Unlocking the Potential of Pick-Up Points in Last-Mile Delivery in Relation to Gen Z: Case Studies from Greece and Italy

Abstract

Pick-up points (PUPs) have become a very attractive alternative for conventional home deliveries due to the growth of e-commerce. This paper investigates the level of satisfaction of the students (Gen Z) as well as the research, teaching, and administrative staff of the Aristotle University of Thessaloniki (AUTH), Greece, and the University of Enna “Kore”, Italy, implemented in November 2023. Optimizing the PUP users’ satisfaction is contingent upon various aspects, including but not limited to location accessibility, expedient pick-up procedures, unambiguous communication, and ensured item availability. The research recorded information about the users’ knowledge about the specific service, their level of satisfaction, their preferences on when and how they use the service, and information about the types of goods they order using the PUPs as their point of collection. The analysis of the collected data revealed very interesting findings that could be useful to the providers of this service, especially when taking into consideration that the majority of the poll’s participants are familiar with the existence of the PUPs in the Municipality of Thessaloniki, that they use this service mainly occasionally, and that the majority are quite pleased with the level of the provided services (accessibility, availability, safety, and security). For the case of Enna in Sicily, similar trends are shown: a high percentage of respondents are familiar with PUPs, and they use pick-up points occasionally and are pleased with the provided level of service. The comparative statistical analysis makes it possible to compare two contexts located in areas of the Mediterranean, i.e., two urban areas with different population sizes but with similar habits on the part of the university student cluster.

1. Introduction

What once was considered a futuristic vision concerning online shopping from the comfort of each person’s home has become a ubiquitous reality. E-commerce has exploded, transforming the retail landscape at a breakneck pace. Although shopping in stores is still the predominant purchasing method, e-commerce platforms are changing the behavior of the final consumer.

Technological and communication evolution has facilitated the propensity for e-commerce, producing a significant impact on the traffic of goods in urban areas because purchases must be delivered to customers (at home or at collection points such as lockers or pick-up points) through delivery tours that cannot always be optimized [1]. While the convenience of home delivery remains a cornerstone of the online shopping experience, a challenge has emerged, offering a unique blend of control and efficiency: the pick-up point. Generation Z, the cohort born between the mid-1990s and early 2010s, is significantly shaping the e-commerce landscape [2,3,4]. Known as digital natives, they have grown up with technology and the internet as integral parts of their daily lives. Their preference for convenience, efficiency, and environmentally friendly options drives them towards innovative shopping solutions like pick-up points. This generation’s comfort with digital platforms and their demand for instant gratification further bolster the adoption of such alternatives in the e-commerce sector [5].

Pick-up points represent a paradigm shift in e-commerce fulfillment, as they provide, among other things, the ability to discover a coveted item online, finalize the purchase with a few clicks, and, instead of waiting days for a delivery that might not always fit someone’s schedule, choose to collect it from a designated location near her/his work or home. This seemingly simple concept, however, has far-reaching implications, altering the e-commerce landscape for both retailers and consumers.

The increase in home deliveries of goods ordered online has led to an increase in urban logistics facilities, including pick-up point. A study conducted in Belgium highlights the need to make the movement of users to/from these pickup and drop-off points more eco-friendly, while calling on urban governments to facilitate the use of these multi-operator pick-up point by providing and/or improving supporting infrastructure as well as safeguarding dedicated spaces in urban plans [6].

The appeal of pick-up points is multifaceted. Retailers benefit from potentially reduced delivery costs by consolidating deliveries to designated locations. This translates to potential savings that can be passed on to customers or reinvested in other areas of the business. Additionally, PUPs can lead to increased sales. Furthermore, physical pick-up locations can provide valuable opportunities for customer interaction, potentially leading to upselling or strengthening brand loyalty.

For consumers, PUPs offer a multitude of advantages, such as the freedom of choosing a location that fits a person’s busy schedule and eliminating the need to wait at home for deliveries or deal with missed parcels. Pick-up points can also offer faster turnaround times, especially for customers residing near designated locations. Additionally, PUPs can be a cost-effective option, particularly for smaller items, as they often come with free pick-up or significantly lower fees compared to traditional home delivery. For those with security concerns about leaving packages unattended or who are frequently away during business hours, PUPs offer a secure and convenient alternative.

However, simply offering PUPs is not a guarantee of customer satisfaction. While convenience is a key factor, several other elements play a crucial role in shaping customer perception. This paper will delve deeper into the critical factors that differentiate a seamless and positive experience from a frustrating one based on data collected through a questionnaire-based poll, focused on qualitative features of this service.

University students are attracted to e-commerce for several reasons, each of which contributes to the sector’s recognition, namely:

• Business opportunities, i.e., e-commerce opens doors for students. It showcases their entrepreneurial spirit.
• Skill development, i.e., e-commerce serves as a dynamic mastering ground for students, offering opportunities to acquire skills in virtual advertising and marketing, network layout, record evaluation, and customer service.
• The flexibility and convenience of online shopping attracts students, offering them the opportunity to shop anytime, anywhere. E-commerce not only saves time and money, but also puts a wide range of products and services at their fingertips.

In this regard, it is essential to analyze the collection of these goods purchased online to improve services and make the last-mile logistics sector more sustainable.

In many cities, pick-up points are crucial for the pick-up/delivery of goods, and therefore, it is essential to understand which goods classes, but also which modes of transport, but also with modes of transport can reach the PUPs in order to be able to improve the mobility of the areas being investigated.

It is essential in terms of sustainability to be able to assess the potential impact produced in terms of carbon emissions by users. Research conducted by [7] highlights that these impacts are greater when collection points are established in urban contexts, while in rural contexts, the benefits in terms of carbon emissions resulting from greater efficiency of the delivery route are quickly offset by the carbon footprint associated with customer travel.

