A bi-annual journal from GRG School of Management … · ISSN : 0974-908X Volume: 3 Issue: 2...

ISSN : 0974-908X

Volume: 3 Issue: 2September 2011

A bi-annual journal from GRG School of Management Studies

Coimbatore, India

Journal of Management Thought and Practice

An Empirical Study on Emotional Competence Kavita Saxena

Indian Bioinformatics: Growth Opportunities and ChallengesPankaj M Madhani

A study on Interrelationships Among Components of Logistics Flexibility for Co-operative Dairies Dr. Mahendra Sharma and Tejas Shah

Factors Influencing the Buying Behaviour of ChocolatesDr. G.Muruganantham and Priom Biswas

Job Satisfaction of Knitwear Employees in Tirupur DistrictS. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass

PRERANA in Sanskrit means hope and inspiration. The primary objective of PRERANA journal is to enhance the standard of management education by drawing from conceptual and empirical research based articles reflecting current industry practices. PRERANA shall include contributions from eminent members of the academia and sharing of practices by experts from industry. The Journal will also contain book reviews, editorial abstracts and executive summaries of recent publications in management.

Prof. S. Balasubramanian, DirectorGRGSMS, Coimbatore

Dr. D. Dhanapal, Group Director KPR Institutions, Coimbatore

Mr. P.M. Jagatheesan, Vice President-HR Sara ELGI Group, Coimbatore

Dr. Rajesh Haldipur, Dean and Professor SDM Institute for Management Development, Mysore

Mr. Rajeev Kamineni, DirectorKamineni Retail Enterprises, Coimbatore

Dr. R. Mahadevan, DirectorIndia Pistons, Chennai

Dr. R. Nandagopal, DirectorPSG Institute of Management, Coimbatore

Dr. Gail K Naughton, Dean College of Business, San Diego State University, USA

Executive Editor: Dr. B. Sripirabaa, Faculty, GRG School of Management Studies

Dr. Alford H Ottley, ProvostNorthwestern College, USA

Mr. Benedict Paramanand, DirectorManagement Next, Bangalore

Dr. T.S. RagunathanChair, Informations Operations Technology ManagementCOBA, University of Toledo, USA

Dr. Ms. Sheela Ramachandran, Vice ChancellorAvinashilingam University for Women, Coimbatore

Dr. A. Senthil Kumar Associate Professor of Mechanical EngineeringNational University of Singapore, Singapore

Dr. Monideepa Tarafdar, Associate Professor, Informations Operations Technology ManagementCOBA, University of Toledo, USA

Dr. Ms. N. Yeshodha Devi, PrincipalPSGR Krishnammal College for Women, Coimbatore

The Editorial Board

ISSN : 0974-908X

Volume: 3 Issue: 2 September 2011PRERANA

Journal of Management Thought and Practice

A bi-annual journal from GRG School of Management Studies

Coimbatore, India

This issue is not for sale and is for limited circulation only

CONTENTS

Title

An Empirical Study on Emotional Competence

Indian Bioinformatics: Growth Opportunities and Challenges

A study on Interrelationships among Components of Logistics Flexibility for Co-operative Dairies

Factors Influencing the Buying Behaviour of Chocolates

Job Satisfaction of Knitwear Employees in Tirupur District

CXO Interview

PRERANA September 2011

Author(s)

Kavita Saxena

Pankaj M Madhani

Dr. Mahendra Sharma and Tejas Shah

Dr. G. Muruganantham and Priom Biswas

S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass

Page No.

1

7

19

31

43

55

i


ii


From the Executive Editor

PRERANA: Journal of Management Thought and Practice. The objective of PRERANA is to disseminate contemporary developments in the field of management in the form of empirical research that tests, extends or builds management theory and contributes to management practice. PRERANA encourages manuscripts that present strong empirical and theoretical contributions to the management field. Preference is given to submissions that test, extend, or build strong theoretical frameworks while empirically examining issues with high importance for management theory and practice. The Journal will also enable industry in applying the most recent developments in the evolution of management thought.

PRERANA's international and multi-disciplinary review team ensures maintenance of standards of excellence with regard to the quality of contents. The current issue publishes five articles in the areas of Finance, Marketing and Human Resources.

Kavita Saxena in her article has used the framework developed by Verbeke et al., (2004) to identify the dimensions of Emotional Competence in sales people. Factor analysis revealed 5 Emotional Competence proficiencies namely - Satirical Approach, Truthfulness, Empathy, Emotional Acceptability and Ethical Emotional Flexibility which when focused upon appropriately and provided training programs would help to enhance the Emotional Competence skills of sales people. Pankaj Madhani in his article has highlighted the Indian and global bioinformatics scenario and the role of India in the emerging global bioinformatics market. The SWOT analysis examines India's competitive position in the global scenario.

Mahendra Sharma and Tejas Shah in their study examined the relationship among the various components of logistics flexibility for co-operative dairies in Gujarat. The results of the study reveal that flexible logistics competence supports the flexible logistics capability which eventually enhances customer satisfaction. Muruganantham and Priom Biswas in their study attempted to investigate the factors influencing the chocolate buying behaviour of young consumers in India. The results of the study show that the taste of the chocolate, occasions and offers and ingredients are the most important factors influencing the chocolate buying behaviour among the youth.

Jayaraman, Chandran and Mohandass in their article have analyzed the factors that can lead to job satisfaction and the relationship between the job factors and job satisfaction among the knitwear employees of Tirupur district. The findings of the study revealed that maximum knitwear employees were generally satisfied with their jobs and the job factors were negligibly related to overall job satisfaction. The factors namely pay packet and gratitude, job character, work environment and job impact emerged as the salient factors that had an impact on the overall level of job satisfaction.

It is my pleasure to place before you the second issue of the third volume of

iii


iv

I thank all the authors for their contributions towards the second issue of the third volume of PRERANA. I look forward for novel, insightful and crafted conceptual work that challenges conventional wisdom concerning all aspects of organizations from academicians, bureaucrats and business executives for publishing in PRERANA. The focus of the articles could be on diverse aspects of management focusing on contemporary issues and future challenges.

- Dr. B. Sripirabaa


1Kavita Saxena

AN EMPIRICAL STUDY ON EMOTIONAL COMPETENCE 1Kavita Saxena

1

University, Kherva, Mehsana, Gujarat. E-mail: [email protected], [email protected], Handset: 98242 54590

Kavita Saxena, Faculty for Marketing, Centre for Management Studies (Ahmedabad), Ganpat

ABSTRACTOver the past few years, Emotional Competence has started catching

everyone's attention. Organizations today are in search of competent sales people who are skillful, talented and passionate about their work. But present day work environment puts lots of stress on them and there are instances where even highly talented sales people are not able to combat stress. Various past researches suggest that Emotional Competence is particularly important in sales, where the sales persons are required to be adaptive and cope with difficult customers (Weitz, Castleberry and Tanner, 2000). The purpose of this research work is to study Emotional Competence proficiencies of sales people in the city of Ahmedabad, Gujarat, India. The framework developed by Verbeke et al., (2004) is used to identify the dimensions of Emotional Competence in sales people. Sample of 100 sales people were selected randomly and the results of factor analysis revealed 5 Emotional Competence proficiencies namely - Satirical Approach, Truthfulness, Empathy, Emotional Acceptability and Ethical Emotional Flexibility.

INTRODUCTIONEmotions are nothing but feelings that people encounter along with their

conscious thoughts. At times, these emotions are so subtle that they go unnoticed but at times they are so strong and dominating that they lead to instant reaction or decision-making by an individual. Emotional Competence (EC) is a life skill that is vital for flourishing and well-being in or out of professional life. EC is all about how we manage our emotions. Emotional Competence refers to one's ability to express or release one's inner feelings or emotions (Gibson, 1997). Level of EC varies from person to person and even from day to day. An emotionally competent person(s):lHas ready access to their emotions/feelingslCan express their emotions freely and can even hold on their feelings depending

upon the requirement of the situationlCan easily release strong emotions

Many organizational experts have started to believe that EC is one of the most important factors for sales persons' as well as organization's success. Gibson (1997) and Salovery et al., (2000) have suggested that as we move towards the era of networked organizations, people's ability to manage self and others emotions will be the key. Level of EC can have a decisive influence on the daily life of people in several professional roles.

This research paper is divided into 3 sections. First section deals with the theoretical background and review of literature pertaining to past research work in the area of EC. Second section includes research methodology adopted to study EC proficiencies of sales people in Ahmedabad. Finally at the end of the paper, findings, suggestions and direction for future research work are discussed.


Kavita Saxena2

LITERATURE REVIEWAccording to Goleman (1995), the foundation of EC is self-awareness,

knowledge of one's abilities and limitations as well as understanding of factors or situations that evoke emotions in one's self and others. Goleman (1995) and Salovey and Mayer (1990) identified the 5 key characteristics to explain EC which are stated below in table 1.

Table 1 Five key EC characteristics

Emotionally competent sales people help in enhancing the performance of the organization (Kerry et al., 2002). Spencer et al., (1997) has identified six emotional competencies: influencing capacity, team leadership, organizational awareness, self-confidence, achievement drive, and leadership. Many studies have been conducted in the past to show the impact of sales people's EC on their interactions with customers. There is an association between salesperson's attributes and customer emotions in buying situations that entail substantial customer/salesperson interaction.

Salespeople manage their emotions in an attempt to develop a new and better self, which in turn can manage emotions (Abraham, 2004; and Schweingruber and Berns, 2005). Certain studies revealed that many store managers were able to handle stress in a better way and believed that laughter facilitates in closing the sales deal successfully and combating stress (Keltner and Bonanno, 1997). Seligman (1990) concluded that self-confidence, empathy and positive attitude are the key EC proficiencies that lead to increased performance and productivity.

Verbeke et al., (2004) studied the role of EC in sales people's relationships with colleagues and customers. The study discovered that salespeople who score high on EC cope differently with socially challenging situations and achieve better social relationships than those who score low on EC. Verbeka et al., (2004), in their study explored the effects of EC on coping, social capital and performance of sales people and found that sales people with high EC scores coped effectively with envy and pride and achieved high social capital and performed well.

However, there is lack of research in the area of identifying key skills that constitute the EC of sales people in Indian context. So this study was conducted to fill the gap by studying the dimensions of important EC skills of sales people in the city of Ahmedabad.

OBJECTIVE OF THE STUDYObjective of the study was to explore the key EC skills that can be used to

improve the performance of sales people.

Self awareness Mood management

Self motivation Empathy Managing

relationshipsRecognizing one’s emotions and feelings and being able to differentiate between the two.

Reacting according to the situation and controlling one’s emotions at times.

Directing oneself towards achievement of goals and giving up on inertia and apprehension.

Putting oneself in others shoes and understanding their emotions.

Handling interpersonal relations and being able to deal with emotions which accompany cooperationand negotiation.


Kavita Saxena 3

METHODOLOGYFor the purpose of the study, exploratory research was conducted to gain better

understanding about the research area being studied. The study uses framework of EC developed by Vereke et al., (2004) to explore the key EC skills that can be used to improve the salespeople's performance. Convenience sampling method is used for data collection. A random sample of 100 sales persons is taken from the city of Ahmedabad. A structured, self-administered questionnaire with a 5 point Likert scale was used for data collection. The responses were collected on various statements related to handling and managing of emotions ranging from “strongly agree” to “strongly disagree”. The collected data was subjected to factor analysis using SPSS to identify the key EC skills of sales persons.

ANALYSIS AND DISCUSSIONThis section presents the analysis of the data that was collected from the

respondents. Table 2 gives the demographic characteristics of the respondents.

13 statements pertaining to EC were subjected to Factor Analysis. Correlation matrix was computed to test the suitability of the data for factor analysis and the results were found to be satisfactory to go ahead with factor analysis. Bartlett's Test of Sphericity was calculated to find whether the number of correlations among variables is statistically significant or not. Overall Kaiser-Meyer-Olkin was found to be 0.720 and Bartlett's test of Sphericity was also significant (Chi-square value = 822.522, df=78, significance=0.000) indicated the suitability of data for factor analysis.Principal Component Analysis (PCA): PCA was used for extracting factors. All factor loadings greater than 0.5 have been considered for analysis. The results of PCA with Varimax Rotation are shown in Table 3. Factors with Eigen value more than 1 were considered for analysis. The table shows Eigen values for 5 factors as 5.354, 1.645, 1.436, 1.153 and 1.043 respectively. It is observed that the percentage of variance explained by each of the 5 factors is 41.184, 12.652, 11.043,

Demographic factor Variable No. of respondentsMale 78GenderFemale

22Below 25 years

4125 – 35 years

5936 – 45 years

0

Age

Above 45 years

0

Below graduation

6Graduation 22

Qualification

Post graduation 72Less than 10000 510000 – 20000

29

21000 – 30000

39

31000 – 40000

4

Monthly income (Rs)

More than 40000

23Fresher

231 – 5 years 406 – 10 years 26

Experience

More than 10 years 11

Table 2 Demographic profile of the sample


Kavita Saxena4

8.87 and 8.024. Total of 5 factors account for 81.773% of variance. Communality shows the amount of variance each variable in the analysis shares with other variables included in the analysis.

Table 3 Principal Component Analysis with Varimax Rotation

Naming of factors: Each of the 5 factors extracted were given appropriate names based on the variables represented in each case. Table 4 summarizes the names of the factors along with the statements which represent each factor and their respective factor loadings along-with the % of variance explained by each one of them.

Table 4 Naming of factors

EC Statements Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Communality

11 0.678 0.879

12 0.757 0.832

13 0.743

0.911

3

0.832

0.817

9

0.725

0.755

6

0.549

0.775

7

0.891

0.892

1 0.875 0.835

5

0.816

0.864

2

0.705

0.818

10

0.878

0.825

4

0.62

0.812

8

0.762

0.616

Eigen value 5.354 1.645 1.436 1.153 1.043

% of variance 41.184 12.652 11.043 8.87 8.024

Cumulative variance 41.184 53.836 64.879 73.749 81.773

Factor Factor Name EC statements Factor loading

11 When I laugh, I am relieved of the worries/work pressure.

0.678

12 When I laugh at myself, it helps me in not getting very emotional.

0.757

1 Satirical Approach (41.184)

13 Whatever happens, I have learned to accept and look at my emotions with some humor.

0.743

3

I can easily show a variety of emotions when the situation asks for it.

0.832

9

I am acquainted with creating emotions to facilitate the process of sales interaction.

0.725

6

My expressed emotions are a part of what I see as my truthful way of dealing with customers.

0.549

2 Truthfulness (12.652)

7

I feel that my expressed emotions are a reflection of my inner self.

0.891

1

I can easily put myself in place of my customers and their needs.

0.8753 Empathy (11.043)

5

I can tune my emotions to those of the customer.

0.816

2

Others have learned to accept the way I manage my emotions.

0.7054 Emotional Acceptability (8.87)

10 I feel that people can quickly feel my emotions.

0.878

4 I am flexible with my emotions by only up to a limit.

0.625 Ethical emotional flexibility (8.024)

8 I manage my emotions keeping in mind certain societal/moral norms.

0.762


Kavita Saxena 5

Factor 1 – Satirical Approach: This factor has got highest factor loading and explains 41.184% of variance. There are 3 statements (No. 11, 12 and 13) which load on this factor. Highest loading is on the statement – “When I laugh at myself, it helps me in not getting very emotional.” Satirical Approach refers to Ironic perspective i.e. the sarcastic nature of the sales people and it enables them to tolerate emotionally ambiguous situations. They are able to handle stressful situations by normally laughing at themselves and adding some humor to make stressful situations lighter.Factor 2 – Truthfulness: This factor explains 12.652% of variance. Highest loading is on the statement – “I feel that my expressed emotions are a reflection of my inner self.” This proficiency states that sales people don't fake their emotions while interacting with customers. Though they manage their emotions as per the need of the situation, they strongly feel that their expressed emotions are a part of what they see as their truthful way of dealing with customers.Factor 3 – Empathy: Empathy refers to putting yourself in others' shoes. It explains 11.043% of variance. There are two statements which explain this factor. The highest loading (0.875) is for the statement – “I can easily put myself in place of my customers and their needs.” These sales people are good listeners; they tend to sense their customers' feelings and show lots of concern towards them. They are able to satisfy their clients by mere ability to sympathize with them.Factor 4 – Emotional Acceptability: This factor explains 8.87% out of the total variance explained. The statement – “I believe that people can quickly feel my emotions” gets the highest loading of 0.878. These sales people are highly acceptable among their clients because of the credibility they have earned during pervious sales interactions. They are accepted for what they are and are not required to change their emotions as per the requirement of the situation or the customer.Factor 5 – Ethical Emotional Flexibility: 8.024% of the total variance is explained by this factor. There are two statements which refer to ethical emotional flexibility of sales people and the statement “I manage my emotions keeping in mind certain societal/moral norms” has highest loading of 0.762. It showcases that the personal and ethical values of a sales person influence his emotional responses. A sales person with high EC will stretch his emotions up to a certain limit to which his personal values permit during the sales interactions. They don't give up their honesty and integrity for meeting the expectations of their managers. They are competent enough to handle stressful situations that demand emotional flexibility.

The study identified 5 key EC skills of the sales people namely satirical approach, truthfulness, empathy, emotional acceptability and ethical emotional flexibility. These findings are in line with the previous studies done in the same area by renowned researchers. Goleman (1995) and Salovey and Mayer (1990) identified


Kavita Saxena6

5 key characteristics to explain EC; Seligman (1990) concluded self-confidence, empathy and positive attitude are the 3 key EC proficiencies that lead to increased performance and productivity. Though the number of skills identified differed from researcher to researcher, the nature and kind of competencies identified are almost similar across all the research studies.

CONCLUSIONThe framework developed by Verbeke et al., (2004) provided valuable inputs in

identifying key EC skills in sales people. The data collected was subjected to factor analysis and the findings indicated satirical approach, truthfulness, empathy, emotional acceptability and ethical emotional flexibility as the important EC skills of sales people.

Various researchers have claimed that enhanced EC skills can lead to improved performance and low employee turnover. Hence efforts must be made to heighten the emotional competence of sales people by imparting appropriate training to them. •Special role plays can be organized for sales people wherein they can value and

learn the virtue of Empathy i.e. putting yourself in shoes of your customers.•They should be subjected to certain case studies which demonstrate use of

humor and sarcasm in combating stress and winning over the customers.•Videos and clippings of successful sales people with high EC can be shown to

others to demonstrate the importance of being ethical in one's dealings and viewing experiences from newer perspectives.

•They should also be trained in the area of impression management by which they can learn to amend their emotions to their advantage and create favorable image during sales interaction with customers.

•Certain management games can help employees to vent their emotions and provide an appropriate place for catharsis (emotional release).Future research can investigate EC proficiencies of sales people in specific

industries and can also find out the impact of various EC skills on the performance of sales people across different industries.

ReferencesAbraham, Rebecca (2004), EC as Antecedent to Performance: A Contingency Framework. Genetic, Social, and General Psychology Monographs, 130, 117 – 145.

Gibson, D. E. (1997), The Struggle for Reason: The Sociology of Emotions in Organizations. Social Perspectives on Emotion, 4, 211-256.

Goleman (1998), Working with Emotional Intelligence. Bantam Books, New York.

Goleman Daniel (1999), Emotional Competence. Executive Excellence, 16(4), 19.

Keltner, D. and Bonanno, G. A. (1997), Study of Laughter and Dissociation: Distinct Correlates of Laughter and Smiling During Bereavement. Journal of Personality and Social Psychology, 73,687-702.

Ritika Sharma (2008), A Study of Exploring Emotional Competence: Its Effect on Coping, Social Capital and Performance of Sales People. Unpublished Ph. D Thesis, Chandigarh: Punjab University, India.

Verbeke, W., Belschak, F. and Bagozzi Richard, P. (2004), Exploring Emotional Competence: Its Effects o n C o p i n g , S o c i a l C a p i t a l , a n d P e r f o r m a n c e o f S a l e s p e o p l e . Av a i l a b l e a t http://papers.ssrn.com/sol3/papers.cfm?abstract_id =513775).


Pankaj M Madhani 7

INDIAN BIOINFORMATICS: GROWTH OPPORTUNITIES AND CHALLENGES

Pankaj M Madhani

ABSTRACTThis paper focuses on India's emerging role in global bioinformatics market.

Bioinformatics is growing as an independent discipline and is fundamental to the growth of biotechnology. India has achieved remarkable success in the software industry. It would add nine per cent to India's GDP by 2010. With India's strong presence in world software market, India is expected to play a leading role in the bioinformatics revolution. This paper highlights Indian and global bioinformatics scenario. It also discusses bioinformatics challenges and opportunities and examines India's competitive position in bioinformatics by using SWOT analysis framework. BIOINFORMATICS: MEANING AND DEFINITIONS

Bioinformatics is a highly interdisciplinary, using techniques and concepts from applied mathematics, informatics, statistics, chemistry, biochemistry, physics, linguistics and computer science to solve biological problems and is the interface between the biological and computational sciences. Bioinformatics is an integration of mathematical, statistical and computer methods to analyze biological, biochemical and biophysical data. It is the application of computer technology to the management and analysis of biological data such as the sequences of DNA (Deoxyribonucleic acid) and RNA (Ribonucleic acid) in genes and amino acids in proteins.

Bioinformatics is the science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research. Bioinformatics is the field of science in which biology, computer science, and Information Technology (IT) merges into a single discipline. The ultimate goal is to enable the discovery of new biological insights and to create a global perspective from which unifying principles in biology can be distinguished. Simply put it is the marriage between biology and IT. More narrowly it is defined as the use of computer technology to organize and analyze genomic, biological, and chemical data in order to support the drug discovery process.

Bioinformatics is a science of developing and utilizing computer databases and algorithms to accelerate and enhance biological research. Bioinformatics deals with methods for storing, retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein genomic, biological and chemical data to support the drug discovery process. Bioinformatics uses computers to store, organize, generate, retrieve, analyze and share sequences, structures, functions, pathways and genetic interactions. Growth of biotechnology has accelerated particularly during the last decade due to accumulation of vast information as a result of sequencing of genomes

1

1 Prof. Pankaj M Madhani, Associate Professor, ICFAI Business School (IBS), IBS House, Near GNFC Tower, SG Road, Bodakdev, Ahmedabad. 380054. E-mail: [email protected]. Handset: 9328949568


8

and solving of crystal structures. This, coupled with advances in IT has made biotechnology increasingly dependent on computationally intensive approaches. This has led to the emergence of a super- specialty discipline, called Bioinformatics. The term 'bioinformatics' is the short form of 'biological informatics', just as biotechnology is the short form of 'biological technology'.

Bioinformatics can be defined as application of computation to the field of biology, including data management, algorithm development, and data mining. Clearly, a number of divergent areas, many of them outside biotechnology, come under bioinformatics. Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned. Bioinformatics is also defined as the application of computer technology to the management of biological information. It involves the development of software tools for the management and treatment of biological information. The definition of bioinformatics is not universally agreed upon. Generally speaking, it is defined as the creation and development of advanced information and computational technologies for problems in biology.

HUMAN GENOME PROJECT (HGP): MAIN GROWTH DRIVER OF BIOINFORMATICS

Bioinformatics tools were used to analyze the huge amount of data obtained from Human Genome Project (October1990-April 2003) attracting many companies round the globe to venture into this field. Bioinformatics became the BUZZ word, after the human genome was deciphered in February 2001. The sequence information generated by the human genome research, has now been stored as a primary information source for future applications in medicine. The available data was so huge that if compiled in books, the data would run into 200 volumes of 1000 pages each and reading alone (ignoring understanding factor) would require 26 years working around the clock.

For the population of about 5 billion human beings with two individuals differing in three million bases, the genomic sequence difference database would have about 15,000,000 billion entries. The present challenge to handle such a huge volume of data is to improve database design, develop software for database access and manipulation, and device data-entry procedures to compensate for the varied computer procedures and systems used in different laboratories. The HGP's information management challenge involves tracking the sequencing of the entire human genome - approximately three billion base pairs of DNA that make up our 23 pairs of chromosomes - and the precise mapping of the 100,000 or so genes that are interspersed on these chromosomes. The explosion of information resulting from the Human Genome Project has propelled the rapid development of bioinformatics as a discipline.

With the mapping of the human genome completed, bioinformatics is undergoing a sea change. Now that researchers and scientists possess maps of the human genome and those of several other animal species, they can look for divergence and similarities between all the genes of multiple species, with the

Pankaj M Madhani


9

ultimate goal of gaining a holistic view of biological systems as a whole. Genome mappings have generated a vast amount of biological data, and the pace at which new biological data is being generated is creating a deluge and the use of bioinformatics methods is the only way to cope with this flood of information.

EMERGING ROLE OF BIOINFORMATICS

Bioinformatics is growing as an independent discipline and helping immensely to accelerate the growth of Biotechnology. Its ultimate goal is to uncover the wealth of biological information hidden in the mass of data and to obtain a clearer insight into the fundamental biology of organisms. Bioinformatics has become a frontline applied science and is of vital importance to the study of new biology, which is widely recognised as the defining scientific endeavour of the twenty-first century. The genomic revolution has underscored the central role of bioinformatics in understanding the very basics of life processes. The growth in full genomic sequencing, structural genomics, proteomics, micro-array etc., will be very slow without application of bioinformatics. In fact usefulness of these areas to solve complex biological problems will be limited without bioinformatics and thus very high importance to bioinformatics (Dureha, 2004).

Bioinformatics is the application of IT and sciences to the organization, management, mining and use of life-science information. Bioinformatics is the buzzword for this new era of biological data analysis. The biotechnology sector depends on bioinformatics to deal with analyzing, interpreting, organizing and storing biological information related to DNA and proteins. Bioinformatics plays a central role in the development of bioindustries in general, including biotechnology, pharmaceutical, agrifood, environment management, medical diagnostics, bioprocessing, bioprospecting and others. Indeed, it is impossible to imagine a modern bioindustry sector without a competitive bioinformatics sector.

IMPORTANCE AND APPLICATIONS OF BIOINFORMATICSthHistorically, the 20 century belonged fundamental science such as physics and

stchemistry while the 21 century belongs to biology. Fusion of biology, statistics and computer science has created exciting field of bioinformatics in recent years. This interdisciplinary field drives an exciting process of understanding the secret of life. Bioinformatics is a set of enabling technologies responsible for the annotation, storage, analysis and retrieval of nucleic acid sequence, protein sequence and structural information. Bioinformatics is used in many areas of life sciences such as drug discovery, molecular medicine, microbial genome applications, and agriculture.

It is now possible, through computer algorithm based bioinformatics procedures, to identify and structurally modify a natural product, to design a drug with the desired properties and to assess its therapeutic effects, theoretically. It's commonly referred as dry lab work which accelerates the wet lab work drastically. Such procedures, similar to an architect's on board plan before construction, are described as in silico (in the computer, based on silicon chip technology), as opposed to the earlier in vitro (in experimental models) and in vivo (in clinical trials) methods.

Pankaj M Madhani


10

The risk involved in the earlier random processes of drug discovery methods is largely removed by bioinformatics.

Bioinformatics is being applied to speed up the drug discovery process by moving towards data-driven drug discovery, to improve efficiency, trim down costs and the time-lines and provide wider access to the entire life sciences sector.

Bioinformatics in itself has been a catalyst and turbocharger in helping make drug discovery more cost effective and less time consuming. Bioinformatics is an integral part of drug discovery and development. Most of the pharma-biotech R&D is moving in silico. The fastest growing segment, in silico modelling allows for the testing of compounds through computer programs simulating the reaction of cells and organisms. This eliminates significant laboratory work and expenses.

As for the application of bioinformatics, within 10 years, all new drug designs will be genomics-related. Growth of the bioinformatics market is primarily attributed to its increased usage in the pharmaceutical industry. The application of bioinformatics in drug discovery and development is expected to reduce the annual cost of developing a new drug by 33 percent, and the time taken for drug discovery by 30 percent. In order to gain a competitive advantage in the drug discovery process, pharmaceutical and life sciences companies are placing increasing emphasis on bioinformatics as it reduces the time and costs of developing medicine because of its facilitation with filtering data and with choosing the best way to proceed. With the ever-rising costs of discovering new drugs and taking them to the market, the average cost of discovering a new drug is about one billion dollars. Bioinformatics has the potential to hugely decrease the risk, cost, and expertise required for the early stages of drug development, target selection and validation.

BACKBONE OF INDIAN BIOINFORMATICS: ROBUST IT AND PHARMA INDUSTRY

India is having global IT leadership leverage advantage. India has robust IT industry with 82 SEI CMM (Capability Maturity Model) level '5' companies - three fourth of the world total (Madhani, 2008). India holds a 44 percent share of the global offshore outsourcing market for software and back-office services. India has achieved remarkable success in the software industry. More than 95 countries outsource their work to India. With liberalization in the 1990s, the Indian software and IT industry prospered. The Indian software industry grew at a rate of over 37.5% per year between 1995 and 2000. The Indian software segment's exports accounted for 72% of total IT industry sales between 2000 and 2001 and grew from $131.2 million in 1990 to $7.8 billion in 2001–2002 (D'Costa, 2003).

Exports are a major source of revenues for the Indian software industry, and exports have been a main focus of the industry (Sahoo and Patnaik, 2009). Furthermore, the number of firms in the industry has grown over time. According to Athreye (2005), there were 420 firms in 1994–1995, 900 firms in 1996–1997, 948 firms in 1999–2000, and 1,828 firms in 2000–2001. The Indian software industry itself had an annual compound growth rate of over 50% per year from 1994 to 1999 (Nirjar and Tylecote, 2005), and grew 250% between 1999 and 2005 (Mathur, 2007). With India's strong presence in world software market, India is expected to play a leading role in the bioinformatics revolution.

Pankaj M Madhani


11

Indian bioinformatics is supported by vibrant domestic pharmaceutical thindustry. Indian pharmaceutical industry is 4 largest pharmaceutical industry in

thterms of volume and 13 position in terms of value in the world. Indian biotech rdindustry is 3 largest in Asia-Pacific region. Exports have been growing at > 20%

annually over the last several years. India has the largest number of US Food and Drug Administration (FDA) approved drug manufacturing facilities outside the US.

India is having more than 70 FDA-approved plants and 200 manufacturing facilities certified as having good manufacturing practices (GMP), more than any other foreign country and 20% of all NDAs (New Drug Applications) filed in the U.S. are from Indian companies. The global pharmaceutical industry is set to grow by a compounded annual rate of nearly 10% over the next five years. The United States accounts for 46% of the world's pharmaceutical market. Some of the factors that will drive this growth are escalating healthcare expenditures, growing prevalence of lifestyle related diseases and ballooning aged population. The India bioinformatics market is expected to exceed US$1 billion by 2011, while the market for Indian biotech R&D products and services will rise to around US$ 3 billion by 2011. The R&D being outsourced to India in these segments relates to areas such as molecular biology, DNA sequencing, molecular biology software packages, molecular modelling, among others.

Complementary competencies in BT and IT make India well positioned to exploit the bioinformatics wave. India, with its enormous strengths in the IT software and services industry and its wide experience in the Biotechnology sphere, has also begun gearing up for this emerging market. India has the potential to become a leading hub of bioinformatics projects.

BIOINFORMATICS: INDIAN SCENARIO The Bioinformatics sector in India have grown up rapidly as IT companies have

also stepped up their focus on the life sciences vertical. Companies like Infosys, Cognizant Technologies, HCL, MphasiS, and TCS have been made significant strides in this space.

Indian Bioinformatics companies can look forward to garnering a large chunk of the world market for bioinformatics services such as data mining, mapping and DNA sequencing, functional genomics, proteomics and molecule design simulation. Growing volumes of genomics data and an expanding number of participants contracting work to Indian companies have encouraged many pharmaceutical, IT, and Biotechnology (BT) companies to enter the bioinformatics sector.

Indian IT companies such as Tata Consultancy Services (TCS), Cognizant Technologies, Infosys, and Wipro have already set up their bioinformatics divisions. Indian pharmaceutical such companies such as GVK Biosciences, Dr. Reddy's Laboratories, Biocon, Astra Zeneca, Ranbaxy, Biological E, and Nicholas Piramal too, are making rapid moves into the bioinformatics arena. India is also witnessing the emergence of pure-play bioinformatics companies such as Strand Genomics.

BIOINFORMATICS: WORLD SCENARIOThe global bioinformatics market is expected to grow at an average annual

growth rate of 25% to reach by 2011. The largest markets for bioinformatics include USA, Europe and Japan while emerging large markets include Australia, China and India. A number of US based companies, like IBM, SGI, Sun Microsystems etc., are

Pankaj M Madhani


12

actively working towards developing IT based bioinformatics tools. Content, mostly in the form of specialized databases on microarrays, ESTs (expressed sequence tag), SNPs (single nucleotide polymorphism) and so on, represents most of the bioinformatics industry. Analysis software and services are anticipated to be the fastest growing market in bioinformatics, primarily driven by the need for improved and sophisticated tools for analyzing and using biological data for developing therapeutic drugs. Pharmaceutical companies are expected to increase their R&D expenditure in the future, mainly focused towards bioinformatics.

BIOINFORMATICS IN INDIA: KEY COMPETITIVE ADVANTAGESIn India, major government organizations, such as Biotechnology Information

System (BTIS) and Department of Biotechnology (DBT) are promoting bioinformatics. DBT had identified bioinformatics as an area of high priority during

ththe 10 plan period (2002-2007). Government of India is also providing numerous tax incentives at par with IT to develop bioinformatics sector. India has combined its strength in biotechnology and IT to attract outsourcing contracts in bioinformatics by building Bio-IT park. The Bio-IT Park would be the launch pad for bioinformatics industry as STPs (Software Technology Parks) were for IT and position itself as a global hub for bioinformatics. These parks would be a conglomerate of academic-industry-research initiatives, thereby opening up new vistas for the Indian bioinformatics market and making it a sunrise industry for the future. The Department of Biotechnology, Government of India has been working with other departments to set up these parks, which is expected to position India in the global hub of bioinformatics. Establishment of Bio-IT parks and new biotech policy acts as growth catalyst for bioinformatics sector.

India has several unique capabilities that may allow it to become a forerunner and important international player. India took early steps in 1986 and established a nationwide bioinformatics system. The Bioinformatics program, as a distributed database and network organization, was launched during 1986-1987. The program has become a successful vehicle for transfer and exchange of information, scientific knowledge, technology packages, and references in the country. In 1987, the government took a major step forward in establishing a national grid of bioinformatics centres as the Biotechnology Information System Network (BTISNet). The network has presently grown to 65 bioinformatics centres, institutes and universities covering all parts of the country with more than 12,000 users and 100 databases. Bioinformatics centres coordinate bioinformatics activities, facilitate collaboration and improve awareness of bioinformatics. Major benefits of BTISNet are: It avoids duplication, builds a network of critical mass, and allows each of the various regional centres to offer customized services for local bioinformatics needs.

Department of biotechnology has also launched BIOGRID INDIA, a high-speed, high bandwidth network in the form of Virtual Public Network (VPN) for resource sharing. It has mirror sites of globally recognized databanks and public domain bioinformatics software tools. Dedicated virtual private network connects the major centres and share data as well as computing resources. Sun Microsystems Inc. a leading US company in IT sectors, has established the world's first state-of the-art dedicated Center of Excellence (CoE) for medical bioinformatics at Hyderabad, in collaboration with the Center for DNA Fingerprinting and Diagnostics (CDFD),

Pankaj M Madhani


13

August 2005. SUN 'Center of Excellence' (CoE) provides major thrust to bioinformatics research in India.

India has large pool of IT professionals, fundamental and life science researchers. These research talents are available at low cost compared to very high cost of western countries. With its large resource pool of molecular biologists,statisticians, and software programmers, good network infrastructure and an emerging venture capital industry, India is well positioned to obtain a fairly large share of the global bioinformatics market. Indian companies can play a significant role in areas such as data handling, data-mining, genotyping and fingerprinting, DNA sequencing etc. The cost of setting up and running a bioinformatics company in India is a fraction of the cost in the US. Indian players will have to leverage upon the lower costs of infrastructure and human resources, while focusing on value added products and services. The Indian bioinformatics sector is gradually trying to garner a good share of the global revenues. India has great potential for a vibrant bioinformatics industry.

India is having strong and high quality educational infrastructure i.e. IIT's, IISC, NIT's, IBB (Institute of Bioinformatics and Biotechnology), IOB (Institute of Bioinformatics), BII (Bioinformatics Institute of India) etc. Government of India has established five academic Center of Excellence (CoE) in the field of bioinformatics i.e. JNU New Delhi, IISC Bangalore, University of Pune, Madurai Kamraj Uni. Madurai and Bose Institute, Kolkata. India is also having a vast network of public national research laboratories which provide world class support for R&D. The Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) set up at IIT Delhi in July 2002, connects 12 academic institutes on VPN. Increasing trend of partnerships between the private sector and the institutions such as New Millennium India Technology Leadership Initiative has fuelled the growth of bioinformatics in India. India's new product patent regime in line with TRIPS (Trade Related Aspects of Intellectual Property Rights) agreement of WTO will enhance export prospects. SWOT analysis of Indian bioinformatics sector is given at Annexure- II.

BIOINFORMATICS IN INDIA: LOOKING AHEADThe Indian bioinformatics industry is highly progressive in many areas such as

contract research and development services, clinical trials, contract manufacturing and drug development. Many IT services companies are joining the race for a share of the bioinformatics pie. Accelrys, Makro Technologies, IBM, Mascon Global, Systat Software, Cherrysoft Technologies and Oracle have their units in India working in the bioinformatics space. IT is an indelible part of the bioinformatics industry. A number of companies, like IBM, SGI, Sun Microsystems etc, are actively working towards developing IT as a tool to expedite research and discovery.

In a developing country like India, bioinformatics has a key role to play in areas like in the pharmaceutical sector, agriculture sector etc. It can be used to reduce the time and cost involved in drug discovery process particularly for third world diseases, Malaria, Tuberculosis etc. It can also be used for various application developments in agriculture like pest resistant and water stressed crop and modifying certain traits in existing crops. Bioinformatics would help to generate significant economic benefits for India such as high skill and high value jobs, growth of firms (e.g. in contract

Pankaj M Madhani


14

research, clinical trials and investigation, product and software development), additional private and public sector R and D spending, higher export revenues and increased share of global KPO (Knowledge Process Outsourcing) market. India is having growing acceptance as a global player in knowledge-based industry.

CONCLUSIONIndia's position as a global software powerhouse and as a leading IT nation with

growing biotechnology expertise gives Indian companies many advantages with the bioinformatics challenges. As IT is an indelible part of the bioinformatics industry the future outlook for Indian Bioinformatics looks bright. India has great potential to develop competitive research and innovative technologies. It is envisaged that, India will emerge as a significant contributor to the world bioinformatics market and position itself as a global hub for bioinformatics. Indian bioinformatics sector has numerous strengths and competitive advantages to make bioinformatics sector a sunrise industry of India. With the improvements in the IPR regime, increasing support from the government and continuing efforts of the private sector companies, it is very much likely that India could repeat its IT success story in bioinformatics too. Indian Bioinformatics market is expected to grow exponentially to take a major pie in the global bioinformatics sector in next few years.

ANNEXURE - ISWOT Analysis of Indian Bioinformatics

Strengths•World's first state-of the-art CDFD – SUN 'Centre of Excellence'(CoE) for

medical bioinformatics set up in 2005 at Hyderabad at cost of $5 million – gives major research thrust

•Launch of BIOGRID INDIA, a high-speed, high bandwidth network in the form of Virtual Public Network (VPN) for resource sharing. It has mirror sites of globally recognized databanks and public domain bioinformatics software tools

•The Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) set up at IIT Delhi in July 2002, connects 12 academic institutes on VPN

•International recognition of scientific strengths and inventions

•Good pool of IT professionals and life science researchers

•A robust IT industry with 87 SEI CMM (Capability Maturity Model) level '5' companies - Three fourth of the world total

•Established Technology Clusters

•R&D Strengths - A vast network of public national research laboratories provides world class support

•High Quality Educational Infrastructure i.e. IIT's, IISC, IBB (Institute of Bioinformatics and Biotechnology), IOB (Institute of Bioinformatics), BII (Bioinformatics Institute of India) etc.

•Rich biodiversity and large datasets of commercial value

•Strong clinical research base

Pankaj M Madhani


15

th•Well-developed user/base industry – 4 largest pharmaceutical industry in the rdworld and 3 largest Biotech industry in Asia-Pacific

•First country in the world to establish a Biotechnology Information System

Network (BTISNet) in 1987•Vast network of 65 Bioinformatics centres with more than 12,000 users - avoids

duplication, builds a network of critical mass, and allows each of the various regional centres to offer customized services for local bioinformatics needs.

•A strong multinational presence (i.e. Sun Micro, IBM, Oracle etc.) enables

Indian talent to work on the most state-of-the-art research projects and experience cutting edge technologies

•Abundance of qualified research talent at low costs

•Five academic Center of Excellence (CoE) established by GOI i.e. JNU New

Delhi, IISC Bangalore, University of Pune, Madurai Kamraj University Madurai and Bose Institute, Kolkata.

Weakness•The results of the recent National Eligibility Test on Bioinformatics (NETBI)

showed the need to improve and reform the quality of bioinformatics education •Few R&D centres having high performance computing infrastructure facilities

•Non-availability of adequate risk capital venture fund

•Lack of nationally streamlined and fully integrated bioinformatics database

•Lack of nodal agency for better coordination as Bioinformatics comes under the

jurisdiction of various government agencies and departments•Absence of national lobbyist organization for Bioinformatics such as

NASSCOM (National Association of Software and Service Companies) for IT•Biotech industry cannot be developed in isolation like IT industry as major

chunk of research has been done by Government labs•Reduced basic life science research due to lack of local bioinformatics support

industry

Opportunities •New product patent regime in line with TRIPS (Trade Related Aspects of

Intellectual Property Rights) agreement of WTO to enhance export prospects

•Establishment of biotech (Bio-IT) parks and new biotech policy acts as

growth catalyst for bioinformatics sector•Global IT leadership leverage

•Fusion of biotechnology and ICT (Information and Communication

Technology) industry growth to create an even stronger bioinformatics industry

•Strong education infrastructure to tap foreign student markets

Pankaj M Madhani


16

•Cost of developing a new drug and a genetically modified product remains very high in western countries.

•Growth of domestic bioindustries will result in increasing demand for bioinformatics products and services

•Movement of medical research into using genomic will see a convergence of medical informatics and bioinformatics, increasing the need for bioinformatics industry players

•Large expatriate community ready to come home to fuel the industry if local conditions change

•Tax incentives from Government - at par with IT

•Large growth potential with exports and increased KPO trend

•Rapidly improving infrastructure

•Growing acceptance as a global player in knowledge-based industry

•Growing government support as Bioinformatics is identified as an area of high thpriority in 10 planning period (2002-2007)

•Promising investment growth opportunities in India

•Growing collaborative partnerships to pursue domestic and global commercial bioinformatics ventures

•Increasing trend of partnerships between the private sector and the institutions such as New Millennium India Technology Leadership Initiative

•Development of Bio-Suite, an indigenous end-to-end world class CMM level '5'Bioinformatics software tool by TCS

•Growing leadership due to large resource pool of molecular biologists, statisticians and software programmers

•Extensive clinical trials opportunities and growth of CROs (Clinical Research Organizations) to fuel Bioinformatics

Threats •Competition from IT savvy emerging biotech countries i.e. Singapore, Taiwan,

Japan, and Korea•Reliance on IT sector business model i.e. focus on low cost high volume and not

on high value niche products and services•Continued 'brain drain' of Indian expertise

•Insufficient trained talent pool to meet demand

•Significant lead time in training the available talent

•Complexity of IP related ownership and access issues of Bioinformatics database

•Increased competition from emerging bioinformatics market i.e. Australia and China

(Source: Madhani, 2007)

Pankaj M Madhani


17

References “Advantage India”, A special report by Department of Information Technology, Government of India, www.mit.gov.in

Ahmed, Mushtaq (2005), Bioinformatics Scenario in India and Collaboration Opportunities. The Nucleus.

“All about the human genome project”, National Human Genome Research Institute (USA), www.genome.govAthreye, S. (2005), The Indian software industry. In A. Arora and A. Gambardella (Eds.). From underdogs to tigers: The rise and growth of the software industry in Brazil, China, India, Ireland, and Israel: 7-40. New York: Oxford University Press. “Biotechnology - A report by Ernst and Young for IBEF”, India Brand Equity Foundation, www.ibef.org

“Bioinformatics Policy of India (BPI–2004)”, National Bioinformatics Policy Draft by Dept. of Biotechnology, Ministry of Science and Technology, Government of India, http://dbtindia.nic.in

D'Costa, A. (2003), Uneven and combined development: Understanding India's software exports. World Development, 31(1), 211–226.

Dureha, Rolly (2004), Widening horizons in Bioinformatics. BioSpectrum,

“Guide to Bioinformatics at Stanford University”, http://smi-web.stanford.edu

Madhani, P. M. (2008), Indian IT and ITeS industry: Emerging strategies in an era of appreciating rupee. Global CEO, 39-45.

Madhani, P. M. (2007), India's Emerging Role in Bio-Informatics. SCMS Journal of Indian Management, 4(2), 105-113.

Mathur, S. (2007), Indian IT and ICT industry: A performance analysis using data envelopment analysis and malmquist index. Global Economy Journal, 7(2), 1-40.

“NIH working definition of Bioinformatics and Computational Biology”, National Institute of Health (USA), www.nih.org

Nirjar, A. and Tylecote, A. (2005), Breaking out of lock-in: Insights from case studies into ways up the value ladder for Indian software SMEs. Information Resources Management Journal, 18(4), 40-61.

Sahoo, P., and Patnaik, R. (2009), Emergence and growth of the Indian software industry: A strategic study. IUP Journal of Business Strategy, 6(3), 81-94.

Pankaj M Madhani


18


19Dr. Mahendra Sharma and Tejas Shah

A STUDY ON INTERRELATIONSHIPS AMONG COMPONENTS OF LOGISTICS FLEXIBILITY FOR

CO-OPERATIVE DAIRIES 1 2Dr. Mahendra Sharma Tejas Shah

1

2

Dr. Mahendra Sharma, Dean, Faculty of Management Study, Ganpat University, Kherva, Mehsana, Gujarat. E-mail: [email protected]. Tejas Shah, Faculty in Marketing, Shri Chimanbhai Patel Institute of Management and Research, Ahmedabad, Gujarat. E-mail: [email protected]

ABSTRACTFlexibility is conceptualized differently in literature. Flexibility seeks to

increase range/variety, improve mobility/responsive and achieve uniform performance. The framework of Zhang et al., (2005) has been used in this study to define components of logistics flexibility. This study examines the relationships among the various components of logistics flexibility for co-operative dairies in Gujarat. The sample unit includes the senior managers of procurement and purchase department (25 nos.), production department (33 nos.) and marketing department (24 nos.) of various co-operative dairies in Gujarat. The reliability test shows that all the components of logistics flexibility scale have been found reliable to use for this study. The Regression analysis is used to find out the degree of relationships among various components of logistics flexibility scale. Based on results, it is empirically confirmed that flexible logistics competence supports the flexible logistics capability, which ultimately enhances customer satisfaction. A firm can achieve customer satisfaction by developing logistics flexibility.

INTRODUCTIONToday, customer responsiveness and flexibility are the keys to responding to

markets and gain critical competitive advantage (Cunningham, 1996). Firms need to respond to changing customer needs in terms of special treatment in design, production, and delivery, which requires firms to view flexibility from a supply chain perspective rather than on equipment or process perspective (Day, 1994).

Gujarat is one of the important states in India in terms of production of fluid milk and milk products. The total milk procurement in India was 10,84,63,000 tonnes, while in Gujarat it was 83,86,000 tonnes, which constitutes around 7.73

thpercent of total milk production in India. Gujarat is ranked 5 among the states producing milk in India. GCMMF is the apex body of all the co-operative dairies in Gujarat. Under GCMMF, 13 co-operative dairies are operating with 19 dairy plants in Gujarat. In Gujarat, there were 13646 organized District co-operative societies (DCS) centers in the year 2008-09, constituting 2839000 farmer members out of which 784000 women members (NDDB, n.d.). Sharma and Thaker (2010) reported that the share of milk procured by co-operatives in Gujarat is much higher at 39 percent as against the national average of 8 percent and has decreased significantly during 2000s. They further noted that the share of Gujarat in total milk procurement by co-operative sector in India is the highest at 32.90 percent in 2008-09.


20 Dr. Mahendra Sharma and Tejas Shah

LITERATURE REVIEWFlexibility can be defined as the reactive capability of the management to the

uncertainty faced by an organization, ignoring the performance dimensions such as cost, time and quality. The coordination of sourcing, making and distribution enhances the company's ability to respond to market changes by eliminating redundant activities and reducing response time. Logistics flexibility includes many activities such as organizing inbound and outbound shipments, providing manufacturing support, and supplying information to coordinate these efforts. Gerwin (1987) defined flexibility as an ability to respond effectively to changing circumstances. Gupta and Somers (1992) were of the view that flexibility is the ability to cope with changing circumstances or instability caused by the environment. Day (1994) defined flexibility as the ability of a firm to respond quickly and efficiently to changing customer needs in inbound and outbound delivery, support, and services. Upton (1994, 1995b) defined it as the ability of a manufacturing system to change states across an increasing range of volume and/or variety, while adhering to stringent time and cost metrics. Zhang et al., (2002) defined flexibility as the organization's ability to meet an increasing variety of customer expectations without excessive costs, time, organizational disruptions or performance losses. Holweg (2005) defined it as a generic ability to adapt to internal and/or external influences.

Kumar et al., (2006) defined the flexibility from supply chain point of view as the ability of supply chain partners to restructure their operations, align their strategies, and share the responsibility to respond rapidly to customers' demand at each link of the chain, to produce a variety of products in the quantities, costs, and qualities that customers expect, while still maintaining high performance. Reichhart and Holweg (2007) defined flexibility as the ability of any system to adapt to internal or external influences, thereby acting or responding to achieve a desired outcome. Bernardes and Hanna (2009) defined flexibility as enabling ability to change status within current configurations and predetermined constraints.

TYPES OF FLEXIBILITYFlexibility can be reactive or proactive or adaptive or redefined (Bernardes and

Hanna, 2009). A number of classifications have been developed to describe flexibility as shown in table 1 below:

Table 1 Types of Flexibility

Product performance, product mix, quality, volume and delivery Slack (1983) – Five types (Manufacturing Level)

Mix flexibility, Changeover flexibility, modification flexibility, rerouting flexibility, volume flexibility, material flexibility and sequencing flexibility

Gerwin (1987) – Seven types (Operational Level)

Basic flexibilities (machine, material handling , and operation), system flexibilities (process, routing, product, volume, expansion) and aggregate flexibilities (programme, production, market)

Sethi and Sethi (1990) – Eleven types (System Level)

Product flexibility, volume flexibility, new product flexibility, distribution flexibility and responsiveness flexibility

Vickery et al. (1999) – Five types (SCM level)

Operations system, market, logistics, supply, organizational and information system flexibility

Vokukra et al. (2003) – Six Elements (SCM level)

Delivery flexibility, production flexibility, product development and sourcing flexibility

Pujawan (2004) – Four level (SCM Level)

Sourcing, developing new products, product customization, responsiveness and delivering the finished products

Kumar et al. (2006) – Five level (SCM level)



COMPONENTS AND ATTRIBUTES OF LOGISTICS FLEXIBILITYFrom the literature, it is difficult to see a totally consistent view concerning the

types of dimensions of flexibility. Basically, Logistics flexibility includes internal competences, that means what the firm can do and control but customers cannot see, and external capabilities, that customers see and value (Zhang et al., 2002 and Zhang et al., 2005). Upton (1995a/b) described two types of logistics flexibility, internal flexibility that focuses on internal competence and external flexibility that focuses on market responsiveness. He found that internal flexibility is the internal means by which external flexibility can be achieved. Competence and capability correspond to secondary and primary flexibility respectively. Flexible competence provides the processes and infrastructure that enable the firm to achieve the desired levels of capability. Logistics competence and capabilities contribute to a firm's competitiveness through creating economic (cost leadership) and market-based (differentiation) values.

The field of flexibility is defined by four building blocks of range-number, range-variety, mobility and uniformity (Bernardes and Hanna, 2009). Flexibility seeks to increase range/variety, improve mobility/responsive and achieve uniform performance (Zhang et al., 2005). Range is the firm's ability to design, make and distribute different products (Slack, 1983). Mobility is the speed at which a firm can change from one product to another (Slack, 1983). Uniformity is the ability to maintain performance standards as a firm switches among products, which implies the ability to maintain high quality as the product is changed (Upton, 1995).

Logistics flexibility has four components: physical supply flexibility and purchasing flexibility, which are competences and physical distribution flexibility and demand management flexibility, which are capabilities. Physical Supply Flexibility is the ability of a firm to provide a variety of inbound materials and supplies for production, quickly and effectively. Purchasing Flexibility is the ability of a firm to make agreements to buy a variety of materials and supplies, quickly and effectively. Physical Distribution Flexibility is the ability of a firm to adjust the inventory, packaging, warehousing and transportation of physical products to meet customer needs, quickly and effectively. Demand Management Flexibility is the ability of a firm to respond to the variety of customer needs for service, delivery time and price, quickly and effectively. Customers value the visible capabilities, physical distribution flexibility and demand management flexibility rather than the supply side competences because customers see how capabilities are deployed to meet their needs. Physical distribution flexibility and demand management flexibility cannot be achieved without flexible logistics competences that support the manufacturing process. This dichotomy of flexible logistics competence and capability enables managers to develop a comprehensive view of flexibility.

Logistics flexibility enables firms to customize product and service offerings without increasing stock levels (Zhang et al., 2005). While functional products with predictable demand benefit most from “physically efficient processes, innovative products demand “market responsive” processes that are focuses on speed and flexibility rather than on cost (Storey et al., 2005). With flexible logistics competence/capability, bundles of services such as adding product features or providing specific packaging, labeling and product configuration can be aligned with individual customer needs.


Dr. Mahendra Sharma and Tejas Shah22

NEED FOR THE STUDYThe logistics flexibility plays crucial role in supply chain of fluid milk and milk

products. Out of all product categories of fast moving consumer goods sector; the fluid milk and milk products segment has the lowest shelf life, higher inventory carrying cost and logistics cost and disposal of outdated products are vital issues. The significant growth of economy of Gujarat and the flourishing development of co-operative network of fluid milk and milk products demand perfect synchronization of supply of various fluid milk and milk products with market opportunities. This issue is equally vital and critical for all manufacturers (co-operative dairies) and marketer (GCMMF) of Fluid Milk and Milk products irrespective of their scale of productions and operations. Therefore, it becomes important to examine the relationships between internal competences and customer perceptible capabilities that comprise logistics flexibility to develop appropriate relationships to describe logistics flexibility at supply chain level with reference to co-operative dairies in Gujarat.

OBJECTIVES OF THE STUDY1. To study the concept of logistics flexibility2. To find out the various components of logistics flexibility3. To examine the relationships among the various components of logistics

flexibility for Fluid Milk and Milk Products in co-operative dairies in Gujarat

RESEARCH METHODOLOGYThis study used a descriptive research design to determine the relationships

among various components of logistics flexibility and interrelationships among them. The non-probability quota sampling method was used for data collection. The managers of different departments at various supply chain levels of co-operative dairies in Gujarat like procurement and purchasing, processing and marketing and distribution were considered as sample universe for sampling procedure. In total, the researcher contacted ninety senior managers of procurement and purchase department, processing department and marketing and distribution department of fifteen co-operative dairies in Gujarat, out of which, eighty two completely filled questionnaires were received. The sample unit includes the senior managers of procurement and purchase department (25 nos.), production department (33 nos.) and marketing department (24 nos.) of various co-operative dairies in Gujarat.

Structured questionnaire was used having close-ended questions. To identify an initial set of components to measure logistics flexibility, an extensive literature review is completed. Based on the findings of Zhang et al., (2005), four dimensions of logistics flexibility at supply chain level named physical supply flexibility, purchasing flexibility, physical distribution flexibility and demand management flexibility creating significant impact customer satisfaction dimension were identified. The reliability and validity of these five factors have been proved in the previous study conducted by Zhang et al., (2005). In this study, a further reliability of these five factors has been checked to confirm the consistency and accuracy of results using these factors. The final part contains the four components of logistics flexibility with total of five factors, which include Physical Supply Flexibility,


Dr. Mahendra Sharma and Tejas Shah 23

Purchasing Flexibility, Physical Distribution Flexibility, Demand Management Flexibility and Customer Satisfaction. These five factors contain 29 statements. All 29 statements are rated using Likert scale with 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree. The managers are asked to rate the each question in terms of the strength of their agreeableness or disagreeableness with respect to Logistics Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

ANALYSIS AND DISCUSSIONThe Cronbach Alpha measurement of reliability analysis has been used to check

reliability of logistics flexibility scale. The Regression analysis is used to find out the degree of impact of various dimensions of logistics flexibility scale for fluid milk and milk products in co-operative dairies in Gujarat (Malhotra and Das, 2010). The analysis is done using 95% confidence level. In addition to regression analysis, Pearson correlation and variance inflation factor (VIF) statistics were used to assess both pair wise and multiple collinearity among independent variables in regression analysis (Keith, 2006; Lewis-Beck 1980 and Menard, 1991). The Durbin-Watson test is also used to assess the degree of autocorrelations among various variables in regression equation (Kutner et al., 2005).

RELIABILITY ANALYSISReliability analysis is done to check whether the variables used to study

logistics flexibility will produce consistent results. The Cronbach Alpha is used to check the reliability. The results are shown in the table 2 below:

Table 2 Reliability of Logistics Flexibility Scale

As shown in the above table, the calculated Cronbach Alphas for all the variables are well ahead of the cut off rate of 0.70 to prove good reliability (Hair et al., 2009). So, it can be concluded that all the factors used to measures the logistics flexibility for fluid milk and milk products in Co-operative dairies in Gujarat are found to be reliable. It means these factors will produce consistent results irrespective of time period.

REGRESSION ANALYSISRegression of Physical Supply Flexibility (PSF) and Purchasing Flexibility

(PF) on Physical Distribution Flexibility (PDF). Hypothesis:H(PSF)0: There is no significant impact of Physical Supply Flexibility on Physical

Distribution Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

Variables Cronbach Alpha

Physical Supply Flexibility

0.788

Purchasing Flexibility

0.802Physical Distribution Flexibility 0.734Demand Management Flexibility

0.711

Customer Satisfaction

0.800

Logistics Flexibility Scale 0.780


24 Dr. Mahendra Sharma and Tejas Shah

H(PSF)1: There is a significant impact of Physical Supply Flexibility on Physical Distribution Flexibility for fluid milk and milk products in co-operative dairies in Gujarat

H(PF)0: There is no significant impact of Purchasing Flexibility on Physical Distribution Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

H(PF)1: There is a significant impact of Purchasing Flexibility on Physical Distribution Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

The regression co-efficient of the independent variables with their respective direction, values and significance level are given in the table 3 below:

2Dependent Variable: PDF R = 0.799Independent Variables: PSF, PF N = 82 numbersDurbin-Watson = 2.091ANOVA (F Value) = 161.528 (Sig. 000), df = (2,79)Correlation Coefficient (PSF and PF) = 0.504

The table 3 shows Physical Supply Flexibility (PSF) exerts significant influence over Physical Distribution Flexibility (PDF). An increase in Physical Supply Flexibility (PSF) will bring about a significant increase in Physical Distribution Flexibility (PDF) by number of times the value of regression co-efficient. Purchasing Flexibility (PF) exerts significant influence over Physical Distribution Flexibility (PDF). An increase in Purchasing Flexibility (PF) will bring about a significant increase in Physical Distribution Flexibility (PDF) by number of times the value of regression co-efficient. The variance analysis shows F = 161.528 at a significance level of 0.000 with df (2, 79), which indicates that all regression co-efficients will be non zero.

The emerging Multiple Regression Equation is as under:PDF = + 0.102 + 0.752 (PSF) + 0.238 (PF)

2The adjusted R , i.e. the co-efficient of determination stands at 0.958 indicating that the equation can explain 79.9% variations in Physical Distribution Flexibility (PDF). For remaining variations, i.e. unexplained variations, some other variables are responsible. The co-efficient of correlation among two independent variables (Physical Supply Flexibility and Purchasing Flexibility) have the value of 0.504. The

Table 3 Regression coefficients

Regression coefficients

Direction

Value t-value

Significance

Level VIF

Statistic

Constant + 0.102 0.480 0.633

PSF +

0.752

13.035

0.000

1.340

PF + 0.238 4.127 0.000 1.340


25

VIF statistics in table 3 also depicts the value of 1.340 for Physical Supply Flexibility and 1.340 for Purchasing Flexibility, which are very much far from cut off rate of 10 for VIF statistics. Hence there is no cause of concern from viewpoint of multicollinearity among the independent variables. The D (Durbin-Watson) statistic stands at 2.091. The corresponding table values for D statistic stand at 1.61 for lower limit (D ) and 1.66 for upper limit (D ). As a result D (2.091) is greater than D (1.66) U L U

and 4 – D (1.909) is greater than D (1.61). Hence it can be concluded that either L

positive or negative autocorrelation does not exist.Physical Supply Flexibility exerts significant positive impact on Physical

Distribution Flexibility because of following reasons: (1) Delivering multiple kinds of material (range) in response to operations requirements on time (mobility) creates positive impact on Physical Distribution Flexibility in terms of distributing varieties of fluid milk and milk products in different pack sizes on time (range and mobility). (2) On time and quick delivery (mobility) of required materials to manufacturing department brings accuracy in terms of producing range of products as per planning (mobility), which in turn develops the ability to deliver varieties of products at the required time (range and mobility) to retailers and customers. So, more flexibility in Physical Supply creates more ability to maintain flexibility in Physical Distribution of fluid milk and milk products in co-operative dairies of Gujarat.

Purchasing Flexibility exerts significant positive impact on Physical Distribution Flexibility because of following reasons: (1) The ability to obtain multiple kinds of materials that meet specification (range, mobility and uniformity) brings ability to distribute varieties of fluid milk and milk products with specification (range and uniformity). (2) The ability to fill multiple materials orders quickly (mobility) leads to more accurate distribution of various products on time (mobility). (3) The co-ordination and close communication with suppliers in terms of specification of required materials (range and uniformity) leads to more accurate physical distribution of varieties of fluid milk and milk products as per specifications (range and uniformity).

Regression of Physical Supply Flexibility (PSF) and Purchasing Flexibility (PF) on Demand Management Flexibility (DMF)Hypothesis:H(PSF)0: There is no significant impact of Physical Supply Flexibility on Demand

Management Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

H(PSF)1: There is a significant impact of Physical Supply Flexibility on Demand Management Flexibility for fluid milk and milk products in co-operative dairies in Gujarat

H(PF)0: There is no significant impact of Purchasing Flexibility on Demand Management Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

H(PF)1: There is a significant impact of Purchasing Flexibility on Demand Management Flexibility for fluid milk and milk products in co-operative dairies in Gujarat.

Dr. Mahendra Sharma and Tejas Shah



The regression co-efficient of the independent variables with their respective direction, values and significance level are given in the table 4 below:

2Dependent Variable: DMF R = 0.606Independent Variables: PSF, PF N = 82 numbersDurbin-Watson = 1.937ANOVA (F Value) = 63.177 (Sig. 000), df = (2,79)Correlation Coefficient (PSF and PF) = 0.504

The table 4 shows that Physical Supply Flexibility (PSF) exerts significant influence over Demand Management Flexibility (DMF). An increase in Physical Supply Flexibility (PSF) will bring about a significant increase in Demand Management Flexibility (DMF) by number of times the value of regression co-efficient. Purchasing Flexibility (PF) exerts significant influence over Demand Management Flexibility (DMF). An increase in Purchasing Flexibility (PF) will bring about a significant increase in Demand Management Flexibility (DMF) by number of times the value of regression co-efficient. The variance analysis shows F = 63.177 at a significance level of 0.000 with df (2, 79), which indicates that all regression co-efficients will be non zero.

The emerging Multiple Regression Equation is as under:DMF = + 0.397 + 0.396 (PSF) + 0.506 (PF)

2The adjusted R , i.e. the co-efficient of determination stands at 0.606 indicating that the equation can explain 60.6% variations in Demand Management Flexibility (DMF). For remaining variations, i.e. unexplained variations, some other variables are responsible. The co-efficient of correlation among two independent variables (Physical Supply Flexibility and Purchasing Flexibility) have the co-efficient of correlation 0.504. The VIF statistics in table 4 also depicts the value of 1.340 for Physical Supply Flexibility and 1.340 for Purchasing Flexibility, which are very much far from cut off rate of 10 for VIF statistics. Hence there is no cause of concern from viewpoint of multicollinearity among the independent variables. The D (Durbin-Watson) statistic stands at 1.937. The corresponding table values for D statistic stand at 1.61 for lower limit (D ) and 1.66 for upper limit (D ). As a result D L U

(1.937) is greater than D (1.66) and 4 – D (2.063) is greater than D (1.61). Hence it U L

can be concluded that either positive or negative autocorrelation does not exist.Physical Supply Flexibility exerts significant positive impact on Demand

Management Flexibility because of following reasons: (1) The ability to deliver


Direction Value t-value Significance

LevelVIF

Statistic

Constant + 0.397 1.299 0.198

PSF +

0.396 4.902

0.000 1.340

PF + 0.506 6.270 0.000 1.340




multiple kinds of materials in terms of requirements as per specifications (range and uniformity) creates ability to respond to multiple retailers' and customers' requirements in terms of specifications (range and uniformity). (2) The ability to pick and assemble multiple kind of materials (range) and delivering it with accurate transport system on time (mobility) leads to respond to multiple delivery requests of retailers and customers quickly and on time (range and mobility).

Purchasing Flexibility exerts significant positive impact on Demand Management Flexibility because of following reasons: (1) It is important to maintain co-ordination among the various members throughout supply chain so that the customers' and retailers' feedback and requirements can be known not only to organization, but also to suppliers of organization. So, ability to maintain close communication and co-ordination with suppliers in terms of specifications of required materials creates ability to respond to feedback and specifications from retailers and consumers (uniformity) quickly. (2) The ability to fill multiple purchasing requests quickly (mobility) with specifications (range and uniformity) leads to flexibility in terms of delivering varieties of required products at required time period (range and uniformity).

Regression of Physical Distribution Flexibility (PDF) and Demand Management Flexibility (DMF) on Customer Satisfaction (CS)Hypothesis:H(PDF)0: There is no significant impact of Physical Distribution Flexibility (PDF)

on Customer Satisfaction (CS) for fluid milk and milk products in co-operative dairies in Gujarat.

H(PDF)1: There is a significant impact of Physical Distribution Flexibility (PDF) and Demand on Customer Satisfaction (CS) for fluid milk and milk products in in Gujarat

H(DMF)0: There is no significant impact of Demand Management Flexibility (DMF) on Customer Satisfaction (CS) for fluid milk and milk products in in Gujarat.

H(DMF)1: There is a significant impact of Demand Management Flexibility (DMF) on Customer Satisfaction (CS) for fluid milk and milk products in

in Gujarat.The regression co-efficient of the independent variables with their respective

direction, values and significance level are given in the table 5 below:

co-operative dairies





Direction Value t-value Significance

LevelVIF

Statistic

Constant

+

0.142

0.066

0.947

PSF

+

0.639

14.070

0.000 1.698

PF + 0.405 8.922 0.000 1.698



2Dependent Variable: CS R = 0.902 Independent Variables: PDF, DMF N = 82 numbers Durbin-Watson = 1.883 ANOVA (F Value) = 372.440 (Sig. 000), df = (2,79) Correlation Coefficient (PSF and PF) = 0.641

The table 5 shows that Physical Distribution Flexibility (PDF) exerts significant influence over Customer Satisfaction (CS). An increase in Physical Supply Flexibility (PSF) will bring about an increase in Customer Satisfaction (CS) by number of times the value of regression co-efficient. Demand Management Flexibility (DMF) exerts significant influence over Customer Satisfaction (CS). An increase in Demand Management Flexibility (DMF) will bring about an increase in Customer Satisfaction (CS) by number of times the value of regression co-efficient. The variance analysis given in table 4 above shows F = 372.440 at a significance level of 0.000 with df (2, 79), which indicates that all regression co-efficients will be non zero.

The emerging Multiple Regression Equation is as under:CS = + 0.142 + 0.639 (PDF) + 0.405 (DMF)

2The adjusted R , i.e. the co-efficient of determination stands at 0.902 indicating that the equation can explain 90.2% variations in Customer Satisfaction (CS). For remaining variations, i.e. unexplained variations, some other variables are responsible. The co-efficient of correlation among two independent variables (Physical Distribution Flexibility and Demand Management Flexibility) have the co-efficient of correlation 0.641. The VIF statistics in table 5 also depicts the value of 1.698 for Physical Distribution Flexibility and 1.698 for Demand Management Flexibility, which are very much far from cut off rate of 10 for VIF statistics. Hence there is no cause of concern from viewpoint of multicollinearity among the independent variables. The D (Durbin-Watson) statistic stands at 1.883. The corresponding table values for D statistic stand at 1.61 for lower limit (D ) and 1.66 L

for upper limit (DU). As a result D (1.883) is greater than D (1.66) and 4 – D (2.117) U

is greater than D (1.61). Hence it can be concluded that either positive or negative L

autocorrelation does not exist.Physical Distribution Flexibility exerts significant impact on Customer

Satisfaction due to following reasons: (1) The ability to quickly assemble multiple customer orders (range) accurately in Physical Distribution leads to on time delivery (mobility) of varieties of fluid milk and milk products. (2) The varieties of various fluid milk and milk products in different pack sizes (range) also provide customization up to certain extent in terms of specific quantity and variant out of varieties of products available. (3) The accurate transport system also makes it possible to deliver the varieties of products at required time, which improves loyalty towards products. (4) The uniformity in terms of standardization of transport services in physical distribution makes it possible to carry varieties of fluid milk and milk products accurately.


Dr. Mahendra Sharma and Tejas Shah 29

Demand Management Flexibility exerts significant impact on Customer Satisfaction due to following reasons: (1) The ability to manage to multiple retailers' and customers' delivery time requirements leads to greater availability of products on time (mobility), which improves loyalty towards products. (2) The ability to manage multiple customers' and retailers' specific requirements (range) leads to greater customer satisfaction and loyalty. (3) The ability to respond to customers' and retailers' feedback and suggestions leads to greater customer satisfaction and reputation of products in the market.

(* Significant at 0.05 level of significance)

Figure 1: Relationships among various dimensions of Logistics Flexibility Model for Milk and Milk Products in Gujarat

CONCLUSION AND IMPLICATIONSThis research brings the systematic way of analyzing the logistics flexibility

from supply chain point of view in terms of its attributes and components. This study has proved that proper management of customer demand must be in co-ordination with proper physical distribution system of dairy co-operatives to gain customer satisfaction. The complex structure of dairy co-operatives in Gujarat makes it more critical to properly manage varieties of customers' demand as well as distribution of varieties of dairy products.

The competence and capability theory brings a systematic resource-based view of logistics flexibility management in co-operative dairies in Gujarat. It is empirically verified that flexible logistics competence supports the flexible logistics capability, which ultimately enhances customer satisfaction. Firms can achieve customer satisfaction by developing logistics flexibility, which enable quick replenishment of incoming materials and rapid delivery of finished product to customers. Customers value the visible capabilities, physical distribution flexibility and demand management flexibility, rather than the supply-side competences because customers see how capabilities are deployed to meet their needs.

This research helps managers to understand that flexible competence may not be sufficient to build competitive advantage. Customer value expression of these competences, which is the capability of the dairy co-operatives to provide the right product, at the right time and in the correct quantity, is more important. The dichotomy of flexible logistics competence and capability can help managers to differentiate the sub-dimensions of logistics flexibility that are critical to their customers from the sub-dimensions that support these capabilities. While

Physical Supply Flexibility

Purchasing Flexibility

Physical Distribution Flexibility

Demand Management

Flexibility

Customer Satisfaction

0.752*

0.369*

0.693*

0.405*

0.506*

0.238*



competences are important, customers do not value them directly. This dichotomy of flexible logistics competence and capability enables managers to develop a comprehensive view of flexibility.

References Bernardes, E.S. and Hanna, M.D. (2009), A theoretical review of flexibility, agility and responsiveness in the operations management literature. International Journal of Operations and Production Management, 29 (1), 30-35

Cunningham, J.B. (1996), Designing flexible logistics systems: a review of some Singaporean examples. Logistics Information Management, 9 (2), 40-48

Day, G.S. (1994), The Capabilities of Market-Driven Organizations. The Journal of Marketing. 58 (4), 37-52

Gerwin, D. (1987), An Agenda for Research on the Flexibility of Manufacturing Processes. International Journal of Operations and Production Management, 7 (1), 38-49

Gupta, Y.P. and Somers, T.M. (1992), The measurement of manufacturing flexibility. European Journal of Operational Research, 60, 166-82

Hair, J.H. Jr., Balck, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2009), Multivariate Data Analysis. Sixth Edition, Pearson Education

Holweg, M. (2005), The three dimensions of responsiveness. International Journal of Operations and Production Management, 25 (7), 603-622

http://www.nddb.org/

Keith, T.Z. (2006), Multiple Regression and Beyond. Allyn and Bacon, Boston, Pearson Education.

Kumar, V., Fantazy, K.A., Kumar, U. and Boyle, T.A. (2006), Implementation and management framework for supply chain flexibility. Journal of enterprise Information Management, 19 (3), 303-319

Kutner, M.H., Nachtsheim, C.J. and Neter, J. (2005), Applied Linear Statistical Models. Fifth Edition, McGraw-Hill, Singapore

Lewis – Beck, M.S. (1980), Applied Regression An Introduction. Sage Publication, Thomas and Oak, New Delhi.

Malhotra, N.K. and Dash, S. (2010), Marketing Research An Applied Orientation. Sixth Edition, Pearson Education, New Delhi.

Menard, S. (1991), Applied Logistics Regression Analysis. Sage Publication, New Delhi

Sethi, A.K. and Sethi, S.P. (1990), Flexibility in manufacturing: a survey. International Journal of Flexible Manufacturing Systems, 2 (4), 289-328

Reichhart, A. and Holweg, M. (2007), Creating customer responsive supply chain: a reconciliation of concepts. International Journal of Operations and Production Management, 27 (11), 1144-1172

Sharma V.P. and Thaker H. (2010), Livestock Development in Gujarat in 2000s: An assessment. In Dutta, S.K. and Dholakia, R.H. (Ed.), High Growth Trajectory and Structural Changes in Gujarat Agriculture. Macmillan Publishing India Ltd.

Slack, N. (1983), Flexibility as manufacturing objectives. International Journal of Operations and Production Management, 3 (3), 4-13.

Upton, D.M. (1994), The management of manufacturing flexibility. California Management Review, 36 (2), 72-89

Upton, D.M. (1995a), Flexibility as process mobility: the management of plant capabilities for quick response manufacturing. Journal of Operations Management, 12 (3/4), 205-24

Upton, D.M. (1995b), What really makes factories flexible?. Harvard Business Review, 73 (4), 74-84

Vickery, S., Calantone, R. and Droge, C.L. (1999), Supply chain flexibility: an empirical study. The journal of Supply Chain Management, 16-24

Zhang, Q., Vonderembse, M.A. and Lim, J.S. (2002), Value Chain Flexibility: a dichotomy of competence and capability. International Journal of Production Research, 40 (3), 561-583

Zhang, Q., Vonderembse, M.A. and Lim, J.S. (2005), Logistics Flexibility and its impact on customer satisfaction. International Journal of Logistics Management, 16 (1), 71-95


Dr. G. Muruganantham and Priom Biswas 31

ABSTRACTChocolate is a word that transcends the barriers of age and language and is

fascinating to kids and their grandparents alike. It is one which is consumed and liked by almost every one. Chocolate accounts for a small share in the sugar and confectionary market in India. According to Technopak report, the chocolate market in India is pegged at Rs 2,000 crores and is growing at the rate of 18 – 20% per annum. The industry caters to a variety of consumers and is extremely fragmented in terms of range of products. This study is an attempt to investigate the factors influencing the chocolate buying behaviour of young consumers in India. Our study results show that the taste of the chocolate, occasions and offers and ingredients are the most important factors influencing the chocolate buying behaviour among the youth.

INTRODUCTIONChocolate is a raw or processed food produced from the seed of the tropical

Theobroma cacao tree. Cacao has been cultivated for at least three millennia in Mexico, Central and South America, with its earliest documented use around 1100 BC. Pure, unsweetened chocolate contains primarily cocoa solids and cocoa butter in varying proportions. Much of the chocolate consumed today is in the form of sweet chocolate, combining chocolate with sugar. Milk chocolate is sweet chocolate that additionally contains milk powder or condensed milk. White chocolate contains cocoa butter, sugar, and milk, but no cocoa solids. The chocolate industry in India is in the hands of two major players with Cadbury holding 70% of the market share and Nestle holding another 20%. Cadbury (India) has for long been the leading player in the chocolate industry. It is virtually a household name with leading brands such as Five Star and Dairy Milk. Amul is the third largest player. They have chosen the phrase “A gift for someone you love” to market their chocolate products.

Chocolate consumption was earlier concentrated in the developed parts of the world but gradually, its sales has grown exponentially in many developing countries, including India. More than 65% of the consumption takes place in the urban market. Global chocolate consumption has doubled in the past 20 years, and increased over 14% in the last five years. As per a Euro monitor study (or mention study), currently the global chocolate market is worth $75 billion annually. In such a scenario, India being the second most populous country in the world with its urban population set to increase by 46 million in the period 2010-2015, there will be increased demand for processed food items such as chocolate and dairy (Euromonitor, 2009).

FACTORS INFLUENCING THE BUYING BEHAVIOUR OF CHOCOLATES

1 2Dr. G. Muruganantham and Priom Biswas

1

2

Dr. G. Muruganantham, Assistant professor, Department of Management Studies, National Institute of Technology, Tiruchirappalli. E-mail: [email protected] Biswas, Management Trainee, Tata TinPlate Company of India Ltd, Jamshedpur. E-mail: [email protected]


32 Dr. G. Muruganantham and Priom Biswas

The range and variety of chocolates available seems to be growing day by day, which leads to a lot of impulse sales for chocolate companies. Chocolates which used to be considered unaffordable, is now considered mid-priced.

Cadbury's initial promotion for its Celebrations brand was concentrated on festivals like Diwali and Rakshabandhan. Over the last seven to eight years, the brand has emerged as a good gift proposition for various occasions and has enabled people to become closer. Research done by Cadbury suggested that they should extend the plank of occasion-based gifting to social gifting i.e., all-year-round gifting options.

The Premium chocolate market in India is pegged at 10% of the overall chocolate sales. These overall chocolate sales of over Rs 2000 crores is still small compared to the market size of the country but is certainly showing a steady growth of about 30 to 40% per annum, as per industry estimates. Premium brands are those products whose average price is Rs 100 and above (Archisman Dinda, 2011).

This research is an academic attempt to find out the factors which influence the buying decision of chocolates among college students. This study will be useful to academicians for research purposes and to chocolate marketers for developing their marketing strategy with better insights into the consumers' minds to gain increased market share.

REVIEW OF LITERATURELia and Harri (2011) indentified that Chocolate generates rich and complex

consumption experiences as a function of various contextual forces. Seven main contextual chocolate consumption categories are physiological need, sensorial gratification, memories and nostalgia, escapism, materialism, chocoholism, interpersonal and self-gifts. On the basis of chocolate consumption categories and ideas from past consumer behaviour research, four more general contextualized chocolate consumption experience types were extracted. They are chocolate consumption experience as medicine, as mind maneuvering, as regression and as ritual enhancement.

Manoj, Preeti and Amit (2010) observed that chocolate being a product that is often consumed by people, especially kids, customers are very particular about the ingredients of the chocolate, for instance, dry fruits, cream, wine, etc. They also stated that the different shapes and designs of chocolate influence customers. Consumption of chocolate varies according to the season and popularity of a particular chocolate brand. Chocolate is an instant energy supplier, hence the preference of many sportspersons. Packaging, taste, ingredients, purpose and consumption period have shown highly significant influence on consumers' buying behavior.

Vrontis and Vignali (2001) reported a very interesting study related to Cadbury Dairy Milk in the UK which revealed that, through the PEST (Political, Economical, Social, and Technological) market analysis, it has been established that 'chocolate ingredients' and 'chocolate cost' are among the major consideration factors for consumers, before buying chocolate.

Sinclair (2007) stated that chocolates were mainly purchased for self-consumption and gift giving for certain holidays and special occasions. His study findings from western countries reveal that, both male and female consumers were



more likely to purchase chocolates for the major Christian holidays: Easter and Christmas. The data also indicated that both chocolate-makers and their consumers believe that the most significant attribute of the chocolate is the flavour, followed by the visual appearance of the chocolate.

It has been over 10 years since the first mention in a medical journal about cocoa and chocolate as potential sources of antioxidants for health. During this time, cocoa has been found to improve antioxidant status, reduce inflammation and has been correlated with reduced heart disease risk. With these results, and its popularity, it has received wide coverage in the press. It is an appealing idea that a food commonly consumed for pure pleasure could also bring tangible benefits for health (Cooper et al, 2007).

Renata and Jacques (2001) conducted a market survey across Belgium and Poland about chocolate consumption behaviour. Research findings reveal that the quality of the chocolate (i.e., the source of origin of the cacao beans), its package, price, novelty, etc. influence the consumption of chocolate among the users.

Pilgrim (1957) stated that food choice is a complex process which involves many different factors. Physiological factors of the individual together with external factors affect perception of the sensory characteristics of foods. Shepherd (1985) developed the model which includes three factors related to choice: (1) food: its physical properties and nutrient content, (2) the individual: his/her previous experience and learning associated with foods, which in turn will lead to different beliefs, values and habits, (3) social-economic environment: attitudes to sensory properties of food or healthiness of food. The health benefits of perceived naturalness, good fat quality, vitamin, mineral, fiber and low fat content of foods were found to be important characteristics for healthy foods, while sensory appeal and taste were naturally important for pleasure-giving foods.

The most frequently mentioned reasons for choosing either apples or chocolate bars were related to health, energy content, good taste, momentary desire, and price. (Roininen, 2001). Pelchat (1997) found that American females were more likely than males and elderly to report at least one craving for chocolates. Furthermore, chocolate has been found to be the food most craved among American and Canadian females.

Packaging plays an important role in a firm's marketing strategy and has the ability to influence all other elements of the marketing mix. Product packaging is a cross-functional and multi-dimensional aspect of marketing that has become increasingly important in consumer need satisfaction, cost savings and the reduction of package material usage leading to substantial improvements in corporate profits (Lonergan, Gosevski, and Gemma, 2010).

Impulse purchasing is not confined to any type of marketing institution, but probably most frequently refers to food purchasing decision. Many studies have used impulse behaviour to view segments of consumer behavior (Kollat and Willet, 1984). The term “impulse buying” is generally considered to be synonymous with unplanned buying, that is, it describes any purchase the shopper makes but has not planned in advance (Stern, 1962). 75% of the chocolate bought in India is an impulse purchase. Retailer's try to keep chocolates in an area easily visible to the shopper, near the payment counter (Disha Kanwar, 2011).


Dr. G. Muruganantham and Priom Biswas34

Kinder Joy, introduced in India by the Italian confectioner Ferrero International, has captured the minds of the Indian kids in a short time to become one of the leading brands in the kids chocolate market. In most stores, Kinder Joy chocolate boxes are placed near the billing counter and that encourages impulse purchase by customers. Also its distinct egg-shape has helped it to stand out from other chocolate brands (Siva Rajendran, 2009). These earlier studies provide a good scope to study the various dimensions of chocolate buying habits among the Indian youth.

RESEARCH OBJECTIVEThe specific objectives of the study are to find out the various factors that affect

the decision making process for buying of chocolates by young consumers and to find out the association between various variables considered in the study.

RESEARCH METHODOLOGYA descriptive research methodology has been used. The study is based on

primary data derived from a convenient sample survey using a pre-tested structured questionnaire. The questionnaire was prepared with help from previous studies. Manoj, Preeti and Amit (2010) have identified 12 variables influencing the buying behavior of soaps and chocolates and Cooper et al., (2007) have identified another four variables like health benefits, impulse buying, gift and premium aspects of chocolates. In our study we used all 16 variables (Refer Table 1) to understand the factors influencing buying pattern of chocolates in India. Data was collected from 134 students in the age group of 18 to 24 years at the National Institute of Technology (NIT), Trichy. NITT consists of students from all over India.

Table 1 Measure of Constructs

Variables Source Questions

Demographic profile

Age and sex of the respondents

Advertisement My choice of a chocolate is affected by the advertisements and marketing of that product

Packaging Attractive packaging styles of a chocolate affects my decision to buy a particular chocolate

Cost Effective My choice of a brand is largely based on the price of that productSchemes My choice of a chocolate is affected by the promotional schemesPopularity The popularity of a chocolate affects my buying decisionUsage Period I prefer to choose a product (chocolate) that I have been using for

a considerable amount of time and do not

prefer to go for new brands or products

Availability My choice of a product is based on the availability of that particular chocolate in the market.

Ingredients The ingredients of a chocolate play a very important role when I choose a particular chocolate

Shape I get attracted towards the stylish shapes and designs of the chocolate and this affects my decision of buying a Particular chocolate.

Purpose I eat chocolate for energy and refreshment

Season I prefer having more chocolates in winters than in summers.Taste

(Patwardhan, Flora, and Gupta, 2010)

My choice of a particular chocolate is mainly due to the unique taste that it provides compared to other chocolates available

Health benefits I buy chocolates for health benefitsImpulse purchase behaviour

Impulse purchase and in -store display affects my decision to buy chocolate

gifts I buy chocolates to gift it to my friends and family

Premium (costly, imported)

(Cooper, Donovan, Waterhouse andWilliamson, 2007)

I buy more premium (costly, imported) chocolates


35

We used non-comparative scaling techniques. Respondents evaluate only one object at a time, and for this reason non-comparative scales are often referred to as monadic scales. Non-comparative techniques consist of continuous and itemized rating scales. In itemized rating scale, the respondents are provided with a scale that has a number or brief description associated with each category. The categories are ordered in terms of scale position, and the respondents are required to select the specified category that best describes the object being rated. The commonly used itemized rating scales are the Likert, Semantic differential, and Stapel scales. In this study Likert scale was used. The Likert scale requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects. SPPS version 17 package was used to analyse the data for factor analysis and correlation.

Reliability test was carried out by applying Cronbach's Alpha method using SPSS software. The reliability of the questionnaire was found to be 0.619 and the number of items was 16.

ANALYSIS AND DISCUSSION

Table 1 Factor Analysis Output

Variable Advertising PopularityPackaging, Style and

ShapeIngredient Unique Taste Brand Lower Price

Promotional schemes

and Discounts

Pearson Correlation

1 .585** .517** 0.008 0.104 .212* 0.148 0.09

Sig. (2-tailed)

0 0 0.924 0.234 0.014 0.088 0.301Advertising

N 134 134 134 134 134 134 134 134

Pearson Correlation

.585** 1 .641** 0.04 0.123 .179* .185* 0.084

Sig. (2-tailed)

0 0 0.648 0.155 0.039 0.033 0.336Popularity

N 134 134 134 134 134 134 134 134

Pearson Correlation

.517** .641** 1 -0.03 0.047 .299** 0.14 0.151

Sig. (2-tailed)

0 0 0.733 0.592 0 0.106 0.081

Packaging, Style and

ShapeN 134

134

134

134

134

134

134 134

Pearson Correlation

0.008

0.04

-0.03

1

.373**

0.166 -.219* -.344**

Sig. (2-tailed)

0.924

0.648

0.733

0

0.055 0.011 0Ingredient

N 134

134

134

134

134

134

134 134

Pearson Correlation

0.104

0.123

0.047

.373**

1

.208*

-0.041 -.301**

Sig. (2-tailed)

0.234

0.155

0.592

0

0.016 0.641 0Unique Taste

N 134

134

134

134

134

134

134 134

Pearson Correlation

.212*

.179*

.299**

0.166

.208*

1

-0.048 -0.136

Sig. (2-tailed)

0.014

0.039

0

0.055

0.016

0.581 0.118Brand

N 134

134

134

134

134

134

134 134

Pearson Correlation

0.148

.185*

0.14

-.219*

-0.041

-0.05

1 .346**

Sig. (2-tailed)

0.088

0.033

0.106

0.011

0.641

0.581 0Lower Price

N 134

134

134

134

134

134

134 134

Pearson Correlation 0.09

0.084

0.151

-.344** -.301**

-0.14

.346** 1

Sig. (2-tailed)

0.301

0.336

0.081

0 0

0.118 0

Promotional schemes

and Discounts

N 134

134

134

134

134

134

134 134

Pearson Correlation

0.017

.172*

.233**

0.143

0.05

0.007 0.094 .309**

Sig. (2-tailed)

0.843 0.047 0.007 0.099 0.564 0.937 0.281 0Premium

N 134 134 134 134 134 134 134 134


Variable Advertising PopularityPackaging, Style and

ShapeIngredient Unique Taste Brand Lower Price

Promotional schemes

and Discounts

Pearson

Correlation

0.017

.172*

.233**

0.143

0.05

0.007 0.094 .309**

Sig. (2-tailed)

0.843

0.047

0.007

0.099

0.564

0.937 0.281 0Premium

N 134

134

134

134

134

134

134 134

Pearson Correlation

-.222**

-0.137

-.306**

0.12

0.05

-.179* -.170* 0.047

Sig. (2-tailed)

0.01

0.114

0

0.168

0.568

0.039 0.049 0.591

Energy, refreshing

and cravingN 134

134

134

134

134

134

134 134

Pearson Correlation

.214*

.177*

0.022

-0.139

0.134

-0.02

0.053 .222**

Sig. (2-tailed)

0.013 0.041 0.801 0.108 0.123 0.839 0.54 0.01Impulse Purchase

N 134 134 134 134 134 134 134 134

Pearson Correlation

-0.094 -0.118 -.230** -0.151 -0.13 -0.03 .184* .450**

Sig. (2-tailed)

0.279 0.176 0.008 0.081 0.135 0.74 0.033 0Health

benefits

N 134 134 134 134 134 134 134 134

Pearson Correlation

0.031 .170* 0.111 -.272** 0.001 0.076 0.125 .301**Gifts

Sig. (2-tailed)

0.718 0.049 0.202 0.001 0.993 0.383 0.151 0

N 134 134 134 134 134 134 134 134

Pearson Correlation

Time

Availability

Season

-.174* -0.084 -.175* -.194* -0.005 -0.06 0.008 .231**

Sig. (2-tailed)

0.044 0.337 0.043 0.025 0.952 0.521 0.928 0.007

N 134

134

134

134

134

134

134 134

Pearson Correlation

.211*

.281**

.290**

0.03

-.213*

0.089

.237** .289**

Sig. (2-tailed)

0.014

0.001

0.001

0.728

0.013

0.306

0.006 0.001

N 134

134

134

134

134

134

134 134

Pearson Correlation

-0.01

0.151

0.102

-0.065

-0.035

-0.02

0.127 .296**

Sig. (2-tailed)

0.906

0.081

0.243

0.453

0.688

0.805

0.143 0.001

N 134

134

134

134

134

134

134 134

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).



Initial Eigen values

Extraction Sums of Squared Loadings

Component Cumulative %

Total

% of Variance Cumulative %

1 18.854 3.017 18.854 18.854

2 36.181

2.772

17.327

36.181

3 47.127

1.751

10.946

47.127

4 54.429 1.168 7.302 54.429

5 60.712 1.005 6.282 60.712

Extraction Method: Principal Component Analysis.

Table 2 Total Variance Explained

Table 3 Component Matrix

ComponentVariable

1 2 3 4 5

Advertising .376 .638 .062 -.228 .354

Popularity .502 .624 .164 -.088 .216

Packaging, Style and Shape

.451

.707

-.001

.013 -.046

Ingredient -.328

.245

.577

.544 .094

Unique Taste -.156

.236

.692

-.153 -.070

Brand .062

.423

.289

.078 -.322

Lower Price .462

.068

-.333

.034 .049

Promotional schemes and Discounts

.718 -.262 -.283 .059 -.007

Premium .521 -.048 .349 .227 -.407



ComponentVariable

1 2 3 4 5

Energy, refreshing and

craving.053

-.554

.462

.040 .334

Impulse Purchase .442

-.132

.377

-.499 .229

Health benefits .482

-.588

.103

.199 .085

Gifts .532 -.103 .091 -.238 -.568

Time .288 -.543 .220 -.185 .085

Availability .458 .256 -.229 .475 .195

Season .527 -.255 .194 .346 .078

Extraction Method: Principal Component Analysis.

Table 4 Total Variance Explained

Initial Eigen values

Extraction Sums of Squared Loadings

Component Cumulative %

Total

% of Variance Cumulative %

Occasions and offer

18.854

3.017

18.854

18.854

Popularity and packaging

36.181 2.772 17.327 36.181

Taste 47.127

1.751

10.946

47.127

Affinity 54.429

1.168

7.302

54.429

Marketing and purpose 60.712 1.005 6.282 60.712

Extraction Method: Principal Component Analysis

Table 5 Correlation Output of the 16 Variables considered

Premium

Energy, refreshing

and craving

Impulse Purchase

Health benefits Gifts Time Availability Season

0.017 -.222** .214* -0.094 0.031 -174* .211* -0.01

0.843 0.01 0.013 0.279 0.718 0.044 0.014 0.906

Advertising

134 134 134 134 134 134 134 134

.172* -0.137 .177* -0.118 .170* -0.08 .281** 0.151

0.047 0.114 0.041 0.176 0.049 0.337 0.001 0.081

Popularity

134 134 134 134 134 134 134 134

.233** -.306** 0.022 -.230** 0.111 -.175*

.290** 0.102

0.007 0 0.801 0.008 0.202 0.043 0.001 0.243

Packaging, Style and Shape

134 134 134 134 134 134 134 134

0.143 0.12 -0.139 -0.151 -.272**

-.194*

0.03 -0.065

0.099 0.168 0.108 0.081 0.001 0.025 0.728 0.453

Ingredient

134

134

134

134

134

134 134 134

0.05

0.05

0.134

-0.13

0.001

-0.01 -.213* -0.035

0.564

0.568

0.123

0.135

0.993

0.952 0.013 0.688

Unique Taste

134

134

134

134

134

134 134 134

0.007

-.179*

-0.018

-0.029

0.076

-0.06 0.089 -0.022

0.937

0.039

0.839

0.74

0.383

0.521 0.306 0.805

Brand

134

134

134

134

134

134 134 134

0.094

-.170*

0.053

.184*

0.125

0.008 .237** 0.127

0.281

0.049

0.54

0.033

0.151

0.928 0.006 0.143

Lower Price

134

134

134

134

134

134 134 134

.309**

0.047

.222**

.450**

.301**

.231** .289** .296**

0

0.591

0.01

0

0

0.007 0.001 0.001

Promotional schemes and Discounts 134

134

134

134

134

134 134 134

1

0.058

.257**

.226**

.346**

0.125 0.114 .272**

0

0.505

0.003

0.009

0

0.15 0.188 0.001

Premium

134

134

134

134

134

134 134 134


38

Energy, refreshing

and craving

Impulse Purchase

Health benefits Gifts Time Availability Season

0.058 1 .264** .355** 0.081 .264** -0.106 .213*

0.505 0 0.002 0 0.352 0.002 0.221 0.013

Energy, refreshing and craving

134

134

134

134

134

134 134 134

.257** .264** 1 .172* .234** .244** 0.049 0.109

0.003 0.002 0.047 0.006 0.005 0.577 0.212

Impulse Purchase

134

134

134

134

134

134 134 134

.226**

.355**

.172*

1

.198*

.425** 0.091 .402**

0.009 0 0.047 0.022 0 0.298 0

Health benefits

134 134 134 134 134 134 134 134

.346** 0.081 .234** .198* 1 0.097 0.105 .187*

0 0.352 0.006 0.022 0.263 0.228 0.03

Gifts

134 134 134 134 134 134 134 134

0.125 .264**

.244**

.425**

0.097 1 -0.115 .275**

0.15

0.002

0.005

0

0.263

0

0.186 0.001

Time

134

134

134

134

134

134 134 134

0.114

-0.106

0.049

0.091

0.105

-0.12 1 0.163

0.188

0.221

0.577

0.298

0.228

0.186 0 0.06

Availability

134

134

134

134

134

134 134 134

.272** .213

* 0.109 .402** .187

* .275** 0.163 1

0.001

0.013

0.212

0

0.03

0.001 0.06 0

Season

134 134 134 134 134 134 134 134

Premium

Our study results show interesting findings which can be helpful to marketers and chocolate manufacturing companies to successfully market their brands in the Indian Market. Out of the 134 respondents, more than half the sample size consists of female respondents (54%) and the most preferred brand (by 65% of respondents) is Cadbury. 68% of the respondents are in the age group of 18-20 years.

The main applications of factor analytic technique is to reduce the number of variables and to detect structure in the relationships between variables. Factor analysis based on principle component method was applied on the raw score of data collected from the questionnaire. In principle component method, all the 16 variables were used and each respondent's score is a different sum total for all the 16 variables, with the scores being averages over some common pair of values, which results in linear combinations; these values correspond to the resultant factors.

This method is used to find the corresponding factors which show linear combination contributing towards affecting respondents buying behaviour. From the linear combinations derived through this method, we arrive at the corresponding factors which affect the respondents' buying behaviour. Mainly five components were observed from the data. These 5 components explain 60.712 of variance of the collected data. They are as explained below:1. Occasions and offers: This component or factor combines mainly three

variables having comparatively high loading value in the component matrix. The combining factors are promotional schemes and discounts (Factor Score=0.718), buying of chocolates for the purpose of gifting to friends and family (Factor Score 0.532) and season (Factor Score 0.527). Analysis reveals that while buying chocolates the young consumers specially take into




39

consideration the promotional schemes or discounts offered by the brand. They particularly buy chocolates during festive seasons like Christmas, Valentines' day, Diwali, New Year. Another important reason is for gifting the chocolate to friends and family during birthdays and special occasions.

2. Popularity and packaging: This factor consists of attractive packaging, shapes and style (Factor Score 0.707) and popularity of the chocolate in the market (Factor Score 0.624). Study respondents are easily attracted to the stylish packaging, shapes and styles. The popularity of the chocolate also influences the consumers to select a specific brand.

3. Taste: This component can itself be described by the preference of unique taste of the chocolate (Factor Score 0.692) which appeals to the consumers. The consumers buying decision mainly is affected by the unique taste of that particular chocolate as compared to the other available chocolates in the market.

4. Affinity: This factor consists of availability of the chocolate in the market (Factor Score 0.475) and ingredients of the chocolate (Factor Score 0.544). Our findings were also supported by previous research. Consumers reach to the product decides the product affinity. The composition of the chocolates also decides the consumer's preference towards that particular chocolate (Manoj, Preeti and Amit, 2010).

5. Marketing and purpose: This factor significantly showed the effect of advertisement (Factor Score 0.354) and the purpose of energy, refreshment and increased craving (Factor Score 0.334) while buying a chocolate. Mostly the female consumers bought chocolates for increased craving. Chocolate is an instant energy supplier and hence the preference of sportspersons (Patwardhan, Flora, and Gupta, 2010). Young consumers in colleges play sports and are involved in other outdoor activities, buy chocolates mainly for this reason.

From the correlation output we found more interesting results as explained below. The variables having significant contribution to the buying behaviour and having mutual correlation were considered.•Advertising (correlation co-efficient 0.585) is highly and mutually

correlated with the popularity (correlation co-efficient 0.585) of chocolate and affects the buying decision of the consumers. Advertising is also mutually correlated with attractive packaging and style (correlation co-efficient 0.517). It was also observed that popularity is also influenced by the attractive packaging and style of the product (correlation co-efficient 0.641).

•Promotional schemes and discounts are mutually negatively correlated with ingredients (correlation co-efficient - 0.344) and unique taste (correlation co-efficient - 0.301) of the product. It shows that consumers who are more involved in getting better offers and discounts do not take into consideration the actual ingredients in the chocolates. It rather shows highly positive correlation with the lower price of the product. With regard to respondents looking for lower price of the product, this has a very high positive correlation with promotional offers and discounts (correlation co-efficient



0.346). Promotional schemes and discounts are also positively correlated with availability of the product in the market (correlation co-efficient.289) and season (correlation co-efficient 0.296) while being negatively correlated with health benefits (correlation co-efficient -0.230) concluding the fact that consumers looking for health benefits from the product are not much involved in the appearance and attractive packaging of the product.

•Availability of the brand is positively correlated with the packaging style (0.290) and popularity (correlation co-efficient 0.281). It can be concluded that popularity of the product improves the availability of that product in the market or vice versa.

•Gifts are negatively correlated with ingredients (correlation co-efficient - 0.272) showing the tendency of consumers to ignore or be unaware of the ingredients while buying chocolates for others. Meanwhile it is positively correlated with the premium (costly, imported) chocolates (correlation co-efficient 0.346) and season (correlation co-efficient 0.272) showing the consumer tendency of buying premium chocolates as gifts for friends and family during festive season.

•While unique taste and ingredients are highly correlated (correlation co-efficient 0.373) showing that taste and ingredients both are major factors which influences the consumer's buying decision.

CONCLUSIONChocolate is one of the most popular FMCG products and the various factors

which influence the buying decision of young consumers was studied in this research. Occasions and offers play a very important role in the buying behaviour of young consumers. Chocolates serve as a very cost effective and popular gift option for their friends and family. Chocolate manufacturers and their marketers can take into consideration this fact and design their offerings accordingly.

Cadbury is already following this strategy with their “celebrations” offering in which an assortment of their popular chocolates is sold in an attractively packaged box and at a considerably lower price. This serves as an ideal gift for the consumers during birthdays and festivals like Christmas, Diwali, Rakshabandhan, Valentine's Day and New Year. All these festivals falls during the winter season hence chocolate consumption increases during this period of the year. Therefore chocolate marketers can provide more promotions and offer during winter season.

Our research reveals that taste plays a crucial role while selecting a chocolate brand, respondents look for unique taste in the product they choose as compared to the other available products in the market. Such an emphasis on the unique taste implies that the ingredients of the chocolate are also very important for the consumers. The buying of chocolates is affected by its reach to the consumers and its availability in the market. Cadbury and Nestle have already made available their market offerings with greater variety in taste and composition, to tap in as many consumers as possible across various geographical regions of India.

Advertising and marketing influence the consumer choice. Advertising increases the popularity and liking for the chocolates. In the recent years, the huge



surge of investment in the advertisement of chocolates and positioning them in new ways have increased their appeal to consumers, ranging wide from 8 to 80 years of age (Cadbury's “shubh arambh” and “pappu pas ho gaya” campaigns are very good examples to demonstrate this fact).

The other purpose for young consumers to buy chocolates are for energy, refreshment and increased craving since they are physically more active, have their own choice of snacks and have cravings for sweet foods. These factors could be considered as a point of difference in communication message to get a competitive advantage.

The correlation of the various factors as described in the findings also suggests that factors like advertisings, popularity, promotional schemes and discounts, season, gifts, packaging and design and composition of the chocolate, impulse buying are few major variables which influence the consumer buying decision.

Chocolate marketers can target the segment of young consumers during festive seasons and give cheaper and more attractive deals of chocolates to be purchased for the purpose of gifting. The chocolate manufacturers can improve their packaging, style, and design to attract more consumers and improve the sales. Increased information about the products' health benefits and ingredients should be made available both on the packaging and marketing communications.

In a country like India with its diversified culture, taste and interest, critically examining the factors affecting consumers' buying decisions is extremely complex. This study is an attempt to explore the youth buying behaviour of chocolates in India. Our study findings and suggestions will be a value addition to the academic literature and chocolate marketers.

References

Archisman Dinda (2011), Premium chocolate market rising in India, http://www.newzglobe.com /article/20100910/premium-chocolate-market-rising-india

Cooper, K. A., Donovan , J. L., Waterhouse, A. L., and Williamson, G. (2007), Cocoa and health :a decade of research, British Journal of Nutrition.

Disha Kanwar (2011), Sumptious story ahead for chocolate, predicts study, http://www.business-standard.com/india/news/sumptious-story-ahead-for-chocolate-predicts-study/439904/, June 21.

Euromonitor, (2009), http://www.ryerson.ca/~rmichon//IndustryReports/Consumer Lifestyles .pdf

Januszewska, R., and Viaene, J. (2001), Acceptance of chocolate by preference cluster mapping across Belgium and Poland, Journal of Euro Marketing, 11, 6-63.

Lia Zarantonello and Harri T. Luomala (2011), Dear Mr Chocolate - Constructing a typology of contextualized consumption experiences through qualitative dairy research. Qualitative market research:An international Journal, 14, 55-82.

Lonergan , Gosevski., and Gemma (2010), Socially conscious packaging and its influence on brnad image, http://smib.vuw.ac.nz:8081/www/anzmac2001/anzmac/authors/pdfs/lonergan. pdf.

Manoj, Preeti and Amit (2010), Identification of secondary factors that influence consumer's buying behaviour for soaps and chocolates. The IUP Journal Of Marketing Management, 9.

Kollat, D. T., and Willet, R. (1984), Chocolate pattern, Journal ofMarketing Research, 21-22.

Kraak, V., and Pelletier, D. L. (1998), Commercialism on the food purchasing behaviour of children and teenage youth, Family Economics And Nutrition Review, 11.



Pelchat, M.L. (1997), Food cravings in young and elderly adults. Appetite, 28, 103-113.

Pilgrim, F. (1957), The components of food acceptance and their measurement. Americn Journal of Clinical Nutrition, 5, 171–175.

Renata Januszewska and Jacques Viaene (2001), Acceptance of chocolate by preference cluster mapping across Belgium and Poland, Journal of Euro Marketing, 11, 61-64.

Roininen, K. (2001), Evaluation of food choice behaviour : development and validation of health and taste attitude scale, http://ethesis.helsinki.fi/julkaisut/maa/elint/vk/roininen /evaluati.pdf

Shepherd, R. (1985), Dietary salt intake. Nutrition Food Science, 96, 10-11.

Siva Rajendran, (2009), Brand Kinder Joy - Changing the chocolate market in India, http://www.bizdewz.com/brand-kinder-joy-changing-the-chocolate-market-in-india/

Sinclair, J. (2007), A comparison of material preference by chocolatiers and consumers, Social Cognition, 26.

Stern, H. (1962), The significance of impulse buying today, The Journal of Marketing, 26, 59-62.


S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass 43

ABSTRACT Due to larger demands of foreign buyers, the Indian garment industry was able

to flourish in multifold. The Tirupur knitwear industry has problems relating to labor, production and marketing and as a result the performance of the garment industry gets affected. In addition to technological and competitive problems, the labor problem poses a threat to the knitwear industry and hence it becomes necessary for this industry to concentrate on Human Resource related problems. The extended working hours, compulsory overtime, committed deliveries by the firms, shorter manufacturing cycles etc. increases the stress level of the employees at their job and lead to job dissatisfaction which ultimately end up with higher attrition rates and low morale. In this context, it would be more relevant to make an attempt to study the problems related to job satisfaction. The satisfaction of employees of any organization makes it victorious while the dissatisfaction leads to more problems. Hence organizations are trying to eliminate the factors that lead for job dissatisfaction; by the way of concentrating on stimulating factors of job satisfaction that can enhance the job satisfaction as well as they can be the successful. The knitwear industry is one of the major Forex earning resource and wide job opportunity provider for Tamilnadu, so the sensation about job satisfaction of knitwear employees in Tirupur district is more important. This research aims to find out the job satisfaction of the knitwear employees and the relationship between the job factors and job satisfaction of knitwear employees in Tirupur District. Primary data used in this research, was collected from 306 Knitwear employees from Tirupur District. Questionnaire was the main tool used to collect the pertinent data from the selected sample respondents and a well structured questionnaire was framed with the help of experts and experienced knitwear employees of various knitwear units in Tirupur District and used for the study. The respondents were chosen by simple random sampling method. The analytical procedures of factor analysis and chi square were utilized to find the job factors relativity with job satisfaction. Maximum knitwear employees were generally satisfied with their jobs. The job factors were negligibly related to overall job satisfaction. The factors “Pay packet and gratitude”, “Job Character”, “Work Environment”, “Job impact” emerged as the salient factors that had an impact on the overall level of job satisfaction.

INTRODUCTIONIn the earlier days the concept of job is to satisfy the person's basic needs are

fading out (Lu et al., 2005), presently the work environment has changed radically. The essential requirements are getting changed and expanding according to

JOB SATISFACTION OF KNITWEAR EMPLOYEES IN TIRUPUR DISTRICT

1 2 3S. Jayaraman Dr. C. Chandran and Dr. S. Mohandass

1 S. Jayaraman, Assistant Professor, Department of Management Studies, PSNA College of Engineering and Technology, Kothandaraman Nagar, Dindigul- 624 622, Tamilnadu, India.E-mail: [email protected], [email protected], Handset: 919976135244

2 Dr. C. Chandran, Professor and Head, Department of Management Studies, Madurai Kamaraj University, Madurai, Tamilnadu, India.

3 Dr. S. Mohandass, Director, SVS Institute of Management Studies, JP Nagar, Myleripalayam, Coimbatore.


44 S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass

the evolution of the work system and standards of living of a workforce (Spector, 1997). The present work force of Tripur knitwear industry were not satisfied with their financial needs, they also give lot of importance to social and psychological aspects related to their job. Today's workforce is much concerned about their job satisfaction in terms of flexible working hours, recognition, job autonomy and job stress apart from their monetary benefits. If the above mentioned job factors are disturbing to their job as well as their performance they are ready to switch over. Organizations failing to meet the above factors may lose their human assets.

Organizations who want to sustain their growth have to support and facilitate their workers and make them feel satisfied in their jobs. The job satisfaction approach considers people as an 'asset' to the organization rather than as 'costs'. India is in the growing phase with certain limitations, which limits the potential and strengths of the entire work force. The non availability of infrastructure, reduced resources and limitations creates the pressure, underpay, long working hours, excessive time away from home and family, unsystematic working hours and stress on the knitwear employees leads to job dissatisfaction. The dissatisfaction does not simply end at the workplace; it will affect the life and work of the employee and make them unhappy. The dissatisfaction cost of employers is also through increased turnover of laborers, more absences, more work complaints and more problems with co-workers. So organizations have to consider and eliminate the factors that lead to job dissatisfaction, by the way of concentrating on stimulating factors of job satisfaction they can enhance the job satisfaction and manage the work force.

JOB SATISFACTIONAhsan et al., (2009) feels that job satisfaction has relationship with job stress.

Job stress factors are management role, relationship with others, workload pressure, homework interface, role ambiguity, and performance pressure. Barbash (1976) indicates that the job satisfaction of workers employed in various occupations was not primarily a function of the work environment but rather a function of demographic characteristics.

European Working Conditions Observatory (2007) explains that job satisfaction was influenced by individual well-being, working hours, work orientation, financial variables, the employment contract, market and job mobility. Education and job security are the strongest correlation with job satisfaction. Job autonomy leads to higher job satisfaction.

Jennifer (2000) in her article has mentioned that job satisfaction is represented by several related factors that have been highly correlated, but two traits have significant correlations, locus of control and negative affectivity. Locus of control means, how much control they have over their job. Negative affectivity means negative emotions, independent of the situation. Lee and Wilbur (1985) in their article examined job satisfaction in the context of personal characteristics. Older workers have exhibited more satisfaction with their jobs, while younger workers



have indicated less satisfaction with the intrinsic characteristics of work. Lora and Nikki (2003) provides a picture of changing structure of the global

apparel production due to the stage out of quotas, and trends in trade policies. Today,

many garment workers are concerned about how free trade policies will impact

thedaily lives of the workers. Mansour and Achoui (2008) in their study found that

the employees perceived their work as challenging and secure. They are comfortable

with their co-workers, and get enough resources, support from their superiors.

Environment characteristics and job characteristics are influencing employees'

satisfaction.NASFAA (2008) express that work load outweighs salary, recognition,

appreciation are correlated with job satisfaction. Work, proud of their job, personal

satisfaction from their roles, current position increases or decreases the job

satisfaction. Richard (1999) feels that pay and promotion are two most important

factor of job satisfaction. These factors highly influence the job satisfaction. Rupa Mathan, (2004), pointed out that industrial relations is not a one sided

game but depends on the mutual understanding, faith and goodwill of all the

employees in the industrial relation system. Sharma (2004) studied the satisfaction of

public and private sectors employees on related issues for which 459 employees were

selected of which 311 employees were from public sector enterprises and 148

employees from private sector enterprises. The result revealed that the public sector

employees were in better position in terms of their job satisfaction than the private

sector organization. Work environment, nature of rewards and benefits they derived

and recognition by superiors influenced job satisfaction. Sirgy et al., (2001), in his

article identified the factors influencing job satisfaction as employee satisfaction

with a variety of needs through resources, activities, and outcomes stemming from

participation in the workplace.Study by Timothy (2004) reveals that employees quickly change their jobs, if

they are dissatisfied with their employers, lack of professional respect, recognition,

and challenge, job rotation, job stress, since they perceived these factors as highly

influencing factors of the job satisfaction. Watson et al., (2003) in his study

highlighted that job satisfaction is an employee's level of positive effect towards job

or job situation. Job satisfaction of an employee differs in meaning and importance in

relations to the facets of work. Some may feel pay and fringe benefits that meet his

expectations to be extremely important; for another, it may be essential to have a job

that provides an opportunity for challenging assignment. Many different perceptions

are there about the job satisfaction, such as pay, promotions, supervision, fringe

benefits, one's co-workers support, and excessive working hours. White (1981) in his study found that salary, education and job tenure did not

alter their satisfaction with the job. Contradictory results have been reported on the

differences in satisfaction between men and women.


S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass46

SCOPE OF THE STUDYThe scope of the study is confined only to the knitwear employees in Tirupur

district. The research aims to find out the influence of the job factors on job satisfaction of Knitwear employees in Tirupur district. Therefore, researcher has made a humble attempt to assess the job factors, conditions and its influence on job satisfaction of knitwear employees in Tirupur district of Tamil Nadu.

OBJECTIVES OF THE STUDYlTo assess the overall level job satisfaction of knitwear Employees in Tirupur

district.lTo identify the relationship between knitwear employees job satisfaction and

the job factors.

METHODLOGY The sample respondent for the study comprised of knitwear employees of

Tirupur district. The present study used primary data collected through a questionnaire, convenience sampling technique was adopted to select a sample of 306 knitwear employees from the universe. Data pertaining to the study was collected between April 2010 and June 2010. The study used cross tabulation, frequency analysis, chi-square analysis and factor analysis to identify the overall level job satisfaction and the relationship between job factors and job satisfaction of knitwear employees of Tirupur District.

HYPOTHESIS OF THE STUDYNull Hypothesis: There is no significant relationship between the job factors and level of job satisfaction of knitwear employees in Tirupur district.

ANALYSIS AND DISCUSSIONBased on the responses from the knitwear employees the job satisfaction scale

ranging from highly dissatisfied to highly satisfied, the overall level of job satisfaction was 80.1% (n =245) and 8.5% of the knitwear employees were dissatisfied with their profession and remaining 11.4% of employees were not able to say about their job satisfaction level. 31.05% of females (n = 95) and 49 % of males (n = 150) were satisfied with their job.

65.4% of the respondents were males and 34.6% were female employees. Among the female employees 11.4% were in the age group of 31– 40 years and 41- 50 years and 8.2% in the age group of 20 – 30 years. Among the male employees 24.8 % belongs to the age group of 31 – 40 years, 17% belongs to the age group of 21 - 30 years and 15.4% in the age group of 41 – 50 years.

Among the Tirupur knitwear employees 43.8% of the workers had their educational qualification below higher secondary and 21.6% of knitwear employees have completed their diploma and 11.1% holds a master's degree and 3.3% hold professional degrees.



47

64.7% of employees (n=198, Male=107, Female=91) are having experience

less than 5 years, 21.9% (n= 67, Male=57, Female=10) of the employees are having

experience between 5 to 10 years. Regarding the work timings 70.6% of the employees felt the work timings are

flexible, but 16.3% are not comfortable with their work timings. Among the knitwear

employees 53.9% of the employees said they are able to take sufficient breaks

between the work, but another 46.1% of the employees said they are struggling in it.With regard to the subject of work knowledge 63.7% of the employees of

Tirupur district replied that they perfectly know about what they have to do in their

job, 28.1% of respondents replied that they know partially and another 4.2% repliedthat are not clear what they are expected to do. Based on the responses from the

employees regarding support and recognition from the superiors, 75.8% of the

employees expressed that the superiors are good and cooperative and only 13% felt

that they are critical. 94.4% of the knitwear employees are comfortable with the work

environment and 68% felt that their job is secured. In term of rigorous work 41.5% of

the employees opined as always they have to work intensively, another 47.3%

employees responded as rarely they have to work intensively and remaining 11.1%

expressed that hardly ever they have to work rigorously. Regarding pressure to work long hours from supervisors, 56% of the employees

said as based on the need only they are pressured to work for long hours, 7.2% of the

respondents are not getting any disturbance but another 24.1% felt that they are

pressured to work long hours and the remaining 12.4% expressed that they are not

pressured to work for long hours. Only 15% of the employees felt proud to be in this

profession, another 40.4% employees said that this is their expected position.

Another 23.9% employee replied that without another option they turned towards

this profession. Regarding enjoyment of work 73.2% of the employees agreed they

are enjoying their work, 9.8% are not having any opinion about enjoyment of job and

the remaining 16.9% expressed that they do not enjoy their job. Regarding salary aspects 58.5 % of the employees said that their salary in

between Rs.1 – Rs.5000, another 29% are getting Rs.5000 – 10000 and remaining

12.4% are getting the salary above Rs.10000. In terms of salary satisfaction 78.8% of

the employees are satisfied with the current salary, specifically 49.3% are highly

satisfied but 13.1% are not satisfied with their salary. Among the 306 employees

70.6% felt that they are getting a fair salary based on their experience and another

19.5% of the employees felt that their salary is not based on their experience.


S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass48

Regarding the stress among the textile employees only 26.5% (n=81) of the

employees said that their job is a stressful profession and remaining 64% (n=196) of

textile employees said that, this is a joyful one and 7.2% (n=22) of the employees said

that the work load in the job is heavy. In the job satisfaction survey of Tirupur district

textile employees, only 24 (7.8%) respondents felt that their job affects their life and

health because of that they are taking off from the job frequently, but remaining

respondents' declined that statement.

Chi-square analysis:The job satisfaction is dependent on various job related factors; if those factors

affect the job, the employees' job satisfaction level will come down. In order to verify

that chi-square test was used. The results are shown in the following.

Table1 Results of Chi-Square analysis

Factor Calculated values Table value Degrees of freedom

Job nature

23.910

26.296

16

Work load

15.686

26.296

16

Job security

42.875

26.296

16

Long working hours 31.556 21.026 12

Fair pay 27.597

26.296

16

Sufficient work breaks

34.625

26.296

16

Job stress 22.028 26.296 16

The Nature of Job is one of the important factors for determining the job

satisfaction level of the employee. If the job nature is favourable for the employees

the work will be completed with the fullest satisfaction. The chi-square test reveals

that the calculated value 23.910 is lesser than the table value and hence we can infer

that the Nature of job is not having much impact on the job satisfaction.

If the Work load is more it will affect the job satisfaction level of the employees.

The Chi-square test reveals that the calculated value 15.686 is lesser than the table

value and hence we infer that Work load is not having that much impact on the job

satisfaction of Tirupur district knitwear employees.

Job security is one of the vital factors among the job satisfaction determinants.

If the job is not secured it may affect the mindset of the employees and the

productivity. The Chi -Square test reveals that the calculated value 42.875 is greater

than the table value we can infer that the Job security determines the job satisfaction

of the knitwear Employees.


S. Jayaraman, Dr. C. Chandran and Dr. S. Mohandass 49

When the working hours are extending beyond the normal working hours certainly it

may create the problem and affect the job satisfaction level of the employees. The

Chi-square test reveals that the calculated value 31.556 is greater than the table value

hence it is inferred that long working hours affects the job satisfaction of knitwear

employees.

If the pay is given in the fair manner based upon the experience and

performance of the employees, it may motivate the employees and increase the job

satisfaction of the employees. The Chi-square test reveals that the calculated value

27.597 is greater than the table value and it is inferred that fair pay increases the level

of job satisfaction of employees.

During the work if the employees are able to take sufficient breaks for their

needs it will improvise job involvement and satisfaction from the job. The Chi-

Square test reveals that the calculated value 34.625 is greater than the table value and

it is inferred that sufficient break increases the level of job satisfaction of employees.

The requirements and needs of the job may create stress among the employees.

The job stress certainly affects the job satisfaction level of the employees. The Chi-

Square test reveals that the calculated value 22.028 is lesser than the table value and it

is inferred that job stress is not having much impact on the job satisfaction of knitwear

employees.

Factor Analysis:

Factor analysis is a data reduction and statistical analysis technique that tries to

explain observed relationships among multiple outcome measures as a function of

some underlying variables, or factors. Factor analysis differs from other analysis, in

that it focuses on multiple dependent variables and tries to uncover patterns of

relationships.

Table 2 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.636

Approx. Chi-Square 279.970

df 66

Bartlett's Test of Sphericity

Sig. .000


50

The KMO and Barlett's test of Sphericity revealed a Chi-square value of

276.97 with 66 degrees of freedom and 0.000 level of significance. The Kaiser-

Meyer-Olkin Measure of Sampling Adequacy value was 0.636 revealing the validity

of factor analysis.

Extraction Method: Principal Component Analysis

Table 4 Factor Analysis - Component Matrix

Table 3 Factor Analysis - Total Variance Explained

Component Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1 2.216

18.465

18.465

2.216

18.465

18.465

2.195

18.289 18.289

2 1.294

10.783

29.248

1.294

10.783

29.248

1.281

10.673 28.962

3 1.156

9.630

38.878

1.156

9.630

38.878

1.157

9.641

38.604

4 1.121

9.340

48.218

1.121

9.340

48.218

1.154

9.614

48.218

5 .992

8.264

56.482

6 .978

8.149

64.631

7 .841

7.011

71.641

8 .825 6.871 78.513

9 .755 6.293 84.806 10 .701 5.841 90.647 11 .693 5.774 96.421

12 .429 3.579 100.000

ComponentVariables

1 2 3 4

Support and recognition from superiors .717

Salary for the job .671 Relationship towards peers .663 Time flexible

.631

Enjoyment towards the job

.503

-.405

Job stress

.649

Job secured

.572

Job nature

.511

-.471

Job kills family time

.563

Work environment .676

Job force to work long hour -.517 .559

Workload in job .453

Extraction Method: Principal Component Analysis. 4 components extracted.




Table 5 Factor Analysis - Rotated Component Matrix

ComponentVariables

1 2 3 4

Support and recognition from superiors

.721

Salary for the job

.670

Relationship towards peers

.668

Time flexible .655 Enjoyment towards the job .457 Job nature

.663

Job secured

.566

Job stress

.548

Work environment

.733

Workload in job .565

Job force to work long hour .724

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 6 iterations.

The factors of the job satisfaction were treated to factor analysis. As a first step, KMO and Bartlett test for sample adequacy were done and the results shown in the table 2 reveals that samples are enough and adequate and factor analysis is valid.

On the extraction initially, the factor one is able to explain 18.465 % of variance, the factor two explains 10.783 % of variance, the factor three explains 9.630 % variance and the factor 4 is able to explain 9.340% variance. All the four factors are able to explain 48.218 %. On the initial extraction factor one explains high percentage of total variance and also explains more variance comparative to other factors. So it is considered more important for determining the job satisfaction of knitwear employees in Tirupur district. The initial and extracted communalities were computed along with the rotated components using varimax rotation and are displayed in the above tables.

Extraction method used was principal component analysis. Rotation method adopted was varimax with Kaiser Normalization. Rotation converged in 6 iterations. Based on the rotated component matrix we can classify the factors in to 4 major factors. They are as follows,•5 components were loaded on factor one. On the basis of factor loading the

factor was labeled as Pay packet and Gratitude•3 components were loaded on factor two. On the basis of factor loading the

factor was labeled as Job Character.•2 components were loaded on factor three. On the basis of factor loading the

factor was labeled as Work Environment•1 component was loaded in factor four and on the basis of factor loading the

factor was labeled as Job Impact.From the above factors and its loading components we can infer that the factors

Pay packet and Gratitude, Job character, Work environment and Job impact play a


52

vital role in determining the job satisfaction level of the knitwear employees in Tirupur district.

CONCLUSION Demographically, knitwear employees in the Tirupur district are male

dominated. Male domination is evidenced in the results of the studies which indicated that knitwear employees are very younger, have less tenure in their job, and most of them have the higher secondary school education only. Hence efforts to increase the job skills must continue among the knitwear employees. Since the supervising category is more too much controlling and monitoring will affect the job satisfaction, hence it is suggested to use the supervision category effectively.

Knitwear employees in the Tirupur district were generally satisfied with their jobs. But few employees were less satisfied which implies that there may be some systems in place which fail to take into consideration the entire knitwear employees. Focusing on the job factors, administrators should use the results of this study to investigate particular systems for improving the job satisfaction. Among the knitwear employees only 15% of them responded that they feel proud to be an employee of that industry, so efforts to change the attitude about the job must go on.

The salary range of knitwear employees is very low and most of them are paid on weekly basis. The knitwear employees are suffering from intensive work, long work hours and expressed that their job affects their life and health and job kills the time for the family. The knitwear companies and administrators should consider this for the improvisation of job satisfaction, but at the same time majority of the employees expressed that they are enjoying the work, the work timings are flexible, get proper recognition and support from the colleagues and superiors, the working environment is so comfortable. The findings of Chi-square and factor analysis indicated that there was a strong association between job satisfaction and job factors. The above findings reveal that job factors are strongly deciding the job satisfaction level of the knitwear employees of Tirupur.

In conclusion there are many variables in the job which led to the satisfaction and dissatisfaction among the knitwear employees, if we want to keep our employees satisfied and loyal to our organization, we have to improvise the factors which maximize the satisfaction and suppress the factors which affect the satisfaction.

References

A study of organizational change and its impact on employee job satisfaction and customer satisfaction in few selected public sector banks in and around Chandigarh - http://www.docstoc.com/docs/2393784/

Ahmed S. Al-Aameri, Saudi (2000), Job satisfaction and organizational commitment for textile employees, Medical Journal 2000, 21 (6), 531-535.

Andre Bishay (Fall 1996), Teacher Motivation and Job Satisfaction: A Study Employing the Experience Sampling Method, Journal of Undergraduate Sciences, 3, 147-154.

Atkinson, Tom. (1983), Differences between Male and Female Attitudes toward Work. The Canadian Business Review, 10(2), 47-51.

Barbash, Jack (1976), Job Satisfaction Attitudes Surveys. Paris: Organization for Economic Co-operation and Development.



