1. Introduction

With the rapid development of e-commerce, online shopping has become an important part of consumers' daily consumption.

In 2023, global e-commerce sales reached $6.3 trillion, accounting for 19 per cent of total global retail sales. Traditional retailers are now facing the challenge of maintaining their offline appeal while also needing to expand their online channels to cope with market changes.

Research has demonstrated that factors influencing consumer decision-making on e-commerce platforms, including price, ratings, promotions, and personalised recommendations, differ significantly from those in traditional retail environments, where factors like in-store experience, promotions, and interpersonal interactions play a key role. However, systematic research on the differences between online and offline consumer decision-making is still relatively limited.

Therefore, this paper aims to explore the differences in factors affecting consumers' purchase decisions in traditional retail settings and e-commerce platforms through case studies, providing a theoretical basis for the optimisation of corporate marketing strategies.

2. Literature Review

The study of consumer behaviour is mainly based on three classical theories.

First, the Theory of Rational Behaviour (TRA), introduced by Fishbein and Ajzen, posits that an individual's behaviour is determined by their attitudes, which refer to the individual's positive or negative evaluation of the outcome of the behaviour, and subjective norms, which refer to the perceptions of the social pressures[1]. Second, the Theory of Planned Behaviour (TPB), based on the TRA theory, adding concept of perceived behavioural control, which suggests that an individual's perception of how easy or difficult a behavior is can also influence their intentions to perform it. Finally, the Technology Acceptance Model (TAM), proposed by Davis, focuses on the field of technology, suggesting that perceived usefulness and ease of use are the key factors in users' acceptance of new technologies[2]. This model is often used to study user behaviour on e-commerce platforms.

With the popularity of e-commerce, consumer behaviour has changed significantly. In offline environments, consumers obtain information through in-store experience and interactions with salespersons, while online consumers rely more on user reviews and product descriptions. In terms of risk perception, online shopping is perceived as riskier due to the lack of physical product interaction, making consumers' trust in the e-commerce platform particularly important. Social influence also differs, offline shopping is shaped by peers and salespeople, while online relies more on user reviews and social media recommendations. In addition, online shopping provides more price comparison tools, making consumers more price-sensitive than in traditional retail environments.

There are five major factors that influences the purchase decisions of e-commerce customers: product price, user evaluation, promotional strategies, website user experience and personalized recommendation. Price remains one of the most important factor for consumers, and with fierce price competition on e-commerce platforms, price comparison becomes easier and more prevalent. User evaluation and electronic word-of-mouth (e-WOM) also has a significant impact on consumer decision-making. Positive reviews and high ratings can significantly enhance a product's credibility and attractiveness. Promotional strategies including discounts, coupons and other promotional tools are highly effective in driving consumer purchasing decisions. A smooth, user-friendly web design and seamless payment process greatly enhance consumer satisfaction. Finally, recommendation system powered by big data can improve consumers' purchase rate by meeting consumers' personalized needs and enhancing the overall shopping experience.

In addition, the rise of e-commerce platforms has significantly changed traditional patterns of consumer behaviour. E-commerce platforms such as Amazon, Alibaba and eBay have created a digital marketplace that is open 24/7, offering unprecedented shopping convenience and product diversity [3]. The shift from brick-and-mortar storefronts to online platforms has eliminated geographic barriers and enabled consumers to shop from global suppliers anytime, anywhere, changing the spatio-temporal nature of shopping behaviour [4].

One of the important changes is that the ease of access to information empowers consumers. E-commerce platforms provide detailed product information, user reviews, and price comparisons, enabling consumers to make more informed purchasing decisions [5]. This contrasts with the asymmetry of information in traditional retailing, where the transparency of online platforms raises consumer expectations about product quality, service and value [6]. Furthermore, personalisation and customisation have become core features of e-commerce platforms. Through data analytics and artificial intelligence, these platforms can analyse consumer behaviour and provide personalised recommendations and promotions [7]. This level of personalisation, which is difficult to achieve in traditional retail environments, increases consumer engagement and loyalty [8]. There has also been a shift in social interaction. Whereas traditional consumer behaviour relied on face-to-face interactions and word-of-mouth recommendations, e-commerce platforms have fostered the development of online communities and social networks in which consumers can share shopping experiences and influence each other's purchasing decisions [9]. User-generated content, such as reviews and ratings, has become a key factor influencing consumer trust and purchase intentions [10]. Moreover, the rise of m-commerce and the integration of e-commerce platforms with mobile devices have made shopping more convenient and readily available [11]. Consumers can now shop anytime, anywhere, which has led to a change in shopping patterns and an increase in impulse buying [12].

