Individuals’ Motivation for Joining a Social Group: Examining Their Homogeneity

Abstract

Human, being a social animal, has desired to be part of a social group. The present study explores motives/reasons of an individual for joining a social group/club through surveys conducted at various cities of India. The result of data analysis suggests that individual desired to join social groups/clubs for self-development, social service, extraversion, social voracity, self and social gratification, and for emotional support. Furthermore, convergence in individuals was also explored on the basis of their responses. From the analysis, three clusters emerged, namely, inquisitive individuals, extrovert individuals and voracious individuals. The study also discusses the implications of the outcome for founders/managers/marketers of groups and researchers.

Keywords

Individuals social groups motivation cluster analysis homogeneity

Introduction

The very essence of human existence is a group. A group is a number of people or things that are located, gathered or classed together. Groups are created to reinforce the accomplishments of human society (Hogg, Hohman, & Rivera, 2008). There is a large variety of groups in existence. These groups are formed on the basis of society, ability, gender, population, faith, political ideology, occupation, family, hobbies, etc. Groups are created by individuals to synchronise their actions and achieve goals which cannot be accomplished alone (Hogg et al., 2008). These groups have a great impact on individual’s lives. Association with groups develops our senses and enhances self-awareness. Membership with groups regulates the behaviour of the individuals. Every action taken by an individual is associated with the group (Levine, Moreland, & Hausman, 2005). When distanced from a group, an individual feels threatened for existence (Williams, 2001). Buss (1999) mentioned that motivation of being affiliated with a group is deeply rooted in the psychology of an individual. Campbell (1958) mentioned that groups have been an important vehicle for achieving individual objectives throughout human history. Psychologists in the past have described a large number of rudimentary, frequently overlapping, human motivations to be part of the groups (Geen, 1995). Hogg et al. (2008) stated that pleasure and pain, life and death, and needing to have certain knowledge about the world and an individual’s place inside it are considered relevant for group membership.

Attempts have been made to identify the motives that influence an individual’s decision for joining a group. Aronson and Mills (1959) stated that people who have went under severe initiation for a task, are more inclined towards joining that particular group. Individual also joined group (unions) to be part of the recruitment process and increase their chance for employment (Waddington & Whitston, 1997). Berry (2015) mentioned that people with politically aligned motives formed and worked together as a group. The studies have been more specific in nature and addressed preferences towards a type of group only. The present study attempts to empirically identify the reasons which propel an individual to join a group. The present study fills this gap and provides insights to the present stakeholders by providing a comprehensive understanding of motivation of an individual behind joining a group.

Literature Review

Hogg et al. (2008) compared three modern social psychological theories which emphasised on the reason why people join and identify with groups. They examined the sociometer model, the terror management theory and the uncertainty–identity theory. They also critically analysed motivations to join groups. As per the authors, selfuncertainty reduction seemed to be a very powerful motive for identifying and joining the groups.

Waddington and Whitston (1997) in their empirical study examined the reasons which propelled people to join unions in Britain. The reasons cited by the people were classified into two broad categories: collective reasons and individual benefits. The results of the study indicated that collectives reasons were dominant than individual benefits received by them. Total six reasons were mentioned by the people which motivated them to join unions. The authors also found that the membership of unions declined when power shifted to the employers.

Zheleva and Getoor (2009) in their empirical examination found that people refrained themselves from becoming members of groups on social media due to the fear of losing their privacy. The main reason cited by the authors in the article which prompted people to join groups on social media was to have friendship with the people having like-mindedness.

Berry (2015) stated that people create and become members of group for ‘collective good’. The study undertaken by the author was qualitative in nature and was focused towards identifying the reasons why people joined or were lobbied to join groups having public interest. The author also discussed origins and development, resources, patterns of recruitment, decision-making processes and lobbying tactics by the public interest groups.

Klandermans (2001) identified the problem in Olson’s (1965) logic of collective action that individuals take their judgements in isolation, they do not consult with people they are working together or with whom they have to keep their promises. The author extended the views of Olson (1965) and found why social movements take place and also why people join them. As per the author, people join social movements as they are aggrieved, have resources to mobilise, want to seize political opportunity and want to create identity.

