From Technology to Traffic: How Website Technological Sophistication, Brand Recognition, and Business Model Innovation Drive Consumer Traffic in Korean E-Commerce

Abstract

As e-commerce continues to expand, understanding the factors that drive consumer traffic to business-to-consumer (B2C) websites is crucial. This study investigates the interplay between website technological sophistication, brand recognition, and business model innovation in influencing website traffic among Korean B2C companies. Drawing on data from 9003 companies across seven key sectors—finance, retail, healthcare, technology, food, education, and media—we employ Ordinary Least Squares (OLS) regression analysis to test our hypotheses. Our findings reveal that website technological sophistication is positively associated with monthly website visits. This relationship is particularly pronounced for companies with innovative business models, highlighting the synergistic effect of advanced website features and novel business strategies in attracting consumers. Conversely, the positive impact of website technological sophistication on traffic is less significant for well-established brands with high recognition levels, indicating that strong brand equity can mitigate the need for highly sophisticated websites. These results align with the Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and Signaling Theory (ST), providing a nuanced understanding of how technology, branding, and innovation intersect to drive online consumer behavior. Our study offers valuable insights for e-commerce firms seeking to optimize their digital presence and underscores the importance of investing in advanced website functionalities, particularly for lesser-known brands and companies with innovative business models. Future research should explore these dynamics in different cultural and industry contexts to enhance the generalizability of our findings.

1. Introduction

The exponential growth of e-commerce has transformed company websites into indispensable touchpoints for consumer engagement in the dynamic digital marketplace. As sophisticated technologies continue to evolve, these online platforms have become highly interactive environments that facilitate seamless transactions and shape the overall customer journey. In this intensely competitive online landscape, understanding the key drivers of effective website design and their impact on consumer traffic has become critical for e-commerce success [1,2].

Extant research has extensively examined various facets of website sophistication and their influence on consumer behavior. Studies have demonstrated that website interactivity, design sophistication, and advanced functionality positively impact performance metrics such as site traffic and sales [3,4]. Website features like personalization engines and multimedia content foster greater site stickiness and lead generation [5]. The underlying technological infrastructure also plays a crucial role, with PHP (Hypertext Preprocessor) frameworks creating dynamic user experiences [6] and responsive design using CSS (Cascading Style Sheets) ensuring cross-device consistency [7]. Security aspects are also critical in building user trust, with protocols like HTTPS (HyperText Transfer Protocol Secure) and SSL (Secure Sockets Layer)/TLS (Transport Layer Security) protecting against data vulnerabilities [8].

Collectively, these studies suggest that technical sophistication enhances both utility and competence perceptions among online consumers, potentially driving website traffic and user engagement. Building on these technological aspects, researchers have recognized the importance of incorporating technological functionality and usability with social and trust elements in e-commerce websites to enhance user engagement. Beldad et al. provided a comprehensive review of online trust antecedents, demonstrating how website elements contribute to building user trust and engagement [9,10].

Despite these valuable insights, a significant gap remains in our understanding of how website technological sophistication operates within the broader context of a company’s overall strategy and market position. Specifically, the potential relationships between technological attributes and other critical features of the company, such as brand recognition and business model innovation, have been largely overlooked. This oversight leaves several crucial questions unanswered:

• How does brand recognition moderate or amplify the effects of website sophistication on consumer behavior? That is, do highly sophisticated websites provide greater benefits to well-established brands, or do they offer a competitive advantage to lesser-known brands?
• What are the potential synergies or conflicts between a company’s innovative business model and its website’s technological features?

Addressing these questions is vital for several reasons. First, it would provide a more holistic understanding of the factors driving e-commerce success, moving beyond isolated examinations of technology or user experience. Second, it could offer valuable insights for businesses seeking to optimize their digital strategies, particularly in balancing investments in technological sophistication with branding efforts and business model innovation. Third, understanding these complex relationships can help explain variations in e-commerce performance across different industries and business models, which is particularly relevant in today’s rapidly evolving digital landscape.

Our study aims to examine the interconnected relationships between website technological sophistication, brand recognition, and business model innovation in driving consumer traffic. To this end, we draw upon a combination of theoretical frameworks, including the Technology Acceptance Model (TAM) [11,12] and the Innovation Diffusion Theory (IDT) [13], to understand how websites’ technological sophistication might attract more consumer traffic in an e-commerce context. Concurrently, we consider research on branding dynamics across digital channels, which highlights how brand equity drives recognition, differentiation, and profitability in e-commerce contexts [14,15,16]. We also incorporate insights from studies on the role of digital channels in facilitating the adoption of novel offerings [17,18] and theoretical perspectives on business model innovation [19,20].

Our hypotheses are derived from these theoretical foundations and existing empirical evidence. We propose that website technological sophistication will be positively related to monthly website visits, based on the TAM and IDT frameworks [11,12,13]. Furthermore, we hypothesize that this relationship will be stronger for companies with innovative business models. Finally, drawing on Signaling Theory (ST) [21] and research on the relationship between technology and branding, we posit that the effect of website sophistication on visits will be weaker for companies with stronger brand recognition.

Our study tests these hypotheses using data on consumer traffic from Korean B2C e-commerce companies, collected from a unique, multi-source dataset. By leveraging a comprehensive dataset of 9003 Korean B2C companies across seven key sectors—finance, retail, healthcare, technology, food, education, and media—we employ Ordinary Least Squares (OLS) regression analysis to test these hypotheses.

