Sustainable Solutions in E-Commerce: An Examination of Customer Acceptance of Automated Delivery Stations in Vietnam. (2024)

Author(s): Jung-Fa Tsai [1]; Hong-Ngoc Ngo [2]; Zhen-Hua Che [2]; Ming-Hua Lin (corresponding author) [3,*]

1. Introduction

The logistics industry is putting in a lot of effort into being more sustainable, which is expected to save energy, costs, and time and minimize environmental harm. A pivotal aspect of this effort is enhancing last-mile delivery efficiency. Modern consumers in Vietnam prefer automated delivery due to the flexibility, safety, and convenience offered by the delivery process [1]. Typical supply chain processes involve supplying raw materials to the manufacturing industry, from which the final product is transferred to a warehouse and subsequently delivered to consumers either through traditional channels of supermarkets or a delivery system. The trend of automated delivery service was particularly popular among Vietnamese consumers during the pandemic period. It is estimated that this market size exceeded USD 20 billion in 2023 and exhibits an average growth rate of 29% from 2020 to 2025 [2]. In the e-commerce industry, logistics is a significant factor that determines the companies’ profit generation.

Over the last few years, the number of items delivered online has increased in Vietnam, enhancing the significance of logistics in e-commerce. The rise of e-commerce has promoted the trend of online shopping and automated delivery services in Vietnam, leading to the progressive trend in Vietnam’s e-logistics industry. The increasing demand for deliveries has made the e-logistics industry of Vietnam highly competitive at both the domestic and the global levels, leading to the integration of modern technology to provide enhanced quality and reliability of delivery services to consumers [3]. Several large logistics companies are operating in Vietnam, which include “Tiki”, “DHL”, “Lazada”, “Sendo”, and “TNT” [3]. The leading e-commerce enterprises in Vietnam have internal logistics departments to provide excellent delivery service to consumers. For instance, Tiki is the first and leading company in Vietnam, which provides fast delivery services to consumers. The most common delivery methods in practice in Vietnam include self-provided delivery service and the use of third-party logistics [3].

The present study seeks to investigate the factors affecting consumer intentions to use automated delivery stations (ADSs) in Vietnam. The study focuses on the impact of innovativeness, location convenience, and perceived time pressure on consumer intention to use ADS. Moreover, the study also analyzes the mediating effect of service convenience. The present study seeks to answer the following research questions:

* How does location convenience influence consumer attitudes toward adopting parcel locker services?

* What role does the perceived time pressure play in shaping consumer intentions to use ADSs?

* Is service convenience a significant mediator in the relationship between innovativeness, location convenience, perceived time pressure, and consumer intention to use ADSs?

Previous studies on the factors affecting consumer intention to use specific delivery services have reported performance expectancy for online delivery services [4], optimism and the need for human interaction [5], and compatibility and innovation [6] for self-service parcel delivery service and convenience, security, and reliability as significant factors that influenced consumers’ intentions to use smart parcel locker services [7]. However, there is a significant scarcity of empirical research focused on the determinants of consumers’ intention to use ADS in the context of Vietnam. Therefore, the present study targets the logistics sector in Vietnam to fill this research gap and intends to provide valuable findings regarding the current scenario of ADS in Vietnam. In addition, there exists a critical gap in the studies focusing on the impact of innovativeness, location convenience, and perceived time pressure on consumer’s intention to utilize ADS. Additionally, the mediating effect of service convenience has yet to be analyzed. By examining the role of service convenience, the study aims to highlight how the logistics industry can transform its delivery system into a consumer-centric delivery channel that prioritizes the comfort and convenience of consumers. Thus, this study is of immense significance due to its potential to fill the research gap in existing studies. The study holds several significant implications for the logistics industry in Vietnam and suggests ways to enhance the quality of delivery service by influencing consumer intentions. The findings of this study ma have valuable implications and suggest effective methods to influence consumer behavior and encourage them to subscribe to automated delivery services, thereby enhancing sustainability in last-mile delivery operations. The present study consists of six sections, which include the introduction, literature review, methodology, results, discussion, and conclusion. The introduction section outlines the background, research questions, research gaps, and the significance of the study. Section 2 provides a critique of existing studies, and Section 3 describes the methods adopted in the study. The key findings are presented in Section 4, while Section 5 discusses these findings with their implications. Finally, Section 6 concludes the study, discusses its limitations, and provides directions for future research opportunities.

2. Literature Review

Relevant studies are reviewed to propose the main effects of innovativeness, location convenience, and perceived time pressure on consumers’ intention to use ADS as well as the mediating effect of service convenience on these relationships.

