Purpose: The purpose of this study includes two parts. First, it introduces a machine-based method for model and instrument development and updating that integrates large sample qualitative data. Second, a new model and instrument for e-commerce customer satisfaction are developed.Design/methodology/approach: The research occurs in two phases. In Phase 1, data collection occurs with a literature-based quantitative model and instrument that includes at least one qualitative scale item per construct. Data analysis of the resulting data includes factor analysis (FA) and latent semantic analysis text mining to generate an updated model and instrument. In Phase 2, data collection uses the new model and instrument. Data analysis in Phase 2 includes exploratory data analysis with FA, exploratory structural equation modeling and partial least square modeling.Findings: As a result of the information gained by the integration of qualitative scales in the literature-based survey, the final model departs substantially from the initial research-based research model. It integrates many of the constructs known to impact a website and software usability from information systems research into a new e-retail satisfaction model.Originality/value: The research method, as presented here, offers a strategy for integrating large scale qualitative data for refinement of models and the development of instruments. It is essentially a method of gaining the wisdom of crowds economically while simultaneously reducing the biases and laborious effort commonly associated with qualitative research.

There is a surge in the development of decision-oriented analysis tools intended to extract actionable information from text. These tools integrate various text-mining methods that were performance tested in a manner that was often biased toward the new system. Those tests primarily utilised descriptive measurement criteria and test datasets that are inconsistent with most business corpora. We propose and test a user-oriented judgment approach that allows testing under controlled customer-oriented corpora and generates effect size measures. To illustrate the approach, customer relations data was analysed by latent semantic analysis and latent Dirichlet analysis with results evaluated by prospective business analysts. Reporting includes comparisons of results with published literature. While the research centres on the context-region text-mining systems, literature comparisons include word-embedding methods. The analysis concludes that none of the systems reviewed possess a repeatable statistical advantage over the others. Instead, distribution attributes, algorithm configuration, and the evaluation task drive results.

Background/Objectives: Vast amounts of textual data are generated by healthcare organizations every year. Traditional content analysis is time-intensive, error-prone, and potentially biased. This study demonstrates how freely available large language model (LLM) artificial intelligence (AI) tools can efficiently and effectively analyze qualitative healthcare data and uncover insights missed by traditional manual analysis. Interview data from chief nursing officers (CNOs) at top-performing academic medical centers were analyzed to identify factors contributing to their operational and patient quality success. Methods: Semi-structured interviews were conducted with CNOs from top-performing academic medical centers that achieved top-decile quality measures while using resources most efficiently. Interview transcripts were analyzed using a mix of traditional text mining in LSA and Gemini 2.5. The capability of four freely available AI platforms—Gemini 2.5, Scholar AI 5.1, Copilot’s Chat, and Claude’s Sonnet 4.5—was also reviewed. Results: LLM AI analysis identified ten primary factors, comprising twenty-four subtopics, that characterized successful hospital performance. Notably, AI analysis identified a theoretical connection that manual analysis had missed, revealing how the identified framework aligned with Donabedian’s seminal structure, process, outcomes quality model. The AI analysis reduced the required time from weeks to nearly instantaneous. Conclusions: LLM AI tools offer a transformative approach to unlocking insight from the analysis of qualitative textual data in healthcare settings. These tools can provide rapid insight that is accessible to personnel with minimal text-mining expertise and offer a practical solution for healthcare organizations to unlock insight hidden in the vast amounts of textual data they hold.