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The impact of demographics on the membership level of member-owned private clubs
Tourism Management Perspectives 16 (2015) 51–57
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Tourism Management Perspectives journal homepage: www.elsevier.com/locate/tmp
The impact of demographics on the membership level of member-owned private clubs Leonard A. Jackson a,⁎, Clayton W. Barrows b, Raymond R. Ferreira c a b c
J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, Suite 216, Atlanta, GA 30303, USA Peter T. Paul College of Business and Economics, University of New Hampshire, 10 Garrison Avenue, Durham, NH 03824, USA Ferreira Company, P.O. Box 874, Atlanta, GA 30301, USA
a r t i c l e
i n f o
Article history: Received 21 June 2015 Accepted 12 July 2015 Available online xxxx Keywords: Private clubs Demographics Private club performance Club memberships Location
a b s t r a c t This study examined the impact of demographics on the membership level of member owned private clubs. Specifically, the study analyzed the effect of demographic variables (number of: residents; owner occupied households; properties of over $300,000 in value; households with over $100,000 income; businesses; CEOs/executives/ professionals) within a ten mile radius of sampled clubs. Findings indicate that demographic variables had an influence on the number of full-privilege members at clubs, total initiation fees collected, average member spending amount, and total revenue. Private clubs are encouraged to carefully and continuously assess the demographic profile of their locale, and develop strategies to leverage its demographics. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Demographics play an important role in predicting customer expectations and demand for goods and services. In fact, there is increasing sentiment and a resounding call by leading club executives for private clubs to better understand the demographics of the locales in which they operate. An understanding of demographics will allow clubs to develop better positioning and pricing strategies as well as lead to a better understanding of their member categories (Carpenter and Miller, 2014). For several clubs, usage of this type of information to aid in strategic and operational decisions making is simply a matter of survival in an increasingly competitive and evolving industry. Demographic characteristics have been associated with consumers' needs, wants, preferences, usage rates, consumption and purchasing habits (Lamb et al., 2013; Kotler, 2000). As such, understanding of customer demographics remains a central tenet of businesses in their ongoing quest to solicit, secure and keep customers. In conjunction, the location of a business relative to consumer segments possessing specific demographic characteristics is also critical in its ability to attract, retain and generate profits from these segments. Location, or place as it is referred to in the marketing mix, is also one of the key components in the development of a firm's marketing strategy. Additionally, the location of a business can be a strength, weakness, or have no effect on performance. For most hospitality related businesses, location is often a key ⁎ Corresponding author. E-mail addresses: [email protected] (L.A. Jackson), [email protected] (C.W. Barrows), [email protected] (R.R. Ferreira).
http://dx.doi.org/10.1016/j.tmp.2015.07.005 2211-9736/© 2015 Elsevier Ltd. All rights reserved.
determinant of success. Hence, hospitality business' location could be considered a strength based on several factors including: proximity to a customer's place of business; proximity to a customer's place of residence; convenience to or from one's residence; and the physical attractiveness of the location (Kotler et al., 2014). Equally important for hospitality businesses is the generally accepted notion that typically, customers are only willing to travel short distances to patronize hospitality establishments. Often, if the market is not in close proximity to the hospitality business' physical location, it will not travel or travel as frequently to purchase goods and services. Hence, in selecting a location, most businesses would prefer one that is close to its intended market. However, determining if the market segment a business wants to capture is present in specific locations is often a daunting and difficult procedure. To overcome this challenge, some hospitality businesses have been able to successfully identify their market segments based on demographic descriptors such as: age, gender, profession and income level (Andreasen, 1988). The hospitality industry's reliance on demographic description of its market segments has led several large hospitality organizations to make key decisions on acquisitions and development of properties based on the demographics of the location being considered. For example, some restaurant franchises mandate that locations under consideration for acquisition or development must meet a minimum demographic profile prior to expending resources, financial or otherwise on the project. This typically involves acquiring demographic information within a certain radius of the proposed facility, such as three miles, to determine if there is a strong and sustainable customer base that satisfies the company's market profile. The profile generally includes demographic characteristics such as
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number of households within a certain income level, number of certain type of businesses in the area, number of people working in the area and types of employment and traffic volume past the proposed location. The minimum demographic requirements are often derived from a company's records as well as demographic profiles of its successful versus unsuccessful ventures. These demographic data are placed in the company's model to forecast potential sales for a proposed location (Melanipju and Sexton, 2007). To date, there is paucity of academic studies addressing the private club industry as a whole (Barrows and Ridout, 2010). Furthermore, due to the exclusive nature of member owned private clubs, historically, the topic of marketing is one that many clubs have been reluctant to address. However, in recent years, the member waiting lists and membership levels of several private clubs have dwindled, and these clubs have experienced an associated significant decline in revenues. The decline in customers and revenues have been attributed to several factors including: overbuilding of private clubs (and competing facilities); the poor performance of the economy; companies downsizing management positions; companies moving from downtown areas to suburban regions and; increased competition from other hospitality businesses (Kaspriske, 2003). Collectively, these factors have changed the competitive dynamics of the industry which has evolved from one in which private clubs faced primarily indirect and direct competition to one in which they now face replacement competition in addition to both indirect and direct competition. Furthermore, today's private club demographics continue to evolve (Fjelstul, Jackson and Tesone, 2011) and knowledge about demographics can aid clubs in their production systems (Tesone, Jackson and Fjelstul, 2009). Given the abovementioned factors and the state of the industry, it is imperative that an analysis of the variables that could possibly influence the membership size of private clubs is undertaken. Additionally, given that the literature suggests paucity of studies addressing the effect of demographic on membership levels at private member-owned clubs, it is hoped that the present study will attempt to fill this gap in the literature and understanding by analyzing the effect of demographic variables on membership levels in the private club industry. It is also important that such an analysis is conducted since information on market penetration rates, market share and the impact of demographics on member-owned private club industry is not replete (Ferreira and Gustafson, 2014; Ferreira and Gustafson, 2006a; Ferreira and Gustafson, 2006b; Ferreira, 1998a; Ferreira, 1998b). As such, the present study examines the impact of demographics on private club membership. Specifically, the primary objective of this empirical study is to increase the private club industry's understanding on the influence that demographic variables have on the number of members for private clubs (e.g.: country clubs, city dining clubs, yacht clubs, and athletic clubs). To accomplish this task, data, including demographic information were collected within a ten mile radius of sampled private clubs, located in thirty US cities. The relationship between the dependent measurement (membership number) and the independent measurements (demographic variables and private club type) was explored using regression and other statistical techniques. A total of seven demographic variables were considered important since they were previously reported by Ferreira (1998a, 1998b), to be related to performance levels of private proprietary clubs. 2. Literature review 2.1. Demographics and firm performance The importance of demographics as a predictive tool is well accepted in the service industry (Kassim, 2006). The relationship between demographics and (1) company performance and (2) company location has been explored in the business literature and has focused on multiple industries including community banks, pawnbrokers, title lenders, life insurance agencies, and payday lenders, among others. Clapp et al. (1990), examined the effect of location on profitability of life insurance agencies. The authors found that there were significant differences between
and among metropolitan area locations, however, profitability and growth of the city were positively related while profitability and population of the city were negatively related. DePrince et al. (2011), looked at the effect of state demographics on the performance of community banks, between 1994 and 2008. They looked at a variety of variables including population growth, per capita income growth and the ratio of single family housing starts to the population, among others. Their study produced two models: one based on simple average ROA and the other on a weighted average ROA. Population growth had a positive and powerful effect in the second model. Growth in personal income and housing starts were both found to have significant and positive in the weighted average model as well. Wealth had greater explanatory power in the second model as well. Following the work by Burkey and Simkins (2004) and Wheatley (2010) on payday lenders, Wheatley and Parker (2011) studied the factors affecting location of pawnbrokers and title lenders in Mississippi. The author looked at the number of these two types of companies, rather than their financial performance. The author considered factors relating to income, population, education, income dispersion, household composition and race. The log of the population, rising poverty rates of households headed by younger persons, education (some college) and percent of population that it Native American were all found to be statistically significant in predicting the number of title lenders in an area. In predicting the number of pawnbrokers in an area, the results largely supported the hypotheses, but fewer variables were statistically significant. Overall, studies examining the relationships between demographics and location and profitability, often found strong (and significant) relationships, suggesting that a variety of industries/companies are the beneficiaries of favorable demographics. Club specific studies, discussed below, are far fewer in number. 2.2. Demographics and club location There is paucity of research about the causal relationships between demographics, performance, and location with private clubs. Ferreira has written two articles in these areas: one on the location effect on clubs in Atlanta (Ferreira, 1998a) and one on the demographic effect on private non-equity clubs (1998b). In his study on location effect, Ferreira examined the financial impact that the 1996 summer Olympics had on Atlanta area private clubs. He found that they had a much more profound effect on clubs within the “Olympic ring”, defined as those located within a 1.5 mile radius of the downtown center. Managers of clubs both within and outside this ring were surveyed before and after the event to determine financial performance and effects from the games. Results suggest that clubs located within the designated ring benefitted from increased income, generated in a variety of areas. Clubs outside of the ring, at best, were able to meet financial benchmarks equal to the previous summer, despite actions taken to leverage the summer games. The second article by Ferreira (1998b) examined the effects of demographics (a factor related to location) on the membership size and financial performance of clubs. Ferreira found significant relationships (and effects) between and among several demographic variables and the financial performance of clubs. Most notably, initiation fees and member spending was affected by: the number of households with income levels of over $100,000; the number of properties valued at over $150,000 and; the number of owner occupied households. Ferreira concluded that select demographic variables (including those mentioned earlier) have the ability to explain a significant portion of critical performance measures. 2.3. Membership and marketing in clubs Two related areas, marketing and membership, are gaining increasing attention in the literature. This is a direct result of ongoing economic challenges, shifting demographics and an increasingly competitive environment. With a less favorable economic environment, clubs are
