Influence of Selected Manufacturing Firm Characteristics on Choice of Inventory Costing Methods

Influence of Selected Manufacturing Firm Characteristics on Choice of Inventory Costing Methods By Supanya Suvannasing An Independent Study Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Business Administration Accounting Emphasis, Faculty of Business Administration Asia-Pacific International University Year (2019-2020)

Table of Contents Page ABSTRACT....................................................................................................................i ACKNOWLEGEMENTS............................................................................................ iii CHAPTER 1 INTRODUCTION ...................................................................................1 CHAPTER 2 LITERATURE REVIEW ........................................................................6 2.1 Sectors of Study...................................................................................................6 Food Industry ......................................................................................................6 Steel Industry.......................................................................................................8 2.2. Inventory Classification .....................................................................................9 2.3 Inventory Costing Methods.................................................................................9 First-in-First-Out (FIFO)...................................................................................10 Weighted Average Cost.....................................................................................11 Moving Average Cost Method ..........................................................................12 2.4. Advantages and Disadvantages of Inventory Costing Method ........................13 2.5 Characteristics of Manufacturing Firm .............................................................14 Firm Size ...........................................................................................................14 Inventory Size....................................................................................................15 Financial Ratio Analysis ...................................................................................16 Profitability of the Firm.....................................................................................16 Financial Leverage of the Firm .........................................................................17 Current Ratios....................................................................................................18 Inventory Turnover............................................................................................18 2.6 Influences of the Choice of Inventory Costing Methods ..................................19 2.7 Prior Studies......................................................................................................20 2.8 Conclusion.........................................................................................................21 Research Questions ...........................................................................................23 2.9 Significance of the Research .............................................................................23 Managers...........................................................................................................23 Investors ............................................................................................................24 Researchers........................................................................................................24 2.10 Scope and Limitation.......................................................................................24

2.11 Conceptual Framework ...................................................................................25 2.12 Definition of Terms.........................................................................................26 CHAPTER 3 RESEARCH METHODS ......................................................................27 3.1 Research Design ................................................................................................27 3.2 Population, Sample Size and Sampling Techniques.........................................28 3.3. Conditions for Analysis....................................................................................31 3.4 Variables............................................................................................................33 3.5 Data Collection..................................................................................................33 3.6 Statistical Formula.............................................................................................33 Explanation of Variables in Equation ...............................................................34 3.7 Analysis of Data ................................................................................................35 3.8 Ethical Issues.....................................................................................................35 CHAPTER 4 RESULTS..............................................................................................36 4.1 Description of 2016 to 2018 Food and Beverage Sector Characteristics..........36 2016-2018 Inventory Costing Methods.............................................................36 2016-2018 Firm Size .........................................................................................36 2016-2018 Firm Financial Leverage .................................................................38 2016-2018 Inventory Size .................................................................................39 2016-2018 Profitability .....................................................................................40 2016-2018 Inventory Turnover .........................................................................41 2016-2018 Current Ratio...................................................................................42 4.2. 2016-2018 Model Analysis Food and Beverage Sector Characteristics..........43 2016-2018 Model Summary..............................................................................43 2016-2018 Hosmer-Lemeshow Test .................................................................44 2016-2018 Classification Table.........................................................................45 2016-2018 Logistic Equation for Variables, Food and Beverage Firms...........45 4.3 2016-2018 Manufacturing Firms in the Steel Sector ........................................51 2016-2018 Inventory Costing Methods.............................................................51 2016-2018 Firm Size .........................................................................................51 2016-2018 Firm Financial Leverage .................................................................52 2016-2018 Inventory Size .................................................................................53 2016-2018 Profitability .....................................................................................54

2016-2018 Inventory Turnover .........................................................................54 2016-2018 Current Ratio...................................................................................55 4.4. 2016-2018 Model Analysis for Steel Sector Characteristics............................56 2016-2018 Model Summary..............................................................................56 2016-2018 Hosmer and Lemeshow Test...........................................................57 2016-2018 Classification Table.........................................................................58 2016-2018 Logistic Equation for Variables, Steel Firms..................................59 CHAPTER 5 CONCLUSIONS ...................................................................................64 5.1. Discussion and Conclusions.............................................................................64 5.2 Recommendations.............................................................................................69 5.3 Limitations.........................................................................................................69 REFERENCES ............................................................................................................71 APPENDICES .............................................................................................................80 Appendix 1...................................................................................................................80 Appendix 2...................................................................................................................80 Appendix 3...................................................................................................................81

List of Tables Page Table 1. 2017-2018 Manufacturing Sector Growth Rates ........................................1 Table 2. Changes in 2017-2018 Employment and Unemployment Rates ................2 Table 3. Related Research Studies..........................................................................22 Table 4.1. 2016-2018 Thailand Stock Exchange ....................................................28 Table 4.2. Thailand Stock Exchange for the Period of 2016-2018 (Cont.).............29 Table 5. Population of Manufacturing Firms..........................................................30 Table 6. 2016-2018 Variance Inflation Factor Results for Food & Beverage Sector .........................................................................................32 Table 7. 2016-2018 Variance Inflation Factors Results for Steel Sector................32 Table 8. 2016-2018 Inventory Costing Method ......................................................36 Table 9. 2016–2018 Firm Size ................................................................................37 Table 10. 2016-2018 Firm Financial Leverage .......................................................38 Table 11. 2016-2018 Inventory Size .......................................................................39 Table 12. 2016-2018 Profitability (Return on Assets, ROA)..................................40 Table 13. 2016-2018 Inventory Turnover...............................................................41 Table 14. 2016-2018 Current Ratio.........................................................................42 Table 15. 2016-2018 Model Summary....................................................................43 Table 16. 2016-2018 Hosmer-Lemeshow Test .......................................................44 Table 17. 2016-2018 Classification Table ..............................................................45 Table 18.1. 2016-2018 Logistic Equation for Variables, Food and Beverage Firms........................................................................................46 Table 18.2. 2016-2018 Logistic Equation Variables, Food and Beverage Firms (Cont.)...........................................................................48 Table 19. 2016-2018 Inventory Costing Method ....................................................51 Table 20. 2016-2018 Firm Size...............................................................................51 Table 21. 2016-2018 Firm Financial Leverage .......................................................52 Table 22. 2016-2018 Inventory Size .......................................................................53 Table 23. 2016-2018 Profitability ...........................................................................54 Table 24. 2016-2018 Inventory Turnover...............................................................55 Table 25. 2016-2018 Current Ratios.......................................................................56

Table 26. 2016-2018 Model Summary....................................................................56 Table 27. 2016-2018 Hosmer and Lemeshow Test.................................................58 Table 28. 2016-2018 Classification Table ..............................................................58 Table 29.1. 2016-2018 Logistic Equation for Variables, Steel Firms.....................59 Table 29.2. 2016-2018 Logistic Equation for Variables, Steel Firms (Cont.) ........61 List of Figures Page Figure 1. 2015-2018 Employment Figures and Unemployment Rates.....................2 Figure 2. Conceptual Framework............................................................................25

i Research Title: Influence of Selected Manufacturing Firm Characteristics on Choice of Inventory Costing Methods Author: Supanya Suvannasing Research Advisor: Dr. Paluku Kazimoto Program: MBA (Accounting Emphasis) Academic Year: 2019-2020 ABSTRACT The purpose of this study was to explore the use of inventory costing methods in manufacturing sectors listed in the Stock Exchange of Thailand from 2016 to 2018, and to examine the influence that selected characteristics of manufacturing firms have on the choice of inventory costing methods. There were two sectors in this study. The first sector was the food and beverage sector consisting of 36 manufacturing firms, and the second was the steel sector consisting of 24 manufacturing firms. These firms adopted inventory costing methods such as First-In-First-Out (FIFO), Weighted Average Cost Method, and Moving Average Cost Method, and the characteristics such as firm size, inventory size, profitability, firm financial leverage, inventory turnover and current ratios were examined. A statistical software package was used to perform a logistic regression test to find the influence that independent variables (firm characteristics) have on dependent variables (inventory costing methods). The results indicated that for the food and beverage sector, the FIFO Method was adopted by 10 firms, and 26 firms adopted the Weighted Average Cost Method and Moving Average Cost Method. In the steel

ii sector, only 4 firms out of 24 firms adopted the FIFO Method. For the food and beverage sector, only three characteristics – profitability, inventory turnover and current ratio – influenced the choice of FIFO Method for 2017 and 2018, while no statistically significant factors were found in the steel sector. Keywords: characteristics, firm size, inventory size, profitability, financial leverage, inventory turnover, current ratio, inventory costing methods, First-In-First-Out, Weighted Average Cost Method and Moving Average Cost Method.