Understanding and comparing data on specific population clusters that live, study, or work in different areas and states, but with similar economic and social aspects, are essential. This research focuses on comparing data on a specific cluster of university students and academic staff and their pick-up point usage habits.

The two cities examined fall within the Mediterranean basin and in Greece and Italy, respectively. Considering the Greek case study, the poll was distributed online after obtaining the necessary licenses from the respective authorities of the Aristotle University of Thessaloniki (AUTh). The poll was implemented in the framework of an under-graduate Thesis in the School of Rural and Surveying Engineering during November 2023. In total, 1332 questionnaires were filled out by the students and teaching, research, and administrative staff of AUTh (however, only 1117 were analyzable).

Table 1. Main features of two case studies.

	Metropolitan Area of Thessaloniki	City of Enna
Population (residents)	1,092,919 [8]	153,589 [9]
Total area (km2)	1285.608 [10]	2575 [9]
University	Aristotle University of Thessaloniki	University of Enna Kore
Number of students	90,299 [11]	4900 [12]
Number of academic staff	2728 [11]	300 [12]
Number of questionnaires analyzed (percentage over total number of students and academic staff)	1117 (1.2%)	240 (4.6%)
Confidence level (margin of error ± 5%)	95%	88%

presents the scale of the two case studies in order to better understand the reasons for the difference between the two samples examined:

Considering the Italian case study, it is important to underline that the city of Enna, located in the center of Sicily, is characterized by approximately 27,500 inhabitants and the presence of two universities, namely, the Kore University of Enna and the Dunarea de Jos University.

By 2023, there were 6800 over-65s compared to all inhabitants [13]. The presence of young university students confirmed a good percentage of Generation Z in this city.

The city is characterized by three main areas: the upper part of Enna, where most of the historical center is located; the lower part of Enna, with a residential vocation and where the university poles are located; and finally, the hamlet of Pergusa, with a tourist vocation. The research was conducted for the first university, which was characterized by about 4900 students enrolled for the academic year 2022/2023 and about 300 employees (of which 188 were employed as teaching/research staff) [12].

In the Italian context, approximately 240 questionnaires were collected through online acquisition. For out-of-town university students, parcel delivery by family members is an invaluable financial help. Despite the difference between the percentage of the questionnaires filled out compared to the total number of students and academic staff in both cases (see Table 1), the poll provides useful and insightful results. Based on the fact that, for our analysis, we considered a 95% confidence level (error rate) for the Greek case, the minimum number of responses in relation to the number of students and academic staff should be over 385, which was covered by almost four (4) times [14]. For the Italian case, the minimum number should have been 358 responses, which was not met. Therefore, the confidence level of the Italian case is calculated to 88%, assuming, in both cases, a ±5% margin of error [14].

Choosing the right mailing service can make a big difference in terms of savings. Therefore, university students often find themselves comparing rates, using online platforms, and choosing suitable packaging and planning, which are key strategies for sending large parcels while spending little considering the ease of reaching delivery/pick-up locations. By analyzing these key aspects and incorporating research on users’ preferences, this paper aims to provide valuable insights into the evolving landscape of e-commerce fulfilment and its impact on the users’ experience. Through a deeper understanding of PUPs, the factors that influence satisfaction, and the potential for future advancements, a path is proposed towards a future where convenience and efficiency seamlessly intertwine with unparalleled user satisfaction.

Section 2 of the present paper describes the key findings of the analysis of the most recent and relevant literature review on PUPs, while Section 3 describes the results of the statistical analysis (descriptive and in depth) of the data collected through the questionnaire-based poll. Section 4 describes the main findings, challenges, limitations, and future steps related to PUP users’ level of satisfaction. Finally, Section 5 presents the main conclusions of this research.

2. Materials and Methods

A pick-up point is a location where customers can select where they would like to pick up their orders. Known by another name, “out-of-home delivery”, this service offers greater flexibility than “home delivery” as the final receivers may select the pickup place based on their schedules and convenience, thus providing diversity, flexibility, cost and time savings, and failsafe delivery [15]. There are different kinds of PUPs provided though the same service: (a) click and collect; (b) parcel lockers; and (c) retail shops/collection points. All these PUPs serve the same idea: a place where the carrier can leave the package and the customer/user can pick it up whenever he/she wants to or can.

Throughout the international literature, PUPs have been examined according to different aspects, as presented in

Table 2. Recent (last 5 years) literature review related to PUPs.

Topic	Reference
Pick-up Point Recommendation	[16,17,18,19,20]
Consumer Behavior Regarding Pick-up Points	[21,22,23]
Pick-up Point Location and Optimization	[18,24,25,26]
Pick-up Point Service Quality	[27,28,29,30]
Pick-up Point Lockers	[31,32,33]

. However, most of the examined references agree that the increase in e-commerce has been the main reason for the increased popularity and usage of PUPs.

In February 2024, a report [34] was published regarding digital commerce, of which e-commerce is a subset, in Greece. It underlines that, during the period of 2018–2022, the number of Greek companies employing ten or more employees and providing e-commerce services increased by 50%. According to the International Trade Administration [35], which uses data from the Hellenic Statistical Authority (ELSTAT), during the period of 2017–2022, the total e-commerce in Greece increased by 76% (from USD 5.5 billion to USD 22.55 billion). Furthermore, based on reports from EUROSTAT and ELSTAT for the year 2022, “83% of Greeks are Internet users and 69% are online shoppers and 7% of consumers make at least one purchase per quarter and 38% make six or more times”. It is quite evident based on the above that e-commerce in Greece has undergone a significant transformation without reaching its peak yet, as it is still developing, even on a weekly basis [36]. According to data from EUROSTAT [37], the percentage of internet users who bought or ordered goods or services for private use increased from 40.12% for the year 2013 to 68.52% for the year 2021, and then slightly decreased to 66.72% for the year 2023. During 2023, 73.55% of the people who had purchased online in the last 3 months purchased clothes, shoes, or accessories [37], items that can easily be picked up from PUPs. Consumers become a key component in the transformation of the city logistics sector in the post-pandemic era, playing a major role in addressing the last-mile challenges and developing a more sustainable e-commerce business [38].

