AI-powered marketing strategies: elevating e-commerce brand recognition in Malaysia Open Access

Brands like Harvard (created in 1636) and Pepsi (created in 1898) are long-term assets. When handled appropriately, a brand has the potential to endure across generations, spanning decades or even centuries (Yen & Valia, 2022). Brand recognition is the concept in marketing plans that are employed by businesses that are considered successful when consumers can easily and quickly able to identify, differentiate or recall the brand through logo, tagline, slogan or packaging (Ward, Yang, Romaniuk, & Beal, 2020). Businesses frequently engage in market research to assess the effectiveness of their strategies aimed at enhancing brand recognition. This is because, brand recognition is important for the company for consumers to distinguish a specific brand by its features or characteristics over its rivals, to drive consumer decisions, build their trust, launch a new product, encourage repeat buyers, to increase market share and to earn maximum profits (Shaily & Emma, 2021).

Artificial intelligence (AI), as a groundbreaking and transformative technology, carries immense potential to revolutionize marketing. Its capacity allows for the creation of novel products and services directly integrated into production systems, catering to consumer demands (Davenport, Guha, Grewal, & Bressgott, 2020). Additionally, AI serves as a powerful tool for recognizing brand logos and unlocking pathways for analyzing the interests of social media users. With AI adoption, brands need to keep themselves up-to-date with this evolving technology to avoid being left behind. The integration of AI marketing strategies plays a crucial part in increasing brand recognition. AI would keep businesses up to speed with the latest technology and would also assist in the improvement of customer interaction and engagement activities (Capatina et al., 2020). Industry leaders like Google, Microsoft, Apple, Amazon and Alibaba are among the popular global brands that have been regulated to focus on AI research in their business operations. Nowadays, AI not only has changed the business model and business operation but has influenced and diversified brand management strategies; as well as consumer behavior (Lundvall & Rikap, 2022).

AI’s effectiveness in marketing varies across different market segments and consumer demographics, often enhancing personalization and engagement (Shanahan, Tran, & Taylor, 2019). In younger, tech-savvy demographics, AI-driven strategies like personalized recommendations and dynamic content can significantly boost engagement and conversion rates. Millennials and Gen Z consumers, familiar with digital experiences, respond well to AI’s ability to predict preferences and deliver tailored content (Hagan, Jahankhani, Broc, & Jamal, 2021). In contrast, older demographics may exhibit lower adoption rates for AI-driven marketing due to privacy concerns and less familiarity with technology. However, when approached thoughtfully, AI can still offer value by simplifying user experiences and providing relevant product suggestions (Rivas & Zhao, 2023). Moreover, market segments such as luxury goods or niche products also benefit from AI’s ability to target specific consumer groups with precision. AI can analyze purchase patterns and behaviors to identify high-value customers and deliver bespoke marketing messages (Rhie, 2019). Hence, AI’s ability to analyze vast datasets and predict consumer behavior makes it a powerful tool across diverse market segments and demographics, though its effectiveness is contingent on strategic implementation and audience receptiveness.

The e-commerce landscape in the USA expanded to $861 billion in 2020, boasting an overwhelming 1.8 million domestic online retailers and 7.1 million worldwide. Amazon’s dominance in the US e-commerce space, commanding a 49% market share. Conversely, Eurostat data indicates that 32% of European consumers engaged in online shopping once or twice within three months (Benmamoun, Singh, Lehnert, & Lee, 2019). Within Malaysia, the e-commerce sector made a significant impact, contributing RM163.3 billion to the country’s gross domestic product (GDP) in 2020. By the third quarter of 2021, Malaysia witnessed a surge in e-commerce revenue, reaching RM801.2 billion. The government recognizes the transformative influence of advanced technologies like AI, acknowledging their role in reshaping global economic dynamics and modern societal lifestyles (Chung, 2023). Hence, the industry’s players should be more aware of the success factors influencing their e-commerce industry. As per the Ayden Malaysia Retail Report 2022, findings indicate that 87% of Malaysian consumers exhibit a preference for retailers leveraging technology to enrich the shopping journey, influencing their likelihood of making purchases (KPMG, 2023).

Nowadays, most businesses often carry out extensive market research to ascertain the effectiveness of their strategies for brand recognition. Businesses devote significant amounts of money, effort and resources to make sure that their brand recognition strategies are successful. However, some businesses that expand with this strategy still fail to retain their consumers (Rhie, 2019). Several studies have shown that e-commerce businesses experience hardship and struggle due to a lack of funding and financial support. A major challenge in adopting AI lies in the constrained budget available to advance and evolve innovative technologies further (Shaily & Emma, 2021). Regarding business readiness, challenges emerge from companies not being internally equipped to embrace the shift toward a digital business culture. Despite the immense potential value offered by AI components, there remains a scarcity of studies exploring why businesses fail to harness AI within their marketing strategies (Mikalef, Islam, Parida, Singh, & Altwaijry, 2023).

