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Article

The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior

by
Leise Kelli de Oliveira
1,
Rui Colaço
2,
Gracielle Gonçalves Ferreira de Araújo
3 and
João de Abreu e Silva
2,*
1
Department of Industrial and Transport Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil
2
CERIS, Civil Engineering Research and Innovation for Sustainability, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
3
Department of Civil Engineering, Federal University of Pernambuco, Recife 50740-530, Brazil
*
Author to whom correspondence should be addressed.
Logistics 2025, 9(3), 88; https://doi.org/10.3390/logistics9030088
Submission received: 26 March 2025 / Revised: 16 June 2025 / Accepted: 27 June 2025 / Published: 1 July 2025
(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)
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Abstract

Background: As e-commerce expands and delivery services diversifies, understanding the factors that shape consumer preferences becomes critical to designing efficient and sustainable urban logistics. This study examines how perceived walkability influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points). Method: The analysis is based on structural equation modeling and utilizes survey data collected from 444 residents of Belo Horizonte, Brazil. Results: The findings emphasize the importance of walkability in supporting weekday store visits, encouraging pickup for online purchases and fostering complementarity between different modes of purchase and delivery services. Perceived walkability positively affects the preference to buy in physical stores and increases the likelihood of using pickup points. Educated men, particularly those living in walkable areas, are the most likely to adopt pickup services. In contrast, affluent individuals and women are less likely to forgo home delivery in favor of pickup points. Conclusions: The results highlight the role of perceived walkability in encouraging in-person pickup as a sustainable alternative to home delivery, providing practical guidance for retailers, urban planners, and logistics firms seeking to align consumer convenience with sustainable delivery strategies.

1. Introduction

The growth in e-commerce has significantly impacted urban freight, resulting in an increase in the number of trips to deliver packages, which poses new challenges for carriers in meeting the demands of digital consumers [1]. This phenomenon has been reported in the literature, highlighting the importance of using unassisted delivery services, such as pickup points or lockers, as an effective alternative to manage this growing demand [2,3,4,5]. Encouraging shoppers to pick up their purchases could prove a sustainable alternative to home deliveries [1] while helping to reduce delivery failures [6].
However, the Brazilian market is still emerging in terms of unassisted delivery alternatives [7]. On the other hand, it is one of the fastest-growing markets in the world, having reached 66.6 million e-consumers by 2024 [8]. To succeed, carriers must know customers’ shopping and delivery preferences [9]. Online retailers that offer alternatives to home delivery tend to provide their customers with a more extensive and often less expensive selection of delivery services [10].
Given this context, it is essential to understand how the rapid expansion of e-commerce intersects with consumers’ shopping behaviors and delivery preferences. Although shopping and delivery choices are often studied separately, emerging research has begun to explore how urban form, mobility patterns, and individual perceptions jointly shape these decisions. This integrated perspective offers a more nuanced understanding of how perceived walkability and retail environment characteristics influence not only where people shop but also how they choose to receive their purchases.
Following the framework established by Salomon [11], several studies have explored the complementary, substitution, modification, or neutral effect of online shopping on traditional shopping and related travel. Research has highlighted the importance of understanding online and in-store retail experiences in an integrated manner [12,13], as individuals often shop through multiple channels [14]. Therefore, the choice of shopping channel—online versus in-store—is primarily influenced by socioeconomic and attitudinal factors, as well as land-use attributes [15], which may also impact consumer preferences regarding delivery methods [16,17].
Another aspect that deserves attention and is explored in this article is the relationship between walkability and its influence on shopping behavior. Walkability refers to the extent to which the built environment supports and encourages walking, typically through a combination of connectivity, diversity of land use, pedestrian infrastructure, and perceptions of safety [18]. Highly walkable environments often provide residents with convenient access to a mix of commercial, recreational, and institutional destinations, thus reducing the need for motorized travel and encouraging more localized and frequent trips [19]. This accessibility not only promotes sustainable mobility patterns but also influences where and how people choose to shop [20,21].
Moreover, while the existing literature has established that the presence of commerce positively affects perceived walkability [22,23,24], this study posits the inverse relationship, that is, that perceived walkability may encourage in-store shopping—following Arranz-López et al. [25]—and consequently influence delivery channel choices. This inverse relationship highlights the dynamic interplay between built environment attributes and consumer behavior. By conceptualizing walkability as an antecedent rather than merely a consequence of retail activity, this study offers a novel perspective on how urban design can influence online and in-store retail.
The purpose of this paper is to examine how perceived walking influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points). Using structural equation modeling based on survey data from Belo Horizonte, Brazil, this study explores how spatial (walkability), temporal (weekday vs. weekend behavior), socioeconomic, and attitudinal factors shape these choices. The main contribution of this paper is twofold: theoretically, it introduces the novel perspective that walkability can act as a driver—not just an outcome—of shopping and delivery behavior; and practically, it offers actionable insights for policymakers, urban planners, and logistics providers aiming to promote more sustainable delivery models through improved pedestrian infrastructure and strategic placement of pickup points.
This article is structured in five sections. Following this brief introductory section, Section 2 presents the literature review and conceptual model. The data and the research method are detailed in Section 3, while the results and discussion are presented and discussed in Section 4. The conclusions of the study are described in Section 5.