In summary, the development of e-commerce platforms has changed traditional consumer behaviour by enhancing access to information, enabling personalisation, transforming social interactions and improving shopping convenience. These changes have important implications for businesses wishing to understand and adapt to the modern consumer [13].

3. Methodology

3.1. Research Design

This study adopts the case study method as a qualitative research method.

The reasons for choosing this method includes applicability, flexibility and availability of multiple data resource. First, the case study method is suitable for in-depth research on complex and real phenomena, especially when the research questions involve "how" and "why" [14]. Since the study aims to explore the differences in factors affecting consumers’ purchasing decision between traditional consumption and e-commerce platforms, and the case study method can provide in-depth understanding. Second, qualitative research offers flexibility, allowing the researcher to adapt and investigate deeper into potential influencing factors throughout the research process[15]. Third, the case study method permits the use of multiple data sources to enhance the reliability and validity of the findings.

In addition, the study utilizes a multiple case comparison study. By comparing and analyzing several representative business cases, it reveals the key factors that influence consumers' purchasing decisions in different consumer environments and highlights the differences between these environments. To ensure the representativeness of the study and the availability of data, the cases are selected based on the following criteria: First, industry representativeness, meaning that the enterprises chosen have significant influence in both traditional retail and e-commerce platforms. Second, data availability, meaning that the selected enterprises have abundant public information, including annual reports, market reports, and news reports, etc. Third, diversity, meaning that the cases cover different industries and market positioning to enhance the generalizability of the research results. Based on these criteria, Walmart, Nike and Jingdong are selected as research cases.

3.2. Data Collection

This study adopts a mixed research methodology combining quantitative and qualitative approaches, aiming to provide insights into how e-commerce platforms change traditional consumer behaviours and the key factors that influence consumers' purchasing decisions.

The data collection process was carefully designed to ensure the comprehensiveness of the study and the reliability of the data.

A large collection of academic papers, industry reports, company annual reports and news reports related to e-commerce, consumer behaviour and market trends. These sources provide us with in-depth insights into corporate strategies, market dynamics and industry developments. Reports from authoritative market research organisations (e.g. Nielsen, Avery Dennison) and data from third-party analysts are collected. These data include consumer survey reports, market share data, consumption trends, etc., which help us understand consumer behavioural characteristics and market conditions. Through e-commerce platforms (e.g., Taobao, Amazon), social media platforms (e.g., Weibo, Twitter) and official corporate websites, a large amount of user comments and feedback are collected. These data reflect current consumer views and market reactions.

In order to effectively analyse the data collected, key indicators were quantified:

3.2.1. Price competitiveness

Collect the selling prices of target products on different platforms and calculate the unit price of each product to facilitate side-by-side comparison. Record the original price and discounted price of the product and calculate the discount rate with the formula:

\( Discount rate =(1-\frac{Discounted pripe}{Original price})×100\% \)

Statistics on the number and duration of promotional activities over time. Based on the above indicators, a standardised methodology was used to normalise the price competition forces into a score of 0-10.

3.2.2. User Comments

Sentiment analysis of a large number of user comments using Natural Language Processing (NLP) techniques. Using Python's NLTK library, the comment text is processed with word splitting, deactivation and word shape reduction. Sentiment scores (positive=1, negative=-1, neutral=0) are assigned to each comment using sentiment lexicon methods or machine learning algorithms (e.g., plain Bayesian classifiers). The average sentiment score of all comments is calculated to reflect the overall user sentiment tendency. Use Latent Dirichlet Allocation (LDA) topic model to identify frequently mentioned topics in comments and quantify the main factors that users focus on.

