Digital transformation has become integral to achieving agility and resilience in manufacturing, with Big Data Analytics (BDA) playing a pivotal role in enabling Smart Supply Chain Management (SmSCM) through real-time tracking, predictive analytics, and demand forecasting (Castro Benavides et al., 2022; Qin et al., 2024). As firms increasingly adopt digital systems, the need for psychometrically sound instruments to assess technology usage and performance impact becomes critical. However, many studies using UTAUT2-based frameworks often rely on adapted instruments without formal content validation, risking poor construct alignment and measurement bias (Shi et al., 2012; Polit & Beck, 2006). This study addresses that gap by developing and validating a UTAUT2-based instrument, extended with Trust, Perceived Risk, and Technology Readiness—factors essential in data-intensive environments (Almanasreh et al., 2022; Narmaditya et al., 2024). It adopts the Content Validity Index (CVI) approach, using expert judgment to assess item relevance and clarity through I-CVI and S-CVI/Ave metrics. While CVI is well-established in health and education domains (Farsi et al., 2025; Dueñas Zorrilla et al., 2024), its application in technology adoption research in manufacturing remains limited.

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Keywords: Content Validity Index, S-CVI/Ave, Smart Supply Chain Management, Technology Adoption, UTAUT2