iii ACKNOWLEGEMENTS The research would not have been completed without the help of important persons who always give advice, encouragement and support. The researcher would like to express appreciation to Dr. Paluku Kazimoto and Dr. Danny Rantung, the Independent Study Advisors, who always gave advice and suggestions for the topic. The researcher also wants to thank Dr. Wanlee Putsom, Mr. Alwyn Chacko and Dr. Kazimoto for help in applying the statistical tests in a proper way that suited the research data, and in the interpretation of the data. Additionally, the researcher would like to appreciate all the comments and suggestions from Dr. Wayne Hamra and Dr. Henry Foster, along with the MBA committee members, who gave direction to improve this paper. Moreover, the researcher would like to express his sincere thanks to Dr. Wayne Hamra and Mrs. Anita Sundaresan for their editorial review work on the final report. The researcher also appreciates the work of previous researchers and book authors for helping the researcher to understand the concepts and to complete the literature review. Thank you for providing guidelines and examples for the researcher to complete this research paper. Lastly, the research could not be done without the data from the Stock Exchange of Thailand (www.set.or.th and www.settrade.com), as well as the companies’ annual reports from their website that the researcher used for data analysis. The researcher appreciates all this kind support in helping to complete this Independent Study. Above all, the researcher thanks God for all wisdom. “For the Lord gives wisdom, and from His mouth come knowledge and understanding” (Proverbs 2:6). Supanya Suvannasing

1 CHAPTER 1 INTRODUCTION Manufacturing sectors play an important role in the Thai economy. The Office of the National Economic and Social Development Council (2019) stated that in 2018, Gross Domestic Product (GDP) rose from 3.2% in the third quarter to 3.7% in the fourth quarter, accelerated by the manufacturing and service sectors, while agricultural production seemed to decline. The non-agricultural sector rose from 3.2% in the third quarter to 4.0% in the last quarter due to a rise of 3.3% in the manufacturing sector, which increased from 1.6% in the third quarter because of the higher domestic and external demands. This contribution came from light industries, which expanded 7.2% due to the production of food, beverages, tobacco, leather and furniture. For exports, manufacturing goods rose because of the increase of major products such as metal products, vehicle parts, chemicals, petrochemicals and petroleum products. Table 1. 2017-2018 Manufacturing Sector Growth Rates Manufacturing sectors create job opportunities and reduce the unemployment rate in the country. The Macroeconomic Strategy and Planning Office (2018) stated that in 2018, employment increased in both agricultural and non-agricultural sectors, which caused unemployment to decrease to its lowest rate in 12 quarters. Non-

2 agricultural employment grew from 1.6% in the third quarter to 1.7% in the fourth quarter of 2018. However, growth of 4.6% came from employment in the manufacturing sector, which improved continually for three quarters due to increased manufacturing production, plus a 1.5% rise in employment in the wholesale and retail trade sector. Figure 1. 2015-2018 Employment Figures and Unemployment Rates Table 2. Changes in 2017-2018 Employment and Unemployment Rates In the manufacturing sector, the main source of income is from the sale of inventory, which has an important role in company operations. The cost of inventory affects both the amount reported in the balance sheet and the cost of goods sold

3 reported in the income statement. Given that there are several choices of inventory costing methods available, managers have some flexibility in selecting the firm’s inventory costing method. All decisions regarding the choice of inventory costing methods, however, have economic consequences. Weygandt, Kimmel and Kieso (2009) stated that there are many reasons why firms adopt different methods, but one of the following three factors are commonly involved: (1) income statement effects, (2) balance sheet effects, and (3) tax effects. According to Porter and Norton (2017), the purpose of each inventory costing method is to match costs with revenues. Simeon and John (2018) stated that as firms buy their inventories in different periods of time, the prices of inventories are likely to be different too. As a result, the choice of selecting inventory costing methods, to assess the value of inventory at the end of the period are chosen by the firms. Needles and Powers (2018) indicated that to facilitate keeping track of inventories in companies, three widely used costing methods are First-In-First-Out (FIFO), Last-In, First-Out (LIFO), and Average Cost Method. Each method has its advantages and disadvantages. Managers tend to select and consider an inventory costing method that shows price trends, along with the consequences that each method has on financial statements, income tax, and cash flows. For Rojkurisathian (2013), however, there is a limitation imposed by Thai Accounting Standard TAS 2 (revised in 2009) on inventories set by the Federation of Accounting Professions of Thailand, which does not allow the LIFO Method to be used to measure the cost of inventories. Ross (2018) explained that the FIFO Method is a regular inventory method applied for perishable goods, pharmaceuticals, or other goods with short period of expiration. Simeon & John (2018) also stated that when there are inventories with

4 expiration dates, companies will likely adopt the FIFO Method. The FIFO Method shows a higher profit for the year than the Weighted Average Cost Method during times when prices are rising. The Chartered Financial Analyst (CFA) Institute (2018) stated that the choice of inventory costing method also influences the calculations of financial ratios that involve the levels of cost of goods sold, gross profit, net profit, inventories, current assets, and total assets. It also affects many financial performance ratios including the current ratio, return on assets, gross profit margin, and inventory turnover. Stice and Stice (2012) stated that many firms adopted more than one inventory costing method for different classes of inventory. Many manufacturing firms in both the food and beverage and steel sectors of the Stock Exchange of Thailand have adopted more than one method. Therefore, this study aimed to investigate and analyze the different characteristics of manufacturing firms that might potentially influence the choice of inventory costing method such as firm size (total assets), inventory size (Inventory/Total Assets), profitability (Net Income/Average Total Assets), firm financial leverage (Debt/Equity), inventory turnover (COGS/Average Inventory), and current ratio (Current Assets/Current Liabilities). These characteristics were tested to see if the relationship existed between them (Independent variables) and the choice of inventory costing method such as FIFO and Average Cost Method (dependent variable). The study selected two manufacturing sectors consisting of the food and beverage sector (sample size of 36 firms) and the steel sector (sample size of 24 firms). The two sectors represent totally different types of businesses, and it caught the attention of the

5 researcher to conduct a study to make a comparison between both sectors to observe if they have similar effects, in order to enhance knowledge and understanding of the potential reasons behind the choice of inventory costing methods. The data is taken from the Stock Exchange of Thailand from 2016 to 2018.

6 CHAPTER 2 LITERATURE REVIEW This section presents the results of reviewing the literature that dealt with the characteristics of manufacturing firms and the inventory costing methods. For this study the food and beverage and steel sectors and the following four characteristics of manufacturing firms: firm size, inventory size, firm financial leverage and profitability were considered. Two other key concepts included were the inventory turnover ratio and current ratio. The firm’s characteristic represents the independent variables. The inventory costing methods represent the dependent variables and consists of the First-In-First-Out (FIFO) and the Average Cost Methods. 2.1 Sectors of Study Food Industry The food industry production volume in 2018 had an increase as compared to the data of 2017 for agriculture production in Thailand. In addition, the food industry has improved the export and the demand for food for domestic consumption (Ministry of Industry, 2018). In 2018, the food production was 40,212,383.241 tons with an increase of 14.21% from 2017. This increment was mainly the production of raw sugar, white sugar, and pure sugar. Sugar cane production increased by 40% to 45% from the previous year. Crude palm oil and refined palm oil productions, chilled, frozen and processed chicken, canned tuna, canned sardines were increased more than previous year which supported domestic and international demand that continues to expand. However, in 2018, the imports also increased by $14,007.78 million, an increase of 7.15% from 2017 because of the increase of import volume of fresh and frozen tuna which supported the expansion of canned tuna industry. Moreover,the import of oil waste plant, milk, and dairy products were increased which supported

