INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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The Effect of Last Mile Delivery Performance on E-Commerce

Customer Loyalty

Md Roshaid Ahmed Tamim*

; Md Al Amin Ali

; Ekaterina Pogonina

; Md Sazzad Hossain

Graduate Student, School of Economics and International Trade, University of Science and Technology

Beijing, China

Doctoral Student, School of Economics and Management, Beihang University, China

Master’s Student, Department of Tourism Management, Yangzhou University, China

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000012

Received: 20 September 2025; Accepted: 26 September 2025; Published: 27 October 2025

ABSTRACT

This study investigates the impact of last-mile delivery performance on customer loyalty in the e-commerce

sector. Using a quantitative research approach with data collected from 386 e-commerce customers, we

examine how delivery speed, delivery accuracy, and return handling influence customer loyalty. Multiple

regression analysis reveals that all three independent variables significantly predict customer loyalty (R² =

0.672, p < 0.001). Delivery accuracy emerged as the strongest predictor (β = 0.412, p < 0.001), followed by

return handling (β = 0.298, p < 0.001) and delivery speed (β = 0.247, p < 0.001). These findings provide

valuable insights for e-commerce retailers and logistics providers seeking to enhance customer retention

through improved last-mile delivery performance. The study contributes to the growing body of literature on e-

commerce logistics and offers practical implications for strategic decision-making in supply chain

management.

Keywords: last-mile delivery, customer loyalty, e-commerce, delivery speed, delivery accuracy,

INTRODUCTION

The exponential growth of e-commerce has fundamentally transformed retail landscapes worldwide, with

global online sales projected to exceed $6.8 trillion by 2028 (Miglani, 2024). This digital revolution has

elevated last-mile delivery from a peripheral logistics function to a critical strategic differentiator that directly

shapes customer experience and loyalty. The last-mile delivery segment, representing the final step in the

fulfillment process where products reach customers' doorsteps, accounts for up to 53% of total shipping costs

and significantly influences brand perception (Closing the Logistics Loop with Last-Mile Delivery, n.d.).

In today's hypercompetitive e-commerce environment, where switching costs are minimal and alternatives are

readily accessible, customer loyalty has become increasingly elusive yet essential for sustainable business

success. Research indicates that acquiring new customers costs five to seven times more than retaining existing

ones, while a 5% increase in customer retention can boost profits by 25-95% (Noor et al., 2024).

Consequently, understanding the mechanisms through which last-mile delivery performance influences

customer loyalty has become paramount for e-commerce success.

The COVID-19 pandemic has further accelerated e-commerce adoption and heightened customer expectations

for delivery services. Consumers now demand faster, more reliable, and flexible delivery options, with 87% of

online shoppers citing delivery experience as a key factor in their repurchase decisions (Singh, 2022). This

paradigm shift has prompted retailers to reimagine their last-mile strategies, investing heavily in technologies

and capabilities that enhance delivery performance.

Despite substantial industry investments and academic interest, the relationship between specific last-mile

delivery attributes and customer loyalty remains inadequately understood. While previous studies have

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

www.rsisinternational.org

Page 110

examined various aspects of logistics service quality, most have adopted broad perspectives without isolating

the distinct effects of critical delivery performance dimensions. This research addresses this gap by

systematically investigating how three key last-mile delivery performance factors delivery speed, delivery

accuracy, and return handling influence customer loyalty in e-commerce contexts.

Research Questions

This study seeks to answer the following research questions:

RQ1: To what extent does delivery speed influence customer loyalty in e-commerce transactions?

RQ2: How does delivery accuracy affect customer loyalty in online retail environments?

RQ3: What is the impact of return handling efficiency on e-commerce customer loyalty?

RQ4: Which last-mile delivery performance dimension has the strongest effect on customer loyalty?

