A cross cultural study of antecedents on career preparation behavior: Learning motivation, academic achievement, and career decision self-efficacy

Journal of Hospitality, Leisure, Sport & Tourism Education 13 (2013) 19–32

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A cross cultural study of antecedents on career preparation behavior: Learning motivation, academic achievement, and career decision self-efficacy KyuHwan Choi a, Dae-Young Kim b,n a b

Department of International Tourism, Dong-A University, Busan, South Korea Department of Hospitality Management, University of Missouri, 220 Eckles Hall, Columbia, MO 65211, USA

a r t i c l e i n f o

Keywords: Cultural difference Learning motivation Academic achievement Career decision self-efficacy Career preparation

abstract The purpose of this study is to identify salient factors on students' career preparation behavior in the context of the hospitality and tourism education. A correlational study examined relationships between learning motivation, academic achievement, career decision selfefficacy, and career preparation behavior for 188 American students and 234 Korean students who major hospitality and tourism. The results reveal cultural differences between American and Korean students in terms of career preparation. A series of multiple regression analyses confirm the proposed relationship between the antecedents and career preparation behavior. The possible implications for students' career preparation are discussed. & 2013 Elsevier Ltd. All rights reserved.

1. Introduction Previous research has shown that career preparation among college and university students is a significant concern and represents a major developmental task (Skorikov, 2007). According to Jenkins (2001), about 80% of the surveyed college students in the hospitality and tourism management were looking for a job in the major-related field after graduating. Due to the applied characteristics of study, hospitality and tourism college students are greatly interested in issues and information relating to their future career (Richardson, 2009; White, 2006). Especially recent weak job market makes college students more concerned about their career following college graduation. In terms of career decision making, most hospitality and tourism college students have relied on career service programs and others' advice (Chuang & DellmannJenkins, 2010). For better understanding career decision making, it is essential to examine salient constructs in college students' career preparation (Healy & Reilly, 1989; Sandler, 2000). In response, recent studies have attempted to identify multidimensional constructs for career preparation (e.g., Chuang & Dellmann-Jenkins, 2010; Niu, 2010). With the realization, this study posits three important constructs in career preparation behavior to provide a more comprehensive picture of hospitality and tourism college student's career preparation behavior. In career decision-making, self-efficacy has received the most research attention relative to other domains of career preparation because it is central to successful educational and career outcomes (e.g., Betz & Luzzo, 1996). Another line of research focuses on learning motivation (e.g., Sandler, 2000) and academic achievement (Ryan & Deci, 2000). To investigate the causal effects on college students' career preparation, thus, the constructs used in this study are career decision self-efficacy, learning motivation, and learning achievement.

n

Corresponding author. Tel.: +1 573 884 7185; fax: +1 573 882 0596. E-mail addresses: [email protected] (K. Choi), [email protected] (D.-Y. Kim).

1473-8376/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jhlste.2013.04.001

