ISSN - 0975-4032

Volume III

Issue I

Jan - June, 2011

Sameer S. Pingle Occupational Health and Safety at Mahindra and Mahindra Ltd:

Vrinda Sood An Empirical Study

L. S. Sridhar Price Discovery in Commodity Market –

M. Sathish An Empirical Study on the Indian Gold Market

Ramesh Kumar Miryala An Empirical Study of Gap Analysis of Service Quality in

Select Private Sector

Salabh Mehrotra Islamic Banking in India: An Innovative Way of Doing Banking

Pankaj Mohanty Business Statistics as Viewed by B-School Students

Chandra Sekhar S F

Prateek Gupta Affordable housing: The Need of the Hour

Amit Kumar Arora (A study of Ghaziabad, U.P.)

Bhavannarayana Kandala Insights into Network Marketing: An International Perspective

Manohar Kapse Case Study: Human Resource Perspective: Delay in Solution

Jayant Sonwalkar

Book Reviews:

R.M. Naidu From Third World to First

Vidya Bhandarker In Search of Change Maestros

Chief Patron Mrs. Aarathy SampathyPresident and CEOSiva Sivani Group of Institutions, Secunderabad.

Patron Mr. Sailesh SampathyVice President and Deputy CEOSiva Sivani Group of Institutions, Secunderabad.

Editor Dr. V. G. ChariDirector - AcademicSiva Sivani Institute of Management.

Assistant Editor Dr. Shahaida PAssoc. Professor, Marketing AreaSiva Sivani Institute of Management.

Editorial Advisory and Review Panel

Dr. Ashish Sadh, Professor, Marketing area, IIM IndoreDr. B. Brahmaiah, Vice President, Industrial Relations, Sujana Group of Industries. HyderabadDr. Cullen Habel, Lecturer in Marketing, The University of Adelaide Business School,South Australia

Dr. D. Dhanapal, CEO, KPR Educational Institutions, CoimbatoreDr. C. Gopalkrishnan, Director In charge & Professor of Strategic Management, Instituteof Management, Nirma University of Science & Technology, Ahmedabad

Dr. H.K. Jayavelu, Professor- HR, IIM KDr. S. Hanuman Kennedy, Professor - HR, PESIT, BangaloreDr. Prashanth N Bharadwaj, Dean’s Associate and Professor, Indiana University of Pennsylvania,

USA

Dr. B. S. R. Rao, International Institute of Insurance and Finance, HyderabadDr. Jayasimha K.R, Asst. Professor, Marketing Area, IIM IndoreDr. B. Rajashekar, Reader, School of Management Studies, University of Hyderabad,Dr. Rajendra Nargundkar, Director, IMT Nagpur, NagpurDr. Srinivas Murthy, Professor - Finance, IPE, HyderabadDr. G.B. Reddy, Associate Professor, Department of law, Osmania University, HyderabadDr. Nilanjan Sen Gupta, Professor, SDM-IMD, MysoreDr. S.M. Vijaykumar, Professor - OB & HRM, Chairperson - Research & Ph.D. IMT Nagpur Dr. Yerram Raju. B, Regional Director, PRMIA, HyderabadProf. V. Venkaiah, Professor and Head, Department of Business Management, Dr. B. R. Ambedkar

Open University

Prof. M. Kamalakar, Operations and IT Area, SSIMDr. V. G. Chari, Director- Academics, SSIM,1st shiftDr. P.V. S. Sai, Director, Training and Consultancy, SSIMDr. S. F. Chandrashekar, Head-HR, SSIMDr. Anil Ramesh, Director-Academics SSIM, 2nd Shift, SSIM

ContentsTitle Page #

Occupational Health and Safety at Mahindra and Mahindra Ltd:An Empirical Study

Sameer S. Pingle and Vrinda Sood 5

Price Discovery in Commodity Market – An Empirical Study on theIndian Gold Market

L. S. Sridhar and M. Sathish 19

An Empirical Study of Gap Analysis of Service Quality in Select Private SectorRamesh Kumar Miryala 30

Islamic Banking in India: An Innovative Way of Doing BankingSalabh Mehrotra 39

Business Statistics as Viewed by B-School StudentsPankaj Mohanty and Chandra Sekhar S F 55

Affordable housing: The Need of the Hour (A study of Ghaziabad, U.P.)Prateek Gupta and Amit Kumar Arora 67

Insights into Network Marketing: An International PerspectiveBhavannarayana Kandala 80

Case Study: Human Resource Perspective: Delay in SolutionManohar Kapse and Jayant Sonwalkar 89

Book Reviews:

From Third World to FirstR.M. Naidu 93

In Search of Change Maestros

Vidya Bhandarker 95

Copyright: Siva Sivani Institute of Management, Secunderabad, India.SuGyaan is a bi-annual publication of the Siva Sivani Institute of Management,NH-7, Kompally, Secunderabad- 500 014.

All efforts are made to ensure correctness of the published information. However, SivaSivani Institute of management is not responsible for any errors caused due to oversightor otherwise. The views expressed in this publication are purely personal judgments ofthe authors and do not reflect the views of Siva Sivani Institute of Management. All effortsare made to ensure that published information is free from copyright violations. However,authors are personally responsible for any copyright violation.

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4

Editorial...

It is with great satisfaction that we present to you the first issue of SuGyaan in 2011.

In its third year of existence SuGyaan has received a tremendous response. Our

sincere gratitude to the authors and reviewers for their support.

The first paper titled Occupational Health and Safety at Mahindra and Mahindra Ltd:

An Empirical Study by Sameer S. Pingle and Vrinda Sood focuses on the various

issues related to safety in a manufacturing company.

The second paper, Price Discovery in Commodity Market – An Empirical Study on the

Indian Gold Market by L. S. Sridhar and M. Sathish explores very interesting trends in

the gold market

The third paper, An Empirical Study of Gap Analysis of Service Quality in Select

Private Sector

By Ramesh Kumar Miryala discusses the implications of service quality in the banking

sector.

The fourth research paper, Islamic Banking in India: An Innovative Way of Doing

Banking by Salabh Mehrotra explores a current topic of interest and its future in

India.

The fifth paper, Business Statistics as Viewed by B-School Students by Pankaj Mohanty

and Chandra Sekhar S F explores the attitudes of students towards the statistics course.

The sixth paper, Affordable housing-The Need of the Hour (A study of Ghaziabad,

U.P.) by Prateek Gupta and Amit Kumar Arora addresses an important need in urban

markets.

The seventh paper Insights into Network Marketing: An International Perspective by

Bhavannarayana Kandala explores the pros and cons of multilevel marketing in India

Next we have a Case Study in Human Resource Perspective: Delay in Solution by

Manohar Kapse and Jayant Sonwalkar.

Lastly, we have two reviews of the books, “Third World to First and In Search of

Change Maestros” by R.M. Naidu and Vidya Bhandarker

We hope you find this issue interesting and look forward to your feedback.

SuGyaan 5

Volume III, Issue I

Occupational Health and Safety at Mahindra and MahindraLtd: An Empirical Study

Sameer S. Pingle and Vrinda Sood

Abstract

Occupational health and safety promotes and maintains the social, mental and physical well-being of workers. Rapid industrialization is imperiling the life and health of workers. Each year,an estimated two million people die as a result of occupational accidents and work-related diseases,making occupational accidents and work-related diseases a growing area of scholarly attention.The tangible and intangible costs associated with occupational accidents have amplified theemphasis on pre-emptive and proactive undertakings at various organizations. Information onoccupational accidents is needed so that companies may understand its prominence thus, statisticaldata is crucial for accident prevention as it acts as a preliminary point for the safety at work. Thepresent study has been undertaken to analyse the occupational accidents and safety concerns atMahindra and Mahindra. The data was collected from primary (survey questionnaire) andsecondary (records maintained at M&M) data sources. Based on the conclusions from analysisrecommendations are cited to implement effective workplace health and safety programmes thatwould help to save the lives of workers by reducing threats and their consequences. Such initiativeswill result in affirmative effects on both worker’s morale and productivity.

Introduction

Rapid industrialization is threatening thelife and health of the workers. Each year,an estimated two million people die as aresult of occupational accidents andwork-related diseases and often havemany direct and indirect negativeconsequences for workers and theirfamilies. A single accident or illness canmean enormous financial as well as socialloss to both workers and employers.Effective workplace health and safetyprogrammes can help to save the lives ofworkers by reducing hazards and theirconsequences and can also have positiveeffects on both worker morale andproductivity.

Occupational health and safetyencompasses the social, mental andphysical well-being of workers. The mainobjectives are:

• Promotion and maintenance of thehighest degree of physical, mentaland social well-being of workers inall occupations

• Prevention among workers ofadverse effects on health caused bytheir working conditions

• Protection of workers in theiremployment from risks resultingfrom factors adverse to health

• Placing and maintenance ofworkers in an occupationalenvironment adapted to physicaland mental needs

• Adaptation of work to humans.

The Constitution of India containsspecific provisions for the occupationalsafety and health of workers in the formof three articles, that is, 24, 39 and 42.The Directorate General of Mines Safety

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(DGMS) and Directorate General ofFactory Advice Service and LabourInstitutes (DGFASLI) strive to achieveoccupational safety and health in mines,factories, and ports. The programsrelating to occupational safetyconcentrate on improvement of the workenvironment, employee–machineryinterface, control and prevention ofchemical hazards, development ofprotective gear and equipment, trainingin safety measures, and development ofsafety and health information systems.