2. Walkability, Shopping Behavior, and Delivery Services

Consumption patterns have changed in recent years, transforming how people travel for shopping purposes [14]. Most studies that analyze the choice of purchasing channels consider sociodemographic, economic, and accessibility attributes as determinants of the choice between e-commerce and traditional retail [26]. Numerous studies have linked socioeconomic factors to the profile of the e-commerce consumer [7,16,27,28,29,30]. Internet experience and the time spent online also influence shopping decisions [31,32,33,34,35]. Attitudinal factors significantly shape consumers’ preferences toward online and in-store purchases. Research indicates that people who enjoy the shopping experience itself tend to visit physical stores more frequently [36]. Conversely, e-commerce appears to be driven primarily by practical considerations, with time constraints and price sensitivity emerging as stronger motivators [7,37,38].
E-commerce may induce complementarity, substitution, modification, or neutrality effects in shopping-related travel. According to Weltevreden [39], complementarity occurs when online commerce complements in-store purchases. Substitution occurs when online shopping replaces trips to physical stores. Modification refers to cases where online shopping alters the frequency or nature of visits to physical stores, while neutrality refers to scenarios where online shopping has no impact on trips to physical stores. These effects have evolved and vary depending on the type of product and the broader context in which they are used. However, complementarity and substitution have been the most frequently reported effects, mainly because they have the most significant impact on travel and urban structure [40,41].
In addition to these effects, empirical evidence suggests that purchasing behavior varies significantly between weekdays and weekends [41,42]. These temporal differences may stem from several underlying factors, such as working professionals facing greater time constraints on weekdays, which favors weekend physical store visits, often combined with other trips and activities. This paper assumes that these constraints may also influence the choice between home delivery and unattended delivery services. For example, increased time constraints on weekdays may lead to greater reliance on home delivery options, while weekend trip-chaining behavior may favor unattended pickup points.
The spatial context of shopping behavior reveals two critical dimensions. First, geographic characteristics, such as the size of the city and the availability of services near work or home, create fundamental constraints on channel choices [43,44]. Second, emerging research highlights the increasing significance of perceived walkability in influencing these decisions. In areas with high walkability, consumers are more likely to buy in-store due to the ease and enjoyment of accessing physical retail spaces [20,23]. The presence of well-maintained sidewalks, attractive streetscapes, and a variety of nearby retail options enhances the practicality and appeal of spontaneous or planned in-person shopping [45].
Recent evidence suggests that the number of stores within walking distance significantly enhances perceived walkability [22,46], which, in turn, can influence the choice of shopping channel. Furthermore, the presence of retail establishments also contributes to the “urban ambiance” [47], creating environments that further enhance perceived walkability [20,22]. Therefore, the physical environment may become a determinant of the frequency and mode of shopping, especially for everyday goods and services [25,48]. These place-based factors interact with the previously noted temporal differences to shape complex decision patterns.
Walkability may also influence consumers’ channel preferences, determining whether they opt to shop online or in-store. Although e-commerce offers convenience and expanded product access, the physical accessibility and experiential advantages of walkable retail environments can reduce the perceived benefits of online alternatives. This preference becomes even more pronounced among people who prioritize social interaction, routine, or physical activity as part of the shopping experience [25,48].
Moreover, walkability may affect not only the shopping channel but also the preferences of the delivery service. Consumers in walkable areas may be more willing to use alternative delivery options, such as click-and-collect or parcel lockers, which are typically located within or near commercial areas [49]. These modes can offer greater flexibility and cost efficiency, particularly when the trip to retrieve a package is perceived as convenient or aligned with existing travel routines [3,5,50,51]. Therefore, walkability and urban design may have a critical impact on consumer decision-making in the context of last-mile logistics, with direct implications for service design and accessibility.
Building on established relationships between shopping behaviors and (a) socioeconomic characteristics, (b) attitudinal motivations, (c) temporal patterns, and (d) spatial contexts, this study develops an integrated model examining how perceived walkability interacts with weekday/weekend shopping dynamics to shape channel choices, and particularly the tradeoffs between in-store shopping, home delivery, and unattended pickup points.
Incorporating walkability into models of consumer behavior and delivery preferences may provide valuable insights for both researchers and practitioners. For urban planners, enhancing walkability may promote more sustainable shopping behaviors and reduce the dependence on home delivery, which often contributes to traffic congestion and emissions. For retailers and logistics providers, understanding the behavioral implications of walkable environments can inform more effective location strategies, last-mile delivery models, and omnichannel retailing approaches.

