Awareness Among Consumers About Green Marketing In Tanjore District

 

 Dr M. Mary Anbunathy

  ABSTRACT

             According to the American Marketing Association, green marketing is the marketing of products that are presumed to be environmentally safe. Thus green marketing incorporates a broad range of activities, including product modification, changes to the production process, packaging changes, as well as modifying advertising.  The movement of green marketing has been expanding rapidly in the world, no exception to India particularly in Tamilnadu. Consumers’ awareness and motivational champion are the driving force in the market, they go for green marketing. Now a day the environment has been changed and the mindset of the consumers also changed go for green marketing. When compare to other countries in India, the level of awareness is lower about the green marketing like organic food and eco friendly products ect.  The Indian consumer has much less awareness of global warming issues. Initiatives from industry and the government are still ice blue. Green is slowly and steadily becoming the symbolic color of eco-consciousness in India. The growing consumer awareness about the origin of products and the concern over impending global environmental crisis there are increasing opportunities to marketers to convince consumers. With this background data have been collected to know the level of awareness’ of the consumers in Tanjore town. For the purpose of the study both primary data and secondary data have been collected and chi square test is used for testing the hypothesis. The study reveals that there is a relationship between the educational qualification and their income level of the consumers in Tanjore town.

IMPORTANCE OF THE STUDY Green marketing definitions can be a little confusing, since green marketing can refer to anything from greening product development to the actual advertising campaign itself. Going by alternative names such as sustainable marketing, environmental marketing, green advertising, eco marketing, organic marketing, all of which point to similar concepts though perhaps in a more specific fashion, green marketing is essentially a marketing message in order to capture more of the market and services that are better for the environment. There are many environmental issues impacted by the production of goods and rendering of services, and therefore there are also many ways a company can market their eco-friendly offerings. Green marketing can appeal to a wide variety of these issues such as the items can save water, reduce greenhouse gas emissions, cut toxic pollution, clean indoor air, and be easily recyclable. Now a day there is awareness among the consumers about the green products. With this back ground the study is considered as an important one.

Review of Literature

  • Merilänen, S., Moisander, J. & Personen, S. (2000). The Masculine Mindset of Environmental Management and Green Marketing. Business Strategy and the Environment, 9(3), pp. 151-162. Environmental management systems and green marketing programmes have gained increasing popularity in western market economies.  They are viewed as cost-efficient, effective and just means of tackling problems associated with the impact of economic activity on the environment.  It is argued in this article, however, that these optimistic views are based on a number of ideas, images and metaphors that retain many and centric and inadequate assumptions about self, society and nature that may be incompatible with long-term environmental protection goals.
  • Prothero, A. & Fitchett, J.A. (2000). Greening Capitalism: Opportunities for Green Community. Journal of Macromarketing, 20(1), pp. 46-56. In this paper, the authors argue that greater ecological enlightenment can be secured through capitalism by using the characteristics of commodity culture to further progress environmental goals.  The authors reject both naive ecological romanticism and revolutionary idealism on the grounds that they fail to offer any pragmatic basis by which greater environmental responsibility can be achieved.  Drawing on the now well-established theoretical tradition of post-Marxist cultural criticism, the authors offer a conceptual justification for the development and implementation of a green commodity discourse.  For this to be achieved and implemented, prevailing paradigms regarding the structure, nature, and characteristics of capitalism must be revised.  Marketing not only has the potential to contribute to the establishment of more sustainable forms of society but, as a principle agent in the operation and proliferation of commodity discourse, also has a considerable responsibility to do so.
  • Oyewole, P. (2001). Social Costs of Environmental Justice Associated with the Practice of Green Marketing. Journal of Business Ethics, 29(3), Feb, pp. 239-252. This paper presents a conceptual link among green marketing, environmental justice, and industrial ecology.  It argues for greater awareness of environmental justice in the practice for green marketing.  In contrast with the type of costs commonly discussed in the literature, the paper identified another type of costs, termed ‘costs with positive results,’ that may be associated with the presence of environmental justice in green marketing.  A research agenda is finally suggested to determine consumers’ awareness of environmental justice, and their willingness to bear the costs associated with it.

Objectives of the study

  1. To know the evaluation of green marketing
  2. To know the contribution of companies towards the green marketing
  3. To know the challenges for green marketing
  4. To know the level of awareness of consumers about the green marketing
  5. To know the attitude among the consumers towards green products.

Methodology of the study   For the purpose of the study, both secondary and primary data have been collected and analyzed. The secondary data have been collected from articles, reports and professional information concerning green marketing studies in general using the internet and academic databases.  The primary data was collected through questionnaire. The statistical methods used for the analysis are percentage analysis and chi square test

Hypotheses for the study

  • There is no significant relationship between the Income and Awareness about the green products
  • There is no significant relationship between the occupation and Awareness about the green products.
  • There is no significant relationship between the educational level and Awareness about the green products.

Evolution of Green Marketing Green marketing term was first discussed in a seminar on ―Ecological Marketing‖ organized by American Marketing Association (AMA) in 1975 and took its place in the literature. The term green marketing came into prominence in the late 1980s and early 1990s. The first wave of green marketing occurred in the 1980s. The tangible milestone for the first wave of green marketing came in the form of published books, both of which were called Green Marketing. They were by Ken Pattie (1992) in the United Kingdom and by Jacquelyn Ottman (1993) in the United States of America. According to Peattie (2001), the evolution of green marketing has three phases.

  • First phase was termed as “Ecological” green marketing, and during this period all marketing activities were concerned to help environmental problems and provide remedies for environmental problems.
  • Second phase was “Environmental” green marketing and the focus shifted on clean technology that involved designing of innovative new products, which take care of pollution and waste issues.
  • Third phase was “Sustainable” green marketing. It came into prominence in the late 1990s and early 2000concerned with developing good quality products which can meet consumers need by focusing on the quality, performance, pricing and convenience in an environment friendly way.

Characteristics of Green Products

  1. Products those are originally grown.
  2. Products those are recyclable, reusable and biodegradable.
  3. Products with natural ingredients.
  4. Products containing recycled contents and non toxic chemical.
  5. Products contents under approved chemicals.
  6. Products that do not harm or pollute the environment.
  7. Products that will not be tested on animals.
  8. Products that have eco-friendly packaging i.e. reusable, refillable containers etc.

Initiatives Taken Up By Business Organizations’ towards Green Marketing

  • Going Green: Tata’s New Mantra Tata Motors is setting up an eco-friendly showroom using natural building material for its flooring and energy efficient lights. The Indian Hotels Company, which runs the Taj chain, is in the process of creating Eco rooms which will have energy efficient mini bars, organic bed linen and napkins made from recycled paper. And when it comes to illumination, the rooms will have CFLs or LEDs. and Paper Sector. The initiatives undertaken by this top green firm in India includes two Clean Development Mechanism projects and a wind farm project that helped generate 2,30,323 Carbon Emission Reductions earning Rs. 17.40 Crore.
  • Oil and Natural Gas Company (ONGC) India’s largest oil producer, ONGC, is all set to lead the list of top 10 green Indian companies with energy-efficient, green crematoriums that will soon replace the traditional wooden pyre across the country. ONGC’s Mokshada Green Cremation initiative will save 60 to 70% of wood and a fourth of the burning time per cremation.
  • Wipro Green It. Wipro can do for you in your quest for a sustainable tomorrow- reduce costs, reduce your carbon footprints and become more efficient – all while saving the environment.
  • Wipro’s Green Machines (In India Only) Wipro Infotech was India’s first company to launch environment friendly computer peripherals. For the Indian market, Wipro has launched a new range of desktops and laptops called Wipro Greenware. These products are RoHS (Restriction of Hazardous Substances) compliant thus reducing e-waste in the environment.
  • India’s 1st Green Stadium The Thyagaraja Stadium stands tall in the quiet residential colony behind the Capital’s famous INA Market. It was jointly dedicated by Union Sports Minister MS Gill and Chief Minister Sheila Dikshit on Friday Dikshit said that the stadium is going to be the first green stadium in India, which has taken a series of steps to ensure energy conservation and this stadium has been constructed as per the green building concept with eco-friendly materials.
  • Suzlon Energy The world’s fourth largest wind-turbine maker is among the greenest and best Indian companies in India. Tulsi Tanti, the visionary behind Suzlon, convinced the world that wind is the energy of the future and built his factory in Pondicherry to run entirely on wind power. Suzlon’s corporate building is the most energy-efficient building ever built in India.
  • Tata Metaliks Limited (TML) Every day is Environment Day at TML, one of the top green firms in India. A practical example that made everyone sit up and take notice is the company’s policy to discourage working on Saturdays at the corporate office. Lights are also switched off during the day with the entire office depending on sunlight.
  • Tamil Nadu Newsprint and Papers Limited (TNPL) Adjudged the best performer in the 2009-2010 Green Business Survey, TNPL was awarded the Green Business Leadership Award in the Pulp soon replace the traditional wooden pyre across the country. ONGC’s Mokshada Green Cremation initiative will save 60 to 70% of wood and a fourth of the burning time per cremation.
  • IndusInd Bank Green banking has been catching up as among the top Indian green initiatives ever since IndusInd opened the country’s first solar-powered ATM and pioneered an eco-savvy change in the Indian banking sector.

Present trends in Green Marketing in India  Governmental Bodies are forcing Firms to become more responsible. In most cases the government forces the firm to adopt policy which protects the interests of the consumers. Competitors’ Environmental Activities pressure the firms to change their Environmental Marketing Activities.

The Future of Green Marketing There are many lessons to be learned to avoid green marketing myopia, the short version of all this is that effective green marketing requires applying good marketing principles to make green products desirable for consumers. Evidence indicates that successful green products have avoided green marketing myopia by following three important principles

  1. Consumer Value Positioning
  • Design environmental products to perform as well as (or better than) alternatives.
  • Promote and deliver the consumer desired value of environmental products and target relevant consumer market segments.
  • Broaden mainstream appeal by bundling consumer desired value into environmental products.
  1. Calibration of Consumer Knowledge
  • Educate consumers with marketing messages that connect environmental attributes with desired consumer values.
  • Frame environmental product attributes as “solutions” for consumer needs.
  • Create engaging and educational internet sites about environmental products desired consumer value.
  1. Credibility of Product Claim
  • Employ environmental product and consumer benefit claims that are specific and meaningful.
  • Procure product endorsements or eco-certifications from trustworthy third parties

Challenges of Green Marketing Implementing green marketing is not going to be an easy job. The firm has to face many problems while trading products of green marketing. Challenges which have to be faced are listed under

  • Green marketing encourages green products / services, green technology, green power / energy.
  • The firm ensures that they convince the customer about their green product, by implementing
  • Eco labeling schemes. Eco labeling schemes offer its “approval” to “Environmentally harmless” products and they are very popular in Japan and Europe. Convincing the Indian customer’s is a great challenge.
  • The profits will be very low since renewable and recyclable products and green technologies are more expensive. Green marketing will be successful only in long run.
  • Many customers may not be willing to pay higher price for green products which may affect the sales of the company.

Analysis of Primary Data

       The following table gives the socio economic back ground of the respondent those who are purchasing the green products for their use in Tiruchirapalli district.

TABLE – 2  DEMOGRAPHICAL   PROFILE OF THE RESPONDENTS
Particulars No. of the Respondent % of the respondent
Age of the respondent Up to 25yrs 18 18
  25-35yrs 39 39
  35-45yrs 17 17
  45-55yrs 15 15
  Above 55 years 11 11
  Total 100 100
Gender of the respondent Male 53 53
  Female 47 47
  Total 100 100
Education  level of the respondent Up to 12th std 12 12
  Graduate 36 36
  PG 41 41
  Professional 7 7
  Others  4  4
  Total 100 100
Marital status of the respondent Married 72 72
  Unmarried 28 28
  Total 100 100
Occupation of the respondent Student 6 6
  Housewife 27 27
  Employed 38 38
  Entrepreneur 26 26
Retired persons 3 3
Total 100 100
Monthly income of the respondent No income 4 4
  Below Rs.10000 22 22
  10001-20000 34 34
  20001-30000 27 27
  Above30000 13 13
  Total 100 100

Sources primary data

        With the help of the above table it is observed that 39% of the respondents are from the age group of 25 – 35. 53 percent of the respondents are male. 41 percent of the respondent have been completed their post graduation.72 of them are married. 38 of them are working people, of which majority of them are in private sector institutions. Majority of them are getting a monthly salary of Rs more than 10000 and less than 20000 per month.

 

TABLE – 2

SOURCES OF INFORMATION ABOUT THE GREEN PRODUCTS

Sl.No Particulars No. of Respondent % of Respondent
1 Friends and Relatives 36 36
2 News paper and Magazines 22 22
3 Television and Radio 9 9
4 Internet 26 26
5 others sources 7 7
  Total 100 100

              Sources primary data

       With the help of the above table, it is observed that 36 of the respondent have got the information about the green products from their friends and relatives. The major media of spreading the awareness is ward of mouth.  The web site is another media among the youngsters for getting information.

 

TABLE -3

 AMOUNT SPEND FOR A MONTH FOR PURCHASING THE GREEN PRODUCTS

                                                                                                          Rs in Hundreds

Sl.No Particulars No. of Respondent % of Respondent
1 Below 500 18 18
2 500 -750 27 27
3 750 – 1000 32 32
4 1000-1250 14 14
5 above 1250 9 9
  Total 100 100

             Sources primary data

With the help of the above table, it is observed that 32 percent of the respondent spending up to 1000 for their monthly purchase of green products.

TABLE -4

NATURE OFGREEN PRODUCTS PURCHASED IN A MONTH

Sl.No Particulars No. of Respondent % of Respondent
1 Organic Food items like Vegetables, Rice, Fruits etc 34 34
2 Cosmetics(soap, Shampoo ect) 47 47
3 Toiletries 9 9
4 Electricals 6 6
5 others 4 4
  Total 100 100

               Sources primary data

               With help of the above table 4 shows the purchase of type of Eco friendly products. 34% of respondents purchase organic food items like rice, vegetables, and fruits only. 47% of the respondent purchased cosmetic items and minority of them are purchased toiletries, electrical and others.

Testing of Hypotheses

  • There is no significant relationship between the Income and Awareness about the green products
  • There is no significant relationship between the occupation and Awareness about the green products.
  • There is no significant relationship between the educational level and Awareness about the green products.

 

                Factors                  Method Calculated value Table value(5% level significance, 12 Degree of freedom) Result
Income Awareness about the green products   42.47 21.026 Rejected
Occupation Awareness about the green products 38.96 21.026 Rejected
Educational level Awareness about the green products 28.96 21.026 Rejected

 

FINDINGS The findings of the study were summarizes and presented.

  • 39% of the respondents are from the age group of 25 – 35
  • 53 percent of the respondents are male.
  • 41 percent of the respondent have been completed their post graduation.
  • 72 of them are married
  • 38 of them are working people, of which majority of them are in private sector institutions.
  • Majority of them are getting a monthly salary of Rs more than 10000 and less than 20000 per month.
  • 36 of the respondent have got the information about the green products from their friends and relatives. The major media of spreading the awareness is ward of mouth. The web site is another media among the youngsters for getting information.
  • 32 percent of the respondent spending up to 1000 for their monthly purchase of green products.
  • There is a significant relationship between the Income and Awareness about the green products
  • There is a significant relationship between the occupation and Awareness about the green products.
  • There is a significant relationship between the educational level and Awareness about the green products.

