From Clicks to Conversions: How Machine Learning Is Shaping E-Commerce Performance in the Information Society

From Clicks to Conversions: How Machine Learning Is Shaping E-Commerce Performance in the Information Society | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Clicks to Conversions: How Machine Learning Is Shaping E-Commerce Performance in the Information Society Syed A. Kazmi, Yassir M. Samra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8526225/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study examines how machine learning (ML) adoption reshapes consumer purchasing behavior and value creation across major e-commerce platforms within the broader context of the information society. Specifically, it investigates the impact of ML technologies on conversion rates and Average Order Value (AOV) in digitally mediated marketplaces. Methods Using secondary data from Amazon, Alibaba, and Etsy covering the period 2020–2023, the study applies descriptive statistics, t-tests, analysis of variance (ANOVA), regression analysis, and difference-in-differences techniques to compare platform performance before and after ML adoption. These methods allow for cross-platform comparison while controlling for marketing expenditure and seasonality. Results The findings reveal statistically significant increases in both conversion rates and AOV following ML adoption across all three platforms. However, the magnitude of these effects differs significantly by platform, reflecting variations in market structure, consumer access mechanisms, and platform-specific personalization strategies. Conclusion The results demonstrate that machine learning functions as a critical infrastructural force shaping consumer access, engagement, and economic outcomes in contemporary digital marketplaces. These findings contribute to understanding how algorithmic systems influence value formation in the information society and raise important implications for platform governance, ethical personalization, and digital inclusion. Machine learning E-commerce Conversion rate Average Order Value Digital platforms Information society 1. Introduction This research aims to investigate the transformative role of machine learning (ML) in shaping consumer purchasing behavior within the e-commerce sector. As digital transformation advances across industries, ML and big data analytics have become essential tools for understanding and predicting consumer needs, preferences, and trends. In the U.S. alone, e-commerce accounted for 22% of total retail sales in 2023, marking the highest share on record and emphasizing the demand for optimized online shopping experiences (Haleem, 2024 ). According to Caroline et al. ( 2023 ), “the e-commerce industry has played a key role in redefining the way consumers shop and interact with the market.” Yet, with such accelerated industry growth, e-commerce platforms face mounting challenges in tracking and responding to evolving consumer behavior. This study aims to address these challenges by examining how ML technologies affect critical performance metrics—specifically, conversion rates and Average Order Value (AOV)—on e-commerce platforms. ML encompasses a variety of methods that enable businesses to forecast consumer actions by analyzing vast volumes of data in real-time. As consumer needs become more immediate and complex, ML provides a solution by enabling platforms to deliver personalized recommendations, implement dynamic pricing, and create segmented marketing strategies based on in-depth behavioral insights (Grewal et al., 2017 ). For example, L’Oreal Luxe achieved substantial growth in Customer Relationship Management (CRM) revenue by using ML to tailor communications, demonstrating the potential of ML to drive business expansion through precise targeting (Databricks, 2023 ). Major brands like Burger King have reported similar successes; by analyzing customer data to personalize recommendations, the chain doubled customer lifetime value (LTV) and increased monthly profits (Braze, (n.d.)). These cases underscore ML's power to improve customer loyalty and revenue through personalized consumer experiences, making it a vital asset for e-commerce businesses seeking sustainable growth. Despite ML's demonstrated potential, implementing it in e-commerce presents unique challenges and varied outcomes across platforms. Research indicates that while ML-driven personalization is highly effective, its impact can differ significantly depending on platform characteristics, target demographics, and product offerings (McAfee & Brynjolfsson, 2017 ). For instance, Amazon’s diverse product catalog and large customer base allows it to maximize ML capabilities, from personalized recommendations to adaptive pricing strategies that engage a wide audience. Alibaba, on the other hand, leverages ML for social commerce and gamified shopping, which caters to its Asian market by enhancing consumer interaction and loyalty but may shape different purchasing behaviors compared to Amazon (Attar et al., 2022 ). In contrast, platforms like Etsy must approach ML carefully to maintain their niche market appeal. Focused on artisanal, handcrafted products, Etsy might limit the extent of personalization to preserve the organic, community-driven shopping experience valued by its customers (Lusch & Nambisan, 2015 ). These platform-specific differences highlight the need for tailored ML applications that align with each platform’s unique market dynamics, enhancing both customer satisfaction and business outcomes. This study contributes to the literature by providing a comparative analysis of ML’s impact on conversion rates and AOV across Amazon, Alibaba, and Etsy. By analyzing “before and after” data for each platform's ML implementation, the research assesses how ML technologies influence key performance metrics and consumer engagement across distinct e-commerce models. Furthermore, the study bridges gaps in understanding how ML strategies can be customized to benefit different types of e-commerce platforms, from broad-market giants to niche-focused businesses. In doing so, this research offers a strategic framework for optimizing ML investments in e-commerce, providing insights for industry practitioners aiming to balance innovation with customer-centric approaches to increase engagement, loyalty, and revenue. This study contributes to the literature on the information society by empirically demonstrating how machine learning systems operate as mediating infrastructures that shape consumer access, choice architecture, and economic participation across heterogeneous platforms. 2. Literature Review 2.1 Machine Learning and Big Data in E-Commerce Machine learning (ML) and big data have revolutionized e-commerce by enabling platforms to analyze and respond to consumer preferences in real-time. ML is a branch of artificial intelligence (AI) that allows systems to learn from data patterns and make decisions based on these insights, transforming industries that rely on extensive consumer data, such as e-commerce (Chen et al., 2012 ). In an era where data is abundant, e-commerce platforms leverage big data—large volumes of structured and unstructured information on consumer interactions and behaviors—to refine and automate their services. According to Grewal et al. ( 2017 ), the integration of ML in e-commerce offers unprecedented capabilities for personalized marketing and customer engagement, allowing businesses to predict purchase intent, personalized recommendations, and optimize inventory. The use of big data provides a foundation for ML’s predictive capabilities, as platforms such as Amazon and Alibaba gather extensive data on browsing history, purchase frequency, and product interactions. This data informs ML algorithms that can segment customers, predict next purchases, and adjust pricing dynamically (Aryafar et al., 2017 ). However, while big data provides the resources ML relies on, the success of ML-driven strategies depends on the quality of data and the platform's ability to process it efficiently. Big data in e-commerce is estimated to grow by 20% annually, which necessitates sophisticated ML models to manage and extract actionable insights from these massive datasets (McAfee & Brynjolfsson, 2017 ). Thus, ML and big data have become the cornerstone of data-driven e-commerce, setting the stage for highly targeted and efficient customer interactions. 2.2 Impact of Machine Learning on Consumer Behavior Research consistently shows that ML-driven personalization strategies can have a significant impact on consumer behavior, particularly in increasing conversion rates and Average Order Value (AOV). Conversion rate, a measure of visitors who complete a purchase, is directly influenced by the relevancy of product recommendations—an area where ML excels. Through collaborative filtering, content-based filtering, and hybrid recommendation systems, ML algorithms can provide highly personalized shopping experiences that resonate with individual consumers (Schafer et al., 2001 ). For instance, Amazon reports that its recommendation engine, which relies heavily on ML, contributes to approximately 35% of its sales (Evdelo, 2020 ). Such personalized systems can make the shopping experience more relevant and engaging, driving higher conversion rates by aligning product suggestions with consumer interests. ML also plays a role in enhancing AOV by implementing dynamic pricing and upselling strategies. AOV reflects the average amount spent per transaction, and platforms that use ML to tailor product bundles or suggest complementary items tend to see higher values per transaction (Chen et al., 2012 ). L’Oreal Luxe, for example, reported a doubling in its Customer Relationship Management (CRM) revenue through ML-powered upselling and personalized recommendations (Medium, 2021 ). By optimizing pricing and product recommendations, e-commerce platforms can increase customer spending, which directly impacts profitability. These effects highlight ML’s capacity not only to drive individual transactions but also to foster long-term consumer loyalty through relevant and customized experiences. 2.3 Platform-Specific Applications of Machine Learning The application of ML in e-commerce varies considerably based on platform characteristics, market focus, and user demographics. Amazon, a global e-commerce giant with a broad consumer base, uses ML extensively for dynamic pricing, predictive inventory management, and advanced personalization. Amazon’s recommendation engine, which employs collaborative filtering, is a prime example of how ML can be used to maximize conversion rates across a diverse user base by providing individualized product suggestions (McAfee & Brynjolfsson, 2017 ). Alibaba, on the other hand, integrates ML to support social commerce features that encourage consumer engagement through gamified experiences. Alibaba’s “Double 11” shopping festival, which uses ML algorithms to drive targeted promotions and interactive experiences, illustrates how platform-specific ML applications can cater to cultural and demographic factors (Zhao et al., 2020 ). Conversely, niche platforms like Etsy approach ML differently. Focused on artisanal and handmade products, Etsy’s value proposition centers around curation rather than broad-market algorithms. While ML is still used for search optimization and general recommendations, the platform limits aggressive personalization to avoid undermining the organic discovery process that appeals to its user base (Lusch & Nambisan, 2015 ). These differences emphasize the need for e-commerce platforms to align ML strategies with their unique market goals, as highly tailored ML applications allow each platform to optimize engagement while staying true to its brand identity. This comparative perspective reveals how ML’s potential is maximized when tailored to each platform's specific consumer and market characteristics. 