Unveiling the Impact: AI-Driven Marketing tactics on Implementation Strategy – Systematic Literature Review (SLR) Approach

Introduction

Artificial Intelligence (AI) is undoubtedly a buzzword in today’s corporate world, considering the diverse benefits that firms are argued to derive from the adoption of AI models, solutions, and systems in their business operations. This probably explains why Kumar et al (2019) assert that AI is a technological solution for addressing the contemporary problems faced by organizations and the benefit as contended by Paschen et al (2019) is not limited to any particular sector of the local or international economy. This understanding explains why researchers, policymakers, and strategists have constantly explored ways of leveraging AI to optimize outcomes across different organizations. – Additionally, developers of AI solutions have consistently delivered, offering a wide range of AI products. These products, both free and paid, address the operational pain points of companies of all sizes – small, medium, and large-scale enterprises. (Jianjun et al., 2021). With the proliferation of AI solutions, more business owners are searching for the most suitable AI tools that can meet their business needs as it is given that such solutions can deliver more than the conventional human capabilities in most cases (Molsa, 2016). To this end, the question of what and, most importantly, how AI affects the implementation of marketing strategy is one that Chief Executive Officers might want to consider before approving the integration of AI in marketing which is a core function of the organization.

The American Marketing Association (2017) saw marketing as a set of institutions, activities, and systematic processes that organizations use to create value, communicate, and deliver value to their customers, partners, society, and other stakeholders. Marketing cannot be tagged as successful when it does not contribute positively to the bottom line of the organization, and this explains why organizations deploy marketing strategies to drive the achievement of certain outcomes. That notwithstanding, what is learnt from research is that marketing strategy may not guarantee optimal outcomes unless it is effectively implemented (Mohammed & Ghaleb, 2022; Rinku, Priyanka & Sajid 2021). Unfortunately, implementing strategies has been a challenge for organizations and understanding how AI contributes to successful implementation is non-negotiable at this point.

Existing research already shows that artificial intelligence addresses some of the strategic marketing questions of organizations, fostering innovativeness and efficiency in the marketing process which is critical to achieving the short, medium, and long-term goals of the organization (Enholm, Papagiannidis, Mikalef and Krogstie, 2021). Also, the position of Darlington (2023) suggests that the introduction of AI in the marketing process has contributed to improving customer referral and brand association which was a herculean task for marketers to address before now. The studies by Irene (2019) still showed that AI was becoming a fundamental aspect of marketing strategy performance which has enabled most organizations to compete effectively in the increasingly competitive business environment. More marketers are shifting from the use of traditional planning tools, metrics, and channels to AI given their conviction that better outcomes can be achieved from the application of AI (Rinku, Priyanka and Sajid, 2021). Irrespective of the extent of the application of AI in the marketing system, Mohammed and Ghaleb (2022) maintained that it would have a multiplier effect on the organizational performance which is the result that organizations seek to achieve. While these and other authors’ positions validate the fact that AI was being used as a part of a successful marketing strategy (Kavya, Hariharan and Chandrakanthan, 2020), there is a huge gap in “how” AI affects marketing strategy implementation.

In addressing the clearly defined gap, this paper was inclined towards understanding how AI affects marketing strategy implementation towards exploring ways of optimizing outcomes. The study argues that optimizing the implementation of marketing strategy is strongly dependent on understanding how AI affects it. Without such understanding, knowledge about the best approach for leveraging AI in the marketing strategy implementation process will be difficult. Hence, the research intends to unearth knowledge regarding how AI affects the implementation of marketing strategy which will open new conversations around enhancing the success of marketing strategies through AI. This will empower AI solution providers with knowledge of what features to upgrade in their value proposition, empowering business owners with insight that will assist in optimizing their AI investment, improve marketing outcomes and inspire further research on the subject.

