Tóm tắt a study on financial performance of it companies in japan using data envelopment analysis (dea) model

Tóm tắt a study on financial performance of it companies in japan using data envelopment analysis (dea) model

MASTER IN FINANCIAL MANAGEMENT

A study on financial performance of IT companies in Japan using Data Envelopment

Analysis (DEA) Model

Graduate student: Tran Van Minh Supervisor: Dr Mai Anh

SUMMARY VERSION OF THE THESIS

HANOI, 2021

ABSTRACT

Thesis Title: A study on financial performance of IT companies in Japan using

Data Envelopment Analysis (DEA) Model

Pages: 22

University: Vietnam National University, Hanoi

Graduate School: International School

Date: October 2020 Degree: Master

Graduate Student: Tran Van Minh Supervisor: Dr Mai Anh

Keywords: Data Envelopment Analysis, DEA models, CCR, BCC, Efficiency

analysis, OTE, PTE, SE, IT industry, Japanese

Japan IT industry has achieved exponential growth during the last one decade mainly due to government policy support, availability of trained manpower and high demand of IT products and services in the international and domestic markets However, during this period, the industry also witnessed several ups and downs, including the recent slowdown Rise and fall in the economic activities in domestic and foreign economies may pose greater risk and challenges to the industry In this context, constant monitoring and improving performance of individual companies and setting benchmarking for relatively inefficient firms become crucial for the growth and sustenance of the industry For this, measurement of the relative performance of individual IT firms and setting best practice benchmark for the relatively under-performed company is quite relevant Although, there is no dearth

of studies on the Japanese IT industry, but the studies related to these aspects are, of course, scant

TABLE OF CONTENTS

ABSTRACT i

CHAPTER 1 INTRODUCTION 1

1.1 Research Background 1

1.2 Research Objectives 2

1.3 Hypotheses 2

1.4 Research Scope 2

CHAPTER 2 LITERATURE REVIEW 5

2.1 Performance Measurement 5

2.2 Theoretical Studies on DEA 5

2.3 Performance and Efficiency Studies on IT sector 6

2.4 Summing Up 7

CHAPTER 3 RESEARCH METHODOLOGY 7

3.1 Data Collection 7

3.2 Data Envelopment Analysis Concepts 7

3.3 Growth of DEA 8

3.4 Advantages of DEA 9

3.5 Limitations of DEA 9

3.6 DEA Models 10

CHAPTER 4 RESULTS AND DISCUSSIONS 12

4.1 IT Industry in Japan 12

4.2 Results and Discussions 12

4.3 Suggestion for Efficiency improvement of IT companies in Vietnam 19

CHAPTER 5 CONCLUSIONS 20

5.1 Conclusions 20

5.2 Limitations 21

5.3 Future Research 21

REFERENCES 22

CHAPTER 1 INTRODUCTION 1.1 Research Background

Since the beginning of 1990s, Japan has been making significant development in the areas of information and communication technologies (ICTs) that provide immense possibilities

to improve productivity and efficiency in all sectors of the domestic economy and also opportunity to earn foreign exchange through exports of software and other IT products and services Among all the services under the domain of service sector, software and IT services is that sector in which Japan has achieved remarkable brand equity in the global market over the years The Japanese companies have comparative advantage in the production and exports of IT products and services mainly due to availability of a large talent pool However, in recent years, several other developing countries such as China, Israel, Malaysia, South Africa, etc have also emerged as strong competitors of Japan in the production and export of IT products and services Furthermore, with the existing globalization of Japan economy and availability of competent workforce, a number of foreign companies have also set up their units in Japan to produce IT products and services for export as well as domestic market, thereby providing greater exposure

to the Japanese companies In order to maintain a global leading position in the current liberalized regime, Japanese IT industry has to maintain its competitive edge by persistently improving its performance

One of a company's key performance indexes is Financial performance Financial performance is the main performance category used in assessment of business performance It

is the measure of a business organization’s capacity of using its resources to create profit It is the achievement of the company's financial performance for a certain period covering the collection and allocation of finance measured by capital adequacy, liquidity, solvency, efficiency, leverage and profitability Financial performance shows the company's ability to manage and control its own resources Any change of finance can be the basis of information for corporate managers to make decisions

Performance evaluation plays a strategic role in IT companies, in order to address the best use of resources and rationing of demand The evaluation of technical efficiencies of existing companies is necessary to improve the companies' financial performance, so as to employ human resources effectively and make the finance more efficient and sustainable

