Consumer Buying Behavior of Solar Energy Products

A REPORT ON

CONSUMER BUYING BEHAVIOR OF SOLAR ENERGY PRODUCTS AT MULTI ENERGY LIMITED Dissertation submitted in partial fulfilment of the Requirement for the award of the degree of PGDM By RAMAKA. SAGAR 12M090 (MARKETING & HR, BA)

PROJECT REPORT SUBMITTED TO

Dhruva College of Management-Hyderabad, India (Approved by AICTE, Ministry of HRD, Govt. of India) In partial fulfillment of PGDM Class of 2012- 2014 Dhruva College of Management

Page 1

DECLARATION I do hereby give the undertaking that the present study is a bonafide work and I have not submitted it for the award of any degree or diploma in any college or University. On my honor as a person of integrity, I have given nor received aid on this work.

R. SAGAR

Dhruva College of Management

Page 2

CERTIFICATE This is to certify that the study titled “A report on Consumer Buying Behavior of solar energy products” has been carried out by R. SAGAR as a part of the curriculum of PGDM program of Dhruva College of Management, Hyderabad during the period 13th June, 2013 to 13th August, 2013 under my supervision.

Mr. SK Balasubramaniam Founder & CEO STAMINA COMPANY


Page 3

ACKNOWLEDGEMENT It is said, the most important single word is we and the zero important single word is I. This is true even in today‟s modern era. It is absolutely impossible for a single individual to complete the assigned job without help and assistance from others.

It is my greatest pleasure to acknowledge sincere gratitude towards Mr. SK Balasubramaniam and Mr. Shyam Penumaka of STAMINA Company for the completion of the project work.

I would also like to acknowledge my sincere gratitude to the chairman of my institute Dr. S PRATAP REDDY and my project guide Prof.Shveta Bahadur and Prof. Ratna Geethika for helping me in this project work.

I am thankful to my all of my friends and batch mates for their help in completing this project work.

Finally, I am thankful to my entire family members for their great support and encouragement.

R. SAGAR 12M090


Page 4

TABLE OF CONTENTS

S.NO

TOPICS

PAGE NO

1

EXECUTIVE SUMMARY

6

2

INTRODUCTION

7

3

LITERATURE REVIEW

9

4

COMPANY PROFILE

12

5

OBJECTIVES OF STUDY

14

6

RESEARCH METHODOLOGY

14

7

ANALYSIS OF THE COLLECTED DATA

15

8

FINDINGS OF THE ANALYSIS

23

9

SUGGESTIONS AND RECOMMDATIONS

24

10

WEBLIOGRAPHY

25

11

ANNEXURES

26


Page 5

EXECUTIVE SUMMARY

In this project I considered most common factors influencing the consumers buying behavior on solar energy products. It includes factors that make him/her to buy particular solar energy products. The factors describe pre-purchase and post-purchase behavior. The methodology adopted was descriptive research. Descriptive research is done using factor analysis with the help of SPSS software. A standard questionnaire was made and used during interviews as a tool for research to find the level of importance of each factor. The response of common people was collected. They were asked to rate the factors on a five point scale in agreement with statements, ranging from 1= Very important and 5= not at all important. A total of 50 samples was collected in a period of two months.


Page 6

INTRODUCTION

Indian solar market Overview: It is believed that the Indian Photovoltaic market has huge potential. The future generation of gadgets will mostly comprise those powered by the solar energy. Depleting non-renewable energy resources, rising electricity bill and increasing awareness about green energy sources, have prompted people to adopt technologies to harness the abundantly available solar energy. It is clean, reliable and safe and if used skillfully, can reduce our dependency on conventional forms of energy and save money as well as the environment. Today, there is a wide range of solar devices available in the market including solar mobile phones, solar chargers, solar shaver, solar candles, solar lamps, solar headphones, solar exhaust fans, solar heating devices, solar energy saver etc. India today is the world‟s fourth largest economy. Its economy has grown steadily over the last 30 years, averaging 7% annually since 2000. Electricity demand is growing at 8% annually, similar to the growth of the economy. According to some articles, there is a 92 GW electricity demand over the next 10 years. India has a power generation capacity of about 170k MW of which only about 8 – 10% is generated through renewable sources. The country has an estimated renewable energy potential of around 85 GW from commercial exploitable sources: Wind: 45 GW, small hydro: 15 GW and biomass/bio-energy: 25 GW. India has the potential to generate 35 MW/km2 using solar photovoltaic and solar thermal energy. The Government of India and its state governments have created a major initiative called „The National Solar Mission‟. One of the main features of the Mission is to make India a global leader in solar energy and the mission envisages an installed solar generation capacity of 20 GW by 2022. This could in fact be much larger due to private initiatives that will no longer need state aid. India is endowed with rich solar energy resources. Because of its location between the Tropic of Cancer and the Equator, India has an average annual temperature that ranges from 25°C –27.5 °C. Being a tropical country, India has huge potential for solar power generation. The average intensity of solar radiation received in India is 200 MW/km2 with 250–300 sunny days in a


