ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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Assessing the Impact of Reverse Logistics Service Quality on
Customer Satisfaction: A Servqual-Based Analysis
Mohamad Afiff Johar
1
, Atikah Saadah Selamat
2
, Mehran Doulatabadi
3
, Nor Azah Abdul Aziz
4*
1,2,3,4
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800016
Received: 08 November 2025; Accepted: 14 November 2025; Published: 18 December 2025
ABSTRACT
Reverse logistics has become a critical component of modern supply chains due to increasing activities of
logistics locally and globally. Hence, frequency of product returns also increase because of various reasons,
including damaged or defective items, items not meeting expectations (wrong size, color, or fit), incorrect items
being sent, misleading advertisements, buyer's remorse, or difficulty using the product defects. Due to these,
customers expect for efficient service recovery. This study investigates the factors that influence customer
satisfaction in returning defective products. Using survey-based quantitative analysis, this research examines the
roles of quality of replacement product, timeline of refunds, customer service quality, clarity of return policy,
and demonstration of understanding as independent variables and customer satisfaction in returning defective
products as dependent variable. By applying SERVQUAL model as underpinning theory, this study found that
all the hypotheses are supported except for the timeline of refunds. The findings provide theoretical
contributions in growing body of literature on reverse logistics and customer satisfaction by providing empirical
evidence on the factors influencing customer satisfaction in the context of returning defective products. For
practical contributions, companies may enhance their return processes and improve customer loyalty.
Keywords: Reverse Logistics; Determinants of Customer Satisfaction; SERVQUAL Model
INTRODUCTION
Reverse logistics, especially in managing the return of defective products, has become a vital component of
customer service and operational effectiveness. As e-commerce expands and customer expectations rise,
businesses face pressure to optimize return procedures for smooth experiences. The rapidly expanding e-
commerce sector and heightened rivalry among retailers have profoundly altered consumer expectations and
habits, especially in relation to returns of products (Smith, 2020). Customers in today's market anticipate a simple
return process, particularly when they are faced with the disappointment of getting defective products. Reverse
logistics, which handles the return of items from customers to the source for a variety of uses such recycling,
disposal, or repair, has become more significant as a result of this change in expectations (Johnson, 2019).
Although reverse logistics is essential for addressing customer concerns, there is a significant research gap about
the customer's experience and the difficulties involved in returning defective products through these channels
(Robinson, 2017). The whole return trip, from making the request to getting the final answer, is included in
reverse logistics. It is essential to comprehend the customer's experience throughout this procedure for several
of reasons. Firstly, customer satisfaction and loyalty may be greatly increased by having a simple and effective
return policy for defective products. Consumer satisfaction with the entire shopping experience and repeat
business are positively correlated with a prompt and simple resolution of their issue (Garcia, 2019). Secondly,
manufacturers and merchants may get useful information from research on consumer experiences with returning
defective products. Reverse logistics operations may be improved by firms by identifying unique difficulties that
consumers confront throughout the return process (Lee, 2020). This may result in customer-focused and more
effective procedures. Last but not least, efficient reverse logistics can guarantee the correct recycling or disposal
of defective products, helping to minimize waste and the environmental impact of modern society (Clark, 2022).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 152
www.rsisinternational.org
Research explicitly looking at customer satisfaction and the difficulties involved in returning defective products
through these channels is still lacking, although the fact that reverse logistics are essential in handling product
returns. Previous research recognises the significance of reverse logistics in resolving customer issues; yet, there
is a deficiency of information about the intricacies involved in returning defective products (Smith, 2020).
According to Brown (2021) firms find it challenging to manage customer complaints and improve their reverse
logistics strategies if they lack a comprehensive comprehension of the aspects that influence customer experience
and the challenges consumers face throughout the return process. The difficulties and consumer experience
associated with returning damaged items via reverse logistics hence make empirical study imperative. Hence,
the purpose of this research is to close this knowledge gap by examining the difficulties and customer experiences
associated with using reverse logistics to return defective products. The study hopes to clarify the particular
difficulties faced in the return procedure by focusing on the particular context of defective products, which will
eventually lead to better customer service strategies. Hence, the Research Objectives are:
1. To examine the determinants of customer satisfaction for reverse logistics.
2. To analyse the relationship between the determinants and the customer satisfaction for reverse logistics.
3. To investigate the most significant determinant of customer satisfaction for reverse logistics.
This research focuses on understanding customer experience within reverse logistics, specifically for returning
defective products in Melaka, Malaysia. We exclude broader aspects like product sorting and disposal. Our
investigation centres on key areas influencing customer satisfaction: ease of initiating returns, speed and
convenience of return shipping options, clarity of the return policy regarding defective items, processing speed
for returned products, and the quality of customer service interactions throughout the return journey. By
identifying these factors' impact on customer satisfaction, the study aims to help businesses in Melaka, Malaysia
refine their reverse logistics strategies and ultimately enhance customer experience when returning defective
products.
