8. Hypotheses generated from road and neighborhood characteristics.

Hypothesis	Statement
H52:	Congestion has a negative effect on API
H53:	Building of highways has a positive effect on API
H54:	Difficulty in parking has a negative effect on API
H55:	Population density has a negative effect on API
H56:	Residential arrangement in surrounding locations has a negative effect on API
H57:	Road infrastructure has a positive effect on API
H58:	Pollution has a negative effect on API

3.8. Travel Characteristic–Related Factors

Travel characteristics include two things, distance to work (or trip length) and estimated time to travel (Ababio-Donor et al. [125], Zhou et al. [126], and Anable et al. [127]). Private vehicles are perceived to be more useful when the trip length or estimated time of travel is high, given that they save time. However, there are some conflicting opinions too; covering long trip lengths or large distances in private vehicles increases the cost of travel, thereby reducing the usefulness of the vehicle. Overall, these factors are components of perceived usefulness as mentioned in the TCB, the TAM, and its newer version TAM2. Table 9 shows the hypotheses we draw herein.

Table 9. Hypotheses generated from travel characteristics.

Hypothesis	Statement
H59:	Distance to work has a positive effect on API
H60:	Travel time has a positive effect on API

3.9. Operational Peripheral–Related Factors

Fuel is an important operational peripheral for automobiles (Dargay [110, 128] and Gupta [104, 116]). A lower price of fuel encourages people to purchase personal vehicles and vice versa. This discussion generates hypotheses of Table 10.

Table 10. Hypothesis generated from operational peripheral–related factors.

Hypothesis	Statement
H61:	Fuel cost has a negative effect on API

3.10. Requirement-Related Factors (Depreciation-Related, Situational, Weather-Related, and Environmental)

During COVID-19, people preferred personal vehicles as they perceived them to be safer against the spread of contagion as compared to public transport. This had a positive impact on the purchase intention of private vehicles (Yan et al. [12]). Depreciation of vehicles already owned by a customer also motivates one to purchase a new car (Shugin and Gang [129]). Commo et al. [130] found that bad weather encourages a person to buy a private vehicle. Therefore, all these are components of perceived usefulness and have a positive impact on API of customers as specified by the TCB and the TAM. This discussion generates the hypotheses of Table 11.

Table 11. Hypotheses generated from requirement factors.

Hypothesis	Statement
H62:	Epidemic (such as COVID-19) severity has a positive effect on API
H63:	Bad weather condition has a positive effect on API
H64:	Depreciation has a positive effect on API

4. Meta-Analysis

4.1. Significance of Occurrences of Factors of API

Table 12 presents the statistical significance of each API factor in terms of a number of articles which find the factor as significant or nonsignificant. The table also includes composite effect size, 95% CI–based range of effect size, measure of heterogeneity (I²), statistical significance value (ρ), and reliability index (Cronbach α). The variable “price” has the highest number of significant mentions (25). This is followed by “income” (21), “social influence” (16), “design” (13), “safety” (12), and others. “Annual household debt,” “highway buildings,” “test driving facility,” and “traffic management” have each been mentioned only once. A few relationships are categorized within the nonsignificant category. Heterogeneity is denoted by I² and defined by variation among the studies analyzed in the meta-analysis. Fletcher [78] and Deeks et al. [131] categorize heterogeneity of 0%–40% as negligible, 30%–60% as moderate, 50%–90% as substantial, and 75%–100% as considerable. The lowest heterogeneity in our analysis is 58%, which lies near the intersection of the moderate and substantial range; both of which are acceptable. ρ2 denotes the statistical significance of the overall effect size analysis and ρ1 denotes the average of the significance values mentioned in the article. β specifies the standardized effect size denoting the impact of a given antecedent on the API of a customer. It is calculated as a weighted average of effect sizes of individual studies as per the following equation:

()

where d_j is the effect size in the jth study and w_j is the weight of the study. The weight of each study is the inverse of its variance, which includes variation between the true effect sizes and sampling variation for each study (Noortgate and Onghena [132]).

Table 12. Statistical measure of heterogeneity and correlation.

