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Based on a strategic group concept, the purpose of this paper is to examine the effects of resource employments on persistent performance in the pharmaceutical industry.

In departing from previous research, this paper takes an inverted approach to mapping firms into heterogeneous groups with distinct long-term performance trajectories, given that strategic profiles and characteristics were unknown. The methodology used is latent class growth analysis, a person-centred approach focussing on the relationships among individuals. Regression models were subsequently used to examine the strategy variables-performance relationship between groups and within groups.

First, firms were grouped into upper-performance and lower-performance trajectory subpopulations. Second, the effects of marketing and R&D on performance significantly differed within subpopulations and presented a U shape or an inverse U shape relation. Third, the employment of R&D resources was more effective in the lower-performance trajectory group, the average scale of which is smaller than in the upper-performance trajectory group. On the contrary, the employment of marketing resources had a greater benefited in the upper-performance trajectory group.

Intangible strategy features are ignored due to measure problem in the long period.

Strategic competition is more significant among intragroup members than inter groups. That the U-shape or invert U-shape effects of resource employments on performance among intragroup members reminds the researchers that the law of diminishing return or increasing return should not be ignored when test the group-performance relationship in future research.

The current study introduces an effective approach to investigate the strategic group concept.

Previous studies have suggested that one may trace the factors (i.e. sources of the competitive advantage) that cause the firm performance by examining the performance itself. Financial ratios have been used to trace the sources of competitive advantage, that is, the resource configuration, but the key resources driving superior performance remained undiscovered. The present study seeks to reduce the number of dimensions in the resource configuration to a two‐dimensional map to capture firms' relative resource positions and identify the resources and capabilities that lead to the superior performance.

Factor analysis is used to extract the resource bundles and management capabilities of the online game industry in Taiwan from financial ratios included in the expanded Du Pont identity. These resource and capability bundles are subsequently verified by discriminant analysis to distinguish firms with competitive advantage from firms with competitive disadvantage. Factor scores are then used as inputs for multidimensional scaling to draw the resource positioning of the competitive firms.

The competitive advantage of online firms can be determined using two dimensions of intellectual property and relationship assets. In addition, firms with advantage in upstream (game developers) and downstream (channels) relationships perform better than other firms.

Private online game firms are excluded from the empirical study because their financial data are not available.

Using financial ratios, the present research identified the resource and capability bundles essential to the superior performance of the online game firms.

The purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective.

In total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Taiwan Economic Journal data bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees.

Many of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms.

The financial factors used to detect fraudulent reporting are helpful for predicting business failure.

The objective of this paper is to stress the importance of detecting financial frauds in predicting business failures disclosed by the unexpected financial crisis brought by Enron, Worldcom and other corporate distresses involving accounting irregularities.

The most frequently used methodologies in predicting business failures, discriminant analysis and neural network (NN) (based on the Kolmogorov‐Gabor polynomial Volterra series algorithm) are used. This paper suggests a two‐stage NN procedure: the first stage detected the false financial statements, which were excluded from samples that used to predict the business failures at the second stage. The one‐stage discriminant analysis and the NN model are used to contrast the two‐stage approach in terms of accuracy rate.

The one‐stage NN model has a higher accuracy rate in identifying failed firms than the discriminant analysis, while the two‐stage NN approach has an even higher accuracy rate than the one‐stage NN model.

Detecting the fraudulent reporting in advance can effectively improve the accuracy rate of business failure predictions.

The paper draws attention to the importance of excluding fraudulent financial reporting to increase the accuracy rate in predicting business failures.

This study aims to explore the impact of the movement from “authoritarian democracy” to full democracy on the relationships between trust with economic growth and investment.

Simple regression models were applied to Taiwan as a case study.

Results indicate that: the direct effect of social trust on growth was significant regardless of the democratic power changeover; the indirect effect through fixed investment was significant only after the transfer of political power; and the direct effect of political trust on growth and the indirect effect through fixed investment were both significant only after the transfer of political power.

The time span of the data used for the regression models in this paper is only ten years, which constrains the number of control variables used in the model.

This study indicates that the political regime plays as a contingency to the essay of social capital and economic growth.

This paper first provides a detailed investigation to specify the effects of social trust on economic growth during the first democratic power changeover.