Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 180 https://internationalpubls.com Enhancing Sales Performance in Henan’s Media Industry through the Synergy of Digital Marketing and AI 1Zhao Weiguang, 2Johar MGM, 2Ali Khatibi, 2Jacquline Tham, 2S. M. Ferdous Azam 1Office of International Exchange, Shangqiu Normal University, No.55, Pingyuan Middle Road, Shangqiu, Henan, China 2Postgraduate Centre, Management and Science University (MSU), University Drive, Off Persiaran Olahraga, 40100 Shah Alam, Selangor Article History: Received: 23-10-2024 Revised: 06-11-2024 Accepted: 14-12-2024 Abstract: This study examines how digital marketing and artificial intelligence (AI) can improve sales performance in the media industry of Henan Province, China. As the market undergoes rapid digital transformation, understanding the role of these technologies in driving sales is crucial for maintaining competitiveness. The research explores three main areas: the impact of digital marketing and AI on sales performance, employee perceptions of these technologies, and the interaction between digital marketing and AI in enhancing sales outcomes. Data was gathered from 240 participants using a structured questionnaire distributed via JotForm and popular Chinese social media platforms. The demographic analysis revealed that 74.2% of respondents were female, with 48.3% aged between 30-39 years. Various statistical methods, including normality tests, descriptive analysis, T-tests, and bivariate correlation, were applied to ensure the reliability and validity of the data. The normality test indicated well-distributed data, while descriptive analysis showed high mean scores, reflecting a strong understanding of the survey items. The T-test found no significant difference in the impact of digital marketing and AI on sales performance, suggesting equal contributions of both technologies. The bivariate correlation revealed a moderate positive relationship between digital marketing and AI, highlighting their complementary roles in boosting sales. A Cronbach’s Alpha value of 0.94 confirmed the high internal consistency of the data. The study concludes that integrating digital marketing and AI into a unified strategy is critical for media organizations to optimize their sales performance. It underscores the need for innovation and healthy competition within media networks to drive digital transformation. Future research should investigate the relationship between innovative marketing strategies and sales performance in non-Western contexts. Keywords: Digital marketing, Artificial intelligence, Sales performance, Media industry, China INTRODUCTION The media industry, as an essential part of the social system, plays a crucial role in disseminating information and shaping public discourse. However, the rapid technological advancements driving societal evolution have necessitated continuous adaptation within media organizations. To stay relevant and effective in this ever-evolving environment, media organizations must embrace innovative communication methods and evolve their strategies to align with changing societal needs and technological innovations (Tham et al., 2017; Pambreni et al., 2019; Herath et al., 2023). One notable trend emerging in the media landscape is media convergence, characterized by the integration of various platforms such as newspapers, radio, television, mobile phones, and the Internet. This Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 181 https://internationalpubls.com convergence marks a shift from competitive relationships between these platforms to collaborative alliances (Udriyah et al., 2019; Horani et al., 2023). Media convergence represents a significant transformation in how information is produced, distributed, and consumed, transcending traditional boundaries and reshaping the media ecosystem. While the concept of media convergence is now widely recognized, its foundational elements have been emerging in various countries and industries for some time. Today, media convergence has moved from being a theoretical idea to a practical reality, becoming a central feature of modern media development (Wulandari et al., 2023; Ranawaka et al., 2023). The transformation of traditional platforms like newspapers, radio, and television, alongside the proliferation of digital platforms such as the Internet and smartphones, has positioned media convergence as a defining characteristic of contemporary media phenomena. Despite its growing prominence, the media industry faces challenges related to effectively managing and integrating diverse platforms and technologies (Zheng et al., 2023; Sudha et al., 2023). Media organizations must navigate complex technological ecosystems and seamlessly integrate multiple communication channels to deliver cohesive and engaging content to their audiences. Furthermore, as media convergence blurs the lines between different forms of media, concerns regarding journalistic integrity and media ethics have surfaced. With information now disseminated across numerous platforms at rapid speeds, ensuring accuracy, objectivity, and accountability is increasingly challenging. This situation calls for the development of detailed frameworks and standards to uphold journalistic principles in the digital age. Moreover, the rise of digital technologies has democratized both media production and consumption, enabling individuals to actively participate in shaping media narratives (Rajapakse et al., 2022; Zhou & Azam, 2024). While this democratization presents opportunities for more inclusive media production, it also challenges traditional media organizations to adapt their strategies to engage and retain increasingly empowered audiences within a saturated information environment. Media convergence, therefore, presents both opportunities and challenges. While it has the potential to improve communication efficiency and extend