SL. no.	Name of the antecedent	Total number of studies	No. of studies which found this antecedent significant	No. of studies which found this antecedent nonsignificant	95% CI range	Cronbach’s α	β	ρ1	I²	ρ2	Comment	∑kεωvεσσ
1	After-sales service	4	3	1	[0.27, 0.47]	0.923	0.37	0.001	76%	0.000	H43 is supported	0.61
2	Age	4	3	1	[0.06, 0.15]	0.859	0.1	0.001	72%	0.000	H2 is supported	0.54
3	Attachment	1	1	0	[0.25, 0.51]	0.832	0.4	0.001	71%	0.003	H8 is supported	—
4	Annual household debt	1	1	0	[−0.21, 0.19]	0.907	−0.01	NA	NA	0.000	H7 is not supported	—
5	Attitude	12	12	0	[0.27, 0.50]	0.987	0.39	0.001	65%	0.000	H9 is supported	0.73
6	Availability of spare parts	10	7	3	[0.05, 0.34]	0.887	0.19	0.001	78%	0.001	H31 is supported	0.48
7	Brand association	4	3	1	[0.12, 0.30]	0.876	0.21	0.003	69%	0.001	H41 is supported	0.29
8	Brand awareness	8	8	0	[0.21, 0.50]	0.923	0.36	0.001	73%	0.000	H39 is supported	0.87
9	Brand image	11	10	1	[0.25, 0.5]	0.91	0.38	0.001	65%	0.004	H42 is supported	0.31
10	Brand loyalty	16	16	0	[0.20, 0.55]	0.855	0.37	0.001	82%	0.001	H40 is supported	0.12
11	Comfortability	8	8	0	[0.45, 0.76]	0.953	0.56	0.001	76%	0.000	H32 is supported	0.57
12	Comparative product quality	2	2	0	[0.32, 0.55]	0.895	0.42	0.003	69%	0.004	H21 is supported	0.52
13	Congestion	8	8	0	[−0.6, −0.3]	0.907	−0.43	0.001	82%	0.000	H52 is supported	0.17
14	Conspicuousness	2	2	0	[0.05, 0.3]	0.895	0.16	0.001	78%	0.000	H30 is supported	0.42
15	Depreciation	3	2	1	[0.44, 0.56]	0.875	0.50	0.001	84.50%	0.000	H64 is supported	0.19
16	Design	13	12	1	[−0.1, 0.43]	0.931	0.26	0.001	68%	0.004	H22 is not supported	0.27
17	Discount	3	2	1	[0.28, 0.53]	0.853	0.41	0.001	83.50%	0.005	H33 is supported	0.33
18	Distance to work	3	2	1	[−0.58, 0.53]	0.902	−0.03	0.01	98%	0.002	H59 is not supported	0.52
19	Ease of information acquisition	2	2	0	[0.05, 0.13]	0.806	0.09	0.003	87%	0.000	H47 is supported	0.69
20	Easy installment	2	2	0	[0.35, 0.52]	0.894	0.43	0.001	87%	0.001	H34 is supported	0.92
21	Education	8	7	1	[0.04, 0.13]	0.845	0.09	0.001	77%	0.000	H3 is supported	0.24
22	Electronic word of mouth	6	6	0	[0.05, 0.35]	0.895	0.23	0.001	76%	0.000	H29 is supported	0.54
23	Engine horsepower	6	5	1	[0.22, 0.45]	0.899	0.33	0.001	73%	0.000	H28 is supported	0.67
24	Entertainment	2	2	0	[0.19, 0.32]	0.876	0.26	0.001	78%	0.000	H16 is supported	0.44
25	Environmental awareness	8	8	0	[−0.5, −0.2]	0.864	−0.32	0.001	79%	0.000	H10 is supported	0.67
26	Epidemic severity	2	2	0	[0.46, 0.6]	0.897	0.53	0.003	67%	0.000	H62 is supported	0.77
27	Fall in income	2	2	0	[−0.44, 0.27]	0.935	−0.1	0.001	70%	0.000	H6 is not supported	0.91
28	Fuel cost	2	2	0	[−0.26, −0.01]	0.915	−0.14	0.001	64%	0.008	H61 is supported	0.80
29	Fuel efficiency	6	5	1	[0.05, 0.34]	0.911	0.2	0.001	88%	0.001	H27 is supported	2.18
30	Gender	6	4	2	[0, 0.23]	0.923	0.11	0.001	74%	0.002	H1 is supported	0.59
31	Government policies	4	4	0	[−0.35, −0.03]	0.897	−0.2	0.001	77%	0.004	H19 is supported	0.44
32	Hedonism	4	4	0	[0.17, 0.41]	0.878	0.27	0.001	85%	0.001	H11 is supported	0.78
33	Highway building	1	1	0	[0.01, 0.15]	0.935	0.08	NA	NA	0.001	H53 is supported	—
34	Important life events	1	1	0	[0.05, 0.17]	0.883	0.11	NA	NA	0.001	H17 is supported	—
35	Income	21	21	0	[0.32, 0.63]	0.948	0.48	0.001	86%	0.000	H4 is supported	0.46
36	Insurance	3	2	1	[0.23, 0.45]	0.972	0.34	0.001	83%	0.001	H35 is supported	0.67
37	Lifestyle	5	5	0	[0.19, 0.32]	0.845	0.26	0.002	58%	0.007	H15 is supported	0.73
38	Maintenance cost	6	5	1	[−0.2, −0.01]	0.916	−0.31	0.001	77%	0.000	H38 is supported	0.57
39	Parking diff.	4	3	1	[−0.45, −0.17]	0.911	−0.3	NA	NA	0.004	H54 is supported	0.82
40	Perceived risk	7	6	1	[−0.58, −0.3]	0.959	−0.45	0.001	76%	0.006	H12 is supported	0.46
41	Personal norms	6	6	0	[−0.35, −0.10]	0.971	−0.23	0.001	73%	0.001	H13 is supported	0.52
42	Persuasion capability	3	2	1	[0.20, 0.40]	0.897	0.29	0.001	68%	0.000	H45 is supported	0.61
43	Pollution	4	3	1	[−0.25, −0.06]	0.822	−0.1	0.001	58%	0.000	H58 is supported	0.85
44	Population density	3	2	1	[−0.25, 0]	0.933	−0.12	0.001	76%	0.001	H55 is supported	0.78
45	Price	25	25	0	[−0.65, −0.3]	0.929	−0.55	0.001	89%	0.001	H37 is supported	0.26
46	Product quality	8	8	0	[0.41, 0.73]	0.737	0.6	0.001	79%	0.000	H20 is supported	0.44
47	Public transport quality	8	7	1	[−0.48, −0.25]	0.879	−0.37	0.001	73%	0.004	H48 is supported	0.36
48	Quality of advertising	4	3	1	[0.04, 0.23]	0.833	0.14	0.002	71%	0.002	H46 is supported	0.29
49	Rail transit	4	4	0	[−0.70, −0.40]	0.992	−0.54	0.001	76%	0.001	H49 is supported	0.32
50	Resale value	5	5	0	[0.20, 0.39]	0.914	0.29	NA	NA	0.001	H26 is supported	0.42
51	Residential arrangement in the surrounding location	2	1	1	[−0.32, −0.11]	0.92	−0.22	NA	NA	0.005	H56 is supported	—
52	Rise in income	3	2	1	[0.15, 0.33]	0.89	0.23	0.001	81%	0.004	H5 is supported	0.51
53	Road infrastructure	2	2	0	[0.17, 0.39]	0.951	0.28	0.001	83%	0.000	H57 is supported	0.72
54	Safety	12	12	0	[0.28, 0.58]	0.916	0.43	0.001	76%	0.000	H24 is supported	0.62
55	Social influence	16	16	0	[0.38, 0.69]	0.958	0.54	0.001	75%	0.000	H18 is supported	0.11
56	Space	6	5	1	[−0.15, 0.55]	0.909	0.36	0.001	74%	0.004	H25 is not supported	0.27
57	Subjective norms	8	8	0	[0.10, 0.48]	0.937	0.33	0.001	62%	0.005	H14 is supported	0.39
58	Test driving facility	1	1	0	[0.02, 0.23]	0.933	0.13	NA	NA	0.002	H44 is supported	—
59	Traffic management	1	1	0	[0.28, 0.40]	0.921	0.34	0.001	68%	0.001	H50 is supported	—
60	Travel time	4	4	0	[0.15, 0.50]	0.91	0.32	0.001	83%	0.000	H60 is supported	0.23
61	Uniqueness	3	3	0	[0.09, 0.4]	0.895	0.22	0.001	83%	0.000	H23 is supported	0.65
62	Vehicle sharing experience	9	7	2	[−0.48, −0.21]	0.952	−0.35	0.001	75%	0.008	H51 is supported	1.07
63	Warranty condition	4	3	1	[0.26, 0.48]	0.915	0.38	0.002	62%	0.005	H36 is supported	0.32
64	Weather condition	5	5	0	[0.04, 0.3]	0.969	0.17	0.001	69%	0.002	H63 is supported	0.41

Note: Hypotheses, which are not supported, are in bold. Also, values higher than 1 in skewness field are in bold.