Suggestions

  • Manufactures’ should concentrate to produce recyclable products, reuse of packaging and they can use energy saving equipments for production and other purpose.
  • More green products should be offered to the retailer, and then they can sell green products to the consumers.
  • Government should offer subsidies for purchasing the equipments and machinery helping in keeping environment green. The manufacturers can be offer loans from the banks to install the equipments at lower rate of interest.
  • Word of mouth and internet (social networks face book, whats app) play a vital role in promoting the awareness about the green products and the advantages of green products. The advertisement should be modified and explain in detail about the green products and then it will reach the consumers.
  • Government should make necessary for creating the awareness about the benefit of green products.

Conclusion

                   The current low levels of consumer awareness about global warming, environmental pollution the Government of India, manufacturers, and retailers need to help raise consumer consciousness. Indian manufacturers have yet to find a market for green products, even as consumers have a low awareness of them because of the insufficient efforts made by the marketers.  Overall, it is clear that the Indian consumers especially Tanjore consumers are having less awareness about the usage of green products. Now a day consumers are spending lesser amount to purchase green products. But they ready to pay more prices for the products which are causing less environmental pollution. They also prefer promotional campaign which protects the environment, and distribution channels which are not causing environmental pollution. Government, companies, consumers and other stockholders have to join hands to come out of the situation. The opinion of the retailers is green products are liked by consumers but because of poor awareness and high prices have not been fully adopted by them. As far as consumers are concerned the awareness level is increasing and has started implementing them in their normal life.  The intermediaries should include consumer’s attitude measurement programme in their marketing plan and adopt all aspects of green marketing, then only they can achieve their goal and fulfill the social responsibility of their business concern. There is a need in this situation to save our earth is  joint hands actions from Government, NGOs, Manufactures’, retailers regulators, scientific community and environmental education groups should create an awareness programmes among the consumers at regular intervals for reviving, maintaining and safeguarding the earth’s eco system.

RERFERENCES

  • Ina landau (2008) – “Gaining Competitive Advantage through Customer Satisfaction, Trust and Confidence in Consideration of the Influence of Green Marketing “Master Thesis- University of Gavle
  • Kanupriya Gupta and Rohini Somanathan (2011), – “Consumers Responses to Incentives to reduce plastic bag use: Evidence from a field experiment in Urban India” – Thesis – Delhi school of Economies., Delhi – 110 007
  • Merilänen, S., Moisander, J. & Personen, S. (2000). The Masculine Mindset of Environmental Management and Green Marketing. Business Strategy and the Environment, 9(3), pp. 151-162.
  • Oyewole, P. (2001). Social Costs of Environmental Justice Associated with the Practice of Green Marketing. Journal of Business Ethics, 29(3), Feb, pp. 239-252.
  • Polonsky, Michael Jay. 1994. “An Introduction to Green marketing” – Electronic Green Journal, 1(2)-Article 3 (1994) – Pg2
  • Prothero, A. & Fitchett, J.A. (2000). Greening Capitalism: Opportunities for Green Community. Journal of Macromarketing, 20(1), pp. 46-56
  • Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). A DESCRIPTIVE STUDY ON THE PROSPECTS OF E-COMMERCE IN INDIA.Golden Research Thoughts, 3 (9), 17.
  • Renee Wever (2009) – “Thinking about the Box – A holistic approach to a sustainable design engineering of packing for Durable consumer goods “–Thesis– Delft University of Technology – Delft, Netherland.
  • Soren Bohne and Rikke Thomson (2011) – “Influencing consumer perception of and attitudes towards CO2 neutral and biodegradable carrier bags“ – Thesis – Department of Business administration – Aarhus University.

Challenges Faced By The Select Urban Public Sector Bank Customer’s While Using Atm/ Debit Card –

A Descriptive Analysis

* S.Bulomine Regi.,

ABSTRACT

“Banking is essential, banks are not”. It is noted that, traditional bank branches (bricks and mortar) are going to vanish through innovative banking services i.e. electronic banking and plastic cards which continue to attract new users. The main objective focused in this paper is to measure the challenges faced by the customers’ using ATM/Debit Card offered by selected public sector banks i.e. State Bank of India and Canara Bank. 360 respondents were selected using purposive stratified random sampling. This paper mainly focused on the challenges faced by the customers using ATM/Debit card.

KEYWORDS: ATM, Debit Card, ATM user, Challenges, Public Sector Banks

INTRODUCTION

ATMs are now a routine part of banking transactions but when they were introduced in 1960s, they were the high- tech technology. The Automated Teller Machine (ATM) is now such a normal part of daily life that it’s strange to think it was ever cutting-edge technology. But in 1960s, when the first cash-dispensing ATM was installed at a branch of Barclays Bank in London, it was innovative and revolutionary. What’s more, over the decades, ATMs have become much more than just cash dispensers. They also allow customers to carry out a range of banking activities, including deposits and mobile phone top-ups. Given that the ATM is such a prominent feature in people’s lives, it’s important to understand its background, technical development and its capabilities. Here’s a quick introduction to the ATM and its global significance.

While the first card-accepting ATM was introduced by Barclays in London in 1968, this was not in fact the very first incarnation of the automated teller. CitiBank, then known as First National City Bank, launched a version of the ATM called the Bankograph in American branches in 1960. This machine did not let customers withdraw money but instead allowed them to pay bills without the assistance of bank staff. Moreover, Barclays’ 1968 addition was not foolproof and cards were regularly swallowed by these early ATMs.

Following these early developments, growth in North America and Western Europe was rapid. In 1969, the first machine to use magnetically encoded plastic was installed at Chemical Bank in New York, although initial take-up was slow as the running costs for these machines, known as Docutellers, outstripped the cost of hiring a human teller. However, as the modified Total Teller was introduced in the early 1970s, ATMs began spreading in banks across the two continents.

Today, ATMs have been popularised across the globe. Experts estimate that developed countries like the USA, Canada, the UK and Japan have a high concentration of ATMs per capita, while steady economic growth in India and China has meant that the number of bank machines in these countries has been growing in the last decade. However, it’s not just the number of ATMs throughout the world that has increased but also its functions. As well as withdrawing and depositing cash, modern ATMs also allow you to put credit on a mobile phone just by entering your phone number of the keypad. What’s more, some machines will let pay money into a beneficiary’s account, while others will print mini bank statements of your last few transactions.

However, as software changes, it does concern over ATM security. Today’s biggest worry for ATM industry professionals is how to maintain the security of global systems beyond the traditional advice to consumers to keep their PIN secret. The development of chip cards and Chip and Pin technology has helped to combat ATM fraud but there are still advances to be made.

STATEMENT OF THE PROBLEM

            Nowadays majority of the customers are using ATMs to withdraw cash from their account. The debit cards are used in very occasion for payments made through online, payments for purchases in shopping mall and so on. The use of ATM is increasing day-by-day, it is important to study the challenges towards use of ATM services. The customers were facing different types of problems with which ATM is directly related. Machine complexity, machine breakdown, poor quality notes, network failure, unsuitable location, forgot ATM pin number, High frequency of use, safety and security are the major problems of ATM users. Customers do not like ATMs because of impersonality, vision problem, fear of technology and reluctance to change and adopt new mode of delivery of service.

 

 

OBJECTIVES OF THE STUDY

The following are the objectives of the study:

  1. To study the socio-economic conditions of the respondents using ATM/Debit Card from select public sector banks in Tirunelveli District.
  2. To identify the challenges faced by the customers while using ATM/Debit Card from select public sector banks in Tirunelveli District.
  3. To give suggestions for the improvement of using ATM/Debit Card.

METHODOLOGY

Research design

A  research  design  is  a  plan  of  the  research  project  to  investigate  and  obtain answers  to research questions. Three types of research designs identified from the literature are exploratory, descriptive and explanatory design.[1] In  the  beginning  of  the  study, an  exploratory research  was  undertaken by an  in-depth review of literature in order to identify the research  problem,  constructs  and  to formulate hypotheses. Descriptive  research design  was  used  in  the  next  stage  of  the research for the purpose of describing the profile of the respondents and to determine the frequencies,  percentages, mean and standard  deviation  of  the  measures  and constructs used  in  the  study. Descriptive research could not explain the relationship among the variables [2] and therefore, to establish relationship and association between variables used in the study, explanatory research was used.

Survey  method  using  a  pre-structured  interview schedule was  used  for  collecting  primary data from the respondents because it offers more accurate means of evaluating information about  the  sample  and  enables  the  researcher  to  draw  inferences about  generalising  the findings  from  a  sample  to  the  population.[3]  The study  also  made  use  of secondary  data  collected  from  published  sources  such  as  records  and  reports of RBI and IRDBT, books, bank official websites, bank magazines, reports, newspapers, journals and websites.

Two banks were selected for the study and 180 customers were selected from each bank purposively those who are using innovative banking services namely ATM/Debit Card, Credit Card, Internet Banking and Mobile Banking. Two banks were selected based on IBA Banking Technology Awards 2014-2015.[4] The select banks are State Bank of India and Canara Bank which are public sector.

Sample design

Details of customers using innovative banking services (IBS) could not be obtained from the banks due to banks’ privacy issues and topic sensitivity. Therefore, the researcher decided to contact the respondents from ATM outlets of the select banks and other urban ATM outlets in the district. Simple random sampling method was adopted to select the ATM outlets and purposive sampling method was adopted to select the respondents. Customers who are using innovative banking services (IBS) visiting ATM outlets on the days of survey were selected as sample respondents. The respondents were selected after having ensured that they have account with any of the two banks and they are using all the two selected IBS. It was also ensured that the respondents have been using IBS for a minimum period of two years.

 Determination of Sample Size

Where

         Z       =       Standardized value corresponding to a confidence level of 95% = 1.96

         S       =       Sample SD from Pilot study of 60 sample = 0.484

         E       =       Acceptable Error =5% = 0.05

         Hence, Sample size = n = (ZS/E) 2

                                                = (1.96*0.484/0.05)2

                                                = 359.96

Hence, Sample Size n= 360

360 respondents who were selected for the study out of those 180 respondents are from State Bank of India and 180 respondents are from Canara Bank. The collected data were analysed with the help of SPSS 21 and AMOS. In order to obtain the score of the attitude of customers Likert Five Point Scaling Technique was used.

Results of Reliability Test Using Cronbach’s Alpha

Variables No. of Items Cronbach’s Alpha
Measuring  level of attitude of  the customers’ towards IBS 28 0.892

LIMITATIONS OF THE STUDY

Each research work is subjected to certain limitations and this study is also not an exception. The present study has the following limitations:

  • The responses for the study have been solicited from the District of Tirunelveli in Tamilnadu alone. The expectation and attitude of the customers in Tirunelveli may vary from those of the rest of the Districts in Tamilnadu and other states in India.
  • The study may suffer from the element of biasness.
  • The customers of two banks were selected for the study to study the attitude towards IBS. As a result, the generalisation of the findings of the present research has to be done with utmost care.
  • Furthermore, the sample was restricted to commercial banks. The other major banks like private, co-operative banks and foreign banks are excluded from the study.
  • The analysis of innovative banking services offered to corporate banking customers are excluded from the study.
  • No published data were available on number of customers availing all the four select services and no banks provided much data.
  • As regard users of card, no categorisation has been done such as users of classic, platinum and alike.
  • The study was restricted to urban customers only.

ANALYSIS AND INTERPRETATION

MILIEU OF THE RESPONDENTS- A DESCRIPTIVE ANALYSIS

  • Majority (61.4%) of the customers using innovative banking services (IBS) are male.
  • Majority (31.7%) of the respondents using innovative banking services (IBS) belongs to the age group of 31-35.
  • Majority (75.3%) of the customers using innovative banking services (IBS) are married.
  • Majority (45.8) of the IBS users are graduates.
  • Majority (44.7) of the respondents are employed.
  • In public sector, customers using innovative banking services (IBS) are earning above
  • Overall 78.3 per cent of the respondents are having savings account.
  • Majority (47.2%) of the respondents are having account with the bank between 2-5 years.

 

Problems faced by the customers while using ATM/Debit Card

         Customers are using maximum ATM/Debit Card service at the maximum in their day to day transactions. It is evident that, majority of the customers are using Debit Card up to 5 times. While using ATM/Debit Card customers are facing problems in performing their task. The below table shows the major problems faced by the customers while accessing ATM/Debit Card.

Table No. 1

Mean of Problems Faced by the Respondents while using ATM/Debit Card

ATM/DEBIT CARD Public Sector of Bank
Poor network 2.05
Lack of infrastructure 1.99
Long waiting queue 3.40
Machine out of service 2.52
Out of cash 1.81
Limited ATM centres 2.52
Unable to print statement 2.41
Letters printed in the statement disappear after few days 3.62
Card blocked 2.58
Misuse of card and frauds 2.27
Lack of confidence 2.18
Swiping is difficult 1.86
ATM centre doors are always open 3.67
Without security guards 3.44
Non-availability of CCTV  (Inside and Outside ATM centre) 2.37
Damaged Currency 2.23
Reduction of balance without cash disposal 2.36
Over/Under value of withdrawal amount 2.29
Location of ATM centre  is safety 2.56
No proper Air Conditioner 1.74
No parking  facilities in front of ATM centres 3.60
2 or more people in a single machine 3.57
Not giving proper intimation about charges 3.54
Magnetic Strip easily damaged 1.89
If misplaced, blocking card is difficult 2.29
Prompt service to get new card and PIN 3.12
Time Consuming 2.29
ATM premises are full of Receipts on the floor. 3.17
Shoppers also charging for using card 2.76

    Source: Primary Data

            Based on the mean score, public sector customers using ATM/Debit Card services are facing problems like ATM outlet doors are always open (3.67), letters in printed statement disappear after few days (3.62), no parking facilities in front of ATM outlets (3.60), two or more people tend to use a machine at a time (3.57), banks are not giving proper intimation about charges (3.54), lack of security guards (3.44) in the ATM outlets and long waiting in queue (3.40).

Inference: It is inferred that, public sector customers are facing the similar problems i.e. lack of infrastructure facilities and not proper maintenance of ATM outlets. It is evidence that, urban ATM outlets in the study area are accurately having these types of problems cited by the sample respondents.

SUGGESTIONS

  • Nowadays, there is sufficient number of ATMs but no proper facilities to access the ATM outlets like parking, shed to stay in queue, paper free ATM center, Air Conditioner, Security guards and CCTV camera in and out of ATM outlets to avoid physical attack and theft occurred in the place of ATM outlet. So, proper care should be given to maintain ATM outlets.
  • The banks should instruct the outsourcing agents to put quality paper for printing receipt. Because, the letter in the printed receipt disappear after few days.
  • The customers should follow the security guidelines given by the banks while accessing ATM/Debit Card.
  • The customers should not disclose the PIN to anybody.
  • The customers should avail ATM/Debit Card with utmost care.

CONCLUSION

Banking sector plays a vital role in the growth of economic development in India. Banking is still under evolutionary stage as it is adopting new technologies to facilitate further the customer convenience in the secured environment. IBS is becoming popular amongst customers who are familiar with the technology up graduation but it is gradually spreading to mass especially at metropolitan and urban cities. Few banks have taken an early lead by introducing technology based banking services. The study on the customers’ attitude towards innovative banking services (IBS) in banking sector reveals that customers are satisfied in some aspects and they want to continue with their respective banks. The shift from cutomerised service to personalized services is highly essential to satisfy all groups of customers. The findings of the study stress upon the importance of the security and safety expected by the customers especially in the case of innovative banking services (IBS) like ATM/Debit Card. The future of internet banking lies in offering personalized internet based services that are not only valued by their customers but are also unique to them. This would help distinguish themselves in the crowd. This would also help them evolve continuously to meet customers’ needs, capitalizing on new technology to build stronger customer relationship.