Research Objectives

The primary objectives of this research are:

To empirically examine the relationship between delivery speed and customer loyalty in e-commerce settings

To investigate the impact of delivery accuracy on customer retention and repeat purchase intentions

To assess how return handling procedures influence customer loyalty behaviors

To determine the relative importance of different last-mile delivery performance dimensions in predicting

customer loyalty

To provide actionable insights for e-commerce practitioners seeking to optimize their last-mile delivery

strategies

Significance of the Study

This research makes several important contributions to theory and practice. Theoretically, it extends the

service quality literature by decomposing last-mile delivery performance into distinct, measurable dimensions

and empirically testing their differential effects on customer loyalty. The study integrates perspectives from

operations management, marketing, and consumer behavior to develop a comprehensive understanding of how

operational performance translates into customer retention.

From a practical standpoint, the findings offer valuable guidance for e-commerce managers and logistics

service providers in resource allocation and capability development. By identifying which delivery

performance dimensions most strongly influence loyalty, the study enables more targeted investments in last-

mile infrastructure and processes. Furthermore, the research provides benchmarks for performance

measurement and competitive positioning in rapidly evolving e-commerce markets.

The study also contributes to sustainable business practices by highlighting how efficient return handling can

reduce environmental impact while enhancing customer satisfaction. This dual benefit aligns with growing

consumer consciousness about sustainability and corporate social responsibility in e-commerce operations.

LITERATURE REVIEW

Last-Mile Delivery in E-Commerce

Last-mile delivery represents the final and often most complex stage of the e-commerce fulfillment process,

encompassing all activities from the last distribution center to the customer's location (Lim et al., 2018). This

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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segment has emerged as a critical battleground for competitive advantage, with retailers recognizing that

delivery experience significantly shapes overall customer satisfaction and brand perception. Recent industry

data indicates that the global last-mile delivery market reached $161.20 billion in 2024, with projections

suggesting continued double-digit growth through 2030 (Last Mile Delivery Market Size, Share & Growth

Report, 2030, n.d.)

The complexity of last-mile delivery stems from multiple factors, including urban congestion, delivery density

variations, customer availability, and the need for real-time coordination(Alverhed et al., 2024). These

challenges are compounded by rising customer expectations for faster, cheaper, and more flexible delivery

options. Identified five key trends shaping last-mile delivery: same-day delivery proliferation, autonomous

vehicle adoption, crowd-sourced delivery models, smart locker deployment, and sustainability initiatives. Each

trend reflects attempts to balance operational efficiency with customer satisfaction while managing cost

pressures (Lyons & McDonald, 2023).

The strategic importance of last-mile delivery extends beyond operational considerations to encompass brand

differentiation and customer relationship management. (Pourmohammadreza et al., 2025) demonstrated that

delivery experience accounts for 48% of the variance in overall e-commerce satisfaction, surpassing product

quality and price as determinants of customer evaluation. This finding underscores the transformative role of

last-mile delivery in shaping competitive dynamics within digital commerce ecosystems.

Customer Loyalty in Digital Commerce

Customer loyalty in e-commerce contexts exhibits distinct characteristics compared to traditional retail

environments. The absence of physical interactions, reduced switching costs, and information transparency

create unique challenges for building lasting customer relationships (Liu et al., 2011). E-commerce loyalty

manifests through multiple behavioral and attitudinal dimensions, including repeat purchases, positive word-

of-mouth, resistance to competitive offers, and willingness to pay premium prices.

Contemporary loyalty research distinguishes between transactional loyalty, driven by convenience and habit,

and emotional loyalty, rooted in genuine brand attachment and identification (Hung, 2014). While

transactional loyalty may generate short-term repeat purchases, emotional loyalty provides more sustainable

competitive advantages through increased customer lifetime value and advocacy behaviors. The challenge for

e-commerce firms lies in transcending purely transactional relationships to forge deeper emotional connections

with customers.

Digital technologies have enabled sophisticated loyalty measurement and management approaches. Advanced

analytics allow firms to track customer journeys, predict churn probability, and personalize retention

interventions. However, (Kumar & Agrawal, 2024) caution that technological capabilities alone cannot

guarantee loyalty; rather, they must be complemented by genuine value creation through superior service

delivery and customer experience management.

Delivery speed has emerged as a primary competitive weapon in e-commerce, with major retailers investing

billions in infrastructure and capabilities to accelerate fulfillment. The proliferation of same-day and instant

delivery services reflect recognition that temporal utility represents a key value driver for online shoppers.