Our study adds significant value to the existing research in several ways. First, by integrating multiple theoretical perspectives (TAM, IDT, and ST), it provides a more comprehensive understanding of e-commerce success factors. Specifically, it examines the combined effects of website sophistication, business model innovation, and brand recognition on actual website traffic, moving beyond the perceptual measures or purchase intentions commonly used in previous studies. Second, by using a large-scale, multi-industry dataset from the Korean market, our study offers insights into how these relationships may vary across different business contexts, addressing the limitations of single-industry or small-scale studies prevalent in the literature. Finally, by analyzing the relationships between website sophistication, business model innovation, and brand recognition using real-world data, this study sheds light on traffic optimization strategies that can inform digital investment and branding decisions. The findings provide actionable evidence to help B2C companies enhance consumer engagement, which is closely tied to revenue generation.

2. Theoretical Background and Hypotheses Development

2.1. Technology Adoption and Innovation Diffusion in E-Commerce Consumer Traffic

The rapid growth of e-commerce has elevated website technological sophistication as a critical factor in online consumer behavior. As businesses increasingly compete in the digital marketplace, understanding the impact of website technological sophistication on consumer behavior, specifically website visits, has become paramount. To comprehensively explore this relationship, we draw upon two seminal theories that have been widely applied in technology adoption and e-commerce research: the Technology Acceptance Model (TAM) proposed by Davis (1989) and the Innovation Diffusion Theory (IDT) introduced by Rogers (2003) [11,12,13]. These theories, while distinct in their origins and primary focuses, offer complementary perspectives that together provide a valuable framework for understanding the dynamics of consumer interaction with e-commerce platforms.

The TAM posits that technology adoption is primarily determined by two key factors: perceived usefulness and perceived ease of use [11]. In the context of e-commerce, perceived usefulness refers to the degree to which a consumer believes that using a particular website will enhance their shopping experience or outcomes, while perceived ease of use reflects the perceived effort required to navigate and interact with the website. These constructs are particularly relevant in the online retail environment, where consumers’ perceptions of a website’s utility and usability can significantly influence their willingness to engage with and ultimately transact on the platform. The application of TAM in e-commerce settings has been extensively validated through numerous empirical studies, each contributing to our understanding of how website characteristics influence consumer behavior.

For instance, Gefen et al. (2003) demonstrated that online purchase intentions are influenced by both the assessment of the IT interface (aligning with perceived ease of use) and trust in the e-vendor, which is partly built through perceived usefulness [22]. This study highlighted the interrelationship between technological features and consumer trust, emphasizing the complex and varied nature of e-commerce adoption. Building on this foundation, Ha and Stoel (2009) found that perceived usefulness and ease of use significantly influence consumers’ attitudes towards online shopping, which in turn predict their intentions to purchase from e-commerce websites [23]. Their research underscored the importance of developing websites that not only offer valuable functionalities but also present them in an accessible and user-friendly manner. Extending this line of inquiry, Hernández et al. (2009) showed that TAM constructs are robust predictors of online shopping adoption, particularly among experienced e-commerce users [4]. This finding suggests that as consumers become more familiar with online shopping, their expectations for website functionality and ease of use may evolve, necessitating ongoing technological innovation to maintain and enhance user engagement.

While TAM provides valuable insights into the cognitive processes underlying technology adoption, the IDT offers a complementary perspective by focusing on the characteristics of innovations that influence their rate of adoption. IDT proposes that the adoption of innovations is influenced by five perceived attributes: relative advantage, compatibility, complexity, trialability, and observability. In the e-commerce context, these attributes can be applied to understand how consumers perceive and adopt technologically sophisticated websites, offering a broader lens through which to examine the diffusion of e-commerce technologies among consumer populations.

The application of IDT to e-commerce has yielded significant insights into consumer behavior. Slyke et al. (2004) found that relative advantage, compatibility, and complexity significantly influence consumers’ intentions to use e-commerce websites [24]. This study highlighted the importance of developing e-commerce platforms that offer clear benefits over traditional shopping methods, align with consumers’ existing values and needs, and minimize perceived complexity. Building on this foundation, Chiu et al. (2014) demonstrated that perceived innovation, encompassing elements of relative advantage and compatibility, positively affects consumers’ attitudes and intentions towards online shopping [25]. Their work bridges IDT concepts with consumer behavior outcomes, illustrating how innovative website features can drive adoption and engagement.

The integration of TAM and IDT in e-commerce research has provided a more comprehensive understanding of consumer behavior in digital marketplaces. This synergistic approach allows researchers to examine both the individual-level cognitive processes emphasized by TAM and the innovation-specific characteristics highlighted by IDT. For example, Ganguly et al. (2010) found that website design elements such as navigability, visual appeal, and information quality positively affect consumers’ online trust and purchase intentions. These elements can be interpreted through both TAM (as contributors to ease of use and usefulness) and IDT (as factors influencing relative advantage and complexity) lenses, demonstrating the complementary nature of these theoretical frameworks [26].

Similarly, Kim and Lennon (2013) showed that website aesthetics and layout significantly impact consumers’ emotional and cognitive responses, ultimately shaping their attitudes and behaviors towards e-retailers [27]. This work supports the importance of visual and functional aspects of website design in influencing consumer behavior, relating to both TAM constructs and the complexity and observability attributes of IDT. The study’s findings underscore the need for e-commerce businesses to invest in sophisticated design elements that not only enhance usability but also create positive emotional responses among users.

As e-commerce has evolved to encompass mobile platforms, researchers have adapted these theoretical frameworks to address new technological contexts. Agrebi and Jallais (2015) highlighted the importance of perceived enjoyment and satisfaction in smartphone shopping adoption, particularly among experienced users [28]. This study extends the application of TAM to mobile commerce while demonstrating the compatibility aspect of IDT, illustrating how theoretical models can be adapted to explain consumer behavior across different technological platforms. The increasing prevalence of mobile commerce underscores the need for businesses to ensure that their websites are not only technologically sophisticated but also optimized for mobile devices to capture and retain user engagement across all platforms.