REFERENCES

  1. Eugine, F. D. C. & Regi, S. B., “Advantages and Challenges of E-Commerce Customers and Businesses: In Indian Perspective” International Journal of Research–Granthaalayah,4(3), 7-13.
  2. Golden, S. A. R. (2015). Regional Imbalance affecting quality of e-banking services with special reference to Tuticorin District-An Analysis.International Journal of Research2(3), 788-798.
  3. Golden, S. A. R., & Regi, S. B. (2014). Attitude of Rural People Towards Technology Inclusion In Banking Services At Tirunelveli District, IGJAE – Indo Global Journal Of Applied Management Science, 2(2).
  4. Golden, S. A. R., & Regi, S. B. (2014). Customer Preference Towards E- Channels Provided By State Of Bank Of India, Kongunadu College Of Arts And Science, Special Edition 1(1).
  5. Golden, S. A. R., & Regi, S. B. (2015). Satisfaction of Customers towards User Friendly Technological Services offered by Public and Private Sector banks at Palayamkottai, Tirunelveli District.International Journal of Research2(3), 775-787.
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  2. http://worldwidejournals.com/paripex/file.php?val=July_2013_1374047900_e453d_54.pdf
  1. Regi, S. B., & C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  2. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards Innovative Banking Practices Available In State Bank Of India At Palayamkottai.Sankhya International Journal Of Management And Technology, 3 (11 (A)), 3133.
  3. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards E-Channels Provided By State Of Bank Of India.
  4. Regi, S. B., and Dr.C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  5. Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). ROLE OF COMMERCIAL BANK IN THE GROWTH OF MICRO AND SMALL ENTERPRISES.Golden Research Thoughts, 3 (7), 15.

[1] Cooper, D.R. and Schindler, P.S. (2001).  Business Research Methods (7th edition). Singapore: McGraw-Hill- Irwin.

[2] Zikmund, W.G. (2000).  Business Research Methods (6th edition). Chicago: The Dryden Press.

[3] Creswell,J.W.  (1994). Research Design: Qualitative and Quantitative Approaches. Thousand Oaks: Sage Publication

[4] http://www.iba.org.in/Documents/FINAL_AWARDS.pdf dated 10/04/2015 time 23.59 p.m

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

 

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

Department of Social Science, Faculty of Economics

 Ethiopia, Aksum University

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

Selected power plants in three regions of Andhra Pradesh

Power Plant by

Fuel Type

Rayalaseema Region Telangana Region Coastal Region
Thermal Rayalaseema Thermal Power Plant .Kothagudaem Thermal Power Station  O & M

 

.Kothagudaem Thermal Power Station Stage V

 Narla Tata Rao Thermal Power Plant
Hydel Nagarjuna Sagar Main Power House

 

Nagarjuna Sagar Left Canal Power House

 

Nagarjuna Sagar Right Canal Power House

Srisailam Left canal power house

 

Srisailam right Canal Power House

 

 

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

APPENDIX TABLES

Table: A1: Nagarjuna Sagar Main Power House

Variables Entered/Removed  
Model Variables Entered Variables Removed Method  
1 acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_discharge_cums  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .997a .995 .995 512.92868  
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 7291771208.745 5 1458354241.749 5543.053 .000a
Residual 38675087.446 147 263095.833    
Total 7330446296.191 152      
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity
b. Dependent Variable: water_discharge_cums      
 

Coefficientsa

 
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) -146.238 1555.816   -.094 .925  
Reser_elevation -.080 .093 -.012 -.865 .389  
Storage capacity -.258 .065 -.091 -3.966 .000  
tail_water_level .350 .111 .031 3.144 .002  
Gross_feet .133 .094 .026 1.419 .158  
acutal_generation 50.669 .423 1.041 119.869 .000  
a. Dependent Variable: water_discharge_cums        

Table: A 2 Nagarjuna Sagar Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 evaporation, energe_bus, twl_ft, storage capacity, reservior_levela . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_drawals  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .864a .747 .739 2350.84646  
a. Predictors: (Constant), evaporation, energe_bus, twl_ft, storage capacity, reservior_level  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 2626964399.664 5 525392879.933 95.068 .000a
Residual 889763133.646 161 5526479.091    
Total 3516727533.310 166      
a. Predictors: (Constant), evaporation, energy bus, twl_ft, storage capacity, reservior_level  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 1660.770 501.102   3.314 .001  
reservior_level -3.516 3.411 -.157 -1.031 .304  
storage capacity -21.705 5.219 -.538 -4.159 .000  
twl_ft 9.653 2.510 .394 3.846 .000  
energy bus 491.286 30.765 .987 15.969 .000  
evaporation .130 .508 .015 .255 .799  
a. Dependent Variable: water_drawals        

Table: A3 Nagarjuna Sagar Right Canal Power House

Model Variables Entered      
1 generation bus, reservior_level, evaporation, storage capacity, tailwaterlevela      
b. Dependent Variable: water_drawals

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .885a .784 .777 3767.05581  
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 8246365913.182 5 1649273182.636 116.222 .000a
Residual 2270513515.133 160 14190709.470    
Total 10516879428.315 165      
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 6133.252 838.604   7.314 .000  
reservior_level .628 7.571 .016 .083 .934  
storage capacity -58.029 9.570 -.832 -6.063 .000  
Evaporation .414 .810 .027 .511 .610  
Tailwaterlevel 37.493 21.598 .263 1.736 .084  
generation bus 486.057 29.945 1.045 16.232 .000  
a. Dependent Variable: water_drawals        

Table:  A4 Srisailam Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 inflow, Reservoir, evaporat, Actual generation, Tail water, storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_withdra  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .981a .963 .959 1454.18057  
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity  
                                                                                   ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 2815082375.894 6 4.692E8 221.872 .000a  
Residual 107846697.597 51 2114641.129      
Total 2922929073.491 57        
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity    
b. Dependent Variable: water_withdra          
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -2243.501 2527.275   -.888 .379
Reservoir -.766 .337 -.239 -2.272 .027
storage capacity 1.195E-6 .000 .000 .004 .997
Actual generation 57.476 3.055 .953 18.814 .000
evaporat .592 .939 .081 .631 .531
Tail water 4.237 1.572 .248 2.695 .010
inflow .000 .002 -.017 -.339 .736
a. Dependent Variable: water_withdra        

Table: A5 Srisailam Right Canal Power House

Model Variables Entered Variables Removed Method  
1 Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir . Enter  
a. All requested variables entered.    
b. Dependent Variable: water withdrawals  
 

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .998a .995 .995 631.39218
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir

ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 1.099E10 6 1.832E9 4.596E3 .000a  
Residual 5.222E7 131 398656.090      
Total 1.105E10 137        
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir  
b. Dependent Variable: water withdrawals        
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -7630.380 1817.341   -4.199 .000
Reservoir -.178 .322 -.027 -.553 .581
storage .000 .000 -.068 -4.288 .000
actual generation 56.314 .459 1.022 122.651 .000
Evaporation .051 .139 .005 .365 .716
Tailwaterlevel .627 .334 .059 1.874 .063
Gross head .289 .320 .036 .904 .368
a. Dependent Variable: water withdrawals      

Table: A6 Kothagudaem Thermal Power Plant O &M

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler) a . Enter  
a. All requested variables entered.    
b. Dependent Variable: Total water consumption  
 

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .747a .558 .517 289298.132  
a. Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 8.032E12 7 1.147E12 13.710 .000a  
Residual 6.361E12 76 8.369E10      
Total 1.439E13 83        
a.     Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
       
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -787978.047 1.334E6   -.591 .557
Condenser Cooling 1.021 .313 .551 3.259 .002
DM Water & Boiler Feed back -2.130 5.717 -.038 -.373 .710
Colony domestic -12.190 15.642 -.250 -.779 .438
(Drin, Sani, Firefigh, Backwarhfiler) 146.699 201.477 .467 .728 .469
Ash Disposal 1.152 .300 .409 3.841 .000
cooling temp 4616.497 10000.955 .039 .462 .646
energy generation -817.112 1096.318 -.295 -.745 .458
a. Dependent Variable: Total water consumption      

Table:  A7 Kothagudaem Thermal Power Plant Stage V

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: TOTAL CONS.  (MT)  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .989a .979 .977 64726.513  
  a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 14792454121098.932 5 2958490824219.786 706.164 .000a
Residual 322593153570.889 77 4189521474.947    
Total 15115047274669.820 82      
a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)
b. Dependent Variable: TOTAL CONS.  (MT)      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 98233.879 76676.230   1.281 .204  
  COOLING TOWER MAKEUP        (MT) .873 .042 .577 20.912 .000  
  ASH DIS-POSAL (MT) 1.186 .078 .484 15.247 .000  
  Boiled Feed and DM plant Regeneration .111 .978 .003 .114 .910  
  Cooling Temperature -1688.373 2158.260 -.014 -.782 .436  
  Energy Generation 32.019 115.619 .005 .277 .783  
  a. Dependent Variable: TOTAL CONS.  (MT)          

Table: A 8 Rayalaseema Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCWa . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Water consumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .934a .873 .846 1324.085  
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
  ANOVAb  
  Model Sum of Squares df Mean Square F Sig.  
  1 Regression 3.487E8 6 5.811E7 33.145 .000a  
  Residual 5.084E7 29 1753200.788      
  Total 3.995E8 35        
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 10334.674 3861.078   2.677 .012
Condenser cooling, BCW .745 .248 .432 3.007 .005
Boiler feed 8.725 4.628 .244 1.885 .069
Ash slurry .847 .501 .230 1.692 .101
Power Generation -.595 .388 -.138 -1.532 .136
Actual Generation -4.143 5.478 -.077 -.756 .456
Cooling Temp -145.408 94.141 -.109 -1.545 .133
a. Dependent Variable: Water consumption        

Table : A 9 Narla Tata Rao Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Totalwaterconsumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 1.000a 1.000 1.000 50290.302  
  a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 11480277367590772.000 5 2296055473518154.000 907849.564 .000a
Residual 42994946072.977 17 2529114474.881    
Total 11480320362536844.000 22      
a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)
b. Dependent Variable: Totalwaterconsumption      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 139993.709 137540.088   1.018 .323  
  Condenser cooling & BCW (KL) 1.002 .001 .987 1277.966 .000  
  Colony Domestic (KL) -.863 .584 -.001 -1.476 .158  
  Ash slurry water (KL) 1.031 .052 .018 19.879 .000  
  Cooling Temperature -373.483 3763.081 .000 -.099 .922  
  Energy Generation -56.843 138.469 .000 -.411 .687  
  a. Dependent Variable: Totalwaterconsumption        

Econometric Competencies and Entrepreneurship Development 

Adebayo G ADEBAYO

Department of Accountancy

Rufus Giwa (Formally Ondo State) Polytechnic.

Owo, Ondo State, Nigeria

Abstract

This study is designed specifically to demonstrate the application of econometrics or quantitative analysis especially in helping the numerous small and medium enterprises in Nigeria in critical decision making. An Agro-business labour saving fabricating firm in Nigeria was selected as a case and pilot study for all other small or medium enterprises fabricates labour saving machines such as the cassava frying machine (CFM) and palm-oil extracting machine (PEM). The agro-business firm also fabricate cassava grater (GRT) and appliances (APP). The entrepreneur is worried on some critical problems. First, whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM. Second is what would be the total sales taking into consideration especially the seasonal fluctuations and third how to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand. Three models were designed to find solution to each of the problems. The first model was the two-stage least square regression. Findings revealed that the special offer sales GRATER and APPLIANCES) had no negative effect on the sales of the major products (CDM and PEM). All the products significantly contributed to the total sales. The second model was forecasting. The model satisfied forecasting requirements and was able to forecast total sales from April to December 2016 taking into consideration seasonal variations referred to as fluctuations. The third model is the recency, frequency and monetary (RFM) analysis. This marketing tool is used to classify customers according to how recent they patronize, how often and how much is involved in individual customer’s cumulative patronage. The RFM analysis carried out on the customers identified the firm’s customers that would likely respond to new offer. An RFM score of 300 and above qualified a customer to be selected. Part of the recommendations was that entrepreneurs should avail themselves of the decision making tools for better management of their enterprises.

 Keywords: 

 Introduction

The world is facing many economic challenges and issues. These do not isolate the developed economies. Both are debt ridden, with regional economic imbalances and geo-political challenges. There is general economic meltdown in the world market. The Nigeria economy has been acutely affected because of the fall in the price of crude oil in the world market as a result of these economic imbalances and trade policies that are not conducive to Nigerian oil market. Since revenue from crude oil takes about 90% of the Federal government total revenue, there is the critical need to raise non-oil revenue to ensure fiscal sustainability while maintaining infrastructure and social spending.

The Federal government has taken a bold step towards revamping agriculture and overhauling its solid mineral resources. From an entrepreneurial perspective, the present economic meltdown in Nigeria would eventually be a blessing in disguise. The Central Bank of Nigeria has been instructed by President Buhari to create a “synergy” and organize soft loans to agro-based industries and other export businesses.

The main objectives of the 2016 Buhari government’s budget are to make the “synergy” work among all the different players in the country’s economy. These include the banking system, financial institutions, government entities, regulators and other arms of the government.

  • Entrepreneurial Development.

The credibility of the Buhari government among the Nigerian populace as a result of his zero tolerance to corruption and his “Big Bang” [a rapid reform which is economically necessary as a result of severe macroeconomic imbalances (Gelb, Jefferson and Singh 1993) such as this period of economic crisis in Nigeria] approach to major economic reforms, has spurred many investors into agro-based industries. They believe that the government meant business.

One of these respondents is a medium scale firm in Nigeria that fabricates machines to boost agricultural produce. The Firm has recently reactivated two of its machines-the Cassava Frying Machine (CFM) and the Automated Palm oil Extracting Machine (PEM)

  • Objectives of the Firm

The major objectives of the Firm in fabricating these labour saving machines is the issue of health hazard on the one hand. Many local manual cassava frying and palm oil producers especially women, had been subjected to untimely death due to incessant heat from the local frying system and the palm oil production. These machines would reduce drastically the feminine life wasted on daily basis. On the other hand, these machines will boost agricultural produce of “GARI”(a local name for the fine grain output from the frying process of cassava) and palm oil to contribute to increase the recent low GDP rate in Nigeria. [Economic growth in the last quarter of 2015 was 2.1% while total growth in the year was 2.8%, the slowest since 1999 to date (NBS, 2016). This statistics seems to toe the line of the global GDP growth projection of 2.5% which is 0.3% point less than November 2015 outlook (GEO, 2016)]. Therefore any entrepreneurial effort to boost the Nigerian GDP at this trying period is a right decision in the right direction.

  • The Cassava Frying Machine

The CFM is powered by electricity and is capable of frying about 5-10-kg of already peeled, washed and had been cut into smaller sizes and loaded into the machine drum. The machine grinds the cassava, presses it cause fermentation and fry the cassava into very fine grains. Adjustments by the use of some special appliances sold by the firm to its customers may cause the machine to dry cassava from the normal “GARI” into smoother form or into powder. It is an automated machine with 100% local components. A single CFM costs N45, 000 ($225)

  • The Automatic Palm oil Extracting Machine..

The PEM is an automated machine that produces fine, clear, well heated palm oil. Palm fruits are removed from the bunch after some period for partial fermentation, washed and then loaded into the machine tank or drum to the brim. The machine twists and separate kernels from tissues, extract the paste and heat into fine glossy oil. The slag is released from an outlet. It is also powered by electricity. The machine is made with 90% local components, that is, 10% components have to be imported. A single machine costs N50, 000 ($250), about N5000 ($25) more than CFM probably as a result of the cost of the imported components.

  • Statement of the Problem.