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focusing much more on supply and demand factors, data mining, managing waiting lists, maximizing member usage of facilities and services, and other services aimed at increasing patronage and revenues. Based on the literature, it appears as though strategic marketing is currently driving much of the decision making in clubs. The changing state of waiting list is of concern to clubs. Ferreira and Gustafson (2006a) examined the change in memberships and length of waiting lists, due to the (2001) recession. The purpose of the study was to measure how memberships, attrition, retention, and new memberships changed between 2000 and 2003. Data were collected via an online survey which was sent out to 1000 randomly selected Club Managers Association of America (CMAA) members. The authors noted a decline in memberships and emphasized the need for clubs to increase marketing strategies during economic downturns (Ferreira and Gustafson, 2006a). As a result of the decline in membership, decreased dues and initiation fees, and less revenue received from food and beverage outlets, clubs experienced a significant drop in their revenues. Clemenz et al. (2006) also examined the current state of waiting lists, noting that the number of clubs with waiting lists was diminishing as well as the length of waiting lists. In their study of 1000 randomly selected CMAA members, sixty-nine percent of responding clubs reported that they did not have a waiting list. Those with waiting lists tended to be “over 50 years old, member-owned, tax-exempt, and had 500–1000 members and gross revenues minus initiation deposits of between five and ten million dollars per year” (Clemenz et al., 2006, p. 19). The authors concluded that waiting lists in private clubs are less prevalent today than they have been historically. They recommended that marketing to new members as well as maintaining the current ones are essential if clubs want to remain viable and profitable. Non-American clubs are facing similar challenges as their U.S. counterparts in addressing and catering to the needs of non-traditional market segments. For example, Baker (2006), noted that Australian clubs, like American clubs, are faced with the challenge of attracting, serving and retaining a younger demographic segment. The author noted that one Australian club blamed the “troublesome youth” instead of the actual “contributors to the under-18's product failures” (Baker, 2006 p. 211– 212). Some private clubs have been innovative and have introduced non-traditional products and services to attract and satisfy the needs of newer market segments. For example, two such innovative product that have been introduced at private clubs are live rock shows and disco in hopes of attracting twelve-eighteen year olds as well as the eighteenthirty five year old crowds. Although these examples failed, they provided information to private clubs about how to better target non-traditional demographic segments and the importance of understanding demographics in providing clubs' products and services (Baker, 2006). Another article written by Ferreira and Gustafson (2006b) focused on determining the structures of private clubs, equity versus non-equity, and the advantages or disadvantages of each. Attributes of equity memberships that were examined are as follows: 1) the amount/% of entrance fee that the club receives, minus a transfer fee; 2) the amount/% of the entrance fee the member receives as a refund; 3) whether equity amount is set by the club or by market conditions and the amount the resigning member would accept; 4) overall entrance fee; and 5) number of individuals waiting to buy a membership versus those waiting to resign the membership (Ferreira and Gustafson, 2006b, p. 65). For this study, the authors administered a survey which was distributed to club managers at a Canadian Society of Club Managers meeting in Ontario, Canada. Fiftyseven percent of the eighty-one responses were returned and responses from equity versus non-equity clubs were compared (Ferreira and Gustafson, 2006b). Ferreira and Gustafson reported that non-equity membership clubs were more likely to have a waiting list (74%), and a longer list. Seventy-five percent of equity clubs had more openings for memberships available and a list of members actually waiting to resign, an average of thirty four waiting to leave (Ferreira and Gustafson, 2006b). Overall, newer clubs were those initiating the equity membership structure, believing the target markets of members will want a portion of
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the fees refunded. Those older clubs were more established and exclusive, with no need to extend equity to attract members. Thus, the new equity memberships find themselves at a financial disadvantage. They have waitlists of members wanting to resign and get a refund; lower transfer fee revenues are received; and frequent assessments are required to fund capital improvements (Ferreira and Gustafson, 2006b). The authors suggested that successful managers have unique, high quality experiences to satisfy current members while attracting new, at the same time being fiscally responsible (Ferreira and Gustafson, 2006b). Drew and Kriz (2006) utilized a consumer behavior approach to investigate the wants, needs and expectations of private club members. Five segments of club patrons were reviewed: full members, social members, visitors, Baby Boomers and Generation X'ers. The authors suggested that clubs must remain true to historical traditions while still catering to the evolving market. They also highlighted the fact that an important issue facing private clubs today is that the original purpose for formation may not be as relevant to the growing segments of society, with different wants, needs and expectations (Drew and Kriz, 2006). The also authors argued that clubs provide members with a sense of belonging to specific reference groups sharing values, emotions, perceptions, attitudes, motives and behavior patterns (Drew and Kriz, 2006, p. 201). Thus, understanding the intrinsic nature and interests of these reference groups will help in developing club marketing programs for the club as a whole as well as specific portions. Additional relevant findings uncovered included the conclusion that Generation X'ers are more interested in flexibility and are less likely to commit to clubs which are governed by the Baby Boomers. However, the study asserted that for private clubs, it is necessary to attract and cater to this younger sub-group, even while Baby Boomers are still active members, since retirement and loss of older members through natural causes are certain future prospects for private clubs. The authors concluded that the consumer behavior approach to changing patron expectations is appropriate and that differing needs, wants and expectations are grounded in the areas of demographics, culture, values, reference groups and attitudes (Drew and Kriz, 2006, p. 209). A final article concerning membership and marketing investigates the relationships among perceived value, member satisfaction, switching costs and member loyalty in the country club industry (Back and Lee, 2009). “The study hoped to realize the mediating role of member satisfaction in relationships centered on value-loyalty and image congruenceloyalty as well as the moderating role of switching costs in the member satisfaction-loyalty relationship” (Back and Lee, 2009, p. 528). Back and Lee (2009) designed an instrument to measure the characteristics of the theoretical model. Image congruence was measured with a separate direct question to the respondents, concerning the image of that respondent as compared to other members'. The sample population for the study comprised members at a mid-tier private club in the Western U.S. with six hundred members, and a $4,000 to $8,000 initiation fee, $400 to $600 monthly fee. The survey was distributed to the club members and two hundred sixty one usable responses were received. The moderating effect of the switching costs was tested, as well as the structural model. The authors determined that switching costs seemed to have no extreme moderating effect on the satisfaction or loyalty of members within private clubs. As a result, the authors suggested that managers provide high value to their members to achieve satisfaction and retain these members. Hence, closing the gap between what club members perceive and expect in accordance with high value will enhance member value. Finally, image congruence determined member satisfaction since private clubs have membership requirements such as social status, relating to social and ideal self-images. This creates emotional attachments, sense of belonging and value for the club member (Back and Lee, 2009). The authors concluded that both value and image congruence are significant initiators of club member satisfaction and as a result club member loyalty. The general business literature identified several strong relationships among location, demographics and financial performance. Research in the area of private clubs indicate waning waiting lists, declining
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memberships, importance of loyalty of current members, and the growing importance of marketing and membership development and retention strategies. With the exception of two articles by Ferreira (1998a, 1998b), the relationship between demographic variables, location, and financial performance has not been thoroughly examined in the hospitality literature. As such, the need for a comprehensive (and current) study would help address this gap in the literature. 3. Methodology The major objective of this study is to analyze the effect of demographic variables on private club membership level or size. Specifically, the study attempted to provide answers to the following two questions: 1. Which demographic variables, if any, account for the variance in a nonprofit tax exempt private club's membership size? (These variables were measured within a ten mile radius of the sampled private club.) The demographic variables collected and utilized were the number of: • • • • • • •
Residents Owner occupied households Properties of over $300,000 in value Households with over $100,000 income Businesses CEOs/executives/professionals Individuals with a college degree.:
2. Which demographic variables have any relationship with the following financial performance measurements at private clubs?: • • • • •
Total revenue Total initiation fees Dues income Average member spending Gross operating profit.
