New Paradigm · Journal of Retailing and Consumer Services, 2026

The Conductive Approach & Agnoseology: A New Research Logic and Paradigm for the AI Era

Beyond inductive, deductive and abductive — how knowledge is created when human interpretation and artificial intelligence work in dialogue.

What is the conductive approach? Conductive coding is a new approach to qualitative coding, introduced by Naeem (2026), in which coding is shaped through a dialogue between human input — the research context, methodology, theory and philosophy — and recommendations generated by artificial intelligence. Unlike inductive, deductive or abductive logics, it is simultaneously iterative and dialogical, designed for research conducted with AI as a collaborator.

Why a fourth logic was needed

For decades, qualitative researchers have had three reasoning logics to choose from: induction (theory emerges from data), deduction (data tests existing theory) and abduction (inference to the best explanation). All three assume a single knowing agent — the human researcher. But when generative AI enters the analysis, something new happens: the machine proposes patterns, keywords and meanings the researcher did not anticipate, and the researcher responds with theoretical and contextual judgement the machine does not possess. Neither induction nor deduction nor abduction describes that conversation. The conductive approach does.

“The definition of conductive coding refers to the integration of the theoretical, contextual, major research aim, objectives, and methodological background of this research through AI-aided coding, where the coding is shaped by recommendations generated by artificial intelligence in response to human input related to the context, methodology, and philosophy, and this combined process is what is called conductive coding.”
— Naeem (2026, p. 6), Journal of Retailing and Consumer Services

“What is unique about the coding approach in this study is that, as opposed to Inductive, Deductive or Abductive logics, conductive coding was used as an approach that is simultaneously iterative and dialogical.”
— Naeem (2026, p. 6), Journal of Retailing and Consumer Services

Agnoseology: a new paradigm for exploring the unknown

Underneath the conductive approach sits a deeper philosophical contribution. Traditional epistemology asks how we know what we know. Agnoseology — introduced in the same paper — asks the question the AI era makes urgent: how do we come to know what we do not initially know? When researchers use AI in thematic analysis, coding and theme development, the AI surfaces hidden data, unrecognised meanings and unknown patterns; Agnoseology names, and gives philosophical structure to, that exploratory process from identifying research gaps to addressing them.

“Agnoseology has been developed as a philosophical concept in which researchers seek to explore the area of unknowns, from identifying research gaps to addressing those gaps… researchers receive recommendations from AI that help uncover unknown or hidden data and previously unrecognised meanings within the dataset, and this exploratory process is what is referred to as Agnoseology.”
— Naeem (2026, p. 6), Journal of Retailing and Consumer Services

“This definition of Agnoseology emerges from the idea of understanding how we come to know what we do not initially know.”
— Naeem (2026, p. 6), Journal of Retailing and Consumer Services

How the two fit together

Conductive coding is the methodological application of Agnoseology: the paradigm explains why human–AI dialogue produces new knowledge; the approach specifies how to conduct that dialogue rigorously — a data-embedded analytical process for generating meaning through a conversation between human interpretation and machine discovery. In this framing, uncertainty is contextual rather than obstructive: it becomes co-constructed material from which large-scale ideas emerge.

“Both Agnoseology and conductive coding contribute to a new understanding of AI as a methodological and philosophical collaborator… Together, they turn qualitative analysis into a conversation between the AI and human interpretation, from which new philosophical questions can then develop as an adventure into the unknown.”
— Naeem (2026, p. 6), Journal of Retailing and Consumer Services

Where this sits in the research programme

The conductive approach builds directly on the published AI-assisted methodology papers — the step-by-step ChatGPT processes for thematic analysis (Naeem, Smith & Thomas, 2025) and case study research (Naeem & Thomas, 2025) — which showed how AI can connect with human analysis within a theoretically informed research domain and philosophy. The 2026 paper takes the final step: it names the reasoning logic those processes enact, and grounds it in a paradigm fit for an era in which, as the paper argues (drawing on Barad), knowledge and reality exist through intra-actions.

Read and cite the source article

Naeem, M. (2026). Fashion retailing and consumers in the digital age: E-semiotics and the co-creation of luxury brand narratives on Instagram. Journal of Retailing and Consumer Services, 90, 104695. https://doi.org/10.1016/j.jretconser.2025.104695

Frequently asked questions

What is the difference between inductive, deductive, abductive and conductive approaches?

Inductive reasoning builds theory from data; deductive reasoning tests theory against data; abductive reasoning infers the best explanation for surprising data. The conductive approach (Naeem, 2026) is different in kind: it is iterative and dialogical, generating knowledge through structured conversation between human theoretical judgement and AI-generated recommendations — the reasoning logic of AI-assisted qualitative research.

Is Agnoseology a replacement for epistemology?

No — it is a complement. Epistemology concerns the nature and justification of what we know; Agnoseology concerns the systematic exploration of what we do not yet know, particularly the unknowns AI can help surface within qualitative data.

Can I use conductive coding in my own research?

Yes. Combine the published AI-assisted six-step process (Naeem, Smith & Thomas, 2025) with explicit reporting of how AI recommendations and your theoretical, contextual and philosophical inputs shaped each coding decision, and cite Naeem (2026) for the approach.