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Marketing | review article

Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2348728

Artificial intelligence in marketing: exploring current and future trends

ebtisam labib

Faculty of Business studies, arab open university, saudi arabia

ABSTRACT artificial intelligence (ai) has revolutionized marketing domain, driving rapid digital transformation by enhancing processes, accelerating growth, and transforming the business landscape. Despite the growing attention towards artificial intelligence review studies, there remains a dearth of comprehensive reviews within the marketing domain. thus, the current study aims to explore the use of artificial intelligence in marketing as an emerging research topic using a systematic literature review (Slr) method. a corpus of 522 studies between 2015 and July 2023 was gathered and finalised from the web of Science (woS) database. Furthermore, the current study expanded the Slr using a bibliometric analysis. Observably, a growing trend of artificial intelligence exists in the marketing domain. the bibliometric analysis findings depicted six emerging clusters of artificial intelligence in marketing research, namely psychosocial dynamic, artificial intelligence-enhanced market dynamic strategies, artificial intelligence for consumer services, artificial intelligence for decision-making, artificial intelligence for value transformation, and artificial intelligence for ethical marketing. the findings highlighted future research avenues in terms of context, methods, and theory. the study also discussed the outcomes for academics and practitioners and proposed a future research agenda to examine the ongoing shift driven by rapid artificial intelligence implementation in marketing.

Introduction

the convergence of modern technologies in this digitalisation era has triggered a transformative wave of digitalisation across industries. Črešnar and nedelko (2020) defined industry 4.0 or i4.0 as a technology revolution for firms to advance firm technology and change other aspects. the i4.0 is a comprehensive term that categorises numerous technologies, such as big data, artificial intelligence (ai), and the internet of things (iot) (ali & Johl, 2023). the i4.0 concept involves reshaping the traditional business and man- agement landscape. the Fortune Business insights (2023) report stated that the global market size of i4.0 was $114.55 billion in 2021 with a projected growth of $377.30 billion by 2029. From a literature per- spective, anshari et  al. (2019) argued that industry 4.0 technologies empower firms to enrich customer engagement, acquisition, and retention strategies. this underscores the enduring impact of i4.0 technol- ogies on marketing evolution, suggesting a potent synergy between marketing and artificial intelligence to drive substantial outcomes (chintalapati & Pandey, 2022).

customer interaction with i4.0 technologies, especially ai has developed massively in the last decade. this situation altered how companies make decisions and engage with customers. the ai offers essential consumer input on goods and services essential for retaining and attracting new customers. Siau (2017) described ai as intelligence exhibited by machines. De Bruyn et  al. (2020) argued that the importance of clearly defining ai as machine intelligence and establishing boundaries to mitigate uncertainty. in the rapidly evolving landscape of consumer behaviour and market dynamics, leveraging ai in marketing

© 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group

CONTACT ebtisam Labib [email protected] Faculty of Business studies, arab open university, saudi arabia this article has been corrected with minor changes. these changes do not impact the academic content of the article.

https://doi.org/10.1080/23311975.2024.2348728

this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent.

ARTICLE HISTORY received 12 October 2023 revised 12 april 2024 accepted 24 april 2024

KEYWORDS artificial intelligence; industry 4.0; marketing; Slr; bibliometric

REVIEWING EDITOR len tiu wright, De Montfort University Faculty of Business and law, United kingdom

SUBJECTS Marketing Management; Marketing; internet/ Digital Marketing/ e-Marketing

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research becomes imperative (ali et  al., 2023). the ability of ai to rapidly analyze extensive datasets empower businesses to extract invaluable insights, facilitating the development of targeted strategies that profoundly resonate with customers (Fu et  al., 2023; li et  al., 2023). theoretically, Xu (2020) argued that global ai spending is anticipated to surge to US$98 billion by 2023, marking an unprecedented 28.4% annual growth. likewise, Paschen et  al. (2019) claimed that ai has transformed the human-centric sales process in the business-to-business (B2B) market. Practically, Balakrishnan et al. (2020) from Mckinsey analytics, half of the firms have integrated ai into at least one of their business functions. among these ai adopters, 75% have seen a 10% improvement in customer experience. Past reports indicated that ai exhibits substantial growth at the firm level with an anticipated 99% return on investment in five years and an impressive 187% in 10 years (Davenport et  al., 2020).