Literature Review

Organizations understand that marketing is fundamental to the achievement of their corporate objectives (Muhammad and Gang, 2019). Beyond this realization, empirical evidence suggests that marketing is only as good as the marketing strategy of the organization and it is not the strategy on paper that delivers results, it is the implementation. This, according to Sundarman and Lailla (2023), involves the allocation of resources, determining capabilities, managing the marketing activities and ensuring the right capacity is maintained. In contrast, marketing strategy implementation deals with managing the marketing mix variables, price, product, place, promotion, people, physical evidence, and process to ensure that it meets the goals of the organization (Yusuf, Astuti, and Ariani, 2022). According to Hailemariam (2020), marketing strategy implementation describes all the activities that a company needs to carry out to execute its marketing strategy effectively and efficiently. Consequently, it is the effectiveness of the implementation of a marketing strategy that guarantees the performance of the organization in the marketplace (Elefachew, 2021). If marketing strategy is an instrument, the implementation of the marketing instrument as claimed by Mustapha (2017) is what is essential to the success of the organization.

From the position of some existing research, there is an emphasis on integrating AI to understand the preferences of customers which can enable the organization to align its operations towards delivering the desired results (Kavya, Hariharan and Chandrakhanthan, 2020). It was even argued by Rinku, Priyanka and Sajid (2021) that marketing strategy requires significant integration of AI to enable effective personalization of the marketing communication process. From a conceptual standpoint however, Yadav and Dwivedi (2023) described artificial intelligence as systems with demonstrated cognitive abilities including Autonomous driving cars, Voice Assistants, Chat GPT, Google Siri, etc. These computerized robotic systems process data that deliver results by solving complex problems logically. Within the context of AI application, it was argued by Marinchak et al (2018) that AI supports the transformation of the traditional marketing approach, enabling organizations to achieve an efficient marketing process. Similarly, Toorajipour et al (2021) recognized the value of AI in saving money and time wasted on irrelevant transactions thereby addressing the productivity and profitability needs of the firm. More so, Ansari and Riasi (2016) highlight the valuable role of AI in making predictions about the size of the market and making other strategic decisions towards maximizing the scarce budget of the organization.

According to Gonzales (2023), it could be claimed that the application of AI to business and management studies started within the last few decades, but it has undoubtedly facilitated the development of products, transformation of marketing outcomes and the achievement of strategic organizational objectives. Empirically, Singh et al (2023) sought to understand AI’s impact in social media contexts using a quantitative analytic approach. The findings confirmed that online marketing significantly improved because of AI. Chintalapati and Pandey (2022) further argued that AI-powered marketing tends to deliver superior experiences and outcomes for the business and the targeted customers. In essence, other research conducted on the subject showed that AI addresses the bulk of the major marketing and marketing strategy questions facing contemporary organizations (Nandan and Nath, 2020; Shih-Yu, 2019; Thiraviyam, 2018). What appears to be a gap, though covered saliently in most existing research, is ‘how’ AI contributes to the implementation of marketing strategy. The construct for critical success factors in marketing strategy implementation includes having clearly defined objectives, effective customer segmentation and targeting, optimization of the marketing mix, integration of digital channels, and openness to continuous improvement, among other factors (Khanna, Ahuja & Popli, 2020; Micu, Capatina, & Micu, 2018).

Research Question

These under-listed questions shall be addressed in this study:

1. How has AI affected the implementation of marketing strategies?
2. What are the critical success factors for improving the effect of AI on the implementation of marketing strategies?

Materials & Methods

The researcher understands that extensive studies have been carried out on the link between marketing and AI but with salient emphasis on how marketing strategy implementation has been affected by, AI which is a gap. Hence, the secondary qualitative method was deemed suitable for the investigation and the Systematic Literature Review (SLR) approach was chosen. The SLR is a structured, effective, and reliable approach to finding, analyzing, and synthesizing evidence from existing research reports towards providing answers to a research question (Lame, 2023). The SLR procedure follows the process of developing the inclusion and exclusion criteria of the study, developing a search strategy which includes the database for the search, selection of study, data extraction, assessment of quality, and data synthesis as it relates to the research question and discussion.