Rothenberg (1989) deployed choice-based probability methods to combine data from the 1980 ‘National Election Study’ along with analogous data on ‘Common Cause members’. The results of the study revealed that political interest and policy preferences are considered important for membership choice by the individuals. The authors stated that citizens who are politically interested and have preferences that coarsely match an organisation’s repute join as members. An important finding in the study was that the ability to pay along with the cost of communication was considered irrelevant, irrespective of education and high incomes of members.

Backstrom, Huttenlocher, Kleinberg and Lan (2006) explored the ideologies by which groups develop and evolve in large-scale social networks. In the empirical study, authors found that the probability of individuals joining is not dependent only on the number of friends in the group but also on the ways in which they are connected to one another. They found that the exchange of desired information was also a reason which prompted individuals to join a group along with trusted friends already present in the group.

Broderick, Caswell, Gregory, Marzolini and Wilson (2002) explored the reasons cited by young people suffering from Asperger syndrome for joining youth social groups. The research indicated that young people suffering from Asperger syndrome joined youth social groups for learning social skills. The results exhibited that young people maintained excellent attendance at social skills groups. Interestingly, these young people also visited the youth social groups to make new friends.

Skarlicki, Lucas, Prociuk and Latham (2000) conducted an exploratory research on psychologists who are members of the Canadian Psychological Association (CPA), Canada. The objective of the research was to develop understanding reasons of why psychologists joined, remained in or left CPA. After using factor analysis, survey items converged into five factors: outcomes, advocacy, organisational justice, annual convention and professional recognition. Remarkably, it was found that present members differed from want to be members in opinion to join CPA.

Dein and Barlow (1999) examined the reasons why people joined religious groups (ISKCON) promoting Hare Krishna Movement. The authors in their conceptual study stated that people joined this religious movement to obtain what others possessed and which they believe they should have, too (theory of deprivation). The authors argued that people who became part of these religious movements wanted to experience a feeling of deprivation.

Finholt and Sproull (1990) proposed a framework which could be used for analysing groups, formed by electronic distribution lists. The authors also examined how computer-based communication technology, specifically electronic group mail, might affect group behaviour in organisations. The authors suggested formation of electronic groups at a workplace as a new social phenomenon which also contributes prominently to organisational behaviour.

Turner (1982) asserted that an individual’s desire to become a member of a group was dependent upon self-concept and social behaviour. The author in the empirical study assumed that individuals were motivated to become the member of group on the basis of their shared self-stereotyping to make their membership seem relevant. The results of the study also indicated that these groups became a vehicle of social influence for an individual.

Lott and Lott (1965) in their empirical examination studied cohesiveness of small groups and defined it in terms of intermember attraction. The authors proposed that group membership is attributed to interpersonal attraction among the group members. This article examines how liking between persons developed and what were the effects of liking which one has upon behaviour was also examined.

Rotolo (2000) in an interesting empirical study proposed that individuals differ in their motivation for joining social groups as per the stages of life from which they pass through. Separate models for men and women were proposed and additional analysis was done on joining and leaving rates by group types. The results of the study suggested that joining and leaving rates among genders are effected by marriage or parenthood on the joining and leaving a group.

Lai, Doong and Yang (2006) in an experimental study analysed the impact of consumer’s intention to join group-buying transactions after using the transaction efficacy. The authors found that individuals joined various consumer groups on perception of price benefits which they are receiving due to negotiation power of the group. The consumer’s perception of transaction utility was strongly correlated with intention to join group for buying.

Jaloba (2009) in a qualitative study, examined the reasons contributing to the joining of online discussion forum on breast cancer by individuals. The results indicated that individuals joined online forum due to lack of offline discussion forums. The article also highlighted the various stages on which the individuals participated in discussion were in key stages of their cancer treatment/journey and they visited these forums to get information, support and companionship.

Gottlieb (1982) surveyed members of 18 self-help groups in Canada and identified the various reasons which motivated individuals to join a social group. Three type of individuals seeking benefits by joining the groups were found by researcher: (a) those who were dealing with loss-transition, (b) those with members one step removed (OSR) from the problem and (c) those who joined groups for decreasing stress, coping and support (SC&S). The author concluded that mutual help becomes a critical source of support and education for individuals who joined social groups.

Walter (1992) examined the researches done in the past and analysed the motives of an individual for joining a musical choir in a church. The results of the study indicated that the Christians who joined and sang in choirs perceived that they are distant from their religious writings and they wanted to exhibit their faith towards their religion and considered choirs singing religious pieces of music socially and culturally important.