Conceptual Model

Based on the literature review, we designed the conceptual model shown in Figure 1. The theoretical framework draws upon interdisciplinary theories from urban planning, consumer behavior, and transportation research to construct a theoretically grounded model that explores the relationships among perceived walkability, shopping channel preferences, and last-mile delivery choices. The aim is to go beyond empirical associations and provide a conceptual foundation for understanding how urban form, individual characteristics, and digital behaviors interact to shape retail-related mobility and delivery patterns.
Considering consumer behavior, we maintain the fundamental premise that attitudinal factors shape shopping channel preferences, as posited by Colaço & de Abreu e Silva [13]. Therefore, consumers who derive pleasure from the shopping experience may demonstrate a stronger preference for physical stores, while those who prioritize time efficiency and cost sensitivity tend to shop online. Related to temporal variation, weekday versus weekend differences often arise from time constraints faced by working individuals. During weekdays, consumers may prefer online shopping and home delivery to save time, while weekends allow more flexible trip chaining [13], which may include visits to physical stores and pickup points.
Additionally, individual and household characteristics such as income, age, gender, and education influence shopping preferences and mobility choices. These characteristics often determine consumers’ access to digital tools, propensity to travel for shopping, and flexibility in choosing delivery options. Previous studies have established that higher income and education levels are correlated with increased e-commerce and delivery services [12,52].
The use of the internet has a significant impact on shopping behavior [53]. Individuals with more extensive internet experience are more likely to adopt e-commerce due to increased familiarity, perceived ease of use, and trust in digital platforms. These users may also be more inclined to explore and evaluate alternative last-mile delivery solutions [54].
Considering shopping responsibility, people responsible for household provisioning (e.g., grocery shopping) develop different shopping habits [55]. Those with high shopping responsibilities often use a combination of online and in-store channels to effectively manage their time and effort [56]. This mixed channel behavior influences both the frequency of in-store purchases and the volume of home deliveries or unattended delivery services [39].
Finally, it is assumed that perceived walkability can significantly affect the choice of shopping channels [20,22]. A walkable environment may enhance perceived behavioral control over in-store shopping, thereby encouraging consumers to visit physical stores more frequently [18,19,21].
In the conceptual model, it is assumed that perceived walkability influences the shopping channel preference and the choice of the delivery channel for online purchases (e.g., a less “walkable” environment may lead to an online preference and opting for online purchases to be delivered home). On the other hand, the number of in-store purchases made on different weekdays is influenced by shopping channel preferences and affects the choice of delivery channel (e.g., if an individual shops at a physical store, it may influence the choice of pickup point over home delivery). In addition, these relationships are influenced by the individual’s socioeconomic characteristics, internet use, and (household) shopping responsibility, which enter the model as exogenous variables.
The conceptual model thus incorporates potential complementarity, substitution, and modification effects between online and in-store shopping. These interactions reflect how increases in one shopping mode may influence the other. For example, consumers may reduce in-store visits as online purchases rise (substitution), or they may use both channels for distinct types of products (complementarity). The preferences of the shopping channel and delivery services suggest that consumers choose delivery modes based on convenience, reliability, and cost. The preferences of the shopping channel, shaped by the factors above, mediate the choice between home delivery and unattended pickup points. Walkable environments may support the use of pickup points as consumers integrate them into daily routines or on the weekends [57].
By integrating socioeconomic, behavioral, spatial, and temporal factors, this framework offers a comprehensive explanation for the interrelationships between walkability, shopping behavior, and delivery preferences. This approach lays the groundwork for structural equation modeling (SEM), which evaluates not only statistical associations but also theoretically justified causal pathways.

3. Data and Research Method

3.1. Data

The survey design was based on the conceptual model outlined in the previous section. The questionnaire consisted of three sections: (i) characterization of the interviewee (gender, age, education, and other factors that can influence purchasing behavior and delivery preferences); (ii) characterization of purchasing patterns (frequency of purchase and type of products purchased, both online and in-store, and whether the product was picked up or delivered at home); and (iii) assessment of attitudinal aspects related to in-store and online shopping preferences, delivery preferences, and perceived walkability using Likert-scale questions. The statements in these questions were based on previous studies [13,25,58].
The survey was conducted in September 2023 among residents of Belo Horizonte, Brazil, collecting responses from 465 individuals using a targeted convenience sampling approach. Following rigorous data cleaning to remove incomplete or inconsistent responses, the final analytical sample comprises 444 respondents. Table 1 presents the descriptive statistics of the sample.
According to the 2022 census [59], 53.5% of Belo Horizonte’s population is female, with an average age of 41.9 years. The percentage of people with higher education is 60% “https://fjpdados.fjp.mg.gov.br/educacao/, (accessed on 5 March 2025)”, and the average income is 7480 Brazilian reais (BRL) or 1249.6 euros (EUR) it represents 3.4 minimum wages according to “https://censo2022.ibge.gov.br, (accessed on 5 March 2025)”. The average income of the sample is 7857.8 BRL/1335.8 EUR. While research resources constrained participant selection, we implemented data validation protocols, including comparison with municipal and national demographic benchmarks (e.g., age and income brackets). Therefore, the sample is similar to the population of Belo Horizonte in terms of gender, but it is younger and has a higher income.

