Is the national economy affecting the stock market - Free Essay Example

Abstract: Whether national economy is affecting the stock market or other way round? A lot of studies have done on the past what are relationship of these variables. In my work I have used cointegration and Granger Causality method to find out the relationship between the stock index price and Economic growth indicator GDP. Introduction The debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia: causality test). The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth. If we focus on some related literature published on this topic one question arises: Is economic development is affected by stock market development? Even though there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE) It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appr opriate method which would meaningfully measure whether stock price is really effecting the economic growth or not? In my work I have tried to find out the co integrating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP. The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method. To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts P art two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and conclusion of the whole study. In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries. Literature Review: Stock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views. If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of gro wth argues that financial sectors provides of reallocating capital to minimize the potential losses. Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Another study from Levine and Zervos (1996) using the data of 24 countries found that a strong positive correlation between stock market development and economic growth. Their expanded study on 49 countries from 1976-1993, they used Stock Market liquidity, Economic growth rate, Capital Accumulating rate and output Growth Rate. They found that all the variables are positively correlated with each other. Demiurgic and Maksimovic (1996) have found positive causal effects of financial development on economic gro wth in line with the à ¢Ã¢â€š ¬Ã‹Å"supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks. Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using the cointegration method and Granger caus ality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper). An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani). Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. C ausal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market. A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock pricesà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper) An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998) The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. The study was done on 47 countries data using cross sectional analysis. In theory the conventional literature on growth was not sufficient enough to look for the connection between financial development and economic growth and the reason is they were focused on the steady state level of capital stock per workerof productivity. And they were not really concentrated on the r ate of growth. Actually the main concern was legitimated to exogenous technical progress. (Levine and Zervos 1998) Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935; basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and econo mic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005) Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets: A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As, they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strong ly suggests that greater stock markets create more liquidity or at least continue economic growth. (Levine. R A spur to economic Growth) A lot of research has established that future economic growth is influenced by countrys financial growth, stock market index returns are another factor of economic growth. The researcher focused to extend their study; they tie together these two strings and started analyzing the relationship between banking industry, stock returns and future economic growth. Research was done on 18 developed and 18 emerging markets and the results are positive and noteworthy relationship between future GDP and stock returns. Few important features can also be predicted such as bank-accounting-disclosure standards, banking crises, insider trading law enforcement and government ownership of banks. (Bank stock returns and economic growth q Rebel A. Cole a, Fariborz Moshirian b,*, Qiongbing Wu c a Department of Finance, DePaul University, Chicago, IL 60604, USA b School of Banking and Finance, The University of New South Wales, Sydney, NSW 2052, Australia c Newcastle raduate School of Business, The University of Newcastle, Newcastle, NSW 2300, Australia Received 29 September 2006; accepted 26 July 2007Available online 21 September 2007) Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa. ( Title:  The nexus between stock market and economic activity: an empirical analysis for India Author(s): Purna Chandra Padhan Journal: International Journal of Social Economics Year: 2007 Volume: 34 Issue: 10  Page: 741 à ¢Ã¢â€š ¬Ã¢â‚¬Å" 753 DOI: 10.1108/03068290710816874 Publisher: Emerald Group Publishing Limited) Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation: 23 Jul 2003 Tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results. A study by Randall Filler(2000) using 70 countries data over the period 1985-1997 proves that there is a very little relationship between economic growth and stock market especially in developing countries and currency appreciation has occurred. From the result of the study we can see that an important role may be played by the stock market in an economy, and these are not essential for economic growth. However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development. Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalizat ion. (Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences; MAM November 2004 Economic Development Financial Market Working Paper No. 2 ) Data: The empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and USA share price is taken from the DOW Jones industrial share price index. The nature of the Data is series used for the time series regression. List of Variables: UGDP UK GDP USP UK Share price LUGDP Log of UK GDP LUSP Log of UK Share price USGDP USA GDP USSP USA (DOW Jones) Share price LUSGDP Log of USA GDP LUSSP Log of USA Share price MGDP Malaysia GDP MSP Malaysia Share price LMGDP Log of Malaysia GDP LMSP Log of Malaysia Share price JGDP Japan GDP JSP Japan Share Price LJGDP Log of Japan GDP LJSP Log of Japan Share price Methodology: Cointegration long term common stochastic trend between non stationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called co integrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses. Meanwhile, cointegration does not imply high correlation; two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows wh ether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009) Series Stationary Test: In this study I have used Augmented Dickey Fuller Test (ADF) to test the stationary of variables. The unit root test is usually used to confirm stationary of a series. The ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). In this study I have used Augmented Dickey Fuller Test (ADF) to check whether the series is stationary or not. ADF test is based on the estimate of the following regression: is in this case variable of interest = , is the differencing operator, t is the time trend and is the random component of zero mean and constant variance. The parameters to be estimated are { } Null and alternative hypothesis of unit root test are: , () () Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The coà ¢Ã¢â€š ¬Ã¢â‚¬Å"integration test can only be performed if both the sequences are all integrated of order I (1). Cointegration Test: Engle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series. Cointegration Long term common random trend between non stationary time series. The linear combination of both the non stationary series can be stationary if both the variables are integrated in same order. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run. If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run. The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met. According to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method). So from the above method we can find the equation By regressing with And after that and is denoted as the estimated regression coefficient vectors. After that I saved the residual from the above equation. Then, = à ¢Ã¢â€š ¬Ã¢â‚¬Å" is representing the estimated residual vector. If the residual is integrated with order z ero that means the series for the residual is stationary, and and are then co integrated and vice versa. I have checked it by performing Augmented Dickey fuller test on the residual series on level value with intercept only of each country. An in this situation (1, -) is called co-integrating vector if the series is stationary. Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and. Granger causality test: Granger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables. In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this is strong relationship. Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely. Granger causality approach (1969), lets think the variable y is Economic Growth (GDP) and x is Stock price index, if it is possible to predict the past values of y and x than from the lagged values of y alone. X is said to be granger caused by and y is helping in predicting it. in case of a simple bivariate model, causality can be tested between stock market growth and economic growth. Granger causality run on the basis of the following bivariate regressions of the form: (1) (2) Where GDP denotes economic growth and SP denotes the stock price index and they explain the changes in growth. Variables are expressed in logarithm form. The distribution of and are uncorrelated by assumption. From the equation one it can be said that current GDP is related to lagged values of itself and as well as that of SP. And equation 2 postulates same kind of behaviour for SP. Both the equations can be obtained by ordinary least squares (OLS). The f statistics are the Wald statistics for the joint hypothesis: and F test is carried out for the null hypothesis of no Granger causality. The formula of f statistic is Lagged term is defined here b y m; number of parameter is defined as k. Test result for Unit Root: Augmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Variables level/1st Difference  Augmented Dickey Fuller Statistic(ADF) test Japan  t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -2.653258 -3.522887 Â  -2.901779 -2.588280   -2.693600   -4.088713   -3.472558 -3.163450 1st Difference -9.053185 -3.524233   -2.902358 -2.588587 -9.003482   -4.090602   -3.473447 -3.163967 Share Price Level   -2.116137 -3.522887 Â  -2.901779 -2.588280   -2.203273   -4.088713   -3.472558 -3.163450 1st Difference   -6.899295 -3.524233   -2.902358 -2.588587   -6.844396   -4.090602   -3.473447 -3.163967 Table 01: Unit root test for stationary Japan If we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value in all level and they are integrated in order one. Variables level/1st Difference  Augmented Dickey Fuller Statistic(ADF) test Malaysia  t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -1.195020 -3.522887 Â  -2.901779 -2.588280 -1.933335   -4.088713   -3.472558 -3.163450 1st Difference -5.951843 -3.524233   -2.902358 -2.588587 -5.923595   -4.090602   -3.473447 -3.163967 Share Price Level   -1.900406 -3.522887 Â  -2.901779 -2.588280   -1.891183   -4.088713   -3.472558 -3.163450 