Statistical Approach to Consumer Decision Making

Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-317...
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Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

Statistical Approach to Consumer Decision Making Gautam Narang, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India. Email: [email protected] Mili Mishra, Department of Electronics and Instrumentation, Birla Institute of Technology and Science, Goa, India. Email: [email protected]

___________________________________________________________________________

Abstract The aim of this paper is to analyze various modes of decision making and apply statistical models to predict consumer behavior in various situations and areas of application. We have studied various case studies for decision making by consumer and thematically classified them and also researched the quantitative techniques underlying each of them to assess the relevance. We also aimed at formulating a model based on real time data and subsequently comparing, analyzing and interpreting scientifically the data used in the model. ___________________________________________________________________________ Key words: consumer behavior, consumer decision models, decision making, decision making models, decision making styles, probabilistic approach to decision making, statistical analysis of decisions. JEL Classification: Marketing : Buyer Behavior

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Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

1. Introduction Consumer decision making is a complicated procedure due to the presence of many seemingly abstract and unrelated variables. The effort to observe and study patters in cognitive behavior is at the heart of many research studies. Probability theories are used in the understanding of decision making in the presence of incomplete and inaccurate information. There are five steps which have been identified and recognized in decision making processes. These steps are recognizing the problem, searching for suitable alternatives, evaluating existing choices, choosing among the options and evaluating the outcome of the choice. (Schiffman & Kanuk, 1994:566-580; Solomon,1996:268; Du Plessis et al, 1991:27; Foxall, 1983:75). Understanding consumer decision making process is extremely valuable for any business. Manipulating consumer decision choices can help businesses as well as policymakers by maximizing their profit under the same amount of investment. Understanding these processes will help with developing marketing strategies targeted to the consumer. One of the most important aspects of marketing is to have a complete understanding of the buyer’s journey. Knowing every cognitive, psychological, and sociological aspect that led to the customer’s end decision would mean you’ve achieved success. This process of decision making is a guideline for studying the way consumers make decisions, but it is important to remember that the stages are not rigid and vary to a great extent depending on many factors which may be environmental and manifest without any prior warning. Problem recognition occurs when a consumer is faced with an unsatisfied need and desires a product that satisfies this need. The goal of a marketer should be to make consumers aware of possible unsatisfied needs, and to show the consumer how the product or service will fulfill that need. The consumer generally develops a certain set of criteria against which he will base his decision. Attributes which plays on emotions (such as perceived trustworthiness, perceived comfort, perceived excellence or perceived status) weigh the heaviest in decision making processes. The level of satisfaction with the purchase dictated a consumer’s future behavior towards the product. Depending on whether his expectations were met he would be more likely to go for the same product or different. In our study we have tried to see the various factors which play in the Indian consumer’s mind while purchasing a certain product.

2. Description of Research The study of the work done in this direction has immensely helped us go forward with our research. Besides helping us understand the existing ideas and concepts on the topic, it has helped understand the areas where work is yet to be done. The following studies are directed at understanding the consumer behavior targeted towards a certain product. We have tried to

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Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

observe the factors at play in the Indian consumer’s minds while going forward with a purchase. Also we have tried to quantify the impact of external factors on the decision making process. These studies can have a direct impact on the marketing strategy of any product and can help with managerial decisions regarding the strategy to target certain demographics. The effect of consumer decision making in the field of technology products is immense. The understanding of consumer’s mindset can help the company increase the revenue manifold. In the Indian context, there are many factors which influence the buying process. We have taken the example of mobile phones. The influence of technology on our lives is very immense. The technology market is considered to be very complex. In order to capture the right sentiments, all companies are competing in the Indian market for their share. The consumer base being huge in India, it has become a necessity to strategize using the right analyses.

3. Research methods The purpose of the research was to identify the factors dominant in the decision making of buying of mobile phones among consumers who purchase in shopping malls. Consumer decision making theory has been developed within the disciplines including psychology, marketing, and consumer and organizational behavior. The focus of this research is predicting consumer’s decision making. In consumer behavior research there are two main types of researches: quantitative and qualitative. Based on the requirements, a quantitative research method is adopted to predict behaviors of consumers. This section will outline the research method. A detailed analysis of a sample crowd for the study of consumer decision making is done. We have used a questionnaire for the implementation of the same. From research experience of the previous researchers, survey becomes the most significant method for the study of Consumer decision making. This purpose of this survey is to evaluate the Indian consumers' attitudes toward mobile phones. Based on the data on the Indian consumers' attitudes towards mobile phones, the impact and the interpretation of the research will be understood. 3.1 Theory It is a persistent question that is being asked that whether the consumer’s behavior differs when it comes to purchasing a technology product. The competition in the technology market is huge resulting in innovative and newer products being launched in the market almost every day. Rapidly changing technological conditions lead to shorter life cycles and the need for rapid decisions. This has not only lead to exceeding the expectations from the customer, it has also caused an over choice scenario for the consumer. Owing to the dynamic market conditions in this area, companies are frequently depending on a product focus and targeting a particular demographic. A number of consumer decision making models have been proposed 18 www.globalbizresearch.org