3.2. Research Method

In the first stage, exploratory factor analysis (EFA) was performed to identify the variables that should be incorporated into the latent constructs, considering the conceptual model. In the second stage, we turn to structural equation modeling (SEM) to fully implement the conceptual model. The empirical data is analyzed using structural equations modeling (SEM) using maximum likelihood (ML) estimation as implemented in AMOS 29™ software [60,61]. SEM is a well-known statistical modeling technique adapted to model complex and indirect relationships and simultaneously accommodate latent variables. More detailed information about SEM can be found in previous works [62,63].
The model in this study contains both a measurement sub-model and a structural sub-model. It can be described by the following set of equations, where Equation (1) represents the structural sub-model and Equation (2) the measurement sub-model:
η = Βη + Γx + ζ
y = Λ y η + ε
where η is a vector (6 × 1) of latent endogenous variables, B is a matrix (6 × 6) of coefficients of η variables, Γ is a matrix (6 × 6) of coefficients of exogenous variables, x is a vector (6 × 1) of observed exogenous variables, ζ is a vector (6 × 1) of errors from structural relation, y is a vector (6 × 1) of observed endogenous variables, Λy is a matrix (6 × 6) of regression coefficients of y on η, and ε is a vector (6 × 1) of measurement and errors on y.
The sample size of 444 respondents is adequate for ML estimation, given the model’s complexity and the number of freely estimated parameters [60,61]. Model fit is evaluated using standard indices: the chi-square divided by degrees of freedom (CMIN/DF), the comparative fit index (CFI), goodness-of-fit index (GFI), and root mean square error of approximation (RMSEA). Values above 0.90 for CFI indicate a reasonable fit, while values above 0.95 for GFI and below 0.05 for RMSEA indicate an excellent fit [64,65].

