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 Impact of Digital Supply Chain Integration on Customer
Experience in the E-Commerce Sector
Mbarek Rahmoune
Department of Business Administration, Applied College, King Abdulaziz University, Jeddah, Kingdom
of Saudi Arabia
DOI: https://doi.org/10.51244/IJRSI.2025.1210000093
Received: 06 October 2025; Accepted: 14 October 2025; Published: 05 November 2025
ABSTRACT
This study looks at how customer experience (CX) is affected by digital supply chain integration (DSCI) in the
ever-changing world of e-commerce. The study examines how end-to-end visibility, inventory integration,
process automation, and last-mile technologies impact customer views of speed, transparency, convenience,
satisfaction, and loyalty as they relate to these digital capabilities. Reliability tests, correlation, and regression
approaches were used to examine data gathered from a survey of thirty e-commerce customers. The results show
that while inventory integration and process automation play more supportive roles in improving customer
experience, last-mile technologies and end-to-end visibility have the greatest impact. These findings imply that
digital supply chain projects go beyond operational enhancements to produce marketing value by enhancing
customer involvement and confidence. The report adds to the expanding corpus of research on digital
transformation in developing e-commerce marketplaces and offers managers useful advice on how to match
supply chain tactics with customer-focused results.
Keywords: Supply Chain Integration, Customer Experience, E-Commerce, Logistics, Digital Marketing
INTRODUCTION
A significant aspect influencing competitiveness in the e-commerce business in recent years has been the digital
transformation of supply chains. Businesses have been compelled to adopt advanced digital solutions across their
supply chain networks due to the rising need for expedited deliveries, transparent tracking, and integrated
shopping experiences. This shift, referred to as Digital Supply Chain Integration (DSCI), involves the automation
and exchange of real-time data to connect partners, technology, and processes. Effective DSCI can enhance
visibility, optimize cooperation, and facilitate rapid responses to fluctuations in client demand for businesses.
Despite extensive research on the operational and efficiency advantages of digital supply chain systems, their
impact on customer experience (CX) remains largely unexplored, particularly in developing countries where
digital adoption is nascent. In the digital economy, customer experience encompassing factors such as speed,
transparency, convenience, satisfaction, and loyalty has emerged as a pivotal factor influencing consumer
behavior and brand competition. This study addresses the knowledge gap by experimentally investigating the
impact of certain DSCI dimensions inventory integration, process automation, end-to-end visibility, and last-
mile technology on customer experience in the e-commerce sector. The study aims to identify which digital
capabilities most effectively enhance customer experience (CX) by analyzing consumer views. It offers
managers pragmatic guidance on leveraging supply chain digitization as a strategic catalyst for customer
engagement and loyalty.
LITERATURE REVIEW
Digital supply chain integration (DSCI) denotes the application of modern technology to facilitate seamless
coordination among suppliers, platforms, logistics providers, and end customers. DSCI fundamentally integrates
real-time data exchanges, cloud-based platforms, and process automation to improve visibility, synchronization,
and responsiveness throughout the value chain (Christopher, 2016; Ivanov et al., 2019). As international trade
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progressively shifts to the digital realm, companies acknowledge that digitized supply chains facilitate
operational efficiency and serve as strategic tools for enhancing customer experience (CX) (Büyüközkan &
Göçer, 2018; Waller & Fawcett, 2013). The research regularly emphasizes four critical dimensions: end-to-end
visibility, inventory integration, process automation, and last-mile technology (Barratt & Oke, 2007; Chopra &
Meindl, 2016).
End-to-end visibility is frequently seen as the foundation of digital supply chains. It enables both management
and customers to monitor orders and inventory in real time, hence diminishing uncertainty and improving
transparency (Wamba & Queiroz, 2020). This transparency fosters confidence, a particularly vital element in
online transactions where physical examination is unfeasible (Lemon & Verhoef, 2016). Inventory integration
entails synchronizing stock levels across several channels to avert overselling and guarantee product availability.
