Introduction: Trust is one of the most valuable intangible assets in modern organizations and a fundamental prerequisite for sustainable business relationships. With the rapid expansion of digital transformation, platform ecosystems, electronic commerce, artificial intelligence, and data-driven business models, trust has evolved beyond interpersonal relationships to encompass organizational trust, institutional trust, technological trust, and digital trust. Despite the rapid growth of trust-related research, existing studies have primarily focused on specific contexts or individual dimensions of trust, resulting in fragmented knowledge and the absence of a comprehensive understanding of the semantic structure of trust in business research. Furthermore, traditional literature review approaches have become increasingly inadequate for synthesizing the rapidly expanding body of scientific publications. To address this gap, the present study employs large-scale text mining to identify, classify, and analyze the semantic patterns of trust across the business literature.



Methods: This study adopts a text-mining approach supported by natural language processing techniques. The dataset consists of 15,920 scientific articles indexed in the Web of Science and Scopus databases published between 2000 and 2024. After removing duplicates and preprocessing the textual data, titles, abstracts, and keywords were analyzed using tokenization, stop-word removal, stemming, and term filtering within RapidMiner. Extracted terms were manually coded according to their conceptual meanings and organized into 53 semantic categories, which were subsequently consolidated into five major dimensions: trust types, antecedents and contextual factors, trust formation processes, trust outcomes, and trust management and development strategies. Term frequencies, document frequencies, and longitudinal trends were then analyzed to reveal the evolution of trust-related knowledge over time.



Finding: The results indicate that antecedents, contextual conditions, and determinants of trust represent the dominant research stream within the business trust literature. Organizational trust, technology trust, and social trust emerged as the most extensively investigated trust types. Professional competence, communication quality, environmental conditions, and prior interactions were identified as the most influential antecedents of trust. In contrast, trust formation mechanisms received comparatively limited scholarly attention, highlighting an important research gap. Among trust outcomes, improved organizational performance, relationship sustainability, and stakeholder satisfaction were the most frequently investigated consequences. Within trust management research, constructive interactions, transparent organizational processes, and accountability represented the most prominent managerial strategies. Longitudinal analysis further revealed substantial growth across all five dimensions between 2000 and 2024, with accelerated expansion after 2015, particularly in technology-related trust, digital environments, and trust management research.



Conclusions: This study presents a comprehensive large-scale semantic Analysis of trust research within the business literature using text-mining techniques. The findings demonstrate that trust research has gradually evolved from identifying trust antecedents toward broader investigations of trust types, organizational outcomes, and management strategies. Nevertheless, important research gaps remain in understanding trust formation mechanisms, trust recovery, trust culture, and the long-term management of trust throughout its life cycle. The proposed conceptual framework contributes to theory development by providing an integrated semantic structure of business trust and offers practical implications for managers and policymakers seeking to strengthen stakeholder relationships, enhance organizational performance, and support trust-building strategies in digital business environments.