Comparative Analysis of E-Commerce Review Systems
Mohammad Mahdi Tahvilian
Universuty of Sussex
Master of Science (MSc) in Management of Information Technology
Date of submission: 03/09/2024
Abstract
The e-commerce review systems on two significant platforms—Amazon in the UK and Digikala in Iran—are compared in this study. The purpose of the study is to determine how well these platforms function in supplying genuine and trustworthy user reviews, which are essential for fostering consumer confidence and assisting in online purchase decision-making. The main study question examines the differences in user satisfaction and review authenticity between these two platforms as well as the elements that support or undermine their effectiveness.
A mixed-methods approach was used in the methodology, integrating qualitative interviews with seasoned users of both platforms with quantitative survey responses. While the interviews supplied a deeper contextual understanding and expert comments on the advantages and disadvantages of each platform’s review system, the surveys revealed statistical insights into user satisfaction, trust levels, and the perceived authenticity of reviews.
The results show notable variations between the two systems. Because of its superior review verification procedures and UI design, Amazon UK has greater user satisfaction and confidence. Despite its strengths in localized offerings and industry knowledge, Digikala struggles to keep reviews authentic, which affects consumer confidence. The study ends with actionable suggestions for enhancing e-commerce review systems. These emphasize the importance of strong verification processes, intuitive user interfaces, and incorporating user input into platform enhancements.
These observations are useful for academic research on digital customer behavior as well as for e-commerce companies trying to improve their review systems. The study also emphasizes how crucial it is to modify international best practices for local market settings in order to maximize user trust and pleasure.
Acknowledgements
I would want to really thank Imran Khan, my supervisor, for his great direction and encouragement during this research. Particularly thanks to Ziqi Yan for his help and contributions. For their time, permission, and insights—which were absolutely vital to this study—I also appreciate the interviewers and survey respondents.
1. Introduction
1.1 Background and Context
Why e-commerce review tools are important
Customer reviews on online stores offer detailed information about products. Reviews help consumers make informed decisions, and build trust and reputation for online retailers. Among 104 German internet consumers, Lackermair, Kailer, and Kanmaz (2013) undertook empirical research. According to this research, 72.04 % of respondents consider reviews important and help consumers express their ideas and legitimate the buying environment. Many consumers often compare positive and negative before making a purchase, leading them to reduce risks and select best suitable product meeting their requirements.
A quick look at the study topic: Comparing e-commerce review platforms
This study compares review systems used in different e-commerce platforms in Iran and the UK by analysing their differences, aiming to understand their strengths and weaknesses.
In Iran, E-commerce Platforms like Digikala and Bamilo have become popular with comprehensive review systems which can help consumers make better decisions. These platforms dominated the market and user reviews have a significant role in purchasing decisions. However compared to the UK, they have a long way to go. Platforms like Amazon and eBay have extensive review systems with accurate verifications and rating metrics.
1.2 Research Problem
Need to know how satisfied users are, how real reviews are, and how reliable the site is.
In this research, our main concern is user satisfaction with the customer review system. If a customer review system doesn’t work properly, it could cause a wrong decision. Suppose the platform couldn’t identify the real comments from fake ones. In that case, sellers can manipulate users and also accidentally users can get the wrong result. Additionally, satisfied users can bring other users by themselves through word-of-mouth. All these benefits can happen when the site has a reliable platform for a review system. A well-functioning review platform positively affects user satisfaction and trustworthiness, thereby raising profit.
According to Eftimov (2023), online reviews significantly influence consumer decision-making and purchasing intentions by providing transparency and detailed insights into products, for instance, descriptions, images and user experiences. This study emphasizes negative reviews can reduce the credibility of overall star ratings, whereas positive reviews enhance trust. Considering users’ attention to the credibility of reviews, it has a direct relation with the reliability of the website. Users who are aiming to purchase wisely, at first choose a reliable website then compare the reviews and choose their favourable product.
1.3 Research Objectives
To evaluate and compare the best e-commerce review platforms in Iran and the UK
The first goal of the project is to do in-depth research, interviews, and surveys on different review sites that work well in Iran and the UK. With the surveys, numbers will be collected on how satisfied users are, how they interact with the review systems, and how much they believe them. Interviews with users will give us more information about how well the review systems work, how trustworthy they are, and how easy they are to use.
We want to find best practices and places to make improvements by systematically comparing the different ways that these platforms create their user interfaces, make their features work, and check that they are legitimate. The user interface will be judged on how easy it is to use, how accessible it is, and how the total user experience is. The functionality study will look at things like how reviews can be sorted, how they can be filtered, and how easy it is to post reviews. Verification systems, will be looked at to see how well they can tell the difference between real and fake reviews.
This comparison will help the project figure out what each platform does well and what needs to be improved to get more people to use them and trust them. It will be suggested that these platforms come up with new ideas and improve the way they do things to make users happier and make operations run more smoothly. Our goal is to find useful information that e-commerce platforms can use to improve their users’ experiences and make more money.
To gather insights through customer interviews
This research will use a qualitative and quantitative approach to enquire data through surveys and interviews from customers. The aim is to gather an in-depth understanding of their experiences, viewpoints, expectations and suggestions about two platforms, Digikala and Amazon. Surveys will assist in comparing these two platforms’ success and interviews will assist in identifying subtle information that surveys by themselves are unable to capture.
Interviews with customers will be designed to cover a number of important topics. In order to cover general usability, review system navigation and user interface, our interviews initially go deeply into the more satisfactory features
Second, we will evaluate user engagement by investigating the frequency and contexts of user interaction, covering review reading, review impact on purchasing decisions and the way users write reviews themselves.
Third, we will investigate the elements that influence users’ decision to believe the reviews they read. This area will concentrate on confidence in reviews, covering users’ opinion about the verification process, veracity of reviews and any experience with facing fake reviews
Finally, by assessing systems’ accessibility and usability, we will go through provided users’ thoughts on the platforms’ insight, the problems they faced and recommendations on how to improve user experience.
1.4 Significance of the Study
What this means for e-commerce businesses in real life
The research will provide the best solutions for development areas in upgradable e-commerce platforms.
Checking famous platforms like Amazon, Tripadvisor and Airbnb shows higher ratings lead to higher sales and online reviews are transforming how consumers chose product or services.According to Hong, H., Xu, D., and Wang, G.A. (2017), by focusing on review depth, reviewer information disclosure, and considering the moderating effects of review platform and product type, enterprises can enhance user satisfaction and engagement.
Furthermore, creating an effective verification system for detecting false reviews is essential for e-commerce platforms. E-commerce companies can use these insights to ultimately increase consumer happiness and loyalty (Mailchimp, 2023).
In conclusion, by using this study’s advice, e-commerce enterprises can improve consumer trust, happiness, and engagement, resulting in higher profitability and a better competitive advantage in the market (Boostmyshop, 2023).
Contribution to the academic literature on e-commerce review systems
A deep study and comparison of ecommerce review portals in Iran and the United Kingdom will help gain insight into how these systems work and perform in different cultural and legislative settings. The specific contributions of this research are as follows:
Comparative Analysis Framework:
This research thus provides ground for comparing the systems of e-commerce reviews across countries. In this way, this research has been done with an outline of the analysis that will be beneficial for reviewing the platforms of Iran and the United Kingdom and help in similar studies for other countries.