audience reach, media organizations must also address the complexities of technological integration, maintain journalistic standards, and evolve their strategies to meet the shifting expectations of audiences (Abeywardana et al., 2023; Rasheed et al., 2024; Nordin et al., 2024). Effectively managing these challenges while leveraging the opportunities that media convergence offers will be crucial for media organizations striving to maintain relevance and success in an ever-changing media landscape. In the digital age, traditional media have undergone significant transformations, evolving from print media to radio broadcasts and television. This transformation has led to a shift from competition to collaboration between traditional and new media platforms. With the rapid expansion of media and supportive government policies, media integration has become a strategic national priority. Media integration in the digital era encompasses both technological convergence and the merging of the media and capital industries, promoting multi-level development across culture, communication, technology, and art. Against this backdrop, this study seeks to explore the role of digital marketing and artificial intelligence (AI) in enhancing sales performance within China’s media industry. Despite the growing significance of these technologies, some customers and employees may still lack full understanding of their impact. However, research indicates that the adoption of digital marketing and AI strategies can Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 182 https://internationalpubls.com significantly improve customer relationships, build trust, and enhance sales performance in the media sector. As highlighted by Indriasari, Gaol, and Matsuo (2019), customer loyalty is a critical factor for the survival and success of service organizations, including media entities. Understanding how digital marketing and AI contribute to improved sales performance can offer valuable insights for media organizations looking to strengthen customer relationships and foster business growth. Additionally, the study investigates the effects of AI-consumer interactions on firm performance, as noted by Payne, Dahl, and Peltier (2021). Examining the relationships between customers, media industry players, and fintech companies within the service ecosystem is essential to identifying how value is co-created among multiple stakeholders. Moreover, research by Aladayleh (2020) underscores the challenges faced by certain demographics, such as Jordanian citizens, in accessing media services. Overcoming these challenges requires innovative approaches, including the use of AI technologies to bridge technical gaps and reach underserved populations. This study aims to contribute to the existing body of knowledge by examining how digital marketing and AI enhance sales performance in China’s media industry. By exploring the relationships between customers, media players, and fintech companies, and investigating AI’s potential to address accessibility challenges, this research provides valuable insights for industry practitioners, policymakers, and researchers. A deeper understanding of these dynamics will help media organizations navigate the complexities of the digital landscape and drive sustainable growth in an increasingly competitive marketplace. LITERATURE REVIEW Numerous studies have explored the significance of digital marketing and artificial intelligence (AI) within the media industry, emphasizing their transformative potential and strategies for effective implementation. Boateng, Damptey, and Otu-Larbi (2020) highlighted the growing importance of digital marketing capabilities in the media sector, asserting that adopting these strategies is crucial amidst increasing competition. They argued that digital marketing strategies should be implemented not only at the organizational level but also within individual branches to better engage diverse customer segments. This approach could enhance customer outreach and foster stronger relationships, which, in turn, would contribute to improved business performance. In this context, digital marketing becomes a tool for media companies to extend their reach, connect with various customer groups, and strengthen their market presence. Similarly, Prokopis and Theodoridis (2019) underscored the critical role of effective data management and AI-driven marketing strategies in ensuring the success of digital marketing efforts. They emphasized how AI can facilitate personalized content creation, targeted audience segmentation, and adaptive services through the timely delivery of relevant information. By leveraging these capabilities, businesses can significantly enhance consumer engagement, providing more personalized experiences that resonate with their target audience. This ability to target and engage specific customer groups is invaluable in an era where consumer preferences are increasingly diverse. Emeh, Ahaiwe, and Okoro (2017) also supported the idea of utilizing diverse social media platforms, including Twitter, LinkedIn, and Sina Weibo, in marketing strategies. They highlighted that using multiple channels not only Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 183 https://internationalpubls.com increases a brand’s visibility but also amplifies audience engagement. With consumers increasingly engaging across various platforms, businesses in the media industry must adapt by embracing a multi- channel marketing strategy that extends their influence across various digital spaces. The transformative potential of digital marketing technologies is further supported by Banon, Alamsjah, and Elidjen (2021), who emphasized that digital marketing and AI talents can significantly enhance business performance. They advocated for integrating digital technologies into customer interactions, pointing out that the digital transformation