73% of consumers consider delivery speed when choosing between competing retailers, with younger

demographics showing particularly strong preferences for rapid fulfillment (Omnichannel Retail Statistics:

Current State and Future Insights, n.d.)

The psychological mechanisms underlying speed preferences involve both utilitarian and hedonic

considerations. From a utilitarian perspective, faster delivery reduces waiting costs and enables immediate

consumption. Hedonically, rapid fulfillment satisfies instant gratification desires and creates positive surprise

when expectations are exceeded. (Baldi et al., 2024) demonstrated that delivery speed influences not only

satisfaction with the logistics service but also product evaluation and overall brand perception, highlighting the

spillover effects of delivery performance.

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Delivery Accuracy and Service Quality

Delivery accuracy encompasses multiple dimensions, including delivering the correct items, meeting promised

delivery windows, and ensuring products arrive in acceptable condition. This performance dimension directly

affects service reliability perceptions and trust formation in e-commerce relationships. Studies consistently

show that delivery failures, even when subsequently resolved, significantly damage customer confidence and

reduce repurchase intentions (Harter et al., 2024).

The impact of delivery accuracy extends beyond individual transactions to shape long-term relationship

quality. (Norouzi, 2024) found that accuracy consistency across multiple orders strongly predicts customer

lifetime value, with high-accuracy performers enjoying 40% higher retention rates compared to industry

averages. This finding emphasizes the cumulative nature of accuracy effects and the importance of operational

excellence in building customer loyalty.

Technology adoption has dramatically improved delivery accuracy capabilities through real-time tracking,

predictive analytics, and automated quality control systems. However, (Ekuma, 2023) note that technology-

enabled accuracy improvements must be balanced with human touchpoints that provide flexibility and problem

resolution when errors occur. The optimal approach combines systematic accuracy enhancement with

responsive service recovery mechanisms that maintain customer trust despite occasional failures.

Hypothesis Development

Drawing on service quality theory and the expectancy-disconfirmation paradigm, this study proposes that last-

mile delivery performance dimensions influence customer loyalty through their effects on service evaluation

and satisfaction. When delivery performance meets or exceeds expectations, positive disconfirmation

generates satisfaction and strengthens loyalty intentions. Conversely, performance failures create negative

disconfirmation that erodes trust and increases defection probability.

Based on the literature review, we propose the following hypotheses:

H1: Delivery speed positively influences customer loyalty in e-commerce transactions.

H2: Delivery accuracy positively influences customer loyalty in e-commerce transactions.

H3: Return handling efficiency positively influences customer loyalty in e-commerce transactions.

H4: Among the three delivery performance dimensions, delivery accuracy has the strongest effect on customer

loyalty.

METHODOLOGY

This study employs a quantitative research design using cross-sectional survey methodology to examine the

relationships between last-mile delivery performance dimensions and customer loyalty. The quantitative

approach enables systematic hypothesis testing and generalization of findings across the e-commerce

population. Cross-sectional data collection provides efficiency while capturing current market dynamics and

customer perceptions.

The target population comprises adult consumers (aged 18 and above) who have completed at least three e-

commerce purchases within the past six months. This criterion ensures participants possess sufficient

experience to evaluate delivery performance meaningfully. The sampling frame includes consumers from

diverse demographic backgrounds, product categories, and geographic locations to enhance external validity.

Sample size determination followed established guidelines for multiple regression analysis. With three

independent variables and assuming a medium effect size (f² = 0.15), power analysis indicated a minimum

sample of 119 participants for 0.95 statistical power at α = 0.05. To account for potential data quality issues

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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and enhance precision, we targeted 400 responses. After data screening, 386 valid responses were retained for

analysis, exceeding recommended thresholds for robust statistical inference.

Participants were recruited through a professional online panel provider using stratified random sampling to

ensure demographic representativeness. Stratification variables included age, gender, income, and geographic

region, with quotas established based on national e-commerce user statistics.

All constructs were measured using established scales adapted from prior research and modified for the e-

commerce context. Seven-point Likert scales (1 = strongly disagree, 5 = strongly agree) were employed for all

items to provide sufficient variance and discrimination between response categories.