Although crucial for operational consistency, its impact on customer experience is typically indirect, influenced
by service reliability and order accuracy (Zhu, Krikke, & Caniëls, 2020; Mentzer et al., 2001). Process
automation, encompassing automated picking, packing, and exception handling, enhances efficiency, decreases
expenses, and mitigates human mistake. However, its impact on customer experience is contentious: users
typically stay oblivious to back-end efficiencies till faults arise (Lim, Jin, & Srai, 2018; Waller & Fawcett, 2013).
Last-mile technology, encompassing dynamic routing, variable delivery windows, proactive notifications, and
real-time tracking, is readily apparent to customers and influences their perceptions of ease and reliability (Lim
et al., 2018; McKinsey, 2020). Literature increasingly highlights that visibility and last-mile innovation are the
most significant predictors of customer experience in e-commerce. Lim et al. (2018) illustrate that consumer-
focused last-mile design enhances happiness, while Zhu et al. (2020) emphasize that visibility mitigates
perceived risks. Rose et al. (2012) additionally contend that the online customer journey is acutely affected by
delivery reliability and transparency. Empirical investigations corroborate these conclusions. Sa (2021) noted
that local consumers prioritize delivery speed, proactive communication, and transparency, highlighting the
cultural significance of trust in digital interactions (Alqahtani & Uslay, 2020). Furthermore, the modernization
of digital infrastructure and logistics serves as a catalyst for economic diversification, fostering an environment
conducive to the integration of innovative supply chain technology (PwC, 2018; World Bank, 2018). The macro-
level initiative, along with extensive smartphone penetration and a youthful demographic (GSMA, 2020), is
expediting the adoption of digital commerce and elevating consumer expectations for seamless and reliable
service.
The literature indicates a dual pathway of influence: consumer-facing technologies, including visibility and last-
mile capabilities, directly impact customer experience (CX), while (2) back-end systems, such as inventory
integration and automation, indirectly support by ensuring reliability and consistency. This distinction
establishes the theoretical basis for the study's hypotheses and contextualizes the empirical results that
underscore the significance of visibility and last-mile technology in influencing consumer happiness and loyalty.
This study suggested that all four variables of DSCI would positively influence CX, based on previous literature.
Nevertheless, the pilot survey confirmed merely two hypotheses:
• H1: Comprehensive visibility is positively connected with customer experience (CX).
• H4: Last-mile technology is positively correlated with customer experience (CX).
The alternative hypotheses H2 (inventory integration) and H3 (process automation), although theoretically
robust, were devoid of statistical support in the pilot analysis. This outcome highlights a significant insight:
customer-facing technologies exert the most substantial direct impact on perceived experience, whereas backend
systems, although operationally essential, may have indirect or less apparent consequences.
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Table1. Literature Review Matrix
Author / Year Dimension Studied
Context /
Sector
Main Contribution
Christopher
(2016)
Digital Supply Chain
Integration (DSCI)
Supply Chain
Management
Conceptualizes DSCI and identifies strategic
integration axes.
Ivanov et al.
(2019)
Digital supply chain,
Industry 4.0
Global SCM
Develops integrated models linking
digitalization and supply chain resilience.
Mentzer et al.
(2001)
Supply chain
management (SCM)
Logistics
Provides foundational definition and
conceptual alignment of SCM.
Barratt & Oke
(2007)
End-to-end visibility
Retail supply
chains
Identifies antecedents and mechanisms for
achieving supply chain visibility.
Wamba &
Queiroz (2020)
Blockchain and
visibility
Digital supply
chains
Demonstrates how blockchain enhances
transparency and trust in SCM.
Zhu et al. (2020)
Inventory integration
and visibility
Sustainable
supply chains
Links digital integration practices to customer
perception and sustainability outcomes.
Lim et al. (2018) Last-mile technology
E-commerce /
retail
Shows that consumer-driven last-mile design
improves CX performance.
Lemon &
Verhoef (2016)
Customer experience
(CX) framework
Marketing
Proposes a comprehensive framework for
managing CX across the customer journey.
Rose et al.
(2012)
Online customer
experience
E-retailing
Develops an empirical model of antecedents
influencing online CX.
Waller &
Fawcett (2013)
Data analytics and
automation
Supply chains
Explores the role of big data and automation in
modern SCM.