of media organizations should extend beyond online and mobile services. Rather than focusing solely on transactional services, a holistic approach to customer engagement is necessary. This comprehensive strategy should address all touchpoints of the customer experience to drive sustained customer satisfaction and loyalty. Sceulovs and Lorencs (2017) also underscored the economic efficiency of digital marketing channels. They argued that businesses must carefully analyze the cost-effectiveness of digital channels to make informed decisions, ensuring that resources are allocated efficiently to maximize marketing impact. By doing so, media organizations can optimize their marketing efforts, which in turn improves their overall performance in the marketplace. Pradhan, Nigam, and Ck (2018) echoed the multifaceted benefits of digital marketing, particularly its ability to streamline operations, improve campaign effectiveness, and drive sales with minimal effort. They noted that digital technologies offer operational efficiency across various business functions, enabling businesses to save time and reduce costs while boosting their marketing effectiveness. On the other hand, Amelda, Alamsjah, and Elidjen (2021) emphasized that providing online and mobile services is only one aspect of digital transformation. Media companies need to go beyond these transactional platforms to innovate in customer engagement. By leveraging new digital tools, companies can ensure their sustained relevance in the rapidly changing digital landscape. These studies collectively highlight the profound impact of digital marketing and AI on the media industry. As the digital environment continues to evolve, businesses must remain agile in adopting emerging technologies to meet the changing demands of consumers. AI, in particular, is a key driver of transformation across industries. Rouhiainen (2018) explained that AI allows machines to learn from data through sophisticated algorithms, enabling them to make autonomous decisions that enhance productivity and efficiency. In marketing, AI’s ability to process large volumes of data quickly and continuously improves decision-making processes, optimizing the performance of marketing initiatives. Crittenden, Biel, and Lovely III (2019) pointed out AI’s role in performing tasks traditionally requiring human intelligence, including machine learning, robotics, and human-like interactions. Within media organizations, these AI-driven capabilities can be harnessed to deliver more personalized and targeted content, increasing consumer engagement and boosting audience retention. The role of digital marketing in today’s business environment cannot be overstated. Pradhan, Nigam, and Ck (2018) defined digital marketing as the use of technology to promote goods, services, and ideas via electronic media. According to Gkikas and Theodoridis (2019), digital marketing encompasses all marketing efforts that use the internet or electronic devices to present, promote, or sell products. The growing reliance on digital channels underscores the need for businesses to adapt their marketing strategies to remain competitive. Brand reputation, as Che, Katayama, and Lee (2020) noted, is also a critical factor in shaping consumer perceptions. It influences consumers’ willingness to purchase Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 184 https://internationalpubls.com products and services, and in this regard, digital marketing plays a crucial role in building and maintaining a positive brand reputation. CommSights (2019) further highlighted that a strong reputation enhances customer confidence, loyalty, and trust, all of which are essential for long-term business success. The convergence of AI and digital marketing is a pivotal development for businesses today. AI-driven technologies enable companies to harness data for personalized marketing campaigns, predictive analytics, and enhanced customer experiences. Digital marketing platforms, which include social media, search engines, and email marketing, provide multiple channels through which businesses can engage with their target audiences. This integration of AI and digital marketing creates new opportunities for companies to strengthen brand loyalty and customer relationships. By utilizing AI- powered analytics, businesses can gain insights into consumer behavior and sentiment, allowing them to tailor marketing efforts to meet customer needs more effectively. This deeper understanding of consumers enhances the relevance of marketing campaigns, which can ultimately increase conversion rates and sales. In addition to AI and digital marketing’s role in improving customer engagement, they are also crucial tools for driving organizational growth in a highly competitive marketplace. Dimitrieska, Stankovska, and Efremova (2018) predicted that AI bots would soon handle conversational functions and direct-to- consumer interactions, leading to significant time and cost savings for businesses. Wirtz (2019) reinforced this idea, emphasizing that the cost-effectiveness of AI technologies contributes to superior financial performance by reducing operational costs and increasing productivity. Conversion rates, which Zumstein and Kotowski (2020) defined as the percentage of website visitors who complete a purchase, are a key focus for businesses, particularly in e-commerce. Davide Di Fatta, Patton, and Viglia (2018) emphasized the importance of optimizing conversion rates to drive sales, while Zohra and Barman (2019) proposed techniques such as page optimization to improve these rates. AI and machine learning tools, such as Sales Qualified Leads (SQL) and Marketing Qualified Leads (MQL), allow marketers to measure real-time impacts on revenue growth, providing insights that can lead to faster, more effective customer service (Columbus, 2018). Shane Barker (2021) also emphasized AI’s ability to attract