Introduction: Knowledge-based products that are developed and launched without comprehensive market research are more likely to fail or underperform in the marketplace. Despite the recognized importance of market research in supporting successful product development, the existing literature lacks a systematic framework that clarifies the causal relationships among different market-research strategies and, more importantly, incorporates the structural and cognitive role of the internal research unit as a knowledge-management hub. Accordingly, this study aims to identify and prioritize market-research strategies for the development of knowledge-based products while explicitly modeling the role of the research unit in transforming tacit market knowledge into explicit and actionable intelligence for research and development (R&D) and marketing teams. The primary research question is: What market-research strategies are employed in the development of knowledge-based products, and how are the causal relationships among these strategies structured, particularly with respect to the role of the research unit?
Methodology: This applied, exploratory study employed a mixed-methods design integrating qualitative and quantitative approaches within an inductive–deductive research paradigm. The study population comprised university professors and managers of knowledge-based companies. Using purposive sampling and the principle of theoretical adequacy, 29 experts were selected to participate in the study. In the qualitative phase, data were collected through semi-structured interviews. The validity of the qualitative findings was established through content and theoretical validity, while reliability was assessed using intra-coder and inter-coder agreement, yielding Cohen’s kappa coefficients of 0.92 and 0.85, respectively. The qualitative data were analyzed using thematic analysis with the support of MAXQDA software. In the quantitative phase, a pairwise-comparison questionnaire was administered to the same group of experts. Content validity was assessed using the content validity ratio (CVR) and content validity index (CVI). Inter-rater reliability was evaluated using the intraclass correlation coefficient (ICC = 0.87) and Kendall’s coefficient of concordance (W = 0.85, p < 0.001). Quantitative data were analyzed using fuzzy cognitive mapping (FCM). Triangular fuzzy numbers were employed to transform linguistic judgments into fuzzy values, followed by defuzzification using the fuzzy mean method. For each identified strategy, the outdegree (influence), indegree (dependence), and centrality index were calculated. The validity of the resulting model was further evaluated through leave-one-out cross-validation, which yielded a mean absolute error (MAE) of 0.041, indicating a high level of predictive accuracy.
Findings: The qualitative analysis identified 20 distinct market-research strategies, ranging from competitor analysis and direct observation to AI-based sentiment analysis, big-data mining, ethnographic studies, A/B testing, and sales-funnel analysis. The quantitative fuzzy cognitive mapping (FCM) analysis identified three strategies with the highest centrality indices: (C4) conducting one-on-one interviews with customers or experts to explore their deeper needs and motivations (centrality = 19.69); (C1) analyzing competitors’ products, pricing, marketing, and distribution strategies to identify market gaps and opportunities for differentiation (centrality = 19.56); and (C11) testing products or prototypes with users to evaluate usability, performance, and user experience (centrality = 19.55). These three strategies demonstrated consistently high levels of both influence and dependence, highlighting their pivotal positions within the overall network of market-research relationships. Rather than functioning hierarchically, they appear to form a mutually reinforcing feedback loop: insights from in-depth interviews inform prototype-testing scenarios; test results reveal gaps and opportunities for competitor analysis; and competitor analysis generates new questions and directions for subsequent interviews.
This cyclical interaction requires a coordinating mechanism, namely the research unit, which functions as a “Ba,” or interactive context, for knowledge conversion. Drawing on the SECI model of knowledge creation (Nonaka & Takeuchi, 2007), the research unit facilitates four interconnected processes: (1) socialization, through which tacit customer knowledge—including needs, pain points, and perceptions—is communicated to the research team through structured interactions; (2) externalization, whereby user experiences obtained through prototype testing are transformed into explicit usability reports, performance indicators, and improvement recommendations; (3) combination, through which competitor intelligence and market-trend data are integrated into structured outputs such as SWOT analyses, positioning maps, and dashboards; and (4) internalization, whereby synthesized knowledge is transferred to R&D and marketing teams and transformed into operational competencies and dynamic capabilities. Thus, the three leading strategies should not be viewed as isolated market-research techniques but as interdependent components of a continuous knowledge-conversion cycle. Within this cycle, the research unit serves as a central coordinating mechanism that connects market intelligence, organizational learning, and product-development activities.
Discussion and Conclusion: The findings indicate that, for knowledge-based products, interactive and exploratory market-research methods may be more effective than purely quantitative approaches in reducing market uncertainty. The three highest-ranked strategies complement one another: in-depth interviews help uncover latent customer needs, competitor analysis supports strategic positioning, and prototype testing provides evidence regarding real-world usability and user experience. Their integrated application, coordinated by a dedicated research unit, can contribute to reducing commercialization risks and improving product–market fit. The findings also extend the existing literature by providing a detailed operational framework that incorporates methods that have received comparatively limited attention in the context of knowledge-based products, including ethnographic research, A/B testing, customer-journey mapping, and sales-funnel analysis. More importantly, the study addresses a theoretical gap by conceptualizing the research unit as an active knowledge-management actor rather than merely a data-collection function. In this role, the research unit coordinates the transformation of tacit market knowledge into explicit, actionable knowledge that can inform R&D and marketing activities. From a practical perspective, managers are advised to prioritize resources for the three core strategies while complementing them with additional techniques such as AI-driven sentiment analysis, big-data analytics, scenario simulation, and continuous customer-feedback collection. The research unit should institutionalize a continuous feedback loop linking customers, R&D, and marketing teams, while fostering a data-driven and customer-centered organizational culture and investing in specialized analytical capabilities and tools. The study has several limitations. First, its focus on the Iranian innovation ecosystem may limit the generalizability of the findings to other institutional, cultural, and industrial contexts. Second, the model relies partly on expert judgments, which may introduce an element of subjective bias. Future research could therefore validate the proposed model across different cultural and industrial settings, incorporate longitudinal performance data to examine the relationship between market-research strategies and actual product outcomes, and employ agent-based or other dynamic simulation approaches to investigate how interactions among strategies evolve over time.