1. Introduction
The Internet has contributed to the expansion of e-commerce as online personalised purchases have increased considerably in the last decades. From all sales worldwide, e-commerce sales account for 18%. China was the world leader in e-commerce sales in 2020, and the highest percentage growth was observed in Argentina [
1]. Brazil and Mexico were the Latin-American leaders in 2020, with 31% and 28%, respectively [
1]. Moreover, US
$ 112.4 billion were spent in the online market in Brazil, followed by Mexico (31.5 billion) and Colombia (14.5 billion in 2020) [
1]. Fashion, shoes, and cosmetics are the leading product categories among Brazilian shoppers. This paper addressed e-commerce in Brazil, an emergent market, which reached 8% of total sales in 2020.
The demand for delivery services has increased due to online shopping [
2]. However, the increase of business-to-consumer (B2C) deliveries has worsened urban freight-related problems, such as negative impacts on traffic and pollution and urban mobility reduction [
2]. B2C deliveries benefit from the high demand in urban cities [
3], although these services generally consider just one package per delivery, thus not allowing for economies of scale [
4].
Despite the growth of e-commerce in the Brazilian market, few studies have analysed the impacts of e-commerce deliveries. The focus is usually on the assessment of sustainable last-mile alternatives. Nogueira et al. [
5] analysed the awareness of e-consumers for sustainable last-mile delivery decisions and Oliveira et al. [
6] analysed the demand for pick-up points as a sustainable solution for last-mile deliveries. However, the factors influencing delivery services should be known to fulfil customers’ expectations and needs [
3]. The most common delivery factors reported in the literature are delivery time [
5,
7,
8,
9], delivery reception, and return possibility [
7]. Nonetheless, e-consumer behaviour plays a vital role in e-commerce [
10], once urban freight policies and practices require understanding how the e- consumers react to it [
11]. Therefore, understanding e-consumer behaviour is key to solving the last-mile problem.
The literature related to the effects of e-commerce delivery attributes on consumer behaviour is limited and focuses on supply chain management [
12] and e-commerce purchases [
8]. Moreover, some research shows delivery’s influence on e-shopping [
13,
14,
15,
16]. More specifically, short delivery times influence e-shopping decisions [
12]. Furthermore, Amorim et al. [
17] found that delivery time, delivery reception, and the flexibility of delivery reception are critical attributes that considerably influence customer preferences for home deliveries of e-grocery products.
The research question addressed in this paper is how delivery attributes affect e-consumers’ behaviour. More specifically, we aim to analyse the effects of delivery attributes (delivery time, delivery fee, and delivery reception) on e-shopping consumer behaviour. Firstly, we estimated a regression model to identify which factors related to e-shopping consumer behaviour related to the delivery attributes. Next, we used artificial neural networks to identify patterns regarding the effects of delivery attributes on e-shopping consumer behaviour. The use of these techniques brings novelty to this study since the usual approaches found in the literature are discrete choice modelling [
6,
7,
8,
9,
18,
19,
20,
21,
22], conjoint analysis [
8], cluster analysis [
8,
23], the mixed structural equation model [
10], and statistical tests [
5].
Thus, the contribution of this paper is threefold. Firstly, we evaluated the effects of delivery attributes on e-shopping consumer behaviour. Second, we used alternative methods to analyse e-customers behaviour. Finally, we analysed the consumer behaviour in e-commerce in an emerging market, where the number of studies is still minimal [
24]. Findings support the development of sustainable delivery strategies. Most e-commerce deliveries are destined for the home/workplace of the buyer. While strategies such as pick-up points are still incipient in Brazil [
18], the absence of strategies to meet home delivery needs efficiently is unsustainable in the long term with the growth of e-commerce and the increasing number of orders, and thus the number of deliveries, by the free shipping strategy [
25]. Therefore, it is essential to identify how delivery attributes have influenced e-consumer behaviour in this context.
The outline of this paper is as follows.
Section 2 presents the literature review and the hypotheses development.
Section 3 describes the data used in this study.
Section 4 covers the research method, and
Section 5 presents the results. Finally, the discussions and the conclusions are presented in
Section 6.
2. Literature Review and Hypotheses Development
This paper analyses delivery fee, delivery time, and delivery reception as delivery factors. Delivery fee is a marketing strategy that influences consumer patterns [
8]. Delivery time is a critical factor of e-shopping decisions [
12] and is related to the time required to deliver the product to the customer [
8]. Product type, age, and education level influence the potential for accepting/rejecting longer delivery times [
5]. Also, faster deliveries increase consumer satisfaction, especially when buying hedonic products or purchases out of impulsive behaviour [
8]. In addition, e-consumers are more flexible to delivery times than delivery fees [
8]. Finally, delivery reception is related to how/when the deliveries are received, including during daytime and the date of deliveries [
7,
8]. The following sections outline the hypotheses analysed in this paper.
The influence of delivery on e-shopping was evaluated by [
5,
8]. Nguyen et al. [
8] analysed consumer preferences in the following delivery attributes: delivery fee, delivery time, and delivery reception. The authors used a conjoint analysis considering the consumer segments (price, time, and value for money) and their contexts (product type, demographic data, and purchase frequency). Nogueira et al. [
5] found that delivery time, delivery fee, and environmental information influence e-commerce purchases. They used the Mann-Whitney test, the Kruskal-Wallis test, and the Spearman correlation test. In addition, Buldeo Rai et al. [
7] evaluated the consumer preferences related to delivery price, delivery time, delivery reception, and return opportunities in last-mile deliveries.
Several aspects of e-consumer preferences have been studied, including last-mile options [
6,
10,
18,
20,
26,
27], customer-driven central last-mile micro depot [
28], and crowd logistics [
23]. Liu et al. [
29] evaluated customers travel choices to collect delivery points. Oliveira et al. [
6] analysed the importance of various delivery factors (delivery destination, delivery time, information and traceability, and delivery fee) to identify the potential demand for automated delivery stations, representing an alternative to e-commerce deliveries. Yuen et al. [
26] studied the factors that influence self-collection services. Xiao et al. [
10] examined the effects of final delivery solutions on e-shopping behaviour. Oliveira et al. [
18] evaluated the accessibility of collection and delivery points (CDP) considering customer preferences and the coverage area. Iannaccone et al. [
20] estimated the market share of pick-up points using customer preferences. From the customers ’ perspective, Hagen and Schell-Kopeinig [
28] examined the acceptance and willingness-to-pay of deliveries for a central last-mile micro depot. Buldeo Rai et al. [
23] investigated the preferences for crowdsourcing among end-consumers.
The environmental and social impacts of B2C deliveries also influence e-customers [
9,
21] and make them choose sustainable delivery options [
9]. The environmental factor increases the tolerance for longer delivery times [
9,
21] and delivery costs [
9]. Consumers choose less convenient delivery destinations, especially when additional economic benefits are offered [
9]. Positive environmental information might encourage sustainable delivery choices [
19]. Ignat and Chankov [
9] evaluated the role of information on e-customers preferences and identified that consumers tend to accept longer delivery times and pay more for environmentally sustainable deliveries.
Sociodemographic characteristics have already been studied in the context of e-shopping consumption [
30]. For example, Xiao et al. [
10] identified that e-shopping frequency is positively influenced by gender, marital status, and the educational level of the consumers. However, previous research has also shown diverging results. For example, Irawan and Wirza [
31] identified an association between sociodemographic characteristics and e-shopping frequency. On the other hand, Yuen et al. [
26] did not find an association between consumers demographic characteristics and e-shopping. Also, demographic characteristics do not affect customers intention to use self-collection services [
26]. Iannaccone et al. [
20] included socioeconomic characteristics to estimate the demand for pick-up points. Buldeo Rai et al. [
23] reported that socioeconomic characteristics do not influence crowdsourced last-mile services. However, Liu et al. [
27] found that psychographic variables explain e-consumer preferences across different product categories. Also, the sociodemographic variables might capture the indirect effects of delivery attributes [
29].
Despite the importance of socioeconomic characteristics for e-commerce, such attributes are mainly analysed only considering new delivery alternatives [
23,
26], i.e., the influence of socioeconomic characteristics on e-shopping deserves further exploration.
Few studies considered the influence of delivery attributes on e-shopping [
13,
14,
15,
16]. Further, the influence of e-shopping characteristics has not been largely explored [
8]. Some e-shopping characteristics such as product information, customer service [
15,
16,
32], brand selection, privacy, and promotions [
16], quality [
13,
15], pricing [
13,
15,
16], convenience [
13,
16,
32], and security [
13,
16] present positive impacts on customer satisfaction. Most of the literature focused on analysing the influence of these factors on e-shopping.
According to Cherrett et al. [
33], frequent shoppers are more likely to use a greater variety of delivery options, reducing delays to receive the products. The product type and purchase frequency influence consumer perceptions regarding delivery services [
16]. Also, Mehmood and Najmi [
34] identified positive impacts of service convenience (decision, access, transaction, general benefits, and post-benefits) on customer satisfaction of home deliveries.
Based on the literature, we propose the following hypotheses to answer the research question addressed in this paper: (1) delivery attributes affect e-shopping behaviour, and (2) delivery attributes affect e-consumers’ behaviour according to their sociodemographic characteristics.
5. Discussion
The estimated coefficients of the logistic regression models provide insights regarding the effects of the delivery attributes according to the sociodemographic characteristics of e-consumers. For instance, gender is considered when the impacts of delivery time and the influence of delivery fee and delivery reception are assessed. Women are more likely to wait longer for the convenience of online shopping. The influence of delivery reception and delivery fees have a greater effect on e-consumers in the 35–49-year-old age group. The purchase of product types (especially books and leisure products) is influenced by delivery fee and delivery reception, while delivery reception only affects the shopping frequency. Also, delivery fees affect the purchase of electronic products, while all delivery attributes are related to privacy, promotion, and pricing when e-shopping.