Following the above SLR protocol, the papers to be included must focus on “AI” and “Marketing Strategy Implementation”, studies must be conducted between 2015 and 2024, the publication shall be a journal and the language must be English. The electronic database to be used shall be Google Scholar and Boolean, strings such as “AI AND Marketing Strategy OR Marketing Strategy Implementation” were used. Before choosing the final paper for the analysis, the researcher screened the title, and abstracts of the journal, and scanned through the content to determine their relevance. After this, the researcher subjected the pre-final papers to quality assessment using the CASP Appraisal framework before shortlisting only quality papers for the research purpose (Long et al, 2020).

Procedures

The search on Google Scholar as clarified in the previous section took one week with constant iterations in the search keywords and strings until a total of 10 papers were selected as suitable and valuable for the research. The flow chart of the overall procedure is shown in Figure 1 below:

Figure 1. Search flowchart.

Source: Author (2024)

The qualitative approach to analysis shall be used in this study whereby the researcher followed the thematic analytic approach to identify keywords from the papers. These keywords also known as codes were grouped to form themes which were reviewed by a colleague. This was subsequently used as the foundation for discussing and presenting the findings of the research under relevant research questions.

Result

Table 1 below shows the demographic characteristics of the papers which were selected for this research. It is observed that all the selected studies have a global focus, indicating a gap in terms of conducting country-specific investigations about the impact of AI on market research strategy implementation. Also, most of the studies have used a qualitative secondary methodology with only one of the studies using quantitative. In terms of focus, there has been a focus on the nexus between AI and the implementation of marketing strategies among firms (Shaw et al, 2019), decision-making among firms in their marketing process (Stone et al, 2020), and the opportunities and challenges that exist (Bruyn et al, 2020). Some other notable studies sought to explore the future of marketing in the domain of AI (Davenport et al, 2020; Yegin, 2020) and the diverse applications of AI to marketing were explored amidst their relationship with the marketing strategy process (Sabharwal, Rittu and Verma, 2022; Shaik, 2023).

Table 1: Demographics of selected papers

Source: SLR (2024)

The above-selected papers, therefore, formed the basis for the answers provided to the research questions hereinafter.

First Research Question

The first question to be addressed is: How has AI affected the implementation of marketing strategies?

Following the analysis, it was observed that very few studies have focused their studies on understanding how marketing strategy implementation is being affected by AI. The first is in terms of scenario analysis where Stone et al (2020) studies confirmed that implementation is effective where marketing strategy is supported by AI wherein different scenario analyses will form the basis for improvement in the implementation process. Secondly, other evidence suggests that prediction which is a challenge in the implementation process has also been affected by AI, wherein Bruyn et al (2020) research confirmed that prediction has been improved through AI.

Most of the authors believed that the implementation process, practice, and systems for marketing strategy have been optimized by AI adoption (Liu and Chen, 2021; Shaik, 2023; Verma et al., 2021). Fourthly, the evidence identified suggests that complex implementation challenges were being addressed by AI integration in the marketing strategy process which the study of Davenport et al (2020) confirmed.

Second Research Question

The second question to be addressed is: What are the critical success factors for improving the effect of AI on the implementation of marketing strategies?

Following the analysis, five critical success factors were identified as fundamental to achieving effective outcomes from the integration of AI in the implementation of marketing strategy. First, stakeholder support is crucial as confirmed by Shaw et al (2019) research which asserts that meaningful implementation of AI in marketing strategy requires the support of internal and external stakeholders as required for implementation.

Another success factor is continuous evaluation since there is a potential that marketing strategy implementation may not be effective despite AI integration (Stone et al, 2020). Hence, with a system of continuous evaluation, issues can be spotted and addressed in good time towards exploring feasible options for resolution (Shaik, 2023).

A third factor observed from existing research is the need for staff to have the capacity to effectively integrate and manage AI in their marketing process. The staff need to be able to ensure that the marketing strategy implementation objectives are clearly defined towards ensuring the appropriate deployment of the solution (Bruyn et al, 2020). Similarly, it was contended by Liu and Chen (2021) that the marketing team need to be in a structured and realistic learning environment to be able to manage the AI solution effectively.

