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A consecutive number of studies on the adoption trend of logistics technology since 1988 revealed that logistics organizations are not in the frontier when it comes to adopting new technology and this delayed adoption creates an information gap. In the advent of supply chain management and the strategic position of logistics, the need for accurate and timely information to accompany the logistics executives became more important than ever before. Given the integrative nature of logistics technology, failure to implement the technology successfully could result in writing off major investments in developing and implementing the technology or even in abandoning the strategic initiatives underpinned by these innovations. Consequently, the need to employ effective strategies and models to cope with these uncertainties is rather crucial. This thesis addresses the aspect of uncertainty in implementation success by process and factor research models. Process research approach focuses on the sequence of events in the technology transfer process that occurs over time. It explains the story that explains the degree of association between these sequences and implementation success. Through content analysis, this research gathers, extracts, and categorizes process data of actual stories of logistics technology adoption and implementations in organizations that are published in literature. The extracted event sequences are then analyzed using optimal matching from natural science and grouped using cluster analysis. Four patterns were revealed that organizations follow to transfer logistics technology namely, formal minimalist, mutual adaptation, development concerned, and organizational roles dispenser. Factors that contribute to successful implementation in each pattern were defined as the crucial and necessary events that characterized and differentiated each pattern from others. The factor approach identifies the potential predictors of successful technology implementation and tests empirical association between predictors and outcomes. This research develops a logistics technology success model. In developing the model, various streams of research were investigated including logistics, information systems, and organizational psychology. The model is tested using a questionnaire survey study. The data were collected from Australian companies which have recently adopted and implemented logistics technology. The results of a partial least squares structured equation modeling provide strong support for the model constructs and valuable insights to logistics/supply chain managers. The last study reports a convergent triangulation study using multiple case study of three Australian companies which have implemented logistics technology. A within and a cross case analysis of the three cases provide cross validation for the results of the other two studies. The results provided high predictive validity for the two models. Furthermore, the case study approach was so beneficial in explaining and contextualizing the linkages of the factor-based model and in confirming the importance of the crucial events in the process-based model. The thesis concludes with a research and managerial implications chapter which is devoted for logistics/supply chain managers and researchers. |