Table 6 summarises the estimated effects of the delivery attributes per sociodemographic and e-shopping characteristics obtained from the neural network models. The sign ‘+’ stands for positive effects (increasing patterns), and ‘−’ stands for negative (decreasing patterns). Moreover, constant and complex (concave or convex functions) effects are explicitly indicated. Finally, the degree of dependence for each explanatory variable was estimated as low, medium, or high by comparing THE slopes of the curves: the higher the slope, the greater the importance of the effect of the delivery attribute regarding the independent variable (sociodemographic characteristic, e-consumption behaviour, or e-shopping characteristic).
For the sociodemographic characteristics, age resulted in complex patterns for the importance of delivery fees, which may increase, stabilise, and decrease as age increases. Also, it presents negative medium effects for most of the other delivery attributes. On the other hand, income presented different effects for each delivery attribute, and it showed great relevance related to the importance of delivery times. Additionally, gender showed a low relevance or constant effect for the delivery attributes (except for the delivery reception influence on the e-shopping decision). For e-consumption behaviour, the frequency of consumption showed a constant relationship for most of the delivery attributes. The product type presented different patterns for the different delivery attributes, despite its low relevance or constant effect for most of the delivery attributes. Privacy is relevant mainly for the importance of delivery fees, while pricing is highly relevant to the importance of delivery reception.
While the logistic regression added robustness to the analysis by statistically validating the estimated coefficients per variable, ANN identified patterns employing a training and learning approach considering the non-linear and multidimensional characteristics about the relations between the variables. Results showed the effectiveness of combining these techniques to analyse consumer behaviour.
Table 7 shows the accuracy of the results for these techniques. Although the logistic regression presented better accuracy, the ANN allowed pattern identification complemented by the PDPs.
The results converge to the literature. For instance, sociodemographic characteristics and gender are related to delivery attributes, as pointed out by [
10], who studied e-shopping frequency. Also, the purchase of different types of products is related to delivery attributes, as shown by [
16]. Nevertheless, the influence of delivery attributes regarding e-shopping characteristics has been studied in this paper as a novel approach in the literature contributing to the gap pointed out by [
8]. Furthermore, privacy, pricing, and promotion are related to the delivery attributes, as shown by [
18,
20,
21,
29], who investigated the impacts of consumer satisfaction on their behaviour.
6. Conclusions
This paper analysed the influence of delivery attributes on e-shopping consumer behaviour. The research hypotheses were: (1) the delivery attributes affect e-consumers’ behaviour according to their sociodemographic characteristics; and (2) delivery attributes affect e-shopping behaviour. The analyses were carried out with Brazilian e-consumers.
For the first hypothesis, the results indicate that the delivery attributes (mainly the delivery fee) affect the behaviour of middle-aged consumers (35–49 years old). For the second hypothesis, we identified that the delivery fee influences the purchase of electronics and books and leisure products. In turn, the importance of delivery attributes is not considered in e-shopping frequency. In summary, the e-consumer gives less importance to delivery fee than to delivery time or delivery reception, and the delivery attributes are considerably related to privacy. In contrast, pricing is more affected by delivery time than delivery fees. Finally, as the importance of promotion increases among e-consumers, it decreases the influence of the delivery attributes.
Consumer behaviour is dynamic and may change over time. Data analysed in this paper was obtained from a survey before the COVID-19 pandemic. In this context, restrictive measures to reduce the virus spread contributed to e-commerce deliveries. It is worth mentioning that the COVID-19 pandemic changed the population’s consumption patterns. In this context, in which the online market has gained more importance, meeting the needs of consumers has become essential to increasing market share. Therefore, delivery attributes are increasingly essential to attract new buyers and/or maintain existing customer loyalty. Among the attributes analysed in this article, the delivery fee has become a factor in retaining customers in marketplaces. Many marketplaces have offered subscriptions in exchange for free shipping and reduced delivery time. Therefore, these attributes are being used to increase online shopping.
On the other hand, the Brazilian market is still restricted in terms of delivery reception alternatives, since most deliveries are still destined for the end customer. Alternatives such as pick-up points or lockers are still timid initiatives that are being disseminated, especially in large cities such as São Paulo and Belo Horizonte. Alternative delivery reception could contribute to sustainable urban freight transport. The results found in the Brazilian context would be generalized to other growing markets in Latin America and developing countries such as China, where similar e-consumer behaviour is observed and few alternatives to home deliveries are available.
Changing consumer behaviour and greater awareness of the collective benefits of this initiative need to be further disseminated to consumers. Thus, further research would analyse the changes in consumer behaviour in the post-pandemic scenario and identify the influence of spatial characteristics on e-commerce deliveries. Finally, further analysis would include the proposition and assessment on the effects of public policies on consumer behaviour in urban freight transport and, more specifically, in e-commerce deliveries.