new customers, increase revenue, and enhance customer retention through strategies like chatbots, personalized recommendations, and AI-powered website builders. The integration of AI and digital marketing is reshaping modern marketing practices. As Kaur (2017) noted, leveraging digital marketing insights enables companies to align their strategies more effectively with business goals. By utilizing AI-driven technologies and digital marketing platforms, businesses can adapt to evolving consumer needs, driving growth and success in a competitive environment. AI and digital marketing offer diverse strategies for engaging target audiences, including email marketing, social media marketing, and search engine optimization (Saura, Palos-Sánchez, & Cerdá Suárez, 2017). These tools provide opportunities for businesses to enhance customer interaction, improve sales, and ultimately achieve sustainable success in the digital age. Brand reputation, as highlighted by Agmeka, Wathoni, and Santoso (2019), significantly influences consumer purchasing decisions. Consumers are more likely to purchase products or services from brands with a strong reputation, as this enhances their social status and perceived wealth. Companies that invest in building and maintaining a positive brand reputation are more likely to attract customers Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 185 https://internationalpubls.com and achieve higher sales performance. This highlights the growing importance of brand perception in today’s competitive business environment, where consumer trust and loyalty are essential for success. Digital marketing and AI also offer a cost-saving advantage over traditional marketing methods. Compared to conventional methods such as billboards and print media, digital marketing offers a more cost-effective way to reach a wider audience. Additionally, AI-powered tools can automate various tasks, reducing the need for manual labor and saving businesses both time and resources (Bala & Verma, 2018). This efficiency enables businesses to allocate their marketing budgets more effectively, maximizing their return on investment. Personalization in digital marketing is another critical factor in improving sales performance. Research indicates that personalized marketing efforts are more likely to resonate with target audiences, leading to higher conversion rates (Behera et al., 2020). Personalization allows businesses to tailor their messages and products to individual customer needs, increasing the likelihood of converting leads into sales. Therefore, businesses that adopt personalized digital marketing strategies are more likely to achieve their sales objectives. The theoretical underpinnings of digital marketing and AI adoption can be explored through models such as the Technology Acceptance Model (TAM). According to Mutoni (2018), TAM explains how individuals adopt new technologies, emphasizing the roles of perceived usefulness (PU) and perceived ease of use (PEOU). TAM suggests that users are more likely to adopt a technology if they believe it will improve their performance and if the technology is easy to use. This model has been widely used to understand consumer behavior toward digital marketing and AI technologies. TAM provides valuable insights into adoption trends, helping businesses identify factors that may hinder the acceptance of new technologies and design interventions to overcome these barriers. In addition to TAM, the Innovation-Decision Process Model offers another perspective on technology adoption. This model, described by Mutoni (2018), outlines five stages of innovation adoption: knowledge, persuasion, decision, implementation, and confirmation. It emphasizes the importance of information dissemination and the social dynamics that influence adoption decisions. The Technology- Organization-Environment (TOE) framework, introduced by Torn atzky and Fleischer (1990), also plays a critical role in understanding the challenges associated with AI adoption. This framework considers technological, organizational, and environmental factors, helping businesses identify barriers to innovation and develop comprehensive strategies for integrating AI into their operations. Together, these theoretical frameworks offer valuable insights into the factors influencing technology adoption, enabling businesses to design more effective strategies for implementing digital marketing and AI technologies and ensuring long-term success in the digital age. Figure 1: Conceptual Framework Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 186 https://internationalpubls.com Research Hypothesis H1: Digital Marketing (DM) is positively related to Innovative Marketing (IM) H2: Artificial Intelligence (AI) is positively related to Innovative Marketing (IM) H3: Digital Marketing (DM) is positively related to Sales Performance (SP) H4: Innovative Marketing (IM) is positively related to Sales Performance (SP) H5: Artificial Intelligence (AI) is positively related to Sales Performance (SP) H6: There is a Reciprocal Relationship between Digital Marketing (DM) and Artificial Intelligence (AI) RESEARCH METHODOLOGY In research, the concept of "population" refers to the entire group of individuals or entities relevant to the study, characterized by traits such as demographics, preferences, or behaviors, as Azam et al. (2021) suggest. For this study, the population includes employees of media companies and customers in Henan province, China, aligning with the aim of exploring how digital marketing and artificial intelligence impact sales performance in the media sector. The research targets both internal (employees) and external (customers) dynamics within the media industry. Employees in roles such as journalists, editors, and marketers are included to capture insights from those directly involved in media operations, while customers represent the external