3.2.3. Brand image and reputation and shopping experience

Questionnaire: A questionnaire containing a five-point Likert scale was designed to collect consumer evaluations of brand awareness, trust and satisfaction.

Score quantification: Convert respondents' evaluations into numerical values (1=strongly disagree, 5=strongly agree) and calculate the average score for each indicator. User experience indicators: collect objective data such as website loading speed, page response time, and number of steps in the payment process. User evaluation: collect user evaluation on website ease of use, navigation convenience, customer service quality, etc., and quantify it as user experience score.

In addition, to improve the reliability and validity of the data, we used data triangulation validation: multi-source data cross-validation: data from different sources were compared and validated to ensure data consistency and accuracy. Data reliability test: for data from questionnaires and scales, Cronbach's alpha coefficient was used to test their internal consistency and ensure the reliability of the measurements.

3.3. Analysis

A comparison matrix is constructed to systematically compare the purchase decision factors between traditional retail environments and those on the e-commerce platforms.

The comparison dimensions include: price strategy (e.g. pricing, discounts, promotions, etc), product diversity and quality, brand image and reputation, user reviews and word-of-mouth, and the overall shopping experience (e.g. service quality, convenience, environment, etc).

Based on consumer behavior theories (e.g., Theory of Rational Behavior, Theory of Planned Behavior, and Technology Acceptance Model), the data are coded and categorized to identify key themes that influence consumer purchasing decisions [16].

Using the results of the case study, SWOT analysis is used to assess the influence of different factors on consumer purchase decisions. This analysis also explores their applicability and effectiveness of these factors in various consumer environments.

(1)Strengths

Price advantage: e-commerce platforms are usually able to offer more competitive prices due to lower operating costs. Commodity richness: the platform has a wide range of commodities, which can meet the diversified needs of consumers. Convenience: Consumers can shop anywhere, anytime and enjoy home delivery services.

(2)Weaknesses

Lack of physical experience: Consumers are unable to directly touch and try the products, which may affect the confidence of purchase. Trust issues: Consumers' trust in the platform may be reduced due to the problem of fake products and uneven quality.

(3)Opportunities

Technological innovation: the development of big data and artificial intelligence offers the possibility of personalised recommendations and refined operations. Market growth: the e-commerce market continues to expand, and consumers' online shopping habits are gradually forming.

(4)Threats

Fierce Competition: There are many competitors in the market, and new entrants continue to emerge, intensifying market competition. Policy Risks: Increased government regulation of online transactions and data privacy may increase compliance costs.

Qualitative analysis software, such as NVivo, is used to assist in the coding and thematic extraction of the data, enhancing the efficiency and systematic nature of the analysis.

3.4. Research Steps

For each case enterprise, detailed descriptions of both traditional retail and e-commerce operations are provided.

These descriptions cover operation mode (business model and channel structure), market positioning (target market and competitive strategy), marketing strategy (brand promotion and promotional activities), and consumer group characteristics (behavior and preferences of major consumer groups).

Based on the literature review and case data, the main factors affecting consumers' purchasing decisions are identified, including price (pricing strategy and price sensitivity), evaluation (user reviews and rating system), promotion (discounts, coupons, and membership system), user experience (shopping environment, service quality and website ease of use), and technology application (personalized recommendation, big data analysis and mobile application).

The factors identified are compared across different cases to analyze their significance in traditional retail versus e-commerce environments. The analysis results are then correlated with established consumer behavior theories such as the Theory of Rational Behavior, the Theory of Planned Behavior, and the Technology Acceptance Model. This correlation verifies the applicability of the theories and to explore possible extensions or modifications.

The case study methodology provides an in-depth exploration of complex phenomena and provides a wealth of background information. The use of multiple case comparisons enables a broader understanding of the research topic, increasing the reliability of research findings. Additionally, the qualitative research method is flexible and suitable for exploratory research.

However, there are some limitations. Due to the small sample size, the findings may not be widely generalizable. Furthermore, qualitative analysis may be subject to the researcher’s subjective interpretation.