The impact of demographics on the financial performance measures was also deemed important since financial performance measures are indicators or proxies of levels of club membership. Furthermore, enterprise financial performance is an indicator of a club's viability, vibrancy, attractiveness and consequently, membership levels. 3.1. Hypotheses The general hypothesis for this study is that the different performance levels for private clubs will be influenced by certain demographic variables within a ten mile radius of the club. Below are six specific directional hypotheses which were developed and tested: H1. Demographic variables will have an influence on the number of fullprivilege members at clubs.
the Club Managers Association of America (CMAA) and have been in operation for five or more years. Participating clubs were located in 30 greater metropolitan areas in the United States as are listed below in Table 1. The demographic information for each of the private club's location was provided by PCensus Analyst (Tetrad). The club managers provided information on the six annual performance measurements for their clubs. The data collected from the private clubs and the demographic variables for each club was entered in a computer data file. The data were analyzed using the IBM SPSS Statistics package. Significant relationships among the performance measurements and the demographic variables were explored through correlation coefficients and stepwise regression analysis.
4. Results and discussion Table 2 displays the means and standard deviations for the six performance variables and seven demographic variables for the 302 private clubs. Table 3 lists the correlation values among the variables. The following statistically significant correlations existed between the dependent (performance) variables and the independent (demographic) variables. Initiation fees significantly correlated with household income levels (.63) and property values (.60). The number of full-privilege members significantly correlated with household income levels (.64), property values (.61), owner occupied households (.60), number of CEO's/executives (.53), and number of businesses (.52). Total revenue was significantly related to owner occupied households (.61) and the number of businesses (.59). Average member spending highly correlated with household income levels (.61) and property values (.60). Dues income only had significant correlation values with other performance variables and not with any demographic variables: gross operating profit (.78), total revenue (.71), number of full-privilege members (.63), and initiation fees (.61). Gross operating profit had significant correlation values only with performance variables as well: dues income (.78), total revenue (.71), number of full-privilege members (.59), and initiation fees (.57). The variance in four of the six private club performance measurements could be explained by the demographic variables, measured within a ten mile radius of the club. The percentage of variance explained by the demographic variables for the following performance measurements: number of full-privilege members, initiation fees, total revenue, and average member spending, ranged between 50 and 58%. In Table 4, the results of a stepwise regression analysis indicated that four of the six dependent variable's variance could be significantly explained by a select number of demographic variables. The variance in the number of full-privilege members club (R2 = .58) was related to the
H2. Demographic variables will have an influence on the total initiation fees collected at clubs. H3. Demographic variables will have an influence on the total dues income at clubs. H4. Demographic variables will have an influence on the amount of member spending at clubs. H5. Demographic variables will have an influence on the total revenue at clubs. H6. Demographic variables will have an influence on the gross operating profits at clubs. 3.2. Data and data collection Data were collected from 302 private clubs (country clubs, city clubs, city-athletic clubs, and yacht clubs) whose managers were members of
Table 1 US metropolitan areas. • • • • • • • • • • • • • • •
Atlanta, GA Austin, TX Baltimore, MD Boston, MA Charlotte, NC Chicago, IL Cincinnati, OH Cleveland, OH Dallas, TX Denver, CO Detroit, MI Houston, TX Jacksonville, FL Las Vegas, NV Los Angeles, CA
• • • • • • • • • • • • • • •
Kansas City, MO Minneapolis, MN New York, NY Philadelphia, PA Pittsburgh, PA Phoenix, AZ Portland, OR San Antonio, TX San Diego, CA San Francisco, CA Seattle, WA St. Louis, MO Tampa, FL San Francisco, CA Washington, DC
L.A. Jackson et al. / Tourism Management Perspectives 16 (2015) 51–57
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one, two, four and five were accepted and hypotheses three and six were rejected.
Table 2 Private club variables. Characteristic
n
Mean
Std. dev.
Performance variables Initiation fees Dues income Number of full-privilege members Total revenue Average member spending Gross operating profits
302 302 302 302 302 302
$333,876.45 $2,507,525.64 932.95 $5,417,531.99 $12,062.40 $1,098,793.24
$307,458.23 $2,211,144.14 804.69 $3,396,261.01 $8,139.59 $855,153.49
Demographic variables within ten mile radius Residential population 302 Owner occupied households 302 Property values 302 Household income levels 302 Number of businesses 302 Number of CEO's/executives 302 Number with college degree 302
1,197,439.96 528,045.13 $152,065.25 $70,283.37 144,739.89 159,253.63 248,603.36
548,809.27 276,289.77 $86,353.97 $50,183.58 76,670.99 88,318.44 113,807.54
number of households having income levels over $100,000, the number of property values over $300,000, the number of owner occupied households, the number of CEOs/executives/professionals, and the number of businesses. The change in initiation fees (R2 = .53) that a private club collected was explained by the number of households having income levels over $100,000 and the number of property values over $300,000. The variance in total revenue (R2 = .52) in a private club could be explained by the number of owner occupied households and the number of businesses. Finally, the average member spending amount (R2 = .50) was related to the number of households having income levels over $100,000 and the number of property values over $300,000, which were the same demographic variables as those related to the initiation fees performance variable. The variance for three of the performance measurements was explained by two demographic variables: the number of households with income over $100,000 and the number of property values over $300,000. The number of businesses within a ten mile radius and the number of owner occupied households influenced two performance measurements, while the number of CEOs/executives/professionals influenced one performance variable. Two of the demographic variables investigated: the number of residents and the number of individuals with a college degree did not significantly affect the variance of any performance variables investigated. Based on these results, hypotheses
5. Summary and conclusions This research had similar findings to the 1998 club study with proprietary clubs (Ferreira, 1998a; Ferreira, 1998b). Other hospitality businesses, especially restaurants, have also found that the demographic makeup of a location has an impact on its business. In this study, two demographic variables had an impact on the financial performance of private clubs. As the number of households with income levels of over $100,000 and the number of properties valued at over $300,000 increased, so did the amount of initiation fees paid by members, as well as the amount spent by members at their club. As the demographic variables that typically measure wealth (income level and residential property value) increased, so did the initiation fees that clubs charged and the average amount that members spent at their club. Total revenue was highest for clubs located in areas that have a large number of businesses and a majority of owner occupied households within ten miles of the club. Typically revenues at clubs (e.g., dues, food and beverage purchases, banquet business, and athletic fees) are affected by the number of businesses in the area and individuals who can afford to own their own home and not rent. This finding was expected since the amount of profitable revenues generated by meetings and banquets is often predicated by businesses that entertain clients or host company parties/events at clubs. Moreover, the number of owner occupied households is another indicator of wealth and this impacts revenue at clubs because typically more home owners will join a club than renters. The number of full-privilege members was highly related to the two demographic wealth indicators mentioned earlier: households with income levels over $100,000 and the number of properties valued over $300,000. Apparently, when there are a large number of households with disposable income living near a private club, there will also be a sizable consortium of candidates for membership that can pay to join and become a member at a club. In addition to proximity to a member's resident, proximity to a member's place of business may be as important, if not more important for some club members. According to Ferreira (1997) many individuals are members at a club that is conveniently located to their residence, their place of business, or both. Club members are employed in professional capacities and typically are CEOs, executives, owners, or managers. The larger the pool of nearby
Table 3 Correlation of private club variables. Variables
Correlation coefficients 1
2
Performance variables 1 Initiation fees 2 Dues income 3 Number of full-privilege members 4 Total revenue 5 Average member spending 6 Gross operating profits
– .61⁎⁎ .30 .58⁎⁎
– .63⁎⁎ .71⁎⁎
.29 .57⁎⁎
−.10 .78⁎⁎
Demographic variables 7 Residential population 8 Owner occupied households 9 Property values 10 Household income levels 11 Number of businesses 12 Number of CEOs executives/mgrs. 13 Number with college degree
.17 .24 .60⁎⁎ .63⁎⁎ .14 −.12 .17
−.04 .27 .18 −.09 .17 −.09 .07
⁎⁎ Significant at .001 level.