7 the expansion of the animal feed industry, the dairy industry, and other foods industry. Furthermore, in 2018, the domestic food sales were 21,546,159.471 tons which was an increase of 4.37% from 2017. The sales of sugar, vegetable oil, milk and instant noodles increased due to increased spending on consumption in the country, since purchasing power continued to expand in 2018. In terms of rice, frozen chicken, processed chicken, canned tuna, canned sardines, raw sugar, fresh tapioca starch, durians, and food seasonings were exported to the major trading partners such as ASEAN, China, Japan, and Europe. In 2018, the export worth $31,447.72 million which was an increase of 8.5% compared to that of 2017 (Ministry of Industry, 2018). The Production, Sales, Export, and Import for the Food Industry for 2014 to 2018 may be found in Appendix 1. To summarize, in 2018 the production in ton boost up from previous years due to high demand of agricultural products from domestic and international consumption. In addition, in 2018, the imports of agricultural products also increased due to the demand of canned fish industry and the animal feed industry. Moreover, the sales of agricultural products had a high demanded by domestic consumption, thus, increase the spending for consumption and increase of purchasing power in the country. Furthermore, Thailand also has high demand of agricultural products from the major trading partners such as ASEAN, China, Japan and Europe. Therefore, food and beverage sector was part of many sectors that helped to improve the economy of Thailand in 2018.

8 Steel Industry The Ministry of Industry of Thailand (2018) showed that the steel industry production index of 2018 decreased compared to that of 2017. Appendix 2 indicates that the production of long steel, rod wire, high tensile steel wire and hot-rolled structural steel has increased primarily due to the expansion of the construction industry, government infrastructure construction, and the construction of private residences. The Ministry of Industry of Thailand (2018) also explained that production in the fourth quarter of 2018 declined compared to that of 2017 with the manufacturing production index at 112.2. This was a decrease of 1.8% compared to the same quarter of 2017 and a decrease of 11.2% in the third quarter compared to the same quarter of 2017. The production of flat steel decreased about 2.8% including tin plate steel decreased by 35.7%, followed by hot-rolled steel coil declined by 13.2% because the manufacturers continued to import these cheaper products from China and Vietnam. For long steel the production increased by 0.8%, rod wire production increased by 9.5%, followed by high tensile steel wire and hot-rolled structural steel increased by 5.9% and 3.8% respectively because these products were used by the construction industry, government infrastructure construction, and the construction of private residences. The Sales Volume and Import Value of Iron and Steel for Q4/2017-Q4/2018 may be found in Appendix 2. The Ministry of Industry of Thailand (2018) indicated that imports in the fourth quarter of 2018 was $2.8 billion which was an increase of 18.2% compared to the same quarter of 2017. The imports also increased by 2.2% in the third quarter compared to the same quarter in 2017 due to the increased import of long steel by 21.2%. In addition, increased imports included seamless steel tubes 69.1% from

9 China, Japan and South Korea, followed by alloy steel and rod wire increased by 58.7% and 11.2%, flat steel increased by 16.9%. In addition, tin coated steel sheets increased by 58.8% which were imported from china, South Korea and Spain, followed by chromium coated steel sheet and hot rolled steel increased by 48.2% and 23.2% respectively. Production Index for Q4/2017 to Q4/2018 may be found for in Appendix 3 2.2. Inventory Classification In manufacturing firms, there are some inventory which are not ready for sale, thus, three categories are classified for manufacturing inventory such as finished goods, work in process, and raw materials. Finished goods inventory is the goods that are waiting for sale. Work in process is the portion of raw material that has been put into the production process and has not been complete. Raw materials are unprocessed material that will be used to produce manufactured goods but have not yet been put into the production process (Weygandt, Kimmel, & Kieo, 2010). 2.3 Inventory Costing Methods Aiello (2007) demonstrated that for accounting purposes, inventory is an asset in the financial statement of position until it is transformed into a finished goods and sold to a customer. Once sold, the inventory transfers to the income statement. Warren, Reeve, and Ducha (2016), have expressed that companies also report: (1) which inventory costing method a company uses to determine the cost of the inventory such as First-In-First-Out (FIFO), Last-In-First- Out (LIFO), or Average; (2) and which inventory valuation method a company uses (such as the lower of cost or market).

10 Department of Industrial Promotion (2016), stated that in Thailand the accounting standard accepts only four inventory costing methods as follows: specific identification, FIFO, Weighted Average, and Moving average. It does not accept LastIn, First-Out (LIFO) because the companies that sell technology equipment may take the advantage from using LIFO Method, making it easier to create a gap for creative accounting. Wahlen, Jones and Pagach (2012) demonstrated that the LIFO Method generates the highest cost of goods sold and the lowest gross profit. According to Needles, Powers and Crosson (2010), “In accounting for inventories, management must choose the type of processing system, costing method, and valuation method the company will use. Because the value of inventory affects a company’s net income, management’s choices will affect not only external and internal evaluations of the company, but also the amount of income taxes the company pays and its cash flows (p.337).” First-in-First-Out (FIFO) When FIFO is used, the oldest merchandises purchased (or produced) are accepted to be sold first and the most up-to-date merchandises (or produced) are expected to remain in inventory (Robinson, Henry, Pirie, & Broihahn, 2015). Authors also said that cost of goods in beginning inventory and costs of the first merchandises purchased (or produced) go into cost of goods sold first as if the earliest merchandises purchased are sold first. Ending inventory would then contain the latest purchase. When the FIFO Method is used, goods are sold in same order as they were purchased (Warren et al., 2016). Authors also said that it is often similar to the physical flow of goods. Therefore, the FIFO regularly gives outcomes that are similar to those that would have been obtained using the specific identification method.

11 When FIFO is used, the first supplies that have been kept in the stock must be used before the new items were purchased (Gough & Gough, 2008). They also told that older items must be rotated to the front of the shelves in order for the new items to be placed at the back of the shelves behind the older goods. FIFO assumes that the oldest items are sold first and they are mostly used by firms that are usually concerned about spoilage or out of date (Epstein, 2012). He also gives the example that FIFO is used by grocery stores because they have an expiration date, so the older goods must be sold first. Computer technology firms also apply FIFO because technology is developing rapidly and the older technology will become obsolete. FIFO Method affects the bottom line of the income statement because, assuming prices are rising, the older items cost lesser than newer ones, thus the older items carry lower costs which inflates the profit. FIFO is often used for most perishable and nonperishable goods (Dopson & Hayes, 2015). According to the authors, failure to apply a FIFO system in storage management, could result in product loss because of spoilage, decline, and a weakening of the quality of the product. Flood (2019) stated that FIFO is a costing method that is well represented in the balance sheet because it provides the most accurate estimate of the current cost of inventory during times of movement. During the time of rising prices, FIFO will provide higher income taxes than any other methods, while in a period of deflation, FIFO gives lower income taxes. Weighted Average Cost Nikolai, Bazley and Jones (2009) explained that when applying the periodic inventory system, the Average Cost Method is also called as the Weighted Average Method. Robinson et al. (2015) indicated that the Weighted Average Cost Method

12 allocates the average cost of goods available for sale to the units sold and those units remaining in inventory. For Bragg (2005), the Weighted Average Costing Method is computed by weighted average of the cost in inventory. According to Bragg (2008), “The weighted-average costing method has the singular advantage of not requiring a database that itemizes the many potential layers of inventory at the different costs at which they were acquired. Instead, the weighted-average of all units in stock is determined, at which point all of the units in stock are accorded that weighted-average value (p.57).” However, Bragg (2005) added that the Weighted Average Cost Method has no precision related to income taxes, and income recognition based on increasing or decreasing cost trends. It makes a better choice for companies that do not want to plan for tax purpose. The Department of Industrial Promotion (2016) stated that the Weighted Average Method is suitable for manufacturing firms that purchase raw materials for production because the product cost is averaged and shows the overall cost of the manufactured product better than other methods. However, the appropriateness of using the Weighted Average Method must also be related to the characteristics of the raw materials because it would not be appropriated to use this method if raw materials are of high value and have unique characteristics. Moving Average Cost Method The Moving Average Cost Method is also known as an Average Cost Method when the assumption of a Weighted Average Cost is applied with a perpetual inventory system (Porter & Norton, 2016). Thus, Moving Average Method for each item purchased, needs to be recalculated for the cost of goods sold to be recorded

