<![CDATA[Micro, small, and medium enterprises (MSMEs) selling through TikTok Shop operate in one of the most data-rich environments available to small firms, yet many struggle to convert dashboard data into competitive advantage. This study examines the first-order question of what market signals sellers actually monitor and how their digital readiness shapes the way they sense and prioritize dashboard anomalies during a live session. Anchored in the Resource-Based View, the Knowledge-Based View, and the sensing microfoundation of Dynamic Capabilities Theory, the study uses a qualitative descriptive design. Primary data were collected through semi-structured in-depth interviews with five informants, comprising four TikTok Shop sellers across the fashion, food, beauty, and smartwatch categories and one Multi-Channel Network consultant, and were triangulated with a direct analysis of the LIVE Dashboard. Data were analyzed through manual thematic analysis following the interactive model of Miles, Huberman, and Saldaña. The findings show that, despite operating in different categories, the sellers converge on a small set of conversion-oriented signals, namely Click-Through Rate, check-out rate, Gross Merchandise Value, and advertising Return on Investment, while consciously ignoring the majority of available metrics. The way this sensing is organized, however, diverges into two configurations of digital readiness: an embedded configuration, in which a trained host senses and acts in a single step, and a centralized configuration, in which the owner or a dedicated data staff member senses and instructs the host. The intensity of sensing is found to be endogenous to platform design, rising as the Gross Merchandise Value based algorithm increased the financial risk of each session. The study contributes a seller-side, real-time account of the sensing capability and argues that competitive advantage in livestream commerce rests not on the dashboard data, which is available to all, but on the interpretation capability, which is rare and difficult to imitate.]]>