More recent research has begun to explore the cultural dimensions of e-commerce adoption, recognizing that technological sophistication may be perceived differently across diverse consumer populations. Chopdar et al. (2018) revealed that privacy and security risks moderate mobile shopping app adoption differently across cultures, influenced by factors such as a country’s level of technological support and integration [29]. This research underscores the importance of considering cultural factors and security concerns in website technological sophistication, which relates to the compatibility attribute of IDT and the perceived usefulness construct of TAM. These findings suggest that e-commerce businesses operating in global markets must tailor their technological offerings to align with diverse cultural expectations and varying levels of technological infrastructure.

While these studies have established clear links between website characteristics and consumer attitudes or purchase intentions, there remains a significant gap in understanding how these factors translate into actual website traffic. Our study aims to address this gap by examining the direct relationship between website technological sophistication and monthly website visits. This focus on concrete behavioral outcomes (website visits) rather than attitudinal measures or behavioral intentions represents an important step in validating the practical implications of TAM and IDT in the e-commerce context.

Drawing on the theoretical foundations of TAM and IDT, we argue that technologically sophisticated websites enhance both perceived usefulness and perceived ease of use, while also offering relative advantages, compatibility with user needs, and reduced complexity. These factors should collectively contribute to increased user acceptance and, consequently, higher website traffic. Sophisticated features such as personalized recommendations, advanced search capabilities, and interactive product visualizations can enhance perceived usefulness by improving the efficiency and effectiveness of the online shopping process. Simultaneously, intuitive navigation systems, responsive designs, and streamlined checkout processes can increase perceived ease of use, reducing the cognitive effort required to interact with the website. From an IDT perspective, these advanced features represent relative advantages over less sophisticated e-commerce platforms and may be more compatible with the evolving expectations of tech-savvy consumers. Therefore, synthesizing insights from TAM, IDT, and the body of empirical research on e-commerce adoption, we propose the following hypothesis:

Hypothesis 1 (H1). 

Website technological sophistication will be positively associated with monthly website visits.

2.2. Website Technological Sophistication and Business Model Innovation

Building upon the foundation of technology adoption and innovation diffusion in e-commerce consumer behavior, we consider the role of business model innovation in the context of website technological sophistication and its impact on consumer traffic. By considering the moderating effect of business model innovation, we aim to uncover the potential synergies between website technology and novel business approaches in driving e-commerce success.

The concept of business model innovation has gained increasing attention in the e-commerce literature as companies seek to differentiate themselves and create unique value propositions in a highly competitive online marketplace. Amit and Zott [30] identified four key sources of value creation in e-business: efficiency, complementarities, lock-in, and novelty. These sources of value creation are closely tied to the technological capabilities of e-commerce websites. For instance, advanced website functionalities can enable new forms of efficiency by streamlining transactions and providing easy access to information. Complementarities can be achieved through the bundling of related products or services, which can be facilitated by sophisticated recommendation systems and personalization technologies. Lock-in, which refers to the ability of e-commerce firms to create high switching costs and encourage customer loyalty, can be enhanced through the use of customer relationship management (CRM) tools and loyalty programs integrated into the website. Finally, novelty, which represents the introduction of new products, services, or business processes, can be supported by the adoption of cutting-edge technologies such as virtual and augmented reality, chatbots, and artificial intelligence.

Empirical research has demonstrated the importance of business model innovation in driving e-commerce success. Zott and Amit [31] found that the design of an e-business model, particularly its degree of novelty and efficiency, significantly impacts firm performance. Similarly, Chesbrough [18] argued that successful business model innovation requires a focus on customer value creation and a willingness to experiment with new value propositions and revenue streams. These findings suggest that e-commerce companies that leverage innovative business models and create unique value for customers are more likely to achieve a competitive advantage and attract a larger customer base.

Researchers have also assessed technology’s role in effectively showcasing innovative products, facilitating the trialability of novel services, and enabling viral diffusion in contexts such as mobile apps, fintech, and health-tech platforms [28,29,32]. Innovative business models rely heavily on digital channels for clarifying complexity, reducing uncertainty, and accelerating adoption. In these cases, the website’s technological sophistication becomes crucial for communicating the value proposition and facilitating consumer understanding and acceptance of the innovative offerings. Amit and Zott [30] noted that the Internet and related technologies have greatly expanded the possibilities for value creation in e-business by enabling new forms of efficiency, complementarities, lock-in, and novelty. Similarly, Teece [20] argued that advances in digital technologies have facilitated the development of novel business models that disrupt traditional industry boundaries and create new markets.

Empirical studies have provided support for the link between website technological sophistication and business model innovation. For instance, Wu et al. [33] found that e-commerce firms’ adoption of advanced website features such as personalization and online communities positively influences their ability to innovate their business models and achieve superior performance. Likewise, Krotov et al. [34] demonstrated that the use of big data analytics and artificial intelligence in e-commerce websites enables firms to develop innovative value propositions and personalized customer experiences, leading to increased customer satisfaction and loyalty.

The IDT also offers a compelling framework for understanding how new ideas are accepted over time [13]. Innovative business models often face challenges in gaining market acceptance due to their novelty and the higher levels of uncertainty that accompany them. The theory suggests that attributes like relative advantage, compatibility, trialability, and observability can significantly influence adoption rates. Websites for innovative business models can be designed to highlight these attributes, providing a clear advantage over competitors and making the new concepts more palatable to consumers.

Sophisticated websites with high-level technological features can offer interactive experiences, virtual trials, and simulations that allow users to experience the innovation firsthand, thus enhancing trialability and observability [35]. Advanced website functionalities can also provide educational content, such as video tutorials, webinars, and detailed FAQs, which facilitate the diffusion of innovation by making learning about the new model more accessible and engaging [36]. Furthermore, adaptive communication supported by sophisticated website technology allows innovative companies to gather real-time feedback and tailor their messaging accordingly, ensuring that the innovation is communicated effectively [37]. Given the critical role of website technology in enabling business model innovation and the importance of innovation attributes in facilitating the adoption of innovative business models, we propose the following hypothesis:

Hypothesis 2 (H2). 