About the Company and the Division

Mahindra & Mahindra is one of the fewgroups that are closely identified withIndia’s industrial progress. Mahindra &Mahindra Limited (M&M) engages inautomotive components, trade, retail andlogistics, financial services, informationtechnology, infrastructure development,and after-market sectors in India andinternationally. Its farm equipmentbusiness manufactures and sellsagricultural tractors; sells DG sets andengines; provides supply chain servicesto retail, export, and domestic markets forfruits and vegetables, and food processingindustries. Mahindra’s Farm EquipmentSector (FES) is the no. 1 tractor brand inIndia, since 1983. Mahindra & Mahindrahad acquired a majority stake in PunjabTractors Limited (PTL) in early 2007.Punjab Tractors, Ltd. manufactured,marketed, and serviced tractors primarilyfor the farming sector in India and alsoprovided harvester combines, ricetransplanters, forklifts, castings, andcomponents and spare parts, agriculturalimplements. The company was foundedin 1970 and post Mahindra – PTL merger,PTL is now a part of Mahindra FES and

is known as Swaraj Division.

Literature Review

Occupational health and Safety is anexceptionally broad topic (CCH, 1992;Glendon, McKenna & Clarke, 2006), asis protection of environment (Guha,1999). Occupational health hazardbroadly means any injury, impairment,or disease affecting a worker or employeeduring his course of employment. Itencompasses community health- relatedfactors too (Snell, Bohlander & Vohra,2010). Occupational illness is defined asany abnormal condition or disordercaused by exposure to environmentalfactors associated with employment(Dessler & Varkkey, 2009).

Employee safety reduces the possibilityof industrial accidents by installing thenecessary safety devices properly andeducating the employees about the safetyaspects. It reduces and then preventsdirect and indirect costs incurred by theorganization due to serious industrialaccidents. It promotes an occupationalenvironment that provides adequateemployee satisfaction and motivation. Itbrings cordiality and harmony in thelabour-management relations. Employeesafety complies with all the lawsgoverning safety and health of theemployees at the workplace. Properlyaddressing the employees’ concern forphysical, mental and psychological wellbeing has become an importantprerequisite for a successful humanresource management (Durai, 2010).

Occupational health and safety has beenreceiving attention from many years, butthe management research and textbookshave focused on stress management and

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legal issues, rather than workenvironment and job satisfaction research(Brief & George, 1991; Sjoberg & Drottz-Sjoberg, 1991). Makin and Winder(2008) have introduced a conceptualframework for Occupational Health andSafety Management. They have focusedupon three areas, which include:

• The facilities, infrastructure,hardware and operatingenvironment that people inorganizations use or convert inorder to produce goods and/orservices;

• The people to whom a duty of careis owed; and

• The management strategies,methodologies and systemsemployed to organize and direct thetransformation of resources intoorganizational outputs. They havealso suggested safe place, safeperson and safe systems approachesfor prevention and controlstrategies in organizations.

Objectives and Hypothesis

The objective of the current study is tounderstand and analyze the incidence ofoccupational accidents and injuries, thecauses of such accidents and theirdependence on various factors like age,department, educational qualification etc.This study also aims at identifying theepicentres of occupational accidents atMahindra and Mahindra’s SwarajDivision (Farm Equipment Sector).Keeping in view the objectives, it is thefollowing hypotheses are formulated:

Ho: Occupational Accidents/ Injuries atM&M are independent of age,

department, educational qualification,work load and lack of process training

H1: Occupational Accidents/ Injuries atM&M are not independent of age,department, educational qualification,stress and lack of process training

Utility of the Study

Health and safety are important aspectsof an organization’s smooth and effectivefunctioning. Good health and safetyperformance ensures an accident-freeindustrial environment. Awareness ofOccupational Health and Safety (OH&S)has improved in India considerably.Organizations have started attaching thesame importance to achieve high OH & Sperformance as they do to other keyaspects of their business activities. Thisdemands adoption of a structuredapproach for the identification of hazards,their evaluation and control of risks. Thepresent study will enable the company intaking effective measures in accidentprevention.

Methodology

Instrument

Two questionnaires were devised forcarrying out the data collection oninjuries and accidents. The availableliterature is used for designing thequestionnaires. The factors identified by(Leveson, 2007), Makin and Winder(2008), (Dessler & Varkkey, 2009) and(Durai, 2010) are used for designingquestionnaires. In addition to the factorsidentified from literature, additionalquestions were framed based on theinteraction with officers and executivesat M & M. The questionnaire was a mixof open ended and multiple choices

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questions and has 5 point Likert scaledquestionnaires. Questionnaires weretranslated into vernacular language(Punjabi) as majority of the workersunderstood only this language. Apartfrom this personal interviews were alsoconducted.

Sources of Data

The project work began with anexploratory research on occupationalhealth and safety. Various measurablefactors were identified. Based on thesevariables, primary and secondary sourceswere identified. Primary sources of dataincluded the feedback from employeesand workers of M&M. Secondary datasources included the official recordsmaintained at M&M (like the accidentand injuries reports) and books andinformation from the web.

Sampling

Sampling involves selecting units from apopulation of interest so that by studyingthe sample one can fairly generalize theresults back to the population from whichthey were chosen. The target populationwas M&M’s employees and workers.

Population: Employees of Mahindra andMahindra Ltd, India

Sampling Frame: Mahindra &Mahindra Ltd, FES Sector, SwarajDivision, Mohali, India

Sample Type: “Non Probabilistic”judgemental sampling was followed topursue people who had met withoccupational injuries/accidents.

Sample size: 42

Pilot Testing:

Each questionnaire was tested with thetotal of 20 respondents which was a small

sample of the total target population. Thedifficulties that were faced by therespondents were noted down andrelevant changes were made by revisingthe questionnaires.

Reliability

Cronbach’s alpha test determines theinternal consistency or averagecorrelation of items in a surveyinstrument to gauge its reliability. A“high” value of alpha is often an evidencethat the items measure an underlyingconstruct. The reliability tests for all thequestionnaires were carried out usingSPSS software. The results for thequestionnaires are as under:

Table 1

Reliability Statistics for OccupationalInjuries and Accidents

Cronbach’s Alpha N of Items

.845 37

The values of Cronbach’s Alpha aresignificantly high (above .70) whichindicate a high value of internal reliability.

Results and Discussion

Based on the interaction with employeesof M & M, the following results areobtained

(1) Frequency Distribution ofnumber of accidents and injuriessustained at M&M:

The analysis clearly indicated that around65% of the workers have sustainedoccupational injuries between 1 to 3times. Very few people have been injuredmore than 7 to 10 times, though aconsiderable percentage, 25%, hassustained injuries while working around

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4 to 6 times. In case of occupationalaccidents (higher degree of severitycompared to injures, consisting of bothreportable ad non reportable accidents),it showed that very few workers have metwith accidents and the number of times,a worker has met with an accident isbetween 1 to 3. This is indicative of thefact that there are very few accidents.

(2) Percentage of the body partsinjured while working:As shown in Figure 1 below surveyindicates that around 71% of times,injury was sustained in hand, followedwith injury of leg at 13 % and injuries offoot at 6%.

While comparing the survey results to the

Fig 1

(Survey Based)accident/injury records maintained atM&M, similar conclusions were drawni.e. the results from survey data and actualrecords were comparable which indicatesthe data of all the workers injured atM&M. This too clearly indicates thatmaximum times hand (79%), followed byfeet (9%) were injured while working.This finding is very crucial as thisindicates the appropriateness of thesurvey and correctness of the datacollected.

(3) Reason for the OccupationalInjury/Accident:

Fig 2 indicates the reasons behind theoccupational mishaps. As is evident fromthe chart, a majority of the accidents

occurred due to personal negligence. Thisnegligence could be lack of attentionwhile carrying out the activity, notwearing proper PPEs required whileworking etc. Task related error, i.e. usingincorrect method of carrying out the taskis also closely related to the aforesaidreason of negligence. By not following thestandard operating procedures, manyaccidents in the workshop have occurred.The other major reason for occupationalaccidents is faulty equipments like sharpedges, broken/cracked tools, breaking ofmachine parts while working etc.Environmental factors included slipperyfloors, oil spills etc. have been found tobe the reason for accidents at M&M.

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The reason for accidents based on actualrecords of M&M indicates a considerabledifference in the ‘Task Related’ errors insurveyed (18%) and actual data (30%)was because mostly respondentscategorized eye related injuries (falling ofchips etc) under faulty equipment whileanalysing the actual records this putunder task related errors. All the otherreasons as per survey data werecomparable to the results of the actualrecords i.e. personal negligence attributed39% of the reason followed by taskrelated errors at 30 % and faultyequipment at 18%.

(4) AGE versus Number of Times Metwith Accident/Injuries

Most of the surveyed respondents gotinjured 1 to 3 times, especially the agegroup above 51 years, i.e. older work forcehas met with more number of accidents/injuries due to their prolonged tenurewith the company.

(5) Highest Qualification vs. Numberof Times Met with Injury

The distribution of accidents/ injuries

with respect to Literacy levels indicatesthat majority of the workers (45%) arenot very well read (10th pass) and thus,might not understand the technicalitiestold in MSDS sheets and other safetyinstructions/ signage displayed on theshop floor.

(6) Sufficiency of Treatment given atOHC vs. Injuries/Accidents Met with

The analysis indicated that majority ofthe workers (60%) were satisfied ornearly content (21%) with the treatmentprovided by the health centre as first aidtowards accident treatment.

(7) Satisfaction with safety officer vs.Knowledge of Availability of SafetyOfficer

83% of the respondents were not verysatisfied with the safety officers’performance of his duties. Only 48% ofthe respondents are aware of the presenceof the safety officer.

(8) Task Training sufficiency vs.Occurrence of Accidents

The output signifies that 95% of the

Fig 2

(Survey Based)

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respondents felt that there is sufficienttraining provided to the employees on thecorrect usage of their machines. Theyhave been trained effectively and there isno lack of process knowledge even withthe workers who met with accidentswhile at work.