AI marketing has benefited greatly to businesses as it enhances consumer engagement. AI marketing elements include big data analytics (BDA), machine learning (ML) and AI platform (AIP) solutions. However, such modern technology also comes with some challenges. The era of digitalization has globalized the whole world and information and data have become more valuable (Ha & Chuah, 2023). However, data privacy is a great concern when AI marketing is involved. AI is a relatively recent field and complicated technology. The public has a broad skepticism and lack of trust in AI as a result of the media’s hype surrounding it. To avoid the risk of a negative brand image, marketing teams must use consumer data ethically (Rivas & Zhao, 2023). Empirical studies concentrating on emerging markets like Malaysia are notably scarce, posing a gap in the understanding of these dynamic economic landscapes. Most studies were conducted in wealthy countries such as the US and European countries. Despite efforts by the Malaysian government, businesses still grapple with the challenge of acquiring the necessary IT literacy skills and resources. The government holds the belief that the post-pandemic economy will heavily lean on technology and AI, shaping into a more knowledge-based structure reliant on IT and AI. Notably, AI’s capacity to facilitate external market insights becomes invaluable during swift and unforeseen shifts in the external landscape, such as the COVID-19 pandemic (Ullah, Haji-Othman, & Daud, 2021).

Despite the numerous benefits of implementing AI in marketing strategies, it also comes with significant limitations and challenges. One primary concern is the ethical considerations surrounding consumer data analysis (Du & Xie, 2021). AI systems rely heavily on vast amounts of data to deliver personalized marketing campaigns. This often involves collecting, storing and analyzing consumer data, which raises significant privacy concerns. Ensuring that data collection methods are transparent and that consumers consent to the data being used is crucial to maintaining trust. Moreover, data breaches and unauthorized access to sensitive information can lead to severe reputational damage and legal repercussions for businesses. Not only that, another challenge is the potential for bias in AI algorithms. According to De Bruyn, Viswanathan, Beh, Brock, and Von Wangenheim (2020), these systems can unintentionally perpetuate existing biases if the training data are not representative or if the algorithms are not carefully designed and monitored. This can lead to discriminatory practices, such as targeting ads to specific demographic groups while excluding others, thus reinforcing social inequalities.

Technical limitations also pose a challenge. Developing and maintaining advanced AI systems requires significant investment in technology and expertise, which may be prohibitive for smaller businesses. Accordingly, Huang and Rust (2021) highlighted that integrating AI tools with existing marketing systems can be complex and time-consuming, requiring substantial changes to infrastructure and workflows. Furthermore, there is a risk of over-reliance on AI, which can restrain creativity and human intuition in marketing strategies. While AI can analyze data and predict trends, it lacks the human touch needed to create emotionally resonant and innovative campaigns. Not to mention that regulatory compliance is an ongoing challenge. Laws and regulations regarding data privacy and AI use are continually evolving and businesses must stay informed and compliant to avoid penalties and ensure ethical practices (Babatunde, Odejide, Edunjobi, & Ogundipe, 2024).

While AI-powered marketing strategies hold promise, businesses must navigate ethical, technical and regulatory challenges to implement them effectively and responsibly. Consequently, this study aims to bridge the existing gap by investigating factors influencing brand recognition within the realm of e-commerce, drawing upon cognitive dissonance theory (CDT) as its foundational framework. The paper unfolds in several sections. It begins with a wide-ranging review of past literature on brand recognition and proceeds to the development of hypotheses (Section 2). Section 3 delineates the research methods. Moving to Section 4, the paper delves into data analysis and extensively explores study implications. Finally, the paper concludes with implications for scholars and practitioners, accompanied by a thorough discussion on limitations and future study recommendations (Section 5).

