Introduction

The consumer need for wellness products and various services have continued to evolve in India as the income levels are growing along with it awareness is rising. The lifestyle of a consumer is an important part and with every passing day each and every consumer is getting known to this. Health/wellness which was recently considered as a niche concept has managed to gather a mainstream audience in today’s time. Consumers today want to have total control about their look, how they feel and this is driving purchase decision across major categories like food, beverages , personal care and services.
In response to this marketers have launched major products and even services that contribute round 600 billion INR to the wellness market in India .This industry is continuing to grow and it still remains a fraction which is about 4 % or less of the overall consumer expenditure in India. Indian consumers have shown a wide display of behaviour characteristics right from passive where demand of such wellness products is very less to believers for whom wellness is an integral part. Thus it is very important for the wellness players to identify their target segment or core consumer target, so that they can align their value proposition with specific needs of this segment.
The scope in the wellness market in India is immense–even a 1% increase in consumer expenditure can potentially create an additional opportunity of six billion INR for wellness players. Today’s young consumers are increasingly driven by wellness choices. They are likely to form the foundation for a more evolved and mature consumer base in a decade. This evolving consumer base will provide the momentum for growth, propelling the wellness market to cross one trillion INR in the next four years the wellness industry has given rise to the evolving Indian consumer. The wellness market in India continues to grow and reach a value of 600 billion INR .The rate at which it is growing is 20 % and products constitute around 60 % of the market. The Indian wellness consumer can be broadly defined into 4 categories:
1) Believers
2) Actives
3) Beginners
4) Passives

The passives represent the largest opportunity for the wellness players to tap into and beginners are the fastest growing category.
Some of the key consumer trends are as follows:

• Despite the fact that wellness market has evolved and managed to gather the largest audience as consumer price still plays a major role in deciding which to opt for.
• This market needs significant investment and long term focus .

• Increase in awareness via celebrities and advertisements.

• Traditional products and services need modern sensibilities.

•Brands are becoming important for every consumer so every company has to be clear with their positioning and get the most out of it.

• Over the next four years, wellness industry is reportedly growing at a CAGR of 18 to 20% and reaches 950 billion INR by 2014.

Innovation is the need of the hour and targeting appropriate consumer is also equally important.

Few major guidelines about the wellness market: * Wellness market in India in 2011 was worth 590 billion INR is expected to reach 950 billion INR by the end of 2014 * Products continue to comprise a majority of 55 to 60% of the market. The service industry like gym and saloon has also seen phenomenal growth in the last few years * Currently it less than 4% of overall consumer expenditure in India and even a 1% increase in consumer expenditure can potentially create an additional opportunity of six billion INR for wellness players * Factors driving the market are the increased awareness in health and the increasing affordability which drives consumer acceptance
Roger Deromedi, CEO of Kraft Foods, who said: “The growing importance of health and wellness has altered buying patterns to a degree that I have not seen before in the food business”

Defining the Healthy Lifestyle Consumer:
The healthy lifestyle consumer has an orientation towards preventing health problems and maximizing personal wellbeing .Most healthy research articles have focused on diet and exercise as a measure. This was defined by Bloch(1984) and a complete review of the literature resulted in two major conclusions. The consumers who maintain a healthy lifestyle do not constitute a large segment and secondly there is a much larger segment who are yet to adopt some aspects of a healthy lifestyle and are yet to incorporate both a healthy diet and exercise regime. Thus the impact of healthy lifestyle consumer on the market place is due to growing demand for healthy products .This demand is not only from people who are maintaining a healthy lifestyle but also from mainstream people, the larger segment. The larger segment is attempting to live healthier and in terms of market impact it is better to look at this from a broader perspective who incorporate exercise and diet in their lives.