The positive association between website technological sophistication and monthly visits will be stronger for companies characterized by innovative business models.

2.3. Website Technological Sophistication and Brand Recognition

Extending the discussion on the factors influencing e-commerce success, we now move to consider the interaction between website technology and brand recognition. While the previous sections focused on the direct impact of website technological sophistication (H1) and its interaction with business model innovation (H2), we turn our attention to the interaction effect between technological sophistication and brand recognition in driving consumer traffic.

Prior research has extensively examined the nexus between technology, branding elements, and their collective impact on digital performance, particularly through the lens of consumer trust and purchase behavior [38]. For instance, Karimov, Brengman, and Van Hove [39] provide an integrative review of how different web design cues, categorized into visual, social, and content design dimensions, influence initial trust in a B2C e-commerce context. Their findings underscore that not only do well-designed web elements boost consumer trust towards unfamiliar online vendors, but specific features such as human-like cues and internal e-assurance structures also play pivotal roles in enhancing this trust. Similarly, Ganguly, Dash, and Cyr [40] explore how website characteristics affect online transactions in the Indian market, highlighting the crucial mediating role of trust between website features and consumer purchase intentions. These studies collectively suggest that effectively integrated technological and branding strategies can significantly impact online consumer behavior by fostering trust and reducing perceived risks in online shopping environments.

Notably, a study by Corbitt et al. [16] and Hernandez et al. [4] implies that lesser-known companies can particularly benefit from technical website sophistication as a means to build trust and compensate for lower brand recognition. This suggests that website technology can serve as a signaling mechanism when brands lack established equity markers such as familiar logos or trademarks. In other words, when consumers are not familiar with a brand, the technological sophistication of the website can act as a surrogate for brand trust, influencing consumer perceptions and behaviors.

ST, proposed by Spence [21], provides a theoretical foundation for understanding this phenomenon. The theory posits that entities send out signals to convey their quality and trustworthiness in the absence of direct experience. In the context of e-commerce, the website serves as a critical signaling mechanism, especially for brands with lower recognition [41]. Consumers rely heavily on the available information, such as the technology and design quality of the website, to assess the credibility and reliability of the brand. When consumers encounter an unfamiliar brand, their perceived risk increases [42,43,44]. However, a highly functional and sophisticated website can mitigate this risk by providing a superior user experience, thereby compensating for the lack of brand recognition. Flavián, Guinalíu, and Gurrea [38] confirmed that the perceived quality of the website can enhance the credibility of lesser-known brands, influencing consumer behavior and increasing traffic.

Moreover, for less familiar brands, the website serves as a platform for customer education and engagement. By incorporating informative content, interactive elements, and user-friendly design, these websites can facilitate the learning process, allowing consumers to become more familiar with the brand [42,43,44]. This is particularly important for new entrants in the market, where the website often forms the first point of contact with potential customers. On the other hand, for brands with greater existing familiarity and recognition, consumers already have quality cues and rely less on the website as a signal of trust. Well-known brands have established perceptions in the minds of consumers, and, as a result, the technological sophistication of the website plays a less significant role in influencing traffic and visits. With strong brand equity, deficiencies in website technological sophistication can be compensated for by the overall brand goodwill and loyal customer base. Hence, this leads to Hypothesis 3.

Hypothesis 3 (H3). 

The positive association between website technological sophistication and monthly visits will be weaker for companies with relatively higher brand recognition and recognition levels.

Figure 1 presents the research model, incorporating our hypotheses 1 through 3.

3. Methodology

3.1. Data Sources and Sample

The research context for this study is the diverse and dynamic business landscape of South Korea, focusing on companies across seven key industries: technology, media, retail, healthcare, finance, food, and education. These industries were specifically chosen due to their high level of digital disruption and the extensive online interactions they have with their customers, making them ideally suited for investigating the research questions at hand.

The data for this study were collected from various sources to ensure a comprehensive and reliable dataset. We focused on companies with websites founded in South Korea from 1980 to September 2022. Crunchbase served as the primary platform for identifying these companies, as it is one of the largest databases documenting private and public companies globally, with a particular focus on startups and technology firms. Crunchbase provides detailed information on companies, including founding dates, funding rounds, industries, business descriptions, locations, and more.

To enhance the depth and breadth of our dataset, we leveraged Crunchbase Pro, which afforded access to connected data across multiple platforms. This included SEMrush for traffic insights, BuiltWith for technology profiling, and IPQwery for trademark intelligence. The linked data integration with these platforms enabled seamless matching of companies across the proprietary sources, allowing us to compile a comprehensive and integrated dataset for analysis.

SEMrush (Boston, MA, USA), a leading online visibility management and competitive analytics platform, provided valuable data on organic and paid search performance, online advertising metrics, and web traffic volumes across domains. With a proprietary database of over 700 million domains, SEMrush offers monthly traffic estimates by leveraging crawling bots, third-party data, and analytics. This made SEMrush an ideal source for obtaining the core monthly visits data for company websites across the seven industries.

BuiltWith (Sydney, Australia), on the other hand, empowers businesses to identify the technologies used by websites through automated tech stack discovery. In this research, BuiltWith furnished the website sophistication index, which was quantified by the active technology count currently adopted across companies in the sample. This metric signaled the technological sophistication of the websites.

IPQwery (Montréal, QC, Canada), a technology and data company, provided trademark registration intelligence using information that includes registration numbers, assignee names, and dates. By leveraging IPQwery, we were able to identify trademark counts for the sampled companies, which served as a proxy for brand recognition levels. Queries were then used to connect these registrations to specific companies.