Data collection occurred over a four-week period using an online survey platform. Participants received an

email invitation explaining the research purpose, voluntary participation, and confidentiality assurances. The

survey began with screening questions to verify eligibility, followed by the main questionnaire sections. To

minimize order effects, question blocks were randomized across participants.

Several measures enhanced data quality. Attention check questions identified careless responding, reverse-

coded items detected response patterns, and completion time screening removed surveys completed

unreasonably quickly. IP address verification prevented duplicate responses, while incomplete surveys were

excluded from analysis.

Data analysis proceeded through multiple stages using SPSS 30 statistical software. Preliminary analyses

included data screening, missing value assessment, and assumption testing for multiple regression. Descriptive

statistics characterized the sample and variables, while reliability analysis confirmed scale internal

consistency.

Correlation analysis examined bivariate relationships between variables and identified potential

multicollinearity issues. Multiple regression analysis tested the research hypotheses, with customer loyalty as

the dependent variable and delivery performance dimensions as independent variables. Hierarchical regression

enabled assessment of incremental variance explained by each predictor.

Additional analyses included mediation testing to explore indirect effects through customer satisfaction,

moderation analysis examining demographic influences, and robustness checks using alternative estimation

methods. These supplementary analyses strengthen confidence in the findings and provide deeper insights into

the studied relationships.

This research adhered to established ethical guidelines for human subjects research. Institutional Review

Board approval was obtained prior to data collection. All participants provided informed consent after

receiving comprehensive information about the study purpose, procedures, and rights. Participation was

voluntary with the option to withdraw at any point without consequence.

Data confidentiality was maintained through anonymous survey design and secure data storage. Personal

identifiers were not collected, and responses were aggregated for analysis. Findings are reported at the

aggregate level without possibility of individual identification. These ethical safeguards ensure participant

protection while maintaining scientific rigor.

RESULTS AND DISCUSSION

Sample Characteristics

The final sample of 386 participants exhibited diverse demographic characteristics representative of the e-

commerce consumer population. Gender distribution was balanced with 52.3% female and 47.7% male

participants. Age ranged from 18 to 67 years (M = 36.4, SD = 11.8), with the largest cohort being 25-34 years

(31.6%), followed by 35-44 years (27.2%), 45-54 years (19.4%), 18-24 years (13.5%), and 55+ years (8.3%).

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

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Table 1: Demographic information

Characteristic

Value

Gender

Female

52.3%

Male

47.7%

Age

18–24

13.5%

25–34

31.6%

35–44

27.2%

45–54

19.4%

55+

8.3%

Education

Bachelor’s degree or higher

68.4%

Below Bachelor’s

31.6%

Annual Household Income

< 40,000

24.1%

40,000–70,000

35.2%

70,000–100,000

26.9%

>100,000

13.8%

E-commerce Experience

M = 7.3 years, SD = 3.2

Purchases per Month

M = 4.6, SD = 2.8

Frequently Purchased Categories

Clothing/Accessories

78.2%

Electronics

64.5%

Books/Media

59.3%

Home Goods

52.8%

Groceries

41.7%

Educational attainment was relatively high, with 68.4% holding bachelor's degrees or higher, reflecting the

digital literacy requirements for e-commerce participation. Annual household income showed wide variation,

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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with 24.1% reporting less than 40,000, 35.2% between 40,000-70,000, 26.9% between 70,000-100,000, and

13.8% exceeding 100,000.

Participants reported substantial e-commerce experience, averaging 7.3 years of online shopping (SD = 3.2)

and 4.6 purchases per month (SD = 2.8). Product categories purchased most frequently included

clothing/accessories (78.2%), electronics (64.5%), books/media (59.3%), home goods (52.8%), and groceries

(41.7%). This diversity ensures findings generalize across various e-commerce segments.

Descriptive Statistics

Table 2 presents descriptive statistics for all study variables, including means, standard deviations, and scale

reliabilities.