13 (Epstein, Nach & Bragg, 2009). According to Nikolai et al. (2009), “Average Cost is used to determine the cost of each sale made until the next purchase, when a new average cost is calculated (p.381).” 2.4. Advantages and Disadvantages of Inventory Costing Method The choice of inventory valuation method selected by a company has an effect on the taxes (Simeon & John, 2018). Lower amounts of closing inventory generate higher cost of sales. The authors indicated that when there are perishable inventories, the companies may use FIFO to show the higher profits for the year in comparison with the Weighted Average Cost Method. The FIFO Method not only favors the firm in terms of higher profits, but also favors the government since it generates higher payable taxes. Furthermore, higher profits and the resultant dividends, are favorable for shareholders. FIFO results in higher net profit and thus, attracts more investors based on the profit declaration. FIFO leads the firm to establish a reliable statement of financial position, and shows the ending inventory that is closest to current values and therefore, provides a more accurate view of a firm’s current assets (Needles et al., 2010). In the case of rising prices, FIFO results in higher profits and income taxes (Gu, 2013). FIFO is the most considered method used by management of firms that generates and shows higher net income for the investors and creditors in the capital market (Pratt, 2010). However, LIFO generates the highest cost of goods sold and the lowest gross profit (Wahlen et al., 2012). In times of endless inflation, FIFO is one of the best inventory methods to show clearly the inventory profits (Ross, 2015). On one hand, according to him, FIFO is regularly used for perishable goods such as foods, pharmaceuticals, or other goods

14 with a short shelf live. Stock keeping in a FIFO system requires that oldest goods at all times be switched to the front upon receipt of new inventory. On the other hand, Average Cost Method takes the current cost and the quantity of existing inventory of an item and combines it with the cost and quantity of the receipt of the newest item, and then averages them to regulate a new inventory value. FIFO represents the true inventory values showing the earliest inventory sold and maintaining the latest inventory for balance sheet (Harris, & Dilling, 2012). Simeon and John (2018) demonstrated that the Weighted Average Cost Method presents a more satisfactory outcome during price fluctuation, all inventories are valued at the same cost without taking into consideration whether the dates of purchase nor the amount are different. Garrison, Noreen, and Brewer (2009) stated that FIFO Method and Weighted Average Cost Method are different which regard to the process costing in two ways. First, the way they calculate the equivalent units. Second, the way they treat the cost of beginning inventory in terms of the cost reconciliation report. A comparison between the two methods, shows that FIFO Method gives more accurate results than the Weighted Average Cost Method, but FIFO is also more complex. However, the complexity is not an issue for computers, but it is somewhat more difficult to understand and to learn than the Weighted Average Cost Method. 2.5 Characteristics of Manufacturing Firm Firm Size Firm size is measured by the total assets and total revenue of a firm (Van Frederikslust, 2012; Saunders & Walter, 1994). An increase of total assets indicates that a firm has generated net income from its operation and added the profit back to its long-term assets for the future expansion, while a decrease of total assets expresses a

15 decline of its assets, which have been sold or the proceeds to shareholders (Gildersleeve, 1999). Firm size is the logarithm of the market value of equity (Haltiwanger, Manser & Topel, 2007). Salman and Yazdanfar (2012) revealed that the total number of employees, sales and the amount of property are the main factors of the measurement of a firm’s size. Another widespread measurement of firm size is the net worth or the book value of Shareholders’ Equity determined from the total assets minus total liabilities (Hirschey, 2008). Mottershead, Kelt and Grant (2012) indicated that the methods used to consider firm size should fit into the type of business, such as: the number of employees, turnover and profitability, number of shops/outlets, stock market valuation, and capital employed. Inventory Size Inventory to total assets determines the percentage of inventory tied up in the total assets of the firm. This ratio indicates how effective a firm is in controlling inventory as a percentage of the total assets (Rachlin, 1997) as shown in the formula below: = The inventory to asset ratio indicates the portion of assets tied up in inventory. Normally, the lower the ratio, the better the performance (Narsale, 2016). Inventory is one of most important activities for assets of a firm (Wisner, Tan & Leong, 2019). Authors also explain that even though firms in manufacturing sector commonly hold more inventory than service firms in the service sector, effective inventory management for both manufacturing and service companies is important. Effective

16 inventory management attempts to create perfect harmony in production and sales areas concerned with inventories in physical aspects to maintain on storing adequate quantities (Prasad & Sinha, 1990). Financial Ratio Analysis Financial analysis is a procedure that provides information for managers’ decision making. Financial analysis is based on the use of financial ratios that enable managers to interpret financial statement accounts characterized with significant relationships or differences across different periods (Riahi-Belkaoui, 1992). Profitability of the Firm According to Robinson, Greuning, Henry and Broihahn (2015), the ability to generate profits on capital investments is a fundamental factor of overall value of a company and the value of the securities it issues. As a result, therefore, many equity analysts would consider profitability to be the main focus of their analysis for competitive position of a company in the market, and by extension, the quality of its management. The income statement reveals the sources of earnings and the components of revenue and expenses. Earnings can be given to shareholders or reinvested in the firm (Robinson et al., 2015). The authors indicate that reinvested earnings improve solvency and meet short-term obligations as they are due. Return on Assets (ROA) measures the returns generated by the assets of a firm. The higher the ratio, the more income is earned by a given level of assets. Most databases compute this ratio as follows: Net Income

17 Robinson et al. (2012) research indicates that using the Weighted Average Cost Method would result in lower return on assets ratios since the incremental profit added to the net income has bigger impact than the incremental increase to the total assets. Financial Leverage of the Firm This refers to the debt-to-equity ratio that compares the level of borrowing with the level of shareholders’ invested capital. The lower the ratios, the better a business’s capability of repaying off the principal on its borrowings, thus it is calculated from the following equation (Engle, 2010): = Total Liabilities Applying the Weighted Average Cost Method would result in higher debt-toequity ratios since it gives a lower retained earnings than when applying FIFO Method (Robinson et al., 2012). Pratt (2010) clarified this information by indicating that the reason for applying FIFO could be for the increment of the credit rating of the firm to offer better terms on its borrowings and greater prices for its outstanding debt securities. Agtarap-San Juan (2007) stated that the total debt to total assets ratio show the creditors share level in the total assets of a firm. According to Platt (1999), debt ratios show which firms relatively have more debt as compared to other firms and to themselves. Kumar and Sharma (1998) reported that the relationship between borrowing and owner capital is the measure of company’s long-term solvency. This ratio reveals the relative claims of creditors and owners against the company’s assets.

18 One approach is to indicate debt-to-equity ratio in terms of the relative proportion of long-term debt and shareholders’ equity, as follows: − ( ) = − Shareholders Equity The debt-to-equity ratio shows the margin level of safety to long-term creditors (Tulsian, & Tulsian, 2016). According to the authors, a low debt-to-equity ratio indicates that a firm used more equity than debt, which means a larger safety margin for creditors. A debt-to-equity ratio of 2:1 is usually considered to be acceptable, but of course, it depends on the particular industry. Current Ratios Current ratio represents the ability to pay short-term debts when they are due. The formula is current assets divided by current liability (Stickney, Weil, Schipper, & Francis, 2009). The benchmark of this ratio has been of 2:1 (Troy, 2008). Robinson et al. (2012) also indicated that using the Weighted Average Cost Method lowers the current ratio since inventory gives lower carrying value than FIFO. Creditors, however, usually compute the current ratio to determine whether a firm has enough liquidity to pay its current liabilities, and thus, would favor a high current ratio because it is a positive indicator that the business has the capacity to pay back its debt obligations (Bragg, 2012; Jagels, & Ralston, 2006). Inventory Turnover Vinturella and Erickson (2013) stated that inventory turnover is highly industry dependent. However, high levels of inventory ties up cash and reduces the firm’s profitability. According to Chang (2010), the inventory turnover ratio refers to the cost of goods sold divided by the average inventory. Thus, it shows the number of