One of the main issues arising from the increase in e-commerce and the necessary changes and adjustments it brings to the city logistics sector concerns how satisfied consumers are by the level of the provided services. In this framework, the authors of the present paper implemented an online questionnaire-based poll addressed to the students and research, teaching, and administrative staff of AUTh. The poll was implemented in November 2023, resulting in 1332 filled-out questionnaires, of which 1117 were complete and analyzable. The analysis of the collected data concerns a descriptive statistical analysis as well as an in-depth statistical analysis. The platform used for the specific poll ensured the anonymity of the participants as well as the fact that their IP (Internet Protocol) addresses were not recorded.

The questionnaire developed specifically for the purpose of this research was divided into two sections.

The first section contained five (5) questions about the following attributes of the participants:

• Gender;
• Age;
• Monthly income;
• Education;
• Residence (at the municipal level).

The second section contained ten (10) questions about the following:

• Usage of courier services (Yes/No);
• Familiarity with the existing PUPs network in the Municipal of Thessaloniki (Yes/No);
• Level of usage of the PUPs network (5-point Likert scale);
• Types of goods purchased and picked up from PUPs (choosing from 11 types of goods);
• Criteria affecting their choice of PUP from which to pick up their purchased goods (choosing from 12 criteria—up to 5 choices);
• Evaluation of their experience using PUPs concerning how fast they received their purchased goods (5-point Likert scale);
• Expansion of the existing PUPs network (Yes/No);
• Evaluation of their experience using PUPs concerning the perceived level of safety and security while using PUPs (5-steps Likert scale);
• How environmentally friendly PUPs are relative to home deliveries (5-point Likert scale);
• Whether accessibility and easiness of use of PUPs are factors affecting their choice to purchase goods online (Yes/No).

3. Results

The statistical analysis of the collected data concerned (a) a descriptive statistical analysis of the questions of section A of the questionnaire and (b) an in-depth statistical analysis concerning the questions of section B.

This section presents the results of both statistical analyses.

Descriptive Statistical Analysis

As mentioned above, the total number of questionnaires used for the statistical analysis was 1117 out of 1332 filled out. For the descriptive statistical analysis, all filled-out questionnaires were analyzed, while for the in-depth analysis, 1117 were analyzed. For the Italian case study, 240 questionnaires were filled out (7 of which were incomplete). The total number of respondents considered for subsequent analysis was 233. The questionnaire was distributed through the free Google platform by forwarding it to specific addresses of students at the Italian University. The data acquisition period lasted approximately 4 months, from January to April 2024.

The results regarding the distribution of the participants related to the following attributes are as follows (blue color represents the Greek case, green represents the Italian case, and red represents negative replies for both cases):

• Gender: For the Greek case study, 60.7% were female and 37.0% were male, while for the Italian case study, 49.4% of respondents were male and 50.6% were female (see Figure 1a).
• Age: For the Greek case study, most of the participants were between 18 and 24 years of age (59.1%). Eight out of ten were between 18 and 39 years of age (80.0%), while the other 19.2% was distributed between 40 and 64 years of age. Finally, just 0.8% of the participants were over 65 years of age. For the Italian case study, more than one out of two respondents were in the age group between 18 and 24 years old (51.1%); 20.6% of respondents were between 25 and 39 years old, so the total number of respondents under the age of 40 was 71.7%; and 28.3% were 40 or older, so only 3% were older than 64 (see Figure 1b).
• Monthly Income: For the Greek case study, over half of the participants stated that their monthly income was no more than EUR 400 (54.2%). Almost one out of four (26.0%) stated that her/his monthly income was somewhere between EUR 401 and EUR 1.200. Finally, only 14.0% of the participants stated that their monthly income was somewhere between EUR 1.291 and EUR 2.000, and 5.7% stated that their monthly income exceeded EUR 2.000. It is quite evident that most of the participants’ monthly income was below half of the minimum monthly salary in Greece (EUR 830 as of 1 April 2024 [39]). For the Italian case study, 36.5% of respondents stated that their personal income was lower than EUR 800/month. Most respondents stated that, somehow, their monthly income was between EUR 800 and 2000/month (52.8%). The remaining 10.7% declared income higher than EUR 2000/month (see Figure 1c).
• Education: For the Greek case study, over half of the participants (as expected) were students at the Aristotle University of Thessaloniki (58.4%). Another 27.0% were highly educated, holding MSc or/and PhD degrees, while only 12.2% held a university degree without any further educational evolution. For the Italian case study, almost half of the respondents (as expected) were university students (49.4%). In addition, 33% were university graduates, while only 3.9% declared a higher level of education, and 13.7% of respondents did not declare an educational level higher than secondary school (Figure 1d);
• Residence (Municipality): For the Greek case study, over 60% of the participants stated the Municipality of Thessaloniki as their location of residence (61.8%), 8.1% the Municipality of Kalamaria, and 5.9% the Municipality of Neapoli-Sykies. The remaining 14.2% was distributed in the rest of the Municipality of Thessaloniki’s metropolitan area. For the Italian case study, all respondents worked or studied at University of Enna “Kore”; therefore, all respondents frequented the same city. Among them, 45.1% were residents of Enna, while the remainder were residents of other Sicilian provinces. In particular, the largest proportion related to the provinces closest to Enna, and of those with the largest population shares, 12.9% come from Catania, 12% from Palermo, and 12.9% from Caltanissetta (see Figure 1e).