Page 7

year. As per government estimates, India receives 5,000tn kWh per year, with most parts of the country receiving 4-7 kWh per square meter per day. Solar is an important, although currently underutilized, energy resource in India with the potential to offer an improved power supply (especially in rural and remote areas) and increase the security of India‟s energy supply. Solar energy intensity varies geographically with Western Rajasthan receiving the highest annual radiation energy and the north-eastern regions receiving the least. India has a good level of solar radiation, receiving the solar energy equivalents of more than 5,000 trillion kWh/year. Depending on the location, the daily incidence ranges from 4 - 7 kWh/m2, with the hours of sunshine ranging from 2300 to 3200 per year. The annual global radiation varies from 1600 to 2200 kWh/m2, which is compared with radiation received in the tropical and sub-tropical regions. The equivalent energy potential is about 6000 million GWh of energy per year.


Page 8

LITERATURE SURVEY

Solar energy is the energy derived from the sun through the form of radiation. A number of solar thermal applications have been developed, which include water/air heating, cooking, drying of agricultural and food products, water purification, detoxification of wastes, cooling and refrigeration, heat for industrial processes, and electric power generation. This technology route also includes solar architecture, which finds utility in designing and construction of energy efficient buildings. Photovoltaic (PV) cells have a low efficiency factor, yet power generation systems using photovoltaic materials have the advantage of having no moving parts. PV cells find applications in individual home rooftop systems, community street lights, community water pumping, and areas where the terrain makes it difficult to access the power grid. Photovoltaic (PV) cells are placed on the rooftop of houses or commercial buildings, and collectors such as mirrors or parabolic dishes that can move and track the sun throughout the day. The efficiency of solar photovoltaic cells with single crystal silicon is about 13 % - 17%. High efficiency cells with concentrators are being manufactured which can operate with low sunlight intensities.

GOVERNMENT INITIATIVES AND SCHEMES FOR SOLAR DEVELOPMENT: • The world‟s first exclusive ministry for renewable energy development, the Ministry of NonConventional Energy Sources (MNES was established in 1992. Since Oct 2006 it has been known as „Ministry of New and Renewable Energy‟) implements various schemes to help the Renewable Energy (RE) sector. The government has the following plans for development of RE in India: • National Action Plan on Climate Change (NAPCC) induced Government‟s policy mandating an increased percentage of electricity to be generated from Renewable Energy (NAPCC envisages renewable energy to constitute approximate 15% of the energy mix of India till 2020) • Subsidy Scheme to Promote Solar Energy through the Jawaharlal Nehru National Solar Mission (JNNSM–With a target of 20,000MW till 2022) Dhruva College of Management

Page 9

 30% subsidy is given by central government for home solar power plants and AP Govt. to offer 20% extra subsidy for solar units.  National Bank for Agriculture and Rural Development (NABARD) is giving 50% loan for solar unit‟s installations.  For households below the poverty line, 100% capital subsidy would be provided as per norms of the Kutir Jyoti Program.

SOLAR DEVELOPMENT SCHEMES: The power deficient regions with regular blackouts of 8-10 hours and large power demand from India‟s fast Growing Economy triggered government‟s attention to invest considerable time and money in this sector. There is a list of some of the existing MNRE schemes for solar energy development: • Generation based incentive for grid interactive solar power generation projects (PV and Thermal both). • Programme on “Accelerated development and deployment of solar water heating Systems in domestic, industrial and commercial sectors”. • Revised Scheme on “Demonstration and Promotion of Solar Photovoltaic Devices/Systems in Urban Areas and Industry”. • Continuation of the Scheme on “Promotion of Solar Thermal Systems for air heating/Steam generating applications, Solar buildings and Akshay Urja Shops”. • Implementation of the programme on “Development of Solar Cities” during 11th Plan period including 2010-11.