Literature Review

Base Paper : Analysis of the healthy lifestyle consumer
Richard L. Divine and Lawrence Lepisto
Department of Marketing and Hospitality Services Administration, Central Michigan University,
Mt Pleasant, Michigan, USA

The basic purpose of this research paper is to study and have a better understanding of the healthy lifestyle consumer by closely examining the demographic, personal values and psychographic antecedents .The methodology adopted in this paper Is cluster analysis which was further used to divide the consumers into healthy and unhealthy lifestyle segment based on their diet and exercise regime .Post this a logistic regression was run on the segment to test the impact of 17 hypothesized antecedents on it .The result of this analysis indicated that people who maintain a healthy lifestyle tend to be female , older and more educated. Coming to the value antecedents they place more importance to self fulfilment and less to fun coefficient. The financial impact of the healthy lifestyle consumer on a number of industries is widely documented and this research is highly beneficial for the companies to know which consumer segment they should target. A demographic profile of the consumer is also obtained helping them to target the most suitable segment .The psychographic predictors are beneficial so as to suggest a way to look forward to expansion of market size of healthy products .The managerial implication of this research paper is that there is still an untapped demand for products and services .We need to look at creating products that form a part of the healthy lifestyle of a consumer but also capture the mainstream elements like fun, pleasure and excitement .The work done by the author is extremely helpful to the marketers in determining the nature of current market for healthy products and to see how the market is growing.

Research Objective
The main objective of the research is to understand the healthy lifestyle of the consumer by investigating the demographic, psychological and value antecedents. Based on the literature review we seek to test these three parameters and derive the patterns of healthy lifestyle.
This analysis specifically seeks to identify who this emerging segment of consumer is, what personal values distinguish those who are more likely to choose a product based on the health considerations and to determine whether a number of hypothesized psychological antecedents do in fact influence whether one maintains a healthy lifestyle.
Methodology
Sample and Data Collection Method
The current study is an exploratory research where the primary data is collected using a survey. The instrument used is an online questionnaire which is sent across through mails and social media platforms. The sample respondents are across India and a convenient based sampling is used. It is assumed that the targeted audience is well educated and has good knowledge of using internet tools. This was considered a safe assumption as the poor and not so well educated Indians are less likely to make a choice of product based on health considerations and will play a little importance in analyzing the behavior of a healthy lifestyle consumer.
Sampling Technique
The sampling technique used was convenience based sampling and due to the scope of the project, a target of 60 respondents was considered efficient for our objective. The respondents varied across geographies and include our friends, families, previous co-workers and fellow class mates which were approached using an internet based questionnaire.
Instrument
The questionnaire included the questions on 3 parameters which are Demographic, value and physiological antecedents. All the questions have been previously validated and used in the white paper that is mentioned in the literature review. Also we tracked the original base papers to further validate and have greater understanding of each question.
Healthy lifestyle
The healthy lifestyle of a respondent was checked using a series of questions on their consumption of meat, alcohol, fruits and vegetables and their exercise frequencies. All the questions were open-ended where the respondents were asked to fill their consumption and exercise frequency in the previous month. The following were the categories: 1) White meat( fish and chicken) 2) Red meat( hamburger, steak and bacon) 3) Alcohol 4) Fruits and vegetables 5) Snack chips 6) Soft Drinks 7) Exercise activities(jogging, swimming, walking etc)