2.4 Ethical and Societal Implications of ML Adoption The deployment of machine learning (ML) in e-commerce brings forth significant challenges and ethical concerns that businesses must address to maintain consumer trust and ensure long-term sustainability. Chief among these is the issue of data privacy. E-commerce platforms collect vast amounts of consumer data to fuel ML algorithms, yet this data collection raises concerns regarding its usage, security, and potential misuse. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States require platforms to ensure transparency and compliance in their data practices. Despite these safeguards, data breaches and unauthorized sharing of information remain critical risks, undermining consumer confidence (Mühlhoff, 2021 ). Platforms must balance the need for extensive data to train sophisticated ML models with principles of data minimization to avoid overreach (McAfee & Brynjolfsson, 2017 ). Algorithmic bias also presents a notable challenge in the application of ML. Biases embedded in training data can result in discriminatory outcomes, where specific consumer groups are unfairly prioritized or excluded in recommendations or pricing strategies. For example, platforms using historical purchasing data may unintentionally replicate societal inequities, leading to biased results (Binns, 2018 ). Coupled with the opacity of many ML models, which often function as "black boxes," these biases can damage consumer trust. Addressing these issues requires the development of explainable AI (XAI) systems that make algorithmic decisions more transparent and accountable, fostering greater consumer acceptance and regulatory compliance (Chen et al., 2012 ). Another ethical concern revolves around the risks of over-personalization. While personalization is a cornerstone of ML-driven e-commerce, excessive tailoring of recommendations can stifle consumer autonomy and discovery. Platforms that rely heavily on ML to predict consumer preferences risk creating a "filter bubble," where users are only exposed to options that align with past behaviors (Pariser, 2011 ). This not only limits the diversity of consumer experiences but also diminishes the excitement of serendipitous discoveries—an essential element for niche platforms like Etsy, which prioritize organic and curated shopping journeys (Lusch & Nambisan, 2015 ). Moreover, over-personalization can border on manipulation, with platforms subtly influencing purchasing decisions in ways that may feel intrusive or exploitative (Voinea, 2017 ). Balancing personalization with consumer privacy adds another layer of complexity. While personalization requires detailed consumer data, overly invasive practices, such as aggressive retargeting ads, can alienate users and create a perception of constant surveillance. To address these concerns, platforms are exploring privacy-preserving ML techniques, such as federated learning, which enables models to learn from decentralized data without compromising individual privacy (McMahan et al., 2017 ). These approaches offer a promising avenue for maintaining the benefits of personalization while respecting consumer boundaries. Operational challenges further complicate the deployment of ML systems in e-commerce. The computational demands of training and running ML models require significant energy resources, raising concerns about environmental sustainability (Chen et al., 2012 ). As platforms grow and handle increasing data volumes, scalability becomes another pressing issue, necessitating regular updates and retraining of algorithms to stay effective in a rapidly evolving marketplace (Sharma et al., 2023 ). To navigate these challenges, e-commerce businesses must adopt a holistic approach to ML implementation that prioritizes ethical considerations alongside technical efficiency. This includes transparent communication with consumers about data usage, addressing algorithmic biases, and designing systems that align with privacy regulations and societal expectations (Binns, 2018 ). By fostering trust and accountability, platforms can harness ML's potential while mitigating its risks, ensuring sustainable and equitable practices that benefit both businesses and consumers. 2.5 Research Gaps and Opportunities Despite the growing interest in ML within e-commerce, significant gaps remain in understanding its differential impact across diverse platform types. Much of the existing research focuses on the successes of ML in general terms, yet there is limited comparative analysis examining how ML strategies vary between large and niche e-commerce platforms. While studies have shown the effectiveness of ML in broad-market platforms like Amazon (Aryafar et al., 2017 ), there is less empirical evidence on ML’s nuanced effects on platforms with a focused or niche audience, such as Etsy. Such cross-platform studies would enrich our understanding of how ML applications can be tailored to suit different market segments and consumer behaviors, addressing the unique needs of niche and specialized markets. Additionally, there is a need for longitudinal studies to evaluate ML’s impact on consumer behavior over time, particularly as platforms continue to evolve and consumers grow more accustomed to personalized interactions. Understanding ML’s role in fostering long-term customer loyalty, rather than focusing solely on immediate conversion metrics, could provide valuable insights for e-commerce strategists. Moreover, further research is needed on the ethical implications of ML in e-commerce, particularly around consumer trust, data transparency, and the mitigation of algorithmic biases. Exploring these areas would advance the field, providing a foundation for more refined and ethically responsible ML practices in e-commerce. 3. Hypothesis Development Conversion Rate Increases with ML Adoption Conversion rate—defined as the percentage of site visitors who make a purchase—is a critical success metric for e-commerce platforms. Before implementing machine learning, conversion rates on these platforms typically rely on standard recommendation engines, which offer generalized product suggestions based on broader purchasing trends. However, studies suggest that ML algorithms enhance personalization by analyzing real-time customer data, such as browsing history, click behavior, and past purchases, to predict individual preferences with greater precision (Caroline et al., 2023 ). This personalized approach increases the likelihood that consumers find relevant products quickly, improving conversion rates by catering directly to their interests and needs (Schafer et al., 2001 ). Research on ML applications in retail further supports this relationship, demonstrating that ML-powered recommendation systems can drive substantial engagement and, consequently, purchase behavior (Grewal et al., 2017 ). For instance, Amazon’s personalized recommendation engine accounts for 35% of its sales, a testament to ML’s power in converting casual browsers into buyers by presenting relevant suggestions. This shift from static to dynamic, personalized recommendations reflects a meaningful improvement in user experience, which is essential for higher conversion rates post-ML adoption (Evdelo, 2020 ). Thus it is hypothesized that: H 1 Conversion rates are expected to be significantly higher after the implementation of machine learning. Increase in Average Order Value with ML Adoption Average Order Value (AOV)—the average spent per customer transaction—is a revenue-driving metric that can benefit significantly from ML implementation. Prior to adopting ML, platforms may offer limited upselling options, often restricted to simple bundling or blanket discounts. Machine learning, however, transforms this approach by dynamically adapting pricing and personalizing upsell opportunities based on real-time insights into customer purchasing behavior. ML algorithms can evaluate factors like item popularity, inventory levels, and individual customer profiles to present tailored upsell offers, which can effectively encourage higher-spending purchases (Chen et al., 2012 ). Dynamic pricing, another ML application, adjusts prices based on demand, competitor pricing, and user activity, leading to an optimized pricing strategy that aligns with consumer willingness to pay. For example, Uber's dynamic pricing model has set a precedent, showing how adjusting prices based on demand can increase transaction value (Banerjee et al., 2015). This personalization of both upsell suggestions and prices aligns with consumer preferences, leading to a higher AOV after ML adoption as customers are incentivized to spend more per transaction (Ban & Bora, 2020). Thus, it is hypothesized that: H 2 Average Order Values are expected to be significantly higher after the implementation of machine learning. Differential Impact of ML on Consumer Behavior by Platform E-commerce platforms like Amazon, Alibaba, and Etsy differ significantly in their consumer demographics, product diversity, and market positioning. Consequently, ML adoption is expected to have varied impacts on consumer behavior across these platforms. Amazon, for instance, serves a broad consumer base with a vast inventory and utilizes ML for extensive personalization and dynamic pricing. This broad reach and large dataset allow Amazon to maximize ML-driven insights to cater to a diverse audience, likely leading to substantial changes in conversion rates and AOV (McAfee & Brynjolfsson, 2017 ). In contrast, Alibaba operates heavily within the Asian market and leverages ML primarily for user engagement and social commerce features. Studies show that Alibaba’s emphasis on gamification and interactive shopping experiences can lead to unique consumer behaviors, such as increased brand loyalty and frequency of visits, which may influence conversion but in ways distinct from Amazon’s approach (Attar et al., 2022 ). Etsy, with its niche artisanal market, might experience a more nuanced impact of ML adoption. As Etsy’s shoppers are often drawn to unique, hand-crafted goods, over-personalization might not resonate as strongly. Research on niche markets suggests that highly personalized, non-algorithmic suggestions may better maintain the platform’s artisanal appeal (Lusch & Nambisan, 2015 ). Therefore, the effectiveness of ML on consumer metrics will likely vary according to each platform’s user expectations and positioning, resulting in distinct levels of change in conversion and AOV. Thus, the final group of hypotheses are: H 3 Post-ML adoption conversion rates differ significantly across platforms due to platform-specific market structures and consumer access mechanisms. 4. Methodology This research aims to explore the impact of machine learning (ML) on consumer behavior and market dynamics within e-commerce platforms, specifically Amazon, Alibaba, and Etsy. These platforms have long incorporated ML technologies, such as product recommendations, dynamic pricing, and logistical optimization, to enhance consumer experiences. The study focuses on two core hypotheses: the first examines how consumer behavior has evolved due to the adoption of ML, while the second investigates the subsequent effects on market dynamics and consumer engagement. 4.1 Data Sources This study uses secondary data obtained from publicly available annual and interim financial reports published by Amazon, Alibaba, and Etsy. Specifically, the analysis draws on Form 10-K filings, annual reports, and investor disclosures covering the period from 2020 to 2023. These documents provide consistent reporting of key performance indicators, including conversion rates, Average Order Value (AOV), and related engagement metrics, enabling cross-platform comparison. 4.2 Variables and Measures Conversion rate is defined as the proportion of platform visits that result in a completed purchase within a given reporting period. Average Order Value (AOV) is defined as the average monetary value of completed transactions during the same period. Machine learning adoption is operationalized as a binary indicator distinguishing periods before (2020–2021) and after (2022–2023) the widespread deployment of ML-driven personalization, pricing, and recommendation systems. Control variables include reported marketing expenditure and seasonality. 