Erkki K. Laitinen

A dynamic performance measurement system: evidence from small Finnish technology companies

The purpose of this paper is (1) to present a new dynamic integrated performance measurement system (IPMS) based on a managerial view, and (2) to present preliminary empirical evidence on the importance of performance measures in small Finnish technology companies using the IMPS as the framework for the survey. The aim is to develop a useful managerial tool for measuring and improving performance in business firms. The system is intended to include a comprehensive set of relevant factors and dimensions, which together form an integrated managerial system of performance measurement. The proposed IPMS is linked to the idea of activity-based costing (ABC). It consists of seven main factors and the causal chain connecting these factors. The factors are classified as two external factors (financial performance and competitiveness) and five internal factors (costs, production factors, activities, products, and revenues). The main idea of the IPMS is to follow the use (transformation) of resources from the point of the very first (elementary) resource allocation to the point when the results of the allocation are realized as revenues. In the causal chain, the factor at any point along the chain is regarded as a determinant of the factor that succeeds it. Moreover, the next resource allocation decision is dynamically affected by the results of the former decisions, thus allowing for learning-by-doing. The IPMS is also used as a framework for a postal questionnaire completed by 93 small Finnish technology firms. These companies put great emphasis on the importance of the employee motivation (production factors dimension), customer satisfaction (products), product profitability (revenues), company profitability, liquidity, and capital structure (financial performance) in the measurement of performance. Factor analysis is used to classify the companies into three groups on the basis of performance measurement.

Bankruptcy prediction: Application of the Taylor's expansion in logistic regression

Abstract The purpose of the present study is to test whether Taylors series expansion can be used to solve the problem associated with the functional form of bankruptcy prediction models. To avoid the problems associated with the normality of variables, the logistic model to describe the insolvency risk is applied. Taylors expansion is then used to approximate the exponent of the logistic function, or the logit. The cash to total assets, cash flow to total assets, and shareholders equity to total assets ratios operationalize the factors affecting the insolvency risk. The usefulness of Taylors model in bankruptcy prediction is evaluated applying the logistic regression model to the data from the Compustat database. The classification accuracy in the test data for the first and second years before bankruptcy show that the classification accuracy of a simple financial ratio model can be increased using the second-order and interaction terms of these ratios. However, in the third year, for the test data, Taylors expansion is not able to increase the classification accuracy when compared with the first-order model.

Impact of enterprise resource planning systems on management control systems and firm performance

In this study, we extend existing research on enterprise resource planning systems by exploring the effects of enterprise system adoption on subsequent non-financial and financial performance of a firm. Specifically, we investigate the role of formal and informal management control systems as mechanisms which mediate the effect of enterprise resource planning systems adoption on firm performance. Our empirical analyses are based on survey data drawn from 70 Finnish business units. Overall, our findings demonstrate that formal types of management control systems act as intervening variables mediating the positive lagged effect between enterprise systems adoption and non-financial performance. Informal types of management control systems, however, do not show similar mediating effects. We also predict and find a significant relationship between non-financial and financial firm performance. These results are important because the evidence on the joint roles of enterprise systems and management control system on improving the firm performance is very limited in prior literature. Our results show that the use of enterprise systems results in improved firm performance in the long run, and that more formal than informal types of management controls help firms achieve future performance goals.

Prediction of failure of a newly founded firm

Abstract The rate of mortality among newly founded firms is very high. Failure statistics universally show that over 50% of newly founded firms will fail during their first five years. The economic, financial, and social losses resulting from these failures are significant. Thus, it is valuable to try to develop methods to predict and to avoid such failures. However, there are only very few studies dealing with failure prediction methods for newly founded firms. The aim of this study was to develop such methods that would also have practical value. Failure was defined as cash insolvency. The aim was to develop a failure prediction model based on financial statement data from newly founded firms. The ratios based on these data are hard measures and easy to calculate also for a person forced to only use publicly available information. A prediction model based on these ratios could, then, be a practical method for financial analysts and other interest groups in evaluating the failure probability of a new firm. This model does not require information about management, products, or markets because it rather deals with the symptoms of failure than the causes. The study is based on a supposition that failure process in a newly founded firm is characterized by a too high initial indebtedness and by too low revenue financing as compared with the budget. This supposition was supported by the empirical results based on the comparison of the financial ratios in first four years between 20 failed and 20 nonfailed small, newly founded, entrepreneurial, industrial firms. These results may give an explanation as to why some newly founded firms will fail and some will survive. This explanation is, of course, based on the development of financial variables in the very first years. The results showed that it is possible, to some degree, to predict the failure of a newly founded firm already in the first year after foundation. The prediction accuracy will increase, when the data of failure is approached. The best univariate predictors proved to be stockholders capital to total capital ratio (indebtedness), cash flow to net sales ratio (revenue financing), and cash flow to total debt ratio (sufficiency of revenue financing to pay financial obligations). The prediction accuracy can be increased by adjusting the critical value of the ratio stepwisely to the year of operation. Thus, the optimal critical value will change year by year after foundation. The prediction accuracy can slightly be improved by a multivariate analysis. This kind of multivariate model also included the logarithmic size (net sales) of the firm as a variable so that the probability to fail increased when the size increased. The results also showed that the same multivariate model estimated for older small firms, can be used as a failure prediction model for newly founded firms. However, this requires that quite different critical values are selected for both types of firms. To summarize, the results show that the failure of a newly founded firm is, to some degree, predictable by univariate or multivariate analyses. The failure risk is increased with high indebtedness, insufficient revenue financing, and large size in the first years. Hence, the risk to fail can be reduced by using less debt as initial financing and by paying special attention to the generation of a sufficient amount of revenues in initial stages. Furthermore, it may be less risky to start business operations with a smaller, rather than larger initial size.