LITERATURE
Supply Chain Management (SCM)
It is the process of managing and overseeing the whole manufacturing flow of products and services. From the
point of origin to the point of consumption, this covers the transportation and storage of completed items,
inventory control, and raw materials. SCM attempts to optimize a business's supply-side operations in order to
increase customer value and acquire a competitive edge in the market. SCM's main objectives are to satisfy end
users and every supply chain link by delivering the right good at the right place at the right time at the right price
at the right cost. The objectives of supply chain management (SCM), which are to ensure supply chain efficacy
and efficiency in order to gain a competitive advantage, may be characterized in a variety of ways. While supply
chain management typically prioritizes improving the movement of products from production to end users, there
is an increasing interest in reverse logistics, particularly in handling returned items (Rogers & Tibben-Lembke,
2001). Ensuring customer happiness is essential, especially when handling faulty products. The overall
satisfaction customers have with a brand is greatly influenced by their experience when returning defective
products. Goodman et al. (2017) found that a good return experience can result in repeat purchases and positive
word-of-mouth referrals, despite product faults. On the other hand, a cumbersome or challenging return
procedure can harm customer retention and brand reputation (Helm et al., 2020). Hence, it is vital for businesses
in the field of SCM to comprehend the factors that impact customer satisfaction when dealing with returns of
defective products. By recognizing these elements and making enhancements, companies can improve customer
satisfaction and possibly secure a competitive edge in the market.
Reverse Logistics (RL)
In reverse logistics, products are moved returned to the manufacturer or other locations in the supply chain after
being delivered to their ultimate destination for recycling, disposal, repairs, or remanufacturing. It covers
handling returns of merchandise because of flaws, complaints from customers, or objects that are nearing the
end of their useful life. Murphy and Poist (1989) described RL as ‘the transfer of goods from the consumer to
the producer in the distribution channel’. Council of logistics management (1999) stated that the focus of forward
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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logistics is the movement of materials from the point of production to the point of use; oppositely, the focus of
RL is the movement of materials from the point of consumption to the point of production.
Defective Products
Products that don't live up to the manufacturer's or customers' expectations for quality or performance are
considered defective. These defects may be the result of poor design, bad production, or damage sustained during
handling and transportation. Returns, repairs, or replacements are often necessary when dealing with faulty items
in order to satisfy customers and follow to legal requirements. Product defects lead to the subsequent evaluation
of raw materials, labour, money, and effort invested in these items as waste. Even worse, the company's
reputation will suffer if consumers get into possession of these defective items.
Independent Variable
Below are variables that explore customer satisfaction during the return process for faulty products.
Quality of Replacement Product
Customers anticipate that replacement products will equal or surpass the quality of the original items, as this
showcases the company’s dedication to addressing problems successfully. Substandard replacements can result
in disappointment, eroded trust, and unfavourable views of the company's dependability. Past research has
emphasised that consumers appreciate top-notch replacement items and link them to a firm's service reliability
(Kumar & Kaushik, 2021). This variable assesses the impact of replacement product quality on customer
satisfaction, emphasizing factors like durability, consistency, and flawless replacements.
Timeliness of Refunds
Timely refund processing is essential for assessing customer satisfaction. Holds in providing refunds or
replacements can lead to annoyance and reduce confidence in the business. On the other hand, quick refunds
reveal operational effectiveness and a focus on the customer. The rate at which refunds are processed has been
identified as a key factor influencing satisfaction in service recovery situations, particularly in the e-commerce
and retail industries (Nguyen et al., 2021). This variable assesses how refund speed affects customer satisfaction,
emphasising the importance of quick resolutions to improve the customer experience in reverse logistics.
Customer Service Quality
The quality of customer service is fundamental to customer satisfaction during the return process. Attentive,
supportive, and reachable customer service can greatly improve customer experiences and cultivate loyalty.
Customers appreciate straightforward communication and prompt solutions to their issues when returning faulty
items. Research indicates that the quality of customer service, particularly in post-sale procedures, significantly
influences satisfaction and customer loyalty (Rahman et al., 2022). This variable explores how the
responsiveness and problem-solving skills of customer service personnel affect satisfaction rates.
Clarity of Return Policy
An open and clear return policy is vital for establishing customer trust and contentment. Customers anticipate
return policies to be straightforward and clear, as this minimizes confusion and stress during the return process.
Studies have shown that transparent return policies greatly enhance customer satisfaction and boost trust in
businesses (Liu & Wang, 2021). This variable examines how the transparency of return policies affects
satisfaction, emphasizing aspects like rules, processes, and terms associated with returning defective products.
Demonstration of Understanding
Demonstration of Understanding is an essential element in guaranteeing customer contentment throughout
reverse logistics. Businesses that efficiently recognize customer issues and dissatisfaction throughout the return
procedure can build trust and loyalty. Through offering tailored and considerate solutions, companies can
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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showcase their dedication to addressing problems, creating a favourable impact on the client. Studies indicate
that grasping customer expectations and responding to their individual issues is crucial for improving satisfaction
and fostering enduring relationships (Kumar et al., 2022).