Page 10

INVESTMENT OPPORTUNITIES: There is a large scope for investments in solar energy sector and Government of India is taking all the necessary measures to promote the solar energy generation in the country. The policy measures and incentives taken by the government of India to promote investment in the solar energy sector are as follows:

• Joint Ventures: A number of companies have entered into joint ventures with leading global PV manufacturers. There are no specific conditions laid down by MNES for the formation of joint ventures. General conditions laid down by the Ministry of Industry, Secretariat for Industrial Approvals and the Reserve Bank of India are applicable for this sector. • Export-oriented Units (EOQ): It is possible to set up a manufacturing plant as a 100 per cent EOU. Generally, these are permitted duty-free import of raw materials and components. They are also eligible to sell up to 20% of their production in domestic markets. Technology Transfer Indian PV industry is interested in seeking technology for the manufacture of PV modules, especially based on thin film materials, and is able to offer technology for the manufacture of silicon solar cells, PV modules and PV systems. • Technology Development: R&D projects are supported by the government at Central/state government research organizations, autonomous societies, universities, recognized colleges, IITs, industries (with suitable infrastructure for R&D) and NGOs.

TOP 20 SOLAR GIANTS IN INDIA: Some of the top players in the solar industry are Central Electronics, Emmvee Solar Systems, Jupiter Solar Power, Lanco Solar, Mahindra Solar One, Moser Baer Solar, Reliance Industries Ltd-Solar Group, Surana Ventures, Tata Power Solar Systems and Vikram Solar.


Page 11

COMPANY PROFILE: Multi Energy Pvt Limited, established in 2012 under the brand name “STAMINA”, is a company based out of Hyderabad. Multi Energy is a leading “Systems Integration” company with demonstrated experience in the design, development, construction and operations of Solar Solutions. Our continuous efforts to provide our clients with the best-in-class solar products have kept STAMINA ahead of the competition.

Our Vision: To create new value, excite and delight our customers through the best products and services.

Our Mission: To continuously provide our domestic, agricultural and commercial segment clients with world class innovative solar solutions and create a sustainable future for our Mother Earth.

STAMINA PRODUCTS: 1. 2. 3. 4. 5. 6.

Hybrid Batteries Hybrid Solverters Solar Panels Solar Charger Solar Heaters Solar lights  Solar Street Lights  Solar LED lamps 7. Solar Pumps


Page 12

PROBLEM CHOSEN: In this research, the problem is that STAMINA Company wants to know the common factors influencing the consumers buying behavior of solar energy products.

APPROACH TO SOLVE: They had decided to know the pulse of the consumers and thus conducted a market research. Through the research the data have been collected using a standard questionnaire and the questionnaire is analyzed using statistical tools like Factor Analysis to implicate the results of the research.

STATISTICAL TOOLS USED Reliability analysis: Reliability analysis is used to measure the internal consistency (reliability) of the scale. For this cronbach‟s Alpha it is the most common measure to know the reliability is used.

Cronbach’s Alpha: Lee cronbach developed this in the year 1951. It is used to measure the coefficient of reliability.

Factor analysis: Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.


Page 13

OBJECTVE OF STUDY To study the major factors which make a consumer to buy a solar energy product out of several factors

RESEARCH METHODOLOGY The research method adopted was both descriptive and exploratory. Population of Study:

Hyderabad city

Sample size:

50 respondents.

Sampling technique:

Convenient Sampling

Data collection: Primary Data: Questionnaire And Personal Interview, which are the two basic methods of collecting primary data, which suffices all research objectives.A questionnaire was designed keeping in view all those possible factors which can determine the awareness among consumers. Secondary data: Secondary data were collected through websites.


Page 14

ANALYSIS KMO and Bartlett’s Test:

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

.792 349.552 36 .000

Conclusion:  From the above table it is evident that the measure of sampling adequacy is mediocre since it is 0.792.  Hence factor analysis is a good analytical tool to describe sample data.  Bartlett‟s test of spericity shows that the R - matrix is not an identity matrix hence variables are homoscedastisitic.