Despite the number of advantages of the integration of ai in various domains like marketing, there is a growing recognition of significant concerns regarding its potential to lead to dehumanization and com- promise privacy (lobschat et  al., 2021). lobschat et  al. (2021) argued that researchers should focus on organizational privacy failures like data leaks, profiling, micro-targeting, surveillance, and hacking. Furthermore, services literature should address the role of ai in decision making process (lobschat et  al., 2021; anayat & rasool, 2024). Prior studies highlighted a disparity between firm interest in ai and its actual adoption (Han et  al., 2021; Mcelheran et  al., 2024; Polisetty et  al., 2023; alwadain et  al., 2024). although ai research has expanded significantly, a substantial gap remains in comprehensively investigating the current and future potential of ai techniques in marketing strategies (Haenlein & kaplan, 2019). Despite the prom- inence of ai in marketing, limited research has outlined the subject. this gap is concerning as it limits insights and practical recommendations for firms seeking to utilise ai for marketing growth (Han et  al., 2021). a thorough synthesis of available studies is required due to the emphasis on ai marketing research (Peyravi et  al., 2020). although past research has summarised ai and marketing research using Slr and interpretive analysis, these approaches may lack objectivity and can be affected by author bias (talwar et  al., 2020; vlacic et  al., 2021; wisetsri et  al., 2021). Furthermore, the insufficient application of the biblio- metric method with other techniques in ai marketing research outlines the need for an in-depth and fair investigation of this critical topic (ismagiloiva et  al., 2020). Hence, the current study attempted to examine and analyse the existing literature on ai in marketing by employing bibliometric analysis with other approaches. given the ever-evolving nature of this topic, the current study comprehensively outlined cur- rent trends and emerging research avenues. the primary research objectives are listed below:

rO1: to identify the development of publications within ai in marketing.

rO2: to identify the research focus, methodological stances, and analytical strategies within ai in marketing.

rO3: to identify the key themes in the use of ai in marketing.

rO4: to identify the future research avenues in ai in marketing.

the remainder of the study comprises three sections. the subsequent section highlights the research methodology adopted for the Slr. an analysis of the collected documents is presented descriptively and the future research agenda is proposed. the final section extensively discusses the results and concludes the study.

Overview of AI in marketing

chui et  al. (2018) stated that the revolutionary potential of ai resonates highly in marketing and sales. the prowess of ai is evident in personalised service supply (Davenport et  al., 2019) and predicted cus- tomer behaviour analysis (verma et  al., 2021), which introduces a novel phase of marketing effectiveness. the advancement of ai in marketing presents an opportunity to revamp outdated techniques and enhance client interaction (Forrest & Hoanca, 2015). the dynamic spectrum of ai applications in market- ing has included comprehensive studies of consumer purchase habits (chatterjee et  al., 2020) and a developing customer demands via data analysis (wirth, 2018). according to Bughin et  al. (2017), using ai capabilities for data-driven advertising strategies helps firms to navigate the vast amount of internet data. additionally, Davenport et  al. (2019) claimed that ai system has the ability to assess customer data

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and offer tailored recommendations that go beyond buying choices. Finally, nanayakkara (2020) con- tented that ai offers marketing personnel by automating repetitive work, which allows them to focus on significant interactions that generate favourable client connections.

incorporating ai-powered tools, such as emotion detection technologies and intelligent robots improves marketers’ ability to provide exceptional consumer experiences, which supports customer reten- tion gradually (vlacic et  al., 2021). the numerous possible uses of ai have developed the notion of con- textualised marketing, where firms strategically deliver information that connects with the specific circumstances of their customers. the effect of ai differs among industries, where sectors involving tan- gible products, travelling, and finance witness a broader range of ai marketing applications following their regular connection with different consumers and big data collection (Davenport et  al., 2019, Zhang et  al., 2022; kanwal et  al., 2024).

Research methodology

Study design

an Slr is a form of research that manages existing publications and follows a systematic methodology for synthesising published data (tranfield et  al., 2003). Based on kraus et  al. (2020), an Slr is a review of an existing body of literature based on a transparent and reproducible methodology in searching, assessing its quality, and synthesising it with high objectivity. Snyder (2019) recommendations outline four phases of the literature review process: designing, conducting, analysing, and writing the review. the current study followed this four-step process to complete the literature review.

Scope of the study

the study scope includes the initial step of Slr based on the research objectives and questions. according to Denyer and tranfield (2009), the scope should follow the ciMO logic, which involves context, inter- vention, mechanism, and outcomes. in this context, the focus area is ai in marketing from 2015 to 2023.

Search strategy and data sources

the second step of Slr involved selecting the appropriate search string and database. Based on past literature, a combination of keywords with Boolean operators was utilised to identify the relevant docu- ments. additionally, a two-word combination approach was adopted to search the relevant documents. the first keyword is related to ai and the second concerns marketing and related terms. the current study used woS to gather articles for analysis as it is a reputable source for identifying high-quality journals globally (elaish et  al., 2023). the articles in this database are also well-organised in terms of research quality (elaish et  al., 2023). conducted in July 2023, this research extracted over 1000 docu- ments. a total of 522 articles were selected for final analysis by limiting the search to journal articles.

Selection criteria and quality assessment

the current study examined ai within the marketing domain and excluded research published in unre- lated domains. the included articles focused on management, business, and operational management domains. Moreover, review articles were excluded from the final review. the review was also confined to english language articles published between 2015 and July 2023, which ensured a contemporary and credible understanding of this niche domain. table 1 lists the complete inclusion and exclusion criteria.

apart from the inclusion and exclusion criteria, the selected articles were evaluated using a quality assessment comprising nine criteria based on kitchenham and charters (2007), which was not intended to criticise the work of any scholars. each criterion was rated on a three-point scale where ‘Yes’ received 1 point, ‘no’ received 0 points, and ‘Partially’ received 0.5 points. the resulting score ranged from 0 to 9 with higher scores indicating that the study was more effective in addressing the research questions. Furthermore, the Preferred reporting items for Systematic reviews and Meta-analyses (PriSMa) diagram was used to present the Slr (see Figure 1).