The two machines fabricated by the firm are the Cassava Frying Machine (CFM) and Palm oil Extracting (PEM) and are sold to customers on demand. Apart from these machines, there are two special offers that are also sold. These are grater (GRT) and appliances (APP). The APP is a device to enhance either the CFM or PEM, especially as a power saving device, at the customers’ option and is sold at the rate of N12, 000 per unit. These appliances are capable of making adjustments possible to CFM and PEM and other agro-based machines.  The GRT powered by electricity, the pealed, and washed cassava are loaded into its receptor and it grinds cassava very well. This is sold at the rate of N15, 000 per unit. Every month the Firm makes these special offers to customers who need the APP on previously bought machines or on a proposed purchase of CFM and /or PEM. The GRT is mostly purchased by customers who could not afford CFM. The firm is now concerned about the following problems:

  1. Whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM.
  2. How to make a good forecast of the total sales from major products and the special offers.
  3. How to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand.
  • Objectives of the Study.

The primary objective of this study is to underscore the importance of econometric or quantitative analysis in solving most of the problems of entrepreneurs and hence enhance entrepreneurship development in Nigeria. In realization of this objective, the study had focused on helping to collect all relevant data on the Firm’s customers. These include each customer’s date of transactions with the Firm, amount of purchases each time, total number of transaction and the most recent transaction. There will also be collection of data on total monthly sales on CFM, PEM, APP and GRT for the past 51 months. Other secondary objective, in addition are:

  1. To create compactible models to find solution to each of the Firm’s area of concern.
  2. To discuss the findings and give expert recommendations on the findings.

  1. Review of Related Literature

2.1 The Nigerian Cassava

Cassava is well known as manihot esculenta or manilot utilissima (Yakasi, 2010). In Nigeria, cassava is grown in all the ecological zones andit is planted all the year round on the availability of moisture (Odoemenem and Otanma, 2011). Production is vital to the economy of Nigeria as the country is the world’s largest producer of the commodity. The crop is produced in 24 of the country’s 36 states. In 1999, Nigeria produced 33 million tonnes, while a decade later, it produced approximately 45 million tonnes, which is almost 19% of production in the world. The average yield per hectare is 10.6 tonnes.(Wikipedia, n.d.)

In Nigeria, cassava production is well-developed as an organized agricultural crop. It has well- established multiplication and processing techniques for food products and cattle feed. Cassava is processed in many processing centres and fabricating enterprises set up in the country. Cassava is used in the preparation of several household foods and derivatives such as paste, biscuits, bread sagos and sauce. Its starch is for industrial use such as baby food, jelly, custard poeders and confectioneries (Echebiri and Edeba, 2008).  Roots or leaves are made into flours. Flours are of three types, yellow garri, white garri, or intermediate colour. These varieties are a matter of choice and traditional attachment. Therefore it may be erroneous to classify any type as the best product in Nigeria. Its other products are as dry extraction of starch, glue or adhesives, modified starch in pharmaceutical as dextrines, as processing inputs, as industrial starch for drilling, and processed foods.

 2.2 Palm Oil in Nigeria

 

Palm oil is as old as Nigeria itself and has been an important subsistence, but until recently a supportive factor in the diet of many Nigerians. Palm oil is the world’s largest source of edible oil, accounting for 38.5 million tonnes or 25% of the global edible oil and fat production (MPOC, 2007). Palm oil is a product extracted from the fleshy mesocarp of the palm fruit (Elaeis guineensis). The global demand for palm oil is growing thus, the crop cultivation serves as a means of livelihood for many rural families, and indeed it is in the farming culture of millions of people in the country. Akanbge et al (2011), referred to this product as capable of having multiple values, a feature that underscores its acclaimed economic importance. Eventually oil graduated from domestic use to industrial application which had appreciated its production geometrically (Omereji,2005). Ekine and Onu(2008) estimated palm oil consumption of about two litres per a family of five per week for cooking. Today, consumption must have tripled since Nigerian house hold now uses palm oil beyond normal consumption. Palm oil is also an essential multipurpose raw material for both food and non-food industries (Armstrong, 1998). Palm oil is used in the manufacturing of margarine, soap candle, base for lipstick, waxes and polish bases in a condense form, confectionary (Embrandiri et at., 2011; Aghalino, 2000), and other uses in pharmaceuticals.

2.3 Forecasting

Because economic and business conditions vary over time, managers must find ways to keep abreast of the effects that such changes will have on their organizations. One technique that can aid in plan­ning for future needs is forecasting. Although numerous forecasting methods have been devised, they all have one common goal—to make predictions of future events so that projections can then be incorporated into the planning and strategy process.

2.3.1 Time-series forecasting meth­ods involve the projection of future values of a variable based entirely on the past and present obser­vations of that variable. Examples of economic or business time series are the monthly publication of the Consumer Price Index, the quarterly statements of gross domestic prod­uct (GDP), and the annually recorded total sales revenues of a particular company.(Levine et al, 2005)

2.3.2 Least-Squares Trend-Fitting and Forecasting. The component factor of a time series most often studied is trend. Trend is studied as an aid in making intermediate and long-range forecasting projections. As depicted in Figure 1 to obtain a visual impression of the overall long­term movements in a time series, a chart is constructed in which the observed data (dependent variable) are plotted on the vertical axis, and the time periods (independent variable) are plotted on the horizontal axis.(See figure 1 below)

The Forecasting add-on module provides two procedures for accomplishing the tasks of creating models and producing forecasts.

The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and produces forecasts. The procedure includes an Expert Modeler that automatically identifies and estimates the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus eliminating the need to identify an appropriate model through trial and error. Alternatively, one can specify a custom ARIMA or exponential smoothing model.

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.

MA(1)       = a first-order moving  average model ,i.e., regressing the dependent error with linear combination of its past error or lagged error. It also corrects serial correlation.

2.4 Research Questions.

The following research questions are formulated by the researcher

  1. What are the total monthly sales of the Firm for 51 months?
  2. How many are the numbers of customers of the Firm are their variable such as name, address, city, state- province, post code, gsm numbers, country, gender, age category are available?
  3. How can customers that would respond to new offer be identified?
  4. What is the forecast total sales from April to December 2016?

2.5 Research Hypotheses.

The following research hypotheses are formulated by the researcher at 5% level of significance.

  1. There will be no significant relationship between total sales of special offers and CDM sales.
  2. There will be no significant relationship between total sales of special offers and POE sales.
  3. There will be no significant relationship between total sales of special offers and APP sales.
  4. There will be no significant relationship between total sales of special offers and GRTsales.

 

  1. Methodology

 3.1 Data Collection

The data collected for this study is a demonstrative data only on behalf of the firm. It is characteristic of most small and medium enterprises of nearly the same status. This case study Firm is having about 90 customers. Sales records on CFM, PEM, APP and GRT were collected for 51 months (January 2012 to March 2016). In the Table below, the author computed the total sales for CFM, PEM, APP and GRT and special offer total sales (SPSALES). Others computed are discount on CDM at 3.5% from records and discount on PEM at 4.8%. A log discount on CFM and PEM and their lagged variables are automatically computed in the SPSS 21 and E-View 7.1 work files. The data are presented in Table 1 below.

 Table 1    Company Data for 51 Months – from January 2012 to March 2016

tsales cfm pem spsales grt app discfm dispem
N N N N N N N N
109.50

123.50

130.00

147.00

154.10

174.00

166.50

178.00

166.50

153.20

132.50

132.70

148.80

167.80

180.50

192.50

192.00

202.50

193.50

183.20

196.70

198.80

179.10

169.80

193.10

194.10

213.00

241.40

231.80

226.40

226.50

245.70

231.20

207.50

219.90

214.00

241.30

265.00

267.50

246.90

237.00

234.20

251.20

266.00

270.80

264.80

258.00

243.80

244.00

246.50

263.30

57.50

60.00

62.50

67.50

75.00

78.00

75.00

73.00

58.00

55.00

56.00

57.00

58.00

65.00

84.00

78.00

77.50

74.50

62.50

68.00

83.00

87.50

78.50

73.00

71.00

73.00

83.00

95.00

92.50

82.00

87.00

95.00

100.00

97.00

100.00

95.00

103.00

112.00

113.00

110.00

105.00

97.00

94.00

95.00

94.00

93.00

100.00

103.00

102.00

102.50

103.00

40.00

50.00

52.50

65.00

64.00

60.00

75.00

86.50

87.50

77.50

60.00

52.00

65.00

73.00

67.00

85.00

85.00

97.50

100.50

85.00

83.00

80.00

68.00

60.00

82.50

80.00

90.00

110.00

108.00

105.00

100.00

110.00

90.00

80.00

78.00

72.00

90.00

105.00

107.50

90.00

85.00

90.00

108.00

120.00

125.00

120.00

105.00

85.00

86.00

85.00

100.00

12.00

13.50

15.00

14.50

15.10

16.00

16.50

18.50

21.00

20.70

22.50

13.70

25.50

29.80

29.50

29.50

29.50

30.20

30.20

30.70

30.70

31.30

32.60

36.80

39.60

41.10

40.00

39.40

39.30

39.40

39.50

40.70

41.20

40.50

41.90

47.00

48.30

48.00

47.00

46.90

47.00

47.20

49.20

51.00

51.80

51.80

53.00

55.50

57.00

59.00

60.30

4.00

5.00

6.00

6.00

7.00

8.00

8.50

10.00

12.00

11.50

13.00

14.00

16.00

20.00

19.50

19.00

18.50

18.50

18.50

17.80

18.00

18.80

19.60

20.00

20.80

21.40

22.00

22.40

22.80

23.00

22.50

22.00

21.20

21.50

23.50

26.00

26.30

26.00

26.00

26.10

26.50

27.00

27.20

28.00

27.00

26.80

27.00

29.00

29.00

29.20

29.30

8.00

8.50

9.00

8.50

8.10

8.00

8.00

8.50

9.00

9.20

9.50

9.70

9.80

9.80

10.00

10.50

11.00

12.00

12.00

12.40

12.70

12.50

13.00

16.80

18.80

19.70

18.00

17.00

16.50

16.40

17.00

18.70

20.00

19.00

18.40

21.00

22.00

22.00

21.00

20.80

20.50

20.20

22.00

23.00

24.80

25.00

26.00

28.80

28.00

29.80

31.00

2.01

2.10

2.19

2.36

2.63

2.73

2.63

2.56

2.03

1.93

1.96

1.99

2.03

2.28

2.94

2.73

2.71

2.61

2.19

2.38

2.91

3.06

2.77

2.56

2.49

2.56

2.91

3.33

3.24

2.87

3.04

3.33

3.50

3.40

3.50

3.33

3.61

3.92

3.96

3.85

3.68

3.40

3.29

3.33

3.29

3.26

3.50

3.61

3.57

3.59

3.07

1.92

2.40

2.52

3.12

3.07

2.88

3.60

4.15

4.20

3.72

2.88

2.50

3.12

3.50

3.22

4.08

4.08

4.68

4.82

4.08

3.98

3.84

3.26

2.88

3.41

3.50

4.32

5.28

5.84

5.04

4.80

5.28

4.32

3.84

3.14

3.46

4.32

5.04

5.16

4.32

4.08

4.32

5.18

5.76

6.00

5.76

5.04

4.08

3.57

4.08

4.80

       *The average Sales discount on CFM is 3.5% and on PEM is 4.8%. No discounts for APP and GRT.

            The LAG variable for CFM and LAG for PEM are automatically Created Series in the SPSS 21 and E-                           View 7.1 work files.

    Figure 1    Graph of the Relationship between Total Sales, and the sales from Cassava Frying Machine, Palm oil Extracting Machine, Appliances and Grater

3.2 Models Specification

Three models are specified to the three major problems faced the Firm as stated under the statement of the problem. These are (1) Two-Stage Least Squares Regression (2) Forecasting Models and (3) The RFM Customer Analysis

 

3.2.1 Model 1.Two Stage Least Square Regression

A careful observation of the relationship between CFM, PEM and the special offers shows that there is a feedback loop between the response and the two major products which are predictors. One of the basic assumptions of the ordinary least-squares (OLS) regression model is that the values of the error terms are independent of the values of the predictors. When this “recursivity assumption” is broken, the two-stage least-squares (2SLS) model can help solve these problematic predictors. The 2SLS model assumes that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term.

Given the existence of instrument variables, the 2SLS model:

  1. Computes OLS models using the instrument variables as predictors and the problematic predictors as responses.
  2. The model-estimated values from stage 1 are then used in place of the actual values of the problematic predictors to compute an OLS model for the response of interest.

Fifty two months of sales information is collected. The file also includes a variable, special offer, displaying each month’s special offer, which has also been recorded into two indicator variables, Appliances offer and Grater offer that can be used as predictors in the regression procedures. Lastly, the monthly discounts (and log-discounts) offered to customers are also listed. Since the monthly discounts are chosen independent of special offer sales but do not affect CDM and POE sales, they should make good instrument variables. Additionally, the lagged CDM and POE should also make good instruments. The independent, predictors and instrumental variables are in the model description as in Table 2 below.

Table 2: Model Description

   Variables Type of Variable
SpecialOfferSales Dependent
CDM Predictor
POE Predictor
Appliances predictor & instrumental
Grater predictor & instrumental
Logdiccfm Instrumental
Logdispoe Instrumental

Special Offer Sales = α0 + α1CFM + α2POE + α 3APP + α4GRT + ε                                                        Eq 1

where

α 0             =    the intercept or constant term

α      α 4   =    the coefficients of both the predictors and instrumental variables

 ε            =    the stochastic error term

All other variables are as described in the model description in Table 2 above.

3.2.2 Model 2. Forecasting Models

The Model of Forecasting from an Equation, can be dynamic or Static. The static model is chosen because the static forecasting model performs a series of one-step ahead forecasts of the dependent variable:

For each observation in the forecast sample:

 yg+k = c(l) + c(2)xs+k+c(3)zs+k+c(4)ys+k1                                                         Eq 2

Such equation is always using the actual value of the lagged endogenous variable. This is translated into sales forecast of the Firm:

Total Sales    = βo + β1Total Sales (-1) + β2Pm Ar(1) + µi                                                 Eq 3

Where:

Total Sales        =     the total sales from CFM, POE, APP and GRT for 51 months. This is the dependent variable.

Total Sales (-1) = a lagged variable of the dependent variable. This is a one step ahead static forecasts that makes the static forecast more accurate than the dynamic forecast since, each period, the actual value of Total Sales(-1) is used in forming the forecast of Total Sales.

Pmi                  =     the period expressed in months with a total of 51 months. This is the independent variable.

  β 0                            =     the constant term or the model intercept.

  β1                   =     the coefficient of the lagged variable.

  β2                    =     the coefficient of the independent variable.

 µi                    =     the stochastic or error term

The tolal sales above is transformed into:

tsalesFp+k = Ф0 + Ф1 tsalesFp+k-1 2PMp+k + AR(1) +  ε                                                    Eq 4

after being subjected to the static forecasting model

where

tsalesF = total sales forecast

 tsalesFp+k-1 =   the previous month’s (lagged) sales to be added to current sales forecast.

Ф I              = constant and coefficients.

p                 = the base period (month) of start of forecast.

k                 = any month from the forecasting period (April to December 2016)

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.  .

ε i               = the error term.

For seasonal adjustment of the forecast (fitted) sales.

tsalesfSA  = ⨍i(tsalesFi)                                                                                                     Eq 5

where

⨍i = multiplicative scoring factor for a 12 month period.

tsalesFi = total sales forecast for April to December.

The tsalesfSA  = the seasonally adjusted tsalesF for months (p52-60) i.e. 52, 53, 54, 55, 56, 57, 58, 59 and 60 for April, May, June, July, August, September, October, November and December.

The actual trend base is P51. In Table 1 the total sales corresponding to P51 is N263300. A forecast for p52-60 is required; i.e. April – December 2016.