2.1. Theoretical Background

The innovation diffusion theory or diffusion of innovation theory, developed by Rogers et al. (2014), is a primitive theory in social sciences that provides insights into the process of innovation adoption within a society [8]. This theory explains how, why, and at what rate an innovation spreads in a society. Innovation refers to the new ideas, processes, technologies, and practices that increase the efficiency of the system in which they are implemented. The innovation diffusion theory, therefore, deals with the diffusion or spread of innovation in a population [9]. According to [10], innovations or new ideas can be diffused into a population in five stages: knowledge about innovation, persuasion, decision about the adoption of innovation, implementation, and confirmation. These five stages shape the process of innovation decisions [11]. The innovation diffusion depends upon the compatibility and relative advantage of the innovation. Moreover, the complexity, trialability, and observability of the innovation are also present in the process of diffusion of innovation. Through innovation diffusion, one can understand the prevailing trends in the market that have proven to be successful. Decisions regarding innovation adoption are impacted by the diffusion of innovation [12]. ADSs in practice today use such new ideas and innovations that bring out the self-service delivery system with benefits such as saving time, ease of use, and accessibility. The innovations employed in ADSs include the use of automation software to process orders and customer data and smart parcel lockers to establish a self-sustaining delivery system with accuracy and efficiency to reduce delivery time [13]. Therefore, the diffusion of the innovation of ADSs in a population or society is the key factor influencing consumers’ intention the use parcel lockers [14].

2.2. Innovativeness and Consumer Intention to Use Automated Delivery Systems (ADSs)

Innovativeness refers to the ability to develop new ideas and skills that are used to create and launch new products and services in the market. It involves implementing new techniques and practices that have not been used before, bringing positive change by using new ideas and processes, which is the source of creating value and quality [15].

On the other hand, consumers’ intention to use an automated delivery system is defined as their willingness or desire of the consumer to use an automated system of parcel delivery. ADS relies less on manual procedures and more on self-service, as well as self-support for delivery methods, differing from traditional home delivery techniques [4,16]. ADS is therefore considered a sustainable logistics solution for urban areas [17,18].

There is a strong correlation between consumer intentions to use ADSs and innovativeness. According to [19], the intention of consumers to use ADS is positively impacted by innovativeness. An ADS is essentially an innovative, self-driven delivery system. Significant innovative methods employed by ADSs include the utilization of automation software for order processing and smart parcel lockers to support a self-sustaining delivery system with accuracy and efficiency [20]. A crucial additional cutting-edge method employed in ADSs is the usage of advanced parcel storage spaces [21]. Compared to traditional order processing, computer software processes orders more quickly. Additionally, automated parcel sorting saves time and increases accessibility. Furthermore, smart storage spaces increase parcel security and enable self-service delivery without requiring a courier or delivery person to supervise or verify. Consumers’ intention to use ADSs is greatly influenced by the innovativeness being incorporated into it [22]. Customers greatly benefit from ADS innovations, which include quick delivery, safe packages with innovative locks, and correctly processed orders. These advantages encourage customers to adopt an ADS mindset and modify their opinion of it [23]. The following hypothesis is then proposed:

H1.

The impact of innovativeness on the consumer intention to use ADSs is significant.

2.3. Location Convenience and Consumer Intention to Use Automated Delivery Systems

Location convenience (LC) refers to the location being easy to access. It explains spots that are readily accessible to delivery mechanisms or that are comfortable. Additionally, LC shows a place that is accessible via a map and that the tracking device can effortlessly trace [24]. The delivery processes find it simpler to deliver packages on schedule and to the precise location because of the spot’s convenience. Convenient locations have a big influence on consumers’ intentions to use ADSs. According to [7], it has been discovered that consumers’ desire to use an ADSs has a positive relationship with place convenience.

ADSs use cutting-edge technology to improve the delivery of packages by maximizing the dispatch as well as routing processes. Programmed package delivery systems employ smart methods and procedures integrated with modern developments such as AI to improve delivery system efficacy. It makes shipment easier, more cost-effective, and faster than traditional delivery systems [25]. LC improves the ADS’ performance, and the delivery method is more likely to benefit from the self-service delivery procedure. Since ADSs involve software for order processing, route optimization, shipment scheduling, and shipment tracking, LC contributes to the fast delivery, easy tracking, and accurate address delivery of parcels [26]. LC thus significantly impacts the efficiency and output of the automated or self-service delivery systems, which in turn significantly impact the consumer intention of using ADSs. Consumer intention to use ADSs, which is shaped by the consumer’s attitude or behavior towards the automated delivery systems, is therefore increased with location convenience due to the increased efficiency of ADSs [23]. The hypothesis below is therefore posited:

H2.

The impact of location convenience on the consumer intention to use ADSs is significant.

2.4. Perceived Time Pressure and Consumer Intention to Use Automated Delivery Systems

Perceived time pressure (PTP) refers to the time stress or pressure that one experiences even when there are no certain time limits. Perceived time stress is a psychological state of mind where one feels stress related to time and is said to be under time pressure that is one has less time to do a certain task, and it is called perceived time pressure when even there are no genuine time limits regarding a certain task [27]. PTP is found to be effective in completing the tasks in time to avoid any future inconvenience. PTP is also found to be effective in ADSs that consider time as a crucial factor impacting the performance of the delivery system [13]. PTP is found to impact significantly the consumer intention to use ADSs [28].

According to [23], there is a direct relationship between the perceived time pressure and consumers’ intention to use ADSs. PTP causes the delivery system to track locations, process orders, and ship the orders within a suitable time or as soon as possible so that the consumer does not face any inconvenience regarding the parcel delivery time. So the PTP is found to enhance the performance of ADSs, which in turn significantly impacts consumers’ intention to use ADSs [29]. Thus, it is postulated that

H3.