Regarding the analysis of the collected data on Section’s B questions, it must be noted that the numbers of questionnaires filled out that were analyzable were 1117 for the Greek case and 233 for the Italian case. Here are the main findings:

• Usage of courier services: For the Greek case study, almost 95% (94.6%) of the participants stated that they used courier services to receive and/or send small parcels, while the remaining 5% (5.4%) stated the opposite. For the Italian case study, almost all the respondents used courier services (96% against 4%) (Figure 2a).
• Awareness of existing PUPs network: For the Greek case study, the percentage of the participants that were aware of the existence of PUPs in the city of Thessaloniki was considered as high (83.4%). However, 16.6% of the participants were not aware of this network, leaving significant room for the administrators of the PUPs network to bring it to their knowledge. For the Italian case study, awareness of existing networks was commonly spread across the sample. In total, 87% of respondents stated that they knew of the possibility of picking up parcels at specific points (Figure 2b).
• Frequency of PUPs usage: For the Greek case study, although most of the participants used courier services and were aware of the existence of PUPs, they rarely/occasionally used them (62.8%). Daily, only 0.4% of the participants used PUPs, while 3.3% used them once per week, 1.5% more than once per week, 21.6% once per month, and 10.45 more than once per month. For the Italian case study, a large share of respondents (40.8%) used pick-up points only scarcely, even if a larger share was aware of pick-up points and used courier services. In total, 40.3% of respondents used pick-up points monthly, while 18.5% declared that they used pick-up points on a weekly basis. Only one respondent used pick-up points daily (Figure 2c).
• Types of products purchased using PUPs: For the Greek case study, the next question concerned the types of products the participants prefer to purchase using the PUPs network. It must be mentioned that for this question, each participant could choose up to five answers from a specific list of products (electric devices, clothing/footwear, products for personal care, home decoration, office consumables, pet supplies, sport equipment, pharmaceuticals and parapharmaceuticals, toys, gifts, and others). The most preferred type of goods was clothing/footwear, followed by products for personal care and electric devices. The least preferable products were pet supplies, toys, and home decoration. For the Italian case study, twelve categories were indicated, and each respondent was asked to choose up to five. The most requested categories were clothing (indicated by 61.4% of the total) and books (52.8%). The least requested were pet supplies and sport accessories (both less than 10%) (Figure 2d).
• Criteria for choosing a specific point to pick up goods: For the Greek case study, again, for this question, the participants had the ability to choose up to five answers from a provided list. The criterion with most answers concerned the distance of PUPs from the participant’s home location, followed by the lower cost of using PUPs compared to traditional home delivery services and the freedom PUPs provide to the people to pick up their goods whenever they want. The least preferable criterion was the accessibility of the PUP by public transport modes, followed by the availability of parking space for their private cars near the PUP. For the Italian case study, most respondents (86.3%) indicated distance from home, indicating that proximity to home is a real driving reason for choosing a pick-up point. The least indicated criterion was linked to environmental practices, indicated by 8.2% of respondents (Figure 2e).
• Rate the experience of using PUPs in terms of speed of service: For the Greek case study, over 50.0% (51.2%) of the participants rated, using a 5-point Likert scale (Bad, Fair, Good, Very Good, and Excellent), their experience using PUPs in terms of speed of service as Very Good or Excellent. The percentage of participants having a positive experience increased to 85.2% by including those that rated their experience as Good. Thus, the percentage of participants with a negative experience with using PUPs (always in relation to the speed of service) was almost 15.0% (14.8%). For the Italian case study, most responses were concentrated in the central category (Good, 68.7%), indicating an overall good relationship without exaggerated enthusiasm. The percentage of respondents who indicated Very Good or Excellent was, overall, 18.9%, while the remaining 12.4% indicated Poor or Fair (Figure 2f).
• Expanding the existing PUPs network: For the Greek case study, over ¾ of the participants had a positive attitude regarding expanding the existing network of PUPs (76.4%), while the other 23.6% were negative toward such a possibility. For the Italian case study, a vast majority (90.1%) of the analyzed sample declared themselves to feel positive regarding the possibility of extending the pick-up point network (Figure 2g).
• Perceived level of safety and security while using PUPs: For the Greek case study, most participants (79.6%) felt secure and safe while using PUPs (a 5-point Likert scale was used for this question, allowing the participants to choose among Bad, Fair, Good, Very Good, and Excellent). However, a significant percentage of the participants (20.4%) felt insecure and unsafe while using PUPs, which is a rather important issue for the administrators to address. For the Italian case study, although most respondents indicated a Good or higher condition (85.7%), there was a significant minority who declared a feeling of insecurity (Figure 2h)
• Environmental footprint: In this question, the participants were asked to evaluate the environmental footprint of PUPs compared to traditional home deliveries. A 5-point Likert scale was used (Low, Neutral, Good, Very Good, Excellent), allowing the participants to estimate whether PUPs are more environmentally friendly than home deliveries concerning the produced environmental footprint from both the couriers and the users. If a person chose 1, that means that, in his/her estimation, home deliveries are more environmentally friendly than PUPs. For the Greek case study, almost 95% of the participants (94.6%) estimated that PUPs are more environmentally friendly compared to traditional home deliveries, while the other 5.3% estimated the opposite. For the Italian case study, most respondents (57.9%) indicated a good environmental impact, comparable to that of deliveries, while a significant minority, 31.3% overall, indicated a better environmental impact. The remaining 10.7% believed that home deliveries have an overall better environmental impact than pick-up points (see Figure 2i).
• Easiness and availability of PUPs: For the last question, the participants were asked whether PUPs’ easiness of use, as well as their 24/7 availability, are factors that are taken into consideration by them in purchasing online goods. For the Greek case study, most of the participants replied positively (66.2%), while the remaining 33.8% replied negatively. For the Italian case study, approximately 81.5% of respondents believed that 24/7 accessibility and convenience is a factor that influences purchases at pick-up points (see Figure 2j).