Note: If the R - matrix is an identity matrix then all correlation co-efficients would be zero.

Cronbach's Alpha: Reliability Statistics Cronbach's N of Items Alpha .855 9

Conclusion: From the above table it is evident that the data is good for analysis.


Page 15

Correlation Matrix: Correlation Matrix Price Maintenance Style Durability Brand Availability Performance serivce quality Price 1.000 .421 .042 .426 .041 .174 .363 .372 .316 Maintenance .421 1.000 -.083 .745 .138 .492 .738 .668 .577 Style .042 -.083 1.000 .033 .425 .150 .040 -.101 -.049 Durability .426 .745 .033 1.000 .227 .631 .763 .724 .920 Brand .041 .138 .425 .227 1.000 .567 .223 .111 .042 Availability Performance serivce quality

.174 .363 .372 .316

.492 .150 .738 .040 .668 -.101 .577 -.049

.631 .920 .763 .724

.567 .223 .111 .042

1.000 .679 .668 .515

.679 1.000 .870 .814

.668 .870 1.000 .880

.515 .814 .880 1.000

Conclusion: The above table shows the relationship between the variables.  Performance and durability are strongly correlated i.e., Is 0.920 among all the variables.


Page 16

Communalities: Communalities Initial Extractio n Price 1.000 .245 Maintenanc 1.000 .671 e Style 1.000 .599 Durability 1.000 .845 Brand 1.000 .792 Availability 1.000 .738 Performanc 1.000 .914 e Service 1.000 .868 Quality 1.000 .763 Extraction Method: Principal Component Analysis.

Conclusion: From the above table it is evident that price is not very influential in study, it is not as strong as expected.


Page 17

Total Variance Explained:

Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 4.853 53.927 53.927 4.853 53.927 53.927 4.726 52.512 52.512 2 1.581 17.571 71.498 1.581 17.571 71.498 1.709 18.986 71.498 3 .895 9.949 81.447 4 .628 6.974 88.421 5 .436 4.844 93.265 6 .239 2.656 95.922 7 .231 2.568 98.490 8 .089 .985 99.475 9 .047 .525 100.000 Extraction Method: Principal Component Analysis.

Conclusion: From the above table it is evident that, there are two components in the study, these two components are available to explain 71.4% of variance in the data.


Page 18

Component Matrix:

Component Matrixa Component 1 2 Performanc .955 -.030 e Durability .919 -.025 Service .916 -.173 Quality .848 -.211 Maintenanc .805 -.151 e Availability .760 .400 Price .475 -.138 Brand .294 .840 Style .033 .773 Extraction Method: Principal Component Analysis. a. 2 components extracted.

Conclusion:  From the above table it is evident that, variables that are strong in component 1 are service, durability, equality and maintenance. Variables that are poor in component 1 are style, brand, and price.  Brand and style are strong in component 2 and the rest of the variables in component 2 are poor.


Page 19

Rotated Component Matrix:

Rotated Component Matrixa Component 1 2 Performanc .943 .159 e Service .932 .011 Durability .906 .156 Quality .873 -.040 Maintenanc .819 .010 e Availability .666 .542 Price .493 -.041 Brand .123 .882 Style -.120 .764 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations.

Conclusion: From the table it is evident that, after rotation most of the values consistent except style. Which means all the variables are strictly coherent each other as explained before.


Page 20

Scree Plot:

Conclusion: From the above graph it is evident that first two components are sufficient to explain variance in the study rest of the components are not so important.


Page 21

Media:

word of mouth tv advertisement Valid Newspaper

Media Frequenc Percent y 20 40.0

social network Total

Valid Percent 40.0

Cumulative Percent 40.0

13

26.0

26.0

66.0

7

14.0

14.0

80.0

10 50

20.0 100.0

20.0 100.0

100.0

Conclusion: From the above table it is evident that, the most influential media is word of mouth.


Page 22

FINDINGS 1) The consumers who prefer the brand of the product are indirectly preferring the style of the product. 2) The consumers are giving more importance to the performance of the product than the price. 3) The Consumers have given more importance to word of mouth than other promotional media as it is the most influential media these days. 4) The consumers who prefer the performance of the product are indirectly preferring the durability of the product.