(9) Workers need additional trainingon safety vs. satisfied with earliertraining

It was evident from the data that previoustrainings have benefitted (44%) theworkers and they have shown great levelof satisfaction and interest in conductingsuch trainings in the future too. Thedemand and significance of such safetyrelated trainings is high.

(10) Trend Analysis for OccupationalAccidents/ Injuries based on Recordsfor the period 2007 to March 2010:

Figure 3

Trend Analysis for OccupationalAccidents/ Injuries (As per Recordsof M&M)

Figure 3 shows that there has been aconsiderable decrease in the number ofaccidents, both reportable and nonreportable. Later half of 2009 has seenalmost negligible number of accidents;this could be due to the newer policies onsafety and welfare implemented by M&Mmanagement at Swaraj Division. Even thereportable accidents (resulting in greaterthan 48 hours of man hour loss due toaccident) have shown a decreasing trend.

Table 2

Number of Injuries of various bodyparts across 2007 – 2010

Body 2007 2008 2009 2010* TotalPart

Arm 2 5 2 1 10Eye 9 9 13 5 36Foot 7 3 6 4 20Hand 20 18 14 7 59Head 1 5 3 2 11Leg 1 5 5 0 11Face 1 2 0 0 3Others 2 3 1 0 6

Total 43 50 44 19 156

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The Table 2 shows a decreasing trendin the number of hand injuries sustainedfrom year 2008 to 2010 in M&M‘s SwarajDivision. Similar trends have beenwitnessed in number of foot, leg, eye and

face injuries. There has been aconsiderable fall post 2009 primarily dueto M&M implementing strict safetynorms at Swaraj Division.

Figure 4

Figure 4 shows the distribution ofaccidents across the various departmentsof production at M&M. The highestnumber of accidents were recorded indepartment number ‘74’ i.e. LMS of LightMachine Shop where the percentage hasbeen 36%. This department is followedby department number ‘78’ or Assembly.The percentage of accidents reported herehas been 26%. All the other departmentshave shown a very low value of accidents.Thus, it is inferred that over 60% of theaccidents occur in Assembly and LightMachine shop alone. A further insight isrequired to understand what kind ofinjuries the workers are subjected toespecially in these two departments. Forthis the data for the last four years wasanalysed to see the part wise injuries inboth these departments i.e. in LMS andAssembly. The data for 2010 is till themonth of March and as stated in therecords of M&M.

Table 3

Distribution of injuries acrossVarious Departments at M&M From

2007 – 2010

LMS 2007 2008 2009 2010* Total74

Eye 5 4 6 2 17

Face 1 0 0 0 1

Hand 4 4 4 1 13

Head 1 2 1 4

Foot 1 2 3

Leg 2 2

Total 11 10 14 5

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Assembly2007 2008 2009 2010*78Eye 3 2 5 10Face 0Hand 7 8 4 4 23Head 1 1Foot 4 1 2 2 9Leg 4 1 5

Arm 2 1 1 4

Other 1 1

Total 17 17 13 6 53

It is clear from the Table 3 that the partsthat received maximum injuries werehand, followed by eyes. The reason forhigher eye injuries sin LMS is due to thepresence of ‘Chips’ or scrap generated

from tools during their manufacturing.Though there are appropriate chipdisposal mechanisms provided, but attimes due to a gush of air, these chips tendto fall into the eyes of the workers. Thetrend of injuries in LMS department hasseen a significant drop in the injuriesreported. A similar trend was witnessedin Assembly department too. Theabsenteeism (number of man days lost)resulting due to the accidents, bothreportable (occupational accidents thatresulted in a man hour loss of more than48 hours) and non reportable ones (theoccupational injuries resulting in a manhour loss of less than 48 hours). Thoughthe reportable accidents show peaks ofhighest loss of man hours, there is noconsistent trend.

Figure 5

This increased loss was witnessed due tomajor accidents that resulted in morethan a month’s leave for the workers.Though the frequency of such accidentsis less, their magnitude is big.

Hypothesis Testing

Age, lack of training and work load hassignificance of 0.073, 0.742 and 0.602respectively which is more than 0.05(Table 5(a & b)). That is Alpha < p

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value which implies ‘Accept NullHypotheses: Ho’. The occurrences ofoccupational accidents independent ofage, qualification, lack of process trainingand work load i.e. there is no relationshipbetween them. But the significance ofDepartment is 0.04 and that of highestqualification is 0.029 which is less thanalpha’s value of 0.05 (Table 5(a & b)).

This means that Alpha > p valuewhich implies ‘Reject NullHypotheses: Ho’. The occurrence ofoccupational accidents is dependent onthe department in which the workerworks and on highest qualification .i.e.there is a significant relationship in thedepartment, highest qualification and thenumber of accidents reported.

These conclusions are based on analysisof the data collected from survey andfrom official records maintained atM&M’s Swaraj division.

• Majority of the workforce hassustained injuries around 1 to 3times and the most affected area hasbeen the hand .i.e. hand has beeninjured maximum number of times,followed by injuries of foot and eye.

• The reason for these accidents hasbeen personal negligence followedby task errors. Workers have been

careless at times while working orhave followed the incorrect methodof performing the task which hasled to accidents.

• Though the age of workers is notlinked to probability of gettinginjured, it was noticed that workersserving a longer period at M&Mhave sustained more injuries thanthe ones serving a shorter tenure

• A general dissatisfaction with theaccessibility and availability of thesafety officer was brought out in the

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analysis

• The number of injuries/ accidentsalso varied with departments. Fewdepartments reported highernumber of accidents compared tothe rest. Maximum accidents werereported in LMS and in Assemblydepartment

• It was brought out that trainingson safety and first aid wereconsidered helpful in accidentprevention

• The workers have been providedenough process trainings and areaware of the correct usage of themachines. Thus, no accidents/injuries were reported due to thisreason.

• The other factors that are notresponsible for accidents arequalification and age of workers.

• There has been significantdecrease in the number ofaccidents, loss in man hours i.e.absenteeism across all departmentsfrom 2007 to 2010.

Recommendations for AccidentPrevention and Higher SafetyWorkplace

M&M’s management is committed to thesafety and wellbeing of its employees.This is evident from the plethora ofinitiatives undertaken in this regard. Asubstantial drop in the number ofreportable accidents at Swaraj has beendue to the new safety culture beinginfused amongst the workers. A fewmore measures if taken can well make ita Zero Accident workplace.

• As concluded, personalnegligence and incorrectmethodology are the mostcommon reasons for accidents/injuries, thus, workers need to beeducated about the importance ofsafety and their role in it.Employees’ involvement in thesafety and health initiatives areof utmost importance. This canbe brought about through theirparticipation in safety initiatives.

• Innovative initiatives in thisregard can be Safety Fairs, whichcan be entertaining as well asinformative. Having safety games(Forklift competitions, safetybingo, safety jackpot etc), guestlectures and safety poster makingcompetitions involving familiesand on the spot awards can be apart of such safety fairs. Safetyfairs can be clubbed with Safetyand Health Week where a weeklong activities involving talks onoccupational health hazards likeStress and fatigue, Hearing loss,Healthy eating, Smoking, Alcoholuse, Diabetes, Asthma, Cancer,Heart disease, Physical fitness,Reproductive health andWomen’s health issues can beundertaken. Having displays likecharts, posters, banners, andpersonalized badges can be doneduring work safe week. DuringWork Safe Week, M&M couldaward “spot prizes” foremployees who are seen to actsafely or with attention to health.

• Involving families in safety fairsand weeks, fostering team work,

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educating the workers about safetyand health issues with an elementof fun, introducing friendlycompetitions and providingrewards and recognition can resultin a positive atmosphere whereworkers feel connected to themanagement and thus, can workcongenially together towards a Zeroaccident workplace.

• A change in mindset needs to bebrought amongst the workers. Theworkers feel that

“I’ve been doing it this way a long timewithout getting hurt.”

“Getting hurt is just a part of the job.”

“I can’t do anything to prevent an injury.”This mind-set needs to be changed to

“I can work safely.”

“Injuries do not have to be a part of myjob.”

“I can address the hazard and not justassume the risk.” This change can bebrought through constant counsellingsessions, use of display charts and byproviding safety trainings

• It was found that workers havebenefited significantly from thefirst aid trainings undertaken anddemand more of them on a regularbasis. The knowledge of generalmedicines for daily ailments (painrelievers, fever etc) could also beincluded in such trainings.

• Workers adhering to safety normscan be rewarded and recognized.They can be given a range of itemsthat can be customised with the

company’s name and logo and aSafety message, such as pens,notepads, folders, badges, key rings,coffee mugs, fridge magnets,bottles, flags, beach umbrellas, teeshirts, caps, jackets, sun glasses,playing cards, paperweights,calendars and many more. Suchnames can be displayed at relevantplaces to infuse a sense of pride inthe worker and to motivate the rest.These names can be printed inSurbhi and also M&M’s newsletter.

• Some companies set formal targets,e.g. working 100,000 hours withouta lost-time injury (which is roughlyequivalent to 50 people workingfulltime for one year, or one personworking over a lifetime). Anotherapproach is to set targets in termsof reductions in accidents. If, forexample, there was a high rate ofmanual handling accidents and thetarget might be to reduce thoseinjuries by 50%. On achieving suchtargets, workers can beappropriately rewarded.

• Based on the general unhappinesswith the safety officer’ availability,the safety officer should visit theshop floor and interact morefrequently with the workers. Heshould also inspect machines,equipment, tools and PPEs andcheck for damages. A moreproactive rather than a reactive roleis expected of the safety officerwhich will help in identifying thesigns of potential accidents

• Also, the supervisor and the safety

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officer should be vested with morepowers and should be madeaccountable to incidence ofaccidents. Frequency of internaland third party audits should beincreased. This would keep thesupervisors and safety officers ontheir toes to implement and ensurea safe working area

• Frequent analysis of emerging risksshould be done so that correctiveaction can be taken before seriousdamage is done. This can be doneby visits of safety officer and themedical officer to gauge dangersfrom machines and any healthrelated risk.