The impact of perceived time pressure on the consumer intention to use ADSs is significant.

2.5. The Mediating Role of Service Convenience

Service convenience (SC) can be conceived as the convenience provided to consumers to attain or acquire a service with little or no expenditure of energy, time, or effort. SC is the consumer perception and thought about how much energy and time are required or will be used to acquire a particular service [30]. SC is therefore a fundamental process that makes a service conveniently or easily available for the consumer. It makes sure that there is no or little effort and time expenditure on behalf of the consumer to avail of a service. SC is found to significantly impact consumers’ intention to use automated delivery systems by being influenced by innovativeness, LC, and PTP [30]. While a key interest in this study is the consumers’ intention to use ADSs, which is a downstream outcome variable, perception of SC, by its definition above, is a psychological process rather than a downstream behavioral outcome. Thus, we do not treat SC as an outcome variable in our model. Rather, SC would serve as a psychological mechanism that might mediate the impacts of innovativeness, location convenience, and perceived time pressure factors on consumers’ intention to use ADS.

Specifically, innovativeness impacts the consumer intention to use the ADSs by impacting the SC of the ADSs. SC aims to reduce the efforts of the consumers and provide value to them. It aims to lessen the consumer input and to provide the best output to the consumer, which leaves a positive impact on the mind of the consumer [31]. Innovativeness greatly imparts SC to the ADSs by optimizing the delivery processes, using smart innovative technology such as smart order processing, smart tracking, and smart locker for secure and delivery-person-free delivery [20]. All these aspects enhance the SC, which in turn significantly impacts consumers’ intention to use ADSs [22].

SC promotes the consumer intention to use ADSs also by highlighting the influence of LC. LC makes ADSs work efficiently and brings value to their customers, creating a better market position for ADSs. LC provides the timely delivery of parcels, which reduces the time and effort at the end for consumers and thus impacts their intention to use ADS [32].

PTP also contributes to SC. With PTP the ADS tries to process and deliver the parcels in time which enhances the accuracy and effective functionality of the ADSs. Therefore, with PTP, the ADSs bring value to the consumer, which is a goal attained by service convenience, which in turn significantly impacts consumers’ intention to use ADSs [33]. The following hypotheses are then posited:

H4.

The mediating role of service convenience between the innovativeness and consumer intention to use ADSs is significant.

H5.

The mediating role of service convenience between location convenience and consumer intention to use an ADSs is significant.

H6.

The mediating role of service convenience between the perceived time pressure and consumer intention to use an ADSs is significant.

Figure 1 indicates the research model of this study with the related hypotheses.

3. Materials and Methods

Quantitative research involves systematically studying a phenomenon by gathering numerical data and analyzing them using statistical tools. Quantitative research originates from the positivist worldview and includes procedures such as questionnaires, systematic protocols, and hypothesis testing [34]. Considering that the research is based on the philosophies of objectivism and positivism, the quantitative methodology was considered the most suitable strategy for this investigation. In particular, this study adopted the structural equation modeling (SEM) method, which is a well-validated methodology in marketing research, for testing the proposed hypotheses.

3.1. Data Collection

A questionnaire was created to gather empirical data from a relevant sample. Consequently, the study utilized primary data acquired from questionnaires. The survey was split into two different parts: the first part gathered demographic information, while the second part consisted of questions relevant to the selected variables. The closed-ended queries guarantee their clarity and ease of understanding. By disseminating inquiries to participants, verifiable data are collected. The results of this data-collection approach serve as a reliable method of study, as a questionnaire tailored to each participant is created. The study participants were requested to fill out a questionnaire that was designed with a standard structure. The study utilized an online Google Doc as well as a physical self-administered questionnaire to carry out the survey. The survey activity had obtained prior authorization from the top authorities of the organizations catering to the target group. Respondents were assured that their data usage would be kept confidential, anonymous, and genuine. The collected data described above were the main emphasis of this investigation. After collecting the data, the researcher analyzed themn to examine the responses given by the specific group of people in the demographic response sheet and the important questions related to the variables of interest.

3.2. Participants and Sampling

This study adopted a non-probability sampling approach, and a sample was selected using a convenience sampling technique. Respondents were chosen based on their affinity for the usage of parcel delivery services within Vietnam. An online survey was designed that included filter questions to gauge the frequency of parcel delivery usage from respondents. During the survey, participants must have confirmed if they had used the parcel-delivery service. Those who answered “yes” proceeded with the questionnaire. The study aims to explore intentions to use automated delivery services, influenced by factors like innovativeness, location convenience, perceived time pressure, and service convenience. The survey was collected via a Google form and shared via various networking applications like Facebook, Zalo, and WhatsApp. In total, 245 completed questionnaires were received from the participants. These 245 valid responses were subsequently included in the final analysis.

3.3. Measures

The survey instrument employed in this investigation has been modified from past studies. The Likert scale was utilized to evaluate the items that represent observed variables. The degree of data obtained is evaluated on the following five-point Likert scale, from strongly disagree (1) to strongly agree (5). The questionnaire of the study consists of 21 items in total, and the items for each variable are presented in Table 1. The variables were assessed using the following scale in the current investigation.