To empirically evaluate the impacts of demographic characteristics on individuals’ behavior related to courier delivery services, we estimated logistic regression models using a consistent set of independent demographic variables across various binary dependent variables [40]. Age and area of residence were the primary and most basic variables taken into consideration in our analysis, as the majority of the participants in both cases represent Gen Z and because the area of residence is directly connected to how the Urban Freight Transport system and, specifically, the courier will react locally regarding the installation of new PUPs (and mainly lockers) and the way these areas will be served. Furthermore, income was analyzed due to the fact that using PUPs is a cheaper option than traditional home delivery services, and we wanted to analyze whether it affects the users’ perception and level of satisfaction, providing useful insights for courier service providers.

The logistic regression model was employed to analyze binary dependent variables, enabling us to assess how demographic factors such as age, gender, education, and income level affect the probability of a specific outcome occurring for a dependent variable. For example, if the dependent variable of interest is the use of courier services, the outcome is “use of courier services” (coded as 1), versus “does not use courier services” (coded as 0). The logistic regression model then estimates the probability that a given individual will fall into one category (1) versus the other (0) (for example, the likelihood of an individual using a courier service) based on their demographic characteristics. The baseline logistic regression model is formulated as follows:

$$\begin{array}{cc}{Dependent}_i=& b_0+b_{1}Gende{r}_i+b_{2}Ag{e}_i+b_3{Age}_i^2+b_4{University student}_i\\ & +b_5{Low income class}_i+b_6{High income class}_i+b_7{\epsilon }_i\end{array}$$

where Dependent_i is the dummy variable of interest, taking the value 1 if individual i’s response falls into the corresponding category, and 0 otherwise. Gender_i is a dummy variable that takes a value of 1 for male and 0 for female. Age_i represents the age category of individual i, with ${Age}_i^2$ included to account for potential non-linear effects of age on the dependent variable. University Student_i is a dummy variable that takes the value 1 for university students and 0 for graduates. The dummy Low income class_i takes a value of 1 if the individual’s monthly income ranges from EUR 0 to 800 and 0 otherwise. Similarly, the dummy High income class_i takes value 1 if the individual’s monthly income exceeds EUR 1600 and 0 otherwise. Finally, ε_i corresponds to the error term.

We estimate the baseline model for each of the different dependent variables, using logistic regressions with clustered standard errors at the municipality level. This approach controls both heteroscedasticity and correlation of the error terms within clusters (Clustered standard errors provide more accurate estimates when observations within the same cluster (i.e., municipality) are more similar to each other than to those from different clusters. This adjustment is necessary because individuals within the same municipality may exhibit correlated behaviors). A more detailed description of all the variables employed in the analysis is presented in

Table 3. Description of the variables employed in the analysis.

Variable Name	Variable Label	Categories and Label Values	# Observations Greek Case Study	# Observations Italian Case Study
Dependent Variables
Use of courier service	Do you Use courier services?	(1) Yes (0) No	1095	233
Weekly use of pick-up points	If you use pick-up points, do you use them at least once per week? (i.e., this includes the answers: once per week; more than once per week; daily)	(1) Yes (0) No	1055	233
How do you evaluate the environmental footprint of delivery pick-up points compared to home delivery?
Environmental footprint of pick-up points: Larger	Larger or somehow larger	(1) Yes (0) No	1081	233
Environmental footprint of pick-up points: Same	Neither larger nor smaller	(1) Yes (0) No	1081	233
Environmental footprint of pick-up points: Smaller	Smaller or somehow smaller	(1) Yes (0) No	1081	233
Which are your criteria for choosing a specific pick-up point? (Only those who indicated making use of pick-up points can answer)
Time flexibility	Flexibility in delivery time	(1) Yes (0) No	1055	225
Distance from home	Distance from your home	(1) Yes (0) No	1055	225
Lower cost	Lower cost compared to home delivery	(1) Yes (0) No	1055	225
Delivery speed	Speed of delivery and service	(1) Yes (0) No	1055	225
User-friendliness	Easy use of the service	(1) Yes (0) No	1055	225
Independent Variables
Gender	Gender of respondent	(1) Male (0) Female	1300	233
Age	Age category of the respondent	(1) 18–24 (2) 25–39 (3) 40–54 (4) 55–64 (5) 65+	1300	233
University student	Education level of the respondent	(1) University student or completed secondary education (0) Graduate of tertiary education	1298	233
Low income class	Monthly income of the respondent ranging from EUR 0 to 800	(1) Yes (0) No	1299	233
High income class	Monthly income of the respondent above EUR 1600	(1) Yes (0) No	1299	233

.

In both

Table 4. Empirical results from logistic regression models (Greek case study).

	(1)	(2)	(3)	(4)	(5)
	Use of Courier Service	Weekly Use of Pick-Up Points	Environmental Footprint of Pick-Up Points: Larger	Environmental Footprint of Pick-Up Points: Same	Environmental Footprint of Pick-Up Points: Smaller
Gender	−0.435 ***	0.611 ***	−0.462	0.015	0.070
	(0.166)	(0.216)	(0.283)	(0.112)	(0.097)
Age	0.034	1.276	−0.594 ***	−1.187 ***	1.257 ***
	(0.395)	(1.008)	(0.171)	(0.229)	(0.216)
Age2	0.001	−0.235	0.148 ***	0.240 ***	−0.261 ***
	(0.099)	(0.180)	(0.048)	(0.039)	(0.038)
University student	−0.494 **	−0.205	0.113	−0.090	0.065
	(0.250)	(0.302)	(0.284)	(0.142)	(0.131)
Low income class	−0.298	−0.074	0.043	0.079	−0.082
	(0.186)	(0.357)	(0.396)	(0.153)	(0.103)
High income class	−0.104	0.605 ***	0.343	−0.778 ***	0.624 ***
	(0.440)	(0.228)	(0.344)	(0.243)	(0.219)
Observations	1094	1054	1080	1080	1080
Nagelkerke R2	0.019	0.066	0.011	0.035	0.034

Notes: All regressions are estimated with robust-clustered standard errors at the municipality level. Standard errors are given in parentheses. **, and *** denote statistical significance at 5%, and 1%.

and

Table 5. Empirical results from logistic regression models (Italian case study).