Figure 3.  Graphical Relationship between Total Sales (tsales) and Fitted Sales (tsalesF)

3.2.3 Model 3.  RFM Analysis         

RFM (Recency, Frequency and Monetary) analysis is a direct marketing option that provides a set of tools designed to improve the results of direct marketing campaigns by identifying demographic, purchasing, and other characteristics that define various groups of consumers and targeting specific groups to maximize positive response rates.

It is a technique used to identify existing customers who are most likely to respond to a new offer. This technique is commonly used in direct marketing. RFM analysis is based on the following simple theory:

The most important factor in identifying customers who are likely to respond to a new offer is RECENCY. Customers who purchased more recently are more likely to purchase again than are customers who purchased further in the past.’The second most important factor is FREQUENCY. Customers who have made more purchases in the past are more likely to respond than are those who have made fewer purchases.

The third most important factor is total amount spent, which is referred to as MONETARY. Customers who have spent more (in total for all purchases) in the past are more likely to respond than those who have spent less.

How RFM Analysis Works

Customers are assigned a recency score based on date of most recent purchase or time interval since most recent purchase. This score is based on a simple ranking of recency values into a small number of categories. For example, if you use five categories, the customers with the most recent purchase dates receive a recency ranking of 5, and those with purchase dates furthest in the past receive a recency ranking of 1. The recency ranking for this Firm is based on the past 20 months as below:

Month Interval Ranking
Jan to Apr 2016 5
Sept to Dec 2015 4
May to Aug 2015 3
Jan to Apr 2015 2
Sept to Dec 2014 1

In a similar fashion, customers are then assigned a frequency ranking, with higher values representing a higher frequency of purchases. For example, in a five category ranking scheme, customers who purchase most often receive a frequency ranking of 5.

 

The number of times a customer made purchases up to a maximum of 5,(or simply, the Transaction Counts) represent frequency ranking for the Firm.

 

Finally, customers are ranked by monetary value, with the highest monetary values receiving the highest ranking. Continuing the five- category example, customers who have spent the most would receive a monetary ranking of 5. The monetary ranking for the firm is:

Naira Value of Purchases Ranking
N120,000 and above 5
N100,000 to N120,000 4
N75,000 to N100,000 3
N40,000 to N75,000 2
Less than 40,000 1

The result is four scores for each customer: recency, frequency, monetary, and combined RFM score, which is simply the three individual scores concatenated into a single value. The “best” customers (those most likely to respond to an offer) are those with the highest combined RFM scores. For example, in a five-category ranking, there is a total of 125 possible combined RFM scores, and the highest combined RFM score is 555.

 Results and Discussion

The results of the models had produced numerous tables in their outputs. The option of  table by table explanation and discussion had been taken.. Where appropriate, results had been discussed. Only the summary points need be discussed further. .Summary of the findings revealed that the special offer sales did not affect the sales of CFM and PEM. The assumption that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term may not hold. When CFM, PEM GRT AND APP were regressed on the total sales, they were all significant at less than 5%.

A multiplicative scoring factor to produce the total sales forecast with seasonal adjustments from April to December 2016.

The RFM analysis produced a combined RFM score for each customer at the concatenation of the three individual scores, computed as (recencyx100) + (frequencyx10) _ monetary. The recency, frequency, monetary score is 5 5 5 respectively. A customer must score at least 3 points for recency to qualify for customers that are likely to respond to new offer on CFM and PEM.

 

Conclusion and Recommendations

This study had been able to highlight the importance of econometric applicaton in business decision making especially for small and medium scale enterprises. Decision making is a prerogative of the entrepreneur but using the tools would help the effective management of their enterprises. I is important to add a caveat that professionals should be used and that the outcome of decision making tools are to assist entrepreneur only and should not be forced on them.

Notwithstanding, entrepreneurs are advised to avail themselves of the decision making tools from professionals with econometric competences. The present day business environment needs the sixth sense which business decision making tools afford entrepreneurs.

 

APPENDIX: (Al, A2 and A3 for Models 1, 2 and 3 respectively.)

 MODEL 1: TWO-STAGE LEAST SQUARES REGRESSION*

 

Table Al-1 The Lagged Variable Infusion into the Model

Series Name Case Number of Non-Missing No of Valid Creating
Values Cases Function
First Last
1 cfm_1 2 51 50 LAGS(cfm,1)
2 pem_1 2 51 50 LAGS(pem,1)

 

 

Table A1-2 Model Description

Type of Variable
                          Spsales Dependent
                                Cfm Predictor
                                Pem Predictor
Equation 1              Grt predictor & instrumental
                               App predictor & instrumenta  l
                       Logdiscfm instrumental
                      Loadispem instrumental

The model description table gives a summary of the model being fit. Variables specified as predictor will be regressed on the instrumental variables, and the model- estimate4d values will then be used in place of the actual values of these problematic predictors when computing the model for the dependent

 

Table A1-3 Model Summary

                                          Multiple R .995
                                           R Square .990
Equation 1
                             Adjusted R Square .989
                    Std. Error of the Estimate 1.410

The model summary table reports the strength of the relationship between the model and the dependent variable.

Multiple R, the multiple correlation coefficient, is the linear correlation between the observed and model- predicted values of the dependent variable. Its relatively high value indicates a very strong relationship.

R Square, the coefficient of determination, is the squared value of the multiple correlation coefficients. It shows that about 99 percent of the variation in Special offer sales is explained by the model.

Adjusted R Square is an r-squared statistic that is “corrected” for the complexity of the model, and is useful for comparing competing models. The larger values of the statistic indicate better models. It showed that the predictors were able to explain 98.9% of the variations in special offer sales.

Std. Error of the Estimate is the standard error in estimates of Special offer sales based on the model. The value this standard error of estimate compare to the standard deviation of 13.649 of Special offer sales shows that the model has reduced the uncertainty in the “best guess” for next month’s sales.

TableA1-4 ANOVA

Sum of Squares Df Mean Square F Sig
Regression 9221.545 4 2305.386 1160.333 0.000
Equation 1 Residual 91.394 46 1.987
Total 9312.940 50

The ANOVA table tests the acceptability of the model from a statistical perspective

The Regression row displays information about the variation accounted for by the model

The Residual row displays information about the variation that is not accounted for by the model

The regression sum of squares is considerably higher than the residual sum of squares, which indicates that about most of the variation in Special offer sales is explained by the model

The significance value of the F statistic is less than 0.05, which means that the variation that is explained by the model is not simply due to chance.

While the ANOVA table is a useful test of the model’s ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship

Table A1-5 Coefficients

Unstandardized Coefficients Beta T Sig.
B Std. Error
(Constant) -3.237 8.202 -.395 .695
cfm .032 .202 .039 .158 .875
Equation 1 pern .026 .030 .036 .857 .396
grt .916 .146 .484 6.276 .000
app .988 .254 .481 3.892 .000

Dependent Variable  Special Sales

This table shows the coefficients of the regression line. It states that the expected special offer sales is equal to : -3.237 + 0.032CFM + 0.026PEM = 0.916GRT + 0.988APP.

The significance value for GRT and APP are less than 0.05, indicating that the effect of GRT and APP distinguishable from sales of CFM and PEM. In other words, the sales of GRT and APP may not be affecting sales from CFM and PEM. When CFM, PEM GRT and APP are regressed against Total Sales, in Table A1-6, all the variables are significant at 1% level. This shows that they all contribute and no “special sales’’ is limiting the sales of CFM and PEM.

 

 Table A1-6 Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -.193 3.180 -.061 .952
cfm 1.054 .053 .404 19.745 .000
pem .991 .036 .437 27.209 .000
grt .812 .173 .133 4.702 .000
app 1.000 .176 .151 5.683 .000
a. Dependent Variable: tsales

Table A1-7 Coefficient Correlation

cfm Pem Grt App
Equation 1 Correlations cfm 1.000
pem -.866 1.000
grt -.885 .661 1.000
app -.966 .843 .757 1.000

*SPSS 21 OUTPUT

 

 

MODEL 2: FORECASTING MODELS**

TableA2-1 Sales Forecast and Seasonally Adjusted Trend

S/N p MONTHS Tsalesf (rounded to a whole number) N’000 Multiplicative scoring Factor(𝒇i) TsalesfSA (rounded to a vhole number) N’000
1 0.89
2 0.99
3 51 MARCH 1.02
4 52 APRIL 269 1.05 282
5 53 MAY 273 1.06 289
6 54 JUNE 275 1.02 281
7 55 JULY 278 1.02 284
8 56 AUGUST 280 1.01 283
9 57 SEPTEMBER 283 1.05 297
10 58 OCTOBER 286 1.02 292
11 59 NOVEMBER 288 0.96 276
12 60 DECEMBER 291 0.91 265

The model equation is: tsalesF = 93.79 + 0.341tsalesf (-1) + 1.64pm +[(AR(1)= 0.233]

  For example April Forecast: tsalesF 52 = 93.79 + 0.341tsalesF51 + 1.64(Mouth 52) + 0.233      (See Eq 4)

                                                     = 93.79 + 0.341(263.3 = March Total Sales) + 1.64(52) + 0.233

                                                     = 93.79 + 89.78 + 85.28 + 0.233

                                                     = 269.09

                                    tsalesF53  = 93.79 + 0.341(269.08) + 1.64(53) + 0.233

                                                    = 272.7  e.t.c

Table A2-2     Augumented Dickey-Fuller Unit Root Test
Test Statistics Coefficients
Variable Intercept Intercept &Trend None Intercept Intercept& Trend None
At Level Tsalesf(Total Sales Forecast) -1.53 -4.152 1.183 -0.065 -0.577 0.0095
1st Diff -6.59 -6.557 -6.385 -0.982 -0.989-1 -0.939
2ndDiff -7.34 -7.253 -7.426 -1.835 -1.835 -1.834
Critical

Value

1%    -3.59

 

5%    -2.93

 

10%    -2.6

1%     – 4.18

 

5%     -3.51

 

10%    -3.19

1%        -2.62

 

5%        -1.95

 

10%       -1.61

TsalesF is stationary at First Difference in Table A2-2 above. This satisfies its expected forecasting property.

   Table A2-3 Model is Stationary at the First and Second difference:

 

 

 

AR Root(s)

  0.233330  0.233330
 No root lies outside the unit circle.
 ARMA model is stationary.
MA Root(s) Modulus Cycle
 -0.236191  0.236191
 No root lies outside the unit circle.
 ARMA model is invertible.

 

 

      **E-View 7.1 OUTPUT

MODEL 3: THE RFM ANALISIS

 

The Firm is having a total of about 90 customers on records. A random sample of 14 customers were made using Random Numbers, but customer ID-01 was purposive. RFM Analysis was limited to customers who made purchases between September 2014 and March 2016.

Table A3-1 Transaction Data

ID DATE AMT N’000 ID DATA AMT N ‘000
01 08/24/2014 212 16 03/15/2015 15
16 09/09/2014 50 83 03/20/2015 105
75 09/30/2014 12 30 04/12/2015 15
75 10/15/2014 15 01 04/14/2015 50
39 10/20/2014 50 72 04/15/2015 30
72 11/15/2014 15 35 05/18/2015 15
28 11/20/2014 50 16 05/25/2015 12
24 11/22/2014 12 89 07/16/2015 45
28 11/25/2014 12 16 07/18/2015 45
24 12/03/2014 15 49 07/25/2015 195
28 12/14/2014 15 08 08/10/2015 12
77 12/18/2014 15 01 18/18/2015 15
16 12/20/2014 15 72 08/20/2015 30
75 12/22/2014 12 35 08/22/2015 30
77 01/15/2015 15 81 09/14/2015 27
02 01/19/2015 62 08 10/20/2015 12
02 03/04/2015 69 08 01/13/2016 12
30 03/10/2015 15 89 01/20/2016 12

When you compute RFM scores from transaction data, a new dataset is created that includes the new RFM scores.

By default, the dataset includes the following information for each customer:

  • Customer ID variable(s)
  • Date of most recent transaction
  • Total number of transactions
  • Summary transaction amount (the default is total)
  • Recency, Frequency, Monetary, and combined RFM scores

The new dataset contains only one row (record) for each customer. The original transaction data has been aggregated by values of the customer identifier variables. The identifier variables are always included in the new dataset; otherwise you would have no way of matching the RFM scores to the customers.

 

Table A3-2 Customers with Transaction Summaries

ID DATE AMT N’000 ID DATE AMT N’000
01 08/24/2014 12 30 10/202014 50
01 04/14/2015 50 30 03/10/2015 15
01 08/18/2015 15 30 04/12/2015 15
02 01/19/2015 62 35 05/18/2015 15
02 03/04/2015 69 35 08/22/2015 30
08 08/10/2015 12 49 07/25/2015 195
08 10/20/2015 12 72 11/15/2014 15
08 01/13/2016 12 72 04/15/2015 30
16 09/09/2014 50 72 08/20/2015 30
16 12/20/2014 15 75 09/30/2014 12
16 03/15/2015 15 75 10/15/2014 15
16 05/25/2015 12 75 22/12/2014 12
16 07/18/2015 45 77 12/18/2014 15
24 11/22/2014 12 77 01/15/2015 15
24 12/03/2014 15 81 09/14/2015 27
28 11/20/2014 50 83 03/20/2015 105
28 11/25/2014 12 89 07/16/2015 45
28 12/14/2014 15 89 01/20/2016 12

The dataset must contain variables that contain the following information:

  • A variable or combination of variables that identify each case (customer).
  • A variable with the date of each transaction.
  • A variable with the monetary value of each transaction.

Table A3-3 RFM Analysis from Transaction Data -The New Data Set.

ID Date Most Recent Transa

ction

Counts

Amount

N’000

Recency Frequency Monetary

Score

RFM

Score

Customer with High Response to an Offer
01 08/18/2015 3 77 3 3 3 333 ü
02 03/04/2015 2 131 2 2 5 225
08 01/13/2016 3 36 5 3 1 531 ü
16 07/18/2015 5 137 3 5 5 355 ü
24 12/03/2014 2 27 1 2 1 121
28 12/12/2014 3 77 1 3 3 133
30 04/12/2015 3 80 2 3 3 233
35 08/22/2015 2 45 3 2 2 322 ü
49 07/25/2015 1 105 3 1 5 315 ü
72 08/20/2015 3 75 3 3 3 333 ü
75 12/22/2014 3 39 1 3 1 131
77 01/14/2015 2 30 2 2 1 221
81 09/14/2015 1 27 4 1 1 411 ü
83 03/20/2015 1 105 2 1 4 214
89 01/20/2016 2 57 5 2 2 522 ü

The combined RFM score for each customer is simply the concatenation of the three individual scores, computed as: (recency x 100) + (frequency x 10) + monetary.

For example, the RFM score for ID-16 is (3×100 + 5×10 + 5) = 300 + 50 + 5 = 355

The marked customers are those that are likely to respond to new offer on CFM and PEM. To qualify, a customer must score at least 3 points for recency.

 

References

Aghalino, S.O. (2000). British Colonial Policies and the Oil Palm Industry in the Niger Delta

        Region of Nigeria, 1900- 1960. African Study Monographs 21(1): 19-33.

Akangbe, J. A., Adesiji, G. B., Fakayode, S. B. and Aderibigbe, Y. 0. (2011). Towards Palm Oil

      Self-sufficiency in Nigeria: Constraints and Training needs Nexus of Palm Oil Extractors. J. Hum. Ecol. 33(2): 139-145.

Echebiri R.N. and Edeba, M.E.I. (2008) Production and Utilisation of Cassava in Nigeria:

      prospects for Food   Security and Infant Nutrition; PAT:Production Agriculture and

     Technology; 4 (1); 38-52

Embrandiri, A., Singh, R.P., Ibrahim, H. M. and Ramli, A.A.(2011). Land application of

      biomass residue generated from palm oil processing: its potential benefits and threats.