Seidman, London and Karsh (1951) investigated the reasons which prompted workers to join labour unions. The sample was collected from 14,000 workers employed in steel mills in the USA. The reasons due to which workers formed labour unions were (a) pressure of informal groups and (b) dues of inspection line. Based upon feedback given by workers, it is argued that workers joined these unions as they sought that they also could create pressure on mills’ management and could avail benefits. Also, at last, the authors compiled various reasons citing non-joining of workers in union.

Research Methodology

Sample

A close-ended, self-administered questionnaire was utilised to record participant responses. Responses were obtained from individuals who wanted to be members of various social groups from four cities in Madhya Pradesh, a state in India, namely, Bhopal, Indore, Gwalior and Jabalpur. The study period was 2 months, that is, from October 2016 to December 2016. The list containing contact details of individuals who inquired for the membership of the social group was obtained. Overall, 1,500 prospective members were randomly selected and contacted. Out of which 441 agreed to be part of the survey. A questionnaire was sent on their email addresses and 350 questionnaires were duly filled. Fourteen responses were not considered, as data were not fully recorded. Twenty more responses were excluded as they were considered outliers, making them sensitive for further analysis. A total of 316 responses were utilised for the study, with a response rate of 71.65 per cent. Babbie (1990) stated that the response rate over 70 per cent is considered good. Thus, data could be utilised further for analysis.

Development of Instrument

First, reasons why individuals joined social groups were collected by extensive review of literature (Berry, 2015; Dein & Barlow, 1999; Hogg et al., 2008; A. J. Lott & B. E. Lott, 1965). Focus group discussions were conducted for the purpose. In all, 20 attributes were selected to collect the responses of consumers. These reasons were utilised to develop a self-administered, close-ended questionnaire consisting of a five-point importance scale using ‘extremely important’ at one end of the range and ‘most unimportant’ at the other.

Data Analysis

The demographic profile of the respondents was described. The reliability of the data was calculated using Cronbach’s Alpha Reliability Coefficient. The importance to the reasons given by the respondent was subjected to factor analysis with Varimax rotations to reduce potential multicollinearity among the items. The dimensions as identified by the factor analysis were used cluster analysis instead of the original ratings on variables. The identified cluster structure was then subjected to analysis of variance (ANOVA) and discriminant analysis to double-check in part the classification reliability.

Out of a total of 316 respondents, 163 respondents (51.6%) were men and 153 respondents (48.4%) were women. Around 177 respondents (56%) were aged between 18 years and 23 years, and 47 respondents (14.9%) aged between 24 years and 29 years. Around 14 respondents (4.4%) were aged between 30 years and 35 years. Twenty-nine respondents (9.2%) were aged between 36 years and 41 years. Out of the total respondents, 134 respondents (42.4%) were students, 22 respondents (7%) were self-employed, 23 (7.3%) were professionals, 63 (19.9%) were in private service, 29 (9.2%) were in government service and 45 (14.2%) were retired (refer to Table 1).

Table 1.

Demographic Profile of Respondents

Characteristics	Frequency	Percentage
Gender
Men	163	51.6
Women	153	48.4
Age
18–23 Years	177	56.0
24–29 Years	47	14.9
30–35 Years	14	4.4
36–41 Years	29	9.2
Profession
Student	134	42.4
Self-employed	22	7.0
Professional	23	7.3
Private Service	63	19.9
Government Service	29	9.2
Retired	45	14.2

Source: Compiled by the authors.

The reliability of the data set was calculated using Cronbach’s Alpha Coefficient. As per Nunnally (2010), a value greater than 0.7 is good and data set can be further used for analysis. The value of α = 0.791, which is far much greater 0.7. Thus, the data were found suitable for further analysis (refer to Table 2).

Table 2.

Reliability Analysis

Cronbach’s Alpha	N of Items
0.791	20

Source: Computed by the authors.

Step 1. Factor Analysis

The size of the sample met the already established standards as it was greater than 100 (Hair et al., 2009). Kaiser–Meyer–Olkin (KMO) was utilised for checking sample adequacy and the results indicated that the value of KMO was meritorious (0.781). The Bartlett test of sphericity valued at χ2 = 1266.842, significant at 0.000 level, indicating that the correlation matrix is not an identity matrix and the data set could be used for factor analysis (refer to Table 3).