• Oil spillage, slippery floors wasanother reason for accidents, thus,maintaining a clean work area ismust. Not only will it remove manyhazards from a work area bykeeping it clean, but will alsoprovide a more productive workenvironment for the workers. Thefrequency of cleaning can beincreased and the house keepingstaff could be asked to work moreeffectively.

Managerial Implications

Health at work and healthy workenvironment are amongst the mostvaluable assets of individuals,communities and countries. In the lightof rapid economic growth and industrialprogress in our country, it becomesimperative that safety and health at theworkplace be given its due importance.However, with stress being laid on quickprofits, safety aspects are generallyignored. It is only with the increase inthe number of people killed and injured

at work that the significance of theproblem has been realised. Instead ofinvestigating accidents after they haveoccurred, taking a high toll of human life,it is now felt that preventing theoccurrence of industrial disasters andoccupational diseases is a much betteridea. Reduction in occupational accidentswould not only save the pains and troublefor the employees, but it saves the crucialman hours, increases productivity andsaves the monetary and non-monetarycosts attached with the accident.Providing a safe and health workenvironment motivates the employees forhigher productivity. From managerialpoint of view, not only does a safe andhealth work environment helps inmaintaining higher levels of productionbut it also helps in keeping the employeeshappy and motivated to give in their best.

Scope for Future Research

The presents study is conducted at FarmEquipment Division (FES) at Mahindra& Mahindra Ltd., Mohali. The results cannot be generalized for other divisions.The smaller sample size and time was amajor constraint during the study. Thebiasness of respondents and theirwillingness to respond to the surveyinstrument also affected results of thestudy. The academic background andorientation of authors might have affectedthe outcomes of this research. There is alot of future scope in research in this area.Lately more methodical models have beenformulated to understand the trends inaccident analysis namely the FunctionalResonance Accident Model (FRAM)(Hollnagel, 2004) and the Systems-Theoretic Accident Model and Processes(STAMP) (Leveson, 2007). These modelswill augment in a more systematic and

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innovative mechanism to look forpremature pointers of potential safetythreat. Also apart from the accidentanalysis, research on various healthhazards at workplace can also beundertaken in similar capacity.Comparable studies can be undertaken atother manufacturing and automobileindustries and a relative analysis ofaccidents can be done based on location,type of industry, demographic and othervariables and their impact onoccupational accidents. A study like thiswill help them in better accidentprevention.

AcknowledgementThe guidance, support andencouragement given by Dr. RamandeepKaur, Medical Officer, Mr. Jagdish Singh,Assistant Manager, HR, and allemployees of Mahindra & Mahindra Ltd.,Swaraj Division, Mohali, India is dulyacknowledged.

ReferencesBrief, A.P. & George, J.M. (1991),Psychological Stress and the Workplace:A Brief Comment on the Lazarus,Outlook, Journal of Social Behaviour andPersonality, Vol.6, issue, 7, pp. 15-20.

CCH (1992), Managing OccupationalHealth and Safety, Sydney, NSW: CCHAustralia.

Dessler, Garry. Varkkey, Biju (2009),Human Resource Management (IndiaEdition), New Delhi: Pearson Education,pp. 635-639.

Durai, Pravin (2010), Human ResourceManagement, New Delhi: PearsonEducation, pp.377-380.

Glendon, A.I., McKenna, E.F., Clarke,S.G. (2006), Human Safety and Risk

Management, Boca Raton, FL: CRC Press.

Guha, R. (1999), Environmentalism: AGlobal History, Boston, MA: Addison-Wesley.

Makin, A.M.and Winder, C. (2006), DoSelf assessment Tools Assist theEffectiveness of Performance BasedLegislation? Journal of Occupationalhealth and Safety, Australia and NewZealand, Vol. 22, pp. 261-267

Sjoberg, L. & Drottz-Sjoberg, B. (1991),Knowledge and Risk Perception amongNuclear Power Plant Employees, RiskAnalysis, Vol.11, pp. 607-618.

Snell, Scott. Bohlander, George andVohra, Veena (2010), Human ResourcesManagement (India Edition), Delhi:Cengage Learning, pp. 471-491.

Hollnagel, E. (2004), Barrier andAccident Prevention. Hampshire,England: Ashgate.

Leveson, N. H. M.S. Owens, B. Ingham,M. & Weiss, K.A. (2007), Safety-DrivenModel-Based Systems EngineeringMethodology Part I, MIT Dept. ofAeronautics and Astronautics.

http:// www.planningcommission.nic.in,Accessed on April 29, 2010, 16:40

Authors

Sameer S. Pingle, Assistant Professor &Chairperson- OB & HR Area, Institute ofManagement, Nirma University, e mail:sameer@imnu.ac.in.

Vrinda Sood, Management Trainee(HR), Ranbaxy Laboratories Ltd.,Hyderabad, e mail: vrinda.sood@gmail.com

#MJSSIM 3 (I) 01, 2011

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Introduction

Price discovery in futures marketcommonly refers to the use of futuresprice to determine the expectations offuture cash market prices. Price discoveryand hedging are the major economic usesof futures contract. Many theoretical aswell as empirical attempts have beenmade by academicians, practitioners, andregulatory bodies. Many studies firstexamine this relationship on the basis ofprice or return. The returns on a varietyof futures contracts generally lead spotreturns.

Over the years, researchers have focusedon different issues in commodities marketwith particular emphasis on modeling inpricing. Hathway et al (1974) has foundthat there is a strong relationship betweenfood prices and inflation. Wiese & Lake(1978) studied that Price Discovery refersto the use of futures price for pricing cashmarket transactions. The significance oftheir contributions depends upon a closerelationship between the prices of futurescontract and cash commodities. Cornelland Reinganum (1981) and French

(1983) found empirically that thedifferences between futures and forwardprices for metals and foreign exchangewere small and were not explained bymodels of the daily vs. terminalsettlement features. In the equitiesmarket, Kawaller et al. (1987), and Stolland Whaley (1990) find that S&P500futures price lead spot price. Chan et al.(1991) and Pizzi et al. (1999) observe bi-directional causality between S&P 500futures and stock index, but the futuresmarket has a stronger lead effect.Likewise, commodities futures prices arefound to lead spot prices. Garbade andSilber (1983) followed by Engle andGranger (1987), since then most of theprice discovery process has identifiedthrough co integration test. This processis applicable to equity, debt and forexfutures and spot markets. Unlike anequity market, we cannot conclude orgeneralise the results for all commodityproducts since each commodity has itsown features and various on differentfactors.

The majority of empirical studies of price

Price Discovery in Commodity Market –An Empirical Study on the Indian Gold Market

L. S. Sridhar and M. Sathish

Abstract

This research examines whether precious metal futures serve as a price discovery vehicle for spotmarket movement. The co-integration test shows that gold futures and spot prices are cointegratedand silver futures and spot prices are cointegrated. The Error Correction model and GrangerCausality test show that gold futures serve as a price discovery for gold spot prices. There is anempirical evidence to show that spot prices appear to play a dominant and significant role in thefutures market. The Error Correction Estimates, in the case of Gold, shows that spot price (gold)does not cause by itself but it influences the future price (gold) in 2 lags. On the other hand, futureprice (gold) cause by itself in 2 and 4 lags. The spot price serves as a price discovery tool for Gold.

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discovery are confined to the analysis ofcash and futures market and in relationto equity index futures. Moreover, in theIndian context, though price discoveryhas been experimented with respect tostock futures and stock options not muchevidence on price discovery process.Hence, in this project an attempt is madeto examine the price discovery for goldprices in spot and futures market.

Commodity prices, many researchershave used notions of co-integration [Engleand Granger (1987)] to investigate pricediscovery in futures market. Thedevelopments in co-integration theoryhave provided a new framework toexamine the existing relationshipbetween cash and future commoditymarkets. Price discovery process has beendone on agricultural products for storableand non storable commodities in all otherinternational markets.

Schroeder and Goodwin (1991) used cointegration procedures to examine thatdaily cash and futures prices did not sharea long-run relationship. They found ashort-run relationship between cash andfutures prices based on Garbade-Silber(1983) model, but failed to find a long-run relationship using either Granger-causality or co integration procedures. Aslightly different approach was adoptedby Koontz et al (1990) to study the pricediscovery in the livestock market. Usingweekly US cash and futures prices from1973 through 1984, they investigatednature of the price discovery process.

In the recent years Praveen andSudhakara (2006) attempted to study acomparison of price discovery betweenstock market and the commodity future

market. They have taken Nifty futuretraded on National Stock Exchange(NSE) and gold future on MultiCommodity of India (MCX). The resultempirically showed that the one monthNifty future did not have any influenceon the spot Nifty, but influenced by futureNifty itself. The casual relationship testin the commodity market showed thatgold future price influenced the spot goldprice, but not the contrary. So this impliesthat information is first disseminated inthe future market and then later reflectedin the spot market

Fu and Qing (2006) examined the pricediscovery process and volatility spilloversin Chinese spot-futures markets throughJohansen cointegration, VECM andbivariate EGARCH model. The empiricalresults indicated that the models providedevidence to support the long-termequilibrium relationships and significantbidirectional information flows betweenspot and futures markets in China, withfutures being dominant.

Gupta and Belwinder (2006) examinedthe price discovery mechanism in theNSE spot and future market. The studyuses the daily closing values of indexfuture S&P CNX Nifty, from June 2002to February 2005. By using the techniqueslike Johansen and VECM, it wasempirically found that there was bilateralcausality between the Nifty index andfutures.

Objectives of the study

To examine the Price Discovery inCommodity Market with emphasis ongold

To examine the existing relationshipbetween spot and future price of gold

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Research Methodology

Data

The data for the study consist of 3 monthsfutures prices and spot prices: Gold - 10th

January, 2007 to 31st March, 2009comprising 581 observations. All thetimes series are obtained from NCDEX(National Commodities and DerivativesExchange) database. Most of the investorsprefer to invest in Bullion market not onlybecause it is a safe investment but also,because it hedges against inflation andpolitical uncertainties and it is easy toliquidate. In this study, only futures andspot price are considered and the logreturns are used.

The research design used here isdescriptive in nature, where the study isdone based on analyzing the Spot priceand future price. We have obtained 27months daily data series from January 10,2007 to 31st March 2009 for spot price andfuture prices. More than 24 months’ datawere taken for this research, the basicidea being future and spot prices canshare long run relationship. The studyperiod selected for spot price of goldduring the period April 2002 to June 2005showed that the Indian gold pricevolatility is relatively higher than globalmarket (Praveen and Sudhakara, 2006).

Methodology

Given the time series nature of data, thefirst step in the analysis is to determinethe descriptive statistics and the variablesare tested for normality using Jarrque-Bera test. Then, the price linkage betweenfutures market and spot market would beinitially investigated using AugmentedDickey Fuller Test and Phillips-Perron

Test. Cointegration analysis will be doneusing Johansen Cointegration Test thatmeasures the extent to which twomarkets have achieved long runequilibrium. The Causality will bechecked using Granger Causality Test.Error Correction dynamics characterizethe price discovery process, wherebymarkets attempt to find equilibrium.

Testing for Stationarity and Cointegration

The first step in the analysis is todetermine the descriptive statistics andthe variables are tested for normality.Then the stationarity of the time seriesis tested using the Augmented Dickey-Fuller test and Schmidt-Phillips test. Thenull hypothesis to be used is that there isa unit root in the series (i.e. series is non-stationarity) while the alternativehypothesis is that there is no unit root. Ifspot and futures prices are found to beintegrated of the same order, cointegration test using the Johansenprocedure are performed. One of the mostwidespread unit root test is theAugmented Dickey Fuller (ADF) test.The standard Dickey Fuller test estimatesfollowing equation:

The case which corresponds to therandom walk which is non-stationarity.The Dickey Fuller test tests whether thist-statistic does not converge to the normaldistribution but instead to thedistribution of a functional of Wienerprocess.

The Dickey Fuller test is only valid forAR (1) processes. If the time series iscorrelated at higher lags, the augmented

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Dickey Fuller test constructs a parametercorrection for higher order correlation, byadding lag differences of the time series:

The order of p could be chosen byminimising information criteria such asAkaike or Schwarz.

The basic idea is that futures and cashprices can share a long-run relationshipif they are found to be cointegrated, i.e. ifthere is a linear combination of themwhich is stationarity. There are severalmethods available for conducting the cointegration test, the most widely usedmethod include the residual based Engle-Granger (1987) test and Johansen-Juselius (1990) tests. Then Engle-Granger co integration test consists of atwo stop procedure. In the first step, theresidual error is tested for stationarity.Variables Y and X might individually benon-stationarity but if the estimate oftheir residual error is stationarity, Y andX are said to be cointegrated. It impliesthat Y and X form a long run relationshipand the regression is not spurious. Engleand Granger (1987) have shown that anycointegrated series has an error correctionrepresentation. In the second step, if theresidual error or the estimation in the firststep is stationarity, the error correctionmode is estimated, which represents theshort run dynamics of the model. If spotand futures prices are found to beintegrated of the same order, cointegration test using Johansen procedureis performed. The basic idea is thatfutures and cash priced can share a long-run relationship if they are found to be

cointegrated, i.e. if there is a linearcombination of them which isstationarity. In this study, Grangercausality test and Johansen test is appliedfor price discovery performance.

Testing for Stationarity

The following hypothesis is postulated

Null Hypothesis H0 – Futures price has aunit root in the series (Non- stationary)

Alternate Hypothesis H1 – Futures pricehas no unit root in the series (stationary)

Testing for Causality with Error-Correction Models

The application of Granger causality testsin economics and finance hasproliferated. On an intuitive level, thestandard Grange causality test examineswhether past changes in one variable ‘y’help to explain current changes inanother variable ‘x’. If not, then oneconcluded that ‘y’ does not Granger cause‘x’. In order to determine whethercausality runs in the direction from ‘x’ to‘y’, the experiment is repeated with ‘x’and ‘y’ interchanged. Four findings arepossible: (1) neither variable Grangercauses the other; (2) ‘y’ causes ‘x’, but notvice versa (3) ‘x’ causes ‘y’ but not viceversa, (4) ‘x’ and ‘y’ cause each other.

In more formal terms, the standardGranger causality test is based on thefollowing regression:

p p“xt = á0 + “ âxi”xt-i + “ âyi”yt-i + å t (1)

i=1 i=1.

Where, “ is the first-difference operatorand “x and “y are stationary times series.The null hypothesis that ye does notGranger cause x is rejected if the

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coefficients, âyi in equation (1) are jointlysignificant based on a Standard F-test Thenull hypothesis that x does not Grangercause y is rejected if the âxi are jointlysignificant in equation (1) when “xreplaces “y as the left side dependentvariable.

Granger (1986) and Engle and Granger(1987) provide a more comprehensive testof causality, which specifically allows fora causal linkage between two variablesstemming from a common trend orequilibrium relationship. More,specifically, this alternative to thestandard test for Granger causalityconsiders the possibility that the laggedlevel of variable ‘y’ may help to explainthe current change in another variable ‘x’even if past changes in ‘y’ do not. Theintuition is that if ‘y’ and ‘x’ have acommon trend, then the current changesin ‘x’ partly is the result of ‘x’ moving intoalignment with the trend value of ‘y’. Suchcausality may not be detected by thestandard Granger causality test, whichonly explains whether past changes in avariable help to explain current changesin another variable. As long as ‘x’ and ‘y’have a common trend, however, causalitymust exist in at least one direction. Thefinding of no causality in either direction-one of the possibilities with the standardGranger causality test is ruled out whenthe variables share a common trend. Inmore formal terms, this alternative testfor Granger causality is based on error-correction models that incorporateinformation from the cointegratedproperties of time series variables. Two(or more) variables are cointegrated (havean equilibrium relationship) if they sharecommon trend(s). To test for causality

when variables are cointegrated, thefollowing error correction equation isused:

p p“xt = á0 + “ âxi”xt-i + “ âyi”yt-i + á1 + µt-1+ å t (2)

i=1 i=1

Where xt and yt have been identified asfirst differenced stationary, co integratedtimes series and µt-1 is lagged value of theerror term from the followingcointegration equation

xt = ãyt + µt (3)

The inclusion of µt-1, which must bestationary if the, first differentiatedstationary ‘x’ and ‘y’ series arecointegrate, differentiates the errorcorrection model form the standardGranger causality regression. Byincluding µt-1, the error correction modelintroduces an additional channel throughwhich Granger causality can emerge.Based on equation (2), the null hypothesisthat ‘y’ does not Granger cause ‘x’ isrejected not only if the âyi s are jointlysignificant, but also if the coefficient onµt-1 is significant. Thus in contrast to thestandard Granger causality test, the error-correction approach as discussed byGranger (1987) allows for the finding that‘y’ Granger causes ‘x’, even if thecoefficient on lagged changes in ‘y’ is notjointly significant.

If spot and futures prices are found to beintegrated of the same order,cointegration tests using Johansenprocedure are performed. Provided thespot and futures prices are cointegrated,they are expected to return to the long

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run-equilibrium after possible short rundeviations. Using cross correlogram, fivelags are identified or both futures and spotprice. The cointegrated variables can berepresented by an error correction mode,in which the “error” refers to thedisequilibrium responses. Since theresidual {et-1} from Ft-1 = á + â.St-1+ et-1, represents an estimation of thedeviation from the long run equilibriumin period t-1, it can be used in the errorcorrection term in the model.

q q“Ft= á + ð.e t-1 + “ âi”Ft-i + “ ãj”St-j + å t

(5)

i=1 i=1

q q“St = á’ + ð’.e t-1 + “ â’i”Ft-i + “ ã’j”St-j +å t (6)

i=1 j=1

Where F and S stand for futures and spotprices, respectively and here q=5,specifying the lag structure for bothfutures and spot price has been identifiedby SBC. The null hypothesis of non-causality is given by

H0 = ð = ã1 = ã2 = ã3 = ...... = ãq = 0in equation (4) and

H0 = ð’ = â’1 = â’2 = â’3 = ...... = âq =0 in equation (5), and

the test statistic follows a chi squaredistribution with degrees of freedom tothe number of restrictions.

Results and Discussion

Descriptive statistics and StationarityTests

Table -1

Descriptive Statistics

Gold Future Gold SpotPrice Price

Mean 12261.33 12788.30

Median 12241.50 12794.18

Maximum 17988.00 17900.00

Minimum 8675.000 8581.250

Std. Dev. 2464.379 2591.420

Skewness 0.196542 -0.016599

Kurtosis 1.855529 1.899900

Jarque-Bera 35.38794 29.27360

Probability 0.000000 0.000000

Descriptive statistics, using theobservations 2007/01/10 - 2009/12/16for the variable ‘Gold Future price’ and‘Gold Spot Price’ (580 valid observations)

The Descriptive statistics shows that allthe variables are not normally distributed.The Skewness and Kurtosis are clearlyobserved in both the data series, which isa confirmation of the stylized fact, relatedto fat tails and extreme values with highfrequencies data. Skewness measuresasymmetry of a distribution. It is alsonoticed that the gold futures and spotmarket seems to be more volatile on theconsidered period regarding standarddeviation.