Demographic antecedents
The demographics across which the respondents were measured across were gender, age, family income, education level. All the questions were multiple choices where the options were on a nominal scale.
Values antecedents
Values are defined as deeply held feelings about what is important in life (Goldsmith et al. 1995). Values play an important role for an individual while making the product decision. With regards to values it is believed that individuals who place high level of importance to values of fun enjoyment and excitement will be less likely to have a healthy lifestyle. For example, it is believed that people who tend to eat outside junk food tend to be happier but lack a healthy lifestyle. Hence we tried to measure these values in our analysis.
The value antecedents were measured on a nine point scale with 1 being least important and 9 being most important. The values which were rated by the respondents were: 1) Fun enjoyment and excitement 2) Security 3) Sense of accomplishment 4) Self fulfillment 5) Self respect 6) Warm relationships with others 7) Being well respected 8) Sense of belonging
All the value antecedents were taken from the literature review which originally was derived from Maslow’s hierarchy of needs.
Psychological antecedents
Locus of control is defined as the extent to which one believes they are in control of their life (internal orientation) as opposed to believing their life is being controlled by outside forces (external orientation) (Villani and Wind, 1975).Locus of control was measured by a five items which were previously validated by Villani and Wind (1975). The questions asked were based on the personality traits that a person possesses. The sense that a person has control over their own health and wellness should lead to a better exercise and diet and hence be internally oriented. Externally oriented people tend to attribute their health to other factors beyond their own control. The measure was based on a five point Likert scale.
Temporal orientation is a trait that indicates if a person plans their life ahead or lives on a day to day basis. It is assumed that a person who plans his/her life ahead tends to lead a healthier life and hence place a product choice based on health considerations.
Analysis Techniques employed
Cluster analysis
A cluster analysis was performed in order to divide respondents into segments based on the healthiness of their lifestyles. The classification variables used in this analysis were the seven measures of a healthy lifestyle; the frequency of consumption within each of the six food/drink categories (white meat, red meat, alcohol, fruit and vegetables, regular soft drinks and snack chips) and the number of times a respondent exercised each month.
The following text explains how a cluster analysis is performed and tries to understand what a cluster analysis is. It is a tool which is used by Multivariate (> 1 variables) for grouping cases using key variables. It can also reveal new moderators and capitalizes on Within group to Between group variance (B>> W). The steps involved in cluster analysis are: Step 1: Identify clustering variables * Based on Problem Objectives * Address Multicollinearity using Factor Analysis * Examine Descriptives (similar range & SDs)
Step 2: Examine the data for: * Multivariate normality * Detection and, if necessary, deletion of outliers
Step3: Estimate initial cluster seeds using hierarchical clustering * Select a method (Ward’s method) * Select a clustering algorithm (Squared Euclidean distance) * Use agglomeration schedule to find the “most” appropriate number of clusters. Let it be N. * Test discriminating strength of N clusters using One-way ANOVA * Estimate centroids for N clusters (cluster seeds for the next stage of clustering).
Step 4: Estimate clusters and membership using Nonhierarchical (K-means) clustering: * Split sample into 2 sub-samples ‘Split 1’ and ‘Split 2’. * Use cluster centroids generated in the hierarchical process * Run K-means on ‘Split 1’ data with the option of ‘updating’ the clustering centroids for ‘N’ number of clusters. Save centroids of N clusters generated. Let it be C. * Use C centroids on Split 2 data. Do so twice; once with “update” options and then with ‘noupdate’ option. Save the cluster members from both options. * Estimate Cohen’s Kappa by using patterns from “update” and “noupdate” options. * Repeat for number of clusters N1, N2 and N3 * Compare Cohen’s Kappa values for N1, N2 and N3 clusters. The cluster solution with “highest” Kappa value is preferred.
Step5: Validate the clusters using MANOVA
Regression
The hypotheses in the research were tested using regression. The 14 hypotheses that were formed are listed and explained below.
Previous research (Shiu et al., 2004) suggests that women are more likely to consume health oriented products and also maintain healthier lifestyles. It has been common observation that women are more conscious about their diet while men are more likely to exercise. Therefore the following hypothesis:
H1: Women are more likely to maintain a healthy lifestyle.
There are researches which suggest that older people tend to have a healthier lifestyle and maintain healthier diets
H2: Age will have a positive effect on maintaining a healthy lifestyle.
With regard to income, research data suggest that healthy lifestyle consumers are likely to be wealthier. Income seems to be positively linked to consumption of fruits and vegetables.
H3: Income will have a positive effect on maintaining a healthy lifestyle.
The people with higher education level are more likely to watch their diet and are more likely to exercise. Interest in nutrition and education seems to be positively linked.
H4: Education will have a positive effect on maintaining a healthy lifestyle.
Regarding the list of values that were previously mentioned in the report it is assumed that people who place a high level of importance on fun and enjoyment are led likely to maintain a healthy lifestyle.
H5: The importance of fun, enjoyment and excitement will have an adverse effect on maintaining a healthy lifestyle
With the rest 7 values it is believed that they have a positive impact on maintain a healthy lifestyle. People which lay higher importance to external social values tend to be more concerned about their looks and appearances and thus assumed to maintain a healthy lifestyle. These external social values are sense of belonging, being well-respected and warm relationships with others. Therefore, it is hypothesized:
H6: The importance of sense of belonging will have a positive effect on maintaining a healthy lifestyle.
H7: The importance of being well respected will have a positive effect on maintaining a healthy lifestyle.
H8: The importance of warm relationships with others will have a positive effect on maintaining a healthy lifestyle.
The internal social values considered self-respect, self-fulfillment, sense of accomplishment and security. It is assumed that people who have high internal values are resistant to lure of instant gratification and take up long term healthy personal lifestyle which makes them feel better about themselves.
H9: The importance of self-respect will have a positive effect on maintaining a healthy lifestyle.
H10: The importance of self-fulfillment will have a positive effect on maintaining a healthy lifestyle.
H11: The importance of a sense of accomplishment will have a positive effect on maintaining a healthy lifestyle.
H12: The importance of security will have a positive effect on maintaining a healthy lifestyle.
The psychological antecedents in question are Locus of control and temporal orientation. It is assumed that individuals with internal orientation for locus of control are more likely to maintain a healthier lifestyle since it leads them to take responsibility of their own health
H13: The more internal one’s locus of control the more likely one will maintain a healthy lifestyle.
It is believed that people who tend to plan their life ahead are more likely to maintain a healthier lifestyle. Hendrix found that planners are more likely to sacrifice for the future and do things they do not want to do if they think doing these things will make their lives better in the future.
H14: A planning temporal orientation will have a positive effect on maintaining a healthy lifestyle.
Data analysis The current study analyzed the data collected from a nationwide sample of consumers in order to examine the impact of 3 types of antecedents on the healthy lifestyle of the consumer. This healthiness was assessed using a 2 group cluster analysis which was performed on a series of survey questions regarding exercise and diet behavior. Hierarchical Cluster Analysis : Cluster Case Processing Summarya,b | Cases | Valid | Missing | Total | N | Percent | N | Percent | N | Percent | 49 | 100.0 | 0 | .0 | 49 | 100.0 | a. Squared Euclidean Distance used | b. Ward Linkage |
Ward Linkage Agglomeration Schedule | Stage | Cluster Combined | Coefficients | Stage Cluster First Appears | Next Stage | | Cluster 1 | Cluster 2 | | Cluster 1 | Cluster 2 | | 32 | 11 | 46 | 3046.115 | 31 | 0 | 40 | 33 | 2 | 38 | 3439.356 | 29 | 23 | 38 | 34 | 5 | 12 | 3847.106 | 25 | 24 | 46 | 35 | 7 | 19 | 4309.406 | 16 | 10 | 42 | 36 | 16 | 30 | 4887.573 | 28 | 26 | 41 | 37 | 3 | 14 | 5521.806 | 30 | 0 | 42 | 38 | 2 | 6 | 6314.699 | 33 | 27 | 45 | 39 | 1 | 41 | 7199.199 | 0 | 21 | 43 | 40 | 11 | 17 | 8145.866 | 32 | 0 | 44 | 41 | 10 | 16 | 9247.199 | 0 | 36 | 43 | 42 | 3 | 7 | 10609.657 | 37 | 35 | 44 | 43 | 1 | 10 | 12503.957 | 39 | 41 | 45 | 44 | 3 | 11 | 14493.666 | 42 | 40 | 46 | 45 | 1 | 2 | 17821.947 | 43 | 38 | 47 | 46 | 3 | 5 | 21574.686 | 44 | 34 | 48 | 47 | 1 | 35 | 29996.723 | 45 | 0 | 48 | 48 | 1 | 3 | 40192.898 | 47 | 46 | 0 |

