ISSN (Print): 0976-8629 ISSN ...€¦ · BGMEA University of Fashion and Technology (BUFT), Dhaka, Bangladesh Panel Data Study of Dividend Policy Effect on Firms’ Value: Study on

1Volume 9, Issue 1 • January-June 2018

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IITM Journal of Management and ITVolume 9 Issue 2 July - December 2018

CONTENTS

Research Paper & Articles

• Panel Data Study of Dividend Policy Effect on Firms’ Value: Study onManufacturing Sector of Bangladesh 1

Mohammad Shahidul Islam

• Determining Factors of Attitude towards Green Purchase Behavior of FMCG Products 10Vaishali Sethi

• Comparing the Volatility of Returns in Indian and Chinese Information Technology Sector 26Parul Tyagi

• Distinguishing Countries on the Basis of Modified HDI 35Ambica Sharma & Hardik Beniwal

• Social Responsibility and Ethics in Marketing 50Anupreet Kaur Mokha

• Job Satisfaction of Employees in Multispecialty Hospitals in Delhi and NCR 57Suniti Chandiok

• Strategies v/s Consumer Perception of Brand Zara / India 68Saloni Saraswat

1Volume 9, Issue 2 • July-December 2018

D r. M ohammad Shahidul Islam* AssociateProfessor, Department of Business Administration,BGMEA Universi ty of Fashion and Technology(BUFT), Dhaka, Bangladesh

Panel Data Study of Dividend Policy Effect onFirms’ Value: Study on ManufacturingSector of BangladeshMohammad Shahidul Islam*

Abstract

The impact of dividend on market price of share is a controversial issue. To solve this issue inour market perspective, this study is done whether there is impact of DPR on PE or not. The paneldata analysis (FE and RE model) is used to find out the impact of dividend on market price. Thestudy is conducted on manufacturing sector and is found that the DPR has impact on PE. Thereis other co factors (age of the firm), which also have impact on PE. So, the findings support therelevance theory of dividend on shareholders’ wealth. This finding will help the dividend decisionmakers and investors for taking corrective dividend decision.

Keywords: Dividend, Market price, Panel Data Analysis, EPS, PE, DPR.

Introduction

The dividend policy has significant importance in thefinancial decisions of the corporation. It is a guidelinefor financial managers, how to pay dividend to theshareholders. Net earnings are divided into two parts.One is retained earnings and the other is dividends. Theretained earnings of the business may be reinvested andused for growth of the business. The dividend isdistributed to the shareholders in order to meet theirexpectation of being made better off financially. So, theproblem is to take decision that how much earningsshould be given in the form of dividend payout andhow much earnings should be kept as retained earnings.

In the modern and complex environment,globalization and privatization have brought deepcompetition in every field of activity. It is very difficultfor the companies to compete in the markets ofstunning nature. To cope with this competitiveness andto add value to the companies; today, the financemanagers have to make critical financial decisions. Theprimary objective of any organization is to maximize

the wealth of shareholders. Financial manager’s aim isto take a decision in such a way that shareholdersreceive a high contribution of dividend, which leadsto increase in the price of share. Dividend policy playsa vital role for a company in financial markets and itdirectly affects the stock price of the company. If acompany pays handsome return to its shareholders itwill attract new investors to invest their money in thecompany and vice versa. The dividend policy causesto increase the wealth of shareholders, finance managermakes different financial decisions and dividend policydecision is one of them (Baker &Powell, 1999).Dividend decision has great impact on firm financialdecision and stock price. The stock price increaseswhen there is smooth payment of dividend exist.Investors do not prefer to purchase the shares of suchtype of companies, which cannot make paymentregularly and of which the dividend decisions havevariability because of the risk of loss associated withthese variations. Simians (1995) argued thatshareholders’ wealth is largely influenced by theorganization’s dividend policy.

The dividend decisions can donate to the value of firmor not which is a controversial issue. There are mainlytwo schools of thoughts available in the field of financethat presented two different opinions about the

2 IITM Journal of Management and IT

dividend policy. One school of thought followed theopinion of Miller and Modigliani (1961) andconsidered dividend policy irrelevant while the secondschool of thought followed the point of view ofGordon (1963) and considered dividend policyrelevant. Since the half century passed, the questionstill remains i.e. whether dividend policy is relevant ornot. The impact of dividend on share price is a vitalissue. If there is impact of dividend, the companyshould aware for dividend payment. For this reason,this study has been undertaken to analyse therelationship between dividend and market value ofshares and to identify the degree of influence ofdividend on market value of firm.

Prior Theoretical and Empirical Evidences

Prior Theoretical and Empirical Evidences ofForeign Context

Dividend policy is one of the most discussed topicsand an essential theory of corporate finance which stillhas its significance. Many researchers presentednumerous theories and pragmatic evidences, however,the problem is quiet unsettled and open for furtherdebate. It is among the top ten unsettled issues ineconomic literature that does not have satisfactoryclarification for the observed dividend behavior of thefirms (Allen and Michaely, 2003; Black, 1976).Discussion of dividend policy cannot be completedwithout including the work of Linter (1956). Linter(1956) raised the question, which is still important,“what choices made by managers do affect the size,shape and timing of dividend payments?” After thecontribution of Linter (1956), Miller & Modigliani(1961) introduced the concept of Dividend Irrelevancetheory in which they explained that dividend policydoes not affect the stock prices. Many researchers likeChen, Firth, & Gao (2002), Uddin & Chowdhury(2005), Denis & sobov (2008) and Adesola &Okwong (2009) provided the strong evidence in thefavor of dividend irrelevance theory and did notconsider its relevance to the stock prices. Gordon(1963) gave another view about the dividend policyby presenting the concept of dividend relevance theory.He said that the dividend policy affects the value offirm and market price of shares. Investors always prefersecure and current income in the form of dividends

over capital gains. Studies conducted by Travlos,Trigeorgis, &Vafeas (2001), Baker, Powell &Veit(2002), Myers & Frank (2004), Dong, Robinson &Veld (2005) and Maditinos, Sevic, Theriou, &Tsinani(2007) support dividend relevance theory. Black &Scholes (1974) found no relationship betweendividend policy and stock prices. Their results furtherexplain that dividend policy does not affect the stockprices and it depends on investors’ decision to keepeither high or low yielding securities.

Barclay and Smith (1995) found that high growthcompanies have lower dividend payouts and debt ratiosthan the low growth companies, which have higherdividend payouts and debt ratios. So, investors preferhigher dividend payouts and consider it less risky thancapital gain. Allen &Rachim (1996) found norelationship between the dividend yield and stockmarket price even after studying Australian listed stocksbut, it shows positive relation between stock prices andsize, earnings and leverage and negative relation stockprices and payout ratio.While, Baskin (1989) examined2344 U.S common stocks from the period of 1967 to1986 and found a significant negative relationshipbetween dividend yield and stock price. Another study,conducted by Ho (2002) relevant to the dividendpolicy in which he used the panel data approach andfixed effect regression model. Results of his studyshowed the positive relation between dividend policyand size of Australian firm and liquidity of Japanesefirms. He found the negative relation between dividendpolicy and risk in case of only Japanese firms. Theoverall industrial effect of Australia and Japan is foundto be significant. Baker, Powell &Veit (2002) providednew evidence of managers’ decision about dividendpolicy. They conducted a survey of managers ofNASDAQ firms that were consistently paying cashdividends. Their survey result shows that managers aremostly aware of historical pattern of dividends andearnings. So, they design their dividend policies afterconsidering it.

Pradhan (2003) also explained the effect of dividendpayment and retained earnings on stock market priceof the Nepalese companies. Results of his study showthat dividend payment has strong relation with stockprice; while, retained earning has very weak relationwith stock market price. His results further explain that


Nepalese stockholders give more importance todividend income than capital gains. Nishat & Irfan(2003) studied 160 companies listed at Karachi StockExchange for the period of 1981-2000. Their resultswere based on cross sectional regression analysis andshowed that dividend yield and payout ratio ispositively related to the share price volatility. Adefila,Oladipo & Adeoti (2004) studied the factors affectingthe dividend policy of Nigerian firms. Results of theirstudy show that Nigerian firms prefer regular dividendpayouts that can be in accordance with the expectationsof their shareholders. Their results also conclude thatthere is no relation between dividend payments, netearnings and stock prices. Nigerian firms pay dividendsto their shareholders regardless of their level of profitsfor satisfaction of their shareholders. Myers & Frank(2004) conducted a study by using the data of 483firms from Multex Investor Database and concludedthat there is a positive relationship between the priceearnings ratio and dividend payout ratio. Their resultsfurther show that there is a significant positive relationbetween debt to equity ratio and dividend payout.

Hussainey, Mgbame, & Chijoke-Mgbame (2011)studied the impact of dividend policy on stock prices.The results of their study show the positive relationbetween dividend yield and stock price changes andnegative relation between dividend payout ratio andstock price changes.

The academicians also engaged in finding out the factsand issues relating to dividend policy and they madedifferent theories on this topic. According to Hayn(1995), dividend payments reduce the earning of anycorporation if there are low earnings are realized, itmakes the decision uneven, which enables managersto take strong decision for dividend and earning infuture. Whereas, DeAngeb et al. (1992) & Charitou(2000) described the change in dividend policy makethe managers informative about the cost of dividendpayment. Spencer (1973) argued that dividends payoutincreases the investors’ confidence in the company.Thus, the company can make future decisions ofdividends payout on the basis of the past dividendspolicies. The study conducted by Farley and Baker(1989) suggests that dividends policy has a significantimpact on stock prices. Dividend payout ratio is basedon current and last year earnings, the changes in yearwise earning and increasing rate of earnings. The past

year dividend payments have great influence on currentpolicy (Pruitt and Gitman 1991).

Prior Theoretical and Empirical Evidences ofBangladeshi Context

Studies related to dividends impact on share price inthe context of Bangladesh are mentioned below.

Uddin (2009) analyzed to identify what determinesthe share prices and there is a significant linearrelationship between market price of stock and net assetvalue per share; dividend percentage; earning per share.

Ali (2011) examined the long-run equilibriumrelationship and the direction of causality betweenstocks. He found that the DSI, in anyway, do notgranger cause dividend yield; but DSI has bi-directional causal relation with market price earningsmultiples and the first lag of the monthly averagetrading volume. On the other hand, unidirectionalcausality is found from DSI to the first lag of monthlyaverage market capitalization but no causality is foundfrom the opposite direction.

Kabir, Bhuiyan and Chowdhury (2013) attempted toidentify the economic and psychological factors thatimpact the market price of shares of the listedpharmaceutical companies in Dhaka Stock Exchange(DSE). They found that the percentage of shares heldby public, and bad news about a particular companynegatively influence the market prices of shares of thatparticular company.

Masum (2014) analyzed to find the relation betweenthe shares market price and the dividend policy of thebanks. He found that the Model shows significantnegative relation between dividend yield and stockprice while, retention ratio has a negative butstatistically insignificant relationship with stock marketprices. He further showed that return on equity andearnings per share have statistically significant positiveimpact on stock price and profit after tax has asignificant negative impact on stock market prices ofthe commercial banks of Bangladesh.

So, it is observed that the dividends policy implicationson shareholders wealth carry diverse arguments fromthe previous researchers. One school of thought holdthe notion that dividend policy does help maximizing


the shareholders’ wealth, however, the other argues thatthere is no such impact that can be arguably supported.Very few papers are found in the context of Bangladesh,which motivates me to study the impact of dividendon share prices and to justify the relevance of dividendin financial decision making.

Research Design

Sample

The study is based on secondary data obtained frompublished annual reports of sample firms, monthlyreview of Dhaka stock exchange and website of DSE.It has taken 86 companies from manufacturing sectorsas sample. The study period is 20 years from 1994 to2013 for study.

From the population (117), it is taken 86 companiesas sample through sample size determinationtechniques.

(n = 2N

1 + N(e) n = Sample size, N = Population size,

e = level of precision)

Hypothesis

H0: There is no association between wealth ofshareholders and dividend policy.

Variables Used in Study

Dependent Variable: PE ratio

Independent Variables: Independent variables areDividend payout ratio (DPR), Capital structure,Investment opportunity, liquidity, ownership(institution), age of the firm, size of the firm.

Model and Methods

The studies conducted by Miller and Modigliani(1961), Friend and Puckett’s (1964) and Chawla andSrinivasan (1987) have influenced this paper. Thistheoretical statement could be framed as:

PEit= +1DPRit+ 2AGEit+ 3LIQit+ 4SIZEit+5OWN(INSTITUTION)it+ 6INVESTOPPit+7CAPITAL STRUCTUREit +uit

Where,

Dependent Variable

PE ratio=Market price per share/Earnings per share

Independent Variables:

DPR (Dividend Payout Ratio) = Cash dividend pershare/Earning per share*100

Firm age (AGE): Natural log of No. of years of listingon the stock exchange

LIQ (Liquidity) = Quick ratio ((Current Asset-Inventory)/Current liability)

SIZE (Size) = Log of Total Assets

OWNIST (Institutional ownership) = No. of Shareheld by institution/total no. of share

INVESTOPP (Investment Opportunity)= (Net fixedassett-net fixed assett-1)/ net fixed assett-1*100

CAPITAL STRUCTURE: Total liabilities/ Equity

Methods: In this study, the panel data approach is usedto analyze the impacts of dividend policies onshareholder’s wealth. Descriptive statistics and panelregression analysis (Fixed effect and random effect) areused to analyze the results.

Conceptual Framework

Panel Data Analysis: Manufacturing Sector

A panel data regression differs from a regular time-series or cross-section regression in that it has a doublesubscript on its variables:

yit = a + X’it b + uit ( i= 1, …, N; t = 1, …, T)

The i subscript denotes the cross-section dimension andt denotes the time-series dimension. Most of the paneldata application utilizes a one-way error componentmodel for the disturbances, with: uit = i + it.

There are several different linear models for panel data.The fundamental distinction is that between fixed-


effects and random-effects models. In the fixed-effects(FE) model, the i is permitted to be correlated withthe regressorsxit, while continuing to assume that xit isuncorrelated with the idiosyncratic error it. In therandom-effects (RE) model, it is assumed that i ispurely random; a stronger assumption implying thati is uncorrelated with the regressors.

Descriptive Statistics

The descriptive statistics is shown in table-1 whichrepresents the mean, standard deviation, minimum,and maximum of variables.

Table 1: Descriptive statistics

Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- DPR | 1292 50.91884 80.35464 -485.4369 985.9155 Investoppo~y | 1133 15.25584 69.10146 -91.77528 988.6974 Capitalstr~e | 1191 1.217675 10.78562 -160 115.6156 Liquidity | 1200 1.943313 3.180107 .0018081 45.78755 Owninstitu~n |1256 15.83767 14.40954 -2 71.57 -------------+-------------------------------------------------------- PE | 1107 32.41737 57.89823 -119.64 881.73 Ageoffirm | 1298 2.383588 .8109209 0 3.637586 Size | 1202 6.556505 1.594046 2.288354 11.59599

Serial Correlation

Because serial correlation in linear panel-data modelsbiases the standard errors and causes the results to beless efficient, researchers need to identify serialcorrelation in the idiosyncratic error term in a panel-data model. While, a number of tests for serialcorrelation in panel-data models have been proposed,a new test discussed by Wooldridge (2002) is veryattractive because it requires relatively few assumptionsand is easy to implement. Wooldridge’s method usesthe residuals from a regression in first-differences. Notethat first differencing the data removes the individual-level effect, the term based on the time-invariantcovariates and the constant.

Table 2: Wooldrige test

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F(4, 865) = 2.485

Prob> F = 0.710

Null hypothesis has been accepted that there are nofirst order autocorrelations in the model (From thetable 2).

Heteroscedasticity

The standard error component assumes that theregression disturbances are homoscedastic with thesame variance across time and individuals. This maybe a restrictive assumption for panels. Whenheteroscedasticity is present the standard errors of theestimates will be biased and I should compute robuststandard errors correcting for the possible presence ofheteroscedasticity.

The fixed-effects regression model estimated by xtreg,feinvokes the OLS estimator under the classicalassumptions that the error process is independently andidentically distributed. Also, the command xtreg,feestimates this model assuming homoscedasticity.Themost likely deviation from homoscedastic errorsin the context of pooled cross-section time-series data(or panel data) is likely to be error variances specificto the cross sectionalunit.

In the linear regression, the error term is assumed tobe homoscedastic constant across observations.Violation of this assumption is pernicious. Estimatesof standard errors for the regression coefficients arebiased and the direction of the bias is not known apriori may inflate or deflate t-tests. So, thehomoscedasticity assumption means that the varianceof the error terms is constant for each observation. TheBreusch- Pagan/ Cook-Weisberg test is used to testheteroscedasticity in this study as shown in table -3by using STATA. A large chi-square would indicatethat the heteroscedasticity is present.

Table 3: Breusch-Pagan/Cook-Weisberg test forheteroscedasticity

Breusch-Pagan/Cook-Weisberg test for heteroscedasticity

Ho: Constant variance

Variables: fitted values of PE

chi2(1) = 0.26

Prob>chi2 = 0.651

From the table 3, it is observed that the chi- squarevalue is small, indicating heteroscedasticity is probablynot a problem. Here, the chi-square value is0.26(p=.651) and indicates the insignificancy whichindicates that the errors have a constant variance (thedata does not suffer from heteroscedasticity).


Multi Collinearity

The panel data analysis drops the variables which havecollinearity. Besides this, collinearity problem ofvariables with multiple regression analysis with SPSShas been verified. The Tolerance is simply the reciprocalof VIF (Variance Inflation Factor) and is computedas: Tolerance=1/VIF. The large values of VIF areunwanted and undesirable. The larger values oftolerance are indicating of lesser problem withcollinearity. The theoretical maximum value oftolerance is 1.00 and minimum value of tolerance iszero. It is observed that the tolerance of the variablesDPR, SIZE, AGE, INVT.OPP, LIQ, CAPST,OWNINST are 0.82, 0.81, 0.72, 0.80, 0.54, 0.61,0.59, respectively, which are highly positive and nearto 1. So, it is concluded that the variables are free frommulti-collinearity.

The Hausman Test

The Hausman principle can be applied to allhypothesis testing problems, in which two differentestimators are available, the first of which b^ is efficientunder the null hypothesis, however inconsistent underthe alternative, while the other estimator b~ isconsistent under both hypotheses, possibly withoutattaining efficiency under any hypothesis. Hausmanhad the intuitive idea to construct a test statistic basedon q = bˆ “b~. Because of the consistency of bothestimators under the null, this difference will convergeto zero, while it fails to converge under the alternative.Hausman suggested the statistic m = q’(var q)-1 q,where var q = varb~ “varbˆ follows from the knownproperties of both estimators under the null hypothesisand from un-correlatedness. The statistic m isdistributed as 2 under the null hypothesis, withdegrees of freedom corresponding to the dimensionof b.

In the concrete case of panel models, It is known thatthe FE estimator is consistent in the RE model as wellas in the FE model. In the FE model, it is even efficientand in the RE model, it has good asymptoticproperties. By contrast, the RE–GLS estimator cannotbe used in the FE model, while it is efficient byconstruction in the RE model. The inconsistency ofthe RE estimator in the FE model follows from thefact that, as T , the individual fixed effects i are

not estimated but are viewed as realizations of randomvariables with mean zero. The violation of theassumption E = 0 for the regression model leads toan inconsistency. In Stata, the Hausman test statisticcan be properly computed based upon the contrastbetween the RE estimator and fixed effects (FE).

Table 4: Hausman test

---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------- DPR | .1113619 .1173229 -.0059611 .0086804 Investoppo~y | .0228262 .0221168 .0007094 .0055006 Capitalstr~e | .2020514 .1904061 .0116453 .0578876 Liquidity | .5178672 1.129106 -.6112384 .8960415 Owninstitu~n | -.3782005 -.2048128 -.1733877 .1560135 Ageoffirm | 20.42063 8.826631 11.594 3.96594 Size | .560498 .8034145 -.2429165 3.432448 ------------------------------------------------------------------------------ b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 21.49 Prob>chi2 = 0.0031

The probability is 0.0031(less than 0.05), so, the nullhypothesis has been rejected that individual effects arerandom and that RE provides consistent estimates.Concluding that author have a fixed-effects model, andcontinued with the estimation of the model using thewithin estimator, the most commonly used with thistype of models.

Fixed Effect Model (FE)

The FE explores the relationship between predictor andoutcome variables within an entity. Each entity has itsown individual characteristics that may or may notinfluence the predictor variables. When using FE it isassumed that something within the individual mayimpact or bias the predictor or outcome variables andwe need to control for this. This is the rationale behindthe assumption of the correlation between entity’s errorterm and predictor variables. The FE removes the effectof those time-invariant characteristics from thepredictor variables so we can assess the predictors’ neteffect. Another important assumption of the FE modelis that those time-invariant characteristics are uniqueto the individual and should not be correlated withother individual characteristics. Each entity is differenttherefore the entity’s error term and the constant(which captures individual characteristics) should notbe correlated with the others.


Table 5: Fixed Effect Model

Fixed-effects (within) regression Number of obs = 939 Group variable: Company Number of groups = 86 R-sq: within = 0.765 Obs per group: min = 1 between = 0.266 avg = 11.3 overall = 0.595 max = 20 F(7,849) = 7.27 corr(u_i, Xb) = -0.3787 Prob> F = 0.0000 ----------------------------------------------------------------------------------- PE | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------+---------------------------------------------------------------- DPR | .1113619 .0246447 4.52 0.000 .0629902 .1597335 Investopportunity | .0228262 .0332137 0.69 0.492 -.0423644 .0880168 Capitalstructure | .2020514 .1744354 1.16 0.247 -.1403238 .5444266 Liquidity | .5178672 1.252381 0.41 0.679 -1.940259 2.975994 Owninstitution| -.3782005 .2107083 -1.79 0.073 -.7917708 .0353698 Ageoffirm | 20.42063 4.649984 4.39 0.000 11.29381 29.54744 Size | .560498 3.66059 0.15 0.878 -6.624369 7.745365 _cons | -25.2651 19.95572 -1.27 0.206 -64.43344 13.90324 ------------------+---------------------------------------------------------------- sigma_u| 25.883871 sigma_e| 50.574622 rho | .20756602 (fraction of variance due to u_i) -----------------------------------------------------------------------------------

Coefficient of Multiple Determinations (R2):

The summary of the model is shown in table 5. TheR2 shows the amount of variance of PE of explainedby DPR, SIZE, AGE, INVT.OPP, LIQ, CAPST,(OWNINST). The value of R2 of the model is.765(within) which indicates that the independentvariables explain 76.5% of the dependent variable (PE).This represents satisfactory result for interpreting themodel.

Significant of the Model: F-Test

The table 5 represents the significance of the modelthrough the F-test. It tests whether R2 is different fromzero. The F value of model is 7.27(p=0.00) which isstatistically significant. It is interpreted that the modelsignificantly improves the ability to predict theoutcome variable (dependent variable).The F-statisticsof the model is significant at 5 percent level ofsignificant indicating that the model providessignificant explanation of variation in the market priceof nonfinancial sector.

Significant of the Variables/Model Parameter:

The result of the model parameter is shown in table5. The coefficient indicates the individual contributionof each predictor to the model. The coefficient valuestell about the relationship between PE and eachpredictor. If the value is positive, it indicates that thereis a positive relationship between the predictor and the

outcome whereas a negative co-efficient represents anegative relationship. The coefficient values also tellus to what degree each predictor affects the outcomeif the effects of all other predictors are held constant.The beta values have an associated standard errorsindicating to what extent these value would vary acrossdifferent sample and these standard error are used todetermine whether or not the beta values differsignificantly from zero. In the model, the Coefficientvalues of DPR, AGE are .1113, 20.42 respectivelywhich are positive in nature. It infers that the DPR,AGE of the firm have positive impact on the PE.