Cronbach’s α determines the internal uniformity or average correlation of items in a survey mechanism to measure its reliability. It varies from zero to one and explains the reliability of factors obtained from dichotomous and/or multipoint-designed opinions on the dimension; a value of 0.7 and higher is considered reliable. Cronbach’s α associated with each variable in Table 12 is the average of the Cronbach α of all the studies where the particular variable has been mentioned. In our case, all the Cronbach α values are higher than 0.8; this denotes that the considered studies are sufficiently reliable. 95% CI range specifies a range of effect sizes for a given antecedent on API. Skewness is mentioned in the last column of this table, and the procedure of its calculation is shown in Section 4.2.

4.2. Detection of Publication Bias

Systematic reviews and meta-analyses may suffer from the file drawer problem, also called publication bias, where studies with significant results are more likely to be published as compared to those which fail to reject the null hypothesis, leading to an inaccurate representation of the effects of interest¹. The literature provides multiple methods to detect and measure publication bias, for example, funnel plots [133], Egger’s regression test [134], and skewness-based measurement [135]. A funnel plot is a scatter plot of the effect estimated from individual studies against some measure of each study’s size on precision. The vertical axis is that of the standard error of the effect estimates, and the horizontal axis is that of the Fisher’s Z-transformed correlation. In case of the absence of publication bias, studies with high precision are plotted near the average, and those with low precision are spread evenly on both sides of the average, generating a funnel-type of distribution (Zwetsloot et al. [136]). According to Tang et al. [137] and Pan et al. [138], publication bias can be determined with 6 studies. Egger’s regression test can quantify publication bias, but it is based on the “measurement of regression intercept which basically estimates the average of study-specific standardized deviates; it does not account for the shape of the deviates, which is skewed in the presence of publication bias. This limits the statistical power of the test” (Lin and Chu [135]). The skewness-based measurement method [135] involves the computation of the value of skewness or Ts for all antecedents, where Ts is computed as

()

where s is the sample’s standard deviation and m₃ is the sample’s third central moment as shown in the following equations:

()

Here, ε_i = −T_I, is the study’s specific standardized effect and T_I is its mean. ∈^′ is the mean of all , that is, the deviations of errors. The value of the skewness is interpreted as the estimate of publication bias. As per Lin and Chu [135], “If the skewness is less than 0.5 in absolute magnitude, the distribution of the standardized deviates is approximately symmetric; the skewness is deemed considerable if it is between 0.5 and 1, and it may be substantial if its absolute value is greater than 1.”

In this study, out of the 64 antecedents extracted, 9 are supported by 10 or more studies, and 29 are supported by 5 or more studies. Given this, we draw funnel plots for antecedents mentioned in six or more studies to check for publication bias (Figure 3) (Figures 3(a), 3(b), 3(c), 3(d), 3(e), 3(f), 3(g), 3(h), 3(i), 3(j), 3(k), 3(l), 3(m), 3(n), 3(o), 3(p), 3(q), 3(r), and 3(s)). In addition, given the smaller number of studies involved in the case of some antecedents, we compute the skewness individually for the antecedents mentioned to be significant in at least 2 studies, that is, we compute the Ts as suggested in the studies of Lin and Chu [135], which we have incorporated as the last column in Table 12. As can be seen from Table 12 (Row 29 and Row 62), the Ts value is greater than 1 for fuel efficiency and vehicle sharing experience, indicating substantial publication bias for these two antecedents. Hence, these need to be further explored, including here the unpublished works as well; something that can be taken up as future research.

4.3. Composite Antecedent Impact Diagram

The composite antecedent impact diagram is presented in Figure 4. Among the 64 antecedents, product quality (0.6) has the highest positive impact, whereas highway building (0.08) has the lowest positive impact on API. Comfortability (0.56), depreciation (0.5), epidemic severity (0.53), income (0.48), easy installment (0.43), and comparative product quality (0.42) also have high impacts on API.

Comfortability (0.56), depreciation (0.5), epidemic severity (0.53), income (0.48), easy installment (0.43), and comparative product quality (0.42) also have high impacts on API or automobile purchase much more than government policies (−0.2). Links colored in green specify that they are moderated by more than one variable, as shown in Figure 4. Of the 14 antecedents that have a negative impact, price has the highest negative impact (−0.55), whereas pollution has the lowest negative impact (−0.1). Rail transit (−0.54), public transport quality (−0.37), and vehicle sharing experience (−0.35) have a strong negative impact. Environmental awareness (−0.32) is capable of restricting automobile purchases.

Furthermore, Figure 5 presents a scatter plot of all the collated antecedents plotted against β (standardized effect size) and the logarithm of the number of studies that found these antecedents significant, indicating whether our hypotheses involving them are supported or otherwise. The logarithm was considered for the sake of the visible clarity of the plot. The lower part of the plot (below the without pub bias horizontal line, in gray area) has the antecedents which failed to find presence as significant in six or more studies.

4.4. Moderator Analysis

Many of the antecedents mentioned in this article have appeared as moderators to the link between fellow antecedents and API, as revealed from relevant literature. Mo and Ming [139] collected samples from Los Angeles County in the United States. They found that income moderates the link between (gender and API) and (age and API). The authors utilized the theory of reasoned action to establish the linkages. The moderation role of income has also been supported by Dias [140] in the link between age and API. Karunanayake and Samarasinghe [141] identified perceived risk as a moderator in between the links (social norms to API), (price to API), (performance to API), and (comfort to API). They had analyzed the influence of these factors based on the unified theory of the acceptance and use of technology. Zhao, Furuoka and Rasiah (Zhao et al. [142]) studied the influence of psychological factors on the purchase intention of customers in China. Based on the self-determination theory, their analysis revealed the moderating role of test drive experience between the links (perceived risk to API) and (perceived performance to API). Huang et al. [143], in their analysis based on the theory of planned behavior, found that the perceived service standard of public transportation moderates the links between (social norms, API), (perceived behavioral control, API), and (attitude, API). Based on this discussion, we can claim that various demographic, psychocognitive, and experiential factors play the role of moderators in the context of different countries. Others belonging to the same or different factors may also have a moderating influence on the links between other fellow factors and API. Therefore, we have tested all factors in the role of moderators between the links from abovementioned actors and API.

