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Analysis of the Impact of Delivery Efficiency on E-Commerce
Adoption by Courier Companies in Lagos Metropolis, Nigeria
Ayantoyinbo B. B., and Oyewale V. D.
Department of Transport Management, Ladoke Akintola University of Technology, Ogbomoso, Oyo
State, Nigeria.
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000409
Received: 12 October 2025; Accepted: 19 October 2025; Published: 13 November 2025
ABSTRACT
The rapid expansion of e-commerce has revolutionized the global retail landscape, offering consumers
unprecedented convenience and access to a diverse array of products. This study examines the influence of
delivery time accuracy, dispatch and routing efficiency, real-time tracking, cost competitiveness, and last-mile
delivery effectiveness on e-commerce adoption among DHL customers in Lagos. The study employed a
descriptive survey research design with structured questionnaires administered across major commercial and
logistics hubs, including Ikeja, Lagos Island, Lekki, Surulere, and Apapa. According to DHL Nigeria Report
(2024), the estimated annual customer base in Lagos was 110,012, from which a sample size of 399 respondents
was determined using stratified random sampling and Slovin’s formula. Inferential statistics of multiple
regression analysis were applied to analyze the data. Result of multiple regression analysis on the impact of
delivery efficiency on e-commerce adoption among DHL customers in Lagos State showed that four (4) out of
five (5) explanatory variables were positively statistically significant in explaining the variation of delivery
flexibility. These variables were delivery time accuracy (p = 0.000), dispatch and routing efficiency (p = 0.010),
real-time tracking capability (p = 0.001), and last-mile delivery effectiveness (p = 0.003). These results
emphasize that customers value speed, accuracy, and visibility in delivery logistics more than cost
considerations. The study concludes that delivery efficiency plays a statistically significant role in enhancing e-
commerce adoption in metropolitan Lagos. DHL should prioritize targeted investments in AI, data analytics, and
technology-driven logistics solutions to strengthen delivery time accuracy, routing efficiency, real-time tracking,
and last-mile operations.
Keywords: Delivery Efficiency, E-commerce Adoption, Courier Services, DHL, Lagos State
INTRODUCTION
The rapid expansion of e-commerce has revolutionized the global retail landscape, offering consumers
unprecedented convenience and access to a diverse array of products (Alzahrani and Alzahrani, 2024). Central
to this transformation is the efficiency of last-mile delivery, the final step in the supply chain where goods are
transported from a distribution hub to the end consumer (Viu-Roig and Alvarez-Palau, 2020). This critical phase
not only determines operational success but also profoundly influences customer satisfaction and loyalty (Oyama
et al., 2022). As the e-commerce sector continues to evolve, understanding the nuances of customer last-mile
experience becomes imperative for businesses aiming to enhance adoption rates and maintain a competitive edge
(Yindi et al., 2021).
In the rapidly evolving landscape of e-commerce and logistics, the last-mile delivery experience has emerged as
a critical component of overall customer satisfaction and business competitiveness (Yindi et al., 2021). The "last
mile" refers to the final leg of a product's journey from a distribution center to the customer's doorstep. This
phase, although short in distance, is often the most complex, costly, and impactful on customer perception (Lim
et al., 2023). Customer last-mile experience encompasses the end-to-end process that consumers undergo from
the moment an order is dispatched to its final delivery at their doorstep (Alzahrani and Alzahrani, 2024). This
experience is multifaceted, involving elements such as delivery speed, reliability, real-time tracking, and the
professionalism of delivery personnel (Oyama et al., 2022). A seamless and positive last-mile experience can
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significantly elevate customer satisfaction, fostering trust and encouraging repeat purchases (Viu-Roig and
Alvarez-Palau, 2020). Conversely, challenges such as delayed deliveries, lack of transparency, or mishandled
packages can lead to dissatisfaction, negative reviews, and ultimately, customer attrition (Zhou et al., 2007).
Therefore, optimizing the last-mile delivery process is paramount for e-commerce businesses striving to meet
and exceed customer expectations (Yindi et al., 2021).
Ngugi and Ochieng (2021) define e-commerce adoption as “the organizational or individual acceptance and
effective utilization of digital platforms for commercial activities, influenced by technological readiness,
regulatory environments, and socio-cultural factors.” In many Sub-Saharan African countries, for example,
mobile penetration and digital payment innovations have spurred a significant rise in online retail activity,
although infrastructural and regulatory challenges persist. According to Chatterjee and Kumar (2022), e-
commerce adoption refers to “the process by which businesses and consumers begin to use internet-based
platforms for commercial activities, including transactions, marketing, and customer engagement.” This
adoption has become critical for businesses seeking operational efficiency, expanded market reach, and
improved customer service. Several factors influence this adoption, including technological infrastructure,
internet accessibility, payment security, and cultural attitudes towards online transactions (Yindi et al., 2021).
Notably, the logistical capabilities of an e-commerce platform, particularly in last-mile delivery, play a crucial
role in shaping consumer perceptions and acceptance (Viu-Roig and Alvarez-Palau, 2020). Efficient and reliable
delivery services can act as a catalyst for e-commerce adoption, as they directly impact the overall shopping
experience (Oyama et al., 2022). In contrast, inconsistent or subpar delivery performance can deter potential
customers, hindering the growth and penetration of e-commerce markets (Alzahrani and Alzahrani, 2024).
Delivery efficiency is a crucial determinant of e-commerce adoption, as it directly influences customer
satisfaction and loyalty (Hübner et al., 2016). Consumers expect timely and reliable deliveries, yet many e-
commerce businesses struggle to meet these expectations due to supply chain inefficiencies and infrastructural
challenges (Xing et al., 2021). Inconsistent delivery performance, lengthy transit times, and last-mile delays have
been cited as major factors that discourage customers from making repeat purchases online (Alzahrani and
Alzahrani, 2024). While some research has explored the role of delivery speed, there remains a gap in
understanding how overall delivery efficiency including packaging quality, route optimization, and courier
reliability impacts the rate of e-commerce adoption in different regions.
LITERATURE REVIEW
Delivery Efficiency
Delivery efficiency refers to the capability of a logistics or transportation system to ensure that goods and
services reach their intended destinations in a timely, cost-effective, and reliable manner (Hübner et al., 2021).
It involves the optimization of multiple supply chain components, including order processing, packaging,
transportation, and final delivery, with the goal of minimizing delays, reducing costs, and enhancing customer
satisfaction (Mangiaracina et al., 2019). Efficient delivery is especially crucial in industries such as e-commerce,
retail, and manufacturing, where timely and accurate product distribution significantly impacts consumer trust
and overall brand reputation (Esper et al., 2022).
The efficiency of delivery services is determined by several key factors, including speed, accuracy, flexibility,
and sustainability. Speed refers to how quickly products are transported from warehouses to customers, directly
influencing customer satisfaction and purchase decisions (Nguyen et al., 2020). Accuracy ensures that the correct
items are delivered in the expected condition, reducing return rates and enhancing customer trust (Ramanathan
et al., 2021). Flexibility in delivery services, such as offering multiple shipping options or same-day delivery,
allows businesses to cater to diverse consumer needs, thereby increasing competitiveness in the market (Pantano
and Timmermans, 2022). Additionally, sustainability is becoming an integral component of delivery efficiency,
with businesses adopting eco-friendly logistics solutions such as electric delivery vehicles, optimized route
planning, and carbon-neutral shipping methods to reduce their environmental impact (Hassan et al., 2022).
Several external and internal factors influence delivery efficiency. Externally, the availability of transportation
infrastructure, urban traffic congestion, and government regulations can affect delivery timelines and costs
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(Sharma et al., 2021). Internally, the level of technological integration in logistics operations, such as the use of
automated warehouses, GPS tracking systems, and artificial intelligence-driven route optimization, plays a
significant role in improving delivery speed and reliability (Tang et al., 2022). Companies that invest in advanced
logistics technologies and strategic partnerships with reliable delivery providers can achieve higher levels of
efficiency, thus enhancing their competitive edge in the market (Kim et al., 2020).
Furthermore, effective delivery management can lead to substantial cost savings, improved inventory control,
and a seamless customer experience. Businesses that optimize their delivery networks not only minimize
operational costs but also prevent revenue losses due to mismanaged shipments, lost goods, and failed deliveries
(Zhang et al., 2021). In the modern digital economy, where consumer expectations for rapid and reliable
deliveries are increasing, organizations must continuously innovate their logistics strategies to maintain market
relevance and ensure long-term customer retention (Gong et al., 2020).
Delivery efficiency is a fundamental aspect of modern supply chain management, influencing customer
satisfaction, business profitability, and overall market competitiveness. With the rise of e-commerce and
evolving consumer expectations, companies must leverage technology, enhance logistical processes, and adopt
sustainable delivery practices to ensure optimal efficiency in their supply chain operations (Janjevic and Ndiaye,
2022).