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and studied to understand this area. There are several advantages offered by formulation of models for any process. The consumer decision making models provide the ability to understand better the effects of changing a variable on the other dependent variables. Such models also logically indicate the interrelationship between variables for research purposes. They also help to understand processes and strategies and provide the foundation for establishing theories. Walters (1978:43) claimed that consumer decision-making models specify exact cause and effect that relate to consumer behavior. Around 300 years ago, Bernoulli gave the first official explanation of consumer decision-making. It was later modified and extended by von Neumann and Morgenstern. It is known as the Utility Theory. This theory proposed that consumers make decisions based on the expected outcomes of their decisions. Consumers were considered as logical characters that possess the ability to estimate the probable outcomes of decisions in the face of uncertainty and choose the alternative which provides them maximum benefits. However, this idealistic model is not practically possible. Consumers are generally neither rational nor consistent. This Utility model, notwithstanding its shortcomings, had been viewed as the dominant decision making model. Nobel Laureate Herbert Simon proposed an alternative, simpler model in the mid-1950s. This model was called Satisficing. Here the consumers got approximately where they wanted to go and then stopped the decision-making process. This theory corrects the pitfalls of Utility Theory. However, it still could be improved. Additional efforts were made by researchers to develop better consumer decision-making models. In the late 1970s, two leading psychologists, Daniel Kahneman and Amos Tversky, developed the Prospect Theory. Two major elements were added. Firstly the concept of value replacing the utility found in Utility Theory. Secondly endowment, in which an item is more precious if one owns it than if someone else, owns it. Value provided a reference point for comparison and was useful in evaluating profit and loss. 3.2 Method Design Designing method will refer to the detailed from survey question design to data analysis. This sector will cover research framework, survey questionnaire and data collection, research environment and sampling, data analysis, and validity and reliability. There are certain factors which may influence the every stage of consumer decision making process. 3.3 Survey Questionnaire and Data Collection The respondents who have purchased mobile phones were chosen as the population of this study. The research was conducted by distribution of questionnaires to respondents. The sample consists of predominantly the undergraduates and young working individuals who have bought cell phones from a mall in New Delhi during the busy festival time of Diwali. Data over a period of ten days was collected and used for this study. The survey questionnaire was arranged by Likert Scale. In terms of Likert (1932 p.3) proposed an accumulated scale for 19 www.globalbizresearch.org

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evaluating the survey respondents' attitudes, opinions, psychic and mental dispositions and preference. Likert scales are easy to conduct, analyze and most importantly “simple for consumers to answer. In Likert Scale questionnaire, NO. 1 in the left side of scale indicates the “powerful disagree” of the respondents, and the NO. 5 in the right side of scale indicates the “powerful agree” of the respondents. For demographics questions single selection scale was used where the respondents tick the option that fit. For example, the sample will show as follow. 12345 “Powerful Disagree” “Powerful Agree” Respondents were selected by using convenience sampling method (Schiffman et al 38). Respondents were chosen based on whether their e-mail or other internet based contact information was available to the researcher or not. Furthermore Snowball sampling (Hart, 2007) was used to support convenience sampling. With every e-mail or message that contained the survey, the participants were nicely asked to send or forward the message or the link itself to other people they knew. VARIABLES

FREQUENCY %

Gender

Male

82

53.94

Female

70

46.06

Under 11

0

0

11-20 yrs

30

19.74

21-30 yrs

81

53.29

31-40 yrs

21

13.82

41-50 yrs

12

7.89

Above 50 yrs

8

5.26

Marital

Single

89

58.55

Status

Married

63

41.45

Monthly

Less than Rs. 10,000

0

0

Family

Rs 10,000 to Rs 20,000 32

21.05

Income

Rs 20,000 to Rs 30,000 82

53.94

Rs 30,000 to Rs 40,000 22

14.47

More than Rs 40,000

10.54

Age

16

Table 1: Source – Primary Data

Sproles (1985) developed a 50-item tool to describe the decision making styles of consumers. Using data collected from 111 undergraduate women in two classes at the University of Arizona and employing a factor analysis technique, Sproles(1985) found six consumer decision-making style traits. He described these traits as: (1) Perfectionism, (2) 20 www.globalbizresearch.org

Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

Value Conscious, (3) Brand Consciousness, (4) Novelty-Fad-Fashion Consciousness, (5) Shopping Avoider-Time Saver (6) Confused, Support-Seeking Decision-Maker. In a later study, Sproles and Kendall (1986) developed a comprehensive tool called Consumer Style Inventory(CSI) to measure consumer decision making styles. The instrument was used with 482 students in 29 home economics classes in five high schools in the Tucson, Arizona area. (ref. Fan, J.X., 1998). This instrument measures eight important characteristics consumer's

decision

making: perfectionism,

of

brand consciousness, novelty-fashion

consciousness, recreational, price-value consciousness, impulsiveness, confused by over choice, and brand-loyal/habitual, (ref. Mitchell, Vincent-Wayne, 2001). FACTORS

1

2

3

4

5

2

16

39

72

23

1.2 I am extremely careful on getting a good price for 5

20

39

70

18

23

67

38

17

38

54

69

15

1.Price Conscious 1.1 I like to get the best deals for the products I buy

even the smallest items 1.3 I keep comparing the prices from different brands and 7 stores 1.4 I do not bother to change to stores just to get a little 3 discount

2. Quality Conscious 2.1 I usually try to buy the best overall quality.

9

13

21

52

64

2.2 Buying a good quality product is very crucial for me

13

22

65

46

13

2.3 I make extra effort to buy the best quality

8

21

44

71

15

2.4 I always buy the best quality.

10

29

59

44

14

3.1 I like to shop and have fun during the activity

15

20

69

44

11

3.2 I feel store hopping wastes my time

8

27

50

60

16

3.3 I make shopping trips fast and efficient.

10

25

47

57

30

17

27

60

49

6

25

38

69

16

3. Recreational

4. Novelty Conscious 4.1 It’s fun and exciting to buy new products

4.2 To get variety, I shop different stores and choose 7 different brands.

5. Variety Seeking 21 www.globalbizresearch.org

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5.1 If use the same brands over and again, I get tired of 16

16

60

48

19

them

20

65

49

15

10

5.2 I buy different brands to get some variety. Table 2: Source – Primary Data

3.4 Assumptions Sample Size 400 or a greater sample size is ideal for adequately using Maximum Likelihood as the estimation technique. This study has only 152 observations, as a result might contain some sampling error and failure to identify small differences.

4. Analysis It has been found (as discussed by Bozinoff, 1982:481 based on work by Lachman et al, 1979) that consumers are frequently engaged in non-conscious behaviour during decisionmaking. Actual consumer decision-making processes might also, in some cases, appear to be unrelated and random. In such cases the opportunistic approach might be at play which is not in alignment with traditional decision-making models. It is clear from studies that a lot of factors like product, situation, context, previous experience affect the decision making strategy. Some researchers have concluded that consumers do not typically apply analytical decision rules to optimize decisions but relied on heuristics that would lead to satisfying decisions instead (e.g. an acceptable price or trusted brand name).Consumers are driven by emotional concerns as much as by analytical thinking and the contribution of each is unknown and depends on many factors Consumer decision making is complex. A number of aspects of consumer decision making have been studied by researches. The most widely used consumer decision-making theory was written by Mowen and Minor (2000). There were five steps in decision making process that included recognizing problems, searching for solutions, evaluating alternatives, choosing among options, and evaluating the outcomes of the choice. The Consumer Styles Inventory (CSI) characterized a consumer’s approach in making choices. This model has been used internationally to identify the different shopping characteristics of consumers. 4.1 Methodology This research applies a quantitative research design and surveys the consumer’s decisionmaking styles, using the adapted Consumer Style Inventory (CSI), to examine consumer’s purchasing behavior in mobile phones’ market. More importantly, this study also focused on the relationship among age, gender, income etc. Quantitative analysis is an appropriate technique for the analysis of data gathered from the questionnaire. Statistical analysis is the dominated method in the methods of quantitative analysis. Statistics Package for the Social

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Science (SPSS) is well known statistical software for social and science researchers to process the data on quantitative research. The alpha coefficients for each factor in this study are presented in Table 1. Cronbach's α is defined as ……………………………. (Equation 1) where K is the number of components (K-items or testlets), total test scores, and

the variance of the observed

the variance of component i for the current sample of persons.

