Customer service during peak (in season) and non-peak (off season) times: a multi-country (Austria, Switzerland, UK & USA) examination of entrepreneurial tourist focused core personnel.

Carraher, Shawn ; Parnell, John A.

ABSTRACT

This study examines the customer service of front line core service personnel in entrepreneurial businesses service businesses that serve the tourist markets in four countries. We assess the influence that seasonality might have on the utility of a selection inventory for predicting levels of customer service. Subjects included 309 core employees servicing the tourist industries in San Francisco, United States; 257 in Vienna, Austria; 250 in Zurich, Switzerland; and 255 in London, United Kingdom. Subjects were surveyed and assessed along dimensions of customer service orientation by trained assessors while serving customers. Response rates exceeded 90 percent as the business owners saw the study as an opportunity to understand how to earn greater revenues from their businesses. The mean service orientation ratings ranged from 3.53 in Austria to 3.62 in Switzerland during off-season or the non-peak tourist time and from 3.92 in Austria to 3.99 in the United Kingdom during the peak tourist season. Results support the use of a biodata inventory as a cost-effective means for small businesses to develop and retain competitive advantage relative to their larger rivals.

INTRODUCTION

Changes in marketing in recent years have affected small and large businesses alike (Garg & Chan, 1997). Many small firms have discovered that an emphasis on service orientation is more important today than ever before. Because customers have become more selective and conservative in their buying habits and larger companies are more forceful in attaining target markets, small businesses often focus on meeting customer needs effectively in order to retain their loyalty (Oh, 2000; Skogland & Siguaw, 2004). Attention to personal service can provide competitive advantage vis-a-vis larger, less personal competitors. In some respects, the small business manager or owner can no longer think of service as one aspect of the business but rather as the reason for its existence (Mill, 1986; Potter, 1988).

An organization's success depends on effective customer relations, a role played predominantly by its customer service employees. As such, firms often attempt to shape their images with customers by managing the types of behaviors employees display (Froehle & Roth, 2004; Hipkin, 2000). This is especially important in small- and medium-sized enterprises (SMEs) where nearly all employees have contact with customers on a daily basis (O' Gorman & Doran, 1999; Parnell, Carraher, & Odom, 2000; Zinger, LeBrasseur, & Zanibbi, 2001). Research suggests that firms emphasizing customer service report higher profitability, return on assets, return on investments, return on sales, and profit growth than those reporting less of an emphasis on customer service by the employees (Wright, Pearce, & Busbin, 1997).

This paper uses front line core personnel from entrepreneurial businesses operating in tourist areas to examine the influence that seasonality might have on the utility of a selection inventory for predicting levels of customer service. This type of instrument could be used for both selection and developmental purposes in order to increase the average levels of customer service within a population of employees. In this industry core employees are typically defined as permanent employees who stay with the business year in and year out as opposed to seasonal employees who are only hired during the "busy" season. Most core employees are extended family members of the entrepreneur or entrepreneurs who own the business.

REVIEW OF THE LITERATURE

Service-Orientation

Customer orientation can be viewed as a "set of basic individual predispositions and an inclination to provide service, to be courteous and helpful in dealing with customers and associates" (Harvey-Cook & Taffler, 2000 p 103). Successful organizations should be customeroriented (Parasuraman, Zeithaml, & Berry, 1985). Indeed, since the introduction of the marketing concept more than four decades ago, customer orientation has been recognized as the underlying foundation of marketing theory and practice (Jaworski & Kohli, 1993. Evolving from this recognition is the assumption that customer-oriented firms should outperform competitors by anticipating customer needs and responding with products and services that have superior value. Organizations improve their abilities to do so as their employees gain more experience and become more entrenched in a customer-oriented culture (Zinger et al., 2001).

Most businesses compete based on service, at least to some degree (Lynn, Lytle, & Bobek, 2000). As such, customer service is a critical success factor for many organizations (Phillips, 1990;

Bowen, Siehl, & Schneider, 1989). Service quality is the foundation of the service pledge that firms offer its customers and embodies an important part in overall value from which the customers judges a businesses performance (Harvey-Cook, & Taffler, 2000). Numerous studies have demonstrated that profit, growth, customer satisfaction and loyalty are enhanced by effective service orientation (Doyle & Wong, 1998; John, 1996). Those businesses whose policies and practices support service excellence generally have a competitive edge in most markets (Lynn et al., 2000).