From the discovered antecedents, random effect sizes of “design,” “annual household debt,” “distance to work,” and “fall in income” cross the line of null effect (Table 12, column 95% CI range), indicating probable positive, negative, or zero impact of these antecedents on API. These thus require further investigation to understand their influence, if any, in influencing one to buy a personal vehicle, or otherwise. In addition, there might be possible moderation of other variables to API by these antecedents. We thus conducted moderator analysis, using the SEMinR package in R. We developed a questionnaire with 65 questions, one each for 64 antecedents and one for API. Each individual effect size is (score out of 5)/5. We administered the questionnaire to 155 individuals and received 91 responses from 59 males and 32 females. 46 had completed master’s degrees, while others had done bachelor’s; income ranged from USD 0.4 million/annum to USD 2.5 million/annum. For each two antecedents x1 and x2, we first computed the interaction term Z = x1 × x2. This was followed by a multiple regression model, fitted with x1, x2, and Z as predictors. The interaction effect size f² is calculated as

()

where

and

denote values of R² associated with endogenous construct when the interaction term of the moderator is included in and excluded from the path model, respectively. In our analysis, we set α = 0.05, which specifies that CI is 95% and computed f² and T statistic values of the specific interactions between the antecedents being investigated and API (the detailed summary output of our analysis is placed in Appendix 1 (see Table A1)). With T > 1.96, all the moderation tests are statistically significant. The following rules govern the identification moderation effect:

i.
If f² < 0.005, the moderation effect is negligible
ii.
If 0.005 ≤ f² ≤ 0.01, the moderation effect is small
iii.
If 0.01 ≤ f² ≤ 0.025, the moderation effect is medium
iv.
If f² ≥ 0.025, the moderation effect is large

From the analysis outcome (Appendix 1), we see that

•
Annual household debt ⟶ API link
- i.
  This is largely moderated by income and a rise in income; financial capability enables a person to withstand debt and still have a luxurious life. Price also has a large moderation effect on this link; a person who is under debt and still buying a personal vehicle will probably opt for a less expensive one.
- ii.
  Medium moderation effect can be noticed for antecedents such as attachment, lifestyle, easy installment, and gender. Attachment is an emotional connection to an object that often provokes a person to intentionally ignore pre-existing monetary constraints and opt for the purchase of an expensive luxurious item such as a personal vehicle. This is seen to be more prominent for men who consider it as an extension of their existence, unlike women. This is supported by Gossling [144] and Balkmar and Mellstrain, [145]. A prevalent lifestyle may habituate a person to use his own vehicle and may even persist in spite of debt.
- iii.
  Age, discount, and education are observed to have a small moderation impact. Young people are more at ease in taking the risk of lucrative purchases; they have a pretty long professional life ahead unlike the aged who might prefer to save more for their retirement. Discounts reduce pressure on the buyer’s pocket. Education may somewhat make people think more rationally and dampen impulsive purchases when under debt.
•
Design ⟶ API link
- i.
  Medium moderation effect is noticed for antecedents, depreciation and uniqueness. Buyers are often attracted to the uniqueness of the latest models. People also tend to change existing vehicles after they depreciate or get older.
- ii.
  Electronic word of mouth is seen to have a small moderation effect. People who have special preferences for look or décor are luxurious and fanciful in general, and they expect appreciation for the items they purchase [85, 146]. Prepurchase assessment and estimation of this appreciation is possible through analyzing social media reviews; if most of the reviews are positive, then that may motivate a hedonistic person to buy a specific vehicle.
•
Space ⟶ API link
- i.
  Design is observed to have a small moderation effect; a spacious vehicle is designed to maximize comfort for occupants. Without proper layout and installation of components, the utilization of space is not possible.
•
Fall in income ⟶ API link
- i.
  Income has a large and price has a medium moderation effect on this link. Despite a fall in income, if a person had a high income earlier, he may still think of owning a private vehicle. Motivation of the potential customer will be higher if the price of the vehicle is low.
- ii.
  Attachment has a small moderation effect; this antecedent in no way relieves financial pressure on the customer.
•
Distance to work ⟶ API link
- i.
  Price, fuel efficiency, and maintenance cost are found to have a medium moderation effect. If distance to work is driving the aspiration to buy, it is more of a necessity-initiated purchase than satisfying a hedonistic desire. The choice of such customers will be governed by cost-related constraints such as price, fuel efficiency, and maintenance cost.
- ii.
  A small moderation effect is noticed for highway building, road infrastructure, and weather. Highway building and road infrastructure increase the convenience of transportation, whereas bad weather strengthens the necessity of purchase.

To evaluate the heterogeneity that is explained by moderator variables, we applied the R package meta-CART, as it is more efficient in terms of interaction effects and does not require model selection; moreover, its time complexity is lesser than meta-regression (Li et al. [147]). While selecting moderators listed in Appendix 1, we have included only those that have large and medium moderation effects, ignoring those with small impacts. The underlying goal of meta-CART analysis is to identify subgroups defined by the moderators that can explain the heterogeneity in the study effect sizes. Appropriate data for meta-CART include an outcome variable of interest (i.e., study effect size), the within-study sample variance of the effect size, and the potential moderator variables that may influence the effect size. RE meta-CART takes the residual heterogeneity unexplained by moderators into account. The RE model is expressed as

()

where τ_k is the variation introduced by the residual heterogeneity. The residual heterogeneity is denoted by

, with τ_k distributed as N (0,

). It may be noted that there are two random components in the RE model: τ_k and ∈_k, where τ_k is the difference between the true effect size of the kth response and the mean of the population from which all effect sizes are sampled and ∈_k is the sampling error of the observed effect size d_k as an estimate of the true effect size of the kth response. While implementing the model, for a strict control of the Type I error (less than or equal to 0.05), the pruning parameter value of 1 (the default value recommended by Breiman et al. [148]) can be applied when the number of responses K < 80, and a value of 0.5, when K ≥ 80 [149]; we thus used the value of 0.5 for the pruning parameter. Table 13 lists out the results obtained.

Table 13. Illustration of heterogeneity explained by the moderator variables.

