Paper Type
ERF
Abstract
AI-CRM integration remains a complex challenge for Small and Medium-sized Enterprises (SMEs). This research investigates how Technological, Organizational, and Environmental (TOE) factors configure with Dynamic Managerial Capabilities (DMC) to shape AI-CRM readiness. Drawing on ten interviews with New Zealand SME owner-managers, the study employs the Gioia methodology to analyze how leaders "sense" and "seize" AI opportunities. Preliminary findings suggest that readiness is expressed through two managerial logics: AI-as-Adaptive Agent (an autonomous teammate bridging skill gaps) and AI-as-System Feature (a standardized layer for operational efficiency). We propose a dual-method roadmap: having established a qualitative data structure through abductive coding, we outline a future phase using Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to identify the specific causal configurations of structural constraints and managerial capabilities. This research offers a configurational understanding of AI readiness in a resource-constrained environment.
Paper Number
1136
Recommended Citation
Chung, Claris and Gong, Yan, "Divergent Managerial Logics of AI-CRM Adoption in New Zealand SMEs" (2026). AMCIS 2026 Proceedings. 3.
https://aisel.aisnet.org/amcis2026/sigadit/sigadit/3
Divergent Managerial Logics of AI-CRM Adoption in New Zealand SMEs
AI-CRM integration remains a complex challenge for Small and Medium-sized Enterprises (SMEs). This research investigates how Technological, Organizational, and Environmental (TOE) factors configure with Dynamic Managerial Capabilities (DMC) to shape AI-CRM readiness. Drawing on ten interviews with New Zealand SME owner-managers, the study employs the Gioia methodology to analyze how leaders "sense" and "seize" AI opportunities. Preliminary findings suggest that readiness is expressed through two managerial logics: AI-as-Adaptive Agent (an autonomous teammate bridging skill gaps) and AI-as-System Feature (a standardized layer for operational efficiency). We propose a dual-method roadmap: having established a qualitative data structure through abductive coding, we outline a future phase using Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to identify the specific causal configurations of structural constraints and managerial capabilities. This research offers a configurational understanding of AI readiness in a resource-constrained environment.
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