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Analysis and results

the corpus of 522 studies was analysed and presented via descriptive analysis.

Performance analysis

Publication trends the ai in marketing has witnessed a dynamic change and a significant increase in research and scholarly activities. a review of the publishing pattern from 2015 to 2023 denotes a consistent and impressive development in scholarly production. initially, several publications were produced in 2015 and 2016, totalling four publications with rather low citation counts. nevertheless, the following years revealed a remarkable increase in the number and effect of research. Observably, 2018 was a watershed moment, which ushered in a significant increase in publications amounting to 25 and a total of 2193 citations.

Table 1. study inclusion and exclusion criteria. inclusion exclusion

should involve ai and related keywords in marketing, business, management, and operational management domains

ai used as another context

should involve organisational context the paper was published in languages other than english should be written in the english language should be published between 2015 and July 2023

Figure 1. PRisMa diagram.

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this tendency intensified in 2019 and 2020 when research production increased to 42 and 90 papers with 2564 and 3500 citations, respectively. the rising trend continued in 2021 with 110 articles and 2687 citations, which emphasised the expanding importance of the field. the year 2022 denoted a strong continuation of this pattern with 143 publications and 1108 citations. Meanwhile, 2023 disclosed a con- sistent pace with 105 papers and 213 citations. these figures amounted to an outstanding 522 papers and a total citation count of 12,574, hence verifying the increasing scholarly interest in the subject and the effect of ai on marketing. Figure 2 depicts the publication trends.

Most influential journals table 2 demonstrates the most influential journals in ai in marketing based on woS, impact Factors (woS iF), and australian Business Deans council (aBDc) ratings. the ‘Journal of Business research’ and ‘industrial Marketing Management’ are influential journals. the ‘Journal of retailing and consumer Services,’ also emphasises consumer-oriented ai debates. Moreover, the ‘annals of Operations research’ and ‘european Journal of Marketing’ provide insights into operational ai aspects and european perspectives. collectively, these publications serve as crucial platforms for advancing the impact of ai on marketing.

Most influential authors table 3 presents prominent authors who have made significant contributions to the discipline, as out- lined by the number of publications. For instance, cHatterJee S and cHaUDHUri r have eight and seven publications, respectively, which indicates their significant commitment to the subject. Bag S, gUPta S, and van eScH P contributed six publications each, which further extended the topic.

Figure 2. Publication trends.

Table 2. Most influential journals. Journal name tP Wos (iF) aBDC

Journal of Business Research 54 11.3 a industrial Marketing Management 54 10.3 a* Journal of Retailing and Consumer services 16 10.4 a annals of operations Research 15 4.8 a european Journal of Marketing 15 4.4 a*

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Furthermore, grewal D, kietZMann J, lUO X, SHarMa a, and wang Y contributed five publications, which demonstrated their active involvement in promoting discussions on ai in marketing.

Descriptive analysis

a dual strategy was used with the assistance of vOSviewer and rStudio tools to determine the top 20 most referenced works (Donthu et  al., 2021). Subsequently, the study performed a manual evaluation that involved a quick review of the technique and abstract parts. authors were later selected for inclu- sion in the analysis if they received five or more citations within each cluster.

Research focus the implementation of ai-based technologies in marketing is the core focus of over 90% of the research. although most studies focused on the potential of these technologies, De Bruyn et  al. (2020) examined the possible drawbacks of marketers implementing ai without caution. the significant privacy issue aris- ing when using consumer data for marketing is a crucial concern. given the relative novelty of ai in the marketing sector, scholarly focus has been on its applications and the revolutionary possibilities for the corporate world. nevertheless, the impact of ai on marketing and customer relationship management (crM) is actively examined by researchers. this trend may result from the widespread use of ai technol- ogies to improve consumer engagement and increase the standard of goods and services.

Research methodology Depending on the study objectives, researchers can apply various research techniques, including descrip- tive, explanatory, exploratory, conceptual, and experimental techniques. in order to systematically describe situations, issues, events, or activities, descriptive research employs data that is frequently gathered to support hypotheses or provide a sound understanding of the subject matter (goundar, 2012). exploratory research investigates uncharted territory, while explanatory studies attempt to comprehend the relation- ships between components within occurrences or phenomena (goundar, 2012). conceptual studies explain relationships thoroughly by exploring existing data and frequently proposing new connections between variables (Jaakkola, 2020). contrarily, experimental research unveils causal links by adjusting factors and contrasting outcomes (goundar, 2012).

exploratory approaches are preferred when addressing the question of regularly used research method- ology in ai marketing, specifically considering the dynamic nature of this field (verma et al., 2021). researchers often use this strategy to analyse this emerging field, which aligns with the emergent nature of ai (nueman, 2014). nonetheless, several technical articles in the field have emphasised conceptual frameworks and cre- ative models that incorporate ai technologies into marketing. the frequency of exploratory design parallels the development stage of ai in marketing given its current state of development (verma et  al., 2021).