According to Wamba-Taguimdje, Fosso Wamba, Kala Kamdjoug, and Tchatchouang Wanko (2020), brand name and reputation are intangible resources for the organization. AI-based solutions designed for brand logo identification open up novel avenues for scrutinizing the interests of social media users. For instance, the images shared across these platforms serve as compelling indicators of associated behaviors, needs and desires, often overlooked by businesses. AI possesses the capability to enhance brand value by nurturing a sense of intimacy through personalized experiences, aiding in the purchasing journey and mitigating post-purchase dissatisfaction or dissonance. Jarek and Mazurek (2019) showcased how luxury brands effectively offer personalized customer care through the utilization of AI elements, replacing conventional human-to-human interactions. AI has introduced novel strategic paradoxes, notably linking the strengths of luxury or premium brands with mass-market appeal and aligning niche markets with the advantages of larger markets via e-commerce. The perception of AI’s role in brand recognition seems intricately tied to users’ individual experiences. AI in marketing introduces various layers of complexity in branding, potentially influencing consumers’ purchasing behaviors (Rahman et al., 2023). Integrating AI elements can offer highly personalized, efficient and engaging experiences. Hagan et al. (2021) highlighted that this not only enhances consumer satisfaction and loyalty but also significantly boosts brand recognition by ensuring that consumers have positive, memorable interactions with the brand.

AI elements, which include ML, AIP, BDA, chatbots and virtual assistants, predictive analytics and natural language processing (NLP), have significantly impacted consumer behavior and brand recognition. ML and AIP analyze vast amounts of consumer data to understand preferences, allowing brands to offer personalized recommendations and experiences. This enhances customer satisfaction and loyalty (Rhie, 2019). Moreover, BDA processes large datasets to uncover valuable insights into consumer trends, enabling businesses to make data-driven decisions, innovate products and optimize marketing strategies. Chatbots and virtual assistants provide instant customer support, streamline purchasing processes and offer personalized interactions, enhancing customer engagement and brand perception (Trivedi, 2019). Whereas, predictive analytics forecasts future consumer behavior based on historical data, enabling brands to anticipate trends, tailor offerings and stay ahead of competitors. NLP interprets and generates human language, facilitating more natural interactions between consumers and brands, improving communication and fostering brand trust. Together, these technologies create seamless, personalized experiences, strengthen brand–consumer relationships and drive brand recognition and loyalty in today’s competitive market landscape (Rivas & Zhao, 2023).

On the other note, the effectiveness of AI marketing strategies can vary significantly across different industries and cultural contexts, shaped by varying consumer behaviors, market dynamics and regulatory environments. For instance, in the retail and e-commerce industry, AI marketing strategies are particularly effective due to their ability to provide personalized shopping experiences. AI-driven recommendation engines analyze consumer behavior to suggest products, increasing sales and enhancing customer satisfaction (Khrais, 2020). Companies like Amazon and Alibaba use AI to predict what customers want based on past purchases and browsing habits, leading to higher conversion rates and customer retention (Krishnan & Mariappan, 2024).

In the financial services sector, AI is utilized for personalized financial advice, fraud detection and customer service through chatbots. AI-driven analytics provide insights into customer spending patterns and investment behaviors, enabling financial institutions to offer tailored financial products. However, this industry faces strict regulatory scrutiny, necessitating robust data protection and compliance measures (Mogaji & Nguyen, 2022). Furthermore, the healthcare industry also benefits from AI marketing strategies, particularly in personalized patient engagement and telehealth services. AI can analyze patient data to provide personalized health recommendations and reminders for medication adherence. However, according to Liu, Gupta, and Patel (2023), privacy concerns are paramount, as sensitive health data must be handled with the utmost care to ensure patient confidentiality and trust.

Moreover, cultural context plays a critical role in the effectiveness of AI marketing. In culturally diverse markets like Asia, AI strategies need to account for regional preferences and languages (Robinson, 2020). For example, in Japan, AI-powered virtual assistants are popular for customer service, reflecting the cultural acceptance of technology in everyday life. In contrast, in European markets, stringent general data protection regulation (GDPR) regulations impact how AI can be used, emphasizing the need for transparency and consent in data usage. In emerging markets, AI adoption in marketing may be slower due to technological infrastructure limitations and lower consumer trust in digital transactions. However, mobile-first strategies leveraging AI can still be highly effective in these regions, where smartphones are the primary internet access point. Dorotic, Stagno, and Warlop (2024) emphasized that the effectiveness of AI marketing strategies is highly context-dependent, requiring careful consideration of industry-specific dynamics, cultural nuances and regulatory frameworks to optimize results and maintain consumer trust.

Studies from countries such as Indonesia and Singapore share similar e-commerce dynamics, challenges and opportunities (Santosa & Surgawati, 2024). These studies highlight varying levels of AI adoption in e-commerce, revealing that technological infrastructure and consumer behavior are significant factors in shaping the effectiveness of AI strategies in these countries. Furthermore, the latest trends in AI adoption in e-commerce include the rise of AI-driven personalization and recommendation systems (Raji et al., 2024; Volkova, Kuzmuk, Oliinyk, Klymenko, & Dankanych, 2021). These trends emphasize how AI is transforming e-commerce in developing countries, aligning with Malaysia’s ongoing digital transformation efforts and providing valuable insights for future research.