Excellent customer service emanates from quality, customer-oriented employees. Indeed, poor customer service is ascribed to many factors but one area is that of the human resources employed in the customer service delivery, and improving it often centers on selection of the right employees to do the job. Businesses need to cultivate a climate for service that crafts, nurtures, and rewards services practices and behaviors to meet customer needs (Lytle, Hom, & Mokwa, 1998). Today more than ever, firms are keenly aware of the customer-orientations of its employees (Baird & Carraher, in press; Carraher, Buckley, Scott,, Parnell, & Carraher, 2002; Carraher, Parnell, Carraher, Carraher, & Sullivan, 2006; Carraher & Sullivan, 2003).

Marketers who advocate the marketing concept believe that organizations eventually attain success by satisfying customer needs (Deshpande, Farley, & Webster, 1993; Kotler, 1997; Paridon, Carraher, & Carraher, 2006). As competition becomes more intense, small businesses recognize that adopting a strong service orientation in their marketing efforts not only gives them a differential advantage, but is also a prerequisite for their survival. Small businesses can no longer adhere to the role of being simply movers of merchandise between manufacturer and consumer. They must ask the customers what they want and then obtain it for them (McDermott, 1990). Successful adoption and implementation of service-oriented business philosophy lies not in the size of the business but in its attitude towards the customer.

Organizations--especially those in the retail sector--are placing increased emphasis on hiring individuals with a strong customer service orientation (Gresham, Heneman, Fox, & McMasters, 2002). Retailers can differentiate themselves by pursuing service orientation as a business strategy (Homburg, Hoyer, & Fassnacht, 2002). Broadly, speaking, individual service workers are direct participants in implementing the marketing concept. A service worker's personal attention is the most important component of service delivery and is directly leads to overall customer satisfaction (Rust, Zahorik, & Keiningham, 1996). Unfortunately, the relative attention given to this topic in the literature has been relatively limited (Arias-Aranda & Alvarez-Gil, 2004; Homburg, Hoyer, & Fassnacht, 2002).

The work of Hogan and associates (Hogan, Hogan, & Busch, 1984) more than two decades ago remains seminal. To measure the traits from their definition of service-orientation, they developed the Service-orientation Index (SOI), an instrument that at that time consisted of 87 true-false items covering issues of agreeableness, adjustment, conscientiousness, and sociability. Validation of the SOI on a group of healthcare workers revealed a significant correlation (p <.05) of r = .31 between SOI scores and service-orientation ratings (Hogan & Hogan, 1992). Following this work, several consulting companies such as Personnel Decisions, Questar Data Systems, and CORE Corporation have developed their own instruments to measure service-orientation and Hogan and Hogan have refined the SOI (Carraher & Sullivan, 2003). These instruments are based on measures of attitudes and/or behaviorally based personality questions, but research published in scholarly journals performed by individuals not affiliated with the consulting companies has yet to substantiate the efficacy of these instruments (Gibelman & Gelman, 2003). Others have recommended the use of more behaviorally based selection procedures such as biographical information blanks, also called biodata (Bowen, Siehl, & Schneider, 1989; Schneider, 1997).

Seasonality and Customer Service

This study reports findings on businesses that go through seasonal changes in customer demands and employment. The seasonal nature of tourism is widely recognized in many different countries (Ashworth & Thomas, 1999; Kemperman, Borgers, Oppewal, & Timmermans, 2000). While some fluctuations can be deterministic due to calendar and weather effects, seasonal variations are also caused by economic agents and hence may not be constant across time. Thus, seasonal patterns may shift due to changing habits and utility functions of consumers (Frances, 1996). As competition continues to intensify with businesses developing new strategies to survive, service orientation becomes a major tool that can assist them in meeting profit and growth goals while an organization undergoes seasonal variations in demand and in changes in the demand curves for services provided (Kimes, 2006; Kasikci, 2006; Dickson, Ford, & Laval, 2005). With rapid changes occurring in all tourist markets, managers and entrepreneurs have to guard against retaining old practices while implementing new service oriented ideas. This is a major culture change in building a service climate that will enhance the business competitiveness (Doyle & Wong, 1998; John, 1996). As such, this study provides a perspective that can assist businesses and marketers as they think about new service orientation strategies to deal with seasonal variations in demand and in changes in the patterns of demand.