1st Difference   -7.842122 -3.524233   -2.902358 -2.588587   -7.779757   -4.090602   -3.473447 -3.163967 The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are highe r than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order one. Variables level/1st Difference  Augmented Dickey Fuller Statistic(ADF) test UK  t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -0.690866 -3.522887 Â  -2.901779 -2.588280 -2.377333   -4.088713   -3.472558 -3.163450 1st Difference -7.474388 -3.524233   -2.902358 -2.588587 -7.439027   -4.090602   -3.473447 -3.163967 Share Price Level -1.711599 -3.522887 Â  -2.901779 -2.588280 -1.261546   -4.088713   -3.472558 -3.163450 1st Difference -7.254574 -3.524233   -2.902358 -2.588587 -7.391821   -4.090602   -3.473447 -3.163967 The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is à ¢Ã¢â€š ¬Ã¢â‚¬Å"0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 with intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary. V ariables level/1st Difference  Augmented Dickey Fuller Statistic(ADF) test USA  t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -3.244801 -3.522887 Â  -2.901779 -2.588280   2.866507   -4.088713   -3.472558 -3.163450 1st Difference -5.010864 -3.524233   -2.902358 -2.588587 -5.750546   -4.090602   -3.473447 -3.163967 Share Price Level -2.074732 -3.522887 Â  -2.901779 -2.588280 -0.359637   -4.088713   -3.472558 -3.163450 1st Difference -8.181234 -3.524233   -2.902358 -2.588587 -8.735399   -4.090602   -3.473447 -3.163967 Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. The level t statistic value for LUSGDP is -3.244801 and -2.866507 r espectively with intercept and with intercept and trend. The result for USA is same as the other country which is higher than the critical values. Proves that the series is not integrated with order one and is non stationary. Whereas the first difference t statistic value for LUSGDP is less than the critical value. The t statistic value LUSGDP with intercept is -5.010864 and -5.750546 with intercept and trend. In this case both the values are lesser than the critical value implies that the series is integrated with order one in first difference. While taking the values in level and 1st difference in case of LUSSP the t statistic value in level are -2.074732 and -0.359637 in level respectively with intercept and wit intercept and trend. Still the series is showing the same nature in level as they are higher than the critical values and the series is not integrated in order 0. The first difference value for LUSSP series with trend and with trend and intercept is -8.181234 and -8.73539 9 respectively which is less than the critical value implies the series is integrated with order one. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that the series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one. Co integration Test: Two step procedure of Engle-Granger cointegration is to check for the long run relationship between the variables. The first stage was run by using traditional OLS method. To do this we need to check whether the series is stationary or not. Which we have checked before by doing ADF test on each series. where the result shows that the series is integrated with order (1). Engle-Granger representation theorem that might have an error correction mechanism is the series is integrated. JAPAN: In this case the long run OLS model is as follows in case of Japan: LJGDP = 7.97824432568 + 0.163668097988*LJSP Dependent Variable: LJGDP Method: Least Squares Date: 12/17/09 Time: 20:30 Sample: 1991Q1 2009Q2 Included observations: 74  Coefficient Std. Error t-Statistic Prob. C 7.978244 0.120791 66.04995 0 LJSP 0.163668 0.048847 3.350602 0.0013 R-squared 0.134891 Mean dependent var  8.381114 Adjusted R-squared 0.122876 S.D. dependent var  0.10605 S.E. of regression 0.099321 Akaike info criterion  -1.75426 Sum squared resid 0.710261 Schwarz criterion  -1.69199 Log likelihood 66.90753 Hannan-Quinn criter.  -1.72942 F-statistic 11.22653 Durbin-Watson stat  0.310636 Prob(F-statistic) 0.001287    From the above model I have saved the residual series and performed ADF test with trend and without trend and the values are as follows in the table: Unit Root test for residual Series saved residual RJP T statistic Test critical values: 1% level 5% level 10% level   With intercept   -2.831807 -3.522887   -2.901779   -2.588280   With intercept and trend   -3.040627   -4.088713   -3.472558   -3.163450 From the above table we can see that the result is significant only in 10% level. Which suggest that there might be a long run relationship between the variables. But there is no long run relationship at 1% and 5% significant level as both the values are higher than the critical value. 2nd stage regression result: LJGDP = 7.96681067902 + 0.170453164194*LJSP + 0.819211725701*RJP(-1) Dependent Variable: LJGDP Method: Least Squares Date: 12/31/09 Time: 18:51 Sample (adjusted): 1991Q2 2009Q2 Included observations: 73 after adjustments  Coefficient Std. Error t-Statistic Prob.      C 7.966811 0.064529 123.4601 0 LJSP 0.170453 0.026119 6.525992 0 RJP(-1) 0.819212 0.064206 12.75915 0      R-squared 0.747462 Mean dependent var  8.384005 Adjusted R-squared 0.740246 S.D. dependent var  0.103806 S.E. of regression 0.052906 Akaike info criterion  -3.00038 Sum squared resid 0.195932 Schwarz criterion  -2.90625 Log likelihood 112.5137 Hannan-Quinn criter.  -2.96286 F-statistic 103.5928 Durbin-Watson stat  1.958683 Prob(F-statistic) 0    2nd stage regression suggest that there is short run relationship between stock market and economic growth. As from the table values after running the regression with the help of one intercept and lagged value of the residual save from the first stage regression. Here we can see that the all the coefficient has positive values and r-sruared (0.747462) is less than the Durbin-Watson value (1.958683). So form the results we can see that there exists a short run relationship between stock market and economic growth. Malaysia Following the same stages on Malaysia, by running the regression on OLS to check the long run relationship between stock market and economic growth in Malasia. The equation to check the first stage regression is: LMGDP = 8.2331829641 + 0.340689829517*LMSP The result from the above regression are described in the following table: Dependent Variable: LMGDP Method: Least Squares Date: 12/17/09 Time: 21:00 Sample: 1991Q1 2009Q2 Included obse rvations: 74  Coefficient Std. Error t-Statistic Prob. C 8.233183 0.644484 12.77484 0 LMSP 0.34069 0.116332 2.928597 0.0046 R-squared 0.106441 Mean dependent var  10.11598 Adjusted R-squared 0.094031 S.D. dependent var  0.407894 S.E. of regression 0.388243 Akaike info criterion  0.972285 Sum squared resid 10.85275 Schwarz criterion  1.034557 Log likelihood -33.97453 Hannan-Quinn criter.  0.997126 F-statistic 8.576678 Durbin-Watson stat  0.054361 Prob(F-statistic) 0.004557    Unit Root test for residual Series T statistic Test critical values: 1% level 5% level 10% level   With intercept   -1.301997 -3.522887   -2.901779   -2.588280   With intercept and trend   -3.975164   -4.088713   -3.472558   -3.163450 From the above regression and after saving the residual I performed and ADF test with trend and without trend on the residual series. Here the result suggests that the t statistic value is higher than the critical values of 1%, 5% and 10% level. Which suggest that residual series is non stationary and there is no relationship between the variables in long run. The estimated equation in error correction model is as follows: LMGDP = 8.13761928798 + 0.360964712114*LMSP + 0.965225800038*R(-1) Dependent Variable: LMGDP Method: Least Squares Date: 01/01/10 Time: 23:15 Sample (adjusted): 1991Q2 2009Q2 Included observations: 7 3 after adjustments  Coefficient Std. Error t-Statistic Prob. C 8.137619 0.147701 55.09505 0 LMSP 0.360965 0.02665 13.54478 0 R(-1) 0.965226 0.027335 35.31042 0      R-squared 0.952382 Mean dependent var  10.12619 Adjusted R-squared 0.951022 S.D. dependent var  0.401091 S.E. of regression 0.088766 Akaike info criterion  -1.96541 Sum squared resid 0.551553 Schwarz criterion  -1.87128 Log likelihood 74.7374 Hannan-Quinn criter.  -1.9279 F-statistic 700.0218 Durbin-Watson stat  2.075716 Prob(F-statistic) 0    2nd stage results are suggesting about the short run relationship between the variables. As we can see from the is less than the Durbin-Watson Statistic. So from the result we can say that there exist a co-integrating relationship between stock market and economic growth in short run. UK Considering the case of UK to find out the relationship both in long and short run I used the same procedure to find out the relationship. As all the variables are integrated with order one which suggests the variables are stationary. Now by applying the Engle Granger cointegration method to estimate the co integrating vector in OLS and then examining the residual series. Cointegration for the long run depends on the residual series. Here I defined the residual series a RUK for the variables LUGDP (log of UK GDP) and LUSP(log of UK share price). If we look at the table of the unit root test for the residual series of the Co-integrating regression of LUGDP and LUSP the residual series RUK is -1.355485 with intercept and   -2.426938 with intercept and trend. Where both the result for unit root test by applying Augmented Dickey Fuller test suggests that the residual series has a nonstationary behaviour in both the case with intercept and with intercept and trend. As the critica l value for at 1%, 5% and 10% is -3.522887, -3.522887 and -2.588280 respectively with intercept and -4.088713, -3.472558 and -3.163450with intercept and trend. As the t statistic value is higher than the critical values in both the case, so from the result we can say that the residual series in non stationary and there is no long run relationship between the variable. Dependent Variable: LUGDP Method: Least Squares Date: 12/17/09 Time: 21:10 Sample: 1991Q1 2009Q2 Included observations: 74  Coefficient Std. Error t-Statistic Prob.      C 6.41427 0.52629 12.18771 0 LUSP 0.790239 0.064275 12.29475 0 R-squared 0.677363 Mean dependent var  12.87916 Adjusted R-squared 0.672882 S.D. dependent var  0.332711 S.E. of regression 0.190291 Akaike info criterion  -0.45386 Sum squared resid 2.607181 Schwarz criterion  -0.39159 Log likelihood 18.79298 Hannan-Quinn criter.  -0.42902 F-statistic 151.1608 Durbin-Watson stat  0.149084 Prob(F-statistic) 0    Unit Root test for residual Series residual saved t statistic Test critical values: RUK  1% level 5% level 10% level   With Intercept   -1.355485 -3.522887   -2.901779   -2.588280   With intercept and trend   -2.426938 -4.088713 -3.472558 -3.16345 2nd stage Dependent Variable: LUGDP Method: Least Squares Date: 01/04/10 Time: 17:57 Sample (adjusted): 1991Q2 2009Q2 Included observations: 73 after adjustments       Coefficient Std. Error t-Statistic Prob.      C 6.375942 0.207063 30.79235 0 LUSP 0.795176 0.025265 31.47291 0 RUK(-1) 0.937553 0.046342 20.23103 0 R-squared 0.952647 Mean dependent var  12.88329 Adjusted R-squared 0.951294 S.D. dependent var  0.333094 S.E. of regression 0.073512 Akaike info criterion  -2.3425 Sum squared resid 0.378285 Schwarz criterion  -2.24837 Log likelihood 88.50121 Hannan-Quinn criter.  -2.30499 F-statistic 704.1223 Durbin-Watson stat  2.248029 Prob(F-statistic) 0    USA In case of USA to find out the relationship between stock market and economic growth using Engle Granger cointegration method we find the following results. LUSGDP = 6.422388123 + 0.32041281224*LUSSP Dependent Variable: LUSGDP Method: Least Squares Date: 12/31/09 Time: 02:02 Sample: 1991Q1 2009Q2 Included observations: 74  Coefficient Std. Error t-Statistic Prob.      