The CDT underpins this study as it is viewed to influence consumers’ cognitive process and as an important role in purchase decisions or product usage experiences (Festinger, 1959). CDT suggests that a fundamental psychological conflict arises when an individual’s behaviors diverge from their internal thoughts and beliefs, creating an underlying tension. Castelo, de Montes, and Larrañaga (2023) contended that people’s psychological perspectives toward technology are primarily shaped by two pivotal factors: the technology’s efficacy and the discomfort associated with its usage. Specifically, technological effectiveness represents a cognitive facet, encapsulating individuals’ cognitive assessments of the technology’s capabilities. Conversely, discomfort with its use denotes an affective element, encapsulating subjective feelings of unease while employing the technology. The experience of discomfort serves as an indicator of an adverse state of psychological well-being, so individuals strive to avoid things that make them uncomfortable. Li, Cao, Ye, and Yue (2021) argue consumers’ discomfort with the usage of AI elements can have a negative psychological impact. Whereas AI elements’ effectiveness represents consumers’ positive cognitive perceptions of AI elements, discomfort with the usage of AI elements reflects their negative affective perceptions. Consumers might initially maintain neutral attitudes toward AI elements before integrating them. However, if consumer experience discomfort with AI elements post-adoption, their subsequent negative emotions conflict with their earlier attitudes. This dissonance, as highlighted by Hinojosa, Gardner, Walker, Cogliser, and Gullifor (2017), leads to a shift in attitudes, potentially cultivating a negative stance toward these elements. Consequently, the discomfort experienced by consumers could instigate reluctance to further utilize the technology.

BDA delivers invaluable business intelligence, driving beneficial transformations like product enhancements and augmented revenue per customer. Additionally, BDA plays a pivotal role in bolstering marketing endeavors, particularly in brand recognition. By leveraging BDA insights, businesses can adeptly deliver customer-specific content at optimal times and locations, thereby enhancing both online and brick-to-mortar brand visibility (Yen & Valia, 2022). Similarly, BDA allows businesses to be as effective as established brands even with a limited marketing budget. The power of BDA allows businesses to analyze a mix of data sets from numerous sources to gain important insights into customer behavior and put in an application those results to business strategy and provide better customer service (Wahab, Hamzah, Sayuti, Lee, & Tan, 2021). According to Hagan et al. (2021), analyzing transactional data which records the consumer’s historical payments may ascertain to determine the amount and frequency of a consumer’s purchases. Additionally, determining a consumer’s spending habits via an e-commerce platform illustrates a business may forecast the products or services that the consumer would like and then offer great bargains. BDA, therefore, offers a chance for companies to interact with the consumer and translate it into customer satisfaction and increased brand recognition. Given the aforementioned factors, the initial hypothesis of the study can be articulated as follows:

The best way for brands to build brand recognition is by delivering great and positive user experiences. ML equips businesses to refine their content at the bottom of the sales funnel, ensuring its relevance to consumers. This not only substantiates their relevance but also showcases their capability to deliver desired products, services and experiences (Micu et al., 2019). Moreover, ML holds the potential to bolster brand recognition by identifying prospective customers most likely to be acquainted with the brand for future transactions. Additionally, it delves into discerning behaviors and traits that differentiate potential customers from one-time purchasers (Pandey & Mishra, 2023). ML also helps businesses to determine which potential customers are likely to be attracted and interested in the brand. This facilitates an ongoing process for brands to perpetually reinforce the brand recognition cycle, empowering personalized one-to-one marketing capabilities. This is because, each individual possesses unique shopping habits, which businesses have to understand in greater depth in order to retain the consumers. Hence, ML is the best and ideal technology for businesses to segment and target potential consumers for brand recognition because it is able to identify complex patterns on a scale far beyond what humans are capable of (Policarpo et al., 2021). As a result, the following hypotheses in this study can be outlined:

AIP has improved drastically over the years, leading to many new channels for customer interaction. AIP can benefit businesses by streamlining essential features on a company website, therefore saving time and money. Moreover, AIP can work alongside a sales team and increase e-commerce revenue by being the customer’s initial point of contact (Barron, 2023). By leveraging AIP, which includes Siri, Alexa and Chatbots to interact with website visitors and potential prospects, it may increase brand recognition. This helps businesses in creating higher brand recall value. Hence, AIP helps the company to deliver a distinct vibe, just like good marketing should ensure that AIP encompasses the current branding for better brand recognition (Trivedi, 2019). With a combination of built-in features and adaptability, the next generation of AIP is capable of analyzing user input and satisfying customer demand. AIP allows customers to engage with the brand online and receive information and product suggestions. Being fast and efficient, AIP helps to increase customer satisfaction and help the company stand out from the competition, as a solution to enhance brand recognition (Kirkby, Baumgarth, & Henseler, 2023). Through AIP, businesses can uphold an unvarying, seamless and favorable customer experience. Building upon this foundation, we propose the following hypotheses:

Malaysian e-commerce consumer is chosen for this study. According to the DOSM, Malaysia’s current total population is 32.7 million people and 81% of this figure are e-commerce consumers. However, due to the large number of populations, it creates a limitation to the number of surveys that can be conducted. In addition to limited knowledge of marketing and AI, therefore, 600 surveys have been distributed. Around 84% of confidence level responses with a minimum required of 504 sample sizes have been selected as a base for the questionnaire received from the respondents for this study. The 504 responses were collected using random and convenience sampling techniques through online surveys. Random and convenience sampling techniques in online surveys were chosen to enhance a study’s rigor by broadening participant diversity and minimizing selection bias. According to Hamzah, Wahab, Abd Rashid, and Voon (2023), random sampling ensures that every individual has an equal chance of being selected, enhancing generalizability. Convenience sampling, while less rigorous, offers practicality and quick data collection from readily available participants. Combining both techniques can improve replicability by balancing comprehensive coverage with logistical feasibility, ensuring that findings are consistent and can be validated through repeated studies (Safian, Osman, Wahab, Othman, & Azhar, 2021).

Furthermore, convenience sampling and online surveys may introduce biases due to self-selection and limited demographic representation. Those with strong opinions or easy access are more likely to participate, skewing results (Hassan & Yong, 2019). Hence, the mitigation strategies involve random sampling techniques to ensure diverse representation, stratification to balance demographics and weighting to adjust for over- or underrepresented groups. Additionally, careful design, pre-testing and transparency regarding survey purpose can improve reliability. In the case of this study, utilizing multiple data sources and analytical methods further enhances validity, minimizing the impact of biases and producing more accurate insights.

The constructs were established through an extensive review of literature, drawing from the Shaily and Emma Model (Shaily & Emma, 2021). Factors impacting brand recognition were derived from CDT literature (Castelo et al., 2023; Festinger, 1959; Hinojosa et al., 2017). Content validity assessments were conducted to ensure instrument accuracy and appropriateness. The evaluation involved two experts: one, an experienced individual from a local multinational e-commerce firm with over 30 years of industry expertise and the other, a professor specializing in marketing and supply chain management. Insights from both experts were instrumental in refining the instruments. All measures were gauged on a five-point Likert scale, ranging from 1 [strongly disagree] to 5 [strongly agree]. Additionally, the study has been conducted with the highest standards of rigor and integrity. The method of data collection was internally checked to enhance the quality. However, ethical review and approval were not required for this study as it adhered to the institutional requirements in place at the time of the study.

Data collection spanned from May to November 2023. Following Faul, Erdfelder, Buchner, and Lang (2009) recommendation of a minimum sample size of 92 for robust analysis using G*power 3.1, this study aimed for a sample size estimation with a medium effect size of 0.15, a power of 0.80 and an α error of 0.05, as per Cohen (2016). With 92 respondents obtained, surpassing the required minimum, the study sample size of 504 aligns with established standards. The analysis to validate causal relationships among latent constructs, outlined in the previous section’s framework, was conducted using the SPSS approach.

As shown in Table 1, the majority of the respondents who participated in this study were female (64.3%) and aged between 31 to 40 years old. A large group of Malay respondents volunteered to participate in this study (55.2%). Most of them (n = 177) hold Bachelor’s degrees and have a monthly income between RM8,001 to RM10,000. Additionally, most of the respondents had used the internet between 1 and 5 years, with TikTok Shop, Shopee and Lazada among the most favorite e-commerce platform. The study’s findings highlight that AI-powered marketing strategies in Malaysia’s e-commerce sector effectively target a predominantly female, middle-aged and well-educated Malay demographic. Most respondents favor platforms like TikTok Shop, Shopee and Lazada and these insights are crucial for enhancing brand recognition and tailoring marketing efforts to this specific audience.