Dependent Variable
The dependent variable for this research is Customer Satisfaction, which indicates how well the return process
aligns with or surpasses customer expectations. Customer satisfaction includes essential elements such as the
quality of substitute items, promptness of refunds, customer service responsiveness, transparency of return
policies, and the company’s ability to show understanding. Elevated satisfaction rates result in beneficial effects
such as returning customers, recommendations through word-of-mouth, and increased loyalty. Studies indicate
that customer satisfaction in reverse logistics is crucial for assessing service quality and addressing post-purchase
issues effectively (Han et al., 2023). This research examines the impact of independent variables on customer
satisfaction, providing companies with insights to enhance their reverse logistics approaches and elevate
customer experiences. According to Oliver (2014), consumer satisfaction is the response of a buyer to being
satisfied. The customer's review of a product or service tells you if it met their wants and expectations. Customers
will be satisfied with a product or service if it meets their wants and expectations. Customer satisfaction is
influenced by the perception of service quality, product quality, and price as well as personal factors and
situational factors (Zeithaml, Bitner, & Gremler, 2013). According to Ogunleye's (2013) research, the experience
of returning a product can also change how a customer feels about the service they receive. When the customers
do not satisfy the product due to the product did not meet their need and expectations, the product will be
returned, and a return may make customer dissatisfied
THEORETICAL FRAMEWORK
This study looks at customer satisfaction in the context of faulty product reverse logistics, but it's also important
to consider the frameworks that are now in place for measuring service quality. SERVQUAL is one framework
of this kind that may provide useful information for comprehending the customer experience around returns.
SERVQUAL is a technique for measuring the quality of services provided and intended to quantify the
discrepancy between the expectations and perceptions of customers, which was created by Parasuraman,
Zeithaml, and Berry in 1988. When assessing customer experience in relation to returning damaged items, it
highlights five essential service quality elements that are very relevant:
Figure 1: SERVQUAL Theoretical framework created by Parasuraman, Zeithaml, and Berry (1988)
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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SERVQUAL dimensions impact returns as follows:
Empathy:
Providing personalized, caring attention to customers is a key factor. If a customer feels ignored or that their
concerns are not being treated individually, their satisfaction can decrease, potentially leading to a return.
Tangibles:
The physical appearance of the product and its packaging, as well as the professional appearance of staff, can
influence a customer's initial perception of quality, impacting the likelihood of a return.
Assurance:
The knowledge and courtesy of employees, and their ability to convey trust, can make a customer feel confident
in their purchase. If this assurance is lacking, especially during a problem, a customer may feel less secure and
be more inclined to return the item.
Responsiveness
How quickly and willingly a company handles customer inquiries or problems directly impacts satisfaction. A
slow or unhelpful response to an issue can lead to a return, while prompt assistance can resolve it.
Reliability
A company's ability to deliver a product that functions as promised and to provide accurate service information
is critical. Errors or failures to perform as promised can lead to dissatisfaction and returns.
By considering these SERVQUAL dimensions in the context of customer experience with returning defective
products, businesses can gain valuable insights into potential areas for improvement. The framework can help
identify gaps between customer expectations and their perceptions of the service received, allowing businesses
to develop strategies to enhance customer satisfaction throughout the return process.
Conceptual Framework for this Study
The conceptual framework for this study integrates the identified independent variables (quality of replacement
product, timeline of refunds, customer service quality, clarity of return policy, demonstration of understanding)
and the dependent variable (customer satisfaction). The framework posits that effective management of reverse
logistics processes can lead to higher customer satisfaction. Figure 2 shows the conceptual framework for this
study.
Figure 2: Conceptual Framework for this Study
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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Hypotheses Development
Customer satisfaction in returning defective products through reverse logistics is influenced by various factors,
as identified in this study. This section discusses each independent variable, its relevance to the return process,
and its hypothesized relationship with customer satisfaction.
Quality of Replacement Product
The quality of the replacement product is a critical factor in determining customer satisfaction during the return
process. Customers expect replacement products to meet or exceed the quality of the original item, which fosters
trust and confidence in the company's commitment to resolving defects (Parasuraman et al., 1988).
H1: The quality of replacement products has a significant influence on customer satisfaction in returning
defective products.
Timeline of Refunds
Timeliness in processing refunds or replacements is a key determinant of customer satisfaction. Prompt action
reflects a company’s reliability and efficiency, minimizing frustration and dissatisfaction caused by delays
(Zeithaml et al., 2000).
H2: The timeline of refunds has a significant influence on customer satisfaction in returning defective products.
Customer Service Quality
The responsiveness and helpfulness of customer service representatives are pivotal in shaping customer
experiences. Clear communication and swift resolutions enhance satisfaction and demonstrate the company’s
dedication to addressing customer concerns (Rahman et al., 2018).
H3: Customer service quality has a significant influence on customer satisfaction in returning defective products.