E-Commerce Adoption
E-commerce adoption refers to the process by which consumers embrace online shopping platforms as an
alternative to traditional retail shopping. It involves the willingness and readiness of individuals to purchase
goods and services through digital platforms, facilitated by internet connectivity, online payment systems, and
logistics networks. The adoption of e-commerce has transformed the global retail landscape, offering consumers
a convenient, diverse, and accessible shopping experience (Bashir et al., 2021).
The growth of e-commerce is driven by multiple factors, including advancements in technology, changes in
consumer preferences, and improvements in logistical efficiency (Nguyen et al., 2019; Oyama et al., 2022).
Digital transformation has made it easier for businesses to establish online storefronts, provide personalized
recommendations, and ensure seamless transactions (Alzahrani & Alzahrani, 2024). The widespread availability
of smartphones and mobile applications has further accelerated the adoption of e-commerce, enabling consumers
to shop from anywhere at any time (Yindi et al., 2021).
Despite its numerous benefits, e-commerce adoption varies across different regions and consumer demographics.
While developed economies have high e-commerce penetration rates due to established technological
infrastructure and digital literacy, developing economies face challenges such as poor internet access, lack of
trust in online transactions, and inefficiencies in last-mile delivery (Viu-Roig & Alvarez-Palau, 2020; Zhou et
al., 2007). Understanding consumer behavior and the key determinants of e-commerce adoption is crucial for
businesses looking to expand their online presence and improve customer satisfaction (Hübner et al., 2016; Xing
et al., 2021).
Empirical Review
Huang et al. (2020), in their study titled "The Role of Logistics Performance in E-commerce Adoption,"
employed a quantitative approach by analyzing survey data from 300 e-commerce customers in China. Using
regression models, the study examined the relationship between delivery efficiency and consumer satisfaction.
The findings showed that fast and reliable delivery services significantly influenced e-commerce adoption,
whereas poor delivery services led to negative customer perceptions and reduced trust in online shopping.
However, the study focused solely on customer perceptions and did not analyze how logistics firms themselves
perceived and tackled delivery efficiency challenges.
Nguyen and Huynh (2021) carried out a study titled "The Effect of Delivery Speed and Reliability on Online
Shopping Growth," using a case study approach to analyze the logistics strategies of top e-commerce platforms
in Vietnam. Through primary interviews and secondary data analysis, the study revealed that shorter delivery
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times and improved real-time tracking services positively impacted consumer retention. However, it also
identified poor last-mile delivery networks as a major limitation. Despite these insights, the study did not explore
how government infrastructure investments could enhance delivery networks for improved e-commerce
adoption.
Akinyemi and Adebayo (2022) investigated the relationship between e-commerce logistics and customer
satisfaction in Nigeria in their study titled "E-commerce Logistics and Customer Satisfaction in Nigeria." The
researchers conducted a survey with 200 Nigerian e-commerce customers and used statistical analysis to assess
key logistics challenges. The findings indicated that delays in order fulfillment, poor handling of goods, and high
logistics costs negatively affected consumer trust and, consequently, e-commerce adoption in Nigeria. However,
the study did not consider how emerging technologies such as AI and blockchain could be integrated to improve
delivery efficiency in Nigeria’s e-commerce sector.
METHODOLOGY
The study area focused on Lagos State, Nigeria, a major economic and commercial hub in West Africa with a
dense population, vibrant markets, and diverse industries, including finance, manufacturing, shipping, and a
rapidly growing digital economy. Major commercial and logistics locations included Ikeja, Lagos Island, Lekki,
Surulere, and Apapa, where DHL operates service centers to support e-commerce activities. These locations
were selected for their socio-economic and infrastructural significance, capturing diverse consumer, business,
and corporate logistics patterns across Lagos. The study employed a descriptive survey research design with the
use of structured questionnaires administered to DHL customers across Lagos during 2024. According to the
DHL Nigeria Report (2024), the total estimated customer base in Lagos was 110,012 annually, with significant
participation from individual consumers, small business owners, and corporate clients. Using stratified random
sampling and Slovin’s formula, a sample size of 399 respondents was determined and proportionally distributed
across the three customer groups. Inferential statistics of multiple regression analysis were applied to analyze
the data.