A survey of 504 e-commerce respondents indicates a high level of awareness and understanding of AI. All 504 respondents claim to know what AI is, reflecting a widespread familiarity with this technology. Furthermore, the same number of respondents express comprehension regarding the implications of AI in the context of e-commerce. Notably, a significant majority (n = 444), report firsthand experience with AI in marketing as shown in Figure 1. This suggests that a substantial portion of the surveyed e-commerce consumer have encountered AI applications within their marketing practices. These results emphasize the prevalent recognition of AI’s relevance and utilization within the e-commerce industry. The findings demonstrate that AI-powered marketing strategies are well-recognized and understood among Malaysian e-commerce consumers. With all 504 respondents aware of AI and 444 having firsthand experience, it highlights the technology’s significant role in enhancing brand recognition, emphasizing its importance in modern e-commerce marketing strategies in Malaysia.

Based on the result summarized in Table 2, all independent variables (BDA, ML and AIP) showed a positive and significant correlation with the dependent variable (brand recognition). This means that the relationship between the variables supports each other and has a strong relationship with the dependent variable. The strongest positive relationship with the dependent variable is AIP (0.669) and the weakest positive relationship is BDA (0.436). The analysis indicates that AI-powered marketing strategies significantly enhance brand recognition in Malaysia’s e-commerce sector. All independent variables positively correlate with brand recognition, with AIP showing the strongest impact. This emphasizes AI’s crucial role in marketing effectiveness. Evidently, AI enhances marketing effectiveness by analyzing vast data sets, predicting consumer behavior, personalizing experiences, automating tasks and optimizing campaigns, leading to improved targeting, engagement and conversion rates.

The model summary shown in Table 3 determined that BDA, ML and AIP dimensions influence brand recognition. The R² represents 0.555 or 55.% as the contributor factor toward brand recognition. It is also found that only two dimensions of independent variables, ML (t = 4.234, p = 0.001) and AIP (t = 8.046, p = 0.001) have significant relationships with brand recognition. While BDA (t = 0.117, p = 0.907) is not significant towards brand recognition. Comparatively, AIP (β = 0.533) has a strong relationship with brand recognition, followed by ML (β = 0.348) and BDA (β = 0.010). Therefore, the dimension relating to BDA, ML and AIP should be emphasized in order to increase brand recognition in e-commerce platforms. Based on the findings, it can be concluded that H2 and H3 are accepted and show a positive relationship with brand recognition, meanwhile, H1 is rejected. The result reveals that AI-powered marketing strategies, particularly ML and AIP, significantly enhance brand recognition in Malaysia’s e-commerce sector. While BDA shows no significant impact, ML and AIP are crucial, explaining 55.5% of the variance in brand recognition. Apparently, ML and AIP are able to enhance brand recognition by predicting consumer behavior, personalizing user experiences and optimizing marketing strategies, leading to higher engagement and better brand recall.

Based on the results, the three main independent variables play an important role in creating a great opportunity for brand recognition on the e-commerce platform. Besides that, there is proof that ML and AIP as the main reasons for the brand to gain brand recognition on the e-commerce platform. Among all the independent variables, ML (t = 4.234, p = 0.001, β = 0.348) has the strongest relationship with the dependent variable, followed by AIP (t = 8.046, p = 0.001, β = 0.533). Meanwhile, BDA shows an insignificant relationship (t = 0.117, p = 0.907, β = 0.010), but still shows a strong relationship. BDA encompasses analytical tools designed to gather, assess, analyze and communicate data to online visitors, enabling a comprehensive understanding and enhancement of e-commerce platform utilization that may lead to brand recognition (Micu et al., 2019). The findings indicate that there is no significant relationship between BDA and brand recognition toward e-commerce platforms. Hence, the result indicates that the Hypothesis 1 is rejected. Therefore, without integration with BDA, e-commerce still can gain brand recognition among online shoppers in Malaysia. This study hypothesized that although effectively practised, it did not contribute to and improve the brand recognition of e-commerce (Shaily & Emma, 2021).

The extension of this study also examined the relationship between ML and brand recognition. In this scenario, ML entails the techniques essential for intelligently managing extensive data sets, involving the creation of valuable algorithms to synthesize, categorize or arrange this information effectively (Micu et al., 2019). ML in e-commerce platforms involves smart features such as image recognition (lens), facial recognition and voice recognition that are able to understand or mimic human emotion and then offer emotional comfort or other better communication styles to consumers. The positive results from the analysis prove that the respondent agrees and feels that ML is among the best elements of AI marketing which e-commerce uses when developing its platforms that lead to brand recognition. To a certain extent, the perception of the ML is also associated with the algorithms which is also another factor that influences brand recognition. The evolving digitalization and computer enhancement machines have enabled the brand to be noticed, hence a high-quality ML is able to perform its function without any error or accident (Allal-Chérif, Simón-Moya, & Ballester, 2021). It is hypothesized that building, training and deploying ML will result in improvement in brand recognition and a possibility to develop a relationship between the customer and the brand, especially on the e-commerce platform. Therefore, Hypothesis 2 is accepted which results in brand recognition.

