Limited studies systematically examine the impact

The gap

Evidence profile

Sourced from the future work and recommendations and conclusions and stated research gap of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 5 representative gaps

• The chief marketing officer is dead, long live the chief algorithmic officer: AI’s transformation of marketing strategy (2026) · Frontiers in Research · doi

  The proposed framework unveils several critical avenues for future scholarly inquiry. Longitudinal, quasi-experimental studies are urgently needed to empirically validate the performance impact of the Chief Algorithmic Officer role and to delineate the conditions under which it thrives or falters. The development of such research would be bolstered by standardized metrics for customer investment, which provide a scientific foundation for cross- firm comparison and robust hypothesis testing (Bendle et al., 2024). Simultaneously, a unified theory of brand equity must be advanced to better translate qualitative consumer perceptions into the quantifiable financial metrics required to justify algorithmic stewardship at the board level (Davcik et al., 2015). Research must also explore how advanced intelligence systems can be leveraged to assess macroeconomic impacts and foster broader economic resilience through technological integration (Nguyen, 2026). In parallel, the refinement of multi-touch attribution and media mix modeling remains a pressing need, providing a robust taxonomic foundation for optimizing marketing ROI in increasingly fragmented digital landscapes (Liu et al., 2025b). The second research frontier concerns the neuro-organizational and behavioral dynamics that govern trust in AI-led strategies. Investigating the psychological and structural antecedents that overcome algorithm aversion at organizational frontlines is crucial for successful human- AI collaboration (Haupt et al., 2024). This work should be coupled with research into strategic frameworks designed to avert digital analytics myopia, ensuring that long-term brand health is not systematically eroded by short-term algorithmic optimization (Vollrath & Villegas, 2021). A specific practical problem requiring attention is the trust deficit engendered by friction in virtual interaction (“Zoomification”) and skepticism toward automated reporting; addressing this is critical for the widespread organizational adoption of algorithmic leadership (Dzreke et al., 2025n; Dzreke & Dzreke, 2025h). Future studies should also examine the longitudinal effects of delegating creative authority to AI on consumer perceptions of brand 302 S. S. DZREKE & S. E. DZREKE authenticity. Finally, as strategic decision-making becomes more embedded in systems, research must explore the potential for skill degradation in traditional marketing competencies and propose mechanisms to ensure organizational resilience and human capital development in the algorithmic age.

  generalfuture work
  Keywords: algorithmic dzreke organizational brand must critical future longitudinal development metrics foundation robust advanced consumer perceptions
• Measuring the Impact of Artificial Intelligence on Customer Engagement and Experience in the Digital Era (2026) · Economic Sciences · doi

  systems, personalized service companies should conduct regular reviews of the delivery, and rapid problem resolution enhance the performance of AI in order to constantly improve its ease with which customers can obtain effectiveness. Consumers should have an increased products/services and create seamless online understanding of the capabilities and limitations of AI, experiences. and they should also provide organisations that work While the findings indicate numerous advantages with AI with the necessary feedback to enhance their associated with using AI, the study did uncover operations. Data should be exchanged in a secure additional areas of concern regarding their use, manner in order the protect the confidential nature of including data protection, emotional limits of AI use, individual data. and occasional inaccuracies within AI systems. Policymakers must establish robust regulations to Results demonstrate that consumers prefer a blended ensure that AI is employed in an accountable manner. approach that incorporates an automated response (AI) They must also create transparency for platforms that for less complex/simple tasks and human intervention utilise algorithms and establish strict data protection for more complex/sensitive issues. legislation that complies with worldwide standards. Overall, the findings of this study support the assertion that AI positively influences customer engagement and experience. Furthermore, the study points to the need for organizations to utilize AI methods very ethically and transparently regarding customer data handling. For businesses, legislators, and researchers looking to enhance the quality of their customer interactions through AI, this study provides relevant data.