CLUSTER Redmeat Chicken fruitnveg softdrink snacks alcohol exercise

ANOVA | | Sum of Squares | df | Mean Square | F | Sig. | Redmeat | Between Groups | 17.186 | 2 | 8.593 | .401 | .672 | | Within Groups | 985.917 | 46 | 21.433 | | | | Total | 1003.102 | 48 | | | | Chicken | Between Groups | 8746.052 | 2 | 4373.026 | 48.084 | .000 | | Within Groups | 4183.499 | 46 | 90.946 | | | | Total | 12929.551 | 48 | | | | fruit n veg | Between Groups | 6379.983 | 2 | 3189.991 | 23.469 | .000 | | Within Groups | 6252.548 | 46 | 135.925 | | | | Total | 12632.531 | 48 | | | | softdrink | Between Groups | 46.116 | 2 | 23.058 | .213 | .809 | | Within Groups | 4988.374 | 46 | 108.443 | | | | Total | 5034.490 | 48 | | | | snacks | Between Groups | 306.315 | 2 | 153.158 | 3.742 | .031 | | Within Groups | 1882.664 | 46 | 40.927 | | | | Total | 2188.980 | 48 | | | | alcohol | Between Groups | 49.336 | 2 | 24.668 | 1.056 | .356 | | Within Groups | 1074.664 | 46 | 23.362 | | | | Total | 1124.000 | 48 | | | | exercise | Between Groups | 3073.225 | 2 | 1536.612 | 32.027 | .000 | | Within Groups | 2207.020 | 46 | 47.979 | | | | Total | 5280.245 | 48 | | | |
According to Hierarchical Cluster Analysis we saw that in 3 solution Cluster we get the maximum number of significant variable.
Now we apply K mean Cluster Analysis to give only a single cluster solution containing the user specific number of clusters .It minimizes the distance of terms within a group and maximizes the distance between different groups.