Furthermore, this study also stretches out to investigate the relationship between AIP and brand recognition. AIP stands as an AI-driven programmatic creative platform, streamlining the advertising creative workflow by producing personalized and context-specific advertising messages at scale, in real time (Rahman et al., 2023; Rhie, 2019). AIP can automate responses to frequently asked customer inquiries, handle repetitive tasks during customer interactions, efficiently manage a higher volume of customer queries automatically and engage with customers round the clock, ensuring continuous interaction. The positive and significant relationship between AIP and brand recognition has been proven and confirmed and it is supported by previous literature (Shaily & Emma, 2021). That describes AIP as having a positive influence on brand recognition in e-commerce as well as in social business industries. AIP nowadays are taking into consideration brands when developing their product to get brand recognition. Hence, it can be concluded that AIP has an impact on brand recognition especially in the e-commerce market subsequently supported by Hypothesis 3. Thus, managing and strategizing AIP is essential in improving brand recognition, especially in an e-commerce market in Malaysia.

To strengthen the study, a comparison with research from countries with similar market conditions, such as Singapore, Thailand and Indonesia, has been incorporated. These countries share common challenges and opportunities in AI-powered marketing, but differ in their outcomes due to variations in technological adoption and market maturity. For instance, studies indicate that in Singapore, AI-driven marketing has a stronger impact on brand loyalty, while in Indonesia, traditional marketing methods are still more prevalent due to lower technological adoption (Santosa & Surgawati, 2024). Additionally, the limited impact of BDA on brand recognition in Malaysia is due to various factors including lower BDA maturity, insufficient data infrastructure and the lack of integration between traditional marketing and data-driven strategies (Chong, Abdul Rasid, Khalid, & Ramayah, 2024; Zian, Zulkarnain, & Kumar, 2024).

The outcome of this study indicates the significance of AI marketing elements as a factor toward brand recognition on e-commerce platforms. This study has proven that there is a positive relationship between the contributing factors (ML and AIP) and brand recognition which is supported by Festinger’s CDT. This study extended similar to previous studies conducted by Shaily and Emma (2021), who found significant and positive contributions of BDA and AIP in influencing brand recognition on social business in Bangladesh. Therefore, it further developed and strengthened the theoretical view by confirming and contradicting previous studies on the two factors of ML and AIP as important drivers that lead to brand recognition in e-commerce in Malaysia by improving the consumer’s perception of technology.

The results from this study might be useful to businesses that are looking to incorporate AI in their business operations to increase brand recognition for e-commerce in Malaysia. This study also recognizes the significant relationship that exists between BDA, ML and AIP toward brand recognition among e-commerce users in Malaysia. Throughout the study, the findings have proven beneficial for businesses to examine and make a checklist to identify which factors are underutilized and underperformed. Decision makers could make better decision-making and execute a proper strategy based on the three factors that have been identified to achieve brand recognition. The result of this study could help the management to re-assess their strategy which could further improve their business structure and achieve brand recognition. Businesses can practically implement AIP and ML by collecting and cleaning relevant customer data and integrating them into their e-commerce platforms for real-time personalized experiences. Continuous monitoring and optimization will ensure the accuracy and relevance of data management. Throughout the process, businesses must adhere to data privacy regulations and ethical guidelines. Businesses can effectively leverage AIP and ML to enhance customer engagement, drive sales and elevate brand recognition in the competitive e-commerce landscape.

Furthermore, some brands are not adaptive to the technology change which may result in loss of brand recognition in the competitive digital market. The requirement to become more innovative and tech-savvy has become necessary to remain competitive in this digital era. The result of this study could also create a master plan for policymakers and practitioners. As the increase of e-commerce activity is increasing greatly, Malaysia is focusing on building infrastructure such as 5G which widens the use of connectivity in terms of Internet. 5G infrastructure is vital for e-commerce, offering faster data transfer and reduced latency. This enables seamless browsing, quicker transactions and enhanced user experiences, crucial for retaining customers and staying competitive. With improved connectivity, businesses can innovate with immersive technologies, personalized services and efficient supply chain management, optimizing e-commerce operations. Moreover, the government should also encourage more small-scale SMEs to sell their product on e-commerce websites through subsidies and tax relief on all stages of e-commerce activities. By doing so, most of the local products sold in Malaysia can be recognized internationally and even be exported to other countries.