The need for effective data protection and ethical compliance cannot also be overemphasized when exploring ways of optimizing the way AI impacts the implementation of marketing strategy (Vlacic, 2021). It was confirmed that AI requires access to confidential information about customers, partners, suppliers, and other details which may result in reputational damage when that information gets out to the public domain.

Lastly, the need for continuous development of AI solutions was found to be a critical success factor for the effective implementation of marketing strategy.  Davenport et al (2020) assert that implementation issues may arise where existing AI solutions do not fit with the context, hence the need to constantly improve the system (working with collaborators or partners) to ensure appropriate alignment which is fundamental to achieving the desired results.

Discussion

The findings of this research have been crucial in understanding how AI affected the implementation of marketing strategies among organizations. The effect was observed in terms of the help provided by AI in analyzing different scenarios, leading to better implementation strategies (Stone et al, 2020). This has also improved the accuracy of predictions, a crucial factor in successful implementation while tackling intricate implementation hurdles, leading to smoother execution (Bruyn et al, 2020; Davenport et al, 2020), also affected the optimization process wherein the research suggests that AI optimizes the marketing strategy implementation process itself (Liu and Chen, 2021; Shaik, 2023; Verma et al., 2021). These findings and evidence from the literature suggest that AI plays a significant role in enhancing marketing strategy implementation. While it was established that AI has affected marketing strategy implementation in terms of scenario analysis, prediction, addressing complex implementation challenges, and improving customer referral and brand association (Stone et al., 2020; Bruyn et al., 2020; Davenport et al., 2020; Darlington, 2023), it could be deduced from general literature that AI makes positive contribution in transforming the marketing approach, achieving efficiency, and delivering superior experiences and outcomes (Marinchak et al., 2018; Chintalapati and Pandey, 2022).

Concerning research question two, some critical success factors were observed, including the discovery that internal and external stakeholder buy-in are crucial for successful AI implementation while continuous evaluation was linked to effective implementation (Shaw et al, 2019; Stone et al, 2020; Shaik, 2023). It was also discovered that staff requires contemporary skills and knowledge to manage AI effectively in marketing processes (Bruyn et al, 2020; Liu and Chen, 2021). Other results point to the need for continuous improvement and data protection as fundamental to achieving effective outcomes. Some of the results, especially the need for continuous evaluation and improvement of a solution for effective marketing strategy implementation, converged with the position of previous literature. For instance, while some scholars argue that AI plays a critical role in addressing strategic organizational objectives, such studies implicitly highlight the need for continuous improvement (Singh et al., 2023; Gonzales, 2023). As a point of departure, while this research focused on identifying critical success factors for improving the effect of AI on marketing strategy implementation, most literature tends to focus on providing a broader overview of AI’s impact on marketing strategies. Hence, most studies were not bothered about how critical success factors such as stakeholder support, continuous evaluation, staff capacity, data protection, and ethical compliance could determine marketing outcomes (Shaw et al., 2019; Shaik, 2023; Vlacic, 2021), but were rather concerned with discussing the role of AI in addressing major marketing questions, transforming marketing outcomes, and achieving strategic organizational objectives (Nandan and Nath, 2020; Shih-Yu, 2019).

Notwithstanding the findings about research questions one and two above, it is clear it has been established by this study that AI significantly improves marketing strategy implementation by affecting core areas of implementation such as analyzing different scenarios, predicting outcomes more accurately, and even streamlining the implementation process. To achieve optimal outcomes from this process, it was discovered as part of the critical success factors that an organization needs to focus on ensuring constant stakeholder support, continuous evaluation, and proper data protection among others. It is on this premise that the key conclusion of the research is advanced in the next section.

Conclusion

This study investigated the impact of artificial intelligence (AI) on implementing marketing strategies and the analysis revealed that AI positively affected the implementation process of marketing strategy. This is achieved through its effectiveness in considering various scenarios (scenario analysis), making more accurate predictions, and overcoming complex implementation challenges. This was corroborated by research such as Stone et al. (2020) validation of AI’s critical role in optimizing the marketing strategy implementation process. Furthermore, it aligns with existing literature in demonstrating the positive contributions of AI to transforming marketing approaches, achieving efficiency gains, and delivering superior results (Marinchak et al., 2018; Chintalapati & Pandey, 2022), leading to increased customer referrals and stronger brand associations among others (Darlington, 2023).