4. Results

In the process of analyzing the dataset, several steps were undertaken to ensure the validity and reliability of the results. Specifically, in Section 4.1, the data were screened for outliers and missing values, followed by a normality test that confirmed the data distribution was normalized. In Section 4.2, The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test were conducted, confirming the data’s suitability for factor analysis and lack of redundancy. An exploratory factor analysis was performed in Section 4.3, showing good factor loadings for all items, indicating high-quality data, while correlation analysis was used in Section 4.4 to determine statistical significance among variables. In Section 4.5, reliability and validity tests were adopted to ensure robustness for hypothesis testing. Lastly, model fit measures were calculated using Confirmatory Factor Analysis (CFA) in Section 4.6, and hypotheses were tested using Structural Equation Modeling (SEM) in Section 4.7.

4.1. Data Screening and Normality

Prior to the data analysis, the data screening was conducted to detect any outliers and missing values. Next, the data normality test was carried out using the skewness test. Since all values were between -1 and +1, the data distribution was normalized. Table 2 presents the results of the descriptive analysis.

4.2. Sphericity and Sampling Adequacy

The Kaiser–Meyer–Olkin (KMO) test was conducted to test if the data are appropriate for factor analysis. Since the KMO test value of 0.924 is above the cut-off of 0.8 (see Table 3), this indicates that the data are suitable for factorized analysis. Meanwhile, Bartletts test was performed to assess the data redundancy; a significant value of 0.000 indicates that this test was also passed.

4.3. Factor Analysis

The rotated component analysis was performed, and the exploratory factor analysis was carried out to assess the quality of the gathered data about their parent variable. The values were compared to a benchmark value of 0.7, and any problems related to cross-loading were looked into. Furthermore, every value was higher than 0.7 as seen in Table 4, indicating that the items had good factor loadings.

4.4. Correlation Analysis

The correlational analysis was carried out to assess the degree of statistical significance for the correlations among variables in the model. Table 5 displays the correlation values of all the variables:

4.5. Reliability and Validity

To guarantee the robustness of the data for testing hypotheses, the validity and reliability of the variables must be assessed. As a result, the study calculated reliability as well as discriminant and convergent validity. The convergent validity was computed using average variance extracted (AVE) and a benchmark value of 0.5, and the reliability was calculated using the composite reliability (CR) indicator, which has a threshold range of 0.7. Values exceeding 0.7 were used to assess the discriminant validity, along with their significance level. Table 6 shows that the data had good validity and reliability.

4.6. The Model Fit Measures

Several model fit indicators were calculated, and the Confirmatory Factor Analysis (CFA) test was carried out to calculate the designed model’s fitness. The model was rated as having excellent status by all model fit indicators, and the results are presented in Table 7. Furthermore, Figure 2 presents the graphical output of the CFA.

4.7. The Hypotheses Testing

Lastly, the testing of the hypotheses was carried out in the final stage of analysis. The results of the Structural Equational Modeling (SEM) were compiled. The independent variables in the model were innovativeness, location convenience, and perceived time pressure. Consumer intention to use ADSs was the dependent variable. Service convenience acted as a mediator between the independent and dependent variables. The outputs of the results are displayed in Table 8 and Table 9.

The results show that innovativeness and location convenience positively influence consumer intention to use ADSs while perceived time pressure does not have an impact on consumer intention to use ADSs. Furthermore, the proposed hypotheses related to mediation were also accepted, which means that service convenience successfully mediates the associations between innovativeness, location convenience as well as perceived time pressure, and the consumer intention to use ADSs. Figure 3 illustrates the SEM output:

5. Discussion of Findings

The study looks into how consumers in Vietnam are adopting automated delivery locations as a sustainable solution. It focuses on several important determinants that affect consumers’ intention to use automated delivery methods. In addition to examining the intermediary function of service ease in these relationships, the approved hypotheses illuminate the positive associations among innovativeness, place convenience, and consumer intention. The perceived time pressure hypothesis, however, is disproven. According to H1, innovativeness has a positive impact on consumers’ intentions to use ADSs. The results corroborate this hypothesis, showing that consumers are more inclined to accept ADS if they display a greater level of inventiveness. This supports the hypothesis that people who are inclined to embrace technological advances are more likely to choose creative options for their package-delivery requirements. Moreover, consumer intention to use ADSs is positively influenced by location convenience, according to H2. This hypothesis is supported by the study, which emphasizes how crucial easy access to ADSs is in determining customer intentions. It follows that when it comes to automated delivery choices, consumers give priority to accessibility, along with convenience, which highlights the importance of strategically placed stations.

The relationships among innovativeness, location convenience, and consumer intention, are suggested to be mediated by service convenience, according to H4 as well as H5. The results support these hypotheses and highlight how important service convenience is in shaping customer intentions. This implies that a smooth and intuitive user experience at programmed delivery points increases the possibility of customer adoption by serving as a bridge between inventive inclinations, practical locations, and the desire to use. The concept presented in H6 is that customer intention and viewed time pressure are mediated by service convenience. The findings do lend credence to this hypothesis, indicating that time constraints play a major role in influencing consumers’ choices to accept ADSs via the intermediary process of service ease. Nevertheless, H3, which implies that consumer decisions to use ADSs are impacted positively by their view of time limitations, was rejected. The findings call into question the belief that individuals under time constraints are inclined to take on programmed delivery options, demonstrating that perceived time constraints have no immediate influence on consumer decisions. This unexpected result makes one reconsider how time limitations influence customer habits when it pertains to package shipment.

Numerous studies have investigated the popularity of ADSs for self-service shipment delivery in a variety of settings, resulting in insights regarding comparable deductions to this present Vietnamese study. For example, a research study conducted by [41] looked at consumer preferences for automated package storage spaces and discovered that convenient, quick, and safe delivery were major factors in adoption. Likewise, research carried out by [42] found that clients cherished ADSs’ flexibility and accessibility. Moreover, a study conducted by [23] showed patterns regarding how customers view self-service shipments, emphasizing the value of reliability and simple interfaces. Furthermore, a study conducted by [43] emphasized how consumer attitudes about technology’s acceptability and usability affect how they behave when it comes to automated shipment delivery. Together, these studies provide a more thorough comprehension of the factors influencing customer perceptions of programmed self-service shipment stations by demonstrating the universal relevance of some key factors in a range of regional and cultural contexts.

The adoption of ADSs in Vietnam and other regions, as highlighted by the various studies, is not just a trend in logistics but also a move towards a more sustainable future. It underscores the importance of innovative and sustainable solutions in addressing modern-day challenges while also meeting consumer needs and preferences. This aligns with global sustainability goals, emphasizing the role of innovation and infrastructure in sustainable development. It also highlights the importance of building resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation, which are key targets of Sustainable Development Goal 9 set by the United Nations (Industry, Innovation, and Infrastructure). Therefore, the adoption and further development of ADSs can be seen as a significant contribution to achieving these sustainability goals.

6. Conclusions

6.1. Recapitulation of the Study

To put it briefly, the goal of this study was to determine the variables influencing Vietnamese customers’ use of controlled delivery points for last-mile delivery at the self-service level, which is expected to promote sustainability among logistics operations. Although the accepted hypotheses provided insights regarding significant variables affecting consumers’ intentions of employing automated systems for shipment, the ruled-out hypothesis emphasized the complexity of user perception in this scenario. The findings supported hypothesis 1, which stated that innovation had a major impact on customer motivations for adopting ADSs. As customers embraced innovative as well as creative options, the appeal of programmed shipment stations increased, positively influencing adoption motives. In a similar vein, hypothesis 2, which focused on the influence of place convenience on user adoption of ADSs, was validated. Customers cited accessibility and closeness to these programmed places as crucial determining elements, which were consistent with the importance of comfort along with convenience in today’s busy lifestyle. In contrast to predictions, hypothesis 3 was rejected, indicating that consumers’ plans to use programmed delivery systems were not directly influenced by their perception of time constraints. This suggested that consumer behaviors towards programmed delivery solutions might be more significantly influenced by other variables, like convenience and innovation. Furthermore, the research study also confirmed that service convenience played an intermediary function in the associations that were suggested in hypotheses 4, 5, and 6. The relationships between innovativeness, place convenience, viewed time stress, and customer plans to utilize automatic delivery methods were found to be mediated by service convenience. The study also provided various theoretical as well as practical implications. With such findings and insights, the current study brings several key implications for both theory and practice, as explained below.

6.2. Theoretical Contributions

This study makes a substantial contribution to marketing literature by analyzing how consumers in Vietnam adopted robotic delivery stations for the self-service delivery of packages. It does so by revealing the complex factors that shape this technology’s acceptance in a distinct socioeconomic and cultural setting. Regarding the interaction of ease, trust, and technological familiarity, the research sheds light on the variables that influence consumer attitudes and preferences. This study bridges a significant gap in the literature by exploring the unique opportunities and obstacles within Vietnam’s developing e-commerce landscape. It highlights the effects of urbanization, as well as the growing reliance on e-commerce, while capturing the subtleties of consumer behavior toward robotic delivery stations, an alternative that has dynamic potential to significantly reduce carbon emissions and contribute to more sustainable logistics practices. This investigation is particularly relevant when considering an emerging market, as it adds to an even more thorough comprehension of the worldwide uptake of sustainable package delivery solutions. Furthermore, the study contributes to the body of literature by examining how consumer behavior and technological innovations interact, revealing trends specific to the market of Vietnam. It provides a deeper comprehension of the root causes, as well as barriers that impact customer choices in the setting of automated delivery, going beyond a simple analysis of the rate of adoption.