The t test associated with coefficient value is significantthen that predictor is making a significant contributionto the model (if the value is less than 0.05). The smallerthe value of significance, p value (the larger the valueof t) is the greater the contribution of that predictor(independent variable). From the table 5, it is observedthat the t value of DPR, AGE are 4.52(p=.000),4.39(p=.000) respectively which are significant at 5percent level of significant. The p values of theindependent variables DPR, AGE are less than .05which also indicates the significance of the variables.So, finally it is concluded that among the independentvariables-DPR, AGE have positive impact on the PE.This result supports the findings of Grdon (1963), Ho(2002), Gul and others(2012).

Model: PEit= -25.26+0.11DPR it+ 20.42AGEit+.517LIQit+ .56SIZEit-.37 OWN(INSTITUTION)it+0 . 0 2 2 I N V E S T O P P i t + 0 . 2 0 2 C A P I TA LSTRUCTUREit +uit

Summary of Findings and Recommendations

The DPR, AGE of the firm have positive impact onthe PE. The t value of DPR, AGE are 4.52(p=.000),4.39(p=.000) respectively which are significant at 5percent level of significant. The dividend payout ratiois derived from formula of Gordon growth model asone of the direct determinant factors to P/E ratios.When the dividend payout ratio is high, the expectedreturns investors gained will be correspondingly high,which will further lead investors make a high measureof stock values, the companies’ P/E ratios will thenrise. Conversely, the P/E ratios will decline. Therefore,it is supposed that there is a positive correlationbetween dividend payout ratios and companies’ P/E


ratios. The DPR has positive impact on the PE(t=4.52), which indicates that the dividend has theimpact on the market price of firms.

This result infers the relevance theory of dividendpolicy which is supported by many other researchers,findings like Myers and Frank (2002), Friend andPuckett (1964), John and Willians (1985), Richardsonand Thompson (1986).These findings will help thedividend decision maker for taking corrective dividenddecision. The companies should follow continuousdividend policy practices with a view to boostinginvestor morale as well as keeping stock market as safeharbor for investment and financing sector.

Conclusion

The impact of dividend on market price of share is acontroversial issue. To solve this issue in our marketperspective, this study is done whether there is impactof DPR on PE or not. Findings support the relevancetheory of dividend on shareholder wealth. The studyis conducted separately on manufacturing sector andhas found that the DPR has impact on PE. Thereare other co factors such as age, capital structure,which have also impact on PE (market price share/Earnings per share). These findings will help thedividend decision maker for taking corrective dividenddecision.

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Determining Factors of Attitude towards GreenPurchase Behavior of FMCG ProductsVaishali Sethi*

Abstract

The study aims to propose and test a model of the effects of explicit attitudinal constructs on thefrequency of green purchase behavior. Environmental concern, Green advertisement and perceivedquality of green products are operationalized by a path model hypothesizing effects of theseantecedents on attitude leading to purchase intention and finally the purchase behavior of greenproducts. The measures are obtained from a survey of a representative sample of 501peopleand analyzed in a structural equations model framework.

The research employs a survey-based method to test a theoretically grounded set of hypotheses.A well-structured and closed ended questionnaire with Nine-point Likert scales that were wellestablished from previous researches was administered to collect responses from 501 respondents.The data from the respondents were processed and analyzed with the statistical programmeSPSS and AMOS using confirmatory factor analysis and Structural equation modeling.

The best predictor of the intention to purchase green products is attitude towards the behavior.This study shows that the intention of consumers to purchase green products is determined byhaving a positive attitude toward green products. The results from the structural-equation modelingshow the environmental concern has the highest influence on attitude towards green productsamong three personal variables including environmental concern, green advertising and perceivedquality of green products.

The major limitation of the study is that it is undertaken in a single Indian city i.e. in a particulargeography hence the study may not have included all the relevant ecological diversities at amore aggregate level. The present study has been conducted by considering only one constructof the theory of planned behavior which is attitude and its antecedents. Future studies need toinvestigate the role of other two important constructs which are subjective norms and perceivedbehavioral control.

The findings can help the marketers to formulate their policy with regard to actions which wouldenhance the purchase and usage behavior of the consumers towards green products. Marketersneed to keep in mind that environmental concern, green advertising and quality of the greenproducts have significant and positive impact on the attitude towards green products. It providesthem with the opportunity to design their communication content in line with requirements toenhance the knowledge level of target audience.

Keywords: Green marketing, green purchase behavior, attitude, environmental concern, greenadvertising, perceived quality of green products

Introduction

There has been a growing concern on environmentalissues internationally. Today’s customers have

commenced to recognize that their purchasingbehavior surely can cause a large effect on environment.Therefore, being socially accountable with the aid ofpresenting environmentally friendly products andservices have to be a practice of any companies whowant to sustain an aggressive benefit in the business

Dr. Vaishali Sethi* Assistant Professor, VivekanandaInstitute of Professional Studies, GGSIPU, Delhi


world (Abdul Wahid et al., 2011). Environmentaltroubles as so important that many governmentalagencies round the world were looking to prepareessential laws and rules to protect the environment.Meanwhile, consumers are paying more attention topurchase eco-friendly products and materials. They aremore and more willing to purchase environmentallyfriendly or so-called green products even though thosemerchandise are often pricier (Sua et al., 2012).

The latest situation for the consequences of worldclimate exchange has sensitized the purchaser to lookfor the well-being of destiny generations. The requiredresponses to the heightened environmental worries are,therefore, not sincerely limited to the environmentalregulations and government tasks. Instead,organizations too want to commit to theenvironmental issues of their business decisions andbe part of the bandwagon termed green motion.However, the motivation to adopt the concept of greenmotion of their businesses rests on purchasersdemonstrating an excessive diploma of environmentalmind-set which translates into green purchase behavior.The improved demand for green products could actas a pressure factor on enterprise corporations to turngreen and start marketing green products.

This paper will focus specifically on the purchasebehavior of green products. In addition, the purposeof this study is to seek to broaden the understandingof what factors influence on intentions of purchasinggreen products by consumers further leading to actualpurchase behavior.

Review of Literature

The term ¯green marketing includes the marketingsports of an enterprise wherein all marketingundertakings are taken underneath the environmentaldifficulty structures (Alsmadi, 2007, p.342). Inaddition, green marketing is the marketing techniquewhere entrepreneur’s intention is to discoverenvironmental responsive customers. Displaying andpositioning green merchandise in the front of thecustomers, is taken into account as consumer productadvertising and marketing (Leigh et al., 1988,mentioned in McDaniel & Rylander, 1993, p.4). Theterm ̄ Green product is used to describe product, whicharen’t dangerous for the environment or merchandise

which are environment pleasant. Chemicalcompositions of the products are also surroundingsfriendly and suitable to recycle. (Alsmadi, 2007, p342).

The theory of planned behavior forms the theoreticalframework of this research because it offers a clearlydefined structure/model that allows the investigationof the influence that attitudes, personal and culturaldeterminants and volitional control have onconsumers’ intentions to buy green products.

Theory of Planned Behavior

The theory of reasoned action propounded by Ajzenand Fishbein (1977) paved the path for the Theory ofplanned behavior by Ajzen (1991). The theory ofplanned behavior has been used in this study forexamining the purchasing behavior towards greenproducts. The theory of planned behavior enables uswith a complete framework for exploring the factorswhich influence the decision to engage in behaviorrelated to environmental issues such as recycling(Chan, 2001) and the same can be applied insystematically understanding different factors affectingthe purchase behavior for environmentally friendlyproducts.

According to the Theory of Reasoned Action (TRA),intention of undertaking or not undertaking thebehavior is the direct predecessor to the behavior. Theintention under discussion is often a result of actionsundertaken by individual to evaluate the favorable orunfavorable performance of the behavior. In manycases, it enunciates disposition of the attitude and thesubjective norm wherein the subjective norm isbasically the perception formed by the individual aboutundertaking or not undertaking that behavior due tothe social pressure. One prominent assumption of TRAis that behavior under consideration is volitional innature i.e. person can decide whether he or sheperforms that behavior or not (Ajzen, 1991). Althoughtrue in many cases, behavior may also depend on otherfactors such as availability of appropriate opportunitiesand resources, which collectively correspond to thepeople’s actual control over the behavior (Liska, 1984).The theory of planned behavior (TPB) is one stepahead of the theory of reasoned action in the sensethat it takes care of the original model’s limitation todeal with incomplete volitional control (Ajzen, 1991).


TPB includes a third variable known as perceivedbehavioral control (PBC), which indicates the abilityof a person to undertake the behavior underconsideration under the assumption that individualbehaves in a rational manner considering theramification of his or her actions (Ramayah, Lee &Lim, 2012). In fact, perceived behavioral controlmanifests the difficulty and controllability to executespecific behavior (Ajzen, 1985).

Symbolically, the TPB model is presented in figurebelow where it is illustrated that each of thedeterminants of intention, i.e. attitude to behavior(AB), subjective norm (SN) and perceived control(PBC) is, in turn, determined by underlying beliefstructures.

Source: Adapted from (Ajzen, 1991)

The know-how of the intention, attitudes closer to thebehavior, subjective norms, perceived behavioralcontrol and peripheral persuasion can help to exposedthe special aspects of the behavior or apprehend betterthe conduct, if you want to then help the marketersin designing the marketing strategies to be capable ofconvincing the customers to make the purchases ofthe products.

In order to understand the purchase behavior of greenproducts, various factors like green advertising,environmental concern, and perceived quality of greenproducts will be studied intensively to find their impacton the attitude of consumers towards green productswhich leads to intent of purchasing them.

Attitude

Fishbein and Ajzen (1975) defined attitude as one’spositive/negative evaluation of a specific behavior.Hoyer and Macinnis (2001) also considered attitudeto be the positive or negative evaluation of an object,

action, issue, or person. Attitudes are sets of beliefsabout a certain object or an act, which may translateinto intention to carry out the act. Intention on theother hand is a determination to act in a certain way(Ramayah etal., 2010). Attitudes are the favorable orunfavorable evaluation an individual forms of aspecified behavior. Attitudes impact the intentions heldand the more favorable the attitude, the bigger theintention to perform the behavior will be. In addition,attitudes are predictors of purchase intentions andconsequently purchase behavior. Moreover, attitudesare necessary, as consumers require an understandingof their attitudes and motivations in order to overcomethe perceived purchase barriers they face (Smith&Paladino, 2010).

The attitudes of consumers to purchase green productsare made up of their beliefs, knowledge and concerntowards the concept of green products, which theyaccumulate from their lifetime. Recognizing theseriousness of environmental problems possibly causedby excessive use of energy and non-renewable naturalresources, copious supplies of foods and products,environmentally unfriendly production processes, andenvironmental disasters, increasing numbers ofindividuals are aware of environmental issues and feelour natural resources are limited and the environmentis more fragile than we once believed. Suchenvironmental awareness instills in the public a positiveattitude toward eco-friendly activities, and encouragespeople to more frequently engage in ecologicalbehaviors in their everyday lives (Han & Hsu, 2011).

There are many studies in the context of Indianconsumers exploring the consumer attitude andbehavior towards green practices, green buyingbehavior and opportunity and challenges in greenconsumerism (Jauhari & Manaktola, 2007; Jain &Kaur, 2004; Mishra & Sharma, 2010; Datta, 2011).Many studies show that the attitude–behaviorrelationship has been strengthened when attitudestowards performing specific environmentally friendlybehavior (e.g., recycling), rather than towards generalenvironmental issues. In general, empirical studies haveimplied a substantial positive relationship betweenecological intention and behavior (Chan, 2001). Thebigger the positive attitudes, the more likely theintention to buy will be and therefore, the greater thelikelihood that consumer will purchase green products


over conventional alternatives. These studies havelooked upon a range of issues in green consumerismand environmental aspect of marketing providing somegood knowledge about Indian consumers but there isa gap in the literature when it boils down to findinga predictive relationship between different aspectsduring the process of adoption and purchasing of greenproducts right from the formation of attitude topurchase intention finally leading to purchase behaviorin a complete cycle.

Environmental Concern

Fundamental to environmental research is anindividual’s concern for the environment. Based on thepioneering research of Dunlap and Van Liere (1978),environmental concern is defined as a global attitudewith indirect effects on behavior through behavioralintention. Some other researchers mentioned thatenvironmental concern is a strong attitude towardspreserving the environment (Kaufmann et al.,2012).

Grunert and Juhl (1995) define an environmentallyconcerned consumer as one ‘‘who knows that theproduction, distribution, use, and disposal of productslead to external costs, and who evaluates such externalcosts negatively, trying to minimize them by her/hisown behavior”. Environmental concern is often citedas a strong motivator for purchase. Huangrecommends that the increased consumption oforganic produce is related to an increased concern forenvironmental issues within society. This is supportedby Tregear et al. who found that purchasers of greenproducts were more likely to engage in environmentallyfriendly activities like recycling in order to demonstratetheir environmental concern (Smith & Paladino,2010). In general, evidence from different sectorsshows that people’s purchasing behavior and attitudetowards it is influenced by environmental concerns(Mayer et al., 2012).

Heightened environmental concern has been reflectedin increased intention to purchase green products.More specifically it has been suggested that consumerswith a higher level of environmental concern will bemore likely to engage in green consumer behavior(Antil, 1984; Roberts, 1991; Sheltzer et al., 1991;Shabecoff, 1993). These claims have been supportedby a number of surveys carried out recently which

reported a dramatic increase in the number ofconsumers expressing environmental concerns andclaiming to have purchased green products.

Among psychographic measures, environmentalconcern was one of the first variables to have beenconsistently reported as impacting on some forms ofattitude towards green purchase behavior (Ellen et al.,1991)and its role was further confirmed in severalstudies (Ellen, 1994, Kim and Choi, 2005,Mostafa,2007, Roberts and Bacon, 1997, Straughan andRoberts, 1999), albeit with different degrees ofintensity, depending on other variables included in theexplaining models.

H1: Environmental concern has a positive effecton consumer’s attitudes towards green products.

Green Advertising

According to many researches being done in the pastgreen advertising has emerged out to be an importantfactor affecting the attitude of consumers towardspurchase intention of green products. “Greenadvertising is an advertising that claims the advertisedproducts or services are environmental friendly or thattheir production process conserves resources andenergy” (Chang C. 2011, p. 23) Green advertising canbe varied in addressing issues from the “environmentalissues, environmental friendliness of the products,corporate image campaigns and emphasis on theenvironmental credential of large companies, to publiccampaigns promoting environmental responsiblebehaviors” (Hartmann and Apaolaza-Ibanez, 2009,p.717) Advertising of the green products as safe forthe environment influences the consumer’s attitude topurchase the products, for instance, “more than halfof the Americans say that they have purchased aproduct because the advertising or label indicated thatit was environmental safe or biodegradable (Ginsbergand Bloom, 2004 p.84). Advertising cannot influencethe consumer’s attitude without highlighting theattribute of the green product.

However, another survey made on Malaysianconsumers demonstrated that there is no relationshipbetween environmental advertisements and purchaseintention of green products (Rahbar and Wahid, 2011,p. 80). Indeed consumers pay attention to greenadvertisement and that permit them to obtain more


information about eco-friendly products but it doesnot lead to purchase behavior. However this surveyonly considered 250 Penang’s consumers (Malaysia)so findings are not really generalized to consumers fromindustrialized countries.

‘Green advertising’ must meet one or more of thefollowing criteria:

• Explicitly or implicitly addresses the relationshipbetween a product or service and the biophysicalenvironment;

• Promotes a green lifestyle with or withouthighlighting a product or service; and

• Presents a corporate image of environmentalresponsibility (Gomon, 2005).

Audience behavior towards the advertising can beindicated through consumers’ favorable or unfavorableresponses towards a particular advertisement.According to Mehta, consumers’ attitude towardsadvertising is one of the influential indicators ofadvertising effectiveness because consumer’s cognitiveability towards the advertising are reflected in theirthoughts and feelings and subsequently will influencetheir attitude towards advertising (Ling et al., 2010).

H2: Green advertising has a positive effect onconsumer’s attitudes towards green products.

Perceived Quality of Green Products

Product attributes such as convenience, availability andquality play an important role in the consumers’purchasing decision process (Gan et al., 2008). Aconsumer’s choice for or against organic products canbe framed as a social dilemma, in which he or she mustweigh individual motives, such as qualityconsiderations. The perceived level of quality, whichis an overall evaluative judgment of a product’s itemsand a key dimension in product choice and attitudetowards purchase intention of that product (Doorn &Verhoef, 2011).

The product quality can be a good starting point forproviding customer satisfaction and producingcustomer loyalty. Johnson and Ettlie explained thatproduct quality as the result of performance, which,in turn can be labeled as the degree of customizationand freedom from defects or how reliably the product

met customer requirements. The product qualitydimension included product packaging, productdesign, product features, warranties, etc. Manycompanies can not only embody green orenvironmental concept in the feature, design, andpackage of their product to increase productdifferentiation, but they should also satisfy theenvironmental requirements of customers’ and furthercreate customer loyalty as well as a competitiveadvantage (Chang & Fong, 2010).

H3: Quality has a positive effect on consumer’sattitudes towards green products.

Purchase Intention and Attitude

Intention is the likelihood that a person will engagein a specific behavior. Intention is the best predictorof behavior, and hence, to change a specific behavior,one must first change the intention to perform thatbehavior. Ng and Paladino (2009) described behavioralintentions as a measure of a person’s relative strengthof cause to execute certain conduct. Nik Abdul Rashid(2009) described green purchase intention as theopportunity and willingness of a person to providedesire to green product over traditional products intheir purchasing concerns. Whereas, Ramayah, Lee andMohamad (2010) stated green purchase intention asdedication to behave in a positive manner.

According to Allport (1935), attitude has beendescribed as an intellectual and neural state ofreadiness. This kingdom of thoughts essentiallyimpacts the response of the target market in thedirection of all objects and conditions with which thetarget audience is faced. One extension of thisphenomenon has been aptly undertaken by means ofSchultz and Zelezny (2000), who define it with theaid of contemplating the mindset closer toenvironmental concerns. They describe it as the deep-rooted concept in a person’s self with a belief of thediploma of bonding between self and the surroundings.Irland (1993) mentions that a purchaser’s purchasingintentions are dependent upon his or herenvironmental attitudes. A beneficial mindset towardsa product which is environmentally friendly ads tosustainable consumption behavior as talked about innumerous research (Chan, 2001; Verbeke & Viaene,2006; Tanner &Kast, 2003; Vermeir, &Verbeke,


2004). The mind-set acts as a vital antecedent to thebehavioral intention that is defined as the degree offavorable or unfavorable evaluation of the behaviorunderneath a study (Ajzen, 1991). Cheng, Lam, &Hsu (2006) concluded that a person willing to showa specific behavior may additionally adopt the costbenefit analysis due to the movement undertaken andfavorable mindset is connected with advantageousevaluation of the action (Ajzen, 1991; Cheng et al.,2006). Ajzen (1991) emphasized that positive mindsettoward a particular behavior strengthens the intentionto carry out that behavior. Under this discussion, it’sfar hypothesized that:

H4: An attitude towards green products has positiverelationship with the purchase intention for theproducts.

Green Purchase Behavior and Purchase Intention

Behavior can be determined from the intention withbig accuracy (Ajzen, 1991). Many studies have showedthe connection between intention and real behavior(Ajzen & Fishbein, 1980; Sheppard, Hartwick,&Warshaw, 1988). Historically, Intention has beenassumed to be robust predictor of behavior but in afew instances, it can no longer act in constant manner.In a study on the behavior bearing on the usage ofinformation technology, Venkatesh et al. (2003)suggested a small to medium impact size of intentionto use information technology on the actual behavior.This may be attributed to as intention - behavior gapand the equal has been showed in study by Grunertand Juhl (1995) also who concluded that intentionmay not always cause the favorable behavior. However,numerous researchers consisting of Sheppard et al.(1988) mentioned an excessive degree of correlationbetween intention and behavior. Researchers analyzingthe buying behavior for natural food have determinedsignificantly positive relationship between purchaseintention and purchase behavior (Saba & Messina,2003; Thøgersen, 2012). In view of the discussion, itis hypothesized that:

H5: Purchase intention is positively related to thepurchase behavior for green products.

The present study attempts to fill the research gap byincorporating the theory of planned behavior (Ajzen,1991) to understand the behavior of the consumers

towards purchasing of green products in the contextof Indian consumers using Structural EquationModelling (SEM). The study looks at the relationshipof purchase intention with variables like attitude whichis construct dependent on factors namely,environmental concern, green attitude and perceivedquality of the products finally leading to purchasebehavior.

Hence, the main contribution of this study is to findout the relationship between attitude towards greenproducts and purchase intention by incorporatingother determinants also which affect the purchaseintention and eventually purchase behavior in acomplete framework in the context of Indianconsumers.

Conceptual Framework

With reference to the foregoing literature review, aconceptual model is proposed in Fig. 1 to explainIndian consumers’ green purchase behavior.

Figure 1: Conceptual Model to Explain IndianConsumers’ Green Purchase Behavior

Research Objectives

The objectives of the research are:

1. To examine the determinants of consumerattitude towards green FMCG Products.

2. To ascertain the relationship between consumerattitude and purchase intention leading topurchase behavior of the green FMCG products.

Research Methodology

Data were collected from the respondents from NewDelhi with varied age groups and different annualincomes. A self-administered questionnaire was givento respondents to collect the data. All items wereborrowed from the existing literature of differentcountries with a slight modification to fit the specificcontext of Green purchasing behavior in India. The


questionnaire contained 9-point Likert scale items forwhich the respondents were required to provide theirresponses with values ranging from ‘strongly disagree:1’ to ‘strongly agree: 9’. The respondents were alsorequired to provide the information related to theirdemographic profiles for the purpose of classification.

The final version of the questionnaire was an adaptedversion of the scale mentioned in Marketing scaleshandbook: A compilation of Multi-Item Measures forConsumer Behavior & Advertising Research, RobertD. Straughan & James A. Roberts(1999), Baker andChurchill (1977) and Kaman Lee, (2008). Somemodifications in terms of content as well as the numberof questions were done to adapt the same for consumermarkets in Indian context. This paper works towardsthe assessment of factors that lead to attitude towardspurchase intention of green FMCG products thatultimately leads to green purchase behavior.

A pilot study was done on the 24-items measurementinstrument to examine the initial reliability of theinstrument. Sample size for this test was kept very small(65) as this was an initial test. The modified self-administrated questionnaires consisting of 24statements for five sub-scales was formulated thatmeasured green advertising, environmental concern,perceived quality of green products, purchase intentionand green purchase behavior were distributed in theprinted form as well as online to the respondents.

A total of 501 pieces of usable questionnaire out oftotal sample size of 550 was collected on whichCronbach’s alpha test was used to test the reliability ofthe data that came out to be 0.92 (Table 1).

Table 1: Reliability Statistics

Variable Cronbach’s Alpha

Environmental Concern 0.923

Green Advertising 0.901

Perceived quality of green products 0.925

Purchase Intention 0.853

Purchase Behavior 0.827

Analysis of the Data

The questionnaire was personally administered andmonitored while collecting the data so that no field isleft unanswered. Due precautions were taken while

designing the same to have specific scale with options.This eliminated the discrepancies arising out of missingfrequencies and outliers from the data. This furtherhelped in getting rid of any coding errors in the data.