The research also identified critical success factors for successful AI implementation, including the need for stakeholder buy-in, continuous evaluation, staff capacity in AI utilization, continuous improvement of the AI solution, ethical compliance, and data protection. In alignment with these, Shaw et al. (2019) emphasized the importance of similar factors for effective AI integration into marketing processes. Aside from this, most of the literature tends to have focused on a more comprehensive view of the role of AI in transforming marketing outcomes and achieving strategic objectives (Nandan & Nath, 2020; Shih-Yu, 2019). However, this study and existing literature converge on the critical importance of continuous improvement and evaluation to optimize the contribution of AI to marketing strategy implementation.

Managerial Implication

The findings of the research result in some fundamental managerial implications for organizations seeking to leverage artificial intelligence (AI) effectively in their marketing strategy implementation. First, the findings strongly suggest that AI significantly improves the analysis of various scenarios, leading to more informed decision-making processes. This suggests the need for managers to explore further investment in AI tools and platforms that enable scenario analysis capabilities to optimize their marketing strategies. Similarly, the findings confirmed that organizations must invest in developing the skills and knowledge of their staff to effectively utilize AI in marketing processes. This could be achieved through structured training programs and workshops to upskill their marketing teams in AI technologies and methodologies. Also important is the need for organizations to establish mechanisms for ongoing evaluation of AI solutions to identify areas for improvement and optimization. This is justified on the premise that continuous evaluation and improvement are identified as critical success factors for effective AI implementation in marketing strategies. The findings further stress the need for organizations to engage internal and external stakeholders to garner support for AI initiatives and ensure compliance with data protection regulations. This should also function within a framework where a robust data protection measure is implemented to safeguard sensitive information and maintain trust with customers and partners

Limitations

This research is limited in terms of the dependence on secondary data while ignoring options such as surveys or interviews which may have resulted in in-depth insight into the subject. Also, the research focused primarily on literature published between 2015 and 2024, resulting in the exclusion of studies conducted on the subject before the period. Similarly, the selected studies were not focused on any specific industry, region or country, thereby making it difficult to generalize the results. The use of the CASP appraisal framework may have enhanced the quality of the findings, but inherent methodological issues of the selected papers limited the findings. On account of these, future research needs to explore a primary research approach suitable for addressing the research question from a contemporary perspective.