Clarity of Return Policy
A clear and transparent return policy provides customers with a sense of security and confidence in the return
process. It eliminates uncertainty and ensures customers are well-informed about their rights and obligations
(Smith & Bolton, 2002).
H4: The clarity of the return policy has a significant influence on customer satisfaction in returning defective
products.
Demonstration of Understanding
Empathy shown by the company during the return process highlights its commitment to addressing customer
needs. Personalized assistance and understanding responses enhance trust and satisfaction, making the
experience more positive for the customer (Grönroos, 1994).
H5: Demonstration of understanding has a significant influence on customer satisfaction in returning defective
products.
METHODOLOGY
Research Philosophy
The positivist philosophy of the study is in line with the quantitative approach. To test theories and provide
objective, observable, and measurable evidence, positivism employs systematic approaches. This approach
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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makes sense for the research as it aims to quantify certain factors and create statistical connections between
customer satisfaction and reverse logistics procedures (Saunders et al., 2007; Bryman, 2012).
Research Approach
The study takes a deductive approach, which is related to quantitative research in general. According to Saunders
et al. (2007) and Bryman (2012), this method entails creating a theoretical framework and hypotheses based on
the body of current literature, which are then verified by gathering empirical data. The research intends to
examine certain theories on the effect of reverse logistics on customer satisfaction by using a deductive method.
Methodological Choice
The research uses a quantitative design with a single approach. This decision is motivated by the need to gather
and evaluate quantitative data in order to understand the connection between consumer happiness and reverse
logistics procedures (Creswell, 2014; Saunders et al., 2007). To test hypotheses and establish the statistical
significance of the correlations between variables, the quantitative technique is suitable.
Research Strategy
Surveys conducted via Google Forms are used to collect data from respondents in Melaka, focusing on their
experiences with reverse logistics processes. This survey approach is selected for its efficiency in gathering
standardized data and providing a comprehensive understanding of customer perceptions (Fowler, 2013;
Saunders et al., 2007).
Time Horizon
This study adopts a cross-sectional time horizon, whereby data is collected at a single point in time. A cross-
sectional approach is appropriate for understanding the current state of customer satisfaction in returning
defective products through reverse logistics in Melaka Tengah. This design enables the researcher to efficiently
analyse relationships between variables and draw conclusions within the constraints of the project timeline.
Techniques and Procedures
The data collection technique involves distributing a structured questionnaire to consumers who have
participated in reverse logistics processes, utilizing Google Forms to reach a broad audience. This questionnaire
is meticulously designed to measure key variables such as the ease of the return process, clarity of the return
policy, timeline of refunds or replacements, quality of customer service, and overall customer satisfaction.
Rigorous data analysis will be performed using advanced statistical techniques, including correlation and
regression analysis, to comprehensively assess survey responses and test the hypotheses (Field, 2013; Pallant,
2020).
Quantitative Study
This research employs a quantitative approach to study customer satisfaction in the context of returning defective
products through reverse logistics. The quantitative method is suitable for identifying relationships between
independent variables (e.g., quality of product, timeliness of refunds) and the dependent variable (customer
satisfaction). This approach allows for the collection of data in numerical form, facilitating statistical analysis to
test hypotheses and draw objective conclusions (Noyes et al., 2019). Quantitative research provides factual and
reliable outcome data that can often be generalized to larger populations. Unlike qualitative research, which
focuses on participant perspectives, quantitative methods ensure measurable, consistent results that align with
the study’s objectives. This approach enables a comprehensive understanding of the factors influencing customer
satisfaction during the return process for defective products (Verhoef & Casebeer, 1997).
Data Collection
The primary data for this study is collected through the distribution of structured questionnaires to customers
residing in urban areas of Melaka who have experienced returning defective products via reverse logistics. The
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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questionnaire is designed to capture key factors that influence customer satisfaction, including the quality of
replacement products, the timeliness of refunds, the responsiveness and helpfulness of customer service, the
clarity of the return policy, and the level of empathy demonstrated by the company. These variables are measured
to understand their relationship with customer satisfaction during the return process. By gathering responses
directly from customers, this study aims to provide insights into how businesses can improve their reverse
logistics strategies to enhance customer satisfaction. The collected data will serve as the foundation for analysing
correlations between the independent variables and the dependent variable.
Secondary data is utilised to provide additional context and support for the research findings. This includes
reviewing academic literature, government policies on logistics, market analyses, and industry reports. These
sources highlight global and local best practices in reverse logistics, customer satisfaction trends, and challenges
faced by companies in managing returns. Integrating primary and secondary data ensure a well-rounded
understanding of the factors influencing customer satisfaction during the return of defective products.
Questionnaires
The primary tool for data collection in this study is a structured questionnaire designed to gather quantitative
data from respondents who have experienced returning defective products through reverse logistics in Melaka.
Questionnaires are widely regarded as an effective and efficient method for collecting data in research, as they
enable the researcher to gather responses from a large sample within a short time frame. The questionnaire used
in this study consists of close-ended questions, making it easier to standardise and analyse the responses
statistically. The questionnaire is divided into three sections, each with a distinct purpose.
Section A focuses on collecting demographic information about the respondents, such as their gender, age,
educational level, monthly income, and frequency of product returns. This section also includes a screening
question to ensure that all respondents have prior experience with returning defective products, ensuring the
relevance of their responses to the study's objectives.
Section B contains questions designed to assess the independent variables in this study, which include the quality
of replacement products, timeliness of refunds, customer service quality, clarity of return policy, and
demonstration of understanding (empathy). Each variable is measured using multiple items to ensure the
reliability and validity of the data. The questions are framed to capture respondents’ perceptions and experiences
with reverse logistics services, such as whether replacement products met their expectations, whether refunds
were processed promptly, and whether customer service representatives were helpful and responsive during the
return process.
Section C focuses on measuring the dependent variable, which is customer satisfaction. The questions in this
section are designed to evaluate the overall satisfaction of respondents with the return process for defective
products. Items in this section address aspects such as whether the return process met their expectations, whether
they felt valued by the company, and whether they are likely to recommend the company based on their return
experience.
Table 1: Likert Scale Points 1 to 5
5 Point Likert Scale
1
Strongly Disagree
2
Disagree
3
Neutral
4
Agree
5
Strongly Agree
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Table 2: Construct Measurement
Variables
Sources
Quality of Product
Parasuraman et al.(2020) Oliver et al. (2022
Timeliness of Refunds
Zeithaml et al. (2021) Yuen & Thai (2023)
Customer Service Quality
Rahman et al. (2022) Nguyen et al. (2021)
Clarity of Return Policy
Smith & Bolton (2022) Zhang et al. (2021)
Demonstration of Understanding
Kumar et al. (2022) Grönroos & Voima (2020)
Customer Satisfaction
Han et al. (2023) Singh et al. (2022)
Pilot Testing
A pilot test is conducted prior to the main survey to validate the questionnaire and ensure its reliability, clarity,
and effectiveness in capturing relevant data. The pilot test involves a small sample of 15 to 30 respondents from
the target population, which consists of customers in Melaka Tengah who have experienced returning defective
products through reverse logistics. The primary purpose of the pilot test is to identify any ambiguities or
inconsistencies in the questionnaire and to make necessary revisions before full-scale data collection.
Cronbach’s Alpha will be used to measure the internal consistency of the items within each construct, with a
threshold of 0.7 or above indicating acceptable reliability. Additionally, feedback from the pilot test participants
will be reviewed to refine the wording, structure, or flow of the questionnaire as needed, ensuring that the final
version is comprehensive and well-suited to meet the study's objectives.
Population
The target population for this study consists of consumers residing in Melaka Tengah who have engaged in
returning defective products through reverse logistics. Melaka Tengah, as the central district of Melaka, features
a high density of retail activities and e-commerce usage, making it an ideal location for the study. The
respondents’ direct experience with product returns makes them a valuable source of data for analysing customer
satisfaction and identifying areas for improvement in reverse logistics.
Sampling Design
This study employs a probability sampling approach, specifically simple random sampling, to ensure each
individual in the target population has an equal chance of being selected. By adopting this method, the study
minimizes sampling bias and enhances the generalizability of the results. Respondents will be selected based on
their relevance to the study's focus, ensuring that only individuals with experience in returning defective products
are included in the sample.
Sample Size
The sample size is determined using Krejcie and Morgan’s (1970) formula, which is widely recognized in social
science research. For an estimated population size of 100,000, a sample size of 384 respondents is sufficient to
achieve statistically significant results. To account for non-responses or incomplete surveys, an additional buffer
will be included, bringing the target sample size to approximately 400 respondents.
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Table 3: Krejcie and Morgan’s Table
Source: Krejcie and Morgan, (1970).
Research Location
The research is conducted in Melaka Tengah, the central district of Melaka. This location was chosen due to its
status as a hub for retail, e-commerce, and logistics activities, providing a rich context for studying reverse
logistics and customer satisfaction. Melaka Tengah’s urban characteristics make it an ideal environment for
gathering diverse responses from consumers with varying demographics and experiences in returning defective
products.
Data Analysis
The data collected through the survey is analysed using the Statistical Package for the Social Sciences (SPSS),
a widely used software for quantitative research. SPSS provides tools for managing, analysing, and visualizing
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data, allowing for both descriptive and inferential statistical analysis.
Descriptive Analysis
Descriptive analysis is used to summarise the demographic characteristics of the respondents, such as gender,
age, education level, and income. Additionally, descriptive statistics provides an overview of the responses to
each variable, including the quality of replacement products, timeliness of refunds, customer service quality,
clarity of return policy, and demonstration of understanding.