3
4
5
6
– .27 −.21 .59⁎⁎
– .20 .71⁎⁎
– .11
–
.12 .61⁎⁎ .33 .34 .59⁎⁎ .24 .11
.13 .38 .60⁎⁎ .61⁎⁎ −.19 .16 .14
−.07 .15 .19 .21 .18 .11 .04
.21 .60⁎⁎ .61⁎⁎ .64⁎⁎ .52⁎⁎ .53⁎⁎ .02
7
8
9
10
11
12
13
– .29 −.10 −.11 .23 .06 −.16
– .26 .35 .26 .14 .05
– .30 .12 −.19 −.09
– .19 .15 .12
– .31 .19
– .40
–
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Table 4 Results from stepwise regression analyses of the relationship among the characteristics of private clubs and the performance variables. Dependent variable
Independent variable
B
Beta
Number of full-privilege members
Household income levels Property values Owner occupied households Number of CEO/executives Number of businesses Constant Household income levels Property values Constant Owner occupied households Number of businesses Constant Household income levels Property values Constant
.0029 .0024 .0023 .0019 .0018 54.13 .0961 .0782 −68.03 .6787 .4006 −32.11 .0361 .0167 −236.78
.86 .52 .34 .33 .29
Initiation fees
Total revenue
Member Spending
.67 .48 .57 .45 .56 .39
F
R square
38.21⁎
.58
31.74⁎
.53
30.58⁎
.52
21.99⁎
.50
⁎ Significant at .01 level.
employed professionals, the greater the number of candidates a club will have for membership (Ferreira, 1995). Driving membership growth is becoming a critical issue for clubs in the aftermath of the recession. Existing clubs are constantly looking for new ways to market their clubs to prospective members. These clubs could use the results of this study as a way to better understand the demographic profile of their communities and to more effectively leverage this information. Clubs under consideration of development could use the same information to identify suitable (or even optimal) locations. The relationship between demographics and company performance and company location has been explored in the business literature (across multiple industries) but deserves more attention in the hospitality literature, and specifically in clubs. Studies continue to show important and significant relationships among these factors. Further, successful marketing is largely based upon adequate knowledge of local demographics. This research project was one of few of its type in the private club industry. Other businesses including those in the hospitality industry (hotels and restaurants) have completed similar market research studies. The information is urgent given the recent economic downturn and highly competitive environment within the private club industry (Woodworth, 2009). Based on the results of this study and the earlier study by Ferreira (1998a); Ferreira (1998b), private clubs need to evaluate the demographic make-up of the zip codes in close proximity to the club to see what impact their location is having on the club's performance. The analysis of the area can help to explain a club's performance, as well as provide information about its membership. Clubs should analyze the demographic makeup of the zip codes within a ten mile radius of the club. Moreover, additional analysis of the change in the demographic variables over the past ten years can be compared to the changes in the club's performance.
5.1. Limitations and future research The findings of this study are limited in their application. The first limitation is that the sample size of 302 private clubs in thirty major cities is small. Another limitation is that all the clubs in the study were member-owned, non-profit private clubs whose managers were members of CMAA and did not include any other member-owned, non-profit private clubs. Because of the small sample size, differences among the types of clubs could not be explored (i.e. the performance of city (dining) clubs may be influenced more by the demographics within a narrower radius of the club (e.g., 1 mile), while golf clubs may be impacted more by the demographics farther out (e.g., 10 miles)).
Future studies should explore different distances from the club's location and other demographic or psychographic variables. The additional variables at different distance lengths may better clarify the variance in a club's performance level. Moreover, future studies should be expanded to include clubs in smaller metropolitan areas to see if the variables that affect clubs in the thirty large size metropolitan areas affect small metropolitan size clubs in the same manner. Another variable that should be explored is the club type to see if there are differences among city clubs, country clubs, golf clubs, yacht clubs, etc.
References Andreasen, A. R. (1988). Cheap but good marketing research. Homewood, IL: Irwin. Back, K. J., & Lee, J. S. (2009). Country club members' perceptions of value, image congruence, and switching costs: An exploratory study of country club members' loyalty. Journal of Hospitality and Tourism Research, 33(4), 528–546. Baker, L. (2006). The trouble with today's young one: A case study of one club's failed attempt at attracting the youth market. International Journal of Hospitality and Tourism Administration, 7(2/3), 211–225. Barrows, C. W., & Ridout, M. (2010). Another decade of research in club management: A review of the literature in academic journals for the period 1994–2005. Journal of Hospitality Management & Marketing, 19(5), 421–463. Burkey, M. L., & Simkins, S. P. (2004). Factors affecting the location of payday lending and traditional banking services in North Carolina. The Review of Regional Studies, 34, 191–205. Carpenter, J., & Miller, E. (2014). A letter to the director, officers, owners and managers of private clubs, 1–4 Available at: http://www.privateclubadvisor.com/_filelib/FileCabinet/ Jackie_Backup/PCA_2014/PCA.11.14_Final.pdf Clapp, J. M., Fields, J. A., & Ghosh, C. (1990). An examination of profitability in spatial markets: The case of life insurance agency locations. The Journal of Risk and Insurance, 57(3), 431–454. Clemenz, C., Kim, S., & Weaver, P. (2006). An exploratory study of waiting lists in private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 19–45. DePrince, A., Ford, W., & Morris, P. (2011). Some causes of interstate differences in community bank performance. Journal of Economics and Finance, 35(1), 22–40. Drew, A., & Kriz, A. (2006). Understanding changing patron expectations of club offerings: A consumer behavioral approach. International Journal of Hospitality and Tourism Administration, 7(2/3), 195–210. Ferreira, R. (1998a). The location effect: How some Atlanta clubs won the Olympic Ring. Cornell Hotel and Restaurant Administration Quarterly, 39(5), 50–58. Ferreira, R., & Gustafson, C. (2006a). Declining memberships during an economic downturn in U.S. private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 3–17. Ferreira, R., & Gustafson, C. (2006b). Select performance differences in equity and nonequity membership structures within private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 63–79. Ferreira, R., & Gustafson, C. (2014). Memberships levels in U. S. private clubs during the 2008–2010 economic downturn. International Journal of Hospitality and Tourism Administration, 15(1), 38–59. Ferreira, R. R. (1995). The relationship of select performance variables among private clubs. Poster presentation at the International Council on Hotel, Restaurant and Institutional Education Conference, Nashville, TN. Ferreira, R. R. (1997). How large is your market for potential members. Proceedings of the Club Managers Association of America's Conference, Orlando, FL (pp. 107–111).
L.A. Jackson et al. / Tourism Management Perspectives 16 (2015) 51–57 Ferreira, R. R. (1998b). The demographic effect on the performance level of private clubs. Journal of Hospitality and Leisure Marketing, 5(4), 23–32. Fjelstul, J., Jackson, L. A., & Tesone, D. (2011). Increasing minority participation through PGA education initiatives. SAGE Open, http://dx.doi.org/10.1177/2158244011416009, http://sgo.sagepub.com, 1-5. Kaspriske, R. (2003). Buyer's market: A sluggish economy has left many private golf clubs desperate for new members. Golf Digest, 54(11), 111. Kassim, N. M. (2006). Telecommunication industry in Malaysia: Demographics effect on customer expectations, performance, satisfaction and retention. Asia Pacific Business Review, 12(4), 437–463. Kotler, P., Bowen, J., & Makens, J. (2014). Marketing for hospitality and tourism (6th ed.). Upper Saddle, NJ: Prentice Hall. Kotler, P. (2000). Marketing management: Millennium edition (10th ed.). Upper Saddle, NJ: Prentice Hall. Lamb, C., Hair, J., & McDaniel, C. (2013). Marketing (12th ed.) South-Western, Mason, Ohio. Melanipju, J., & Sexton, J. (2007). The restaurant location guidebook (1st ed.). Chicago, Illinois: International Real Estate Location Institute, Inc. Tesone, D., Jackson, L. A., & Fjelstul, J. (2009). Charting production systems for golf and club operations. Journal of Retail & Leisure Property, 8(1), 67–76. Wheatley, W., & Parker (2011). Pawnbrokers and title lenders in Mississippi: Economic, regional, and demographic factors affecting their location and placement. Southern Business & Economic Journal, 34(½), 1–23. Wheatley, W. P. (2010). Economic and regional determinants of the location of payday lenders and banking institutions in Mississippi: Reconsidering the role of race and other factors in firm location. The Review of Regional Studies, 40, 53–69. Woodworth, R. M. (2009). The recession is over (maybe)! Cornell Hospitality Quarterly, 50(4), 407–412. Leonard A. Jackson, PhD, teaches graduate and undergraduate classes in the Hospitality Management Department, Robinson College of Business, Georgia State University. His research interests include sustainability and financial performance, hotel REIT performance, business combinations, entrepreneurship and executive transition impact.
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Clayton Barrows is a Professor of Hospitality Management at the University of New Hampshire where he teaches classes in hospitality management with an emphasis on private club management. He has worked in the hospitality industry and hospitality education in the US and Canada for 30 years. Prior to joining the faculty at the University of New Hampshire sin 2006), he was a Professor in the School of Hospitality and Tourism Management at the University of Guelph in Canada. While at Guelph, he also served as the coordinator of the MBA program in Hospitality and Tourism. Professor Barrow's areas of expertise are food and beverage management and private club management.
Dr. Raymond Ferreira, founder and president of the Ferreira Company, is an internationally recognized academic and industry research expert in the private club industry. He worked full-time as a club manager for 12 years, and has taught at Georgia State University's School of Hospitality Administration and University of Houston's Hilton College of Hotel and Restaurant Management.
