Does the U.S. Patent and Trademark Office Grant Too Many Bad Patents?: Evidence from a Quasi-Experiment

Many believe the root cause of the patent system’s dysfunction is that the U.S. Patent and Trademark Office (PTO or Agency) is issuing too many invalid patents that unnecessarily drain consumer welfare. Concerns regarding the Agency’s overgranting tendencies have recently spurred the Supreme Court to take a renewed interest in substantive patent law and have driven Congress to enact the first major patent reform act in over sixty years. Policymakers, however, have been modifying the system in an effort to increase patent quality in the dark. As there exists little to no compelling empirical evidence the PTO is actually overgranting patents, lawmakers are left trying to fix the patent system without even understanding the root causes of the system’s shortcomings. This Article begins to rectify this deficiency, advancing the conversation along two dimensions. First, it provides a novel theoretical source for a granting bias on the part of the Agency, positing that the inability of the PTO to finally reject a patent application may create an incentive for the resource-constrained Agency to allow additional patents. Second, this Article attempts to explore, through a sophisticated natural experiment framework, whether the Agency is in fact acting on this incentive and overgranting patents. Our findings suggest that the PTO is biased toward allowing patents. Moreover, our results suggest the PTO is targeting its overgranting tendencies toward those patents it stands to benefit from the most—that is, those patent applications directed toward technologies that have historically had high repeat-filing rates, such as information, computer, and health-related technologies. Our findings provide policymakers with much-needed evidence that the PTO is indeed overgranting patents. Our results also suggest that the literature has overlooked a substantial source of Agency bias; hence, recent fixes to improve patent quality will not achieve their desired outcome of extinguishing the PTO’s overgranting proclivities

The Relationship Between HR Practices and Firm Performance: Examining Causal Order

Significant research attention has been devoted to examining the relationship between HR practices and firm performance, and the research support has assumed HR as the causal variable. Using data from 45 business units (with 62 data points), this study examines how measures of HR practices correlate with past, concurrent, and future operational performance measures. The results indicate that correlations with performance measures at all three times are both high and invariant, and that controlling for past or concurrent performance virtually eliminates the correlation of HR with future performance. Implications are discussed

Peeling Back the Onion Competitive Advantage Through People: Test of a Causal Model

Proponents of the resource-based view (RBV) of the firm have identified human resource management (HRM) and human capital as organizational resources that can contribute to sustainable competitive success. A number of empirical studies have documented the relationship between systems of human resource policies and practices and firm performance. The mechanisms by which HRM leads to firm performance, however, remain largely unexplored. In this study, we explore the pathways leading from HRM to firm performance. Specifically, we use structural equation modeling to test a model positing a set of causal relationships between high performance work systems (HPWS), employee retention, workforce productivity and firm market value. Within a set of manufacturing firms, results indicate the primary impact of HPWS on productivity and market value is through its influence on employee retention

ILR Research in Progress 2006-07

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.Research_in_Progress_2006_07.pdf: 18 downloads, before Oct. 1, 2020

Synthesizing diverse evidence: the use of primary qualitative data analysis methods and logic models in public health reviews

Objectives: The nature of public health evidence presents challenges for conventional systematic review processes, with increasing recognition of the need to include a broader range of work including observational studies and qualitative research, yet with methods to combine diverse sources remaining underdeveloped. The objective of this paper is to report the application of a new approach for review of evidence in the public health sphere. The method enables a diverse range of evidence types to be synthesized in order to examine potential relationships between a public health environment and outcomes. Study design: The study drew on previous work by the National Institute for Health and Clinical Excellence on conceptual frameworks. It applied and further extended this work to the synthesis of evidence relating to one particular public health area: the enhancement of employee mental well-being in the workplace. Methods: The approach utilized thematic analysis techniques from primary research, together with conceptual modelling, to explore potential relationships between factors and outcomes. Results: The method enabled a logic framework to be built from a diverse document set that illustrates how elements and associations between elements may impact on the well-being of employees. Conclusions: Whilst recognizing potential criticisms of the approach, it is suggested that logic models can be a useful way of examining the complexity of relationships between factors and outcomes in public health, and of highlighting potential areas for interventions and further research. The use of techniques from primary qualitative research may also be helpful in synthesizing diverse document types. (C) 2010 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved

Conceptualisation of intellectual capital in analysts’ narratives: a performative view

Purpose: This study tests the performativity of Intellectual Capital (IC) from the perspective of sell-side analysts, a type of actor who consumes and creates IC information and in whose practice IC information plays a significant role. Design/methodology/approach: The empirical component of the study comprises a narrative analysis of the text of a large corpus of sell-side analysts’ initiation coverage reports. We adopt Mouritsen’s (2006) performative and ostensive conceptualisations of IC as our theoretical framework. Findings: We find that the identities and properties of IC elements are variable, dynamic and transformative. The relevance of IC elements in the eyes of analysts is conditional on the context, temporally contingent and bestowed indirectly. IC elements are attributed to firm value both directly, in a linear manner, and indirectly, via various non-linear interrelationships established with other IC elements, tangible capital and financial capital. Research limitations/implications: This study challenges the conventional IC research paradigm and contributes towards a performativity-inspired conceptualisation of IC and a resultant situated model of IC in place of a predictive model. Originality/value: This is the first study to apply a performative lens to study IC identities, roles and relationships from the perspective of a field of practice that is external to the organisation where IC is hosted. Examining IC from analysts’ perspective is important because not only can it provide an alternative perspective of IC, it also enables an understanding of analysts’ field of practice

Mapping knowledge management and organizational learning in support of organizational memory

The normative literature within the field of Knowledge Management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on Organizational Learning however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (Fuzzy Cognitive Mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an Information Systems Evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizng inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between Knowledge Management (KM) and Organisational Learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization

Soft computing for intelligent data analysis

Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. The paper briefly looks at some of the key issues in intelligent data analysis, discusses the opportunities for soft computing in this context, and presents several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies