Towards a calculative model of supply chain enabling IT implementation
针对供应链信息技术实施动机的不一致研究，提出并验证了第三种“算计模型”，认为企业实施IT是基于局部利益算计而非理性渐进或情境驱动，对管理者理解IT决策复杂性有启示。
Purpose Research provides 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. We propose a third, calculative IT implementation model and empirically assess the validity of these diverging models. Design/methodology/approach We 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: (1) that IT implementation will follow logical stages, (2) that implementation of these technologies represents a rational choice, and (3) 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 Our findings make a significant theoretical and practical contribution and provide the basis for isolating and operationalising the calculative model for further empirical testing.