4.3 Machine Learning Adoption Timeline and Analytical Strategy Descriptive statistics are used to find broader trends, such as changes in conversion rates or AOV. We will use statistical tests such as t-tests, F-tests, and ANOVA to evaluate trends in key metrics from times of high ML adoption to earlier times. These tools help measure whether the implementation of ML has had a real impact on consumer behavior and platform performance. The F-test provides depth to the calculation by analyzing variance distributions before and after ML use. The formula for the t-test & F-test is as follows: T-test formula : $$\:t=\frac{(X̄1\:-\:X̄2)}{\surd\:\left[\right(S1²\:/\:n1)\:+\:(S2²\:/\:n2\left)\right]}$$ where X̄1 and X̄2 are the sample means, and S1² and S2² are sample variances. F-test formula : $$\:F=\frac{Variance\:of\:pre-ML\:group}{Variance\:of\:post-ML\:group}$$ A significant F-value (p < 0.05) indicates that ML adoption has introduced meaningful variability in the performance metrics, suggesting more dynamic impacts across consumer segments. Additionally, regression analysis will be conducted to control for exogenous factors such as marketing expenditures and seasonality. The regression model is specified as: Y = β 0 ​ + β 1 ​(ML Adoption) + β 2 ​(Marketing Spend) + β 3 ​(Seasonality) + ϵ This model isolates the effects of ML adoption on key metrics such as AOV, conversion rates, and CLV, providing a precise interpretation of these effects. For instance, a positive effect on conversion rates or AOV would suggest that ML-driven product recommendations are more relevant to consumers. In contrast, negative effects could arise from operational changes that affect consumer trust. The second sub-research objective focuses on the implications of ML on market dynamics. This will be assessed using several metrics, including market share, customer segmentation, click-through rates, time spent on the platform, and sentiment analysis drawn from market research and company reports. Natural language processing (NLP) will be employed to analyze sentiment from reports such as Etsy’s 2022–2024 annual reports, using tools like TextBlob to classify consumer attitudes toward the platform and its offerings. For customer segmentation, k-means clustering will be applied with the objective function: Minimize \(\:{\sum\:}_{i=1}^{n}{\sum\:}_{k=1}^{K}\mid\:\mid\:{x}_{i}-{{\mu\:}_{k}\mid\:\mid\:}^{2}\) This technique allows the representation of distinct consumer cohorts based on their response to ML-driven personalization, thereby linking specific segments to different levels of engagement and satisfaction. To understand the structural effects of ML on competition, a difference-in-differences (DID) approach will be used to isolate the changes in market structure attributable to the adoption of ML. The DID model is specified as: $$\:\varDelta\:Y=({Y}_{post\:treated}-{Y}_{pre\:\:treated})-{(Y}_{post\:control}-{Y}_{pre\:control})$$ This method quantifies the effects of ML on market share, customer engagement, and other metrics, translating the impacts of ML adoption into observable changes in market behavior. 4.4 Robustness and Replicability All analyses are conducted using standard econometric techniques commonly applied in digital platform and information systems research. The use of publicly available data ensures that the study can be independently replicated. Model specifications were checked for consistency across platforms to confirm the robustness of the results. 4.5 Ethical Considerations This study relies exclusively on secondary data obtained from publicly available corporate disclosures. It does not involve human participants, personal data, or any form of human subject experimentation. As such, no ethical approval was required. 4.6 Data Availability Statement The data supporting the findings of this study are publicly available through annual reports, financial disclosures, and investor communications issued by Amazon, Alibaba, and Etsy. By comparing the performance indicators of Amazon, Alibaba, and Etsy during periods of accelerated growth and adoption of ML, this study will reveal how machine learning technologies have driven improvements in conversion rates, AOV, CLV, and market share. These findings will support the hypothesis that ML has been a pivotal factor in the platforms' early success and future growth and that its applications can potentially be scaled to other segments of e-commerce. 5. Results When studying consumer purchasing trends, key metrics such as conversion rates, Average Order Value (AOV), retention rates, purchase frequency, and Customer Lifetime Value (CLV) are crucial. The data presented in this analysis was sourced from the annual and interim financial reports of Amazon (2020–2023), Alibaba (2020–2023), and Etsy (2020–2024). Descriptive Statistics Descriptive statistics provide a useful snapshot of each platform’s performance, highlighting important trends. Between 2020 and 2023, Amazon demonstrated strong performance, maintaining a substantial market share and user growth. Alibaba, while also expanding rapidly, faced regulatory challenges in its home country. Etsy capitalized on niche markets, benefiting from an increased demand for handmade and vintage goods. The following table summarizes key metrics for each platform: Table 1 Descriptive Statistics Metric Amazon Conversion Rate Amazon AOV Alibaba Conversion Rate Alibaba AOV Etsy Conversion Rate Etsy AOV Mean 0.123 82.57 0.0971 72.36 0.0847 57.66 Standard Deviation 0.0194 5.77 0.015 5.62 0.0115 5.88 Min 0.098 73.32 0.0761 63.52 0.0664 47.92 Max 0.1542 91.56 0.122 81.21 0.1029 65.87 T-tests for Pre- and Post-ML Adoption To assess the impact of machine learning (ML) adoption, t-tests were conducted comparing the means of conversion rates and AOV for the pre-ML period (2020–2021) and post-ML period (2022–2023). The results of these tests, shown in Table 2 , reveal statistically significant improvements across all platforms. Table 2 T-test results Platform Metric T-Statistic P-Value Significance Amazon Conversion Rate -14.23 < 0.001 Significant Amazon AOV -11.98 < 0.001 Significant Alibaba Conversion Rate -10.75 < 0.001 Significant Alibaba AOV -13.88 < 0.001 Significant Etsy Conversion Rate -13.52 < 0.001 Significant Etsy AOV -13.14 < 0.001 Significant The negative t-statistics indicate that the conversion rates and AOV were significantly lower before the adoption of ML, and the improvements in both metrics post-ML adoption are highly significant (p-values < 0.001). The results for each platform suggest that ML-driven initiatives, such as personalized recommendations and dynamic pricing, played a critical role in enhancing performance, thus supporting H 1 & H 2 . The second hypothesis proposed that ML adoption would result in an increase in AOV across all platforms. The results from the data analysis fully support H 2 . The t-statistic for Amazon’s conversion rate (-14.23) demonstrates a significant increase in post-ML adoption. The platform's use of ML for product recommendations, customer segmentation, and personalized advertising contributed to the increase in conversions by delivering more relevant products to customers, thus speeding up the purchasing process. Similarly, Amazon’s AOV showed a significant increase (-11.98) post-ML, driven by dynamic pricing and personalized upselling strategies that led to higher customer engagement and increased revenue per purchase. Alibaba’s conversion rate and AOV both experienced significant improvements following ML adoption. The t-statistics for conversion rate (-10.75) and AOV (-13.88) confirm these results. Alibaba’s ML-powered product recommendations, regional product suggestions, and marketing campaigns helped to personalize the shopping experience, boosting conversions and increasing transaction values. For Etsy, the t-statistics for conversion rate (-13.52) and AOV (-13.14) also indicate statistically significant improvements post-ML adoption. Etsy leveraged ML for personalized product recommendations, particularly in its niche markets of handmade and vintage goods. This increased customer engagement and, in turn, pushed customers to purchase more expensive items, boosting AOV. F-tests for Post-ML Adoption To evaluate H 3 , an ANOVA F-test and Tukey's Honest Significant Difference (HSD) test were used. It was a set of monthly Amazon, Alibaba, and Etsy conversion rates after the period of implementation of machine learning (2022–2023). A one-way ANOVA analysis revealed significant differences in conversion between all three platforms (F-statistic of 120.75 and p-value < 0.001). Since the p-value is well below the significance level (= 0.05), the null hypothesis of equal conversion rates between platforms was discarded. This finding also confirms that the platforms have statistically different conversion rates. To see which platform pairings showed a significant difference, we performed Tukey’s HSD test. The findings are compiled in Table 3 : Table 3 F-test results (Pairwise Comparisons) Group 1 Group 2 Mean Difference P-value Lower Bound Upper Bound Significant Alibaba Amazon 0.0288 < 0.001 0.0222 0.0354 Yes Alibaba Etsy -0.0131 < 0.001 -0.0197 -0.0065 Yes Amazon Etsy -0.0419 < 0.001 -0.0485 -0.0353 Yes The average conversion rate of Amazon was much higher than Alibaba’s (mean difference 0.0288, p < 0.001). That outcome means that in the test period, Amazon’s use of ML and overall e-commerce approach was more successful at converting visitors into buyers than Alibaba’s. The confidence interval of the mean difference (0.0222–0.0354) confirms the validity of this difference. It might be that Amazon’s superior performance is a combination of its powerful recommendation system, product range, and global reach. In the Amazon vs. Etsy analysis, the average difference in conversion rate was largest (-0.0419), with Amazon winning (p < 0.001). The confidence range (-0.0485 to -0.0353) shows an unchanging, large difference. This huge disparity makes Amazon a better bet for data-driven personalization and efficiencies. Etsy, on the other hand, which is aimed at a very specific audience of handmade, exclusive goods, might prefer conversion rate over engagement and customer experience. Despite being somewhat comparable, Etsy’s conversion rate was even higher than Alibaba’s (mean difference − 0.0131, p < 0.001) in terms of the size of the conversions). As the confidence interval (-0.0197–0.0065) indicates, this variance is smaller than Amazon’s but still significant. Alibaba’s focus on wholesale markets and international trade probably means that the conversion can be more accurate than with Etsy’s niche, smaller-scale e-commerce platform. These results strongly suggest that when a platform implements ML tactics, the conversion rate on that platform is indicative of the audience characteristics and product approach. The largest average conversion rate is Amazon, which has the advantage of its dominance in the market and probably better personalization algorithms. Alibaba’s conversion rate is slightly lower than Amazon’s, but it is higher than Etsy’s (which has a specialized segment of small-batch products with fewer buyers). These large disparities make the importance of the relative performance of ML-based solutions for different platform objectives clear. Amazon’s numbers are probably a reflection of its global product portfolio and scale, while Alibaba’s numbers correspond to wholesale and international e-commerce. Etsy’s low conversion rate could be a reflection of its own unique segmentation, which favors artisanal and handmade items, which are inherently more targeted to a narrower market. Regression Analysis Further insights into the impact of ML on conversion rates and AOV are provided by the regression analysis shown in Table 4 . This analysis accounts for additional factors such as marketing spend and seasonality, offering a deeper understanding of the role of ML compared to traditional marketing efforts. Table 4 Combined Regression Analysis Platform Metric ML Adoption Coefficient ML Adoption P-Value Marketing Spend Coefficient Marketing Spend P-Value Seasonality Coefficient Seasonality P-Value Amazon Conversion Rate 0.048 < 0.001 0.001 0.674 0.003 0.552 Amazon AOV 10.34 < 0.001 0.21 0.738 0.43 0.611 Alibaba Conversion Rate 0.036 < 0.001 0.002 0.598 0.002 0.569 Alibaba AOV 8.74 < 0.001 0.17 0.655 0.31 0.605 Etsy Conversion Rate 0.029 < 0.001 0.001 0.722 0.001 0.632 Etsy AOV 5.62 < 0.001 0.12 0.711 0.25 0.612 The last set of hypotheses posited that the impact of ML adoption on conversion rates and AOV would vary across platforms (Amazon, Alibaba, and Etsy), given their different business models and market positions. The data reveal that ML adoption has a significant effect across all platforms, but the magnitude of the effect varies. Amazon shows the highest increase in both conversion rate and AOV, which aligns with its broad market, data-driven approach to pricing, and personalized recommendations. This is reflected in the higher coefficients for Amazon in both the t-tests and regression analysis. Alibaba, similarly, experiences a significant increase in both metrics, with slightly lower increases than Amazon, reflecting its focus on dynamic pricing and regional product recommendations, which still outperforms traditional marketing spend. Etsy, while a smaller platform, also shows substantial improvements in conversion rates and AOV, with a slightly smaller increase than the other two platforms, which aligns with Etsy’s focus on niche, personalized product recommendations. 