Table 3.

KMO and Bartlett’s Test

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.781
Bartlett’s Test of Sphericity	Approx. Chi-square	1,266.842
	Df	190
	Sig.	0.000

Source: Computed by the authors.

The rotation converged in eight iterations. Twenty items were converged by Varimax rotation into six orthogonal factors. All the factors having Eigen values greater than or equal to one were taken in to consideration. The factors accounted for an overall of 55.405% variance. Total 17 items converged in to six factors which were heavily loaded (loading ≥ 0.5).

The first factor was labelled as ‘for self-development’. The factor comprised of five items. All the retained factors were labelled in accordance with their combined characteristics. The Eigen value of the factor is 4.204. The factor explained 14.774 per cent variance. The second factor was labelled as ‘for social service’. The factor comprised of two items. The Eigen value of the factor is 1.964. The factor explained 11.096 per cent variance. The third factor was labelled as ‘for extraversion’. The factor comprised of three items. The Eigen value of the factor is 1.477. The factor explained 8.942 per cent variance. The fourth factor was labelled as ‘for social voracity’. The factor comprised of three items. The Eigen value of the factor is 1.477. The factor explained 8.942 per cent variance. The fifth factor was labelled as ‘for self and social gratification’. The factor comprised of three items. The Eigen value of the factor is 1.147. The factor explained 6.838 per cent variance. The sixth factor was labelled as ‘for emotional support’. The factor comprised of two items. The Eigen value of the factor is 1.023. The factor explained 5.919 per cent variance (refer to Table 4).

Table 4.

Factor Analysis

Factors	Loading	Communality	Mean	Eigen Value	Variance	Cumulative Variance
Factor 1: For Self Development				4.204	14.773	14.773
Personal Growth & Development	0.795	0.489	3.92
Leadership Development	0.767	0.676	3.53
For Job Opportunities	0.607	0.628	4.01
For Meeting Experts	0.579	0.396	3.53
Develop Confidence	0.511	0.591	4.24
Factor 2: For Social Service				1.964	11.056	25.829
Social Services	0.708	0.51	3.67
Social Welfare	0.672	0.607	3.89
Factor 3: For Extraversion				1.477	8.942	34.771
Friendship	0.734	0.557	4.08
Entertainment	0.669	0.598	4.18
For Fun Activities	0.663	0.627	3.97
Factor 4: For Social Voracity				1.267	7.877	42.649
Status Quo	0.741	0.525	3.60
To Avail Privileges	0.692	0.59	3.53
To Become Famous	0.5	0.676	3.47
Factor 5: For Self & Social Gratification				1.147	6.838	49.486
Opportunity to Interact	0.719	0.565	4.07
For Self-satisfaction	0.561	0.665	4.03
Factor 6: For Emotional Support				1.023	5.919	55.405
For Getaways	0.716	0.543	3.70
To Avoid loneliness	0.575	0.61	3.52

Source: Computed by the authors.

Note: a Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalisation. Rotation converged in eight iterations.

Step 2. Cluster Analysis

The complete sample collected was clustered using a hybrid approach which was a combination of hierarchical and non-hierarchical methods. For the purpose of clustering all of the outliers were deleted from the data (n = 20) to make it usable for cluster analysis. Based on the guidelines for the identification of clusters in the literature, a visual inspection was carried out of the Horizontal Icicle dendrogram (on the computer printout) and the sudden jumps in the algorithm schedule were pointed out. This initial analysis indicated three- to five-cluster solutions. Subsequently, k-mean clustering procedure was used to develop three-, four- and five-cluster solutions based on the seed points suggested by the earlier hierarchical clustering.

The k-mean cluster analysis resulted in 316 respondents being grouped into three clusters that are labelled as ‘inquisitive individuals,’ ‘extrovert individuals’ and ‘voracious individuals’ (refer to Table 5). ANOVA tests also indicated that all six factors aggregating restaurant preferences make a significant contribution to differentiate the three clusters (p < .000). The sample included 90 inquisitive individuals, 101 extrovert individuals and 125 voracious individuals. Table 6 presents the profile of the three clusters with factor scores of culinary preferences and significant level of ANOVA tests.

