Towards a calculative model of supply chain enabling IT implementation

Purpose – The purpose of this paper is to illustrate inconsistent findings regarding motivations for the implementation of information technology (IT) in the supply chain. Two main theoretical perspectives emerge from the literature. The first predicts a logical progressive implementation of technology over time. The second views situational factors as moderating progressive implementation over time. The authors propose a third, calculative IT implementation model and empirically assess the validity of these diverging models. Design/methodology/approach – The authors took a qualitative, theory-testing approach. Cross-sectional surveys conducted in 2001 and 2011 – yielding responses from 62 matching firms – showed dynamic IT implementation patterns over time and allowed the selection of nine case studies for comparison. Findings – Results provide substantial support for a calculative model alongside the situational and progressive perspectives. This model addresses three problematic assumptions that underpin the progressive and situational perspectives: that IT implementation will follow logical stages; that implementation of these technologies represents a rational choice; and that managers implement these technologies to improve the performance of entire supply chains. Research limitations/implications – The empirical investigations were limited to Australian manufacturers. Further studies should extend the generalisability of the findings and study the phenomenon in different contexts. Practical implications – The study enhances practitioners’ understanding of the difficulties and complexities of IT implementation decisions among supply chain partners. Such an understanding may motivate managers to pursue IT adoption that goes beyond addressing locally focused performance metrics. Originality/value – The findings make a significant theoretical and practical contribution and provide the basis for isolating and operationalising the calculative model for further empirical testing.