Management & Change by IILM Institute for Higher Education

Number 1 & 2

Volume 21

2017

Management & Change

Ajeet Kumar Maurya Shraddha Mishra

Estimating Volatility of Indian Corporate Debt Market

Manisha Behal Pavleen Soni

Understanding Attitude of Young media users in Indiaâ&#x20AC;&#x201D;Is an ardent TV viewer also an ardent Internet user?

Luxmi Ashu Vashisht

Knowledge Management (KM) mediates the relationship between Organizational Learning (OL) and Organizational Performance (OP): A study of few selected Indian Organizations

Harpreet Kaur G S Bhalla

College Effectiveness and Teachersâ&#x20AC;&#x2122; Satisfaction: A Study of Government Colleges in Punjab

Anjala Kalsie Shikha Mittal Shrivastav

Valuation of Distressed Firms: A Case Study of Unitech Limited

Book Reviews Journal of IILM Institute for Higher Educaion

Management & Change

Editor Dr. Smitha Girija Director and Associate Professor - Marketing IILM Institute for Higher Education

Journal of IILM Institute for Higher Education (Listed in Cabell’s Directory of IILM Institute for Higher Education Publishing Opportunities, Texas, USA & EBSCO, Ipswich, MA) Email: management.change@iilm.edu

Email: smitha.girija@iilm.edu

EDITORIAL ADVISORY BOARD Gopal, Gurram

Industry Professor, Industrial Technology and Management (INTM), School of Applied Technology (SAT), Illinois Institute of Technology, 3424 S. State Street, Suite 4001, Chicago IL - 60616, United States of America.

Padam, Sudarsanam

Former Dean, Administrative Staff College of India, Hyderabad, India

Raghu Ram, T. L.

Professor of Strategy, XLRI Jamshedpur, Circuit House Area, Sonari, Jamshedpur, Jharkhand, India.

Wadhwa, B D

Senior Director, IILM Graduate School of Management, 16, Knowledge Park – 2, Greater Noida – 201306, India.

Shahi, Sujata

Senior Director & Professor, Organizational Behaviour & Human Resource Management, IILM Institute for Business and Management, DLF Golf Course Road, Sector – 53, Gurgaon – 122003, Haryana, India

Manuscript Submission Contributions are invited in diverse areas of management from interested authors. In each issue of the journal it is normally planned to include research papers, case studies, original conceptual papers/perspectives, short communications, management cases and book reviews. For contributor’s guidelines, authors may refer to the inside back cover. Enquiries should be electronically made to the Editor, Management & Change, IILM Institute for Higher Education or E-mail at: management.change@iilm.edu Frequency and Subscriptions Management & Change is published bi-annually i.e. twice a year (No.1: Summer; No.2: Winter). Annual subscription rates are as follows: Within India – Institutional: Rs. 750; Individual: Rs. 500 Overseas – Asian Countries: $50; Other Countries: $150 (Air mail) Demand Draft should be drawn in favour of: IILM Institute for Higher Education, payable at New Delhi. Advertisement rates full page Rs. 20,000; half page Rs. 10,000. Editorial/Subscription Information For editorial queries, please write to the Editor, Management & Change, IILM Institute for Higher Education, Tel: 91-11-40934356, Fax: 91-11-40934335, E-mail: management.change@iilm.edu . For subscription related queries please contact Editorial Coordinator (aarti.sharma@iilm.edu). Order for print copies to be made at management.change@iilm.edu . Copyright @ 2017 IILM Institute for Higher Education. All Rights Reserved.

Chronology of Editorial Team of ‘Management & Change’

Volume & Issue (Year)

Editor

Associate Editor

Editorial Coordinator

Vol. 1 No. 1 (1997)

Prof. Debi S. Saini

Sami A. Khan

Zafar H. Anjum

Vol. 1 No. 2 (1997)

Prof. Debi S. Saini

Sami A. Khan

Zafar H. Anjum

Vol. 2 No. 1 (1998)

Prof. Debi S. Saini

Sami A. Khan

Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui

Vol. 2 No. 2 (1998)

Prof. Debi S. Saini

Sami A. Khan

Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui

Vol. 3 No. 1 (1999)

Prof. Debi S. Saini

Sami A. Khan

Zafar H. Anjum

Vol. 3 No. 2 (1999)

Prof. Debi S. Saini

Sami A. Khan

Lincy Sebastian Yusuf Siddiqui

Vol. 4 No. 1 (2000)

Prof. Gautam Bhattacharyya

Sami A. Khan

Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui

Vol. 4 No. 2 (2000)

Prof. Gautam Bhattacharyya

Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui

Vol. 5 No. 1 (2001)

Prof. Gautam Bhattacharyya

Yusuf Siddiqui

Vol. 5 No. 2 (2001)

Prof. Gautam Bhattacharyya

Yusuf Siddiqui

Vol. 6 No. 1 (2002)

Prof. Gautam Bhattacharyya

Yusuf Siddiqui

Vol. 6 No. 2 (2002)

Prof. Gautam Bhattacharyya

Yusuf Siddiqui

Vol. 7 No. 1 (2003)

Dr. Irfan A. Rizvi

Prof. M.K. Moitra

Yusuf Siddiqui

Vol. 7 No. 2 (2003)

Dr. Irfan A. Rizvi

Prof. M.K. Moitra

Yusuf Siddiqui

Vol. 8 No. 1 & 2 (2004)

Dr. Irfan A. Rizvi

Johnson E.P

Vol. 9 No. 1 (2005)

Dr. K.M.Mital

Dr. Siri D. Vivek

Johnson E.P

Vol. 9 No. 2 (2005)

Dr. K.M.Mital

Dr. Rajesh Pilania

Johnson E.P

Vol. 10 No. 1 (2006)

Dr. K.M.Mital

Dr. Rajesh Pilania

Johnson E.P

Vol. 10 No. 2 (2006)

Dr. K.M.Mital

Johnson E.P

Vol. 11 No. 1 (2007)

Dr. K.M.Mital

Johnson E.P

Vol. 11 No. 2 (2007)

Dr. K.M.Mital

Johnson E.P

Vol. 12 No. 1 (2008)

Dr. K.M.Mital

Johnson E.P

Vol. 12 No. 2 (2008)

Dr. K.M.Mital

Johnson E.P

Vol. 13 No. 1 (2009)

Dr. K.M.Mital

Arun Thomas

Vol. 13 No. 2 (2009)

Dr. K.M.Mital

Arun Thomas

Vol. 14 No. 1 (2010)

Dr. K.M.Mital

Ms. Deepa Khanna Ms. Sarla Rawat

Vol. 14 No. 2 (2010)

Dr. K.M.Mital

Ms. Deepa Khanna Ms. Sarla Rawat

Vol. 15 No. 1 & 2 (2011)

Dr. P. Malarvizhi

Mr. George Skaria

Ms. Deepa Khanna Ms. Shipra Jain

Vol. 16 No. 1 & 2 (2012)

Dr. Sangeeta Chopra

Ms. Aarti Sharma Ms. Shipra Jain

Vol. 17 No. 1 & 2 (2013)

Prof. Vandana Srivastava

Dr. Sangeeta Chopra Dr. Silky Kushwah

Ms. Aarti Sharma

Vol. 18 No. 1 (2014)

Dr. Vandana Srivastava

Dr. Sangeeta Chopra Dr. Silky Kushwah

Ms. Aarti Sharma

Vol. 18 No. 2 (2014)

Dr. Vandana Srivastava

Dr. Silky Kushwah

Ms. Aarti Sharma

Vol. 19 No. 1 (2015)

Dr. Vandana Srivastava

Dr. Moumita Acharyya Ms. Aarti Sharma

Vol. 19 No. 2 (2015)

Dr. Vandana Srivastava

Dr. Moumita Acharyya Ms. Aarti Sharma

Vol. 20 No. 1 & 2 (2016)

Dr. Sangeeta Chopra

Dr. Deepika Dhingra

Ms. Aarti Sharma

Vol. 21 No. 1 & 2 (2017)

Dr. Smitha Girija

Ms. Aarti Sharma

ACKNOWLEDGEMENT TO REFEREES Following management professionals acted as referees for contributions made for Management & Change, Vol. 21 No. 1 & 2 (2017). Management & Change acknowledges their valuable comments and suggestions for improving papers included in the following issue. Management & Change, Vol. 21 No. 1 & 2 Sujata Shahi

Professor, Organizational Behaviour & Human Resource Management, IILM Institute for Business & Management, 1 Knowledge Centre, Golf Course Road, 71-1 Sector 53 Gurgaon – 122003, India.

Shyamali Satpathy

Associate Professor, Organizational Behaviour & Human Resource Management, IILM Graduate School of Management, 16, Knowledge Park-2, Greater Noida - 201306, India.

Shraddha Mishra

Assistant Professor, Finance and Accounting Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003, India

Sanyukta Jolly

Associate Professor, Organizational Behaviour & Human Resource Management, IILM Undergraduate Business School, 3 Lodhi Institutional Area, New Delhi – 110003, India.

Reenu Bansal

Assistant Professor, Finance and Accounting Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003, India

P. Malarvizhi

Professor, Finance and Accounting Management, IILM Institute for Business & Management, 1 Knowledge Centre, Golf Course Road, 71-1 Sector 53 Gurgaon – 122003, India.

Gunjan Rana

Associate Professor, Marketing, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003, India

Daisy Mathur Jain

Associate Professor, Technology and Innovation, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003, India

Management & Change VOLUME 21

NUMBER 1 & 2

2017

Estimating Volatility of Indian Corporate Debt Market

Ajeet Kumar Maurya Shraddha Mishra

Understanding Attitude of Young media users in Indiaâ&#x20AC;&#x201D;Is an ardent TV viewer also an ardent Internet user?

Manisha Behal Pavleen Soni

Knowledge Management (KM) mediates the relationship between Organizational Learning (OL) and Organizational Performance (OP): A study of few selected Indian Organizations

Luxmi Ashu Vashisht

College Effectiveness and Teachersâ&#x20AC;&#x2122; Satisfaction: A Study of Government Colleges in Punjab

Harpreet Kaur G S Bhalla

Valuation of Distressed Firms: A Case Study of Unitech Limited

Anjala Kalsie Shikha Mittal Shrivastav

ARTICLES

Contributors Ajeet Kumar Maurya

Senior Research Fellow, Institute of Management Studies, Banaras Hindu University, Varanasi. Email: jeetraj12345@gmail.com

Shraddha Mishra

Assistant Professor, Finance and Accounting Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi â&#x20AC;&#x201C; 110003, India. E-mail: shraddha.mishra@iilm.edu

Manisha Behal

Research Scholar, University Business School, Guru Nanak Dev University, Amritsar, Punjab, India. E-mail: manishabehal@yahoo.co.in

Pavleen Soni

Assistant Professor, University Business School, Guru Nanak Dev University, Amritsar, Punjab, India. E-mail: topavleen@yahoo.co.in

Luxmi

Professor, University Business School, Panjab University, Chandigarh -160025, India. Email: luxmimalodia@yahoo.com

Ashu Vashisht

Senior Research Fellow, University Business School, Panjab University, Chandigarh -160025, India. Email: vashishthashu@.com

Harpreet Kaur

Research Fellow, Department of Commerce, Guru Nanak Dev University, Amritsar (Punjab) -143001, India. Email: preeti356@yahoo.co.in

G S Bhalla

Professor, Department of Commerce, Guru Nanak Dev University, Amritsar (Punjab) -143001, India. Email: hellogsbhalla@gmail.com

Anjala Kalsie

Assistant Professor, Faculty of Management University of Delhi, Delhi â&#x20AC;&#x201C; 110007. Email: kalsieanjala@gmail.com

Shikha Mittal Shrivastav

Assistant Professor, Finance and Accounting Management, IILM Graduate School of Management, 16, Knowledge Park2, Greater Noida - 201306, India. E-mail: shikha.shrivastava@iilm.ac.in

Studies,

From the Editor’s Desk

Redefining the Management Education in Today’s Era:

The study of management has become integral to functioning of all the businesses across and outside domestic boundaries. It is imperative to look at management education from the marketoriented perspective and take a strategic view to better align business education with the requirement of the global market. The development of management education though can be traced back to 18th century but it is currently at the boom in India with more than 1000 B-Schools coming up. The competition in the managerial/leadership job market has increased manifolds and the approach of management education is evolving steadily with Artificial Intelligence paving its way. It will bring new criteria and approach for success by expanding “collaboration capabilities, information sharing, experimentation, learning and decision-making effectiveness, and the ability to reach beyond the organization for insight”1 The present issue of the journal deals with a variety of areas related to management ranging from finance, marketing to determinants of teacher’s satisfaction. The paper titled “Valuation of Distressed Firms: A Case Study of Unitech Limited” introduces a new valuation model which addresses the various issues and risks that a firm faces based on its unique characteristics. The adapted model was applied to Unitech Limited, India’s leading Real Estate player which is presently facing the distress condition. The management education is not only concerned with managing distress but also on managing finance specifically in today’s era of increased volatility in the Indian corporate debt markets. The paper titled “Estimating volatility of Indian corporate debt market” discusses the issue of optimum utilization of debt funds and how to generate money out of it. The paper further discusses the aspects of volatility which will safeguard the interest of investors. The next paper titled “College Effectiveness and Teachers’ Satisfaction: A Study of Government Colleges in Punjab” aims at studying the effectiveness of Government colleges in Punjab. The authors study the determinants of the effectiveness of a government college from a teacher’s perspective. The authors highlighted 11 determinants of teacher’s satisfaction amongst which financial administration had a very high impact. The paper titled “Knowledge Management (KM) mediates the relationship between Organizational Learning (OL) and Organizational Performance (OP): A study of few selected Indian Organizations” studies the relation in context of manufacturing and service 1

Vegard Kolbjørnsrud, Richard Amico, Robert J. Thomas (2016), How Artificial Intelligence will Redefine Management, Harvard Business Review

sector firms in India to understand the reasons behind the exponential growth in these sectors. The authors concluded that knowledge management along with organizational learning plays a key role in achieving higher levels of organizational performance. The last paper titled â&#x20AC;&#x153;Understanding Attitude of Young media users in Indiaâ&#x20AC;&#x201D;Is an ardent TV viewer also an ardent Internet user?â&#x20AC;? gives new insights on attitudinal dimensions of TV content and internet content and media profile of respondents. Further, this paper offers useful insights to marketers that will help them design media campaigns in accordance with the audience The management graduates can learn and make informed decisions derived from these emerging areas and diverse themes. I hope the present issue provides useful insights to management graduates and young entrepreneurs. IILM Institute for Higher Education

Dr Smitha Girija

ESTIMATING VOLATILITY OF INDIAN CORPORATE DEBT MARKET Ajeet Kumar Maurya1

Shraddha Mishra2

Volatility is a central focus of modern financial market researches. The objective of this paper is to empirically analyze the volatility pattern of Indian corporate bond Market. The daily price value of corporate debt market is used for the period of April 2011 to March 2016. As capital market volatility is effectively depicted with the help of ARCH class models, the estimations of the ARCH (1) and GARCH(1,1) models have been performed so as to produce the evidence of time varying volatility which shows clustering, high persistence and predictability and responds symmetrically for positive and negative shocks. The stationarity of data and existence of autocorrelation in the data series were checked. The ADF test results that the data series of select period are stationary. While auto-correlation test shows that there is auto-correlation in residuals of the data and that was removed by using AR (1). Both ARCH and GARCH parameters are statistically significant at the 5% significance level which means that the “news” parameter and the persistence coefficient are significant. On the basis of study, it can be concluded that the Indian corporate debt market is highly volatile and there is the evidence of high persistence of time varying volatility. Keywords: Volatility, Debt market, GARCH model, Autocorrelation INTRODUCTION Volatility is a concept which measures variability or dispersion to a central tendency. It shows the variability of an asset price. It could be defined as the degree to which the price of an asset fluctuates. In financial phenomenon, it can be described as a method which measures magnitude of changes in the current price of an asset from its average price of past. Greater the deviation between the current price of an asset and its past average past price, greater will be volatility and vice-versa. In finance, it is also referred as risk. Nobel Prize winner in economics, Merton Miller (1991) has defined volatility in his book. He wrote “By volatility public seems to mean days when large market movements, particularly down moves, occur. These precipitous market wide price drops cannot always be traced to a specific news event. Nor should this lack of smoking gun be seen as in any way anomalous in market for assets like common stock whose value depends on subjective judgment about cash flow and resale prices in highly uncertain future. The public takes a more deterministic view of stock prices; if the market crashes, there must be a specific reason.” The volatility is a central focus of modern financial market researches as well as academic researches. The stock market of any economy is very uncertain. Usually the stock market index/ 1

Senior Research Fellow, Institute of Management Studies, Banaras Hindu University, Varanasi. Email: jeetraj12345@gmail.com 2 Assistant Professor, Finance and Accounting Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003, India. E-mail: shraddha.mishra@iilm.edu

price goes up one day and then goes down for next day or for next some days. Then the market again goes up and goes down next. These ups and downs continue for days to days or years to years and this fluctuation is termed as stock market volatility. The same types of volatility pattern in corporate debt market also. But corporate debt market volatility is not necessarily a bad thing. In fact, volatility is usually used as basis for efficient price discovery of the corporate debt market. The importance of volatility can be seen in the area of financial economics. The equilibrium price of an asset is widely affected by changes in volatility. The investment management depends upon mean-variance theory. Portfolio managers, risk arbitrageurs and corporate financial dealer continually and closely watch volatility trend of the stock market because the changes in the prices of bonds have a major impact on their investment and returns.

THE IMPORTANCE OF VOLATILITY FORECASTING Volatility plays a very important role in financial market of a country. Academicians and practitioners of financial market have much focused towards the study of this over the last few decades. First of all, the importance of volatility can be imagined that this has become a key point to many investment decisions and portfolio selections. Investors and portfolio managers always try to select less risky securities or securities which have a certain level of risk which can be borne by them. To assess the investment risk of a security, it is necessary a good forecast of volatility of assets prices over the investment holding periods. In recent derivatives securities have captured much growth in trading volume. Volatility is also the most important variable in its pricing. Anyone who wants to set a price of derivative security, he need to know the volatility of the underlying asset form the purchase date to expiry date. Nowadays, one can buy derivatives that are clearly written in terms of definition and measurement of volatility. In these contracts, volatility now becomes the underlying asset. So in this forecasting of volatility is much needed to price the derivative contracts. Next, now financial risk management is playing a key role in securities market since the time when the first Basle Agreement was constituted in 1996. The establishment of Basle Agreement makes essential to the risk management to forecast volatility of securities for financial institutions. To assess the risk involved in a security portfolio, the banks and other financial institutions have to fix and keep aside the reserve capital of at least three times that of value-at-risk (VaR). Value-at-risk (VaR) is a minimum expected loss with a 1% confidence level for a given time horizon (usually 1 or 10 days) or sometimes, a 5% critical value is used. VaR is a mean estimate which is very helpful for volatility forecast. Financial market volatility has also a vital consequence on an economy of a country or whole world. This can be evidenced by many incidents like terrorist attack on September 11, 2001 and recent financial crisis in United States have a big and negative impact on the economy of whole world. This is a clear consequence and proof of important link between financial market uncertainty and economic performance along with investorâ&#x20AC;&#x2122;s confidence in security market.

And lastly, volatility is also an important parameter for the policy makers while making any policy. They do consider the market volatility as a guide and parameter for the susceptibility of financial markets and the economy. Every authority of policy maker like Reserve Bank of India strongly consider the volatility of securities, currencies and derivatives commodities in making its monetary policy.

HETEROSCEDASTIC TIME SERIES As it is usually known that normally the return/price of a security follows the high and low volatility both. During some periods, it goes up and falls quickly and for some periods it moves not at all or very little. During the periods when the prices go down quickly are mostly followed by prices even going down more quickly or going up very sharply. It is also that when prices go up quickly often followed by the prices even going up more sharply or falling more quickly or by an unusual amount. Normally these patterns can be seen in a long time series data of a price/return of security or stock market or whenever these patterns usually remained for a long time a data series, this pattern is term as Autoregressive Conditional Heteroscedasticity (ARCH). Most of the time, there is found heteroscedasticity (unequal variance or variance vary with time) in many financial time series. The heteroscedastic time series have some special features. The first is fat-tail behavior (leptokurtic distribution) means the probability distribution of time series often shows fatter tail than the normal distribution. This also can be judged by the Kurtosis value of time series. If the kurtosis value is found more than 3, we can say that the time series data are following leptokurtic distribution. The second is volatility clustering. The high variances are followed by high variances and low variances are followed by low variances or sometimes the variances from one period to the next period cannot be predicted. Third, they follow a squared series autocorrelation. Although the autocorrelation function (ACF) of time series themselves are largely uncorrelated but the ACF of their squared series shows some autocorrelation. And the last, they may have some leverage effect. This effect can be seen in assets returns when returns are found negatively correlated with the changes in volatility. Thus is one of the problems commonly encountered in cross-sectional data is heteroscedasticity (unequal variance) in the error term. There are various reasons for heteroscedasticity, such as the presence of outliers in the data, or incorrect functional form of regression model or incorrect transformation of data or mixing observations with different measures of scale (e.g. mixing high- income households with low-income households) etc. The classical linear regression model (CLRM) assumes that the error term Îźi in the regression model has some homoscedasticity (equal variance) across the time series which is denoted by Ď&#x192; 2 . For instance, in studying the consumption expenditure in relation to income, this assumption would imply that low-income and high-income households have the same disturbance variance even though their average level of consumption expenditure is different. However, if the assumption of homoscedasticity or equal variance is not satisfied, we have the problem of heteroscedasticity or unequal variance denoted by Ď&#x192;2i. Thus, when the low-income 12

households are compared with high income households, this has not only higher consumption expenditure but also greater variability in their consumption expenditure. As a result, in regression of consumption expenditure in relation to household income, we are likely to encounter heteroscedasticity. Heteroscedasticity has the following consequences: 1. Heteroscedasticity does not alter the unbiasedness and consistency properties of OLS estimators. 2. There is also a fact that OLS estimators are no longer of minimum variance of efficient. Therefore, they are not the best linear unbiased estimators (BLUE). They are simply linear unbiased estimators. 3. As a result, the t and F tests based under the standard assumptions of classical linear regression model (CLRM) may not be reliable because it will result in erroneous conclusions regarding the statistical significance of the estimated regression coefficients. 4. In the presence of Heteroscedasticity, the best linear unbiased estimators (BLUE) are provided by the method of weighted least squares (WLS). Because of these consequences, it is import that we check for heteroscedasticity which is usually found in cross-sectional data or in time series data. METHODOLOGY The objective of this paper is to empirically analyze the volatility pattern of Indian corporate bond Market. The daily price value of corporate debt market is used for the period of April 2011 to March 2016. The required data are collected for the sample period from the National Stock Exchange (NSE) India database. Daily returns are calculated as: Rt = Log (Pt / Pt-1)* 100 As capital market volatility is effectively depicted with the help of ARCH class models, the estimations of the ARCH (1) and GARCH(1,1) models have been performed so as to produce the evidence of time varying volatility which shows clustering, high persistence and predictability and responds symmetrically for positive and negative shocks. RESULTS AND DISCUSIONS The descriptive statistics (table 1) shows that the average daily return of Corporate Debt market is 0.0213%. The standard Deviations of the daily return is 0.98% which says that the variation in daily return is very high. The coefficient of the skewness is found to be insignificant and negative. The negative values indicate that the average investor in the equity market prefers negative symmetry as compared to positive asymmetry. This indicates that a rational investor prefers portfolios with lower probability of large payoffs. (Bordoloi & Shankar-2008) The coefficients of kurtosis of daily return is 5.09 which is higher than 3. The coefficient of kurtosis more than 3 shows that the returns of corporate debt market have highly leptokurtic distribution compared to the normal distribution. Because the investorâ&#x20AC;&#x2122;s preferences for higher moments are important for security valuation and thus such preference take positive values. The Jarque-Bera statistic also indicates the lack of normal distribution in the equity returns. The 13

Jarque-Bera statistic is significant at 5% level and this indicates much higher distributions than the normal distribution. Table: 1 Descriptive Statistics Statistic CBMR Mean 0.000213 Median 0.000116 Maximum 0.038613 Minimum -0.057136 Std. Dev. 0.009858 Skewness -0.190282 Kurtosis 5.098225 Jarque-Bera 234.7583 Probability 0.000000 Sum 0.263402 Sum Sq. Dev. 0.120298 Observations 1239 It is very apparent from the Figure 1 that the amplitude of the daily stock returns is changing in corporate debt market. The magnitude of this change is sometimes large and sometimes small. It is also clear that periods of high volatility are followed by periods of high volatility and periods of low volatility are followed by periods of low volatility. This suggests that residuals are conditionally heteroscedastic and it can be represented by ARCH and GARCH Model. Figure: 1 Residuals of Daily CDM Return for the period of five years CDMR .04

.02

.00

-.02

-.04

-.06 250

500

750

1000

Unit Root Test Before moving further in analysis of data, it is necessary to check whether the data are stationary or not because any econometric model asks for a stationary time series data. The results of regression may be spurious if we analyse it without checking the stationarity of data. Therefore unit root test is used to check the stationary of data. For conducting this test, Augmented Dickey Fuller Test (ADF test) is employed. The results of unit root of all the variables are presented in table 2. As P Value indicates that it is less than 0.05, it implies that daily return of Corporate Debt Market is stationary in nature. It also suggests however there may be some periods of high variance but the daily return of CDM will revisit to its mean level in the long run. Table 2: Unit Root Test Null Hypothesis: CBMR has a unit root Lag Length: 0 (Automatic - based on SIC, maxlag=22)

Augmented Dickey-Fuller test statistic Test critical values: 1% level 5% level 10% level

t-Statistic

Prob.*

-29.21123 -3.435419 -2.863666 -2.567952

0.0000

*MacKinnon (1996) one-sided p-values. ARCH Model A simple measure of asset return volatility is its variance over time. If we have data for stock returns over, say, a period of 1000 days, we can compute the variance of the daily stock return by subtracting the mean value of stock returns from their individual values, square the difference and divide it by the number of observation. But it does not capture volatility by itself because it is a measure of what is called unconditional variance, which is a single number for a given sample. It does not take into account the past history of returns. So it does not take into account time varying volatility in asset returns. And a measure that take into account the past history of asst returns in known as autoregressive conditional heteroscedasticity (ARCH). A simple way to measure the volatility is the following regression: Rt = c + ut Where Rt is daily return, c is a constant and ut represents the error term. Here the return is measured as log changes in the price over the successive days. ARCH should only ever be applied to series that do not have any trends or seasonal effects, i.e. that has no (evident) serially correlation. ARIMA is often applied to such a series (or even seasonal ARIMA), at which point ARCH may be a good fit.