  generalrecommendationsevidence 5/5
  Keywords: enhance customer systems order consumers create regarding manner protection must establish complex personalized service companies
• SOCIAL MEDIA MARKETING REDEFINED: THE ROLE OF CHATBOTS AND VIRTUAL INFLUENCERS IN AI-DRIVEN INFLUENCER MARKETING (2026) · ShodhKosh: Journal of Visual and Performing Arts · doi

  Future research should focus on measuring the effectiveness of the AI tools and their ethical implications by ensuring that consumer trust is the top priority in this ever-changing digital environment Nicolescu and Tudorache (2022)Chatbots and virtual influencers are transforming social media marketing and customer engagement by providing brands more better and innovative ways to easily get in touch with their audience.

  generalconclusionsevidence 5/5
  Keywords: future focus measuring effectiveness tools ethical implications ensuring consumer trust priority ever changing digital environment
• AI-driven marketing tactics and consumer behavior: a bibliometric review and systematic literature review (2026) · Frontiers in Research Metrics and Analytics · doi

  influenced by heuristics and algorithmic that there is a need to integrate psychological frameworks, such as self-determination theory and affordance theory, to better understand intrinsic motivation, perceived control and resistance toward AI-enabled tactics. Furthermore, there is a need to adopt interdisciplinary theoretical frameworks as existing literature applies theories in isolation. Future studies could integrate marketing, psychology, information systems, and human-computer interaction to develop comprehensive models. For instance, combining the stimulus- organism-response (SOR) model with technology adoption may offer a richer understanding of how AI-driven marketing tactics shape internal psychological states and behavioral outcomes. In addition, limited attention has been given to ethical and trust-based frameworks despite growing concerns of privacy, fairness and algorithmic bias. Future research can develop ethical AI frameworks that explain how transparency, fairness, accountability, and responsible AI tactics influence consumer trust and behavioral outcomes in this digital era. 6.2 Future directions: contexts In terms of context (Figure 6), this review indicates that most studies focused on digital marketing communication and e- commerce, especially social media marketing, online advertising Frontiers in Research Metrics and Analytics 12 frontiersin.org Ahuja et al. 10.3389/frma.2026.1914129 and retail platforms. However, fewer studies examined contexts such as fashion, community services, non-profit organizations and charity advertising. This concentration limits the generalizability of findings and highlights the need to examine AI-enabled marketing industries. In the service sector, tactics in a wide range of existing literature predominantly focuses on banking, tourism, and service failure management, while other emerging sectors such as public services, professional consultancy, entertainment, supply chain, and logistics remain underexplored. Future researchers may examine the influence of AI-driven tactics in diverse service sectors. Furthermore, current research primarily focuses on Gen Z and active digital users, with less emphasis on older and less digitally engaged populations. In a same way, a substantial proportion of empirical research originates in India and China, while developing economies and cross-cultural studies remain underexplored. 6.3 Future directions: methodology The methodologies section (Table 5) indicates that most of the prior studies used quantitative methods, such as survey and experimental research designs. However, these studies mainly collected cross-sectional data, which may limit the generalizability of the results in a dynamic world. Conversely, a limited number of studies used qualitative methods, mixed methods and conceptual research, which indicates inadequate investigation of consumer experiences, perceptions and psychological mechanisms associated with AI-driven marketing tactics. Fut

  generalrecommendationsevidence 5/5
  Keywords: tactics marketing future frameworks need psychological driven digital indicates service algorithmic there integrate theory enabled
• AI-Driven Consumer Behavior Prediction and Brand Relationship Marketing: Technological Applications and Theoretical Mechanisms (2026) · Exploring Science Academic Conference Series · doi

  Limited studies systematically examine the impact of AI-driven consumer behavior prediction on long-term brand relationship marketing - Prior research primarily focuses on technological performance improvement rather than long-term relational outcomes - The moderating mechanism of trust and data privacy concerns is underexplored in existing AI marketing literature

  generalstated research gapevidence 5/5
  Keywords: limited studies systematically examine impact ai-driven consumer behavior

Questions about this gap