Table 2 Descriptive Statistics and Scale Reliabilities

Variable

Mean

Cronbach's α

Skewness

Kurtosis

Delivery Speed

5.23

1.14

0.87

-0.42

0.18

Delivery Accuracy

5.67

1.03

0.91

-0.58

0.31

Return Handling

4.92

1.31

0.89

-0.34

-0.22

Customer Loyalty

5.31

1.21

0.93

-0.51

0.27

All scales demonstrated satisfactory internal consistency with Cronbach's alpha values exceeding the 0.70

threshold recommended for established scales. Customer loyalty showed the highest reliability (α = 0.93),

followed by delivery accuracy (α = 0.91), return handling (α = 0.89), and delivery speed (α = 0.87).

Mean scores indicate generally positive evaluations across all dimensions, with delivery accuracy receiving the

highest ratings (M = 5.67) and return handling the lowest (M = 4.92). The relatively high standard deviations

suggest meaningful variation in customer experiences and perceptions across the sample.

Normality assessment through skewness and kurtosis values indicated approximate normal distributions for all

variables, with values falling within the acceptable range of -2 to +2. This supports the appropriateness of

parametric statistical techniques for hypothesis testing.

Correlation Analysis

Table 3 displays the correlation matrix examining relationships between study variables.

Table 3. Correlation Matrix

Variable

1. Delivery Speed

1.00

2. Delivery Accuracy

0.52***

1.00

3. Return Handling

0.44***

0.48***

1.00

4. Customer Loyalty

0.58***

0.71***

0.62***

1.00

***p < 0.001

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All variables showed significant positive correlations, supporting the hypothesized relationships. Customer

loyalty demonstrated strong correlations with all three delivery performance dimensions, with delivery

accuracy showing the strongest bivariate relationship (r = 0.71, p < 0.001), followed by return handling (r =

0.62, p < 0.001) and delivery speed (r = 0.58, p < 0.001).

Correlations among independent variables ranged from 0.44 to 0.52, indicating moderate relationships that

warrant attention for multicollinearity but fall below concerning thresholds. Variance inflation factor (VIF)

values in subsequent regression analyses ranged from 1.42 to 1.58, well below the conservative cutoff of 5.0,

confirming absence of problematic multicollinearity.

Hypothesis Testing

Multiple regression analysis was conducted to test the research hypotheses. Table 4 presents the regression

results with customer loyalty as the dependent variable.

Table 4. Multiple Regression Analysis Results

Variable

VIF

Constant

0.847

0.238

3.56

<0.001

Delivery Speed

0.262

0.047

0.247

5.57

<0.001

1.42

Delivery Accuracy

0.484

0.054

0.412

8.96

<0.001

1.51

Return Handling

0.275

0.041

0.298

6.71

<0.001

1.58

R² = 0.672, Adjusted R² = 0.669, F(3, 382) = 261.23, p < 0.001

The regression model explained 67.2% of the variance in customer loyalty (R² = 0.672, F(3, 382) = 261.23, p <

0.001), indicating strong predictive power. All three delivery performance dimensions significantly predicted

customer loyalty, supporting hypotheses H1, H2, and H3.

Hypothesis 1 proposed that delivery speed positively influences customer loyalty. The results support this

hypothesis (β = 0.247, t = 5.57, p < 0.001), indicating that faster delivery significantly enhances customer

loyalty. Each unit increase in perceived delivery speed is associated with a 0.262 unit increase in loyalty,

holding other factors constant.

Hypothesis 2 suggested that delivery accuracy positively influences customer loyalty. This hypothesis

received strong support (β = 0.412, t = 8.96, p < 0.001), with delivery accuracy emerging as the strongest

predictor. The standardized coefficient indicates that a one standard deviation increase in delivery accuracy

corresponds to a 0.412 standard deviation increase in loyalty.

Hypothesis 3 posited that return handling efficiency positively influences customer loyalty. The results

confirm this hypothesis (β = 0.298, t = 6.71, p < 0.001), demonstrating that effective return processes

significantly contribute to customer retention. The magnitude of this effect exceeded that of delivery speed,

highlighting the importance of post-purchase service quality.