ARCH Test Variable C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

Coefficient

Std. Error

t-Statistic

Prob.

0.000213

0.000280

0.759128

0.4479

0.000000 Mean dependent var 0.000000 S.D. dependent var 0.009858 Akaike info criterion 0.120298 Schwarz criterion 3966.019 Hannan-Quinn criter. 1.633780

0.000213 0.009858 -6.400354 -6.396220 -6.398799

The above ARCH Model shows that this mean equation is not significant as the p value is more than 0.05. The R-squared and adjusted R-squared also do not represent the model. So there is need to study the volatility of corporate bond market through GARCH model which may capture volatility pattern in right manner. Testing for ARCH effects Table: 4 Heteroscedasticity Test: ARCH F-statistic Obs*R-squared

9.027640 8.976642

Prob. F(1,1235) Prob. Chi-Square(1)

0.0027 0.0027

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample (adjusted): 3 1239 Included observations: 1237 after adjustments Variable C RESID^2(-1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

Coefficient

Std. Error

t-Statistic

Prob.

8.60E-05 0.085185

6.19E-06 0.028351

13.88456 3.004603

0.0000 0.0027

0.007257 Mean dependent var 0.006453 S.D. dependent var 0.000197 Akaike info criterion 4.77E-05 Schwarz criterion 8802.709 Hannan-Quinn criter. 9.027640 Durbin-Watson stat 0.002713

9.40E-05 0.000197 -14.22912 -14.22084 -14.22600 2.021576

In this regard, the ARCH test was used to test for ARCH effects on the residuals. The results are presented by table 4. It is found that P value is less than 0.05. Thus we can reject the null hypothesis that there is no ARCH effect. It means there is ARCH effect in the corporate bond market return. In other words, the zero probability value strongly shows the presence of heteroscedasticity in the residuals which makes more apparent to use GARCH Model.

Autocorrelation test A common problem in regression analysis involving time series data is autocorrelation. As we know that one of the assumptions of the classical linear regression model (CLRM) is that the error term Îźt , are uncorrelated. It means that error term at time t is not correlated with error term at time (t -1) or any other error term in the past. If the error terms are correlated, the following consequences will be: 1. The ordinary least square (OLS) estimators are still unbiased and consistent. 2. They are still normally distributed in large samples. 3. They are no longer efficient. It implies that there are no longer best linear unbiased estimators (BLUE). In most cases OLS standard errors are underestimated which means the estimated t values are inflated and it gives the evidence that a coefficient is more significant than it actually may be. 4. As a result, as in case of heteroscedasticity, the hypothesis testing procedure becomes suspect because the estimated standard errors may not be reliable, even asymptotically (i.e. in large samples). As a result, the usual t and F tests may not be valid. As in case of heteroscedasticity, we need to find out if autocorrelation exists in a specific model or regression and if exists we should to take corrective action or find alternative estimating procedure that will produce best linear unbiased estimators (BLUE).

Correlogram Q- Statistics Test Q statistic is widely used to test the auto-correlation of the equation errors in regression. This is used to test the joint hypothesis of no auto-correlation up to a specified number of lags. If the Qstatistic is greater than the critical value from the Chi-square distribution then the null hypothesis of no auto-correlation is rejected. The hypothesis of no auto-correlation is also verified by the associated probability value of Q-statistic. The last two columns reported in the correlogram are the Ljung-Box Q-statistics and their p-values. If P value is low form the specified level of significance, we do not reject the null hypothesis of no auto-correlation and if p value is higher than the specified level of significance then we reject the null hypothesis of no auto-correlation. The rejection of null hypothesis confirms that there is serial correlation of errors which says that the present value is dependent on the previous value. The hypothesis of correlogram Q- statistics is as follows: H0 : There is no auto-correlation at lag (n) in residuals. 17

The Q-statistic is calculated as follows:

Where n = Number of observations K = Number of lags Ď&#x2C6; = Auto-correlation Coefficient And if the auto-correlation of errors exists in a regression, it is an indication of heteroscedasticity (terms ARCH) and before moving toward ARCH family model for testing the volatility, it is necessary to remove this auto-correlation existence in the residuals for the validity of the regression. So after discussing the importance of testing of existence auto-correlation in a time series data, the Q-statistics is applied to know whether the auto-correlation is existed in present data series which are used for the this study. H0: There is no auto-correlation in the residuals. The table 5 exhibits that the first 36 lagged auto-correlation coefficients and Q- statistics of daily data of corporate bond market in the selected period. The result reveals that the autocorrelation coefficients at lag 1 to lag 36 are insignificant as the autocorrelation values are not close to zero in all lagged. While the p values of Q-statistics are zero which is less than 0.05 (at 5 % level of significance), thus we reject the null hypothesis of no auto-correlation in residuals. Thus we can say that there is the presence of auto-correlation in the series or residuals. Before moving the ARCH family model, we need to remove this so that the regression used for the study may be righty specified and valid.

Table 5: Auto-correlation Test Autocorrelation

Partial Correlation

|* | | | | | | | | | | | | | | *| | | | | | | | | | | | *| | | | | | | | |

|* | | | | | | | | | *| | | | | *| | | | | | | | | | | | *| | | | | | | | |

| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

PAC

0.183 -0.024 -0.047 -0.025 0.015 0.004 -0.006 -0.008 0.001 0.013 -0.062 -0.028 -0.007 0.028 0.001 -0.077 0.017 0.015 0.048 -0.026 -0.043 -0.043 0.000 0.010 -0.006 -0.020 -0.033 -0.076 -0.012 0.029 -0.000 0.014 0.042 0.013 -0.062 -0.013

0.183 -0.059 -0.033 -0.012 0.020 -0.006 -0.006 -0.005 0.004 0.011 -0.070 -0.002 -0.004 0.025 -0.015 -0.074 0.050 -0.003 0.042 -0.047 -0.020 -0.035 0.010 -0.002 -0.008 -0.014 -0.038 -0.067 0.009 0.035 -0.023 0.009 0.039 -0.001 -0.058 0.006

Q-Stat 41.565 42.273 45.052 45.842 46.130 46.146 46.195 46.271 46.272 46.487 51.325 52.285 52.339 53.318 53.318 60.697 61.063 61.332 64.212 65.084 67.379 69.682 69.682 69.817 69.863 70.394 71.736 79.020 79.202 80.235 80.235 80.501 82.736 82.959 87.803 88.012

Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

As the above auto-correlation test results that there is some auto-correlation existence in data series. Therefore, to remove the auto-correlation existence in present data series, we insert ARMA 1 (AR 1) in the mean equation of ARCH Model. Thus after inserting AR (1) in mean equation, the new equation will be as follows: Mean equation Rt = c + ar(1) + ɛt…… (1.1) After adding AR (1) in mean equation, the result of ARCH model is presented in table 6. From the table 6 we can see that constant is not found significant but AR (1) is found to be significant as p value is less than 0.05. The overall ARCH model is also significant. The Rsquared and adjusted R-squared also show that this model is representing approx. 33 percent of the data. Table 6: ARCH Effect Variable

Coefficient

Std. Error

t-Statistic

Prob.

0.000214 0.183000

0.000337 0.027969

0.634182 6.543031

0.5261 0.0000

C AR(1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted AR Roots

0.033477 Mean dependent var 0.032695 S.D. dependent var 0.009699 Akaike info criterion 0.116270 Schwarz criterion 3983.396 Hannan-Quinn criter. 42.81126 Durbin-Watson stat 0.000000

0.000213 0.009862 -6.431981 -6.423707 -6.428869 1.978007

.18

Test of Auto-correlation in new mean equation To verify the model that there is no serial correlation in new mean equation, the existence of auto-correlation was again tested. The result of Q-Statistics is presented in table 7. The result shows that now there is no autocorrelation in new mean equation as p value of all 36 lagged residuals are found more than 0.05 which do not reject the null hypothesis of no auto-correlation in series. Thus we can say that this new equation ARCH model is valid and rightly specified.

Table 7: Correlogram of Residuals Q-statistic probabilities adjusted for 1 ARMA term(s) Autocorrelation Partial Correlation | | | | 1 | | | | 2 | | | | 3 | | | | 4 | | | | 5 | | | | 6 | | | | 7 | | | | 8 | | | | 9 | | | | 10 | | | | 11 | | | | 12 | | | | 13 | | | | 14 | | | | 15 *| | *| | 16 | | | | 17 | | | | 18 | | | | 19 | | | | 20 | | | | 21 | | | | 22 | | | | 23 | | | | 24 | | | | 25 | | | | 26 | | | | 27 *| | *| | 28 | | | | 29 | | | | 30 | | | | 31 | | | | 32 | | | | 33 | | | | 34 *| | | | 35 | | | | 36

AC 0.011 -0.051 -0.041 -0.021 0.020 0.002 -0.006 -0.007 0.000 0.026 -0.064 -0.016 -0.007 0.031 0.010 -0.085 0.030 0.003 0.054 -0.029 -0.033 -0.038 0.007 0.012 -0.005 -0.015 -0.017 -0.072 -0.004 0.033 -0.008 0.007 0.039 0.018 -0.066 0.005

PAC 0.011 -0.051 -0.040 -0.023 0.017 -0.002 -0.006 -0.006 0.001 0.024 -0.065 -0.013 -0.011 0.026 0.003 -0.083 0.036 -0.004 0.050 -0.034 -0.020 -0.040 0.003 -0.000 -0.005 -0.008 -0.027 -0.073 -0.011 0.037 -0.018 -0.001 0.039 0.017 -0.056 0.004

Q-Stat 0.1460 3.3905 5.5062 6.0540 6.5716 6.5769 6.6186 6.6855 6.6855 7.4991 12.591 12.931 12.993 14.195 14.319 23.463 24.592 24.606 28.227 29.271 30.627 32.411 32.465 32.647 32.674 32.943 33.291 39.925 39.945 41.304 41.384 41.454 43.413 43.815 49.385 49.419

Prob 0.066 0.064 0.109 0.160 0.254 0.358 0.462 0.571 0.585 0.247 0.298 0.370 0.360 0.426 0.075 0.077 0.104 0.059 0.062 0.060 0.053 0.070 0.087 0.111 0.133 0.154 0.052 0.067 0.065 0.081 0.099 0.086 0.099 0.053 0.054

GENERALIZED ARCH (GARCH) MODEL Bollerslev (1986) has generalized the ARCH model to perform the function of the previous period squared errors. Along with this Bollerslev used the past conditional variance in the

GARCH Model. The GARCH (1,1) Model is widely accepted and proved to be an excellent model to exam the volatility of stock market return. GARCH (1, 1) Model For volatility estimation, the GARCH (1, 1) model is used by Bollerslev (1986). The model for daily stock return is specified as under:  

Mean equation Rt = c + ɛt…… Variance equation σ2t = ω + α1ɛ2t-1 + β1σ2 t-1

Since σt2 is the one-period ahead forecast variance based on past information, it is called the conditional variance. The above specified conditional variance equation is a function of three terms: a constant term (c), news about volatility from the previous period, measured as the lag of the squared residual from the mean equation (ɛ2t-1) which is ARCH effect and the last period’s forecast variance (σ2 t-1) which is called GARCH effect. The summation of GARCH and ARCH term denotes the persistent of volatility. If the value of α + β is less than 1, the model is considered to be stationary and volatility shock will be persistent. However the value of α + β is greater than 1, would be termed as non-stationarity in variance and if α + β = 1 then it denoted that there is Integrated GARCH (IGARCH) or Unit root in Variance (Brooks, Chris; 2002). But while checking the ARCH effect, it was found that there is found the existence of autocorrelation in the original data series and to remove the serial correlation AR (1) term was added in the mean equation of the ARCH Model so that the model can be rightly specified and valid. So there is also need to be inserted AR (1) term in GARCH Model. After adding the AR (1) term in GARCH model the new equation will be as below:  

New Mean equation Rt = c + ar(1) + ɛt…… New Variance equation σ2t = ω + ar(1) + α1ɛ2t-1 + β1σ2 t-1

The GARCH (1, 1) model assumes that the effect of a return shock on current volatility declines geometrically over time. This model is consistent with the volatility clustering where large changes in stock returns are likely to be followed by further large changes. The table 8 shows that the three coefficient ω (constant), α (ARCH term) and β (GARC term) have shown significant result for the corporate debt market. High GARCH term (0. 880495) that the past variance terms have a strong impact on the conditional variance or we can say that the information/news of previous days has high impact of today return i.e approx 88%. The high persistence (0.954712 which is approximately to unity) shows that the volatility of the stock returns dies down slowly. This implies that shocks to the conditional variance will be highly persistent. Both ARCH and GARCH parameters are statistically significant at the 5% 22

significance level which means that the “news” parameter and the persistence coefficient are significant. Thus, the volatility of the Corporate Debt Market will significantly change after the introduction of any new but significant information. Thus there is clear evidence that corporate bond market returns exhibit considerable time varying and time-correlated volatility whether we use the ARCH or the GARCH model and thus we can say that the corporate debt market is predictable and affected by the news of previous period. Table 8: GARCH Test GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) Variable C AR(1)

Coefficient

Std. Error

z-Statistic

Prob.

0.000353 0.179191

0.000310 0.029839

1.136545 6.005262

0.2557 0.0000

3.583750 4.964154 35.81390

0.0003 0.0000 0.0000

Variance Equation C α β R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots

4.30E-06 0.074217 0.880495

1.20E-06 0.014951 0.024585

0.033329 Mean dependent var 0.032547 S.D. dependent var 0.009700 Akaike info criterion 0.116288 Schwarz criterion 4040.247 Hannan-Quinn criter. 1.970562

0.000213 0.009862 -6.518978 -6.498294 -6.511198

.18

Testing of Auto-correlation existence in GARCH (1,1) Model Now we check this equation is rightly specified or not. Do the GARCH (1,1) still contain any auto-correlation in residuals? Because the correct model of ARCH Family should not contain any auto-correlation in its series. If there is no auto-correlation in the data series then that model is rightly specified.

Table 9: Correlogram Standardized Residuals (Q Statistics) Q-statistic probabilities adjusted for 1 ARMA term(s) Autocorrelation | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

PAC

0.022 -0.022 -0.020 -0.024 0.012 0.010 -0.019 -0.005 -0.008 0.017 -0.056 -0.019 -0.006 0.036 0.007 -0.055 0.023 -0.011 0.061 -0.007 -0.031 -0.027 0.002 0.006 -0.003 -0.020 -0.021 -0.065 -0.005 0.026 0.002 0.015 0.026 0.018 -0.049 0.002

0.022 -0.022 -0.019 -0.024 0.012 0.008 -0.020 -0.004 -0.008 0.017 -0.059 -0.016 -0.007 0.035 0.002 -0.054 0.029 -0.015 0.060 -0.015 -0.024 -0.027 0.001 0.001 -0.005 -0.014 -0.026 -0.065 -0.010 0.033 -0.003 0.010 0.022 0.016 -0.043 0.002

Q-Stat 0.5931 1.1828 1.6686 2.4059 2.5914 2.7243 3.1720 3.2084 3.2934 3.6626 7.6015 8.0474 8.0943 9.7485 9.8175 13.606 14.294 14.460 19.119 19.174 20.419 21.333 21.337 21.379 21.390 21.917 22.473 27.901 27.938 28.819 28.823 29.118 30.008 30.414 33.485 33.489

Prob 0.277 0.434 0.493 0.628 0.742 0.787 0.865 0.915 0.932 0.668 0.709 0.778 0.714 0.775 0.556 0.577 0.634 0.385 0.446 0.432 0.439 0.500 0.558 0.616 0.641 0.663 0.416 0.468 0.475 0.527 0.563 0.568 0.596 0.493 0.541

The above table shows that all the p values associated with Q-statistics are more than 0.05 ,

so we reject null hypothesis of no auto-correlation in the series. Thus we can say that this GARCH equation is rightly specified. The table 10 exhibits the Correlogram Standardized Residuals Squared. All the p values are more than 0.05, so we reject the null hypothesis and say that GARCH Variance model is rightly specified. Table 10: Correlogram Standardized Residuals Squared Q-statistic probabilities adjusted for 1 ARMA term(s) Autocorrelation Partial Correlation | | | | 1 | | | | 2 | | | | 3 | | | | 4 | | | | 5 | | | | 6 | | | | 7 | | | | 8 | | | | 9 | | | | 10 | | | | 11 | | | | 12 | | | | 13 | | | | 14 | | | | 15 | | | | 16 | | | | 17 | | | | 18 | | | | 19 | | | | 20 | | | | 21 | | | | 22 | | | | 23 | | | | 24 | | | | 25 | | | | 26 | | | | 27 | | | | 28 | | | | 29 | | | | 30 | | | | 31 | | | | 32 | | | | 33 | | | | 34 25

AC 0.012 -0.013 0.025 -0.037 -0.025 0.034 0.001 -0.002 0.004 -0.042 0.005 0.025 0.022 0.019 -0.014 -0.016 -0.004 -0.021 0.005 0.061 -0.038 -0.003 0.004 -0.016 0.027 -0.021 0.000 0.011 -0.026 -0.016 0.009 -0.009 0.028 0.068

PAC 0.012 -0.013 0.025 -0.037 -0.024 0.033 0.001 -0.002 0.001 -0.041 0.008 0.023 0.024 0.016 -0.017 -0.012 -0.002 -0.020 0.005 0.056 -0.038 0.001 0.002 -0.009 0.026 -0.031 0.005 0.009 -0.023 -0.008 0.002 -0.009 0.027 0.063

Q-Stat 0.1752 0.3815 1.1438 2.8166 3.6239 5.0560 5.0565 5.0632 5.0874 7.2951 7.3218 8.1170 8.7308 9.1665 9.4153 9.7567 9.7742 10.351 10.381 15.038 16.864 16.873 16.898 17.240 18.190 18.749 18.749 18.909 19.748 20.056 20.153 20.263 21.250 27.143

Prob 0.537 0.564 0.421 0.459 0.409 0.537 0.652 0.748 0.606 0.695 0.703 0.726 0.760 0.804 0.835 0.878 0.888 0.919 0.720 0.662 0.719 0.769 0.797 0.794 0.809 0.847 0.873 0.873 0.891 0.912 0.930 0.926 0.753

| |

35 36

-0.042 -0.032

-0.037 -0.032

29.393 30.669

0.693 0.677

CONCLUSION The objective of this chapter is to examine the volatility of Indian corporate debt market (CDM) by applying ARCH and GARCH Model. The study time period was five years (April 2011 to March 2016) and the data were taken from NSE Wholesale Debt Market database. The study first analyses descriptive statistics which resulted that the standard deviations of the daily return is 0.98% which says that the variation in daily return is very high. The coefficient of the skewness is found to be insignificant and negative. The negative values indicate that the average investor in the equity market prefers negative symmetry as compared to positive asymmetry. The coefficient of kurtosis more than 3 shows that the returns of both indices have highly leptokurtic distribution compared to the normal distribution. Next the stationarity of data and existence of autocorrelation in the data series were checked. The ADF test results that the data series of select period are stationary. While auto-correlation test shows that there is auto-correlation in residuals of the data and that was removed by using AR (1). Both ARCH and GARCH parameters are statistically significant at the 5% significance level which means that the “news” parameter and the persistence coefficient are significant. On the basis of above examination, it can be concluded that the Indian corporate debt market is highly volatile and there is the evidence of high persistence of time varying volatility. The finding is in line with the earlier findings of Thenmozhi (2002), Shenbagaraman (2003), Gupta and Kumar (2002) and Raju and Karande (2003). LIMITATIONS This study also has its limitations. First limitation is time constraint for the availability of data because the data are used only for the period of five years from April 2011 to March 2016. The Second is the sample data taken from only NSE wholesale Debt Market while other sources/platforms of data for corporate debt market were not used. The third limitation is that the current analysis is limited to the fixed effects in the GARCH equation. We limited this study with the corporate debt market returns variable and mostly focused on the univariate kind of analysis.