19 times in a year a company can turn the average inventory into sale activities. Moreover, the ratio indicates how efficient the company manages the inventory to meet the need of customers and the shortage of appropriate inventory. The higher ratio, the better the performance. The inventory turnover is computed as follows: Higher inventory turnover ratio or lower average or lower average days in inventory suggests that management is reducing the amount of inventory on hand, relative to cost of goods sold (Kimmel, Weygandt, & Kieoso (2010). Days for recovery of inventory are computed as follows: = 365 Wahlen, Baginski and Bradshaw (2011), have expressed that by using FIFO, the inventory turnover ratios give a better explanation on the turnover of inventory items since it divides a cost of goods sold by an average inventory which reflects current costs and recent costs. 2.6 Influences of the Choice of Inventory Costing Methods Robinson et al. (2012) indicated that the selection of inventory costing method potentially impacts inventory carrying amounts and cost of goods sold significantly. The methods have influence on other financial statement items such as current assets, total assets, gross profit, and net profit. Thus, the financial statements and associated notes give vital information about inventory accounting policies of a firm for analystto evaluate financial performance and to make a comparison with other firms (Robinson et al., 2012).

20 2.7 Prior Studies Gopalakrishnan (1994) stated that there is a relationship between both size and profitability and the choice of both depreciation and inventory methods. In addition, inventory method choice seems to be supported by both the size and debt-to-equity assumptions. Furthermore, for smaller companies, the size was likely to be positively related to the use of FIFO Method. Moreover, the result found that the higher the leverage, the greater the likelihood that a company would select the FIFO Method. However, the findings did not support the hypothesis that larger the company, the lesser the likelihood that a company would select the FIFO Method. Zinkevičienė and Rudžionienė (2005) in his study found that there was no impact of firm’s leverage on its choice of FIFO Method. However, the findings did not support the hypothesis that the higher a firm’s financial leverage, the more likely are its managers to use straight-line depreciation and the FIFO Method. Simeon and John (2018) found that a significant correlation between profitability, tax, and closing inventory and the choice of inventory cost method. Murdoch, Dehning and Krause (2013) found that the ability of FIFO and LIFO earnings predicted operating cash flows in a specific industry and LIFO indicated significantly more variation in future operating cash flows than does FIFO earnings. However, the study did reveal that FIFO indicated more variation in firm future operating cash flows than did LIFO earnings for LIFO retail trade firms. Asgari, Mehdizade and Hassani (2014) stated that there was no significant relationship were found between the inventory costing method (FIFO) and profitability and debt-to-equity ratios.

21 Furthermore, Ibarra (2018) stated that firms which use FIFO realize statistically significant lower tax savings, larger tax loss carried forwards, higher leverage, lower current ratios, and greater variability in inventory levels and are smaller in size. Hapsari (2016) expressed that firm size, financial leverage, liquidity ratio and income before tax did not have any statistically significant effect on the selection of inventory cost flow assumption in retail companies listed in the Indonesia Stock Exchange for the period 2012-2014. 2.8 Conclusion The manufacturing firms contributed a significant portion to Thailand GDP. In 2018, the increase was about 3.7% in the fourth quarter. In addition, firms in this sector create many job. As a result, this sector helps to reduce the country’s unemployment rate. Both agricultural and non-agricultural sectors decreased the unemployment to the lowest rate in 12 quarters. Manufacturers’ main sources of income is from the sales of inventory. Because of that, the accounting method (in term of inventory costing method) that is being used is important to the manufacturing firms since the cost of inventory affect both the amount reported in the balance sheet and the cost of goods sold reported in the income statement. Therefore, this study examined the two manufacturing sectors, which have different type of business, including food and beverage and steel sectors (listed in the Stock Exchange of Thailand). The two sectors have been producing, selling, importing, and exporting a huge amount of inventory. Furthermore, in accounting, there are many accounting choices for managers to apply based on the need and appropriateness of nature of the business.

22 Thus, the study analyzed the characteristics of the manufacturing firms (Firm size, inventory size, profitability, firm financial leverage, current ratio, and inventory turnover) to see if relationship existed between them and the choice of inventory costing method (FIFO and Average Cost Method). There were many related studies from many countries. The researchers would like to study about manufacturing sectors in Thailand to see if the similar results would be found in this study. Table 3. Related Research Studies Gopalakrishnan (1994) 1. There is a relationship between both size and profitability and the choice of both depreciation method and inventory method. 2. Inventory method choice supports both the size and debtto-equity assumptions. 3. The size is likely to be positively related to use of the FIFO Method. 4. The higher the leverage, the greater the likelihood that a company would select the FIFO Method 5. The findings did not support the hypothesis that larger the company, the lesser the likelihood that a company would select the FIFO Method. Zinkevičienė and Rudžionienėv (2005) 1. The findings did not support the hypothesis that the higher a firm’s financial leverage, the more likely are its managers to use straight-line depreciation and the FIFO Method Simeon and John (2018) 1. There is a very significant correlation between profitability, tax, and costing inventory Asgari et. al. (2014) 1. There was no significant relationship between inventory accounting method (FIFO) and profitability. 2. There is no significant relationship between inventory costing method and debt-equity ratios. Ibarra (2018) 1. Firms that use FIFO realize statistically significant lower tax savings, larger tax loss carried forwards, higher leverage, lower current ratios, greater variability in inventory levels, and are smaller in size. Hapsari (2016) 1. Firm size, financial leverage, liquidity ratio and income before tax did not have any statistically significant effect on the selection of inventory cost flow assumption in retail companies listed in Indonesia Stock Exchange for the period 2012-2014.

23 Research Questions The aim of this research study was to determine the influences of selected manufacturing firm characteristics on the choice of inventory costing methods. The study sought to answer the following questions: 1. What inventory costing methods are used by selected? 2. How do firm characteristics (size, inventory, leverage, profitability, current ratios and inventory turnover) influence the choice of inventory costing methods? 2.9 Significance of the Research This research is designed to examine the influences that selected characteristics of firms have on inventory-costing methods adopted by manufacturing companies. The findings of this research contribute to the body of knowledge, as it indicates the characteristics of Thai firms that have chosen different inventory costing methods. The findings of this research can be used by managers, investors, and researchers. Managers Because accounting provides many choices for managers, making the right decision when selecting an inventory costing method is important. Managers should make the choice that is the best for the firm. The selection of an inventory costing method affects user decisions, the financial statements, investors, creditors, as well as the amount of income tax expense. The findings of this study may be helpful to management in understanding more about how certain characteristics of manufacturing firm may affect the choice of inventory costing method. In manufacturing firms, the sale of inventory is the main source of revenue. To manage

24 the inventories held in stock, there is also a need to reduce the inventories that will be out of date, and the manager should understand the nature of the inventories well. Therefore, choosing the correct inventory costing method can reduce such problems. Investors This research may help investors to be aware of how to interpret financial statements for their investment decisions, because financial statements cannot be compared or analyzed when different companies apply different inventory costing methods or other accounting choices that are different. Investors may see that the FIFO Method produces higher profits and higher declared dividends. The Average Cost Method is more conservative, since it produces lower profits during periods of rising prices than does the FIFO Method. Researchers Researchers may discover findings useful for improving knowledge and understanding of the nature of manufacturing firms regarding which inventory costing methods were chosen by the majority of manufacturing firms. Researchers understand more regarding the accounting choices made by companies. 2.10 Scope and Limitation The purpose of this research was to analyze the influence of selected characteristics of manufacturing firms with respect to the choice of an inventorycosting method. Limitation: use of secondary data. Limited to inventory costing methods in manufacturing sectors listed in the Stock Exchange of Thailand from 2016 to 2018. Results may not be generalizable. In addition, Rojkurisathian, (2013), stated that Thai Accounting Standards TAS 2 (revised in 2009) on Inventories stated by the