Dependent variable	Independent variable	Moderators	Estimated residual heterogeneity (tau-squared)	Between subgroups heterogeneity (in percentage)	P value
API	Annual household debt	Income, rise in income, attachment, lifestyle, easy installment, gender, and price	0.1405	47.11	< 0.0001
	Design	Depreciation and uniqueness	0.2067	36.53	< 0.0001
	Distance to work	Price, maintenance cost, and fuel efficiency	0.1119	33.18	< 0.0001
	Fall in income	Income and price	0.1347	51.47	< 0.0001

In Table 13, all p values are less than 0.000, that is, significant. All tau-squared values are higher than zero, hence there is non-negligible heterogeneity or diversity between characteristics of the subgroups. Residual heterogeneity between subgroups is 47.11% for annual household debt (and associated moderators), 36.53% for design (and allied moderators), and 51.47% for fall in income (and associated moderators), whereas for distance to work (and associated moderators), it is 33.18%. This specifies the heterogeneity between subgroups which cannot be explained by the moderators. In the case of three of the antecedents, annual household debt, design, and distance to work, most of the heterogeneity is explained by the moderators as the residual heterogeneity is less than 50%, which is moderate. Therefore, the mentioned moderators are important for respective antecedents. For the fall in income, however, residual heterogeneity is 51.47%; hence, heterogeneity explained by moderators is 48.53%, which is also non-negligible.

5. Conclusion and Future Work

In this study, we have reviewed articles on factors that have an impact on the API of customers. As far as the theoretical framework of antecedents of product purchase intentions is concerned (in general), some of the articles are associated with theory, while the others are not associated with any theory. We have thus discussed components of API considering several theories and specified average effect size along with the 95% CI range. We distinguished between the antecedents that generate positive impact, negative impact, and yet-undecided impact. The impact is undecided for antecedents for which the 95% CI effect range crosses the link of the null effect. In Tables 14 and 15, we summarize the findings of our article. Table 14 shows the type of impact (confirmed positive, confirmed negative, or undecided) on API, names of associated antecedents, and names of antecedents with and without publication bias along with names of antecedents for which publication bias could not be determined due to the small number of studies. Table 15 shows the antecedents with undecided impact on API and associated moderators with medium or large moderation effects.

Table 14. Antecedents, their impacts on API and publication bias.

Type of impact on API	Antecedents extracted from the literature	Antecedents with publication bias	Antecedents without publication bias	Antecedents for which publication bias could not be determined (the number of articles which found this antecedent to be significant was less than 6)
Confirmed positive impact	Gender, age, education, income, rise in income, attachment, attitude, hedonism, subjective norms, lifestyle, entertainment, important life events, social influence or pressure, product quality, comparative product quality, design, uniqueness, safety, resale value, fuel efficiency, engine horsepower, product reputation in social media or electronic word of mouth, conspicuousness, availability of spare parts, comfortability, discount, facility of installment, vehicle insurance, warranty, brand image, brand awareness, brand loyalty, brand association, after-sales service, availability of test driving facility, persuasion capability of company personnel, quality of advertisements, ease of information acquisition, traffic management quality, highway building, road infrastructure, travel time, epidemic severity, weather condition, and depreciations	Nil	Attitude, availability of spare parts, brand awareness, brand image, brand loyalty, comfortability, congestion, perceived risk, engine horsepower, income, environmental awareness, personal norms, price, product quality, safety, social influence, subjective norms, vehicle sharing experience, electronic word of mouth, design, and education	Gender, age, rise in income, attachment, hedonism, subjective norms, lifestyle, entertainment, important life events, comparative product quality, design, uniqueness, resale value, fuel efficiency, conspicuousness, discount, facility of installment, vehicle insurance, warranty, brand association, after-sales service, availability of test driving facility, persuasion capability of company personnel, quality of advertisements, ease of information acquisition, traffic management quality, highway building, road infrastructure, travel time, epidemic severity, weather condition, and depreciations
Confirmed negative impact	Congestion, environmental awareness, fuel cost, government policies, maintenance cost, perceived risk, personal norms, parking difficulty, pollution, population density, price, public transport quality, rail transit, residential arrangement in the surrounding location, and vehicle sharing experience	Nil	Congestion, perceived risk, environmental awareness, personal norms, price, and vehicle sharing experience	Fuel cost, government policies, maintenance cost, parking difficulty, pollution, population density, public transport quality, rail transit, and residential arrangement in the surrounding location
Undecided impact	Fall in income, annual household debt, space, distance to work, and design	Nil	Design	Fall in income, annual household debt, space, distance to work, and design

Table 15. Antecedents with undecided impacts on API and associated moderators.

Independent variable	Possible moderators with medium effect	Possible moderators with a large effect
Annual household debt	Attachment, lifestyle, easy installment, and gender	Income, rise in income, and price
Design	Depreciation and uniqueness	Nil
Distance to work	Price, maintenance cost, and fuel efficiency	Nil
Fall in income	Price	Income
Space	Nil	Nil

We distinguished among the antecedents that generate positive impact, negative impact, and yet-undecided impact. The impact is undecided for those antecedents which have crossed the link of null effect. Those factors with undecided impact (Table 15) are annual household debt, design, distance to work, fall in income, and space. These are possible areas for future research. Furthermore, there are some antecedents that have been found significant in less than six studies (last row of Table 14) , which need to be explored further.

5.1. Contributions to Theory

The study contributes to theory in several dimensions. The meta-analysis adds value to the existing knowledge on the various factors of API of customers. The study will help researchers with more dimensions to understand and deduce the type of variables that can be used for analyzing the impact of influencing API in different socioeconomic contexts. The study provides a consolidated view of relevant influencers along with their effect sizes coupled with reliability, statistical significance, and theoretical background.

Based on the findings mentioned above, we hereby add to the TCB by proposing the automobile consumer behavior (TACB), where factors are derived from antecedents for which there is no publication bias and the 95% CI of the effect size does not cross the line of null effect. The factors that influence the process in various stages are as follows:

•
Some factors contribute to problem recognition thereby triggering the potential outcomes of vehicle purchase, we term these factors as Influencer set A.
•
Some other factors promote product selection; we club them into two subphases: Subphase 1 is associated with information acquisition, which we term as Influencer set B, while Subphase 2 deals with the evaluation of alternatives. This evaluation may be performed based on the qualities of products (we term these as Influencer set C1) and qualities of brands (termed here as Influencer set C2).
•
This is followed by a purchase decision, and we term the corresponding factors as Influencer set D.
•
Factors which influence postpurchase experience, which may impact future product selection, are included in Influence set E.

The involved factors across the influencer sets are illustrated in Figure 6.