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UNDERSTANDING ATTITUDE OF YOUNG MEDIA USERS IN INDIAâ&#x20AC;&#x201D;IS AN ARDENT TV VIEWER ALSO AN ARDENT INTERNET USER? Manisha Behal1

Pavleen Soni2

Media planners are interested in understanding attitude of media users in order to frame messages effectively. Therefore, the present study endeavors to explore and compare the attitudinal dimensions of TV content (programs/ads) and internet content (use/ads) and presents media (TV and internet) profile of respondents. A sample of 714 young media users has been taken from schools and colleges in three cities of Punjab (India) and data have been analyzed through factor analysis, cluster analysis and chi-square test. The results reveal that respondents demonstrate positive attitude towards it. But, they view TV use (programs) negatively. Conversely, they view TV ads more positively in contrast to internet ads and exhibit mixed attitude towards internet ads. The study used attitudinal segmentation approach for categorizing TV viewers and internet users and profile of young media users for TV and internet content also reveals significant differences across clusters with respect to demographic variables, TV viewing frequency and internet usage pattern. The paper offers insights to marketers that can help them in designing media campaigns for young media users. Also, the study suggests that effectiveness of the media campaign is likely to increase if it is designed to address specific needs of clusters. Keywords: Attitude, Young media users, Television, Internet, India. INTRODUCTION Youth constitute an important market segment and are a key target for marketers. In India, majority of population is in the age group of 15-34 years and is termed as worldâ&#x20AC;&#x2122;s youngest country with 430 million youth which is expected to reach 464 million by 2021 (Shivakumar, 2013). They seek new ways to spend their leisure time, as a result electronic gadgets and multimedia environment in home is created at very early age. Although data from India is limited about the time spend by youth on media, yet significant proportion of youth spend considerable time (more than two hours daily) viewing television which is a dominant mass medium in Indian families (Census, 2011). In spite of heavy TV viewing among young viewers, time spent on watching TV on traditional media has decreased from 3 hours to 22/3 hours daily. But overall consumption of TV has increased by 38 minutes a day, as many young viewers watch TV online with the help of mobile phones and computers (Rideout et al., 2010). Thus, internet has become 1

Research Scholar, University Business School, Guru Nanak Dev University, Amritsar, Punjab, India. E-mail: manishabehal@yahoo.co.in 2

Assistant Professor, University Business School, Guru Nanak Dev University, Amritsar, Punjab, India. E-mail: topavleen@yahoo.co.in

an important means of entertainment as well as a channel for speedy communication in India. The base of internet users in India is currently estimated to be about 120 million users and it ranks third largest in the world after China and United States. Although, Indian users spend less time online per capita as compared to the users in developed nations, yet, internet is frequently used for social networking and communication (Gnanasambandam et al., 2012). Media diet of youth has grown steadily day by day as the bedrooms of these people have become media emporium in which they use two or more than two media at a same time. Moreover, media has enormous impact on our beliefs and perceptions, marketers need to communicate such information to the massive audience particularly to the young people who are termed as ‘digital generation’ to represent ‘the future’ in an appropriate manner. As Indian society becomes increasingly information-based, it is required to study the impact of media on youth because media are playing an extremely greater role in children’s leisure. More so, no such study has been conducted, to the best of researchers’ knowledge, so far to address this issue. Therefore, the present study endeavours to assess the attitude of youth towards TV and internet content (wherein one media is traditional and established while the other one is an emerging and new age media) and reveals media profile of respondents using personal and demographic variables. PREVIOUS RESEARCH Attitude towards TV content (programs and advertisements) Attitude is defined as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favour or disfavour” (Eagly and Chaiken, 1993; pp.1). Attitude of children towards TV programs has been assessed from its effects on them. Ward and Rivadeneyra (1999) reported that exposure to sexual content on TV for children cause stronger endorsement of recreational attitudes of students towards sex. Similarly, Brown (2002) and Ward and Friedman (2006) found that TV exposure particularly to sexy prime time programs and talk shows were associated with greater sexual attitudes and beliefs among high school students. They found that greater exposure to such programs was related with greater endorsement of their attitude toward sexual roles and relationships. Fitzpatrick et al. (2012) revealed that early childhood television exposure was associated with antisocial symptoms, emotional distress and lower academic achievement. Hale and Guan (2015) through a review based study concluded that screen time (television, computer, mobile devices and video games) reasoned to be a significant predictor of sleep outcomes primarily shorten duration and delayed timing among school-aged children and adolescents. Anschutz et al. (2011) studied the effects of thin ideal focused television clips on body dissatisfaction among preadolescent girls. They found that thin ideal movie clips were positively related to greater body dissatisfaction among girls as compared to clips with neutral content. Rey-Lopez et al. (2008) found an association between TV viewing and increase in weight of children. They revealed that children who watch TV independently in their bedroom faced greater chances of being overweight.

Nonetheless, researchers have also found that TV programs give children/adolescents an opportunity to learn social skills (Ozdemir, 2006), tend to entertain them (Valkenburg and Janssen, 1999), help in relieving daily stress (Verma and Larson, 2002) Previous researchers have found that children reveal positive attitude towards TV advertising. They consider ads to be credible (Chan, 2001) that tend to entertain as well as inform them (D’Alessio et al., 2009). Ertike (2011) surveyed Turkish college students and found that TV ads were seen to be exciting and amusing. Ads were also reported to depict life styles that students would aspire to live. Chan (2001) examined Chinese children’s perceived truthfulness, liking and attention toward TV advertising in Hong Kong. She reported that nearly equal proportions of children found ads to be true and untrue. While, majority of them liked TV ads but their liking decreased with age. D’Alessio et al. (2009) uncovered three determinants of attitude towards television advertising viz., enjoyment, credence, purchase intention and investigated differences in attitude towards ads across demographic variables and found significant differences. Yang (2000) observed that four beliefs of advertising—good for personal economy/consumer benefits, good for economy, product information, and hedonic or pleasure significantly contributed towards positive attitude towards advertising among college students. Ling et al. (2010) uncovered five aspects of advertising—credibility, informative, hedonic, pleasure, and good for economy that demonstrated positive attitude of undergraduates toward TV advertising. On the other hand, Alwitt and Prabhakar (1992) found that respondents perceived ads to be deceptive and irrelevant to people’s needs which contributed to formation of negative attitude among them. Some studies also addressed the viewpoints of young and middle aged adults about TV ads who reported that ads interrupt during viewing. Shobiye (2017) through a comparative study on South African and Nigerian viewers revealed that Nigerian TV viewers consider television commercials as irritating and interrupting so they felt that commercials should not be included in the programs. Whereas, South African viewers found to be more accommodating to acceptance of commercials during viewing time. Cotte et al. (2005) however reported that both positive and negative attitude towards TV advertising exist simultaneously. Attitude towards internet content (use and advertisements) It has been found that majority of families considered internet as a useful source for children homework support (Cranmer, 2006) as well as adults view internet as a useful, interesting and relevant media to get learning information (Hong et al., 2003). Sam et al. (2005) also observed that undergraduates used internet for educational purposes such as for doing research, downloading e-resources and e-mail interactions. They found that positive attitude towards internet use was also enforced in undergraduates due to high computer self-efficacy and modest computer anxiety about using computers. However, Valkenburg and Soeters (2001) found that affinity towards computers was the most prominent reason for using internet followed by its informational and entertainment aspect. On the other side, they reported that a computer crash or virus was the most disturbing experience, followed by violence and pornography and it 31

contributed to formation of negative attitude among users. Leino (2006) observed that students considered internet as an easy way to access current information. Finding new friends, chatting with old ones and downloading facility of internet were other key advantages perceived by them. But, the study also revealed that internet use was reported to pose some threats to students such as lack of reliability of information and the risk of computer and internet addiction that contribute to formation of negative attitude in them. Recent research also points to negative outcomes of internet use on young internet users. For example, greater use of internet leads to internet addiction, poor academic performance (Jiang, 2014) and leads to depression in young internet users (Selfhout et al., 2009). Previous research has explored attitude towards internet advertising by exploring beliefs about it. Wolin et al. (2002) explored the beliefs that influence attitude of consumers towards online advertising. The beliefs viz., informative, hedonic/pleasure, social role and image contributed to positive attitude towards online advertising. Conversely, negative beliefs viz., materialism, falsity/nonsense, and value corruption were found to be associated with negative attitude towards internet advertising. Karson et al. (2006) segmented consumers based on their beliefs about online advertising into three attitudinal groupsâ&#x20AC;&#x201D;pros, ambivalents, and critics. It was found that critics were likely to use internet less frequently for searching information as well as viewed it as less utilitarian and hedonic than their pro and ambivalent counterparts. Similarly, Yang (2004) segmented internet users on the basis of their lifestyles into three clusters namely, experiencers, traditionalists and self-indulgents and found that traditionalists held more positive attitude toward internet advertising and viewed it as informative, good for consumers and providing hedonic value/pleasure than experiencers and self-indulgent. Korgaonkar and Wolin (2002) examined the differences in attitude of web users and its impact on internet usage patterns. They segmented users into three categories viz., heavy, medium and light web users and revealed that heavy users held more positive attitude toward web advertising, which led to more frequent online purchasing than their counterparts. Cardoso and Cardoso (2011) confirmed that adolescentsâ&#x20AC;&#x2122; attitude towards internet advertising was a four dimensional construct comprising of informativeness, entertainment, irritation and trustworthiness and adolescents generally held neutral or even negative attitude towards internet advertising. Dufflett (2015) reported that young adults who used internet more frequently depicted favourable attitude towards advertising on facebook. Previous literature also suggests that attitude of children and adolescents towards media is associated with demographic variables (Dâ&#x20AC;&#x2122;Alessio et al., 2009; Peng et al., 2006; Teo and Lim, 2000; Tsai et al., 2001; Wolin and Korgaonkar, 2003). For example, boys hold more positive attitude towards internet use and surf internet for downloading activities and obtaining information than female users. On the other hand, younger children watch television more frequently and perceive it more positively than older ones.

RESEARCH QUESTIONS Based on review of previous studies, need was felt to assess the dimensions of media users’ attitude towards media content (television and internet) particularly, in Indian settings where youth spend considerable time in media related activities. Therefore, the following research questions were proposed. 1. What are the attitudinal dimensions of television content (programs and advertisements) and internet content (use and advertisements)? 2. What are the potential attitudinal segments of television viewers and internet users in India? 3. Does the profile of Indian media users (television viewers and internet users) with different attitudinal segments vary across demographic variables? RESEARCH METHODOLOGY The present study is based on primary data collected with the help of structured and pre-tested questionnaire from three cities of the Punjab—Amritsar, Jalandhar and Ludhiana, incorporating sample as per population proportion in age category 15 to 24 years with respect to these three cities (Census, 2001). Viewers in this age category have sufficient cognitive capabilities to understand marketplace stimuli (John, 1999). D.A.V. schools and colleges have been approached to collect the data as they run a chain of schools and colleges in respective cities. With the permission of Principal of each school and college, the questionnaires were personally administrated to the respondents in their respective classrooms and they were supervised to fill in responses with respect to the issues addressed in questionnaire. Of the 800 questionnaires, 714 (89.25%) usable questionnaires were returned. Fifty one per cent respondents belong to the age category of 15 to 19 years, most of them are boys (51.3%) with weekly pocket money ranging from INR 251—500 (46.8%), with monthly family income less than INR 50000 approximately (59.8%). 61.6 per cent of them reported that mothers are their primary caregiver. Moreover, 48.7% mothers of young media users were graduates while 44.4% fathers were graduates. Data were analyzed using descriptive statistics, Exploratory Factor Analysis, Cluster Analysis and Chi-squaretest (2) through SPSS 19.0. MEASURES USED Attitude of young viewers towards TV content (programs and advertisements) A battery of nineteen statements has been devised on the basis of previous literature (Fitzpatrick et al., 2012; Hale and Guan, 2015; Ozdemir, 2006; Valkenburg and Janssen, 1999; Verma and Larson, 2002). Responses were measured on five point Likert scale (Cronbach’s alpha () = 0.735).An array of seven bi-polar adjective scales was prepared on the basis of dimensions described by D’Alessio et al. (2009) to assess the attitude towards TV advertisements. (Cronbach’s alpha () = 0.622). Attitude of internet users toward internet content (use and advertisements) 33

An 18 item scale has been adapted from Tsai et al. (2001) (Cronbach’s alpha () = 0.745). A 24 item scale was adapted from Yang (2004) to assess attitude towards internet advertisements (Cronbach’s alpha () = 0.858). Responses were measured on 5-point likert scale. DATA ANALYSIS Attitudinal dimensions of media content (TV and internet) Factor analytical technique has been applied to the nineteen statements of TV programs and seven bi-polar adjectives of TV ads (refer Table 1) in order to explore the dimensions of young viewers’ attitude towards television content. The analysis yielded a five-factor solution and total variance explained equaled to 59.75%. A three factor solution was obtained in case of TV advertisements explaining 66.32% of total variance. From Table 1 it can be seen that respondents view TV programs to have negative psychological effects on them and promote sexuality but do not lead to aggressive behavior in them. They also view TV programs to inform them about latest life style trends and help reduce daily stress and to act as a source of entertainment. Lastly, they report that watching TV programs also tend to have negative physiological effects. Conversely, Table 1 also shows that young viewers watch TV ads with interest because they think that ads tend to entertain them. Therefore, they enjoy watching ads. Nonetheless, they also report that TV ads are not truthful as well as trustworthy and consider the role of TV ads as an important information purveyor in making different purchase decisions. Similarly, in order to explore the dimensions of attitude of internet users towards internet content, Factor analytical technique has been applied to seventeen items of internet use and twenty four statements (see Table 1) of internet ads. Four factors were identified explaining 56.69% of total variance in case of internet use. In case of attitude toward internet ads, six factors were obtained with 55.58% of total variance explained. Results of factor analyzes are shown in Table 1. First factor of attitude towards internet use (refer Table 1) measures internet users’ feelings and anxiety about internet use. They express anxiety and low self-efficacy in using internet. Apart from this, they also find internet as a useful tool as it provides relevant information whenever required. Through the third factor, young users use internet independently without assistance of others but they do not spend a lot of time using internet. Lastly, they report that internet is good for people as it allows the users to do more interesting and imaginative work and enlarges their scope. The factor structure for internet advertising suggests that online advertising act as an important source of information as well as perceived to be good for consumers. Along with this, internet advertising manipulates users, distorts values among them and promotes a materialistic society. Forming attitudinal groups of young media users (TV and internet) Cluster analysis has been used to segment the attitudinal groups of young media users who are alike in their responses toward TV and internet content. Attitudinal dimensions of television and 34

internet content explored from factor analysis have been used as input variables for cluster analysis. Firstly, the existence of outliers was assessed, as cluster analysis is sensitive to outliers. Six (TV content) and fifteen observations (internet content) were identified as outliers, hence deleted from analysis. Secondly, segments were formed using average linkage method in hierarchical clustering approach with squared euclidean distances as the dissimilarity measure for defining an initial cluster solution. Three clusters were identified in case of TV content and two natural clusters were identified in case of internet content based on the highest percentage change in the agglomeration coefficient. Thirdly, result obtained from non-hierarchical cluster analysis i.e. K means method further confirmed the existence of same number of clusters. TV viewers clusters were named as, Focused viewers, Leisure oriented viewers and Skeptical viewers while internet users clusters, viz., Pros and Critics. Table 2 presents an overview of the characteristics of the clusters of young TV viewers and internet users as revealed through final cluster centres. Clusters of TV viewers Focused viewers: The cluster of focused viewers is comprised of approximately 33% respondents and represents respondents who hold positive attitude towards outcomes of television content. But, these young viewers score relatively low on viewing TV content negatively. This implies that these set of young viewers perceive TV content to have positive impact on them and at the same time they are well aware of the negative outcomes of TV content. Leisure oriented viewers: The leisure oriented viewers comprise of respondents who score low on negative outcomes of TV content but moderate on positive attributes of TV content representing nearly 39% of the sample. They consider television as a positive tool and view that TV content is very effective in informing and influencing them. But they feel that TV ads are moderately enjoyable and credible and also view attributes of TV programs as moderately positive. Therefore, this suggests that they consider TV content positively and think about it as a source of entertainment, information, and as a tool to relieve stress. Skeptical viewers: Those respondents who are low on positive attributes of TV content but high on negative outcomes of TV content compose the cluster of skeptical viewers representing approximately 27% of respondents. These viewers consider TV content as boring as well as tiring. Furthermore, positive attributes of TV programs, enjoyment, credence, and effectiveness dimensions of TV ads are perceived negatively by skeptical viewers. On the whole these viewers treat TV content negatively.

Clusters of internet users Pros: This segment is composed of young internet users (46.78%) who are high on positive attitudinal dimensions of internet ads. They view internet ads as informative, reliable/hedonic, good for consumers and enriching. They also perceive internet content to be useful and express 35

that they can independently control their use behavior. These respondents also score low on negative dimensions of internet ads as well as of internet use. It implies that pros perceive internet as a positive tool as well as express that they are confident in using internet. Hence, they hold positive attitude towards internet content. Critics: Second segment of internet users (53.22%) exhibit negative orientation with respect to the dimensions of internet content. They think that internet ads are manipulative and distorting values, exaggerating and fostering materialism. These respondents also score low on the positive dimensions of internet content. This implies that these set of respondents perceive that internet has negative impact on them. Table 3 presents the profile of attitudinal clusters of media users across demographic variables, TV viewing frequency and internet usage. The results from Table 3 indicate that differences across age and education level of mothers are statistically significant in case of TV viewer clusters. The table further shows that as compared to skeptical TV viewers, focused TV viewers and leisure oriented TV viewers are older. Mothers of leisure oriented viewers have the highest education level as nearly 8% of them have obtained a post-graduate degree or a higher degree, while 5% of the mothers of focused viewers segment and nearly 7% of the mothers of skeptical viewers have obtained a postgraduate degree or a higher degree. Table 3 also indicate statistically significant differences in the internet usersâ&#x20AC;&#x2122; age, gender, education status of fathers, as to who acts as primary caregiver to users and internet usage pattern between attitudinal clusters of internet users. It states that as compared to pros, critics are younger. Male critics account for 26% of the total sample as compared to female pros who account for 20% of the total sample. Fathers of critics have the highest education level as nearly 15% of them have obtained a post graduate degree or a higher degree, whereas only 9% of the fathers of pros segment have a post graduate degree or a higher degree. Approximately thirty three per cent critics report that mothers are the primary caregiver in contrast to 28.6% pros. Moreover, the proportion of critics who report that grandparents and siblings act as primary caregivers is three times the percentage of pros who report that grandparents and siblings act as primary caregiver to them. Pros (29.9%) report to use internet for more than three hours in a day in contrast to critics (33.4%) who use internet for more than three hours daily. Moreover, nearly 20% critics report to use internet according to need in contrast to 17% of pros who use internet according to need. Thereafter, interaction between clusters of TV viewers and internet users has been gauged with the help of cross tabulation. The results depicted in Table 4 represent that 51.54% focused viewers are also pros (35.77%) and 48.46% are critics (29.57%). However, majority of leisure oriented viewers (57.88%) are critics which are 42.47% of the total sample. On account of skeptical viewers, 52.26% of them are also critics (27.96%). At the same time, 47.74% of them are pros which are 29.05% of the total sample.

DISCUSSION AND CONCLUSIONS Findings depicted that young media users have more positive attitude toward internet use which is an emerging media than TV use (programs). This may be so because they consider internet to play an important role in one-to-one and interactive socialization of youth. Moreover, as it helps them in doing more interesting and imaginative work, facilitates learning, provides pleasure in the form of online games, social networking (facebook, orkut, twitter, wechat, whatsapp etc.) and is a source of leisure activities, they hold more positive attitude towards it. However, media users report mixed attitude (positive and negative) toward internet advertisements, while they view TV ads mainly as a positive tool. They think that TV ads are more entertaining and effective but internet ads are perceived by them as informative as well as exaggerating. Through attitudinal segmentation approach, internet users get segmented into two clusters namely Pros and Critics while, TV viewers get grouped into three segments namely, focused viewers, leisure oriented viewers and skeptical viewers. These attitudinal clusters predict media usersâ&#x20AC;&#x2122; attitude toward media content. Focused viewers who depict positive attitude towards TV content also consider internet content to be positive. Also, at the same time, some of the focused viewers hold negative attitude towards internet content. But, the proportion of respondents who depict positive attitude toward TV and internet content is higher than the ones who depict negative attitude toward these two media. However, majority of leisure oriented viewers are critics. These respondents view TV content as moderately positive but exhibit negative attitude toward internet content. A greater proportion of skeptical viewers also hold negative attitude toward internet content. At the same time, more respondents in the pros category respond to be skeptical about TV content. This suggests that TV viewership is more diffuse whereas polarization of attitude is high in case of internet content. Previous research reveals that the ability of marketers in understanding the TV and internet population improve through demographic segmentation approach. But, marketers are interested in learning as to why different groups of TV viewers and internet users respond to TV and internet content differently. Therefore, attitudinal segmentation as outlined in the present study is a critical step in understanding the heterogeneity of the growing young TV and internet population in India. The factor structure of media content shall help marketers in designing media campaigns for young media users. TV content should be framed to lessen the effects of negative outcomes in youth such as sexuality whereas internet messages should not sound exaggerating and should be targeted to suggest that marketers do not promote materialism in youth. They should also plan advertising messages which are neither misleading nor distorting values in youth. Skeptical TV viewers are found to be pro internet users. This may suggest transition towards new media owing to somewhat negative attitude towards traditional media. A large opportunity lies for marketers to develop positive attitude towards internet use, as a large chunk of TV viewers who hold positive attitude toward TV content are critical about internet use.

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List of Tables Table 1: Results of Factor analysis of attitude towards TV and internet content Attitude towards TV programs

Factor loadings

Commu -nalities

S16 TV programs frighten as well as cause nightmares (e.g. horror shows) *

0.654

0.490

S17 TV programs lead to obesity in youth due to eating disorders*

0.654

0.522

S19 Watching excessive TV programs cause sleep disturbances and psychiatric symptoms*

0.646

0.450

S18 TV programs increase desire for immediate gratification in youth*

0.640

0.513

S15 TV programs encourage illegal or risky behavior*

0.530

0.452

S9 Sexual content in the TV programs force the youth to indulge in such activity*

0.856

0.798

S8 TV programs display sexual content that spoil innocence of youth*

0.844

0.759

S6 Television programsâ&#x20AC;&#x2122; characters increase aggressive behavior in me*

0.857

0.766

S7 Television programsâ&#x20AC;&#x2122; characters excite me to imitate violent behavior*

0.845

0.762

S5 TV programs inform me about latest life style trends.

0.702

0.509

S4 TV programs relieve me of daily stress.

0.675

0.509

S1 TV programs act as a source of entertainment for me.