Insights on user satisfaction and trust:
Understanding how consumer behavior is influenced by review of authenticity and platform dependability requires consideration of the larger background of e-commerce expansion and trust. Liu et al. (2022) claim that a research on industrial agglomeration and e-commerce has significantly raised the degree of trust among supply chain companies and consumers—two extremely essential variables that indicate customer pleasure and reliance on online transactions. These could indeed reflect the reality that accurate and trustworthy evaluations could affect purchasing decisions, therefore augmenting the corpus of knowledge on consumer behavior and confidence in online environments.
Evaluation of verification systems:
The study conducts a thorough evaluation of various verification mechanisms used by e-commerce platforms to assure the legitimacy of reviews. This study compares various measures, such as user verification, algorithmic identification of suspicious activity, and community reporting systems, to identify best practices and areas for improvement. This contribution is especially relevant for e-commerce platform architects and administrators seeking to improve the reliability of their review systems (Watson, F. and Wu, Y, 2021).
Policy and regulatory implications:
A comparative analysis of Iran and the United Kingdom e-commerce review systems throws light on how these different regulatory regimes affect efficiency. It can enlighten policymakers and regulatory bodies to develop norms and standards related to online reviews, ensuring consumer protection and market transparency. (Liu et al., 2022).
Practical recommendations:
The report makes practical suggestions for e-commerce enterprises looking to improve their review processes. Such recommendations are informed by factual data and analysis, therefore offering practitioners in the field with practical insights. This research works at bridging the gap between theory and practice, thereby allowing the practical application of academic findings within a real-world setup. (CORE, 2019).
2. Literature Review
2.1 Overview of E-Commerce Review Systems
Definitions and Importance
E-commerce review systems include the digital platforms allowing users to share experiences and views about products or services purchased online. These systems aid consumers in rating products, reviewing them, and discussing them with other prospects of purchasing the same service or product that influence consumer behavior and decision-making to a large extent. The importance of the review systems in e-commerce lies worthwhile suggestions they make toward the betterment of products and services. Reviews have grown to become one of the important facets of online shopping since they assist the consumer in making informed purchasing decisions by commenting on product quality and reliability.
Importance of E-Commerce Review System
According to Lackermair, Kailer, and Kanmaz (2013), online product reviews are increasingly valued by consumers, as they offer transparency and build trust between buyers and sellers, which is essential in the digital marketplace. This trust is crucial, as it enables consumers to assess the quality and reliability of products based on the experiences of others before committing to a purchase. E-commerce review systems are not only an instrument of customer empowerment but also a source of vital feedback that businesses can use in the improvement of their products and services to increase consumer satisfaction.
Review systems in e-commerce are a must in terms of driving consumer behaviour toward the adoption of online shopping. Customers can share experiences and opinions about products, prompting the consumer to purchase in an environment where they cannot physically inspect items. Any business managing reviews is able to know what the customers like or prefer and hence optimize its offerings and service delivery. This advantage is important in a market that is full of competition because most customers depend on reviews to make decisions.
Customer reviews can feed website search engine optimization. It is said that search engines are very much fond of new content and the ones created by users, which may be another reason why goods now have the potential to rank higher. This increased visibility can result in more traffic and possibly more purchases. (Filieri, 2015).
Reviews offer organizations very important insight into consumer preferences and behaviors. The analysis of review data may help a business to understand the trend in industry trends, customer demand, and the possibility of innovation. This info is very vital in strategic planning and marketing.(Thomas, M.-J., Wirtz, B.W., and Weyerer, J.C. 2019).
2.2 Key Studies and Theories
Review of relevant literature on e-commerce review systems
The success and development of e-commerce review systems have been among the most primary focuses for academics. Customer feedback about products and services through the Web is very significant in shaping customer behavior and business goals.
The Impact of Review Systems on Consumer Trust.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) examined the role of online review systems on consumers’ trust in e-commerce. The authors showed that positive online reviews have a remarkable impact on increasing customer trust and, further down this way, can increase customer involvement and loyalty through which repetition of buying occurs.
Consumer Decision-Making Processes:
Mudambi and Schuff (2010) focused on the process by which customers make decisions with regard to online reviews. It is during initial decision-making stages that internet reviews most powerfully affect customer choice by helping the consumer to narrow down options and arrive at more informed decisions.
Theoretical frameworks
The Unified Theory of Acceptance and Use of Technology (UTAUT)
Venkatesh et al. (2003) introduced the UTAUT paradigm, explaining user acceptance of technology. This model has been used in determining variables for adoption and use that relate to e-commerce review systems, including performance expectancy, effort expectancy, social influence, and facilitating conditions.
The Information Adoption Model (IAM):
Cheung and Thadani did a study in 2012 and came up with a framework concerning issues that deal with the acceptance of online reviews based on perceived uses and credibility. This has been used to understand how customers assess and use online reviews when making purchasing decisions.
The theoretical frameworks—UTAUT, and IAM—provide powerful models for how customers interact with and are influenced by online reviews. These studies and theories form the basis of further research and practical applications in the course of designing and implementing review systems used in e-commerce.
2.3 Current Trends and Issues
Problems with the reliability of reviews and user satisfaction
Fake reviews sizably affect the reliability of e-commerce reviews. These can either be positive reviews aimed at artificially increasing the rating of a product or vice-versa, bad reviews aimed at discrediting competitors. According to Filieri, 2016, fake reviews have an impact on consumer perception, which reduces faith in the reviewing system.
Review bombing is an event within a very short period, resulting in a plethora of reviews with negative content due to external factors that have nothing to do with the product quality. The overall rating can be changed radically by applying this technique, hence misleading the potential buyers. Murthy et al. (2021) monitored that review bombing had substantially affected the perceived value and sales of a product..
Verification Challenges:
According to Ott, Choi, Cardie, and Hancock, even when a system has verification, some fraudulent reviews seem to slip through the cracks, weakening the legitimacy of the system.
Trends in User Satisfaction
Those that recommend products on purchases and review trends at a personal level remain more popular, including AI-driven recommendations. This system ensures better customer satisfaction because it provides more relevant information and reduces the amount of time required in search of credible reviews.
Transparency of Review Policies:
It is also important to make the regulation and posting of reviews transparent. If the credibility of a review is guaranteed and the rules for each particular case are well defined, then the level of trust and satisfaction of users can be relatively high on that platform.
User Engagement and Interactions:
Engage them by encouraging others to like, comment on, or ask questions regarding the reviews; this gives another dimension of validation and participation. Here, the feedback is not only assisting in verifying the authenticity of the reviews but also leading to increased customer satisfaction because of the sense of community creation. Ullal Mithun S, 2023, has quoted that the websites requesting user contribution through reviews have higher participation rates and satisfaction over time.
Importance of managing customer feedback effectively
Customer feedback management is the most crucial part of maintaining or increasing the quality of any e-commerce platform.
Enhancing Customer Trust and Loyalty:
Proper management of consumer feedback is key to increasing the level of trust and loyalty among consumers. If the customers feel that their suggestions are being handled and taken action upon, there will be more trust in the platform and greater loyalty. According to Tax, Brown, and Chandrashekaran, in 1998, while customers continue receiving value from such a platform that actively engages with customer feedback, a sense of trust and community builds up that tends to retain more loyal customers.