4. Results and Discussion

Table 2 presents the results from the EFA used to guide the measurement sub-model. Four factors are extracted using principal component analysis (PCA) and varimax rotation. The first factor refers to perceived walkability, accounting for 58.38% of the explained variance and a Kaiser–Meyer–Olkin (KMO) score of 0.623. The second to fourth factors are related to preferences for shopping channels, explaining 73.96% of the total variance and a KMO score of 0.545. All four factors are incorporated as latent variables in the measurement sub-model and are validated within it, since all coefficients of the statements are statistically significant at the 1% level.
Figure 2 illustrates the standardized direct effects between endogenous variables, providing an overview of the relationships among them. Table 3 presents the direct and total standardized effects between all variables. The model presents a good fit, with CMIN/DF = 1.606, CFI = 0.941, GFI = 0.951, and RMSEA = 0.037.
Perceived walkability has a positive effect (0.200, significant at the 95% level) on the preference to shop in street stores (street store preference), which positively affects (0.108, significant at the 90% level) shopping in stores on weekdays (in-store weekday). In-store weekday, in turn, has a positive effect (0.615, significant at the 99% level) on physical purchases during weekends (in-store weekend). Shopping in-store on weekends positively impacts (0.092, significant at the 95% level) the likelihood of having an online purchase delivered home (online home). This effect can eventually be related to people with a more limited budget for shopping during the week.
Individuals who exhibit a preference for online shopping are more likely to choose home delivery (positive association with online home, coefficient = 0.108, significant at the 95% level) and are significantly less inclined to retrieve their purchases from pickup points (negative association with online pickup, coefficient = −0.126, significant at the 99% level). This result can be understood as the convenience offered by online shopping also being perceived as advantageous when receiving a product at home, which is supported by the literature [66,67].
A preference for shopping malls (shopping mall preference) shows a positive impact on both the street store preference (0.252, significant at the 99% level) and the online preference (0.135, significant at the 99% level). However, the effect of the relationship with street store preference is of a higher magnitude. Those who prefer to shop in shopping malls may eventually shop via different channels, which exogenous variables might better explain. The standardized direct and total effects of the model are presented in Table 3.
The total effects in the model reveal that perceived walkability (0.003, total effect), street store preference (0.014, total effect), and shopping in-store both on weekdays (0.110 direct effect; 0.131, total effect) and weekends (0.033, total effect) positively impact picking up an online purchase (online pickup), which aligns with the perception that picking up a delivery may be easier for those who are already engaged in a shopping trip while having a positive perception of the walking conditions during that trip. Small but significant total effects from walkability and street store preference on home deliveries (online home) highlight hybrid shopping behaviors in walkable environments. These relationships may be further clarified by accounting for exogenous variables.
The effects of the exogenous variables indicate that age has a positive impact on street store preference, both directly (0.142) and in total (0.105), while having a negative effect on shopping mall preference (−0.145, both direct and total) and online preference (−0.185, direct; −0.205, total). Older individuals shop in-store on weekdays and weekends, showing a positive (total) effect with online pickup (0.041) and a negative (total) effect with online home delivery, although nonsignificant (−0.012). Since internet use has a positive effect on online preference (0.100, direct and total) and online home (0.011, total), and a negative effect on online pickup (−0.009, although nonsignificant), (lack of) digital skills may play a role in the choices of older individuals. Older people are also more likely to value more “traditional” street store commerce, as shown elsewhere [13,41].
Gender (in this case, being a woman) has a negative correlation with perceived walkability (−0.112, direct and total). On the other hand, while showing a positive relationship with street stores (0.020, total), shopping malls (0.166, both direct and total), and online preference (0.022, total), the magnitude of the coefficient for shopping malls is substantially higher. While shopping in-store on weekdays (0.002, total, although not significant) and weekends (0.001, total, but not significant), women show a positive relationship with home deliveries (0.003, total) and a negative relationship with picking up purchases (−0.002, total, although not significant). Women may shop through different channels; however, a negative perception of the walking environment may lead them to prefer shopping malls and having their online purchases delivered to their homes. This finding eventually relates to the perception that shopping malls are comfortable and safe (“I enjoy shopping at shopping malls because it is comfortable (AC, plentiful parking, safety)”).
Education has a positive relationship with perceived walkability (0.117, direct and total) and online home (0.093, direct and total). These educated individuals may live in areas that are more likely to be perceived as walkable. However, being more educated, they are more likely to shop online, which is consistent with previous research [16]. However, the total effects of education on street store preference (0.023, total), shopping in-store on weekdays (0.003, total) and weekends (0.002, total), and online pickup (0.034, total) are all positive, being essentially mediated by perceived walkability, which supports the hypothesis that living in walkable neighborhoods may eventually lead (some) individuals to shop in-store or, at least, pick up their online purchases.
The effects of income on endogenous variables are similar to those of gender, but there is no relationship with perceived walkability. More affluent individuals may perceive shopping malls’ “comfort and safety” similarly to women. The same applies to the convenience of having a product delivered home. The difference might be because more affluent individuals may reside in more walkable locations, or that women may perceive the walking environment less favorably due to security and safety concerns, thus preferring shopping centers and home deliveries.
Finally, internet users show an online preference (0.100, direct and total) and prefer their purchases delivered home (0.011, total), which is not surprising and aligns with the literature [13]. At the same time, those with shopping responsibility will be more likely to shop in-store on weekends (0.074, direct and total) and prefer home deliveries (0.157, total) over pickup (0.056, total) (with a higher magnitude for the former). The latter are eventually the individuals with a more limited time budget for shopping during the week, as identified in the discussion of the effects between endogenous variables.

Discussion

This study provides empirical support for the hypothesis that perceived walkability significantly influences shopping behavior and the use of delivery services, aligning with and expanding the literature that connects urban form with retail-related mobility. The results demonstrate that walkability positively influences street store preference, which in turn increases the likelihood of in-store shopping during weekdays, a finding that confirms previous research indicating that walkable environments promote more frequent, localized trips [18,25]. Importantly, this supports our conceptual reversal of traditional directionality. Instead of retail density leading to an increased perception of walkability, perceived walkability emerges as a driver of trips to physical stores.
Additionally, in-store shopping on weekdays increases in-store shopping on weekends, which subsequently correlates with a higher probability of home delivery, a pattern that reveals a complementarity effect rather than the commonly assumed substitution. This finding shows how (e.g.,) time constraints during the week may prompt consumers to adopt hybrid behaviors: shopping in person on weekends and relying on home deliveries for convenience during busier periods.
Consumers with a preference for online shopping show a strong association with home delivery and an aversion to pickup points, confirming previous findings that associate e-commerce with convenience-driven behavior [66]. This underscores the point made by Vural & Aktepe [67]: without strategic placement and user-aligned design, pickup services may struggle to attract core online consumers.
On the contrary, educated males living in walkable areas are more likely to use pickup points, suggesting that walkability can mitigate some of the convenience tradeoffs traditionally associated with unattended delivery. These consumers represent a key demographic for sustainable logistics initiatives, as noted by Kiba-Janiak et al. [68]. Their behavior supports the findings of Morganti et al. [57] and Ma et al. [49] on the viability of integrating parcel lockers and pickup points into pedestrian-friendly urban environments.
The role of attitudes and preferences is also evident. Preference for shopping malls increases both street store and online preferences, indicating a form of channel multiplicity. This finding aligns with Colaço & de Abreu e Silva [13], who emphasize the fluidity of consumer behavior across physical and digital platforms. Women and affluent consumers tend to favor shopping malls and home delivery, which is likely explained by perceptions of safety, comfort, and convenience—attributes frequently associated with mall environments [69].
Interestingly, gender is negatively correlated with perceived walkability, suggesting that urban environments perceived as walkable by men may not be experienced in the same way by women. This finding converges with other studies in Brazil [70]. This reinforces recent arguments that walkability is not only a spatial construct but also a gendered one, with safety perceptions that heavily shape women’s navigation of urban space [45]. These findings highlight the importance of incorporating equity and gender sensitivity into urban mobility and logistics planning.
The influence of internet use on online preferences and home delivery is consistent with previous results, as digital familiarity clearly drives the adoption of not only online shopping but also its preferred fulfilment modes [54]. Meanwhile, shopping responsibility has a positive influence on in-store weekend shopping and preference for home delivery, suggesting that task-based constraints are critical in shaping hybrid behaviors [58].
Taken together, the results of this study provide a clearer understanding of how spatial (walkability), temporal (weekday/weekend), behavioral (preferences), and sociodemographic factors interact to influence complex shopping and delivery decisions. They confirm many patterns observed in the literature while also providing new insights, particularly regarding the mediating role of walkability and the observed complementarity between shopping channels and delivery modes.