To address these limitations, data triangulation is employed to reduce bias by cross-verifying multiple data sources. A transparent research process is maintained by documenting research steps in detail to improve reproducibility. Finally, the use of theoretical frameworks enhances the academic rigor of the study.

4. Results

4.1. Overview of Data Analysis

To deeply explore the differences in factors affecting consumers' purchasing decisions in the traditional consumption context and on e-commerce platforms, this paper selects three representative enterprises, Walmart, Nike and Jingdong (JD.com) as case studies. By quantitatively analysing the key influencing factors of these companies on traditional retail and e-commerce platforms, we can reveal consumers' decision-making preferences in different shopping environments.

4.2. Data collection and processing

We quantified five key factors for each company—price competitiveness, user reviews, in-store experience, personalized recommendations, and logistics speed—using a scale of 1 to 10, where higher scores indicate greater importance in consumers' buying decisions. The data were then categorised by shopping type (traditional retail and e-commerce platforms), and the average score for each factor in both environments was calculated.

4.3. Results of analysis

4.3.1. Comparison of Average Scores of Key Factors

The following table presents the average scores of the influencing factors in traditional retail and e-commerce platform shopping environments:

Table 1: Comparison of scores for traditional retail and e-commerce platform impact factors.

4.3.2. Visualization

To further illustrate the differences in the importance of each factor in different shopping environments, the following bar chart was created:

Figure 1: Comparison of the importance of influencing factors between traditional retail and e-commerce platforms. (Note: Figure 1 illustrates the comparative scores of the key factors across traditional retail and e-commerce platforms, highlighting the disparities in consumer priorities.)

4.3.3. Key Findings

Price Competitiveness and Logistics Speed: These factors scored significantly higher in the e-commerce context (both at 8.67) compared to traditional retail (Price Competitiveness: 6.00; Logistics Speed: 6.67). User Reviews and Personalized Recommendations: These factors also have higher importance on e-commerce platforms (User Reviews: 8.00; Personalized Recommendations: 8.33) than in traditional retail (User Reviews: 3.67; Personalized Recommendations: 4.00). In-Store Experience: This factor scores highest in traditional retail (8.33) versus e-commerce platforms (5.00), indicating its crucial role in the physical shopping environment.

4.4. Analysis of Results

4.4.1. Price Competitiveness and Logistics Speed

The prominence of price competitiveness and logistics speed in e-commerce highlights the modern consumer's expectation for value and convenience in online shopping.

(1) Importance of Price Competitiveness

E-commerce platforms often provide lower prices due to reduced operational costs and economies of scale [3]. Consumers can effortlessly compare prices across multiple sellers, seeking the best deals. This aligns with the Theory of Rational Behavior (TRA), where individuals aim to maximize utility by obtaining the best possible value [17]. The higher score indicates that online shoppers are more price-sensitive, leveraging the transparency and competitiveness of e-commerce pricing.

(2) Significance of Logistics Speed

Logistics speed scores equally high, reflecting consumers' desire for fast and reliable delivery services. In the digital age, immediate gratification has become a normative expectation [12]. The ability to receive products quickly enhances customer satisfaction and influences purchase decisions significantly.

According to the Theory of Planned Behavior (TPB), perceived behavioral control affects intentions and behaviors [1]. Efficient logistics increase consumers' perception of control over the purchase process, reducing uncertainty about delivery times and enhancing the likelihood of transaction completion.

(3) Consumer Expectations

The high importance of logistics speed indicates that today's consumers expect prompt delivery as a standard service feature. With advancements in logistics technology and supply chain management, same-day or next-day deliveries have become feasible, raising consumer expectations [11]. This shift underscores the need for e-commerce platforms to continuously invest in logistics capabilities to meet and exceed customer expectations.

4.4.2. User Reviews and Personalized Recommendations

The elevated importance of user reviews and personalized recommendations on e-commerce platforms reflects consumers' reliance on digital information sources to compensate for the lack of physical product interaction.

