Operational Efficiency and Firm Life Cycle in the Korean Manufacturing Sector

Transcription

1 , pp Operational Efficiency and Firm Life Cycle in the Korean Manufacturing Sector Jayoun Won 1, Sang-Lyul Ryu 2 1 First Author, Visiting Researcher, College of Business, Konkuk University, 120 Neungdong-ro Gwangjin-gu, Seoul 05029, Korea 2 Corresponding Author, Associate Professor, College of Business, Konkuk University, 120 Neungdong-ro Gwangjin-gu, Seoul 05029, Korea, slryu2002@konkuk.ac.kr Abstract. We examine how operational efficiency is different across life cycle stages. We apply Data Envelopment Analysis (DEA) to estimate the efficiency scores for Korean manufacturing firms. The means of the technical, pure technical and scale efficiency scores are 0.847, 0.879, and 0.964, respectively, implying that Korean manufacturing firms have a significant level of inefficiency and still room for improvement. The statistical tests of mean efficiency difference show that the operational efficiency is increasing through the life cycle as a whole. The scale efficiency of the decline stage is lower than those of the growth and maturity stages, which may be due to the unutilized production capacity. 1 Introduction Profitability has been a key object for the Korean manufacturing sector in ages. Miller (1984) decomposes profitability into efficiency and price recovery. Under the circumstance where the price recovery is deteriorated by market competition, efficiency improvement leads to better profitability. This study begins with the idea that operational efficiency may differ across a firm's life cycle stages. Firm life cycle theory identifies how a firm grows, matures, and decline (Mueller 1972). Unlike product or industry life cycle, firm life cycle considers the firm as a combination of distinct life cycle stages (Dickinson 2011). These stages are discerned by firm-specific attributes such as sales growth, capital expenditures, investment opportunities, the number of employees, age, etc. In the growth stage, revenues and capital expenditures grow rapidly, more opportunities for investments, earnings are likely to get behind, and the firm starts to build efficiencies in production and sales activities. It will take more employees to meet operational activities. As the firm moves to maturity, the market for the firm's products becomes more competitive and begins to saturate. The firm focuses on improving efficiency in process and reducing overall operating costs. In the decline stage, revenues and earnings go down, unutilized capacity increases, and managers are faced with a decision whether to downsize their production capacity. A number of researchers have investigated the effects of firm life cycle stages on the performance measures. However, to date, little literature has incorporated a firm's ISSN: ASTL Copyright 2015 SERSC

2 life cycle and its operational efficiency. We address the following research question: How is the operational effficiency of Korean manufacturing firms differnt across life cycle stages? In this context, we explore operational efficiency scores for the manufacturing sector in Korea. 2 Research Methodology 2.1 Operational Efficiency This study uses DEA in estimating the operational efficiency of manufacturing firms. We employ the output-oriented three-input one-output model in which the output is sales revenue, and the three inputs are (i) costs of goods sold, (ii) selling, general and administrative costs (SG&A costs), and (iii) the beginning balance of plant, property and equipments. Calculating efficiency scores for decision making units (hereafter DMUs) is as follows: First, we apply Charnes et al. (1978) to estimate the technical efficiency of the DMUs (hereafter CCR model). Second, we also apply Banker et al. (1984) to measure the pure technical efficiency of the different DMUs (hereafter BCC model). Third, we evaluate scale efficiency by dividing technical efficiency by pure technical efficiency (Banker and Thrall 1992). If a DMU is fully efficient in both the CCR and BCC models, it is operating in the most efficient scale size. 2.2 Firm Life Cycle This study classifies DMUs into three life cycle stages (growth, maturity, and decline) using the following six descriptors commonly used in prior research on life cycle (Anthony and Ramesh 1992; Black 1998): (i) sales growth, (ii) change in capital expenditure, (iii) market to book ratio, (iv) change in the number of employee, (v) retained earnings ratio, and (vi) firm age. Table 1. Life Cycle Descriptor Life Cycle Stage (score) Sales Growth Change in Capital Expenditure Market to Book Ratio Change in the Number of Employee Retained Earnings Ratio Firm Age Growth (1) H H H H L L Overlapped (2) 2nd 2nd 2nd 2nd 2nd 2nd Maturity (3) 3rd 3rd 3rd 3rd 3rd 3rd Overlapped (4) 4th 4th 4th 4th 4th 4th Decline (5) L L L L H H Note: H and L indicate highest and lowest, respectively. Classifying DMUs into the growth, maturity, and decline stages by using the six descriptors is as follows: 152 Copyright 2015 SERSC