6.3. Managerial Implications

This study provides useful information that have impacted the nation’s logistics providers along with companies’ operational plans. First off, by customizing their offerings to match customer preferences, e-commerce companies have benefited from the findings, which have optimized last-mile delivery options. These companies have been able to create user-friendly interfaces, as well as improve customer experience by considering the ease, confidence, and technological familiarity elements. This has resulted in simpler transactions and increased customer satisfaction. Additionally, the study has made it easier for businesses to launch targeted marketing campaigns and create communication plans that appeal to Vietnamese customers. Through targeted solutions to the research’s identified problems, companies can develop more impactful marketing campaigns that emphasize automated delivery points’ advantages. The increased consumer knowledge and embracing of technological advances can be attributed in large part to this focused approach. Moreover, equipped with the knowledge gained from this study, logistics companies have improved the way they implement automated delivery locations. Strategic positioning of stations in regions with greater adoption potential has been made possible by the complicated knowledge of consumer attitudes that have guided decision-making regarding station locations. As a result, last-mile deliveries have become more effective and environmentally friendly, saving money on operations, and raising overall logistics efficiency.

6.4. Limitations and Future Research Directions

This study has certain limitations as well. The geographic as well as demographic limitations of the research have limited its applicability, which could limit the findings’ generalizability to the Vietnamese population as a whole. Moreover, the duration of the study also has an impact on the findings’ applicability as customer preferences and technological adoption trends change. Vietnam’s regional differences and cultural aspects could potentially affect the study’s external validity. Furthermore, the study’s findings’ long-term applicability can be called into question by the quickly evolving technology landscape, as well as the introduction of new delivery methods. Furthermore, the research does not adequately address cross-cultural differences, nor the specific contextual elements within Vietnam, which could affect the applicability of findings to other cultures. In addition, the study has skipped over the nuances that influence adoption behaviors in favor of a shallow exploration of the complex motivations underlying consumer decisions.

Future studies should use a longitudinal method to track changing customer attitudes over time to improve one’s understanding of Vietnam’s automated self-service package delivery stations. Moreover, the applicability of results would be improved by a larger and more varied sample that includes people from a range of socioeconomic statuses and geographical areas. Furthermore, a more nuanced comprehension would be obtained by looking into how external factors, like legal frameworks and technology advancements, affect consumer adoption. Additionally, a comprehensive understanding would benefit from additional research on the impact of social as well as cultural variables on Vietnam’s adoption of controlled delivery systems. In addition, it would be worthwhile to look into how these automated locations affect the effectiveness of last-mile logistics, as well as environmental sustainability. Future studies should also examine the significance of trust and security issues, as these elements have a big influence on how quickly consumers adopt new technologies. It would be insightful to look into the way trust is established or destroyed in the backdrop of controlled self-service package delivery stations, particularly in light of the quickly changing technological environment. Additionally, a comparative study with other nations or areas going through comparable changes in last-mile service delivery will offer a more comprehensive understanding of the variables influencing customer decisions. Comprehending plausible distinctions or affinities in adoption trends will aid in the creation of more broadly applicable models.

Author Contributions

Conceptualization, J.-F.T. and H.-N.N.; methodology, J.-F.T., Z.-H.C. and H.-N.N.; software, H.-N.N.; validation, J.-F.T., H.-N.N. and Z.-H.C.; formal analysis, J.-F.T. and H.-N.N.; investigation, J.-F.T. and H.-N.N.; resources, J.-F.T. and H.-N.N.; data curation, H.-N.N.; writing—original draft preparation, H.-N.N.; writing—review and editing, J.-F.T., H.-N.N., Z.-H.C. and M.-H.L.; visualization, H.-N.N.; supervision, J.-F.T. and Z.-H.C.; project administration, J.-F.T. and M.-H.L.; funding acquisition, J.-F.T. and M.-H.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on request due to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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References

1. D. Phuong Last-Mile Logistics in Vietnam in Industrial Revolution 4.0: Opportunities and Challenges.,

2. A.D. Do; V.L. Ta; P.T. Bui; N.T. Do; Q.T. Dong; H.T. Lam The Impact of the Quality of Logistics Services in E-Commerce on the Satisfaction and Loyalty of Generation Z Customers., 2023, 15, 15294. DOI: https://doi.org/10.3390/su152115294.

3. Q. Nguyen; U. Ho Current Status and Solutions to Develop e-Logistics in Vietnam., LAB University of Applied Sciences: Lahti, Finland, 2023,

4. N. Gunden; C. Morosan; A. DeFranco Consumers’ intentions to use online food delivery systems in the USA., 2020, 32,pp. 1325-1345. DOI: https://doi.org/10.1108/IJCHM-06-2019-0595.

5. Y. Chen; J. Yu; S. Yang; J. Wei Consumer’s intention to use self-service parcel delivery service in online retailing: An empirical study., 2018, 28,pp. 500-519. DOI: https://doi.org/10.1108/IntR-11-2016-0334.

6. K.F. Yuen; X. Wang; L.T.W. Ng; Y.D. Wong An investigation of customers’ intention to use self-collection services for last-mile delivery., 2018, 66,pp. 1-8. DOI: https://doi.org/10.1016/j.tranpol.2018.03.001.

7. K.F. Yuen; X. Wang; F. Ma; Y.D. Wong The determinants of customers’ intention to use smart lockers for last-mile deliveries., 2019, 49,pp. 316-326. DOI: https://doi.org/10.1016/j.jretconser.2019.03.022.