     Springer Science. Environmentalist, DOI 10.1007/s 10669-011-9367-0.

Gelb, A.; Jefferson, G. and Singh, I. (1993) Can Communist Economies Transform                         Incrementally? China’s Experiwnce: World Bank Working Papers WP 1189, October.

Levine, D.M.; Stephan, D.; Krehbiel, T.C. AND Berenson, M.L.(2005) Statistics for Managers,

      Upper-Saddle River, NJ. Pearson Educational Inc.

NBS (National Bureau of Statistics)(2015) Growth of Gross Domestic Product in Nigeria .

       Annual Abstract of Statistics. Federal Republic of Nigeria.

Odoemenem, I.U.,and Otanma, L.B. (2011) Economic Analysis of Cassava Production in Benue

        State, Nigeria. Current Research Journal of Social Sciences; 3(5), 406-411

Omereji, G.O. (2005) The Oil Palm Industry in Nigeria: cultivation, Processing and Trade.

       Mindex  Publishers, Benin City. Pp. 131-156

Cassava Production in Nigeria (n.d.) in Wikipedia. Reviewed April 10, 2016 from

          http://en.wikipedia.org/wiki/cassava production

Yakasi, M.T. (2010) Economic Contribution of Cassava Production (A Case Study of Kuje Area

        Council, Federal Capital Territory , Abuja Nigeria; Bayero Journal of Pure and Applied

       Sciences; 3 (1) ; 215-219.

 

The Effect of Project Management Information System on  Project Managers and Project Success

 

Shadi Fallah

Department of  management,  Islamic Azad University, Qaemshahr, Iran

shfallaah@gmail.com

Yousef Gholipour-Kanani

Department of Industrial Engineering, Islamic Azad University, Qaemshahr, Iran

Abstract. Project management information system (PMIS) helps managers in decision making, planning, organizing and controlling the project. Based on the importance of decision making, the aim of this study is assessing the effects of PMIS on the project management success. This study designed a comprehensive model to evaluate the impact of PMIS on the project management functions. This model included five factors such as: PMIS quality, the quality of output information of PMIS, PMIS application, PMIS impact on project management and the impact of PMIS on the project success. This study conducted in Iran in three dependent firms in Tehran petroleum. Data of this research is collected by using questionnaires from project managers. Results demonstrated the crucial role of PMIS on the project management success due to better planning, proper scheduling, regulating and controlling the project. At the end limitation and suggestion for future study is discussed.

Keywords: Project management information system, decision making, planning, project control.

INTRODUCTION

Information is one of the most important capitals in the organizations, because all physical facilities and environmental decision making are affected by information. Information can change competitive aspects withing the organization and lead to competitive advantage. Morever, successful organizations in information technology can change competition characteristics in the industry and benefit by being pioneer. Due to the importance of project management information system (PMIS) to implement project faster, less expensive and with higher quality, the general expectation from project managers enhanced (Welsch, 2006). Organizations apply information systems in their organizations to improve effeciency and profitibility; through this way they can adopt themselves easier to environmental changes and attain updated information. Also managers can estimated the project costs,  profits and budget by using PMIS. In general, PMIS can be used in Implementing activities, goods and materials management, collecting and classifying financial and non-financial information, and storage of information.

Despite of the importance of PMIS, there are still limited studies in this filed. Previous studies mostly considered on implementing information technology instruments in the organizations. Morever, due to importance of oil and gas projects more research about PMIS is needed. Because, in this industry PMIS can play the key role in recognizing the suitable resources, planning and scheduling, this study has considered on the effect of project management information system on  project managers and project success.

Forasmuch as PMIS accompanies managers in all process of implementing project, this study aims to assess the effect of PMIS on  project managers. Also  this study targeted to evaluate the effect of  PMIS on project success. Furthermore, the effects of PMIS on its application, project management characteristics and quality of outputs.

LITERATURE RIVIEW

An information system defined as ‘’ a set of related components that assist to collect, recovery, processing, storage and distribution of information within the organization’’ (Olson, 2004). This information is very useful for decision making and controlling the operation in an organization. Managerial team can use this information for analysis and making decision about future product line. Also Management information system defined as ’’ series of tools which provide to managers the required information in their professional fields on time, accurate and in appropriate conditions’’. Study of Welsch (2006) indicated that MIS assists middle managers through the provision of information in appropriate formats according to what they need.

According to Olson (2004) projects due to their diverse and uniqueness are structurally different from the current organizational routines. Project management is always more difficult than regular administration and requires more attention to different aspects of organization. The project combines the demands of the custodians, contractors and other stakeholders; so to create unity between all factors involved in the project, appropriate techniques and utilities should be exploited (Baker et al., 2009). Belout (2003) in an empirical study considered on the effects of PMIS on multi-project environment. Results showed that using project management information system is beneficial for project managers. However, no adverse effects have been observed due to the project, and information overload.

Commonly projects implement in certain time, cost and quality; they mostly done only for one time. Therefore it is essential to apply an instrument which helps organization to do projects more effective (Dietrich and Lehtonen, 2005).  Also another study has investigated the Challenges in information systems projects (Finch, 2003). Adams and Barndt (2008) in their study showed that PMIS has a positive effect on the management and execution of construction projects and managers should give careful consideration to overcome the weaknesses of the project. According to study of (Cooper et al., 2001) during the projects some changes may happen in process. Despite the theoretical accuracy in the preparation of plans and administered programs, managers should carefully pay attention to using on time and accurate information. Also project control team requires different expertise to accomplish the responsibilities as good as possible (Dai and Wells, 2004). For the purpose of control, various units should send information to control project department under proper discipline and coordination. Particularly, the activities which locate on the critical path of the project should regularly monitor and inspect to ensure that occurred delays don’t lead to delay in entire project. Bozeman and Kingsley (2007) showed that data quality of PMIS is related positively to the correct decisions, managers’ satisfaction from PMIS and increasing usage of PMIS within the organization. Also this study discussed about implementing multiple projects simultaneously causes that the project managers expand the results of the quality information for a project to all existing projects. Based on abovementioned literatures the following hypotheses were proposed:

H1. PMIS quality is positively related to output information quality.

H2. PMIS quality is positively related to PMIS application and project management factors.

H3. Output information quality of PMIS is positively related to PMIS application and project management factors.

H4. PMIS application influences positively on project management factors.

H5. PMIS application influences positively on project success factors.

H6. Project management factors influences positively on project success.

METHODOLOGY

Data of this research attained by distributing questionnaire which included 20 questions based on 6 main categories. This study targeted project control department of three firms which dependent to Tehran Petroleum Company. Targeted employees were 80 and all of them participated in this research. According to table of Morgan number of participants is 63. This study applied SPSS to test normality of study population and hypothesized relationships. Testing normality of population evaluated by using Kolmogorov–Smirnov test; also binominal test applied to assess the hypothesized relationships.

To consider on validity of questionnaire, all questions reviewed by several scholars and their opinions applied in the study instrument. After discussion and making some changes, the validity of questionnaire approved by them.  To test reliability of questionnaire, this study applied Cronbach’ alpha and all values ranged between .76 and .85; Therefore, result demonstrated the reliability of questionnaire. Scale format of questionnaire was based on five-point Likert scale (1=very low to 5=very high).

Table 1. Respondents information

                              Number           Percentage

Gender:

Male                                                      51                      81%

Female                                      12                  19%

Total                                  63                100%

Organizational tenure:

<5 years                                                19                   30%

6-10 years                       30                       48%

>11 years                                              14                        22%

Total                                        63              100%

Educational level:

Bachelor degree                 42                  67%

Master degree                              21                  33%

Total                                       63                100%

 

RESULTS

Results of Kolmogorov–Smirnov test showed that data of this study is not normal; therefore assessing data is done by using Binominal test which is one of the nonparametric tests. As can be seen in Table 2, P-Value related to H1 is less than 0.05; this result indicated that there is significant difference between two groups. Furthermore, according to the reality that 95% of responses were ‘’high’’ ad ‘’very high’’; it conclude that PMIS has positive and significant effect on quality of output information. Thus, H1 is supported. Similarly, P-Value related to H2 is less than 0.05 and majority of responses (94%) were ‘’high’’ ad ‘’very high’’. This finding reveals that quality of PMIS influences positively and significantly on application of PMIS and project management factors. Therefore, H2 is supported. Also table 2 shows that the responses of participants about H3 were mostly (93%) above “fair’’ with P-Value<0.05. This result indicates that quality of PMIS output information is positively and significantly related to PMIS application and project management factors. Therefore, it reveals the empirical support for H3. To assess H4 which indicates to the positive relationship between PMIS application and project management factors; Table 2 shows that 90% of responses were ‘’high’’ ad ‘’very high’’ and P-Value<0.05. Thus, H4 is empirically supported. Also 86% responses related to H5 are above ‘’fair’’; also P-Value related to H5 is less than 0.05; this result indicated that there is significant difference between two groups. Abovementioned values indicated the positive relationship between PMIS application and project success factors. Therefore, H5 is supported. The last hypothesis is also empirically approved with majority of ‘’high’’ and ‘’very high’’ responses (94%) and P-Value<0.05. Therefore, there is positive significant relationship between project management factors on project success. Generally, all hypotheses were supported.

Table 2. Results

                        Maen          SD         %Responses ≤3       %Responses>3          α          P-Value

H1                     4.56           .33             5%                     95%                  .05            0.00

H2                     4.40           .39                 6%                           94%                .05            0.00

H3                     4.43           .48                 7% 93%                .05                           0.00

H4                     4.46           .50                10%                         90%                 .05            0.00

H5                     4.44           .40                14%   86%                 .05            0.00

H6                     4.55           .47                  6%                          94%                .05            0.00

Discussion

This study is done based on direct and indirect effects of PMIS on project success. Easy usage of system, flexibility, respond time, easy learning, integrity of system, all have critical roles on quality of provided information. When quality of PMIS is high, information is more Reachable, reliable, accurate, comprehensive and secure. Result demonstrated that quality of information directly and intensively is related to applying PMIS and its effects on project management. But quality of information is not the only factor that should be considered; because it will be useful when it applies in organization through qualified managers. Using affective PMIS makes performance more professional due to its application in planning, controlling, setting and reporting in different steps of projects.

Results indicated that PMIS quality influences positively on output information quality, PMIS application and project management factors, project management project factors and project success factors. Results of this study were consistent with the result of existing empirical researches (e.g., Belout, 2003; Bozeman and Kingsley, 2007; Olson,  2004).

Limitation and suggestion for future research

The first limitation of this study is related to generalizability because data of this study collected from three dependent firms to Tehran Petroleum Company. It is beneficial if future studies consider on larger number of participants.   Also this study conducted in Iran which is known as developing country; therefore to generalizability of results to developed countries some problems may occur.  Also data of this study collected on limited period of time and does not cover information and changes may happen over time.  Therefore, it is useful to collect data using longitudinal design. Finally, further consideration should be done about other internal and external factors may effect on the quality of PMIS.

References

– Baker, B., Murphy, D., Fisher, D. 2009. Factors affecting project success. In Cleland, D., King, W. (Eds),Project Management Handbook, Wiley, New York, NY, 902-19.

– Kostalova, J., Tetrevova, L., Syedik, J. 2015. Support of Project Management Methods by Project Management Information System. Procedia – Social and Behavioral Sciences, 210, 96-104.

– Bozeman, B., Kingsley, G. 2007. Risk culture in public and private organizations. Public Administration Review, 58 (2), 109-18.

– Iyer, K.C., Banerjee, P.S., 2016. Measuring and benchmarking managerial efficiency of project execution schedule performance. Measuring and benchmarking managerial efficiency of project execution schedule performance, 34 (2), 216-236.

– Dietrich, P., Lehtonen, P., 2005. Successful management of strategic intentions through multiple projects. International Journal of Project Management 23 (5), 386–391.

– Ghaffari, M., Sheikhahmadi, F., Safakish, G. 2014. Modeling and risk analysis of virtual project team through project life cycle with fuzzy approach. Computers & Industrial Engineering, 72 (June), 98-105.

– Jafarzadeh, M., Tareghian, H.R. Rahbarnia, F., Ghanbari, R. 2015. Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon. European Journal of Operational Research, 243(2), 658-664.

– Liu, S., Wang, L. 2016. Influence of managerial control on performance in medical information system projects: The moderating role of organizational environment and team risks. International Journal of Project Management, 34(1), 102-116.

– Welsch, W. 2006. “Input: state and local IT spending surge begins”, available at: http://www.washingtontechnology.com/news/1_1/daily_news/28292-1.html (accessed July 29, 2006).

 

ACCOUNTABILITY IN EDUCATION IN KENYA: CHALLENGES AND STRATEGIES

Main Author: Dr. Reuben Nguyo Lecturer under mentorship Programme

Department of Educational, Administration and Planning University of   Nairobi

Co-Author: Jedidah  Nyawira  Kimathi, North-Eastern Hill University, India. Department of Education P.O. Box 793022, Meghalaya, Shillong

Abstract

School accountability—the process of evaluating school performance on the basis of student performance measures—is increasingly prevalent around the world. Kenya has not been left behind. Therefore, the paper will explore the challenges and strategies of accountability in education in Kenya. Accountability in education is a broad concept that could be addressed in many ways, such as using political processes to assure democratic accountability, introducing market-based reforms to increase accountability to parents and children, or developing peer-based accountability systems to increase the professional accountability of teachers. The most commonly considered definition of accountability involves using administrative data-based mechanisms aimed at increasing student achievement. The study found out that the Government of Kenya is struggling with many challenges related to accountability in education. The challenges include: enrolment policy, education for individuals with disabilities, staff and performance, quality assurance and standards, management and governance among others. As far strategies to enhance accountability include devising performance indicators through The National Education Sector Plan (NESP) 2013-2018.

Key Terms: Accountability, Indicators of accountability, challenges

 Introduction

As the economies of nations compete for strong positions within a competitive global market place, many governments have become increasingly interested in the performance of all aspects of their education systems. This trend, coupled with the enormous expenditures that are devoted to education, has also precipitated widespread public requests for higher levels of scrutiny concerning the quality of education. These demands for information about school system performance can only be addressed through the implementation of systematic accountability systems.

Historically, the education profession has conformed to the requirements of regulatory or compliance accountability systems (usually based on government statutes), and has also subscribed to professional norms established by associations of educators. However, at the beginning of the 21st Century, accountability systems have also been required to respond to demands that professional performance be judged by the results that have been achieved (UNESCO,2005).

Accountability has been an educational issue for as long as people have had to pay for and govern schools. The term covers a diverse array of means by which some broad entity requires some providers of education to give an account of their work and holds them responsible for their performance.

Anderson (2005) asserts that Educational accountability targets either the processes or results of education. A desired goal is identified (e.g., compliance with the legal mandates of providing special education, highly qualified teachers, improved student performance), and measures are identified for determining whether the goal is met (e.g., a checklist of indicators that the legal mandates have been met, a target of 90% correct for teachers taking a test of current knowledge and skills, a target of 60% of students performing at grade level by the end of each school year). Criteria for determining whether the goal has been met can involve specific determinations of ways that the goal may and may not be met (e.g., deciding how many indicators in the checklist must be marked to be considered meeting the legal mandates, determining the specific content that does or does not count for specific types of teachers, determining how to calculate the percentage of students performing at a proficient level, and how to define grade level performance).

Models of Accountability in Education

A number of models of accountability in education have been developed, chiefly by Kogan (1986), Ranson (1986), Elliot et al. (1981) and Day and Klein (1987). These models illustrate different codes which specify, for example, alternative methods of presenting and evaluating the account. Whilst there are some differences of classification and nomenclature, four main models of educational accountability emerge from the literature: professional, hierarchical, market and public. Although it is unlikely that any of the ideal models will exist in its pure form.

 

(a) Professional Accountability

The emphasis on accountability for process is characteristic of professional accountability. Sockett (1980, p. 11) illustrates this, arguing that ‘the question (professionals) debate is not whether certain results have been achieved, but whether professional standards of integrity and practice have been adhered to’. In this form of accountability, teachers, and hence schools, are accountable to professionals. Ranson (1986) states that the educational process is so complex that only professionals can hold other professionals to account.

(b) Hierarchical Accountability

In contrast to professional accountability where accountability is ‘sideways’, the hierarchical model involves accountability ‘upwards’. This is exercised through the managerial hierarchy and stresses the contractual relationship with the state. Becher et al. (1981) describe it as an obligation to render an account to an employer.

(c) Market Accountability

In the market model, accountability is to the consumer (normally the parent). The emphasis is on accountability for outputs, mainly measured by examination results. In this system, schools are accountable to the consumer who chooses their product or an alternative in the marketplace. In order for the market to operate effectively, information (for example, examination results) needs to be available so consumers know the full specifications of the product they are ‘buying.’

 

(d) Public Accountability

Both market and public accountability involve an active role for parents. In the public model, this active role is required of the community more widely. The method of accounting stresses parental and community participation in determining the purpose and process of education (Ranson 1986). This operates collectively through the democratic process as well as indi-vidually, and therefore involves all individuals within an electoral ward. It stresses mutual accountability and partnership between politicians, professionals, parents and the community.

Forms of Accountability in Education

Accountability occurs in many ways in educational systems:

  1. a) System Accountability

Educational accountability in which the system is held responsible for the results of its students gained popularity in the early 1990s. Schools, local education agencies, and states are held accountable for the performance of all students in the public education system.

  1. b) Accountability for the Process of Education

This is a common form of educational accountability. Schools are required to meet accreditation criteria. Special education programs must demonstrate that they have provided services and maintained Individualized Education Programs (IEPs) in a manner consistent with the law. The desired goal of educational accountability focused on process is to improve the process that is targeted. Special education IEPs are an example of a process targeted for accountability. Meeting the process requirements means demonstrating compliance with a number of requirements in the law and in regulations for IEPs. Measurement occurs through the completion of a checklist, for example, that identifies the requirements (such as providing notice within a certain period of time, having specific signatures on the IEP document, and so on). The criteria for determining when the measures show that the goal has or has not been met are defined in terms of numbers of elements that must be checked. The consequences for not meeting the goal generally include a letter identifying the problems in the process. In some cases, repeated failure to meet the criteria results in penalties, such as reduction of funding, to the educational system.

  1. c) Individual Accountability

Student accountability implemented via promotion or exit exams is a common type of individual accountability in schools. Students are required to pass a test to demonstrate that they are ready to move either from one grade to the next (promotion) or leave the educational system with a credential certifying successful completion (exit). The tests that are administered to students generally cover those topics that the school system or its public have deemed important for individual students to demonstrate at a certain point in time. The criteria for determining when the measures show that the goal has been met (for instance, that the student is ready to move from one grade to the next) are defined in terms of passing scores on the test. In some cases alternative criteria are available to certain students who either are not able to pass the tests or who need to demonstrate that they have met criteria through other means.

Individual accountability for the adults in the education system include such variations as teachers being held responsible for passing tests to obtain or keep jobs, or principals and educators receiving salary bonuses on the basis of student achievement. This type of accountability includes the same components as other educational accountability systems, with goals, measures, and other criteria for determining when the goal has been met, and rewards and sanctions for meeting or not meeting the criteria.

Challenges to Accountability in Education

Enrolment Policy

The Education Sector has been making improvements in terms of access to institutions of basic education and provision of services. However, the challenge of attributing learning outcomes to the investment in the sector still remains. The resource investment over the years, both for development and recurrent expenditures would have by know translated into exemplary results at the ECDE, primary and secondary school levels; however this is not the case.

At the ECDE level for instance, though the enrolment increased from 1.914 million children (967,544 boys and 946,678 girls) in 2009 to 2.37 million (1,175,530 boys and 1,194,518 girls) in 2011, the Gross Enrolment Rate (GER) was still at 65.5 per and at 41.8 percent in 2011. Clearly, this is an indication that many children of nursery school going age were still not enrolled in the

ECDE centers, a clear violation of the children’s right to basic education.

At the primary school level enrollments increased from 8.8 million (4.5 million boys and 4.3 million girls) in 2010 to 9.86 million (4.98 boys and 4.86 girls) in 2011; the gross Enrolment Rate was at to 115.0% while the Net Enrolment Rate was at 95.7% in 2011. While this demonstrate good progress in terms of access from a national point of view, the situation is not the same especially in ASAL areas where many children of school going age can still be seen staying at home during school days. The Completion rate stood at 74.6 per cent in 2011 clearly showing that children are dropping out of school. The question is, why do they drop where do those who drop go to?

In terms of transition from primary to secondary schools, the rate was at 73.3 per cent (68.9 per cent for male and 75.3 per cent for female) in 2011; again it is clear that some learners do not access secondary education; where do they go to? What alternatives do they have? Does the government know where they are?

At the Secondary School level the enrolment grew from 1.18 million students in 2007 (639,393 boys and 540,874 girls) to 1.5 million (804,119 boys and 695,896 girls) students in 2009 to 1.7 million (916,302 boys and 792,818 girls) students in 2010 and further 1.8 million (948,706 boys and 819,014 girls) in 2011. The GER for secondary level was at 48.8 per cent (51.0 per cent for boys and 46.8 per cent for girls) and the NER was at 32.7 per cent (32.6 per cent for boys and 33.1 per cent for girls) in 2011; a clear indication that many children of secondary school going age are not in schools.

 

Education for Individuals with Disabilities

While Kenya government recognizes the need to educate all children, including those with exceptional needs, there lacks a mechanism to ensure and oversee that all students have equal access to education. The crucial question regarding persons with disabilities, especially those with intellectual disabilities is: how will the rights of persons with exceptionalities be protected from economic, social, and political neglect? An estimated 80% of all individuals with disabilities reside in isolated areas in developing countries (Oriedo, 2003) with 150 million of them being children (Eleweke & Rodda, 2002). Disability-related issues affect approximately 50% of the population in these countries (Oriedo, 2003, Mukuria, Korir & Andea, 2007). In most cases, disability problems are compounded by the fact that most of the people with disabilities are extremely poor and live in areas where medical and educational services are not available (Eleweke & Rodda, 2002; Meja-Pearce, 1998; Oriedo, 2003; Mukuria & Korir 2006). According to the 2009 census, this group makes up approximately 20% of the Kenya’s population (Kenya Bureau of Statistics, 2009); unfortunately, only 2% of individuals with disabilities receive any form of special education (Eleweke & Rodda, 2003; Mukuria & Korir, 2007).

 

Staffing & Teacher Performance:

Though the outcry on teacher shortage continues to be heard, additional concerns also revolve

around teacher distribution with allegations of some schools having more teachers than they require while in other schools, at every given time, some classes remain untaught because of teacher shortage. Teachers’ absenteeism also remains an issue that cuts across many schools in the country with concerns that some teachers chose to be away based on a mutual agreement with the head teacher as opposed to an official documentation of leave of absence.

 

Quality Assurance & Standards support:

Teacher performance records are lacking in many schools: While at the Classroom level, it is not easy to determine the extent to which the teachers are delivering the right content; but instead the performance of the teacher is left to be reflected in the performance of the learners (often during external examinations)

Another critical gap is that the school terms often begin with the teachers not aware of the specific dates that the QASOs would be visiting their schools. The criterion that determines which schools to be visited during a particular term is also not readily available. Some schools also indicate that one calendar year ends without any QASO visiting their schools and as such no quality assurance support is received from MoE throughout the year. Most of the QA&SO are not clear on the kind of support teachers require and they also have capacity gaps.

Even though feedback is given to the schools after visits have been conducted by MoE officials, the feedback never trickles down to the learners and their parents / guardians. Most of the time the feedback is discussed at the teachers level while other actors in education service provision are left out. The feedback at times reaches the headquarters of MoE but there are no clear mechanisms of responding to such feedback until a crisis emerge.

Management and Governance:

While some schools have School Management Committees and Board of Management in place, that have undergone trainings conducted by Ministry of Education officials, the functioning of

these committees is not reflected in the manner in which school programmes and activities are implemented. In some instances, the head teachers continue to make decisions by themselves (as

individuals) and the SMC &  BoM members hardly question such decisions. There is also lack of School development Plans in most schools and this creates an opportunity for poor plans.

The information on the funds received that is displayed on the school notice boards has been limited to the FPE funds with many head teachers not displaying any other funds the school receive, especially those collected from parents towards other programmes, for example the school feeding programmes. In terms of purchases of school items, there have been outcry among parents that some head teachers collude with suppliers to increase the prices of goods (often way ahead of the market prices) with the aim of receiving “kickbacks.”

Very few schools do generate annual financial reports for discussion with parents; majority of schools choose to discuss the financial reports with the MoE officials and ignore the parents, guardians and children. In addition, auditing of the funds that the schools receive every year is irregular and such audit reports are never shared with the parents, guardians and children.

 

Access to Information:

Information flow between the school administration and the teachers is another gap that exists in many primary and secondary schools. For instance, some teachers are only aware of the data in regards to the learners in the school and their performance but have no idea on the resource requirements of their schools and the management of resources that the schools receive. The level of awareness of some teachers in regards to various policies and guidelines in education service provision is also minimal; this is however attributed to lack of access to such documents at the school level; there are cases where the school head teachers limit such information to themselves and do not share with the other teachers in the school.

While the children are aware that the government has been financing the Free Primary Education, many of them are not aware of how much they have been entitled to over the years. Worse is the fact that some of the parents and guardians too are not aware of what their children

are entitled to under the FPE programme. This is attributed to lack of clear communication modes between the school administration and the children, and their parents and guardians.

 

Holistic Focus on Learners:

On an annual basis, the schools over concentrate on discussing the performance of the children in regard to KCPE and KCSE results; and very minimal is discussed in reference to performance of the children at other levels (class 1 – 7 & Form 1-3). While many of the school teachers are aware that some of the learners do not transit to secondary schools, it appears that majority of the teachers have no role in following up where such learners go to. For instance, there are cases where some teachers interact with their former pupils in the neighborhood such as in the market centers; while they are very much aware that such children have not enrolled in secondary schools; they do not bother to find out whether such children require support.

Some parents have also  have left their children in the hands of teachers and do not care to follow up on what their children do in school; some do not even attend school meetings throughout the year and do not event care to find out what deliberations and decisions are made in such meetings.

 

Finances:

Though the government supports the Free Day Secondary Education (FDSE) programme, there is a general feeling among the public that secondary education in Kenya is largely expensive. The fact that various categories of schools charge different amounts of fees is something that continues to amaze the public. There are cases of schools that get to acquire the National level status then increases the fees payable by about 50%; goes ahead to demand the same from parents and the government does nothing.

Various secondary school heads continue to incur exorbitant expenditures with completely no oversight. For instance who pays for the cost of Kenya Secondary Schools Heads Association Annual meeting? Is it the Government of Kenya? is it from the Head teachers personal incomes? or from the poor parents and guardians (from the fees paid to schools)? This is something that the public is seeking for accountability on the part of the government. Secondary schools in Kenya continue to manage millions of shillings annually; but majority of the schools do not report to the students, parents and guardians on their incomes and expenditure on annual basis. Reports that are shared publicly are largely in regards to performance and very minimal information on finances. A part from the details of the fees to be paid in the subsequent year, the secondary schools heads often give very minimal information on the expenditure of the previous

years.

 

Public Participation:

 Even though various districts have a culture of annual education stakeholders meetings, the participants in such meetings are often limited to head teachers, teacher unions, FBOs and NGOs. Public participation in such meetings, for example through the Chairpersons of schools and other representatives of parents and children continue to remain very minimal.

 

Strategies towards Accountability in Education

The National Education Sector Plan (NESP) 2013-2018 is an all-inclusive, sector-wide

programme whose prime goal is: Quality Basic Education for Kenya’s Sustainable

Development. The sector plan builds on the successes and challenges of the Kenya Education Sector Support Programme (KESSP), 2005-2010. Sector governance, management and accountability in a decentralized setting with devolved responsibilities and diverse partnerships have been emphasized. Clear guidelines for coordination, transparency, and reporting at the national, county, sub-county and institutional levels are paramount. The focus on improvement of education quality specifically targets: improvement of schooling outcomes and impact of the sector investment; development of relevant skills; improved learning outcomes; and improved efficiency and effectiveness in use of available resources.

 

Four major sets of performance indicators for NESP are identified as;

  1. Social development and economic growth for the 21st century are dependent on a broad base of capable, literate, numerate, confident and motivated citizens. These citizens will actively contribute to a knowledge-based society. The National Education Sector Plan (NESP) sets out to shape the education system to complement and support the national aspirations of Kenya.

  1. The sector plan as set out in NESP emphasizes a holistic and balanced development of the entire education sector, and is embodied in recent legislation, including the Basic Education Act 2013. The NESP Implementation Plan focuses on the urgent need to enroll all students in basic education, raise literacy and numeracy levels, reduce existing disparities, and improve the quality of education with a focus on teacher quality, school level leadership, more effective applications of teacher training in the classroom, increasing resources to the education sector, and targeting improvements and monitoring key results.

  1. NESP sets out to expand on the national aspirations set out in Vision 2030 through a statement of comprehensive goals and objectives. It further aligns a commitment to the UN Convention on the Rights of the Child and the education related goals of the Millennium Development Goals with a vision for the wider educational aspirations of Kenya.

  1. The foundation priorities of the education system provide the strategic implementation processes set out in NESP. Through the extensive consultation processes undertaken for developing NESP, stakeholders elaborated that the vision and goals embody and cluster around four principles:

 

Inclusiveness

This principle is about the fullness of the range of learning opportunities provided for all children, young people and communities. The NESP describes the social circumstances and barriers to learning that present challenges to implementation planning.

 

Integrated and Unified System

This principle is about the characteristics of an efficient education system that effectively and coherently integrates all learning institutions, central authorities and administrative agencies, through their mandates, processes and procedures. NESP describes governance, management and administrative expectations (institutional arrangements) that will ensure all students are exposed to opportunities of shared knowledge and culture.

 

Equitable School Environment

This principle is about the provision of safe, stimulating and innovative learning places of modern pedagogy for all children and young people. NESP sets out goals and objectives for the fair provision of infrastructural, teaching and learning resources and support systems to benefit all learners.

 

Quality of Learning

This principle is about the setting of rigorous quality benchmarks in the curriculum and its delivery and assessment so as to ensure that the learning opportunities for all children and young people are maximized. NESP describes expectations for minimum standards of the physical learning environment, curriculum development, teacher performance, and prescribes the work of agencies to monitor and assure ongoing quality.

  1. NESP also implies four central and interdependent policy pillars to underpin the

development of each of the described implementation strategies:

 

Pedagogy Enhanced by Technology

NESP makes a very strong representation of the role of technology in a modern, vibrant and successful society. NESP envisages a solid technology base through information and communication technology (ICT) to be reflected within the curriculum at all levels, its delivery and the system support mechanisms. The principles described above clearly focus on the fundamental place of pedagogy in lifting and maintaining quality of learning. This policy pillar establishes the place of technology as a powerful support to pedagogy but not the determinant of pedagogy.