	(1)	(2)	(3)	(4)	(5)
	Use of Courier Service	Weekly Use of Pick-Up Points	Environmental Footprint of Pick-Up Points: Larger	Environmental Footprint of Pick-Up Points: Same	Environmental Footprint of Pick-Up Points: Smaller
Gender	0.042	0.161	0.499	−0.147	−0.139
	(0.725)	(0.345)	(0.431)	(0.271)	(0.291)
Age	2.803 ***	−0.674 *	−2.204 ***	0.454	−0.239
	(0.879)	(0.356)	(0.514)	(0.319)	(0.372)
Age2	−0.545 ***	0.096	0.418 ***	−0.046	−0.055
	(0.187)	(0.079)	(0.106)	(0.075)	(0.097)
University student	0.683	−0.917 ***	−0.459	0.146	−0.302
	(0.751)	(0.338)	(0.434)	(0.281)	(0.306)
Low income class	−0.103	−1.804 ***	−0.041	−0.716 **	0.401
	(0.758)	(0.579)	(0.509)	(0.323)	(0.339)
High income class	1.911	0.637	−0.043	−0.162	0.047
	(1.391)	(0.414)	(0.604)	(0.365)	(0.394)
Observations	233	233	233	233	233
Nagelkerke R2	0.152	0.152	0.052	0.068	0.079

Notes: All regressions are estimated with robust-clustered standard errors at the municipality level. Standard errors are given in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%.

, we estimated logistic regression models with robust clustered standard errors at the municipality level.

Table 4 presents the results regarding the utilization of courier services, pick-up points, and perceptions of their environmental impact for the Greek case study. Gender significantly influences the use of courier services, with females more inclined to use such services compared to males. Conversely, males show a higher propensity for weekly use of pick-up points. Age demonstrates varied effects: While it does not significantly impact overall courier service usage, younger individuals tend to perceive pick-up points as resulting in a larger or equivalent environmental footprint compared to other delivery methods, whereas older individuals perceive it as having a smaller environmental impact. The quadratic term of age indicates a non-linear relationship of both age effects. University student status shows a significant effect solely on the use of courier services, where graduates are more likely to make use of them. Income level shows mixed effects, with higher-income individuals more likely to use pick-up points weekly and also more likely to perceive them as having a smaller environmental footprint compared to other delivery methods.

Table 5 presents the results regarding the utilization of courier services, pick-up points, and perceptions of their environmental impact for the Italian case study. The effects on the dependent variables under study seem to be different. Gender does not have a statistically significant effect, while age exerts a significant, non-linear effect on the use of couriers, frequency of weekly purchases, and perception of greater environmental impact. Specifically, as in the Greek case study, younger individuals tend to perceive pick-up points as having a larger environmental footprint compared to other delivery methods, but this perception changes as they grow older. In addition, graduates and lower-income individuals are less likely to make weekly use of pick-up points.

Table 6. Empirical results from logistic regression models: criteria for choosing a specific pick-up point (Greek case study).

	(1)	(2)	(3)	(4)	(5)
	Time Flexibility	Distance from Home	Lower Cost	Delivery Speed	User Friendliness
Gender	0.174 ***	0.116	−0.125	0.001	0.040
	(0.046)	(0.139)	(0.096)	(0.062)	(0.105)
Age	0.707 ***	0.219	0.665 **	0.411	0.108
	(0.244)	(0.702)	(0.304)	(0.295)	(0.179)
Age2	−0.132 **	−0.070	−0.189 ***	−0.109 *	−0.015
	(0.056)	(0.131)	(0.071)	(0.063)	(0.029)
University student	−0.071	0.086	−0.108	0.079	0.172
	(0.106)	(0.215)	(0.116)	(0.129)	(0.112)
Low income class	0.053	0.386 ***	0.074	0.048	−0.054
	(0.125)	(0.147)	(0.092)	(0.084)	(0.124)
High income class	−0.118	0.434	−0.459	−0.405 **	0.235
	(0.260)	(0.336)	(0.305)	(0.159)	(0.173)
Observations	1054	1054	1054	1054	1054
Nagelkerke R2	0.013	0.011	0.032	0.014	0.004

Notes: All regressions are estimated with robust-clustered standard errors at the municipality level. Standard errors are given in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%.

reports the results of the demographic factors affecting individuals’ criteria for choosing a specific pick-up point for courier services for the Greek case study. Males exhibit a stronger preference for time flexibility compared to females, which is statistically significant at the 1% level. Age plays a significant role, with older individuals showing a preference for time flexibility and lower cost, although this effect diminishes as age increases. University student status does not show significant effects across the criteria examined. Regarding income level, lower-income individuals prioritize proximity (distance from home), whereas higher-income individuals are less concerned with delivery speed. Overall, the findings highlight the importance of considering demographic characteristics in understanding consumer behaviors and preferences within the courier delivery service context.

Table 7. Empirical results from logistic regression models: criteria for choosing a specific pick-up point (Italian case study).