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The absolute value of ADF and PP teststatistic is more than the critical value at5% level. Therefore, both the series canbe taken as non-stationary. The nullhypothesis that the Futures price and theSpot Price having a unit root is not

Table -2.1

Augmented Dickey Fuller (ADF) Test - Future Price and Spot Price

Variable Coefficient Std. Error t-Statistic Prob.

Gold Future Price (-1) -0.001080 0.003406 0.317190 0.7512

Constant 27.60341 42.56247 0.648539 0.5169

Gold Spot Price (1) -0.002105 0.002958 -0.711694 0.4769

Constant 39.28758 38.57137 1.018568 0.3088

rejected. It is further found that the boththe gold futures and spot prices areintegrated of order 1. Therefore, thenecessary condition for testingcointegration is satisfied.

Table - 2.2

Philip Perron (PP) Test - Future Price and Spot Price

Variable Coefficient Std. Error t-Statistic Prob.

Gold Future Price (-1) -0.001080 0.003406 -0.317190 0.7512

Constant 27.60341 42.56247 0.648539 0.5169

Gold Spot Price (1) -0.002105 0.002958 -0.711694 0.4769

Constant 39.28758 38.57137 1.018568 0.3088

Table – 3

Johansen Co integration Test - Futures and Spot Price

No. of Cointegration Eigen value Statistic Critical Value Prob.**Equation(s)

None* 0.023268 18.81455 15.49471 0.0882

At most 1 0.000482 0.277240 3.841466 0.5985

Trace test indicates 1 co integrating eqn (s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

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Table - 4

Test for Granger-Causality - Futures and Spot Price

Null Hypothesis F-Statistic P-Value

GSPOTPRICE does not Granger Cause GFUTUREPRICE 7.84021 0.00044

GFUTUREPRICE does not Granger Cause GSPOTPRICE 0.48108 0.61836

Co-integration and Granger CausalityTest Results:

In order to test for cointegration betweenspot and futures prices, the Johansen(1988) procedure is employed. By usingtrace statistics and maximum eigen valuestatistic, it was identified that there existson cointegration equation between thefutures gold and spot gold price and sothe ECM for these series was proceeded.

Error Correction Model

Then Granger causality test primarilyindicated that there is a causalrelationship between futures and spotclose prices. Granger causality test showsthat future price do not Granger cause thespot price but spot price does Grangercause the future price. Therefore, itappears that Granger causality runs one-way from spot price to future price andnot the other way in Gold

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Having found that co integration existsand since the level series are non-stationary, ECM is the appropriate modelto capture the relationship betweenfutures and spot prices. Initially, the rankof the co integration using the Johansen’smethodology is tested. The ErrorCorrection Estimates, in the case of Gold,shows that spot price does not cause byitself but it influences the future price in2 lags. Thus spot price influences thefutures price which is same as the resultobtained by the Granger Causality Test.

CONCLUSION

This study attempts to examine theevidence of price discovery in gold spotmarket movement. The co integration testshows that gold futures and spot prices

are cointegrated and there exists one cointegration equation. The Grangercausality test shows that there is no bi-causal relationship between gold futuresand spot prices. Spot price significantlyinfluences the Future price. The ErrorCorrection Estimates, in the case of Gold,shows that gold spot price does not causeby itself but it influences the gold futureprice in 2 lags. On the other hand, goldfuture price causes by itself in 2 and 4lags.

References

Besseler, D.A., Covey, T. (1991),“Cointegration: Some results on US CattlePrices” The Journal of Futures Market,Vol.11, No.4, pp 461-474.

Cornell, Bradford and Reinganum, Marc

Standard errors in () & t-Statistics in [ ].

t-statistics > 1.76 is significant at 0.10 level of significance

t-statistics > 1.96 is significant at 0.05 level of significance

t-statistics > 2.56 is significant at 0.01 level of significance

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R (1981), “Forward and Futures Prices:Evidence from the Foreign ExchangeMarkets” Journal of Finance, Vol No.36pp. 1035-1045.

Chan, K., etal. (1991), “A FurtherAnalysis of the Lead-lag Relationshipbetween the Cash Market and StockIndex Futures Market”, Review ofFinancial Studies 5, 123-152.

Cox John C, Ingersoll Jonathan and RossStephen A (1981), “The Relationbetween Forward Prices and FuturePrices”, Journal of Financial Economics,Vol. No.9 pp. 521-546.

Engle, R.F., & Granger, C.W.J. (1987),“Cointegration and Error Correction:Representation, estimation and testing”Econometrica, Vol. No. 55, pp.251-276.

Fu.L & Qing, Z.J (2006), “Price Discoveryand volatility spillovers”, Evidence fromChinese spot-futures market, Journal ofFinance, Vol.No:53,pp.211-219.

Franses, Philip Hans, “A ConciseIntroduction to Econometrics: AnIntuitive Guide”, (2nd Edition),Cambridge University Press : 2003.

Fortenbery, T.R. and Zapata H.O., (1993),“An Examination of cointegrationRelations between Futures and LocalGrain Markets” Journal of FuturesMarket, Vol. 1, pp. 921-932.

French Kenneth R (1983), “A comparisonof Futures and Forward Prices” Journalof Financial Economics, Vol.No.12pp.311-342.

Garbade, K.D. and Silber, W.L. (1983),“Price movements and price discovery infutures and cash markets”, Review ofEconomics and Statistics, 65, pp.289-297.

Geweke, J. (1982), “Measurement oflinear dependence and feedback betweenmultiple time series”, Journal of theAmerican Statistical Association 77, 304-313.

Granger, C.W.J. (1986), “Developmentsin the study of cointegrated economicvariables”, Oxford Bulletin of Economicsand Statistics, Vol.No.48, pp.213-228.

Gupta, Kapil., & Singh, Balwinder.(2006). Price Discovery & Causality inspot & Futures Markets in India. TheICFAI Journal of Derivatives Markets,3(1), 30-41

Harvey, A.C. (1981), The EconometricAnalysis of time Series, A Halsted PressBook.

Hull, J C, “Options, Futures, and otherDerivatives”, (7th Edition), PerasonPublishers: 2007

Johansen, S. (1988), “Statistical Analysisof Cointegrated Vectors”, Journal ofEconomic Dynamics and Control, 12,231-54.

Johansen, S. and K. Juselius.(1990),“Maximum Likelihood Estimation andInference and Inference on Cointegration– With Applications for the Demand forMoney”, Oxford Bulletin of Economicsand Statistics, 59, 2, 169-210.

Lutkepohl, H. and H. Reimers. (1992),“Impulse Response Analysis ofCointegrated Systems”, Journal ofEconomic Dynamics and Control, 16, 53-78.

Hathaway Dale E, Hendrik S.Houthakker and John A. Schnittker(1974), “Food Prices and Inflation”,Brookings Paperson Economic Activity,

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Vol. 1974, No.1, pp.63-116

Helmuth, John (1977), “Grain Pricing”Economic Bulletin No.1, Washington:Commodity Futures Trading Comm.

Jarrow, Robert A and Oldfield, George S(1981), “Forward Contracts and FuturesContract”, Journal of FinancialEconomics, Vol.9 pp. 373-382

Johansen, Soren (1988), “StatisticalAnalysis of Cointegration Vectors”,Journal of Economic Dynamics andControl, Vol No.12, pp.231-254.

Kamara, A (1982), “Issues in FuturesMarket: A Survey”, Journal of FuturesMarkets, Vol 2, pp. 169-210

Kawaller, I. G., Koch, P. D. and Koch, T.W. (1987): ‘The temporal pricerelationship between S&P500 futures andthe S&P500 index’, Journal of Finance,Vol.No:53, pp 12-19.

Koontz, S.R., Gracia P., and Hudson,M.A. (1990), “Dominant-satelliterelationships between live cattle cash andfutures markets”, The Journal of FuturesMarket, Vol No.10, pp. 123-136

Ollerman, C.M. and Brorsen, B.W.,Farrris, P.L. (1989), “Price discovery forfeeder cattle”, The Journal of FuturesMarket, 9, pp.113-121

Pizza, M.A. et al. (1998). An examinationof the relationship between Stock IndexCash and Futures Markets: ACointegration approach. The Journal ofFutures Markets, 18(3), 297-305.

Praveen, D.G., and sudhakara, A. (2006),‘Price discovery and causality in theIndian derivativemarket’, The ICFAIJournal of Derivative Market.

Schroeder, T.C., and Goodwin B.K.

(1991), “Price Discovery andCointegration for live hogs”, Journal ofFutures Market, Vol.11 No.4, pp.685-696

Stoll, H. R. and R. E. Whaley, (1990),“The dynamics of stock index and stockindex futures, Journal of Financial andQuantitative Analysis 25, 441-468.

Silber, William (1981), “Innovation,Competition and New Contract Design inFutures Market”, Journal of FuturesMarket, Vol.1 No.1 pp. 123-155

Wiese Virgil (1978), “Use of CommodityExchanges by Local Grain MarketingOrganisations” in A Peck (ed.) Viewsfrom the Trade, Board of Trade of the Cityof Chicago, Chicago

Yang J., Bessler D., and Leatham D.J.,(2001), “Asset sotrabality and pricediscovery in commodity futures markets: a new look”, Journal of Futures Market,Vol. No. 21, pp.279-300

Witherspoon, J.T. (1993), “How PriceDiscovery by Futures Impacts the CashMarket, Journal of Futures Markets”, 11,685-696.