0.660

0.463

S11 TV programs decrease my physical activity as I spend most of the time watching television*

0.800

0.697

S10 TV programs decrease reading habits in me*

0.791

0.674

Factor 1: Negative psychological effects of TV (eigenvalue=3.330, variance explained=15.00%, mean = 2.66, S.D. =0.69)

Factor 2: Sexuality (eigenvalue = 1.392, variance explained = 12.73%, mean=2.68, S.D.=1.12)

Factor 3: Aggression (eigenvalue = 1.378, variance explained = 11.48%, mean=3.68, S.D.=0.97)

Factor 4: Positive attributes of TV programs (eigenvalue = 1.195, variance explained = 10.32%, mean=3.95, S.D.=0.57)

Factor 5: Negative physiological effects of TV programs (eigenvalue = 1.070, variance explained = 10.20%, mean=2.96, S.D.=0.99)

Attitude towards TV advertisements Factor 1: Enjoyment (eigenvalue = 2.241, variance explained = 26.12%, mean=5.07,

S.D.=1.27) A7 Enjoyable/tiring

0.799

0.653

A3 Interesting/boring

0.779

0.660

A4 Like/dislike

0.722

0.606

A1 Trustworthy/ lies

0.869

0.760

A2 Truthful/untruthful

0.860

0.754

A6 Persuasive/ non persuasive

0.856

0.738

A5 Informative/non-informative

0.631

0.471

S13 I am not uncomfortable in using internet*

0.766

0.608

S14 I hesitate to use internet if in case I look stupid*

0.752

0.590

S17 I need an experienced person nearby when I use the internet*

0.679

0.568

S12 If given the opportunity to use internet I am afraid that I might damage it in some way *

0.676

0.474

S15 I feel bored toward using the internet*

0.644

0.513

S16 When using the internet, I am not quite confident about what I am doing*

0.581

0.496

S4 Internet helps me to acquire relevant information I need

0.725

0.589

S5 Internet makes society more advanced

0.710

0.532

S3 Internet makes a great contribution to human life

0.635

0.485

S8 I do not need someone to tell me the best way to use the internet

0.778

0.649

S9 I can use the internet independently, without the assistance of others

0.704

0.625

Factor 2: Credence (eigenvalue=1.341, variance explained=22.37%, mean=3.38, S.D.=1.41)

Factor 3: Effectiveness (eigenvalue= 1.060, variance explained=17.81%, mean=4.92, S.D.=1.23)

Attitude towards internet use Factor 1: Confidence (eigenvalue=3.532, variance explained=21.29%, mean=3.67, S.D.=0.76)

Factor 2: Perceived usefulness (eigenvalue=1.942, variance explained=14.36%, mean=4.34, S.D.=0.55)

Factor 3: Independent control and use behavior (eigenvalue=1.425, variance explained=11.00%, mean=3.46, S.D.=0.73)

S11 I spend much time on using the internet

0.586

0.562

S1 Internet allow me to do more interesting and imaginative work

0.758

0.641

S2 Internet enlarges my scope

0.649

0.606

S2 Internet advertising is a valuable source of information about local sales

0.819

0.719

S3 Internet advertising is a valuable source of information about products/ services

0.788

0.718

S4 Internet advertising helps me keep up to date with products/services available in the marketplace

0.692

0.570

S1 Internet advertising is a valuable source of information about latest fashion

0.661

0.530

S23 Internet advertising promotes undesirable values in our society*

0.794

0.673

S24 Internet advertising distorts the values of our youth*

0.758

0.628

S22 Internet advertising persuades people to buy things they should not buy*

0.687

0.543

S21 Internet advertising insults the intelligence of the average consumer *

0.601

0.451

S13 Internet advertising provides accurate information about products/services

0.700

0.536

S14 Internet advertising is interesting and attractive

0.675

0.547

S15 Internet advertising are even more enjoyable than other media contents

0.663

0.516

S16 I like to think about what I see on Internet advertising

0.498

0.420

S9 Internet advertising is essential

0.741

0.613

S10 Internet advertising helps raise our standard of living

0.692

0.598

S11 Internet advertising results in better products for the public

0.641

0.578

Factor 4: Enrichment (eigenvalue=1.039, variance explained=10.03%, mean=4.34, S.D.=0.57)

Attitude towards internet advertisements Factor 1: Informative (eigenvalue=5.603, variance explained=10.96%, mean=4.09, S.D.=0.69)

Factor 2: Manipulative and distorting values (eigenvalue=2.438, variance explained=10.16%, mean=2.72, S.D.=0.73)

Factor 3: Reliable and hedonic (eigenvalue=1.344, variance explained=10.03%, mean=3.77, S.D.=0.52)

Factor 4: Good for consumers (eigenvalue=1.276, variance explained=9.06%, mean=3.85, S.D.=0.53)

S12 Internet advertising is a valuable source of information about how to establish personal taste

0.450

0.460

S17 Internet advertising does not provide a true picture of the product advertised *

0.736

0.583

S19 Internet advertising is misleading*

0.696

0.536

S18 Internet advertising is an impersonal way of selling*

0.653

0.487

S20 Internet advertising makes people buy a lot of things that they do not really need *

0.462

0.428

S8 Internet advertising makes people live in a world of fantasy*

0.675

0.545

S7 Internet advertising encourages people to buy something to impress others*

0.670

0.592

S6 Internet advertising promotes a materialistic society*

0.634

0.513

Factor 5: Exaggerating (eigenvalue=1.116, variance explained=7.99%, mean=2.54, S.D.=0.63)

Factor 6: Materialistic (eigenvalue=1.007, variance explained=7.35%, mean=2.29, S.D.=0.66)

â&#x20AC;&#x153;Source: Researcherâ&#x20AC;? Note: *Reverse coded

Table 2 Profile of young media user clusters and F-test results

Clusters Attitudinal factors of TV content

Focuse d viewers (32.63 %)

Leisure oriente d viewers (38.70 %)

Negative psychological effects of TV programs

2.67 (M)

2.48 (L)

Sexuality

2.80 (M)

2.17 (L)

3.48 (L)

3.64 (M)

Aggression

Clusters Skeptic al viewers

Fvalue

Attitudinal factors of internet content

Pros

Critics

(46.78 %)

(53.22 %)

F-value

(26.67 %) 2.88 (H)

19.923

3.21 (H)

60.829

3.93 (H)

11.655

Informative

4.49 (H)

Manipulative and distorting values

2.37 (L)

Reliable and hedonic

4.15 (H)

3.74 (L) 351.972 *

3.03 (H)

177.650 *

3.44 (L) 620.317 *

Positive attributes of TV programs

4.04 (H)

3.99 (M)

3.80 (L)

Negative physiological effects of TV programs

2.99 (M)

2.86 (L)

3.05 (H)

Enjoyment

5.56 (H)

5.48 (M)

3.97 (L)

Credence

5.33 (H)

3.10 (M)

Effectiveness

5.11 (M)

5.50 (H)

11.545 *

Good for consumers

4.24 (H)

2.417** Exaggerating

3.50 (L) 651.368 *

2.29 (L)

2.75 (H)

108.214

2.66 (H)

408.345

154.84 1*

Materialistic

3.09 (L)

437.70 5*

Confidence

3.62 (L)

3.72 (H)

3.084**

3.92 (L)

138.22 1*

Perceived usefulness

4.52 (H)

4.17 (L)

77.179*

Independent control and use behavior

3.67 (H)

3.28 (L)

54.637*

Enrichment

4.53 (H)

4.17 (L)

76.166*

1.86 (L)

“Source: Researcher” Note: *p< 0.01, **p< 0.10, H= high, M=medium, and L=low

Table 3: Profile of attitudinal cluster groups of TV content and internet content Variables

Attitudinal clusters of TV content

Focused viewers

Leisure oriented viewers N

Chisquare (2)

Attitudinal cluster of internet content

Skeptical viewers N

Pros

Chisquare (2)

Critics

15-19

124

17.5

121

17.1

120

16.9

20-24

107

15.1

153

21.6

11.7

Male

129

18.2

142

20.1

104

14.7

Female

102

14.4

132

18.6

14.0

149

21.3

208

29.8

178

25.5

164

23.5

187

26.6

182

26.0

140

20.0

190

27.2

Age 11.059*

7.458*

Gender

1.157

4.766**

Family income Less than 50,000

143

20.2

169

23.9

111

15.7

200

28.6

220

31.5

50,001-1,00,000

8.9

11.2

8.8

14.0

102

14.6

More than 1,00,000

3.5

3.7

4.2

8.4

7.2

Under-graduate

10.7

103

14.5

6.5

108

15.5

114

16.3

Graduation

119

16.8

117

16.5

108

15.3

160

22.9

182

26.0

Post-graduation

5.1

7.6

6.9

8.4

10.9

Housewife

184

26.0

227

32.1

152

21.5

268

38.3

287

41.1

Govt. service

2.4

2.5

2.4

4.7

Private service

2.0

3.0

2.7

3.0

4.9

Profession/Business

2.3

1.1

2.1

3.0

2.6

Under-graduate

11.0

13.0

7.6

112

16.0

113

16.2

Graduation

101

14.3

124

17.5

12.9

153

21.9

156

22.3

Post-graduation or higher

7.3

8.2

8.9

103

14.7

Govt. service

7.2

11.0

6.8

10.9

14.0

Private service

3.5

4.9

3.0

5.2

6.4

Profession

2.3

1.4

1.0

2.7

1.9

Business

132

18.6

141

19.9

122

17.2

186

26.6

204

29.2

Agriculture

1.0

1.4

0.7

1.4

1.7

Mother

146

20.6

163

23.0

125

17.7

200

28.6

230

32.9

Father

10.2

105

14.8

9.3

118

16.9

120

17.2

4.590

3.733

Mother education

15.595*

0.825

or higher Mother occupation

8.472

6.203

Father education

5.260

7.355**

Father occupation

8.964

3.035

Primary caregiver

Grandparents and

7.336

4.684***

Siblings

1.8

0.8

1.7

5.4

7.3

5.2

115

16.2

117

16.5

13.0

5.9

9.0

5.6

5.1

5.8

4.8

1.3

3.1

No. of hours watching TV daily Less than 1 hour 1-2 hours 3.829

2-3 hours More than 3 hours

Internet usage per day Less than 1 hour 37

5.3

9.0

9.9

101

14.4

7.4

4.6

7.3

5.4

118

16.9

138

19.7

1-2 hours 2-3 hours 18.185*

More than 3 hours According to need

“Source: Researcher” Note: *Significant at 1% level of significance; **Significant at 5% level of significance, *** Significant at 10% level of significance.

Table 4: Crosstabs representing clusters of TV viewers and internet users Clusters of internet users Cluster of TV viewers

Total (n)

Pros

Critics

Focused viewers

117 (51.54a) (35.77b)

110 (48.46a) (29.57b)

227

Leisure oriented viewers

115 (42.12a) (35.18b)

158 (57.88a) (42.47b)

273

Skeptical viewers

95 (47.74a) (29.05b)

104 (52.26a) (27.96b)

199

327

372

699

Total (n)

“Source: Researcher” Note: Figures in Parenthesis of a represent percentages of TV viewer clusters and b represent percentages of internet user clusters. Bold figures represent frequency of TV viewer clusters and internet user clusters

KNOWLEDGE MANAGEMENT (KM) MEDIATES THE RELATIONSHIP BETWEEN ORGANIZATIONAL LEARNING (OL) AND ORGANIZATIONAL PERFORMANCE (OP): A STUDY OF FEW SELECTED INDIAN ORGANIZATIONS 1 Luxmi Ashu Vashisht2 This paper aims to review the current literature on knowledge Management (KM), organizational learning (OL) and Organizational Performance (OP), particularly in relation to the knowledge intensive manufacturing and the services sector firms in India. With the growth story in the two key sectors of the Indian economy and the six questionnaires have been obtained from employees working in public and private sector growing potential, it is imperative to understand how these two sectors are booming at an exponential rate. For the present study, a total of one hundred and ninety manufacturing and service organizations, in total eight organizations were studied. The paper formulates some hypotheses and conceptual model from the literature review. The results revealed that there exists a significant and positive relationship between knowledge management, organizational learning and organizational performance. It was found that knowledge management fully mediated the link between organizational learning and organizational performance. It simply vindicates that organizational learning alone is not sufficient to get the best of performance from organizations; rather managers need to spice it up with the right techniques of knowledge management to achieve surpassing organizational performance. The result of study was found to be consistent with earlier studies of Garratt (1990), Sinkula et al. (1997), McElroy, M. W. (2000), Vera & Crossan (2003), Su et al. (2004), King (2009), Tseng & Kuo (2010), North et al. (2014), Meihami et al. (2014), Aggestam, L. (2015), Haghighi & Bagheri (2015), Valmohammadi & Ahmadi (2015), Leal-Rodriguez et al. (2015), Ali et al. (2017), Uddin et al. (2017) and others. Keywords: Knowledge Management, Organizational Learning, Organizational Performance, Manufacturing and Service Sector, India. INTRODUCTION Knowledge Management (KM) is a very critical factor in the success and in providing a competitive advantage to organizations. To gain and sustain competitive advantage firms deploy valuable resources, as per the resource-based view (RBV), Barney (1991; Grant (1996), which states that accumulation of resources is a vital factor in influencing business success. However, the direct utilization of the resource-based view approach in foreseeing firm achievement is excessively simple and shortsighted. In todayâ&#x20AC;&#x2122;s knowledge driven world, knowledge 1

Professor, University Business luxmimalodia@yahoo.com

School,

Panjab

University,

Chandigarh

-160025,

India.

Email:

Senior Research Fellow, University Business School, Panjab University, Chandigarh -160025, India. Email: vashishthashu@.com

management is an important factor along with land, labor, machine and entrepreneurship. There are certain Knowledge management capabilities such as knowledge acquisition, knowledge conversion, and knowledge application which are derived from assortment of organizational structure and culture of the firm and are deeply rooted in the firm’s operations, Grant (1996). For strategic success of any business it is important to utilize knowledge management (KM) and organizational learning (OL) for achieving long term organizational performance (OP). However, only knowledge acquisition or dissemination doesn’t positively affect performance directly, however there are certain knowledge management processes which positively affect performance directly or indirectly, Darroch, (2005), thus indicating that there must exist some other factors that may increase the success rate of organizational learning, and from literature review one can find that knowledge management process may prove to be a crucial factor that may act as a catalyst to increase the effect of knowledge management. But knowledge management and organizational learning are the two of most jugate concepts that have been confused by the managers. So this paper will tend to differentiate between these two concepts and will re-examine the relationship between knowledge management and organizational performance, and propose a mediating conceptualization of OL. Indian manufacturing and services sector has been selected for the study as in an economy, these two sectors are regarded as knowledge-intensive due to vast quantum of knowledge as input, scrimpy life cycle of products, increasing demand for customizable products, will be selected as the target of study because of having large amount of knowledge input, shorter product life cycles, high demand for customized products, and great quantity of production value. Thus, the results of this study depend on Indian knowledge-intensive firms to provide a rich data set of information regarding knowledge management and organizational learning behaviours in growing, unstable & competitive business environment so as to amplify organizational performance. Knowledge Management Information is turning out to be ever more imperative in our economy now, and most businesses have recognized that knowledge when managed properly can lead competitive advantage. But as there is plethora of information available to most of business, however, it has become arduous to handle such an exuberance of available data. Knowledge management (KM) may help to extricate this carking paradox for us, Liao (2003). Gold et al. (2001) have stated the issue of effective management of knowledge by keeping in view of organizational capabilities, which states that for effective knowledge management there exists certain organizational capabilities or ”preconditions” which are exigent such as knowledge infrastructure consisting of technology, structure, and culture along with a knowledge process architecture of acquisition, conversion, application, and protection, which results in the competitive success of a firm. Various studies have elucidated different aspects of knowledge management processes such as: capture, transfer, and use, Delong (1997); acquire, collaborate, integrate, experiment, Leonard (1995); create, transfer, assemble, integrate, and exploit, Teece (1998); create, transfer use and create, process, Skyrme (1998), Spender (1996). In depth examination of the available characteristics enables us 49

to cluster them into four broad dimensions of process capability-acquisition process, conversion process, application processes, and protection process, Gold et al., (2001). Knowledge management capabilities befall within three interrelated processes: knowledge acquisition, knowledge conversion, and knowledge application, Gold et al. (2001), Cui et al. (2005). Organizational Learning In this rapidly changing volatile and uncertain economy, numerous organizations are endeavouring to survive and remain competitive. In order to achieve protracted organizational success, one of the available strategic means is developing organizational learning (OL), Senge (1990); Crossan et al. (1999), Harung (1996); Marsick and Watkins (2003). Hence, it becomes important to analyze this epochal construct of organizational learning which has become a prerequisite for determining the success of organizations. Previous literature has defined this construct from numerous viewpoints. Jerez-Gomez et al. (2005) mentioned about multitudinous studies focusing on this construct which applied a psychological approach such as Cyert and March, (1963); Daft and Weick (1984), and a sociological approach such as Nelson and Winter (1982); Levitt and March (1988), or from the view point of Organizational Theory such as defined by Cangelosi and Dill (1965); Senge (1990); Huber (1991). ‘Learning curves’ as specified by Yelle (1979); Lieberman (1987) and “experience curves” as specified by Boston Consulting Group (1968) are the traditional methods to measure learning, however, Garvin, (1993) stated these curves as “incomplete measuring tools”. Organizational learning is an intricate multidimensional construct encompassing multiple sub processes, Slater and Narver (1994). So, Jerez-Gomez et al. (2005) consider organizational learning to be a latent multidimensional construct including managerial commitment, systems perspective, openness and experimentation, and knowledge transfer and integration. In order to face the current precarious and revulsive business environment, organizations must keep learning so as to be competitiveness and gain strategic success. Organizational Performance A recurring theme in most branches of management is the subject of performance which is of sapidity to both academic scholars and practicing managers. Inspite of the ponderability of the concept of performance lying within the broader area of organizational effectiveness, still dealing with the study of performance in research studies is presumably one of the thorniest issues confronting the academic researcher at present. There exists plethora of studies available on this subject and the number is still increasing, but a terminology and definition is still missing on this subject. Venkatrman & Ramanujam (1986) stated that financial performance, operational performance, and organizational effectiveness should be included in the single concept of performance. However, a traditional outlook views organizational performance as a construct which only includes financial performance with consideration on budgets, assets, operations, products, services, markets and human resources as a crucial factor in influencing the success of an organization Dixon (1999); Easterby et al. (1999), thereby associating the financial benefits of 50

organizational performance with the success of organization, Thurbin (1994). However, the need of the moment is to develop a far wider dimension of interpretations embracing the concept of performance. The importance of performance measurement system is manifold. Not only does it demonstrate how an organization does, how well it does it and how much progress it makes over time in archiving its goals, most importantly, it helps to manage organizational change, Yeo (2003), Darroch, (2005). REVIEW OF LITERATURE The following part consists of review of previous studies regarding the variables under study. Relationship between Organizational Learning and Knowledge Management The previous literature on knowledge management discusses its numerous influences on organizational learning. Various authors have stated that these two concepts are cause and effect simultaneously, while others stated organizational learning is a cause, knowledge management is an effect; or may be opposite. In various studies, researchers have implicitly assumed a perspective where knowledge management exerts a direct effect on organizational learning where the causal relationship direction turns primarily from knowledge management to organizational learning, while various other studies have assumed a perspective where organizational learning exerts a direct effect on knowledge management where the causal relationship direction turns primarily from organizational learning to knowledge management which could also account for the associations between these two variables, Su and Hsieh (2003, 2004). In this perspective, knowledge management is viewed as a reaction to organizational learning rather than an action that contribute towards organizational learning. So, from this perspective, the present study adopted the model in which organizational learning exerts a direct effect on knowledge management which views knowledge management is a reaction to organizational learning. Garratt (1990) in his studies found that a learning organization is the result of consistent effort in the direction of organizational development and learning. In order to generate consistent organizational performance, organization must evolve personal or group learning proficiency, and to develop learning capabilities, organization must adhere to well developed process of knowledge management. In the absence of knowledge management an organization may stumble to cultivate personal or group learning capabilities, Garratt (1990), Su et al. (2004). Jerez-Gomez, et al. (2005) concluded that the strategic resource leading to organizational learning is the management of knowledge and, more specifically, its acquisition or creation, along with its dissemination and integration within the organizations. The base of a well structured knowledge in organizations may act as a pillar to develop organizational learning, Nonaka and Takeuchi (1995). Organizational learning is a dynamic process which is based on proper management of knowledge, which moves among the diverse levels of action, moving from the individual to the group level, and then to the organizational level and back again, Huber (1991); Crossan et al. (1999). Various studies have found significant and positive relationship between these two variables, such as Garratt (1990), Sinkula et al. (1997), McElroy, M. W. (2000), Vera et al. 51

(2003), Lin & Lee (2004), Su et al. (2004), Liao et al. (2008), King, W. R. (2009), North et al. (2014), Aggestam, L. (2015), Erkelens et al. (2015), Kamasak et al. (2016), Jalali & Rezaie (2016), Serrat (2017). and others. Relationship between Organizational Learning and Organizational Performance Learning whether it is individual, group or organizational is not only an important resource for a firm, but also it serves as a basic source of competitive advantage, therefore, organizational learning capabilities which are related to the process of knowledge management in an organization, Gold et al. (2001) thereby ultimately leads to organizational performance. With effective and efficient learning process, most organizations claim that it is helpful to organizational performance. From a strategic point of view, various studies have considered organizational learning as a major source of heterogeneity among various organizations thereby leading to competitive advantage, Grant, (1996) and ultimately enhanced organizational performance in different context may it be financial performance, marketing performance or partnership performance, Tippins and Ravipreet, (2003). An organization with an obdurate backbone of organizational learning is not only a collector or storehouse of knowledge but also a processor of it. Learning from the knowledge available from the feedback loop from customers, channels, and competitors must be used to develop core competence, which will allow businesses to earn long-run supernormal profits. ). Various studies have found significant and positive relationship between these two variables, such as Su et al. (2004), Yang et al., (2004), Khandekar and Sharma (2006); Tseng (2010), Chien et al. (2015), Hussein et al. (2016), Miraz et al. (2016), Pollanen et al. (2017), Xue (2017) and others. Relationship between Knowledge Management and Organizational Performance Effective knowledge management is an epochal organizational resource which helps in development of capabilities and ultimately leads to key aspects of organizational performance. With greater knowledge management capabilities, firms can obtain and utilize knowledge more effectively and efficiently, which results in above-normal performance. Also, when organizations develop higher knowledge management capabilities, they can more effectively develop strategies related to marketing, financial or related to partnership offerings to meet their strategic needs and can ultimately enhance performance in different contexts. Various studies have proved this relationship such as Valmohammadi & Ahmadi (2015), Darroch (2005) found that acquisition of knowledge has more indirect than direct influence on organizational performance. In an organization knowledge management can be regarded as a bottom up approach, when this knowledge is shared between organization boundaries, it leads to cumulative effect thereby it gets embedded within the processes, products and services of the organization which ultimately leads to learning organization. Thus it becomes important to harness the collective knowledge of the organization which has been gained through experiences and competence. Thus organizations must develop a culture of knowledge management to tap into the vast knowledge base of the employees in order to develop a culture of organizational learning so as to preserve and expand 52

the core competencies of the employees. Various studies have proved this relationship such as those conducted by Birasnav (2014), Meihami et al. (2014), Haghighi & Bagheri (2015), Valmohammadi & Ahmadi (2015), Ha et al. (2016), Masaâ&#x20AC;&#x2122;deh et al. (2017), Soon et al. (2017) and others. Relationship between Knowledge Management, Organizational Learning and Organizational Performance Also, the organization where there prevails the culture of learning organization stresses on the fact that they must encourage individuals to foster creation, sharing and exploitation of knowledge. The organizations that are successfully managing this process have shown to have better organization performance and hence achieve competitive advantage. Thus, organizations must revise their organization learning cycle to create knowledge and ultimately boost organization performance, Dixon (1994). Thus it becomes important to identify and harness the collective knowledge of the employees and support it with right strategy, structure, system, culture and policies and practices of the people management in order to tap vast knowledge base and create a learning environment in the organization and utilize it to enhance organizational performance. A systematic way to deal with performance management may help in distinguishing the areas where the deployment of knowledge management technology is lacking. A powerful system of performance management may enhance the process of acquiring and deploying necessary knowledge management technology to a required spot where investigation and exploitation of information could be progressed. As contended by Davenport & Prusak (2011), the way to a superior productivity and profitability of knowledge workers is applying organization learning through technology all the more precisely. Various studies have proved this relationship such as Yang et al., (2004); Khandekar and Sharma (2006); Tseng (2010), LealRodriguez et al. (2015), Liebowitz & Frank (2016), Bhatti et al. (2016), Kasemsap (2016), Serrat (2017) and others. METHODOLOGY Present study The base for the present study has been developed by literature review of the above mentioned and other similar studies available. The authors attempt to study knowledge management, learning organization and organisational performance. In all, eight organisations were studied. The paper studies knowledge management, learning organization and organizational performance. The main objectives, conceptual model and hypothesis of the study are as follows: Objectives ď&#x201A;ˇ To study knowledge management, organizational learning and organizational performance in few selected Indian organizations. ď&#x201A;ˇ To examine the relationship between 53

ď&#x201A;ˇ

a) Organizational learning and knowledge management b) Knowledge management and organizational performance c) Organizational learning and organizational performance in few selected Indian organizations. To examine the mediating role of knowledge management between organizational learning and organizational performance in few selected Indian organizations.