	(1)	(2)	(3)	(4)	(5)
	Time Flexibility	Distance from Home	Lower Cost	Delivery Speed	User Friendliness
Gender	−0.613 **	0.432	0.539 *	−0.228	0.581
	(0.278)	(0.391)	(0.305)	(0.306)	(0.299)
Age	0.313	0.519	−1.013 ***	−0.892 **	0.079
	(0.323)	(0.425)	(0.341)	(0.355)	(0.328)
Age2	−0.111	−0.055	0.215 ***	0.149 *	0.047 *
	(0.079)	(0.109)	(0.077)	(0.082)	(0.079)
University student	−0.181	0.727 *	−0.211	0.181	−0.418
	(0.281)	(0.388)	(0.309)	(0.317)	(0.295)
Low income class	−0.492	0.306	−0.514	−0.232	−1.861 ***
	(0.328)	(0.411)	(0.361)	(0.353)	(0.384)
High income class	−0.123	1.121 **	−0.468	−0.259	−0.327
	(0.359)	(0.546)	(0.401)	(0.406)	(0.362)
Observations	225	225	225	225	225
Nagelkerke R2	0.056	0.058	0.031	0.012	0.282

Notes: All regressions are estimated with robust-clustered standard errors at the municipality level. Standard errors are given in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1%.

reports the results of the demographic factors affecting individuals’ criteria for choosing a specific pick-up point for courier services for the Italian case study. Males show a stronger preference for lower cost and a weaker preference for the time flexibility criterion compared to females. In addition, older individuals are more concerned about lower cost and delivery speed; however, the effect of age is, again, non-linear. Finally, both university students and higher-income individuals seem to be more concerned about the distance from home criterion, while lower-income individuals are less concerned about user-friendliness.

Table 8. Summary of the main variables used in the empirical analysis.

Variable	Mean	Std. Dev.	Variance	Min	Max
gender	0.38	0.49	0.23	0	1
age	1.66	0.92	0.84	1	5
student	0.60	0.49	0.24	0	1
income_1class	0.67	0.47	0.22	0	1
income_2class	0.22	0.42	0.17	0	1
income_3class	0.11	0.31	0.10	0	1
courier_service	0.95	0.22	0.05	0	1
pickup_points_weekly	0.05	0.22	0.05	0	1
enviromental_footprint_more	0.05	0.21	0.05	0	1
enviromental_footprint_same	0.34	0.47	0.22	0	1
enviromental_footprint_less	0.61	0.49	0.23	0	1
time_flexibility	0.57	0.50	0.25	0	1
distance_home	0.92	0.27	0.07	0	1
delivery_speed	0.42	0.49	0.24	0	1
user_friendliness	0.41	0.49	0.24	0	1

and

Table 9. Summary of the main variables used in the empirical analysis (Enna case study).

Variable	Mean	Std. Dev.	Variance	Min	Max
gender	0.49	0.50	0.25	0	1
age	1.92	1.14	1.30	1	5
student	0.63	0.48	0.23	0	1
income_1class	0.37	0.48	0.23	0	1
income_2class	0.24	0.43	0.18	0	1
income_3class	0.39	0.49	0.24	0	1
courier_service	0.97	0.18	0.03	0	1
pickup_points_weekly	0.19	0.39	0.15	0	1
enviromental_footprint_more	0.11	0.31	0.09	0	1
enviromental_footprint_same	0.58	0.49	0.25	0	1
enviromental_footprint_less	0.31	0.47	0.22	0	1
time_flexibility	0.37	0.48	0.23	0	1
distance_home	0.86	0.35	0.12	0	1
delivery_speed	0.24	0.43	0.18	0	1
user_friendliness	0.42	0.49	0.25	0	1

present a summary of the main variables used in the above-mentioned empirical analysis for the Greek case and the Italian case, respectively.

4. Discussion

Although awareness of the PUS network was high (83.4%) among the participants, monthly usage was modest (21.6%). Clothing and footwear emerged as the most common items collected at PUPs, while pet supplies and home décor were the least common. Based on the participants’ replies, convenience was a significant factor in their decisions, while proximity to home, lower delivery costs, and flexible pick-up times ranked as the top criteria for PUP selection.

Notably, over 85% of the participants expressed their satisfaction with the speed of goods delivered by using PUPs and recognized the environmental benefits when compared to the traditional home delivery policy. However, the participants raised security concerns; 20.4% of them expressed insecurity while using PUPs, a percentage which must be taken into consideration in order to better understand the reasons and, through targeted actions, eliminate them. It is worth mentioning that age is a demographic factor influencing PUPs’ usage.

Even in the Italian case, the results are interesting. A high percentage of people are aware of the existing network of PUPs (87%), like the Greek case, although the percentage of monthly users is higher (40.3%). Based on the poll’s results, “Clothing and fashion items” was indicated as the most purchased product type; on the other hand, “Pet supplies” and “Sports and outdoor equipment” were the least requested product categories. In this sense, it is noted that, in both the Greek and Italian cases, the most widespread category and the least widespread category were similar. These results are also in line with European data, where clothing is always among the most-sold categories [41].

More than 80% of respondents in the Italian case instead indicated distance from home as the main criterion for utilizing pick-up points. This shows how the target users are interested in minimizing travel; these users could be more interested in the original function of e-commerce, which involves home delivery. Only 10.7% of respondents believed that traditional home delivery has a better environmental impact than pick-up points; in this sense, the percentages between the two analyzed cases are similar. Similarly, 85.7% of respondents said they felt secure at pick-up points; the minority who said the opposite represents a basis on which to evaluate any subsequent research to investigate the reasons for this. It should be considered that the percentages are, overall, similar between the two cases studied.

Intriguingly, the research revealed an interplay between age, gender, income, and PUP usage. Younger demographics, potentially due to their more flexible lifestyles, exhibited lower overall PUP utilization rates. Furthermore, they ascribed less environmental value to PUPs compared to their older counterparts. It can be assumed that this could be attributed to a heightened awareness of environmental issues among older generations of a difference in how environmental impact is perceived across age groups.

The results of the Italian case study showed similar results. While age and use of pick-up points showed a non-linear relationship, weekly use of pick-up points decreased. Similarly, a certain awareness of the better environmental impact of pick-up points compared to traditional delivery was more widespread among young people. The research also unveiled gender-based disparities in PUP selection criteria. Men prioritized the flexibility of pick-up times, potentially reflecting busier schedules and a greater need to accommodate work or social commitments. Conversely, for older adults and individuals with lower incomes, proximity to home emerged as the most critical factor. In this case, it can be strongly assumed that this is due to limitations in mobility or a greater reliance on walking or public transportation as a cheaper way to move.