References

• Ansari, A., & Riasi, A. (2016). ‘Modeling and Evaluating Customer Loyalty Using Neural Networks: Evidence From Startup Insurance Companies,’ Future Business Journal, 1(2), 15–30.
• Arsenijevic, U., & Jovic, M. (2019). ‘Artificial intelligence marketing: Chatbots’ In International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) (pp. 19–193). IEEE.
• Bruyn et al (2020) Artificial Intelligence and Marketing: Pitfalls and Opportunities. [Online] Available from: https://journals.sagepub.com/doi/abs/10.1016/j.intmar.2020.04.007 [Accessed March 02, 2024]
• Chintalapati, S. and Pandey, S. (2022) Artificial intelligence in marketing: A systematic literature review. [Online] Available from: https://journals.sagepub.com/doi/pdf/10.1177/14707853211018428 [Accessed March 02, 2024]
• Darlington, N. (2023). ‘Evaluating The Influence Of Artificial Intelligence Marketing On Customer Satisfaction With Products And Services Of Telecommunication Companies In Nigeria,’ International Journal of Management and Marketing Systems, 13(9), 1–31.
• Davenport et al (2020) How artificial intelligence will change the future of marketing. [Online] Available from:https://link.springer.com/article/10.1007/s11747-019-00696-0 [Accessed March 02, 2024]
• Elefachew, G. (2021). The Effect of Marketing Strategy on Small and Medium Enterprises Performance: In The Case of Debre Berhan Town [Doctoral Dissertation, Debre Berhan University].
• Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). ‘Artificial intelligence and business value: A literature review,’ Journal of Information Systems Frontiers, 24(6), 1–26.
• Gonzales, J. (2023) ‘Implications of AI innovation on economic growth: a panel data study,’ Journal of Economic Structures, 2(4), 1 – 8
• Hailemariam, S. (2020). The effect of marketing strategy on micro and small enterprises sales performance: Evidence from the service sector in Addis Ababa, in the case of Kirkos sub city. Doctoral dissertation, St. Mary’s University.
• Jianjun, H., Yao, Y., Hameed, J., Hafiz, W. K., Nawaz, M. A., Aqdas, R., & Patwary, A. K. (2021). ‘The Role of Artificial and Non-Artificial Intelligence in the New Product Success with Moderating Role of New Product Innovation: A Case of Manufacturing Companies in China,’ Complexity, 1–14.
• Kavya, G., Hariharan, B., & Chandrakhanthan, J. (2020). ‘Impact Of Artificial Intelligence In Marketing: A Perspective Of Marketing Professionals Of Pakistan,’ Global Journal of Management and Business Research: E-Marketing, 19(2), 2249-4588.
• Khanna, V., Ahuja, R., & Popli, H. (2020). ‘Role Of Artificial Intelligence In Pharmaceutical Marketing: A Comprehensive Review,’ Journal of Advanced Scientific Research, 11, 54–61.
• Lame, G. (2023) Systematic Literature Reviews: An Introduction. [Online] Available from: https://hal.science/hal-02196760/file/systematic_literature_reviews_an_introduction.pdf [Accessed March 02, 2024]
• Liu, Y. and Chen, W. (2021) Optimization of Brand Marketing Strategy of Intelligent Technology under the Background of Artificial Intelligence. [Online] Available from: https://www.hindawi.com/journals/misy/2021/9507917/ [Accessed March 02, 2024]
• Long et al (2020) Optimising the value of the critical appraisal skills programme (CASP) tool for quality appraisal in qualitative evidence synthesis. [Online] Available from: https://journals.sagepub.com/doi/full/10.1177/2632084320947559 [Accessed March 02, 2024]
• Marinchak, C. L., Forrest, E., & Hoanca, B. (2018). ‘The Impact Of Artificial Intelligence And Virtual Personal Assistants On Marketing,’ Journal of Information Science and Technology, 4, 5748–5756.
• Micu, A., Capatina, A., & Micu, A.-E. (2018). ‘Exploring Artificial Intelligence Techniques’ Applicability In Social Media Marketing,’ Journal of Emerging Trends in Marketing and Management, 1, 156–165.
• Mohammed, T., & Ghaleb El. R. (2022). The role of artificial intelligence, marketing strategies, and organizational capabilities in organizational performance: The moderating role of organizational behaviour. In Uncertain supply chain management (pp. 1457–1466). UAE.
• Muhammad, Z., & Gang, L. (2019). ‘Impact of Artificial Intelligence In Marketing: A Perspective Of Marketing Professionals Of Pakistan,’ Global Journal of Management and Business Research: E-Marketing, 19(2), 2249-4588.