Predicting a corporate credit analyst's risk estimate by logistic and linear models

Abstract The purpose is to predict corporate credit analysts risk estimate by the weighted logistic (binary response) and linear regression (20-class risk estimate) analyses. The data comprise filed register information from Finska (Suomen Asiakastieto Oy) including 35 variables from 3200 companies. The coefficient of concordance was 95% and the rate of multiple determination 75% for the logistic and linear models, respectively. In a binary classification the differences in performance between the models were insignificant provided that the linear model is rotated. Both of the models give a classification accuracy of 90% in the estimation sample and 96% in the test sample.

Qualified audit reports in Finland: evidence from large companies

The purpose of the study is to develop a logistic model based on financial statement information to identify qualified audit reports. The empirical data are retrieved from audit reports from thirty-seven publicly-traded companies (HeSE) in the years 1992, 1993 and 1994. Thus, there are in all 111 audit reports of which only eight are qualified in the way of including remarks or supplementary information. These eight qualifications concerned three companies during the period of study. The qualification decision (0/1) is explained by sixteen financial ratios and by the audit lag. Univariate analysis showed that the qualification of an audit report is mainly associated with low profitability, high indebtedness and low (negative) growth. The multivariate logistic model showed that the likelihood of receiving a qualification is larger, the lower the growth of the firm, the lower the share of equity in balance sheet and the smaller the number of employees. The total error rate of the model in Lachenbruch validation was only 5.4%. When two qualified reports containing remarks on an additional and a separate auditor were considered as non-qualified, the corresponding total error rate for a re-estimated logistic model was as low as 1.8%. This model also included the audit lag as an explanatory variable. The results of the study indicate that an efficient model to explain qualifications in the audit reports of Finnish publicly-traded companies can be found.

Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model

This paper assesses the classification performance of the Z-Score model in predicting bankruptcy and other types of firm distress, with the goal of examining the models usefulness for all parties, especially banks that operate internationally and need to assess the failure risk of firms. We analyze the performance of the Z-Score model for firms from 31 European and three non-European countries using different modifications of the original model. This study is the first to offer such a comprehensive international analysis. Except for the United States and China, the firms in the sample are primarily private, and include non-financial companies across all industrial sectors. We use the original Z-Score model developed by Altman, Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy (1983) for private and public manufacturing and non-manufacturing firms. While there is some evidence that Z-Score models of bankruptcy prediction have been outperformed by competing market-based or hazard models, in other studies, Z-Score models perform very well. Without a comprehensive international comparison, however, the results of competing models are difficult to generalize. This study offers evidence that the general Z-Score model works reasonably well for most countries (the prediction accuracy is approximately 0.75) and classification accuracy can be improved further (above 0.90) by using country-specific estimation that incorporates additional variables.

Financial predictors for different phases of the failure process

The purpose of the study is to find financial predictors for alternative phases of failure process. For this purpose the typical process is divided into starting, intervening and final phases. The hypotheses presuppose that the differences, trends and levels are significant predictors in these phases, respectively. The aim is to use a separate model to identify the beginning of each phase. This kind of warning system makes it possible to identify the phase in which the firm possibly lives and to assess its risk to fail already years before failure. The main statistical method is regression analysis since both survival status and survival time are used as the dependent variable. The hypotheses were supported by empirical evidence.

Management accounting systems in Finnish service firms

Abstract This study deals with management accounting practices in small and medium sized service firms in Finland. The recent trends of Management Accounting Systems (MAS), such as Activity Based Costing (ABC), are not frequently used in Finnish service organizations, although these practices have been realized by the majority of respondents to be an important tool to understand real product costs, decrease production costs, modernize cost accounting systems and identify activity costs. An empirical study indicates that a few Finnish small and medium sized service organizations have implemented recent developments of MAS such as ABC. The practices of MAS do not vary with differences in type of service firms that were included in this study. This paper demonstrates that MAS are not very successful in the Finnish service organizations investigated to achieve the goal of decision making, planning and management control, and to improve the information system within the organizations.

Cash Management Behavior and Failure Prediction

The purpose of this study is to evaluate the information contained in static and dynamic inventory cash management models to predict failure in a sample of 41 small and middle-sized Finnish bankrupt firms and their nonbankrupt counterparts. The results indicate that the estimates of the (scale) elasticity of cash balance with respect to the volume of transactions (approximated by net sales) is significantly lower for the failed firms. Furthermore, only the scale elasticity appears to be a statistically significant discriminating variable, and only in the first year before bankruptcy. This estimate remarkably increased the Lachenbruch validated classification accuracy based on traditional financial variables. Copyright Blackwell Publishers Ltd 1998.