Novel food technologies, such as genetic editing, cell-cultured foods, and controlled environment agriculture, offer potential solutions to global challenges like climate change and resource scarcity, yet their success depends on consumer readiness to adopt them. This research examines how cultural uncertainty avoidance and religiosity jointly shape adoption readiness for these technologies. Two studies were conducted with participants from Japan (a high uncertainty avoidance culture; n = 1214) and Singapore (a low uncertainty avoidance culture; n = 1189). Study 1 showed that in Japan, highly religious consumers were more willing to pay for and support novel food technologies than their secular counterparts, whereas in Singapore, secular consumers expressed greater support than more religious consumers. Study 2 demonstrated that symbolic value mediates these cross-cultural patterns, revealing how consumers interpret novel food technologies through identity- and value-based meanings that vary by cultural and religious context. These findings offer theoretical insight into the sociocultural predictors of technology adoption and provide practical guidance for tailoring strategies for adoption readiness across diverse markets. • Religiosity and cultural uncertainty avoidance shape consumer readiness for novel food technologies. • Consumers in Japan and Singapore display contrasting adoption patterns based on religiosity. • Symbolic value mediates the relationship between religiosity and technological acceptance. • High religiosity groups in high uncertainty cultures show greater willingness to adopt novel foods. • Low religiosity groups in low uncertainty cultures are more receptive to novel foods.

The growing complexity and uncertainty of sustainable supply chain environments demand advanced digital capabilities that sustain competitiveness through effective decision-making. This study investigates how Generative AI (GenAI)-enabled digital dexterity strengthens decision-making performance and, in turn, contributes to organizational future performance. Integrating dynamic capabilities theory with institutional theory, we develop and test a model in which decision-making quality and decision-making efficiency represent distinct dimensions of decision-making performance, while ethical identity and regulatory governance effectiveness shape the strength of relationships. Survey data were collected from 296 Chinese firms across multiple industries and analysed using partial least squares structural equation modeling. The results show that GenAI-enabled digital dexterity significantly enhances both decision-making quality and decision-making efficiency, and that both dimensions are positively associated with organizational future performance. Ethical identity further amplifies the positive effects of GenAI-enabled digital dexterity on decision-making outcomes, highlighting the importance of normative commitments for responsible and credible use of GenAI. Regulatory governance effectiveness exhibits asymmetric boundary effects, dampening the performance value of decision-making quality while strengthening the contribution of decision-making efficiency to future performance. This study advances research on digital transformation and sustainable operations by clarifying the distinctive role of GenAI-enabled digital dexterity, identifying decision-making performance as a central mechanism, and explaining how institutional governance conditions shape value realization. It also offers practical guidance on how firms can build and govern GenAI-enabled capabilities to support future-oriented performance in sustainability-focused supply chains. • Shows how GenAI-enabled digital dexterity improves decision-making in supply chains. • Identifies ethical identity as a normative amplifier of GenAI decision benefits. • Reveals regulatory governance as a boundary condition shaping GenAI decision outcomes. • Conceptualises and validates GenAI-enabled digital dexterity as an organizational capability. • Offers managers guidance on extracting value from GenAI in complex environments.