6. Discussion Machine learning has proven to be a transformative force in the e-commerce sector, especially in driving key metrics like conversion rates and Average Order Value (AOV). The findings of this study confirm that ML adoption has significantly enhanced both of these critical performance indicators across Amazon, Alibaba, and Etsy. Personalized recommendations, dynamic pricing, and automated decision-making systems powered by ML have allowed these platforms to fine-tune customer experiences and maximize revenue opportunities. As the results show, Amazon, Alibaba, and Etsy all experienced substantial increases in conversion rates and AOV, underscoring the potency of ML in improving customer engagement and transaction values. However, the findings also reveal a nuanced understanding of the relationship between ML and traditional marketing strategies. While marketing efforts alone (such as ad spending) were not found to have a statistically significant effect on conversions and AOV, integrating marketing with ML-driven insights shows promise for boosting e-commerce performance even further. Targeted marketing campaigns that complement personalized recommendations and dynamic pricing could lead to even more substantial gains. In terms of future research, a key area to explore is how marketing strategies and ML can be integrated to maximize their joint impact. Marketing, when informed by ML data, could optimize offers, discounts, and product recommendations to more precisely target consumers, enhancing the overall shopping experience. Furthermore, as ML technologies evolve and new tools like artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) emerge, exploring how these technologies can be combined with ML to provide a more personalized and immersive shopping experience could be an exciting avenue for future studies. Another critical area of investigation concerns the long-term sustainability of ML-powered improvements. Given the rapid pace of technological advancements and evolving customer preferences, it is important to understand how ML systems can adapt over time to continue delivering value. Research into the adaptability of ML systems to new market conditions and consumer demands will be vital to ensuring the ongoing success of ML in e-commerce. Lastly, ethical concerns surrounding ML’s use in e-commerce, particularly regarding data privacy and consumer trust, demand immediate attention. As platforms collect and process vast amounts of consumer data to fuel their ML algorithms, ensuring data security and transparency is paramount. Future research should address how platforms can balance the benefits of ML personalization and dynamic pricing with the ethical responsibility to protect user privacy and maintain trust. This study is limited by its reliance on secondary financial data and its focus on large, established platforms. Future research could extend this analysis to smaller or emerging digital marketplaces and incorporate longitudinal consumer-level data 7. Conclusion This study highlights the transformative role of machine learning in shaping the future of e-commerce. By leveraging real-time customer data, platforms like Amazon, Alibaba, and Etsy have significantly improved conversion rates and AOV, resulting in increased revenue and customer satisfaction. However, the findings also emphasize the importance of integrating ML with broader marketing strategies to unlock its full potential. As e-commerce continues to evolve, understanding how ML can be sustained, ethically implemented, and combined with new technologies will be essential for platforms to maintain a competitive edge. The adoption of machine learning is undeniably a game-changer for the e-commerce industry, and its impact will only grow as technology advances. Future research should focus on how ML can evolve to meet the changing demands of the market, while also addressing the ethical implications of its widespread use in personalized shopping experiences. The continued success of e-commerce platforms depends not only on the innovations brought by ML but also on how these innovations are responsibly integrated into business practices and customer relationships. Overall, the findings underscore the role of machine learning as a structural force shaping access, visibility, and value creation in the contemporary information society. Declarations Funding This research received no external funding. Author Contribution S.A.K. conceived the study, developed the research design, conducted the literature review, performed the data analysis, and wrote the main manuscript text.Y.M.S. contributed to the theoretical framing, methodological refinement, interpretation of results, and critical revision of the manuscript.Both authors reviewed, edited, and approved the final version of the manuscript. Data Availability The data supporting the findings of this study are publicly available through annual reports, financial disclosures, and investor communications issued by Amazon, Alibaba, and Etsy. References Aryafar, K., Guillory, D., Hong, L.: An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. ArXiv. (2017). https://doi.org/10.1145/3124749.3124758 Attar, R.W., Almusharraf, A., Alfawaz, A., Hajli, N.: New Trends in E-Commerce Research: Linking Social Commerce and Sharing Commerce: A Systematic Literature Review. Sustainability. 14 , 16024 (2022). https://doi.org/10.3390/su142316024 Ban, G.-Y., Keskin, N., Bora: Personalized Dynamic Pricing with Machine Learning: High Dimensional Features and Heterogeneous ElasticityApril 20, Management Science, Vol. 67, No. 9, September 2021, pp. 5549–5568, Available at SSRN: https://ssrn.com/abstract=2972985 or (2020). http://dx.doi.org/10.2139/ssrn.2972985 Banerjee, S., Riquelme, C., Johari: Ramesh, Pricing in Ride-Share Platforms: A Queueing-Theoretic Approach (February 10, 2015). Available at SSRN: https://ssrn.com/abstract=2568258 or http://dx.doi.org/10.2139/ssrn.2568258 Binns, R. Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on, Fairness: Accountability, and Transparency, 149–159. (2018) Braze. (n.d.). Burger King: Driving app installs and monthly active users through personalized marketing. Retrieved November 26: from (2024). https://www.braze.com/customers/burger-king-client-story Caroline, N., Yuswardi, N., Rofi’i, Y.U.: Int. J. Softw. Eng. Comput. Sci. (IJSECS). 3 (3), 352–364 (2023). https://doi.org/10.35870/ijsecs.v3i3.1840 Analysis of E-Commerce Purchase Patterns Using Big Data: An Integrative approach to understanding consumer behavior Chen, H., Chiang, R.H.L., Storey, V.C.: Business Intelligence and Analytics: From Big Data to Big Impact. MIS Q. 36 (4), 1165–1188 (2012) Databricks: L’Oréal amplifies consumer experience leveraging Databricks Lakehouse. Databricks. Retrieved November 26, 2024, from (2023). https://www.databricks.com/company/newsroom/press-releases/loreal-amplifies-consumer-experience-leveraging-databricks Evdelo: Amazon’s recommendation algorithm drives 35% of its sales. Evdelo. (2020)., July 3 https://evdelo.com/amazons-recommendation-algorithm-drives-35-of-its-sales/ Grewal, D., Roggeveen, A.L., Nordfält, J.: The Future of Retailing. J. Retail. 93 (1), 1–6 (2017) Haleem, A.: US e-commerce sales reached $ 1.119 trillion in 2023. Digital Commerce 360. (2024)., June 27 https://www.digitalcommerce360.com/article/us-ecommerce-sales/#:~:text=U.S.%20ecommerce%20sales%20grew%20to,22.0%25%20of%20total%20retail%20sales Lusch, R.F., Nambisan, S.: Service Innovation: A Service-Dominant Logic Perspective. MIS Q. 39 (1), 155–175 (2015) McAfee, A., Brynjolfsson, E.: Machine, Platform. Harnessing Our Digital Future. Norton & Company, Crowd (2017) McMahan, B., Moore, E., Ramage, D., Hampson, S.,y, Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1273–1282. (2017) Medium: How L'Oréal wins big online, one shopper at a time. Medium. Retrieved November 26, 2024, from (2021). https://medium.com/rosetta-ai-global/how-lor%C3%A9al-wins-big-online-one-shopper-at-a-time-f9adc4a2ab43 Mühlhoff, R.: Predictive privacy: Towards an applied ethics of data analytics. Ethics Inf. Technol. 23 (3), 501–514 (2021). https://doi.org/10.1007/s10676-021-09606-x Pariser, E.: The Filter Bubble: What the Internet is Hiding From You. Penguin (2011) Sharma, M.K., Nachappa, M.N., Kumar, R.: Personalized Treatment Recommendations for Mental Health Disorders Using AI and Big Healthcare Data. IEEE International Conference on ICT in Business Industry & Government, Indore, India. 1–6. (2023). 10.1109/ictbig59752.2023.10455991 Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce Recommendation Applications. Data Min. Knowl. Disc. 5 (1–2), 115–153 (2001) Voinea, D.V.: Ethical implications of filter bubbles and personalized news-streams. Int. J. Social Sci. Educational Stud. 3 (3), 190–189 (2017) Zhao, X., Chen, X., Song, X., Zhang, W., Gao, L., De Ciel, R.: A look at Alibaba Double 11 Shopping Festival. J. Student Res. 8 (1) (2020). https://doi.org/10.47611/jsr.v8i1.527 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8526225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571308490,"identity":"a260b1d5-fe75-458d-ba94-0789d8112a30","order_by":0,"name":"Syed A. 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Introduction","content":"\u003cp\u003eThis research aims to investigate the transformative role of machine learning (ML) in shaping consumer purchasing behavior within the e-commerce sector. As digital transformation advances across industries, ML and big data analytics have become essential tools for understanding and predicting consumer needs, preferences, and trends. In the U.S. alone, e-commerce accounted for 22% of total retail sales in 2023, marking the highest share on record and emphasizing the demand for optimized online shopping experiences (Haleem, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to Caroline et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u0026ldquo;the e-commerce industry has played a key role in redefining the way consumers shop and interact with the market.\u0026rdquo; Yet, with such accelerated industry growth, e-commerce platforms face mounting challenges in tracking and responding to evolving consumer behavior. This study aims to address these challenges by examining how ML technologies affect critical performance metrics\u0026mdash;specifically, conversion rates and Average Order Value (AOV)\u0026mdash;on e-commerce platforms.