After running it for 2 clusters we see we get only 3 significant factors ;

ANOVA | | Sum of Squares | df | Mean Square | F | Sig. | Redmeat | Between Groups | 9.644 | 1 | 9.644 | .456 | .503 | | Within Groups | 993.458 | 47 | 21.137 | | | | Total | 1003.102 | 48 | | | | Chicken | Between Groups | 680.304 | 1 | 680.304 | 2.610 | .113 | | Within Groups | 12249.247 | 47 | 260.622 | | | | Total | 12929.551 | 48 | | | | fruit n veg | Between Groups | 6372.441 | 1 | 6372.441 | 47.844 | .000 | | Within Groups | 6260.089 | 47 | 133.193 | | | | Total | 12632.531 | 48 | | | | softdrink | Between Groups | 12.506 | 1 | 12.506 | .117 | .734 | | Within Groups | 5021.984 | 47 | 106.851 | | | | Total | 5034.490 | 48 | | | | snacks | Between Groups | 147.253 | 1 | 147.253 | 3.390 | .072 | | Within Groups | 2041.726 | 47 | 43.441 | | | | Total | 2188.980 | 48 | | | | alcohol | Between Groups | 41.607 | 1 | 41.607 | 1.807 | .185 | | Within Groups | 1082.393 | 47 | 23.030 | | | | Total | 1124.000 | 48 | | | | exercise | Between Groups | 2932.420 | 1 | 2932.420 | 58.703 | .000 | | Within Groups | 2347.825 | 47 | 49.954 | | | | Total | 5280.245 | 48 | | | |

Symmetric Measuresa | | Value | Asymp. Std. Errorb | Approx. Tc | Approx. Sig. | Measure of Agreement | Kappa | .903 | .095 | 4.766 | .000 | N of Valid Cases | 21 | | | | a. half = 2.00 | b. Not assuming the null hypothesis. | c. Using the asymptotic standard error assuming the null hypothesis. |

The Kappa value for 3 cluster solution is 0.903.We find the Kappa value in case of 2 and 4 cluster solution as well and come to a conclusion as to which cluster solution is the most feasible.

Symmetric Measuresa | | Value | Asymp. Std. Errorb | Approx. Tc | Approx. Sig. | Measure of Agreement | Kappa | .763 | .127 | 5.067 | .000 | N of Valid Cases | 21 | | | |
We see the Kappa value for 4 cluster solution is 0.763 .

Finally we see the Kappa value is the highest in case of 2 cluster solution so we choose this as our solution ;The value is 1 in case of 2 cluster solution.

Symmetric Measuresa | | Value | Asymp. Std. Errorb | Approx. Tc | Approx. Sig. | Measure of Agreement | Kappa | 1.000 | .000 | 4.583 | .000 | N of Valid Cases | 21 | | | | | | c. Using the asymptotic standard error assuming the null hypothesis.The final cluster solution is as follows : Final Cluster Centersa | | Cluster | | 1 | 2 | fruit n veg | 14 | 36 | snacks | 10 | 13 | exercise | 4 | 25 | a. half = 2.00 | Number of Cases in each Clustera | Cluster | 1 | 15.000 | | 2 | 6.000 | Valid | 21.000 | Missing | .000 | a. half = 2.00 | |

Thus the consumption of fruit and vegetable was a major determining factor for the difference in two clusters. As we can see the healthy segment is the one with a consumption of 36 in case of fruit and vegetable even exercise is a major differentiator .We see that exercise regime also leads to differentiate the two clusters into healthy and unhealthy .The exercise behaviour of un healthy is only 4 compared to 25 in case of the healthy segment. Thus the biggest determinant if whether one was classified as healthy or the unhealthy cluster was their fruit and vegetable consumption followed by the frequency with which they exercised.