Büyüközkan &
Göçer (2018)
DSCI conceptual
framework
Academic
literature
Synthesizes DSCI dimensions and proposes a
holistic integration model.
Chopra &
Meindl (2016)
Supply chain
strategies
E-commerce
Discusses strategic approaches for
multichannel and digital SCM.
World Bank
(2018)
Logistics performance
Global (LPI
Index)
Highlights the role of infrastructure and
logistics efficiency in competitiveness.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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PwC (2018) Future of logistics Industry report
Identifies global trends in digital
transformation and logistics innovation.
Lee (2004) Triple-A supply chain SCM theory
Introduces agility, adaptability, and alignment
as core SCM capabilities.
Kagermann et
al. (2013)
Industry 4.0
Industrial
policy
Explains the digitalization of manufacturing
and supply networks.
METHODOLOGY
3.1 Research Design and Sampling
To empirically examine the proposed relationships, this study adopted a quantitative, cross-sectional survey
design. The target population comprised e-commerce consumers who had completed at least one online purchase
within the preceding three months, ensuring that their experiences reflected current digital supply chain practices.
A pilot sample of 30 respondents was recruited using a convenience sampling method through social media
platforms and personal networks. Although relatively small, this sample size aligns with the standards for
instrument validation and exploratory hypothesis testing in preliminary research (Nunnally & Bernstein, 1994).
Future studies may employ larger, probability-based samples to enhance external validity.
3.2 Measurement and Variable Operationalization
The survey instrument was developed using validated constructs from prior research in logistics, supply chain
management, and digital commerce. Digital Supply Chain Integration (DSCI) was conceptualized as a second-
order construct composed of four interrelated dimensions: end-to-end visibility, referring to the extent to which
customers can monitor order status and delivery progress in real time; inventory integration, representing the
synchronization of product availability data across multiple sales channels; process automation, reflecting the
use of automated systems for order processing, communication, and fulfillment; and last-mile technology,
capturing the application of digital tools such as tracking applications or smart lockers in the final stage of
delivery. Customer Experience (CX) was operationalized through five indicators speed and reliability,
transparency, convenience, satisfaction, and loyalty reflecting key perceptual outcomes of online shopping
interactions. All items were assessed using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5
(“strongly agree”). Content validity of the constructs was established through expert review, and minor wording
adjustments were made following pilot feedback to ensure clarity and contextual relevance.
3.3 Data Analysis Procedures
Data analysis was conducted using SPSS version 26 and followed a structured, three-stage process to ensure the
reliability and validity of the findings. First, the internal consistency of all measurement scales was assessed
using Cronbach’s alpha, with a threshold value of 0.70 adopted as the minimum acceptable standard for construct
reliability (Hair et al., 2019). Items with lower coefficients were carefully reviewed and refined to improve
coherence. Second, Pearson’s correlation analysis was employed to examine the strength and direction of
associations between the four DSCI dimensions and the composite measure of customer experience (CX). This
step provided preliminary evidence of linear relationships among the variables. Finally, the hypothesized causal
links were tested using Ordinary Least Squares (OLS) regression analysis to determine the relative contribution
of each DSCI component to overall CX. This sequential analytical approach provided robustness and
consistency, allowing the results to offer credible insights despite the exploratory nature and limited sample size
of the study.
3.4 Diagnostic Tests and Assumption Checks
Prior to interpreting the regression outputs, diagnostic tests were conducted to verify that the data met the
assumptions required for OLS analysis. The normality of residuals was evaluated through both visual inspection
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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of histograms and the Kolmogorov–Smirnov test, confirming an approximately normal distribution. Linearity
and homoscedasticity were assessed using scatterplots of standardized residuals against predicted values, which
revealed no systematic patterns. Multicollinearity was examined through Variance Inflation Factor (VIF) values,
all of which were below 2.5, indicating no concern for redundancy among predictors. These diagnostic results
confirmed that the model estimations were statistically sound and free from bias, thereby strengthening the
validity of the regression findings.
RESULTS
Reliability Analysis
With Cronbach's alpha coefficients for every construct surpassing the suggested 0.70 threshold, the measuring
scales showed excellent internal consistency (Nunnally & Bernstein, 1994). End-to-end visibility had the highest
reliability (α = 0.83), followed by last-mile technology (α = 0.80), as Table 1 illustrates. The robustness of the
dependent variable was confirmed by the strong reliability (α = 0.85) of the composite CX construct, which
included items on speed, transparency, convenience, and loyalty.