\u003c/p\u003e \u003cp\u003eML encompasses a variety of methods that enable businesses to forecast consumer actions by analyzing vast volumes of data in real-time. As consumer needs become more immediate and complex, ML provides a solution by enabling platforms to deliver personalized recommendations, implement dynamic pricing, and create segmented marketing strategies based on in-depth behavioral insights (Grewal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, L\u0026rsquo;Oreal Luxe achieved substantial growth in Customer Relationship Management (CRM) revenue by using ML to tailor communications, demonstrating the potential of ML to drive business expansion through precise targeting (Databricks, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Major brands like Burger King have reported similar successes; by analyzing customer data to personalize recommendations, the chain doubled customer lifetime value (LTV) and increased monthly profits (Braze, (n.d.)). These cases underscore ML's power to improve customer loyalty and revenue through personalized consumer experiences, making it a vital asset for e-commerce businesses seeking sustainable growth.\u003c/p\u003e \u003cp\u003eDespite ML's demonstrated potential, implementing it in e-commerce presents unique challenges and varied outcomes across platforms. Research indicates that while ML-driven personalization is highly effective, its impact can differ significantly depending on platform characteristics, target demographics, and product offerings (McAfee \u0026amp; Brynjolfsson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, Amazon\u0026rsquo;s diverse product catalog and large customer base allows it to maximize ML capabilities, from personalized recommendations to adaptive pricing strategies that engage a wide audience. Alibaba, on the other hand, leverages ML for social commerce and gamified shopping, which caters to its Asian market by enhancing consumer interaction and loyalty but may shape different purchasing behaviors compared to Amazon (Attar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, platforms like Etsy must approach ML carefully to maintain their niche market appeal. Focused on artisanal, handcrafted products, Etsy might limit the extent of personalization to preserve the organic, community-driven shopping experience valued by its customers (Lusch \u0026amp; Nambisan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These platform-specific differences highlight the need for tailored ML applications that align with each platform\u0026rsquo;s unique market dynamics, enhancing both customer satisfaction and business outcomes.\u003c/p\u003e \u003cp\u003eThis study contributes to the literature by providing a comparative analysis of ML\u0026rsquo;s impact on conversion rates and AOV across Amazon, Alibaba, and Etsy. By analyzing \u0026ldquo;before and after\u0026rdquo; data for each platform's ML implementation, the research assesses how ML technologies influence key performance metrics and consumer engagement across distinct e-commerce models. Furthermore, the study bridges gaps in understanding how ML strategies can be customized to benefit different types of e-commerce platforms, from broad-market giants to niche-focused businesses. In doing so, this research offers a strategic framework for optimizing ML investments in e-commerce, providing insights for industry practitioners aiming to balance innovation with customer-centric approaches to increase engagement, loyalty, and revenue. This study contributes to the literature on the information society by empirically demonstrating how machine learning systems operate as mediating infrastructures that shape consumer access, choice architecture, and economic participation across heterogeneous platforms.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Machine Learning and Big Data in E-Commerce\u003c/h2\u003e \u003cp\u003eMachine learning (ML) and big data have revolutionized e-commerce by enabling platforms to analyze and respond to consumer preferences in real-time. ML is a branch of artificial intelligence (AI) that allows systems to learn from data patterns and make decisions based on these insights, transforming industries that rely on extensive consumer data, such as e-commerce (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In an era where data is abundant, e-commerce platforms leverage big data\u0026mdash;large volumes of structured and unstructured information on consumer interactions and behaviors\u0026mdash;to refine and automate their services. According to Grewal et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the integration of ML in e-commerce offers unprecedented capabilities for personalized marketing and customer engagement, allowing businesses to predict purchase intent, personalized recommendations, and optimize inventory.\u003c/p\u003e \u003cp\u003eThe use of big data provides a foundation for ML\u0026rsquo;s predictive capabilities, as platforms such as Amazon and Alibaba gather extensive data on browsing history, purchase frequency, and product interactions. This data informs ML algorithms that can segment customers, predict next purchases, and adjust pricing dynamically (Aryafar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, while big data provides the resources ML relies on, the success of ML-driven strategies depends on the quality of data and the platform's ability to process it efficiently. Big data in e-commerce is estimated to grow by 20% annually, which necessitates sophisticated ML models to manage and extract actionable insights from these massive datasets (McAfee \u0026amp; Brynjolfsson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, ML and big data have become the cornerstone of data-driven e-commerce, setting the stage for highly targeted and efficient customer interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Impact of Machine Learning on Consumer Behavior\u003c/h2\u003e \u003cp\u003eResearch consistently shows that ML-driven personalization strategies can have a significant impact on consumer behavior, particularly in increasing conversion rates and Average Order Value (AOV). Conversion rate, a measure of visitors who complete a purchase, is directly influenced by the relevancy of product recommendations\u0026mdash;an area where ML excels. Through collaborative filtering, content-based filtering, and hybrid recommendation systems, ML algorithms can provide highly personalized shopping experiences that resonate with individual consumers (Schafer et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For instance, Amazon reports that its recommendation engine, which relies heavily on ML, contributes to approximately 35% of its sales (Evdelo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such personalized systems can make the shopping experience more relevant and engaging, driving higher conversion rates by aligning product suggestions with consumer interests.\u003c/p\u003e \u003cp\u003eML also plays a role in enhancing AOV by implementing dynamic pricing and upselling strategies. AOV reflects the average amount spent per transaction, and platforms that use ML to tailor product bundles or suggest complementary items tend to see higher values per transaction (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). L\u0026rsquo;Oreal Luxe, for example, reported a doubling in its Customer Relationship Management (CRM) revenue through ML-powered upselling and personalized recommendations (Medium, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By optimizing pricing and product recommendations, e-commerce platforms can increase customer spending, which directly impacts profitability. These effects highlight ML\u0026rsquo;s capacity not only to drive individual transactions but also to foster long-term consumer loyalty through relevant and customized experiences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Platform-Specific Applications of Machine Learning\u003c/h2\u003e \u003cp\u003eThe application of ML in e-commerce varies considerably based on platform characteristics, market focus, and user demographics. Amazon, a global e-commerce giant with a broad consumer base, uses ML extensively for dynamic pricing, predictive inventory management, and advanced personalization. Amazon\u0026rsquo;s recommendation engine, which employs collaborative filtering, is a prime example of how ML can be used to maximize conversion rates across a diverse user base by providing individualized product suggestions (McAfee \u0026amp; Brynjolfsson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Alibaba, on the other hand, integrates ML to support social commerce features that encourage consumer engagement through gamified experiences. Alibaba\u0026rsquo;s \u0026ldquo;Double 11\u0026rdquo; shopping festival, which uses ML algorithms to drive targeted promotions and interactive experiences, illustrates how platform-specific ML applications can cater to cultural and demographic factors (Zhao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, niche platforms like Etsy approach ML differently. Focused on artisanal and handmade products, Etsy\u0026rsquo;s value proposition centers around curation rather than broad-market algorithms. While ML is still used for search optimization and general recommendations, the platform limits aggressive personalization to avoid undermining the organic discovery process that appeals to its user base (Lusch \u0026amp; Nambisan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These differences emphasize the need for e-commerce platforms to align ML strategies with their unique market goals, as highly tailored ML applications allow each platform to optimize engagement while staying true to its brand identity. This comparative perspective reveals how ML\u0026rsquo;s potential is maximized when tailored to each platform's specific consumer and market characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ethical and Societal Implications of ML Adoption\u003c/h2\u003e \u003cp\u003eThe deployment of machine learning (ML) in e-commerce brings forth significant challenges and ethical concerns that businesses must address to maintain consumer trust and ensure long-term sustainability. Chief among these is the issue of data privacy. E-commerce platforms collect vast amounts of consumer data to fuel ML algorithms, yet this data collection raises concerns regarding its usage, security, and potential misuse. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States require platforms to ensure transparency and compliance in their data practices. Despite these safeguards, data breaches and unauthorized sharing of information remain critical risks, undermining consumer confidence (M\u0026uuml;hlhoff, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Platforms must balance the need for extensive data to train sophisticated ML models with principles of data minimization to avoid overreach (McAfee \u0026amp; Brynjolfsson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlgorithmic bias also presents a notable challenge in the application of ML. Biases embedded in training data can result in discriminatory outcomes, where specific consumer groups are unfairly prioritized or excluded in recommendations or pricing strategies. For example, platforms using historical purchasing data may unintentionally replicate societal inequities, leading to biased results (Binns, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Coupled with the opacity of many ML models, which often function as \"black boxes,\" these biases can damage consumer trust. Addressing these issues requires the development of explainable AI (XAI) systems that make algorithmic decisions more transparent and accountable, fostering greater consumer acceptance and regulatory compliance (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother ethical concern revolves around the risks of over-personalization. While personalization is a cornerstone of ML-driven e-commerce, excessive tailoring of recommendations can stifle consumer autonomy and discovery. Platforms that rely heavily on ML to predict consumer preferences risk creating a \"filter bubble,\" where users are only exposed to options that align with past behaviors (Pariser, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This not only limits the diversity of consumer experiences but also diminishes the excitement of serendipitous discoveries\u0026mdash;an essential element for niche platforms like Etsy, which prioritize organic and curated shopping journeys (Lusch \u0026amp; Nambisan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, over-personalization can border on manipulation, with platforms subtly influencing purchasing decisions in ways that may feel intrusive or exploitative (Voinea, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBalancing personalization with consumer privacy adds another layer of complexity. While personalization requires detailed consumer data, overly invasive practices, such as aggressive retargeting ads, can alienate users and create a perception of constant surveillance. To address these concerns, platforms are exploring privacy-preserving ML techniques, such as federated learning, which enables models to learn from decentralized data without compromising individual privacy (McMahan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These approaches offer a promising avenue for maintaining the benefits of personalization while respecting consumer boundaries.