Conceptual model Knowledge Acquisition Knowledge Application Knowledge Conversion

Knowledge management H1a

H2a

H4a

Openness and System Perspective Experimentation Management commitment Knowledge Transfer and Integration

Organizational learning H3a

Financial Market Performance Performance Partnership Performance

Organizational performance

Figure 1: Conceptual model linking Knowledge Management and Organizational Performance with mediating role of Organizational Learning Hypothesis H1a. There is a significant correlation between knowledge management and organizational learning in few selected Indian organizations. H2a. There is a significant correlation between knowledge management and organizational performance in few selected Indian organizations. H3a. There is a significant correlation between organizational learning and organizational performance in few selected Indian organizations. H4a. Organizational learning acts as a mediating variable between knowledge management and organizational performance in few selected Indian organizations. Research Design The study is descriptive and empirical in nature. Eight Indian organizations were chosen. Then a sample of 196 respondents was chosen from a sample frame of eight organizations using random sampling.

Data Collection Questionnaire survey method was utilized for primary data collection. Data collection tool used were as follows: a) Knowledge Management: To measure this variable, multidimensional scale developed by Gold et al. (2001) comprising of three subscales, that is, Knowledge Acquisition, Knowledge Application and Knowledge Conversion was used. Where knowledge acquisition defined as the procedure to scrutinize and obtain maiden knowledge, fabricate new knowledge out of extant knowledge through collaboration between individuals and business partners; knowledge conversion defined as the astuteness to make knowledge utilitarian; and knowledge application is defined as the procedure which is oriented towards the proper utilization of knowledge. b) Organization performance: To measure this variable, multidimensional scale developed by Jerez-Gomez et al. (2005) comprising of three subscales, that is, Financial Performance, Market Performance and Partnership Performance was used. Where financial performance is related to the success of the business programmes with respect to the resources employed in implementing them; market performance is the accomplishment of a business product and programme in existing business and the future positioning of the firm; and partnership performance is the accomplishment of organizational objectives which concerns the strength, stability and sustainability of their relationships with the firmâ&#x20AC;&#x2122;s partners. c) Organization learning: To measure this variable, multidimensional scale developed by Emden et al. (2005) comprising of four subscales, that is, Management Commitment, System Perspective, Openness and Experimentation, and Knowledge Transfer and Integration. Where, management commitment is related to recognition of the importance of learning and developing an organization culture which promotes the acquisition, creation and transfer of knowledge as fundamental values; system perspective stresses to bring together the members of the organization around a common identity; openness and experimentation involves welcoming new ideas and viewpoints from both internally and externally and thereby widening the horizons of individual knowledge; knowledge transfer and integration refers to simultaneously occurring two closely linked processes rather than successively transferring and integration of knowledge internally. The questionnaire also sought demographic information of respondentâ&#x20AC;&#x2122;s i.e. age, education, marital status, hierarchical level, experience in present organization, experience in present position & total work experience. DATA ANALYSIS The data was analyzed using SPSS. Necessary tables encompassing SPSS output is included in the paper at the appropriate places. The Confidence Level of 0.05 is assumed for the study.

Descriptive Statistics Table 1: Descriptive Statistics Dimensions Mean 4.2881 Organization performance Financial Performance 4.4682 Market Performance 4.4181 Partnership Performance 4.1582 4.2775 Organization learning Mgt Commitment 4.0305 System Perspective 4.6497 Openness and Experimentation 4.6843 Knowledge Transfer and Integration 3.9004 4.6699 knowledge Management Knowledge Acquisition 4.6822 Knowledge Conversion 4.6441 Knowledge Application 4.6780 Valid N (list wise) 196

Std. Deviation .43617 .49306 .48956 .58261 .21427 .29425 .34274 .37042 .24050 .29779 .34655 .32315 .33159

The descriptive statistics table 1 above shows the difference in level of organization learning with variables management commitment, system perspective, openness & experimentation, knowledge transfer & integration; the level of organization performance with sub variables financial performance, market performance and partnership performance; and the level of knowledge management with sub variables knowledge acquisition, knowledge conversion and knowledge application. The mean of knowledge management was found highest with score of 4.66, which denotes that the select firms are continuously acquiring or creating potentially utilitarian knowledge which is appropriate for them and providing it to those who can utilize it whenever necessary so as to achieve maximum effective application in order to generate positive organization performance. The mean scores for organization performance was 4.28 with financial performance mean score as 4.46, market performance mean score as 4.41 and partnership performance mean score as 4.15 thereby showing that the organization with higher performance on the financial and market front displayed better overall organizational performance. The mean scores for organization learning was 4.2775, with highest mean scores of sub dimensions as openness & experimentation mean score as 4.68, management communication mean score as 4.03, system perspective mean score as 4.64, Knowledge transfer & integration mean score as 3.90, which denotes that the select organizations are on a learning curve as they were found to be successfully retaining their knowledge and transferring it whenever required and spreading it throughout the various departments of the organization.

.301** .001

.032

.695**

.001

.734

.000

.702** .000

.452** .000

.413** .000

.351** .001

.349** .000

.402** .000

.000

.224* .003

.015

.268

.207* .000

.024

.268

.321** .042

.378** .143

.295** .115

.367** .056

.847** .316

.774** .437

.695** .734

.103

.292** .000

.274** .007

.317** .551

.003

.004

.001

.000

.137

.000

.001

.000

.188* .001

.136

.146

.176

.093

.072

.032

.103

.358**

.247** .000

.055

.000

.018

.313** .000

.261** .000

.300** .000

.330** .000

.138

.357** .012

.301** .015

.292** .003

.358** .007

.429**

.269** .000 .000

.424** .000

.477** .000

.470** .001

.514** .000

.218* .001

.229* .022

.224* .024

.274** .000

.247** .551

.429** .003

.418**

.000

.329** .000

.406** .000

.301** .000

.382** .000

.297** .002

.211* .000

.207* .001

.317** .000

.055

.269** .000

.418** .000

.457** 13

.468** 12

.441** 11

.510** 10

.279** 9

.334** 8

.295** 7

.374** 6

.533** 5

.777** 4

.734** 3

.686** 2

Financial organization Knowledge Openness & System Management organization performance performance transfer & experimentat perspective commitment learning integration ion .001 .295** .000 .374** .000 .533** .000 .777** .000 .734** .000 .686** 1 1

Correlation analysis The following table shows correlation between Knowledge Management and Organisational performance (H1a); Knowledge management and organizational learning (H2a); and Organizational learning and organizational performance (H3a).

Table 2: Correlations

.410** .000 .135 .857** .144 .630** .000 .000 1

.656** .000

.378** .249** .000 .881** .007 .000 1 .630** .000

.759** .000

.322** .000 .172 .759** .656** .000 .000

.000

.922** .063

.412** 1 .922** .881** .000 .857** .000 .000

.249** .063 .007 .135 .144

.172

.027

.203*

.027

.203* .000

.319** 1

.000

1 .319** .412** .000 .322** .000 .410** .000 .000

.378** .000

.702** .349** .000 .402** .000 .000

.351** .000

.413** .000

.452** .000

.774** .847** .000 .367** .000 .295** .000 .321** .000 .000

.378** .001

.072 .437 .093 .316 .176 .056 .146 .115 .188* .143 .042

.136

.357** .000 .138 .330** .137 .300** .000 .313** .004 .001

.261** .001

.229* .218* .012 .514** .018 .470** .000 .424** .000 .000

.477** .000

.211* .297** .022 .382** .001 .301** .000 .329** .000

.406** .001

.334** .279** .000 .510** .002 .441** .000

.000

.468** .000

.457** .000

.000

Knowledge Knowledge Knowledge knowledge Partnership Market application Conversion Acquisition Management performance performance

13 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). These results are discussed further below: The results of Pearsonâ&#x20AC;&#x2122;s coefficient of correlation shows a significant and positive correlation between knowledge management and organization performance (r=.367**, p=.000), at p value less than 0.05. The study finds management commitment, system perspective, openness & experimentation, knowledge transfer & integration dimensions of organization learning as positive predictors of knowledge management practices such as knowledge acquisition, knowledge conversion, and knowledge application. Thus, when top management recognizes the significance of knowledge management as vital to its strategic planning, the employees get a sense of bearing and direction. Employees feel more adjusted also, connected with the organizational objectives identified with knowledge management activities. Besides, at the point 58

when an organization develops a system to manage the performance of its employees by distinguishing the gaps in expected performance and making the new learning accessible to all employees in the hierarchical databases of the organization, it then creates a learning environment which promotes the creation and development of maiden information. A successful administration of employees' performance might increase the success rate of idea generation and innovation. Knowledge creation can also occur otherwise, yet it can't be effectively used in the absence of a system of coordinating employees' performance. These findings can be supported by studies in the field of organizational learning; such as: Zack et al. (2009), Tseng (2010); Huang et al., (2008); O'Dell and Hubert (2011), Haghighi & Bagheri (2015), Meihami et al. (2014), Valmohammadi & Ahmadi (2015), Ha et al. (2016), Masa’deh et al. (2017), Soon et al. (2017) and others. Hence, Hypothesis H1a: There is a significant correlation between Knowledge management and organizational performance is proved. The results of Pearson’s coefficient of correlation shows a significant and positive correlation between Knowledge Management and organization learning (r=.510**, .000) at p value less than 0.05. Thus organizations must facilitate building of a positive working culture which must include learning, sharing, openness and trust, which are the requirements of a good knowledge management culture, Chang and Lee (2007); O’Dell and Hubert (2011). In McKinsey, chief executive officers have developed a culture of trust, openness and support to encourage knowledge management practices, Bartlett (1996). Thus, knowledge-oriented organizations promote the concept of management commitment, system perspective, openness & experimentation, knowledge transfer & integration, knowledge acquisition, knowledge conversion, and knowledge application to improve organizational learning and knowledge management practices. These findings can be supported by various research studies such as Garratt (1990), Sinkula et al. (1997), McElroy, M. W. (2000), Vera et al. (2003), Prusak & Matson (2006), Spender et al. (2008), King, W. R. (2009), WeiLing Ke and Su et al. (2004), North et al. (2014), Aggestam (2015), Erkelens et al. (2015), Garratt (1990), Sinkula et al. (1997), McElroy, M. W. (2000), Vera et al. (2003), Lin & Lee (2004), Su et al. (2004), Liao et al. (2008), King, W. R. (2009), North et al. (2014), Aggestam, L. (2015), Erkelens et al. (2015), Kamasak et al. (2016), Jalali & Rezaie (2016), Serrat (2017) and others. Hence, Hypothesis H2a: There is a significant correlation between Knowledge management and organizational learning is proved. The results of Pearson’s coefficient of correlation shows a significant and positive correlation between organization learning and organization performance (r=.374**, p=.000), at p value less than 0.05.

The results shows that organization learning facilitate openness and experimentation and ownership among employees, which helps in enhancing the firm's performance in terms of knowledge creation and financial performance, these results are consistent to the idea of management commitment in order to empower the workforce to motivate employees to demonstrate learning and performance, Marsick (2009); Dirani (2009). For example, various equipment of an automobile are designed and fabricated in diverse units of its organization and various parameters of equipment directly affect the design of others. Thus culture of organizational learning can help to avoid any fault in the design and assembling of a flawed design and thereby saving the subsequent remedial cost. Moreover, when employees and the organizations have a larger amount of accomplishment orientation and the management is committed in the organization and is willing to support individuals in risk-taking and promptly gives control to individuals over resources which they have to accomplish their work and thereby resulting into higher organization performance including financial performance, market performance, and partnership performance. Tippins and Ravipreet (2003) mentioned that the relationship between IT competency and firm performance is mediated by organizational learning. Results are supportive to the literature about the positive effect of organization learning on organization performance such as Yang et al., (2004); Khandekar and Sharma (2006); Tseng (2010), Chien et al. (2015), Wong et al (2015), Hussein et al. (2016), Miraz et al. (2016), Pollanen et al. (2017), Xue (2017) and others. Hence, Hypothesis H3a: There is a significant correlation between Organizational learning and organizational performance is proved. Mediation analysis The present paper utilized Baron and Kenny method for mediation analysis. The following table presents the results of mediation analysis. Table 3: Role of Knowledge Management as mediator between Organizational Learning & Organizational Performance Dependent Independent Beta t Sig. F Ratio Sig. Adjusted variable variable Value Value R square Organizational Knowledge .301 3.394 .001 11.522 .001 .083 Learning Management Knowledge Organizational .602 8.123 .000 65.977 .000 .357 Management Performance Organizational Organizational .268 3.000 .003 8.998 .003 .064 Learning Performance

Organizational Performance

Knowledge Management

.239

2.435

.016 12.770

.000

.167

Organizational Learning

.252

2.567

.012

The steps adopted for mediation analysis are discussed below: Step-1: Coefficient of organizational learning was found positive and significant for knowledge management (β = .301, p ≤ 0.000), and 8% of the variances in knowledge management was explained by organizational learning. Step-2: Coefficients of knowledge management was found positive and significant for organizational performance (β = .602, p ≤ 0.000), and 36% of the variances in organizational performance was explained by knowledge management. Step-3: Coefficients organizational learning of was found positive and significant for organizational performance (β = .268, p ≤ 0.001) and 7 % of the variances in organizational performance was explained by organizational learning. Step-4: when in the last step knowledge management and organizational learning were added simultaneously in the regression equation, the significance level of knowledge management was found to be reduced, which showed that organization learning mediates the relation between knowledge management and organizational performance. More particularly, to comprehend the interceding impact of organization learning on knowledge management practices and organization's performance in organizations, one needs to consider the perplexing structure of working together or cooperating in this competitively focused business environment particularly in highly innovative areas. For instance, for successful implementation of a power plant project requires coordinated efforts and joint working of several teams spread crosswise over geologies and disciplines. In such settings, complex errands can be refined and accomplished through participation and co-operation, sharing of information, experiences, perspectives and open discussions. Exactly when this happens at the organizational level, it helps in accomplishing financial targets. At the same moment when people share and grasp best practices of distinctive divisions of the organizations, it brings about diminished costs. Organization learning can likewise dismiss immoderate bungles or costly mismatches and reengineering when exchange of crucial information is done straightforwardly in a convenient and advantageous manner on time. This result has been supported by various studies such as Yang et al., (2004); Khandekar and Sharma (2006); Tseng (2010), Leal-Rodriguez et al. (2015), Jain et al. (2015), Liebowitz & Frank (2016), Bhatti et al. (2016), Kasemsap (2016), Serrat (2017) and others. Hence hypothesis, H4a: organizational learning acts as a mediating variable between knowledge management and organizational performance in few selected Indian organizations. CONCLUSION The present study may help in improving the organization learning culture in order to enhance the knowledge management practices so that the performance of the firm could be enhanced and sustained. This study was conducted in order to understand how organization learning can impact organizational performance, as a mediating variable between knowledge management and 61

organizational performance. It can be concluded that the three variables have a significant positive relationship with each other. This study has concluded that there exists a positive as well as a significant relationship between Knowledge management and organizational performance and organizational learning which showcases a reciprocatory relation. It can be understood as if the organization is devising fair strategies of knowledge management and thereby developing organizational learning then it automatically leads to higher organization performance, as organizational learning mediates the relationship between knowledge management and organization performance. Thus, the present research helped in specifying those elements which can help managers and executives find the aspects of organizational learning in relation to knowledge management in order to gain competitive advantage through superior organization performance. Out of all other people management practices in organizations (such as leadership, hiring and selection, and human resource development), performance management practices have received the least attention from the organization learning perspective. However, as the current study shows, there is a substantial relationship between the two. Further relationship between knowledge management and organization learning depicts that organizations which are keen to have a positive overall performance must move towards the path of knowledge management channelizing and activating it towards all the departments of the organization. The mediating role of knowledge management between organizational learning and organization performance shows that organizations which implement knowledge management system along with organizational learning have an edge to achieve higher organizational performance. Various studies have suggested that setting a good performance system in order to guarantee superior organizational performance is not enough. Rather, organizations need to implement organizational learning along with knowledge management into their strategy, structure, culture and operations. This enables the positive impact of organizational learning to occur in the organization. The contribution of organizational learning is not limited to increased performance, in current and future times. As Senge (2006) states, organizational learning allows an organization to innovate, transform and recreate it, as its human capital learns. Implementing a learning system in organizations is more difficult than a performance management system. It is easier for managers to plan performance, support and assess performance at the end, than to manage the available knowledge bulk and assess if employees have really learnt from it throughout the performance cycle. However, such difficulties should encourage organizations to develop learning systems along with proper implementation of knowledge management systems. RECOMMENDATIONS FOR FUTURE RESEARCH There is a noticeable lack of literature on the relationship between organization learning and organization performance and more specifically mediating role of knowledge management between these two variables. Future research on these variables is always welcomed; however, the future researchers should try to overcome the limitations of this study by extending the sample size and geographical scope, in order to obtain a clearer picture regarding the relationship 62

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COLLEGE EFFECTIVENESS AND TEACHERS’ SATISFACTION: A STUDY OF GOVERNMENT COLLEGES IN PUNJAB (INDIA) 1 Harpreet Kaur G S Bhalla2 The Punjab education system has witnessed significant expansion in public higher education, both in terms of number of institutions as well as the students’ enrolment with 10 state universities and 48 government colleges (40 general degree and 8 professional colleges). But, higher education sector in Punjab still confronted various problems. This paper investigates the effectiveness of government colleges in Punjab (India) from the teachers’ perspective. The study explores the various determinants of effectiveness of government colleges and the impact of these determinants on satisfaction of teachers. The sample of the study comprised of 162 teachers from the various general degree government colleges of Punjab and data was analyzed using Descriptive, factor analysis and regression analysis. Results showed 11 determinants of teachers satisfaction i.e. Infrastructure facilities, faculty development, financial administration, academic environment for students, college administration, placement services for students, research environment, teaching environment, learning material, extracurricular activities and students support services. Financial administration has greater impact on teachers’ satisfaction. Keywords: Colleges, Effectiveness, Government, Punjab, Satisfaction, Teachers, INTRODUCTION With a variety of stakeholders the educational system finds itself in a state of confusion over the identification of such design characteristics that would impact the processes part, integrate the interest of various customer groups (Sanhey et al. 2004, 2006). Sanhey et al. (2003) reported that the educational institute should aim to satisfy the needs of various stakeholders by designing of an appropriate system comprising a management system, technical system and a social system. Colleges’ effectiveness is only possible when the satisfaction of all parties is maximized. Teachers are the employees of educational institutions and employee satisfaction with the working environment of the institutions can promote teaching and research quality. Oshagbemi (1997) said that Employee satisfaction has been found to be as important as customer (students’) satisfaction. Employees are the important part of any institution and effectiveness of that institution cannot be improved without the satisfaction of their employees. So, the satisfaction of employees 1

Research Fellow, Department of Commerce, Guru Nanak Dev University, Amritsar (Punjab) -143001, India. Email: preeti356@yahoo.co.in 2

