Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 1 references
AI in Service Interactions
Abstract
Consumers increasingly ask conversational large language models (LLMs) which product or company to choose. When web-search-enabled assistants answer, they ground their recommendations in sources retrieved at query time, yet little is documented about which publications they draw on. We conducted an exploratory study of the sources cited by two widely used assistants, OpenAI's ChatGPT and Google's Gemini, across 100 open "best/top X for a small business" questions spanning ten commercial categories, issued twice to each assistant (398 usable responses). For every response we recorded the source domains the assistant grounded on, excluding search-action links, and separated editorial publications from platform properties. The two assistants drew on almost entirely different sources: among editorial domains cited in three or more responses, their overlap was only 12%. ChatGPT leaned on technology-review and financial press (TechRadar was its most-cited source, in 16% of its responses; followed by CNBC, Tom's Guide, and Yahoo Finance). Gemini grounded heavily on platform properties (YouTube in 53% of its responses, Reddit in 25%) and on consumer-review and comparison sites (Forbes, Trustpilot, ConsumerAffairs, G2). Press-release wire domains appeared in under 3% of responses, marginally more often via ChatGPT. Important limitation: Gemini's grounding interface returns redirect labels rather than verifiable source URLs, so its reported sources cannot be independently confirmed, and part of the observed divergence reflects how each platform reports sources, not only what it reads. We release the full dataset. Given the exploratory sample, results are directional, not definitive.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
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The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
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