\u003c/p\u003e \u003cp\u003eOperational challenges further complicate the deployment of ML systems in e-commerce. The computational demands of training and running ML models require significant energy resources, raising concerns about environmental sustainability (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As platforms grow and handle increasing data volumes, scalability becomes another pressing issue, necessitating regular updates and retraining of algorithms to stay effective in a rapidly evolving marketplace (Sharma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo navigate these challenges, e-commerce businesses must adopt a holistic approach to ML implementation that prioritizes ethical considerations alongside technical efficiency. This includes transparent communication with consumers about data usage, addressing algorithmic biases, and designing systems that align with privacy regulations and societal expectations (Binns, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By fostering trust and accountability, platforms can harness ML's potential while mitigating its risks, ensuring sustainable and equitable practices that benefit both businesses and consumers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Research Gaps and Opportunities\u003c/h2\u003e \u003cp\u003eDespite the growing interest in ML within e-commerce, significant gaps remain in understanding its differential impact across diverse platform types. Much of the existing research focuses on the successes of ML in general terms, yet there is limited comparative analysis examining how ML strategies vary between large and niche e-commerce platforms. While studies have shown the effectiveness of ML in broad-market platforms like Amazon (Aryafar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), there is less empirical evidence on ML\u0026rsquo;s nuanced effects on platforms with a focused or niche audience, such as Etsy. Such cross-platform studies would enrich our understanding of how ML applications can be tailored to suit different market segments and consumer behaviors, addressing the unique needs of niche and specialized markets.\u003c/p\u003e \u003cp\u003eAdditionally, there is a need for longitudinal studies to evaluate ML\u0026rsquo;s impact on consumer behavior over time, particularly as platforms continue to evolve and consumers grow more accustomed to personalized interactions. Understanding ML\u0026rsquo;s role in fostering long-term customer loyalty, rather than focusing solely on immediate conversion metrics, could provide valuable insights for e-commerce strategists. Moreover, further research is needed on the ethical implications of ML in e-commerce, particularly around consumer trust, data transparency, and the mitigation of algorithmic biases. Exploring these areas would advance the field, providing a foundation for more refined and ethically responsible ML practices in e-commerce.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Hypothesis Development","content":"\u003cp\u003e \u003cem\u003eConversion Rate Increases with ML Adoption\u003c/em\u003e \u003c/p\u003e \u003cp\u003eConversion rate\u0026mdash;defined as the percentage of site visitors who make a purchase\u0026mdash;is a critical success metric for e-commerce platforms. Before implementing machine learning, conversion rates on these platforms typically rely on standard recommendation engines, which offer generalized product suggestions based on broader purchasing trends. However, studies suggest that ML algorithms enhance personalization by analyzing real-time customer data, such as browsing history, click behavior, and past purchases, to predict individual preferences with greater precision (Caroline et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This personalized approach increases the likelihood that consumers find relevant products quickly, improving conversion rates by catering directly to their interests and needs (Schafer et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on ML applications in retail further supports this relationship, demonstrating that ML-powered recommendation systems can drive substantial engagement and, consequently, purchase behavior (Grewal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, Amazon\u0026rsquo;s personalized recommendation engine accounts for 35% of its sales, a testament to ML\u0026rsquo;s power in converting casual browsers into buyers by presenting relevant suggestions. This shift from static to dynamic, personalized recommendations reflects a meaningful improvement in user experience, which is essential for higher conversion rates post-ML adoption (Evdelo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eConversion rates are expected to be significantly higher after the implementation of machine learning.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIncrease in Average Order Value with ML Adoption\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAverage Order Value (AOV)\u0026mdash;the average spent per customer transaction\u0026mdash;is a revenue-driving metric that can benefit significantly from ML implementation. Prior to adopting ML, platforms may offer limited upselling options, often restricted to simple bundling or blanket discounts. Machine learning, however, transforms this approach by dynamically adapting pricing and personalizing upsell opportunities based on real-time insights into customer purchasing behavior. ML algorithms can evaluate factors like item popularity, inventory levels, and individual customer profiles to present tailored upsell offers, which can effectively encourage higher-spending purchases (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDynamic pricing, another ML application, adjusts prices based on demand, competitor pricing, and user activity, leading to an optimized pricing strategy that aligns with consumer willingness to pay. For example, Uber's dynamic pricing model has set a precedent, showing how adjusting prices based on demand can increase transaction value (Banerjee et al., 2015). This personalization of both upsell suggestions and prices aligns with consumer preferences, leading to a higher AOV after ML adoption as customers are incentivized to spend more per transaction (Ban \u0026amp; Bora, 2020). Thus, it is hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eAverage Order Values are expected to be significantly higher after the implementation of machine learning.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDifferential Impact of ML on Consumer Behavior by Platform\u003c/em\u003e \u003c/p\u003e \u003cp\u003eE-commerce platforms like Amazon, Alibaba, and Etsy differ significantly in their consumer demographics, product diversity, and market positioning. Consequently, ML adoption is expected to have varied impacts on consumer behavior across these platforms. Amazon, for instance, serves a broad consumer base with a vast inventory and utilizes ML for extensive personalization and dynamic pricing. This broad reach and large dataset allow Amazon to maximize ML-driven insights to cater to a diverse audience, likely leading to substantial changes in conversion rates and AOV (McAfee \u0026amp; Brynjolfsson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, Alibaba operates heavily within the Asian market and leverages ML primarily for user engagement and social commerce features. Studies show that Alibaba\u0026rsquo;s emphasis on gamification and interactive shopping experiences can lead to unique consumer behaviors, such as increased brand loyalty and frequency of visits, which may influence conversion but in ways distinct from Amazon\u0026rsquo;s approach (Attar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEtsy, with its niche artisanal market, might experience a more nuanced impact of ML adoption. As Etsy\u0026rsquo;s shoppers are often drawn to unique, hand-crafted goods, over-personalization might not resonate as strongly. Research on niche markets suggests that highly personalized, non-algorithmic suggestions may better maintain the platform\u0026rsquo;s artisanal appeal (Lusch \u0026amp; Nambisan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, the effectiveness of ML on consumer metrics will likely vary according to each platform\u0026rsquo;s user expectations and positioning, resulting in distinct levels of change in conversion and AOV. Thus, the final group of hypotheses are:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003ePost-ML adoption conversion rates differ significantly across platforms due to platform-specific market structures and consumer access mechanisms.\u003c/p\u003e \u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThis research aims to explore the impact of machine learning (ML) on consumer behavior and market dynamics within e-commerce platforms, specifically Amazon, Alibaba, and Etsy. These platforms have long incorporated ML technologies, such as product recommendations, dynamic pricing, and logistical optimization, to enhance consumer experiences. The study focuses on two core hypotheses: the first examines how consumer behavior has evolved due to the adoption of ML, while the second investigates the subsequent effects on market dynamics and consumer engagement.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Sources\u003c/h2\u003e \u003cp\u003eThis study uses secondary data obtained from publicly available annual and interim financial reports published by Amazon, Alibaba, and Etsy. Specifically, the analysis draws on Form 10-K filings, annual reports, and investor disclosures covering the period from 2020 to 2023. These documents provide consistent reporting of key performance indicators, including conversion rates, Average Order Value (AOV), and related engagement metrics, enabling cross-platform comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variables and Measures\u003c/h2\u003e \u003cp\u003eConversion rate is defined as the proportion of platform visits that result in a completed purchase within a given reporting period. Average Order Value (AOV) is defined as the average monetary value of completed transactions during the same period. Machine learning adoption is operationalized as a binary indicator distinguishing periods before (2020\u0026ndash;2021) and after (2022\u0026ndash;2023) the widespread deployment of ML-driven personalization, pricing, and recommendation systems. Control variables include reported marketing expenditure and seasonality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Machine Learning Adoption Timeline and Analytical Strategy\u003c/h2\u003e \u003cp\u003eDescriptive statistics are used to find broader trends, such as changes in conversion rates or AOV. We will use statistical tests such as t-tests, F-tests, and ANOVA to evaluate trends in key metrics from times of high ML adoption to earlier times. These tools help measure whether the implementation of ML has had a real impact on consumer behavior and platform performance. The F-test provides depth to the calculation by analyzing variance distributions before and after ML use.