Jooh Lee

Operational linkage between diverse dimensions of information technology investments and multifaceted aspects of a firm's economic performance

A number of studies have been performed with the aim of examining the impact of information technology (IT) on the performance of firms and have arrived at multifaceted conclusions that have sometimes been conflicting. Very few studies have been performed with the objective of understanding investment in IT and the diverse dimensions of organizational performance with an attempt at using aggregated measures. An exploratory attempt has been made in this paper into understanding the linkage between IT and a firms economic performance using aggregated measures of accounting-based and market-based performance while considering intervening variables such as firm size, capital intensity, research and development intensity and advertising intensity. The results indicate that various dimensions of IT are significantly and positively linked to a firms performance irrespective of the different criteria of performance measures. The results also show that IT has a significant effect on aggregated composite measures of accounting-based and market-based performances.

Revisiting corporate reputation and firm performance link

Purpose – Corporate reputation is regarded as an intangible asset which differentiates a firm from others and attracts customers to repurchase and willingly pay a premium price for products. However, despite the perceptive association between reputation and financial performance, empirical studies report inconclusive results. The purpose of this study is to investigate this link more comprehensively using four different reputation attributes and firm characteristics in the context of high‐ vs low‐tech companies.Design/methodology/approach – This study operationalizes the corporate reputation as the four measures of Fortunes “Americas Most Admired Companies” of 2008 and matched the companies with financial performance and firm characteristics measures from COMPUSTAT Research Insight for the period between 2001 and 2005. A total of 230 firms (108 in high‐tech vs 122 in low‐tech) over the same period were selected and stepwise multiple regression analysis probed the relationship between the corporate reput...

Two-stage production modeling of large U.S. banks

This study proposes a new approach to model a two-stage production process.A DEA-BPNN method adds flexibility in two-stage production modeling.The proposed model complements two-stage DEA by adding predictive power.The adaptive method can support incremental performance improvement.The proposed model is empirically supported by using data from large U.S. banks. 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.

Comparisons on selected ratios between IFRS and US GAAP companies

Purpose – The purpose of this paper is to provide evidence of the effect of the differences related to reporting inventory, property plant and equipment, intangible assets, and development costs between International Financial Reporting Standards (IFRS) and US Generally Accepted Accounting Principles (US GAAP) companies.Design/methodology/approach – Both univariate tests (t‐tests) and multivariate tests (ANOVA, probit and logit analyses) are used to compare the ratios between IFRS and US GAAP companies.Findings – Results consistently show that IFRS‐country firms have a significantly higher current ratio, a significantly lower asset turnover ratio, and a significantly lower debt‐to‐asset ratio.Research limitations/implications – This paper only focuses on inventory, property plant and equipment, intangible assets, and development costs. Other financial variables are not considered.Practical implications – The results are useful for individuals who are interested in reporting and investing in countries usin...

Strategie Orientation and Export Performance

Abstract The purpose of this paper is to examine the linkage between strategic variables and the export performance of firms in the United States, Germany, and Japan. R&D Intensity, Capital Intensity, Average Collection Period, Debt Leverage, and Labor Productivity are used as measures of strategic variables. R&D intensity and Labor Productivity are found to have a strong and positive association with export performance in all three countries. Capital intensity and average collection period also have significant relationship with export performance in the United States and Japan, respectively.

Best performance modeling using complementary DEA-ANN approach

Purpose – The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement. Design/methodology/approach – Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms. Findings – The combined modeling approach proves effective through sequenti...

Assessing global supply chains: perspectives on performance measures across leading countries

Contingency theory suggests that each performance measure carries different emphases and is subject to contextual factors. Although useful, comparative measures of firm performance are fraught with difficulties in comparison mainly for two reasons: different measures carry dissimilar emphasis, and disparate national financial and business practices can exert innate bearings in the measures. Finding a context-invariant performance measure serves as a platform to conduct global supply chain performance comparison studies. Basing on longitudinal 2,400 international observations, this study explores if there exists significant difference among commonly employed performance measures across major countries and proposes context-invariant measures. While the results confirm the previous studies that accounting- and market-based performance indicators show different emphases among countries and country groups, this study finds sustained growth rate, growth rate in sales, and composite performance scores invariant of locus.

Module defect prediction under the Eclipse platform: the quadratic effect of software size and the influence of prerelease defects

Finding the factors leading to defects in software engineering can provide a competitive advantage for companies, project managers, programmers, and others, enabling them to better manage a project. Relying on findings from prior research in software defects prediction models, this study aims to examine the effects of the size, the complexity, and the number of the prerelease defects of a software module on the postrelease defects. Regression analysis of 327 responses from the PROMISE database reveals that such effects do exist under the Eclipse platform, a Java language development environment. This study confirms the presence of the quadratic effect of module size and the significant influence of the complexity and the number of prerelease defects in predicting postrelease defects.

Corporate Diversification: A Universal Construct Across International Boundaries?

ABSTRACT Although diversification has been the subject of a large amount of research (Bettis & Hall, 1982; Dess, Gupta, Hennart, & Hill, 1995; Montgomery, 1985; Varadarajan & Ramanujam, 1987) very little consensus exists in the strategic management literature. One possible reason for such confusion is the incomplete conceptualization of the construct. The present study proposes the use of an international-based perspective of diversification. Utilizing a multinational sample comprised of firms from the United States, Japan, and Korea, we tested the relationships between a variety of diversification measures and firm performance. Results suggest that countries differ with regard to resource profiles and their inherent perspectives when diversifying.

Neural network modeling for a two-stage production process with versatile variables: Predictive analysis for above-average performance

Abstract 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.