C 6.422388 0.140166 45.82 0 LUSSP 0.320413 0.015722 20.38041 0 R-squared 0.852266 Mean dependent var  9.274948 Adjusted R-squared 0.850214 S.D. dependent var  0.166293 S.E. of regression 0.064359 Akaike info criterion  -2.62203 Sum squared resid 0.29823 Schwarz criterion  -2.55975 Log likelihood 99.01496 Hannan-Quinn criter.  -2.59719 F-statistic 415.3609 Durbin-Watson stat  0.124101 Prob(F-statistic) 0    Unit Root test for residual Series Residual saved (RUS) T statistic Test critical values: 1% level 5% level 10% level With intercept -0.638033 -3.522887 -2.901779 -2.588280 With intercept and trend -1.430799 -4.088713 -3.472558 -3.163450 After saving the residuals from the 1st stage regression RUS I did the ADF test on it where we can see the t statistic value is literally higher than the 1%, 5% and 10% critical value in both the cases with intercept and with intercept and trend. As we can see the critical values are -3.552287, -2.901779 and -2.588280 with intercept, -1.430799, -3.472558 and -3.163450 in 1%, 5% and 10% level respectively. So the possibility for having long run relationship between GDP and stock price doesnt exist in case of USA. 2nd stage regression: Dependent Variable: LUSGDP Method: Least Squares Date: 01/05/10 Time: 21:36 Sample (adjusted): 1991Q2 2009Q2 Included observations: 73 after adjustments       Coefficient Std. Error t-Statistic Prob. C 6.400276 0.051084 125.29 0 LUSSP 0.323107 0.005722 56.46591 0 RUS(-1) 0.972896 0.043361 22.43708 0 R-squared 0.981148 Mean dependent var  9.278975 Adjusted R-squared 0.980609 S.D. dependent var  0.163769 S.E. of regression 0.022805 Akaike info criterion  -4.683433 Sum squared resid 0.036405 Schwarz criterion  -4.589305 Log likelihood 173.9453 Hannan-Quinn criter.  -4.645922 F-statistic 1821.53 Durbin-Watson stat  2.153933 Prob(F-statistic) 0    Granger Causality test: I performed the Granger Causality test using the first difference on series DLJGDP and DLJSP, DLMSP and DLMGDP, DLUSGDP and DLUSSP and between DLUGDP and DLUSP Pair wise Granger Causality Tests Sample: 1991Q1 2009Q2 Lags: 3    Null Hypothesis: F-Statistic Probability DLJGDP does not Granger Cause DLJSP 1.08475 0.3621 DLJSP does not Granger Cause DLJGDP 1.38425 0.2558 DLMSP does not Granger Cause DLMGDP 15.767 0.00000009 DLMGDP does not Granger Cause DLMSP 1.29015 0.2856 DLUSSP does not Granger Cause DLUSGDP 3.36502 0.024 DLUSGDP does not Granger Cause DLUSSP 0.40935 0.7468 DLUSP does not Granger Cause DLUGDP 4.59524 0.0057 DLUGDP does not Granger Cause DLUSP 0.76991 0.5152 From the Granger Causality result table we can see that to reject the null hypotheses the GDP does not because LJSP, here from result we can see the chances to of occurring error type is 1 and is 36.21%. And the probability is too great that GDP does not causing DLJSP to reject the null hypothesis. Moreover, LJSP is causing GDP is also too great to reject the null hypothesis is before. There exist no causal relationship in both the direction. While considering the result from the causality relationship between DLMSP and DLMGDP I founded the same kind of result. Here it is showing that GDP does not cause DLMSP. And in the same way LMSP does not cause the DLGDP. As the f statistic value is too high to reject the null hypothesis. Therefore, there is no causal relationship between GDP growth and stock price index yield in case of Malaysia. However, the Granger causality result in case of USA shows a slightly bit different result than the other countries. Here probability of USSP does not granger causing USGDP is too big to reject the null hypothesis. On the other hand we can reject the null that USGDP does not granger causing USSP. The results suggest the existence of causal relationship between the variable. In case of UK we could find any causal relationship between the variable as in both the cases the probability that UGDP does not granger cause USP and USP does not granger cause UGDP is too great to reject the null hypothesis. So, from the above result we can say there is no causal relationship between the variables GDP and economic growth indicator except UK. Analysis of the result: Analysis: The purpose of the paper was to assess the relationship between stock market and Economic Growth. The empirical study was done on the basis of Cointegration test and Causality frame work. The tests were done using the variables quarterly data on GDP and quarterly data on share price index of four countries Japan, Malaysia, The UK and The USA for the period 1991 Q1 to 2009 Q2. In my study the findings from the empirical results are: No long run relationship between stock market Growth and Economic Growth in Japan No long run relationship between stock market Growth and Economic Growth in Japan No long run relationship between stock market Growth and Economic Growth in The UK and USA While analyzing my work, I found some significance and some insignificance in my results. UK and USA stock markets are considered as developed stock market. Randall Filler (200) stock market activity and future economic growth is related with each other specially in developing economies and there may have some effect of the stock market in developed economy which may not be essential. MY study shows the same result as my results shows that there is no long run relationship. If I consider the Levine Zervos (1998), Beck and Levine (2004), No long run relationship between stock market Growth and Economic Growth in The USA

Different Models of Change Management - Free Essay Example