Cluster 1: Inquisitive Individuals. As in the factor analysis, each cluster was also labelled in accordance with the characteristics of its composites. This cluster, contains 28.49 per cent of the total sample (90 out of 316) and was the smallest cluster. Mean scores (Table 5) suggest that individuals belonging to this cluster joined groups for personal growth and development, leadership development, job opportunities, meeting experts and developing confidence. Generally, they want to be less involved in social service and interactive with other people, rather want to join groups for self-development. To recapitulate, these clusters are interested in joining a social group, but are not interested in social service, socialising and interaction.

Table 5.

Cluster Analysis

	Factor 1: For Self Development	Factor 2: For Social Service	Factor 3: For Extraversion	Factor 4: For Social Voracity	Factor 5: For Self & Social Gratification	Factor 6: For Emotional Support
Cluster 1[90]: Inquisitive Individuals	0.1306	–0.1472	–1.1982	–0.0220	–0.1256	–0.1833
Cluster 2[101]: Extrovert Individuals	–0.1140	–0.4473	0.5690	–0.7783	–0.2601	0.0729
Cluster 3[125]: Voracious Individuals	–0.0192	0.4675	0.4030	0.6447	0.3007	0.0730

Source: Computed by the authors.

Cluster 2: Extrovert Individuals. This cluster contains 31.96 per cent respondents, out of the total sample (101 out of 316). Mean scores (Table 5) suggest that individuals belonging to this cluster joined groups for extraversion. Their objective is to make new friends, to entertain themselves and to have fun activities. Generally, they are not focused towards self-development or social service, rather they want to enjoy company of others and make new friends.

Cluster 3: Voracious Individuals. This cluster comprised of 39.55 per cent respondents, out of the total sample (125 out of 316). This is the largest cluster. Mean scores (refer to Table 5) suggest that individuals belonging to this cluster desired to join the group for social service, social voracity, self and social gratification, and emotional support. Generally, they want to do social service and welfare along with maintaining status quo, to avail privileges and to become famous, to get the opportunity to interact, for self-satisfaction, for getaways and to avoid loneliness.

Step 3. ANOVA

One-way ANOVA was conducted in order to test whether significant differences in motives of an individual to join social groups exist across segments using the three clusters as independent variables and the six factors as dependent variables. There were significant differences observed in motives of individuals desiring to join social groups.

There were statistically significant differences observed between the mean of the clusters. As one-way ANOVA was used to determine the difference in mean scores: (a) for self-development (F (2,313) = 37.592, p = .000); (2) for social service (F (2,313) = 140.281, p = .000); (3) for extraversion (F (2,313) = 44.670, p = .000); (4) for social voracity (F (2,313) = 15.687, p = .000); (5) for self and social gratification (F (2,313) = 15.041, p = .000) and (6) for emotional support (F (2,313) = 25.891, p = .000) (refer to Table 6).

Table 6.

ANOVA

		Sum of Squares	df	Mean Square	F	Sig.
For Self-development	Between Groups	148.893	2	74.446	140.281	0.000
	Within Groups	166.107	313	0.531
For Social Service	Between Groups	61.010	2	30.505	37.592	0.000
	Within Groups	253.990	313	0.811
For Extraversion	Between Groups	69.946	2	34.973	44.670	0.000
	Within Groups	245.054	313	0.783
For Social Voracity	Between Groups	28.698	2	14.349	15.687	0.000
	Within Groups	286.302	313	0.915
For Self & Social Gratification	Between Groups	27.620	2	13.810	15.041	0.000
	Within Groups	287.380	313	0.918
For Emotional Support	Between Groups	44.715	2	22.357	25.891	0.000
	Within Groups	270.285	313	0.864

Source: Computed by the authors.

Step 4. Discriminant Analysis

It was important to double-check the structure of the cluster. For the mentioned purpose, ‘discriminant analysis’ was employed. First, the test of equality of group covariance matrices using Box’s M (Box’s M = 129.941, F = 3.001 with df = 42, 183584.180, p = .000) indicated that the covariance was equal (refer to Table 7).

Table 7.

Box’s M

Test Results
Box’s M		129.941
F	Approx.	3.001
	df1	42
	df2	183,584.180
	Sig.	0.000
Tests null hypothesis of equal population covariance matrices.

Source: Computed by the authors.