Result
A Cluster analysis was formed to divide the respondents into segment based on the healthiness of their lifestyles .The variables used in this analysis is the seven major measures of healthy lifestyle; the number of serving per month of red meat, chicken, fruits and vegetable, snacks ,alcohol ,soft drink and also the measure of exercise performed. From the agglomeration coefficient we have chosen the cluster solution of 3 clusters as it has the maximum jump .Now we will perform ANOVA to see whether the groups formed are significant or not .After performing ANOVA we see as shown in the above table that only 4 out of 6 factors are significant. In case of 2 cluster solution we see that only 3 variables are significant and so we go ahead to fine tune the result by performing K Means Cluster Analysis .There we check the Kappa Value of 2,3,4 cluster solutions. Then we come to a conclusion that only in case of 2 cluster solution we get the highest Kappa value that is 1.So in our case 2 cluster solution is the most ideal .Thus we can split our target customer into two segments : Healthy and Unhealthy Lifestyle segment..The two clusters differ on 3 attributes out of the 6 total attributes. The major differentiator is the Fruit and Vegetable consumption and exercise frequency .Thus our healthy lifestyle consumer segment is the one with a consumption of 36 serving of fruit and vegetable and frequency of exercise of 25.It also suggested that alcohol consumption was not a major indicator of unhealthy lifestyle .For major companies looking to expand the market for healthy lifestyle products they need to tailor their products to better match the wants of people who are extremely time pressed. This result can also be beneficial to help the company’s positioning and define the target consumer. The research hypotheses in the study were tested using the regression technique which is based on one tail test with 95 percent confidence level. The results are depicted in the appendix.

Reference

Richard L. Divine, Lawrence Lepisto, (2005) "Analysis of the healthy lifestyle consumer", Journal of Consumer Marketing, Vol. 22 Iss: 5, pp.275 - 283
Hendrix, P. (1984), “Antecedents and consequences of time use: proposed measures and preliminary evidence”, Advances in Consumer Research, Vol. 11, pp. 35-40
Villani, K. and Wind, Y. (1975), “On the usage of ‘modified’ personality trait measures in consumer research”, Journal of Consumer Research, Vol. 2, December, pp. 223-8
Shiu, E., Dawson, J. and Marshall, D. (2004), “Segmenting the convenience and health trends in the British food market”, British Food Journal, Vol. 106 Nos 2/3, p. 106
Goldsmith, R., Freiden, J. and Hendereson, K. (1995), “The impact of social values on food-related attitudes”, The Journal of Product & Brand Manangement, Vol. 4 No. 4,pp. 6-14.
Analysis of the healthy lifestyle consumer Richard L. Divine and Lawrence Lepisto Department of Marketing and Hospitality Services Administration, Central Michigan University,
Mt Pleasant, Michigan, USA Winds of change: The wellness consumer(PWC 2012 REPORT IN FICCI CONFERENCE)

Appendix Step Summary | | Model | Action | Effect(s) | Model Fitting Criteria | Effect Selection Tests | | | | -2 Log Likelihood | Chi-Squarea | df | Sig. | 0 | Entered | Intercept | 25.127 | . | | | 1 | Entered | Belonging | 10.044 | 15.083 | 7 | .035 | Stepwise Method: Forward Entry | a. The chi-square for entry is based on the likelihood ratio test. |