RESULT AND DISCUSSION
Socio-economic Characteristics of DHL Customers
An analysis of the socio-economic background of respondents, as presented in Table 1, revealed significant
demographic diversity among DHL customers. The gender distribution indicates a slight predominance of male
respondents, who accounted for 54.2% of the sample population, compared to 45.8% female respondents. This
marginal disparity (Mean = 1.46, SD = 0.499) suggests a relatively balanced gender representation among DHL
service users, thereby mitigating the risk of gender bias in customer perceptions.
Marital status analysis illustrates that the majority of respondents were married, representing 85.2% of the total
sample, whereas single individuals constituted 14.5%, and only 0.3% were divorced. The mean score of 1.86
(SD = 0.357) implies a high concentration of married individuals, reflecting the possible association between
marital stability and frequent logistics engagements, such as household or family-related deliveries.
Age distribution indicates that the largest proportion of respondents were between the ages of 30–39 years
(37.9%), followed by the 40–49year bracket (28.0%) and 18–29 years (18.8%). The mean age score (Mean =
3.33, SD = 0.988) falls within the 30–39 range, suggesting that DHL’s clientele base predominantly consists of
active, economically productive individuals. This demographic trend aligns with the general pattern of middle-
aged adults being primary consumers of logistics and delivery services due to career, business, and household
responsibilities.
In terms of academic qualification, a significant proportion of respondents held NCE/OND certificates (49.6%),
followed by those with WAEC qualifications (38.4%). Only 7.4% of the customers had attained HND/BSc
degrees, while 4.6% fell under the 'Others' category. The average educational qualification (Mean = 1.78, SD =
0.771) suggests a moderately educated customer base, likely reflecting the accessibility and appeal of DHL
services across various educational strata.
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The occupational profile of respondents reveals that students form the largest occupational group, accounting
for 37.2% of the sample, closely followed by self-employed individuals (31.0%). Civil servants represented
18.6%, while 13.2% of respondents identified with other occupations. The occupational mean (Mean = 2.39, SD
= 0.936) indicates a strong presence of economically independent users particularly self-employed individuals
and students who may rely on courier services for business operations or academic-related logistics.
Table 1: Socio-economic Characteristics of DHL Customers
Variable Category Frequency Percent (%) Mean Standard Deviation
Gender Male 213 54.2 1.46 0.499
Female 180 45.8
Marital Status Single 57 14.5 1.86 0.357
Married 335 85.2
Divorced 1 0.3
Age Below 18 years 7 1.8 3.33 0.988
18–29 years 74 18.8
30–39 years 149 37.9
40–49 years 110 28.0
50 years and above 53 13.5
Academic Qualification WAEC 151 38.4 1.78 0.771
NCE/OND 195 49.6
HND/BSc 29 7.4
Others 18 4.6
Occupation Civil Servant 73 18.6 2.39 0.936
Student 146 37.2
Self-employed 122 31.0
Others 52 13.2
Total 393 100.0
Source: Researcher’s Computation (2025)
Analysis of Delivery Efficiency Impact on e-Commerce Adoption
Findings from the result of multiple regression analysis provided critical insights into the analysis of delivery
efficiency impact on e-commerce adoption of courier company in Metropolitan Lagos, Nigeria is presented in
Table 2 and Table 3, which provide the Model Summary, ANOVA, and Coefficients for the regression model
assessing the impact of delivery efficiency on e-commerce adoption.
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The Multiple R value of 0.865 indicates a strong positive correlation between the predictor variables (Delivery
Time Accuracy, Dispatch and Routing Efficiency, Real-Time Tracking Capability, Cost Competitiveness, and
Last-Mile Delivery Effectiveness) and the dependent variable, which is Delivery Efficiency. The R Square (R²)
value of 0.748 suggests that 74.8% of the variability in delivery efficiency can be explained by the predictors in
the model. This is a high level of explanatory power, indicating the model is robust. The Adjusted R Square of
0.744 accounts for the number of predictors in the model, confirming that the model fits well even after adjusting
for the degrees of freedom. The Standard Error of 1.418 is the average distance that the observed values fall
from the regression line, which is relatively low, suggesting that the model fits the data with minimal error.
The ANOVA table 2 is used to assess the overall significance of the regression model. The F-statistic value of
226.892 with a p-value of 0.000 (p < 0.05) indicates that the regression model is statistically significant. This
means that at least one of the predictors in the model significantly contributes to explaining variations in delivery
efficiency. Thus, the null hypothesis (H01: Delivery efficiency has no significant impact on e-commerce
adoption) is rejected, and the model as a whole is significant.