Analysis technique the two key types of data analysis are qualitative and quantitative analysis. Statistics are not used in qualitative research. instead, statements, symbols, or observations are employed to collect data, which are evaluated via content analysis, sentiment analysis, or thematic analysis (nueman, 2014). Quantitative analysis employs statistical methods to examine data linkages and evaluate the gathered data (nueman,

Table 3. Most influential authors. authors tP articles fractionalised

CHatteRJee s 8 2.58 CHauDHuRi R 7 2.33 Bag s 6 1.35 guPta s 6 2.23 Van esCH P 6 1.92 gReWaL D 5 1.12 KietZMann J 5 1.32 Luo X 5 1.33 sHaRMa a 5 1.37 Wang Y 5 1.42

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2014). the aforementioned explanation and data insights outline a pattern among the top 20 frequently referenced publications, which collectively highlight a preference for qualitative analysis. Only two writers (Deng et  al., 2019; kaiser et  al., 2020) used statistical analysis, while the majority adopted qualitative approaches, which proves that marketing ai remains in its infancy and that few empirical studies exist. therefore, academics favour qualitative analysis of textual data as a practical means to comprehending this developing field.

Cluster analysis

the cluster analysis was performed based on the corpus of 522 publications. a keyword co-occurrence analysis technique was adopted to develop the cluster (Donthu et  al., 2021). Publication keywords as mentioned in the search strategy were employed as primary unit of analysis. table 4 demonstrates six clusters formed based on the analysis.

Psychosocial dynamic of AI the psychosocial dynamics of ai in marketing involve the multidimensional examination of human actions, feelings, and perceptions in technological integration. the acceptability and uptake of ai-driven methods uncover a complex interplay between consumer involvement and intention where the psycho- logical variables regarding competence and communications impact people’s perceptions of these tech- nologies. as the ability of ai to comprehend and react to user emotions can substantially impact the entire user experience, the world of emotions and psychology becomes even more intertwined with the engagement process. the acceptance and use of this technology are influenced by functional compe- tence and the emotional connections made, which is a complicated web that extends to service robots.

the subtleties of social pressure and corporate social responsibility must be considered when devel- oping strategies to utilise ai in marketing given that these elements significantly influence customer behaviour and perceptions. concepts of behaviour, engagement, and intent are included in the psycho- social domain, which explains the underlying mechanics defining how ai technology and human psy- chology interact in marketing environments. comprehending these dynamic characteristics is vital for developing strategies that cognitively and emotionally connect with customers, which influences the future of marketing interactions as the ai-driven marketing paradigm continues to develop.

The AI-enhanced market dynamics and strategies the cluster ‘ai-enhanced market dynamics & strategies’ is a focal area within ai in marketing, which encapsulates the union of modern technology with the subtleties of market behaviour. this cluster exam- ines the complex web of rivalry, dynamics, and networks where insights and predictions fuelled by ai are revolutionising the area of supply chain management and market regulations. the responding strat- egies of the market change as the predictive capabilities of ai expand, which enhances comprehension of consumer behaviour, affecting sales trajectories, and facilitating reasoned decision-making. word-of- mouth acquires new dimensions through the amplification of ai, which assists in distributing dynamic information and changing market dynamics. this cluster denotes the integration of ai capabilities with the fundamentals of market dynamics, thus forming policies and projections that enable enterprises to

Table 4. Cluster analysis. Cluster no Cluster name Keywords

1st Psychosocial dynamic of ai acceptance, adoption, behaviour, competence, communication, emotions, engagement, intention, perceptions, personality, service robots, responses, usage, strategies, social pressure, corporate social responsibility

2nd ai-enhanced market dynamic & strategies Competition, dynamic, networks, policy, prediction, supply chain management, word-of-mouth, sales, market

3rd ai for consumer services anthropomorphism, e-commerce, trust, satisfaction, augmented reality, brand engagement

4th ai for decision making artificial intelligence, CRM, big data, decision-making, dynamic capabilities, predictive analytics, value-creation

5th ai for value-transformation innovation, knowledge, perspective, transformation, value co-creation 6th ai for ethical marketing analytics, competitive advantage, discrimination, employment

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traverse the dynamic landscape with unmatched foresight and agility. this cluster also emphasises the critical role of ai in navigating the complex web of contemporary marketplaces, which eventually encour- ages growth and success as it continues to redefine marketing paradigms.

AI for consumer services a revolutionary area is present within the ‘ai for consumer services’ cluster, where the fusion of technol- ogy and human-centred interactions reshapes the marketing field. anthropomorphism takes the stage as ai closes the gap between computers and users, which creates personalised connection to e-commerce experiences. trust and enjoyment become interwoven as ai-driven recommendations and interactions are tailored to individual tastes, which encourages dependability and fulfilment. the difference between virtual and real-world encounters is blurred by augmented reality, which adds another level of immersive interaction.

the power of ai to comprehend consumer behaviour and preferences revitalises brand engagement and paves the path for specialised marketing campaigns that connect with them personally. in this clus- ter, technology plays a human-like role in increasing trust, contentment, and brand resonance. this clus- ter also exemplifies the synergy between ai and consumer services. the use of ai changes conventional paradigms in the dynamic field of e-commerce, which fosters an environment, where customer require- ments are predicted and expertly managed, hence raising the customer experience to new heights.

