Thesis (Ph. D.)--York University, 1999. Schulich School of Business. / Typescript. Includes bibliographical references. Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ43429.

Thesis (M. Phil.)--University of Hong Kong, 1977.

Transportation infrastructure, a vital component to sustain economic prosperity, represents the largest public-owned infrastructure asset in the U.S. With over a trillion invested dollars invested into long-lived physical assets such as roads and bridges, transportation agencies are tasked with maintenance and rehabilitation efforts to ensure that the access to transportation facilities is readily available and that the infrastructure is properly preserved. The management of these assets and the determination of their value, however, have been at the forefront of discussions in many state agencies and local governments. As a consequence, asset valuation has become a key component in asset management because it links the performance of infrastructure and deterioration process with the value of the infrastructure and its depreciation, providing critical information for decision makers at various levels to make more informed decisions. A utility-based methodological framework for the valuation of transportation infrastructure is presented along with a case study to demonstrate its applicability. A general framework is presented with emphasis on the valuation of pavement infrastructure. The results from the framework is then compared to existing valuation methods in addition to a series of sensitivity analysis on the variation of performance measures and their effect on the value of an asset. The development of this valuation approach serves as a starting point for assessing, in addition to the physical condition of an asset, the operational measures that can often make an asset less useful to its customers and managing agency. Utility theory can be utilized to combine the effect of performance indicators of varying measures and scales on the value of an asset. The proposed framework can assist state and local transportation agencies in the optimization of resource allocation procedures for better coordination of asset investments, facilitating benefit-cost analyses to quantify the impact of infrastructure investments. This tool allows agencies to detect deficiencies if any, in the management of its assets, providing a feedback mechanism that can foster an introspective review of its current management practices that may need further refinement or possibly elimination. / text

published_or_final_version / Mathematics / Master / Master of Philosophy

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This dissertation examines the perceptual bias of investors and securities analysts (the “audience” in the stock market) in their valuation of public firms’ innovative activities. I suggest that such bias occurs because the audience views a firm’s innovation through the prism of the firm’s categorization in product markets – its industry category – which may only loosely conform to the technological interrelationship among firms in knowledge space. I explore theoretically the conditions under which evaluation bias is most likely to occur – notably, due to innovations that defy the existing categorical structure used by the audience. Based on this theoretical framework, I develop hypotheses for empirical tests. I first argue that both technological opportunities and technological threats residing outside a firm’s industry are more likely to be underestimated by the stock market than those residing within the industry. In a sample of large U.S. manufacturing firms covering the years 1980 through 2000, I collected patent data to reflect innovative activities of firms. I compare the effect of a firm’s innovative activities on its current market valuation and its future cash flows. Consistent with my predictions, I find that firms capitalizing on cross-industry opportunities are more likely to be undervalued, while firms facing cross-industry technological competition are more likely to be overvalued. I further argue that reliance on categorical identity for information cues may reduce the audience’s ability to adequately assess the value-relevance of innovations that deviate from the technological norm in an industry. By analyzing the absolute forecast errors in security analysts’ reports, I find confirming evidence that deviant innovations increase the forecast bias. I further argue and demonstrate that such bias is less prevalent for diversified firms and in industries with less stable categorical “norms,” two conditions in which the audience is less likely to rely on categorical information and more likely to employ firm-specific information. Finally, I discuss the contribution and implication of the findings to studies of categorization and value-relevance of technological innovations.

This dissertation examines the perceptual bias of investors and securities analysts (the “audience” in the stock market) in their valuation of public firms’ innovative activities. I suggest that such bias occurs because the audience views a firm’s innovation through the prism of the firm’s categorization in product markets – its industry category – which may only loosely conform to the technological interrelationship among firms in knowledge space. I explore theoretically the conditions under which evaluation bias is most likely to occur – notably, due to innovations that defy the existing categorical structure used by the audience. Based on this theoretical framework, I develop hypotheses for empirical tests. I first argue that both technological opportunities and technological threats residing outside a firm’s industry are more likely to be underestimated by the stock market than those residing within the industry. In a sample of large U.S. manufacturing firms covering the years 1980 through 2000, I collected patent data to reflect innovative activities of firms. I compare the effect of a firm’s innovative activities on its current market valuation and its future cash flows. Consistent with my predictions, I find that firms capitalizing on cross-industry opportunities are more likely to be undervalued, while firms facing cross-industry technological competition are more likely to be overvalued. I further argue that reliance on categorical identity for information cues may reduce the audience’s ability to adequately assess the value-relevance of innovations that deviate from the technological norm in an industry. By analyzing the absolute forecast errors in security analysts’ reports, I find confirming evidence that deviant innovations increase the forecast bias. I further argue and demonstrate that such bias is less prevalent for diversified firms and in industries with less stable categorical “norms,” two conditions in which the audience is less likely to rely on categorical information and more likely to employ firm-specific information. Finally, I discuss the contribution and implication of the findings to studies of categorization and value-relevance of technological innovations.

No description available.