Case Processing Summary | | N | Marginal Percentage | Cluster Number of Case | 1 | 15 | 71.4% | | 2 | 6 | 28.6% | income | 1 | 7 | 33.3% | | 2 | 7 | 33.3% | | 3 | 7 | 33.3% | Gender | 0 | 17 | 81.0% | | 1 | 4 | 19.0% | age | 2 | 18 | 85.7% | | 3 | 2 | 9.5% | | 4 | 1 | 4.8% | education | 3 | 2 | 9.5% | | 4 | 7 | 33.3% | | 5 | 5 | 23.8% | | 6 | 7 | 33.3% | belonging | 1 | 1 | 4.8% | | 2 | 2 | 9.5% | | 3 | 2 | 9.5% | | 5 | 2 | 9.5% | | 6 | 3 | 14.3% | | 7 | 4 | 19.0% | | 8 | 3 | 14.3% | | 9 | 4 | 19.0% | wellrespected | 2 | 1 | 4.8% | | 4 | 2 | 9.5% | | 6 | 1 | 4.8% | | 7 | 7 | 33.3% | | 8 | 5 | 23.8% | | 9 | 5 | 23.8% | relation | 3 | 2 | 9.5% | | 4 | 2 | 9.5% | | 5 | 2 | 9.5% | | 6 | 1 | 4.8% | | 7 | 5 | 23.8% | | 8 | 4 | 19.0% | | 9 | 5 | 23.8% | selfrespect | 3 | 1 | 4.8% | | 4 | 2 | 9.5% | | 5 | 2 | 9.5% | | 7 | 2 | 9.5% | | 8 | 4 | 19.0% | | 9 | 10 | 47.6% | selffulfilment | 3 | 2 | 9.5% | | 4 | 2 | 9.5% | | 5 | 3 | 14.3% | | 6 | 1 | 4.8% | | 7 | 2 | 9.5% | | 8 | 6 | 28.6% | | 9 | 5 | 23.8% | accomplishment | 4 | 2 | 9.5% | | 5 | 3 | 14.3% | | 6 | 4 | 19.0% | | 7 | 4 | 19.0% | | 8 | 1 | 4.8% | | 9 | 7 | 33.3% | security | 2 | 2 | 9.5% | | 3 | 2 | 9.5% | | 4 | 3 | 14.3% | | 5 | 1 | 4.8% | | 6 | 1 | 4.8% | | 7 | 3 | 14.3% | | 8 | 4 | 19.0% | | 9 | 5 | 23.8% | fun | 1 | 1 | 4.8% | | 2 | 1 | 4.8% | | 3 | 2 | 9.5% | | 4 | 2 | 9.5% | | 5 | 1 | 4.8% | | 6 | 2 | 9.5% | | 7 | 3 | 14.3% | | 8 | 5 | 23.8% | | 9 | 4 | 19.0% | locusofcontrol | -1.5350 | 1 | 4.8% | | -.4600 | 4 | 19.0% | | -.2150 | 1 | 4.8% | | -.1450 | 1 | 4.8% | | .1700 | 1 | 4.8% | | .2700 | 1 | 4.8% | | .3750 | 1 | 4.8% | | .4900 | 1 | 4.8% | | .5150 | 1 | 4.8% | | .9000 | 1 | 4.8% | | 1.5600 | 1 | 4.8% | | 1.6650 | 1 | 4.8% | | 2.0800 | 1 | 4.8% | | 2.9200 | 1 | 4.8% | | 2.9500 | 1 | 4.8% | | 3.3400 | 1 | 4.8% | | 3.6000 | 1 | 4.8% | | 4.4150 | 1 | 4.8% | temporal orientation | 1 | 3 | 14.3% | | 2 | 3 | 14.3% | | 3 | 4 | 19.0% | | 4 | 1 | 4.8% | | 5 | 4 | 19.0% | | 6 | 4 | 19.0% | | 7 | 2 | 9.5% | Valid | 21 | 100.0% | Missing | 0 | | Total | 21 | | Subpopulation | 21a | | a. The dependent variable has only one value observed in 21 (100.0%) subpopulations. |

Model Summaryj | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson | 1 | .000a | .000 | -.053 | .475 | | 2 | .043b | .002 | -.109 | .488 | | 3 | .304c | .092 | -.068 | .478 | | 4 | .305d | .093 | -.134 | .493 | | 5 | .342e | .117 | -.178 | .502 | | 6 | .370f | .137 | -.233 | .514 | | 7 | .401g | .160 | -.292 | .526 | | 8 | .467h | .218 | -.303 | .528 | | 9 | .468i | .219 | -.420 | .552 | 1.127 | a. Predictors: (Constant), income | b. Predictors: (Constant), income , Gender | c. Predictors: (Constant), income , Gender, age | d. Predictors: (Constant), income , Gender, age, education | e. Predictors: (Constant), income , Gender, age, education, belonging | f. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected | g. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation | h. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation, selfrespect | i. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation, selfrespect, selffulfilment | j. Dependent Variable: Cluster Number of Case |

Variables Entered/Removeda | Model | Variables Entered | Variables Removed | Method | 1 | incomeb | . | Enter | 2 | Genderb | . | Enter | 3 | ageb | . | Enter | 4 | educationb | . | Enter | 5 | belongingb | . | Enter | 6 | wellrespectedb | . | Enter | 7 | relationb | . | Enter | 8 | selfrespectb | . | Enter | 9 | selffulfilmentb | . | Enter | a. Dependent Variable: Cluster Number of Case | b. All requested variables entered. |

ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | .000 | 1 | .000 | .000 | 1.000b | | Residual | 4.286 | 19 | .226 | | | | Total | 4.286 | 20 | | | | 2 | Regression | .008 | 2 | .004 | .017 | .984c | | Residual | 4.278 | 18 | .238 | | | | Total | 4.286 | 20 | | | | 3 | Regression | .396 | 3 | .132 | .577 | .638d | | Residual | 3.890 | 17 | .229 | | | | Total | 4.286 | 20 | | | | 4 | Regression | .399 | 4 | .100 | .410 | .799e | | Residual | 3.887 | 16 | .243 | | | | Total | 4.286 | 20 | | | | 5 | Regression | .500 | 5 | .100 | .397 | .844f | | Residual | 3.785 | 15 | .252 | | | | Total | 4.286 | 20 | | | | 6 | Regression | .586 | 6 | .098 | .370 | .886g | | Residual | 3.700 | 14 | .264 | | | | Total | 4.286 | 20 | | | | 7 | Regression | .688 | 7 | .098 | .355 | .913h | | Residual | 3.598 | 13 | .277 | | | | Total | 4.286 | 20 | | | | 8 | Regression | .936 | 8 | .117 | .419 | .888i | | Residual | 3.350 | 12 | .279 | | | | Total | 4.286 | 20 | | | | 9 | Regression | .938 | 9 | .104 | .342 | .941j | | Residual | 3.348 | 11 | .304 | | | | Total | 4.286 | 20 | | | | a. Dependent Variable: Cluster Number of Case | b. Predictors: (Constant), income | c. Predictors: (Constant), income , Gender | d. Predictors: (Constant), income , Gender, age | e. Predictors: (Constant), income , Gender, age, education | f. Predictors: (Constant), income , Gender, age, education, belonging | g. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected | h. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation | i. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation, selfrespect | j. Predictors: (Constant), income , Gender, age, education, belonging, wellrespected, relation, selfrespect, selffulfilment |

Variables Entered/Removeda | Model | Variables Entered | Variables Removed | Method | 1 | accomplishmentb | . | Enter | 2 | securityb | . | Enter | 3 | funb | . | Enter | 4 | locusofcontrolb | . | Enter | 5 | temporal orientationb | . | Enter | a. Dependent Variable: Cluster Number of Case | b. All requested variables entered. |

Model Summaryf | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson | 1 | .165a | .027 | -.024 | .468 | | 2 | .261b | .068 | -.035 | .471 | | 3 | .591c | .350 | .235 | .405 | | 4 | .613d | .376 | .220 | .409 | | 5 | .633e | .400 | .201 | .414 | 1.490 | a. Predictors: (Constant), accomplishment | b. Predictors: (Constant), accomplishment, security | c. Predictors: (Constant), accomplishment, security, fun | d. Predictors: (Constant), accomplishment, security, fun, locusofcontrol | e. Predictors: (Constant), accomplishment, security, fun, locusofcontrol, temporal orientation | f. Dependent Variable: Cluster Number of Case |

ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | .117 | 1 | .117 | .533 | .474b | | Residual | 4.169 | 19 | .219 | | | | Total | 4.286 | 20 | | | | 2 | Regression | .292 | 2 | .146 | .657 | .530c | | Residual | 3.994 | 18 | .222 | | | | Total | 4.286 | 20 | | | | 3 | Regression | 1.499 | 3 | .500 | 3.048 | .057d | | Residual | 2.787 | 17 | .164 | | | | Total | 4.286 | 20 | | | | 4 | Regression | 1.613 | 4 | .403 | 2.413 | .092e | | Residual | 2.673 | 16 | .167 | | | | Total | 4.286 | 20 | | | | 5 | Regression | 1.716 | 5 | .343 | 2.004 | .136f | | Residual | 2.569 | 15 | .171 | | | | Total | 4.286 | 20 | | | | a. Dependent Variable: Cluster Number of Case | b. Predictors: (Constant), accomplishment | c. Predictors: (Constant), accomplishment, security | d. Predictors: (Constant), accomplishment, security, fun | e. Predictors: (Constant), accomplishment, security, fun, locusofcontrol | f. Predictors: (Constant), accomplishment, security, fun, locusofcontrol, temporal orientation |