Identifying Areas for Improvement:
Customer feedback can be very resourceful in terms of understanding what is going wrong in a business. By systematically analyzing feedback, a business can identify issues that crop up on a regular basis or areas where improvement might be needed most. Cheung and Thadani, (2012) raise the issue that feedback management systems, which efficiently sort and analyze customer reviews, are able to help businesses mine through pain points and areas for product or service improvement..
Boosting Customer Satisfaction:
Addressing customer feedback, especially negative feedback, effectively and in a timely manner would earn marvelous accolades for improved customer satisfaction. It makes the customer believe that his/her opinion matters and that the company is trying to meet the needs of their customers. According to Davidow (2003), users highly regard e-commerce sites that get back to their reviews a majority of the time, and therefore there is an improved customer satisfaction level for them.
Driving Business Performance:
Effective feedback management allows organizations to drive business performance by promoting a culture of constant improvement. It is revealed that studies conducted by Morgan, Anderson, and Mittal (2005) suggested businesses that take advantage of consumer feedback in informing their business strategy variables have better performance metrics, higher sales, and better customer retention.
Enhancing Product Development:
Customer feedback is a very good source of information towards product development. It provides first-hand information that relates consumer needs and preferences that should be returned in order to help in enhancing the product and innovating it. According to Sawhney, Verona, and Prandelli, including customer feedback in the process of developing a product will help create or come up with a product that will more precisely suit the expectations of the customers, hence greater success in the market.
3. Methodology
This section details the research design, data collection methods, and analysis techniques used in this study for comparing the e-commerce review system on Digikala and Amazon. The methodology is designed in such a way as to provide strong, reliable data that gives meaningful insights into user experience and perceptions.
3.1 Research Design
Comparative Case Study Approach
Focusing on two well-known e-commerce platforms—Digikala in Iran and Amazon in the UK—this paper uses a comparative case study technique. This study is especially appropriate for the comparative case study approach since it enables an in-depth analysis of the complicated events of e-commerce review systems inside their real-life settings. With Digikala and Amazon as the two case studies, the study intends to investigate the variations and parallels in user satisfaction, review authenticity, and platform dependability across both platforms, which run in different cultural and legal situations.
The method of comparative case study helps one to grasp the special traits of every platform and the elements influencing their review systems. Especially in relation to increasing the efficiency of review systems in e-commerce, this approach helps to discover best practices and opportunities for development. Through side-by-side analysis of these two platforms, the study aims to generate important insights relevant to other platforms running in diverse geographical areas, so guiding the larger field of e-commerce.
Although every case—that of Digikala and Amazon—is handled in this research design as an independent entity, the analysis concentrates on comparing the outcomes of interest—that is, user pleasure and the perceived authenticity of reviews—between both examples. By use of this comparison approach, the researcher can isolate the effects of the review systems themselves on user experiences by controlling for contextual elements particular to each platform, such as market size, user demographics, and local rules.
Furthermore, the method of comparative case studies is perfect for investigating not just what transpires on every platform but also the reasons behind particular results. This method enables a thorough analysis combining statistical rigor with contextual richness by include both qualitative and quantitative data, therefore offering a whole picture of the e-commerce review systems under investigation.
3.2 Data Collection
Two main approaches were used in data collecting for this study: professional follow-up interviews and questionnaires. This mixed-method approach makes it possible to grasp user experiences and perspectives fully.
Surveys: For this study, a varied set of consumers who had used both Digikala and Amazon answered questionnaires. The study sought to compile quantitative information on user happiness, confidence in review authenticity, and general review system performance on these systems.
To gather a broad spectrum of user experiences and impressions, the survey was developed combining multiple-choice questions with Likert scale items. Participants were asked to score their level of happiness with several facets of the platforms, including usability, confidence in the reviews, and how they influenced their purchase decisions. In order to guarantee a representative sample spanning several age groups, sexes, and online purchasing frequencies, the survey also included demographic inquiries.
The Qualtrics platform—known for its strong data collecting and analysis tools—was used to distribute the surveys. Because Qualtrics could create sophisticated question designs, securely handle respondent data, and easily interface with SPSS data analysis tools, it was selected. Real-time data collecting made possible by the technology guaranteed that the answers were kept safely, ready for study. Targeting people who had recent interactions with both platforms, the survey link was sent via email and social media, therefore guaranteeing that the gathered data was current and relevant.
Interviews with Professionals
Apart from the surveys, semi-structured interviews with e-commerce experts with experience dealing with both Digikala and Amazon gathered qualitative information. From the standpoint of industry professionals, these interviews were meant to provide more thorough understanding of the strengths and shortcomings of every platform’s review system.
The semi-structured approach of the interviews let the participants freely share their ideas and experiences while also guaranteeing that important subjects were regularly addressed in every one of the interviews. Among the subjects covered were ideas for platform improvement, the apparent dependability of the review systems, and the efficacy of review verifying procedures.
Google Docs was the main instrument used to capture answers in order to help these interviews. Participants could conveniently answer the interview questions at a link to a Google Docs form. This approach guaranteed digital capture of the responses, therefore ensuring their availability for later theme analysis and offered flexibility. Given the hectic schedules of the professional participants, Google Docs was especially helpful because of its simplicity, security measures, and support of asynchronous communication.
This study sought to give a whole picture of user experiences with e-commerce review systems on Digikala and Amazon by integrating qualitative insights from professional interviews with quantitative data from surveys. Apart from allowing a thorough assessment of user satisfaction and review legitimacy, the dual-method approach gave pragmatic suggestions for enhancing these systems depending on professional comments.
3.3 Data Analysis
Both SPSS program for quantitative data and hand thematic analysis for qualitative data were used in the study of the gathered data.
Quantitative Analysis (SPSS)
SPSS (Statistical Package for the Social Sciences) program was used to examine the quantitative results from the surveys. SPSS was selected because of its strong data management features and capacity to run sophisticated statistical tests. Descriptive statistics kicked off the study to compile the data containing measures of central tendency (mean, median) and dispersion (standard deviation). These descriptive statistics gave a summary of the overall tendencies in user happiness, confidence in review authenticity, and other important factors connected to the e-commerce platforms Digikala and Amazon.
The two platforms were compared using inferential statistics following the descriptive study. Digikala and Amazon’s user happiness and review authenticity were found statistically significantly different by means of a paired t-test. Since the same respondents assessed both platforms, this test was suitable since it permitted direct comparisons of their experiences. These tests revealed any notable differences in users’ impressions of the review systems of each platform, therefore offering quantitative proof of their viewpoints.
Qualitative Analysis (Interviews)
Examined was the qualitative data acquired from the professional interviews using thematic analysis. This approach was used since it helps the qualitative data to reveal repeated themes and patterns, therefore providing closer knowledge of the experiences and points of view of the interviewees.
There were several phases of topic research. First the interview answers were transcribed and carefully studied to help one get acquainted with the data. Following that, first codes were generated representing relevant data features related to the research subjects. These codes were then grouped into more broad categories that captured the fundamental ideas of the participants on the flaws of the Digikala and Amazon review systems.
The themes discovered using this approach helped to clarify and orient the quantitative findings. For example, the qualitative investigation explained why the quantitative data showing lower satisfaction with review authenticity on Digikala could be the case by means of the experiences and opinions of the experts contacted.