Biographical Data

The use of biographical information in the selection of employees dates back to 1894 when T.L. Peters used it to select insurance agents (Ferguson, 1962). Research using biographical questionnaires in multiple-choice formats blossomed during World War II, with much of the research demonstrating that biodata could predict success in several of the branches of the military (McDermott, 1990). Reviews of further work support the empirical validity of biographical data in predicting various criteria including job placement success (Harvey-Cook & Taffler, 2000), turnover (Lynn et al. 2000), and performance for service employees in large organizations (Carraher et al., 2006). The use of biodata differs from measures of values, attitudes, moods, interests, personality, and abilities, but it may assess constructs in all of these domains (Stokes, 1999; Schmidt, Kim, Ramsay, & Gillespie, 2004). Biodata questions are often presented in a standardized self-report questionnaire with a multiple-choice format that asks individuals to describe past attitudes, behaviors, and experiences (Gatewood & Field, 2001). For example "in high school, how easy were your math classes?" and "in comparison with most people you know, how often do your friends come to you for advice or guidance" are biodata questions which could be reflections of perceived cognitive abilities and the personality construct of agreeableness, respectively (Stokes, 1999). Researchers assume that past and present behaviors, attitudes, interests, and intentions are the best predictors of future behavior (Owens, 1976; Stokes, 1999). Biodata responses are generally believed to be reliable, but research has been inconclusive (Law, Mobley, & Wong, 2002).

Others have used samples of students in order to create a parsimonious biodata inventory purported to measure constructs related to service-orientation (McBride, 1988; McBride, 1997; McBride, Mendoza, & Carraher, 1997). McBride developed a 39-item biodata inventory hypothesized to contain the following 7 topical scales: agreeableness, desire to make good impressions on others, life satisfaction, need for achievement, resistance to stress, responsibility, and sociability. Two factors--sociability and good impressions--were found to relate significantly to estimates of service-orientation.

Based upon modifications suggested by McBride et al (1997) Carraher, Mendoza, Buckley, Schoenfeldt, and Carraher (1998) examined whether or not a modified version of McBride's 39-item biodata instrument could be used to measure the service-orientation construct. Using limited information factor analysis, Carraher and associates found support for an eight-factor solution--agreeableness split in to two factors, helpfulness and agreeableness--as well as finding that service-orientation ratings were consistently related with three of the scales: good impression, sociability, and helpfulness.

Two years later, Chait, Carraher, and Buckley (2000) used 605 job applicants to reexamine the relationship that the biodata measure would have with service orientation. Using principal components analysis, they found support for a five-factor model with McBride's biodata instrument. Their five-factor model was similar to the "big five" personality factors of extraversion, conscientiousness, emotional stability, agreeableness, and openness to experience. The results from this five-factor model had a similar multiple R (.46) to the original seven-factor solution McBride hypothesized.

In a cross-cultural study, Carraher, Buckley, Scott, Parnell, and Carraher (2002) examined 704 job applicants from the United States, Canada, and United Kingdom, extending the work done by Chait and Associates across borders. They found that across countries, service-orientation ratings were significantly correlated with agreeableness, extraversion, and openness to experience, and they were also significantly correlated with conscientiousness in both non-American samples but not in the American sample. The six-month, test-retest reliability estimates ranged from .73 (openness to experience) to .84 (extraversion). More recently, Carraher, Carraher, and Mintu-Wimsatt (2005) sampled 403 employees from a global financial information services organization in Unite Kingdom and 295 from Poland, Russia, and Ukraine and examined the effectiveness of the instrument's value for selection and development. They found that the instrument was more effective in Eastern Europe than in the United Kingdom.

The analysis presented herein extends the work of Carraher and associates, and examines whether or not McBride's selection instrument is useful within entrepreneurial tourist businesses with core service workers during in-season and out-of-season times of the year. Specifically, the 39-item inventory developed by McBride (1988) is utilized in order to assess individuals' personality characteristics and compare these assessments to levels of service-orientation determined through actual on-the-job performance. Following this work, (Carraher et al. 2002) we hypothesize that extraversion, openness to experience, agreeableness, and conscientiousness will predict the customer service oriented behaviors of core service workers in all four countries, while emotional stability will not be related to customer service oriented behaviors. It is also expected that employees treat customers better during the off-season than during peak season because they should have more time to devote to each customer.

