This study analyzes current trends in information technology business practices and investments in Argentina. In order to provide a better perspective on these trends, Argentine data has been contrasted with U.S. and Spanish data. We have found a profound effect of IT on organizational practices and several competitive dimensions in Argentina. Many Argentine companies report that their IT investments drive significant improvements in key performance dimensions, such as productivity, internal and external communication efficiency, innovation, market share and profitability. On the other hand, information technologies are changing the way collaborative work is carried out in these companies, creating an internal capability gap that requires both management teams and lower organizational levels to develop new skills. These changes are shaping organizations’ architectures towards flatter structures that seem to contribute to more agile organizations. Argentine data comparison with U.S. and Spanish data reveals the challenges imposed by local conditions on CIOs and their organizations when it comes to sustaining significant IT investments while gauging financially risky bets. However, by comparing benefits in recent years from IT investments reported by Argentine companies with those reported by Spanish and U.S. firms, we conclude that Argentine companies may have large performance improvement gains as they continue to develop stronger IT capabilities. This study is based on data collected through the Business and Information Technologies (BIT) project -a collective international effort to assess IT business trends by analyzing data from a survey conducted in several countries.

 The literature on organizational learning curves has established that plants improve their performance at very different rates of learning. Several empirical studies have identified some of the factors affecting productivity improvement with cumulative experience. Most of these empirical studies have focused on productivity improvement, with the plant as the unit of analysis. However, few empirical studies have focused on quality improvement learning curves, and to our knowledge, no study has analyzed disaggregated intra-plant quality problems as the unit of empirical analysis. This dissertation examines quality improvement within 17 automotive assembly plants within one company that set company-wide quality improvement objectives. Combining field observations with statistical analysis of panel data consisting of intra-plant micro-data on quality faults along the production process, we were able to deconstruct elements of the “learning black-box,” to understand how, and under what circumstances, different production areas within each plant were able to improve their quality performance. In this dissertation we arrived to the following three main conclusions: 1) The problem population within a complex manufacturing process under competitive pressure to improve quality performance (at an aggregated level) is characterized by a large number of problem categories and low rate of defect creation within each problem category. Under these circumstances, the diversity of the plants’ problem population tends to be high, and the higher it becomes, the more it hampers organizational learning. Organizations attempting to fuel their learning through root cause problem solving need to develop robust and relatively decentralized, low-cost root cause problem solving capabilities. 2) Management at the plants was usually biased toward framing the “organizational quality problem” in terms of the aggregated quality defect rate, rather than in terms of the quality problem categories underlying the overall defect rate. This framing of the “organizational quality problem” seems to have biased decisions on quality improvement resource allocation toward “containment countermeasures,” with few resources allocated to follow up containment with root cause problem solving. 3) Some “lean manufacturing system” techniques may improve quality fault feedback, increase organizational responsiveness to quality faults and provide the opportunity to begin root cause problem solving in the context of the problem. However, if the organizations do not develop decentralized root cause problem solving capabilities, those lean manufacturing techniques may result in a “containing organization,” instead of supporting a “learning organization.”