The AI for decision-making the ‘ai for decision-making’ cluster presents a paradigm-shifting area where the effective fusion of mar- keting tactics and ai transforms the environment of informed decision-making. a new era is established by the integration of ai into crM, which employs big data to internalise complex consumer behaviours and empowers organisations with dynamic capabilities. Predictive analytics is a pillar of this cluster, which provides decision-makers with foresight and allows them to foresee trends, determine opportuni- ties, and minimise risks.

a collaboration of ai and decision-making results in value creation as the data-driven insights of ai encourage more strategic and significant decisions. the dynamic interplay of these terms demonstrates the aptitude of ai for deciphering enormous volumes of data to raise the decision-making processes to a new level of precision. the ‘ai for decision-making’ cluster is a catalyst for revolutionary strategies as organisations traverse the dynamic market, thus empowering them to adapt, thrive, and innovate in a period marked by data-driven agility and strategic insight.

AI for value-transformation the ‘ai for value-transformation’ cluster focuses on a crucial area, in which marketing and ai interact to redefine innovation and knowledge-sharing paradigms. this cluster orchestrates a transformation in how value is co-created and views are developed within the marketing world. the importance of the catalytic role of ai in value co-creation cannot be overstated as it uses insights from massive databases to create novel solutions deeply in tune with consumer requirements. combining ai and marketing propel firms towards disruptive strategies that produce outstanding levels of engagement and satisfaction. rising ai-driven innovations ignite a dynamic revolution in conventional thinking, which develops fresh win- dows of opportunity and changes how companies perceive and deliver value. this cluster demonstrates how ai exceeds traditional limitations, which sparks a fundamental transformation in marketing strate- gies that improve value generation, encourage collaboration, and paves an intimately intertwined future.

The AI for ethical marketing the power of analytics unites with competitive advantage within the cluster of ‘ai for ethical marketing,’ while vigilance against discrimination and considerations for ethical employment practises consolidating. this dynamic cluster underlines the value of making thoughtful decisions as it navigates the challenging environment where ai seamlessly integrates with marketing strategies. Using data-driven insights allows businesses to gain a competitive edge via the analytical prowess of ai, which facilitates the development

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of well-informed plans that reflect customer values and preferences. the cluster highlights the need to eliminate prejudice in algorithmic processes and preserve fair employment practices, as the ethical impli- cations of the effects of ai cannot be disregarded. the marriage of ai with ethical marketing represents a dedication to success based on openness, responsibility, and diversity. this cluster functions as a com- pass that directs marketers towards growth-oriented tactics while upholding moral standards, protecting brand reputation, and encouraging long-term consumer trust as firms unveil the potential of ai. innovation and responsibility collaborate to build success in ai for ethical marketing where competitive advantage flourishes without jeopardising equality, fairness, and the larger benefit of society.

Future research direction

Theory

Davenport et  al. (2019) mentioned that the adoption of ai-based systems by people differs from the adoption of traditional technology. terzopoulos and Satratzemi (2020) explained that ai-based systems usually function independently contrary to prior technologies requiring manual operation. in light of the new challenges arising from this technical difference, previous theories can merely describe how ai works in the marketing domain (Davenport et  al., 2019). a promising future research area in manage- ment and computer sciences is emerging as ai transforms the marketing landscapes, which calls for a merger of theories (Dwivedi et  al., 2019). an investigation of the ideal theoretical framework for ai in marketing occurs along different trajectories, which lays the groundwork for future research. the rele- vance of current theories must first be investigated based on how ai is incorporated into marketing strategies. the second question is whether a requirement for developing innovative theories exists that complement this dynamic environment. the focus extends beyond the areas of technology, company management, and consumer behaviour. instead, the project includes unexplored areas that overlap these domains. it is possible to pave the path for the future by creating a comprehensive model or theory that precisely describes the current context of ai system adoption in the field of marketing. this research agenda aims to identify the theoretical foundation that thoroughly supports the disruptive role of ai in marketing through multidisciplinary inquiry and integration.

Context

Marketing has developed a landscape of numerous ai-based solutions to take advantage of the inherent benefits of ai. Huang and rust (2020) mentioned that the deployment includes multiple technologies from collaborative machines that facilitate packaging to drones that plan delivery and service robots that provide assistance. Future researchers are prepared to discover unexplored territory as the story of ai in marketing develops. this investigation requires a complex mapping of the numerous ai-based technolo- gies in use. the priority is identifying the characteristics that differentiate these systems while identifying their similarities. Furthermore, a crucial aspect involves comprehending the contextual variables influenc- ing organisational decisions to implement a certain ai-based technology. this investigation also exam- ined the organisational elements that impact how ai systems are integrated for marketing objectives. the current study also included an analysis of features that distinguish organisations within the same industry and across various sectors and an examination of the factors influencing the adoption trajectory of these transformative technologies to broaden the scope. researchers could shed light on the choices, processes, and variables that integrate ai-based systems in marketing into the contemporary business landscape by initiating these investigative trajectories.