Contents lists available at ScienceDirect
Tourism Management Perspectives journal homepage: www.elsevier.com/locate/tmp
The impact of demographics on the membership level of member-owned private clubs Leonard A. Jackson a,⁎, Clayton W. Barrows b, Raymond R. Ferreira c a b c
J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, Suite 216, Atlanta, GA 30303, USA Peter T. Paul College of Business and Economics, University of New Hampshire, 10 Garrison Avenue, Durham, NH 03824, USA Ferreira Company, P.O. Box 874, Atlanta, GA 30301, USA
a r t i c l e
i n f o
Article history: Received 21 June 2015 Accepted 12 July 2015 Available online xxxx Keywords: Private clubs Demographics Private club performance Club memberships Location
a b s t r a c t This study examined the impact of demographics on the membership level of member owned private clubs. Specifically, the study analyzed the effect of demographic variables (number of: residents; owner occupied households; properties of over $300,000 in value; households with over $100,000 income; businesses; CEOs/executives/ professionals) within a ten mile radius of sampled clubs. Findings indicate that demographic variables had an influence on the number of full-privilege members at clubs, total initiation fees collected, average member spending amount, and total revenue. Private clubs are encouraged to carefully and continuously assess the demographic profile of their locale, and develop strategies to leverage its demographics. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Demographics play an important role in predicting customer expectations and demand for goods and services. In fact, there is increasing sentiment and a resounding call by leading club executives for private clubs to better understand the demographics of the locales in which they operate. An understanding of demographics will allow clubs to develop better positioning and pricing strategies as well as lead to a better understanding of their member categories (Carpenter and Miller, 2014). For several clubs, usage of this type of information to aid in strategic and operational decisions making is simply a matter of survival in an increasingly competitive and evolving industry. Demographic characteristics have been associated with consumers' needs, wants, preferences, usage rates, consumption and purchasing habits (Lamb et al., 2013; Kotler, 2000). As such, understanding of customer demographics remains a central tenet of businesses in their ongoing quest to solicit, secure and keep customers. In conjunction, the location of a business relative to consumer segments possessing specific demographic characteristics is also critical in its ability to attract, retain and generate profits from these segments. Location, or place as it is referred to in the marketing mix, is also one of the key components in the development of a firm's marketing strategy. Additionally, the location of a business can be a strength, weakness, or have no effect on performance. For most hospitality related businesses, location is often a key ⁎ Corresponding author. E-mail addresses: [email protected] (L.A. Jackson), [email protected] (C.W. Barrows), [email protected] (R.R. Ferreira).
http://dx.doi.org/10.1016/j.tmp.2015.07.005 2211-9736/© 2015 Elsevier Ltd. All rights reserved.
determinant of success. Hence, hospitality business' location could be considered a strength based on several factors including: proximity to a customer's place of business; proximity to a customer's place of residence; convenience to or from one's residence; and the physical attractiveness of the location (Kotler et al., 2014). Equally important for hospitality businesses is the generally accepted notion that typically, customers are only willing to travel short distances to patronize hospitality establishments. Often, if the market is not in close proximity to the hospitality business' physical location, it will not travel or travel as frequently to purchase goods and services. Hence, in selecting a location, most businesses would prefer one that is close to its intended market. However, determining if the market segment a business wants to capture is present in specific locations is often a daunting and difficult procedure. To overcome this challenge, some hospitality businesses have been able to successfully identify their market segments based on demographic descriptors such as: age, gender, profession and income level (Andreasen, 1988). The hospitality industry's reliance on demographic description of its market segments has led several large hospitality organizations to make key decisions on acquisitions and development of properties based on the demographics of the location being considered. For example, some restaurant franchises mandate that locations under consideration for acquisition or development must meet a minimum demographic profile prior to expending resources, financial or otherwise on the project. This typically involves acquiring demographic information within a certain radius of the proposed facility, such as three miles, to determine if there is a strong and sustainable customer base that satisfies the company's market profile. The profile generally includes demographic characteristics such as
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number of households within a certain income level, number of certain type of businesses in the area, number of people working in the area and types of employment and traffic volume past the proposed location. The minimum demographic requirements are often derived from a company's records as well as demographic profiles of its successful versus unsuccessful ventures. These demographic data are placed in the company's model to forecast potential sales for a proposed location (Melanipju and Sexton, 2007). To date, there is paucity of academic studies addressing the private club industry as a whole (Barrows and Ridout, 2010). Furthermore, due to the exclusive nature of member owned private clubs, historically, the topic of marketing is one that many clubs have been reluctant to address. However, in recent years, the member waiting lists and membership levels of several private clubs have dwindled, and these clubs have experienced an associated significant decline in revenues. The decline in customers and revenues have been attributed to several factors including: overbuilding of private clubs (and competing facilities); the poor performance of the economy; companies downsizing management positions; companies moving from downtown areas to suburban regions and; increased competition from other hospitality businesses (Kaspriske, 2003). Collectively, these factors have changed the competitive dynamics of the industry which has evolved from one in which private clubs faced primarily indirect and direct competition to one in which they now face replacement competition in addition to both indirect and direct competition. Furthermore, today's private club demographics continue to evolve (Fjelstul, Jackson and Tesone, 2011) and knowledge about demographics can aid clubs in their production systems (Tesone, Jackson and Fjelstul, 2009). Given the abovementioned factors and the state of the industry, it is imperative that an analysis of the variables that could possibly influence the membership size of private clubs is undertaken. Additionally, given that the literature suggests paucity of studies addressing the effect of demographic on membership levels at private member-owned clubs, it is hoped that the present study will attempt to fill this gap in the literature and understanding by analyzing the effect of demographic variables on membership levels in the private club industry. It is also important that such an analysis is conducted since information on market penetration rates, market share and the impact of demographics on member-owned private club industry is not replete (Ferreira and Gustafson, 2014; Ferreira and Gustafson, 2006a; Ferreira and Gustafson, 2006b; Ferreira, 1998a; Ferreira, 1998b). As such, the present study examines the impact of demographics on private club membership. Specifically, the primary objective of this empirical study is to increase the private club industry's understanding on the influence that demographic variables have on the number of members for private clubs (e.g.: country clubs, city dining clubs, yacht clubs, and athletic clubs). To accomplish this task, data, including demographic information were collected within a ten mile radius of sampled private clubs, located in thirty US cities. The relationship between the dependent measurement (membership number) and the independent measurements (demographic variables and private club type) was explored using regression and other statistical techniques. A total of seven demographic variables were considered important since they were previously reported by Ferreira (1998a, 1998b), to be related to performance levels of private proprietary clubs. 2. Literature review 2.1. Demographics and firm performance The importance of demographics as a predictive tool is well accepted in the service industry (Kassim, 2006). The relationship between demographics and (1) company performance and (2) company location has been explored in the business literature and has focused on multiple industries including community banks, pawnbrokers, title lenders, life insurance agencies, and payday lenders, among others. Clapp et al. (1990), examined the effect of location on profitability of life insurance agencies. The authors found that there were significant differences between
and among metropolitan area locations, however, profitability and growth of the city were positively related while profitability and population of the city were negatively related. DePrince et al. (2011), looked at the effect of state demographics on the performance of community banks, between 1994 and 2008. They looked at a variety of variables including population growth, per capita income growth and the ratio of single family housing starts to the population, among others. Their study produced two models: one based on simple average ROA and the other on a weighted average ROA. Population growth had a positive and powerful effect in the second model. Growth in personal income and housing starts were both found to have significant and positive in the weighted average model as well. Wealth had greater explanatory power in the second model as well. Following the work by Burkey and Simkins (2004) and Wheatley (2010) on payday lenders, Wheatley and Parker (2011) studied the factors affecting location of pawnbrokers and title lenders in Mississippi. The author looked at the number of these two types of companies, rather than their financial performance. The author considered factors relating to income, population, education, income dispersion, household composition and race. The log of the population, rising poverty rates of households headed by younger persons, education (some college) and percent of population that it Native American were all found to be statistically significant in predicting the number of title lenders in an area. In predicting the number of pawnbrokers in an area, the results largely supported the hypotheses, but fewer variables were statistically significant. Overall, studies examining the relationships between demographics and location and profitability, often found strong (and significant) relationships, suggesting that a variety of industries/companies are the beneficiaries of favorable demographics. Club specific studies, discussed below, are far fewer in number. 2.2. Demographics and club location There is paucity of research about the causal relationships between demographics, performance, and location with private clubs. Ferreira has written two articles in these areas: one on the location effect on clubs in Atlanta (Ferreira, 1998a) and one on the demographic effect on private non-equity clubs (1998b). In his study on location effect, Ferreira examined the financial impact that the 1996 summer Olympics had on Atlanta area private clubs. He found that they had a much more profound effect on clubs within the “Olympic ring”, defined as those located within a 1.5 mile radius of the downtown center. Managers of clubs both within and outside this ring were surveyed before and after the event to determine financial performance and effects from the games. Results suggest that clubs located within the designated ring benefitted from increased income, generated in a variety of areas. Clubs outside of the ring, at best, were able to meet financial benchmarks equal to the previous summer, despite actions taken to leverage the summer games. The second article by Ferreira (1998b) examined the effects of demographics (a factor related to location) on the membership size and financial performance of clubs. Ferreira found significant relationships (and effects) between and among several demographic variables and the financial performance of clubs. Most notably, initiation fees and member spending was affected by: the number of households with income levels of over $100,000; the number of properties valued at over $150,000 and; the number of owner occupied households. Ferreira concluded that select demographic variables (including those mentioned earlier) have the ability to explain a significant portion of critical performance measures. 2.3. Membership and marketing in clubs Two related areas, marketing and membership, are gaining increasing attention in the literature. This is a direct result of ongoing economic challenges, shifting demographics and an increasingly competitive environment. With a less favorable economic environment, clubs are