As the significance value of Wilks’ Lambda test (which tests the hypothesis that the means of the functions listed are equal across groups) is less than 0.05, it indicates that the groups differ, and, therefore, the function is a significant discriminator (refer to Table 8). This is clearly the case in this study (p < .000), and one can conclude that both the functions are valid discriminators. The three clusters generated in the study are significantly different from each other.

Table 8.

Discriminant Analysis

Wilks’ Lambda
Test of Function(s)	Wilks’ Lambda	Chi-square	df	Sig.
1 through 2	0.152	585.394	12	0.000
2	0.463	239.011	5	0.000

Source: Computed by the authors.

The effectiveness of the discriminant function was decided by its rate of correct classification of original variables among the clusters. To this effect, it can be observed that the discriminant function has been able to classify 94 per cent of original variables into the predefined groups. The high level of accurate classification suggests the robustness of the discriminant function (refer to Table 9). The hit ratio was used to examine the predictive accuracy of discriminant analysis. It is obtained from classification results. The standards of comparison for the hit ratio for unequal groups sizes was calculated using proportion chance criterion. This criterion is deemed suitable by various authors. The formula for the criterion is CPRO= p2 + (1 – p)2 (Hair et al., 2006).

In case of more than two groups, the criterion can be calculated individually for each group (Hair et al., 2006). In all the cases, the predicted group membership of all the groups was higher than the derived standard for comparison for the hit ratio. Thus, it indicated that majority of the members of the groups were classified correctly (refer to Table 9).

Table 9.

Classification Resultsa

		Cluster	Predicted Group Membership			Total
			1.00	2.00	3.00
Original	Count	1.00	77	4	5	86
		2.00	7	70	2	79
		3.00	1	0	150	151
	%	1.00	89.5	4.7	5.8	100.0
		2.00	8.9	88.6	2.5	100.0
		3.00	0.7	0.0	99.3	100.0

Source: Computed by the authors.

Note: a94.0 per cent of original grouped cases correctly classified.

Discussion and Implications

This study examines the reasons which motivated individuals to join social groups in India. The 20 reasons were converged into six latent dimensions. Next, the evolved factor structure was utilised to determine proximity/distance of individuals’ reasons which motivated them to join social groups. The exercise was conducted on individuals who were either members or who desired to be members of various social groups. On the basis of learnings established from the evolved factor structure, the marketing efforts of social groups could be reduced. The origins of the study lie in backdrop of the absence of substantial studies of similar nature in Indian context.

The reasons of individuals for joining social groups are measured on a ‘20-item scale’. The items converged into six broad dimensions that are explained by 17 variables. These dimensions are self-development, social service, extraversion, social voracity, self and social gratification, and emotional support. The study identifies self-development as a primary motive of an individual for joining a social group. These factors offer a preview of reasons motivating an individual to join social groups in India. The study further contributes by suggesting the heterogeneity in individuals’ responses in respect of evolved dimensions. Their classification into ‘inquisitive individuals’, ‘extrovert individuals’ and ‘voracious individuals’ based on the objectives they seek to attain, offers marketers of these social groups a better insight into the assistances sought by each group that can help them calibrate their marketing efforts. The largest group of individuals consists of voracious individuals, in addition being voracious, they also want to do social service. It would be difficult for the managers of groups to offer them activities in which they could perform social service but also could avail benefits for themselves.

Moreover, a simple classification scheme of individuals with the desire to join or have joined social groups in India indicates that increase in the disposable income and continuous exposure to social media has attracted individuals to join social groups formed for specific purposes. The outcome of the present study could be used by managers, founders and members of various social groups for developing a strategy for attracting new members and positioning their group/club accordingly.

Future Research

This study provides and creates a substance for the researches on reasons motivating individuals to join social group and identification of their distinctive segments. The evolved segments could be augmented by supplementary research, which complements the psychographic details of individuals. Finally, the analysis of individuals’ motivation to join social groups in this study has reinforced the structuring of the hypotheses for detailed investigation, how these motivations play an important role in joining social group. Individuals’ motivation to join social clubs can also be studied from the point of view of intrinsic and extrinsic cues triggering the selection of social group that would result in focused marketing efforts by these groups. This study provides a foundation for future research in the profit/non-profit services sector of the Indian economy and social groups/clubs.

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