Page 23

SUGGESTIONS 1) The company must concentrate on performance improvement of their products. 2) The company must concentrate on brand image in the market through launching various promotional activities.

RECOMMENDATIONS 1)

The company must establish R&D department.

2)

Most of the people are not aware of solar products, so the company must bring awareness in people through various promotional activities.

3)

The company must educate the people in rural areas about the solar products.

4)

The company must bring awareness in people about government subsidies on solar products.

5) 6)

The company must also educate the farmers about working of their solar water pumps. The company must improve their distribution channel.

7)

The company must improve after sales service.

8)

The company must recruit efficient engineers.


Page 24

WEBLIOGRAPHY     

http://en.wikipedia.org/wiki/Factor_analysis” http://www.ncl.ac.uk/iss/statistics/docs/factoranalysis.php” http://www.eco-ventures.in/pdfs/indian_solar_market_potential_anita.pdf http://indiagovernance.gov.in/files/solar_energy_economy.pdf http://www.monroecollege.edu/AcademicResources/ebooks/9781111532406_lores_p01_ ch03.pdf  www.electronicsb2b.com


Page 25

ANNEXURE-1

COMPANY PROFILE: Multi Energy Pvt Limited, established in 2012 under the brand name “STAMINA”, is a company based out of Hyderabad. Multi Energy is a leading “Systems Integration” company with demonstrated experience in the design, development, construction and operations of Solar Solutions. Our continuous efforts to provide our clients with the best-in-class solar products have kept STAMINA ahead of the competition.

Our Vision: To create new value, excite and delight our customers through the best products and services.

Our Mission: To continuously provide our domestic, agricultural and commercial segment clients with world class innovative solar solutions and create a sustainable future for our Mother Earth.

STAMINA PRODUCTS: 8. Hybrid Batteries 9. Hybrid Solverters 10.Solar Panels 11.Solar Charger 12.Solar Heaters 13.Solar lights  Solar Street Lights  Solar LED lamps 14.Solar Water Pumps


Page 26

Hybrid Batteries: Solar Batteries (Deep Cycle Batteries) are a key component in a stand-alone renewable energy system. Batteries are the very heart of the solar electric system. They are the reservoir where the power is stored. A battery storage bank allows the power system to deliver a constant level of power to electrical loads. Without batteries there would not be any power after sunset and maybe not even enough during daylight hours, depending on cloud cover, etc. Solar batteries for standby power applications are deep-cycle batteries which can discharge more of their stored energy while still maintaining long life. A deep cycle is when up to 80% of the battery capacity is discharged and recharged. The right choice of the size, type and number of batteries is important to insure that a power system performs properly and maximizes the life of the batteries. Features:     

Unique tubular positive plates designed for longer life. Superior active material & Special alloy used for low maintenance of the battery. Plate designed with special additives to get quick recovery from deep discharge. Float guide vent plug for electrolyte level indication. Virgin Lead used for the manufacturing of electrodes.

Hybrid Solverters (Solar PCU): A solar inverter, or Solar PCU, or Solverter converts the variable direct current (DC) output of a photovoltaic (PV) solar panel into a utility frequency alternating current (AC) that can be fed into a commercial electrical grid or used by a local, off-grid electrical network. It is a critical component in a photovoltaic system, allowing the use of ordinary commercial appliances. Solar inverters have special functions adapted for use with photovoltaic arrays, including maximum power point tracking and anti-islanding protection. Solar inverters use maximum power point tracking (MPPT) to get the maximum possible power from the PV array.


Page 27

Features:         

Pure Sine Wave Online UPS. Hybrid Charging : Charging in both Solar & Grid Powers. Suitable for Rural Conditions. Voltage and Frequency controlled. Double Conversion Technology. Equipped with safety and protection systems. LCD Display. Cold Start and Hot Stand-by. System Bypass facility when the system fails. Low Maintenance, Long Life and Eco-Friendly.