Content

Dwivedi et  al. (2019) highlighted how in the early stages of ai incorporation into marketing, the poten- tial seems limitless and broadens the research arena. researchers could demonstrate the multidimen- sional influence of ai in various marketing scenarios by posing a compelling proposition. Several lines of inquiry promise to reveal new information, such as exploring the influence of organisational capabilities

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on the choice to integrate ai into marketing practices. the complexities surrounding the adoption or exclusion of ai system installation in marketing is an excellent research field. in integrating ai systems into marketing strategies, the ethical factor has increased in significance and demands insights into the responsible measures that firms must implement to secure consumer data and privacy. researchers who follow these lines of study reveal the complex tapestry of the potential of ai in numerous marketing environments, which advances the grasp of the effects, complexities, and potentialities of this technology.

Method

as new technologies entice users’ acceptance (Huang & rust, 2020), a complex interaction of multiple elements emerges and influences how quickly those new technologies are integrated, as highlighted by Hamm and klesel (2021). numerous study disciplines become apparent in light of this dynamic as prom- ising areas for further investigation. an examination could emphasise identifying the best techniques or models for effectively analysing the adoption of ai technologies in marketing environments. researchers are also encouraged to investigate how ai impacts marketing strategies to differentiate between compa- nies that have employed the technology and those that have not. additionally, the area of methodolog- ical variety becomes more prominent, hence raising the question of whether various ai marketing applications require different assessment procedures to comprehend their overall business impact. if so, a new issue presents itself: How can scholars combine these various approaches to holistically compre- hend the entire area? researchers who follow these paths shed light on the complex dynamics of ai integration into marketing, which could radically alter company models, practices, and landscapes.

Research implications

the long-lasting impact of ai on marketing is undeniable given the rapid development of technology based on Davenport et  al. (2019). Dwivedi et  al. (2019) stated that the transformation of the marketing landscape has sparked a quick rise in research interest due to organisations continuing to invest in ai-based technologies. the issue of in-depth analysis of this rising body of information is illuminated by this exponential increase, which is reflected in the expanding publication trend. Hence, this study offered a comprehensive analysis of ai in marketing literature using bibliometric analysis to identify past patterns and prospective future trajectories. the current study provided a compass for scholars to navigate cita- tions and publications by examining key journals, organisations, and countries. this study outlined the future development of ai role in marketing domain by considering the range of approaches, methodol- ogy, context and contents studying issues, and examining problems. this thorough analysis outlines weaknesses in existing approaches and guides scholars towards unexplored directions that could advance the discipline. the convergence of these ideas and an explanation of common methodologies equip researchers with the knowledge and tools required to successfully navigate the complex world of ai in marketing.

Conclusion

artificial intelligence (ai) has revolutionized marketing domain, driving rapid digital transformation by enhancing processes, accelerating growth, and transforming the business landscape. Despite the growing attention towards artificial intelligence review studies, there remains a dearth of comprehensive reviews within the marketing domain. to address these gaps the aim of this review study to explore the use of artificial intelligence in marketing as an emerging research topic using a systematic literature review (Slr) method. the bibliometric analysis of 522 studies highlight that ai in marketing domain has six clusters: psychosocial dynamic of ai, ai-enhanced market dynamic & strategies, ai for consumer services, ai for decision making, ai for value-transformation and ai for ethical marketing. the review study high- lights that there is a possibility to pave the path for the future by creating a comprehensive model or theory that precisely describes the current context of ai system adoption in the field of marketing. Moreover, future researchers could shed light on the choices, processes, and variables that integrate

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ai-based systems in marketing into the contemporary business landscape by initiating these investigative trajectories.

Acknowledgements

the author of this manuscript would like to express her appreciation and gratitude to the arab Open University in Saudi arabia for supporting this research.

Author contributions

concept, writing, analysis, and revision are done by ebtisam labib.

Disclosure statement

no potential conflict of interest was reported by the author(s).

About the author

Ebtisam Labib is a dedicated assistant Professor and Director of the academic advising Unit, bringing over 15 years of experience to the realm of higher education. Her academic journey is anchored by a Ph.D. in Marketing with a specialized focus on consumer Behavior from Universiti Sains Malaysia, complemented by an MBa from king abdulaziz University. Her academic interest areas are artificial intelligence, Digital Marketing, consumer behavior, block chains, social media marketing, blended learning, open and distance learning.

Data availability statement

Data will be available on request.