METHODS

Subjects

Subjects included 309 core service workers in entrepreneurial tourist businesses in San Francisco, United States; 257 in Vienna, Austria; 250 in Zurich, Switzerland; and 255 in London, United Kingdom. Average ages at the beginning of the study were 24.8 years (SD = 4.44) in the United States, 24.8 years (SD = 4.23) in Austria, 26.4 years (SD = 3.92) in Switzerland, and 24.7 years (SD = 4.47) in the United Kingdom. Population sampling was utilized with for all full-time front-line core employees. Males were between 52 percent (Austria) to 59 percent (United Kingdom) of the samples so they were relatively evenly divided between men and women. Subjects were surveyed and assessed along dimensions of customer service orientation by trained assessors while serving customers. Response rates exceeded 90 percent as the business owners saw the study as an opportunity to understand how to earn greater revenues from their businesses. The mean service orientation ratings ranged from 3.53 in Austria to 3.62 in Switzerland during off-season or the non-peak tourist time and from 3.92 in Austria to 3.99 in the United Kingdom during the peak tourist season.

Instrument

The primary instrument was the selection inventory developed by McBride. Using principal components analysis as utilized by Chait and associates (2000) we found support for the five-factor models so we are going them with scales calculated using regressed factor scores. Carraher et al. (2002) and associates found the 6-month, test-retest reliability estimates to range from .73 (Openness to Experience) to .84 (Extraversion). In addition to demographic items (age, sex, etc.), many of the questions contained in the inventory consisted of experiential, attitudinal, and behaviorally based items (e.g. "When you were a member of a small group, how much do you participate?" and "How comfortable are you in new places and situations?"). The response formats for all questions except for age and gender had five response categories.

Customer Interaction & Criterion: Ratings of Service

The customer service interactions took place at the work sites of the entrepreneurial businesses. Three experienced observers rated the performance of the subjects in order to allow a comparison in the ratings. Each of the raters had been trained in order to accurately and consistently identifies differing levels of service-orientation. In addition to having graduate level training in performing behavioral assessments each assessor also received at least ten hours of training in the field specific to assessing customer service orientation. After observing 6-30 actual customer service interactions, raters would rate each of the subjects. They would also speak with 50 percent of the customers after the conclusion of the customer service interaction. Serviceorientation was measured using a 17-item rater evaluation form designed by Schoenfeldt (1999). The criterion scores were obtained by averaging the service-orientation scores given by the raters as has been previously done.

RESULTS AND ANALYSIS

Table 1 presents the means, standard deviations, and correlations between the five dimensions of the customer service instrument and the measurement of customer service at peak and non-peak seasons. Within all four samples the top three variables correlated with customer service were extraversion, conscientiousness, and agreeableness. In addition, openness to experience is relatively unrelated to customer service during the non-peak season and related to customer service during the peak season. The same pattern of results is found for emotional stability with the exception of the United States where emotional stability was related to customer service in both peak and non-peak times. In the other three samples, emotional stability has a weak but significant correlation with customer service in the peak season (r's = .14, .15, and .16, respectively).

Interestingly, the correlations between service levels at peak seasons and non-peak seasons produced a mean correlation coefficient of .545. The measure of customer service itself had coefficient alpha reliability estimates of .97 to .98 within these samples, indicating that customer service may not be the same constructs during peak and non-peak seasons. It may also be noted that while we had proposed that customer service levels would be higher during the non-peak than the peak times we found just the opposite.

Table 2 examines whether these results are significantly different using t-tests. T-values ranged from -7.70 to -11.41 in all four cases, suggesting that the customer service levels were significantly lower during the non-peak season than during the peak season. In order to gauge whether or not these results were atypical we also assessed the performance of 293 of the employees in San Francisco one year after the first assessment during the peak time period and found a mean for customer service of 3.94 which was not significantly different from their scores 1 year earlier (t = .55 and r = .802) and then again a week later with a mean level of 3.93 (r = .96 for one week test-retest reliability).

Results for regressions analyzing all five of the scales on the criterion are presented in Table 3. Extraversion was the only scale consistently correlated with service-orientation ratings across all of the samples during both peak and non-peak seasons. Agreeableness and Conscientiousness were significantly correlated with customer service in all but one situation (Peak time in the United Kingdom for Agreeableness [beta] = .08 and non-peak time in the United States for Conscientiousness [beta] = .08) sample); Emotional Stability was not significantly related to customer service in any of the samples during either peak or non-peak times while Openness to Experience was able to contribute to the prediction of customer service during non-peak times in Switzerland and the United Kingdom ([beta]'s = -.134 and -.123, respectively). The [R.sup.2] values suggest that between 17.8 and 29.9 percent of the variance in customer service levels can be explained with McBride's instrument. Thus, while an instrument such as this one could potentially provide great utility for organizations seeking to use a questionnaire for selection or developmental purposes, we strongly suggest that additional cross cultural research be performed on the construct of service-orientation itself in order to ascertain its meaning between, within, and across cultures- and across time periods.

CONCLUSIONS AND FUTURE DIRECTIONS

A service-oriented philosophy is one of the most logical and cost-effective ways for small businesses to develop and retain a competitive advantage in customer loyalty and satisfaction relative to their larger counterparts (Wredenburg & Wee, 1986). This is supported by the fact that while many small businesses function with limited resources, they generally have information accessible to enhance the effectiveness of their customer relations. The results presented in this study support the use of a biodata inventory as a basis for such an approach.

Broadly speaking, these findings are not surprising. They mirror those found by previous researchers with this instrument for job applicants in the United States, United Kingdom, and Canada (Carraher et al., 1998; 2002), suggesting that it may be useful for developmental purposes so that small business owners across cultures can increase their service-oriented behaviors. In fact, O'Gorman and Doran (1999) noted that it is through the focus on serving the customer well that small and medium sized businesses may more effectively compete with larger organizations that may have a cost advantage.

Certain individuals appear to be better suited for delivering excellent customer service than others. The present study demonstrates that service-orientation may be found most frequently in extraverted individuals who make a conscious effort to actively help others and seek out new ways to satisfy the needs of customers. As a consequence, it does appear that inventories such as the one developed by McBride may be useful for identifying individuals with the tendency to exhibit strong service-oriented behaviors for both developmental and selection purposes.

While the identification of proactive, considerate employees may be important to any organization, it is especially crucial in ones in which customer service may be a major part of the job for most of the positions within the organization as is increasingly becoming the case in the United States and around much of the world. This is especially important in small businesses where most employees may be called upon on a consistent basis to interact with both internal and external customers.

This research sheds light on those areas of service-orientation that may be most important in the identification of individuals likely to behave in a service-oriented manner within small businesses. This type of an instrument can make an important contribution in the identification of individuals across cultures likely to exhibit high levels of service-oriented behaviors and that additional cross-cultural research is performed on the construct of service-orientation.

These findings are important as they support the propositions of Vargo and Lusch about the development of a new dominant logic in the area of marketing that could lead to positive economic outcomes for hospitality businesses and managers. Their central proposition is that marketing has changed from focusing on manufacturing and hard products to focus on provision of services as fundamental to the economic exchange process (Vargo & Lusch, 2004). The shift is leading to changes in the ways that customers are viewed and organizations are valued (Canina & S. Gibson, 2003; Canina, 2001; Canina, 1996; Yuyuenyongwatana, Bansal, & Ellis, 1997). This change could lead to a more accurate valuation of hospitality organizations by investors and underwriting firms during acquisitions and initial public offerings. The valuation process is proposed to have changed from the value being defined by the producer in terms of exchange value in to the value being determined by the consumer based upon the value in use of a service. As hospitality firms can have strong value propositions, this should lead to a more accurate estimate of risks associated with hospitality organizations by investors.

Although these findings are noteworthy, additional research is needed in several areas. First, inventories such as the one developed by McBride may assist managers in hiring, training, and retaining individuals most likely to provide excellent customer service. Additional research--including a confirmation of the present study--is needed to understand the usefulness of such inventories (Buckley, Carraher, Carraher, Ferris, & Carraher, 2008; Carland, Hoy, Boulton, & Carland, 1984).

Second, although the present study suggested that biodata could be useful in predicting customer service-orientation in four nations, its applicability in many other countries remains inconclusive. Cultural differences such as those that exist between European, African, and Asian nations could draw the conclusions of this study into question in other cultural contexts (Carraher, 2005; Carraher & Carraher, 2006; Carraher, Sullivan, & Carraher, 2005; Jusoh & Parnell, 2008).

Third, this study supports the usefulness of McBride's biodata instrument among core employees in entrepreneurial retail establishments. As such, the potential influence of organizational size or organizational level on the link was not addressed. Further research is needed to assess the extent to which organizational size may be a key concern (Carland, Hoy, & Carland, 1988; Keiningham, Aksoy, Daly, Perrier, & Solom, 2006; Parnell & Menefee, 2007).

Fourth, this study assumes a common interpretation of customer service orientation across cultures. Although this appears to be true (Carraher et al. 2005), some differences between service expectations in different nations likely exist as appears to be the case with the construct of customer service itself across time (Carraher, Franklin, Parnell & Sullivan, 2006. Further research should examine the utility of biodata within this framework. It is also likely that other factors, in addition to personality, may influence service-oriented performance. Plethora variables, including general cognitive abilities (Carland & Carland, 1990; Carraher, 1991; Sethi & Carraher, 1993; Carraher & Buckley, 1996; Sturman & Carraher, 2006), motivational levels (Buckley, Mobbs, Mendoza, Novicevic, Carraher, & Beu, 2002), workplace values (Carraher, Carraher, & Whitely, 2003; Carraher, Sullivan, & Crocitto, in press), expectations, (Buckley, Fedor, Veres, Wiese, & Carraher, 1998) and occupational interests (Carraher & Carraher, 2005), may influence an individual's customer service levels and future research could involve examining their potential relationship.

Finally, given the results of our current study, research should be performed which would examine what might cause the construct of customer service to change from peak to non-peak seasons. One possible explanation may be that due to differing levels of employee involvement in the service delivery process during peak and non-peak seasons customer service might truly be different depending on the involvement of the employee and the customers in the process (Guy, 2003; Mascarenhas, Kesavan, & Bernacchi, 2004).

ACKNOWLEDGMENTS

The authors would like to thank Jorge Mendoza, Ralph Alexander, H. John Bernardin, Greg Dobbins, and Michael Harvey for their valuable comments on, and assistance with, earlier editions of this paper--and to the U.S. State Department for their support of the project.

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Table 1: Intercorrelations of Mcbride Instrument Scales and Service
Orientation

 EXTRA CONS EMSTAB AGREE

EXTRAVERSION .85 .59 (c) .14 (a) .36 (c)
CONSCIENTIOUSNESS .56 (c) .78 .38 (c) .36 (c)
EMOTIONAL STABILITY .16 (b) .32 (c) .85 .31 (c)
AGREEABLENESS .35 (c) .39 (c) .34 (c) .75
OPENNESS TO EXPERIENCE .01 .31 (c) .39 (c) .23 (c)
SERVICE ORIENTATION (Non-peak) .37 (c) .31 (c) .16 (b) .32 (c)
SERVICE ORIENTATION (peak) .35 (c) .42 (c) .22 (c) .37 (c)

Coefficient alpha reliability estimates for American sample on the
 diagonal
American sample (n = 309) below diagonal, Austrian sample (n = 257)
 above diagonal

 EXTRA CONS EMSTAB AGREE

EXTRAVERSION .85 .54 (c) .13 (a) .29 (c)
CONSCIENTIOUSNESS .51 (c) .80 .31 (c) .40 (c)
EMOTIONAL STABILITY .07 .25 (c) .85 .40 (c)
AGREEABLENESS .33 (c) .31 (c) .30 (c) .74
EXPERIENCE .00 .29 (c) .35 (c) .22 (c)
SERVICE ORIENTATION (Non-peak) .40 (c) .41 (c) .05 .27 (c)
SERVICE ORIENTATION (peak) .39 (c) .47 (c) .16 (b) .31 (c)

Coefficient alpha reliability estimates for Swiss sample on the
 diagonal
Switzerland sample (n = 250) below diagonal, United Kingdom sample
 (n = 255) above diagonal

 OPEN SVYN SVYP

EXTRAVERSION .12 .42 (c) .48 (c)
CONSCIENTIOUSNESS .37 (c) .39 (c) .43 (c)
EMOTIONAL STABILITY .49 (c) .01 .15 (a)
AGREEABLENESS .25 (c) .26 (c) .33 (c)
OPENNESS TO EXPERIENCE .73 .01 .20 (c)
SERVICE ORIENTATION (Non-peak) .09 .97 .52 (c)
SERVICE ORIENTATION (peak) .20 (c) .55 (c) .97

