发表机构
Sophia University（上智大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出计算KJ-Ho框架，结合领域专用LLM实现无分析师偏差的定性数据见解提取，整合三种方法论并作出五项贡献，属概念性研究。
AI 中文摘要
支撑消费者见解生成的定性研究方法论——KJ法、扎根理论与主题分析——存在共同的结构局限：人类分析师的认知处理能力。重复研究进一步表明，分析相同数据时不同分析师得出的结论存在显著差异（即分析师偏差）。本文提出计算KJ-Ho（即川喜田二郎法），这是一种在计算层面实现KJ法认识论的理论框架——让结构从数据本身浮现，而非强加分析师的先入之见，我们将这种取向称为“无分析师偏差”。该框架采用一种领域专用大型语言模型，该模型通过在营销研究语料库上进行持续预训练（CPT），并在专家 curated 的见解对上进行监督微调（SFT）构建，其架构分为三层：数据结构化、见解提取与策略生成。两项在日本营销背景下开展的初步研究验证了基于CPT的领域专业化的必要性。本文作出五项贡献：（1）将KJ法、扎根理论与皮尔斯溯因推理整合为数据驱动解释生成的单一认识论承诺；（2）利用领域专用嵌入的三层架构，用于跨访谈分析；（3）两种新型评估指标：InsightExtraction-F1与MarketingQA；（4）明确应对WEIRD问题，聚焦非西方方法论；（5）将近三十年营销研究实践中提炼的五个实践衍生问题表述转化为设计要求。人类分析师保留监督角色。本文是一篇在实证验证前提出的概念论文。
英文摘要
The qualitative research methodologies that underpin consumer-insight generation - the KJ method, Grounded Theory, and Thematic Analysis - share a structural constraint: the cognitive processing capacity of the human analyst. Replication research further shows that conclusions vary substantially across analysts analyzing identical data (analyst bias). This paper proposes Computational KJ-Ho (the Kawakita Jiro method), a theoretical framework that computationally realizes the KJ method's epistemology - letting structure emerge from the data itself without imposing the analyst's preconceptions - an orientation we term "analyst-bias-free." The framework employs a domain-specialized LLM built through continued pre-training (CPT) on a marketing-research corpus and supervised fine-tuning (SFT) on expert-curated insight pairs, organized as a three-layer architecture: data structuring, insight extraction, and strategy generation. Two preliminary studies in the Japanese marketing context support the necessity of CPT-based domain specialization. The paper makes five contributions: (1) a theoretical integration of the KJ method, Grounded Theory, and Peircean abduction into a single epistemological commitment of data-driven explanation generation; (2) a three-layer architecture leveraging domain-specialized embeddings for cross-interview analysis; (3) two novel evaluation metrics, InsightExtraction-F1 and MarketingQA; (4) explicit engagement with the WEIRD problem, centering a non-Western methodology; and (5) five practice-derived problem formulations from nearly three decades of marketing-research practice, translated into design requirements. The human analyst retains a supervisory role. This is a concept paper presented ahead of empirical validation.
CommentsConcept paper. 38 pages, 1 figure, 2 tables