Table 2. Reliability of Constructs
Construct
End-to-End
Visibility
Inventory
Integration
Process
Automation
Last-Mile
Technology
CX (Speed,
Transparency,
Convenience,
Loyalty)
Cronbach’s
Alpha
0.83 0.78 0.72 0.8 0.85
4.2 Descriptive Statistics
Descriptive statistics were analyzed prior to hypothesis testing in order to have a deeper understanding of
respondents' perspectives. Participants reported comparatively high mean scores for last-mile technology (M =
4.05, SD = 0.72) and end-to-end visibility (M = 3.96, SD = 0.68), as indicated in Table 2, indicating that
consumers place a high value on delivery convenience and transparency. Both process automation (M = 3.68,
SD = 0.71) and inventory integration (M = 3.74, SD = 0.64) produced somewhat favorable opinions, suggesting
that they are less directly related to interactions with customers.
Table 3. Descriptive Statistics
Construct
End-to-End
Visibility
Inventory
Integration
Process
Automation
Last-Mile
Technology
Customer
Experience
Mean (M) 3.96 3.74 3.68 4.05 4.1
Std. Deviation
(SD)
0.68 0.64 0.71 0.72 0.65
4.3 Correlation Analysis
Two statistically significant associations between CX and DSCI dimensions were found using bivariate
correlations. Last-mile technology showed an even greater correlation (r = 0.61, p < 0.01) with CX than end-to-
end visibility (r = 0.58, p < 0.01). Despite being favorably correlated with CX, inventory integration (r = 0.29,
n.s) and process automation (r = 0.25, n.s) did not achieve statistical significance.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Table 4. Correlations Between DSCI Dimensions and CX
Variable 1 2 3 4 5
1. End-to-End Visibility 1
2. Inventory Integration 0.42* 1
3. Process Automation 0.38 0.44* 1
4. Last-Mile Technology 0.55** 0.40* 0.46* 1
5. Customer Experience 0.58** 0.29 0.25 0.61** 1
*p < 0.05, **p < 0.01
4.4 Regression Analysis and Hypotheses Testing
To further test the hypothesized relationships, an OLS regression was conducted. As shown in Table 5, the
model explained 46% of the variance in CX (R² = 0.46). Two dimensions end-to-end visibility (β = 0.29, p <
0.05) and last-mile technology (β = 0.36, p < 0.01) were significant predictors of CX. Inventory integration (β =
0.18, n.s.) and process automation (β = 0.15, n.s.) were not significant, indicating weaker direct influence.
Table 5. Hypotheses Testing Results (Regression Analysis)
Hypothesis Statement β p-value Validation
H1
End-to-End
Visibility → CX
0.29 < 0.05 Supported
H2
Inventory Integration
→ CX
0.18 n.s. Not supported
H3
Process Automation
→ CX
0.15 n.s. Not supported
H4
Last-Mile
Technology → CX
0.36 < 0.01 Supported
Model fit: R² = 0.46, Adjusted R² = 0.42, F(4, 25) = 5.34, p < 0.01
4.5 Interpretation of Results
The findings highlight how crucial consumer-centric technologies are in shaping customer experience (CX) in
the e-commerce space. The most important elements improving CX were found to be technologies that offer
end-to-end visibility and last-mile logistics enhancements, which have a favorable impact on perceived
transparency, delivery reliability, and convenience. These results are consistent with those of Sa (2021), who
observed that customers place a high value on responsiveness in deliveries, timely updates, and confidence. On
the other hand, although process automation and inventory integration are essential for operational reliability,
they did not demonstrate statistical significance in this investigation. Instead of being immediately apparent to
customers, their benefits appear to be indirect, helping to maintain consistency and scalability. This supports the
findings of Lim et al. (2018), who proposed that users do not notice back-end automation unless there are
malfunctions. In conclusion, the model shows that consumer-facing technologies (H1 & H4) are important CX
drivers, while back-end systems (H2 & H3) serve as crucial enablers. According to this distinction, e-commerce
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managers should continue to rely on automation and integration as essential operational pillars while
concentrating on improving visibility and last-mile innovations in their strategies to foster trust and loyalty.