Solar Panels: A solar panel is a collection of solar cells connected electrically and packaged into a frame. Solar panels are the most noticeable component of a residential solar electric system. Solar panels are joined into arrays and commonly mounted in one of three ways: on roofs; on poles in free standing arrays; or directly on the ground. Lots of small solar cells spread over a large area can work together to provide enough power to be useful. The more light that hits a cell, the more electricity it produces. While solar batteries store energy and release when required solar panels as an extension of PV cells transform sunlight energy into electrical energy. Solar panels are given output ratings in watts. This rating is the maximum produced by the panel under ideal conditions. Output per panel is between 10 and 300 watts, with 100 watts be a common configuration. The average lifetime of a PV panel system is approximately 20 years and it can be used independently or along with other conventional power sources. Features:     

Long Service Life High Efficiency Solar Cells. Special Aluminum Frame Design. High Transmission, Low Iron Tempered Glass. Advanced Cell Encapsulation.


Page 28

Solar Charger: Solar Charger or Solar charge controller controls the level of battery charge. The use of a charge controller is essential for long battery life. Batteries last a lot longer if they aren‟t overcharged or drained too much. Once the batteries are fully charged, the charge controller doesn‟t let current from the PV modules continue to flow into them. Similarly, once the batteries have been drained to a certain predetermined level, controlled by measuring battery voltage, many charge controllers will not allow more current to be drained from the batteries until they have been recharged. Features:      

Always keep the battery on full voltage condition Prevent the battery from over-charging Prevent the battery from over-discharging Prevent the battery from reverse charging to solar panels during nights Reverse Polarity Protection for Battery & Solar Panels When the battery voltage is low, the controller will automatically cut off the load of the system. If the voltage of battery is back to normal and the load will restart working  According to the battery voltage grade, the controller can automatically set charge-off voltage, the load-restore voltage.  The controller will automatically compensate the temperature of the charging voltage according to the changes of ambient temperature  Thunder Protection Solar Heaters: Solar Water Heaters are the most competitive alternatives to the conventional methods of water heating such as electric geysers, fuel fed boilers etc. The non polluting nature, inexhaustible supply and low operational cost make Solar Water Heaters an attractive and sustainable option. Solar Water Heaters run fuel free on sunshine which minimizes the operational cost to nearly zero. Benefits:  Hot water throughout the year  Cut your current bills  Cut your carbon footprint Dhruva College of Management

Page 29

Solar Lights: Solar Street Lights: Solar street Lights are the light sources which are powered by photovoltaic panels. The photovoltaic panels charge a rechargeable battery, which powers a fluorescent or LED lamp. Most solar panels turn on and turn off automatically by sensing outdoor light using a light source. STAMINA Solar Systems design and develop Solar Street Light. Our Solar Street Lights are being used in urban and rural areas as a means for preserving the non-renewable sources of energy. Solar Street Light has been available for a long time, having initially been designed for use in less developed or isolated areas, circumstances where the electricity supply is less. Benefits:  Since the solar lights are independent of the utility grid, the operating costs are minimized  Solar lights require much less maintenance compared to conventional street lights  This is a non-polluting source of electricity.

Solar LED Lamps: A solar lamp is a portable light fixture composed of an LED lamp, a photovoltaic solar panel, and a rechargeable battery. In the era where measures to protect the environment are being taken up, the Solar Lantern we produce is of abundant use. Our solar lantern is perfect for places where there is a dearth of electricity. It is manufactured of high quality raw material. Keeping the international clients in mind, we have the provision of offering customized solar lanterns affordable rates. Solar Pumps: A pump running on the electricity generated by photovoltaic i.e., the heat collected from sunlight is known as Solar Pump. Solar pumps otherwise known as solar powered pumps are supposed to be more economical and has less environmental impact compared to the conventional pumps. Solar pumps are most useful where grid electricity is unavailable. They are also cost effective because of the free availability of sunlight or solar energy. Dhruva College of Management

Page 30

ANNEXURE-2 RESEARCH DESIGN Research design is the plan, structure and strategy of investigation conceived so as to obtain answers to research questions and to control variance The research design can be classified into three broad categories (A) Exploratory (B) Descriptive (C) Casual

(A) Exploratory Research: Exploratory research is conducted to provide a better understanding of a situation. It isn‟t designed to come up with final answers or decisions. Through exploratory research, researchers hope to produce hypotheses about what is going on in a situation. A hypothesis is a statement that describes how two or more variables are related. For example, if sales for a particular line of vehicles dropped during the latest quarter, as a researcher you might use exploratory research to provide insights about what caused the decrease in revenue. Suppose that you conducted interviews with potential car buyers and noticed that they seemed to be more excited about the new styles of other car brands than they were about the brand in question. This might lead to the hypothesis that style preferences had changed, resulting in lower sales. You can‟t really confirm or reject the hypothesis with exploratory research, though. That job is left for descriptive.

