Empirical Investigation of Key Determinants Affecting Emerging Marketing Technologies’ Adoption: Evidence from Kuwait

Abstract

While enterprises in developed countries are motivated to sustain adoption and application of advanced marketing technologies, others, despite a well-designed technological infrastructure and substantial support, continue struggling to adopt and use these technologies. This is predominantly owing to the insufficient understanding of the key determinants that influence their employees’ adoption and use of them. Among these advanced marketing technologies, neuromarketing and AI-related concepts have emerged, yet their effective utilization and adoption approaches remain largely unfamiliar in some regional areas. The state of Kuwait, characterized by a robust technological infrastructure and sustained governmental investment in innovation, comprised specialized enterprises and marketing agencies that are severely constrained by the outlined challenges, impeding their capacities to capitalize on the country’s accelerating digital transformation. Understanding the organizational, structural, and contextual underpinnings regarding the determinants underlying these advanced marketing technologies, alongside proving the efficiency in boosting performance, offers guidance for struggling agencies to mitigate significant expenses and unnecessary interventions. However, research on the key determinants underlying the sustained and effective adoption and usage of these advanced marketing technologies is limited and warrants deeper investigation into its underlying foundations. As a response to such critical gap, the current dissertation has developed a research model anchored in the premise of extending the Decomposed Theory of planned Behavior (Taylor and Todd, 1995), and tested it empirically through a self-administered online questionnaire, using data collected from a sample of marketing employees (n=162) working in marketing and advertising agencies in Kuwait. This model investigates deeply the key determinants affecting the adoption and usage intentions to AI and neuromarketing, functioning as instances of the emerging marketing technologies that need further exploration. The model extended the DTPB by adding decomposed variables alongside introducing the influence of the Perceived Digital Marketing Capabilities ‘PDMCs’ as one of the core belief variables. Partial Least Square Structural Equation Modeling (PLS-SEM) is used as the analytic tool to determine the validity of the investigated key determinants. The findings reveal that all the decomposed variables significantly influence the core belief variables except for the perceived risk towards the perceived behavioral control. Perceived ease of use was the most influential attitude’s decomposition, superior influence was the most influential subjective norm’s decomposed variables, while the facilitating conditions proved to have the most influential impacts on the perceived behavioral control on the usage and adoption of the indicated emerging marketing technologies. Among the core variables, Perceived Digital Marketing Capabilities ‘PDMCs’ is found to be the most influential variable to influence both the AI adoption intention and neuromarketing usage intentions. Subjective norms found to be non-significant on the intention to use neuromarketing techniques, while perceived Behavioral control found to be non-significant on the AI adoption intentions. All the other path coefficients of the core variables were positively significant in influencing both types of the observed behavioral intentions. The findings grant substantial theoretical and practical implications, as they extend DTPB and offer empirical evidence that behavioral intentions can be fundamentally anchored in employees’ cognitive appraisals. Notwithstanding the practical implications of providing empirical validation that explains which precise factors are more influential than the others, that ultimately guide the needed intervention strategies toward the journey of using and adopting such advanced marketing technologies.