Hypothesis 4 predicted that delivery accuracy would have the strongest effect on customer loyalty among the

three dimensions. The standardized coefficients support this hypothesis, with delivery accuracy (β = 0.412)

showing a larger effect than return handling (β = 0.298) or delivery speed (β = 0.247). Pairwise comparisons

using Wald tests confirmed that the difference between delivery accuracy and other predictors was statistically

significant (p < 0.05).

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Additional Analyses

To provide deeper insights, several supplementary analyses were conducted. First, mediation analysis

examined whether customer satisfaction mediates the relationship between delivery performance and loyalty.

Results indicated significant partial mediation effects for all three predictors, with satisfaction accounting for

38-45% of the total effects. This finding aligns with service quality theory suggesting that operational

performance influences loyalty through satisfaction mechanisms.

Second, moderation analysis explored whether demographic factors influence the strength of relationships.

Age significantly moderated the effect of delivery speed (β = -0.087, p = 0.018), with younger consumers

showing stronger loyalty responses to speed improvements. Income moderated the return handling effect (β =

0.094, p = 0.023), suggesting that higher-income consumers place greater value on convenient return

processes.

Third, polynomial regression tested for nonlinear relationships. A significant quadratic term for delivery speed

(β = -0.073, p = 0.041) indicated diminishing returns at high-speed levels, consistent with satisfaction plateau

effects identified in prior research. Linear relationships adequately characterized the accuracy and return

handling effects.

DISCUSSION OF FINDINGS

The results provide compelling evidence that last-mile delivery performance significantly influences customer

loyalty in e-commerce contexts. The substantial variance explained (67.2%) demonstrates that delivery

performance represents a primary driver of customer retention, validating industry investments in logistics

capabilities. These findings extend service quality literature by decomposing delivery performance into distinct

dimensions and quantifying their differential impacts on loyalty.

The preeminence of delivery accuracy as a loyalty predictor aligns with reliability being a foundational service

quality dimension. Customers appear to prioritize consistent, error-free delivery over speed, suggesting that

reliability concerns dominate evaluation processes. This finding has important implications for resource

allocation, indicating that investments in accuracy-enhancing technologies and processes may yield superior

returns compared to speed-focused initiatives.

The significant effect of return handling challenges traditional conceptualizations of delivery as a

unidirectional process. Contemporary e-commerce requires bidirectional logistics capabilities that support the

complete customer journey. The strength of this relationship (β = 0.298) suggests that return experience

substantially influences overall service evaluation and loyalty formation. This finding is particularly relevant

given rising return rates and growing consumer expectations for hassle-free return processes.

While delivery speed showed the smallest standardized coefficient among the three predictors, its effect

remained substantial and statistically significant. The identified nonlinear relationship suggests an optimal

speed range beyond which marginal improvements provide minimal loyalty benefits. This finding challenges

the prevalent "faster is always better" mentality and advocates for strategic speed positioning that balances

customer value with operational costs.

The mediation analysis revealing partial effects through satisfaction confirms that delivery performance

influences loyalty through both direct and indirect pathways. Direct effects may reflect the signaling value of

delivery performance in communicating retailer competence and customer orientation. Indirect effects through

satisfaction align with traditional service quality frameworks linking operational performance to customer

outcomes through evaluation processes.

Moderation effects highlight the importance of customer segmentation in delivery strategy formulation.

Younger consumers' heightened sensitivity to delivery speed reflects generational differences in instant

gratification expectations and time valuation. Higher-income consumers' emphasis on return handling may

stem from higher opportunity costs and greater purchase risk exposure. These findings suggest that one-size-

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fits-all delivery strategies may be suboptimal, with targeted approaches needed for different customer

segments.

CONCLUSION

Theoretical Contributions

This research makes several significant contributions to the e-commerce and service operations literature.

First, it provides empirical evidence for the multidimensional nature of last-mile delivery performance and its

differential effects on customer loyalty. By decomposing delivery performance into speed, accuracy, and

return handling components, the study offers a more nuanced understanding of how operational capabilities

translate into customer retention.

Second, the research extends service quality theory to the e-commerce context by identifying delivery

accuracy as the dominant driver of loyalty. These finding challenges speed-centric perspectives prevalent in

industry discourse and academic research, suggesting that reliability considerations outweigh temporal factors

in customer evaluation processes. The theoretical implication is that service quality hierarchies may differ in

digital versus physical retail environments.