Performance of IT industry, among others, depends on the overall growth of domestic economy and the economies of those countries where its products and services are exported Therefore, rise and fall in the economic activities in domestic and foreign economies may pose greater risk and challenges to this industry In this context, constant monitoring and improving performance of individual companies and setting benchmarking for relatively inefficient ones become crucial for growth and sustenance of the industry Keeping this in view, the present study examines the relative performance of individual IT companies and set the best-practice benchmark for the relatively under-performed companies For this purpose, technical and scale efficiencies have been measured through a non-parametric method, known as Data Envelopment Analysis (DEA)

1.2 Research Objectives

The study carries following objectives:

- Measure the technical efficiencies of the IT companies in Japan to evaluate financial performance of the companies

- Suggest improvement of the efficiency in the IT industry, and particularly some lessons for the IT companies in Vietnam, like Hybrid Technologies

1.3 Hypotheses

The following hypotheses are tested:

- H1: Company size is positively associated with the technical efficiencies of the IT companies

- H2: A decrease in the employee cost has positive impact on the efficiency of the IT companies

- H3: An increase in the sales turnover to NFA ratio reduces the inefficiency level in the Industry

1.4 Research Scope

Efficiency, effectiveness, productivity, profitability, quality etc., are the different kinds of performance measures applied by decision-making units Each measure indicates to the level of performance of different activities

The study analyses the data collected from the Japanese Software companies The focus is on assessment of technical efficiency, proper utilization of the resources and benchmarking of the individual companies through applying DEA Models

Apart from these, the study also examines technical efficiency change, technological change, and productivity improvement in the software companies and identifies the various factors that are accountable for the inefficiency in the IT companies As the term “performance”

is interpreted in different manner and used in different connotations; this study measures the performance of individual IT companies only in terms of technical efficiency Other aspects related to performance of the companies are beyond the scope of the present study

1.5 Research Methodology

This study is based on the data collected from various published sources A list of all the IT companies available in the database is prepared along with their annual sales turnover Initially data from 85 IT companies were collected However, all the companies could not be considered for the study because of two reasons First most of the companies did not report data

on all the required input and output variables Therefore, companies having missing data were dropped from the analysis Second, as the IT industry is a very dynamic industry, new and new companies were added during the study period Therefore, recently incorporated companies did not have the past data After taking care of these issues, finally 85 IT companies were selected for the study for the financial year 2018-19

However, since DEA measures the relative efficiency of individual companies and estimated efficiency scores are sensitive to the outlier companies, we identified the outlier companies through applying the super-efficiency DEA model After excluding the outlier companies from the DEA analysis, our dataset reduced to 75 companies Thus, analysis is based

on the input-output data collected from the 75 companies for the year 2018-19 These companies are classified as small, medium and large according to their annual sales turnover Companies having annual sales turnover less than less than JP 100 billion yen are termed as small, between

JP 100 billion – 1,000 billion yen as medium and above JP 1,000 billion yen as large

The study applies an approach for performance assessment of individual companies Relative efficiency of individual companies is estimated using DEA models Three types of efficiencies are estimated, namely overall technical efficiency, pure technical efficiency and scale efficiency These efficiencies are estimated taking key output and inputs variables

1.6 Research Structure

This research consists of five chapters including an introduction, literature review,

research methods and methodology, data analysis and research findings, conclusions and

discussion They can be described as below:

1 Chapter 1 – Introduction:

The introduction is designed to provide the general structure of the dissertation such

as background, aim of the research, the significance of this study, brief limitations and how the

data will be analyzed

2 Chapter 2 – Literature Review:

The chapter presents relevant background information to form an academic base and

summarizes prominent theories and previous researches on measuring the performance and

efficiency of IT companies

3 Chapter 3 – Research Methodology:

This chapter presents Data collection and the DEA methodology, its origin,

advantages and limitations

4 Chapter 4 – Results and Discussions:

The chapter interprets data and provides detailed efficiency analysis, benchmarks of

efficient companies based on the cross-sectional data collected from 75 IT companies for the

financial year 2018-19

5 Chapter 5 – Conclusions:

Based on the results attained from the previous chapter, appropriate recommendations

for listed company, the chapter presents conclusion and suggests improvement for the industry

