Search

• A combined OLSMR-BPNN is a promising paradigm for explorative analysis. • R&D and efficiency are significant factors for manufacturing firms. • R&D and efficiency are even more influential for the above-average performers. • Tech. innovation and operational excellence are crucial performance drivers. This study explores the potential strategic determinants of firm performance, with an emphasis on R&D investment and operational efficiency in leading U.S. manufacturing firms. In particular, it investigates R&D as a driver of technological innovation, and operational efficiency and as an indicator of the best-practice operations, for their impact relative to Tobin's Q and Market value. The study jointly uses ordinary least square multiple regression (OLSMR) and backpropagation neural network (BPNN), not only to measure the statistical significance of factors, but also to explore new insights into their relative importance, and to determine the differential impact of each factor following the varying performance levels. A major finding is that proactive R&D investments and operational excellence are the most impactful factors on both metrics of performance as compared to other conventional factors used in this study. Another encouraging finding is that both R&D intensity and operational efficiency are even more influential in the above-average performers and yield higher returns in market valuation. Through a combined OLSMR-BPNN approach, this study presents insightful findings on this intriguing subject and highlights prospective research opportunities. [ABSTRACT FROM AUTHOR]

With growing academic interest and pragmatic need, adaptive two-stage production modeling becomes an emergent research topic for decision sciences and production management. Although prior research has addressed sequential production process, the primary focus was limited to efficiency analysis with a narrow scope of applications. Data envelopment analysis (DEA) has been commonly used for earlier studies; however, its lack of learning and deficiency in predictive capability seriously diminish the practical utility of DEA and call for an intelligent information-processing technique for further advancement. This paper uniquely presents an output-focused backpropagation neural network (BPNN) approach with capabilities to capture patterns of high performers, a significant departure from conventional efficiency-driven DEA analysis, as well as a promising analytic paradigm. In so doing, the proposed standalone BPNN can predict above-average performance and supports managerial decision-making in setting progressive performance targets in consecutive stages. The sound empirical application to the two-stage bank production process proves the effectiveness of the proposed analytic paradigm. In brief, the intelligent learning model advances existing two-stage production modeling with a methodological breakthrough and makes significant contributions to the existing literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore an innovative performance model for a two-stage sequential production process by combining data envelopment analysis (DEA) and back propagation neural network (BPNN). Recent literature shows a growing interest on performance modeling of two-stage production process using DEA. But, most previous studies on the scope of two-stage modeling are still limited to the efficiency measurement and also have neglected the progressive direction of predictive value and capacity. As an optimization technique, two-stage DEA model lacks predictive capacity. Despite an adaptive prediction model being a practical necessity, this area has rarely been addressed in the previous studies. This paper demonstrates an integrative approach to constructive performance modeling of a two-stage sequential production process by exploring predictive capacity of BPNN in conjunction with DEA. The effectiveness of our jointly integrated performance model through this study is empirically supported by its practical application to the financial banking operations across large U.S. banks. [ABSTRACT FROM AUTHOR]