Zapata, H.O. and T.R. Fortenbery (1996),“Stochastic Interest Rate and PriceDiscovery in Selected Markets”, Reviewof Agricultural Economics 18, 643-654.

Authors

L. S. Sridhar, Lecturer, PSG Institute ofManagement, PSG College of Technology,Coimbatore, e-mail-sonnappansridhar@yahoo.co.in

M. Sathish, Lecturer, PSG Institute ofManagement, PSG College of Technology,Coimbatore, e- mail-mahendran.sathish@gmail.com

#MJSSIM 3 (I) 02, 2011

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Introduction

Banking services worldwide can broadlybe classified into investment banking andcommercial banking and is primarilyconcerned with helping corporate bodiesraise funds at the best possible rates fromvarious markets. Commercial banking isconcerned with channeling savings toproductive uses. Banking is anintermediary function but one that is veryessential for sustained economic growth.In India, since the nationalization ofbanks in 1969, banking has beenprimarily in the Central Government’sdomain. As part of the Government’sliberalization policy which began in 1991,New Private Sector Banks (NPSBs) wereallowed to be set up. Today, India has nineNPSBs that provide commercial bankingservices. In a relatively short period, theNPSBs have managed to achieve about2% of the market share in terms ofbusiness, a disproportionate of 2% shareof the total income and almost 17% ofthe total net profit earned by the bankingsystem as a whole. This success can beattributed in large measure to the superiorquality of Services that these banks have

An Empirical Study of Gap Analysis of Service Quality inSelect Private Sector

Ramesh Kumar Miryala

Abstract

The present study evaluates the customer perceptions of service quality in select private sector banks.Data was collected from 200 customers of Private Sector Banks using structured questionnaire.Gap analysis and Multi regression were used for analysis of data. The result shows that thedimension of service quality such as Empathy and Accessibility has more gap, as the customerexpectations are high to their perceived service. The result also indicates that Empathy-Reliability-Assurance positively influences the service quality. The study implies that bank should reduce theservice gap to deliver superior quality of service to retain existing customers as well as to attractnew customers.

been able to provide.

Service quality is a concept that hasaroused considerable interest and debatein the research literature because of thedifficulties in both defining it andmeasuring it with no overall consensusemerging on either (Wisniewski, 2001).Nowadays, with the increasedcompetition, service quality has becomea popular area of academic investigationand has been recognized as a key factorin keeping competitive advantage andsustaining satisfying relationships withcustomers (Zeithmal et al, 2000). Servicequality can be defined as the differencebetween customer’s expectations forservice performance prior to the serviceencounter and their perceptions of theservice received (Asubonteng et al, 1996).Service quality can thus be defined as thedifference between customerexpectations of service and e perceivedservice. If expectations are greater thanperformance, then perceived quality isless than satisfactory and hence customerdissatisfaction occurs (Parasuraman et al.,1985; Lewis and Mitchell, 1990).

Objectives

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• To Evaluate the Quality of Servicein Select Banks in Nalgonda District

• To identify the gap betweencustomer expectation andperception

• To identify the areas that need toimprove by banks to deliversuperior quality of service.

Methodology

The data was collected for the study 200from customers of select Private SectorBanks in Nalgonda district in AndhraPradesh, based on convenience andadministered a modified SERVQUALquestionnaire containing two sections:customers’ expectations and customers’perception each consisting of 26questions of 6 dimensions. The studyfollows the SERVQUAL as a frameworkand one dimension (accessibility) wasadded to previous dimensions to fit intothe study (Al-Fazwan, 2005). Therespondents were asked to rate theirexpectations and perceptions of serviceoffered by the respective banks. A sevenpoint Likert scale was used.

Service Quality

Service quality can be defined as thedifference between customer’sexpectations for service performanceprior to the service encounter and theirperceptions of the service received(Asubonteng et al.,1996). Quality servicehas a positive effect on the bottom-lineperformance of a firm and thereby on thecompetitive advantages that could begained from an improvement in thequality of service offering, so that theperceived service exceeds the service leveldesired by customers (Caruana, 2002;

Chumpitaz.2004). Gefan (2002) definedservice quality as the subjectivecomparison that customers make betweenthe quality of the service that they wantto receive and what they actually get.Nowadays, with the increasedcompetition, service quality has becomea popular area of academic investigationand has been recognized as a key factorin keeping competitive advantage andsustaining satisfying relationships withcustomers (Zeithmal et al...2000).

Dimensions of Service Quality

The SERVQUAL scale is the principalinstrument widely utilized to assessservice quality for a variety of services.Parasuraman et al., (1988) haveconceptualized a five dimensional modelof service quality such as: reliability,responsiveness, empathy, assurance andtangibility. Their measurementinstrument is known as SERVQUAL,which has become almost the standardway of measuring service quality. Further,each item of SERVQUAL has been usedtwice: to measure expectations andperceptions of service quality. The centralidea in this model is that service qualityis a function of difference scores or gapbetween expectations and perceptions.The five dimensions of SERVQUALIncludes:

Tangibles: Physical facilities, equipmentand appearance of personnel.

Reliability: Ability to perform thepromised service dependably andaccurately.

Responsiveness: Willingness to helpcustomers and provide prompt service.

Assurance: Knowledge and courtesy of

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employees and their ability to inspire trustand confidence.

Empathy: Caring and individualizedattention that the firm provides to itscustomers.

Literature Review

Koushiki Choudhury (2007) in his studysuggests that customers distinguish fourdimensions of service quality in the caseof the retail banking industry in India,namely, attitude, competence, tangiblesand convenience. Identifying theunderlying dimensions of the servicequality construct in the Indian retailbanking industry is the first step in thedefinition and hence provision of qualityservice. The paper has drawn upon thefindings of the service quality dimensionsto contend the initiatives that bankmanagers can take to enhance theiremployees’ skills and attitudes and instilla customer-service culture. Sandip GoshHasra and BL Srivastava (2009) in theirstudy indicated that the bank should payattention to these dimension of servicequality and pay more attention todimension of assurance-empathy toincrease loyalty to a company, willingnessto pay, customer commitment andcustomer trust.

Sudesh (2007) revealed that poor servicequality in public sector banks is mainlybecause of deficiency in tangibility, lackof responsiveness and empathy. Privatesector banks, on the other hand, were

found to be more reformed in this regards.Above all, the foreign banks wererelatively close to the expectations of theircustomers with regard to variousdimensions of service quality. Further, thestudy revealed that there existed servicequality variation across demographicvariables and suggested that managementof banks should pay attention to potentialfailure points and should be responsiveto customer problems. Joshua andKoshi(2005) in their study on‘Expectation and perception of servicequality in old and new generation banks’,observed that the performance of the newgeneration banks across all the servicequality dimensions are better than thoseof old generation banks. Al-Fazwan(2005) in his study found that the bankshould concentrate on accessibilitydimension. He stated that the particularbank should take maximum efforts toraise the level of services to meet out thecustomer expectations. (Table 1&2)

Inference

The table 2 represents the gap scores forprivate sector banks. The differencebetween the customer’s expectation andperception of service is the gap scorewhich is then averaged for eachdimension.

The unweighted gap score was presentedin the table 2 Average gap score for sixdimensions as calculated in table 2 isaveraged to compute the unweighted gapscore.

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Table 1 : Gap Analysis Score

Statements Expectation Perception Service GAP (E-P)

TANGIBILITYModern looking equipment 6.6 6.1 0.5Physical facility 6.5 6.1 0.4Employee are well dressed 6.6 5.9 0.7Materials are visually appealing 6.7 6.1 0.6

Average gap score 0.55

RELIABILITYDelivers service at promised time 6.7 5.7 1Interest in solving problem 6.6 5.6 1Perform service right first time 6.6 5.7 0.9Follows the promised time 6.6 5.7 0.9Maintain error free records 6.9 6.4 0.5

Average gap score 0.86

RESPONSIVENESSTell you about performance of service 6.7 5.9 0.8Gives prompt service 6.4 5.6 0.8Willingness to help 6.5 5.6 0.9Not busy to respond queries 6.4 5.1 1.3

Average gap score 0.95

ASSURANCEInstills confidence 6.8 5.9 0.9Safe transactions 6.7 6.3 0.4Employees are consistently courteous 6.4 5.3 1.1Employee have enough knowledge 6.6 6.1 0.5

Average gap score 0.73

EMPATHYGives individual attention 6.4 4.8 1.6Convenient operating hours 6.6 5.7 0.9Gives personal attention 6.3 4.9 1.4Best interest in heart 6.6 5.7 0.9Understand customer’s specific needs 6.6 5.2 1.4

Average gap score 1.24

ACCESSIBILITYConvenient branch locations 6.7 5.7 1Extended working hours 6.4 4.9 1.5ATM network 6.8 5.7 1.1Safe net banking and mobile banking 6.3 5.8 0.5

Average gap score 1.03

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Table 2

Average Gap Score of Private Sector Banks (Un weighted)

No DIMENSIONS GAP SCORES

1. Average score for Tangibles 0.55

2. Average score for Reliability 0.86

3. Average score for Responsiveness 0.95

4. Average score for Assurance 0.73

5. Average score for Empathy 1.24

6. Average score for Accessibility 1.03

TOTAL 5.36

Average (total/6) Un-weighted score 0.893

Table 3

Highest Gap Scores of Private Sector Banks

NO ATTRIBUTES DIMENSIONS GAP SCORES

1. Banks will give customers individualattentions EMPATHY 1.6

2. Banks has Extended Working Hours tomeet customer needs ACCESSIBILITY 1.5

3. Banks has employees to give customer’spersonal attention EMPATHY 1.4

4. The employees of banks will understandthe specific needs of their customer EMPATH 1.4

5. Employees of banks will never be too busyto respond to customer’s request RESPONSIVENESS 1.3

Inference

The table 3 represents the attributeshaving the highest gap scores observedfrom the table 1. There exist highest gapbetween customer expectations andperceptions of bank services in theseattributes. This indicates that thecustomers are not satisfied with the

service in these attributes. Theseincludes: giving individual attentions(1.6) [empathy], extended working hoursto meet customer needs (1.5)[accessibility], employees give customerpersonal attention (1.4) [empathy],employees understand the specific needsof the customers (1.4)[empathy],employees are never too busy to respond

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to customer’s request(1.3)[responsiveness]. Hence it was observed

that the more gaps are identified inempathy dimension.