Inferential Analysis
Inferential statistics is employed to examine the relationships between the independent variables and customer
satisfaction. Regression analysis is conducted to determine the strength and significance of these relationships,
while Pearson’s correlation coefficient measure the degree of association between variables. Hypotheses are
tested at a 95% confidence interval to identify statistically significant factors influencing customer satisfaction.
Reliability and Validity
The reliability of the questionnaire is tested using Cronbach’s Alpha to ensure internal consistency among the
items within each variable. A Cronbach’s Alpha value of 0.7 or higher is considered acceptable, indicating that
the items are consistent in measuring the constructs they represent. The validity of the questionnaire is assessed
to ensure it accurately measures the intended constructs. Content validity will be established by aligning the
questionnaire items with prior research and theoretical frameworks related to reverse logistics and customer
satisfaction. Construct validity is tested through factor analysis to verify that each item contributes to its
respective variable.
Table 4: Result of Test Reliability for the Pilot Test (Each Variable)
Variable
Cronba
ch`s
Alpha
Number (N)
of Items
Result
Independent
Variables
Quality of Replacement
Product (QP)
0.874
5
Good
Reliability
Timeline of Refunds (TR)
0.899
5
Good
Reliability
Customer Service Quality
(CSQ)
0.895
5
Good
Reliability
Clarity of Return Policy
(CRP)
0.870
5
Good
Reliability
Demonstration of
Understanding (DU)
0.867
5
Good
Reliability
Dependent
Variable
Customer Satisfaction (CS)
0.878
5
Good
Reliability
Source: Primary data from SPSS Statistics Output
RESULTS AND DISCUSSION
Demographic Analysis
Data Analysis of Demographic Variables are as follows:
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Table 5: Demographic Analysis
Frequency of returning products in the past 12 months are as follows:
Figure 3: Frequency of Returning Products
Source: Original data of SPSS Statistics Output
17%
31%
35%
17%
1–2 times / 1–2 kali
3–4 times / 3–4 kali
More than 4 times / Lebih
daripada 4 kali
Never / Tidak Pernah
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Regression Analysis
ANOVA is used to test for differences in means between groups, while multiple regression is used to model the
relationship between multiple predictor variables and a single outcome variable. A common scenario is to first
use ANOVA to get a global "omnibus" test for group differences, and then use multiple regression (or a series
of t-tests) as a follow-up to understand which specific groups differ and the strength of their relationships. The
specific terms like coefficient, are outputs of the regression model, not separate steps.
Anova
Table 6: ANOVA
Source: Original data of SPSS Statistics Output
Table 5 shows an ANOVA significance level value 0.001 which is lower than 0.05. Hence, this model has an
overall significant difference among group means.
Multiple Regression
Multiple regression is used to predict the Quality of Replacement Product, Timeline of Refund, Customer
Service Quality, Clarity Return Policy, and Demonstration Understanding on Customer Satisfaction. A
correlation coefficient is a number between -1 and 1 that tells the strength and direction of a relationship between
variables. The P-value indicates the probability of observing the calculated correlation coefficient by random
chance if no actual relationship existed in the population, with a small p-value (e.g., < 0.05) suggesting the
correlation is statistically significant (Bhandari.P, 2022).
Table 7 Coefficients Analysis for all Independent and Dependent Variables
Source: Original data of SPSS Statistics Output
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Beta
Unstandardized beta (b): Represents the change in the dependent variable for a one-unit change in the
independent variable. Its interpretation depends on the original units of measurement.
Standardized beta (𝛽) provides a unitless measure, allowing for direct comparison between independent
variables. A higher absolute value indicates a stronger individual effect on the dependent variable.
Positive beta Indicates a positive relationship, meaning as the independent variable increases, the dependent
variable also increases. Negative beta Indicates an inverse relationship; as the independent variable increases,
the dependent variable decreases. The closer the beta coefficient is to 1 or -1, he stronger the relationship is with
the dependent variable.
In this study, standardized beta coefficients for four out of 5 variables in Table 7 show with the values close to
1 indicating a strong positive relationship between the predictor and outcome variable. Only for independent
variable “Clarity Return Policy” is close to 0 indicate a weak or non-existent relationship with outcome variable.
T-Statistics
A t-statistic with an absolute value greater than 1.96 indicates a statistically significant result at the 0.05
significance level (or 95% confidence level) for a two-tailed test, assuming the sample size is large enough for
the t-distribution.
In this study, t-statistics for four out of five variables in Table 7 show with the values greater than 1.96 indicates
a statistically significant result. Only for independent variable “Clarity Return Policy” is less than 1.96 which
is 0.246 indicates a statistically nonsignificant result.
Significance
A P-Value of 0.05 means there is a 5% probability of observing the results you did, or results more extreme, if
the null hypothesis is actually true. It's a threshold for statistical significance, meaning if the p-value is less than
0.05 (p < 0.05), the results are considered unlikely to have occurred by random chance, and the null hypothesis is
typically rejected.
In this study, Table 7 above shows the results coefficients analysis between independent and dependent variables.