The initial dataset comprised information on 9044 Korean companies across the seven industries—finance, retail, healthcare, technology, food, education, and media. All of these companies were registered entities on Crunchbase as of September 2022, covering both digital native and traditional organizations with an online presence. The industries were intentionally chosen based on their high level of digital disruption and the extensive online customer interactions they engage in, enabling a thorough investigation of the research questions.

An examination of Korean companies across these different industries reveals distinct patterns in their digital metrics, collectively painting a picture of a rapidly evolving business environment characterized by technological sophistication, digital engagement, innovation, and a unique blend of traditional and modern business practices. Across all industries, there is a strong trend towards digitalization and innovation, reflecting Korea’s overall technological advancement and the government policies that encourage digital transformation.

Industries such as media and retail demonstrate a significant global influence, which is evident from their high web traffic and social media engagement. Particularly in the technology, healthcare, and finance sectors, there is a marked emphasis on research and development (R&D) and patent holdings, indicating a strategic focus on innovation. The patterns observed in these metrics also reflect broader cultural and economic factors in Korea, including a highly digitalized population, government support for innovation, and a strong emphasis on education and R&D.

3.2. Variable Operationalization

3.2.1. Dependent Variable

The monthly website visits metric from SEMrush constitutes the main outcome variable, offering insights into the total user sessions on a company’s website per month. It signifies an aggregate figure combining visits across all sources, based on SEMrush’s proprietary data compilation methodology leveraging multiple channels. As such, it captures overall website traffic volumes, serving as an appropriate performance yardstick (See Table 1 for details).

3.2.2. Independent Variable

The central explanatory variable is BuiltWith’s “Active Technology Count” metric, which denotes the technological sophistication of websites. This index quantifies the number of distinctly identifiable technologies actively running on companies’ sites, spanning numerous different web tools across categories like content management, e-commerce, analytics, and more. Detected through automated scripts, it indicates the technological sophistication of the websites. Thereby, it offers a robust proxy for quantifying website sophistication levels (See Table 1 for details).

3.2.3. Moderating Variables

First, to assess business model innovation, we developed a variable using Crunchbase’s company descriptions and categorizations. This variable tracks the emergence of new industry category combinations over time, reflecting innovation as novel recombinations of existing elements. We aggregated Crunchbase’s industry/sector categories annually and identified new category combinations each year, comparing them to the previous years’ data. The core measure is the ratio of new dyadic category combinations to all possible dyadic combinations, normalized by the total industry/sector categories appeared per year to account for dataset fluctuations. This approach allows for consistent cross-year comparisons and sector-specific analysis. For instance, pioneering mixes like “AI music therapy” or “subscription snowgear” were dummy coded to signify innovative recombinations.

Second, to quantify companies’ branding recognition, we utilized trademark registration counts obtained from the IPQwery database. This approach leverages the legal protection companies seek for their brand assets as a proxy for their investment in and perceived value of brand recognition. Trademark registrations typically cover company names, logos, slogans, and product names, representing key elements of a company’s brand identity. The number of trademarks a company registers can indicate the breadth and depth of its branding efforts, as well as its commitment to protecting its market position. This metric offers several advantages: objective, quantifiable, and comparable across industries and time periods. Furthermore, the act of trademark registration suggests a company’s confidence in its brand’s market potential and longevity. While not a direct measure of consumer recognition, trademark counts serve as a valuable indicator of a company’s branding strategy and its perceived importance of brand protection in the marketplace (See Table 1 for details).

3.2.4. Control Variables

To account for potential confounding factors, we included several control variables. Company size was defined by the number of employees and categorized as 1 if the company had over 500 employees. Age was measured by the number of years since the company’s founding. Social media presence was operationalized as 1 if the company had Facebook and Twitter followers. Additionally, we controlled for technological assets through patent counts tracked by the IPQwery database. We also included a dummy variable for each of the seven industry categories covered in our study, as classified by the Crunchbase database (See Table 1 for details).

Table 1. Variable descriptions.

Variable Type	Variable Name	Measurement	Data Sources
Dependent Variable	Monthly consumer visits	Monthly website visits metric	SEMrush
Independent Variable	Technological sophistication	Active Technology Count	BuiltWith
Moderating Variable	Business model innovation	Novel keyword combinations in industry categories	Crunchbase
Moderating Variable	Brand recognition	Trademark registration counts	IPQwery
Control Variable	Company size	The number of employees (categorized as 1 if the company had over 500 employees)	Crunchbase
Control Variable	Age	The number of years since the company’s founding	Crunchbase
Control Variable	Social media presence	Coded 1 if the company had Facebook or Twitter followers	Crunchbase
Control Variable	Technological asset	Patent counts	IPQwery
Control Variable	Industry categories	Dummy variables for each of the seven industry categories	Crunchbase

Table 1. Variable descriptions.

Variable Type	Variable Name	Measurement	Data Sources
Dependent Variable	Monthly consumer visits	Monthly website visits metric	SEMrush
Independent Variable	Technological sophistication	Active Technology Count	BuiltWith
Moderating Variable	Business model innovation	Novel keyword combinations in industry categories	Crunchbase
Moderating Variable	Brand recognition	Trademark registration counts	IPQwery
Control Variable	Company size	The number of employees (categorized as 1 if the company had over 500 employees)	Crunchbase
Control Variable	Age	The number of years since the company’s founding	Crunchbase
Control Variable	Social media presence	Coded 1 if the company had Facebook or Twitter followers	Crunchbase
Control Variable	Technological asset	Patent counts	IPQwery
Control Variable	Industry categories	Dummy variables for each of the seven industry categories	Crunchbase

3.3. Statistical Analysis

To test the hypothesized main and interaction effects, we employed multiple Ordinary Least Squares (OLS) regression models. The analysis involved models with the following general specification:

Traffic_i = β₀ + β₁TechSoph_i + β₂BMInnovation_i + β₃Brand_i + β₄TechSoph_i X Brand_i + β₅TechSoph_i X BMInnovation_i + ΣControls_i + ε_i

(1)

In this equation, Traffic serves as the outcome variable for company i, TechSoph denotes the website sophistication measure, and BMInnovation indicates business model innovation. Brand constitutes the branding proxy variable based on trademarks owned. Interaction terms between TechSoph and the two moderators help assess contingent relationships. Control variables aid in isolating the effects of the explanatory variables. ε_i represents the error term.