• Mustapha, B. (2017). ‘Effects of Marketing Mix Strategy on Performance of Small Scale Businesses in Maiduguri Metropolitan, Bomo State Nigeria,’ Journal of Marketing and Consumer Research, 31, 1–6.
• Nandan, S., & Nath, M. D. (2020). ‘Impact of Artificial Intelligence In Making Better Marketing Decisions In Healthcare Industries,’ Our Heritage, 8, 53–59.
• Sabharwal, D., Ritu, S. and Verma, M. (2022) Studying the Relationship between Artificial Intelligence and Digital Advertising in Marketing Strategy. [Online] Available from: https://www.researchgate.net/profile/Manish-Verma-7/publication/371686837_STUDYING_THE_RELATIONSHIP_BETWEEN_ARTIFICIAL_INTELLIGENCE_AND_DIGITAL_ADVERTISING_IN_MARKETING_STRATEGY/links/64902dcd95bbbe0c6ed972ec/STUDYING-THE-RELATIONSHIP-BETWEEN-ARTIFICIAL-INTELLIGENCE-AND-DIGITAL-ADVERTISING-IN-MARKETING-STRATEGY.pdf [Accessed March 02, 2024]
• Shaik, M. (2023) Impact of artificial intelligence on marketing. [Online] Available from: https://journal.formosapublisher.org/index.php/eajmr/article/view/3112 [Accessed March 02, 2024]
• Shaw et al (2019) Artificial Intelligence and the Implementation Challenge. [Online] Available from: https://www.jmir.org/2019/7/e13659/ [Accessed March 02, 2024]
• Shih-Yu, C. (2019). ‘The Era Of Artificial Intelligence: Relationship Between Taiwan’s Machine Tool International Trade Show Marketing And International Agents,’ International Journal of Business and Economic Affairs, 4(3), 116–123.
• Singh et al (2023) Implications & Impact of Artificial Intelligence in Digital Media: With Special Focus on Social Media Marketing. [Online] Available from: https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_07006.pdf [Accessed March 02, 2024]
• Stone et al (2020) Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. [Online] Available from: https://www.emerald.com/insight/content/doi/10.1108/BL-03-2020-0022/full/html [Accessed March 02, 2024]
• Sudirman, D. and Lailla, N. (2023) ‘The Influence Of Marketing Strategy And Marketing Mix On Marketing Performance Of Ready To Drink Beverages Through Brand Image,’ International Journal of Professional Business Review, 8(9), 1 – 15
• Thiraviyam, T. (2018). ‘Artificial Intelligence Marketing,’ International Journal of Recent Research Aspects, 5, 449–452.
• Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). ‘Artificial Intelligence In Supply Chain Management: A Systematic Literature Review. Journal of Business Research, 122, 502–517.
• Tripopsakul, S. (2018). Social Media Adoption As A Business Platform: An Integrated TAM-TOE Framework. Polish Journal of Management Studies, 18(2), 350–362.
• Ullah, S., & Qureshi, Q. (2019). ‘ICTs Adoption Decision in Pakistani SMEs: Mediating Role of Owner/Managers with the Lens of Organizational and Technological Context of TOE Framework,’ International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 10(6), 861–870.
• Urban, B., & Verachia, A. (2019). ‘Organizational Antecedents of Innovative Firms: A Focus on Entrepreneurial Orientation in South Africa,’ International Journal of Business Innovation and Research, 18(1), 128–144.
• Usman, U., Ahmad, M., & Zakaria, N. (2019). ‘The Determinants of Adoption of Cloud-Based ERP of Nigerian’s SMES Manufacturing Sector Using TOE Framework and DOI Theory,’ International Journal of Enterprise Information Systems (IJEIS), 15(3), 27–43.
• Verma, S., Sharma, R., Deb, S. and Maitra, D. (2021) Artificial intelligence in marketing: Systematic review and future research direction. [Online] Available from: https://www.sciencedirect.com/science/article/pii/S2667096820300021 [Accessed March 02, 2024]
• Vlasic, B. (2021) The evolving role of artificial intelligence in marketing: A review and research agenda. [Online] Available from: https://www.sciencedirect.com/science/article/abs/pii/S0148296321000643 [Accessed March 02, 2024]
• Yadav, M. and Dwivedi, N. (2023) ‘Impact of AI on Business,’ International Journal for Multidisciplinary Research, 5(3), 1 – 6.
• Yegin, T. (2020) The Place And Future Of Artificial Intelligence In Marketing Strategies. [Online] Available from: https://dergipark.org.tr/en/pub/sosekev/issue/72156/1161527 [Accessed March 02, 2024]
• Yusuf, A. M., Astuti, M., & Ariani, M. B. N. (2022). ‘The Effect of Digital Marketing Mix Strategy on Marketing Performance Through the Implementation of Customer Relationship Management MSME 4.0 DKI Jakarta,’ International Journal of Business, Technology and Organizational Behavior, 2(4), 381–396.