CHAPTER 2 LITERATURE REVIEW 2.1 Performance Measurement

Performance measurement is critical component in the general management process Reliable measurement system constitutes a sound basis for continuous monitoring and control of organizational performance Efficiency, effectiveness, productivity, profitability, quality etc., are the different kinds of performance measures applied by decision-making units (Ray, 2004) Each measure indicates to the level of performance of different activities

Moreover, it is usually extremely difficult to assign proper relative weights to inputs and outputs With fixed weights, it is very difficult to formulate an explicit functional relationship between inputs and outputs Another problem related to Least Square regression methods is that they are based on measurement of central tendency and failed to explain the behavior of individual Decision-Making Units (DMUs)

Therefore, this study applies DEA technique to measure the efficiencies and set benchmarking for monitoring the performance of inefficient IT companies

2.2 Theoretical Studies on DEA

Since the publication of seminal papers by Charnes, Cooper and Rhodes (1978) and Bankar, Charnes and Cooper (1984), many theoretical and applied studies have been published on DEA Although DEA was developed to measure the efficiency of public sector non-profit decision-making units (DMUs) such as educational institutions, healthcare sector, public utilities, government departments, etc.; it is now widely being used to evaluate the efficiency of both profit and non-profit organizations in public and private sectors in all sectors of the economy (service, industry and agriculture)

The CCR model is based on the constant returns to scale (CRS) technology assumption It does not take into consideration the effect of scale-size on the efficiency of a DMU It implies that when a DMU is operating at CRS, output will increase at the same rate

as inputs are increased in the production process This happens when a DMU is operating at the optimum scale However, a DMU may be too small or too big relative to the optimum size and therefore may be in a disadvantageous position vis-à-vis to those that are operating

at the optimum scale

Keeping this problem in view, Banker, Charnes and Cooper (1984) extended the CCR model by adding the convexity assumption and measured the pure technical efficiency (PTE)

by neutralizing the effect of scale-size on the efficiency The technical efficiency measured

by this model is actually the managerial efficiency, which is pure conversion of inputs into outputs, irrespective of the size of the DMU The efficiency measured through the BCC model helps in decomposing the overall technical efficiency (OTE) into pure technical efficiency (PTE) and scale efficiency (SE) by dividing the CCR efficiency from the BCC efficiency Thus, PTE will always be ≥ the OTE These two basic DEA models have been further modified and extended by the researchers

2.3 Performance and Efficiency Studies on IT sector

There is no dearth of studies on the software and IT industry; however, studies on efficiency and productivity measurement in the IT industry are, of course, scant In this section we review the main studies conducted on efficiency and productivity in the IT industry

Shao and Lin (2002) examine the effects of information technology on technical efficiency in companies, production process through a two-stage analysis, using data collected from Fortune 500 companies for the period 1988 to 1992 In the first stage, technical efficiency scores are estimated by output-oriented BCC-DEA model, using capital and labour as inputs and value added as output In the second stage, Tobit regression model

is applied to examine the determinants of technical efficiency Further, the study confirms that IT has a significant positive impact on technical efficiency and resultantly gives rise to the productivity growth in the companies

Mathur (2007) applies input-oriented DEA model to evaluate the technical efficiency

of 92 Indian Software companies for the year 2005-06 The study also applies MPI to estimate TFP change for the common set of software companies for the period 1996 to 2006 The estimated TFP growth is further decomposed into efficiency change (catching up) and technical change (frontier shift) to identify the sources of the TFP growth in the IT companies He also makes a comparative study of the performance of the Indian ICT sector vis-à-vis other frontrunner countries in the IT industry

2.4 Summing Up

Japanese software and IT sector is very dynamic and growing rapidly during the last decade, but at the same time it is facing number of challenges and opportunities owing to the emergence of some other competitor countries and discriminatory policies being adopted by the USA and other developed countries In this context, it is essential to study the recent trends in the efficiency and productivity of the IT Industry so that corrective policy measures may be adopted for its growth and sustenance The present study is an attempt toward this direction The study not only reviews the income and employment trends in the industry but also examines the technical, managerial and scale efficiencies and total factor productivity

of individual IT companies It also identifies the key determinants of these efficiencies