The use of OLS regression allows for an examination of the relationships between the key variables of interest while controlling for relevant factors. The interaction terms in the model enable the assessment of the moderating effects of business model innovation and brand recognition on the relationship between website sophistication and traffic.

4. Results

4.1. Descriptive Statistics

Prior to conducting the hypothesis testing, we carried out a descriptive analysis to comprehend the distribution of the variables, as detailed in

Table 2. Descriptive statistics.

		Mean	S.D.	Min	Max
1	Monthly consumer visits *	2.70	4.25	0	19.27
2	Technological sophistication *	2.38	1.00	0	5.12
3	Business model innovation	0.42	0.49	0	1
4	Brand recognition	0.76	7.53	0	369
5	Technological assets	1.74	19.81	0	950
6	Social media presence (Twitter)	0.15	0.36	0	1
7	Social media presence (Facebook)	0.48	0.5	0	1
8	Company size	0.03	0.16	0	1
9	Age	12.48	7.76	1	37
10	Industry category_Technology	0.36	0.48	0	1
11	Industry category_Media	0.09	0.29	0	1
12	Industry category_Healthcare	0.08	0.26	0	1
13	Industry category_Education	0.04	0.19	0	1
14	Industry category_Retail	0.08	0.27	0	1
15	Industry category_Finance	0.04	0.2	0	1
16	Industry category_Food and Beverage	0.01	0.12	0	1

* Logged variable.

.

Our dependent variable, monthly website visits, showed a right-skewed distribution, typical for web traffic data. Hence, we applied a natural logarithm transformation to normalize this variable. The average log-transformed monthly website visits was 2.70 with a standard deviation (SD) of 4.25, suggesting a wide range of digital footprints among Korean companies. The average log-transformed count of active technologies on company websites, our independent variable, was 2.38 (SD = 1.00), indicating moderate technological sophistication across the sample. Approximately 42% of companies had innovative business models (dummy variable), reflecting a trend towards novel, digitally driven strategies in Korea. The average count of trademarks was 0.76 (SD = 7.53), with a highly skewed distribution, indicating varying degrees of brand establishment and recognition in the market.

Industry-level comparisons revealed that the retail sector had the highest average website visits and technological sophistication, underscoring its prominence in attracting online traffic and adopting advanced technologies. The finance and education sectors had the highest proportion of innovative business models, reflecting a trend towards modernization and digital transformation. The healthcare industry led in brand recognition, likely due to the established nature of healthcare companies and the importance of brand trust in this sector.

Table 3. Correlation matrix.

	1	2	3	4	5	6	7	8
1	1
2	0.37 *	1
3	0.11	0.07	1
4	0.1	0.07	0.04	1
5	0.06	0.03	0.01	0.29 *	1
6	0.23 *	0.13	0.06	0.07	0.04	1
7	0.24	0.23 *	0.04	0.03	0	0.3	1
8	0.19 *	0.08	0.07	0.19 *	0.13	0.08	0.03	1
9	0.13	0.03	0.22 *	0.07	0.05	0.04	−0.08	0.18
10	−0.06	0.02	−0.04	−0.02	0.01	−0.02	−0.06	−0.05
11	0.04	0.02	−0.02	0	−0.01	0.05	0.07	−0.02
12	0.02	0.01	−0.04	0.08	0.08	−0.01	−0.04	0.03
13	0.03	0.03	0.01	−0.01	−0.02	0.01	0.04	0
14	0.09	0.08	0.01	0.04	−0.02	0.01	0.1	0.02
15	0.05	−0.01	0.01	−0.02	−0.01	0	0	0.06
16	0.02	0.01	0.01	0	−0.01	−0.01	0.04	0.02
	9	10	11	12	13	14	15	16
9	1
10	−0.05	1
11	0.01	−0.24 *	1
12	0.06	−0.21 *	−0.09	1
13	0.01	−0.14	−0.06	−0.06	1
14	−0.04	−0.21 *	−0.09	−0.08	−0.06	1
15	0.01	−0.16 *	−0.07	−0.06	−0.04	−0.06	1
16	−0.01	−0.09	−0.04	−0.03	−0.02	−0.03	−0.02	1

* Significant at the 0.05 level.

presents the correlation matrix among the variables in our study. Notably, the Variance Inflation Factor (VIF) test result is less than 2, which typically indicates low multicollinearity among predictors in a regression model. This suggests that each predictor contributes unique and valuable information to the model without excessive overlap.

4.2. Sectoral Characteristics

Before moving to the hypothesis testing, we conducted a sectoral analysis to understand the characteristics and dynamics of the different industries included in our study.

The strong online engagement in the media industry is likely driven by Korea’s robust entertainment sector, renowned for its global influence in music, film, and online gaming. High web traffic reflects the industry’s consumer-focused nature and the widespread popularity of Korean media content. This aligns with the industry’s need to provide rich, engaging content and interactive user experiences, which are essential in media and entertainment. The media industry in Korea is also known for its innovative approaches, including unique content delivery platforms and monetization models, partly driven by the global K-pop phenomenon and the rise of streaming services. Media companies leveraging social media directly engage with consumers, particularly in Korea’s highly digitalized society.

Korean retail companies in e-commerce have been at the forefront of adopting advanced digital technologies. High web traffic reflects Korea’s highly digitalized consumer base and the popularity of online shopping. The high trademark counts suggest a significant emphasis on brand development and recognition, which is critical in the competitive retail sector. The strong presence on social media platforms indicates retail companies’ strategies to engage consumers and promote online shopping. Innovation in retail often involves customer experience enhancement, digital marketing strategies, and omnichannel sales approaches, reflecting the industry’s adaptability.