audience engaging with media content. Henan, one of China’s most populous provinces, provides contextual relevance by offering a diverse media landscape, which can reflect broader industry trends. The unit of analysis is individual employees and customers, allowing for a detailed examination of perceptions and behaviors within the media ecosystem. To ensure generalizability, probability sampling will be used, offering every member of the population a known chance of selection (Azam et al., 2023). A sample size of 300 employees and 250 customers will be chosen, with surveys serving as the primary data collection tool, ensuring validity and reliability through expert review and pilot testing. DATA ANALYSIS AND FINDINGS The demographic analysis of this study provides important insights into the gender and age composition of the 240 respondents, revealing the gender distribution and its potential influence on study outcomes. Gender is a critical factor in research as it can affect opinions, behaviors, and opportunities, making it essential to consider how it might shape the findings. Of the total respondents, 62 (25.8%) were male, while a significantly larger proportion, 178 (74.2%), were female. This notable gender difference is important for understanding the study’s overall demographic dynamics and ensuring that the gender composition is factored into the interpretation of results. The age distribution is categorized into four groups: 18-29 years, 30-39 years, 40-49 years, and 50 years and above. The largest group, aged 30-39 years, accounted for 48.3% of the sample (116 respondents), followed by the 18-29 years group, which represented 31.3% (75 respondents). The 40-49 years group made up 15% (36 respondents), while the smallest group, those aged 50 and above, constituted the remaining 5.4%. The age distribution suggests a concentration of respondents in the younger to middle-aged categories, likely reflecting the active workforce involved in the media and marketing sectors, where Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 187 https://internationalpubls.com digital marketing and AI integration are more common. The study’s demographic analysis is crucial in providing context to the research, as different age groups and genders may possess varying perspectives, experiences, and attitudes that could influence how digital marketing and AI are perceived, utilized, and evaluated in relation to sales performance. Furthermore, the study employs Principal Component Analysis (PCA) with Varimax rotation to examine the factor loadings of the key constructs such as Digital Marketing (DM), Artificial Intelligence (AI), Innovative Marketing (IM), and Sales Performance (SP). PCA helps clarify how the observed variables align with these latent constructs, providing a deeper understanding of the relationships between different aspects of marketing strategies. High loadings within each dimension indicate strong correlations between the observed variables and the theoretical constructs, thereby validating the research’s framework. The results underscore the media industry’s increasing reliance on digital and innovative marketing strategies, augmented by AI tools, to enhance sales performance in China. This finding highlights the growing trend of using data-driven marketing approaches to stay competitive in the media sector. The PCA results further illustrate the relevance of these constructs and the interconnectedness of digital marketing, AI, and innovation in driving business outcomes. The study also confirmed the internal consistency of the constructs by calculating Cronbach’s Alpha coefficients for the four factors retained by PCA. The reliability of these factors was critical to ensuring that the items used in the questionnaire effectively measure the intended constructs, contributing to the credibility of the research findings. In line with established guidelines (Cronbach, 1951; Sekaran & Bougie, 2010), Cronbach’s Alpha was employed to assess the reliability of the factors, confirming the robustness of the measurement model. The Cronbach’s Alpha values for each factor summarize the reliability statistics. Digital Marketing (DM) achieved an Alpha of 0.909, indicating excellent internal consistency, while Artificial Intelligence (AI) had a Cronbach’s Alpha of 0.833, reflecting good reliability. Innovative Marketing (IM) recorded an Alpha of 0.827, signifying acceptable internal consistency, and Sales Performance (SP) had a Cronbach’s Alpha of 0.833, demonstrating good reliability as well. The overall Cronbach’s Alpha for the 29 items across the four factors was 0.897, which indicates strong internal consistency for the entire scale. These results provide evidence that the scales used to measure DM, AI, IM, and SP are reliable, thus enhancing the credibility of the research findings. Construct reliability is a crucial aspect of quantitative research, ensuring that the items in each factor consistently measure the intended concept. As noted by Cronbach (1951) and Sekaran and Bougie (2010), an Alpha value above 0.70 is generally considered acceptable, with values above 0.80 reflecting good reliability and values exceeding 0.90 indicating excellent consistency. In this study, the Cronbach’s Alpha values for the individual constructs fall within the range of good to excellent, providing strong evidence of the reliability of the measurement instruments. The study’s reliability analysis reveals high internal consistency across all four key factors: Digital Marketing, Artificial Intelligence, Innovative Marketing, and Sales Performance. For the Digital Marketing factor, comprising 11 items, the Cronbach’s Alpha was 0.909, signaling excellent reliability in measuring various aspects of digital marketing strategies, tools, and effectiveness. This is particularly important given the significant role that digital marketing plays in the modern business