Introduction This paper provides a critical discussion of the different models of change management with a focus on the models proposed by Kurt Lewin (1958), John Kotter (1995) and the McKinsey 7S model (1982) developed by Tom Peters and Robert Waterman. Understanding Change Given the wide diversity in the nature and type of change experienced by individuals and organisations, no single definition of change exists. However, there is a general consensus that change is a constant feature of organisational life (Bamford and Daniel, 2005), and that it is constantly increasing in terms of its frequency, magnitude and unpredictability (Burnes, 2009). Jones (2007) defined organisational change as the way in which organisations move from one state to another to increase their effectiveness, and Greenan (2003) stated that it involves a re-distribution of power, information and skills. Similarly, Saif et al (2013) assert that effective change management is essential for organisational development and ultimately survival, and yet studies have shown that around 60% of change initiatives fail (CIPD, 2015) Signià ¯Ã‚ ¬Ã‚ cant work has been done to characterise the nature of change, the forces that drive it and the processes through which it can be achieved, and this has resulted in a number of models and theories that claim to capture change (Saif et al, 2013). All approaches, however, are dependent to some extent on the wider strategic and environmental context in which an organisation operates. According to Pettigrew et al (1992) this context is the why and when of change and takes account of the external context such as the current political, economic and social environment, and also the internal contextual factors such as organisational culture, structure and capabilities. Lewins 3 Step Change Model One of the most widely recognised of these change models was provided by Kurt Lewin (1958) who became the pioneer of planned change with the introduction of his three-step change model in the 1950s. The steps in this model include: unfreezing- where the current equilibrium is destabilised to allow any old behaviours to be discarded and the desired new behaviours to be adopted; moving where individuals are supported to move from less acceptable to more acceptable behaviours through different change initiatives; and re-freezing where the new behaviours become embedded in every-day practice to allow stability at a new equilibrium as shown in Figure 1: Figure 1 Lewins 3-Step Change Model Source: Carpenter, Bauer and Erdogen, 2009 According to Cameron and Green (2009), Lewins model provides a useful tool for those considering organisational change, particularly when used in conjunction with his force field analysis technique which provides a focus for management teams to debate the resisting and driving forces for change. They claim that through using this model, a team can quickly move on to identifying the next steps in the change process. However, Lewins model has attracted major criticism in that it assumes that organisations operate within a stable environment, it is a top-down approach, and fails to give consideration to issues around organisational power and politics (Burnes, 2004). In addition, its linear approach has been found to be too inflexible in certain scenarios such as in times of instability and uncertainty in the external and internal environment (Bamford and Forrester, 2003). In addition, it has been claimed that such a model is only relevant to incremental and isolated change projects which therefore makes it unable to tackle transformational change (Dawson, 1994). Kotters 8 Step Model Lewins model has been adapted and re-created in many different forms (McWhinney, 1992). In particular, the work of John Kotter (1995) can easily be mapped against Lewins model (Higgs and Rowand, 2005), but instead provides a more practical eight-step approach to change management (Todnem By, 2005). Kotter initially developed his change model by observing for-profit businesses, but it is claimed that it has applicability to public and third sector organisations also (Nitta et al, 2009). Kotters model was based upon his observations of the main mistakes made in organisations which were seeking to transform themselves and he proposed eight key steps to success (see Figure 2): Figure 2 Kotters 8 Step Model Source: Adapted from: Department for Children, Schools and Families, 2009 Within Kotters model, the different steps are: Step 1: Increase Urgency: according to Bond (2007) this first step is important in generating the activation energy to start the process of change. External pressures can help to achieve this sense of urgency such as legislative forces or threat of new competition. Kotter (1998) claimed that failure to adequately complete this step is one of the most frequent causes of failure overall. Step 2: Build the Guiding Team: with the sufficient power and influence to lead the change (Appelbaum et al, 2012). Step 3: Get the Right Vision: that clearly articulates what the change is, why it is needed and how it will be achieved. Step 4: Communicate Buy In: by telling all key stakeholders in a range of different ways the what, why and how of the change, so that they understand and support the change initiative. Step 5: Empower Action: by facilitating individuals to support the change. Successful change usually requires sufficient resources to support and empower the process (Fernandez and Rainey, 2006). Step 6: Create Short Term Wins: and giving recognition for the work done. Short-term wins provide visible evidence that the change is worth it and justified. Acknowledging these successes builds morale and momentum whilst also gaining crucial buy-in (Gupta, 2011). Step 7: Dont Let Up: consolidate the gains achieved and create further momentum by developing people as change agents (Appelbaum et al, 2012). Step 8: Make it Stick: and anchor the change within the culture of the organisation. According to Fernandez and Rainey (2006), for change to be enduring, members of the organisation must incorporate the new practices into their daily routine. Kotters model is generally considered to provide a practical and logical approach to managing change, and has been found to have a high level of appeal amongst managers with it still being used extensively today (Cameron and Green, 