Professor, Department of Commerce, Guru Nanak Dev University, Amritsar (Punjab) -143001, India. Email: hellogsbhalla@gmail.com

is also very important to attain the goals of higher educational institutions’ effectiveness. Higher educational institutions can satisfy their students only if they also satisfy their employees, i.e. teachers and administrators. Teachers are the internal customers with the most significant influence on quality of education and their satisfaction should be equally important. Better satisfaction will provide teachers a reason to continue with the institute and stay committed. Various studies proposed that teachers are the greatest assets of educational institution and teachers’ satisfaction influence institution performance (Kusku, 2001; Sanhey et al.2008, Lazibat et al. 2014). Punjab, a well-known state of North India is a leader in providing higher education, but, Government Colleges in Punjab are facing a critical stage in the effectiveness of education. Most of the Government colleges are run substantially through the services of Adhoc teachers or guest faculty. Teachers’ satisfaction is considered an important factor of college effectiveness. This study was conducted to examine the satisfaction of the teachers’ of government colleges in the state of Punjab (India). It is important to inquire about the teachers’ satisfaction of college effectiveness in order to assist administrators in changing policies that will lead to improvement of teaching and learning condition. LITERATURE REVIEW Various studies, both conceptual and empirical have been conducted to examine the satisfaction of faculty towards Services provided by higher educational institutions. Johnstone and Agustias (1983) identified main dimensions which were seen by teaching staff to be important. The researcher found seven dimensions useful for evaluating an institution of higher education i.e. size of an institution, output quantity considerations, students’ success in completing a degree in reasonable time, students’ academic performance, quality of teaching staff, students characteristics and total enrolment. Kusku (2001) explored the satisfaction level of academic staff of state universities and the difference of their perceptions on the basis of demographic variables in Istanbul. Results revealed that job satisfaction was the most important dimension followed by professional satisfaction. There were no serious differences according to gender, age and seniority among satisfaction of academic staff. A significant relation was found in only between salary satisfaction level and gender. Research findings revealed that women’s salary satisfaction level was slightly lower than that of men’s. . Sanhey et al. (2008) examined the faculty perspective about quality in higher education in Delhi. Gap analysis showed that there was a great deal of dissatisfaction amongst faculty and there was a need for improvement. Findings showed 10 factors which were able to meet the faculty requirements.; i.e. 1) effective and efficient leadership; 2) clear and specific goals; 3) strategic and operational planning; 4) budget priorities; 5) emphasis on continuous improvement; 6) management information system; 7) instructional competence; 8) differentiation; 9) customer focused need based; 10) well defined channels of communication. Kaur and Bhalla (2009) explored the perceptions of teachers towards the management of colleges with reference to management of faculty and students related factors and difference of teachers’ perceptions for by dividing the colleges into higher and 70

lower ranked colleges. The survey was conducted 16 colleges run by SGPC (Shiromani Gurdwara Prabandhak Committee) in Punjab (India). Results indicated that faculty related factors i.e. teaching environment, research environment, education material; infrastructure and faculty motivation in higher ranked colleges showed significant difference for all the students’ related factors i.e. education of students, placement of students and extracurricular activities when compared with lower ranked colleges. Kaur (2010) explored the perception of teachers and principals towards the management of colleges and examined the difference between the perception of teachers and principals of the colleges run by the Shiromani Gurdwara Prabandhak Committee (SGPC) in Punjab. Results showed 8 dimensions of college management; Teaching environment; Research environment; Education material; Infrastructure; Faculty Motivation; Education of students; Placement of students; Extracurricular activities of students. Teachers were not much satisfied with the availability of the research environment, educational material, faculty motivation and placement of students in the college, whereas principals mainly dissatisfied with the research environment and placement of students in the college. The Results of the t-test showed that both the groups were significantly different for all the faculty related factors. For students related factors the perception of both groups were significantly different for only one dimension i.e. the education of students. Sandmaung and Khang (2013) measured the perspective of students, teaching staff, managerial staff and employers towards quality indicators of higher education institutions. Quality indicators divided into nine factors: quantitative indicators, education management, faculty competence and attitude, evaluation of examination, tangible and facilities, integration with society, recognition and flexibility, teaching and learning process, knowledge management system, quality of graduates and quality assurance. Results showed that there were significant gaps in the quality perception among students and teaching and managerial staff. Teaching and communication, and evaluation of examination are consistently among the most important quality expectations of all key stakeholders. Lazibat et al. (2014) examined the perception of students and teachers towards service quality in higher education and influence of service quality dimensions on students’ satisfaction in Croatia. HEdPERF was used to examine students and teachers’ perceptions with 7 quality dimensions (Non-academics, academics, access, reputation, programs, buildings and supporting facilities).Results revealed that teachers were satisfied with their job and satisfaction with earnings and working conditions was much lower. Academic and non-academic dimensions rated highest by the teachers. Duzevic and casni (2015) compared the perceptions of students and faculty of service quality in higher education and identified the institutional aspects that may affect the perceptions of students and faculty. Factor analysis was used to explore the service quality dimensions (access, non-academic aspect, academic aspect, facilities and programs and reputation). Results revealed that student’s ratings were slightly lower than faculty’s for all service quality dimensions. Karna and Julin (2015) evaluated the satisfaction of teachers’ and students’ towards university campus facilities and the impact of these facilities on their satisfaction. The questionnaire comprised of eight dimensions i.e. workplace facilities, laboratory facilities, teaching facilities, general purpose facilities, facility maintenance, campus accessibility 71

and movement ,outdoor areas and total satisfaction of students. Factors related to campus accessibility had the highest mean value in both groups. Teaching facility had a greater impact on students and staff satisfaction. NEED OF THE STUDY The purpose of the present study is to examine the various factors of teachers’ satisfaction and impact of these factors on overall teachers’ satisfaction. From the above literature, it has been seen that most of the studies on teachers’ satisfaction conducted in developed countries but limited literature available in case of developing countries. Teachers’ satisfaction has been extensively studied for private institutions or universities. Very few literature is available to the government educational institutions. Moreover, as a consequence of declining various resources, government colleges in Punjab have sought to improve the effectiveness of education in their institutions. It has been seen that the condition of the government colleges have been continuously deteriorating year after year. Poor funding, vacant teaching positions, low rate of enrolment, unequal access, poor quality of infrastructure facilities, pitiable working conditions and political interference are major problems of government colleges in Punjab. Politics in the educational institutions especially during appointments and promotions to the post of teachers’ has deteriorate the standard of education. Majority of the government colleges lack basic facilities including drinking water, sanitation, proper furniture etc. In 2014 the University Grants Commission (UGC) passed clear instructions to the universities and government colleges of Punjab to recruit permanent staff instead of temporary. However , till date government colleges are being run without regular faculty. This is ruining the whole thread of higher education in the state and in the country at large. So, the present research is an attempts to evaluate the teachers’ satisfaction towards effectiveness of the government colleges in Punjab. So, the objectives of the study are:  To study the satisfaction of teachers’ towards various determinants of effectiveness in Government colleges.  To examine the impact of the determinants of effectiveness on satisfaction of teachers’. RESEARCH METHODOLOGY This study has been mainly conducted to examine the satisfaction of teachers’ towards determinants of effectiveness in government colleges and further to explore the impact of determinants on the overall satisfaction of teachers. So, primary data was used to achieve the objectives. The universe of the study comprised of all the teachers of general degree government colleges in the state of Punjab. There are total 40 general degree government colleges in Punjab. The study covered six districts (Amritsar, Jalandhar, Ludhiana, Patiala, Sangrur and Mohali) of Punjab out of twenty two districts. These districts have been selected because more than fifty percent of the total government colleges were situated in these districts. All general degree government colleges were selected from these districts for the purpose of the study. Teachers’ were selected through purposive sampling from various districts of Punjab. A well-structured 72

questionnaire was used to collect the data. An initial pool of 86 statements (31 statements related with faculty and 55 statements related to students related factors ) and 7 statements related to overall satisfaction were developed according to a 5 point Likert scale (1: Strongly Disagree ;2: Disagree; 3: Neutral; 4: Agree; 5 Strongly Agree). A total of 270 questionnaires (Amritsar=40; Jalandhar=45; Ludhiana=50; Patiala=50; Sangrur=50; Mohali=35) were distributed to the teachers. Some questionnaires were sent by post. Survey was conducted in the month of October 2016. A total of 198 questionnaires were collected from teachers and 162 questionnaires had been found useful for the analysis. To test the underlying determinants of college effectiveness from the teachers’ perspective Exploratory Factor Analysis (EFA) was used. Regression analysis was used to explore the impact of determinants on overall satisfaction of teachers’. On the basis of literature the present research has identified various factors of effectiveness of higher educational institutions. On its basis the following hypotheses have been framed:  H1: Infrastructure facilities have significant positive impact on teachers’ satisfaction  H2 : Faculty Development has significant positive impact on teachers’ satisfaction  H3: Financial Administration has significant positive impact on teachers’ satisfaction  H4: Academic environment for students have significant positive impact on teachers’ satisfaction.  H5: College administration has significant impact on teachers’ satisfaction.  H6: Placement services have significant impact on teachers’ satisfaction.  H7: Research environment has significant impact on teachers’ satisfaction.  H8: Teaching environment has significant impact on teachers’ satisfaction.  H9: Learning Material has significant impact on teachers’ satisfaction.  H10: Extracurricular activities have significant impact on teachers’ satisfaction.  H11: Student Support Services have significant impact on teachers’ satisfaction. Table 1 shows the demographic profile of the respondents.

Variables Gender Qualification

Marital Status Age

Table 1: Demographic Profile of the Respondents Categories No. of Respondents Male 87 Female 75 Bachelors’ 28 Masters’ 64 M.Phil 44 PhD 26 Married 99 Unmarried 63 Below 25 12 25-35 45 73

Percentage 53.7 46.3 17.3 39.5 27.2 16.0 61.1 38.9 7.4 27.8

Classification of Job

Total teaching experience

35-45 Above 45 Adhoc Contract Permanent Guest Faculty 1-3 years 3-5 years 5-8 years More than 8 Years

47 58 30 37 40 55 59 45 22 36

29.0 35.8 18.5 22.8 24.7 34.0 36.4 27.8 13.6 22.2

Urban Rural

105 57

64.8 35.2

Location of the colleges

DATA ANALYSIS AND INTERPRETATION Factor Analysis Factor analysis was used for defining the unidimentionality of the factors affecting the satisfaction of teachersâ&#x20AC;&#x2122; towards government colleges effectiveness. In the first step of factor analysis 22 statements were deleted due to low item to total correlation, low communalities and due to cross loadings. Remaining 64 statements were used for factor factor analysis. The KaiserMeyer-Olkin (KMO) measure of sampling adequacy was found to be sufficiently high from the recommended limit of .06 (Nunnally, 1996). Bartlettâ&#x20AC;&#x2122;s test of sphericity was highly significant (p<.01) indicates that there was sufficient correlation among the variables (Table 2).

Table 2: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Approx. Chi-Square Sphericity df Sig.

.794 8062.549 2016 .000

Factor analysis has been run using Principal component analysis (PCA) with varimax rotation. Table 3 shows that eleven factors extracted which have Eigen value more than 1. These factors explained 71.05 percent of total variance. Further, mean score has been calculated to find out the level of agreement or disagreement of the respondents.

Items label Factor 1 Infra14 Infra7 Infra12 Infra8 Infra3 Infra4 Infra9 Infra6 Infra5 Factor 2 FD6

FD9 FD8 FD1 FD4 FD3 FD7 FD10 Factor 3 FA4 FA2 FA5 FA1 FA3

Table 3: Factor Analysis STATEMENTS

Mean

Infrastructure Facilities Hostel facility provided by the college. 3.68 Fresh drinking water is available. 3.48 There is enough space for parking in college. 3.58 Adequate numbers of washrooms are available. 3.35 Waiting rooms available for parents of the 3.14 students. Generator facility is provided by the college. 3.56 Sufficient playgrounds are available 3.36 Transport facility is provided by the college. 2.90 Classrooms are well equipped. 3.24 Eigenvalue = 11.54, variance explained =18.03, Îą=.921 Faculty Development Each faculty member gets sufficient chance to 2.77 attend various seminars, conferences, workshops and refresher courses. Salaries are distributed on due time. 2.82 Salary is provided as per UGC norms. 2.78 Amount of budget spent on staff development is 2.85 satisfactory in the college. There is effective delegation of authority. 2.98 Role of teachers in decision making is good. 3.00 Contribution of the teachers is recognized by the 2.89 college through honor and awards. Faculty is promoted in due course of time. 2.98 Eigenvalue = 6.67, variance explained =10.42, Îą=.951 Financial Administration The number of scholarship available for 3.84 deserving students. Fee charged from students is reasonable. 3.88 The scholarships are paid within reasonable time. 3.64 College is efficiently managing the finances 3.77 available. Fee concession is given by the college to 3.94 deserving Students.

S.D

Factor Loading

.995 1.017 .869 .974 1.057

.836 .788 .772 .763 .749

.905 .964 1.043 .904

.743 .727 .714 .689

.928

.877

1.021 1.009 .914

.874 .867 .864

.925 .946 .991

.859 .855 .836

.990

.834

.811

.810

.829 .875 .858

.800 .783 .770

.858

.764

FA6

Factor 4 AE6 AE5 AE4 AE2 AE3 AE13 AE7

Factor 5 CA3 CA2 CA1 CA5 CA4

Factor 6 Placement 2 Placement3 Placement1 Placement4 Placement5

Factor 7 RE6 RE7

Amount of budget spent on improvement of 3.43 infrastructure is satisfactory. Eigenvalue = 5.53, variance explained =8.64, α=.899 Academic Environment For Students Teachers are easily accessible. 3.95 Teachers in the college inspire students for study. 4.17 Teachers in the college give proper attention to 4.07 students. Teachers in the college are effective in teaching. 4.02 Teachers in the college are innovative. 3.95 The teachers’ pay considerable attention to the 3.96 overall personality development of students Guest lectures are arranged in the college 3.80 frequently for students. Eigenvalue = 4.63, variance explained =7.23, α=.869 College Administration Administration maintains accurate and retrieval 3.54 records. Administrative employees behave cordially. 3.47 Administrative staff provides services without 3.54 delay. Campus security services are good. 3.54 Administrative staff accessible during office 3.54 hours. Eigenvalue = 3.48, variance explained =5.44, α=.894 Placement services Placement services are provided by the college. 3.46 Students actively participate in the placement 3.44 activities. Career counseling sessions are conducted. 3.64 Past placement records of the students are good. 3.34 College students appearing in competitive exams 3.49 have a good passing rate. Eigenvalue = 2.88, variance explained =4.50, α=.906 Research Environment Seminars and conferences are organized by the 3.67 college. Teachers participate in academic seminars, 3.62 conferences and workshops. 76

.898

.754

.918 .733 .889

.791 .768 .767

.682 .746 .690

.755 .716 .663

.855

.610

.820

.851

.953 1.010

.824 .813

.953 .857

.778 .761

1.137 1.046

.849 .823

1.001 1.212 1.017

.792 .783 .684

.945

.842

1.022

.821

RE5 RE8 RE2

Factor 8 TE1 TE5 TE8 TE6

TE3 TE7 Factor 9 LM4 LM5 LM2 LM3 LM1

Factor 10 Extracurri1 Extracurri2 Extracurri3 Extracurri4 Factor 11

Library contains sufficient number of reference 3.54 books, journals, magazines. Sufficient number of research papers published in 3.38 peer reviewed journal by faculty members. Amount of budget spent on research development 3.19 is satisfactory. Eigenvalue = 2.52, variance explained =3.93, α=.880 Teaching Environment The college has good educational environment. 3.06 Teaching work “suffer” due to other assignments 3.55 carried out by teachers. There is good understanding among the faculty. 3.64 In the selection of temporary teaching staff 3.60 extraneous considerations (other than merit) are involved. The teachers have sufficient freedom to choose 3.58 subject related to their specialization and interest. The college has a transparent admission process. 3.82 Eigenvalue = 2.80, variance explained =4.38, α=.811 Learning Material Adequate laboratories are available. 3.30 Sufficient computers are available in the college. 3.29 Libraries are updated with material related to 3.33 subjects. Students are informed regularly about updated 3.21 library collection. Audio visual aids are available for classroom 3.16 teaching. Eigenvalue = 2.09, variance explained =3.27, α=.919 Extracurricular Activities Extracurricular activities are organized by the 4.01 college. Students actively participate in extracurricular 3.94 activities. College makes emphasis on development of 3.93 sports facilities. College has a NCC unit. 4.20 Eigenvalue = 1.81, variance explained =2.82, α=.905 Student Support Services 77

1.010

.774

.927

.759

.998

.753

.925 .849

.751 .739

.903 .866

.734 .731

.976

.720

.863

.713

1.034 1.173 1.051

.805 .743 .739

1.123

.718

1.097

.687

.808

.884

.896

.868

.842

.755

.819

SS3 SS4 SS2 SS1

The college has Internal Quality Assurance Cell. 3.35 Internal Quality Assurance Cell works effectively. 3.20 Grievances are redressed promptly. 3.43 The college has grievances redressal cell. 3.47 Eigenvalue = 1.49, variance explained =2.32, α=.897 Total Variance Explained =71.05%

.916 .911 .918 .934

.850 .823 .795 .736

Factor 1: Infrastructure Facilities It is the most important factor as it accounted for 18.03 percent of total variance with factor loading .836 to .689 and Cronbach’s alpha value .921. In total, nine statements have been loaded on this factor. The satisfaction of teachers on various aspects of infrastructural facilities was found to be neutral. These statements are; Hostel facility provided by the college (Mean=3.68); Fresh drinking water is available (3.48) ;There is enough space for parking in college (3.58);Adequate numbers of washrooms are available (3.35);Waiting rooms available for parents of the students (3.14);Generator facility is provided by the college (3.56);sufficient playgrounds are available (3.36); transport facility is provided by the college (2.90),classrooms are well equipped (3.24). Factor 2: Faculty Development The second factor has been named as faculty development and it accounted for 10.42 percent of total variance with factor loading from .877 to .834 and Cronbach’s alpha value .951. Teachers were dissatisfied with the faculty development programs in the colleges. Teachers indicate their level of disagreement with ‘Each faculty member gets sufficient chance to attend various seminars, conferences, workshops and refresher courses (Mean=2.77) ;Salaries are distributed on due time (Mean=2.82); Salaries is provided as per UGC norms (2.78);); Amount of budget spent on staff development is satisfactory in the college (Mean=2.85); There is effective delegation of authority (Mean=2.98); Role of teachers in decision making is good (Mean=3.00); Contribution of teachers is recognized by the college through honor and awards (Mean=2.89); Faculty is promoted in due course of time (Mean=2.98). Factor 3: Financial Administration The third factor has been emerged as financial administration and it explains 8.64 percent of total variance with 5.53 eigen value. The number of scholarships available for deserving students has high loading (.810) followed by fee charged from students is reasonable (.800); The scholarships are paid within reasonable time (.783); College is efficiently managing the finances available (.770); Fee concession is given by the college to deserving students (.764); Amount of budget spent on improvement of infrastructure is satisfactory (.754). Teachers were given a positive response for financial administration of the college. The factor shows sufficient reliability with .899 Cronbach’s alpha value.

Factor 4: Academic Environment for Students The fourth factor has been accounted for 7.23 percent of variance with 4.63 eigen value. Factor loading ranged from .791 to .610. Teachers are easily accessible has highest loading followed by ‘Teachers in the college inspire students for study (.768), Teachers in the college give proper attention to students (.767); Teachers in the college are effective in teaching (.755) Teachers in the college are innovative (.716); The teachers’ pay considerable attention to the overall personality development of students (.663). Guest lectures are arranged in the college frequently (.610). The teachers were agreed with the teaching environment for students. Cronbach’s alpha for this factor was .869. Factor 5: College Administration This factor contributed 5.44 percent of variance with 3.48 eigen value and the value of Cronbach’s alpha was .894. The variables in this factor are; Administrative maintains accurate and retrieval records (.851); Administrative employees behave cordially (.824); Administrative staff provides services without delay (.813); Campus security services are good (.778);Administrative staff accessible during office hours (.761). The mean score of the variables shows that teachers respond neutral to the administrative services of the college. Factor 6: Placement Services for Students This factor contributes to 4.50 percent of variance with 2.88 eigen value. Placement services are provided by the college has highest loading (.849) followed by Students actively participate in the placement activities (.823 ); Career Counseling sessions are conducted (.792); Past placement records of the students are good (.783); College students appearing in competitive exams have a good passing rate (.684). The mean score of the variables reveals that neutral response was found placement services for students provided by the college. The Cronbach’s alpha for this factor was .906. Factor 7: Research Environment This factor explained 3.93 percent of variance with 2.52 eigen value and the value of Cronbach’s alpha was .880. The highest factor loading was for the statement ‘Seminar and conferences are organized by the college (.842) followed by ‘Teachers participate in academic seminars, conferences and workshops (.821); Library contains sufficient number of reference books, journal and magazines (.774); Sufficient number of research papers published in peer reviewed journal by faculty members (.759); Amount of budget spent on research development is satisfactory (.753). Mean score of the variables shows that teachers respond neutral to the research environment of the college. Factor 8: Teaching Environment This factor explained 4.38 percent of variance with eigen value 2.80. Six statement have been loaded under this factor with factor loading ranged from .751 to .713. These items are the 79

college has good educational environment (.751); teaching work ‘‘suffer’’ due to other assignments carried out by teachers (.739); There is good understanding among the faculty (.734); ‘In the selection of temporary teaching staff extraneous consideration (other than merit) are involved (.731); Teachers have sufficient freedom to choose subject related to their specialization and interest (.720); The college has a transparent admission process (.713). The Cronbach’s alpha value was .811. Mean score of these statements shows that teachers respond neutral for all these statements. Factor 9: Learning Material This factor included five variables which explain 3.27 percent of variance with 2.09 eigen value and .919 Cronbach’s alpha value. Adequate laboratories are available (.805) has highest loading followed by Sufficient computers are available in the college (.743); Libraries are updated with material related to subjects (.739); Students are informed regularly about updated library collection (.718); Audio visual aids are available for classroom teaching (.687). Mean score of the variables indicate that neutral response was found for the facility of learning material. Factor 10: Extracurricular Activities for Students This factor has been labeled as extracurricular activities for students with .905 Cronbach’s alpha value. Four statements have been loaded under this factor with 2.82 percent of variance and 1.81 eigen value. These statements are Extracurricular activities are organized by the college (.884); ‘Students actively participate in extracurricular activities (.868); College make emphasis on development of sports facilities (.842); College has a NCC unit (.819). Teachers response found neutral to agree for all the statements. Factor 11: Student Support Services Student support services explained 2.32 percent of total variance with 1.49 eigen value and .897 Cronbach’s alpha value. Factor loading ranged from .850 to .819. The College has an internal quality assurance cell has a highest factor loading (.850) followed by Internal quality assurance cell works effectively (.823); Grievances are redressed promptly (.795); The college has grievances redressal cell (.736). Teachers are agree with the support services provided to students. Impact of Determinants of College Effectiveness on Teachers’ Satisfaction Regression analysis examines the Impact of independent variables on the dependent variable. Eleven factors of college effectiveness i.e. Infrastructure facilities (9 items), Faculty development (8 items); Financial administration (6 items); Academic environment for students (7 items), College administration (5 items), Placement services (5 items), Research environment (5 items);Teaching environment (6 items); Learning material (5 items), , Extracurricular activities (4 items) and Students support services (4 items) have been taken as independent variable and overall student satisfaction (7 items) has been taken as dependent variable. 80

Regression Equation Y=b0+ b1x1 +b2x2+………………………………..b11x11 +e Where, Y=overall satisfaction (Dependent variable) b0=Constant ,b1x1…………b11x11=Independent variables e=Error Term Table 4: Model Summary Model

.693

R Square Adjusted R Std. Error of Square the Estimate

.480

.464

.51249

The adjusted R square of the model is .46, indicating that a total of 46 percent variance in overall satisfaction of teachers has been explained by five dimensions of college effectiveness. ANOVA table shows the fitness of the model and the significance of the f value is given as zero, which is far below p value of 0.05. This means that the regression model is a good fit.

Regression 37.854

Table 5: ANOVA 5 7.571

Residual

40.973

156

Total

78.827

161

28.825

.000

.263

Dependent Variable: Overall Satisfaction The regression coefficients show that Financial Administration (β=.413, t=6.43, p<0.01), learning material (β =.111, t=2.299, p<0.05), College Administration (β=.158 ,t=2.798, p<0.01), Teaching Environment (β=.202, t=3.265, p<0.01), Placement Services (β=.135, t=2.589, p<0.05) had positive and significant impact on teachers satisfaction. Hence the hypothesis H3, H5, H6, H8, H9, have been accepted and H1, H2, H4, H7, H10 have been rejected. Table 6: Regression Coefficients Unstandardized Coefficients Model

Standardized Coefficients

Std. Error

(Constant) -.115

.324

.413

.064

.111

.048 81

Beta

Sig.

-.354

.724

.412

6.432

.000***

.151

2.299

.023**

.158

.056

.174

2.798

.006***

.202

.062

.199

3.265

.001***

Place

.135

.052

.178

2.589

.011**

Note: ***=1% level;**=5% level,*10% level The importance of the dimensions was indicated by standardized beta coefficients (Clemes et al. 2008). Financial administration of the government colleges were considered the most important factor for Teachers’ satisfaction (Beta=.412) followed by teaching environment. (Beta=.199), Placement services (Beta=.178), college administration (Beta=.174) and Learning material (Beta=.151). CONCLUSION AND DISCUSSION The study has highlighted the importance of teachers’ satisfaction. The most important aspect of teachers’ satisfaction was financial administration of the colleges followed by teaching environment, placement services, college administration and learning material. Teachers were dissatisfied with the faculty development programs in the colleges and neutral response was found for research environment. Teachers’ satisfaction is very important for educational institutions because satisfied employees tend to be more committed towards their organisations. By increasing faculty satisfaction goals and objectives of the institutions can be achieved. The findings of the research useful for higher education authorities because they consider these predictors for their decision making and policy formulation. Institution of higher education need to be saved from political interference. Further government colleges must have basic infrastructure facilities. Greater financial support and better environment should be provided. The policy should be transparent and simple to implement too. In the present era, before joining any educational institution students look at the quality of the education being offered by the institute. So satisfied teachers are the pillar of any institution and the most important aspect of teachers satisfaction seen to be that satisfied teachers tend to be more productive and more committed towards their institution .With regard to managerial contribution the findings of this research may use for developing strategies in order to enhance the satisfaction of teachers’. LIMITATIONS AND DIRECTION FOR FUTURE RESEARCH The study is based on questionnaire so there is always a gap between truth and actual responses. The study is conducted only one state i.e. Punjab. The analysis of the study suffer from small sample size. For further research the study can be extended to other state colleges. The research should be undertaken with large sample. Longitudinal research may be undertaken. REFERENCES Clemes, M. D., Gan, C. E., & Kao, T. H. (2008). University student satisfaction: An empirical analysis. Journal of Marketing for Higher Education, 17(2), 292-325.