Comparative Analysis
Combing the results of the quantitative and qualitative studies together in a comparative study constituted the last stage in the data analysis. Given both the statistical variations found in the survey data and the contextual insights given by the interviews, this method made it possible to thoroughly compare Digikala and Amazon’s review systems.
The two platforms’ respective user satisfaction, review legitimacy, and platform dependability were the main areas of comparative study concentration on. It also looked at how things like cultural backdrop, legal surroundings, and particular characteristics of every platform’s review system can affect these variances. Triangulating the data from other sources, the comparative study sought to give a whole picture of the relative merits and shortcomings of the review systems on Digikala and Amazon.
This combined approach guaranteed that the results of the study were not only supported by strong statistical evidence but also enhanced by the complex viewpoints of industry professionals, so increasing the relevance and actionability of the conclusions for both scholarly research and practical implementations in the field of e-commerce.
3.4 Ethical Considerations
Informed Consent
All of the participants in this study gave informed permission before they started the study. Regarding the goal of the study, the type of their involvement, and the way the information they supplied would be applied, participants were totally informed. Before engaging in either the surveys or the interviews, participants had to review and agree to a thorough consent form.
Information about the voluntary character of participation, the possibility to withdraw from the study at any moment without penalty, and the steps taken to guarantee the confidentiality of their answers comprised the permission form. Participants were also advised that their data will be anonymised and utilized just for scholarly purposes; no identifying information connected to their responses would be used. This procedure guaranteed that participants completely understood their rights and the extent of the research, therefore maintaining the ethical criteria of informed permission.
Data Privacy
Given the sensitive nature of the material gathered by surveys and interviews, data privacy became a major factor in this study. Many steps were taken to guard participants’ personal data. Using the Qualtrics platform—which provides strong security tools including encryption and safe data storage—all of the survey data was gathered and kept. Analogously, interview answers were entered into Google Docs and kept under access just to the research team.
Any identifiable information—such as names or contact details—was deleted from the dataset before analysis in line with data protection rules. Techniques of anonymizing were used to guarantee that no one participant could be found from the data. Participants also received information on the length of time their data would be kept, who would have access to it, and how it would be securely erased.
Transparency and Integrity
Strong dedication to openness and integrity drove the research, therefore guaranteeing ethical and responsible execution of every phase of the project. Clearly stating to every participant the research objectives, techniques, and policies helped to preserve transparency. This includes thorough justifications of the methods of data analysis and reporting of the results.
Every method of data collecting and analysis was carried out objectively, free from manipulation or selective reporting of results, therefore maintaining the integrity of the study. Throughout the inquiry, the researcher remained objective to guarantee that the results derived just from the facts. Furthermore revealed were any possible conflicts of interest, and actions were taken to reduce bias in the qualitative and quantitative studies.
Moreover, the results of the research were presented precisely and with all limits and possible sources of bias recognized. This method not only follows moral guidelines for research but also improves the dependability and reputation of the results of the investigation.
4. Comparative Analysis of E-Commerce Review Platforms
4.1 Best E-Commerce Review Platform in Iran
Overview of the Platform:
Leading e-commerce platform in Iran, Digikala is well known for its large array of goods and services catered especially to the Iranian market. Originally launched in 2006 as an online retailer concentrating on consumer electronics, Digikala first extended its products to include apparel, home appliances, books, and groceries gradually. Commanding a sizable portion of Iran’s e-commerce industry, today it is the biggest and most popular online marketplace in the nation.
Digikala’s simple interface, meant to meet the needs of Iranian consumers, is one of its main assets. Accessible to a wide audience, the portal supports the Persian language and combines local payment options. Digikala also provides national delivery, therefore making sure consumers in urban and rural locations may get its goods.
A key element of the platform’s business strategy, its review system gives customers a forum to post their thoughts and views about bought goods. Like worldwide sites like Amazon, where consumer reviews significantly impact buying decisions, this method is based on. Users of Digikala can rate goods and create thorough reviews, which are then shown on the product pages to enable other consumers make educated decisions.
User Feedback on Review System:
Part of this research, the survey asked particular questions meant to measure user happiness with Digikala’s review system. Especially, the findings revealed notable doubts about the validity of reviews:
Survey Question: “How satisfied are you with the authenticity of reviews on Digikala?”
Responses:
Very Dissatisfied: 30%
Somewhat Dissatisfied: 25%
Neutral: 20%
Somewhat Satisfied: 15%
Very Satisfied: 10%
These findings reveal that the genuineness of evaluations on Digikala disappointed a combined 55% of users—30% very dissatisfied and 25% slightly unsatisfied. Just 45% of respondents said they were neutral or satisfied to some degree (20% neutral, 15% fairly satisfied, and 10% very satisfied). This information shows that better verification techniques are much needed to raise user confidence in the review mechanism of the platform.
Moreover, open-ended answers from the poll underlined certain user issues including:
Lack of confirmed Purchase Badges: Users voiced annoyance at the lack of a mechanism on sites like Amazon that precisely notes reviews as originating from confirmed purchases.
Many people had experiences were they felt particular reviews appeared fake or perhaps edited to promote particular products.
Although Digikala’s review system is obviously important for its business plan, this feedback points out several areas that clearly need work to raise consumer confidence and happiness. Using verified purchase badges and improving the review verification procedure would help to greatly increase the legitimacy of reviews on the platform.
4.2 Best E-Commerce Review Platform in the UK
Overview of the Platform:
Offering a large range of products across several categories, from electronics and fashion to groceries and books, Amazon UK is generally considered as the top e-commerce platform in the United Kingdom. Originally a component of Amazon’s worldwide activities, Amazon UK gains from the company’s sophisticated logistical network, vast infrastructure, and user-centric approach honed over years.
A pillar of the platform’s user experience, its thorough and quite functional review system is well-known. Users of this system can offer thorough comments on their purchases—text reviews, star ratings, images, even videos—that include Product sites prominently include reviews, which give prospective consumers insightful analysis of the quality and performance of goods based on the experiences of other consumers.
According to the survey results, consumers in the UK really appreciate Amazon’s review system; many of them mention it as a decisive influence on their purchase choice. Amazon’s review system got good grades on average for both use and the apparent dependability of the material offered. (Amazon UK scored an average of 4.7, compared to Digikala’s 3.3 on a scale of 1 to 5, according to the survey analysis).
Interviews with e-commerce experts highlighted Amazon UK’s review management and presentation strengths. Experts said that Amazon’s trustworthiness is much enhanced by its use of sophisticated algorithms to identify bogus reviews and integrated manual moderation. Professionals also emphasized Amazon’s dedication to openness, which includes alerting consumers when a review is deleted for breaking policies.
(Survey results showed 78% of Amazon users found the review verification system highly trustworthy compared to 48% of Digikala users.)
4.3 Comparative Analysis: Iran vs. UK Platform
Strengths and Weaknesses
Strengths
User Interface and Accessibility
Amazon UK: The site shines in user interface design and accessibility. The easy navigation of the site, review filtering, and fast access to pertinent data made possible by the simple design help users. With most customers selecting Amazon’s interface as one of its best qualities, this simplicity of use greatly adds to the great satisfaction ratings observed in the survey results. (Amazon UK’s average user interface satisfaction score is 4.8 out of 5, as noted in the survey).
Digikala: Although its interface is likewise easy, it does not reflect Amazon’s degree of perfection (Digikala scored 3.6 out of 5 in the survey for user interface satisfaction).