(1) Role of User Reviews

User reviews scored 8.00 on e-commerce platforms, significantly higher than in traditional retail. In online shopping, consumers cannot physically examine products, leading them to depend on the experiences of other buyers to assess product quality and performance [10]. This reliance aligns with the Subjective Norms component of the TPB, where social influences affect individual intentions. User reviews provide social proof, reducing perceived risk and building trust in both the product and the platform [5]. Positive reviews can enhance attitudes towards a product, while negative reviews may deter purchases.

(2) Impact of Personalized Recommendations

Personalized recommendations scored 8.33 on e-commerce platforms, demonstrating their effectiveness in influencing consumer behavior. Utilizing algorithms and big data analytics, e-commerce platforms offer tailored product suggestions, enhancing the shopping experience [7]. This practice relates to the Technology Acceptance Model (TAM), where perceived usefulness and ease of use determine user acceptance of technology [2]. Personalized recommendations increase perceived usefulness by simplifying product discovery and aligning offerings with individual preferences. This customization increases customer engagement and loyalty [8].

(3) Mitigating Information Asymmetry

User reviews and personalized recommendations help mitigate the information asymmetry inherent in online shopping. They enhance perceived behavioral control by providing consumers with additional information and options, thereby facilitating more informed and confident purchase decisions [6].

4.4.3. The Unique Value of In-Store Experience

In-store experience remains highly significant in traditional retail, with a score of 8.33, emphasizing the continued importance of physical interaction in consumer purchasing decisions.

(1) Importance of Sensory Interaction

Physical stores offer tangible benefits such as the ability to touch, feel, and try products before purchasing. This sensory interaction reduces uncertainty and perceived risk, crucial elements in the TRA where attitudes towards behavior influence intentions.

(2) Interpersonal Engagement

Interactions with knowledgeable sales staff enhance the shopping experience by providing personalized assistance and building rapport [4]. The TPB suggests that perceived behavioral control and subjective norms influence consumer behavior. In-store assistance increases perceived control by offering immediate answers to questions, while social interactions with staff can positively impact subjective norms.

(3) Preference for Certain Product Categories

Certain products, such as apparel or luxury goods, benefit significantly from in-store experiences. Consumers often prefer to assess fit, quality, and aesthetic appeal firsthand before making a purchase decision [13]. This preference underscores the irreplaceable value of brick-and-mortar stores in providing comprehensive product evaluations.

4.5. Strategic Implications and Recommendations

4.5.1. E-commerce Platforms

(1) Enhance Price Competitiveness and Logistics

Dynamic Pricing Strategies: Utilize real-time data to adjust prices competitively. Supply Chain Optimization: Invest in logistics infrastructure to improve delivery speed and reliability, meeting consumers' expectations for prompt service.

(2) Strengthen User Reviews and Personalization

Authenticity Assurance: Implement measures to ensure the authenticity of user reviews, enhancing trust. Advanced Personalization: Leverage artificial intelligence to refine recommendation algorithms, increasing relevance and customer satisfaction.

4.5.2. Traditional Retailers

(1) Focus on Experiential Marketing

Enhanced In-Store Experience: Create engaging, interactive store environments that encourage sensory engagement and exploration. Personalized Service: Train staff to provide exceptional customer service, offering personalized assistance that cannot be replicated online.

(2) Integrate Digital Technologies

Omnichannel Strategies: Blend online and offline experiences through mobile apps, in-store tablets, and digital kiosks. Augmented Reality (AR) and Virtual Reality (VR): Incorporate AR/VR technologies to enrich product demonstrations and customer engagement.

4.6. Conclusion

The analysis highlights the shifting priorities of consumers in different shopping environments. E-commerce platforms are favored for their price competitiveness, efficient logistics, and personalized shopping experiences facilitated by technology. In contrast, traditional retail excels in providing immersive in-store experiences through direct product interaction and personalized customer service.

Understanding these dynamics through the lens of consumer behavior theories allows businesses to tailor their strategies effectively. E-commerce platforms should continue to innovate in logistics and personalization, while traditional retailers should emphasize experiential marketing to leverage their unique strengths.

5. Discussion

Although this study provides valuable insights, there are several limitations.

