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The use of information technologies between supply chain organizations has been shown to promote organizational coordination and have a positive impact on performance. Drawing from organizational theories of learning, we build on this research by proposing a model that relates the pattern of supplier use of IT to specific types of supply chain coordination activities and a comprehensive set of organizational benefits. Specifically, we evaluate how two patterns of IT use by suppliers (exploitation and exploration) relate to two specific types of coordination activities with their buyers (operational and strategic coordination), which in turn are posited to promote specific organizational benefits. Using data from 241 first-tier OEM suppliers in the computer industry, our findings show that each pattern of IT use directly promotes a specific type of coordination activity. Although both types of coordination activities are needed to achieve both strategic and operational benefits, we find each coordination activity to be uniquely promoted by a specific pattern of IT use. IT use for exploitation is found to be an antecedent to operational coordination; IT use for exploration is found to be an antecedent to strategic coordination. No crossover between pattern of use and coordination activities is found. Our findings show that to achieve a complete set of benefits, suppliers must ultimately use IT for both exploration and exploitation. These findings provide a deeper understanding of the mechanism of how the pattern of IT use can result in a comprehensive set of organizational benefits for supplier firms.

The use of e-business technologies between supply chain organizations has been thematic in recent literature. Organizational collaboration, the foundation of supply chain management, has been enabled by the development and use of e-business technologies. Organizational collaboration and information sharing, in turn, are expected to improve organizational performance. We propose and test a model of the relationship between organizational use of e-business technologies, organizational collaboration, and performance, using empirical data. Our model differs from past studies in that collaboration is viewed as two unique constructs, differentiating between intra- and inter-organizational collaboration. Our findings show that use of e-business technologies impacts performance both directly and indirectly by promoting both measures of collaboration. Intra-firm collaboration is also found to have a direct impact on organizational performance. However, the impact of inter-organizational collaboration on performance is found to be only indirect, through the impact of intra-organizational collaboration. These findings reveal the complexity of organizational collaboration, underscore the importance for companies to promote internal collaboration, and invest in information technologies that facilitate it.

This research investigates the benefits in forecast accuracy by combining judgmental forecasts with those generated by statistical models. Our study differs from prior research efforts in this area along two important dimensions. First, two different types of judgmental forecasts are evaluated for combination with statistical forecasts — one based on contextual knowledge and one based on technical knowledge. Contextual knowledge is information gained through experience on the job with the specific time series and products being forecasted. Technical knowledge is information gained from education on formal forecasting models and data analysis. Second, we investigate the conditions under which adding judgment to combination forecasts helps the most. Specifically, we test the improvement as a function of time series variability. Our results show that judgmental forecasts based on contextual knowledge, rather than technical knowledge, are the better input into combination forecasts. Bringing judgmental forecasts based on contextual knowledge into combination forecast improves forecast accuracy over the individual statistical and judgmental forecasts. However, the benefit attained from including contextual knowledge in the combination depends on the amount of inherent variability in the time series being forecast. More contextual knowledge is needed for combination forecasts if a time series has more data variability. If the amount of variability is low, less emphasis should be given to contextual knowledge when making combination forecasts. In general, our findings suggest a linear relationship between the amount of contextual knowledge needed and data variability.