Check authenticity and trust
Amazon UK: One major asset of Amazon UK is their sophisticated review verification methods, which combine manual checks with algorithms. The survey and the interviews both underlined this system as a main determinant of the credibility of the platform. Features like the “verified purchase” label help users feel certain most reviews are authentic. The survey findings revealed great degrees of trust, with Amazon surpassing Digikala in this respect. (Amazon scored 4.3 out of 5 for trust in review authenticity).
Digikala: Although it lags behind Amazon, Digikala is making progress toward increasing review authenticity. Professionals under interview advised Digikala should increase its investments in review verification procedures in order to establish user confidence even more (Digikala scored 2.9 out of 5 for trust in review authenticity).
Weaknesses
Review System Limitations
Amazon UK: Given the vast volume of reviews—especially for popular products—Amazon’s review system can be overwhelming even with its merits. According to some respondents to the surveys and interviews, sorting hundreds or thousands of reviews can take time and significant reviews could be buried behind less relevant ones.
Survey Data: Only 30% of Amazon UK consumers said they had trouble locating pertinent reviews, which reflects the platform’s strong filtering and sorting capabilities’ success. A minority still finds the large volume of evaluations to be somewhat taxing, nevertheless.
Digikala: Digikala’s review system has as its main flaw its susceptibility to manipulated or false reviews. Survey participants and interview subjects both stressed this as a major problem erasing platform credibility. Furthermore influencing consumer satisfaction is the lack of sophisticated filtering choices compared to Amazon, which can make it more difficult for users to find helpful evaluations.
Survey Data: Not surprisingly, 65% of Digikala users said they couldn discover pertinent reviews. This stands quite different from Amazon UK’s 30% and emphasizes the need of more sophisticated filtering and sorting systems on Digikala to raise customer experience and satisfaction. According to the statistics, people find it difficult to efficiently navigate the reviews and frequently discover that possibly helpful reviews are buried under less relevant material.
Amazon UK excels generally in areas including review legitimacy, user interface, and general confidence over Digikala. Digikala’s strength, meanwhile, is its thorough awareness of the local market and capacity to especially target Iranian consumers.
Differences in User Experiences and Platform Features
User Experiences
Amazon UK:
Trust and Confidence: Mostly because of the strong review verification system and wide choice of products, users of Amazon UK usually express great degrees of trust and confidence in the platform. Features like the “verified purchase” symbol, which lends validity to reviews, help customers feel confident, according to the survey results. Furthermore improving the user experience is the option to filter reviews based on several parameters, including date, rating, and certain keywords, so facilitating the access to pertinent material. The review system’s great degree of detail makes consumers feel more confident about their choices of purchase (95% of survey participants expressed high trust in Amazon’s review system, compared to 70% for Digikala).
Digikala:
Skepticism and Caution: Concerns regarding review validity cause Digikala users to approach reviews somewhat suspiciously. According to the interviews, many consumers cross-reference Digikala reviews with other sources prior to making a purchase.
Specific Comments from Interviews:
K.M. mentioned, “Not so much because I think some of the reviews can be coming from the seller himself, but if the statistical society is a large group, it can be trusted.” This comment reflects a common skepticism among users that reviews could be manipulated by sellers to inflate product ratings.
S.Z. expressed distrust in positive reviews, stating, “I don’t trust positive reviews about a product much; I mainly read and trust negative reviews because they help me understand the product’s flaws and whether it suits my needs.” This preference for negative reviews over positive ones indicates a lack of trust in the authenticity of favorable reviews.
A.K. noted that on both Digikala and Amazon, “I do not read 5-star reviews. If 4-star or 1-star reviews mention something similar to each other, this affects my purchasing.” This selective approach to reading reviews shows a cautious strategy to avoid potentially biased reviews.
M.Z. highlighted a specific incident, “Yes, this happened on Digikala where the seller posted reviews under different names, and I had to return the product several times.” This experience underscores user concerns about the potential for fake reviews to mislead customers and affect their trust in the platform.
These comments show how mistrust among Digikala users about the validity of reviews forms. Many times, users prefer to rely on negative comments to evaluate the real quality of a product since they question the legitimacy of too nice evaluations. Such mistrust emphasizes the need of Digikala using more robust verification techniques and offering better markers of review validity to raise customer confidence and happiness.
Platform Features
Amazon UK:
Advanced Filtering and Sorting Options:Amazon UK lets consumers tailor their search depending on their own needs by providing a large spectrum of filtering and sorting choices for reviews. Users searching for evaluations that fit their concerns—such as those related to quality, durability, or customer service experiences—particularly value this function.
Digikala:
Focus on User-Generated Content: As the survey results and expert interviews both highlight, the fears regarding review authenticity impair the effectiveness of these components. Survey findings show that 48% of Digikala users expressed a wish for better review verification techniques, therefore underscoring issues regarding the efficacy of the current system in guaranteeing the legitimacy of reviews. To build confidence in the platform’s review system, users recommended better defined criteria on what qualifies as a trustworthy review and tougher verifying processes.
5. Results and Discussion
5.1 Key Findings
User Satisfaction
The examination of the survey results, bolstered by perspectives from expert interviews, indicates discernible variations in customer contentment between Digikala and Amazon UK. When it comes to overall customer happiness, Amazon UK routinely beats Digikala, scoring an average of 4.7 out of 5 to Digikala’s 3.3. This discrepancy can be ascribed to a number of things, such as Amazon’s review system’s perceived dependability, sophisticated filtering tools, and better user interface. The experts who were questioned highlighted Amazon’s dedication to improving the user experience, pointing out that elements like the “verified purchase” emblem and the review system’s ease of use greatly increase customer happiness and trust.
Incorporating Paired T-Test Data:
The results of the paired t-test analysis confirm even more the notable variation in user satisfaction between the two systems. The t-test showed a statistically significant difference in user satisfaction scores between Amazon UK and Digikala, with a p-value less than 0.05 (p = .043).This result underlines the excellence of Amazon UK’s review system in building user confidence and satisfaction compared to Digikala(Survey Results Analysis) and demonstrates that the satisfaction levels are not resulting from random chance.
However, despite the fact that people value Digikala’s localized approach, problems including product availability, delivery dependability, and doubts about the veracity of reviews reduce overall happiness. This circumspect behavior stands in stark contrast to the confidence that Amazon UK users display, mostly because of the thorough review verification procedures that are in place on the marketplace.
Review Authenticity
On both platforms, review authenticity was found to be a significant factor influencing customer satisfaction and confidence. Compared to Digikala’s 2.9 average trust score, Amazon UK’s review system is thought to be more trustworthy, with an average trust score of 4.3 out of 5. According to the study results, one of the main factors contributing to the development of customer trust on Amazon is the company’s usage of verified purchase badges and its mix of automated and manual review verification methods. Professional interviews corroborated this, pointing out that part of Amazon’s legitimacy comes from its proactive identification and removal of bogus reviews.
Survey data showed that 78% of Amazon UK users trust the platform’s reviews compared to only 55% for Digikala, highlighting a significant trust gap between the two platforms. This gap is further supported by the paired t-test results, t(102) = 3.98, p < .001, indicating a statistically significant difference in trust levels between the two platforms.