2009). However, despite this it has been criticised for a number of reasons. One of the key criticisms is that there is a lack of follow through and that it peaks too early (Cameron and Green, 2004). Other critics suggest that this approach is based on an often unfounded assumption that individuals will resist change (Kelman, 2005), and that where resistance does occur, there is insufficient explanation of the reasons why (King and Anderson, 2002). In addition, Sidorko (2008) argues that Kotter makes no concessions to the fact that his model is ordered sequentially and that all steps must be followed. He claims that from his study of organisational change and the use of the model, there is often a need to build multiple guiding coalitions on multiple occ asions which is something that Kotter fails to acknowledge. Both Lewins and Kotters models focus specifically on planned change and it is this factor that is the target of most criticism. It is claimed that their models are inadequate in a range of circumstances, particularly where the given change is just one of a multiplicity of changes happening within the organisation (Carnall, 2007). Similarly, other critics argue that change cannot be viewed as a linear sequence which can be applied to processes that are in reality messy and untidy (Buchanan and Storey, 1997). McKinsey 7S The McKinsey 7S Model was developed in the early 1980s by Tom Peters and Robert Waterman. It is differentiated from other change theories as instead of proposing steps that must be taken in a particular order, the framework looks at the separate elements and how well they work and interact with each other). The 7S in the model describes the seven variables, termed levers which form the framework (Peters and Waterman, 1982), as shown in Figure 3: Figure 3 The McKinsey 7S Model Source: Jurevicius, 2013 In Figure 3, it can be seen that the seven S variables include: Strategy: which is the plan that is formulated to sustain competitive advantage Structure: which is the way the organisation is structured and its reporting mechanisms Systems: are the daily activities employees undertake to get the job done Shared Values: are the organisations core values that are demonstrated in the corporate culture Style: refers to the leadership style adopted Staff: are the employees Skills: the skills and competencies of the individual employees. Shared Values are located in the centre of the model, to highlight that these are central to the development of all the other critical components, and the seven interdependent factors which are categorised as either hard or soft elements. The hard elements are easier to identify and can be directly influenced including strategy, structure and systems. The soft elements are much less tangible and are more influenced by organisational culture. One of the benefits of the model is that is can be used to understand how the different organisational elements are interconnected and so how a change in one area can impact on the others. To be effective, an organisation must have a high degree of internal alignment amongst all of the seven Ss each must be consistent with and reinforce the others (Saif et al, 2013). In addition, according to Rasiel and Friga (2002), the benefits of the McKinsey 7S model include the fact that it provides a diagnostic tool for managers to identify areas that are ineffective and combines the rational and hard elements of organisations alongside the softer, more emotional elements. Criticisms of the McKinsey 7S model, however, claim that it does not offer any guidance on how to proceed once any areas of non-alignment have been identified (Grant, 2008). In addition, Bhatti (2011) argues that the model fails to take account of the importance of resources. Without additional resources such as finance, information, technology, and the time, any change initiative cannot be effectively implemented (Higgins, 2005). Discussion According to Sidorko (2008) all of these change models have a role to play in supporting organisational change, but advises that they must be implemented cautiously and complemented with effective leadership. He claims that without such leadership, the models are merely a strict prescription for change that may not fit the organisations needs and which may result in more harm than good. He claims that instead of applying such change models prescriptively, they should instead be used selectively and adaptively to accommodate the culture and environment of the organisation. This view is supported by Graetz and Smith (2010) who claim that in practice, it may be useful to account for contextual variables and adapt chosen change models accordingly. MacBryde et al (2014) claim that change models such as those examined in this paper, are too abstract for practical application, and are generalised to the extent where they are at risk of missing the actual detail of what is happening. A further criticism of change management models in general, is that there is a lack of evaluation built into the process and yet critics claim that such evaluation is key to successful and sustainable change (Moran and Brightman, 2000). Conclusion In conclusion, this paper has provided a critical discussion of some of the most commonly cited change management models. It is evident that all three have been considered to have some practical benefit in terms of aiding the process of change in organisations and our understanding of it, and across all three models, it is clear that there is a high level of commonality amongst them. However they have all been subjected to criticism due to their abstract nature. It has been argued that they oversimplify the process of change, lack evaluation, and do not take sufficient account of the often turbulent business context and environment in which organisational change occurs. In addition, it is clear that no matter how robust the change model, it will