Most commonly used computerized statistical tools,SPSS version 20.0 and AMOS, were used for theanalysis of data while the hypothesis was tested usingConfirmatory Factor Analysis (CFA) and DescriptiveAnalysis (DA). The analysis was performed at95%confidence level which is generally accepted levelof confidence in social sciences research. The data thuscollected was transformed into tabular form that is themost suitable form to present the data analysis andthe same was entered in SPSS 20.0 for analysis.

Profile of Respondents

Out of 501 questionnaires collected 51% weremales(256) and 49% were females (245) with 21.4%of the respondents in the age bracket of 18-25 years(107), 28.9% in 26-35 Years, 25.3% in 36-45 yearsand 24.4% in above 46 years. 54.3% (272) of therespondents were married and 45.7% (229) of the totalrespondents were unmarried. 29% of the respondentswere service class and almost equal percentages ofrespondents were students (25.5%) and were inbusiness (21.4%). Majority of the respondentsbelonged to the income group of less than 2.5 lakhs(35.1%) with the least number of respondents in theincome bracket of above 8 lakhs (20.6%).35.5 per centof the respondents purchase eco-friendly products oncea week or more often and a comparable 36.5 per centof the respondents purchase online eco-friendlyproducts at least once a month, whereas 27.9 per centof the respondents purchase eco-friendly products lessthan once a month.

Confirmatory Factor Analysis

On the basis of theory or literature review, CFA is usedto examine the hypothesized relationship betweenconstructs and attitude which is the latent variablefurther leading to purchase intention and greenpurchase behavior. As per the nature of the latentvariables or constructs, key model constructs are shownin the Table 2.


Table 2: Constructs

Environmental Concern EC1: I am worried about the worsening of the quality of India’s environment.

EC2: We are approaching the limit of the number of people the earth can support.

EC3: To maintain a healthy economy, we will have to develop a steady-state economy where industrial growth is controlled.

EC4: Humans need to adapt to the natural environment because they cannot remake it to suit their needs.

EC5: When humans interfere with nature, it often produces disastrous consequences.

EC6: India’s environment is my major concern.

EC7: I often think about how the environmental quality in India can be improved.

Green Advertising GA1: The brands of green products stand for its promises.

GA2: In general, statements shown in advertisements about green product are believable.

GA3: Over time, experiences with the green brand have made me think the brand meets its promises, without exceeding my expectations but without falling below them.

GA4: I can trust in the green brand’s name of its advertising.

Perceived quality of green products PQGP1: The green products meet or exceed the requirements of environmental regulations.

PQGP 2: The green products consume the least amount of resources and energy.

PQGP3: The green products are easy to recycle, disassemble, decompose, and reuse.

PQGP4: The quality of the green products is superior.

Green purchase Intention PI1: I would like to use green products.

PI2: I would buy green products if I happen to see them in a store.

PI3: I would actively seek out green products in a store in order to purchase it.

PI4: I would patronize and recommend the use of green products.

PI5: If I understand the potential damage to the environment that some products can cause, I do not intend to purchase those products.

Green purchase Behavior PB1: I prefer green products over non-green products when their product qualities are similar.

PB2: I buy green products even if they are more expensive than the non-green ones.

PB3: I try to discover the environmental effects of products prior to purchase.

PB4: I don’t buy a product if the company which sells it is environmentally irresponsible.


The present study used AMOS statistical software toconduct confirmatory factor analysis to test theefficiency of the 5 constructs. According to Holtzmanand Leich (2014), when running CFA, there arevarious fit statistics which help to examine the modelfitness for the data as shown in Table 3.

The ratio of goodness of fit to degrees of freedomshould be no more than 3 (Carmines and MacIver,

1981), and the value of RMSEA should be less than0.08, with the GFI, IFI, NFI, CFI exceeding 0.9(Bagozzi and Yi, 1988). Furthermore, RMR whichstands for Root Mean Residual is associated to theresidual in the attitude model. The range of RMR valueis zero to one where a smaller RMR shows better modelfit.

Table 3: Model Fit Indices for 1st order CFA

CMIN/DF GFI CFI IFI NFI RMSEA

Desired Value

3 or lower 0.90 or higher

0.90 or higher

0.90 or higher

0.90 or higher

0.08 or lower

Values from the Model

1.954 0.927 0.969 0.969 0.938 0.044

Table 3 shows that model is fitted to the data as GFI,CFI, NFI and IFI are greater or equal to 0.90 whichis considered acceptable. Moreover, value of RMSEA

is equal or less than 0.08 and value of CMIN/DFshould be less than 3 which are considered acceptable.

The model for first order CFA is shown in Figure 2.

Figure 2: The model for first order CFASource: Output of AMOS


Validity Assessment

Convergent validity assessment: (CR > 0.7; AVE < CR;0.5 < AVE)

Validity of Discriminant: (AVE > MSV, ASV<AVE)

As per Table 4, CR is more than 0.7 and AVE is morethan 0.5 for all the 5 factors. Also, the Table 4 shows

that CR is higher than AVE for all factors. So, we canconclude that the factors in the measurement modelhave sufficient convergent validity. As per themeasurement model, MSV is less than AVE for all thefactors. Also, ASV is less than AVE for all the factors.Hence, we can confirm Discriminant validity of themeasurement model.

Table 4: Convergent and Discriminate Validity of the CFA model

CR AVE MSV Max R(H)

PI EC GA PQGP PB

Purchase Intention 0.853 0.536 0.261 0.853 0.732

Environmental Concern 0.923 0.632 0.281 0.948 0.488 0.795

Green Advertising 0.901 0.695 0.256 0.965 0.506 0.474 0.834

Perceived quality of green products

0.926 0.757 0.281 0.976 0.511 0.530 0.369 0.870

Purchase Behavior 0.828 0.546 0.234 0.979 0.484 0.208 0.249 0.195 0.739

Table 4 shows that there is convergent andDiscriminant validity in the factors of the measurementmodel as the value of all constructs are acceptable. Afterconducting CFA it was found that model fittedsuccessfully in Indian scenario.

Structural Model Analysis

The present study conducted a linear analysis of thestructural relation model using AMOS statisticalsoftware to understand causality and correlation amongthe variables.

According to Holtzman and Leich (2014), SEM is used

to examine how well the assessment predicts thesemeasures. Also, the same fit statistics, which wediscussed for the CFA should be considered whenrunning SEM (Figure 3).

The results show that the value of ÷2/df is 1.971, thevalue of RMR is 0.199, the value of GFI is 0.926, thevalue of AGFI is 0.910, the value of NFI is 0.937, thevalue of RFI is 0.929, the value of CFI is 0.968, andthe value of RMSEA is 0.044. The fit of the model istherefore acceptable. In addition, the analysis showedthat all the hypotheses were supported. The completeresults are presented in Figure 3.

Figure 3: Results of SEM AnalysisSource: Output of AMOS


Regression weights of (Table 5) and Standardized regression weights (Table 6) were found to be satisfactory.Table 5: Regression Weights

Estimate S.E. C.R. P Label

Purchaseintention <--- attitude .879 .092 9.537 *** Significant

Purchasebehavior <--- Purchaseintention .494 .059 8.406 *** Significant

Environmentalconcern <--- attitude 1.028 .102 10.068 *** Significant

Greenadvertising <--- attitude .889 .093 9.527 *** Significant

Perceived qualityofgreenproducts <--- attitude 1.000

Significant

EC7 <--- Environmentalconcern .851 .049 17.277 *** Significant



EC4 <--- Environmentalconcern 1.017 .049 20.890 *** Significant


EC2 <--- Environmentalconcern 1.034 .050 20.812 *** Significant

EC1 <--- Environmentalconcern 1.000

Significant

GA4 <--- Greenadvertising 1.000

Significant

GA3 <--- Greenadvertising 1.018 .048 21.221 *** Significant



PQGP4 <--- Perceived qualityofgreenproducts 1.000

Significant

PQGP3 <--- Perceived qualityofgreenproducts 1.043 .040 25.925 *** Significant



PI1 <--- Purchaseintention 1.000

Significant

PI2 <--- Purchaseintention 1.134 .073 15.537 *** Significant



PB4 <--- Purchasebehavior 1.111 .074 14.941 *** Significant

PB3 <--- Purchasebehavior 1.091 .073 14.878 *** Significant

PB2 <--- Purchasebehavior .999 .072 13.879 *** Significant

PB1 <--- Purchasebehavior 1.000

Significant

PI5 <--- Purchasebehavior 1.060 .072 14.753 *** Significant


Table 6: Standardized Regression Weights

Estimate

Purchaseintention <--- attitude .724

Purchasebehavior <--- Purchaseintention .479

Environmentalconcern <--- attitude .729

Greenadvertising <--- attitude .634

Perceived qualityofgreenproducts <--- attitude .682

EC7 <--- Environmentalconcern .719







GA4 <--- Greenadvertising .837




PQGP4 <--- Perceived qualityofgreenproducts .851




PI1 <--- Purchaseintention .714




PB4 <--- Purchasebehavior .768





Source: Output table of AMOS

Hypothesis Testing and Inductive AnalysisTable 7: Summary of the Structural Model

Path Description Hypothesis Unstandardized Path Estimates

Standardized Path Estimates

Result

Environmental Concern → Attitude H1 1.028*** 0.729 Supported

Green Advertising → Attitude H2 0.889*** 0.634 Supported

Perceived quality of green products → Attitude H3 1.000*** 0.682 Supported

Attitude → Purchase Intention H4 0.879*** 0.724 Supported

Purchase Intention → Purchase Behavior H5 0.494*** 0.479 Supported


The empirical results show that (1) environmentalconcern is significantly positively related to attitude;(2) green advertising is significantly positively relatedto attitude; (3) perceived quality of green products andattitude are significantly positively related; (4) attitudeis significantly positively related to purchase intentionof green products; (5) purchase intention issignificantly positively related to actual purchasebehavior.

H4 and H5 were supported at the 1% significancelevel, these suggest that intentions of green purchasingdo influence green purchasing behavior and attitudesdo influence purchase intentions. These findings arealso consistent with other research conducted (Smith& Paladino, 2010). The results obtained for someantecedents will now be investigated. The first variableexamined was environmental concern, which isconcerned with H1. H1, which specifies therelationship between environmental concern andattitude, was supported at the 1% level. This isconsistent with past research that has found theconsumers purchase green products for environmentalconcern (Smith& Paladino, 2010). The impact ofquality on attitude (H2) was supported at 1% leveland consistent with past research (Chang & Fong,2010). H3 was supported at the 1% significant level.This hypothesis suggests that green advertising has apositive effect on consumer’s attitudes.

Conclusion

The theory of Planned Behavior (TPB) proved itsapplicability in explaining social behavior aimed atgreen products purchases. The TPB model was testedon a large representative sample of consumers fromNew Delhi and NCR region in India and its predictiveability corresponds with other examples. The bestpredictors of the intention to purchase green productsare attitudes towards the behavior. This study showsthat the intention of consumers to purchase greenproducts is determined by having a positive attitudetoward green products. The present study also hasexamined the influence of various personal andmarketing factors on the attitude toward greenproducts of consumers from Delhi. The results fromthe structural-equation modeling show theenvironmental concern has the highest influence on

attitude towards green products among three personalvariables including environmental concern, greenadvertising and perceived quality of green products.The results of this research have implications for themarketing of these products to consumers and forresearchers. Marketers of green products should linkthese products with concern for environmental issueswithin society to promote these products.

Managerial Implications

The findings of this study have provided valuedknowledge on the prerequisite of the purchaseintention which finally leads to purchase behavior forgreen products in Indian context. These findings canhelp the marketers to formulate their policy with regardto actions which would enhance the purchase andusage behavior of the consumers towards greenproducts. It is important for the policy makers workingtowards improvement of environment to understandthe behavioral aspects of the consumption so that theycould make people change and believe in certain aspectof their action leading to betterment of theenvironment and ecology. Marketers need to keep inmind that environmental concern, green advertisingand quality of the green products have significant andpositive impact on the attitude towards green products.

It provides them with the opportunity to design theircommunication content in line with requirements toenhance the knowledge level of target audience. Thereis always a concern towards authenticity of the claimsmade by marketers for the green products. Theapprehension is more enhanced in the absence ofappropriate communication hat tells about thegenuineness of the claim. A right set of knowledgespread through genuine advertisement is expected tohave positive impact on the attitude of the consumersas per the findings of this study.

Limitations and Directions for Future Research

The empirical outcomes acquired in this study areconsistent with the theoretical history and also withthe general belief on the subject matter. Despite this,the present study has few limitations. The study wasundertaken in a single Indian city i.e. in a specificgeography subsequently the study might not haveblanketed all of the relevant ecological diversities at a


more aggregate degree. This unpredictability may relyupon many elements inclusive of price, availability,degree of involvement etc. (Vermeir & Verbeke, 2004).These aspectscan be investigated in future studies.

The present study has been conducted via consideringonly one construct of the theory of planned behaviorwhich is attitude and its antecedents. Future researchwants to investigate the position of different importantconstructs which can be subjective norms andperceived behavioral control. They may additionallyinvolve many important elements such as socialinfluence, word of mouth, motivation to comply,perceived consumer effectiveness and control onavailability of green products which might act asmoderator or mediator with the various constructs ofthe model. Since environmental issue is a vital coveragetrouble on worldwide degree, it is prudent to look atthe position of environmental rules as a moderator tothe purchase behavior in the model. Also, groupmoderation can also be studied keeping as variousdemographic factors as moderators.

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Comparing the Volatility of Returns in Indianand Chinese Information Technology SectorParul Tyagi*

Abstract

The growth in Indian and Chinese economies has been attributed to major reforms in the modusoperandi of the capital market of the two economies. The stock market performance of the twoleading economies of Asia has been a topic of discussion globally; especially after 2008. In thepresent research, the researcher has compared the performance and stock market volatility ofIndian and Chinese I.T. Indices Returns during 2004 to 2017 i.e. thirteen years. InformationTechnology sector forms one of the major industries of any economy and contributes to the GDPof that economy as well. Present study uses advance econometric tools like ADF test to studystationarity, statistical tools to compare performance and Garch (1,1) model to study the volatilitypattern of the I.T. sector indices of the two economies. The results were calculated on E-Views 8software.

Keywords: Information Technology, ADF Test, Stationarity, Volatility, Garch (1, 1), E-Views 8.

Introduction

The two major fastest growing Asian economies i.e.India and China are becoming the area of interestamong researchers. Few questions, which arise in thiscontext are related to performance of these economiesover time, movements in their stock indices and thevolatility spill over mechanism of their stock indicesincluding the sectoral diversification. Analysing thevolatility of stocks, sectors and index as a whole hasbeen one of the popular area of research. With globaldiversification of equity investment and emergence ofglobal mind set in investing fuelled by removal ofrestrictions on capital account, it is obviously both ofacademic and corporate interest to conduct such study.India and China has witnessed a remarkable growthrate since 1980 coupled with poverty reduction. Onethird of the world population is covered by both ofthese economies. In the past many significantdevelopments took place. One among them isemergence of China and India as major economicforces in an international economy. The growth inthese economies has been attributed to major reforms

in the modus operandi of the capital market of thetwo economies. The reforms in the capital marketsbrought about in 1980’s and 1990’s in the twoeconomies have revolutionized the performance oftheir capital markets. The stock market performanceof the two leading economies of Asia has been a topicof discussion globally, especially after 2008. Theresearcher finds that lot of studies had been conductedfocusing on the overall performance and volatility instock returns in these economies but no separate studieshave been conducted targeting the sectoral indexperformances of these economies. In the presentresearch, the researcher has compared the performanceand stock market volatility of Indian and ChineseInformation Technology Index Returns. InformationTechnology forms one of the major industries of anyeconomy and contributes to the GDP of that economyas well. India accounts for 67% of US$ 124-130 billionmarket thus becoming world’s largest sourcedestination for IT sector. The industry is divided intoengineering services, IT services, hardware, softwareproducts and business process management (BPM).This sector is expected to grow at 11% per annumand thus will triple its revenue by FY 2025. Theinternet economy of India is expected to reach 10

Dr. Parul Tyagi* Assistant Professor, Jaipuria Schoolof Business, Indirapuram, Ghaziabad


trillion by 2018 according to BCG (Boston ConsultingGroup) report. Public cloud services revenue hasreached US$ 838 million in 2015, growing by 33 percent year-on-year. The industry has also increased thedemand for computer science engineering in educationsector. The Industry amounts to 12.3 per cent of theglobal market due to massive exports. These exportsincludes 56.12 percent from IT services thus forming56.12 per cent of total IT exports (including hardware)from India. The Business Process Management (BPM)segment covers 23.46 percent of total IT exportsduring FY15.

The size of China’s IT industry is $493 billion, whichis much more in comparison to India. It is mainlyinvolved in developing and publishing software andrelated services in China. The outsourcing market ofChina’s IT Industry is expected to reach $60 billionby 2015, in which 60% exports belongs to Japan. Overthe last few years I.T. sector has witnessed a massivegrowth in China. China is even planning to make thissector as a part of its seven strategic industries whichwill help the economy to flourish growth throughinnovation and creativity. The largest internet users(500 million) from the world come from China. ThusChina’s IT market is the fourth largest in the world.China is second largest software-outsourcingdestination in the world next to India. Although thehardware markets of China earns only small margins.In 2015, revenue from IT sector reached total of$124.5 billion, up 7.5% from 2014.The researchinvestigates the comparison of volatility and stockmarket performance of India and China IT Index fromApril 2004 to March 2017 using advance econometrictools. One of the major reason that these kind of studywere not there is due to lack of availability of sectoralreturns data of Chinese Stock Markets. In this study,Chinese IT Index for thirteen years on daily basis usingWeighted Average Market Capitalization Method hasbeen developed by the researcher.

Literature Review

Starting with the pioneering work of Mandelbrot(1963) and Fama (1965), various features of stockreturns have been extensively documented in theliterature, which is important in modeling stock marketvolatility. It has been found that stock market volatility

is time varying and it also exhibits positive serialcorrelation (volatility clustering). This implies thatchanges in volatility are non-random. Moreover, thevolatility of returns can be characterized as a long-memory process as it tends to persist (Bollerslev, Chouand Kroner, 1992).

Schwert (1989) agreed with this argument. Fama(1965) also found the similar evidence. Baillie andBollerslev (1991) observed that the volatility ispredictable in the sense that it is typically higher atthe beginning and at the close of trading period.

Chan, Karceski & Lakonishok (1998) in their researchpaper concluded that “the macroeconomic factors doa poor job in explaining return covariation. In termsof understanding the return covariation across stocks,widely used factors such as industrial productiongrowth and unanticipated inflation do not seem to bemore useful than a randomly generated series ofnumbers.”

Akgiray (1989) found that GARCH (1, 1) had betterexplanatory power to predict future volatility in USstock market. Poshakwale and Murinde (2001)modeled volatility in stock markets of Hungary andPoland using daily indices. They found that GARCH(1, 1) accounted for nonlinearity and volatilityclustering.

Poon and Granger (2003) provided comprehensivereview on volatility forecasting. They examined themethodologies and empirical findings of 93 researchpapers and provided synaptic view of the volatilityliterature on forecasting. They found that ARCH andGARCH classes of time series models are very usefulin measuring and forecasting volatility.

Li et al.(2005) examined the relationship betweenexpected stock return and volatility based onparametric EGARCH- M model. They found apositive but insignificant relationship between stockreturn and volatility.

Mishra (2010) in his work on the dynamics of stockmarket return volatility of India and Japan usedTGARCH-M model. His findings revealed that thesemarkets are impacted asymmetrically by bad news andgood news. The return volatility persists in bothcountries.


Joshi (2010) investigated the stock market volatility inthe emerging stock markets of India and China usingdaily closing price from 1st January, 2005 to 12th May,2009. The results detect the presence of non-linearitythrough BDSL test; while, conditional Heteroscedasti-city is identified through ARCH-LM test. The findingsreveal that the GARCH (1, 1) model successfullycaptures nonlinearity and volatility clustering. Theanalysis suggests that the persistence of volatility inChinese stock market is more than Indian stockmarket.

Guo & Savickas (2003) found that the high stockmarket returns are the results of combination ofidiosyncratic stock market volatility as well as aggregatestock market volatility. Their research found positiverelationship between risk and return of equity marketsbut this relationship was found negative in case offuture returns of equity markets and idiosyncratic stockvolatility. This idiosyncratic volatility was examined asa macro variable which helped in forecasting futureshare prices.

Engle & Ng (1993) examined the relationship ofmarket news and stock market volatility. Theyevaluated the impact of any market information onstock prices and how this curve tends to slope? Theycompared various parametric and non-parametricmodels ARCH models and applied them on daily shareprice data of Japanese stock market. Non Diagnostictests were performed showing that there is asymmetryin the news and stock market data. They found thatthis type of asymmetry in the data is being successfullycaptured through the models of Runkle, Jagannathanand Glosten(1989) also known as GJR model.

Maheshchandra (2014) studied that there exists longterm volatility in the stock markets of India and China.The daily time series data of the stock exchanges BSE(Bombay Stock Exchange) and SSE (Shanghai StockExchange) were collected for a period of five years. Toprovide the evidence for the same FIGARCH modelswere used which proved out well fitted in the researchand thus, provided a strong evidence for the existenceof FIGARCH. However the property of long termvolatility mechanism for found stronger in BSE ascompared to SSE.

Thenmozhi and Chandra (2013) studied about theVIX (Indian Volatility Index) and how the risk

management mechanism takes place in it. They founda negative relationship between NIFTY index andIndian VIX. They found that VIX captures marketchanges or ups and downs (Volatility) better than thetraditional methods of capturing volatility like ARCH/GARCH models. They concluded that an investor cantake the benefits of high positive returns by investingin Large Cap Portfolios and Mid Cap Portfolios whenthe VIX showing higher returns.

Mobarak (2005) studied how the growth &development & democracy of an economy are relatedto volatility of stock prices. Volatility and averagegrowth were taken as ‘two equation system’. Throughthis the author found that volatility decreases at highlevel of democracy & diversification in countryespecially in Muslim countries and at high volatilityeconomic growth reduces. Author tried to identify analternative link between development and democracyin a country through studying volatility in equitymarkets.

Raju and Ghosh (2004) have made comparison of stockmarket volatility in Indian market with several countriesat international level, which the authors finds importantto be studied as it decides the pricing of securities. Theyhave taken both the mature economies like UK and USas well as emerging economies of Asia like India andChina. The mature economies exhibited high volatilityreturns but the emerging economies exhibited lower aswell as negative returns. Along with this Indian equitymarkets exhibit less skewness and kurtosis and aremoving faster towards information technology.

Birãu and Trivedi (2013) showed how volatility ofcapital markets is studied widely in the field of financeespecially during the period of economic crises in thesemarkets. These economies are a source of attractionfor investors as well as possess higher growth prospects.They have done a deep inspection in these economiesby using GARCH models and studying co-integrationin the markets as a result of diversification of portfolioand becoming financially globalized.


Objectives of Study:

i. To compare the Performance of Indian andChinese IT Stock Indices during April 2004 toMarch 2017.


ii. To compare the Volatility of Indian and ChineseIT Stock Indices during April 2004 to March2017.