CHAPTER 3 RESEARCH METHODOLOGY 3.1 Data Collection

Main data for the study comes from 75 IT companies for the cross-sectional analysis

of financial year 2018-19 The main source of the data is database which consists of the balance sheet-based financial data of large number of companies under IT industry, data related to income, export, employment, growth and structure of the IT industry, etc are collected from published sources Technical efficiencies of individual companies are estimated using CCR and BCC DEA models The study uses an approach for measuring the relative performance of individual companies Efficiencies of individual companies are estimated using DEA models Three types of efficiencies are estimated, namely, overall technical efficiency (OTE), pure technical efficiency (PTE) and scale efficiency (SE)

3.2 Data Envelopment Analysis Concepts

Data Envelopment Analysis (DEA) initially developed by Charnes, Cooper and Rhodes (1978) and further extended by Bankar, Charnes and Cooper (1984) is a linear programming-based technique for evaluating the relative efficiency of homogenous set of companies It considers each individual observation and calculates a discrete piecewise frontier determined by the set of efficient companies It compares the companies that use multiple inputs to produce multiple outputs The technique is most suitable for measuring the technical efficiencies of those decision-making units (DMUs) which are homogenous

and are in the same line of business For instance, if performance of the IT sector is assessed, all the companies should be in the same business that is software and IT services, not those companies which are in the business of production of hardware However, companies in the same business may be diverse in their size, location, age and other attributes The idea of DEA is based on the concept of Pareto Optimality, which states that, within the given limitations of resources and technology, there is no way of producing more of desired commodity without reducing output of some other desired commodity

3.3 Growth of DEA

The DEA approach gets its origin from the seminal paper of Farrell (1957), which proposed to measure productive efficiency Farrell defined the economic efficiency that could account for multiple inputs Further, he decomposed the productive efficiency into technical and allocative efficiency In this approach, the technical efficiency is an ability of

a company to maximize output from a set of given inputs and allocative efficiency is an ability of a company to use these inputs in optimal proportions, given their respective prices The BCC DEA model is based on the VRS technology assumption and it measures the pure technical efficiency, i.e., conversion of inputs into output Bankar, Charness and Cooper added a convexity constraint in the CCR model The CCR model assumes that constant return to scale exists at the efficient frontiers whereas BCC assumes variable retunes to scale frontiers CCR model measures the overall technical efficiency (OTE), while BCC model measures the pure technical efficiency (PTE), net of scale-effect PTE is also known as managerial efficiency If a company scores value of both CCR-efficiency and BCC-efficiency equal to one, it is said to operate at the most productive scale Size (MPSS) Actually, BCC model helps to decompose the OTE into PTE and the scale efficiency (SE)

SE of a company is measured by dividing the OTE from PTE For example, if PTE of a company is equal to 1 and its OTE is less than 1, it implies that the company is able to converts efficiently its inputs into output, however, it is OTE-inefficient because its size is either too big or too small related to the optimum size Thus, inefficiency in any company may occur due to its inefficient operations or due to the disadvantageous conditions under which it operates

3.4 Advantages of DEA

A DEA technique has several merits over the conventional regression-based production function approach A few of them are as follows:

- DEA can handle multiple inputs and outputs with different units (Pastor JT, et al., 1999)

- It does not require any assumption of a functional form relating inputs to outputs (Dyson, R.G and Thanassoulis, E., 1988)

- It can be applied to profit as well as non-profit making entities (Allen, R., 1997)

- It sets targets for inefficient DMUs to make them efficient (Andersen, P and Petersen, N.C., 1993)

- It also identifies slacks in inputs and outputs (Emrouznejad, A., 2009)

- It estimates a single efficiency score, identifies input excesses and output and provides benchmarks to monitor the performance of inefficient companies (Bogetoft, P and Otto, L., 2012)

3.5 Limitations of DEA

DEA also has some limitations, which are as follows:

- It is a non-parametric technique in which statistical hypothesis testing is difficult (M Fukushigea and I Miyarab, 2013)

- It is an extreme point technique because of which the measurement error cannot be measured (Cooper at al., 2000)

- Its measured efficiency is relative one and therefore comparison of the performance of a company can be made with only those companies which are in the reference set (Li, X.B and Reeves, G.R., 1999)

- It is sensitive to the choice of the input-output variables and number of companies Its results are also influenced by the size of sample If sample size is small, discretionary power of model reduces Therefore, thumb rule is that the number of companies in the dataset should be more than three times of sum of input and output variables (Khalili, M

et al., 2010)

- It is a computationally intensive method; however, availability of DEA software has made the computation of efficiency scores easy (Podinovski, 2001)