environment, especially in sectors like media, where technology and digital platforms are central to Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 188 https://internationalpubls.com marketing efforts. The Artificial Intelligence factor, consisting of five items, achieved a Cronbach’s Alpha of 0.833, demonstrating good internal consistency. This result highlights the effectiveness of the items in capturing the role of AI in personalizing marketing efforts, analyzing data, and optimizing business operations. The Innovative Marketing factor, with seven items, showed a Cronbach’s Alpha of 0.827, reflecting good reliability in assessing aspects like marketing innovation, adaptability, and the use of feedback mechanisms to drive strategy. This is significant, as innovation is considered a competitive advantage, particularly in dynamic markets like media, where consumer preferences and technologies evolve rapidly. The Sales Performance factor, with six items, yielded a Cronbach’s Alpha of 0.833, signifying good internal consistency. This construct measures the success of marketing strategies in achieving sales goals and contributing to overall business performance, making it crucial for understanding the relationship between marketing efforts and financial outcomes. The high reliability of these factors supports the validity of the research scales and enhances the credibility of the study’s findings. In addition to Cronbach’s Alpha, the study also examines the correlations between the key constructs using correlation analysis, which offers valuable insights into the relationships between Digital Marketing, Artificial Intelligence, Innovative Marketing, and Sales Performance. The correlation between Digital Marketing and Artificial Intelligence is 0.220, indicating a weak but positive relationship between the two constructs. This suggests that while digital marketing and AI are linked, they operate independently, each contributing uniquely to the overall marketing strategy. The correlation between Digital Marketing and Innovative Marketing is 0.361, reflecting a moderate positive relationship, indicating that digital and innovative marketing strategies overlap in certain areas, such as creative digital platform usage. The correlation between Digital Marketing and Sales Performance is 0.272, showing a moderate positive relationship, meaning that digital marketing efforts are generally associated with improved sales outcomes, although other factors may also influence performance. The correlation between Artificial Intelligence and Innovative Marketing is 0.487, which is relatively strong, suggesting that AI plays a significant role in driving innovation in marketing practices. AI’s ability to analyze data and personalize marketing strategies contributes to more innovative approaches in customer engagement. Similarly, the correlation between Artificial Intelligence and Sales Performance is 0.334, indicating a positive impact of AI on sales, highlighting the importance of data-driven marketing strategies in driving business results. Lastly, the correlation between Innovative Marketing and Sales Performance is 0.487, demonstrating that innovative marketing approaches are strongly associated with improved sales performance through better customer engagement and satisfaction. Overall, these moderate to strong correlations support the discriminant validity of the study, confirming that the constructs are distinct but related, and provide a solid foundation for understanding their impact on marketing strategies and business outcomes. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 189 https://internationalpubls.com Figure 2: Examine Discriminant Validity between Constructs Revision The revised measurement model, as shown in Figure 2, demonstrates significant improvements in its fitness indices. The Root Mean Square Error of Approximation (RMSEA) is reported at 0.070, indicating a moderate fit according to conventional standards (Hu & Bentler, 1999). The Comparative Fit Index (CFI) is 0.870, suggesting a relatively good fit when compared to a null model (Bentler, 1990). Additionally, the Parsimonious Fit Index, represented by the Chi-Square divided by degrees of freedom (ChiSq/df), is 2.457, which is within the acceptable range for model parsimony (Kline, 2016). These enhancements confirm that the modifications to the model were effective, as the observed values align closely with the recommended thresholds for good model fit. Consequently, no further changes are necessary. The findings indicate that the revised model adequately represents the data, meeting the criteria for evaluating model fit in structural equation modeling (SEM) (Byrne, 2010). The full path model, shown in Figure 3, reflects this rigorous evaluation. Figure 3: Fit Indexes and Parameter Estimates of the Revised Hypothesized Model After identifying issues with the initial hypothesized model, the structural model was revised and re- evaluated using AMOS (Version 21.0) to address discrepancies between the sample data and the model’s implied covariance matrix. The revisions led to significant improvements in several key goodness-of-fit indices, including the Normed Chi-square (CMIN/DF), Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI). One of the most notable improvements Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 190 https://internationalpubls.com was observed in the Normed Chi-square, with a CMIN/DF of 2.411, which is below the threshold of 3.0 commonly considered acceptable, indicating a good fit (Tabachnick & Fidell, 2007). This suggests that the revised model better aligns with the observed data and strikes an appropriate balance between complexity and sample size. The RMSEA for the revised model was 0.068, which is below the recommended cut-off of 0.08 for complex models (Hair et al., 2010), indicating a close fit between the sample’s covariance