(B) Descriptive Research: Descriptive research can be used to accomplish a wide variety of research objectives. However, descriptive data become useful for solving problems only when the process is guided by one or more specific research problems, much thought and effort, and quite often exploratory research to clarify the problem and develop hypotheses. A descriptive study design is very different from an exploratory study design. Exploratory studies are flexible in nature; descriptive studies are not. They require a clear specification of the who, what, when, where, why, and how of the research. Dhruva College of Management

Page 31

(c) Casual Research: Sometimes managers need stronger evidence that a particular action is likely to produce a particular outcome. For example, if you were considering a change in product packaging, you might want to test this hypothesis: “A redesign of the cereal package so that it is shorter and less likely to tip over will improve consumer attitudes toward the product.” For really important decisions, sometimes we need stronger evidence than we can get with descriptive research. (Using descriptive research, we might have learned that there was a negative correlation between consumer ratings of likelihood of tipping over and attitude toward the product, but not a lot more.) Descriptive research is fine for testing hypotheses about relationships between variables, but we need causal designs for testing cause-and-effect relationships.

FACTOR ANALYSIS Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, uncorrelated variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer such unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors, plus "error" terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Computationally this technique is equivalent to a low rank approximation of the matrix of observed variables. Factor analysis originated in psychometric, and is used in the behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data. Factor analysis is related to principal component analysis (PCA), but the two are not identical. Latent variable models, including factor analysis, use regression modelling techniques to test hypotheses producing error terms, while PCA is a descriptive statistical technique


Page 32

Terminology Factor loadings: The factor loadings, also called component loadings in PCA, are the correlation coefficients between the variables (rows) and factors (columns). Analogous to Pearson's r, the squared factor loading is the percent of variance in that indicator variable explained by the factor. To get the percent of variance in all the variables accounted for by each factor, add the sum of the squared factor loadings for that factor (column) and divide by the number of variables. (Note the number of variables equals the sum of their variances as the variance of a standardized variable is 1.) This is the same as dividing the factor's eigenvalue by the number of variables. Interpreting factor loadings: By one rule of thumb in confirmatory factor analysis, loadings should be .7 or higher to confirm that independent variables identified a priori are represented by a particular factor, on the rationale that the .7 level corresponds to about half of the variance in the indicator being explained by the factor. However, the .7 standard is a high one and reallife data may well not meet this criterion, which is why some researchers, particularly for exploratory purposes, will use a lower level such as .4 for the central factor and .25 for other factors call loadings above .6 "high" and those below .4 "low". In any event, factor loadings must be interpreted in the light of theory, not by arbitrary cutoff levels. In oblique rotation, one gets both a pattern matrix and a structural matrix. The structure matrix is simply the factor loading matrix as in orthogonal rotation, representing the variance in a measured variable explained by a factor on both a unique and common contribution basis. The pattern matrix, in contrast, contains coefficients which just represent unique contributions. The more factors, the lower the pattern coefficients as a rule since there will be more common contributions to the variance explained. For oblique rotation, the researcher looks at both the structure and pattern coefficients when attributing a label to a factor.

Communality: The sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors, and this is called the communality. The communality measures the percent of variance in a given variable explained by all the factors jointly and may be interpreted as the reliability of the indicator.

Spurious solutions: If the commonality exceeds 1.0, there is a spurious solution, which may reflect too small a sample or the researcher has too many or too few factors.


Page 33

The uniqueness of a variable: That is, uniqueness is the variability of a variable minus its commonality.

Eigenvalues: /Characteristic roots: The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor. The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important factors. Eigenvalues measure the amount of variation in the total sample accounted for by each factor.

Factor scores (also called component scores in PCA): are the scores of each case (row) on each factor (column). To compute the factor score for a given case for a given factor, one takes the case's standardized score on each variable, multiplies by the corresponding factor loading of the variable for the given factor, and sums these products. Computing factor scores allow one to look for factor outliers. Also, factor scores may be used as variables in subsequent modeling.