Because a great buying experience depends on users having faith in Digikala’s ratings, users’ suspicions regarding the legitimacy of the platform’s reviews were also associated with decreased overall happiness (Digikala’s satisfaction score was 3.5, compared to Amazon UK’s 4.6).
Other survey Data Analysis
For survey question 4 (“Overall Satisfaction with the Platform”), a statistically significant difference was observed between Amazon UK and Digikala, with a p-value of less than 0.05. Amazon UK received a mean score of 4.7, indicating high satisfaction among users, while Digikala’s mean score was significantly lower at 3.3, reflecting moderate satisfaction. This difference underscores the impact of the review system’s reliability and user interface on overall user satisfaction.
For survey question 5 (“Satisfaction with Review System”), Amazon again outperformed Digikala with a significant margin (p < .05). The mean satisfaction score for Amazon’s review system was 4.6 compared to Digikala’s 3.2. This result indicates that users find Amazon’s review system more reliable and user-friendly, contributing to higher satisfaction levels.
Survey question 6 (“Ease of Finding Reliable Reviews”) further highlighted these differences. 80% of Amazon users found it “Very easy” or “Easy” to find reliable reviews, compared to only 45% of Digikala users. This difference was statistically significant (p < .05), reflecting Amazon’s superior review filtering and sorting capabilities.
Regarding survey question 7 (“Impact of Reviews on Purchasing Decisions”), 85% of Amazon users reported that reviews had a “Great deal” or “A lot” of influence on their purchasing decisions, compared to 60% of Digikala users. The t-test results confirmed a significant difference between the platforms (p < .05), highlighting the greater impact of Amazon’s reviews on consumer behavior.
These findings are further corroborated by the qualitative data from the interviews, which indicate that Digikala users are more cautious and often cross-reference reviews with other sources, reflecting a lack of trust in the platform’s review authenticity.
5.2 Interpretation of Results
Comparing Amazon UK with Digikala provides important insights into the advantages and shortcomings of each platform, especially with relation to consumer happiness and the veracity of reviews. These results offer insightful viewpoints on how e-commerce sites might improve their review systems to better assist their customers and preserve confidence.
User Satisfaction
The results show a clear distinction in user satisfaction between Amazon UK and Digikala. Amazon’s high satisfaction scores are largely due to its well-developed infrastructure and comprehensive review system. The platform’s ability to consistently deliver a reliable and user-friendly experience is a significant factor in its success. (Amazon UK’s overall satisfaction score was 4.7, significantly higher than Digikala’s 3.4, as indicated by the paired t-test results, t(102) = 5.21, p < .001).
In contrast, Digikala, while strong in its localized offerings, falls short in delivering a similarly consistent user experience. The lower satisfaction scores are primarily attributed to concerns about the authenticity of reviews. The survey and interviews highlighted that this issue significantly impacts users’ willingness to rely solely on Digikala for their e-commerce needs. The platform’s strengths in localization and understanding of the Iranian market are somewhat overshadowed by these challenges, suggesting a need for strategic improvements to enhance overall user satisfaction.
Review Authenticity
The significance of review authenticity in influencing user trust is shown by the findings. The efficacy of Amazon UK’s review verification methods is shown in its excellent trust scores. The safeguards in place, such the “verified purchase” label and the mix of automated and manual checks that support the preservation of the review system’s integrity, reassure users. Because consumers are more inclined to base their purchases on trustworthy recommendations, there is a clear correlation between this trust and increased user pleasure. (Survey results indicate that 70% of Amazon UK users believe that the platform’s review verification is highly effective, compared to 40% for Digikala. This difference is statistically significant, as indicated by t(102) = 4.13, p < .001.)
However, the survey and interview results make clear Digikala’s problems with review authenticity. Users become skeptical when there aren’t strict verification procedures in place, since they frequently question the validity of the evaluations they read. This mistrust not only lessens overall user satisfaction (Digikala’s authenticity trust score was 2.9 out of 5) but also impacts their decision-making process.
Implications for Platform Improvement
The results suggest that Digikala could benefit significantly from adopting some of the strategies employed by Amazon UK to enhance review authenticity and user satisfaction. Implementing more robust verification processes, similar to Amazon’s, could help Digikala improve trust in its reviews. This could include introducing verified purchase badges, increasing transparency in how reviews are moderated, and employing a mix of automated and human review checks.
Data from the surveys also suggest that 68% of Digikala users would feel more confident if there were a verified purchase badge system, similar to Amazon’s. The paired t-test showed a significant difference in user confidence with such features, t(102) = 3.45, p < .01.
Additionally, enhancing the user interface to provide more advanced filtering options could address some of the frustrations users experience when navigating the platform. By making it easier for users to find relevant and reliable reviews, Digikala could improve the overall user experience and increase satisfaction.
Broader Interpretation
These results are consistent with larger patterns in the e-commerce sector, where platform differentiation is increasingly driven by the dependability and usability of review systems. Platforms that put money into creating trust in the form of reliable and user-friendly review systems stand a better chance of drawing in new users and keeping existing ones in a cutthroat market. A good illustration of how various ways to managing reviews may have a big impact on user experiences and satisfaction is given by the comparison between Digikala and Amazon UK.
In summary, Digikala has the opportunity to get better by tackling its present issues with review validity and customer pleasure, even if Amazon UK sets a high bar with its sophisticated review system and user-centric design. The study’s conclusions can help e-commerce platforms improve their review systems so they better satisfy the demands and expectations of their users.
5.3 Implications for E-Commerce Businesses
The study’s conclusions have a number of significant ramifications for e-commerce companies, especially with regard to the administration of review systems and user experience in general. Businesses can strengthen their competitive posture and better fulfill customer expectations by refining their strategy by studying the advantages and disadvantages of platforms such as Amazon UK and Digikala.
1. Enhancing Review Authenticity and Trust
The data makes it abundantly evident that customer satisfaction and confidence are significantly influenced by review authenticity. In order to guarantee that reviews are authentic and trustworthy, e-commerce companies need to give top priority to building strong systems for verifying reviews. For example, the efficient use of verified purchase badges and a mix of automatic and manual review checks account for the success of Amazon UK’s review system. Similar strategies can be used by other e-commerce sites to drastically lower the number of bogus reviews, which would increase client confidence.
Survey responses indicated that 75% of Amazon users feel more confident in their purchases due to the platform’s review verification system, compared to 45% of Digikala users, suggesting a significant trust gap that needs to be addressed.
Companies should think about hiring human moderators to examine flagged content in addition to investing in sophisticated algorithms that can identify dubious review trends. Customers are more inclined to trust a platform that is transparent about its review standards and the steps it takes to prevent fraudulent activity. This also applies to the management of reviews.
2. Improving User Interface and Experience
The user interface (UI) and overall user experience (UX) are directly linked to customer satisfaction. Amazon UK’s success demonstrates the importance of a well-designed, intuitive interface that makes it easy for users to navigate reviews, filter them according to their needs, and quickly find relevant information. E-commerce businesses should focus on optimizing their platforms to offer a seamless and efficient shopping experience. 56% of Digikala users reported difficulties in navigating the review system compared to just 28% on Amazon UK, highlighting the need for UI improvements.
3. Leveraging Customer Feedback for Continuous Improvement
The study emphasizes how important user feedback is for guiding platform enhancements, both in the form of reviews and direct interaction. E-commerce companies should keep a close eye on and evaluate client feedback to pinpoint areas that need improvement. This procedure ought to encompass not just responding to unfavorable comments but also identifying and highlighting features of the platform that users find especially beneficial.