5. Conclusions

This article examines how perceived walkability influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points) through a survey conducted in Belo Horizonte, Brazil. The results of our SEM analysis support the identification of four distinct consumer subgroups with varying preferences and behaviors. The first subgroup consists of older individuals who are more inclined to shop in-store due to lower digital proficiency and a preference for physical shopping experiences (e.g., the opportunity to encounter friends and neighbors, as well as the availability of personalized service). This finding is consistent with previous research, which indicates that older people often prefer traditional “physical” commerce due to its social aspects [13,39]. The second comprises women and (or) more affluent individuals who tend to shop across multiple channels but show a strong preference for shopping malls and home deliveries. These choices appear to be influenced by perceptions of comfort, safety, and security, reinforcing previous findings that associate shopping mall preference with these characteristics [69]. The third includes educated individuals with a positive perception of walkability. These individuals are likely younger men, exhibiting a high propensity to shop online and being more willing to use pickup points, especially in walkable environments. This supports the idea that perceived walkability can indeed encourage sustainable travel behaviors [22]. The fourth and final group consists of consumers who shop in-store on weekends and rely on home delivery for online purchases, suggesting a strong complementarity effect between in-store and online purchases, eventually addressing a very restricted time budget. These individuals may experience time constraints related to domestic responsibilities. Although our research design incorporated potential proxy variables, such as household size and composition, we could not directly relate these to time constraints in our model. We intend to pursue this line of inquiry further in future research.

5.1. Theoretical and Practical Contributions

Theoretically, this research advances the understanding of how perceived walkability functions as a driver, rather than just an outcome, of shopping and delivery behavior. While the existing literature often treats walkability as a passive result of land use and retail density, our model reverses this causality, showing that walkability actively shapes consumers’ preferences for shopping in physical stores and choosing unattended delivery options, such as pickup points. This conceptual shift contributes to the literature by highlighting how subjective spatial perceptions mediate interactions between digital and physical retail environments.
Furthermore, our findings offer empirical support for the complementarity hypothesis in the relationship between in-store and online shopping. Rather than fully substituting physical retail for online shopping, our model reveals that people can engage in hybrid behaviors, such as shopping in stores on weekends and relying on home delivery during weekdays due to time constraints. This study also confirms the role of attitudinal factors and sociodemographic attributes in shaping delivery preferences. In particular, gender perceptions of walkability, digital skills, and shopping responsibility mediate the relationship between urban form and logistics behavior, suggesting the need for more differentiated behavioral models.
Practically, the findings offer actionable guidance for urban planners, logistics providers, and retailers. First, enhancing pedestrian infrastructure in commercial areas can increase consumers’ willingness to shop in person and use sustainable delivery modes, such as pickup points or lockers. In this sense, improvements in walkability can serve both mobility and logistics objectives, particularly in densely populated urban centers. Second, the results suggest that pickup point adoption is most prevalent among younger, educated men who reside in walkable neighborhoods. Strategically placing parcel lockers in locations such as tech hubs, university campuses, or transit-accessible areas may enhance usage rates among this subgroup and support more sustainable last-mile delivery practices. Additionally, retailers may partner with workplace campuses or tech hubs to install pickup lockers at those locations, and urban planners can devise zoning policies incentivizing pickup hubs in office districts. Conversely, women and affluent consumers are more likely to favor shopping malls and home deliveries, potentially due to safety, convenience, and comfort. To reach these segments, integrating pickup solutions into malls and mixed-use developments can improve adoption by aligning with existing behavior patterns.
Finally, the identification of temporal constraints, with weekend shopping linked to the dependency on home delivery on weekdays, highlights the importance of aligning logistics services with urban rhythms. Delivery solutions that account for trip chaining and flexible pickup options can better match the lived realities of consumers, particularly those with limited weekday availability. Together, these contributions highlight the importance of multidimensional planning approaches that integrate urban form, consumer behavior, and logistics innovation to deliver more sustainable, equitable, and efficient last-mile delivery services.