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focusing much more on supply and demand factors, data mining, managing waiting lists, maximizing member usage of facilities and services, and other services aimed at increasing patronage and revenues. Based on the literature, it appears as though strategic marketing is currently driving much of the decision making in clubs. The changing state of waiting list is of concern to clubs. Ferreira and Gustafson (2006a) examined the change in memberships and length of waiting lists, due to the (2001) recession. The purpose of the study was to measure how memberships, attrition, retention, and new memberships changed between 2000 and 2003. Data were collected via an online survey which was sent out to 1000 randomly selected Club Managers Association of America (CMAA) members. The authors noted a decline in memberships and emphasized the need for clubs to increase marketing strategies during economic downturns (Ferreira and Gustafson, 2006a). As a result of the decline in membership, decreased dues and initiation fees, and less revenue received from food and beverage outlets, clubs experienced a significant drop in their revenues. Clemenz et al. (2006) also examined the current state of waiting lists, noting that the number of clubs with waiting lists was diminishing as well as the length of waiting lists. In their study of 1000 randomly selected CMAA members, sixty-nine percent of responding clubs reported that they did not have a waiting list. Those with waiting lists tended to be “over 50 years old, member-owned, tax-exempt, and had 500–1000 members and gross revenues minus initiation deposits of between five and ten million dollars per year” (Clemenz et al., 2006, p. 19). The authors concluded that waiting lists in private clubs are less prevalent today than they have been historically. They recommended that marketing to new members as well as maintaining the current ones are essential if clubs want to remain viable and profitable. Non-American clubs are facing similar challenges as their U.S. counterparts in addressing and catering to the needs of non-traditional market segments. For example, Baker (2006), noted that Australian clubs, like American clubs, are faced with the challenge of attracting, serving and retaining a younger demographic segment. The author noted that one Australian club blamed the “troublesome youth” instead of the actual “contributors to the under-18's product failures” (Baker, 2006 p. 211– 212). Some private clubs have been innovative and have introduced non-traditional products and services to attract and satisfy the needs of newer market segments. For example, two such innovative product that have been introduced at private clubs are live rock shows and disco in hopes of attracting twelve-eighteen year olds as well as the eighteenthirty five year old crowds. Although these examples failed, they provided information to private clubs about how to better target non-traditional demographic segments and the importance of understanding demographics in providing clubs' products and services (Baker, 2006). Another article written by Ferreira and Gustafson (2006b) focused on determining the structures of private clubs, equity versus non-equity, and the advantages or disadvantages of each. Attributes of equity memberships that were examined are as follows: 1) the amount/% of entrance fee that the club receives, minus a transfer fee; 2) the amount/% of the entrance fee the member receives as a refund; 3) whether equity amount is set by the club or by market conditions and the amount the resigning member would accept; 4) overall entrance fee; and 5) number of individuals waiting to buy a membership versus those waiting to resign the membership (Ferreira and Gustafson, 2006b, p. 65). For this study, the authors administered a survey which was distributed to club managers at a Canadian Society of Club Managers meeting in Ontario, Canada. Fiftyseven percent of the eighty-one responses were returned and responses from equity versus non-equity clubs were compared (Ferreira and Gustafson, 2006b). Ferreira and Gustafson reported that non-equity membership clubs were more likely to have a waiting list (74%), and a longer list. Seventy-five percent of equity clubs had more openings for memberships available and a list of members actually waiting to resign, an average of thirty four waiting to leave (Ferreira and Gustafson, 2006b). Overall, newer clubs were those initiating the equity membership structure, believing the target markets of members will want a portion of
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the fees refunded. Those older clubs were more established and exclusive, with no need to extend equity to attract members. Thus, the new equity memberships find themselves at a financial disadvantage. They have waitlists of members wanting to resign and get a refund; lower transfer fee revenues are received; and frequent assessments are required to fund capital improvements (Ferreira and Gustafson, 2006b). The authors suggested that successful managers have unique, high quality experiences to satisfy current members while attracting new, at the same time being fiscally responsible (Ferreira and Gustafson, 2006b). Drew and Kriz (2006) utilized a consumer behavior approach to investigate the wants, needs and expectations of private club members. Five segments of club patrons were reviewed: full members, social members, visitors, Baby Boomers and Generation X'ers. The authors suggested that clubs must remain true to historical traditions while still catering to the evolving market. They also highlighted the fact that an important issue facing private clubs today is that the original purpose for formation may not be as relevant to the growing segments of society, with different wants, needs and expectations (Drew and Kriz, 2006). The also authors argued that clubs provide members with a sense of belonging to specific reference groups sharing values, emotions, perceptions, attitudes, motives and behavior patterns (Drew and Kriz, 2006, p. 201). Thus, understanding the intrinsic nature and interests of these reference groups will help in developing club marketing programs for the club as a whole as well as specific portions. Additional relevant findings uncovered included the conclusion that Generation X'ers are more interested in flexibility and are less likely to commit to clubs which are governed by the Baby Boomers. However, the study asserted that for private clubs, it is necessary to attract and cater to this younger sub-group, even while Baby Boomers are still active members, since retirement and loss of older members through natural causes are certain future prospects for private clubs. The authors concluded that the consumer behavior approach to changing patron expectations is appropriate and that differing needs, wants and expectations are grounded in the areas of demographics, culture, values, reference groups and attitudes (Drew and Kriz, 2006, p. 209). A final article concerning membership and marketing investigates the relationships among perceived value, member satisfaction, switching costs and member loyalty in the country club industry (Back and Lee, 2009). “The study hoped to realize the mediating role of member satisfaction in relationships centered on value-loyalty and image congruenceloyalty as well as the moderating role of switching costs in the member satisfaction-loyalty relationship” (Back and Lee, 2009, p. 528). Back and Lee (2009) designed an instrument to measure the characteristics of the theoretical model. Image congruence was measured with a separate direct question to the respondents, concerning the image of that respondent as compared to other members'. The sample population for the study comprised members at a mid-tier private club in the Western U.S. with six hundred members, and a $4,000 to $8,000 initiation fee, $400 to $600 monthly fee. The survey was distributed to the club members and two hundred sixty one usable responses were received. The moderating effect of the switching costs was tested, as well as the structural model. The authors determined that switching costs seemed to have no extreme moderating effect on the satisfaction or loyalty of members within private clubs. As a result, the authors suggested that managers provide high value to their members to achieve satisfaction and retain these members. Hence, closing the gap between what club members perceive and expect in accordance with high value will enhance member value. Finally, image congruence determined member satisfaction since private clubs have membership requirements such as social status, relating to social and ideal self-images. This creates emotional attachments, sense of belonging and value for the club member (Back and Lee, 2009). The authors concluded that both value and image congruence are significant initiators of club member satisfaction and as a result club member loyalty. The general business literature identified several strong relationships among location, demographics and financial performance. Research in the area of private clubs indicate waning waiting lists, declining
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memberships, importance of loyalty of current members, and the growing importance of marketing and membership development and retention strategies. With the exception of two articles by Ferreira (1998a, 1998b), the relationship between demographic variables, location, and financial performance has not been thoroughly examined in the hospitality literature. As such, the need for a comprehensive (and current) study would help address this gap in the literature. 3. Methodology The major objective of this study is to analyze the effect of demographic variables on private club membership level or size. Specifically, the study attempted to provide answers to the following two questions: 1. Which demographic variables, if any, account for the variance in a nonprofit tax exempt private club's membership size? (These variables were measured within a ten mile radius of the sampled private club.) The demographic variables collected and utilized were the number of: • • • • • • •
Residents Owner occupied households Properties of over $300,000 in value Households with over $100,000 income Businesses CEOs/executives/professionals Individuals with a college degree.:
2. Which demographic variables have any relationship with the following financial performance measurements at private clubs?: • • • • •
Total revenue Total initiation fees Dues income Average member spending Gross operating profit.
