Artificial intelligence in marketing: exploring current and future trends

Labib, Ebtisam Article Artificial intelligence in marketing: exploring current and future trends Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Labib, Ebtisam (2024) : Artificial intelligence in marketing: exploring current and future trends, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-13, https://doi.org/10.1080/23311975.2024.2348728 This Version is available at: https://hdl.handle.net/10419/326261 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Artificial intelligence in marketing: exploring current and future trends Ebtisam Labib To cite this article: Ebtisam Labib (2024) Artificial intelligence in marketing: exploring current and future trends, Cogent Business & Management, 11:1, 2348728, DOI: 10.1080/23311975.2024.2348728 To link to this article: https://doi.org/10.1080/23311975.2024.2348728 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 02 May 2024. Submit your article to this journal Article views: 30138 View related articles View Crossmark data Citing articles: 18 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 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 management 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 perspective, 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 technologies 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 2 e. laBiB 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 compromise 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 prominence 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 bibliometric 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 current 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 customer 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 marketing 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 cOgent BUSineSS & ManageMent 3 and offer tailored recommendations that go beyond buying choices. Finally, nanayakkara (2020) contented 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 retention gradually (vlacic etal., 2021). the numerous possible uses of ai have developed the notion of contextualised 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 tangible 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, intervention, 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 documents. 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 documents. 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 unrelated 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). 4 e. laBiB 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. cOgent BUSineSS & ManageMent 5 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 consistent 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 outlined 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* 6 e. laBiB 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 inclusion 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 arising 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 technologies 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 descriptive, 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 relationships 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 methodology 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 creative 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 cOgent BUSineSS & ManageMent 7 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 psychological variables regarding competence and communications impact people’s perceptions of these technologies. 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 competence 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 developing 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 psychosocial domain, which explains the underlying mechanics defining how ai technology and human psychology 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 examines 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 strategies 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-ofmouth 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