Coefficient alpha reliability estimates for American sample on the
 diagonal
American sample (n = 309) below diagonal, Austrian sample (n = 257)
 above diagonal

 OPEN SVYN SVYP

EXTRAVERSION .08 .39 (c) .43 (c)
CONSCIENTIOUSNESS .28 (c) .43 (c) .51 (c)
EMOTIONAL STABILITY .36 (c) .09 .14 (a)
AGREEABLENESS .29 (c) .27 (c) .28 (c)
EXPERIENCE .74 .01 .17 (c)
SERVICE ORIENTATION (Non-peak) .02 .98 .59 (c)
SERVICE ORIENTATION (peak) .23 (c) .52 (c) .98

Coefficient alpha reliability estimates for Swiss sample on the
 diagonal
Switzerland sample (n = 250) below diagonal, United Kingdom sample
 (n = 255) above diagonal

 USA Austria

 MEANS SD MEANS SD

EXTRAVERSION 3.55 .49 3.59 .49
CONSCIENTIOUSNESS 3.72 .43 3.73 .47
EMOTIONAL STABILITY 3.31 .36 3.30 .34
AGREEABLENESS 3.77 .47 3.73 .47
OPENNESS TO EXPERIENCE 3.37 .47 3.33 .45
SERVICE ORIENTATION (Non-peak) 3.57 .79 3.53 .74
SERVICE ORIENTATION (peak) 3.93 .60 3.92 .66

Coefficient alpha reliability estimates for American sample on the
 diagonal
American sample (n = 309) below diagonal, Austrian sample (n = 257)
 above diagonal

 Switzerland U.K.

 MEANS SD MEANS SD

EXTRAVERSION 3.56 .50 3.55 .49
CONSCIENTIOUSNESS 3.74 .43 3.72 .43
EMOTIONAL STABILITY 3.30 .34 3.35 .48
AGREEABLENESS 3.79 .45 3.79 .48
EXPERIENCE 3.34 .45 3.34 .48
SERVICE ORIENTATION (Non-peak) 3.62 .77 3.55 .76
SERVICE ORIENTATION (peak) 3.96 .61 3.99 .58

Coefficient alpha reliability estimates for Swiss sample on the
 diagonal
Switzerland sample (n = 250) below diagonal, United Kingdom sample
 (n = 255) above diagonal

(a) = p<.05 (b) = p<.01 (c) = p<.001

Table 2: Comparisons of Average Service Orientation Ratings at Peak and
Non-peak Times of the Year

 Non-peak Peak

Sample M M t p

1. United States 3.57 3.93 -9.57 0.0000001
2. Austria 3.53 3.92 -9.21 0.0000001
3. Switzerland 3.62 3.96 -7.70 0.0000001
4. United Kingdom 3.55 3.99 -11.41 0.0000001

Table 3: Regression of Service-orientation with Mcbride Big Five Scales

Scales Beta Weights

 United States Austria

 Non Peak Non Peak

Extraversion .246 (c) .147 (c) .219 (c) .337 (c)
Agreeableness .182 (b) .210 (c) .145 (a) .141 (a)
Emotional Stability .037 .028 -.113 .036
Conscientiousness .080 .215 (c) .290 (c) .152 (a)
Openness to Experience .010 .071 -.118 .084
including 5 topical
 scales .422 (c) .488 (c) .487 (c) .533 (c)
[R.sup.2] .178 .238 .237 .284

Scales Beta Weights

 Switzerland United
 Kingdom

 Non Peak Non Peak

Extraversion .192 (c) .193 (c) .199 (c) .223 (c)
Agreeableness .159 (a) .131 (a) .136 (a) .080
Emotional Stability -.043 .003 -.033 -.050
Conscientiousness .307 (c) .292 (c) .307 (c) .354 (c)
Openness to Experience -.134 (a) .115 -.123 (a) .049
including 5 topical
 scales .494 (c) .525 (c) .490 (c) .547 (c)
[R.sup.2] .244 .276 .240 .299

Multiple R from regression analysis

(a) = p<.05 (b) = p<.01 (c) = p<.001