matrix and the model’s implied matrix. This result suggests that the revised model successfully captures the relationships among the variables without overfitting or underfitting. Furthermore, the CFI for the revised model was 0.879, which, while still below the ideal benchmark of 0.90, represents an improvement over the initial hypothesized model (Byrne, 2010). The CFI measures the improvement in fit relative to a null model, with values closer to 1.0 indicating better fit. While the CFI of 0.879 suggests that model fit could be further enhanced, it indicates that the revisions have brought it closer to an acceptable level, especially considering the model’s complexity and the large sample size. The Chi-square statistic in the revised model may have been influenced by the large sample size (n = 240), as Chi-square tests are sensitive to sample size and can result in significant results even when the model fits reasonably well (Byrne, 2010). In such cases, researchers often rely more on other indices, such as RMSEA and CFI, as they are less affected by sample size and offer a more nuanced evaluation of model fit. The improvements in the goodness-of-fit indices, particularly CMIN/DF and RMSEA, suggest that the revisions effectively addressed the discrepancies identified in the initial analysis. The reduction in the Normed Chi-square value indicates that the revised model is more parsimonious, balancing model complexity with fit. Parsimony is crucial in model evaluation, as overly complex models can lead to overfitting, where the model fits the sample data too closely and fails to generalize to other samples. The RMSEA value of 0.068 further supports the adequacy of the model, as it shows that the model closely approximates the population covariance matrix. This result is particularly important in social science research, where models must account for complex relationships among variables. The improvement in RMSEA from the initial hypothesized model demonstrates that the revisions reduced the error of approximation, making the model more reflective of the underlying data structure. The CFI of 0.879, though slightly below the ideal threshold of 0.90, provides additional evidence that the revised model is an improvement over the initial hypothesized model. In complex models, a CFI slightly below 0.90 may still be acceptable if other fit indices are within reasonable ranges (Hair et al., 2010). The AMOS output after the revisions did not suggest further modifications, indicating that the revised model had reached a point of satisfactory fit where no additional adjustments were necessary. This absence of modification suggestions implies model stability, suggesting that further changes might lead to overfitting the model to the sample data. The improvements in model fit indices following the revisions underscore the importance of iterative model testing and refinement in structural equation modeling (SEM). This iterative process is essential to ensure that the final model is both statistically robust and theoretically sound. Hypothesis testing, an integral part of statistical analysis, was conducted using Maximum Likelihood Estimates (MLE) in SEM to evaluate the relationships among the constructs of Digital Marketing (DM), Artificial Intelligence (AI), Innovative Marketing (IM), and Sales Performance (SP). The results Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 191 https://internationalpubls.com of these tests, provide estimates, standard errors, critical ratios, and p-values for each hypothesized relationship. The first hypothesis, testing whether Digital Marketing significantly impacts Innovative Marketing, yielded an estimate of 0.440, a standard error of 0.119, and a critical ratio of 3.713 (p < 0.001), indicating a positive influence of DM on IM. The second hypothesis, examining the effect of AI on Innovative Marketing, produced an estimate of 0.468, a standard error of 0.084, and a critical ratio of 5.603 (p < 0.001), suggesting that AI significantly enhances innovative marketing strategies. The third hypothesis, focusing on the impact of Innovative Marketing on Sales Performance, provided an estimate of 0.286, a standard error of 0.075, and a critical ratio of 3.823 (p < 0.001), supporting the idea that innovative marketing practices contribute to improved sales performance. The fourth hypothesis tested the direct effect of Digital Marketing on Sales Performance, yielding an estimate of 0.176 (p = 0.067), which indicates a marginally non-significant relationship. Similarly, the fifth hypothesis examined AI’s direct influence on Sales Performance, yielding an estimate of 0.129 (p = 0.065), suggesting a modest effect. Finally, the study tested the correlation between Digital Marketing and Artificial Intelligence, which returned an estimate of 0.045 (p = 0.002), indicating a significant and moderate positive relationship between these two constructs. The hypothesis testing results reveal that both Digital Marketing and Artificial Intelligence positively impact Innovative Marketing, suggesting that organizations leveraging these technologies are better positioned to implement innovative marketing strategies. Additionally, Innovative Marketing significantly affects Sales Performance, indicating that creative marketing approaches enhance sales outcomes, aligning with existing literature on the competitive advantages of innovation (Anderson & Gerbing, 1988; Byrne, 2010). However, the direct effects of Digital Marketing and AI on Sales Performance were not statistically significant, suggesting that their impact on sales is more indirect, operating through the enhancement of Innovative Marketing. The significant correlation between Digital Marketing and AI highlights their complementary roles, as organizations skilled in digital marketing are likely to adopt AI tools, thereby improving overall marketing effectiveness. The main