Alternatively, the Cronbach's α can also be defined as ……………………………….. (Equation 2) where K is as above,

the average variance, and

the average of all covariances

between the components across the current sample of persons. Cronbach’s α (alpha) is a coefficient of reliability. Factors

Cronbach’s Alpha

Price Conscious

.885

Quality Conscious

.764

Recreational

.721

Novelty Conscious

.728

Variety Seeking

.707

Table 3: Reliability for Consumer Styles Inventory(CSI) Questionnaire

Moreover, this research also used Kaiser-Meyer-Olkin (KMO) test to examine whether the data was fit to use factor analysis in analyzing data. From Table 2, the value of KMO (.869) was closed to 1. Also we used Bartlett’s Test of Sphericity. .837

Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett’s Test of

Chi-Square

10767.601

Df

1257

Sig.

.000

Sphericity

Table 4: KMO and Bartlett’s Test

Factors

1(%)

2(%)

3(%)

4(%)

5(%)

Total

Gap

1.1

4.32

7.91

28.06

43.16

16.54

99.99

0.01

1.2

3.35

12.75

27.51

46.97

9.39

99.97

0.03

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Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

1.3

5

15

44.28

23.57

11.4

99.25

0.75

1.4

3.31

16.55

31.78

40.39

7.94

99.97

0.03

2.1

5.67

6.38

8.51

35.46

43.97

99.99

0.01

2.2

8.27

13.79

40.68

28.27

8.96

99.97

0.03

2.3

5.51

14.48

28.27

43.44

8.27

99.97

0.03

2.4

7.09

14.18

39

29.78

9.92

99.97

0.03

3.1

10.27

14.38

40.41

27.39

7.53

99.98

0.02

3.2

5.51

15.17

28.27

41.37

9.65

99.97

0.03

3.3

6.75

16.89

22.29

33.78

20.27

99.98

0.02

4.1

11.33

14

40

30.66

4

99.99

0.01

4.2

6.04

14.09

24.16

45.63

10.06

99.98

0.02

5.1

7.04

11.26

38.73

29.57

13.38

99.98

0.02

5.2

5.55

12.5

43.05

29.86

9.02

99.98

0.02

Table 5: Likert Scale items percentage wise according to Factors

Factors Price Conscious

1

2

3

4

5

Total

0.6875

26

81.25

191.5

17.25

28.1875

2.75

24.25

339.6875

77.5

450.6875

4

8.666667

2.888889

118.2222

66.66667

69.55556

1.555

16

0

144

121

20.25

0.25

1

1

12.25

0.25

62.88889

1

Quality Conscious Recreational Novelty Conscious Variety Seeking

Table 6: The variance of each Factor used in the study

4.2 Results of Analysis As a result of the analysis, some factors were found to be the most appropriate representation of consumer decision-making styles for mobile phones. Furthermore, style of consumer was not significantly different based on gender. This means that genders were not different in their style of shopping.

4.3 Discussions The demographic information indicated most owners purchased. Additionally, consumers tended to purchase well-known brands in the market rather than those “Other” brands that were less known. 4.3.1 Difference by Gender Interestingly, the number of female buyers of technology product is as much as that of males. This is a big conclusion for the marketers to tap this consumer base. Females will be 24 www.globalbizresearch.org

Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

buying cells phones as much as males. It proves that the product design, promotion, and development cannot ignore female buyers. It will be a loss to them as they would not be able to benefit from this customer base. The environmental factors like increase in employment opportunities as well as earning potential of women has played a huge role in the inclusion of women in the active market has increased manifold. 4.3.2 Differences between Age Groups When looking at the numbers of respondents in the 18-24 years age group could be considered as key potential consumers in technology product market. Considering the brands owned by participants aged less than 18 to 34 years old, the research found that the consumers purchased famous brands. Therefore, marketing managers should design and develop new products to attract these consumers by focusing more on the features and innovations in the market as well as quality. The younger age group will switch from their brands if they get better features for lesser price while this kind of behavior is a little uncommon in the older age group demographic who prefer to stick to the well known brands. 4.3.3 Differences between Incomes The results showed that there was a tendency for respondents in the low income groups to own mobile phones. It is possible that the price of mobile phones has come down compared to the past and it has become a common commodity. In addition, as the technology advances, the price is decreasing and the quality is improving. On the other hand, income level seemed to influence the amount spent. Participants who had low monthly incomes were more likely to have spent more money either on purchasing or planning on a future purchase of a mobile phone. A mobile phone is now considered an investment and the lower income groups want to invest in an intelligent manner and do not mind buying a little expensive sets as they are trusting on the quality. The higher income group likes to change between handsets and keep updated with the latest technology so might not be brand conscious.