Antecedents belonging to Influencer sets A, B, C1, C2, D, and E are supported by various existing theories of purchase behavior as shown in Table 16. The relevant theories are as follows: TCB (Hung and Cheng [143], Gokhale et al. [150], and Hackbarth and Madlener [63]), theory of planned behavior (Dargay [110], Choo and Mokhtarian [91], Huang and Cheng [143], Bhutto. et al. [151], and Bearden [120]), theory of reasoned action (Choo and Mokhtarian [91]), SCT (Mo and Ming [139]), FMAM (Bhaskar et al. [37] and Altaf and Hashim [152]) HSM (Ebrahimi et al. [153] and Naidu [133]), Maslow’s hierarchy of needs theory (Yang et al. [12]), VSPM (Karoui et al. [154]), Nicosia’s model (Sohail and Sahin [33]), and MET (Amir and Asad [112]).

Table 16. Components of the automobile consumer behavior (TACB): antecedents in Influencer sets A, B, C1, C2, D, and E along with supporting theories.

Antecedents	Supporting theories
Antecedents	Fishbien’s multiattribute model	Howard–Sheth’s model	Marshallian economic theory	Maslow’s hierarchy of needs theory	Nicosia’s model	Social cognitive theory	Theory of consumer behavior	Theory of planned behavior	Theory of reasoned action	Veblenian’s social psychological model
Attitude							√	√	√
Availability of spare parts	√					√
Brand awareness	√	√
Brand image	√	√
Brand loyalty	√	√
Comfortability	√			√
Congestion							√
Education								√
Electronic word of mouth						√	√
Environmental awareness								√
Income
Perceived risk							√			√
Personal norms					√
Price			√
Product quality	√					√
Public transport quality				√
Safety				√
Social influence							√
Subjective norms								√
Vehicle sharing experience				√

5.2. Implications for Practice

The findings of this study provide some useful implications for practitioners, which can help them guide through the consumer’s decision-making process. Manufacturers as well as marketers can consider the influencers of the 64 factors on API, and can accordingly develop their strategies to reach out to the customer. The number of antecedents identified from the literature is further reduced to 20 based on there being no publication bias and the 95% CI of impact has not crossed the line of null effect. As the practitioners might be looking at influencing one or more stages of the automobile purchase lifecycle, they can look at those factors which exert considerable influence, positive or negative, in the particular stage(s) of the process. The antecedents based on their effect size and their stages of influence are plotted in Figure 7.

The antecedent product quality is the most important one for influencing API among potential customers, and this is looked at during the evaluation of alternatives and postproduct experience stages. This antecedent is followed by comfortability, reduction in price, safety features, brand loyalty, image, awareness, and availability of spare parts in their corresponding stages of the decision-making process. The practitioners can use this granularity of API knowledge as they reach out to their potential customers.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Anuradha Banerjee has contributed to conceptualization, data curation, formal analysis, investigation, validation, and preparation of the initial draft.

Basav Roychoudhury contributed to methodology, validation, visualization, and refinement of the draft for better representation, readability, and understanding.

Bidyut Jyoti Gogoi contributed to the methodology, validation, and refinement of the draft.

Funding

This work was not supported by any funding agency.

Endnotes

¹https://web.ma.utexuds.edu (last accessed on 25th December, 2024).

Appendix 1

Table A1. Analysis of moderation effect.

Antecedent	Moderator	f²	Nature of moderation	T–value
Annual household debt	After-sales service	0.001	Negligible	2.772
	Age	0.006	Small	2.116
	Attachment	0.016	Medium	12.210
	Attitude	0.003	Negligible	4.119
	Availability of spare parts	0.002	Negligible	5.424
	Brand association	0.002	Negligible	3.202
	Brand awareness	0.004	Negligible	2.091
	Brand image	0.004	Negligible	4.125
	Brand loyalty	0.004	Negligible	4.387
	Comfortability	0.001	Negligible	13.167
	Comparative product quality	0.003	Negligible	2.178
	Congestion	0.001	Negligible	2.567
	Conspicuousness	0.001	Negligible	3.191
	Depreciation	0.002	Negligible	2.708
	Design	0.002	Negligible	21.115
	Discount	0.005	Small	3.267
	Distance to work	0.004	Negligible	4.298
	Ease of information acquisition	0.002	Negligible	3.176
	Easy installment	0.011	Medium	1.989
	Education	0.005	Small	3.448
	Electronic word of mouth	0.002	Negligible	2.241
	Engine horsepower	0.004	Negligible	1.997
	Entertainment	0.001	Negligible	6.145
	Environmental awareness	0.002	Negligible	2.231
	Epidemic severity	0.004	Negligible	3.278
	Fall in income	0.004	Negligible	2.452
	Fuel cost	0.004	Negligible	4.775
	Fuel efficiency	0.002	Negligible	3.332
	Gender	0.015	Medium	2.136
	Government policies	0.002	Negligible	2.788
	Hedonism	0.002	Negligible	4.513
	Highway building	0.001	Negligible	10.977
	Important life events	0.001	Negligible	7.802
	Income	0.038	Large	4.886
	Insurance	0.001	Negligible	2.424
	Lifestyle	0.022	Medium	2.167
	Maintenance cost	0.004	Negligible	2.119
	Parking diff.	0.001	Negligible	3.781
	Perceived risk	0.001	Negligible	2.909
	Personal norms	0.002	Negligible	6.487
	Persuasion capability	0.003	Negligible	5.437
	Pollution	0.002	Negligible	3.499
	Population density	0.002	Negligible	3.219
	Price	0.031	Large	2.845
	Product quality	0.002	Negligible	6.081
	Public transport quality	0.001	Negligible	3.242
	Quality of advertising	0.001	Negligible	7.809
	Rail transit	0.004	Negligible	6.717
	Resale value	0.001	Negligible	1.998
	Residential arrangement in the surrounding location	0.001	Negligible	3.213
	Rise in income	0.041	Large	2.279
	Road infrastructure	0.003	Negligible	4.190
	Safety	0.002	Negligible	2.239
	Social influence	0.004	Negligible	2.211
	Space	0.002	Negligible	2.377
	Subjective norms	0.001	Negligible	4.102
	Test driving facility	0.001	Negligible	5.266
	Traffic management	0.002	Negligible	4.037
	Travel time	0.003	Negligible	6.115
	Uniqueness	0.002	Negligible	5.255
	Vehicle sharing experience	0.002	Negligible	2.832
	Warranty condition	0.004	Negligible	2.464
	Weather condition	0.003	Negligible	3.918