25 Federation of Accounting Professions of Thailand does not allow the use of LIFO method to measure the cost of inventories. 2.11 Conceptual Framework A conceptual framework clarifies, either graphically or in narrative form, the most important things to be considered such as key factors, variables, constructs, and the assumed interrelationships among them. Frameworks can be simple or elaborate, rational or theory driven, descriptive or causal (Miles, Huberman, & Saldaña, 2014 ). Figure 2. Conceptual Framework

26 Firm Characteristics B1= Firm Size (characterized by the total Assets for the firm) B2= Inventory Size (Ending inventory/Total Assets) B3= Profitability (Net Income/Average Total Assets) B4= Firm Financial Leverage (Debt/Equity) B5=Inventory Turnover (COGS/Average Inventory) B6=Current Ratio (Current Assets/Current liability) 2.12 Definition of Terms There are some important terms that the researcher used in this paper which need description. Characteristics of manufacturing firms refers to the firm size, inventory size, profitability, firm financial leverage, Inventory Turnover and Current Ratios. Firm size refers to total assets of the firm. Inventory size refers to the ending inventory divided by total assets. Profitability refers to the return on assets ratio (ROA). Firm financial leverage refers to the debt to equity ratio. Inventory Turnover refers to the cost of goods sold divided by average inventory. Current Ratio refers to Current Assets/Current Liabilities. Inventory costing methods refers to the First-In-First-Out (FIFO), Weighted Average Cost Method, and Moving Average Cost Method.

27 CHAPTER 3 RESEARCH METHODS This chapter begins with the research design that was developed for the study, the sample and sampling techniques that were used, the procedures for gathering the data, and statistical formulas that were employed for data analysis and interpretation. The aim of this research was to examine the influence of firm size, inventory size, profitability, and firm financial leverage on the choice of an inventory costing method. 3.1 Research Design The research design used in this study was the descriptive method. According to Travers (1978), this method describes the nature of a situation as it exists during the period of the study, and explores the course of a particular phenomenon. According to Singh and Nath (2007), descriptive research studies include all of the following characteristics: they involve hypothesis formulation and testing, they use logical methods of inductive-deductive reasoning to arrive at generalizations, they often employ methods of randomization so that error may be estimated when population characteristics are inferred from observation of samples, and the variables and procedures are described as accurately and completely as possible so that the study can be replicated by other researchers. For Sigh and Nath (2007), descriptive research is non-experimental, for it deals with the relationships among non-manipulated variables. For Mitchell and Jolley (2010), descriptive research is needed to accurately describe and predict what people think, feel, or do. The key to conducting descriptive research is to get accurate measurements from a representative sample.

28 The descriptive method was used in this research to describe the selected firm’s characteristics (independent variable s). The inventory costing method (dependent variable) was stated in firms financial statements, and described using frequencies and percentages. Logistic regression was used to measure the influence that various manufacturing firm characteristics had on selection of inventory costing method. 3.2 Population, Sample Size and Sampling Techniques A population refers to the universe of units from which the sample is to be chosen, while a sample is a segment of the population that is chosen for investigation or a subset of the population (Hammond and Wellington (2015); Bryman and Bell, (2011). The population for this research study was composed of publicly-traded firms in the food and beverage and steel sectors that were listed on the Stock Exchange of Thailand. They both represent totally different types of businesses because foods and beverages are perishable inventory goods with short shelf lives, while steel products last much longer. Thus, the researcher wished to examine whether firm characteristics in two sectors with very different types of finished products had any common influences on the choice of inventory costing methods. Table 4.1. 2016-2018 Thailand Stock Exchange 2016-2018 Manufacturing Sectors No. Firm Code Steel Food and Beverage Firm Code 1 SAM SAMCHAI STEEL INDUSTRIES PUBLIC COMPANY LIMITED SIAM FOOD PRODUCTS PUBLIC COMPANY LIMITED SFP 2 CSP CSP STEEL CENTER PUBLIC COMPANY LIMITED BURIRAM SUGAR PUBLIC COMPANY LIMITED BRR 3 PERM PERMSIN STEEL WORKS PUBLIC COMPANY LIMITED ASIAN SEAFOODS COLDSTORAGE PUBLIC COMPANY LIMITED ASIAN 4 MCS M.C.S.STEEL PUBLIC COMPANY LIMITED CARABAO GROUP PUBLIC COMPANY LIMITED CBG 5 CITY CITY STEEL PUBLIC COMPANY LIMITED ICHITAN GROUP PUBLIC COMPANY LIMITED ICHI 6 SMIT SAHAMIT MACHINERY PUBLIC COMPANY LIMITED PREMIER MARKETING PUBLIC COMPANY LIMITED PM 7 TGPRO THAI-GERMAN PRODUCTS PUBLIC COMPANY LIMITED THAI UNION GROUP PUBLIC COMPANY LIMITED TU

29 8 GJS G J STEEL PUBLIC COMPANY LIMITED CHIANGMAI FROZEN FOODS PUBLIC COMPANY LIMITED CM 9 THE THE STEEL PUBLIC COMPANY LIMITED CHAROEN POKPHAND FOODS PUBLIC COMPANY LIMITED CPF 10 LHK LOHAKIT METAL PUBLIC COMPANY LIMITED THAI VEGETABLE OIL PUBLIC COMPANY LIMITED TVO 11 PAP PACIFIC PIPE PUBLIC COMPANY LIMITED PRESIDENT BAKERY PUBLIC COMPANY LIMITED PB 12 TIW THAILAND IRON WORKS PUBLIC COMPANY LIMITED SEAFRESH INDUSTRY PUBLIC COMPANY LIMITED CFRESH 13 INOX POSCO-THAINOX PUBLIC COMPANY LIMITED AGRIPURE HOLDINGS PUBLIC COMPANY LIMITED APURE 14 MAX MAX METAL CORPORATION PUBLIC COMPANY LIMITED TIPCO FOODS PUBLIC COMPANY LIMITED TIPCO 15 TWP THAI WIRE PRODUCTS PUBLIC COMPANY LIMITED TAOKAENOI FOOD & MARKETING PUBLIC COMPANY LIMITED TKN 16 TSTH TATA STEEL (THAILAND) PUBLIC COMPANY LIMITED BANGKOK RANCH PUBLIC COMPANY LIMITED BR 17 BSBM BANGSAPHAN BARMILL PUBLIC COMPANY LIMITED CHUMPORN PALM OIL INDUSTRY PUBLIC COMPANY LIMITED CPI 18 AMC ASIA METAL PUBLIC COMPANY LIMITED SUB SRI THAI PUBLIC COMPANY LIMITED SST 19 CEN CAPITAL ENGINEERING NETWORK PUBLIC COMPANY LIMITED SURAPON FOODS PUBLIC COMPANY LIMITED SSF 20 TYCN TYCOONS WORLDWIDE GROUP (THAILAND) PUBLIC CO., LTD. SERMSUK PUBLIC COMPANY LIMITED SSC 21 RICH RICH ASIA CORPORATION PUBLIC COMPANY LIMITED KHON KAEN SUGAR INDUSTRY PUBLIC COMPANY LIMITED KSL 22 GSTEL G STEEL PUBLIC COMPANY LIMITED THAIFOODS GROUP PUBLIC COMPANY LIMITED TFG 23 TMT TMT STEEL PUBLIC COMPANY LIMITED SAPPE PUBLIC COMPANY LIMITED SAPPE 24 MILL MILLCON STEEL PUBLIC COMPANY LIMITED S & P SYNDICATE PUBLIC COMPANY LIMITED SNP 25 KHONBURI SUGAR PUBLIC COMPANY LIMITED KBS 26 MINOR INTERNATIONAL PUBLIC COMPANY LIMITED MINT 27 MALEE GROUP PUBLIC COMPANY LIMITED MALEE Table 4.2. Thailand Stock Exchange for the Period of 2016-2018 (Cont.) 28 PATUM RICE MILL AND GRANARY PUBLIC COMPANY LIMTED PRG 29 FOOD AND DRINKS PUBLIC COMPANY LIMITED F&D 30 THAITHEPAROS PUBLIC COMPANY LIMITED SAUCE 31 THAI PRESIDENT FOODS PUBLIC COMPANY LIMITED TFMAMA 32 KASET THAI INTERNATIONAL SUGAR CORPORATION PUBLIC COMPANY LIMITED KTIS 33 LAM SOON (THAILAND) PUBLIC COMPANY LIMITED LST 34 MK RESTAURANT GROUP PUBLIC COMPANY LIMITED M 35 TROPICAL CANNING (THAILAND) PUBLIC COMPANY LIMITED TC 36 HAAD THIP PUBLIC COMPANY LIMITED HTC