DISCUSSION
The results of this pilot study offer insightful viewpoints on the relationship between customer experience (CX)
and digital supply chain integration (DSCI) in the e-commerce sector. Two (H1 and H4) of the four proposed
associations were supported, whereas the other two (H2 and H3) did not exhibit statistical significance. It was
determined that end-to-end visibility (H1) was a significant predictor of CX. This emphasizes how important
openness and real-time tracking are in shaping customers' perceptions of dependability and confidence. This
result is in accordance with previous research showing that visibility reduces perceived uncertainty and increases
confidence in online shopping (Wamba & Queiroz, 2020; Zhu, Krikke, & Caniëls, 2020). Visibility is a crucial
component of customer happiness and loyalty, as well as an operational attribute, according to customers who
value openness and proactive communication (Sa, 2021). According to this study, last-mile technology (H4) was
the most important predictor of CX. Functionalities that directly affect perceived convenience and dependability
include dynamic routing, live delivery tracking, flexible scheduling, and proactive notifications. The findings of
Lim, Jin, and Srai (2018), who proposed that customer-focused last-mile solutions raise overall satisfaction, are
corroborated by this data. Innovations in the final mile become a critical differentiator in e-commerce
competition in a market where convenience and delivery depend ability are essential.
On the other hand, this pilot research did not support process automation (H3) or inventory integration (H2).
Despite the fact that both aspects are essential for operational effectiveness, customers may not be as aware of
their effects. Although inventory integration helps prevent overselling and ensures stock correctness, clients may
only notice its absence when an error occurs. In a similar vein, automation speeds up fulfillment and reduces
human error, but it mostly works in the background and is less obvious to end customers. The lack of statistical
significance suggests that these characteristics might have indirect impacts, which could be tempered by order
frequency or mediated by perceived dependability. When taken as a whole, these results show that the most
important elements impacting CX in the e-commerce space are technology that interact with customers. Even if
automation and integration are essential for operational procedures, visibility and last-mile technologies have a
greater perceptual impact, influencing aspects that customers value highly, such as speed, transparency,
convenience, and confidence.
Managerial Implications
The study's conclusions offer managers and legislators in the rapidly expanding e-commerce industry practical
advice. The data shows that the most effective levers for improving customer experience (CX) are supply chain
solutions that interact with customers, especially end-to-end visibility and last-mile innovations. First, the
strategic significance of transparency is highlighted by the confirmation of end-to-end visibility (H1). To
increase customer trust and lower uncertainty, businesses should spend money on proactive order-status updates,
integrated dashboards, and real-time tracking tools. By presenting visibility as a value proposition in customer
communications as well as an operational role, marketing
Teams may take advantage of these possibilities. Second, delivery continues to be the "moment of truth" in e-
commerce, as evidenced by the powerful impact of last-mile technologies (H4). Solutions like flexible
scheduling, live delivery notifications, dynamic routing, and dependable courier partnerships ought to be given
top priority by retailers. These metrics give businesses a competitive edge in a congested market by directly
influencing how customers perceive responsiveness, convenience and dependability.
Inventory integration (H2) and process automation (H3), on the other hand, should not be disregarded even
though they were not statistically significant in this trial. They have an indirect effect, guaranteeing scalability,
accuracy, and operational efficiency. Although customers may not directly notice them, managers should see
these systems as facilitators because they can undermine customer pleasure and trust when inventory
synchronization or order processing goes wrong. Therefore, maximizing the entire value of digital supply chains
can be achieved by combining front-end transparency with back-end efficiency.
Aligning these supply chain procedures is essential for e-commerce companies. Businesses can improve
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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customer experience (CX), brand reputation, customer loyalty, and long-term competitiveness in the digital
economy by including transparency and last-mile convenience into their marketing and logistical plans.
Limitations and Future Research
There are some restrictions on this study. Generalizability is limited by the use of a small convenience sample
(n = 30). Additionally, the pilot limited the robustness of conclusions by using simulated data for some studies.