Third, the study contributes to reverse logistics literature by establishing return handling as a critical loyalty

antecedent comparable in importance to forward delivery performance. This finding elevates returns from

operational necessity to strategic opportunity, suggesting that competitive advantage can be achieved through

superior reverse logistics capabilities.

Practical Implications

The findings offer actionable guidance for e-commerce practitioners and logistics service providers. The

primacy of delivery accuracy suggests that firms should prioritize investments in quality control systems,

inventory management, and order fulfillment accuracy over pure speed improvements. Technologies such as

automated picking systems, RFID tracking, and AI-powered quality checks can enhance accuracy while

maintaining operational efficiency.

The strong effect of return handling indicates that firms should view returns as relationship-building

opportunities rather than cost centers. Implementing customer-friendly return policies, offering multiple return

channels, and ensuring rapid refund processing can significantly enhance loyalty. The business case for liberal

return policies is strengthened by evidence that return experience substantially influences retention.

The nonlinear relationship between speed and loyalty suggests that firms should identify optimal speed

thresholds rather than pursuing maximum velocity. Cost-benefit analysis should consider diminishing loyalty

returns when evaluating investments in expedited delivery infrastructure. Strategic speed positioning that

meets customer expectations without overdelivering may optimize profitability.

Customer segmentation based on delivery preference heterogeneity can enhance targeting effectiveness.

Younger, time-sensitive segments may justify premium speed options, while quality-conscious segments may

respond better to reliability guarantees. Personalized delivery options that allow customers to self-select based

on individual preferences can maximize satisfaction across diverse segments.

Limitations and Future Research

Several limitations should be acknowledged when interpreting the findings. First, the cross-sectional design

precludes causal inference, and longitudinal research is needed to establish temporal relationships between

delivery performance and loyalty evolution. Future studies could employ panel data or experimental designs to

strengthen causal claims.

Second, the study focused on three delivery performance dimensions, but other factors such as delivery

flexibility, communication quality, and sustainability may also influence loyalty. Comprehensive frameworks

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incorporating additional dimensions could provide more complete understanding of delivery performance

effects.

Third, the research did not account for contextual factors such as product category, purchase value, or

competitive intensity that may moderate observed relationships. Future research should examine boundary

conditions and contingency factors that influence the delivery performance-loyalty relationship.

Fourth, cultural factors may influence delivery expectations and loyalty formation processes. The study's

single-country focus limits generalizability, and cross-cultural research is needed to identify universal versus

culture-specific effects.

Future research directions include investigating the role of emerging technologies such as autonomous delivery

vehicles, drone delivery, and predictive logistics in shaping customer expectations and loyalty. The integration

of sustainability considerations into delivery performance frameworks represents another promising avenue,

particularly given growing environmental consciousness among consumers.

Concluding Remarks

This study demonstrates that last-mile delivery performance profoundly influences customer loyalty in e-

commerce environments. The findings reveal that delivery accuracy serves as the strongest loyalty predictor,

followed by return handling and delivery speed. These insights challenge prevailing assumptions about the

primacy of speed and highlight the multifaceted nature of delivery performance evaluation.

As e-commerce continues its rapid expansion and evolution, understanding the mechanisms linking

operational performance to customer outcomes becomes increasingly critical. The evidence presented here

suggests that sustainable competitive advantage lies not in singular focus on speed but in balanced excellence

across multiple delivery performance dimensions. Firms that recognize and act upon these insights will be

better positioned to build lasting customer relationships in the digital economy.

The research underscores that last-mile delivery has transcended its traditional role as a back-office function to

become a frontline customer experience differentiator. In an era where product and price advantages are easily

replicated, superior delivery performance may represent the last frontier for competitive differentiation. The

companies that master the last mile will ultimately win the race for customer loyalty in the digital marketplace.

Ethical Approval

The study was conducted in accordance with the ethical principles of voluntary participation, informed

consent, and data protection. Participants were informed about the study's purpose and procedures, and their

participation was considered as implied consent.

Conflict Of Interest

The authors declare that they have no conflicts of interest.

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