The finance sector’s high web traffic reflects the increasing digitalization of the sector in Korea, including online banking, fintech, and digital payment solutions. Nearly half of the companies being innovative indicates a significant shift towards fintech and modern financial services, aligning with global trends and Korea’s advanced IT infrastructure. The focus on patents over trademarks suggests an emphasis on developing new financial technologies and services rather than traditional branding. The presence of larger companies could be due to the scale and trust required in the finance industry, where established institutions often dominate.

The education sector’s moderate web traffic reflects the growing importance of online education and digital learning platforms in Korea, a trend accelerated by the COVID-19 pandemic. The moderate technological sophistication indicates the adoption of advanced digital tools and platforms for educational purposes, aligning with Korea’s emphasis on high-quality education and technological integration. The education sector’s innovation might include new methods of content delivery, e-learning platforms, and educational technology (EdTech) solutions. The moderate social media presence is likely a tool for student engagement, marketing educational programs, and community building. The prevalence of younger companies with few large entities reflects the emergence of new players in the education sector, particularly in EdTech, which is a relatively recent but rapidly growing field.

The healthcare sector’s moderate web traffic may reflect the specialized nature of healthcare services and products, where website traffic is not the primary success metric. This underscores the industry’s focus on research, development, and innovation, particularly in biotechnology and pharmaceuticals, sectors where Korea has made significant investments. The lower social media presence could be due to the regulatory environment and the nature of healthcare services, where direct consumer engagement through social media is less prevalent compared to other industries.

Korean technology companies show a balanced approach to their online presence. This is likely because they focus more on business-to-business (B2B) markets or niche sectors where high web traffic is not the primary indicator of success. Their moderate technological sophistication could reflect their focus on specialized rather than mass-market technologies. The high percentage of innovative business models reflects Korea’s strong emphasis on technological innovation and R&D, especially in areas like semiconductors, telecommunications, and information technology. The focus on patents over trademarks suggests a strategy prioritizing innovation and product development over branding. Korean tech companies often emphasize intellectual property to maintain a competitive edge in fast-evolving tech sectors. The moderate social media presence might be due to the B2B nature of many Korean tech companies, where social media is less critical for customer engagement compared to business-to-consumer (B2C) sectors.

4.3. Hypothesis Testing: OLS Regression Results

The regression models, denoted as Model 1 to Model 4, reported in

Table 4. OLS regression result (H1).

	Model 1
Technological sophistication	1.301 ***
	(0.039)
Business model innovation	0.413 ***
	(0.083)
Brand recognition	0.014
	(0.010)
Technological assets	0.003
	(0.003)
Social media presence (Twitter)	1.521 ***
	(0.141)
Social media presence (Facebook)	1.038 ***
	(0.086)
Company size	3.315 ***
	(0.337)
Company age	0.051 ***
	(0.005)
Industry category_Technology	−0.007
	(0.093)
Industry category_Retail	0.989 ***
	(0.185)
Industry category_Media	0.494 **
	(0.156)
Industry category_Healthcare	0.353 *
	(0.156)
Industry category_Food and Beverage	0.507
	(0.347)
Industry category_Finance	1.174 ***
	(0.226)
Industry category_Education	0.562 *
	(0.226)
Constant	−2.252 ***
	(0.115)
Observations	9003
R-squared	0.229
Log likelihood	−24,640

Robust standard errors in parentheses, *** p < 0.001, ** p < 0.01, * p < 0.05, and + p < 0.1.

and

Table 5. OLS regression result of interaction effects (H2 and H3).

	Model 2	Model 3	Model 4
Technological sophistication	1.135 ***	1.316 ***	1.147 ***
	(0.048)	(0.039)	(0.048)
Business model innovation	−0.550 **	0.423 ***	−0.567 ***
	(0.170)	(0.083)	(0.170)
Technological sophistication X Business model innovation	0.402 ***		0.413 ***
	(0.078)		(0.078)
Brand recognition	0.013	0.191 **	0.193 ***
	(0.010)	(0.059)	(0.059)
Technological sophistication X Brand recognition		−0.049 **	−0.050 **
		(0.016)	(0.016)
Technological assets	0.003	0.002	0.002
	(0.003)	(0.003)	(0.003)
Social media presence (Twitter)	1.500 ***	1.508 ***	1.486 ***
	(0.141)	(0.141)	(0.140)
Social media presence (Facebook)	1.036 ***	1.043 ***	1.040 ***
	(0.086)	(0.086)	(0.086)
Company size	3.293 ***	3.305 ***	3.282 ***
	(0.336)	(0.335)	(0.334)
Company age	0.051 ***	0.050 ***	0.050 ***
	(0.005)	(0.005)	(0.005)
Industry category_Technology	−0.015	−0.003	−0.011
	(0.093)	(0.093)	(0.093)
Industry category_Retail	1.000 ***	0.968 ***	0.979 ***
	(0.185)	(0.186)	(0.186)
Industry category_Media	0.508 **	0.494 **	0.508 **
	(0.156)	(0.156)	(0.155)
Industry category_Healthcare	0.371 *	0.286 +	0.302 +
	(0.156)	(0.156)	(0.156)
Industry category_Food and Beverage	0.500	0.477	0.469
	(0.349)	(0.347)	(0.349)
Industry category_Finance	1.183 ***	1.181 ***	1.191 ***
	(0.226)	(0.226)	(0.226)
Industry category_Education	0.557 *	0.553 *	0.548 *
	(0.226)	(0.227)	(0.227)
Constant	−1.863 ***	−2.296 ***	−1.897 ***
	(0.126)	(0.115)	(0.126)
Observations	9003	9003	9003
R-squared	0.231	0.232	0.234
Log likelihood	−24,628	−24,623	−24,610

Robust standard errors in parentheses, *** p < 0.001, ** p < 0.01, * p < 0.05, and + p < 0.1.

