Exploring AI Awareness Within Small Owner-Operated Businesses

I. Introduction and Research Question

Artificial Intelligence (AI) is a rapidly diffusing technology that spans industries and organizational contexts, increasingly functioning as a general-purpose technology (Liang et al., 2022; Yuxuan & Hussain, 2025). As AI becomes more accessible, its impact extends beyond enterprise capabilities to include small business decision-makers and non-technical users (Yuxuan & Hussain, 2025). Individuals encounter AI through both direct interaction with tools and indirect exposure through media, social platforms, peer networks, and vendor narratives (Puntoni et al., 2021). As a result, understanding of AI is often informal, fragmented, and shaped by experience rather than structured learning.

Yuxuan and Hussain (2025) introduce AI awareness as a construct capturing how individuals understand AI, form attitudes toward it, and evaluate its implications. They conceptualize it as a multidimensional construct consisting of cognitive, behavioral, and ethical dimensions that shape how individuals interpret AI technologies. In this framing, AI awareness functions as a pre-adoption process influencing whether and how AI should be used.

Despite emerging agreement on its multidimensional structure, Yuxuan and Hussain (2025) highlight a lack of consensus in the definition of AI awareness. Existing research often conflates technical knowledge, behavioral attitudes, and ethical considerations, limiting conceptual clarity. Although the three dimensions are widely cited, they are often treated as generic constructs rather than specified in ways unique to AI, leaving their content underdeveloped (Kong & Zhu, 2025).

Current research is disproportionately focused on enterprise environments and workforce dynamics, particularly job insecurity and organizational change (Kong et al., 2021; Li et al., 2019; Zhou et al., 2024). Small business decision-makers remain underexplored despite operating under different conditions and relying on rapid decision-making in resource-constrained environments (Yuxuan & Hussain, 2025).

Existing studies suggest that awareness is shaped by direct and indirect exposure to AI, influencing how individuals interpret information. This implies that AI awareness is constructed through exposure rather than being a fixed attribute (Zhou et al., 2024). Importantly, AI awareness is distinct from AI adoption: adoption reflects a behavioral decision, whereas awareness captures the cognitive, behavioral, and ethical processes that precede that decision (Ajzen, 1991; Davis, 1989; Yuxuan & Hussain, 2025).

This research is important because technology adoption is driven not only by availability, but by how individuals interpret and evaluate that technology (Ajzen, 1991; Davis, 1989). Knowledge and attitudes toward AI influence willingness to adopt and trust in its outputs. Small business decision-makers rely on informal learning and environmental cues to guide decisions under uncertainty, making AI awareness critical to understanding how they evaluate and engage with emerging technologies.

II. Propositions

Building on prior research that conceptualizes AI awareness as a multidimensional construct, this study evaluates whether the cognitive, behavioral, and ethical structure holds in a small business context and how it is shaped through exposure (Yuxuan & Hussain, 2025).

1. P1. AI awareness among small business decision-makers is a multidimensional construct consisting of cognitive, behavioral, and ethical dimensions.
2. P2. Cognitive AI awareness reflects how small business decision-makers understand and interpret AI capabilities and limitations.
3. P3. Behavioral AI awareness reflects how small business decision-makers respond to and engage with AI.
4. P4. Ethical AI awareness reflects how small business decision-makers evaluate the implications of AI use.
5. P5. AI awareness is shaped by exposure to AI tools, media, and external narratives.
6. P6. Variation in exposure leads to differences in the depth and content of AI awareness across its dimensions.
7. P7. Unstructured and informal exposure results in fragmented or inconsistent AI awareness.

III. Methodology

This study employs a qualitative, exploratory research design aimed at construct refinement. A qualitative approach is appropriate given the lack of conceptual clarity surrounding AI awareness and the need to understand both its dimensional structure and formation process in context. The study follows an inductive–abductive hybrid approach, combining deductive grounding in existing literature with inductive refinement through empirical data (Gioia et al., 2012; Corbin & Strauss, 2014).

Data will be collected through semi-structured interviews across two participant groups. AI experts, including AI product leaders, consultants, and researchers, will be interviewed to assess whether the cognitive, behavioral, and ethical dimensions are conceptually distinct and meaningfully specific to AI. Small business decision-makers will be interviewed to examine how these dimensions manifest in practice and whether they hold in real-world decision-making contexts.

The research proceeds in three sequential phases. Construct anchoring evaluates whether the three dimensions identified in prior research are conceptually valid and AI-specific. Contextual grounding examines how these dimensions are expressed among small business decision-makers. Mechanism testing investigates how exposure to AI shapes the formation and variation of awareness. Progression between phases is contingent on sufficient empirical support for the preceding phase, allowing iterative refinement of the construct where necessary.

Interview protocols are aligned to the proposed dimensions and exposure mechanism. Participants describe their understanding of AI capabilities, behavioral responses to AI use, and ethical concerns. Additional questions reconstruct exposure pathways, where participants encountered AI, which sources influenced their understanding, and how perceptions evolved over time.

Coding progression

1. Open coding captures participant language and identifies emergent concepts, remaining closely aligned with informants' terms.
2. Axial coding examines relationships between categories, with attention to whether responses cluster around the proposed cognitive, behavioral, and ethical dimensions or suggest refinements.
3. Selective coding integrates categories into higher-order themes, enabling refinement of AI awareness as a multidimensional construct (Corbin & Strauss, 2014).

This process is supported by a Gioia-inspired approach, facilitating movement from first-order participant terms to second-order theoretical themes and aggregate dimensions (Gioia et al., 2012). The combination of grounded theory techniques within a thematic framework (Guest et al., 2012) allows interpretive flexibility consistent with a constructivist perspective (Charmaz, 2006), while maintaining analytical rigor and traceability. To ensure qualitative rigor, the study systematically demonstrates transparent connections between the raw data and the emergent theoretical concepts, aiming to extract transferable concepts and principles (Lincoln & Guba, 1985).

IV. Expected Contribution

This study contributes to the emerging literature on AI by refining AI awareness as a multidimensional and exposure-driven construct situated within decision-making contexts. While prior research has established cognitive, behavioral, and ethical dimensions, limited attention has been given to how these dimensions are operationalized in practice or how awareness is formed. By specifying how the dimensions manifest in business decision-making and distinguishing AI awareness from related constructs such as AI literacy and AI adoption, this study positions AI awareness as a pre-adoption process that shapes how individuals interpret and evaluate AI prior to behavioral decisions. It further introduces a process-oriented mechanism in which exposure, through tools, media, vendor narratives, and peer interactions, influences interpretation and the formation of awareness.

Empirically, this research extends the study of AI awareness into the context of small business decision-makers, a group that remains underrepresented in current literature. Unlike enterprise environments, small businesses operate under conditions of informal learning, limited technical expertise, and high reliance on external sources of information. As a result, AI awareness in this context is likely to be more fragmented, experience-driven, and shaped by external narratives. By examining how awareness forms under these conditions, this study establishes small business decision-makers as a distinct and relevant context for understanding AI-related cognition.

Methodologically, this study provides a foundation for future scale development of AI awareness by offering a refined conceptual structure grounded in empirical data. From a practical perspective, the findings offer value to small business decision-makers by improving their ability to interpret AI capabilities, evaluate vendor claims, and make more informed decisions regarding AI investment and adoption. By shifting focus from adoption outcomes to the processes that precede them, the study provides insight relevant to policy, education, and organizations shaping how AI is introduced and understood in the marketplace.