8. J.A. García-Avilés Diffusion of innovation., John Wiley & Sons, Inc.: New York, NY, USA, 2020,pp. 1-8.

9. M.K. Okour; C.W. Chong; F.A.M. Abdel Fattah Knowledge management systems usage: Application of diffusion of innovation theory., 2021, 70,pp. 756-776. DOI: https://doi.org/10.1108/GKMC-08-2020-0117.

10. E.M. Rogers; A. Singhal; M.M. Quinlan, 2nd ed. edition; Routledge: London, UK, 2014,

11. L.J. Menzli; L.K. Smirani; J.A. Boulahia; M. Hadjouni Investigation of open educational resources adoption in higher education using Rogers’ diffusion of innovation theory., 2022, 8,p. e09885. DOI: https://doi.org/10.1016/j.heliyon.2022.e09885. PMID: https://www.ncbi.nlm.nih.gov/pubmed/35874064.

12. L. Cirus; I. Simonova Rogers’ Diffusion of Innovation Theory Applied on Primary Education: Case Study of Czech Teachers.,

13. A. Ranjbari; C. Diehl; G. Dalla Chiara; A. Goodchild Do parcel lockers reduce delivery times? Evidence from the field., 2023, 172,p. 103070. DOI: https://doi.org/10.1016/j.tre.2023.103070.

14. Y.-T. Tsai; P. Tiwasing Customers’ intention to adopt smart lockers in last-mile delivery service: A multi-theory perspective., 2021, 61,p. 102514. DOI: https://doi.org/10.1016/j.jretconser.2021.102514.

15. R. Mancha; G. Shankaranarayanan Making a digital innovator: Antecedents of innovativeness with digital technologies., 2021, 34,pp. 318-335. DOI: https://doi.org/10.1108/ITP-12-2018-0577.

16. V. Engesser; E. Rombaut; L. Vanhaverbeke; P. Lebeau Autonomous delivery solutions for last-mile logistics operations: A literature review and research agenda., 2023, 15, 2774. DOI: https://doi.org/10.3390/su15032774.

17. Z. He The challenges in sustainability of urban freight network design and distribution innovations: A systematic literature review., 2020, 50,pp. 601-640. DOI: https://doi.org/10.1108/IJPDLM-05-2019-0154.

18. G. Lozzi; G. Iannaccone; I. Maltese; V. Gatta; E. Marcucci; R. Lozzi On-demand logistics: Solutions, barriers, and enablers., 2022, 14, 9465. DOI: https://doi.org/10.3390/su14159465.

19. J. Hwang; J.J. Kim; K.-W. Lee Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions., 2021, 163,p. 120433. DOI: https://doi.org/10.1016/j.techfore.2020.120433.

20. G. Iannaccone; E. Marcucci; V. Gatta What young e-consumers want? Forecasting parcel lockers choice in Rome., 2021, 5, 57. DOI: https://doi.org/10.3390/logistics5030057.

21. S.S. Kumar; D. Hemanth; S. Dwneeth; K. Dilip; A. Divyatej Automated package delivery accepting system-smart freight box.,

22. Y. Vakulenko; P. Shams; D. Hellström; K. Hjort Service innovation in e-commerce last mile delivery: Mapping the e-customer journey., 2019, 101,pp. 461-468. DOI: https://doi.org/10.1016/j.jbusres.2019.01.016.

23. M. Zhou; L. Zhao; N. Kong; K.S. Campy; G. Xu; G. Zhu; X. Cao; S. Wang Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery., 2020, 52,p. 101911. DOI: https://doi.org/10.1016/j.jretconser.2019.101911.

24. D.L. Widaningrum GIS and SVM approach for convenience store location analysis.,

25. Y.M. Tang; K.Y. Chau; D. Xu; X. Liu Consumer perceptions to support IoT based smart parcel locker logistics in China., 2021, 62,p. 102659. DOI: https://doi.org/10.1016/j.jretconser.2021.102659.

26. I. Almarashdeh; G. Jaradat; A. Abuhamdah; M. Alsmadi; M.B. Alazzam; R. Alkhasawneh; I. Awawdeh The difference between shopping online using mobile apps and website shopping: A case study of service convenience., 2019, 11,pp. 151-160.

27. L. Peng; W. Zhang; X. Wang; S. Liang Moderating effects of time pressure on the relationship between perceived value and purchase intention in social E-commerce sales promotion: Considering the impact of product involvement., 2019, 56,pp. 317-328. DOI: https://doi.org/10.1016/j.im.2018.11.007.

28. E. Thulin; B. Vilhelmson; M. Johansson New telework, time pressure, and time use control in everyday life., 2019, 11, 3067. DOI: https://doi.org/10.3390/su11113067.

29. M.D. Simoni; E. Kutanoglu; C.G. Claudel Optimization and analysis of a robot-assisted last mile delivery system., 2020, 142,p. 102049. DOI: https://doi.org/10.1016/j.tre.2020.102049.

30. P. Raman Understanding female consumers’ intention to shop online: The role of trust, convenience and customer service., 2019, 31,pp. 1138-1160. DOI: https://doi.org/10.1108/APJML-10-2018-0396.