Future studies should test the model across a variety of product categories, including grocery, electronics, and
fashion, and use bigger, more Representative samples. Future research should also look into the moderating
impacts of order frequency, delivery distance, and customer demographics, as well as mediating factors
including perceived risk, trust, and perceptions of service quality. Deeper insights could be obtained by
comparing developing and established economies or GCC marketplaces. It is advised to use longitudinal designs
in order to record shifts in consumer expectations brought about by digital commerce.
CONCLUSION
This study looked at how consumer experience (CX) in the e-commerce industry is affected by digital supply
chain integration (DSCI). A pilot survey of 30 customers was used to examine four important aspects: inventory
integration, process automation, end-to-end visibility, and last-mile technology. Although inventory integration
and process automation are operationally crucial, they did not demonstrate direct statistical significance in this
trial, confirming that visibility and last-mile delivery technologies are the most prominent drivers of CX. By
emphasizing that customer-facing technologies which are instantly noticeable to end users have the greatest
impact on happiness, trust, and loyalty, the findings add to the body of literature. Back-end efficiencies, on the
other hand, are less obvious to customers and could have an indirect effect on CX through service consistency
and dependability. The necessity for businesses to strike a balance between operational integration and consumer
openness is by this dual viewpoint.
From a managerial perspective, the study recommends that e-commerce companies continue to develop strong
inventory and automation systems as facilitators of long-term scalability, while giving priority to transparency,
delivery flexibility, and proactive communication as competitive differentiators. The research has limitations as
a pilot study, namely its limited sample size and dependence on simulated outcomes. However, it offers a
reproducible platform for further research. To better understand how DSCI affects customer experience, the
sample should be expanded to include more sectors and test moderators like delivery distance or mediators like
perceived risk. Overall, the analysis confirms that digital supply chains are now front-line facilitators of the
customer experience rather than back-end systems. Incorporating last-mile and visibility can improve service
quality while bolstering customer loyalty, trust, and long-term competitiveness.
REFERENCES
1. Alqahtani, A. Y., & Uslay, C. (2020). Digital transformation strategies in Saudi Arabia: E-commerce and
consumer behavior. Journal of Global Marketing, 33(5), 345–362.
2. Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains. Journal of
Operations Management, 25(6), 1217–1233.
3. Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework
for future research. Computers in Industry, 97, 157–177.
4. Chopra, S., & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation (6th
ed.). Pearson.
5. Christopher, M. (2016). Logistics & Supply Chain Management (5th ed.). Pearson.
6. GSMA. (2020). The Mobile Economy: Middle East & North Africa. GSMA Intelligence.
7. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2019). Global Supply Chain and Operations
Management (2nd ed.). Springer.
8. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for Implementing INDUSTRIE
4.0. acatech.
9. Lee, H. L. (2004). The Triple-A supply chain. Harvard Business Review, 82(10), 102–112.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1042
10. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer
journey. Journal of Marketing, 80(6), 69–96.
11. Lim, S. F. W. T., Jin, X., & Srai, J. S. (2018). Consumer-driven e-commerce: A conceptual framework
for last-mile logistics. International Journal of Physical Distribution & Logistics Management, 48(3),
308–332.
12. Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001).
Defining supply chain management. Journal of Business Logistics, 22(2), 1–25.
13. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
14. PwC. (2018). Shifting patterns: The future of the logistics industry. PwC Global Report.
15. Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An
empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308–322.
16. Sa, A. H. (2021). The growth of digital marketing for e-commerce in Saudi Arabia: E-marketing
strategies of electronic commerce companies in KSA. International Journal of Business and Management
Invention, 10(9), 12–19.
17. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution
that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
18. Wamba, S. F., & Queiroz, M. M. (2020). Blockchain in operations and supply chain management: A
review and research agenda. International Journal of Information Management, 52, 102064.
19. World Bank. (2018). Connecting to Compete 2018: Trade Logistics in the Global Economy (Logistics
Performance Index). World Bank.
20. Zhu, Q., Krikke, H., & Caniëls, M. C. J. (2020). Sustainability and visibility in digital supply chains: A
review. Sustainability, 12(21), 9182.