30 A purposive sampling technique was used in this research based on the following criteria: 1) The first required criterion was that manufacturing companies had published their annual financial statements for the years from 2016 to 2018. 2) Second, their complete financial statements were fully available on the Stock Exchange of Thailand website (www.set.or.th and www.settrade.com), along with the annual reports from each of the manufacturing companies. 3) Third, manufacturing companies must have adopted an Inventory costing method, either FIFO or Average Cost, for finished goods. 4) Fourth, the firms must have joined the Thailand Stock Exchange on or before 2015, because data from 2015 is useful for the researcher in finding the profitability of the firm in 2016 (Net Income/Average Total Assets). 5) The fifth and final criteria was that the financial statements must be displayed in Baht. Based on these criteria, the total sample for this research consisted of 60 companies, where 24 companies were from the steel sector, and 36 companies were from the food and beverage sector. Table 5. Population of Manufacturing Firms Manufacturing Sectors Number of Firms Sample Size Steel 26 24.41 ≈ 24 Food and Beverage 40 36.36 ≈ 36 Total 66 60 Source: www.settrade.com

31 Sample Size Formula (Steel Industry) n = N / 1+ Ne2 Where, n = Sample Size N= Total Population e = Error The researcher used the confidence level of 95%; thus, expected error equal 5% n = 26 / 1 + (26)*(0.05)2 n =24.41≈24 Sample Size Formula (Food and Beverage) n = N / 1+ Ne2 Where, n = Sample Size N= Total Population e = Error The researcher used the confidence level of 95%; thus, expected error equal 5% n = 40 / 1 + (40)*(0.05)2 n =36.36≈36 The sample size for this research should have been a sample of 67 companies for both food and beverage and steel sectors. However, only 60 firms met the requirements, since only 60 of them had adopted one of the inventory costing methods, either FIFO or Average Cost Method, for all types of inventory. Therefore, the sample size decreased from 66 to 60 companies. 3.3. Conditions for Analysis For independent variable levels, the researcher used ratio and interval scales because the independent variables were measured in Baht and with ratios. The researcher used z-scores to standardize the data.

32 Multicollinearity was calculated for the independent variables to test if there were interrelationships among them. Gregory and Bader (2018) explained multicollinearity as a high level of correlation among the variables of interest. The assumption of multicollinearity is tested using Variance Inflation Factor (VIF) and Condition indices, particularly in regression analyses. A VIF of more than 10 shows that there is multicollinearity, and the assumption is violated. To solve this problem, independent variables with high VIF values are eliminated. The results in the table below report the results for calculation of multicollinearity. Table 6. 2016-2018 Variance Inflation Factor Results for Food & Beverage Sector VIF Results Independent Variables 2018 2017 2016 Firm Size 1.375 1.462 1.584 Financial Leverage 2.367 1.864 2.540 Inventory Size 1.895 1.584 1.473 Profitability 1.801 1.333 1.580 Inventory Turnover 1.982 1.699 1.688 Current Ratio 1.725 1.470 1.648 VIFs for the food and beverage sector were less than 10. Therefore, there is no relationship between the independent variables, and the model is free from bias. Table 7. 2016-2018 Variance Inflation Factors Results for Steel Sector VIFs Result Independent Variables 2018 2017 2016 Firm Size 1.712 3.246 2.795 Financial Leverage 1.212 2.455 2.415 Inventory Size 1.626 2.432 3.441 Profitability 1.269 1.449 1.748 Inventory Turnover 3.514 8.068 2.098 Current Ratio 3.273 8.050 1.522 The VIFs for steel sector were less than 10. Therefore, there is no relationship between the independent variables, and the model is free from bias.

33 3.4 Variables 1. The independent variables (Covariates) were the characteristics of each firm that included firm size, inventory size, profitability, financial leverage, inventory turnover, and current ratio. 2. The dependent variable was the inventory costing method used by each firm that included FIFO and Average Cost Methods. 3.5 Data Collection This research used secondary data. According to Burt, Barber and Rigby (2009), secondary data are data acquired from a source that is not a primary data source. Payne (2005) also stated that secondary data are information from published sources which was collected by someone else. Thus, this research used only secondary data; therefore, it did not use any instruments, such as a questionnaire or interview guide. 3.6 Statistical Formula The statistical formula used in this research was the Logistic Regression Model. This formula examined the impact of firm size, inventory size, profitability, and firm financial leverage on the management choice of inventory-costing method. Allison (2012) stated that a logistic regression model is also known as logit model. A key issue with the linear probability model is that probabilities are bounded by 0 and 1, whereas linear functions are fundamentally unbounded. The answer to this problem is to transform the probability so that it is no longer bounded. When the probability is transformed to an odds, the upper bound is removed. If the logarithm of the odds was taken, the lower bound was also removed. We can develop the logistic

34 model by setting the result equal to a linear function of the explanatory variables. For k explanatory variables and I = 1…, n individuals, the model is log(pi/1-pi) = + 1xi1 + 2xi2 + … + kxik Where pi is the probability that yi = 1. The expression on the left is commonly referred to as the logit or log-odds. α is the intercept, and x’s may be either quantitative variables or dummy (indicator) variables. The Logistic Regression Model was employed to test the impact of the independent variables on the dependent variable as follows: Log(p/1-p) = B0 + B1*x1+B2*x2 +B3*x3 +B4*x4+B5*x5+B6*x6 Where p is the probability of inventory costing method, and Where X1, X2, X3, X4, X5 and X6 are the predictors X1 = Firm Size X2 = Firm Financial Leverage X3 = Inventory Size X4 = Profitability of the Firm X5 = Inventory Turnover X6 = Current Ratio Explanation of Variables in Equation The UCLA Institute for Digital Research & Education (n.d.) explained that “B” refers to the coefficient for the constant, which is also called the intercept in the null model. “S.E” refers the standard error around the coefficient for the constant. “Wald” and “Sig.” refer to the Wald chi-square tests that test the null hypothesis that the constant equals 0. The hypothesis is rejected because the p-value, which in the table is called “Sig.”, is smaller than the critical p-value of 0.05 (or .01). Therefore, it

35 was concluded that the constant is not 0. “df” refers to the degrees of freedom for the Wald chi-square test. In this case, it is only one degree of freedom because there is only one predictor in the model, which is the constant. Exp(B) refers to the exponentiation of the B coefficient, also known as an odds ratio. This value is given by default because odds ratios can be easier to interpret than the coefficient, which is in log-odds units. 3.7 Analysis of Data As soon as the pertinent data were gathered by the researcher, they were compiled, sorted, organized and tabulated. This research used total assets as a proxy for firm size, and debt ratio (debt/equity) as a proxy for financial leverage of the firm. In the case of inventory size, the amount of ending inventory was divided by total assets to eliminate the size affect. Additionally, profitability was calculated by dividing the net income by average total assets, inventory turnover was calculated by dividing Cost of Goods Sold by Average Inventory, and current ratio was calculated by dividing Current Assets by Current Liabilities. The data were organized and tabulated with the help of a software package to find the estimated coefficient for each independent variable. 3.8 Ethical Issues This research paper uses open sources that are available to the public. All financial statements and annual reports of the public companies were available for public use. In addition, credit was given to the original sources and previous researchers for all the quotations and secondary information that was used in this paper.