Dužević, I., & Časni, A. Č. (2015). Student and faculty perceptions of service quality: the moderating role of the institutional aspects. Higher education, 70(3), 567-584. Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Upper Saddle River, NJ: Pearson. Johnstone, J. N. (1983) . An analysis of the perceptions teaching staff hold towards factors useful for evaluating an institution of higher education. Higher Education, 12(2), 215-229. Kärnä, S., & Julin, P. (2015) . A framework for measuring student and staff satisfaction with university campus facilities. Quality Assurance in Education, 23(1), 47-66. Kaur, D. (2010). Effectiveness of College Management–A Comparative View of Teachers and Principals. Asia Pacific Business Review, 6(2), 150-161. Kaur, D. and Bhalla, G.S. (2009). Perceptions of towards College management: A case Study. The Icfaian Journal of Management Research, 8(8) , 59-76. Sandmaung, M., & Ba Khang, D. (2013). Quality expectations in Thai higher education institutions: Multiple stakeholder perspectives. Quality Assurance in Education, 21(3), 260-281. Kusku, F. (2001) . Dimensions of employee satisfaction: A state university example. METU Studies in Development, 28(3/4), 399-430. Lazibat, T., Baković, T., & Dužević, I. (2014). How perceived service quality influences students' satisfaction? Teachers' and students' perspectives. Total Quality Management & Business Excellence, 25(7-8), 923-934. Malhotra, N. K. (2008). Marketing research: An applied orientation, 5/e. Pearson Education India. Nunnally, J. (1994). Bernstein (1994), Psychometric Theory. Aufl., New York Oshagbemi, T. (1997).The influence of rank on the job satisfaction of organizational members. Journal of Managerial Psychology, 12(8), 511-519. Sahney, S., Banwet, D. K., & Karunes, S. (2004). A SERVQUAL and QFD approach to total quality education: A student perspective. International Journal of productivity and performance management.’’ 53(2), 143-166. Sahney, S., Banwet, D. K., & Karunes, S. (2008). An integrated framework of indices for quality management in education: a faculty perspective. The TQM Journal, 20(5), 502-519.

VALUATION OF DISTRESSED FIRMS: A CASE STUDY OF UNITECH LIMITED Anjala Kalsie1

Shikha Mittal Shrivastav2

The objective of the paper is to introduce a valuation model that adapts the traditional valuation model and addresses various issues and risk pertaining to the specific firms’ characteristics. The major factor that caused distortion in the analysis of the traditional method is the improper treatment of the risk of default. The paper, thus addresses the issue by proposing a model that estimates the risk of default and incorporates it in the traditional valuation method. The adapted model was then applied to Unitech Limited, India’s leading Real Estate player, which is currently facing distress conditions. The model developed in the study for the calculation of firm valuation yielded the results which are in line with the current equity market capitalization of Unitech Limited. But the results are highly sensitive to the input parameters. Keywords: Altman Z-Score, Distressed Liquidation Value, Distance to Default, Black and Scholes theory, Firm Value INTRODUCTION During the mature stage, companies try to stretch their maturity as long as possible and some like Coca Cola even manage to do so. But some due to reasons such as emergence of substitutes and technological innovation enter into decline stage. In addition, external factors such as recession in economy and depressed capital markets can worsen the situation. In many cases, human factors such as managerial errors are to blame for failing to innovate in new products and failing to anticipate the change in the market and in consumer behavior [Grant, 2010]. Although not necessarily applicable to all cases, some of the main factors that accompany decline are Stagnant or declining revenues, Shrinking or negative margins, Asset divestitures, Financial Leverage, Liquidity Constraints[Damodaran, 2009]. There are two types of distress, economic and financial. The main difference between a mature company and company in decline is that a mature company still derives its revenues from growth investments while a company in decline actually starts losing value from its growth investments. In that case company’s value as going concern is less than the total value of its assets. This means that the business is no longer feasible or, as the academicians say, it has become economically distressed. In this situation, the assets are not in their highest value use and

Assistant Professor, Faculty of Management Studies, University of Delhi, Delhi – 110007. Email: kalsieanjala@gmail.com 2

Assistant Professor, Finance and Accounting Management, IILM Graduate School of Management, 16, Knowledge Park-2, Greater Noida - 201306, India. E-mail: shikha.shrivastava@iilm.ac.in

it would be more beneficial for the company to close down its operations and divest its assets [Crystal and Mokal, 2006; Damodaran, 2009]. This situation is different from financial distress. A company that is financially distressed may be highly profitable, but the distress comes from insolvency, i.e. illiquidity. This is defined as having difficulties in meeting liabilities such as interest payments or other contractual obligations when they arise. It is possible that a company that is financially distressed may be economically viable. Financial distress can have serious consequences. If firms are unable to meet their debt payments they are usually forced to liquidate their assets at bargain prices and use the proceeds to pay off debt. Also the image of distress may ruin the company’s relationship with its suppliers, employees and lenders and effect its operations. A company in economic distress, if nothing changes, will end up in financial distress. Developing a theory of financial distress, Gordon (1971) suggested that the decrease in the earnings capacity of the firm can result in the possibility of inability of the firm to repay the principal or interest component of debt. Such a state represents the distressed financial condition of the firm. Wruck (1990) also explained “financial distress as a situation, where cash flows are insufficient to cover the current obligations”. Researchers have attempted to unravel the causes and impact of financial troubles, bankruptcy, debt restructuring along with the efforts to predict the distressed conditions of the firms. The seminal studies by Beaver (1966) verified the predictive ability of financial ratios in the event of corporate failure based on the 30 individual ratios or univariate analysis and suggested that ratios have the ability to detect the illness of firms much before the corporate failure. Altman (1968) was the first researcher to apply the Multiple Discriminant Analysis (MDA) approach to the financial distress prediction domain. He developed a Z-score bankruptcy prediction model and determined a cut point of Z-score (2.675) to classify healthy and distressed firms. The model based upon ratio analysis to predict the financial health of the enterprise considers multivariate view along with the interaction of the various independent variables instead of the earlier univariate analysis. The predictive accuracy of the model is reported as 95%. The results showed that the Z-score model had sound prediction performance one year and two years before financial distress, but did not indicate good prediction utility three to five years before financial distress. A number of authors followed atman’s work, and applied the Z-score model into different markets, different time periods and different industries. LITERATURE REVIEW The academic literature offers various valuation techniques. At the same time there are great number of articles dealing with financial distressed firms and the risk of bankruptcy. However, there are very few research studies that combine these two topics and explicitly deal with the valuation of declining and distressed companies. Some of the most notable works on this topic are single chapters of books on valuation or bankruptcy, by authors such as Damodaran 85

(2009); Arzac(2008) and Scarberry (1996). Apart from these, there is also an important research stream within the field of accounting that analyzes the difference between the book value and market value of assets for loss-making firms (Collins et al., 1999; Hsu and Etheridge, 2009). As a consequence of the limited applicability of traditional methods, practitioners are ever more willing to abandon the traditional valuation approaches and rely increasingly on new paradigms based primarily on personal assessments to value distressed securities. The use of personal judgment and new paradigms instead of the traditional theoretical approach resulted in high variation in the estimates (Damodaran, 2009). In fact, research conducted by Gilson et al. (2000) analyzed multiple valuation methods for distressed firms and obtained variations of up to 250%. They analyzed about 60 declining and distressed companies and valued them using different valuation methods. The results obtained were highly varied with standard deviations of up to five times from the mean, represented in that case by the market value. In their study this was the equivalent of a variation ranging from 20% to 250%. (Gilson et al., 2000).These methods fail to account for risk of default, resulting in need of new valuation frameworks that addresses these risks and other characteristics of firms in decline and financial distress. Much of the variations are the result of reasons stated above. When a firm in this situation, many of the future strategies and courses of action they take to turn around or alleviate distress are built upon an initial valuation of the firm. Decisions about the future of the company and strategies such as refinancing, the sale of certain divisions or the whole company, raising new equity or the evaluation of liquidation are all dependent on accurate initial valuation (Houlihan and Lokey, 2011). Furthermore, companies in decline or distress are gaining attention as hedge funds and private equity investors see them as alternative investments. So, there is need for adaptation of traditional valuation methods in order to make them applicable for such cases. Such model would limit use of personal judgments to metrics and improve valuation accuracy. Agarwal & Taffler (2008) compared the performance of two alternative formulations of market-based models for the prediction of corporate bankruptcy with a well-established UKbased z-score model. The results showed that in terms of predictive accuracy, there is little difference between the market-based and accounting models. The study however proved that both of neither the market-based models nor the accounting-ratio based model is a sufficient statistic for failure prediction and both the models carry unique information about firm failure. Altman et al. (2017) assessed the classification performance of the Z-Score model in predicting bankruptcy and other types of firm distress. They analyzed the performance of the ZScore model for firms from 31 European and three non-European countries using different modifications of the original model. It was found that, while there is some evidence that Z-Score models of bankruptcy prediction have been outperformed by competing market-based or hazard models, in other studies, Z-Score models perform very well. The study provided evidence that the general Z-Score model works reasonably well for most countries (the prediction accuracy is 86

approximately 0.75) and classification accuracy can be improved further (above 0.90) by using country-specific estimation that incorporates additional variables. Alexakis (2008) analysed that whether Z-score can predict correctly company failures on the basis of the empirical analysis concentrates on the construction companies listed in Athens Exchange for the period 1995-2006. It was derived from the results that a particular Altman model performs well in predicting failures for a period up to five years earlier. Ray (2011) attempted to investigate the financial health of automobile industry in India for a period from 2003-04 to 2009-10 by analyzing that whether Altman’s Z score model can foresee correctly the corporate financial distress of the automobile industry in Indian context. The results indicated that overall financial performance of automobile sector in India is viable as Z score (Z values for all the seven years were more than 1.81 but less than 3) indicated but may lead to corporate bankruptcy in near future unless regulatory measures are undertaken immediately. Chouhan et al. (2014) in their paper highlighted that using financial ratios as a tool of valuing the financial stress of the companies are not appropriate for ‘Stockholders’ equity position and creditors’ claims as the Stakeholder’s have concerns about the consequences of financial distress for companies, and controls of capital adequacy through the regulatory capital requirement. Thus the paper analyzed and re-examined the Altman Z score. The sample of 30 listed BSE firms is bifurcated into healthy and unstable firms based on the score calculated for companies for a period of 5 years. It was found that change in the z scores is not significant in case of all the companies. OBJECTIVES & METHODOLOGY While the dominant valuation methods have proven to be very reliable for healthy companies with stable future growth prospects, they struggle to yield accurate results for companies that face extreme volatility and uncertainty, such as firms in decline and distress. Several research studies found major deviations in the results from traditional valuation techniques for these kinds of firms. Objectives The objective of this paper is to present a model that specifically addresses various issues and risks pertaining to distressed firms. The presented model will adapt all the identified issues along with dealing with the firm specific characteristics. The model will also deal specifically with risk of default. The model will be based on the following equation:

The idea behind the model is that the value of firm is given by average of firmâ&#x20AC;&#x2122;s value by going concern and its liquidation value, weighted by firmâ&#x20AC;&#x2122;s probability of default. Both the going concern and liquidation value are adapted to the challenges faced by firm in decline and distress phases. The probability of default is derived using metric called Distance to Default which is based on Black-Scholes-Merton Options Pricing Model. Lastly, the model is applied to a case in order to demonstrate its advantages. The objective of this paper is two-fold. The first objective is to analyze the applicability of traditional valuation techniques to firms in decline and distress and to identify the most common problems encountered in declining and distressed firms. Hence, a full analysis and review of the traditional models will be performed. The second objective is to identify possible solutions to these problems and to introduce a model that tackles the characteristics of firms in decline and distress. This adapted model will be presented in detail and applied to a real life case. Methodology This paper focuses on especially valuation of distressed firms. There are several measures used to gauge whether the firm is in distress or not. The secondary sources of data have been used for the current paper. The sources include annual reports of Unitech, internet sources, books and journals. The required accounting information for Altman-Z score analysis is obtained from CMIE Prowess Database. The financial data used are annual and cover a period of 2011-12 to 2015-16. Ratio Analysis Banks generally go for ratios such as interest coverage, debt service ability ratio in order to check financial strength of the firm. Ratios of Unitech Limited from the financial year 2012-2016 have been calculated and analyzed to ascertain the valuation of the firm. Analysis of Company Performance Further, the companyâ&#x20AC;&#x2122;s performance from the year 2012-2016 has been analyzed using the financial measures like Net revenue, EBDITA, Net profit and the share prices over the period of time. Altman Z-Score Altman Z-score is used for predicting bankruptcy of a firm. The formula was first published in 1968 by Edward Altman. The formula is widely used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults and an easy-tocalculate control measure for the financial distress status of companies in academic studies. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.

The Altman Z-score is calculated as follows:

Where: A = working capital / total assets B = retained earnings / total assets C = earnings before interest and tax / total assets D = market value of equity / total liabilities E = sales / total assets A Z-score score below 1.8 means the company is highly likely to for bankruptcy, while companies having Z-score between 1.8-3 is less likely to go for bankruptcy while companies with scores above 3 are not likely to go bankrupt. However Z-score do not work for new companies or startups have low earnings ratio and have a low Z-score. There also have been cases where companies had high Z-score but where still unable to pay its bills and thus had to declare bankruptcy. In all Z-score can be a good variable to see if a company is facing distress. Altman (2007) also estimated the cumulative probabilities of default for bonds in different ratings classes over five and ten-year periods. Valuation The model introduced adapts the traditional valuation techniques in order to include the risks faced by distressed companies. There are several ways to incorporate the effects of this risk into the estimated value of the company. The most widely used methods for companies with distinct features include, among many others, the modified DCF approach, the Adjusted Present Value approach, the Simulation approach and the Relative Valuation approach. Depending on these features and characteristics some valuation methods offer certain benefits over others. All of these methods have shown some very distinct results, also in the case of distressed companies [Damodaran, 2009]. This model although is based on the traditional methods, but specifically addresses the risk of default during the valuation process. This method distinguishes between the going concern assumptions from the bankruptcy situation which is represented by a distressed sale. The goal is to take into account all the risk associated with decline and distress in the probability a forward-looking default metric which is then used to weigh between the going concern value and the liquidation value. Equation 1 represents the crux of the model:

Where, EV = Enterprise Value of firm as going concern 89

pd= probability of distress LV = Liquidation Value of firm in distressed liquidation scenario The model uses adapted DCF to compute the going concern value. The probability of default will be estimated using a model introduced by Moody's KMV, which is based on option pricing theory [Crosbie and Bohn, 2003]. The methodology used to compute distressed liquidation value is explained later. A. Going Concern Value The assumption behind Going Concern Valuation is that firm will continue to operate that is it will somehow survive and return to financial health. Going Concern Value can be estimated using any of the traditional models; the main assumption is company will not default. This study will mainly focus on DCF method. Since we have separated the risk of default, we won’t include this risk while estimating cash flow projections and discount rate. The methodology behing cash flow projection and discount will be explained later. The Enterprise Value will be calculated using normal DCF method.

While estimating cash-flow projections, we will assume firm will be able to manage its operations around and turn profitable. For this we need to estimate reasonable profitable measures, this can be done by analyzing historical margins and comparing them to industry average. There are three metrics that need to be estimated i.e. Revenue Growth, Operating Margins and Tax Rate. We will assume restructuring will depress growth in initial years but over the time growth will pick up and revert back to industry average. Distress usually depresses operating margins so restructuring will initially need to lower or even negative margins but over the time they will come back to industry levels. In most cases financial distress leads to lower effective tax rate or even tax credits. Lastly to estimate free cash flows, we will assume due to depressed financials, firm will avoid reinvestment or postpone it. As the capital structure of the firm will vary, we need to reflect these changes in discount rate. On its path to financial health, reduction in leverage will result in lower cost of capital. To include this change in the capital structure and the change in the cost of capital the different variables have to be re-adjusted several times over the projection period [Damodaran, 2009]. The table-1 includes all these necessary inputs. Discount rate will be computed on WACC estimation i.e. weighted average of cost of equity and debt. Cost of equity is calculated using unlevered β and later to derive firm’s normal β, it needs to be re-levered by firm’s Debt/Equity Ratio. For this both market value of equity and debt has to be calculated. The market value of equity can be easily derived from market prices. 90

The market value of debt is more difficult to obtain directly, since firms have a lot of non-traded debt, which is normally specified in book value terms [Brealey et al., 2011]. One such approach that could be used to get market value of debt would be treating entire book value of debt as one coupon bond. The market value can be computed using the equation below.

Levered beta can therefore be derived using following equation.

Once beta is calculated cost of equity can be derived from CAPM model.

Finally the cost of debt, including tax shield can be calculated using equation below (Default spread is calculated using matrix which is enclosed in the appendix).

Finally cost of capital can be derived from WACC formula.

Terminal Value Calculation To conclude the going concern valuation, terminal value needs to be computed. Since the firm is assumed to in healthy condition, Terminal Value can be calculated without much adaptations to the traditional methods. We will calculate terminal value using stable growth model. The model can be summarized in the equation below.

Going Concern Value can be then calculated using equation (2).

B. Distressed Liquidation Value Generally there are two methods used to compute liquidation value. The choice depends upon the information available. First is to estimate distressed sale proceeds as a percentage of book value. Normally the percentage is derived from historical distress sales in the same industry. There is a significant amount of information available regarding distressed firms, but since every industry is different and every asset has its own characteristics, such information might not be applicable [Brown et al., 1994]. Besides, each firm has industry specific assets which might not be in condition to be used by firm operating in another industry. Second method that could be used is based on DCF. The main idea is no growth is assumed and formula derives the value that a healthy firm is willing to pay for the companyâ&#x20AC;&#x2122;s assets. The formula is given below.

The average of past few years EBIT is used to gauge the earning power of the firm which is then discounted by cost of capital. Probability of Default Probability of default is likelihood of a default over a particular time horizon. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations. There are various parameters that affect probability of default. These generally include Asset Value of Firm, its Risk and Leverage. A firm usually defaults when market value of its asset drops below its book value of debt. This section focuses on finding the probability of such scenario. In this paper itself we had briefly explained Option Valuation Approach on how equities can be seen as options. In a model introduced by Merton (1974) which was later successfully commercialized by Moody's KMV, was based on the very concept of option pricing theory. KMV model uses the Black â&#x20AC;&#x201C; Scholes and Merton structures as inspiration to compute an intermediate phase called Distance to Default (DD) and after this calculate the probability of default. Firstly the Distance to Default has to be calculated and then can be developed and estimated the probability of default of a specific company by results of the values of assets of the company and company's volatility (Black & Scholes, 1973). To calculate DD we assume efficient markets so all the costs associated with distress are assumed to be taken in account by the market. Although different approaches are used to derive the DD, this paper is based on a structural approach of Merton's (1974) model and Black and Scholes' (1973) option pricing model. The first step is to calculate Asset Value and Volatility and then the values obtained are used to derive probability of default. 92

For calculation of probability of default we first need to compute asset value and volatility of firm. The derivation of these formulas that we are about to study and analyzing distribution of our Distance to Default is beyond the scope of the study so I have assumed that they are normally distributed. The market value of asset and the market value of equity are related by the following Black and Scholes expression [Crosbie and Bohn, 2003].

Where,

The model also suggests that equity and asset volatility are related by following equation.

In Distance to Default (DD) model the probability of default is defined as probability that market value of firmâ&#x20AC;&#x2122;s asset will fall below the book value of the firmâ&#x20AC;&#x2122;s debt by the time debt matures. Since we have assumed normal distribution the probability of default is given by the equation below.

Where,

CASE STUDY ON UNITECH LIMITED Established in 1971 by a group of technocrats, Unitech Limited is one of India's leading Real Estate players. It started business as a consultancy firm for soil and foundation engineering and has grown to have the most diversified product mix in real estate comprising of world-class commercial complexes, IT/ITES parks, SEZs, integrated residential developments, schools, hotels, malls, golf courses and amusement parks. Unitech Limited is India's second largest real estate investment company, and has recently claimed to be the largest real estate builder in the country. The company is based in New Delhi and ranks 1484, in Forbes Global 2000 listing of the top 2000 public companies in the world by Forbes magazine, 32nd in India. Its construction business includes highways, roads, powerhouses, transmission lines, and it has residential projects called Unitech Cities/Uni World. RATIO ANALYSIS Earnings per share are declining for the last past 5 years. The company is raising more and more debt as evident from D/E ratio. Although debt to equity is in comfortable range but company is unable to get returns from the capital it’s employing. Return on Capital Employed (ROCE has decreased significantly. Moving forward to current ratio, it’s also following a downward trend. Interest coverage ratio also showed declining trend over the past 5 years. So the firm is facing negative earnings ratio, liquidity issues and unable to get returns from capital employed. Clearly from the above we can conclude the firm is definitely in distress. Complete ratio analysis is presented in table-2. From the table-2, it is evident that Unitech has been facing characteristics of firm in distress. Its profitability is declining and for previous two financial years it has shown negative net profit margins. Returns on assets declined significantly from 1.02% in the year 2012 to 3.09% in the year 2016. There is a decline of 16% in the share prices as compared to financial year 2012. If we take a look at its valuation multiples, they have also declined significantly. EV multiples have been declining so share is being traded at lower EV multiples. Price/BV and Price/Revenues multiple have also declined. This represents the falling investor sentiments looking to invest in Unitech. Adding to its woes interest costs have increased as they piled up new debt and earning margins have reduced that led to negative interest coverage ratio. Clearly firm is not performing well financially and can be said to be in financial distress. Analysis of Company Performance Let’s take a view on firm’s performance over last 5 years. In Figure-1, figures on y-axis are in crore rupees. As we can see that although the revenue increased in FY14, still net profit as well as operating profit declined due to increase in operating and interest costs. Let’s now take a look at EBIT and interest costs. Clearly from the graph (Figure-2), we can observe that EBIT is

decreasing while interest costs have been increasing. The above graph is evidence of the fact that the firm is facing financial distress and is struggling to pay off its debt from its operations.