In terms of criteria for choosing points, the Italian results differed from the Greek ones. In particular, contrary to the Greek case, male respondents were less interested in time flexibility and more interested in lower costs. Younger users preferred lower costs, which is probably related to their lower spending capacity. Also, it should be considered how distance from home was influenced by high-income users, differently from the Greek case study.

The results of the two samples, although with some differences, are very similar. As can be seen, the adoption of e-commerce, awareness of pick-up points, and weekly purchases, but also the most and least purchased categories and the criteria behind purchases, are similar. It is interesting, from this point of view, to recall the notable similarities between the areas considered. The values of Gross Domestic Product per capita of the two regions, Sicily and Central Macedonia, are very similar [41]; likewise, the cultural similarities between the two countries are significant. The two samples chosen reflect a population mainly composed of students and university staff in cities with universities, making the similarity of the results justified. Both Enna and Thessaloniki, in fact, are cities that have their beating heart in the University. The differences in size and in absolute value between the two cities, in terms of both territorial expansion and population, allow us to justify some differences, such as the different parameters that influence the choices of attributes. See the aforementioned case of distance from home, an attribute more affected by the low-income class for the largest city (Thessaloniki) and more affected by the high-income class for the smallest city (Enna). This result could be associated with the different possibilities of movement in a large city of low-income users compared to the same possibilities in a smaller city. The research, overall, reveals valuable insights that can guide strategies to increase the adoption of PUPs and improve the users’ experiences. The results are fully aligned with those of [42,43]. However, it must be mentioned that, for the Italian case, the confidence level is considered to be low (88%), meaning that the poll should be repeated in the future with a higher number of filled-out questionnaires.

5. Conclusions

To enhance the effectiveness of PUPs, several strategic recommendations can be implemented:

• Strategies to bridge the awareness–usage gap: As mentioned above, awareness of PUPs is high, but their usage remains modest. To bridge this gap, educational campaigns highlighting PUPs’ benefits (convenience, cost-effectiveness, environmental friendliness) could be implemented. Collaboration with e-commerce retailers offering PUP incentives and expanding the PUPs network to more convenient locations, especially near residential areas, could further entice users.
• Enhancing security and user comfort: Security concerns surfaced, as some users felt it was unsafe to use PUPs. To address this issue, improving lighting and establishing a dedicated customer support channel for PUP-related inquiries or concerns will allow users to report issues or seek assistance properly. By implementing a multi-layered approach that combines physical security measures, clear communication, and user-friendly technology, PUP services can become a more secure and convenient option for e-commerce deliveries.
• Tailoring PUP services to demographics: Different demographics have varying needs and preferences regarding PUPs. Offering a wider variety of PUP locations, including those close to residential areas for proximity-conscious users, is essential. Partnering with businesses frequented by younger demographics to establish PUP locations within those businesses could be explored.

To better understand PUPs’ environmental and economic benefits, future research should focus on conducting a comprehensive life cycle assessment to compare the environmental impact of PUPs versus home deliveries. This analysis should extend beyond fuel consumption and consider factors like packaging materials, infrastructure requirements, and autonomous-energy PUPs. Moreover, future research should focus on exploring the potential for integrating PUPs with smart city technologies. This could involve real-time traffic data to optimize delivery routes, smart locker systems for secure and convenient pick-up, or integration with public transportation networks to encourage users to combine picking up their goods with their daily commutes. Finally, crowd shipping should be explored as a solution for those that are not willing to use PUPs. Generation Z, known for their digital savviness and environmentally conscious mindsets, can play a crucial role in the adoption and optimization of these technologies. Their preference for sustainable practices and convenience aligns well with the integration of smart city technologies and alternative delivery methods. Understanding the behavior and preferences of Gen Z consumers can provide valuable insights for designing PUP systems that meet their expectations and encourage broader adoption. Incorporating Gen Z’s feedback and usage patterns will be essential in creating efficient, user-friendly, and eco-friendly PUP solutions.

To improve the effectiveness of PUPs, several initiatives can be implemented. Training and information activities include conducting interactive workshops and webinars to educate users on PUPs’ benefits and the efficient use of e-commerce platforms, developing specialized training programs for service managers to understand user behavior and integrate feedback, and creating comprehensive guides and FAQs. Sustainable service activities suggest promoting the use of eco-friendly vehicles for last-mile deliveries; implementing policies to ensure fair wages and safe working conditions for delivery riders; and advocating for the development of bike lanes, pedestrian pathways, and dedicated PUP access points. The validity and scope of the opinion poll involve clearly stating the study’s basis using an opinion poll and acknowledging potential biases while contextualizing findings and highlighting the need for further research to validate and expand upon results. Future research directions recommend planning subsequent research phases to gather data from broader demographics, conducting longitudinal studies to track changes in user behavior over time, and expanding the study to include comparative analyses with other universities or urban areas.

The limitations of the research mainly concern the fact that the poll was addressed only to members of two universities in Greece and Italy and not to the residents of the urban areas where they are located, and secondly, the fact that the structure of the questionnaire did not allow us to explore the underlying reasons behind participants’ behavior and the reasons for their preferences. Moreover, the number of filled-out questionnaires for the Italian case study was lower than that required in order to achieve a confidence level of 95%. However, the Italian case study is considered supplementary to our approach, allowing us to compare the two living areas for future research.

Future research will include a larger sample involving the residents of urban areas instead of only university students and staff, as well as a statistical analysis testing the effectiveness of PUPs through the selection of customer populations; sampling, formulating, and testing hypotheses with confidence intervals; and comparing the results across different groups.