Design	After-sales service	0.002	Negligible	3.776
	Age	0.002	Negligible	4.132
	Attachment	0.002	Negligible	6.800
	Annual household debt	0.003	Negligible	3.679
	Attitude	0.001	Negligible	3.585
	Availability of spare parts	0.001	Negligible	5.266
	Brand association	0.002	Negligible	3.564
	Brand awareness	0.004	Negligible	2.244
	Brand image	0.001	Negligible	6.892
	Brand loyalty	0.003	Negligible	1.996
	Comfortability	0.002	Negligible	1.987
	Comparative product quality	0.003	Negligible	2.232
	Congestion	0.001	Negligible	3.821
	Conspicuousness	0.004	Negligible	2.366
	Depreciation	0.012	Medium	1.982
	Discount	0.002	Negligible	1.987
	Distance to work	0.003	Negligible	2.666
	Ease of information acquisition	0.001	Negligible	2.707
	Easy installment	0.001	Negligible	4.556
	Education	0.003	Negligible	5.101
	Electronic word of mouth	0.005	Small	7.766
	Engine horsepower	0.004	Negligible	2.591
	Entertainment	0.003	Negligible	3.918
	Environmental awareness	0.002	Negligible	3.768
	Epidemic severity	0.001	Negligible	2.132
	Fall in income	0.002	Negligible	2.229
	Fuel cost	0.003	Negligible	3.679
	Fuel efficiency	0.001	Negligible	3.585
	Gender	0.001	Negligible	4.233
	Government policies	0.001	Negligible	4.556
	Hedonism	0.003	Negligible	3.998
	Highway building	0.002	Negligible	2.001
	Important life events	0.004	Negligible	2.591
	Income	0.003	Negligible	3.918
	Insurance	0.004	Negligible	2.464
	Lifestyle	0.003	Negligible	3.918
	Maintenance cost	0.002	Negligible	3.776
	Parking diff.	0.001	Negligible	4.132
	Perceived risk	0.002	Negligible	6.800
	Personal norms	0.003	Negligible	3.679
	Persuasion capability	0.001	Negligible	3.585
	Pollution	0.001	Negligible	5.266
	Population density	0.001	Negligible	2.880
	Price	0.001	Negligible	4.342
	Product quality	0.004	Negligible	4.776
	Public transport quality	0.003	Negligible	5.489
	Quality of advertising	0.001	Negligible	3.811
	Rail transit	0.001	Negligible	1.999
	Resale value	0.003	Negligible	3.879
	Residential arrangement in the surrounding location	0.001	Negligible	4.121
	Rise in income	0.001	Negligible	6.266
	Road infrastructure	0.001	Negligible	2.887
	Safety	0.001	Negligible	4.389
	Social influence	0.004	Negligible	6.786
	Space	0.003	Negligible	5.455
	Subjective norms	0.001	Negligible	3.811
	Test driving facility	0.002	Negligible	2.844
	Traffic management	0.003	Negligible	3.194
	Travel time	0.001	Negligible	3.711
	Uniqueness	0.019	Medium	4.674
	Vehicle sharing experience	0.001	Negligible	2.884
	Warranty condition	0.001	Negligible	4.342
	Weather condition	0.004	Negligible	4.811

Space	After-sales service	0.003	Negligible	5.632
	Age	0.001	Negligible	3.891
	Attachment	0.002	Negligible	2.232
	Annual household debt	0.003	Negligible	3.821
	Attitude	0.002	Negligible	6.661
	Availability of spare parts	0.001	Negligible	1.982
	Brand association	0.004	Negligible	2.897
	Brand awareness	0.004	Negligible	2.668
	Brand image	0.002	Negligible	8.707
	Brand loyalty	0.003	Negligible	4.596
	Comfortability	0.001	Negligible	2.101
	Comparative product quality	0.004	Negligible	7.888
	Congestion	0.001	Negligible	2.591
	Conspicuousness	0.004	Negligible	3.917
	Depreciation	0.002	Negligible	4.768
	Design	0.007	Small	2.167
	Discount	0.001	Negligible	2.229
	Distance to work	0.001	Negligible	3.627
	Ease of information acquisition	0.001	Negligible	3.578
	Easy installment	0.004	Negligible	4.211
	Education	0.003	Negligible	4.956
	Electronic word of mouth	0.001	Negligible	3.998
	Engine horsepower	0.002	Negligible	2.001
	Entertainment	0.003	Negligible	2.591
	Environmental awareness	0.002	Negligible	3.918
	Epidemic severity	0.001	Negligible	2.464
	Fall in income	0.003	Negligible	3.918
	Fuel cost	0.004	Negligible	3.776
	Fuel efficiency	0.003	Negligible	4.132
	Gender	0.003	Negligible	6.800
	Government policies	0.001	Negligible	3.679
	Hedonism	0.003	Negligible	4.342
	Highway building	0.001	Negligible	4.811
	Important life events	0.004	Negligible	5.632
	Income	0.004	Negligible	3.866
	Insurance	0.001	Negligible	2.207
	Lifestyle	0.001	Negligible	3.371
	Maintenance cost	0.001	Negligible	5.457
	Parking diff.	0.002	Negligible	1.982
	Perceived risk	0.003	Negligible	3.444
	Personal norms	0.004	Negligible	2.119
	Persuasion capability	0.002	Negligible	2.233
	Pollution	0.003	Negligible	2.346
	Population density	0.002	Negligible	2.110
	Price	0.004	Negligible	6.969
	Product quality	0.004	Negligible	2.591
	Public transport quality	0.004	Negligible	3775
	Quality of advertising	0.002	Negligible	4.790
	Rail transit	0.003	Negligible	1.998
	Resale value	0.001	Negligible	2.245
	Residential arrangement in the surrounding location	0.004	Negligible	3.918
	Rise in income	0.004	Negligible	3.776
	Road infrastructure	0.004	Negligible	4.132
	Safety	0.003	Negligible	3.918
	Social influence	0.002	Negligible	3.776
	Subjective norms	0.004	Negligible	4.132
	Test driving facility	0.004	Negligible	6.800
	Traffic management	0.004	Negligible	3.679
	Travel time	0.002	Negligible	3.585
	Uniqueness	0.003	Negligible	5.266
	Vehicle sharing experience	0.001	Negligible	3.564
	Warranty condition	0.004	Negligible	2.244
	Weather condition	0.004	Negligible	6.892