Criteria for determining the number of factors: Using one or more of the methods below, the researcher determines an appropriate range of solutions to investigate. Methods may not agree. For instance, the Kaiser criterion may suggest five factors and the scree test may suggest two, so the researcher may request 3-, 4-, and 5-factor solutions discuss each in terms of their relation to external data and theory.

Comprehensibility: A purely subjective criterion would be to retain those factors whose meaning are comprehensible to the researcher. This is not recommended

Kaiser criterion: The Kaiser rule is to drop all components with eigenvalues under 1.0 – this being the eigenvalue equal to the information accounted for by an average single item. The Kaiser criterion is the default in SPSS and most statistical software but is not recommended when used as the sole cutoff criterion for estimating the number of factors as it tends to overextract factors.

The variance explained criteria: Some researchers simply use the rule of keeping enough factors to account for 90% (sometimes 80%) of the variation. Where the researcher's goal emphasizes parsimony (explaining variance with as few factors as possible), the criterion could be as low as 50% Dhruva College of Management

Page 34

Scree plot: The Cattell scree test plots the components as the X axis and the corresponding eigenvalues as the Y-axis. As one moves to the right, toward later components, the eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Cattell's screen test says to drop all further components after the one starting the elbow. This rule is sometimes criticized for being amenable to researcher-controlled "fudging". That is, as picking the "elbow" can be subjective because the curve has multiple elbows or is a smooth curve, the researcher may be tempted to set the cutoff at the number of factors desired by his or her research agenda. Horn's Parallel Analysis (PA): A Monte-Carlo based simulation method that compares the observed eigenvalues with those obtained from unrelated normal variables. A factor or component is retained if the associated eigenvalue is bigger than the 95th of the distribution of eigenvalues derived from the random data. PA is one of the most recommendable rules for determining the number of components to retain, but only few programs include this option. Before dropping a factor below one's cutoff, however, the researcher should check its correlation with the dependent variable. A very small factor can have a large correlation with the dependent variable, in which case it should not be dropped.

Rotation methods: The annotated output maximizes the variance accounted for by the first and subsequent factors, and forcing the factors to be orthogonal. This data-compression comes at the cost of having most items load on the early factors, and usually, of having many items load substantially on more than one factor. Rotation serves to make the output more understandable, by seeking so-called "Simple Structure": A pattern of loadings where items load most strongly on one factor, and much weaker on the other factors. Rotations can be orthogonal or oblique (allowing the facts to correlate).

Varimax rotation: is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, which has the effect of differentiating the original variables by extracting factor. Each factor will tend to have either large or small loadings of any particular variable. A varimax solution yields results which make it as easy as possible to identify each variable with a single factor. This is the most common rotation option.


Page 35

Advantages:  Both objective and subjective attributes can be used to provide the subjective attributes can be converted into scores  Factor Analysis can be used to identify hidden dimensions or constructs which may not be apparent from direct analysis  It is easy and inexpensive to do

Disadvantages:  Usefulness depends on the researchers' ability to collect a sufficient set of product attributes. If important attributes are missing the value of the procedure is reduced.  If sets of observed variables are highly similar to each other and distinct from other items, factor analysis will assign a single factor to them. This may make it harder to identify factors that capture more interesting relationships.  Naming the factors may require background knowledge Cor theory because multiple attributes can be highly correlated for no apparent reason.


Page 36

QUESTIONNAIRE Factors influencing consumer buying behavior on solar power units Name:

Contact Number:

Age:

Occupation:

1) On a scale of 1-5 please rate the importance level of the following factors for choosing the product.

Price

1

2

3

4

5

Maintenance

1

2

3

4

5

Style

1

2

3

4

5

Durability

1

2

3

4

5

Brand

1

2

3

4

5

Availability (place)

1

2

3

4

5

Performance

1

2

3

4

5

After sales service

1

2

3

4

5

Quality

1

2

3

4

5

2) Which media can influence you most? 1) Word of mouth 2) TV advertisement 3) newspapers 4) Social networks 5) Others


Page 37

Consumer Buying Behavior of Solar Energy Products

Recommend Documents