The impact of demographics on the financial performance measures was also deemed important since financial performance measures are indicators or proxies of levels of club membership. Furthermore, enterprise financial performance is an indicator of a club's viability, vibrancy, attractiveness and consequently, membership levels. 3.1. Hypotheses The general hypothesis for this study is that the different performance levels for private clubs will be influenced by certain demographic variables within a ten mile radius of the club. Below are six specific directional hypotheses which were developed and tested: H1. Demographic variables will have an influence on the number of fullprivilege members at clubs.
the Club Managers Association of America (CMAA) and have been in operation for five or more years. Participating clubs were located in 30 greater metropolitan areas in the United States as are listed below in Table 1. The demographic information for each of the private club's location was provided by PCensus Analyst (Tetrad). The club managers provided information on the six annual performance measurements for their clubs. The data collected from the private clubs and the demographic variables for each club was entered in a computer data file. The data were analyzed using the IBM SPSS Statistics package. Significant relationships among the performance measurements and the demographic variables were explored through correlation coefficients and stepwise regression analysis.
4. Results and discussion Table 2 displays the means and standard deviations for the six performance variables and seven demographic variables for the 302 private clubs. Table 3 lists the correlation values among the variables. The following statistically significant correlations existed between the dependent (performance) variables and the independent (demographic) variables. Initiation fees significantly correlated with household income levels (.63) and property values (.60). The number of full-privilege members significantly correlated with household income levels (.64), property values (.61), owner occupied households (.60), number of CEO's/executives (.53), and number of businesses (.52). Total revenue was significantly related to owner occupied households (.61) and the number of businesses (.59). Average member spending highly correlated with household income levels (.61) and property values (.60). Dues income only had significant correlation values with other performance variables and not with any demographic variables: gross operating profit (.78), total revenue (.71), number of full-privilege members (.63), and initiation fees (.61). Gross operating profit had significant correlation values only with performance variables as well: dues income (.78), total revenue (.71), number of full-privilege members (.59), and initiation fees (.57). The variance in four of the six private club performance measurements could be explained by the demographic variables, measured within a ten mile radius of the club. The percentage of variance explained by the demographic variables for the following performance measurements: number of full-privilege members, initiation fees, total revenue, and average member spending, ranged between 50 and 58%. In Table 4, the results of a stepwise regression analysis indicated that four of the six dependent variable's variance could be significantly explained by a select number of demographic variables. The variance in the number of full-privilege members club (R2 = .58) was related to the
H2. Demographic variables will have an influence on the total initiation fees collected at clubs. H3. Demographic variables will have an influence on the total dues income at clubs. H4. Demographic variables will have an influence on the amount of member spending at clubs. H5. Demographic variables will have an influence on the total revenue at clubs. H6. Demographic variables will have an influence on the gross operating profits at clubs. 3.2. Data and data collection Data were collected from 302 private clubs (country clubs, city clubs, city-athletic clubs, and yacht clubs) whose managers were members of
Table 1 US metropolitan areas. • • • • • • • • • • • • • • •
Atlanta, GA Austin, TX Baltimore, MD Boston, MA Charlotte, NC Chicago, IL Cincinnati, OH Cleveland, OH Dallas, TX Denver, CO Detroit, MI Houston, TX Jacksonville, FL Las Vegas, NV Los Angeles, CA
• • • • • • • • • • • • • • •
Kansas City, MO Minneapolis, MN New York, NY Philadelphia, PA Pittsburgh, PA Phoenix, AZ Portland, OR San Antonio, TX San Diego, CA San Francisco, CA Seattle, WA St. Louis, MO Tampa, FL San Francisco, CA Washington, DC
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one, two, four and five were accepted and hypotheses three and six were rejected.
Table 2 Private club variables. Characteristic
n
Mean
Std. dev.
Performance variables Initiation fees Dues income Number of full-privilege members Total revenue Average member spending Gross operating profits
302 302 302 302 302 302
$333,876.45 $2,507,525.64 932.95 $5,417,531.99 $12,062.40 $1,098,793.24
$307,458.23 $2,211,144.14 804.69 $3,396,261.01 $8,139.59 $855,153.49
Demographic variables within ten mile radius Residential population 302 Owner occupied households 302 Property values 302 Household income levels 302 Number of businesses 302 Number of CEO's/executives 302 Number with college degree 302
1,197,439.96 528,045.13 $152,065.25 $70,283.37 144,739.89 159,253.63 248,603.36
548,809.27 276,289.77 $86,353.97 $50,183.58 76,670.99 88,318.44 113,807.54
number of households having income levels over $100,000, the number of property values over $300,000, the number of owner occupied households, the number of CEOs/executives/professionals, and the number of businesses. The change in initiation fees (R2 = .53) that a private club collected was explained by the number of households having income levels over $100,000 and the number of property values over $300,000. The variance in total revenue (R2 = .52) in a private club could be explained by the number of owner occupied households and the number of businesses. Finally, the average member spending amount (R2 = .50) was related to the number of households having income levels over $100,000 and the number of property values over $300,000, which were the same demographic variables as those related to the initiation fees performance variable. The variance for three of the performance measurements was explained by two demographic variables: the number of households with income over $100,000 and the number of property values over $300,000. The number of businesses within a ten mile radius and the number of owner occupied households influenced two performance measurements, while the number of CEOs/executives/professionals influenced one performance variable. Two of the demographic variables investigated: the number of residents and the number of individuals with a college degree did not significantly affect the variance of any performance variables investigated. Based on these results, hypotheses
5. Summary and conclusions This research had similar findings to the 1998 club study with proprietary clubs (Ferreira, 1998a; Ferreira, 1998b). Other hospitality businesses, especially restaurants, have also found that the demographic makeup of a location has an impact on its business. In this study, two demographic variables had an impact on the financial performance of private clubs. As the number of households with income levels of over $100,000 and the number of properties valued at over $300,000 increased, so did the amount of initiation fees paid by members, as well as the amount spent by members at their club. As the demographic variables that typically measure wealth (income level and residential property value) increased, so did the initiation fees that clubs charged and the average amount that members spent at their club. Total revenue was highest for clubs located in areas that have a large number of businesses and a majority of owner occupied households within ten miles of the club. Typically revenues at clubs (e.g., dues, food and beverage purchases, banquet business, and athletic fees) are affected by the number of businesses in the area and individuals who can afford to own their own home and not rent. This finding was expected since the amount of profitable revenues generated by meetings and banquets is often predicated by businesses that entertain clients or host company parties/events at clubs. Moreover, the number of owner occupied households is another indicator of wealth and this impacts revenue at clubs because typically more home owners will join a club than renters. The number of full-privilege members was highly related to the two demographic wealth indicators mentioned earlier: households with income levels over $100,000 and the number of properties valued over $300,000. Apparently, when there are a large number of households with disposable income living near a private club, there will also be a sizable consortium of candidates for membership that can pay to join and become a member at a club. In addition to proximity to a member's resident, proximity to a member's place of business may be as important, if not more important for some club members. According to Ferreira (1997) many individuals are members at a club that is conveniently located to their residence, their place of business, or both. Club members are employed in professional capacities and typically are CEOs, executives, owners, or managers. The larger the pool of nearby
Table 3 Correlation of private club variables. Variables
Correlation coefficients 1
2
Performance variables 1 Initiation fees 2 Dues income 3 Number of full-privilege members 4 Total revenue 5 Average member spending 6 Gross operating profits
– .61⁎⁎ .30 .58⁎⁎
– .63⁎⁎ .71⁎⁎
.29 .57⁎⁎
−.10 .78⁎⁎
Demographic variables 7 Residential population 8 Owner occupied households 9 Property values 10 Household income levels 11 Number of businesses 12 Number of CEOs executives/mgrs. 13 Number with college degree
.17 .24 .60⁎⁎ .63⁎⁎ .14 −.12 .17
−.04 .27 .18 −.09 .17 −.09 .07
⁎⁎ Significant at .001 level.