Distance to work	After-sales service	0.004	Negligible	1.996
	Age	0.001	Negligible	1.987
	Attachment	0.002	Negligible	2.232
	Annual household debt	0.001	Negligible	3.821
	Attitude	0.001	Negligible	2.366
	Availability of spare parts	0.003	Negligible	1.982
	Brand association	0.004	Negligible	1.987
	Brand awareness	0.004	Negligible	2.666
	Brand image	0.002	Negligible	2.707
	Brand loyalty	0.003	Negligible	4.556
	Comfortability	0.001	Negligible	5.101
	Comparative product quality	0.004	Negligible	7.766
	Congestion	0.004	Negligible	2.591
	Conspicuousness	0.004	Negligible	3.918
	Depreciation	0.001	Negligible	3.768
	Design	0.002	Negligible	2.132
	Discount	0.003	Negligible	2.229
	Ease of information acquisition	0.001	Negligible	3.679
	Easy installment	0.004	Negligible	3.585
	Education	0.004	Negligible	4.233
	Electronic word of mouth	0.004	Negligible	4.556
	Engine horsepower	0.001	Negligible	3.998
	Entertainment	0.002	Negligible	2.001
	Environmental awareness	0.001	Negligible	2.591
	Epidemic severity	0.001	Negligible	3.918
	Fall in income	0.003	Negligible	2.464
	Fuel cost	0.001	Negligible	3.918
	Fuel efficiency	0.009	Medium	3.776
	Gender	0.004	Negligible	4.132
	Government policies	0.004	Negligible	6.800
	Hedonism	0.001	Negligible	3.679
	Highway building	0.006	Small	3.585
	Important life events	0.002	Negligible	5.266
	Income	0.003	Negligible	2.889
	Insurance	0.001	Negligible	4.111
	Lifestyle	0.004	Negligible	25.176
	Maintenance cost	0.014	Medium	5.489
	Parking diff.	0.002	Negligible	5.444
	Perceived risk	0.003	Negligible	1.993
	Personal norms	0.001	Negligible	3.456
	Persuasion capability	0.004	Negligible	2.121
	Pollution	0.004	Negligible	6.266
	Population density	0.004	Negligible	11.989
	Price	0.011	Medium	4.187
	Product quality	0.002	Negligible	4.322
	Public transport quality	0.001	Negligible	3.261
	Quality of advertising	0.001	Negligible	3.886
	Rail transit	0.003	Negligible	2.841
	Resale value	0.004	Negligible	7.792
	Residential arrangement in the surrounding location	0.001	Negligible	4.269
	Rise in income	0.003	Negligible	4.115
	Road infrastructure	0.005	Small	2.761
	Safety	0.003	Negligible	2.784
	Social influence	0.003	Negligible	4.887
	Space	0.001	Negligible	7.732
	Subjective norms	0.003	Negligible	3.891
	Test driving facility	0.004	Negligible	4.676
	Traffic management	0.003	Negligible	3.821
	Travel time	0.004	Negligible	7.109
	Uniqueness	0.004	Negligible	1.980
	Vehicle sharing experience	0.003	Negligible	1.999
	Warranty condition	0.004	Negligible	2.668
	Weather condition	0.008	Small	9.719

Fall in income	After-sales service	0.001	Negligible	2.557
	Age	0.001	Negligible	3.872
	Attachment	0.008	Small	3.918
	Annual household debt	0.004	Negligible	3.455
	Attitude	0.004	Negligible	17.918
	Availability of spare parts	0.004	Negligible	5.171
	Brand association	0.003	Negligible	3.189
	Brand awareness	0.004	Negligible	5.610
	Brand image	0.003	Negligible	3.619
	Brand loyalty	0.003	Negligible	4.166
	Comfortability	0.003	Negligible	6.786
	Comparative product quality	0.003	Negligible	6.119
	Congestion	0.002	Negligible	4.299
	Conspicuousness	0.002	Negligible	2.207
	Depreciation	0.004	Negligible	2.257
	Design	0.001	Negligible	5.778
	Discount	0.002	Negligible	1.977
	Distance to work	0.001	Negligible	3.444
	Ease of information acquisition	0.003	Negligible	5.912
	Easy installment	0.002	Negligible	5.214
	Education	0.002	Negligible	2.346
	Electronic word of mouth	0.003	Negligible	7.270
	Engine horsepower	0.002	Negligible	6.969
	Entertainment	0.002	Negligible	22.591
	Environmental awareness	0.004	Negligible	2.109
	Epidemic severity	0.001	Negligible	3.166
	Fuel cost	0.002	Negligible	2.990
	Fuel efficiency	0.001	Negligible	2.245
	Gender	0.001	Negligible	2.267

	Government policies	0.002	Negligible	17.112
	Hedonism	0.004	Negligible	1.999
	Highway building	0.001	Negligible	4.179
	Important life events	0.002	Negligible	3.132
	Income	0.026	Large	6.233
	Insurance	0.002	Negligible	6.959
	Lifestyle	0.002	Negligible	4.400
	Maintenance cost	0.004	Negligible	3.585
	Parking diff.	0.001	Negligible	3.123
	Perceived risk	0.002	Negligible	3.564
	Personal norms	0.002	Negligible	2.244
	Persuasion capability	0.002	Negligible	3.760
	Pollution	0.004	Negligible	7.059
	Population density	0.001	Negligible	7.675
	Price	0.012	Medium	2.232
	Product quality	0.001	Negligible	4.727
	Public transport quality	0.003	Negligible	1.998
	Quality of advertising	0.002	Negligible	1.982
	Rail transit	0.002	Negligible	6.667
	Resale value	0.004	Negligible	2.190
	Residential arrangement in the surrounding location	0.001	Negligible	2.707
	Rise in income	0.002	Negligible	3.812
	Road infrastructure	0.001	Negligible	13.172
	Safety	0.003	Negligible	8.788
	Social influence	0.002	Negligible	3.534
	Space	0.002	Negligible	3.918
	Subjective norms	0.003	Negligible	3.532
	Test driving facility	0.002	Negligible	6.199
	Traffic management	0.002	Negligible	3.107
	Travel time	0.004	Negligible	3.679
	Uniqueness	0.001	Negligible	6.223
	Vehicle sharing experience	0.002	Negligible	8.102
	Warranty condition	0.001	Negligible	7.158
	Weather condition	0.001	Negligible	3.998

Open Research

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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