Table 4

Lowest Gap Scores of Private Sector Banks

NO ATTRIBUTES DIMENSIONS GAP SCORES

1. Customers of banks feel safe withtransaction ASSURANCE 0.4

2. Bank has modern looking equipment TANGIBLES 0.5

3. Material associated with service are visuallyappealing TANGIBLES 0.6

4. Employees in banks tell customers exactlywhen service will be performed RESPONSIVENESS 0.8

5. Employees in banks are always bewilling to help customers RESPONSIVENESS 0.9

Inference

The table 4 represents the attributeshaving the lowest gap scores observedfrom the table 1. These includes:customers feel safe transaction withbanks (0.4) [assurance], bank has modernlooking equipment (0.5) [tangibles],material associated with service arevisually appealing (0.6) [tangibles]

employees tell customers exactly whenservice will be performed (0.8)[responsiveness], employees in banks arealways willing to help customers(0.9)[responsiveness].There exists little gapbetween customer expectation andperception in tangibles and reliabilitydimensions.

Table 5Multi regression [stepwise method]

5A.Model Summary

Mode l R R Square Adjusted Std. ErrorR Square of the Estimate

1 .631(a) .398 .395 .692692 .681(b) .464 .458 .655273 .697(c) .486 .479 .64292

a Predictors: (Constant), empathyb Predictors: (Constant), empathy, reliabilityc Predictors: (Constant), empathy, reliability, assurance

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Coefficients (a)

S Model Unstandardized Standardized t Sig. No. Coefficients Coefficients

B Std. Error Beta

1 (Constant) 1.648 .359 4.596 .000

empathy .774 .068 .631 11.437 .000

2 (Constant) .394 .424 .928 .355

empathy .594 .074 .484 8.055 .000

reliability .378 .077 .296 4.926 .000

3 (Constant) -.442 .504 -.877 .381

empathy .416 .094 .339 4.416 .000

reliability .346 .076 .271 4.550 .000

assurance .329 .112 .219 2.939 .004

a Dependent Variable: service quality

Inference

The multi regression analysis (table 6)tells us that the overall model fits 48 %.The adjusted R square value .479 reflectsthe independent variables (empathy,reliability, and accessibility) predicts 39%variance in the dependent variable(service quality). The R square valuegives the proportion of variance independent variable accounted by the setof independent variables chosen for themodel. Here the R square value depictsthat independent variables (empathy,reliability, accessibility) account for48.6% of variance in service quality. Thebeta value in (coefficient table-4) gives ameasure of contribution of each variableto the model. A larger value indicates thata unit change in this predictor variablehas a large effect on criterion variable(service quality). The stepwise multiregression analysis shows that the

empathy (.339), reliability (.271),assurance (.219) together influences theservice quality to 82% whereas empathyalone by 63%. We can say that empathyis the major dimension influencing thequality of service.

Findings

The gap analysis shows that empathy ishaving more gap between customerexpectation and perception of servicequality. The bank has to reduce this gapgiving individual personal attention tounderstand the customer specific needs.Next to empathy more gap was observedin accessibility dimension. The customersof the banks expect to extend the workinghours in Saturday for their convenience.And also some of the customers aredissatisfied with ATM maintenance. Sothe bank management should concentrateon proper maintenance of ATM. In

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responsiveness dimension, there is moregap in attribute responding customerqueries in busy time. The employeeswillingly come forward to solve thecustomer problem. The Multi regressionanalysis shows that dimension (table 5B)Empathy-Reliability-Assurancepositively influences the banking servicequality.

Conclusion

Banks have to understand the changingneeds of customers, their aspirations andexpectations to create value. Banks shouldalso have a strong customer relationshipmanagement system that would indicatethe worth of the customer and be able tounderstand his needs while interactingwith him, so as to cross sell their products.To manage growth and continuity inbusiness, human resources play animportant role. The new generationprivate sector banks and foreign banksenjoy a lead in this regard when comparedto PSBs and old generation private sectorbanks. Skill sets of employees need upgradation so as to make them morecomfortable with the latest technologythat will increase their comfort levelwhile educating customers to use thesame in their day to day dealings. (Nair,The Hindu-Survey of Indian Industry2010, pp.60-61). Banks may follow afeedback system to know the customerexpectations for improving the level ofcustomer satisfaction to maximum level.Remarks on service reliability should becontinuously obtained from customers.This will enhance their service quality toa large extent.

References

Al-Fazwan (2005) “Assessing ServiceQuality in a Saudi Bank”, Journal of KingSaud University, vol 18, eng.sci (1),pp.101-115.

Asubonteng, P., McCleary, K.J. and Swan,J.E. (1996), “SERVQUAL Revisited: aCritical Review of Service Quality”,Journal of Services Marketing, Vol. 10,No. 6, pp. 62-81.

Caruana, Albert (2002), “Service Quality-The Effects of Service Quality and theMediating Role of CustomerSatisfaction”, European Journal ofMarketing,Vol.36 No.7/8,pp.811-828.

Chumpitaz, Ruben and Paparoidamis,Nicholas.G (2004), “Service Quality andMarketing Performance in B2B:Exploring the Mediating Role of ClientSatisfaction”, Managing Service Quality,Vol.14 No.2/3,pp.235-248.

Dr. Chandrakala.S, (2009), “EffectiveRole of CRM in Banking Sector”, BankingFinance, pp5-8.

Gefen.D (2000) “E-commerce: The Roleof Familiarity and Trust”, InternationalJournal of Management Science, Vol.28N0.6, pp725-37.

Joshua A J, V Moli, P. Koshi (2005),“Expectation and Perception of ServiceQuality in Old and New GenerationBanks”, Indian Journal of Marketing,vol.37(3), pp. 18.

Koushiki Choudhury(2007) , Journal ofAsia-Pacific Business, Volume 8, Issue 4December 2007 , pp 21 – 38.

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lewi S, B.R. & Mitchell, V.W., “Definingand Measuring the Quality of CustomerService”, Marketing Intelligence andPlanning, 1990, 8, pp. 11 - 17.

Nair M.V, “Banking - New Directions ofGrowth”, The Hindu-Survey of IndianIndustry 2010, pp.60-61.

Parasuraman,A.; Berry, Leonard L.;Zeithaml, Valarie A., “A ConceptualModel of Service Quality and ItsImplications for Future Research”,Journal of Marketing, 1985, 49, 4, 41-50.

Pa r a s u r a m a n , A . ; B e r r y, L e o n a r dL.;Zeithaml,Valarie A., “SERVQUAL: AMultiple-Item Scale For MeasuringConsumer Perceptions of ServiceQuality”, Journal of Retailing, 1988, 64,1, 12-40.

Sandip Ghosh Hazra and Kailash BLSrivastava (2009) “Impact of ServiceQuality on Customer Loyalty,Commitment and Trust in the Indian

Banking Sector” ICFAI Journal ofMarketing Management, vol .3 Nos3&4,pp. 75-95.

Sudesh (2007) “Service quality in banks-A study in Haryana and Chandigarh”,NICE Journal of Business, 2(1), pp.55-65.

Wisniewski M; , “Using Servqual toAssess Customer Satisfaction with PublicSector Services”, Managing ServiceQuality, 2001, vol. 11 no. 6 pp. 380-388

Zeithmal, V.A., (2000), “Service QualityDelivery Trough Websites: A CriticalReview of Extant Knowledge”, Journal ofthe Academy of Marketing Science, vol.30no.4,pp.362-75.

Author

Dr. Ramesh Kumar Miryala, Professor,Swami Ramananda Tirtha Institute ofScience & Technology, Nalgonda, A. P. email: miryalaramesh@gmail.com

#MJSSIM 3 (I) 03, 2011

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Introduction

In the present scenario Islamic bankingalong with finance is one of the fastestgrowing industries in the world. Thesurveys made by various researchersreveal that Islamic banking is growingwith an exceptional rate of 20 percentworldwide. Although Islamic banking isfor all communities irrespective ofreligion, but particularly for Muslimsinterest is forbidden. But as far as Muslimpopulation is concerned, then Islam is theworld’s second largest religion afterChristianity with approximate 1.0-1.8billion disciples, that comprising 20-25%of the world population. India is thesecond largest country in the world afterChina as far as population is concerned.As per census 2001 Muslim populationhas been estimated to be 13.4 percent oftotal population in India.

Islamic banking can be simply defined asa banking operation that abides by sharia(Islamic law), under which a key point isthe prohibition of interest or riba.

Generally, Islamic banking is anothername of interest-free banking. Loans area central element of conventionalbanking, with banks borrowing fromdepositors and lending to people in needof finance. Conventional banks thus makemoney from the difference between thelower interest rate they pay on depositsand the higher interest rate they chargetheir customers. Islamic banks, on theother hand, are prohibited from payingor receiving interest. Sharia-compliantbanks do not give out loans; instead, theyuse other modes– sale-, lease- andpartnership-based instruments – to makeprofit.

Besides being prohibited from earningriba, Islamic banks cannot engage inharam activities prohibited under sharia,such as those involving pork, alcohol,pornography and gambling. They cannotbuy stocks of wine and sell them to aclient. No