AMTP Proceedings 2026

Document Type

Conference Proceeding

Publication Date

Spring 2026

Abstract

In contemporary business landscapes, an organization's capacity to deliver groundbreaking innovative products and foster deep, meaningful engagement with its customers stands as the paramount objective of modern strategic practices. This dual focus not only drives sustainable growth but also fortifies competitive positioning in hyper-competitive markets. Yet, despite the extensive empirical evidence highlighting the transformative benefits of innovation—such as enhanced market share and profitability—and customer engagement—evidenced by loyalty, advocacy, and repeat business—the pivotal role of big data-artificial intelligence (BD-AI) technologies remains strikingly underexplored. In particular, scant attention has been paid to how BD-AI training equips firms to harness these tools effectively in pursuit of core outcomes like customer referral engagement (where satisfied customers actively promote the brand) and product innovation (the development of novel, market-leading offerings).

Our empirical findings illuminate these dynamics with clarity. Firms exhibiting a proactive market orientation—characterized by anticipatory sensing of market signals, rapid responsiveness to customer needs, and forward-looking resource allocation—demonstrate a pronounced reliance on BD-AI technologies to accelerate product innovation. For instance, BD-AI enables real-time analysis of vast datasets to uncover unmet needs, predict trends, and streamline R&D processes, thereby catalyzing the creation of disruptive products tailored to evolving demands. This innovation prowess, in turn, cascades into elevated levels of customer referral engagement, as innovative products generate delight, word-of-mouth advocacy, and viral sharing among user networks.

Complementing this pathway, our results emphatically affirm the indispensable role of BD-AI training. Such training—encompassing skill-building in data analytics, machine learning algorithms, ethical AI deployment, and interpretive decision-making—serves as a critical enabler. It empowers employees to not only deploy BD-AI tools proficiently but also to interpret outputs contextually, mitigate biases, and integrate insights into strategic actions. Without it, firms risk underutilizing BD-AI's potential, leading to suboptimal outcomes.

Ultimately, these insights compel organizations to embed BD-AI technologies deeply into their operational fabric, paralleled by robust, ongoing training programs. This integrated approach sustains long-term innovation capacity, bolsters customer-centric engagement, and ensures resilience amid rapid technological and market disruptions in industries ranging from consumer goods to high-tech services. By dissecting the contingencies under which BD-AI falters—such as inadequate training or misaligned market orientation—the study delivers actionable guidance for managers seeking to optimize technology investments. Theoretically, it enriches the discourse on BD-AI's integrative function within modern business strategies, bridging gaps between market orientation, technological capabilities, and performance imperatives.

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