, progressively added variables to examine the effects of website sophistication, business model innovation, brand recognition, and their interactions on website traffic.

H1 proposed that higher technological sophistication would positively relate to monthly website visitation rates. The results provided robust support for H1, with the coefficient for the active technology count being positive and statistically significant across multiple models (Model 1: β = 1.301, p < 0.001; Model 4: β = 1.147, p < 0.001), indicating a strong positive association between technological sophistication and web traffic.

Control variables such as company size (Model 1: β = 3.315, p < 0.001), social media presence (Model 1, Twitter: β = 1.521, p < 0.001; Facebook: β = 1.038, p < 0.001), and company age (Model 1: β = 0.051, p < 0.001) were found to have significant positive relationships with website traffic. These effects remain similar across models.

H2 hypothesized a stronger positive association for companies with innovative business models. This hypothesis was confirmed by the positive and significant interaction term (Model 2: β = 0.402, p < 0.001; Model 4: β = 0.413, p < 0.001), implying that innovative business models enhance the positive impact of technological sophistication on traffic.

H3 anticipated a negative association between website technological sophistication and monthly visits for companies with stronger brand recognition. The negative and significant interaction term between technological sophistication and brand recognition (Model 3: β = −0.049, p < 0.01; Model 4: β = −0.050, p < 0.01) supported H3, suggesting that while technology is a significant driver of website traffic, its effect diminishes as brand recognition increases.

The final model presented an R-squared value of 0.234, indicating that approximately 23.4% of the variance in website traffic could be explained by the model, which is reasonable given the complex nature of web traffic determinants.

5. Discussion and Conclusions

5.1. Summary of Findings

This study investigated the relationships between website technological sophistication, brand recognition, and business model innovation in driving website traffic among Korean companies across seven industries. By leveraging a unique multi-source dataset and employing OLS regression analysis, we uncovered several key findings that contribute to the existing literature on e-commerce success factors.

Our results provide strong support for the positive relationship between website technological sophistication and monthly website visitation rates (H1). Furthermore, our results reveal that the positive impact of website technological sophistication on web traffic is enhanced for companies with innovative business models (H2). The moderating effect of brand recognition on the relationship between website technological sophistication and web traffic (H3) offers new insights into the substitution effect between technology and branding within the e-commerce context.

5.2. Theoretical Implications

Our study contributes to the existing literature on e-commerce success factors in several important ways.

The strong support for H1 aligns with and extends the TAM and IDT by demonstrating their applicability in the context of website technological sophistication and its impact on web traffic. By focusing on actual traffic data rather than perceptual measures, our study offers a more robust validation of these theories in real-world e-commerce settings.

The findings related to H2 extend ST by showing that website technological sophistication can serve as an alternative signaling mechanism, particularly for lesser-known brands. This insight provides a more nuanced understanding of how brand equity interacts with technological investments in shaping consumer behavior online. It contributes to the growing literature on the synergistic effects of technology and branding on digital performance by demonstrating the contingent nature of this relationship.

The support for H3 highlights the role of website technological sophistication in facilitating the adoption and acceptance of innovative business models. This insight is particularly relevant in the context of the growing prominence of digital platforms and the emergence of new disruptive business models across various industries. It contributes to the literature on business model innovation by demonstrating the importance of website technology in enabling the creation and delivery of unique value to customers.

5.3. Practical Implications

Our findings offer several important implications for practitioners. The strong positive relationship between website technological sophistication and web traffic underscores the importance of investing in advanced website functionalities and capabilities, particularly for companies seeking to enhance their online presence and engagement. This suggests that e-commerce firms should prioritize continuous improvement and updating of their website technologies to maintain competitiveness.

For companies with strong brand recognition, the diminishing returns of website technological sophistication suggest that resources might be better allocated to other areas. Conversely, lesser-known brands can potentially compensate for lower brand recognition by investing in sophisticated website technologies. This implies that companies should carefully assess their brand strength when making decisions about technological investments.

Companies with innovative business models should prioritize investment in advanced website technologies, as these can significantly enhance the impact of their novel strategies on web traffic and customer engagement. This highlights the importance of aligning technological capabilities with business model innovation to maximize online performance.

Our multi-industry analysis enables practitioners to develop nuanced, sector-specific strategies for optimizing their digital presence and driving web traffic. For instance, retail companies might focus more on personalization technologies, while B2B firms in the technology sector might prioritize information-rich content delivery systems.

5.4. Limitations and Future Research Directions

While our study offers valuable insights, it is important to acknowledge its limitations. First, our analysis focused on a specific geographic context (South Korea) and a limited set of industries. Future studies could explore the generalizability of our findings by examining these relationships in different cultural and industry settings. Second, our research relied on cross-sectional data, which limits our ability to make causal inferences. Longitudinal studies that track the evolution of website technological sophistication, brand recognition, and business model innovation over time could provide a more nuanced understanding of their dynamic interrelationships. Finally, while we considered several important control variables, future research could explore additional factors that may influence these relationships, such as industry-specific factors, competitive intensity, or regulatory environments.

5.5. Conclusions

In conclusion, our study enriches the existing literature on e-commerce success factors by demonstrating the significant impact of website technological sophistication on web traffic and its contingent relationships with brand recognition and business model innovation. By leveraging a unique multi-source dataset, our research offers valuable insights for both scholars and practitioners aiming to understand the complex dynamics between technology, branding, and innovation in enhancing online performance. As digital channels continue to evolve and gain prominence, our findings highlight the importance of investing in advanced website functionalities and capabilities as strategic tools for differentiation and growth in the highly competitive e-commerce environment.