ORCID

ebtisam labib http://orcid.org/0000-0001-6185-6046

References

ali, O., abdelbaki, w., Shrestha, a., elbasi, e., alryalat, M. a. a., & Dwivedi, Y. k. (2023). a systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 1. https://doi.org/10.1016/j.jik.2023.100333

ali, k., & Johl, S. k. (2023). Driving forces for industry 4.0 readiness, sustainable manufacturing practices and circular economy capabilities: Does firm size matter? Journal of Manufacturing Technology Management, 34(5), 838–13. https://doi.org/10.1108/JMtM-07-2022-0254

alwadain, a., Fati, S. M., ali, k., & ali, r. F. (2024). From theory to practice: an integrated ttF-UtaUt study on electric vehicle adoption behavior. PloS One, 19(3), e0297890. https://doi.org/10.1371/journal.pone.0297890

ali, k., & waheed, a. (2024). Synergistic role of tQM 4.0 toward industry 4.0 readiness: a sociotechnical perspective of selected industries. The TQM Journal. ahead-of-print. https://doi.org/10.1108/tQM-08-2023-0249

anayat, S., & rasool, g. (2024). artificial intelligence marketing (aiM): connecting-the-dots using bibliometrics. Journal of Marketing Theory and Practice, 32(1), 114–135.

anshari, M., almunawar, M. n., lim, S. a., & Mudimigh, a. a. (2019). customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94–101. https://doi. org/10.1016/j.aci.2018.05.004

Balakrishnan, t., chui, M., Hall, B., & Henke, n. (2020). Global survey: The state of AI in 2020. Mckinsey analytics. https:// w w w. m c k i n s e y. c o m / ~ / m e d i a / M c k i n s e y / B u s i n e s s Fu n c t i o n s / M c k i n s e y a n a l y t i c s / O u r i n s i g h t s / globalsurveythestateofaiin2020/global-survey-the-state-ofai-in-2020.pdf

Bughin, J., Hazan, e., ramaswamy, S., chui, M., allas, t., Dahistrom, P., Henke, n., & trench, M. (2017). Artificial intelli- gence: The next digital frontier. Mckinsey&company.

chatterjee, S., nguyen, B., ghosh, S. k., Bhattacharjee, k. k., & chaudhuri, S. (2020). adoption of artificial intelligence integrated crM system: an empirical study of indian organisations. The Bottom Line, 33(4), 359–375. https://doi. org/10.1108/Bl-08-2020-0057

12 e. laBiB

chintalapati, S., & Pandey, S. k. (2022). artificial intelligence in marketing: a systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428

chui, M., Manyika, J., Miremadi, M., Henke, n., chung, r., nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Applications and value of deep learning. Mckinsey global institute discussion paper. https://www.mckinsey.com/

Črešnar, r., & nedelko, Z. (2020). Understanding future leaders: How are personal values of generations Y and Z tailored to leadership in industry 4.0? Sustainability, 12(11), 4417. https://doi.org/10.3390/su12114417

Davenport, t., guha, a., grewal, D., & Bressgott, t. (2019). How artificial intelligence will change the future of mar- keting. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

De Bruyn, a., viswanathan, v., Beh, Y. S., Brock, J. k. U., & von wangenheim, F. (2020). artificial intelligence and market- ing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007

Deng, S., tan, c. w., wang, w., & Pan, Y. (2019). Smart generation system of personalized advertising copy and its application to advertising practice and research. Journal of Advertising, 48(4), 356–365. https://doi.org/10.1080/009 13367.2019.1652121

Denyer, D., & tranfield, D. (2009). Producing a systematic review. Da Buchanan & a. Bryman. Donthu, n., kumar, S., Mukherjee, D., Pandey, n., & lim, w. M. (2021). How to conduct a bibliometric analysis: an

overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 Dwivedi, Y. k., Huges, l., elvera, i., & aarts, g. (2019). artificial intelligence (ai): Multidisciplinary perspectives on

emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(101994), 1–47. http://hdl.handle.net/10454/17208

elaish, M. M., Hussein, M. H., & Hwang, g. J. (2023). critical research trends of mobile technology-supported english language learning: a review of the top 100 highly cited articles. Education and Information Technologies, 28(5), 4849–4874. https://doi.org/10.1007/s10639-022-11352-6

Forrest, e., & Hoanca, B. (2015). artificial intelligence: Marketing’s game changer. in Edward Forres and Bogdan Hoanca Trends and innovations in marketing information systems (pp. 45–64). igi global.

Fortune Business insights. (2023). Artificial intelligence market size, share & growth report [2030]. retrieved november 1, 2023, from https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114

Fu, l., li, J., & chen, Y. (2023). an innovative decision making method for air quality monitoring based on big data-assisted artificial intelligence technique. Journal of Innovation & Knowledge, 8(2), 100294. https://doi. org/10.1016/j.jik.2022.100294

goundar, S. (2012). chapter 3: research methodology and research method. in S. goundar (ed.), Cloud computing. research gate Publications.

Haenlein, M., & kaplan, a. (2019). a brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Hamm, P., & klesel, M. (2021 Success factors for the adoption of artificial intelligence in organizations: A literature review [Paper presentation]. 27th americas conference on information Systems (aMciS). aiS electronic library.