First, the sample scope is limited, as only three companies were selected for case studies, which may not be fully representative of all industries and markets. Second, the scoring of influencing factors is based on subjective judgment, which introduces potential bias and lacks large-scale quantitative data support. Third, the market environment and consumer behavior are constantly evolving, meaning that the research results may lose some of their applicability over time.

To address these limitations, future research can pursue the following approaches: First, expand the scope of the study to include enterprises from more industries and regions, and increase the sample size in order to enhance the generalizability of the research results. Second, adopt quantitative research methods by incorporating large-scale consumer surveys and statistical analyses to obtain more objective and precise data. Third, delve into the impact of emerging factors such as mobile shopping, social media influence and sustainable consumption on consumers' purchasing decisions. Finally, conduct longitudinal research to observe the trend of consumer behavior over time and capture market dynamics through long-term tracking studies.

To optimize strategies across different channels, enterprises should focus on several key areas. For e-commerce platforms, businesses should enhance price competitiveness, optimize logistics systems, and accelerate delivery times. Additionally, they should improve user review mechanisms to ensure the authenticity and reliability of feedback, while using big data and artificial intelligence to enhance the accuracy of personalized recommendations.

For traditional retail, the in-store experience should be strengthened by improving service quality and enhancing the shopping environment. Sales staff training should focus on better customer interactions, and technological elements such as smart fitting mirrors and augmented reality (AR) experiences should be introduced to make shopping more engaging.

Moreover, omni-channel integration is crucial, and companies should work to seamlessly combine online and offline channels. Services like online ordering with in-store pickup or the ability for consumers to experience products in-store before making an online purchase can cater to diverse consumer needs.

Finally, businesses must monitor changes in consumer demand by conducting regular market research to stay informed about shifts in consumer preferences. This allows companies to adjust their marketing strategies and product positioning accordingly, ensuring they remain aligned with evolving consumer expectations.

6. Conclusion

This study highlights that on e-commerce platforms, price competitiveness and logistics speed are the most influential factors in consumer purchase decisions.

Consumers are able to easily compare prices and have a strong demand for fast delivery when shopping online. Additionally, user reviews and personalized recommendations on e-commerce platforms have a significant impact on consumer decisions. As consumers cannot physically experience products on the ground, consumers rely more on feedback from others and personalized product recommendations. In contrast, the in-store experience remains a key factor. Consumers value the opportunity to try out products, interact with sales associates, and enjoy the sensory experience of the shopping environment. The primary distinction between these factors in the two environments is that e-commerce platforms are more focused on digital interactions and efficiency, while traditional retail emphasizes in-person experiences and interpersonal relationships. This fundamental difference directly affects consumers' buying behavior and decision-making process.

This study contributes to the field by comparing consumer decision-making factors in traditional and e-commerce environments. It expands the applicability of consumer behavior theories in different shopping environments, providing new perspectives for academic research. Based on specific cases and data analysis, the study empirically verifies the differences in consumer behavior in different shopping environments, providing reliable data support for subsequent research. Additionally, it provides valuable references for enterprises to formulate marketing strategies and optimize channel layout, helping them to stay competitive in the fierce market competition.

Looking ahead, technological advancements such as artificial intelligence (AI) in e-commerce and augmented reality (AR) in traditional retail are poised to further influence consumer decision-making. AI can enhance personalized recommendations and customer service on e-commerce platforms by analyzing consumer behavior patterns more precisely, leading to even more tailored shopping experiences that can significantly impact purchasing decisions. On the other hand, AR technology allows traditional retailers to offer virtual try-ons, immersive product demonstrations, and interactive in-store experiences. By integrating digital elements into the physical shopping environment, AR can enrich the in-store experience, reduce the uncertainty associated with purchases, and positively influence consumer decisions. These innovations are likely to blur the boundaries between online and offline shopping, creating hybrid experiences that cater to evolving consumer expectations.

In summary, as technology continues to evolve, both e-commerce platforms and traditional retailers must adapt to changing consumer behaviors and preferences. Embracing innovations like AI and AR will be crucial for businesses aiming to enhance customer engagement and influence purchase decisions in an increasingly competitive marketplace.