Measuring Collaborative Maturity in Human–AI Work: Development and Validation of the CIQ Scale

INKUBIS: Jurnal Ekonomi dan Bisnis Volume 8, Issue 1, 387-407 e_ISSN: 2775-3913 https://inkubis.polteksci.ac.id/index.php/ink/indx DOI: doi.org/10.59261/inkubis.v8i1.188 Measuring Collaborative Maturity in Human–AI Work: Development and Validation of the CIQ Scale Andreas Raditya1* Thomas Stefanus Kaihatu2 Timotius FCW Sutrisno3 Universitas Ciputra, Indonesia Universitas Ciputra, Indonesia Universitas Ciputra, Indonesia *Corresponding author: Andreas Raditya, Universitas Ciputra, Indonesia. 🖂 Article Info : Article history: Received: March 18, 2026 Revised: April 23, 2026 Accepted: April 25, 2026 Abstract Background: Collaboration has emerged as an essential capability because it helps us achieve effective collective performance something we are now doing more often in digital and cross-functional organizations. Although the collaborative activities are becoming more intense, a variety of inconsistencies remain in terms of outcomes given the differences in shared cognition, interaction quality, and role integration between human beings and artificial intelligence (AI). This gap signifies the necessity of a holistic measurement tool that is able to quantify collaborative maturity Keywords: in human–AI integrated workflows. adaptive co-learning; collaborative Objectives: This paper aims to develop and validate the collaborative intelligence quotient; cognitive intelligence quotient (CIQ) scale as a supporting diagnostic construct for synchronization; human–ai measuring the collaborative maturity of human–AI integrated workflows complementary intelligence; in the diverse property development and integrated township sector in measurement validation. Indonesia. Methods: A scale-development protocol was conducted using a purposive sample of 32 managerial practitioners in Indonesian property firms. Dimensionality, reliability, and convergent validity were examined sequentially using EFA followed by CFA. Results: The EFA suggested a four-factor structure, and the CFAs conducted for further purification led to a relatively simple measurement model with three latent dimensions (Adaptive CoLearning; Cognitive Synchronization & Fluency Interaction; Human-AI Complementary Intelligence) and nine out of eleven indicators. The final model showed adequate internal consistency and convergent validity. Conclusion: CIQ is a psychometrically reliable tool to systematically chart organizational collaborative maturity in utilizing AI for teamwork, and it could serve as an end-to-end foundation on which subsequent structural testing and capability scaling may be operationalized. To cite this article: Author Name;. (2026). Measuring Collaborative Maturity in Human–AI Work: Development and Validation of the CIQ Scale. INKUBIS: Jurnal Ekonomi dan Bisnis, 8 (1), 387-407. https://doi.org/10.59261/inkubis.v8i1.188 INTRODUCTION Collaboration has become a central competence across organizations of different degrees of digital enablement and cross-functionality that combines distributed expertise into synchronized decisions (e.g., achieving coordination) (Englmaier et al., 2025; Larson & DeChurch, 2020). However, despite high levels of visible coordination, uneven collective outcomes exist because shared understanding and coordination quality differ across teams and conditions or within teams over time, with some behaviors being more easily and precisely orchestrated than others (Kordova & Hirschprung, 2023; Marlow et al., 2017; Niler et al., 2021). These vulnerabilities are exacerbated in high-throughput, parallel work where small bits of information can be lost along handoffs and everyday transitions, causing desynchronized action and 387 | INKUBIS: Jurnal Ekonomi dan Bisnis Andreas Raditya, Thomas Stefanus Kaihatu, Timotius FCW Sutrisno Measuring Collaborative... quantifiable error, demonstrating how seemingly trivial communication gaps manifest as systemlevel performance threats (Paquette et al., 2023). Simultaneously, the presentation of AI as an active team member (as well as a tool) brings into everyday collaboration human–AI teaming, which reorients trust dynamics in new collaborative arrangements, changing communication and shared cognition (Berretta et al., 2023; Jarrahi, 2018; Schmutz et al., 2024). This shift essentially transforms collaboration into a sociotechnical orchestration problem, requiring organizations to integrate human decision-making, collective knowledge processes, and AI-assisted contributions into a unified workflow capable of maintaining trustworthy collective performance capabilities (Ali et al., 2025). Therefore, the observation of systematic differences in collective performance across groups and persistent findings indicating that teams under-realize their potential as coordination and common ground decline further underscore the need for more fine-grained measurement of collaborative maturity in environments where collaboration is frequent but its success remains uncertain (Leblanc et al., 2024; Riedl et al., 2021). In this view, Collective Intelligence Theory frames collective performance less as an outcome and more as an emergent capability that unfolds over time via a team interaction process. These interaction processes, in contemporary organizations, act as an interacting system in which common understanding and aligned information synchrony determine whether top-down or targeted decisions and cross-functional execution remain congruent (Vuchkovski et al., 2023). As this system is more prone to drift and inefficiency consistent with recent quantitative syntheses indicating that group-level intelligence factors correlate with group performance but remain sensitive to team-level coordination and regulation of member-level cognition when that connective tissue weakens, collective potency diminishes (Rowe et al., 2021). These dynamics are compounded by digital transformation, where more work is taking place at a distance and in a technology-mediated fashion, such that sustaining cohesion and coordination becomes an issue of capability rather than routine communication (Janssens et al., 2022). More recently, the accelerating diffusion of AI into knowledge work expands interaction even further away from human–human exchange and toward human–AI teaming, which requires coordinating complementary strengths to achieve common objectives (Raisch & Krakowski, 2021; Seeber et al., 2020). Here, research on human–AI teams shows that common ground and shared mental models remain salient but more difficult to achieve and maintain when AI agents are included as teammates in sociotechnical workflows. Strikingly, present reviews on AI teaming note that coordination, communication, trust, and shared cognition become more susceptible to decay when AI teammates are introduced (Schmutz et al., 2024), underscoring the rationale for an operational assessment perspective capable of evaluating collaborative maturity in human–AIintegrated environments. Established tools measur (...truncated)