31. K. YuSheng; M. Ibrahim Service innovation, service delivery and customer satisfaction and loyalty in the banking sector of Ghana., 2019, 37,pp. 1215-1233. DOI: https://doi.org/10.1108/IJBM-06-2018-0142.

32. X. Wang; Y.D. Wong; C.-C. Teo; K.F. Yuen; K.X. Li Decomposing service conveniences in self-collection: An integrated application of the SERVCON and Kano models., 2019, 49,pp. 356-375. DOI: https://doi.org/10.1108/IJPDLM-10-2018-0336.

33. M.U.H. Uzir; H. Al Halbusi; R. Thurasamy; R.L.T. Hock; M.A. Aljaberi; N. Hasan; M. Hamid The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country., 2021, 63,p. 102721. DOI: https://doi.org/10.1016/j.jretconser.2021.102721.

34. R. Watson Quantitative research., 2015, 29,pp. 44-48. DOI: https://doi.org/10.7748/ns.29.31.44.e8681. PMID: https://www.ncbi.nlm.nih.gov/pubmed/25828021.

35. A.H. Crespo; I.R. del Bosque The effect of innovativeness on the adoption of B2C e-commerce: A model based on the Theory of Planned Behaviour., 2008, 24,pp. 2830-2847. DOI: https://doi.org/10.1016/j.chb.2008.04.008.

36. J.E. Collier; R.S. Moore; A. Horky; M.L. Moore Why the little things matter: Exploring situational influences on customers’ self-service technology decisions., 2015, 68,pp. 703-710. DOI: https://doi.org/10.1016/j.jbusres.2014.08.001.

37. C.-F. Chen; C. White; Y.-E. Hsieh The role of consumer participation readiness in automated parcel station usage intentions., 2020, 54,p. 102063. DOI: https://doi.org/10.1016/j.jretconser.2020.102063.

38. D.L. Coffman; R.C. MacCallum Using parcels to convert path analysis models into latent variable models., 2005, 40,pp. 235-259. DOI: https://doi.org/10.1207/s15327906mbr4002_4. PMID: https://www.ncbi.nlm.nih.gov/pubmed/26760108.

39. M.C. Chen; K.C. Chang; C.L. Hsu; I.C. Yang Understanding the relationship between service convenience and customer satisfaction in home delivery by Kano model., 2011, 23,pp. 386-410. DOI: https://doi.org/10.1108/13555851111143277.

40. M.L. Meuter; M.J. Bitner; A.L. Ostrom; S.W. Brown Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies., 2005, 69,pp. 61-83. DOI: https://doi.org/10.1509/jmkg.69.2.61.60759.

41. A. Rossolov A last-mile delivery channel choice by E-shoppers: Assessing the potential demand for automated parcel lockers., 2023, 26,pp. 983-1005. DOI: https://doi.org/10.1080/13675567.2021.2005004.

42. L.K.d. Oliveira; R.L.M.d. Oliveira; L.T.M.d. Sousa; I.d.P. Caliari; C.d.O.L. Nascimento Analysis of accessibility from collection and delivery points: Towards the sustainability of the e-commerce delivery., 2019, 11,p. e20190048. DOI: https://doi.org/10.1590/2175-3369.011.e20190048.

43. J. Hinzmann; K. Bogatzki Acceptance of Autonomous Delivery Vehicles for Last Mile Delivery in Germany: Extension of the Technology Acceptance Model to an Autonomous Delivery Vehicles Acceptance Model., Jönköping University: Jönköping, Sweden, 2020,

Figures and Tables

Figure 1: Research model. [Please download the PDF to view the image]

Figure 2: Graphical output of CFA (LCN = location convenience, PTPR = perceived time pressure, SCN = service convenience, INNOV = innovativeness, CIN = consumer intention to use ADSs). [Please download the PDF to view the image]

Figure 3: Graphical Output of SEM. [Please download the PDF to view the image]

Table 1: Construct, observational variables, and supporting references.

Table 2: Descriptive analysis results.

Notes. LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADS.

Table 3: Results of KMO and Bartlett’s test.

Table 4: Factor loadings of the items.

Notes. LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADS.

Table 5: Correlation analysis.

Notes. **. Correlation is significant at the 0.01 level (2-tailed). LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADSs.

Table 6: Variables’ reliability and validity.

Notes. *** Correlations of constructs are below the main diagonal. LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADSs, CR = composite reliability, AVE = average variance extracted, MSV= mean square variance, MaxR(H) = Maximum H Reliability.

Table 7: Results of model fit measures.

Table 8: Results of direct effects.

Notes. LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADSs.

Table 9: Results of indirect effects.

Notes. * p-value < 0.05; LC = location convenience, PTP = perceived time pressure, SC = service convenience, INN = innovativeness, CI = consumer intention to use ADSs.

Author Affiliation(s):

[1] Department of Business Management, National Taipei University of Technology, Taipei 106344, Taiwan

[2] Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106344, Taiwan

[3] Department of Urban Industrial Management and Marketing, University of Taipei, Taipei 111036, Taiwan

Author Note(s):

[*] Correspondence: [emailprotected]

DOI: 10.3390/su16114570