This study can be incredibly useful for the concerned businesses, consumers and researchers as it contains detailed information regarding AI marketing, e-commerce and the importance of brand recognition. This study provided insight into the relationships among three key elements of AI marketing towards brand recognition in the e-commerce industry. Despite the limitations, there are possibilities of future study opportunities that could have benefits through the progressive work of this study. It is recommended that future study to further expand on other AI elements such as robotics and Deep Learning. Moreover, future studies may also explore from a consumer perspective, such as consumer perception, consumer readiness for AI, customer satisfaction, customer loyalty toward e-commerce or other alternatives of B2B, B2C or B2B2C platforms such as accommodation, food order applications, e-hailing and many more. It is also recommended that future studies focus on unpopular marketing activities, explore potential challenges to AI adoption and contemplate the challenges and impact and also focus on the cost of adopting AI in B2B marketing activities. It is also recommended for future studies to reserve some time and effort to obtain the target respondent from the whole digital market or even a larger number of respondents throughout Malaysia. The future study may conduct an in-depth investigation and physical interaction, verbal interview or focused group discussion that provides a better and in-depth understanding.

AI marketing and brand recognition are positively correlated and have an impact on one another. Utilizing AI in marketing entails a company utilizing cutting-edge technology and all of its features to enhance customer service handling and consumer engagement (Ray, 2019). If brands can create recognition by ensuring that their customers are satisfied and have a positive experience with their brand, AI marketing can make a significant contribution to improving brand recognition for e-commerce. Therefore, e-commerce in Malaysia can make use of AI marketing to improve their brand recognition. AI algorithms analyze consumer data, enabling personalized recommendations, targeted advertising and tailored promotions. This enhances customer engagement, loyalty and ultimately, brand visibility and recognition in the competitive Malaysian market.

Furthermore, this study’s findings should be able to provide a better understanding of e-commerce user’s perceptions of AI elements that relate to brand recognition. Notably, this study also contributes to a comprehensive understanding of the characteristics of e-commerce users in Malaysia. Findings revealed that AIP was shown to be the most powerful element in AI marketing that can lead to brand recognition in the e-commerce industry. As a result, top management should invest in AIP such as Siri, Alexa or Chatbots to remain competitive in the market. Apparently, AIP in e-commerce utilizes customer data to offer personalized shopping experiences, including product recommendations, customized marketing messages and tailored promotions. By catering to individual preferences and behaviors, AIP enhances customer satisfaction, fosters loyalty and ultimately boosts brand recognition and trust in the e-commerce industry.

On the other hand, BDA did not lead to brand recognition among the e-commerce users, instead, brand recognition is gained through meaningful customer experience infused with sentimental and emotional values that can be felt by the customer via ML. BDA may not directly lead to brand recognition in e-commerce if insights are not effectively translated into actionable strategies. Without targeted marketing efforts, personalized experiences and clear communication of value propositions, BDA insights may remain underutilized, failing to engage users and establish brand recognition effectively. As a result, this study’s finding might assist brands in re-strategising their strategy to obtain brand recognition by expanding the customer experience with emotional values for them to remember and to win the hearts of Malaysian e-commerce users. Because of the highly competitive e-commerce market, this strategy could easily attract a customer who is not yet thinking of switching from one e-commerce platform to another. Even though BDA was rejected in this study, this may be due to the mistrust of AI systems, data privacy and data security.

Furthermore, there is a strong correlation between ML and brand recognition. ML enhances brand recognition in e-commerce by analyzing vast consumer data to personalize recommendations, optimize marketing strategies and predict consumer behavior. Additionally, tailored experiences, targeted ads and efficient product placements based on ML insights foster customer engagement, loyalty and ultimately elevate brand recognition in the competitive e-commerce landscape. Moreover, ML utilizes the latest and modern technologies as marketing tools to assist brands in better marketing. ML helps brands come up with more advanced ways to enhance their promotional activities and campaigns and also helps brands increase consumer engagement. Brands may simply increase their brand recognition if they can use ML effectively and efficiently consequently creating brand intelligence in the e-commerce industry (Mikalef et al., 2023).

The findings from this study show that AIP and ML collectively elevate e-commerce brand recognition by offering personalized experiences, optimizing marketing strategies and uncovering valuable consumer insights. Despite BDA not directly leading to brand recognition, this study advancing study on consumer behavior and technology adoption in emerging markets. Leveraging these technologies enables brands to stay competitive, enhance customer engagement and foster brand loyalty. However, careful consideration of privacy concerns, ethical implications and infrastructure challenges is crucial to maximizing the benefits of AIP, ML and BDA in the Malaysian e-commerce sector.