3
4
5
6
– .27 −.21 .59⁎⁎
– .20 .71⁎⁎
– .11
–
.12 .61⁎⁎ .33 .34 .59⁎⁎ .24 .11
.13 .38 .60⁎⁎ .61⁎⁎ −.19 .16 .14
−.07 .15 .19 .21 .18 .11 .04
.21 .60⁎⁎ .61⁎⁎ .64⁎⁎ .52⁎⁎ .53⁎⁎ .02
7
8
9
10
11
12
13
– .29 −.10 −.11 .23 .06 −.16
– .26 .35 .26 .14 .05
– .30 .12 −.19 −.09
– .19 .15 .12
– .31 .19
– .40
–
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L.A. Jackson et al. / Tourism Management Perspectives 16 (2015) 51–57
Table 4 Results from stepwise regression analyses of the relationship among the characteristics of private clubs and the performance variables. Dependent variable
Independent variable
B
Beta
Number of full-privilege members
Household income levels Property values Owner occupied households Number of CEO/executives Number of businesses Constant Household income levels Property values Constant Owner occupied households Number of businesses Constant Household income levels Property values Constant
.0029 .0024 .0023 .0019 .0018 54.13 .0961 .0782 −68.03 .6787 .4006 −32.11 .0361 .0167 −236.78
.86 .52 .34 .33 .29
Initiation fees
Total revenue
Member Spending
.67 .48 .57 .45 .56 .39
F
R square
38.21⁎
.58
31.74⁎
.53
30.58⁎
.52
21.99⁎
.50
⁎ Significant at .01 level.
employed professionals, the greater the number of candidates a club will have for membership (Ferreira, 1995). Driving membership growth is becoming a critical issue for clubs in the aftermath of the recession. Existing clubs are constantly looking for new ways to market their clubs to prospective members. These clubs could use the results of this study as a way to better understand the demographic profile of their communities and to more effectively leverage this information. Clubs under consideration of development could use the same information to identify suitable (or even optimal) locations. The relationship between demographics and company performance and company location has been explored in the business literature (across multiple industries) but deserves more attention in the hospitality literature, and specifically in clubs. Studies continue to show important and significant relationships among these factors. Further, successful marketing is largely based upon adequate knowledge of local demographics. This research project was one of few of its type in the private club industry. Other businesses including those in the hospitality industry (hotels and restaurants) have completed similar market research studies. The information is urgent given the recent economic downturn and highly competitive environment within the private club industry (Woodworth, 2009). Based on the results of this study and the earlier study by Ferreira (1998a); Ferreira (1998b), private clubs need to evaluate the demographic make-up of the zip codes in close proximity to the club to see what impact their location is having on the club's performance. The analysis of the area can help to explain a club's performance, as well as provide information about its membership. Clubs should analyze the demographic makeup of the zip codes within a ten mile radius of the club. Moreover, additional analysis of the change in the demographic variables over the past ten years can be compared to the changes in the club's performance.
5.1. Limitations and future research The findings of this study are limited in their application. The first limitation is that the sample size of 302 private clubs in thirty major cities is small. Another limitation is that all the clubs in the study were member-owned, non-profit private clubs whose managers were members of CMAA and did not include any other member-owned, non-profit private clubs. Because of the small sample size, differences among the types of clubs could not be explored (i.e. the performance of city (dining) clubs may be influenced more by the demographics within a narrower radius of the club (e.g., 1 mile), while golf clubs may be impacted more by the demographics farther out (e.g., 10 miles)).
Future studies should explore different distances from the club's location and other demographic or psychographic variables. The additional variables at different distance lengths may better clarify the variance in a club's performance level. Moreover, future studies should be expanded to include clubs in smaller metropolitan areas to see if the variables that affect clubs in the thirty large size metropolitan areas affect small metropolitan size clubs in the same manner. Another variable that should be explored is the club type to see if there are differences among city clubs, country clubs, golf clubs, yacht clubs, etc.
References Andreasen, A. R. (1988). Cheap but good marketing research. Homewood, IL: Irwin. Back, K. J., & Lee, J. S. (2009). Country club members' perceptions of value, image congruence, and switching costs: An exploratory study of country club members' loyalty. Journal of Hospitality and Tourism Research, 33(4), 528–546. Baker, L. (2006). The trouble with today's young one: A case study of one club's failed attempt at attracting the youth market. International Journal of Hospitality and Tourism Administration, 7(2/3), 211–225. Barrows, C. W., & Ridout, M. (2010). Another decade of research in club management: A review of the literature in academic journals for the period 1994–2005. Journal of Hospitality Management & Marketing, 19(5), 421–463. Burkey, M. L., & Simkins, S. P. (2004). Factors affecting the location of payday lending and traditional banking services in North Carolina. The Review of Regional Studies, 34, 191–205. Carpenter, J., & Miller, E. (2014). A letter to the director, officers, owners and managers of private clubs, 1–4 Available at: http://www.privateclubadvisor.com/_filelib/FileCabinet/ Jackie_Backup/PCA_2014/PCA.11.14_Final.pdf Clapp, J. M., Fields, J. A., & Ghosh, C. (1990). An examination of profitability in spatial markets: The case of life insurance agency locations. The Journal of Risk and Insurance, 57(3), 431–454. Clemenz, C., Kim, S., & Weaver, P. (2006). An exploratory study of waiting lists in private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 19–45. DePrince, A., Ford, W., & Morris, P. (2011). Some causes of interstate differences in community bank performance. Journal of Economics and Finance, 35(1), 22–40. Drew, A., & Kriz, A. (2006). Understanding changing patron expectations of club offerings: A consumer behavioral approach. International Journal of Hospitality and Tourism Administration, 7(2/3), 195–210. Ferreira, R. (1998a). The location effect: How some Atlanta clubs won the Olympic Ring. Cornell Hotel and Restaurant Administration Quarterly, 39(5), 50–58. Ferreira, R., & Gustafson, C. (2006a). Declining memberships during an economic downturn in U.S. private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 3–17. Ferreira, R., & Gustafson, C. (2006b). Select performance differences in equity and nonequity membership structures within private clubs. International Journal of Hospitality and Tourism Administration, 7(2/3), 63–79. Ferreira, R., & Gustafson, C. (2014). Memberships levels in U. S. private clubs during the 2008–2010 economic downturn. International Journal of Hospitality and Tourism Administration, 15(1), 38–59. Ferreira, R. R. (1995). The relationship of select performance variables among private clubs. Poster presentation at the International Council on Hotel, Restaurant and Institutional Education Conference, Nashville, TN. Ferreira, R. R. (1997). How large is your market for potential members. Proceedings of the Club Managers Association of America's Conference, Orlando, FL (pp. 107–111).
L.A. Jackson et al. / Tourism Management Perspectives 16 (2015) 51–57 Ferreira, R. R. (1998b). The demographic effect on the performance level of private clubs. Journal of Hospitality and Leisure Marketing, 5(4), 23–32. Fjelstul, J., Jackson, L. A., & Tesone, D. (2011). Increasing minority participation through PGA education initiatives. SAGE Open, http://dx.doi.org/10.1177/2158244011416009, http://sgo.sagepub.com, 1-5. Kaspriske, R. (2003). Buyer's market: A sluggish economy has left many private golf clubs desperate for new members. Golf Digest, 54(11), 111. Kassim, N. M. (2006). Telecommunication industry in Malaysia: Demographics effect on customer expectations, performance, satisfaction and retention. Asia Pacific Business Review, 12(4), 437–463. Kotler, P., Bowen, J., & Makens, J. (2014). Marketing for hospitality and tourism (6th ed.). Upper Saddle, NJ: Prentice Hall. Kotler, P. (2000). Marketing management: Millennium edition (10th ed.). Upper Saddle, NJ: Prentice Hall. Lamb, C., Hair, J., & McDaniel, C. (2013). Marketing (12th ed.) South-Western, Mason, Ohio. Melanipju, J., & Sexton, J. (2007). The restaurant location guidebook (1st ed.). Chicago, Illinois: International Real Estate Location Institute, Inc. Tesone, D., Jackson, L. A., & Fjelstul, J. (2009). Charting production systems for golf and club operations. Journal of Retail & Leisure Property, 8(1), 67–76. Wheatley, W., & Parker (2011). Pawnbrokers and title lenders in Mississippi: Economic, regional, and demographic factors affecting their location and placement. Southern Business & Economic Journal, 34(½), 1–23. Wheatley, W. P. (2010). Economic and regional determinants of the location of payday lenders and banking institutions in Mississippi: Reconsidering the role of race and other factors in firm location. The Review of Regional Studies, 40, 53–69. Woodworth, R. M. (2009). The recession is over (maybe)! Cornell Hospitality Quarterly, 50(4), 407–412. Leonard A. Jackson, PhD, teaches graduate and undergraduate classes in the Hospitality Management Department, Robinson College of Business, Georgia State University. His research interests include sustainability and financial performance, hotel REIT performance, business combinations, entrepreneurship and executive transition impact.
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Clayton Barrows is a Professor of Hospitality Management at the University of New Hampshire where he teaches classes in hospitality management with an emphasis on private club management. He has worked in the hospitality industry and hospitality education in the US and Canada for 30 years. Prior to joining the faculty at the University of New Hampshire sin 2006), he was a Professor in the School of Hospitality and Tourism Management at the University of Guelph in Canada. While at Guelph, he also served as the coordinator of the MBA program in Hospitality and Tourism. Professor Barrow's areas of expertise are food and beverage management and private club management.
Dr. Raymond Ferreira, founder and president of the Ferreira Company, is an internationally recognized academic and industry research expert in the private club industry. He worked full-time as a club manager for 12 years, and has taught at Georgia State University's School of Hospitality Administration and University of Houston's Hilton College of Hotel and Restaurant Management.