The Coefficients Table 3 provides the regression coefficients for each predictor, showing how much each variable
influences the dependent variable (delivery efficiency). For the Delivery Time Accuracy predictor, the
Unstandardized Coefficient (B) is 0.912, with a p-value of 0.000, indicating a significant positive relationship
with delivery efficiency. Dispatch and Routing Efficiency has a coefficient of 1.007 (p = 0.010), Real-Time
Tracking Capability has a coefficient of 0.928 (p = 0.001), and Last-Mile Delivery Effectiveness has a coefficient
of 0.677 (p = 0.003). All these variables are significant in explaining delivery efficiency, as their p-values are
less than 0.05. However, Cost Competitiveness of Delivery has a very small coefficient of 0.004, and its p-value
of 0.764 is greater than the threshold of 0.05, indicating that this variable does not significantly contribute to the
model.
Table 2: Model Summary and ANOVAa for Delivery Efficiency
Multiple R .865a
R Square (R2) .748
Adjusted R Square (R2) .744
Standard Error 1.418
Analysis of Variance Table
Model Sum of Squares df Mean Square F Sig.
1 Regression 2279.855 5 455.971 226.892 .000b
Residual 769.693 383 2.010
Total 3049.548 388
Source: Researcher’s Computation (2025)
Table 3: Coefficientsa for Delivery Efficiency
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .463 .376 1.233 .218
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Delivery Time Accuracy .912 .060 .395 15.308 .000
Dispatch and Routing
Efficiency
1.007 .066 .394 15.279 .010
Real-Time Tracking
Capability
.928 .066 .370 14.156 .001
Cost Competitiveness of
Delivery
.004 .012 .007 21.300 .764
Last-Mile Delivery
Effectiveness
.677 .084 .207 8.017 .003
a. Dependent Variable: Delivery Efficiency
Source: Researcher’s Computation (2025)
The results from the multiple regression analysis as presented above revealed a strong relationship between
delivery efficiency and e-commerce adoption among DHL customers in Lagos State. With a multiple correlation
coefficient (R) of 0.865 and a coefficient of determination (R²) of 0.748, it can be inferred that approximately
74.8% of the variance in e-commerce adoption is explained by delivery efficiency components. The adjusted R²
value of 0.744 further confirms the model’s robustness and generalizability. The high F-statistic value (F =
226.892, p = .000) signifies that the model is statistically significant. The regression coefficients in Table 4.5
indicate that all major components of delivery efficiency delivery time accuracy (β = .912, p < .001), dispatch
and routing efficiency (β = 1.007, p = .010), real-time tracking capability (β = .928, p = .001), and last-mile
delivery effectiveness (β = .677, p = .003) have significant positive impacts on e-commerce adoption. However,
cost competitiveness of delivery was not significant (p = .764), suggesting that while cost remains a
consideration, customers prioritize speed, accuracy, and visibility in delivery logistics.
These findings are consistent with recent studies. For instance, Adeniran and Olajide (2020) assert that timely
and accurate delivery significantly boosts customer confidence and drives repeat usage of e-commerce
platforms. Similarly, Oyelami et al. (2021) found that real-time tracking technologies improve customer
experience and foster trust in delivery systems. The importance of dispatch efficiency and last-mile logistics is
echoed by Ekeh and Adebayo (2022), who argued that the effectiveness of these logistics elements is essential
for overcoming infrastructural constraints in Nigerian cities. The insignificant result of cost competitiveness
echoes Chidubem and Aluko (2023), who emphasized that urban e-commerce consumers are increasingly more
concerned with service reliability than cost, especially when dealing with premium logistics services like DHL.
Therefore, while cost may influence adoption at lower income levels, service quality and efficiency remain the
dominant predictors of customer satisfaction and e-commerce growth in Lagos.
CONCLUSION AND RECOMMENDATION
The study therefore concludes that, analysis of delivery efficiency has a statistically significant impact on e-
commerce adoption of courier company in Metropolis Lagos, Nigeria. However, it was recommendations that;
DHL should prioritize improving delivery time accuracy, dispatch and routing efficiency, real-time tracking, and
last-mile delivery effectiveness. Investments in these operational areas would enhance customer satisfaction and
contribute to a higher adoption rate of DHL’s e-commerce logistics services. This could involve leveraging AI
and data analytics to optimize delivery routes and improve dispatch systems.
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