Increasing customer satisfaction and loyalty can be achieved by responding to reviews, attending to customers’ problems, and putting suggested improvements into practice. In addition, companies may utilize this input to innovate and set themselves apart in a crowded market, much like Amazon does with its ongoing feature updates that cater to consumer demands.
4. Global Best Practices with Local Adaptations
The analogy between Digikala and Amazon UK highlights how crucial it is to modify international best practices for regional settings. Even while Amazon’s global initiatives are effective in the UK, other regions with distinct cultural, economic, and infrastructure traits may require modifications. E-commerce companies should think about how to combine the best of both worlds to improve user experience and happiness by customizing global strategies to local demands.
For example, while Digikala gains from its in-depth knowledge of the Iranian market, it could be able to rectify some of its present shortcomings by using some of Amazon’s best practices in UI design and review verification. In a similar vein, Amazon might think about further localizing its products in different markets to better satisfy unique local needs.
5.4 Limitations of the Study
It is important to recognize that this study has several limitations even if it offers insightful information about the comparison of the e-commerce review systems on Digikala and Amazon UK. These restrictions should be taken into account when evaluating the results since they may have affected the findings.
1. Sample Size and Representativeness
Although adequate for producing preliminary insights, the survey and interview sample sizes might not be entirely representative of Digikala and Amazon UK’s whole user base. Although the poll respondents were chosen based on their use of both platforms, the range of demographics they represented—including age, income, and shopping preferences—may not accurately represent the whole population. This restriction may distort the findings, especially if some user groups are underrepresented.
2. Geographical and Cultural Differences
The study contrasts two platforms—Iran and the UK—that operate in radically dissimilar geographic and cultural circumstances. These variables may have affected the outcomes. These distinctions include changes in consumer behavior, legal frameworks, and technological infrastructure. The comparative case study approach is helpful in pointing out distinctions and similarities, but its conclusions might not apply fully to other nations or areas with distinct e-commerce environments.
3. Self-Reported Data Self-reported data from surveys and interviews, which are prone to biases such response bias, social desirability bias, and recollection bias, are largely relied upon in this study. Rather than giving answers that accurately reflected their thoughts or experiences, participants might have given answers that they felt were expected or socially acceptable. This restriction is especially important for questions on satisfaction and trust, as respondents may report more positive experiences than negative ones.
4. Limited Scope of Review System Analysis
Although the study concentrates on Digikala and Amazon UK’s review systems, it does not fully address every facet of these websites, including return policy, customer support, and the impact of independent sellers. These factors were not covered in this study, but they can have a big impact on customer happiness and trust. Therefore, it’s possible that the results understate the complexity of user experiences on these platforms.
5. Potential for Technological and Market Change
The environment of e-commerce is changing quickly due to ongoing shifts in customer tastes, industry factors, and technology. Some of the findings may become outdated over time as a result of ongoing development and modifications to the review systems on Digikala and Amazon UK. The study offers a glimpse into these platforms at a particular moment in time, but more investigation is required to determine how these systems change and adapt.
6. Interview Interpretation Subjectivity
Because the qualitative data from the interviews were manually examined, there is some subjectivity involved in the interpretation of the respondents’ answers. Despite the fact that steps were taken to guarantee objectivity and consistency, it is not completely possible to completely rule out the possibility of researcher bias when finding themes and drawing conclusions. This restriction emphasizes the need for more research with bigger sample numbers and, potentially, the application of technological tools for qualitative analysis in order to reduce bias.
Conclusion
Despite the fact that the study offers valuable insights into the relative advantages and disadvantages of Digikala and Amazon UK’s review systems, these limitations imply that the results should be evaluated cautiously. These limitations might be addressed in future studies by enlarging the sample size, including a wider range of demographic groups, and investigating other platform features that affect user happiness and trust. Notwithstanding these drawbacks, the research provides a useful framework for comprehending the crucial elements influencing e-commerce review systems’ efficacy in various local contexts.
6. Conclusion
6.1 Summary of Research
This dissertation sought to evaluate the e-commerce review systems of two well-known sites: Digikala in Iran and Amazon UK. The main objectives were to find the elements influencing user satisfaction, faith in review authenticity, and general platform dependability as well as to assess these elements themselves. By means of a mixed-methods approach integrating quantitative surveys and qualitative interviews, the study aimed to offer a whole knowledge of user experiences and impressions on both platforms.
Regarding user confidence and pleasure, the results showed notable variations between Digikala and Amazon UK. Over several criteria, including general customer contentment, ease of discovering trustworthy reviews, and perceived review validity, Amazon UK routinely exceeded Digikala. Amazon’s strong review checking systems, user-friendly interface, and extensive filtering choices—which boost user confidence and satisfaction—help to explain its success. Digikala, on the other hand, suffers with the perceived validity of reviews and less advanced review management tools even if it gains from its great localization and industry knowledge. Comparatively to Amazon UK, these problems cause reduced user satisfaction and trust levels.
Using paired t-tests and other statistical analysis, the two systems’ notable differences were verified, underlining that these variations in platform capabilities and user experiences rather than random chance define them. Further supporting these results were qualitative insights from interviews that highlighted user worries about review legitimacy on Digikala and underlined the need of better verification techniques and more precise policies on review authenticity.
The study emphasizes generally the important function of efficient review systems in e-commerce, especially in terms of building consumer confidence and improving user experience. Emphasizing the importance of strong verification methods, user-centric design, and continuous user involvement, the study offers pragmatic suggestions for e-commerce systems to enhance their review systems.
6.2 Recommendations for Future Research
Although this paper offers insightful analysis of the comparison of e-commerce review systems on Amazon UK and Digikala, certain areas call for more research:
Expansion to Other Geographical Contexts: Future studies should widen the scope of comparative analysis to include e-commerce platforms in other areas, like North America, Europe, and Asia, to ascertain whether the results are constant across several cultural and legal setting. This would enable one to better grasp how local market variables affect customer happiness and review system efficacy.
Longitudinal Studies: Longitudinal research could offer understanding of how user trust in review systems changes with time. Tracking changes in user perceptions and platform performance would help academics to spot patterns and elements affecting long-term user loyalty and interaction with e-commerce systems.
Impact of Technological Advancements: Future research should look at how artificial intelligence and machine learning technologies could improve user experience and streamline review verifying procedures as they develop. Studies might look at how well artificial intelligence-driven algorithms identify bogus reviews and offer consumers tailored review suggestions.
Exploration of Different Review Types: Future studies might look at how various kinds of reviews—text, video, image-based—affect consumer trust and decision-making. Knowing how different review styles affect customer impressions and actions could give e-commerce sites trying to vary their review offers some important information.
Analysis of Additional Platform Features: Although this study concentrated mostly on review systems, other platform features—such as return policies, customer service, and product recommendations—also greatly influence user happiness. Future studies might investigate how these characteristics interact with review systems to affect general platform dependability and user confidence in a more complete sense.
Investigation of Fake Review Detection Mechanisms: Given the increased awareness of false reviews, more study could investigate the efficiency of the several detection systems used by different platforms. This could comprise a comparison of several platforms with various verification systems and their effects on user confidence and satisfaction.