5. Conclusion The objectives of this study were to investigate the consumer decision making styles and to study variations in the consumer decision making styles across different demographic variables. Following the study of Sproles and Kendall (1986), an attempt was made to profile the decision making styles of Indian Consumers. Sproles and Kendall (1986) identified nine decision making styles while in this study researcher found only four decision making styles in Indian environment. These decision making styles are price consciousness, quality consciousness, recreational and novelty consciousness. In addition, this study shows that the average Indian shoppers in our sample was not very brand conscious, but quite price and 25 www.globalbizresearch.org

Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) 2014 Vol: 1 Issue 1

quality conscious. It is found that single consumers are more price conscious than married consumers. Indian consumers are recreational in their shopping. Shopping is fun activity for them. Young consumers between the age group of 11- 20 years are most recreational in their shopping. 5.1 Managerial Implications Information on consumers' decision-making style will definitely be useful for retailers targeting Indian markets. As Indian retail Industries are progressing in today’s scenario and increasing number of national and international players are getting interested in the emerging retail market in India, an understanding of Indian shopping behavior, with particular reference to their decision-making styles, can become a competitive advantage. Categorizing consumers by their decision-making styles and demographic variables provides meaningful ways to identify and understand consumer segments and to target each segment with more focused marketing strategies. 5.2 Limitations and Further Research There are several limitations that call for further future research. The study has been conducted in New Delhi city of India. The results of the same, if conducted in other part of the county may vary. It is because a country like India has geographically, economically, socially and culturally very different areas. This difference is too significant to be ignored. The sample consisted of 152 shoppers. The small sample size is also error-prone. Further research may address the following important questions: How do cultural factors influence the consumers’ decision making styles? Do the people from different geographical areas of Indian differ in their decision making styles? Do the people from urban area and rural area differ in their decision making styles?

6. Appendix Spearman’s coefficientThe

Spearman

correlation

coefficient

is

defined

as

the Pearson

correlation

coefficient between the ranked variables. The n raw scores Xi,Yi are converted to ranks xi,yi, and ρ is computed from these:

(Equation 3)

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In applications where ties are known to be absent, a simpler procedure can be used to calculate ρ. Differences di = xi − yi between the ranks of each observation on the two variables are calculated, and ρ is given by:

(Equation 4) Likert ScaleThe format of a typical five-level Likert item is: 1. Strongly disagree 2. Disagree 3. Neither agree nor disagree 4. Agree 5. Strongly agree Mann Whitney U testThe test involves the calculation of a statistic, usually called U, whose distribution under the null hypothesis is known. In the case of small samples, the distribution is tabulated, but for sample sizes above ~20 there is a good approximation using the normal distribution. Some books tabulate statistics equivalent to U, such as the sum of ranks in one of the samples, rather than U itself. The U test is included in most modern statistical packages. It is also easily calculated by hand, especially for small samples. There are two ways of doing this. First, arrange all the observations into a single ranked series. That is, rank all the observations without regard to which sample they are in. For small samples a direct method is recommended. It is very quick, and gives an insight into the meaning of the U statistic. 1. Choose the sample for which the ranks seem to be smaller (The only reason to do this is to make computation easier). Call this "sample 1," and call the other sample "sample 2." 2. Taking each observation in sample 1, count the number of observations in sample 2 that have a smaller rank (count a half for any that are equal to it). The sum of these counts is U. Analysis of variance-There are three classes of models used in the analysis of variance, and these are outlined here. Fixed-effects models (Model 1). The fixed-effects model of analysis of variance applies to situations in which the experimenter applies one or more treatments to the subjects of the experiment to see if the response variable values change. This allows the experimenter to estimate the ranges of response variable values that the treatment would generate in the population as a whole. Random-effects models (Model 2) this model is used when the treatments are not fixed. This occurs when the various factor levels are sampled from a larger population. Because the levels themselves are random variables, some assumptions and the method of contrasting the treatments differ from ANOVA model 1. Mixed-effects models (Model 3) A mixed-effects model contains experimental factors of both 27 www.globalbizresearch.org

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fixed and random-effects types, with appropriately different interpretations and analysis for the two types.

7. Acknowledgement This data collection for the research was done by Miss Vinamrata Agnihotri as part of her academic project in Delhi University, India. The data was used throughout the research work and has been instrumental in working on this paper.

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