36 CHAPTER 4 RESULTS This section presents the results for various characteristics of manufacturing firms that were generated from analysis of descriptive statistics and logistic tests. This information is presented in tables for visibility and ease of analysis. 4.1 Description of 2016 to 2018 Food and Beverage Sector Characteristics 2016-2018 Inventory Costing Methods Results in Table 8 show that the majority of food and beverage manufacturers used an Average Cost Method (72.2%), while only 27.8% of such firms were using the FIFO Method. This shows that though FIFO may be seen as a good inventory method which shows high net income and higher retained earnings, a majority of firms in food and beverage sector preferred to use an Average Cost Method. Table 8. 2016-2018 Inventory Costing Method 2018 2017 2016 Frequency Percent Frequency Percent Frequency Percent FIFO 10 27.8 10 27.8 10 27.8 Average Cost 26 72.2 26 72.2 26 72.2 Total 36 100.0 36 100.0 36 100.0 2016-2018 Firm Size Firm size is determined by the total net assets at year end. Findings in Table 9 reveal that from 2016 to 2018, over 60% of food and beverage manufacturers were relatively small firms, with 100 million Baht ($3.225 Million) to 10,000 Million Baht ($322.5 Million) in total assets.

37 Table 9. 2016–2018 Firm Size * 2018 2017 2016 In Million Baht Frequency Percent Frequency Percent Frequency Percent 100 – 10,000 22 61.11 23 63.89 25 69.44 10,001 – Above 14 38.89 15 36.11 11 30.56 Total 36 100.00 36 100.00 36 100.00 Average 2018 = 37,270 mil Baht| Average 2017 = 17,085 mil Baht| Average 2016 = 17,075 mil Baht * Firm Size equals Total Assets In 2016, the Thai economy encountered many problems: an extensive drought, declining exports, domestic instability, and unpredictable global financial markets driven by political instability (Bank of Thailand, 2016). These economic factors might be reasons why many manufacturing firms did not expand their operations. More recently, delays in forming a new Thai government may also have reduced public and private investment projects according to the World Bank (Sangwongwanich, 2019). Average firm size (total assets) from 2016 to 2017 remained stable (17,075 million Baht and 17,085 million Baht respectively), while in 2018 average firm size more than doubled to 37,270 million Baht when compared to previous years. Many food and beverage manufacturers may have increased firm size in order to boost production due to increased spending on consumption in the country, since purchasing power and international demand continued to expand in 2018. Imports of raw materials also increased in 2018 due to high demand in the canned tuna industry, the animal feed industry, the dairy industry, and other food industries that were expanded rapidly in 2018 (Ministry of Industry, 2018).

38 2016-2018 Firm Financial Leverage Firm financial leverage is determined by total liabilities divided by total equity; this ratio compares the level of borrowing with the level of capital contributed by shareholders (Engle, 2010). The findings in Table 10 showed that in 2016 and 2017, 63.9% of food and beverage firms had debt-to-equity ratio of less than or equal to 1.00, with an average figure of 0.91 for those years. Table 10. 2016-2018 Firm Financial Leverage * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent ≤ 1.00 21 58.33 23 63.89 23 63.89 > 1.01 15 41.67 13 36.11 13 36.11 Total 36 100.00 36 100.00 36 100.00 Average Leverage 2018 = 1.00 | Average Leverage 2017 = 0.91 Average Leverage 2016 =0.91 * Firm Financial Leverage equals Debt Divided by Equity Since a majority of food and beverage manufacturers during were small, this might be the reason why they found it difficult to borrow. Moreover, smaller firms might have been more recently established than larger firms, and so may not have much access to loans, or a higher potential for their loan requests to be rejected (Banternghansa, Paweenawat, & Samphantharak, 2019). However, 36% or more of firms have debt-to-equity ratios that are higher than 1.0, which means that many firms had borrowed extensively. As a result, creditors have more power over these firms, and it is risky for them to carry heavy debt loads because they may not be able to repay them when they are due. In 2018, average firm leverage was equal to 1.00. Increased production, sales, imports and exports due to increases in domestic and international demands might be the reason why many firms expanded their businesses in 2018, and this may have

39 caused average firm leverage to increase. Banternghansa, Paweenawat, and Samphantharak (2019) said, “large firms tend to finance their assets by debt more than small firms.” 2016-2018 Inventory Size Inventory size is determined by ending inventory divided by total assets. This ratio indicates how effective a firm is in controlling inventory as a percentage of the total assets (Rachlin, 1997). Table 11. 2016-2018 Inventory Size * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent Less – 0.10 14 38.89 16 44.44 16 44.44 0.11 – 0.20 14 38.89 11 30.55 11 30.55 0.21 – Above 8 22.22 9 25.01 9 25.01 Total 36 100.00 36 100.00 36 100.00 Average Inv. Size 2018 = 0.16 |Average Inv. Size 2017 = 0.15 |Average Inv. Size 2016 = 0.15 * Inventory Size equals Ending Inventory Divided by Total Assets Findings in Table 11 show that in 2016 and 2017, 44% of food and beverage manufacturers had inventories of less than or equal to 10% of their total assets; the lower ratio, the better a firm’s financial condition (Narsale, 2016). This figure indicates how effectively a firm manages its inventory. The average inventory size was relatively stable from 15–16% for the three-year period that was studied. This shows that food and beverage manufacturers did not store much inventory. They are likely trying to manage their inventory effectively in order to reduce the risk of unsold inventory due to out-of-date or damaged products during the previous years of declining sales. In 2018, average inventory size was 16% of total assets. This modest increase in size might be because production and sales increased to meet growing domestic

40 and international demand. So this may be the reason why food and beverage manufacturers increased the size of their inventories, to store adequate merchandise to meet higher demand. To deal with economic uncertainty, just-in-time (JIT) inventory could be another alternative choice for the company in order to produce inventory only when the management system indicates that it is needed (Rachlin, 1997). This also helps to decrease carrying unnecessary inventory. 2016-2018 Profitability Profitability (Return on Assets, or ROA) is determined by net income divided by average total assets, and indicates how effectively a company generates a profit through use of its total assets. Table 12. 2016-2018 Profitability (Return on Assets, ROA) * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent ≤ 0.00 7 19.45 8 22.22 7 19.44 0.01– 0.10 21 58.33 18 50.00 15 41.67 0.11 – Above 8 22.22 10 27.78 14 38.89 Total 36 100.00 36 100.00 36 100.00 Average Profitability 2018 =0.05 |Average Profitability 2017= 0.07 Average Profitability 2016 = 0.08 * Profitability (ROA) equals Net Income divided by Average Total Assets Findings in Table 12 showed that from 2016 to 2018, over 20% of food and beverage manufacturers were using their total assets to generate net income (ROA) of 10% or higher. However, during this same period of time, over 19% of such firms recorded operating losses. From 2016 to 2018, average profitability gradually declined from 8% to 7% and 5%, respectively as many manufacturers’ levels of profitability declined each year. These results were opposite to increasing production and sales from 2017 to 2018 due to high domestic and international demand for

41 agricultural products from major trading partners such as ASEAN, China, Japan, and Europe. However, the profitability of many food and beverage manufacturers declined. There might be many reasons regarding the decline, but one key reason was that in 2018, the prices of raw sugar and refined sugar were declined 22.5% and 22.7% respectively, compared to 2017 caused Thai sugar exports declined in revenue due to the sugar export subsidy from India. In addition, the domestic market competition also increased and big millers dominated the market since they could better adapt to the demands of food and beverage manufacturers which were the largest domestic customers (Local sugar industry outlook neutral, 2019). Moreover, beverage manufacturers’ profitability declined due to the new excise tax on beverage with high sugar content, alcohol beverages, cigarettes and imported wine from September 16, 2017 (Jitpleecheep & Arunmas, 2019). 2016-2018 Inventory Turnover Inventory Turnover is determined by the cost of goods sold divided by average inventory. It shows the number of times in a year a company can turn its inventory into sales. High levels of inventory tie up cash and reduce the firm’s profitability. Table 13. 2016-2018 Inventory Turnover * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent 1.00 – 5.00 16 44.44 11 30.55 11 30.56 5.01 – 10.00 13 36.11 17 47.22 18 50.00 10.01 – Above 7 19.45 8 22.23 7 19.44 Total 36 100.00 36 100.00 36 100.00 Avg. Inv. Turnover 2018 = 7.07| Avg. Inv. Turnover 2017 = 7.48| Avg. Inv. Turnover 2016 =7.55 * Inventory Turnover equals Cost of Goods Sold divided by Average Inventory