Bibliometric Dataset on the Evolution of Technostress in Organisations (1982–2026)

We built this dataset to explore how the concept and research focus of technostress have transformed over the last four decades, spanning from 1982 to March 2026. The primary goal was to track how technostress shifted from a relatively simple, IT-centered workplace strain into a pervasive, multi-dimensional phenomenon. This evolution was heavily accelerated by major disruption events, particularly the COVID-19 pandemic, and the rapid rise of Artificial Intelligence and algorithmic management in today's workplaces. In total, the dataset gathers 1,620 primary records from 832 scientific sources indexed in Scopus. Looking at the broader trends, research in this field has grown at an annual rate of 9.27%, with more than half of all published work concentrated between 2023 and 2026 alone. The collection clearly maps out three main historical chapters. First, the pre-pandemic era (1982–2019, with 323 papers) focused mostly on early IT adoption, computer anxiety, and foundational organizational stress. Next came the acute COVID-19 period (2020–2022, with 375 papers), which shifted heavily toward forced remote work, digital fatigue, blurred work-life boundaries, and communication overload. Finally, the post-pandemic and AI expansion era (2023–2026, with 922 papers) reflects a major pivot toward generative AI, automated workplace monitoring, and the psychological impact of working alongside intelligent systems. Alongside these thematic shifts, the data also highlights active global collaboration networks, featuring an international co-authorship rate of nearly 24%. The dataset itself consists of raw bibliographic metadata extracted directly from Scopus on March 29, 2026, using the single-descriptor search query TITLE-ABS-KEY ("technostress"). To ensure the sample remained as broad and representative as possible across different disciplines and regions, no restrictions were placed on language or subject areas during the initial search. The exported files include key bibliographic variables such as paper titles, abstracts, author keywords, Keywords Plus, affiliations, citation counts, publication years, and source details. Researchers can easily load these raw CSV or BIB files into popular science mapping tools like R (using Bibliometrix or Biblioshiny), VOSviewer, CiteSpace, or Python libraries to recreate our co-occurrence analyses, structural networks, and thematic matrices. Beyond replicating our findings, this dataset serves as a solid, open-access foundation for anyone looking to conduct systematic reviews, meta-analyses, or future empirical research on how technological advances continue to reshape occupational well-being.