Hypothesis:

i. Ho1: There is no significant difference in theperformance of IT index of Indian and Chinesestock market during April 2004 to March 2017.

ii. Ho2: There is no significant difference in thevolatility of IT index of Indian and Chinese stockmarket during April 2004 to March 2017.

Period of Study

The data was collected on daily basis using the indexvalues of India and China for the time period of 13years i.e. from April 2004 to March 2017. The dataincludes 2894 observations from Indian stock Indicesand 2989 from Chinese stock indices depending upontheir trading days during the period of study. Theperiod has been chosen to capture the volatility effectsin stock markets of IT Sector of the two economiesaccurately.

Data Collection:Table 1: Source of Data Collection for China

Parameters Internet Source Nifty IT Yahoo Finance

Net Income www.morningstar.com Earnings Per Share www.morningstar.com

Share Price http://in.finance.yahoo.com, www.google.com/finance

List of companies http://www.infoseekchina.com

To create the Chinese IT Index, Weighted averagemethodology has been used to create the daily indexvalues for thirteen years. List of top market players ofthis sector has been prepared including 25 companies.Then their daily M-Cap was calculated by using theformula (Share price* No. of outstanding Shares).While, assuming that outstanding shares remainconstant for one year. These outstanding shares werecalculated by using the formula (Net Income / Earningper Share). Once the daily M-Cap of companies forten years was obtained, weighted average method wasapplied to calculate the daily index value. Initially dailyreturns of Stock Indices were calculated using theequation:

/ )*100

Where Ri = Return for the day t

Rt = Closing value of the Index on the trading day t

Rt–1= Closing value of the Index on trading day t-1i.e. immediately preceding the day t.

Then afterwards, In order to check the stationarity inthe returns data series, Augmented Dickey Fuller(ADF) unit root test is being applied and the resultswere obtained through E-Views 8 software.

The first objective of measuring the performance wasduly accomplished by using certain statistical tools likeMean, Median, Standard Deviation, Coefficient ofVariation, Skewness, Kurtosis as well as the probabilityto check the significance of these statistical parametersof performance of stock price index. The outputwindow has been obtained through E-Views 2008.

For the second objective, In order to compute andanalyze the volatility of Indian and Chinese IT Index,Generalized Autoregressive Heteroscedastic, GARCH(1, 1) model is being used.

GARCH (1, 1) has two parts:

i. Mean Equation

ii. Variance Equation

The mean equation is as follows:풓풕 = 풄ퟏ+ 풄ퟐ(풓풕 − ퟏ) + 풆…. (eq1)

Here the variables are (Return of Index on day t)

rt – 1 Return of Index on day t-1)

rt is the dependent variable and rt – 1 is theindependent variable

c1 is constant

c2is coefficient

e is the residual

Returns are calculated taking the daily data of thirteenyears starting from 1st April, 2004 to 31st March, 2017.

The objective behind developing the Model is to checkwhether the return on day t is affected by return onday t-1 i.e. measuring volatility.

Above regression equation or model (eq1) is being runon E-Views 8 using least square method.

Residual derived from mean equation (1) is used inmaking variance equation (2).


푮푨푹푪푯 = 푪ퟑ + 푪ퟒ ∗ 푹푬푺푰푫(−ퟏ)ퟐ + 푪ퟓ ∗ 푮푨푹푪푯(−ퟏ) (eq 2)

Here GARCH = Variance of the residual (error term)derived from eq (1). It is also known as current day’svariance or volatility of index.

C 3 is the constant

RESID (–1)2 is previous day’s squared residual derivedfrom eq (1). It is also known as previous day’s indexinformation about volatility. It is the ARCH term.

GARCH (–1)is the previous day’s residual variance. Itis called the GARCH term.

C4&C5 are coefficients of ARCH & GARCH termsrespectively.

Above GARCH (1, 1) variance equation or model(eq2) is being run on E-Views 8 using normaldistribution.

Data Analysis and Interpretation

Comparing the Performance of Indian andChinese IT Index

The performance of Indian and Chinese I.T. marketshave been measured with the help of descriptivestatistics applied on thirteen years daily returns i.e.from (1st April, 2004 to 31st March, 2017) of Indianand Chinese I.T. Index as shown in following table:

Table 2: Performance Statistics of Indian and Chinese I.T. Indices

Basic Statistics India China Mean 0.044357 0.135

Median 0.076188 0.056108

Maximum 12.60016 72.67681

Minimum -90.5416 -18.6006 Std. Dev. 2.573426 2.415782

Coefficient of Variation 5801.623 1789.468

Skewness -17.4597 10.41743

Kurtosis 618.364 319.7323

Jarque-Bera 39477137 10868780

Probability 0 0

Sum 110.6276 349.5145

Sum Sq. Dev. 16509.94 15103.58

Observations 2494 2589

Note: Calculations were done on E-VIEWS 8; Values are significant at 1% level.

Both the Indices showed positive mean returns duringthe study period but Chinese I.T. Index showed highestmean return of (0.135) as compared to Indian I.T.Index (0.044357). Indian I.T. Index showed maximumreturn of 12.60016 units as compared to Chinese I.T.index depicting a maximum return of 72.67681 units,which is much higher than Indian I.T. Index.

The variation in terms of mean returns was measuredthrough standard deviation, which was a little morein Indian I.T. Index (2.573426) as compared toChinese I.T. Index (2.415782). This depicts that thestock returns vary a little more in case of Indian I.T.Index i.e. the index is more volatile.

CV was found maximum in Indian I.T. Index(5801.623) percent as compared to Chinese I.T. Index

showing (1789.468) percent. Thus, it can beinterpreted that Indian I.T. markets are more risky thanChinese I.T. markets.

The skewness in both the Indiceswas different fromzero; thus, showing that the return distribution seriesof both the Indices is not symmetric.

The value of kurtosis was greater than three in boththe indices thus indicating that the return series of boththe indices when diagrammatically shown will haveheavier tails and both the series are leptokurtic innature.

The computed values of JB (Jarque-Bera) statistics aresignificant at one percent level, thus null hypothesis isrejected i.e. there is no normality found in Indian andChinese I.T. index return series.


The above interpretations can also be seen in the following graph:

Source: Author’s own creation

The graph shows rough co-movement between Indianand Chinese I.T. Index. Although it is clear evidencefrom the graph that performance of Chinese I.T. Indexis better as compared to Indian I.T. index. Hence fromthe investor’s point of view, investing in Chinese I.T.markets will be much profitable and less risky ascompared to investing in Indian I.T. markets. Theresults also reject the null hypothesis as thecorresponding p-values are less than one percentsignificance level, showing that there is significantdifference between the performance of Indian andChinese I.T. Indices.

Comparing the volatility in Indian and ChineseI.T. Indices

Null Hypothesis: Indian and Chinese I.T. Index has aunit root

Table 3: Results of Unit Root Test

ADFTest India China

T-stat Value -50.18540 -50.16489

Critical Values of ADF

Significance Level India China

1% -3.432779 -3.432779

5% -2.862499 -2.862499

10% -2.567326 -2.567326

Source: Author’s own creation, Note: Values are significant at 1% level

Since the t-stat values are greater than all the criticalvalues; hence, the null hypothesis is rejected i.e. Indianand Chinese I.T. Index do not have a unit root andthus, the data series is stationary.

Now the next step in the study was to model thevolatility of Indian and Chinese I.T. index. For thispurpose GARCH (1, 1) model was being applied tothe data series of returns of Indian and Chinese I.T.index.

The regression equation or model (eq1) was being runon E-Views 8 using least square method. The outputwindow showed the following table:

Table 4: Estimates of Mean Equation in Indian andChinese I.T. Index

Index Descriptive Statistics

India China c rt-1 c rt-1

Coefficient 0.044598 -0.005475 0.133161 0.013620

t-value 0.865192 -0.273326 2.800047 0.692834

p-value 0.3870 0.7846 0.0051 0.4885

standard error 0.051547 0.020032 0.047557 0.019659


Residuals can be plotted on the graph with the helpof the above outputs obtained.


Graph 2: Residual Distribution Graph of Indian I.T. Index

-100

-80

-60

-40

-20

0

20

-100

-80

-60

-40

-20

0

20

04 07 09 12 15 17

R e s id u a l A c t u a l F i t t e d

Source: Author’s own creationGraph 3: Residual Distribution Graph of Chinese I.T. Index

-20

0

20

40

60

80

-20

0

20

40

60

80

04 07 09 12 15 17

R e s id u a l A c t u a l F i t t e d


During 1st April, 2004 to 31st March, 2017 theresiduals are fluctuating in Indian and Chinese I.T.index. From the above graphs it can be seen that inIndian ITindex, during April 2004 to mid of 2007the fluctuation is small for a long time period of overthree years. That means small fluctuation is creatinganother small fluctuation for a long time, which derives

that small volatility is causing another small volatilityfor a long time. Again from mid of 2007 till mid of2009 the volatility is peak in Indian IT index coveringalmost about two years. So, high volatility is creatinganother high volatility for a long period. In other wordsperiods of low volatility are followed by periods of lowvolatility and periods of high volatility are followed


by periods of high volatility. This suggests that residualor error term is conditionally heteroscedastic and itcan be represented by ARCH & GARCH model.

But, in case of Chinese I.T. index the returns appearsto be fluctuating during the entire period of thirteenyears. That is the volatility exists in the index but thereis no pattern is no prolonged period of similar volatilityi.e. high or low can be seen in an index. Thus it appearsthat the returns are not heteroscedastic in nature.

Residual derived from mean equation (1) is used inmaking variance equation (2).

GARCH (1, 1) variance equation or model (eq2) isbeing run on E-Views 8 using normal distribution.The output window showed the following table:

Null Hypothesis

HO: ARCH term is not significant to explain theGARCH term.Table 5: Estimates of Variance Equation in Indian and

Chinese I.T. Index

Index Description

India China

C

Coefficient 0.097348 3.774345

Standard Error 0.007015 6.251969

Z-stat 13.87634 0.603705

P-stat 0.0000 0.5460

ARCH

[RESID(-1)2 ]

Coefficient 0.214940 -0.001710


Z-stat 15.90246 -0.531960

P-stat 0.0000 0.5948

GARCH

[GARCH(-1)]

Coefficient 0.816058 0.600786


Z-stat 92.15734 0.907635

P-stat 0.0000 0.3641


The (coefficient of ARCH +coefficient of GARCH)in both the indices are non-zero and very close to butsmaller than unity, therefore it can be interpreted thatthe model is valid that is mean returns on index willrevert back to their previous values slowly. TheseARCH and GARCH term represents the impact ofrecent and historical news/information respectively.The corresponding p-values of both ARCH andGARCH term are significant at 1% level in Indian I.T.index. Hence null hypothesis is rejected i.e. the ARCH

term is significant to explain the volatility of GARCHterm. Thus, it can be concluded that returns in IndianI.T. index are conditionally heteroscedastic. But thecoefficient of GARCH term is significantly higher inIndian I.T. index thus explaining that the index returnsare more affected by historical news. In Chinese I.T.Index the coefficient of ARCH term came out to benegative as well as statistically insignificant. Hence, thenull hypothesis is accepted i.e. ARCH term is notsignificant to explain the GARCH term i.e. currentday’s volatility is not affected by previous days volatilityi.e. the returns are not conditionally heteroscedastic.The negative ARCH term shows negative shock (a badnews). This shows the possibility of asymmetries involatility i.e. the index returns are not heteroscedasticspread. The GARCH term also came out to bestatistically insignificant. Hence, it can be interpretedthat GARCH(1,1) model does not fit in Chinese I.T.index; since, the index returns have very poor volatilityas well as asymmetries in volatility can be seen.

Conclusions and Recommendations

For those investors, whose investment portfolioincludes Indian/Chinese I.T. Index stocks the resultsof present study indicates following recommendations:both the indices are yielding positive returns butChinese I.T index has given higher returns during theperiod 2004-2017 as compared to Chinese I.T. index.Also, the investment was less risky. So, it was a greensignal for investors of Chinese I.T stocks. But sinceStock markets are unpredictable an investor shouldtake decisions accordingly. The probable reason is thatover the last few years China’s I.T. sector has witnesseda massive growth. China is even planning to make thissector as a part of its seven strategic industries, whichwill help an economy to flourish growth throughinnovation and creativity. The largest internet users(500 million) from the world come from China. Thus,China’s IT market is the fourth largest in the world.China is second largest software-outsourcingdestination in the world next to India. Although thehardware markets of China earn only small margins.In 2015, revenue from IT sector reached total of$124.5 billion, up 7.5% from 2014.On the otherhand; Indian I.T. stocks were not able to achieve highreturns probably due to few political and economicscams and scandals in the country. The examples are


2G spectrum scam (2010), The Satyam Computer’sScam (2009), Speak Asia scam (2011), few Ponzischemes, which collected thousands of crores of rupeesfrom investors through online survey. But particularlyif we look into Indian I.T. Stock market, a pattern hasbeen seen that the small and mid-cap companies (Firstsource Solutions, Persistent System, Larsen and TurboLtd, Sonata Software, Mindtree Ltd.), have alwaysstolen the attraction of investors as compared to largecap companies. Thus “Separating Men from the Boys”in the market. As far as volatility in the indices isconcerned, Indian I.T. markets are more volatile ascompared to Chinese I.T. markets. In Indian market,an investor should plan investment accordingly bytaking rational decision. Those who have invested inIndian/Chinese I.T. stocks should hold theirinvestment for a long time. They may curb thevolatility by ignoring short term volatility and thinkingabout future long term returns say for a period and20-30 years.

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Distinguishing Countries on the Basis ofModified HDIAmbica Sharma* & Hardik Beniwal**

Abstract

The well-known Human Development Index (HDI) encompasses only three rather basic aspectsof human welfare. However, development goes beyond these factors and therefore this researchpaper extends to the effect of new dimensions namely political and cultural freedom, control ofcorruption, electric power consumption, leisure conditions measured by number of mobile cellularsubscriptions, quality of life and wellbeing measured by improved water resources and sanitationfacilities and population growth that can be incorporated into HDI for better express progressin development. This paper explores the relationship between these factors of human well-beingand distinguishes 122 countries between low HDI and high HDI. The primary reason of inclusionof additional factors is to provide a holistic measure of progress of countries and thus, yieldinga more precise conclusion of their development status.

Keywords: HDI, Development, Country

Introduction

United Nations Development Programme (UNDP)publishes the human development report as a metricto assess the social and economic development levelsof countries. This index makes it possible to followchanges in the development levels over time and tocompare the development levels of differentcountries.The Human Development Index (HDI)summaries major components like a long and healthylife, being knowledgeable and have a good standardof living.

HDI was established to place emphasis on individuals,more precisely on their opportunities to realizesatisfying work and lives. Evaluating a country’spotential for individual human development providesa supplementary metric for evaluating a country’s levelof development besides considering standard economicgrowth statistics, such as gross domestic product(GDP). This index can also be used to examine thevarious policy choices of nations; if, for example, two

countries have approximately the same gross nationalincome (GNI) per capita, then it can help to evaluatewhy they produce widely disparate humandevelopment outcomes.

The variables used by UNDP for calculating are asfollows:

• Life expectancy at birth: Number of years anewborn infant could expect to live if prevailingpatterns of age-specific mortality rates at the timeof birth stay the same throughout the infant’s life.

• Mean years of schooling: Average number ofyears of education received by people ages 25 andolder, converted from education attainment levelsusing official durations of each level.

• Expected years of schooling: Number of yearsof schooling that a child of school entrance agecan expect to receive if prevailing patterns of age-specific enrolment rates persist throughout thechild’s life.

• Gros s nat ional income (GNI) p ercapita : Aggregate income of an economygenerated by its production and its ownership offactors of production, less the incomes paid for

Ambica Sharma* Student, Sri Guru Gobind SinghCollege of Commerce, Delhi UniversityHardik Beniwal** Student, Sri Guru Gobind SinghCollege of Commerce, Delhi University


the use of factors of production owned by rest ofthe world, converted to international dollars.

Modified HDI

However, this research paper introduces andincorporates the effects of the following additionalmeasures

• Political and Cultural Freedom: As per UNDPidea of development “Freedom is more than anidealistic goal —it is a vital component of humandevelopment”. Sen (2000) states that democracyencourages a society to prioritize, what it aims todo and UNDP (2002) sees it not only as a valuebut also a means with which to achievedevelopment. For this the data has been takenfrom Freedom House by the U.S.-based non-governmental organization which a yearly surveyand report Freedom in the World that measuresthe of civil degree liberties and political rights inevery nation. Each country and territory isassigned between 0 and 4 points on a series of 25indicators, for an aggregate score of up to 100.These scores are used to determine two numericalratings, for political rights and civil liberties, witha rating of 1 representing the most free conditionsand 7 the least free.

• Control on Corruption: Control of Corruptioncaptures perceptions of the extent to which publicpower is exercised for private gain, including bothpetty and grand forms of corruption, as well as“capture” of the state by elites and privateinterests.

• Electric power consumption (kWh per capita):Energy is deeply implicated in each of theeconomic, social and environmental dimensionsof human development. Energy services providean essential input to economic activity. Theycontribute to social development througheducation and public health and help meet thebasic human need for food and shelter. Modernenergy services can improve the environment, forexample by reducing the pollution caused byinefficient equipment and processes and byslowing deforestation. But, rising energy use canalso worsen pollution, and mismanagement ofenergy resources can harm ecosystems. The

relationships between energy use and humandevelopment are extremely complex.

Mobile Cellular Subscriptions (per 100 people):Mobile cellular subscriptions reflect the degree ofleisure conditions and advancement in the country andis thus a measurement of development.

• Percentage of population with access toImproved Water Source and improvedSanitation Facilities is a measure of quality oflife and wellbeing of the citizens and thus reflectsdevelopment of the country.

• Population Growth (annual %): More peoplecould mean more mouths to feed, more healthcare and education services to provide, and soforth. Also, more population for a country richin resources could also mean greater economicdevelopment. After all, the more people you have,the more work is done, and more value is created.Thus, the effect of population on thedevelopment of countries can be mixed.

Literature Review

We have taken reference for the factors and data usedin our research from a few already published reportsincluding: A proposal for a modified HumanDevelopment Index by María Andreina Salas-Bourgoin(1990), Global Energy Futures and HumanDevelopment: A Framework for Analysis by Alan D.Pasternak (2000) and Human Development: Beyondthe Human Development Index by Ranis,, Stewart &Amman (2006). This paper introduces two new factorsfor the measurement of HDI of countries namelyemployment and political freedom in terms (i)employment-to-population ratio; (ii) non-vulnerableemployment as a share of total employment, and (iii)the Democracy Index, with an aim to make HDI betterable to serve as a proxy for progress in humandevelopment. These factors create and expandopportunities and choices. There is an acknowledgedcorrelation between these indicators and humandevelopment. It also explains and calculates modifiedHDI for 117 countries. The modified HDI indicatesthat the countries with overall high HDI suffers interms of employment, while developing countries lagbehind in the quality of employment. It also revealedthat modified HDI scores of countries like Russian


Federation and the Bolivarian Republic of Venezuela,with restricted political freedom decline. Alan D.Pasternak (2000) worked on the relationship betweenmeasures of human well-being and consumption ofenergy and electricity. A correlation is shown betweenthe United Nations’ Human Development Index(HDI) and annual per-capita electricity consumptionfor 60 populous countries comprising 90% of theworld’s population. Inter-country comparisons showthat incomes rise with electricity use beyond the annual4,000 kWh per-capita level. These additional incomescan contribute to higher standard of living. Itconcludes that neither the Human Development Indexnor the Gross Domestic Product of developingcountries will increase without an increase in anelectricity use. Ranis,, Stewart & Samman (2006)identifies 11 new aspects of Human development. 8indicators were found that were highly correlated toHDI. Of these indicators is number of Telephone/Cellphone subscribers, an indicator of leisure conditionsand technological advancements. Another factor usedis Corruption Index, which is the misuse of publicpower for private benefits and hinder the outreach ofbenefits and policies to the target segment of societies.


• Model Specification

Model specification is based on the availableliterature and theory. Such literature summarizedin the review of literature helped us to identifyindependent, dependent and the relationshipbetween them. In our analysis we tried to estimatethe effect of factors like political and culturalfreedom, control of corruption, electric powerconsumption, mobile cellular subscriptions,improved water resources and sanitation facilitiesand population growth in distinguishingcountries between low HDI countries and highHDI countries.

• Gathering of data

Data was gathered from (i) HDI report by UNDevelopment Programme report (ii) FreedomHouse report(iii) World Bank. It is acrosssectionaldata of various determinants of HDI across 122countries of the world.

• Execution of Model- Choosing appropriateeconometric techniques

First, new measures affecting HDI are explained,besides the basic variables, life expectancy at birth,mean years of schooling, expected years ofschooling and GNI per capita and then proceededto determine appropriate econometric techniquesthat could group countries into clusters withsimilar characteristics on the basis of the variableschosen. Cluster analysis divided the countries intotwo clusters (High HDI and Low HDI) withrespect to 11 factors used in our analysis and thenused discriminant analysis to check the accuracyof classification.

• Interpreting the results

We interpreted the results of cluster using theANOVA table, cluster membership table anddiscriminant analysis with the help Test ofEquality Group Means table, Pooled Within-Group Matrices table, Eigen values, Wilks lambdaand classification results at a significance level of5%. The results were significant at the said level,thus analysis, the additional factors introducedand the clusters formed are plausible.

Analysis

Table 1: Cluster Analysis

Cluster

Initial Cluster Centers 1 2

Life Expectancy at Birth 78.3230 61.9360

Expected Years of Schooling 13.4061 5.4227

Mean Years of Schooling 9.7646 1.6580

GNI per capita 129915.6009 889.4556

Freedom Rating 6 4

Control on Corruption .8915 -.6416

Electric Power Consumption 15309.4300 51.4408

Mobile Cellular Subscriptions 159.1317 46.4959

Improved Water Source 100 58

Improved Sanitation Facilities 98 11

Population Growth 4.4126 3.8358



This table shows that the cluster center for lifeexpectancy at birth is greater for cluster 1 than cluster2. Similarly, the cluster center for expected years ofschooling lies around 13.40 for cluster 1 while its 5.42

for cluster 2 implying that citizens belonging tocountries in cluster 1 are expected to receive more yearsof schooling.

Table 2: Cluster Membership

Case Number Countries Cluster Distance

1 Norway 1 20316.978

2 Australia 1 7954.176

3 Switzerland 1 7331.296

4 Germany 1 7430.187

5 Denmark 1 8590.235

6 Singapore 1 27794.309

7 Netherlands 1 6749.407

8 Ireland 1 9203.161

9 Iceland 1 43969.546

10 Canada 1 8723.682

11 United States 1 2881.163

12 Sweden 1 4549.028

13 United Kingdom 1 14354.870

14 Japan 1 13914.112

15 Israel 2 19482.291

16 Luxembourg 1 12081.010

17 France 1 13441.738

18 Belgium 1 10235.886

19 Finland 1 12126.443

20 Austria 1 7824.346

21 Slovenia 2 17042.413

22 Italy 1 18350.919

23 Spain 1 18961.715

24 Czech Republic 2 16418.486

25 Greece 2 12893.199

26 Estonia 2 14838.493

27 Cyprus 2 17275.691

28 Malta 2 17487.733

29 Qatar 1 79439.935

30 Poland 2 12009.309

31 Lithuania 2 13861.265

32 Chile 2 9581.725

33 Saudi Arabia 1 2652.135

(Contd...)


34 Portugal 2 14082.555

35 United Arab Emirates 1 15674.333

36 Hungary 2 11294.454

37 Latvia 2 10431.419

38 Argentina 2 8745.755

39 Croatia 2 8194.877

40 Bahrain 1 15332.187

41 Montenegro 2 4002.951

42 Romania 2 7195.107

43 Kuwait 1 25733.675

44 Belarus 2 3709.525

45 Oman 1 17030.950

46 Uruguay 2 6962.498

47 Bulgaria 2 4755.991

48 Kazakhstan 2 10431.176

49 Bahamas 2 9320.214

50 Malaysia 2 12611.990

51 Panama 2 7226.215

52 Mauritius 2 5703.180

53 Trinidad and Tobago 2 16567.950

54 Costa Rica 2 1773.765

55 Serbia 2 2110.588

56 Cuba 2 4845.596

57 Iran (Islamic Republic of) 2 4364.311

58 Georgia 2 3429.887

59 Turkey 2 6496.698

60 Sri Lanka 2 2186.114

61 Albania 2 1998.059

62 Lebanon 2 1293.327

63 Mexico 2 4138.893

64 Azerbaijan 2 4168.551

65 Brazil 2 1950.489

66 Bosnia and Herzegovina 2 2467.977

67 The former Yugoslav Republic of Macedonia 2 413.061

68 Algeria 2 1519.247

69 Armenia 2 4060.610

70 Ukraine 2 5043.211

71 Jordan 2 2152.315

72 Peru 2 1277.683

(Contd...)


73 Thailand 2 2305.352

74 Ecuador 2 1879.185

75 China 2 2080.350

76 Mongolia 2 1801.754

77 Jamaica 2 4048.984

78 Colombia 2 1014.091

79 Suriname 2 4049.416

80 Tunisia 2 2121.617

81 Dominican Republic 2 776.654

82 Moldova (Republic of) 2 7260.223

83 Botswana 2 2453.631

84 Gabon 2 6870.441

85 Paraguay 2 4107.098

86 Egypt 2 2316.810

87 Indonesia 2 2574.074

88 Philippines 2 4118.305

89 El Salvador 2 4675.718

90 Bolivia (Plurinational State of) 2 6251.014

91 South Africa 2 2073.836

92 Iraq 2 1067.435

93 Morocco 2 5205.019

94 Nicaragua 2 7663.169

95 Guatemala 2 5418.411

96 Namibia 2 2541.816

97 Micronesia (Federated States of) 2 8953.796

98 Tajikistan 2 9668.484

99 Honduras 2 7927.980

100 India 2 6719.911

101 Bangladesh 2 9093.959

102 Ghana 2 8598.526

103 Zambia 2 8901.050

104 Cambodia 2 9342.943

105 Nepal 2 10112.266

106 Myanmar 2 7556.515

107 Kenya 2 9574.549

108 Pakistan 2 7409.398

109 Angola 2 6235.027

110 Tanzania (United Republic of) 2 9993.505

111 Nigeria 2 7095.125

(Contd...)


112 Cameroon 2 9538.146

113 Zimbabwe 2 10780.470

114 Senegal 2 10181.063

115 Haiti 2 10798.934

116 Togo 2 11165.303

117 Benin 2 10470.826

118 Gambia 2 10749.860

119 Ethiopia 2 10924.541

120 Mozambique 2 11275.531

121 South Sudan 2 10578.089

122 Niger 2 11550.317


Cluster analysis has divided 122 countries into twomain groups and that are country with high HumanDevelopment Index and country with low Human

Development Index. The country which is in clusterone are high HDI country and the country in cluster2 are low HDI country. As per the analysis 27 countrieshave High HDI and 95 have low HDI

Table 3: ANOVA

Cluster Error F Sig.

Mean Square Df Mean Square Df

Life Expectancy at Birth 1806 1 39 120 46 .000

Expected Years of Schooling 235 1 6 120 42 .000

Mean Years of Schooling 190 1 6 120 30 .000

GNI per capita 30841593827 1 142994497 120 216 .000

Freedom Rating 27 1 3 120 9 .004

Control on Corruption 64 1 0 120 135 .000

Electric Power Consumption 2026852961 1 22532759 120 90 .000

Mobile Cellular Subscriptions 10002 1 971 120 10 .002

Improved Water Source 2298 1 137 120 17 .000

Improved Sanitation Facilities 13639 1 546 120 25 .000

Population Growth 0.352 1 1.551 120 0.227 .635


According to the ANOVA table, life expectancy atbirth, expected year of schooling, GNI per capita,mean year of schooling, improvement sanitationfacilities, improved water source, electric powerconsumption, control on corruption, are less than

0.005,they are significant. Population growth is morethan 0.005 that is why it is insignificant. Hence it isideal to segregate the respondents of these variables intodifferent groups.


Table 4: Discriminant AnalysisGroup Statistics

Cluster Number of Case Mean Std. Deviation Valid N (list wise)

Unweight Weighted

HIGH HDI

Life Expectancy at Birth 81 3 27 27

Expected years of schooling 16 2 27 27

Mean Years of Schooling 11 2 27 27

GNI per capita 50545 20278 27 27

Freedom Rating 2 2 27 27

Control on Corruption 1 1 27 27

Electric Power Consumption 11980 9598 27 27

Mobile Cellular Subscriptions 134 27 27 27

Improved Water Source 99 1 27 27

Improved Sanitation Facilities 99 2 27 27

Population Growth 1 1 27 27

LOW HDI


Expected Years of Schooling 13 2 95 95


GNI per capita 12245 8295 95 95








TOTAL


Expected Years of Schooling 14 3 122 122


GNI per capita 20721 19917 122 122









It can be interpreted from the table that the mean lifeexpectancy at birth, years of schooling, expected yearsof schooling,gross national income per capita, controlon corruption, electric power consumption, mobilecellular subscriptions, percentage of population withaccess to improved water sourse and improved

sanitation facilities, population growth of cluster 1 isgreater than the mean of that of cluster 2. Also thepolitical and cultural freedom rating of cluster 1 ishigher (2.2037) representing that degree of civilliberties and political rights is higher for cluster 1 thanfor cluster 2.


Table 5: Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

Life Expectancy at Birth .724 46 1 120 .000

Expected Years of Schooling .740 42 1 120 .000

Mean Years of Schooling .800 30 1 120 .000

GNI per capita .357 216 1 120 .000

Freedom Rating .934 9 1 120 .004

Control on Corruption .470 135 1 120 .000

Electric Power Consumption .572 90 1 120 .000

Mobile Cellular Subscriptions .921 10 1 120 .002

Improved Water Source .877 17 1 120 .000

Improved Sanitation Facilities .828 25 1 120 .000

Population Growth .998 .227 1 120 .635


As we can see the p value of all the variables except population growth are less than 0.005. Therefore, there isa significant mean difference between high and low HDI, with respect to all the variables. The null hypothesisof equality of group means among high and low HDI is rejected.

Table 6: Pooled Within-Groups Matrices

Pooled Within-Groups Matrices

Life Expectan

cy at Birth

Expected Years of

Schooling

Mean Years of

Schooling

GNI per

capita

Freedom Rating

Control on Corruption

Electric Power

Consumption

Mobile Cellular

Subscribers

Improved Water Source

Improve Sanitation Facilities

Population Growth

Correlation

Life Expectancy at Birth

1.000 .698 .627 .355 -.408 .510 .192 .292 .739 .808 -.585

Expected Years of Schooling

.698 1.000 .756 .304 -.535 .569 .268 .356 .652 .701 -.639

Mean Years of Schooling

.627 .756 1.000 .369 -.396 .519 .245 .345 .634 .725 -.620

GNI per capita .355 .304 .369 1.000 -.057 .323 .241 .406 .391 .424 -.073

Freedom Rating -.408 -.535 -.396 -.057 1.000 -.660 -.108 -.087 -.335 -.208 .492

Control on Corruption

.510 .569 .519 .323 -.660 1.000 .235 .245 .471 .379 -.434

Electric Power Consumption

.192 .268 .245 .241 -.108 .235 1.000 .142 .216 .233 -.115

Mobile Cellular Subscriptions

.292 .356 .345 .406 -.087 .245 .142 1.000 .561 .445 -.137

Improved Water Source

.739 .652 .634 .391 -.335 .471 .216 .561 1.000 .776 -.553

Improved Sanitation Facilities

.808 .701 .725 .424 -.208 .379 .233 .445 .776 1.000 -.549

Population Growth

-.585 -.639 -.620 -.073 .492 -.434 -.115 -.137 -.553 -.549 1.000



The diagonal from top left to bottom right is equal to1 (because a variable will always be perfectly correlatedwith itself ). Also, there exist no perfect correlationbetween any two factors.

Table 6: Eigen values

Function Eigen Value

% of Variance

Cumulative %

Canonical Correlation

1 3.001a 100.0 100.0 .866


The Eigen values explain the discriminating ability ofa function. An Eigen value of greater than 1 expectedas it would imply that the predictor variables have gooddiscriminating power and the model is significantoverall. It is simply the ratio of explained variation tounexplained variation. Therefore, a higher degree ofexplained variation in the sample used is expected. Thelarger the Eigen value the more variance the functionexplains in the dependent variablevalue of 3.001 is highand shows canonical correlation of 0.866, whichmeasures the linkage between discriminant scores.

Table 7: Wilks' Lambda

Test of Function(s)

Wilks' Lambda

Chi-square Df Sig.

1 .250 158.756 11 .000


Results of Wilk’s Lambda show how well predictionmodel fits in. It is significant and tests whether or notthere are equal variances among groups. If this valueis greater than 0.001 then it can be interpreted thatthere is equal group variance. Although, the nullhypothesis is rejected for equal group variance, stillanalysis is proceeded with caution.

Table 8: Canonical Discriminant Function

Function 1 LifeExpectancyatBirth .045 ExpectedYearsofSchooling .102 MeanYearsofSchooling -.037 GNI per capita .000 FreedomRating .156 ControlonCorruption .889 ElectricPowerConsumption .000 MobileCellularSubscriptions -.004 ImprovedWaterSource -.019 ImprovedSanitationFacilities -.009 PopulationGrowth .189 (Constant) -3.499

Canonical Discriminant Function


This table shows the relative importance of ourpredictor variables. These are unstandardizeddiscriminant functions, which shall be used in the finalequation. In this case, control on corruption has thehighest relative value.

Table 9: Functions at Group Centroids

Cluster Number of Case Function

1

HIGH HDI 3.223

LOW HDI -.916


The functions at group centroids are the meandiscriminant functions scores for each group. It’s goodwhen predicted group membership is accurate. In thiscase it is 94.4% and relatively higher and indicatesthere are few false negatives. This means that 94.4%of the groups were correctly classified.

Table 10: Classification Results

Cluster Number of

Case

Predicted Group

Membership

Total

HIGH HDI

LOW HDI

Original Count HIGH HDI 25 2 27

LOW HDI 1 94 95

% HIGH HDI 92.6 7.4 100.0

LOW HDI 1.1 98.9 100.0


As it can be seen, 92.6% of high HDI are classifiedcorrectly as high HDI and 98.9% low HDI areclassified correctly as low. HDI. Therefore, overall97.5% countries are classified correctly.

Conclusion

The addition of new measures, namely, political andcultural freedom, control of corruption, electric powerconsumption, leisure conditions measured by numberof mobile cellular subscriptions, quality of life andwellbeing measured by improved water resources andsanitation facilities and population growth in definingHDI is an attempt to make the HDI better able toserve as a proxy for progress in human development.

In this study used two techniques have been used-cluster analysis and discriminant analysis. Cluster


analysis helped in grouping the countries into HighHDI and Low HDI with respect to the above stated7 factors besides the basic factors. At a significance levelof 0.5 percent, we found that all the factors, exceptpopulation are significant in determining the clusters.Cluster 1 represented countries with high HDI andCluster 2 represented countries with low HDI. Theanalysis has grouped 27 countries as High HDI and95 as low HDI. Further, to check whether the clustersformed are significantly different from each other ornot and to evaluate the accuracy of classification,discriminant analysis was run. The classification resultsindicate 97.5% accuracy thus, evidencing that thefactors used in discriminating the countries as low andhigh HDI as well as the clusters found are plausible.

We can conclude that the inclusion of additionalfactors can provide a holistic measure of progress ofcountries and thus yielding a more precise conclusionof their development status.

References

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2. Bourgoin, S., & Andreina, M. (2014). A proposal for amodified Human Development Index. CepalL Review, na,29-44.

3. Bravo, G. (2014). The human sustainable developmentindex: New calculations and a first critical analysis.Ecological Indicators, 37, 145-150.

4. C.Dumith, S., C.Hallal, P., S.Reis, R., & W Khol, H.(2011). Worldwide prevalence of physical inactivity andits association with human development index in 76countries. Preventive medicine, 53 1-2, 24-8.

5. Crump, J. A., Ram, P., Gupta, S., Miller, M., & Mintz,E. (1984-2005). Analysis of data gaps pertaining toSalmonella enterica serotype typhi infections in low andmedium human development index countries.Epidemiology and infection, 136 4, 436-48.

6. Despotis, D. K. (2005). A reassessment of the humandevelopment index via data envelopment analysi s.Operational research society, 56 (8), 969-980.

7. Grimm, M., Harttgen, K., Klasen, S., & Misselhorn, M.(2008). A human development index by income groups.World development, 36 (12), 2527-2546.

8. McGillivray, M. (1991). The human development index:Yet another redundant composite development indicator.World Development, 19 (10), 1461-1468.

9. Noorbakhsh, F. (1998). A modified human developmentindex. World Development, 26 (3), 517-528.

10. Noorbakhsh, F. (1998). The human development index:Some technical issues and alternative indices. InternationalDevelopment, 10, 589-605.

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13. Trabold-Nübler, H. (1991). The human developmentindex—A new development indicator. Intereconomics, 26(5), 236-243.


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W H

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APP

END

IX


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Social Responsibility and Ethics in MarketingAnupreet Kaur Mokha*

Abstract

Ethics are our perception regarding what is right and wrong. Although these idealsmay rangefrom one person to a distinctive or one company to another, ethics and social responsibilityareaforemost part to any company’s marketing department. Ethics has a tendency to focus on theindividual or marketing group decision, whilst social responsibility takes into account the overalleffect of marketing practices on society. The main aim of the marketing department is to selectan audience, engage to that audience, and get the audience to buy that unique products orservices. In doing this, a company should make sure that they are first abiding by way of all lawsand regulations, however they ought to also attempt to be sure that they areappearing ethically and honestly. Though the pursuit of ethical marketing and social responsibilitydoes no longer automatically translate into increased profit, it is far nonetheless the responsibilityof the company to ensure it is accountable for its actions and their impact on society. This paperwillhave a look at the significance of integration of ethics in social responsibility marketing andthe ethical issues faced in the marketing.

Keywords: Social Responsibility Marketing, Ethical Marketing, Marketing plan, Ethical issues

Introduction

A variety of phraseshave been used from time to timeinterchangeably, to refer to social responsibility suchas business ethics, corporate accountability,sustainability, corporate citizenship to describecompanies’ efforts to deal with ethical practices,governance,employee-friendly regulations, humanrights, environmental effect, and communityengagement in the core features of the business.

In accordance with the research by the global non-profit organization known as ‘Business for SocialResponsibility’, companies now tends to reportpublicly on their social responsibility activity so as tofulfil the needs and demands of various stakeholdersand other interested people consisting of personnel,consumers, suppliers, clients,shareholders, regulatorsand lawmakers, who are asking companies to be moreresponsible now not only for their own overallperformance, but also for the impact of their products,the performance of their delivery channel, and the well-being of their personnel(Marinova, 2013).A company

that makes use of both ethical and socially responsiblemarketing strategy will definitely benefitted with therespect and trust of their customers they target andinteract with. Over long term, this could translate togreater benefits all spherical.

Objectives

1. To understand the concept of social responsibilitymarketing and Ethical marketing.

2. To develop and implement a socially responsiblemarketing plan.

3. To analyze the benefits of integrating ethics intomarketing strategy.

4. To understand ethical issues faced in marketing.

Literature Review

Social Responsibility Marketing

Socially responsibility marketing is a marketingphilosophy that a business enterprise have to take intoconsideration; “What is within the best interestof society in the present and long term?” It is totallybased on the concept that market offerings should not

Anupreet Kaur Mokha* Assistant professor, SGTBKhalsa College, Delhi University


be simplest profit-driven, however they should alsoreinforce social and ethical values for the advantage ofcitizens. Social responsibility in marketing is oftenconsidered with ethics. The difference among the twois that what is taken into consideration ethical inphrases of business, society and individually might notbe the same thing nor do all business actions necessarilyshould be socially accountableso that you can be takeninto consideration ethical. Social responsibility is a largeconcept that pertains to a business obligation tomaximize its positive impact on society whilstminimizing its negative impact (Anatasia, 2015)

To be socially responsible is when the organization isbasically concerned about individual, its society andenvironment as a whole with whom and where itconducts business. In its most fundamental form,socially responsible marketing is taking ethicalmovements that inspire a positive impact on all of thecompany’s stakeholders, including employees,community, consumers, and shareholders (Hunt et al.,1990). The main responsibility of marketers in thisregard is to package and communicate theorganization’s decisions that will impact the variouscommunities with which they interact(Marinova,2013). Consumers have the right and strength todecide which companies succeed or fail; so marketershave a main duty to make sure their practices are visibleas philanthropic without being phony. BrandKarma isthe right example of one of the means by way of whichconsumers make those decisions.

Socially responsibility marketing is a marketingphilosophy. It shows that a company must consideratewhat is in the best interest of the society in the presentand long time. Socially responsible companies have toproduce desirable products fervently. Consumer’simmediate gratification can reap from this type ofproducts and this form of product can also benefitconsumers and society in the long time. Marketersprovide the right products to the right people at theright time. Ethical marketers make certain productsmeet and exceed their needs, back up their claims andprovide value to the customers over time whilst findingpossibilities to pay it ahead. (Kotler et al., 2013).

Social conscious marketing directs the drawback oftraditional marketing practices and follows thephilosophy of mindfulness and responsibility.

Company-marketing practices must be primarily basedon consumer satisfaction, innovative ideas and offersociety long-term value and benefit(Marinova, 2013).A company that makes use of both ethical and sociallyresponsible marketing strategy will benefit the respectand trust of the customers they approach and interactwith. Over long time, this may translate to greaterbenefits all round. Now a day’s firms could make theirpractices more moral and accountable by perfectingthe following characteristics.

• Honesty: Making sure a product satisfies a needit promises to, or helps in presenting a lifestyle itadvertises. Marketinghave to be transparent aboutviable side effects so customers come to recognizethe honesty of your marketing.

• Safety: Any product or service that might be riskyto the health conditions of people, animals or theenvironment need to have clean advisories andwarnings.

• Transparency: Any strategies to control anddisguise facts and information customers needcould damage a company. The practices of thecompany must be transparent, obvious moral orethical.

• Respecting Customer Privacy: When customersbelieve sufficient to allow you to access to theirinformation, selling it to steer companies oracquiring prospective customers’ informationwithout permission is unethical and breaks trust.

• Ethical Pricing: Accumulating data about yourgoal market will come up with information onhow much they may be inclined to pay in yourproduct. Creating fake shortages and terriblemouthing the competition are consideredunethical marketing practices.

Ethical Marketing

Good ethics is a keystone of sustainable marketing. Inthe long run, unethical marketing damages customersand society at a large. Further, it ultimatelyharms acompany’s effectiveness and reputation. As a result, thesustainable marketing goals of long-term customer andbusiness welfare can be achieved best through ethicalmarketing conduct. Ethical marketing is a philosophythat consciousnessspecializes in honesty, fairness and


responsibility.Ethical marketing is a process throughwhich companies generate customer interest inproducts or services, construct healthier and strongcustomer interest/relationships, and create value for allstakeholders by means of incorporating social andenvironmental considerations in products andpromotions (Anatasia, 2015). All aspects of marketingare considered, from sales strategies to businesscommunication and business development. Thoughwrong and right are subjective, a widespread set ofguidelines can be put in vicinity to ensure thecompany’s intent is broadcasted and accomplished.

Marketing ethics can be defined as formulatingguidelines to establish practices that are obvious andstraightforward. It is basically establishing marketingpolicies and strategies for corporate sectors whereactions show integrity and equity to consumers andall other stakeholders. (Saviour& et al. 1997). Themain reason of marketing ethics is to address principlesand standards for growing desirable conduct inside themarketplace (Mohamed, 2007).

Marketing ethics inculcate decisions about what is rightor wrong within the organizational context of planningand implementing marketing activities in a globalbusiness environment to advantage (i) organizationaloverall performance, (ii) social acceptance andadvancement in the organization,(iii) individualachievement in a work groupand (iv) stakeholders(Hunt et al., 1990). This kind of definition ofmarketing ethics acknowledges that ethical decisionsarise in a complex social network within a marketingorganization. Marketers are usually asked by top-levelmanagement to assist in making the numbers byreaching almost impossible sales targets. In reality, mostmarketing misconduct is accomplished to help theorganization. Being a team player and bending thepolicies and procedures to make targets mayadditionally and effectively result in a promotion.

The following are the principles of ethical marketing:

• All marketing communications share the samestandard of truth and trust.

• Marketing professionals should follow the higheststandard of personal ethics.

• Ethics should be discussed honestly and openlyduring all the minute marketing decisions.

• The privacy of the customer should be utmostimportant and should never be compromised.

• Marketers should be obvious and transparentabout to whom they pay to endorse theirproducts.

• Consumers should be treated with equity basedon the nature of the product as well as consumer(e.g. marketing to children).

• Marketers must comply with the rules, regulationsand standards established by variousgovernmental and professional organizations.

Merging Social Responsibility and EthicalMarketing

Organizations are conscious that consumers now a dayare savvy and opinionated. The company’s activatesdirectly or indirectly which have an effect thestakeholders whether it be individuals, groups orbusinesses. With the continuous growth of business,industry and increased push for marketing efforts,marketing ethics is growing to be the top of thecorporate agenda. (Kotler et al, 2013).

Social responsibility and marketing ethics ought to gohand in hand. It is the social responsibility to formulaterules and regulations which might be applicableethically and which work not for maximizing theprofits of the businesses but which targets at the largerinterest of all the stakeholders (Anatasia, 2015). Sowith this in mind, companiesneed to create an ethicallysound marketing plan and combine it into all elementsof their marketing mix.

• Do good not just to look good – Focus on beingaccountable and how your firm can honestly helpthe community or country. It is in doing so thatyour customers, the press, and all those watchingmay be inspired.

• To acknowledge the social obligations toshareholders- It is the duty of the marketer toaccept the effects of their marketing decisions,policies and strategies and accept the socialobligations to shareholders that include increasedmarketing and economic power.

• Speak up against company policies that do notreflect the ethical profile of the company – As


the face of the organization, marketers shouldvoice their concerns whilst there may be apotential for a practice to be seen as unethical.

• Think about long term effects, not short termgains – Short sighted groups will undervalue theeffect of responsible marketing for instantlyenjoyable growth.

• To try to balance justly the needs of the buyerwith the interests of the seller-The marketersmust constitute their products in a clear way inselling, marketing and other forms ofcommunication. This consists of the avoidanceof false, misleading and deceptive promotion.