3 First, the six life cycle descriptors for each DMU are computed. We selected the median value of the prior five years for each variable to mitigate the yearly variations. Second, descriptor quintiles are calculated for each of the variables. Third, the six descriptor observations for each DMU are assigned to each quintile of the same variable and they are given a score as shown in Table 1. The composite score ranges from six to thirty. Fourth, composite score quintile is calculated, and DMUs in the first, third, fifth quintile are classified into the growth, maturity, and decline stages, respectively. We discarded DMUs in the second and fourth quintile because they are likely to be overlapped between the two stages. 3 Empirical Results 3.1 Operational Efficiency The sample includes all manufacturing firms that are listed on the Korean Stock Exchange from 2002 to We impose the following criteria for our sample: (i) those firms whose fiscal years end on December 31, and (ii) those firms whose financial data can be obtained from database TS2000. Table 2 shows three operational efficiency scores by year. For our pooled data, the means of technical, pure technical, and scale efficiency scores are 0.847, 0.879, and 0.964, respectively, suggesting that there is a significant level of inefficiency and still room for improvement. Table 2. Operational Efficiency Score (N=3,258) Year Sample size Operational Efficiency Technical Pure Technical Scale Mean Standard Deviation Note: Output and inputs are expressed in one million Korean won and deflated to 2010 Korean won using the index of consumer prices. Copyright 2015 SERSC 153

4 3.2 Firm Life Cycle Table 3 shows the number of observations across the different life cycle strata of sample DMUs. The number of DMUs for each stage shows 748, 578, 673 DMUs in the growth, maturity, and decline stage, respectively. The number of DMUs has been increased in the maturity and decline phases, but gone down steadily in the growth phase over the past 11 years. Table 3. Distribution of Life Cycle Stage (N=1,999) Year Growth Maturity Decline Total # of obs. % # of obs. % # of obs. % # of obs. % Pooled , Statistical Test We use t-tests to compare mean differences in the operational efficiency between each pair of stages. The test results are represented in Table 4. Overall, we conclude that operational efficiency is increasing over the entire life cycle stages. However, at the decline stage the scale efficiency is lower than that of the maturity stage. Table 4. Test of Mean Difference in Efficiency by Stages of Life Cycle (N=1,999) Life Cycle Stage Operational Efficiency Growth Maturity Decline Score Technical 0.02 (t=3.685, p<0.01) t-test (t=0.505, p=0.6074) Score Pure (t=2.98, p<0.01) Technical t-test (t=2.604, p<0.01) Score Scale (t=2.164, p=0.031) t-test (t=3.908, p<0.01) 154 Copyright 2015 SERSC

5 4 Concluding Remarks We document how the operational effficiency of Korean manufacturing firms is differnt among life cycle stages. For our pooled data, the mean of the technical, pure technical and scale efficiency scores are 0.847, 0.879, and 0.964, respectively, implying that Korean manufacturing firms have a significant level of inefficiency and still room for improvement. Both of the technical and pure technical efficiencies are the highest scores in This study grouped DMUs into three life cycle stages: growth, maturity, and decline. The statistical tests of mean efficiency difference shows that the operational efficiency is increasing through the life cycle as a whole. The scale efficiency of the decline stage is lower than those of the growth and maturity stages, which may be due to the unused production capacity. References 1. Anthony, J.H., Ramesh, K.: Association between Accounting Performance Measures and Stock Prices: a Test of the Life Cycle Hypothesis. Journal of Accounting and Economics, 15, (1992). 2. Banker, R.D., Charnes, A., Cooper, W.W.: Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), (1984). 3. Banker, R.D., Thrall, R.M.: Estimation of Returns to Scale Using Data Envelopment Analysis. European Journal of Operational Research, 62, (1992). 4. Black, E.L.: Life Cycle Affects the Incremental Value-relevance of Earnings and Cash Flow Measures. Journal of Financial Statement Analysis, 4(1), (1998). 5. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the Efficiency of Decision Making Units. European Journal of Operations Research, 2(6), (1978). 6. Dickinson, V.: Cash Flow Patterns as a Proxy for Firm Life Cycle. The Accounting Review 86 (6), (2011). 7. Miller, D.M.: Profitability Equals Productivity Plus Price Recovery. Harvard Business Review, 62(3), (1984). 8. Mueller, D.C.: A Life Cycle Theory of the Firm. Journal of Industrial Economics, 20(3), (1972). 9. Won, J., Ryu, S-L.: Determinants of Operating Efficiency in Korean Construction Firms: Panel Data Analysis. Information- An International Interdisciplinary Journal, 18(5), (2015). Copyright 2015 SERSC 155