Four variables are significant that has P-Value = 0.001 (<0.05) and one independent variable not significant that
has P-Value = 0.806 (>0.05). The relationship of Quality of Replacement Product to Customer Satisfaction is
significant: P=0.001 (P<0.05). Timeline of Refund to Customer Satisfaction is not significant: P=0.806 (P>0.05).
Customer Service Quality to Customer Satisfaction is significant: P=0.001 (P<0.05). Clarity Return Policy to
Customer Satisfaction is significant: P=0.001 (P<0.05). Demonstration Understanding to Customer Satisfaction
is significant: P=0.001 (P<0.05).
Variance Inflation Factor (VIF)
VIF, or Variance Inflation Factor, is a measure used in multiple regression to detect multicollinearity, which
occurs when independent variables are highly correlated. VIF=1 indicates no multicollinearity. VIF>1 suggests
some degree of multicollinearity. 5<VIF<10 may indicate high multicollinearity that warrants further
investigation. VIF>10 suggests a serious multicollinearity problem where the coefficients are poorly estimated.
In this study refer to Table 7 above, all less than 1, indicates no multicollinearity.
Model Summary
It is shown in Table 8 below:
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Table 8: 4.5.1 Model Summary
Source: Original data of SPSS Statistics Output
R is the Pearson correlation coefficient, which measures the strength and direction of a linear relationship
between two variables (ranging from -1 to +1). Essentially, R tells the relationship's direction (positive or
negative). R-squared (𝑅
2
), the coefficient of determination, is the square of R, and it represents the proportion
of the variance in the dependent variable that is predictable from the independent variable(s) (ranging from 0 to
1).
Table 8 presents the results of multiple linear regression, with a correlation coefficient R of 0.891 indicating a
strong positive relationship between independent variables and dependent variable in this study. A good link
between independent variables and dependent variables is a positive indicator of R.
However, the R square (R
2
) value is 0.794, indicating that 79.4% of the variance in the dependent variable
(Customer Satisfaction) predicted from the independent variables (Quality of Replacement Product, Timeline of
Refund, Customer Service Quality, Clarity Return Policy, and Demonstration Understanding)
Hypotheses Tests Summary
Table below summarizes the results of hypothesis testing using regression analysis:
Table 9: Hypothesis Tests Summary
Hypothesis
Description
P-Value
Result
Conclusion
H1: Quality of
Replacement Product
(QP) influences Customer
Satisfaction (CS)
There is a significant
positive relationship
between quality of
replacement product and
customer satisfaction.
P =
0.001
(<0.05)
Supported
Quality of Replacement
Product significantly
influences Customer
Satisfaction.
H2: Timeline of Refunds
(TR) influences (CS)
There is a significant
positive relationship
between the timeline of
refunds and customer
satisfaction.
p =
0.806
(>0.05)
Not
Supported
Timeline of Refunds
does not significantly
influence Customer
Satisfaction.
H3: Customer Service
Quality (CSQ) influences
Customer Satisfaction
(CS)
There is a significant
positive relationship
between customer service
quality and customer
satisfaction.
p =
0.001(<0
.05)
Supported
Customer Service
Quality significantly
influences Customer
Satisfaction.
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H4: Clarity of Return
Policy (CRP) influences
Customer Satisfaction
(CS)
There is a significant
positive relationship
between clarity of return
policy and customer
satisfaction.
p = 0.00
(<0.05)
Supported
Clarity of Return Policy
significantly influences
Customer Satisfaction.
H5: Demonstration of
Understanding (DU)
influences Customer
Satisfaction (CS)
There is a significant
positive relationship
between demonstration of
understanding and customer
satisfaction.
p = 0.00
(<0.05)
Supported
Demonstration of
Understanding
significantly influences
Customer Satisfaction.
The results indicates that a substantial positive correlation exists between several independent variables which
are Quality of Replacement Product (QP), Customer Service Quality (CSQ), Clarity of Return Policy (CRP),
and Demonstration of Understanding (DU and the dependent variable which is Customer Satisfaction (CS). The
importance of these relationships was assessed via hypothesis testing, with p-values reflecting the intensity of
each correlation. The results of the hypothesis testing revealed that four of the five hypotheses were validated,
suggesting a significant positive connection between these factors and customer satisfaction. In particular,
Quality of Replacement Product (H1), Customer Service Quality (H3), Clarity of Return Policy (H4), and
Demonstration of Understandig (H5) were identified to significantly affect customer satisfaction, with p-values
is 0.001 (<0.05), verifying their substantial influence. Nonetheless, the Timeline of Refunds (TR) (H2) did not
demonstrate a significant connection with customer satisfaction, reflected by a p-value of 0.806 (>0.05),
resulting in the dismissal of this hypothesis. This indicates that although the speed of refunds might influence
customer satisfaction, it is not as significant as other factors. In summary, the findings emphasize the
significance of upholding superior product quality, outstanding customer service, transparent return policies, and
compassionate customer interactions in improving customer satisfaction during the reverse logistics process.
These results offer important perspectives for companies to enhance their return procedures, which in turn
promotes increased customer loyalty and satisfaction.
The SERVQUAL theory relates to product returns by using its five dimensions which are tangibles, reliability,
responsiveness, assurance, and empathy to assess customer expectations. This regarding service associated with
the product's purchase and after-sales support which can heavily influence both product satisfaction and the
decision to return an item. A positive perception of these service dimensions builds satisfaction, which in turn
reduces the likelihood of a return, while dissatisfaction in any of these areas can increase the probability of a
return. SERVQUAL becomes an underpinning theory for this study, however the authors hypothesised a few
other dimensions to suit with the modern supply chains due to increasing activities of logistics locally and
globally and widely used of online transactions. When reverse logistics is to happen, customers under dilemma
of returning when they received defective products. Considering some circumstances that would satisfy them,
returning products would take place.
CONCLUSION
There are a few implications as contributions of this study which are categorised as Theoretical Contributions
and Practical Contributions. This part also highlights the limitations and recommendations for future research.
Theoretical Contributions
This study contributes to the growing body of literature on reverse logistics and customer satisfaction by
providing empirical evidence on the factors influencing customer satisfaction in the context of returning
defective products. The findings validate the applicability of the SERVQUAL model but enhance it in a few
other dimensions to suit with the modern supply chains due to increasing activities of logistics locally and
globally and widely used of online transactions. The study highlights the importance of the roles of Quality of
Replacement Product, Timeline of Refunds, Customer Service Quality, Clarity of Return Policy, and
Demonstration of Understanding as drivers of customer satisfaction in extending existing theories on customer-
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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centric practices. By integrating insights from reverse logistics, this research bridges a gap in the literature,
offering a multidimensional understanding of customer satisfaction.
Practical Contributions
The findings of this study offer valuable and actionable insights for businesses aiming to enhance customer
satisfaction in reverse logistics processes. First, ensuring quality of replacement product is crucial, as businesses
need to guarantee that replacement products meet customer expectations. This will improve perceptions of
tangibility and foster greater trust in the product return process. Additionally, addressing adequate time for
refund processes are vital for strengthening reliability. A seamless and transparent refund process will reassure
customers and improve their overall satisfaction.
Investing in customer service training is another critical implication. Providing comprehensive training programs
for staff to develop responsiveness and problem-solving skills can significantly improve service quality. By
equipping staff with the tools to handle customer concerns effectively, businesses can enhance their
responsiveness and build stronger relationships with their customers. Moreover, refining and clearly
communicating return policies will bolster customer confidence. Accessible, concise, and transparent policies
reduce ambiguity, making it easier for customers to navigate the return process. Finally, by demonstrating
understanding and personalized attention, it is creating positive customer experiences. This relates to empathy-
driven practices which will not only resolve immediate customer issues but also contribute to long-term retention
and trust. All of these, businesses could cultivate loyalty and satisfaction among their customers.
LIMITATIONS
The study has several limitations that should be acknowledged. First, it focuses solely on the region of Melaka,
limiting the generalizability of findings to other regions or countries. Additionally, the data collected is based on
self-reported responses, which may introduce biases such as social desirability or inaccurate recall. The study’s
reliance on a quantitative approach using SPSS analysis, without incorporating qualitative insights, may
overlook deeper contextual factors influencing customer satisfaction. Furthermore, the research examines
specific variables Quality of Replacement Product, Timeline of Refunds, Customer Service Quality, Clarity of
Return Policy, and Demonstration of Understanding while potentially excluding other relevant factors such as
pricing or brand loyalty. Lastly, the cross-sectional nature of the study captures responses at a single point in
time, making it difficult to account for changes in customer satisfaction over time.
RECOMMENDATIONS FOR FUTURE RESEARCH
Based on the findings and limitations of this study, several recommendations are proposed for future research.
First, future studies should explore customer satisfaction in broader contexts within reverse logistics, such as
recycling and disposal processes. Expanding the scope to these areas will provide a more comprehensive
understanding of customer satisfaction across the reverse logistics spectrum. Second, conducting longitudinal
studies will yield valuable insights into how customer satisfaction evolves over time. Such research can help
identify trends and patterns in customer perceptions, enabling businesses to adapt their strategies more
effectively. Third, comparative analyses across different industries and regions can enhance the generalizability
of the findings and uncover industry-specific or regional variations in customer satisfaction. Finally, future
research should investigate the role of technology, such as automation and artificial intelligence, in improving
reverse logistics processes. Exploring how technological advancements can streamline operations and enhance
customer satisfaction could provide businesses with innovative solutions for optimizing their reverse logistics
practices.
ACKNOWLEDGEMENT
The authors would like to thank the Universiti Teknikal Malaysia Melaka (UTeM) for providing the facilities
and support throughout this research. Special appreciation to the Fakulti Pengurusan Teknologi dan
Teknousahawanan (FPTT) and the research group SIR-ED (C-TED) for their guidance and funding.
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