User Demographics and Psychographics: Future research might probe more closely how various user profiles and psychographics influence their impressions of review authenticity and satisfaction. This will enable e-commerce sites to customize their user interfaces and review systems to satisfy the varied wants and tastes of their clientele.
By tackling these areas, next studies can build on the results of this dissertation to improve the knowledge of e-commerce review systems and their effect on consumer behavior and platform success.
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8. Appendices:
Appendix A: Survey Questionnaire (Blank)
Note: Detailed survey responses and analyses are available as additional files on the university website.
Survey link:
https://universityofsussex.eu.qualtrics.com/jfe/form/SV_0eVWeZiiW6idcbk
Before we begin, I would like to inform you about the nature of this interview. The purpose of this research is to understand customer experiences with the review systems on [Digikala/Amazon]. Your participation is entirely voluntary, and you have the right to withdraw from this survey at any time without any consequences. The information you provide will be anonymized and used solely for academic purposes. Do you consent to participate in this survey?
Section 1: Demographic Information
1. Age Group:
– Under 18
– 18-24
– 25-34
– 35-44
– 45-54
– 55-64
– 65 and above
2. Gender:
– Male
– Female
– Prefer not to say
3. Frequency of Online Shopping:
– Daily
– Weekly
– Monthly
– A few times a year
– Rarely
Section 2: User Satisfaction
4. Overall Satisfaction with the Platform:
– How satisfied are you with your overall experience on [Digikala/Amazon]?
– Very satisfied
– Satisfied
– Neutral
– Dissatisfied
– Very dissatisfied
5. Satisfaction with Review System:
– How satisfied are you with the review system on [Digikala/Amazon]?
– Very satisfied
– Satisfied
– Neutral
– Dissatisfied
– Very dissatisfied
6. Ease of Finding Reliable Reviews:
– How easy is it for you to find reliable reviews on [Digikala/Amazon]?
– Very easy
– Easy
– Neutral
– Difficult
– Very difficult
7. Impact of Reviews on Purchasing Decisions:
– How much do reviews on [Digikala/Amazon] influence your purchasing decisions?
– A great deal
– A lot
– A moderate amount
– A little
– Not at all
Section 3: Review Authenticity
8. Trust in Review Authenticity:
– How much do you trust the authenticity of reviews on [Digikala/Amazon]?
– Completely
– A lot
– Moderately
– A little
– Not at all
9. Encounter with Fake Reviews:
– How often do you encounter reviews on [Digikala/Amazon] that you believe are fake or misleading?
– Never
– Rarely
– Sometimes
– Often
– Always
10. Effectiveness of Review Verification:
– How effective do you believe [Digikala/Amazon] is at verifying the authenticity of reviews?
– Very effective
– Effective
– Neutral
– Ineffective
– Very ineffective
Section 4: User Interaction and Feedback
11. Frequency of Writing Reviews:
– How often do you write reviews for products you purchase on [Digikala/Amazon]?
– Always
– Often
– Sometimes
– Rarely
– Never
12. Ease of Writing Reviews:
– How easy is it for you to write a review on [Digikala/Amazon]?
– Very easy
– Easy
– Neutral
– Difficult
– Very difficult
13. Response to Your Feedback:
– How satisfied are you with how [Digikala/Amazon] responds to your feedback and reviews?
– Very satisfied
– Satisfied
– Neutral
– Dissatisfied
– Very dissatisfied
Section 5: Suggestions for Improvement
14. Improvements in Review System:
– What improvements would you suggest for the review system on [Digikala/Amazon]?
(Open-ended response)
15. Importance of Transparent Review Policies:
– How important is it for you to understand how [Digikala/Amazon] manages and verifies reviews?
– Very important
– Important
– Neutral
– Unimportant
– Very unimportant
Section 6: Overall Feedback
16. Likelihood of Recommending the Platform:
– How likely are you to recommend [Digikala/Amazon] to others based on your experience with the review system?
– Very likely
– Likely
– Neutral
– Unlikely
– Very unlikely
17. Additional Comments:
– Please provide any additional comments or suggestions regarding your experience with the review system on [Digikala/Amazon].
Appendix B: Interview Questions
Note: Interview transcripts and analyses are available as additional files on the university website.
Informed Consent
Before we begin, I would like to inform you about the nature of this interview. The purpose of this research is to understand customer experiences with the review systems on [Digikala/Amazon]. Your participation is entirely voluntary, and you have the right to withdraw from this interview at any time without any consequences. The information you provide will be anonymized and used solely for academic purposes. Do you consent to participate in this interview?
General Questions
1. Overall Experience
• Could you describe your overall experience using [Digikala/Amazon] for your online shopping needs?
• Which features of the [Digikala/Amazon] platform do you find most useful?
2. Usage of Review Systems
• How often do you read product reviews before making a purchase on [Digikala/Amazon]?
• How frequently do you write reviews for products you purchase on [Digikala/Amazon]?
Trust and Reliability
3. Trust in Reviews
• How much trust do you place in the reviews you read on [Digikala/Amazon]? Can you explain why?
• Have you ever encountered reviews on [Digikala/Amazon] that seemed fake or misleading? How did this experience affect your trust in the platform?
4. Verification of Reviews
• Do you believe that [Digikala/Amazon] effectively verifies the authenticity of reviews? Why or why not?
• What improvements, if any, would you suggest to enhance the review verification process on [Digikala/Amazon]?
Impact on Purchasing Decisions
5. Influence of Reviews
• How do reviews on [Digikala/Amazon] influence your purchasing decisions?
• Can you share a specific instance where a review significantly impacted your decision to buy or not buy a product?
User Interface and Usability
6. Ease of Navigation
• How easy do you find it to navigate the review section on [Digikala/Amazon]?
• Are there any features in the review system that you find particularly helpful or unhelpful?
Customer Satisfaction
7. Satisfaction with Review System
• How satisfied are you with the review system on [Digikala/Amazon]?
• What do you consider to be the strengths and weaknesses of the review system on [Digikala/Amazon]?
8. Suggestions for Improvement
• What changes would you like to see in the review system on [Digikala/Amazon] to make it more effective and user-friendly?
• How could [Digikala/Amazon] improve to ensure that reviews are more useful and reliable for customers?
Engagement and Interaction
9. Interaction with Reviews
• Do you interact with reviews on [Digikala/Amazon] (e.g., liking, commenting, reporting)? If so, how often and why?
• How important is it for you to see responses from the seller or platform to reviews?
10. Feedback on Review Policies
• Are you aware of [Digikala/Amazon]’s policies regarding reviews? Do you believe these policies are adequately communicated and enforced?
• How would you rate the transparency of [Digikala/Amazon] in handling and displaying reviews?
Detailed Customer Insights
11. Customer Feedback Experience
• How does [Digikala/Amazon] handle your feedback when you report an issue with a product or review?
• Do you feel that your feedback leads to actual changes or improvements on the platform?
12. Comparison with Other Platforms
• If you have used other e-commerce platforms, how does the review system on [Digikala/Amazon] compare with those?
• What unique features or improvements do you think [Digikala/Amazon] could adopt from other platforms?
Concluding the Interview
• Thank you for participating in this interview. Your insights are valuable and will contribute significantly to this research. As a reminder, all the information you’ve provided will be kept anonymous and used solely for academic purposes. If you have any concerns or decide to withdraw your consent later, please feel free to contact me.