Han, r., lam, H. k., Zhan, Y., wang, Y., Dwivedi, Y. k., & tan, H. k. (2021). artificial intelligence in business-tobusiness marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(12), 2467–2497. https://doi.org/10.1108/iMDS-05-2021-0300

Huang, M. H., & rust, r. t. (2020). a strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

ismagiloiva, e., Dwivedi, Y., & rana, n. (2020). visualising the knowledge domain of artificial intelligence in marketing: a bibliometric analysis. in IFIP international federation for information processing (pp. 43–53). Springer nature Switzerland. https://doi.org/10.1007/978-3-030-64849-7_5

Jaakkola, e. (2020). Designing conceptual articles: Four approaches. Academy of Marketing Science Review, 10, 18–16. https://doi.org/10.1007/s13162-020-00161-0

kaiser, c., ahuvia, a., rauschnabel, P. a., & wimble, M. (2020). Social media monitoring: what can marketers learn from Facebook brand photos? Journal of Business Research, 117, 707–717. https://doi.org/10.1016/j.jbusres.2019.09.017

kanwal, n., bin isha, a. S. n., & ali, k. (2024). new ways of working: a comparative empirical analysis appertaining to health and well-being and psychosocial hazards. Technology Analysis & Strategic Management, 36, 1–14. https:// doi.org/10.1080/09537325.2024.2306643

kitchenham, B., & charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (pp. 1–57). Software engineering group, School of computer Science and Mathematics, keele University. https://doi. org/10.1145/1134285.1134500

kraus, S., Breier, M., & Dasí-rodríguez, S. (2020). the art of crafting a systematic literature review in entrepreneur- ship research. International Entrepreneurship and Management Journal, 16(3), 1023–1042. https://doi.org/10.1007/ s11365-020-00635-4

li, P., Bastone, a., Mohamad, t. a., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. evidence from a healthcare institution in the United arab emirates. Journal of Innovation & Knowledge, 8(2), 100340. https://doi.org/10.1016/j.jik.2023.100340

lobschat, l., Mueller, B., eggers, F., Brandimarte, l., Diefenbach, S., kroschke, M., & wirtz, J. (2021). corporate digital responsibility. Journal of Business Research, 122, 875–888. https://doi.org/10.1016/j.jbusres.2019.10.006

cOgent BUSineSS & ManageMent 13

Mcelheran, k., li, J. F., Brynjolfsson, e., kroff, Z., Dinlersoz, e., Foster, l., & Zolas, n. (2024). ai adoption in america: who, what, and where. Journal of Economics & Management Strategy, 33, 375–415.

nanayakkara. (2020). Application of artificial intelligence in marketing mix: A conceptual review [Paper presentation]. international conference on Business and information (icBi) (pp. 530–542).

nueman, w. l. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson. Paschen, J., kietzmann, J., & kietzmann, t. c. (2019). artificial intelligence (ai) and its implications for market knowl-

edge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410–1419. https://doi.org/10.1108/ JBiM-10-2018-0295

Peyravi, B., nekrošienė, J., & lobanova, l. (2020). revolutionized technologies for marketing: theoretical review with focus on artificial intelligence. Business: Theory and Practice, 21(2), 827–834. https://doi.org/10.3846/btp.2020.12313

Polisetty, a., chakraborty, D., g, S., kar, a. k., & Pahari, S. (2023). what determines ai adoption in companies? Mixed-method evidence. Journal of Computer Information Systems, 64(1), 1–18. https://doi.org/10.1080/08874417 .2023.2219668

Siau, k. l. (2017). Impact of artificial intelligence, robotics, and machine learning on sales and marketing impact of arti- ficial intelligence, robotics, and machine. association for information Systems aiS electronic library. https://aisel. aisnet.org/cgi/viewcontent.cgi?article=1047&context=mwais2017

Snyder, H. (2019). literature review as a research methodology: an overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

talwar, S., talwar, M., kaur, P., & Dhir, a. (2020). consumers’ resistance to digital innovations: a systematic review and framework development. Australasian Marketing Journal, 28(4), 286–299. https://doi.org/10.1016/j.ausmj.2020.06.014

terzopoulos, g., & Satratzemi, M. (2020). voice assistants and smart speakers in everyday life and education. Information in Education, 19(3), 473–490. https://doi.org/10.15388/infedu.2020.21

tranfield, D., Denyer, D., & Smart, P. (2003). towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi. org/10.1111/1467-8551.00375

verma, S., Sharma, r., Deb, S., & Maitra, D. (2021). artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data, 1(1), 1–8. https://doi.org/10.1016/j. jjimei.2020.100002

vlacic, B., corbo, l., costa, e., Silva, S., & Dabic, M. (2021). the evolving role of artificial intelligence in marketing: a review and research agenda. Journal of Business Research, 128(May–2021), 187–203. https://doi.org/10.1016/j.jbus- res.2021.01.055

wirth, n. (2018). Hello marketing, what can artificial help you with? International Journal of Market Research, 60(5), 435–438. https://doi.org/10.1177/1470785318776841

wisetsri, w., Julie, c., thakur, v., Pandey, D., & gulati, k. (2021). Systematic analysis and future research directions in artificial intelligence for marketing. Turkish Journal of Computer and Mathematics Education, 12(11), 43–55. https:// doi.org/10.17762/turcomat.v12i11.5825

Xu, r. (2020). A design pattern for deploying machine learning models to production. california State University. Zhang, B. S., ali, k., & kanesan, t. (2022). a model of extended technology acceptance for behavioral intention toward

evs with gender as a moderator. Frontiers in Psychology, 13, 1080414. https://doi.org/10.3389/fpsyg.2022.1080414

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