be ineffectual unless complemented by effective leadership. It has been proposed that given this, change models such as those provided by Lewin, Kotter and the McKinsey 7S, should be used as a guide rather than a panacea, and applied flexibly to best match the culture and environment of the organisation and the nature of the change itself. References Appelbaum, S.H., Habashy, S., Malo, J.L. and Shafiq, H. (2012) Back to the future: revisiting Kotters 1996 change model, Journal of Management Development, Vol. 31 (8), pp. 764-782. Bamford, D. and Daniel, S. (2005) A Case Study of Change Management Effectiveness within the NHS, Journal of Change Management, Vol. 5 (4), pp. 391-406. Bamford, D.R. and Forrester, P.L. (2003) Managing planned and emergent change within an operations management environment, International Journal of Operations and Production Management, Vol. 23 (5), pp. 546 564. Bhatti, O.K. (2011) Strategy Implementation: An Alternative Choice of 8Ss, Annals of Management Research, Vol. 1 (2), pp. 52-59. Bond, M.A. (2007) Workplace chemistry: promoting diversity through organizational change, New England: University Press of New England. Buchanan, D. A. and Storey, J. (1997). Role-taking and role-switching in organizational change: the four pluralities. In McLoughlin, I. and Harris, M. (Eds), Innovation , Organizational Change and Technology. London: International Thompson. Burnes, B. (2009) Managing Change. 5th edn. Edinburgh: Pearson Education Limited. Burnes, B. (2004) Kurt Lewin and the Planned Approach to Change: A Re-appraisal, Journal of Management Studies, Vol. 41 (6), pp. 977-1002. Cameron, E. and Green, M. (2004) Making Sense of change management: a complete guide to the models, tools techniques of organizational change, London: Kogan Page Publishers. Carnall, C. A. (2007) Managing Change in Organizations. Essex: Person Education. Carpenter, M., Bauer, T. and Erdogen, B (2009) Principles of Management, Flat World Knowledge available at: Available at: https://www.web-books.com/eLibrary/NC/B0/B58/047MB58.html CIPD. (2015). Change Management, Chartered Institute of Personnel and Development. Available at: https://www.cipd.co.uk/hr-resources/factsheets/change-management.aspx [accessed 22 May 2015]. Dawson, P. (1994) Organizational Change: A Processual Approach. Paul Chapman Publishing: London. Day, G. and Leggat, S. (2015) Leading and managing health services, Melbourne: Cambridge University Press. Department for Children, Schools and Families. (2009) Change Management Models. Available at: https://www.dcsf.gov.uk/everychildmatters/strategy/deliveringservices/servicedirectories/models/changemanagementmodels/ Fernandez, S. and Rainey, H. G. (2006) Managing Successful Organisational Change in the Public Sector, Public Administration Review, Vol. 66 (2), pp.168-176. Grant, P. (2008) The productive ward round: a critical analysis of organisational change, The International Journal of Clinical Leadership, Vol.16 (4), pp. 193-201. Graetz, F. and Smith, A.C.T. (2010), Managing organizational change: a philosophies of change approach, Journal of Change Management, Vol. 10 (2), pp. 135-154. Greenan, N. (2003) Organisational change, technology, employment and skills: an empirical study of French manufacturing, Cambridge Journal of Economics, Vol. 27 (2), pp. 287-316. Gupta, P. (2011) Leading Innovation Change The Kotter Way, International Journal of Innovation Science, Vol. 3 (3), pp. 141-149. Higgs, M. and Rowland, D. (2005) All Changes Great and Small: Exploring Approaches to Change and its Leadership, Journal of Change Management, Vol. 5 (2), pp.121-151. Higgins. J.M. (2005) The Eight Ss of Successful Strategy Execution, Journal of Change Management, Vol. 5 (1), pp. 3-13. Jones, G.R. (2007) Organisational Theory, Design, and Change, New Jersey: Pearson Prentice Hall. Jurevicius, O. (2013) McKinsey 7s Model. Available at: https://www.strategicmanagementinsight.com/tools/mckinsey-7s-model-framework.html Kelman, S. (2005) Unleashing change: A study of organizational renewal in government, Washington, DC: Brookings Institution Press. King, M. and Anderson, N. (2002) Managing Innovation and Change, Sydney: Thomson. Kotter, J.P. (1995) Leading Change: Why Transformation Effort s Fail, Harvard Business Review, March-April, pp. 59-67. Kotter, J.P. (1998) Winning at Change, Leader to Leader, Vol.10, pp.27-33. Lewin, K. (1958) Group decisions and social change. In Swanson, G.E., Newcomb, T.M. and Nartley, E.L. (Eds), Readings in Social Psychology, Holt, Rhinehart and Winston, New York, NY. MacBryde, J., Paton, S., Bayliss, M. and Grant, N. (2014) Transformation in the defence sector: The critical role of performance measurement, Management Accounting Research, Vol. 25 (2), pp. 157-172. McWhinney, W. (1992) Paths of change, Newbury Park: Sage. Moran, J. and Brightman, B. (2000), Leading organisational change, Journal of Workplace Learning, Vol. 12 (2), pp. 66-74. Nitta, K.A., Wrobel, S.L., Howard, J.Y. and Jimmerson-Eddings, E. (2009) Leading Change of a School District Reorganization, Public Performance and Management Review, Vol.32 (3), pp. 463-488. Peters, T. and Waterman, R. H. (1982). In search of excellence. New York, NY: Harper and Rowe. Pettigrew, A.,Ferlie, E. McKee, L. (1992). Shaping strategic change: making change in large organizations, the case of the National Health Service. London: Sage. Rasiel, E.M. and Friga, P.N. (2002) The McKinsey Mind, US: McGraw-Hill. Saif, N., Razzaq, N., Rehman, S.U., Javed, A. and Ahmad, B. (2013) The concept of change management in todays business world, Information and Knowledge Management, Vol. 3 (6), pp. 28-33. Sidorko, P.E. (2008), Transforming library and higher education support services: can change models help?, Library Management, Vol. 29 (4/5), pp. 307-318. Todnem By, R. (2005) Organisational Change Management: A Critical Review, Journal of Change Management, Vol. 5 (4), pp. 369-380.