Developing and Implementing a SociallyResponsible Marketing Plan

While ethics and social responsibility are usedsometimes interchangeably, there is a distinctionbetween the two terms. Ethics mainly focuses on theindividual or marketing group decision, whereas socialresponsibility takes into account the full effect ofmarketing practices on society. Ethical marketingdoesn’t refer to a plan in and of itself, but it gives toolsfor organization to evaluate the marketing techniquesthey use in the past, present, and future (Gilbertson,1999). If an organizationmakes a decision that anethical marketing strategy can increase their earningsor develop their public image, they can take steps torevise their existing marketing. Social responsibility andethical marketing typically comes into the marketingplan technique in one of two ways i.e. it can be acorporate-level strategy with particular objectives orcan be a part of the marketing mix primarily based onthe situation analysis.

In order to foster a socially responsible and ethicalbehavior pattern among marketers while achievingcompany targets, special care need to be taken todisplay trends and shifts in society’s values and ideals.Next, marketers should forecast the long-time periodeffects of the decisions that pertain to those changes.Bearing in mind that a company cannot fulfil thedesires of an entire society, it best serves marketers tofocus their most costly efforts on their target market,whilst being aware of the values of society at a large(Anatasia, 2015).

Marketing strategy must consider stakeholdersconsisting of managers, employees, customers, businesspartners, industry associations, government regulators,and special-interest group, all of whom makecontributions to accepted standards and society’sexpectations. The most basic of these standards werecodified as laws and policies to inspire organizationsto conform to society’s expectations of businessconduct (Gilbertson, 1999). In response to customerdemands, together with the threat of improvedregulation, increasinglymore companies have included ethics andsocial duty into the strategic marketing planningprocess. Any organization’s recognition may bedamaged by way of poor performance or ethicalmisconduct. However, it is much easier to recover frombad marketing performance than from ethicalmisconduct.

Five simple steps every marketer can take to create asustainable socially responsible market plan are:

Figure-1: Steps to create a sustainable socially responsiblemarket plan


Benefits of Integrating Ethics into your MarketingStrategy

Marketing ethics has been advanced with reference tobusiness ethics that mirror the interest of numerousstakeholders. These ethics describe ideas which mightbe acceptable in marketplace. Marketing is an activitywhich is at the front of enterprise activities with up todate interfaces with customers and the general public.The non-adherence to moral practices in marketinghas paved way for two major actions such asconsumerism and environmentalism (Kotler et al.,2013). These organizations have started exertingpressures on marketers to recall and act in an ethical


manner. Interest in ethical concerns in marketing has,significantly heightened (Marinova, 2013).There is nooverstatement in citing that researches in marketingethics have emerge as a precursor of researches in ethicsin different areas.

Many companies integrate social responsibility andethics into their strategic marketing planning processthrough ethics compliance packages or integrityinitiatives that make legal compliance, socialresponsibility and ethics an organization-wide effort.Such programs establish, communicate, and monitora firm’s moral values and legal requirements throughcodes of behavior, ethics workplaces, educationprograms, and audits. The marketing plan ought toconsist of distinct elements of ethics and socialresponsibility as decided by upper-level marketingmanagers. Marketing strategy and implementationplans ought to be evolved that replicate a knowledgeof (i) the risks related to ethical and legal misconduct,(ii) the values of organizational members andstakeholders, and (iii) the ethical and social effects ofstrategic choices (Gilbertson, 1999).

Increasingly, companies are responding to the need tooffer organization guidelines and policies to assist theirmanagers’ cope with questions of marketing ethics. Ofcourse even the best guidelines cannot resolve all thedifficult ethical decisions that people and corporationsought to make. But there are few concepts thatmarketers can choose amongst(Hunt et al., 1990).Oneprinciple states that such troubles ought to bedetermined by the free market and legal system. Aecond, and more enlightened principle, placesobligation not on the system but in the hands ofindividual companies and managers. Every firm andmarketing manager have to work out a philosophy ofsocially responsible and ethical behavior. Under asustainable marketing concept, managers have to lookbeyond what is legal and allowable and developstandards primarily based on personal integrity, long-time consumer welfare and corporate conscience(Marinova, 2013).

The following are the benefits of integrating ethics inmarketing strategy:

• Improves marketing performance: Sociallyresponsible companies and their personnel canbetter respond to the stakeholder’s demands. A

company’s reputation for social responsibility isvery critical for consumers’ buying decisions.Social responsibility and ethical behavior both canreduce the costs of legal violations, civil litigation,and damaging publicity.

• Moral Marketing Compass: This is significantin economic downturns when unethical practicesought to become tempting.

• Win-win Marketing: The focus of this isprimarily on customer value which will increasecompany value.

• Keeps marketing legal: It reduces the risk ofcutting corners and turning a blind eye.

• Goodwill: Goodwill and strong reputationamong customers are the benefits whichorganizations cannot afford to overlook. Not onlywill customers believe that the organizations caresfor them, but will also accomplice the brand withpleasant feelings and reviews and spread the word.

• Improved quality of recruits and increasesretention: A good company attracts goodpersonnel, customers, suppliers and investors,who will be satisfied to assist the organization toreap its goals. Great marketing practices make newmarketers feel like their time at the job will makea difference and so will be less likely to changejobs.

Ethical Issues Faced in Marketing

Ethical marketing can guide advertising, research andinformation use, strategies for gaining a facet over thecompetition and organization polices (Hunt et al.,1990).Ethical issues in marketing get up from theconflicts and lack of settlement on specific issues.Parties involved in marketing transactions have a setof expectancies about how the business relationshipswill take shape and how diverse transactions need tobe performed. However, there can also be some issuesthat stand up from seeking to employ an ethicalmarketing strategy.

Individuals who have confined business experienceoften find themselves required to make unexpecteddecisions about product satisfactory, marketing,pricing, sales techniques, privacy, hiring practices, andpollution control (Gilbertson, 1999). Whilst personal


values are inconsistent with the configuration of valuesheld through the work group, ethical conflict mayadditionally take place. It is essential that a sharedvision of acceptable conduct develop from anorganizational perspective, to cultivate dependable andreliable relationships with all involved stakeholders. Ashared vision of ethics that is part of an organization’sway of life can be questioned, modified and analyzedas new issues develop. However, marketing ethicsshould relate to work environment decisions and mustno longer control or impact personal ethical issues(Hunt et al., 1990).

Each marketing concept has its own ethical issues suchas:

• Irresponsible Market Research: Impropermarket research can lead to stereotyping that willshape undesirable ideas and attitudes which willultimately affect marketing behavior.

• Selecting Specific Market Audience: The useof selective marketing practice is to select specificcustomers and weeding out the other consumerswhich are less important for company whichcauses social disparity and unrest.

• Unethical Advertising and Promotion: Makingfalse claims about the uses of the product itsimportance is an unethical and immoral way ofcreating profits. These are related with deceptiveadvertising when the consumer is led to believesomething which is not true.

• Pricing issue: It emerges when competitors aremaking similar products jointly to determine itsprice and manufacturers are forcing retailers tocharge very high prices.

• Delivery Channel practices: Marketing likeselling their products through telemarketingcompanies which are not only annoying, but arealso disruptive and not trustworthy. Unsolicitedapproaches now-a-days are almost synonymouswith direct marketing and has left the industrywith a tarnished reputation.

• Dealing with competitors: Many companiesadvertise low prices as a bait and then once theydraw the attention of the customers, they switchthem over to a higher priced product, because theadvertised good was unavailable or not of any

value to the customer. Many online surveys andwork from home opportunities use such kind ofunethical marketing technique.

Conclusion

The ethics of marketing and its bond with the clientsforms a basis to the victory of the company. Ethics arethe sincere values and standards that govern the actionsand verdicts of an entity or cluster. It is normal thatcustomers anticipate to be handled in a fair mannerand with reference. Reliability of service,responsiveness, trustworthiness, understanding andreception of value addition to products are few of theexpectations of the customers. They do not wantunrealistic guarantees, or deceptive services. There arefew ethical dilemmas for marketers in meeting theexpectations of customers. Ethical issues usually arisesdue to the dissimilarity between the individual andorganization’s values and norms. When the productsare not disclosed properly then they are dishonorablyadvertising their product.

Conscientious marketers face many more dilemmas.The best thing to do is often uncertain. Due to thefact that not all managers have excellent ethicalsensitivity, companies need to increase corporatemarketing ethics regulations- broad guidelines thateveryone within the organization must comply with.These regulations should cover distributor relations,advertising standards, pricing, customer support,product development and general ethical standards. Ifsomeone does not follow guidelines then the rightactions have to be taken against them for breachingthe codes of conduct. In sum, marketing ethics showsthat there should be an apparent knowledge of whatis right and what is wrong in business.

References

• Anatasia (2015, February 6).Social Responsibility & Ethicsin Marketing. Retrieved from https://www.cleverism.com/social-responsibility-ethics-marketing/.

• Hunt, S.D. (1990). Commentary on an empiricalinvestigation of a general theory of marketing ethics.Journal of the Academy of Marketing Science, 18, 173-177.

• Kotler, P. & Keller, K.L (2013). Marketing Management.13th edition. New Jersey: Pearson/Prentice-Hall.

• Labbai, Mohamed (2007).Social Responsibility, Ethics &Marketing. International Market ing Conference onMarketing & Society, Vol,8, 17-28.


• N. Marinova (2013). Marketing Ethics and SocialResponsibility. Trakia Journal of Sciences, 11(1), 535-538.

• Saviour L.S Nwachukwua Scott J Vitell Jr. bFaye WGilbertc James H Barnesd (1997). Ethics and SocialResponsibility in Marketing: An Examination of theEthical Evaluation of Advertising Strategies. Journal ofBusiness Research, 39(2), 107-118.

• Thomas F. Gilbertson PhD (1999). Ethics and SocialResponsibility in Marketing. Journal of Professional ServicesMarketing, 20(1), pp: 51-61.DOI: 10.1300/J090v20n01_ 05.

• Why social responsibility important marketing (2018,August 19).Retrieved from https://www.investopedia.com/ask/answers/042215/why-social-responsibility-important-marketing.asp.


Job Satisfaction of Employees in MultispecialtyHospitals in Delhi and NCRSuniti Chandiok*

Abstract

The purpose of this study is to examine how medical and nursing staffs of the hospital are affectedby specific motivational factors, such as satisfaction and motivation at the place of work. Thisstudy of job satisfaction is justified on the basis of its prospective value of understanding and ingenerating the optimistic outcomes from both the organisational and individual perspectives.The study was conducted on the hospital employees as they are one of the most importantstakeholders in hospitals to explore the factors, which influencing their job satisfaction. The datawas collected from 329 employees working in a hospital but all are from different sectors suchas medical consultants (n=52), non-medical (n=110), nursing (n-=128), paramedical(n=39).Previously developed and validated tools are four work-related motivators (organizationalrole, pay and compensation, relationship and co-worker, promotion and carrier Growth) wereused. There are four categories of health care professionals, medical consultant, non-medical,nursing, paramedical participated in job satisfaction and were compared across sociodemographic and occupational variables. The factor analysis was performed using principalcomponent analysis method for extracting factors to establish the features of the job satisfactionvariables measured. To explore the scope of variability in the employees’ job satisfaction explainedby the various factors and additionally identified the correlation of each resulted factor with thejob satisfaction scores.

Keywords: Satisfaction, Motivation, Ethics, Responsibility, Job.

Introduction

Satisfaction of employees used to describe that all theemployees are happy and fulfilling their desires andrequirement at work. Many measures means thatemployee satisfaction is a factor in employee motivation,employee goal achievement, and positive employeemorale at the workplace. As per Vroom, “Employeepleasure is a positive orientation of an individualtowards a job role which he is currently occupying”.

Many researchers accept as true that if you want tomaintain employee satisfaction the best way is to makeyour workers feel like part of a family. According tooffice events, such as get-together, parties, groupoutings, can help build better relationship amongpersonnel. A lot of companies also involve their

employees in team building activities are designed tostrengthen the employees association in work relatedsetting. Camping trips, team building activities andorganize various trips are versions of this type of team-building approach, with which several members havefound success. If possible, provide facilities to improvethe morale of the workers. Basic considerations likeimprove employee satisfaction, as employees will feelcared for by their employers. The base of every body’ssatisfaction is their respect and care for workers andthe work they perform in organization.

In all interaction and dealings with management,employees should be deal with certain courtesy. Anemployee should discuss all the troubles, which theyare facing in organisation top management and shouldbe provide solution and carefully evaluated. Even ifmanagement cannot meet all the targets of employees,then by showing workers that they are being heard andDr. Suniti Chandiok* Associate Professor, Banarsidas

Chandiwala Institute of Professional Studies, Dwarka


putting honest dedication into compromising willoften help to improve morale.

Satisfaction = f (what employee are expecting, whatthey are getting, time and social, economic, culture ofthe employee).

Satisfaction being a nonstop practice starts from thefirst day and gets reinforced with the time whichdepending on the significance of the different factorsconsidered being essential for the all the employee.Devotion towards the organization starts to build upwhen the employee continues to get the positivereinforcements on various important aspects for theduration of the employment. The core values ofemployee satisfaction are the values that have enabledemployees to build a leading company in the industrywhich will also fuel employee’s growth in the comingyears and these are the standards that will driveemployee’s career.

• Professionalism– Demonstrating professionalmethods, character and standards.

• Enthusiasm– Showing excitement, optimism andpassion for your work.

• Resource fulnes s– Acting effectively andimaginatively to produce great results from scarceresources.

• Self- directedness– Working independently andautonomously to achieve the goals set bymanagement.

• Ethics –The accepted norms of what is right andwhat is wrong in our life.

• Unselfishness – Doing work for others with ourexpectation form other and giving them yourvaluable time and clients and co-workers. Showing selfless effortto the team toachieve a common goal and target fororganization.

• Stra teg ic -mindednes s– Suggesting andimplementing long-term improvements springingfrom a sequence of short-term tasks and goal.

Determinants of Employee Satisfaction

Employee satisfaction is a multi-variable andinexpressible concept. There are many of factors that

manipulate employee satisfaction. These factors can beclassified into two categories. 1.Organizationalvariables and 2.Personal variables

A) Organizational variables: The organizationaldeterminants of employee satisfaction play a veryimportant role.

1) Overall individual satisfaction: Employeesbe should satisfy with the organization.

2) Compensation and benefits: Compensa-tion can be described as the amount ofreward that a worker expects from the job.Employees should be provided withcompetitive salary packages.

3) Nature of work: Employee satisfaction ishighly unfair by the nature of work.Employees are satisfied with job thatinvolves intelligence, skills and scope forgreater freedom in work.

4) Work environment and conditions :Employees are extremely motivated withgood functioning conditions as they providea feeling of safety, comfort and motivation.Cleanliness is of utmost importance as thereare a gigantic number of workers workingat a job place.

5) Job content: Factors like acknowledgment,accountability, improvement, success etc.can be referred to as job content.

6) Job satisfaction : Job satisfaction isimpacted by job design and condition ofwork. Jobs that are rich in positivebehavioral fundamentals such asindependence, task implication andfeedback also contribute to employee’ssatis faction. Each element of theenvironmental system can attract or detractfrom job satisfaction.

7) Organizational level: The jobs that are athigher levels are viewed as influential,esteemed and opening for self-control.

8) Opportunities for promotion: Promotioncan be shared as a significant success in thelife. It promises more pay, accountability,power, self-determination and position.


9) Work group: There is a natural desire ofhuman beings to interact with others andso existence of groups in organizations is acommon observable fact. The work groupsalso influences the job satisfaction ofworkers. The satisfaction of an individualis dependent on the connection with thegroup members, group cohesiveness and hisown need for affiliation.

10) Leadership styles: The satisfaction level onthe job can be determined by the leadershipstyles. Democratic leaders promotefriendship, respect and affection inrelationships among the staff.

11) Communicat ion method s : Whenadministrative policies and all importantannouncements are communicated to theemployees, it enhances their confidence.The methods chosen for communicationalso play a vital role in organization.

12) Safety measures: An employer must makesure that he/she provides a safe environmentto his/her employee. The security measuresoutside office include security guards andparking facility. While inside the office,there must be introduced safe environmentfor male and female employees to work.There must be no discrimination orharassment practiced and the employeeshould be given equal opportunity to growas an individual despite being male orfemale.

B) Personal variables: The personal determinantshelp all the employees to maintain the motivationof employees. Employee satisfaction can be relatedto psychological factors and so number ofpersonal factors determines the employeesatisfaction.

1) Persona l ity : The personality of anindividual can be determined by observinghis individual psychological conditions. Thefactors that determine the satisfaction ofindividuals and his psychological conditionsis perception, attitudes and learning.

2) Age: Age is one of the important factor tomotive all the employees.

3) Education: Education plays a significantfactor of employee satisfaction as it providesan opportunity for developing one’spersonality, attitude and intellectual level ofan individual.

4) Gender dif ferences : The gender andcontest of the employees plays importantdeterminants of Employee satisfaction.Women, the fairer sex are more likely tobe satisfied than their male counterpart.

The worker satisfaction can also be determined byother factors like learning, skill autonomy, jobcharacteristics, unbiased attitude of management, socialstatus etc. It is important for all the managers to studyall these factors in assessing the happiness of the staffand escalating their level of employee satisfaction.

Literature Review

According to Partridge in 1981,in Britain, the worksatisfaction level of women was found satisfied ascompared with black men, as they normally have lowexpectations from their jobs. Beumont in 1982highlights the work satisfaction level of generalhousehold in US &Britain. In the study he found thatin U.S there is an optimistic relationship between jobsatisfaction and age whereas, in Britain it wasconsiderably low.

Savery (1987) determined the effect of motivators onjob satisfaction. He states intrinsic motivators’ helpsin achieving job pleasure. The study says stress beingone of the important point to disappointment in lifeand job therefore, it has to be taken care of properlyto reduce the level of dissatisfaction. The boss helps inescalating the satisfaction level by offering intrinsicmotivators like challenging in work and careerdevelopment of subordinates and he can help all ofthem who need their help.

Savery (1989) define satisfaction of nurses in Perth,Western Australia. The job satisfaction level of thenurses was mainly due to interesting and challengingwork, which was followed by a feeling of successwherein, he even talk about salary was ranked as a verylow satisfier. The job satisfaction level increased as theperson grew old where in the variables like gender, timein hospital, position, power held were controlled.Organizations should always focus on satisfying the


three basic needs (Individual motivators, Employeerelationships, personal relationships) of an employeewhich will in return help the employees in achievingjob satisfaction.

Ingram (1992) states that job satisfaction is related towork, co-workers, promotion, pay, supervision relatesto customer orientation. In service industry front linepeople are the one who interact with the customerson a regular basis and influence the customerperception by their behaviors as well as the appearanceof the product /service knowledge. Promotion is veryimportant determined in work satisfaction. It is the dutyof the manger to monitor and improve the employeessatisfaction level related to supervision quality, workingconditions, comparative compensations and variousbenefits and company policies so that it helps in achievingthe desiredsatisfaction in personal and profession life.Melvin (1993) stated that the organizational design ofan organization plays a very important role in the levelof job satisfaction at the same time it also plays animportant role in employee’s high job involvement inorganization. A good quality environmental design ofan institute helps in resolving the misunderstandingof employees.

Tietjen & Myers (1998) define the theories of jobsatisfaction mentioned by Herzberg and Lockers. Jobsatisfaction is always maximum when an employee issatisfied with the work which is assigned to him. Awell-furnished office and the environment of the workplace doesn’t help much whereas the base duty allottedin the job and the intrinsic related feelings of anindividual is a optimistic approach in him about thejob and workplace. MacDermid (1999) said there aresix variables of workaholic patterns i.e. workaholics,enthusiastic workaholics, work enthusiastic, unengagedworkers, relaxed workers and disenchanted workers.

The job satisfaction level and career satisfaction levelwas much more in enthusiastic workaholics, Workenthusiastic, relaxed workers than workaholics,unengaged workers and disenchanted workers becauseof the future career prospects, working involvementand work enjoyment. Oshagbemi (1999) highlights theacademics and their managers’ job satisfaction levels:A comparative Study. Managers and academics are notable to achieve job happiness because they are not

fulfilled with the present pay, research andadministration and management.

Zaki (2003) explains the job pleasure and performanceof Lebanese banking of non-managerial staff. Theresearcher found an important relationship betweenjob satisfaction and gender in relation to pay andsupervision.

Only satisfied people in the organization perform andit is the duty and responsibility of the organization totake proper care of their employees. Warn (2003)highlighted on work place dimensions leading to stress& ultimately reducing job satisfaction in theirprofessional life. Stress is increasing in work place dueto lack of power, role conflict and role ambiguityleading to improve job frustration. A positive workingatmosphere like positive learning environment or noannoyance surroundings or not being scared in workplace helps in reducing stress and achieving worksatisfaction level.

Saari& Judge (2004) discussed on employee positiveattitudes leading to better satisfaction in work place.The employee attitude is related to the job, when aperson has a liking towards to the job the satisfactionpoint increases there by increasing the instituteperformance as whole. Austin (2007) mentions self-fulfillment, independence and job surroundings as thekey reasons to the managers’ job satisfaction.Employers should focus on these three factors i.e. thedemographic variables (age, gender, number of yearsin the organization, public or private sector, numberof employees supervised) independence in work andthe work environment to make the system flawlesslyleading to job satisfaction. Omey (2007) discussed therelationship between educational level and jobsatisfaction. Higher educated workers are alwayssatisfied in comparison with the lower educatedworkers, the fact being higher educated people obtaina job of better quality.

Silverthrone (2008) studied the contribution ofpersonality variable LOC on job satisfaction andrelated outcomes such as performance and job stress.Findings reveal that internal locus of control leads tolower level of job stress and higher level of jobsatisfaction. External LOC doesn’t reduce the job stresswhereas internal locus of control leads to performanceand satisfaction by reducing the job stress.


Artz (2010) studies the link between fringe benefitsand job satisfaction. Fringe benefits always don’t leadto job satisfaction. It is always acceptable to an extentwhere in the employee has a feeling that he is able tosatisfy his needs. Many a times it is found that it doesn’tmatch the requirement of the employee leading todissatisfaction. Therefore, organizations have to reviewtheir system in a better way which will provide fringebenefits as required and provide employees everyopportunity to avail them, ultimately leading to jobsatisfaction. Antvor (2010) discussed the influence ofnational culture on the national job satisfaction leveland its effect on other evaluations of job related aspects.It was stated that although cultural influence was therein national job satisfaction, all job aspects of jobsatisfaction were not cultural context specific.Management has to be careful while comparing theresults from a cross-national job happiness study.

Al-Zoubi (2012) studied the relationship between jobsatisfaction of various private and public Jordanianorganizations and salary. He found that salary is not aprime factor that influences job satisfaction. Thoughfinancial effect is fast but has very sort effect. Jobsatisfaction is always a long-term requirement by anorganization. Therefore, organizations should think ofinnovative ways that will enhance all job aspectsincluding salaries as well as psycho-social variables thatenhances the work life quality.

Witte (2012) highlighted on the group differencesaspect in job satisfaction. The study was done on thebanking sector in Belgium.

A model was created for testing the hypothesis. Themodel was “Job Demand Control support” and theanalysis found says that job demand have the highesteffect in explaining satisfaction in relation to theworking conditions and less in relation to explainingsatisfaction with job content. Singh & Jain (2013)stated that employees’ positive attitude reflects theethics of the organization. Work environment is thekey factor in job satisfaction. Good work surroundingsand good working situation leads to job satisfactionat the same time helps in increasing employee workperformance, prosperity, customer satisfaction as wellas retention. Aristovnik (2014) discussed influence oforganizational and environmental factors on employee

job satisfaction. The police employees rated salary andsecurity as the least motivator and support from themanagement as high.

