The Role of the AMO enhancing HRM practices in Open Innovation performance in the high-tech industry

Abstract

This research investigated how Human Resource Management (HRM) practices, framed within the Ability–Motivation–Opportunity (AMO) model, influence Open Innovation (OI) performance in high-tech organisations. The study asked: How can the Ability-Motivation-Opportunity (AMO) enhancing HRM practices lead to firm-level OI performance in the high-tech sector? It explores how HRM practices can be systematically used to enhance OI performance. For this purpose, 13 in-depth expert interviews were conducted in the fields of biotechnology, AI, semiconductors, and advanced manufacturing, which all fall under the high-tech sector. Key findings show that HRM has an influence on the OI abilities of companies through HRM practices that enhance AMO, including skill-based recruitment (Ability), purpose-driven motivation systems (Motivation), and collaborative platforms (Opportunity). Thematic analysis shows that about 41% of the data fell under motivation-enhancing practices, which makes it central to maintain employee-led OI. Conversely, ability and opportunity practices contributed 34% and 25% respectively, indicating that there was an integrated but uneven impact of each AMO dimension. The study finds that effective HRM not only empowers employees but also enables crossboundary knowledge sharing, team autonomy and alignment of strategies with the OI goals. It focuses more on the interdependence and temporal adjustment of the AMO practices at various phases of the innovation lifecycle. Theoretically, the study expounds the AMO theory by combining it with dynamic capabilities and resource-based views and redefines the role of AMO into a strategic facilitator of OI rather than only productivity. Practically, it offers actionable implications in developing the HRM systems that enhance knowledge absorption, team collaboration and innovation responsiveness. The results underline that motivation, particularly related to autonomy, purpose, and recognition, is the most reliable determinant of the OI performance. The data further shows that organisations can better respond to volatile knowledge-intensive markets when they dynamically deploy AMOenhancing practices across innovation stages and integrate them into their organisational cultures. For practitioners, the findings suggest three priorities for action examined in the thesis: (1) sustain ability through skill-based recruitment coupled with focused up-skilling aligned to each phase of the innovation life-cycle; (2) enhance motivation mainly with non-monetary reward and recognition schemes that focus on autonomy, purpose, and timely feedback; and (3) enhance opportunity by embedding collaborative platforms and cross-boundary project teams so knowledge transfers quickly across functions. These results are derived from a qualitative, cross- ii sectional data set of 13 expert interviews in biotechnology, AI, semiconductor, and advanced manufacturing firms, so their generalisability is necessarily limited. Future research should therefore try to test the AMO–OI model with larger and more diverse samples, consider longitudinal or mixed-method designs in order to be able to examine causality over time, as well as compare the relative salience of ability, motivation, and opportunity between industries and cultural contexts.