findings of the study are confirming that Digital Marketing and Artificial Intelligence significantly influence Innovative Marketing, while the direct influence of these factors on Sales Performance was not supported. Furthermore, the study identified a significant reciprocal relationship between Digital Marketing and AI, underlining their complementary roles in driving business outcomes. These findings underscore the importance of integrating digital and AI technologies into marketing strategies to foster innovation and improve sales performance, particularly within the context of the Chinese media industry. CONCLUSION AND IMPLICATIONS This study investigates the role of digital marketing and artificial intelligence (AI) in enhancing sales performance within the media industry in Henan Province, China. In the context of the increasing digitalization of the industry, digital marketing and AI have become essential tools for media organizations seeking to maintain a competitive advantage. The study explores three key aspects: how digital marketing and AI improve sales performance, employee perspectives on these technologies, and the relationship between digital marketing and AI in contributing to sales outcomes. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 192 https://internationalpubls.com To achieve its objectives, a survey was conducted using a structured questionnaire distributed to 240 respondents via JotForm and popular Chinese social media platforms such as WeChat, Weibo, and Sina Weibo. The demographic profile of respondents revealed that 74.2% were female and 25.8% were male, with the majority aged between 30 and 39 years (48.3%), followed by those aged 18-29 (31.3%), 40-49 (15%), and 50 years and above (5.4%). This demographic diversity provided valuable insights into various perspectives within the media industry in Henan, enhancing the overall analysis. To ensure the reliability and validity of the data, a normality test was conducted, confirming that the data were normally distributed as the skewness values fell within acceptable limits. Although a few outliers were detected, they were removed to maintain the accuracy and reliability of the dataset. The study employed descriptive analysis and T-tests to assess the survey results. Descriptive analysis revealed high mean scores for the survey items, indicating that respondents understood the questions well and that the data were valid and reliable. The T-test was used to determine if there were significant differences between the impacts of digital marketing and AI on sales performance. The results showed no significant difference between the two technologies, with both exhibiting values above the p-value threshold of 0.05. This finding suggests that digital marketing and AI contribute equally to enhancing sales performance, emphasizing that they should be integrated into a unified strategy rather than treated as separate tools. The analysis highlights the critical role of digital marketing and AI in the media industry. The validation of data through normality tests, descriptive analysis, and T-tests ensures that the results are both robust and reliable. The equal contribution of digital marketing and AI to sales performance suggests that media organizations should integrate these technologies to maximize their effectiveness. Additionally, the demographic analysis allows organizations to tailor their digital marketing and AI strategies to different demographic groups, improving the effectiveness of marketing efforts and ultimately driving better sales outcomes. A bivariate correlation test was also conducted to explore the relationship between digital marketing and AI. The results revealed a moderate correlation between the two variables, indicating that while they are related, they each play distinct roles in enhancing sales performance. This finding highlights the complementary nature of digital marketing and AI, as both technologies are essential but have unique contributions to sales outcomes. A reliability test was performed using Cronbach's Alpha, which yielded a value of 0.94, well above the recommended threshold of 0.7, indicating high internal consistency and confirming that the survey items reliably measured the intended constructs (Djakasaputra et al., 2021). This research makes a significant contribution to understanding how digital marketing and AI can improve media sales performance, providing empirical evidence in the context of China’s media industry. It adds to the existing body of knowledge, which has predominantly focused on Western contexts (Gherardi, 2009a; Gherardi, 2009b; Swan, 2007; Tsoukas, 2005). Conducted in China, this study offers a fresh perspective on how digital marketing and AI influence media organizations’ performance in a non-Western setting. Based on the findings, the study recommends that media organizations and policymakers prioritize digital marketing and AI when developing business strategies. The results underscore the importance Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 7s (2025) 193 https://internationalpubls.com of these technologies in driving sales performance and suggest they should be integrated into strategic frameworks. The study also emphasizes the need for constructive competition within media networks, suggesting that organizations foster an environment that encourages competition to optimize their digital marketing strategies. Finally, the study calls for future research on the relationship between innovative marketing and sales performance, particularly within China's media industry. By addressing this gap, the study offers valuable insights into how innovative marketing strategies can enhance sales outcomes. This is especially important in an industry where organizations must continuously adapt to stay competitive in an evolving digital landscape. 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