Statement of the Problem: In Delhi and NCR, lotsof upcoming super specialty hospitals are coming andestablishing their business. They are selecting andrecruiting lots of talented employees. Do thesehospitals managements are giving right salary,environment, culture etc. to their employees; reallythese employees are contented and happy in their jobin hospital or not is a big question. This studyexamined the satisfaction level of employees who areworking in these hospitals. It examined the satisfactionlevel in 4 main factors, i.e. organizational role, relationwith coworkers, pay and compensation and promotionand career growth.

Objective of the Study & Scope of the Study

The objective is to study and measure the happinesslevel of the employees and employers perceptiontowards organization. To understand the attitude ofthe employees towards their work and identify thefactors that motivates the staff. To know the bestpractices and methods to enhance loyalty and retentionof employees in the organisation. The scope of thestudy is to identify employees’ satisfaction level andtheir perceptions’ about the organisational motivation& factors, which affect employees working in one ofthe best super specialty hospital of Delhi


In this study, the data was collected from one of thesuper specialty hospital in Delhi having 1200+ undervarious departments of medical, non-medical, nursingand paramedical, which are further sub-categorizedinto various departments. The data collectiontechnique is both primary and secondary. The primarydata is collected through questionnaires & surveys,interview. Responses are analyzed with quantitativemethods by assigning numerical values to Likert-scale.The pre-test and post-test can be compared andanalyzed in study. A research study is carried out on asample from a population of total of 329 employeeswere surveyed. The 329 employees are from differentdepartment 52 from medical, 110 from paramedical,128 from nursing and 39 from non-medical. A


structured questionnaire has been prepared to get therelevant information from the respondents.

The questionnaire consists of a variety of questionspresented to the respondents for their responses whichare both open ended and multiple choice questions.The Questionnaire used in this study is composed of3 parts. First part includes the demographic questionssuch as the name, age, gender and department ofrespondent. The second part deals with job contentand satisfaction level. Job content and satisfaction ofemployees are measured by structured questionnaire.The third part includes job satisfaction questions andsuggestions. Answer of questionnaires is going to reflectthe ideas and judgment of the respondents. Thequestionnaire has total of 20 questions with 41 subquestions, which were made using Likert scalingtechniques (5. Strongly agree 4. Agree 3. Neutral 2.Disagree 1. Strongly disagree).All the questions wererelated to these factors and also a suggestion box wasalso incorporated to comprehend the needs of theemployees other than these four factors to motivatethem. Analytical representation are represented with

the help of SPSS,MS Excel, Cronbach’s Alpha,frequency tables, descriptive statistics, and factoranalysis.

V Data analysis:

Reliability Test: In order to extract the dimensionsand to test the validity and reliability of the analysis,the exploratory factor analysis and Cronbach’s alphafor internal consistency were employed to determinethe satisfaction of the employees.

1. Reliability AnalysisTable 1: Reliability test

Cronbach’s Alpha N of Items

.892 41

Source: Estimated from primary data, Sept-Jan 2018

Interpretation: According to the table 1, Cronbach’salpha which is the most common measure of internalconsistency. It is the most commonly used to measurethe scale is reliable. The alpha coefficient of the itemsis 0.892, suggesting the items have relatively goodinternal consistency.

2. Demographic Profile of Respondent (Frequency Test):Table 2: Demographic Profile of Respondent

Sl. No Items Response No of Respondent %

1 Gender of Employees Male 156 47.4

Female 173 52.6

2 Age of Employees Valid less than 25 105 31.9

26-35 160 48.6

36-45 56 17.0

46-55 3 .9

50 and above 5 1.5

3 Level of Employees Medical 52 15.8

Non-Medical 110 33.4

Nursing 128 38.9

Paramedical 39 11.9

Source: Estimated from primary data, Sept 17-Jan 2018

Interpretation: According to table no-2 the simplefrequency of employees has been calculated based on3 descriptions i.e. level, Age and Gender so males are

47% and females are 52%,According to age 48.6%are of 26-35 age and rest are less than 25. Level ofemployees in nursing and non medical are more inhospital industry.


3. Descriptive Statistics

a. Complete StatisticsTable 3: Complete Descriptive Statistics

Items N Mean Std. Deviation 1 In the last six months, someone at work has talked to me about my

progress 329 0.057 1.029

2 My organization is dedicated and serious about my professional development

329 0.049 0.897

3 Good work is appreciated and recognized in my organization 329 0.056 1.01 4 My organization has good reward and recognition programme 329 0.049 0.881 5 I feel free to share my view with my seniors and HOD 329 0.052 0.952 6 The leadership clearly shares organizational goals and challenges with

employees 329 0.043 0.789

7 I am aware of my Job Description 329 0.035 0.64 8 I enjoy my work and challenges it provides 329 0.042 0.769 9 My job encourages me to constantly improve my knowledge and skills 329 0.039 0.699 10 I get the training I need to do my job well 329 0.043 0.781 11 I have the opportunity to learn new skills which would help me to

advance in my career 329 0.039 0.701

12 I get proper leaves 329 0.065 1.172 13 Management arranges several activities along with the designated job 329 0.066 1.2 14 I feel happy and proud to work here 329 0.043 0.779 15 I am aware of the hospital’s mission and vision and my day to day

activities are in tune with organization’s vision and mission 329 0.034 0.621

16 Trainings are being provided on organization’s vision and mission 329 0.036 0.653 17 Organization has a reputation of being fair and just 329 0.041 0.749 18 I receive proper guidance and feedback regarding my work 329 0.05 0.901 19 The management treats its employees with respect and dignity 329 0.053 0.96 20 My supervisor or someone at work seems to care about me as a person 329 0.05 0.914 21 I was told clearly about my compensation and benefits when I joined

this organization 329 0.047 0.852

22 Increments and appraisals are fair and transparent 329 0.059 1.07 23 I am encouraged to participate in organizational decision making

process 329 0.05 0.912

24 My supervisor encourages my suggestions and correct decisions are taken

329 0.049 0.883

25 I am satisfied with overall welfare facilities for employees in the organization

329 0.055 1

26 Employee grievance procedure of the organization is adequate and fair 329 0.043 0.778

27 I can count on my team members for help and guidance 329 0.04 0.72 28 Team work is recognized and rewarded in our organization 329 0.047 0.855 30 I would recommend it as a good place to work to my friends 329 0.048 0.88 31 I get a feeling of personal satisfaction from my work 329 0.052 0.938 32 I am satisfied with present working environment 329 0.05 0.905 33 I have a safe and secure work environment 329 0.044 0.803

34 I get clarification and feedback without any delay from my superiors 329 0.047 0.851

35 Management shares important information with us in time 329 0.048 0.878

36 I get all the support from the organization whenever I need it 329 0.065 1.173 37 The management cares about its employees and the patients 329 0.061 1.112

38 I get adequate appraisals on time 329 0.068 1.238 39 I get adequate salary according to the work I do 329 0.063 1.137



Interpretation: This table shows the descriptivestatistics based on the employee satisfaction inhealthcare questionnaire giving higher mean valuesof the task and relationship based questions i.e.4.43,4.26, 4.24 of “I am aware of the hospital’s missionand vision and my day to day activities are in tune

with organization’s vision and mission”, “I am awareof my job description”, “Trainings are beingprovided on organization’s vision and mission”,respectively. And provides higher standard deviationstatistics i.e. 1.238 on “I get adequate appraisals ontime”.

a. Statistics of ESAT construct/ attribute: Organizational RoleTable 4: Descriptive Statistics of Organizational Role

Items N Mean

1 Management cares about its employees and patients. 329 3.2

2 I get all the support from the organization whenever I need it. 329 3.4

3 I am satisfied with overall welfare facilities for employees in the organization. 329 3.78

4 The management treats its employees with respect and dignity. 329 3.85

5 Employee grievance procedure of the organization is adequate and fair. 329 3.88

6 I am encourages to participate in organizational decision making process. 329 3.9

7 I get a feeling of personal satisfaction from my work. 329 3.95

8 I am satisfied with present working environment. 329 3.97

9 I would recommend it as a good place to work to my friends. 329 3.98

10 My supervisor or someone at work seems to care about me as a person 329 4.02

11 My supervisor encourages my suggestions and correct decisions are taken. 329 4.03

12 I receive proper guidance and feedback regarding my work. 329 4.04

13 Organization has a reputation of being fair and just. 329 4.08

14 I have a safe and secure work environment. 329 4.11

15 Trainings are being provided on organization’s vision and mission 329 4.24

16 I am aware of the hospital’s mission and vision and my day to day activities are in tune with the organization’s vision and mission.

329 4.44

Source: Estimated from primary data, Sept-Jan 2018

Interpretation: This table describes the mean statisticsof organization’s role attribute. Here, the mean rangesfrom 3.20 - 4.44. The average mean is 3.929.

b. Statistics of ESAT construct/ attribute: Pay andCompensation

Table 5: Descriptive Statistics of Pay and Compensation

Items N Mean

1 I was told clearly about my compensation and benefits when I joined this organization.

329 4.08

2 Increments and appraisals are fair and transparent.

329 3.54

3 I get adequate appraisals on time. 329 3.43

4 I get adequate salary according to the work I do.

329 3.36

Source: Estimated from primary data, Sept 2017

Interpretation: This table describes the mean statisticsof pay and compensation attribute. Here, the mean

ranges from 3.36 - 4.08. The average mean is 3.6025.

c. Statistic s of ESAT construct/ attr ibute:Relation with Co-WorkersTable 6: Descriptive Statistics of Relation with

Co-Workers

Items N Mean

1 I can count on my team members for help and guidance

329 4.11

2 Team work is recognized and rewarded in our organization.

329 3.99

3 I get clarification and feedback without any delay from my superiors.

329 4.07

4 Management shares important information with us in time.

329 3.95

Source: Estimated from primary data, Sept 2017

Interpretation: This table describes the mean statisticsof relation with co-workers attribute. Here, the meanranges from 3.95 - 4.11. The average mean is 4.03.


d. Statistics of ESAT construct/ attribute: Promotion and Career GrowthTable 7: Descriptive Statistics of Promotion and Career Growth

Items N Mean

1 In the last six months, someone at work has talked to me about my progress. 329 3.74

2 My organization is dedicated and serious about my professional development. 329 3.84

3 Good work is appreciated and recognized in my organization. 329 3.86

4 My organization has a good reward and recognition program. 329 3.88

5 I feel free to share my view with my seniors and HOD 329 3.95

6 The leadership clearly shares organizational goals and challenges with employees. 329 3.99

7 I am aware of my job description. 329 4.26

8 I enjoy my work and challenges it provides. 329 4.09

9 My job encourages me to constantly improve my knowledge and skills. 329 4.19

10 I get training I need to do my job well 329 4.08

11 I have the opportunity to learn new skills which would help me to advance in my career. 329 4.12

12 I get proper leaves. 329 3.33

13 Management arranges several activities along with the designated job. 329 3.36

14 I feel happy and proud to work here. 329 4.14


Interpretation: According to table no-7 it describes the mean statistics of Promotion and Career Growthattribute. Here, the mean ranges from 3.33 - 4.26. The average mean is 3.916.

4. Factor Analysis

Table 8: Principal Component Analysis

Sl. N Factor F1-Pay and Compensation

F2 - promotion and Career

Growth

F3-Relation with Co-Workers

F4-Organisational Role

1 I am aware of the hospital’s mission and vision and my day to day activities Are in tune with organization’s vision and mission

.234

2 Trainings are being provided on organization’s vision and mission

.332

3 Organization has a reputation of being fair and just .354

4 I receive proper guidance and feedback regarding my work

.338

5 The management treats its employees with respect and dignity

.239

6 My supervisor or someone at work seems to care about me as a person

.338

7 I am encouraged to participate in organizational decision making process

.374

8 My supervisor encourages my suggestions and correct decisions are taken

.299

9 I am satisfied with overall welfare facilities for employees in the organization

.237

10 Employee grievance procedure of the organization is adequate and fair

.332

11 I would recommend it as a good place to work to my friends

.359

(Contd...)



Interpretation: It is seen in this table variables1,2,3,4,5,6,13,14,15,16,19,20,23,24,32,33 loadedonto component 1.Variables 7,8,34,35 loaded ontocomponent 2. Variables 17,18,30,31 loaded ontocomponent 3 while variables 9,10,11,12,21,22, 25,26, 27,28,29,36,37,38 loaded onto component 4.The four components were named pay andcompensation, promotion and career growth, relationwith co-workers and organizational role. Also it showsthe maximum of the mean in pay and compensationcomponent followed by promotion and career growth

then relation with co-Workers and in the lastorganizational role.

Findings and Conclusion

Employee job satisfaction is an important feature oforganizations. Hospitals need to ensure that employeesmorale are far above the ground in the worksurroundings they are entrusted. High job pleasureamong the employees is a requirement for increasingtheir productivity and quality of customer service. Thepositive performance of employees in the organizationis an outcome of their suitable job experience. It

12 I get a feeling of personal satisfaction from my work

.289

13 I am satisfied with present working environment .254 14 I have a safe and secure work environment .382 15 I get all the support from the organization

whenever I need it .334

16 The management cares about its employees and the patients

.349

17 I was told clearly about my compensation and benefits when I joined this Organization

.836

18 Increments and appraisals are fair and transparent .883

19 I get adequate appraisals on time .789

20 I get adequate salary according to the work I do .942 21 I can count on my team members for help and

guidance .537

22 Team work is recognized and rewarded in our organization

.554

23 I get clarification and feedback without any delay from my superiors

.580

24 Management shares important information with us in time

.578

25 In the last six months, someone at work has talked to me about my progress

.684

26 My organization is dedicated and serious about my professional development

.662

27 Good work is appreciated and recognized in my organization

.714

28 My organization has a good reward and recognition program

.712

29 I feel free to share my view with my seniors and HOD

.645

30 The leadership clearly shares organizational goals and challenges with Employees

.670

31 I am aware of my Job Description .620 32 I enjoy my work and challenges it provides .639 33 My job encourages me to constantly improve my

knowledge and skills .789

34 I get the training I need to do my job well .623 35 I have the opportunity to learn new skills which

would help me to advance in my career .628

36 I get proper leaves .760 37 Management arranges several activities along with .639


represent that there is a significant association betweenjob satisfaction of employees and relationshipbehaviour factors, pay and compensation factors, andtraining and career growth factors in the perspectiveof employees of private hospitals. The hospitaladministrators should take these variables intoconsideration whenever thinking of employees jobsatisfaction measures. The correlation results indicatethat the most significant component in the employeework satisfaction is the pay and compensation factor.The study exploring the factors influencing the jobsatisfaction of staff analyses the important aspects ofemployees’ motivating factor.

The survey has provided the intensity of job satisfactionof employees in healthcare through certain factors i.e.organizational role, pay and compensation, relationwith co-workers, and promotion and career growth etc.The results exposed that the satisfaction of workforcepossessed more by pay and compensation,organizational role in comparison to promotion andcareer growth and Relation with co- workers. Also, theresult discovered that relation with co-workers isimportant for employee satisfaction. It also revealedthat employee satisfaction helps in becoming moremature and can acquire certain competencies that leadto outstanding performance at workplace. The idea isthat employees value fair handling which causes themto be motivated to keep the fairness within theassociations of their co-workers and the organizationthat determinants employee satisfaction includesoverall individual satisfaction, compensation andbenefits as this is the most important variable foremployee satisfaction.

References

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Strategies v/s Consumer Perception of BrandZara - IndiaSaloni Saraswat*

Abstract

The strategies implemented by Zara provide insightful information to all the brands in attainingcompetitive advantage and sustaining it. Zara is a fashion retail brand that offer products forboth men and women and is popularity known as one the best division of Inditex. Apart fromcompetitors such as H&M (Hennes and Mauritz) Zara is constantly able to retain its first positionacross the globe. According to a recent study (The Economic Times Web site, 2017), the netprofit of the company was $2.7 billion by December 2017 and is further expected to rise by 6%due to stronger market presence, sales and international expansions. Additionally, theimplementation of an efficient supply chain and development of an effective positioning strategymake the brand Zara the most preferred brand amongst others. Zara is an established brand,but its reach is restricted to only some parts of the country in India particularly the metropolitanareas. Most of the respondents had never heard of Zara before, which means that it is requiredby the brand to strategize itself in a manner that it is accepted and recognised by all the targetconsumers.

Keywords: Zara, Strategies, Consumer Perception, Supply Chain.

Introduction

Zara is a flagship brand of Inditex group and comprisesof around 2,200 stores in 96 countries (Forbes Website, 2018). Zara aims at developing shorter responsetime in order to effectively meet the demand ofconstantly fluctuating fashion trends. The responsetime of Zara is 30 days in which it recognizes latesttrends, design apparels and supply them to its stores.Moreover, the big fashion retailers and even smallretailers acquire time period of around 5-6 months inorder to provide its finished products to the endconsumers (Lopez & Fan, 2009). As a result, Zaraholds a competitive edge and unique identity insupplying its products to its consumers in such a smallspan of time. The fashion trends of Zara are timelyupdated by closely monitoring the fashion shows andtranslating the ideas in updated trendy offerings.

Thorough sales analysis of customer demands is doneby the brand in order to provide maximum satisfaction

to its customers. The main advantage of themanufacturing process of Zara is that it eliminateswasteful produce that means the products are notproduced in bulk that might carry the risk of failureand Zara aims in eradicating those failures.Additionally, Zara prefer developing fewer products asit involves less risk and helps in creating more demandby generating artificial scarcity. This process aims atmaking the fashionable offerings more desirablemaking them more profitable leading Zara to earnextra profit. (Roy, 2010)

In 2017, (The Economic Times Web site, 2017) Zarastarted its online services by providing customers accessto its latest trendy products and thus making a firstmover advantage in countries like India. The biggestcompetitor of Zara, H&M (Hennes and Mauritz) hasstarted the same online services in March 2018,following the footsteps of Zara due to major dip inthe market share and revenue of the brand (Hanbury,2018). The major advantage that Zara has over itscompetitors is its low expenditure on advertising(Payton, 2017). The annual advertising expenditure

Saloni Saraswat* Assistant Professor, (SBM, IFTMUniversity), Moradabad


is estimated to be 0.3% of its total revenue, which isextremely low as compared to other retailers that spendalmost 4% of the annual revenue. This strategy enablesZara to cut down its cost and concentrate majorly uponinternational expansions. Zara aims at segmenting alarger customer base in order to expand its reach andits strategies aims at making the brand accessible,affordable and famous.

Literature Review

ZARA has been one of the most successful fashionbrands since last few years. It has been constantlyexpanding its business across the world. In the last fewyears, it opened its stores in India as well. The successof ZARA lies upon a lot of factors. One of them is itsagile supply chain (Zhelyazkov). Since, the globalmarket place demand a much more agile response fromthe organizations and partners in supply chain.Decision about raw materials must be taken in advanceand it is the most risky part of agile supply chain. Inthe fashion world, where companies are competing ontime, the need of new abilities is rising. Agility is theone solution which responds rapidly to unpredictablechanges in demand.

Another factor is the way it involves its customers andprovides them a shopping experience. The“involvement” factor influences the customer’sshopping behaviour, his/her purchase & post purchaseattitude and behaviour towards the brand (Yan & Joey,2011). ZARA’s pricing strategy; design strategy & itsquick responsiveness also play a major role. Itinfluences consumer’s buying behaviour (Yan & Joey,2011).

ZARA’s expansion through internationalization hasalso played a key role in its success across the world.ZARA has very carefully carried out itsinternationalization process by selecting the rightmarket and adopting the right entry strategy for eachinternational market. It also focused on how to marketits brand in different markets of the world (Lopez &Fan, 2009).

Statement of the Problem

ZARA is unique in the way that it does not spendmoney on marketing and instead concentrates onopening new stores. The paper aims at studying how

ZARA is able to sell products at low-price. which arehigh end fashion products. Moreover, how ZARA is asmashing success in India and instead of having hugecompetitors the brand is considered to be mostpreferred among the consumers. According to a recentreport (Malviya, 2017), it has been established thatZara being one of the world’s leading apparel brandentered the country in 2010 and initially doubled salesevery two years. With average sales of Rs.50 crores,Zara’s performance is presently enhanced than India’slargest jewellery chain, Tanishq (Malviya, 2017).

Objective of the Study

• Understanding the marketing strategiesimplemented by Zara.

• Measuring Brand awareness of Zara.

• How do consumers perceive Zara as a brand?

This part of the paper will focus on studying thevarious strategies implemented by Zara in India andthe way they want to position themselves amongst theconsumers in India.

Segmentation, Targeting, Positioning

Segmentation Strategy

The segmentation strategy adopted by Zara in Indiais centred upon the demographics of its customers likegender, age and psychographics (Keller, 2012). Thecustomers are further segmented on the basis of theirfashion sense and style for instance in India Zara offerscontemporary, trendy, classic, grunge and Latino styleof clothing (Lopez & Fan, 2009). The segmentationstrategy of Zara involves a blend of ethnicity in itsproducts with a combination of varied settings andtastes for the Indian customers along with introducingnew products every two weeks. (Marketing tree blog,2017) .

Targeting Strategy

The target audience of Zara comprises of the customersthat are interested in purchasing trendy apparels andhigh end fashion products but are unable to makepurchase from the already existing high end boutiquesand couture. With the increasing disposable incomein the country there will be fashionable and qualityapparels (Case Study: Zara’s Entry into Indian Retail


Fashion Market). Target audience of Zara in thecountry is woman above 17 years to early 40’sparticularly residing in mid income category segmentand who are conscious towards fashion and prefertrendy clothing. (Marketing tree blog, 2017).

Positioning Strategy

Positioning strategy of brand Zara aims atdemocratizing fashion. The main objective of thecompany in India is to provide its customers with highfashion and trendy products at lower prices toaccommodate their needs. The stores and outlets ofZara are particularly located at high end locations tomake its products accessible to large consumer basethat prefer trendy, highly fashionable and qualityproducts at reasonable prices. (Dutta, 2003),(Marketing tree blog, 2017), (Case Study: Zara’s Entryinto Indian Retail Fashion Market).

Marketing Mix

Product:

The ability to respond rapidly to ever changingdemands of customer is the major strength of the brandZara. The manufacturing process of the company isnot outsourced as a result it provide complete authorityand control of the products produced by it (Dutta,2003). The unique selling proposition of this brand isobserved to be developing and imitating latest fashiontrends (Zhelyazkov), (Yan & Joey, 2011). Additionally,the latest trending apparels and offerings are madeavailable at all the stores at a maximum time period oftwo-four weeks. The unsold product from the store ispulled out of the display shelf on an immediate basisand new products are being arranged accordingly. Forinstance, the stores of Zara comprises of severalproducts that are westernized and this is the majordrawback of this brand in India as it is not working toreach the local consumers by innovating anddeveloping designs that collaborates local traditionswith modernism. Secondly, lack of seasonal variationsin the product range is the major setback observed inthe growth of this brand in India.

Price:

The main aim of Zara is to position its product ataffordable price to its consumers. Zara believes that

consumers perceive the prices of its products to bequite reasonable as compared to other existingcompetitors of the brand that fixes premium prices oftheir offerings (Yan & Joey, 2011), (Dutta, 2003).However, after analysing the pricing structure of brandZara it has been observed that Zara adopts a premiumpricing strategy in India. The pricing policy in Indiaby brand Zara is developed by elevating thedevelopment and training costs of the organization.