\u003c/p\u003e \u003cp\u003eThe formula for the t-test \u0026amp; F-test is as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eT-test formula\u003c/b\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:t=\\frac{(X̄1\\:-\\:X̄2)}{\\surd\\:\\left[\\right(S1\u0026sup2;\\:/\\:n1)\\:+\\:(S2\u0026sup2;\\:/\\:n2\\left)\\right]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere X̄1 and X̄2 are the sample means, and S1\u0026sup2; and S2\u0026sup2; are sample variances.\u003c/p\u003e \u003cp\u003e \u003cb\u003eF-test formula\u003c/b\u003e:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:F=\\frac{Variance\\:of\\:pre-ML\\:group}{Variance\\:of\\:post-ML\\:group}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA significant F-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicates that ML adoption has introduced meaningful variability in the performance metrics, suggesting more dynamic impacts across consumer segments.\u003c/p\u003e \u003cp\u003eAdditionally, regression analysis will be conducted to control for exogenous factors such as marketing expenditures and seasonality. The regression model is specified as:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e​ + β\u003csub\u003e1\u003c/sub\u003e​(ML Adoption) + β\u003csub\u003e2\u003c/sub\u003e​(Marketing Spend) + β\u003csub\u003e3\u003c/sub\u003e​(Seasonality) + ϵ\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis model isolates the effects of ML adoption on key metrics such as AOV, conversion rates, and CLV, providing a precise interpretation of these effects. For instance, a positive effect on conversion rates or AOV would suggest that ML-driven product recommendations are more relevant to consumers. In contrast, negative effects could arise from operational changes that affect consumer trust.\u003c/p\u003e \u003cp\u003eThe second sub-research objective focuses on the implications of ML on market dynamics. This will be assessed using several metrics, including market share, customer segmentation, click-through rates, time spent on the platform, and sentiment analysis drawn from market research and company reports. Natural language processing (NLP) will be employed to analyze sentiment from reports such as Etsy\u0026rsquo;s 2022\u0026ndash;2024 annual reports, using tools like TextBlob to classify consumer attitudes toward the platform and its offerings.\u003c/p\u003e \u003cp\u003eFor customer segmentation, k-means clustering will be applied with the objective function:\u003c/p\u003e \u003cp\u003eMinimize\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{k=1}^{K}\\mid\\:\\mid\\:{x}_{i}-{{\\mu\\:}_{k}\\mid\\:\\mid\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThis technique allows the representation of distinct consumer cohorts based on their response to ML-driven personalization, thereby linking specific segments to different levels of engagement and satisfaction.\u003c/p\u003e \u003cp\u003eTo understand the structural effects of ML on competition, a difference-in-differences (DID) approach will be used to isolate the changes in market structure attributable to the adoption of ML. The DID model is specified as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:Y=({Y}_{post\\:treated}-{Y}_{pre\\:\\:treated})-{(Y}_{post\\:control}-{Y}_{pre\\:control})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis method quantifies the effects of ML on market share, customer engagement, and other metrics, translating the impacts of ML adoption into observable changes in market behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness and Replicability\u003c/h2\u003e \u003cp\u003eAll analyses are conducted using standard econometric techniques commonly applied in digital platform and information systems research. The use of publicly available data ensures that the study can be independently replicated. Model specifications were checked for consistency across platforms to confirm the robustness of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study relies exclusively on secondary data obtained from publicly available corporate disclosures. It does not involve human participants, personal data, or any form of human subject experimentation. As such, no ethical approval was required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Data Availability Statement\u003c/h2\u003e \u003cp\u003eThe data supporting the findings of this study are publicly available through annual reports, financial disclosures, and investor communications issued by Amazon, Alibaba, and Etsy.\u003c/p\u003e \u003cp\u003eBy comparing the performance indicators of Amazon, Alibaba, and Etsy during periods of accelerated growth and adoption of ML, this study will reveal how machine learning technologies have driven improvements in conversion rates, AOV, CLV, and market share. These findings will support the hypothesis that ML has been a pivotal factor in the platforms' early success and future growth and that its applications can potentially be scaled to other segments of e-commerce.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cp\u003eWhen studying consumer purchasing trends, key metrics such as conversion rates, Average Order Value (AOV), retention rates, purchase frequency, and Customer Lifetime Value (CLV) are crucial. The data presented in this analysis was sourced from the annual and interim financial reports of Amazon (2020\u0026ndash;2023), Alibaba (2020\u0026ndash;2023), and Etsy (2020\u0026ndash;2024).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDescriptive Statistics\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDescriptive statistics provide a useful snapshot of each platform\u0026rsquo;s performance, highlighting important trends. Between 2020 and 2023, Amazon demonstrated strong performance, maintaining a substantial market share and user growth. Alibaba, while also expanding rapidly, faced regulatory challenges in its home country. Etsy capitalized on niche markets, benefiting from an increased demand for handmade and vintage goods. The following table summarizes key metrics for each platform:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmazon Conversion Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmazon AOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlibaba Conversion Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlibaba AOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEtsy Conversion Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEtsy AOV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eT-tests for Pre- and Post-ML Adoption\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo assess the impact of machine learning (ML) adoption, t-tests were conducted comparing the means of conversion rates and AOV for the pre-ML period (2020\u0026ndash;2021) and post-ML period (2022\u0026ndash;2023). The results of these tests, shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal statistically significant improvements across all platforms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eT-test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-14.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe negative t-statistics indicate that the conversion rates and AOV were significantly lower before the adoption of ML, and the improvements in both metrics post-ML adoption are highly significant (p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results for each platform suggest that ML-driven initiatives, such as personalized recommendations and dynamic pricing, played a critical role in enhancing performance, thus supporting \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u0026amp; \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe second hypothesis proposed that ML adoption would result in an increase in AOV across all platforms. The results from the data analysis fully support \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. The t-statistic for Amazon\u0026rsquo;s conversion rate (-14.23) demonstrates a significant increase in post-ML adoption. The platform's use of ML for product recommendations, customer segmentation, and personalized advertising contributed to the increase in conversions by delivering more relevant products to customers, thus speeding up the purchasing process. Similarly, Amazon\u0026rsquo;s AOV showed a significant increase (-11.98) post-ML, driven by dynamic pricing and personalized upselling strategies that led to higher customer engagement and increased revenue per purchase.\u003c/p\u003e \u003cp\u003eAlibaba\u0026rsquo;s conversion rate and AOV both experienced significant improvements following ML adoption. The t-statistics for conversion rate (-10.75) and AOV (-13.88) confirm these results. Alibaba\u0026rsquo;s ML-powered product recommendations, regional product suggestions, and marketing campaigns helped to personalize the shopping experience, boosting conversions and increasing transaction values.\u003c/p\u003e \u003cp\u003eFor Etsy, the t-statistics for conversion rate (-13.52) and AOV (-13.14) also indicate statistically significant improvements post-ML adoption. Etsy leveraged ML for personalized product recommendations, particularly in its niche markets of handmade and vintage goods. This increased customer engagement and, in turn, pushed customers to purchase more expensive items, boosting AOV.\u003c/p\u003e \u003cp\u003e \u003cem\u003eF-tests for Post-ML Adoption\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo evaluate \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, an ANOVA F-test and Tukey's Honest Significant Difference (HSD) test were used. It was a set of monthly Amazon, Alibaba, and Etsy conversion rates after the period of implementation of machine learning (2022\u0026ndash;2023).\u003c/p\u003e \u003cp\u003eA one-way ANOVA analysis revealed significant differences in conversion between all three platforms (F-statistic of 120.75 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Since the p-value is well below the significance level (=\u0026thinsp;0.05), the null hypothesis of equal conversion rates between platforms was discarded. This finding also confirms that the platforms have statistically different conversion rates.\u003c/p\u003e \u003cp\u003eTo see which platform pairings showed a significant difference, we performed Tukey\u0026rsquo;s HSD test. The findings are compiled in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eF-test results (Pairwise Comparisons)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average conversion rate of Amazon was much higher than Alibaba\u0026rsquo;s (mean difference 0.0288, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). That outcome means that in the test period, Amazon\u0026rsquo;s use of ML and overall e-commerce approach was more successful at converting visitors into buyers than Alibaba\u0026rsquo;s. The confidence interval of the mean difference (0.0222\u0026ndash;0.0354) confirms the validity of this difference. It might be that Amazon\u0026rsquo;s superior performance is a combination of its powerful recommendation system, product range, and global reach.\u003c/p\u003e \u003cp\u003eIn the Amazon vs. Etsy analysis, the average difference in conversion rate was largest (-0.0419), with Amazon winning (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The confidence range (-0.0485 to -0.0353) shows an unchanging, large difference. This huge disparity makes Amazon a better bet for data-driven personalization and efficiencies. Etsy, on the other hand, which is aimed at a very specific audience of handmade, exclusive goods, might prefer conversion rate over engagement and customer experience.