Altman score Letâ&#x20AC;&#x2122;s calculate Altman's Z-score for Unitech: A = working capital / total assets = 0.227 B = retained earnings / total assets = .327 C = earnings before interest and tax / total assets = -.022 D = market value of equity / total liabilities = .066 E = sales / total assets = .074 Z-Score=1.2A+1.4B+3.3C+0.6D+ 1.0E = 0.772 Clearly Z-score indicates the firm is highly likely to go bankrupt in the next two years and is in distress. Letâ&#x20AC;&#x2122;s take a view at past 5 year share price performance of Unitech Limited.The share price has been highly volatile and declined continuously. Shareholders have almost lost 80% of their value as the share traded in the range of 35-40 in FY14 versus now trading in the range of 4-5. It seems market has already incorporated the cost of distress the company is facing at present. (Figure 3) Going Concern Value Now letâ&#x20AC;&#x2122;s take a look at the valuation as done by the model explained earlier. First take a look at going concern valuation. Main assumptions that we used for going concern valuation were: 1. The firm is expected to grow at a higher growth rate during the initial years 2. The growth rate will increase initially and then after growth period return to the stable growth rate. 3. The free cash-flow to equity is the correct measure of expected cash-flows to stockholders. 4. Length of growth period is estimated to be ten years 5. We will assume that growth in Capital Spending, Depreciation and Working Capital will approach towards industry average during the high growth period. 6. We assume capital structure doesn't alter 7. We assume firm will make even in the next financial year and growth in revenues will begin thereafter.

Growth rate assumptions regarding earnings inputs are shown in Table 3. We have assumed that the company would break even in year 1 due to its restructuring effects and later on have high revenue growth rate and later go for 5% i.e. the industry average. We have assumed the same for EBITDA/Revenue. As the companyâ&#x20AC;&#x2122;s restructuring efforts bear fruit EBITDA/Revenue will improve and reach industry average. Similar assumptions have been made for growth in capital expenditure and as capital expenditure growth is negative in initial years depreciation will also decrease. Other inputs for the model are given in Table 4 and 5. Cost of capital Risk free rate has been assumed to be 6.40% i.e. 10 year yield rate of GOI bond. Equity risk premium has been taken as 8.82%*. Effective tax rate is taken as 21.40% i.e. the average industry effective tax rate Default spread for company has been taken as 10%. (Since the company has D rating and default spread is based on estimated cumulative probabilities (see appendix) of default for bonds in different ratings classes from Altman, 2007) Using equation (6) we get cost of debt as After tax cost of debt = (6.40% + 10%) * (1 â&#x20AC;&#x201C; 21.40%) = 16.40% We have taken the industry average unlevered beta as .61 (source: Reuters). Market Value of debt has been calculated using equation 3 and market value of equity by simply taking the product of current market price and no. of shares outstanding. We can calculate beta using equation (4). Beta (levered) = 0.61 * (1-(1-21.40%) * 2.31 = 2.51 Cost of equity (from equation (5)) = 6.40% + 2.51 * 8.82% = 28.56% Also we have assumed that since company beta will change after growth period as risk after restructuring will change. We assumed beta will gradually go down to industry average. Cost of capital can be calculated using equation (7). From these assumptions we get the cost of capital matrix (Table 6). We can discount FCFF using Cumm. WACC and compute Present Value of FCFF in growth phase. Later we can compute Terminal Value using equation (8). We can then simply add both these values to cash and cash equivalents to get final value of the firm. Calculations are shown in Table-7. Liquidated Value of Firm

For liquidated value we first need to normalize EBIT. As shown in Table 8, EBIT for last year was negative so to get a fair value we have used average of past 10 years EBIT to compute liquidated value using equation (9). As above we get average EBIT for last 10 years as 897.648 INR crores.

Adding cash and equivalent balance of INR 217 crore we get liquidated value of 3770 INR crore. This value is at a discount of almost 30% of the book value of firm Probability of default The probability of default is computed by using Distance to Default model and market share price of Unitech Limited in FY 16. The first step is to determine both total asset value and its volatility using volatility of share prices. Table-9 summarizes the inputs used to calculate default probability. In the model one year equity volatility is adjusted for time horizon of 5 years by multiplying it by square root of 5, which results in volatility of 37.32%. Solving for value of asset and asset volatility using equation (10) and (11) we get value of assets as 3323 INR crores and asset volatility as 15.21%. Using these as inputs and solving equation (12) we get probability of default as 11.97%. Firm Valuation From the above outputs we can perform our valuation using equation (1). The going concern value is INR 5810 crore, the liquidation value is INR 3552 crore and the probability of default is estimated to be 11.97%.

So the estimated value as obtained by our model is 5539.717 crores. If we deduct the book value of debt, INR 3917.83 crore, the firmâ&#x20AC;&#x2122;s equity would be valued at INR 1621.887 crores which is almost in line with present market capitalization of its equity. CONCLUSION & RECOMMENDATIONS Conclusion The goal of the study was to study the limitations of traditional methods while valuing distressed firms. Therefore, we begin with studying the traditional methods and why they could not accurately value distress firms. The major limitation was that the firms in distress face very unusual conditions than normal firms and these cannot be fully taken into account by the traditional valuation methods. In addition, the methods completely ignore the risk of default. Based on the findings we introduced a model which takes into account this risk by introducing the concept of probability of default which is derived from distance to default which is based on Black Scholes theory. We established the value of firm is weighted average of both going 97

concern value and liquidated vale weighted by probability of default. Later the model was applied to Unitech Limited a real estate firm currently facing distress conditions. It was established although the model takes into account the risk of default whereas there is no study for the accuracy of the model. Also unfortunately the model is not a universal model to be used under such scenarios as the method of choice is usually determined by the availability and accuracy of information available. Recommendations for future research The model introduced is purely as of now theoretical with large assumptions. The accuracy of the model therefore is still in question due to assumptions made and insufficient information. Therefore the model could be applied to a bigger study with dual time series and cross sectional data. The findings should be analyzed and possible alteration identified. In addition there could be study on sensitivity analyses of various inputs to learn the impact of the inputs taken. In addition, the impact of various assumptions could be analyzed to judge the accuracy of model to learn if it is apt to ignore certain other factors. Based on all this learningâ&#x20AC;&#x2122;s the model could be adapted to improve its accuracy. Limitations of study 1. First we have assumed that value and return follow standard normal distribution. It would be very difficult to construct the model without the assumption of normality of asset returns due to the amount of data needed to predict the possible returns. In addition, the probability of bankruptcy is also assumed to follow a normal distribution. In the original Moody's KMV model the probability of bankruptcy is calculated based on the company's vast data of historical default and bankruptcy frequencies. Since this data is not publicly available, a normal distribution is assumed. 2. Second, we assumed the debt to have a single zero coupon bond characteristics whereas firms usually have different types of long-term bonds according to their seniority, collateral, covenant, or convertibility [Merton, 1974]. Due to this assumption has been made regarding the debt maturity and debt type. 3. Third, Black and Scholes model is based on market data from the very beginning of analysis and market value of debt keeps on constantly changing. Studies show it leads to over- or under-estimation of probability of default. 4. Fourth, the model is based on public firms which trade on markets. Private firms are generally analyzed via comparable analysis based on accounting data. Therefore, the model cannot be applied to analyze private firms. 5. Lastly, the model is based on the Black and Scholes theory, which is based on the following assumptions: no transaction costs, efficient markets, full access to capital markets, and the basic assumption from the Miller and Modigliani theorems [Modigliani and Miller, 1958] [Black and Scholes, 1973]. 98

REFERENCES Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accountingbased bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551. Alexakis, P. (2008). 'Altman Ζ-score model'and prediction of business failures. International Journal of Monetary Economics and Finance, 1(4), 329-337. Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐Score Model. Journal of International Financial Management & Accounting, 28(2), 131171. Altman,E.I(1993) : Corporate Financial Distress and Bankruptcy, Second Edition, John Wiley and Sons, Nu York. Andrade, G., & Kaplan, S. N. (1998). How costly is financial (not economic) distress? Evidence from highly leveraged transactions that became distressed. The Journal of Finance, 53(5), 1443-1493. Annual Reports of Unitech Limited (2006-2016). Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111. Bharath, S. T., & Shumway, T. (2004). Forecasting default with the KMV-Merton model. Master’s Thesis, University of Michigan. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654. Bodie, Z., Kane, A., & Marcus, A. J. (2011). Investment and portfolio management. McGrawHill Irwin. Brealey, R. A., Myers, S. C., Allen, F., & Mohanty, P. (2012). Principles of corporate finance. Tata McGraw-Hill Education. Charitou, A., & Trigeorgis, L. (2004). Explaining bankruptcy using option pricing. Working Paper, University of Cyprus. Chouhan, V., Chandra, B., & Goswami, S. (2014). Predicting financial stability of select BSE companies revisiting Altman Z score. International Letters of Social and Humanistic Sciences, 15(2), 92-105. Damodaran, A. (2006). The cost of distress: Survival, truncation risk and valuation (Monograph). Retrieved from Stern School of Business website: http://pages. stern. nyu. edu/~ adamodar///. pdf. Damodaran, A. (2009). The Dark Side of Valuation: Valuing Young, Distressed, and Complex Businesses (ed.). Financial Times Prentice Hall. Damodaran, A. (2009). Valuing distressed and declining companies. Stern School of Business. Damodaran, A. (2016). Damodaran on valuation: security analysis for investment and corporate finance (Vol. 324). John Wiley & Sons. Gordon, M. J. (1971). Towards a theory of financial distress. The Journal of Finance, 26(2), 347356. 99

Grant, R. M. (2010). Contemporary Strategy Analysis Text Only. John Wiley & Sons. Houlihan and Lokey (2011). Buying and selling a troubled company. Technical report, Houlihan Lokey.Inc. https://www.hl.com/library/bsttcacs.pdf. http://pages.stern.nyu.edu/~adamodar/ http://www.moneycontrol.com/financials/unitech/ http://www.reuters.com/finance/stocks/financialHighlights?rpc=66&symbol=UNTE.NS http://www.unitechgroup.com/investor-relations/financial.asp Li, W. G. (2014). Corporate financial distress and bankruptcy prediction in the North American construction industry. North Carolina, Durham: Duke University. López-Gutiérrez, C., Sanfilippo-Azofra, S., & Torre-Olmo, B. (2015). Investment decisions of companies in financial distress. BRQ Business Research Quarterly, 18(3), 174-187. Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470. Modigliani, F., & Miller, M. H. (1959). The cost of capital, corporation finance, and the theory of investment: Reply. The American Economic Review, 49(4), 655-669. Ray, S. (2011). Assessing corporate financial distress in automobile industry of India: An application of Altman’s model. Research journal of Finance and Accounting, 2(3), 155168. Rosen, H., Nicholson, J., & Rdgers, J., (2011) Going concern versus liquidation valuations, the impact on value maximization in insolvency situations. FTI Consulting International Arbitration Practice Group, April 2011. Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of financial economics, 27(2), 419-444.

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APPENDIX TABLES Table 1: Variables to compute Discount Rate Year t

Beta β

Cost Equity Re

Cost Debt Rd

Debt Ratio D/E

Cost Capital rc

Table 2: Ratio Analysis of Unitech Limited for FY’12- FY’16

Ratio Analysis of Unitech Limited FY 12 FY 13 FY 14 FY 15 FY 16 Basic EPS (Rs.) 0.91 0.8 0.27 -0.49 -3.45 Book Value/Share 45.96 43.54 44.19 41.82 38.46 Revenue from Operations/Share (Rs.) 9.26 9.33 11.21 13.11 7.67 PBDIT/Share (Rs.) 2.05 1.97 1.27 4.06 -2.32 PBIT/Share (Rs.) 1.88 1.82 1.08 3.88 -2.45 PBT/Share (Rs.) 1.67 1.7 0.79 3.61 -3.7 Net Profit/Share (Rs.) 0.94 0.78 0.18 -0.62 -3.46 PBDIT Margin (%) 22.13 21.13 11.34 30.95 -30.2 PBIT Margin (%) 20.34 19.5 9.62 29.62 -31.96 PBT Margin (%) 18.02 18.24 7.01 27.49 -48.27 Net Profit Margin (%) 10.16 8.36 1.57 -4.74 -45.03 Return on Networth/Equity (%) 1.97 1.83 0.6 -1.17 -8.97 Return on Capital Employed (%) 1.45 1.31 0.45 -0.93 -6.99 Return on Assets (%) 1.02 0.87 0.25 -0.46 -3.09 Total Debt/Equity (X) 0.27 0.34 0.36 0.35 0.39 Asset Turnover Ratio (%) 10.45 10.21 10.71 12.31 6.88 EV/Net Operating Revenue (X) 4.33 3.98 2.58 2.28 2.51 EV/EBITDA (X) 19.55 18.84 22.71 7.36 -8.32 MarketCap/Net Operating Revenue (X) 3.11 2.52 1.25 1.24 0.64 Price/BV (X) 0.63 0.54 0.32 0.39 0.13 Price/Net Operating Revenue 3.11 2.52 1.25 1.24 0.64 Current Ratio (X) 2.49 2.21 1.67 1.53 1.41 Quick Ratio (X) 1.75 1.65 1.34 1.26 1.17 Inventory Turnover Ratio (X) 0.48 0.55 0.74 0.9 0.53 Interest Coverage Ratios (%) 8.76 15.59 3.69 13.94 -1.96 101

Table 3: Growth rate assumptions regarding earnings inputs Year

Growth Rate in EBITDA/Revenue Growth Rate in Growth Rate in Working Capital Revenue

Capital Spending

Depreciation

as % of Revenue

-20%

10%

20.00%

1 2

10.00%

4.94%

-50%

10%

20.00%

15.00%

10.00%

-50%

10%

20.00%

25.00%

15.00%

-20%

10%

20.00%

35.00%

20.00%

-10%

-50%

20.00%

25.00%

-30%

20.00%

15.00%

25.20%

10%

20.00%

10.00%

27.80%

15%

10%

20.00%

6.00%

30.40%

20%

15%

20.00%

5.00%

33.00%

25%

20%

20.00%

Table 4: Model Inputs Current EBIT

492.75

Current Net Income =

246.21

Current Interest Expense =

56.28

Current Capital Spending =

47.28

Current Depreciation =

43.40

Tax Rate on Income =

21.40%

Current Revenues =

2,662.05

Current Working Capital =

10,142.36

Chg. Working Capital =

10,142.36

Cash and Non-operating assets =

487.06

Book Value of Debt =

3,206.08

Book Value of Equity =

12,023.84

102

Table 5: Free cash flow for the firm (FCFF) 1

Terminal Year

Revenues

2,161.76

2,377.94

2,734.63

3,418.28

4,614.68

5,768.35

6,633.61

7,296.97

7,734.78

8,121.52

8,527.60

- Operating Expenses

2,161.76

2,260.46

2,461.16

2,905.54

3,691.75

4,326.26

4,961.94

5,458.13

5,584.51

5,652.58

5,713.49

117.47

273.46

512.74

922.94

1,442.09

1,671.67

1,838.84

2,150.27

2,468.94

2,814.11

EBITDA - Depreciation

38.95

42.85

47.13

51.84

25.92

18.15

19.05

20.96

24.10

28.92

30.37

EBIT

-38.95

74.63

226.33

460.90

897.01

1,423.94

1,652.62

1,817.88

2,126.17

2,440.02

2,783.74

15.97

48.44

98.63

191.96

304.72

353.66

389.03

455.00

522.16

595.72

- EBIT*t EBIT (1-t)

-38.95

58.66

177.90

362.27

705.05

1,119.22

1,298.96

1,428.85

1,671.17

1,917.86

2,188.02

+ Depreciation

38.95

42.85

47.13

51.84

25.92

18.15

19.05

20.96

24.10

28.92

30.37

- Capital Spending

37.82

18.91

9.46

7.56

6.81

7.15

7.86

9.04

11.30

11.87

43.24

71.34

136.73

239.28

230.73

173.05

132.67

87.56

77.35

81.22

39.36

144.23

269.81

484.89

899.48

1,137.09

1,308.09

1,596.40

1,858.13

2,125.30

- Chg. Working Capital Free CF to Firm

-37.82

Table 6: Cost of Capital Matrix

Terminal Year

21.40%

2.51

2.32

2.12

1.92

1.73

1.53

Cost of Equity

28.56%

26.82%

25.09%

23.36%

21.63%

19.89%

Cost of Debt

12.89%

Debt Ratio

56.63%

Cost of Capital

19.68%

18.93%

18.18%

17.43%

16.68%

15.93%

Cum. WACC

1.19684

1.43244

1.71441

2.05188

2.45578

2.92074

3.45179

4.05346

4.72955

5.48288

Tax Rate Beta

103

Table 7: Net Value of Firm Calculation

Present Value of FCFF in high growth phase =

2,045.48

Present Value of Terminal Value of Firm =

3,547.02

Value of the firm =

5,592.50

+ Cash and Marketable Securities =

217.62

Net Value of Firm

5,810.12 Table 8: Unitech Limited EBIT FY 2007-2016

EBIT for last 10 years FY 16 FY 15 FY 14 FY 13 FY 12 FY 11 FY 10 FY 9 FY 8 FY 7 EBIT -641.7 25.6 179.35 372.4 492.75 1014.67 1132.82 2042.88 2395.26 1962.45 Average= 897.648 Table 9: Model Inputs Equity 1256

Ï&#x192;E 16.69%

Debtm 2895

rf 6.40%

T 5

FIGURES

Figure 1: Unitech Limited Performance from FY 2012-2016

104

Figure 2: Unitech Limited EBIT & Interest data FY 2012-16

Figure 3: Share Price performance of Unitech Limited FY 2012-16

105

Appendix 1: Distance to Default

Appendix 2: Unitech P&L Statement 2012-16 Profit & Loss account

------------------- in Rs. Cr. ------------------Mar '16 Mar '15 Mar '14 Mar '13 Mar '12

Income Sales Turnover Excise Duty Net Sales Other Income Stock Adjustments Total Income Expenditure Raw Materials Power & Fuel Cost Employee Cost Other Manufacturing Expenses Miscellaneous Expenses Total Expenses Operating Profit PBDIT Depreciation EBIT Interest Profit Before Tax Extra-ordinary items PBT (Post Extra-ord Items) Tax Reported Net Profit Minority Interest Share Of P/L Of Associates Net P/L After Minority Interest & Share Of Associates

106

2,021.91 3,438.07 2,948.35 2,457.87 2,429.57 14.37 6.89 15.03 17.33 7.71 2,007.54 3,431.18 2,933.32 2,440.54 2,421.86 64.73 -702.35 63.57 85.24 208.05 89.49 -79.71 1.99 22.73 32.14 2,161.76 2,649.12 2,998.88 2,548.51 2,662.05 514.01 195.92 304.96 229.29 135.4 0.57 19.08 127.26 111.48 88.21 153.58 181.1 213.1 188.24 161.96 1,872.40 1,707.07 1,869.89 1,381.77 1,422.12 227.49 474.58 253.91 225.49 318.21 2,768.05 2,577.75 2,769.12 2,136.27 2,125.90 -671.02 773.72 166.19 327 328.1 -606.29 71.37 229.76 412.24 536.15 35.41 45.77 50.41 39.84 43.4 -641.7 25.6 179.35 372.4 492.75 327.39 72.93 76.5 30.53 56.28 -969.09 -47.33 102.85 341.87 436.47 -0.32 -3.49 0.33 0.06 -0.66 -969.41 -50.82 103.18 341.93 435.81 -65.34 111.85 57.06 137.77 189.59 -904.07 -162.66 46.12 204.15 246.21 -1.15 -34 -23.45 -4.81 8.07 -0.22 -0.32 -0.17 -0.6 0.77 -902.38 865.88 172.43 313.03 238.04

Appendix 3: Unitech Balance Sheet 2012-16 Mar-16 EQUITIES AND LIABILITIES SHAREHOLDER'S FUNDS Equity Share Capital Total Share Capital Reserves and Surplus Total Reserves and Surplus Total Shareholders Funds Minority Interest NON-CURRENT LIABILITIES Long Term Borrowings Deferred Tax Liabilities [Net] Other Long Term Liabilities Long Term Provisions Total Non-Current Liabilities CURRENT LIABILITIES Short Term Borrowings Trade Payables Other Current Liabilities Short Term Provisions Total Current Liabilities Total Capital And Liabilities ASSETS NON-CURRENT ASSETS Tangible Assets Intangible Assets Capital Work-In-Progress Intangible Assets Under Development Fixed Assets Non-Current Investments Deferred Tax Assets [Net] Long Term Loans And Advances Other Non-Current Assets Total Non-Current Assets CURRENT ASSETS Current Investments Inventories Trade Receivables Cash And Cash Equivalents Short Term Loans And Advances OtherCurrentAssets Total Current Assets Total Assets

Mar-15

Mar-14

Mar-13

Mar-12

523.26 523.26 523.26 523.26 523.26 523.26 523.26 523.26 523.26 523.26 9,539.17 10,418.33 11,036.90 10,867.32 11,500.58 9,539.17 10,418.33 11,036.90 10,867.32 11,500.58 10,062.43 10,941.59 11,560.16 11,390.58 12,023.84 51.19 5.22 39.24 59.29 71.79 2,464.69 2,165.55 2,588.42 2,878.62 2,120.32 8.36 9.83 50.53 33.87 15.39 301.72 547.8 1,105.14 1,607.59 2,090.32 25.48 26.53 27.01 21.2 18.35 2,800.25 2,749.72 3,771.10 4,541.28 4,244.38 1,453.14 1,559.79 13,210.80 5.4 16,229.12 29,143.00 19,029.37

1,635.10 1,416.63 11,102.71 4.79 14,159.24 27,855.76

1,524.02 1,331.14 9,146.11 4.68 12,005.95 27,376.44

1,038.04 1,224.44 5,583.82 48.13 7,894.43 23,885.57

1,085.76 660.85 4,975.77 91.24 6,813.63 23,153.65

698.2 12.21 1,195.23 0 1,905.65 1,320.62 219.96 177.62 7.15 6,299.72

785.5 2,686.74 1,164.51 0 4,636.76 1,337.01 62.96 190.52 8.84 6,236.09

1,617.48 2,657.36 1,362.97 8.25 5,646.07 1,345.50 106.24 243.01 24.35 7,365.17

1,387.87 2,197.95 1,339.50 8.25 4,933.57 1,236.97 54.16 220.62 4.72 6,450.05

1,181.03 2,147.35 1,120.01 6.94 4,455.32 1,579.12 0 161.81 1.41 6,197.66

0.57 3,823.47 1,552.59 217.05 3,494.19 13,755.40 22,843.27 29,143.00

8.88 3,804.82 1,543.19 226.42 3,458.76 12,577.59 21,619.67 27,855.76

7.57 3,951.75 1,279.84 266.53 3,524.01 10,981.56 20,011.27 27,376.44

65.1 4,402.54 1,554.48 406.36 3,879.11 7,127.93 17,435.52 23,885.57

168.51 5,026.58 1,838.42 318.55 4,320.32 5,283.61 16,955.99 23,153.65

107

Appendix 4: Industry Averages Gross Margin Beta Sales - 5 Yr. Growth Rate Current Ratio Operating Margin Effective Tax Rate - 5 Yr. Avg. Return on Assets - 5 Yr. Avg. Return on Equity - 5 Yr. Avg.

Unitech industry -5.8 57.5 3.2 1.53 -9.98 15.02 1.45 3.03 -24.96 28.21 -21.4 -0.44 7.88 -0.91 15.41

Appendix 5: Default Spreads matrix

Appendix 6: Equity Volatility Period

Long Term Daily - One Daily - One Weekly - Weekly - Fortnightly - Monthly Beta * Month Year Range One Year Two Year Two Year Two Year Range Range Range Range Range

Beta

2.79

1.33

1.9

2.27

2.28

3.09

2.08

Mean

13.61

4.67

5.56

5.24

7.6

7.54

6.92

Standard Deviation

22.16%

4.12%

16.69%

10.68%

9.75%

14.44%

21.27%

108

ISSN NO. 0974-0902

RNI NO. 67689/97

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