Promotion:

The marketing strategy of Zara is observed to be themost exclusive and unique one. “Zero investment inmarketing” helps the brand in utilizing advertisementmoney in opening of new stores across the world. Themain promotional strategy of the brand is toemphasize its efforts in searching for differentialpoints and gaining competitive edge in the overallmarket (Yan & Joey, 2011), (Case Study: Zara’s Entryinto Indian Retail Fashion Market). The uniquestrategy of Zara is to ensure the consumers that itsproducts are distinctive, affordable and unique. Thecompany lays specific efforts on word of mouthadvertising as Zara believes it poses greater impacton the minds of the consumers. In 2017, Zara hasopened its online platform for Indian consumers inorder to develop accessibility, awareness, enhancedcustomer satisfaction and first mover advantages ascompared to its biggest competitor H&M. (TheEconomic Times, 2017).

The target population of Zara lies between the agegroup of 17 – 40 years that live in the city. As theconsumer of this age group are considered to be onethat are more averse towards fashion. Zara payattention to every detail of their showrooms such asthe windows is put up in a very elegant manner andthe attendants of the shop are well groomed. Onlineshopping, bar coding and computer aided purchasesare certain measures specifically designed to enhancesales and enable Zara to become global brand. (CaseStudy: Zara’s Entry into Indian Retail Fashion Market)

Place:

Zara is a vertically integrated retailer that designs,produces and distribute its product all by itself (Yan& Joey, 2011; Dutta, 2003). Due to its global presence


Zara is able to achieve this approach in an extremelysuccessful manner and is gradually expanding its basein India. It has been observed that approximately 90%of the stores are owned by Zara and rest are jointventures and franchises, this implies that the experienceof customers remain same in all the stores whileentering Zara stores be it at any place (Ferdows, Lewis,& Machuca, 2003), (Case Study: Zara’s Entry intoIndian Retail Fashion Market) . The stores of Zara areestablished in urban area specifically in a mall that helpsthe brand to gain enhanced popularity and brandimage. In order to provide accessibility and awarenessof its brand Zara started online store for its customersin 2017, in order to make the product reach largercustomer base.

Entry Strategy of Zara in India

The international expansion is primarily focusing uponthree different entry modes joint ventures, subsidiariesand franchising. To enter the Indian market in India,Inditex used the strategy of pursuing a joint venturewith Trent Limited, a Tata Group company, which isone of the highly recognized clothing line distributorsin the country. Amongst this Inditex holds 51% shareof this collaboration and Tata’s subsidiary holds 49%share. In the present scenario, Zara has almost 20 storesall over India and has observed a high double digitgrowth in December 2017 in same-store in the market,where most retailers struggled for a single digit growthdue to slowdown in consumer spending. (Entry ModeOf Zara Into The Indian And Chinese Market, 2016),(Chadha, 2014), (Zara to enter India’s fashion market),(Case Study: Zara’s Entry into Indian Retail FashionMarket), (Entry Mode Of Zara Into The Indian AndChinese Market)

The major restraints that Zara had witnessed whileentering into the Indian market were demography andcultural concerns. Since, the target market is wide inIndia, the brand has projected as the income willbecome larger there will be increasing demand forfashionable and superior quality products.

SWOT Analysis

(Bhasin, 2018), (Zara SWOT Analysis/Matrix),(ZaraSWOT Analysis, Competitors & USP)

Fig 1: SWOT Analysis of Zara

Target market is huge comprising of men & woman of teens to adults.

Free standard and store delivery.

Easy exchange and return policies.

Superior quality products at

Lacks information on item dimensions for instance width and model height, item length and size of the item.

Amount of exchanges or returns can be avoided due to lack of item dimension.

Collaborations with designers could enable the company to expand its base into new markets.

The use of virtual dress up with merchandise and mobile apps are technologies that could proof beneficial if implemented.

Biggest competitor H&M is enhancing collaborations with designers and are attaining broader target market.

Zara is required to develop efficient operating mobile applications.

Demonetisation and lack of awareness of brand in India influence Zara’s affordability and efficiency in the Indian market.

Opportunities Threats

Strengths Weakness

Supply Chain of Zara in India

Zara is signified as one of the most renowned brandfor its ability to efficiently deliver new clothes to storesrapidly and in small batches (Ferdows, Lewis, &Machuca, 2003), (Yan & Joey, 2011). In India it hasbeen observed that almost twice a week store managersorder clothes and on schedule new garments arrive inthe store (Zhelyazkov), (Mazaira, Gonzalez, &Avendano, 2003). It has been estimated that theSpanish brand produces approximately 550 millionitems a year for its nearly 1,770 stores operating inalmost 87 countries. The effective and efficient supplychain of Zara is its competitive advantage. It usesvertical integration, owns its supply chain andembodies the idea of term “fast fashion”. (Dutta,2003).

i. Just in time production

• Zara provides fashionable and trendyproducts to its consumers that cater tovarious tastes of the citizens of Indiathrough an integrated process i.e. just intime. (Ferdows, Lewis, & Machuca, 2003),(Zhelyazkov).

• Zara holds its production that allows theorganization to be flexible in the amount,


frequency and variety of new products thatare to be launched. (Yan & Joey, 2011).

• Zara produces its products almost sixmonths in advance approximately 15 to25% of the seasonal product. Almost 50per cent of the clothes designed andmanufactured by Zara are developed in themiddle of the season. (Ferdows, Lewis, &Machuca, 2003), (Dutta, 2003).

• If a certain style or design of apparel in thecountry observes swelling demand thanZara reacts extensively, designs new stylesand display them into stores at the peaktime of the trend. (Ferdows, Lewis, &Machuca, 2003).

• Continuous feedback is implemented by thestore managers to ensure the likeabilitytowards its offerings, what they dislike andwhat new products they are searching for.The data collected is instantly beingdelivered to the designers of Zara who beginsketching and visualizing the product onthe spot. (Ferdows, Lewis, & Machuca,2003), (Zhelyazkov).

ii. Inventory Management

Zara avoids piling up inventory in any part of its supplychain from raw materials to finished products. Thereis an inventory optimization process that isimplemented by the brand to predict the quantity thatmust be delivered to every single retail store viashipments that comes out twice every week. Zaraavoids holding unpopular stock and create an exclusivebrand image among its consumers (Ferdows, Lewis,& Machuca, 2003), (Yan & Joey, 2011), (Dutta,2003). Moreover, the batch manufactured by the brandis small, so there is no fear of piled up unsold inventorythat is required to get rid of (Ferdows, Lewis, &Machuca, 2003).

iii. Centralized logistics

Centralization is the key to Zara success as it is believedby the visionaries of this brand. Zara develops deep,

predictable and fast pace products to provide orderfulfilment to stores. Trucks leave at specific times andshipments arrive in stores at specific times. Garmentsof the brands are already labelled and priced upondestination. Due to flexible centralization processevery staff associated from design to procurement,production, distribution and retail adheres to thetimeline and performs operations effectively(Ferdows, Lewis, & Machuca, 2003; Yan & Joey,2011). Most importantly at Zara, change does notdisturb the system in spite it is a part of the system.It’s vertically integrated supply chain and crossfunctional operations enables mass production underpush regulator that assists the brand to develop well-managed inventories, higher profitability, lowermarkdowns and value creation for shareholders in boththe short and long term. (Ferdows, Lewis, & Machuca,2003).


Study Population:

100 respondents.

Data Collection Procedure:

Primary data (Questionnaire) and secondary data.

Data Analysis Procedure:

• Creating Questionnaire.

• Collecting Reponses from 80-100respondents.

• Creating parameters for the samerespondents to check how the strategies ofZara are being perceived amongst itsconsumers.

Analysis & Interpretation

This study has eliminated respondents who have notheard about the brand Zara.


Table 1

Question Objective Options Response

How Often do you shop in a month

To know how frequently consumers go for shopping

Once a month 2 -3 times a month More than 3 times a month Other

40% of the respondents go once a month for shopping, 34% go 2 to 3 times a month. 17% go more than 3 times a month & 9% of the respondents go very often.

Have you heard of ZARA To know how famous ZARA is among the respondents

Yes No

86% of the respondents have heard of ZARA. This indicates majority of respondents are aware of ZARA as a famous brand.

How did you hear about ZARA

To know which source of media is most effective in spreading awareness for ZARA

TV or Radio Online Media (Facebook,

etc.) Print Media (Newspaper,

Magazine) Word of Mouth Other

39% of the respondents got to know about ZARA through word of mouth. 30% through print media like newspapers & magazines. 24% of people got to know through online media like Facebook promotions. TV & Radio advertisements are not present at all indicating that ZARA concentrates more on verbalizer strategy.

How often do you visit the ZARA store

To know how often people visit ZARA store

Once a month 2 -3 times a month More than 3 times a month Other

About 59% of the total respondents visit Zara store once a month at least which reflects that Zara has wide acceptance and accessibility.

Where will you do shopping for ZARA products

To know which mode of ZARA store is most effective among consumers

Internet Flagship Stores Stores in High

Street/Shopping Mall All of the above Other

66% of the respondents buy from Stores in High Streets/Shopping malls. 26% buy from flagship stores & 18% buy from Online portals. Zara opens its stores in India mostly in metropolitan cities and mostly in shopping malls to attract huge crowd & to become more visible and accessible.

What categories of clothing would you be most likely to purchase from ZARA (Tick all that apply)

To know which category of clothing from ZARA is most liked by customers

Casual Work/Corporate Parties Shoes Accessories

Most of the respondents buy casuals and party wear from ZARA.

ZARA offers great style and wide variety of options to choose from.

What is your first consideration when purchasing clothes from ZARA (Choose more than one)

To know what do consumers consider the most while shopping from ZARA

Fashion Brand Popularity Color & Design Value for money Quality Service

Respondents feel Zara provides clothes which are inclined towards more of fashionable clothes. The second parameter is Quality. Then the other parameters as well comes one after the other. We can conclude from this that Zara products tend to design fashionable products according to the actual trends

The table summarises an overall analysis of the study.The first column tells us the question which has beenasked to the respondents. The second column tells usthe objective of asking that particular question. It tellsus why that question is asked and what information isneeded from the respondents by asking that question.The third column displays the options which weregiven to the respondents for that particular question.They had to choose any one option to answer thatquestion. The fourth column summarises the response

received from the respondents for that particularquestion.

A. Independent Variable: Age Group

Age Group taken into consideration –

• 18-25 years.• 26-35 years.• 36-46 years.

(Scores are given out of 5)


Table 2

Dependent Variable Null Hypothesis p-value & Average Score

Result

Latest Fashion Trend People of all age groups feel in the same manner about ZARA’s fashion trend.

p-value: 0.0015 Avg Score: 18-25 yrs :3.67 26-35 yrs : 2.22 36-46 yrs : 3.14

Null hypothesis rejected since p-value less than 0.05. It shows people from different age group think differently about ZARA’s fashion trend. Young age group (18-25 yrs) has given highest score indicating that they have positive perception about ZARA’s fashion trend & believe it offers products with the latest fashion trend. On the other hand, mid age group people (26-35 yrs) has given a low score indicating that they feel ZARA is not very fashion trendy.

Quality People of all age groups feel in the same manner about ZARA’s Quality of product

p-value: 0.0066 Avg Score: 18-25 yrs – 3.58 26-35 yrs – 2.31 36-46 yrs – 3.14

Null hypothesis rejected since p-value less than 0.05. It shows people from different age group think differently about ZARA’s quality of product. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s quality of product & believe it offers good quality products. On the other hand, mid age group people (26-35 yrs) has given a low score indicating that they feel ZARA is not offering good quality of products and are not very satisfied with it.

Customer Service People of all age groups feel in the same manner about ZARA’s Customer Service


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group think differently about ZARA’s customer service. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s customer service. On the other hand, mid age group feel negatively about ZARA’s customer service as they have given a low score. Age group 36-46 yrs gave 2.85 score indicating that they are fine with ZARA’s customer service.

Value for money People of all age groups feel in the same manner about ZARA’s value for money


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group think differently about ZARA’s value for money. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s value for money. On the other hand, mid age group feel negatively about ZARA’s customer service as they have given a low score. Age group 36-46 yrs gave 2.85 score indicating that they are fine with ZARA’s customer service.

Wide range of designs People of all age groups feel in the same manner about ZARA’s range of designs.


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group think differently about ZARA’s range of designs. People from age group 18-25 yrs & 36-46 yrs have given a positive score indicating positive perception towards ZARA’s range of designs. They believe it offers quite a good range of designs & styles to choose from. On the other hand, people from age group 26-35 yrs have given a low score indicating dissatisfaction towards the range of designs offered by ZARA.

(Contd...)


Exclusivity People of all age groups feel in the same manner about ZARA’s exclusivity.


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s exclusivity. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s exclusivity. On the other hand, mid age group feel negatively about ZARA’s customer service as they have given a low score. Age group 36-46 yrs gave 3.28 score indicating that they are quite fine with ZARA’s exclusivity of products.

Stylish (Style Quotient)

People of all age groups feel in the same manner about ZARA’s style quotient.


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s style quotient. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s style quotient & believe that ZARA’s products are really stylish. On the other hand, mid age group feel moderately about ZARA’s style quotient as they have given a 2.5 out of 5. Age group 36-46 yrs gave 3.42 score indicating that they are quite fine with ZARA’s style quotient.

ZARA has low prices People of all age groups feel in the same manner about ZARA’s prices.

p-value: 0.54 Avg Score: 18-25 yrs – 2.1 26-35 yrs – 1.77 36-46 yrs – 2

Null hypothesis accepted since p-value greater than 0.05. It shows that people from different age group feel in the same manner about ZARA’s pricing. All of them have given an average score of 2. This means that ZARA does not have a low price and its products are expensive. People from all the 3 age groups have same perception about ZARA’s pricing.

ZARA has a well-known brand name

People of all age groups feel in the same manner about ZARA’s brand name.

p-value: 0.0001 Avg Score: 18-25 yrs – 3.79 26-35 yrs – 2.13 36-46 yrs – 3

Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s brand name. Young age group (18-25yrs) has given highest score indicating that they have positive perception about ZARA’s brand name & feel that it is a well-known & established brand name. On the other hand, mid age group feel negatively about ZARA’s brand name as they have given a low score of 2 out of 5. Age group 36-46 yrs gave 3.0 score indicating that they feel moderately about ZARA’s brand name in the market.

ZARA has a convenient location

People of all age groups feel in the same manner about ZARA’s store location.

p-value: 0.0113 Avg Score: 18-25 yrs – 3.08 26-35 yrs – 2 36-46 yrs – 2.42

Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s store location. It shows that young people (18 to 25 yrs) are moderate about ZARA’s store location and feel that ZARA’s store location is reachable whereas middle aged people (26 to 35 yrs) are not very happy & positive about it. They feel that ZARA’s store location is not convenient & it is difficult to reach. People belonging to the age group of 36-46 years also seem to have a less than satisfactory opinion about ZARA’s store location.

ZARA produces high quality products

People of all age groups feel in the same manner about ZARA’s Quality of product.


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s quality of product. It shows that young people belonging to the age group of 18-25 years feel positively about ZARA and seem to be pretty satisfied with it. But the middle aged people belong to the age group of 26-35 years have given ZARA a low score indicating dissatisfaction with ZARA’s product Quality. People belonging to the age group of 36-46 years also seem fine with the quality aspect of ZARA.

ZARA is a trendsetter People of all age groups feel in the same manner about ZARA’s trend setting capacity.


Null hypothesis rejected since p-value less than 0.05. It shows people from different age group feel differently about ZARA’s trend setting capacity. It can be seen that young people belonging to the age group of 18-25 years feel positively about ZARA and seem to be pretty satisfied with it. But the middle aged people belonging to the age group of 26-35 years have given ZARA a low score indicating low confidence on ZARA’s trend setting capacity. People belonging to the age group of 36-46 years also seem fine with the trend setting


In this table, the analysis is described age group wise(18-25 yrs., 26-35 yrs., 36-46 yrs.). Age group isconsidered as independent variable and variousdependent variables are tested using one way anovaon SPSS software. The analysis is done using p-values.

Interpretation of p-value: When p-value is less than0.05, the null hypothesis is rejected. This means thatthe respondents of different age group think differentlyabout a particular dependent variable.

When p-value is greater than 0.05, the null hypothesisis accepted. This means that the respondents ofdifferent age group think in the same manner about aparticular dependent variable.

The first column tells us about the dependent variable.The second column tells us about the Null Hypothesiswhich is taken into consideration. The third columntells us about the p –value and the average score givenby each age group for that particular test. The fourthcolumn describes the result obtained- whether nullhypothesis is accepted or rejected, its interpretation andaverage scores given by each age group.

B. Independent Variable: Gender

Gender –

• Male.• Female.

(Scores are given out of 5)

Table 3:

Dependent Variable Null Hypothesis p-value & Average Score

Result

Latest Fashion Trend People of both genders feel in the same manner about ZARA’s fashion trend.

p-value: 0.0223 Avg Score: Male – 2.91 Female – 3.67

Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s fashion trend. Female have given a score of 3.67 which means that they have positive perception about ZARA’s fashion trend and beleive that ZARA’s products are fashionable. Whereas, male have given a moderate score of 2.9 (approx 3) which means they feel moderately about ZARA’s fashion trend.

Quality People of both genders feel in the same manner about ZARA’s quality.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s quality. Female have given a score of 3.69 which means that female feel positively about ZARA’s quality and beleive that ZARA’s products are of good quality. Whereas, male have given a moderate score of 2.8 (approx 3) which means they feel moderately about ZARA’s quality and are not very happy with it.

Customer Service People of both genders feel in the same manner about ZARA’s customer service.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s customer service. The scores tell us that female feel moderately about ZARA’s customer service and beleive that ZARA’s customer sevice is okay, but not very good. Whereas, male have given a low score of 2.4 which means they feel negatively about ZARA’s customer service and are not happy with it.

(Contd...)


Value for money People of both genders feel in the same manner about ZARA’s value for money.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s value for money. The scores tell us that female feel moderately about ZARA’s value for money and beleive that ZARA’s products offer a decent value for money which has been paid for it, but still not very happy & satisfied with it. Whereas, male have given a low score of 2.5 which means they feel negatively about ZARA’s value for money and are not happy with it.

Wide range of designs People of both genders feel in the same manner about ZARA’s range of designs.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s range of designs. We can see that female feel moderately about ZARA’s range of designs and beleive that ZARA offers a decent range of designs but needs to offer more.Whereas, male have given a low score of 2.77 which means they feel negatively about ZARA’s range of designs and are not happy with it.

Exclusivity People of both genders feel in the same manner about ZARA’s exclusivity.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s exclusivity. The scores tell us that female feel positively about ZARA’s exclusivity and beleive that ZARA offers an exclusive range of products & can do more.Whereas, male have given a low score of 2.85 which means they feel negatively about ZARA’s exclusivity and are not happy with it.

Stylish (Style Quotient) People of both genders feel in the same manner about ZARA’s style quotient.


Null hypothesis accepted since p-value greater than 0.05. It shows people of both genders feel in the same manner about ZARA’s style quotient. We can see that female & male both feel positively about ZARA’s style quotient. Female have given a high score of almost 4 out of 5, which means they believe that ZARA’s products are stylish and nice to wear. Although, male have given a moderate score of 3.10, but we can say they are on the positive side as well.

ZARA has low prices People of both genders feel in the same manner about ZARA’s pricing.


Null hypothesis accepted since p-value greater than 0.05. It shows people of both genders feel in the same manner about ZARA’s pricing. The results tell us that female & male both feel negatively about ZARA’s pricing. Both gave an average score of about 2 out of 5, which means that ZARA’s pricing is not low. They find ZARA’s products expensive.

(Contd...)


ZARA has a well-known brand name

People of both genders feel in the same manner about ZARA’s brand name.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s brand name. The scores tell us that female feel positively about ZARA’s brand name and beleive that ZARA has a well known brand name in the market. Whereas, male have given a low score of 2.79 which means they are not so positive about ZARA’s brand name & feel that it is not yet a very well known brand.

ZARA has a convenient location

People of both genders feel in the same manner about ZARA’s store location.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s store location. We can see that female feel moderately about ZARA’s store location & believe that stores are not very close to their homes but still are manageable.Whereas, male have given a low score of 2.375 which means they feel negatively about ZARA’s store location and are not happy with it.

ZARA produces high quality products

People of both genders feel in the same manner about ZARA’s quality.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s quality. The scores indicate that female feel positively about ZARA’s quality and beleive that ZARA’s products are of good quality. Whereas, male have given a moderate score of 2.8 (approx 3) which means they feel moderately about ZARA’s quality and are not very happy with it.

ZARA is a trendsetter People of both genders feel in the same manner about ZARA’s trend setting capacity.


Null hypothesis rejected since p-value less than 0.05. It shows people of both genders feel differently about ZARA’s trendsetting capacity The results tell us that female feel positively about ZARA’s trend setting capacity and beleive that ZARA can set a trend for the people. Whereas, male have given a moderate score of 2.73 which means they feel moderately about ZARA’s trend setting capacity & do not seem to have much confidence in ZARA.

ZARA has a contemporary image

People of both genders feel in the same manner about ZARA’s image.


Null hypothesis accepted since p-value greater than 0.05. It shows people of both genders feel in the same manner about ZARA’s image. The results tell us that female & male both feel moderately about ZARA’s image. Both gave an average score of about 3 out of 5, which means that ZARA’s image is moderate. They need to do more to improve its image.


In this table, the analysis is described gender wise(Male, Female). Gender is considered as independentvariable and various dependent variables are testedusing one way anova on SPSS software. The analysisis done using p-values.

Interpretation of p-value: When p-value is less than0.05, the null hypothesis is rejected. This means thatthe respondents of different gender think differentlyabout a particular dependent variable.

When p-value is greater than 0.05, the null hypothesisis accepted. This means that the respondents ofdifferent gender think in the same manner about aparticular dependent variable.

The first column tells us about the dependent variable.The second column tells us about the null hypothesiswhich is taken into consideration. The third columntells us about the p –value and the average score givenby each age group for that particular test. The fourthcolumn describes the result obtained- whether nullhypothesis is accepted or rejected, its interpretation andaverage scores given by each gender.

Conclusion

According to the analysis and interpretation of thisstudy it has been observed that Zara has developed acraze and huge popularity for its products among thefashion fanatics and is efficient in adhering to thechanging needs of the consumers. The unique sellingproposition of the brand is to sell latest and exclusiveapparels at affordable prices. However, consumers ofdifferent gender and age group have differentperception about ZARA, when we look at fashiontrend followed by the brand. According to the analysis,Zara is successful in providing fashionable and trendyproducts to its customers. Furthermore, regarding thequality of products Zara has successfully attainedpositive responses on this parameter on the other handZara is unable to cater to its pricing strategies amongthe consumers. Its key marketing strategy is based onaffordability, exclusivity, differentiation and experience.Moreover, the brand relies heavily on word of mouthadvertising more than anything as it offersdifferentiating feature in its offerings as a result it isquiet popular. According to the analysis almost 39%of the respondents got to know about Zara through

this source, although Zara can come up with certainpromotional events to cater to its huge market.

It can be observed that to provide customer service,Zara is able to cater well in this strategy though it isobserved to be postive in the minds of the female ratherthan the male population. Zara tries to attract oldergeneration to try its products that provides themyouthful feeling by wearing its clothing. Furthermore,the target audience of Zara identifies the brand in asimilar manner. According to this research study it hasbeen inferred that the consumers in the age group of18-25 years are more inclined towards he brand Zararather than the people in the age group of 35 andabove, which means in this strategy Zara is not able toposition itself successfully.

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