 

Systemic Solutions

The NESP principle of an integrated and unified system demands that meeting the challenges requires the design, development and implementation of agencies, approaches and processes that support the interdependencies of all elements within the system. The setting of priorities and sequencing of implementation strategies will take account of the expected growth and impact of the education system. The NESP elaborates the mechanism (the National Education Board, NEB) whereby growth and impact is considered across the social and wider sectors.

 

Collaboration

The achievement of the sector plan requires a high commitment by all key stakeholders in the education system to working together as a team. Collaboration as an approach, however, goes beyond individuals working together for the common goal with a focus on the learner. It includes the establishment of conditions and relationships between the central administration, agencies and learning institutions to facilitate collaborative processes and approaches. The design of new and strengthened strategies is expected to stimulate and maintain a focus on group, rather than individual, effort through to the very top of the system.

Capacity Development

Achieving the NESP goals in a decentralized system requires significant capacity building at all levels of the education system. The strengthening and establishment of new ways of working through clearly defined roles, expectations, responsibilities, accountabilities and mandates are best achieved by capacity building of both human and resources. This policy pillar will strive to incorporate capacity building as a prime driver for reform.

Conclusion

The Government of Kenya is encountering many challenges as it deals with accountability in education owing to the fact that the idea of accountability has not yet been embraced fully neither by the assessors nor those being assessed. Mechanisms have been formulated to enhance accountability but have not yet been implemented fully.

Recommendation

There are should be concerted efforts among all the education stakeholders to ensure there accountability in education. There also needs sensitization about accountability in education as many stakeholders are not aware of their role as far as accountability is concerned. Follow up measures on accountability should be to put in place.

References

Anderson A.J. (2005). Accountability in Education. The International Academy of Education (IAE) and the International Institute for Educational Planning (IIEP).

Becher, T., M. Eraut & J. Knight. (1981). Policies for educational accountability. London: Heinemann Educational.

Day, P. and R. Klein. (1987). Accountabilities: five public services. London: Tavistock

Eleweke, C. J., & Rhoda, M. (2000). The challenge of enhancing inclusive education in developing Countries. International Journal on Inclusive Education, 6 113-126.

Elliot, J., D. Bridges, D. Ebutt, R. Gibson and J. Nias. (1981). School accountability. London: Grant McIntyre.

Farrell, M.C & Law, J.(2002). Changing Forms of Accountability in Education? A Case Study of Leas In Wales. Public Administration77, 2 .Wiley online library.

Kogan, M. (1986). Educational  accountability. An analytic overview. London: Hutchinson.

Korir, J. & Mukuria, G. & Andea, B. (2007). Educating children with emotional and /or emotional Disabilities in Kenya. A right or a privilege? Journal of International Special Needs Education, 10, 49- 57.

Figlio, D & Loeb, S. (2011). School Accountability. In Eric A. Hanushek, Stephen Machin, and Ludger Woessmann, editor: Handbooks in Economics, Vol. 3, The Netherlands: North-Holland, pp. 383-421.

Meja-Pearce, A. (1998). Disabled Africa: Rights not welfare. Index on Censorship, 27, 177-195.

Oriedo, T. (2003). The state of persons with disabilities in Kenya. Council for Exceptional Children: Division of International Special Education and Services. fromhttp//www.cec.sped.org/ind/natlover.html.

Republic of Kenya (2015). Ministry of Education, Science and Technology National Education Sector Plan Volume One: Basic Education Programme Rationale and Approach 2013 – 2018.

Republic of Kenya (2012). The Report of the Task Force on the Realignment of the Education Sector to the Constitution of Kenya.

Republic of Kenya (2011) Education Sector Report

Republic of Kenya (2012) Education Sector Report

Ranson, S. (1986). ‘Towards a political theory of public accountability in education’,Local Government Studies 4, 77–98.

Sockett, H. (1980). ‘Accountability – the contemporary issues’ in H. Sockett (ed.), Accountability in the English educational system. London: Hodder and Stoughton.

World Vision Kenya (2012). Enhancing Responsiveness and Effectiveness of Basic Education Service Delivery in Kenya Project Reports

http://www.education.com/reference/article/accountability/

http://www.unesco.org/iiep

 

Language Awareness in the Workplace

Mustafa Wshyar Abdullah AL-Ahmedi

Lecturer at Koya University – Koya, Erbil/ Iraq

Abstract:

This article is exploring the use of language by the individuals of a group who are active members of a team. Real examples are given to further investigate the language awareness in real life communications.

Key Words: Language, Awareness, Workplace, Team, Individuals

  1. Introduction

      Working in a team can enhance a great experience for a participant in a group work. The enhancements can be new information, the way of dealing with subject matters, and communication with people who may have different ideas. Those features are mostly very important to be existed in a person who looks for a good employment opportunity. It is a reality that most employers try to employ someone with those and some other characteristics like a good command in using technology. Integrating to a work environment is significant for an employee to perform an acceptable performance. To be a successful person in a workplace, being aware of cross cultural and different types of community of practice has its significance.

As a part of Language Awareness in the Workplace, the students had to work in one of the projects which were offered to participate. One of them was “Wish upon a Star” and the other one was “Equality and Diversity at UCLan”. All the students decided to go with the first one because the majority desired to take part in it and the minority of students did not like the idea of creating a small group. “Wish upon a Star” is a project which leads to publishing a book about star and constellation stories for children. The aim of the book is to provide the readers, who are supposed generally to be children, with constellation and star beliefs in India and the UK. The master students in English Language and Literature were requested to contribute some cultural facts and beliefs about sky issues to the content of the book.

The preparation for the project started in early December 2011 and the book is expected to be published around July 2012. This report is written to show what were done to contribute to the project and the way which steps were taken. It explores the writer’s, who was a group member, weaknesses and strengths which are very important to be discovered for improvement. The benefits will also be mentioned as the most participants could obtain new skills or at least improving the existed ones.

  1. Community of Practice

  • An Introduction to Community of Practice

 

The term community of practice (CofP) is frequently mentioned in the modern world, especially when individuals exist in a group form to take a responsibility. Lave and Wenger (1991: 98) define the term: “A community of practice is a set of relations among persons, activity, and world, over time and in relation with other tangential and overlapping communities of practice.” That is to say, communities of practice are groups of codes which understood and used by the same members of a group. In other words, communities of practice are the ways of communication among the members of a group to achieve the common aims.

Hardcastle and Powers (2004) see community of practice as an application to practice skills to make communication in community groups, organizations or institutions. The relation among individuals is based on the community practice. In other words, it is a community of practice which arranges an acceptable relationship among members of a group. The members can communicate with each other through some common communication rules which are known by all the other members as they are familiar with the community they gathered in.

      Communities of practice are everywhere; human beings belong to several communities of practice in different time and places: at home, work, school, or in hobbies (Wenger, 1998). It can be said that a community of practice can be found in all aspects of life and it is the main tool of communicators. It is a tool which helps human beings to communicate and share ideas. They can understand each other through it and they can express themselves easily. As a result of this argument, community of practice involves in all forms of communication or it is the basic and it’s the way which makes communication.  Wenger (1998: 84) claims that there are three dimensions of a community of practice which are: mutual engagement, joint enterprise and shared repertoire. To understand these dimensions, looking at an example can be helpful before defining them. A large company which consists of many departments can be a good sample to give the reader a clear overview about the dimensions. The managers of departments can altogether engage in a group meeting which is arranged for all of them to discuss the goals of the company and they mutually engage. They may have a joint enterprise from discussing the achievements among themselves. The managers can also have shared repertoires by exchanging stories of achievements and other events.

  • Mutual Engagement

This is a regular interaction which people in pairs or groups discuss general issues (Holmes and Meyerhoff, 1999). This type is very simple which usually does not need arguments or debates. It is general discussions and it is the basis of relations. It can be said this one is used when the members have a shared goal and they all work to achieve it. As a result, the members communicate through this type of CofP because the target is the same. The communication can be while drinking a coffee or small meetings.

  • Joint Enterprise

It is more than a shared goal and needs process; complex relationships are involved in this dimension (Wenger, 1998). Members usually debate and have more serious meetings for longer hours.

  • Shared Repertoire

This dimension is the most complex one which linguistic resources involve in and The meetings are more serious in which gestures, pictures and regular meals become a part of the CofP (Holmes and Meyerhoff, 1999). The members of a group negotiate the meaning which shows disagreement sometimes.

  • Community of Practice of the MA English Language and Literature team

The group consisted of different people from different cultural and educational backgrounds. There was a main goal which was a contribution to publish a book and all the members worked for the same aim. It is worth mentioning that there was a main group in which sub groups involved in. My course mates and two of the tutors were members in a subgroup. This sub group mainly arranged meetings for its members and attended once the general meeting of the whole group. The whole group, which was the main one, included MA English Language and Literature team, MA Publishing Team and MA Creative Writing for Children Team.

            The common goal of the main group and subgroups was the same which was preparing the content for the book. Each group and individual had different duties; the duties were divided among the subgroups through their educational backgrounds. My group was supposed to carry out a research and collect data related to cultural beliefs as our educational background relates to language, culture and literature. Our part was exploring the beliefs in India and the UK about constellations and stars. Two members of the group had an Indian background and two members were British who were familiar with the beliefs in the UK. The other three members had no background about both countries and they tried to help those members to collect data and find information about both cultures.

            Regarding the power, all the members were equal in terms of controlling or managing the project. None of the members in the whole group including supervisors were more powerful. All the ideas were discussed and they were arranged to create a good content to the project. In brief, all the members decided on the content of the project and there was not anyone to have a final decision as the group generally decided on the final decision.

  1. Discursive Chosen Community of Practice

Discursive means everyday talk and it is a multi-utterance of talk; in order to analyze a conversation, meeting or talk, discursive should be analyzed (Tracy, 2002). Tracy (2002) argues that a talk is called discursive practice rather than a talk because it leads us to recognize the talk not like just a single thing, but to see it like an activity which has different parts. To interpret a talk, all the aspects like culture, speakers’ power status, politeness/ impoliteness and the others should be analyzed. After considering all these aspects, the exact meaning behind that speech might be explored and understood. As a result, it can be said that discursive practices are mainly used in conversation analysis to see how a conversation is conversed to show how successful it is.

            The language of communication for the MA English Language and Literature team was English as nobody could talk the first language of some other participants. Some of the members of the team had different accents but it did not affect the communication. All members’ speech tone was fine and it was always stayed at an average level which was considered polite. All the members of the team started studying in the same course since September 2011 and continued till late May 2012, but still some members of the group sometimes confused some other members’ names or even they did not know. This case shows that there was a lack of communication among some of the members and they did not have tight relations with each other. If the confusion or not knowing the names does really prove this, that means the relation among members of the team had to be tighter to enhance higher achievements.

In terms of the practices of the MA English Language and Literature team, it can be said that power was essentially equal as all the members worked all together. The decisions were taken through evaluating all the individual ideas and the ones, which were believed to be the best, were chosen. Generally speaking, the power did not have an important role as a community of practice in the group. The tutors who worked with us did not have a power of leading the project to any direction which they might desire. Their role was to guide us and answering our questions about the project to do our part very well as all student members were new for such a research and project.

            The practices like jocular and politeness were very common. There were many different ideas about the project and all the participants tried to show their thoughts to be admitted by other members. All the discussions were done in a very polite and friendly environment. To the best of my knowledge, cultural differences never made a problem. The obstacles or problems were solved through jokes and indirectly without harming any member. For example, a member of the group had always a problem with time managment; the member usually arrived to the meetings late. Other members did not react or sometimes I used jokes as a reaction, like telling that the member was supposed to arrive later and the moment was taken humorously. I personally tried to solve all the problems without making any trouble for the team. As it has been mentioned, the late arrivals or not attending was the biggest issue for the team. In such cases, other members volunteered to do the job which was considered to be done by someone else. In spite of this, those members who had such problems were encouraged to participate the meetings and to share towards the project.

            Collectivism and individualism had their influence on the members but they did not change the direction of the activities. The members of the team were divided equally on both these cultural backgrounds. The members who came from collectivistic cultures desired to arrange more meetings, while people from individualistic cultures did not mind and they thought the discussions could be done by using Facebook and emails. A member who was from a collectivist culture was not very happy as we did not have as many meetings as that member preferred to have. In spite of this concern, all other members including others from collectivist cultures were quite happy about the progress of the project. High context and low context was another difference like collectivism and individualism, but I never felt about its existence. All the members were very friendly and as it had been mentioned earlier, the power was equal which did not make any difference among us to recognize this matter.

  1. Employability Outcomes

The project was an opportunity for me to improve the skills which directly relate to my future employment. Communication skills were among the most important ones that I needed. In my undergraduate study, I did not have any group works; though, this project was the first one in my life which I became a group member to carry out a research. Communicating with people from different academic level and nationalities made me more communicable. This is a very important feature for a person to have it while seeking an employment opportunity. After the period of the project and communicating to the members of the group, now I am more confident in terms of contacting other people. The key point here is confidence because it can be increased through practicing the skills in real workplaces.  To be honest, I was not very confident in my communication in the past, but now a serious improvement can be seen. This improved skill is a significant outcome of working on the project. It can definitely be said that I will communicate easier in the future and it is a very important feature to have to be more employable.

Time management is another issue which I see as an employability outcome of this project. I usually had been good at managing my time, but when started this project, the skill needed improvements. During the work of the project, many weaknesses could be found about the time management skills. Through consulting other members and external sources, some of the negative points were changed to positives. This point is very important for my future employment and career because success can be the result of good time management. I was provided with very useful leaflets and sources by the tutors about this issue. After reading them, I found that I had to manage my time better as it was not great. Now, my time is managed better and I can have more activities as the least amount of time is spent carelessly or without a plan. I have many goals that I work hard to achieve them. My main targets are obtaining higher degrees and learning more to have a better employment opportunity. Trying to reach all the achievements need a good plan which requires managing time very well. This skill can have a main role in my future employability.

The dream employment for me is working in a group which consists of people from different cultures and nations. Such an employment essentials cross cultural skills, that is to say, someone with a good knowledge of different cultural aspects. The group of people which involved in the project was quite diverse. I worked with people who had different ideas and attitudes. It was a good introduction for me to learn some basics of dealing with people who may think differently especially with different customs. It does not look like having a conversation with someone who the same culture is shared with. In this sense, communicators should be more careful to avoid misunderstanding. After communication with all the members of the team, my cross cultural information increased significantly.

We had many meetings to discuss the project; a great number of people from different courses and positions attended to the meetings. In the meetings, I usually could improve my leadership skills as I always tried to participate and contribute something to the meetings. It can be said that being active in a group can enhance leadership skills because usually active people become leaders. Problem solving was another issue which I could recognize it from myself; I could solve some of the problems in a few meetings. While my leadership and problem solving skills were improving, my self-confidence increased significantly. As people relied on me sometimes and they wanted me to speak at the meetings, I became more confident in communicating with other people. This helped me to recommend solutions and new ideas with less hesitation.

Using technology to enhance a group aim was another new thing for me. Facebook was generally used to update group members about the recent changes of the project and sometimes, the ideas were discussed there. We had two Facebook groups: one of them was shared between students of my course and MA publishing students, the other one was dedicated to the students of my course. The second one was not very active as we usually preferred face to face communication, but I usually became an active member of the first group. I used the group to update MA publishing students about our works. It was proven to me that a space on a social network can be very useful for a group to work on a project. People from a work place can have such a group to discuss issues and problems about their job or they can easily ask questions and search for the answers there.

Team work was very new for me and I found that it is more beneficial than doing a type of work individually. It could be seen that a team work was more successful than a work of an individual. In the project, I realized that to get great achievements for a group, a shared aim of the team is very important. That is to say, all the members of a group should work to achieve a target and the group achievements should be more important than individual attainments.

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