5.2. Limitations and Future Research

This study relies on cross-sectional data collected through non-probabilistic convenience sampling in a single Brazilian city. While the sample demographics broadly align with the expected profile of e-consumers (i.e., younger, more educated, and more affluent), the results may not generalize to the broader population or other urban contexts. Future research should aim to replicate this study in diverse urban environments, both within Brazil and internationally, to evaluate the transferability of the findings.
Furthermore, further investigation into gendered perceptions of walkability and the role of household labor divisions in shopping behavior could strengthen our findings. However, our survey was not designed as a time-use study, which limits our ability to fully capture the influence of time constraints on purchasing and delivery decisions. Moreover, a mixed-method approach—for example, combining surveys with interviews—could have provided deeper insight into how gender shapes the perception of walkability. Such approaches could refine our understanding of barriers to adopting more sustainable delivery options and will be considered in future research.

Author Contributions

Conceptualization, L.K.d.O., R.C. and J.d.A.e.S.; methodology, R.C. and J.d.A.e.S.; software, R.C. and J.d.A.e.S.; validation, L.K.d.O., R.C. and J.d.A.e.S.; formal analysis, L.K.d.O., R.C., G.G.F.d.A. and J.d.A.e.S.; investigation, L.K.d.O., R.C., G.G.F.d.A. and J.d.A.e.S.; resources, L.K.d.O. and J.d.A.e.S.; data curation, L.K.d.O., R.C. and G.G.F.d.A.; writing—original draft preparation, L.K.d.O., R.C. and G.G.F.d.A.; writing—review and editing, L.K.d.O., R.C. and J.d.A.e.S.; visualization, L.K.d.O. and R.C.; supervision, L.K.d.O., R.C. and J.d.A.e.S.; project administration, L.K.d.O. and J.d.A.e.S.; funding acquisition, L.K.d.O. and J.d.A.e.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CNPq, grant number 304517/2023-2 and FACEPE, grant number BPG-0347-3.01/23. The authors gratefully acknowledge the Foundation for Science and Technology (FCT) support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020) and for funding the research project PTDC/ECI-TRA/4841/2021 (REMOBIL Research Project, DOI: 10.54499/PTDC/ECI-TRA/4841/2021).

Data Availability Statement

The data set is available on request from the authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BLRBrazilian reais
CFI Comparative fit index
CMIN/DFChi-square divided by degrees of freedom
EFAExploratory factor analysis
EUREuro
GFIGoodness-of-fit index
KMOKaiser–Meyer–Olkin
PCAPrincipal components analysis
RMSEARoot mean square error of approximation
SEMStructural equation modeling