\u003c/p\u003e \u003cp\u003eDespite being somewhat comparable, Etsy\u0026rsquo;s conversion rate was even higher than Alibaba\u0026rsquo;s (mean difference \u0026minus;\u0026thinsp;0.0131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in terms of the size of the conversions). As the confidence interval (-0.0197\u0026ndash;0.0065) indicates, this variance is smaller than Amazon\u0026rsquo;s but still significant. Alibaba\u0026rsquo;s focus on wholesale markets and international trade probably means that the conversion can be more accurate than with Etsy\u0026rsquo;s niche, smaller-scale e-commerce platform.\u003c/p\u003e \u003cp\u003eThese results strongly suggest that when a platform implements ML tactics, the conversion rate on that platform is indicative of the audience characteristics and product approach. The largest average conversion rate is Amazon, which has the advantage of its dominance in the market and probably better personalization algorithms. Alibaba\u0026rsquo;s conversion rate is slightly lower than Amazon\u0026rsquo;s, but it is higher than Etsy\u0026rsquo;s (which has a specialized segment of small-batch products with fewer buyers).\u003c/p\u003e \u003cp\u003eThese large disparities make the importance of the relative performance of ML-based solutions for different platform objectives clear. Amazon\u0026rsquo;s numbers are probably a reflection of its global product portfolio and scale, while Alibaba\u0026rsquo;s numbers correspond to wholesale and international e-commerce. Etsy\u0026rsquo;s low conversion rate could be a reflection of its own unique segmentation, which favors artisanal and handmade items, which are inherently more targeted to a narrower market.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRegression Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFurther insights into the impact of ML on conversion rates and AOV are provided by the regression analysis shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This analysis accounts for additional factors such as marketing spend and seasonality, offering a deeper understanding of the role of ML compared to traditional marketing efforts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombined Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eML Adoption Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eML Adoption P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing Spend Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarketing Spend P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeasonality Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSeasonality P-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlibaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversion Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe last set of hypotheses posited that the impact of ML adoption on conversion rates and AOV would vary across platforms (Amazon, Alibaba, and Etsy), given their different business models and market positions. The data reveal that ML adoption has a significant effect across all platforms, but the magnitude of the effect varies. Amazon shows the highest increase in both conversion rate and AOV, which aligns with its broad market, data-driven approach to pricing, and personalized recommendations. This is reflected in the higher coefficients for Amazon in both the t-tests and regression analysis. Alibaba, similarly, experiences a significant increase in both metrics, with slightly lower increases than Amazon, reflecting its focus on dynamic pricing and regional product recommendations, which still outperforms traditional marketing spend. Etsy, while a smaller platform, also shows substantial improvements in conversion rates and AOV, with a slightly smaller increase than the other two platforms, which aligns with Etsy\u0026rsquo;s focus on niche, personalized product recommendations.\u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eMachine learning has proven to be a transformative force in the e-commerce sector, especially in driving key metrics like conversion rates and Average Order Value (AOV). The findings of this study confirm that ML adoption has significantly enhanced both of these critical performance indicators across Amazon, Alibaba, and Etsy. Personalized recommendations, dynamic pricing, and automated decision-making systems powered by ML have allowed these platforms to fine-tune customer experiences and maximize revenue opportunities. As the results show, Amazon, Alibaba, and Etsy all experienced substantial increases in conversion rates and AOV, underscoring the potency of ML in improving customer engagement and transaction values.\u003c/p\u003e \u003cp\u003eHowever, the findings also reveal a nuanced understanding of the relationship between ML and traditional marketing strategies. While marketing efforts alone (such as ad spending) were not found to have a statistically significant effect on conversions and AOV, integrating marketing with ML-driven insights shows promise for boosting e-commerce performance even further. Targeted marketing campaigns that complement personalized recommendations and dynamic pricing could lead to even more substantial gains.\u003c/p\u003e \u003cp\u003eIn terms of future research, a key area to explore is how marketing strategies and ML can be integrated to maximize their joint impact. Marketing, when informed by ML data, could optimize offers, discounts, and product recommendations to more precisely target consumers, enhancing the overall shopping experience. Furthermore, as ML technologies evolve and new tools like artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) emerge, exploring how these technologies can be combined with ML to provide a more personalized and immersive shopping experience could be an exciting avenue for future studies.\u003c/p\u003e \u003cp\u003eAnother critical area of investigation concerns the long-term sustainability of ML-powered improvements. Given the rapid pace of technological advancements and evolving customer preferences, it is important to understand how ML systems can adapt over time to continue delivering value. Research into the adaptability of ML systems to new market conditions and consumer demands will be vital to ensuring the ongoing success of ML in e-commerce.\u003c/p\u003e \u003cp\u003eLastly, ethical concerns surrounding ML\u0026rsquo;s use in e-commerce, particularly regarding data privacy and consumer trust, demand immediate attention. As platforms collect and process vast amounts of consumer data to fuel their ML algorithms, ensuring data security and transparency is paramount. Future research should address how platforms can balance the benefits of ML personalization and dynamic pricing with the ethical responsibility to protect user privacy and maintain trust. This study is limited by its reliance on secondary financial data and its focus on large, established platforms. Future research could extend this analysis to smaller or emerging digital marketplaces and incorporate longitudinal consumer-level data\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study highlights the transformative role of machine learning in shaping the future of e-commerce. By leveraging real-time customer data, platforms like Amazon, Alibaba, and Etsy have significantly improved conversion rates and AOV, resulting in increased revenue and customer satisfaction. However, the findings also emphasize the importance of integrating ML with broader marketing strategies to unlock its full potential. As e-commerce continues to evolve, understanding how ML can be sustained, ethically implemented, and combined with new technologies will be essential for platforms to maintain a competitive edge.\u003c/p\u003e \u003cp\u003eThe adoption of machine learning is undeniably a game-changer for the e-commerce industry, and its impact will only grow as technology advances. Future research should focus on how ML can evolve to meet the changing demands of the market, while also addressing the ethical implications of its widespread use in personalized shopping experiences. The continued success of e-commerce platforms depends not only on the innovations brought by ML but also on how these innovations are responsibly integrated into business practices and customer relationships. Overall, the findings underscore the role of machine learning as a structural force shaping access, visibility, and value creation in the contemporary information society.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A.K. conceived the study, developed the research design, conducted the literature review, performed the data analysis, and wrote the main manuscript text.Y.M.S. contributed to the theoretical framing, methodological refinement, interpretation of results, and critical revision of the manuscript.Both authors reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are publicly available through annual reports, financial disclosures, and investor communications issued by Amazon, Alibaba, and Etsy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAryafar, K., Guillory, D., Hong, L.: An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. 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Student Res. \u003cb\u003e8\u003c/b\u003e(1) (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47611/jsr.v8i1.527\u003c/span\u003e\u003cspan address=\"10.47611/jsr.v8i1.527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, E-commerce, Conversion rate, Average Order Value, Digital platforms, Information society","lastPublishedDoi":"10.21203/rs.3.rs-8526225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8526225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study examines how machine learning (ML) adoption reshapes consumer purchasing behavior and value creation across major e-commerce platforms within the broader context of the information society. Specifically, it investigates the impact of ML technologies on conversion rates and Average Order Value (AOV) in digitally mediated marketplaces.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing secondary data from Amazon, Alibaba, and Etsy covering the period 2020\u0026ndash;2023, the study applies descriptive statistics, t-tests, analysis of variance (ANOVA), regression analysis, and difference-in-differences techniques to compare platform performance before and after ML adoption. These methods allow for cross-platform comparison while controlling for marketing expenditure and seasonality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings reveal statistically significant increases in both conversion rates and AOV following ML adoption across all three platforms. However, the magnitude of these effects differs significantly by platform, reflecting variations in market structure, consumer access mechanisms, and platform-specific personalization strategies.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results demonstrate that machine learning functions as a critical infrastructural force shaping consumer access, engagement, and economic outcomes in contemporary digital marketplaces. These findings contribute to understanding how algorithmic systems influence value formation in the information society and raise important implications for platform governance, ethical personalization, and digital inclusion.\u003c/p\u003e","manuscriptTitle":"From Clicks to Conversions: How Machine Learning Is Shaping E-Commerce Performance in the Information Society","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 12:43:06","doi":"10.21203/rs.3.rs-8526225/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f4b749d-7416-4b59-8a88-d0f1544cb829","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T12:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 12:43:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8526225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8526225","identity":"rs-8526225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}