53

Boca Raton, Timothy Lee Dore (2004), The relationship between job characteristics, job satisfaction, and turnover intention among software developers, Unpublished doctoral thesis, Faculty of Argosy University, Orange County, Florida USA, ISBN:1-58112-270-5

Eswari, M. and Palanivelu, P. Job satisfaction: A study among hospital textile employees in Coimbatore. Retrieved from indiahttp://ssrn.com/abstract=1428444

European Working Conditions Observatory (2007), Measuring job satisfaction in surveys- Comparative analytical report

Factors contributing to financial aid administrators' job satisfaction - NASFAA 2008 Financial Aid Administrators' Job Satisfaction Survey Results

Jeffrey P. Slattery, Rajan Selvarajan, T.T. (2005), Antecedents to Temporary Employee's Turnover Intentions. Midwest Academy of Management-submitted to the program for the Organizational Behavior and Organizational Theory track at the March 31-April 2, 2005.

Jennifer S. Skibba (2000), Personality and job satisfaction: An investigation of central Wisconsin fire fighters. The Graduate School University of Wisconsin-Stout Menomonie, WI 54751 Thousand Oaks, CA: Sage Publication.

Lee, Raymond and Elizabeth R.Wilbur (1985), Age, Education, job Tenure, Salary, Job Characteristics and Job Satisfaction: A Multivariate Analysis. Human Relations, 38(8), 781-791.

Lise M. Saari and Timothy A. Judge 2004), Employee Attitudes and Job satisfaction, Human Resource Management, Wiley Periodicals, Inc. 43 (4).

Lora Jo Foo and Nikki Fortunato Bas (2003), Free Trade looming threats to the world's garment worker, Sweatshop watch, Oakland, USA, 2003.

Lu, H., While, A., and Barriball, K., (2005), Job satisfaction among nursing employees: a literature review. International Journal of Nursing Studies, 42(2), 211-227.

Mahmoud AL-Hussami R.N. (2008), A Study of Textile employees' Job Satisfaction, European Journal of Scientific Research, 22(2), 286-295.

Mourad Mansour and Mustafa Achoui (2008), Job Satisfaction of Saudi Female Employees: An Exploratory Study, King Fahd University of Petroleum and Minerals (KFUPM) under Internal Research Grant IN070381.

Nilufar Ahsan, Zaini Abdullah, David Yong Gun Fie and Syed Shah Alam (2009), A Study of Job Stress on Job Satisfaction among University Staff in Malaysia. European Journal of Social Sciences, 8(1).

Nwachukwu Prince Ololube (2006), Teachers Job Satisfaction and Motivation for School Effectiveness. University of Helsinki Finland - http://www.usca.edu/essays/vol182006/ololube.pdf

Richard A. Murray (1999), Job Satisfaction of Professional and Paraprofessional Library Staff at the University of North Carolina at Chapel Hill. A Master's paper for the M.S. in L.S. degree.

Rupa Mathan, (2004), Industrial relations in private sector, Finance India, 8(3).

White, Terence A. (1981), The Relative Importance of Work as a Factor in Life Satisfaction. Relations Industries, 36(l), 179-191.

S. Jayaraman, Dr. C.Chandran and Dr. S. Mohandass


54


55

The Executive Editor of PRERANA (PRN) interviewed Mr. K. Ilango, (KIO), Joint Managing Director, RSM Autokast Ltd, Coimbatore on September 7, 2011. He is the President of TiE Coimbatore and the past President of CODISSIA.

PRN: Could you highlight the best management practices followed in your organization?

KIO: We have not really classified the management practices which we have in our organization as best. Our own organization has not grown exponentially, but we had a solid growth, very conservative in our expansion and where we have seen opportunities we never hesitated to grab that opportunity. We are very keen to have a very good technological edge. Ours being essentially an automotive component manufacture industry with foundry background we were the first to introduce indigenous induction modern furnace in the SME sectors around Coimbatore and imported real modern testing facilities ahead of anybody else in Coimbatore. Similarly any technology which came our way, we were always one of the first to adopt. The second thing we always believed is that even in the worst situation we should be able to stay afloat and one of the things which we did in the very early stage is to invest a lot into green energy and it was both as a matter of social responsibility and that had made a lot of business sense, because it really paid back very well even at very early stages. Apart from that in terms of people management we segmented our business into various aspects and always had a team to execute with assigned responsibilities. Our focus was always on improving our productivity flow and improving management practices and the team always contributed.

PRN: Could you highlight the innovative practices adopted by your company that helped you gain a competitive edge in the current scenario?

KIO: We always believe in the technological edge. Secondly we were also very good in identifying markets. We started exporting our brake drums way back in 1994 and we started of with a very challenging place, Australia. It was challenging in one way because straight away it was the first world country. Customers gave a lot of helping hand which helped us to learn and understand. We did a lot of mistakes in the initial stages but we learnt from the mistakes and corrected them and now it's a smooth flow. We have customers in about 30 countries, but few of them buy products once in six months or once a year, but still we have good relationship with them. Our products essentially go to the after markets in majority of the countries we export. Instead of multiple products we went for multiple markets.

PRN: What are the efforts you initiated in your organization to survive in this competitive environment?

KIO: We always believe that our product is the lowest value added casting in the automotive component industry, which means that we have to be super efficient whether we like it or not. So, one of the fundamental things we always did is to keep our costs low. We ensure that we have enough employees and we buy our raw

CXO Interview


56

is investing in green energy resources, which even in the worst situations helped us staying comfortably afloat.

PRN: What are the new ventures that you are planning for the next 2 to 3 years?

KIO: Apart from the existing business, I have been very bullish on two industries. One is the tourism sector. Today with the increasing income levels in our country there is tremendous amount of potential for tourism and I would like to do something to connect people to give them the best offer. The second space in which I am really bullish is food processing. The agriculture economy in India is very significant and any inefficient situation means there is tremendous opportunity. So if you are able to level it out to some extent by buying it at a very low price and storing it, I think it's a great economic opportunity. There is a huge market for processed food. There can be 10000 entrepreneurs in the food processing sector and everybody can do it. It depends on how innovative and what kind of product niche you find out and place yourself, whether as a supplier to a larger player or brand the product. It all depends on your business strategy depending on your core strength. But these are ideally two areas which I want to get into. We are working on it.

PRN: Sir, do you anticipate fast growing compensation would have an impact on the cost competitiveness of the organization?

KIO: As far as compensation is concerned, it has to increase in our country. Wages have to go up. We cannot have only 5% or 2% people in our country having same life style as people in developed countries. They should have all the facilities which each of us enjoys and I strongly believe in that and to that extent I am pretty socialistic. But labour productivity even today is low when compared to Chinese. Chinese employees produce atleast double of what we produce. I think as the compensation of employees goes up we need to get the best out of them. To get highest productivity we need to improve in technology and provide training. With increase in productivity, I think the increasing compensation will not have an impact of the cost competitiveness of the organization.

PRN: Sir, you have been in the business for almost three decades, have you ever been in a dilemma between your business and ethical responsibility?

KIO: Yes of course, you come into the business field like an idealistic young man and unfortunately, the business environment in our country doesn't foster ethics when it comes to practicality. It's very sad and unfortunate but that's the fact. But somewhere on the line, we learned to digest our idealistic notions and got on with what we are doing. But along the line you always harbor your want to cleanse the system and make sure that you follow ethical practices involved in whatever you do. We have been conserving energy and 80% of the energy that is being used by us is produced by our windmills. We purely look at things with the heart. We don't look at things with a very hard and fast rule unless somebody does something very bad like stealing.

PRN: How do you think management students and graduates could contribute to the SME sector in India?


57

KIO: Most of the MBA's are in the corporate at different levels and hardly anybody comes to the SME sectors to participate. Management students and graduates can do a lot because management practices are essential for entrepreneurs today. Many of the entrepreneurs can do better if they get inputs from management graduates. Unfortunately what I have seen is that most of the management graduates are very ill-equipped to come and help us out. There is no point in us coming and teaching you what it is. You have to come and give your inputs, the knowledge which you gain in your institution into practice and match it to the situations here. But unfortunately vast majority of the management graduates today are not yet equipped with the tools to come and convert that learning into process. The curriculum at every level should be oriented towards practicality. Theory in one area and practicality is another area I can tell this because I am an engineering graduate I could never connect most of what I learnt to what I do. But since we had business, I had an advantage. I used to go to our factories and so my perspective was better. Most of my friends never had that idea of what they are learning and how they are going to apply.

Most of the management institutions have the internship wherein they can send students to the SME's so that they can go observe and see how best they can help them in identifying an issue or a concern which would help them enhance the productivity or market. Students should show interest, be sincere and ensure that they learn and also provide some value addition to the organization. Faculties could also work along with students whereby the industry institute partnership will become much stronger and then there will be so much of productivity in the whole thing benefiting both the students and the industry. This will help the cause of innovation which is very essential in our country.

PRN: What are your suggestions for management faculty who seek industrial experience or taking up an assignment in an industry?

KIO: They have to come prepared, be sincere and it'll be great if you can assimilate six months in between the course to work in industry. But faculty should come with a positive attitude and at the end of the day go back feeling satisfied. It should benefit the ends, the faculties and the company. Little bit of learning is also appreciated. There is so much that we have to learn in terms of management practices. Most of the entrepreneurs struggle and fail in their aspect of knowledge, and many of them are good in their own business, they want to become a successful entrepreneur with the possessed skills and knowledge, but they don't know how to keep their books, they don't know how to identify a product that could be sold in the market or that has a demand. If an entrepreneur is already having 25 employees and doing a business and if faculty are willing to discuss with him, explain the challenges and support him on his limitations- accounts, finance and marketing it is of immense help to them. If faculty could help entrepreneurs market his products they become ultimately experienced in trying to do it at the cost of somebody else. The faculty might come out with something that is fabulous, but provided they look at it from the mindset of the entrepreneur. The entrepreneur might have just finished his diploma or not even that, but he is still an entrepreneur. How much of our management students willing to go in for that? If they are willing to partner it's a win-win for everyone.


58

PRN: Can faculties be a part of the SMEs say for 15 days or 20 days and work with them?

KIO: Yes why not? 15 to 20 days you may not be able to succeed in trying to finish a project but can identifying a project. If a faculty comes here and says they want to do a study here, and identify an area of study then you can take it forward with your students or consultants. Most of the consultants come and ask the right questions and put them in the study and give it to you and that's how consultancy works and if they do it in a large scale people pay huge amounts. You can do it at a small level to a local industry but one should have the willingness to do it perfectly. One thing that has to be clear is that any business man or entrepreneur will be willing to spend as long as they see value addition, which may be in terms of market or in terms of product of any other value addition. So at a consultancy level in a local scenario, faculty can definitely do it. As an entrepreneur he may not be able to take it forward, you can help him to achieve the desired study.

PRN: Entrepreneurship has been gaining momentum in India. What is your opinion on it?

KLO: The spirit of entrepreneurs is pretty strong here in Coimbatore but essentially we need 100 other Coimbatores in the country to lift it to a higher level because everybody cannot be a job seeker, they should be job providers and entrepreneurship is the way for that. In the next 20 years, anybody with a vision and willingness to work hard can succeed to any extent. The limitation is one's own mind. Anything existing and expanding or anything new all you need to do is to get hold of the opportunities, so I think there are tremendous opportunities and everybody should aspire to become an entrepreneur. And that's one thing good about Coimbatore because everybody wants to be an entrepreneur at least at the back of the mind. My own employees wanted to become entrepreneur and some of them have already become. There are no issues, they learn business from us when we buy products and deal with the local businesses. I really encourage local business and entrepreneurship. We are willing to take the risk of new technology when it is provided by the local market.

PRN: Would you recommend management students to go straight for their own venture or to gather industrial experience initially before taking up entrepreneurship?

KIO: No it's not necessary to gather industrial experience before taking up entrepreneurship, but it can always help you become an all round entrepreneur where you don't have to pay for your mistakes. If you have the ability and the willing to become an entrepreneur and as long as you are willing to take up a chance, you need not have any experience. Steve Jobs or Markz was not working for anybody before they became entrepreneurs. I don't think it's a prerequisite, if you already have ideas to start off, you can start off. But if you feel that no, I can't afford it and I have only limited resources and let me prudently use it by gaining resources then, so be it. Opportunities are there now and in another 10 years down the line, it will be little difficult to enter but it will still be there.


59

PRN: What qualities should be nurtured among the youth these days, especially management graduates?

KIO: The fundamental thing is the willingness to be open and learn. One thing I strongly believe and which I don't see in the youth these days is the lack of interest in acquiring knowledge. At the end of the day, knowledge, whatever be it, I am not saying this is knowledge and I am not defining knowledge, it can be anything, it can be on current affairs, it can be on science, and as long as you have the inclination to gain knowledge you will have the edge. Many of the students are not taking that extra effort to gain knowledge and I would say that the first step for anything is to gain knowledge. It may be useful anywhere even as a professional, how else are you going to be better than the others and how else can you achieve results, that's only by knowledge and we have an inherent ability to absorb knowledge and the second important thing is, information is available on the click of a button. So it's not like those days when you have to hunt for information and travel around the world for it. It's just that you should have the inclination to do that, and then you are the king. 5% extra in the class than the 50 other students can make a huge difference. It can be in any environment, class room or industry or anywhere.

PRN: What measures should the B-schools take to tap the creative mind of the students?

KIO: They should give them a lot of freedom to explore and experiment, I think, that is essential. The problem with our education system generally is, for instance, you have a lab, the first thing that they say is don't go break anything, don't go do this thing. The don'ts are more than the do's and I think that is the key here, we should allow their creative mind to be opened up and they should be given opportunities. Especially the libraries, labs etc must be opened to them 24/7. I think that kind of a mindset is required, a little more openness and opportunities to use your facilities to try out innovative stuffs. You can take them and show them around and expose them to the real corporate world and make them gain knowledge and allow them to be them as well. Little bit of more freedom is required of course with its own limitations, and I think things are changing but on a slower base. You are creating some opportunities and that is really good. But we have to give them more openness to implement the business samples and plans. Taking risk here is an essential part in which the students have to be strong at. We can help you in this as long as you have the willingness to learn. Students cannot themselves take up and do things. It is the faculties, experts and successful entrepreneurs who have to help them. There are incubation centers to help students. It all depends on what kind of business plan it is. If there is something that we as TiE can do, then, we will. Last year TiE was just formed and we were not prepared, now that we are, we are willing to help. In Coimbatore there are a lot of other opportunities and organizations that can help.


60

PRN: What would be the attitudinal change that should be brought about in the youth?

KIO: Youth have to be youth and they should be allowed to explore and experiment. They should be willing to take the risk also. I think they have already been doing it but the educational system can be changed a bit. In most of the western economies especially in Europe, people, take off one year and go around the world and explore and experiment. The urge to learn must start from the school level. Gaining of knowledge should be from that level. Unfortunately many are insisting on mugging up, getting good grades and getting into good colleges. The kind of system we have these days is not sustainable. IT companies may fall and best of talents may move in to other sectors such as services like tourism and hotels. Usually people in these days prefer IT, but that mindset should change. Though they have become cozy and comfortable in earning regular high pay, they should change and plan on growth rather than sustainability. IT will become more productive and important later only if they concentrate on growth. My only message would be, this is your time, this is your era, make use of it. The next 20 years is going to be a real good time for everybody to grow in the country. Yes we go through pains, we go through difficulties, we go through trials and everything, but all that is a part of the game.

PRN: Thank you for your time Mr Ilango.

Issue Dated Deadline for Submission

Issue No. 1 - March December 31

Issue No. 2 - September June 30

Guidelines to Authors

1. All articles are subject to peer review.

2. The manuscripts should be between 2000 – 3000 words (including figures and tables), typed using MS Word in 1.5 line spacing, Times New Roman, 12-point size, on A4 size papers with 1” (2.54 cm) margin on all sides.

3. All references in the manuscript should be placed at the end and arranged alphabetically. The referencing style suggested by the Amer ican Psycho logy Assoc ia t ion may be fo l lowed (www.apastyle.apa.org).

4. The cover page should contain (a) the title of the paper (b) author's name (c) designation and official address (d) address for communication (e) phone numbers and (f) e-mail address.

5. Author(s) should include a brief profile of themselves in about 50 words.

6. A non-mathematical abstract of about 150 words should be submitted along with the manuscript.

7. Two hard copies of the manuscript along with a soft copy in CD should be sent by courier/post addressed to “The Editor, PRERANA, GRG School of Management Studies, Peelamedu, Coimbatore – 641 004, Tamilnadu” and a soft copy to [email protected].

8. Authors(s) should send a declaration stating that the manuscript is not published, copyrighted, accepted or under review elsewhere. Please note that copyright of all accepted articles will be with PRERANA.

9 Each author will receive a complementary copy of the journal.

Deadlines for submission of Manuscripts

Executive Editor - PRERANA

Peelamedu, Coimbatore 641 004, Tamilnadu, IndiaPhone: +91 422 257 2222 / 429 5959 URL: www.grgsms.com

Email to: [email protected]

GRG School of Management Studies

This issue is not for sale and is for limited circulation only

About GRGSMS:

GRG School of Management Studies (GRGSMS) is a part of the GRG Group of Educational Institutions in Coimbatore under the aegis of the GRG Trust. The GRG Trust was established in 1956 by Shri G.R. Govindarajulu and his wife, Smt. Chandrakanthi Govindarajulu with a motto of empowering women through education.

Established in 1993, GRGSMS is one of the very few b-schools dedicated exclusively to women. The flagship programme of GRGSMS is the two-year, full-time MBA Degree. GRGSMS is approved by the All India Council for Technical Education (AICTE) and the degree is awarded by Bharathiar University, Coimbatore.

The vision of GRGSMS is, “to be a leader in education, training and consulting in management founded on a strong base of core values, holistic perspective, and recognition of human dignity.”

GRGSMS is located on the campus of the GRG Institutions in Peelamedu. The environment-friendly landscape and architecture provides an enabling atmosphere for learning and development. The infrastructure is state-of-the-art, modern and contemporary. Well-equipped classrooms, technology-enabled teaching aids, multimedia lecture halls with video-conferencing facility, commerce and language laboratories, conference and seminar halls, a well-stacked library and 24/7 wi-fi connectivity ensure excellence in education. A spacious and hygienic cafeteria and sports ground help to develop holistic personality of students.

GRGSMS has a core team of qualified and experienced faculty, supplemented by eminent representatives of the industry as visiting faculty. Regular guest lectures by senior executives from industry and scholars from the academia help students to learn the latest in management theory and best industry practices.

The objective of the GRGSMS curriculum is to nurture and develop women leaders with holistic perspective and concern for the society and environment.

Date post:	05-Apr-2018
Category:	Documents
Upload:	trandang
View:	213 times
Download:	1 times

A bi-annual journal from GRG School of Management … · ISSN : 0974-908X Volume: 3 Issue: 2...

Documents