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Logistics 09 00088 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Logistics 09 00088 g002
Figure 2. Structural sub-model—standardized direct effects between endogenous variables. Note: (***) indicates coefficients significantly different from zero at the 99% confidence level; (**) indicates coefficients significantly different from zero at the 95% confidence level; (*) indicates coefficients significantly different from zero at the 90% confidence level.
Figure 2. Structural sub-model—standardized direct effects between endogenous variables. Note: (***) indicates coefficients significantly different from zero at the 99% confidence level; (**) indicates coefficients significantly different from zero at the 95% confidence level; (*) indicates coefficients significantly different from zero at the 90% confidence level.
Logistics 09 00088 g002
Table 1. Descriptive statistics of the sample.
Table 1. Descriptive statistics of the sample.
Variable or StatementDescriptionMean or PercentageStd. Deviation
Socioeconomic
Age Age of the respondent (18 to 70 years old)28.5710.62
Gender1 if female52.7%-
Education 1 if bachelor’s degree or higher60.4%-
Income 1 if income is above the average of the sample (7857.8 BRL/1335.8 EUR)36.5%-
Internet Experience
Internet Use1 if more than 4 h online per day75.9%-
Shopping Responsibility
Shop. Res1 if full shopping responsibility; 0 if shared or no shopping responsibility30.2%-
Shopping Frequency
In-store WeekdayTotal number of in-store purchases on weekdays2.422.97
In-store WeekendTotal number of in-store purchases on weekends1.231.89
Online HomeTotal number of online purchases delivered home0.951.80
Online PickupTotal number of online purchases collected at a store or pickup point0.230.92
Shopping Preferences1 = completely disagree to 5 = completely agree
I enjoy shopping at local stores because I get to encounter friends and neighbors2.331.2632
I enjoy shopping locally because it’s quicker (closer)3.721.31
I enjoy shopping locally, because of the “local store” environment and the personalized service.2.661.22
I enjoy shopping at shopping malls because it’s more “comfortable” (parking, A/C, security/safety)3.301.37
I enjoy shopping at shopping malls, because of the variety of products sold under the same roof3.541.30
I enjoy shopping online, because of the diversity of the items sold online3.821.06
I enjoy shopping online because I find cheaper products online4.161.00
Perceived Walkability1 = completely disagrees to 5 = completely agrees
The streets are well lit at night.3.411.23
The streets have broad sidewalks and there are plenty of trees and nice-looking houses3.021.35
There are crosswalks, traffic signs and traffic lights which make crossing busy streets easy3.331.34
Table 2. Latent variables, exploratory factor analysis and measurement submodel.
Table 2. Latent variables, exploratory factor analysis and measurement submodel.
Latent ConstructStatementsExploratory Factor AnalysisMeasurement Sub-Model
KMO and Total Var ExplainedLoadingsCoefficient *
Perceived walkabilityThe streets are well lit at night.KMO = 0.623, TVExp = 58.38%0.7690.578
The streets have broad sidewalks and there are plenty of trees and nice-looking houses0.8220.806
There are crosswalks, traffic signs and traffic lights that make crossing busy streets easy.0.6950.466
Street store preferenceI enjoy shopping in local stores because I get to meet friends and neighbors.KMO = 0.623, TVExp = 58.38%0.7500.631
I enjoy shopping locally because it is closer.0.7280.528
I enjoy shopping locally, due to the “local store” environment and personalized service.0.8170.730
Shopping mall preferenceI enjoy shopping at shopping malls because it is more “comfortable” (parking, A/C, security/safety).0.9220.644
I enjoy shopping in malls because of the variety of products sold under the same roof.0.9071.079
Online preferenceI enjoy shopping online because of the diversity of the items.0.8810.624
I enjoy shopping online because I find cheaper products.0.9011.007
Note: * All coefficients are significantly different from zero at the 99% confidence level.
Table 3. Standardized direct and total effects of the model.
Table 3. Standardized direct and total effects of the model.
Variables Perceived
Walkability		Street Store PreferenceShop. Mall PreferenceOnline PreferenceIn-Store WeekdayIn-Store WeekendOnline HomeAgeGenderEducationIncomeInternet UseShop. Respons.
Perceived walkabilityDirect −0.112 **0.117 **
Total −0.112 **0.117 **
Street store preferenceDirect0.200 ** 0.252 *** 0.142 **
Total0.200 ** 0.252 *** 0.105 **0.0200.023 **0.022 **
Shop. mall preferenceDirect −0.145 ***0.166 *** 0.087 **
Total −0.145 **0.166 *** 0.087 **
Online preferenceDirect 0.135 *** −0.185 *** 0.100 **
Total 0.135 ** −0.205 ***0.022 ** 0.012 **0.100 **
In-store weekdayDirect 0.108 * 0.164 ***
Total0.022 *0.108 *0.027 * 0.176 ***0.0020.003 **0.002 *
In-store weekendDirect 0.615 *** 0.074 **
Total0.013 *0.066 *0.017 * 0.615 *** 0.108 ***0.0010.002 **0.001 * 0.074 **
Online homeDirect 0.108 ** 0.092 ** 0.093 ** 0.150 ***
Total0.001 *0.006 *0.016 **0.108 **0.0570.092 −0.0120.003 **0.093 *0.001 **0.011 *0.157 ***
Online pickupDirect −0.126 ***0.110 ** 0.358 *
Total0.003 **0.014 **−0.008−0.088 *0.131 **0.033 *0.358 *0.041 **−0.0020.034 **−0.001−0.0090.056 ***
Note: *** Indicates a coefficient significantly different from zero at the 99% confidence level; ** indicates coefficients significantly different from zero at the 95% confidence level; * indicates coefficients significantly different from zero at the 90% confidence level.
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MDPI and ACS Style

Oliveira, L.K.d.; Colaço, R.; Araújo, G.G.F.d.; de Abreu e Silva, J. The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior. Logistics 2025, 9, 88. https://doi.org/10.3390/logistics9030088

AMA Style

Oliveira LKd, Colaço R, Araújo GGFd, de Abreu e Silva J. The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior. Logistics. 2025; 9(3):88. https://doi.org/10.3390/logistics9030088

Chicago/Turabian Style

Oliveira, Leise Kelli de, Rui Colaço, Gracielle Gonçalves Ferreira de Araújo, and João de Abreu e Silva. 2025. "The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior" Logistics 9, no. 3: 88. https://doi.org/10.3390/logistics9030088

APA Style

Oliveira, L. K. d., Colaço, R., Araújo, G. G. F. d., & de Abreu e Silva, J. (2025). The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior. Logistics, 9(3), 88. https://doi.org/10.3390/logistics9030088

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