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In career development research, understanding cross cultural differences in different countries has provided insights to fully comprehending and promoting industries' strategies that address college students' career preparation (Hartung, Fouad, Leong, & Hardin, 2010). This comparative study has further implications for fostering adaptive learning strategies for success among college students. Although some studies have investigated limited constructs to evaluate when considering a career (e.g., Richardson, 2009; White, 2006), relatively little attention has been paid to relations between multidimensional constructs and career preparation within the hospitality and tourism contexts. Therefore, this empirical study focuses on identifying the relationships among salient predictors (i.e., learning motivation, academic achievement, and career decision self-efficacy) of career-preparation behavior among Korean and American college students within the context of the hospitality and tourism industry. The discussion provides insights into the construction of a conceptual framework and offers suggestions on how it can be applied in follow-up studies. 2. Literature review 2.1. Individualism and collectivism Human behavior is defined as a function of both individual and environmental factors (Hui, 1988). Cultural theorists have agreed that cultural systems can be examined as the product of the interaction of people with their ecological and geographical environments and their climate (e.g., Kemmelmeier et al., 2003). Hofstede (1980) observed differences in certain behaviors and attitudes because individuals live in different cultures and societies. Hofstede's (1980) individualism and collectivism constructs have been discussed in many contexts in the social sciences. For example, individualism and collectivism appear to be semantic opposites (Hartung et al., 2010; Hui, 1988). Societies high in individualism are simultaneously low in collectivism and vice versa (e.g., Hofstede, 1980; Hui, 1988). In individualistic cultures and societies, people are autonomous and independent from their in-groups. “People prefer interdependent relationships with others and subordinate the goals of their in-groups to their own personal goals” (Shavitt, Lalwani, Zhang & Torelli, 2006). They behave primarily on the basis of their attitudes rather than in-group norms. In general, exchange theory adequately predicts the social behavior of individuals in individualistic cultures (Triandis, 2001). On the other hand, in collectivistic cultures, “people prefer interdependent relationships to others and subordinate their personal goals to those of their in-groups” (e.g., Hofstede, 1980; Triandis, 1995). They place more value on collective goals and are guided by group norms and traditional authority figures (Oyserman, Coon, & Kemmelmeier, 2002). The utility of the individualism–collectivism construct is indisputable (Chiou, 2001). Hofstede's (1980) cultural values approach, which provided numerical values to measure culture, has been the most popular measure. This approach allows cultural differences to be used directly as independent variables to explain differences in behaviors in psychology and consumer behavior settings across different cultures (i.e., individualism and collectivism) (e.g., Hui, 1988; Nelson & Shavitt, 2002; Soh & Leong, 2002; Walker, 2010). Follow-up studies, however, have noted that even within individualistic or collectivistic cultures differences exist. In particular, Triandis (1995) argued that culture difference is multidimensional. For example, U.S. individualism emphasizes competition and status, whereas Sweden's individualism emphasizes equality (Soh & Leong, 2002; Triandis, 1995). In addition, according to Triandis (2001), many varieties of collectivism also exist. For example, Korean collectivism, which emphasizes hierarchy, is not the same as the collectivism of the Israeli Kibbutz, which emphasizes equality. Thus, according to Triandis (1995), both individualism and collectivism may be delineated further as either horizontal (i.e., emphasizing equality) or vertical (i.e., emphasizing hierarchy). This creates four typologies: horizontal individualism, vertical individualism, horizontal collectivism, and vertical collectivism, which are categorized as follows (Chiou, 2001; Nelson & Shavitt, 2002; Triandis & Gelfans, 1998): in horizontal individualism (HI), people tend to view as equal to others in status. In vertical individualism (VI), people prefer to distinguish themselves from others. In horizontal collectivism (HC), people merge themselves with their in-groups, whereas in vertical collectivism (VC), people submit to the authorities of the in-group and are willing to sacrifice themselves for their in-group (Shavitt et al., 2006; Triandis, 2001; Triandis & Gelfans, 1998). Several researchers have investigated the validity of the distinctions among the four cultural patterns. Using a total of sixteen items, they measured the four patterns and subsequently supported both convergent and divergent validity (Triandis & Gelfans, 1998). The following types of studies have investigated the four patterns using various scales: studies to verify reliability and validity of the scales of the four cultural patterns (e.g., Chiou, 2001; Kemmelmeier et al., 2003); studies to measure the relationship between learning motivation and related values in cultural differences (e.g., Walker, 2010); and studies comparing the relationships between academic achievement and related values in cultural differences (e.g., Nelson & Shavitt, 2002; Soh & Leong, 2002). Based on previous studies, the present study aims to investigate the relationships between learning motivation and the four cultural patterns (HI, VI, HC, and VC). Cultural differences are used directly as independent variables to explain differences in individuals' behaviors. Learning motivation is formed by individual differences. Thus, this study hypothesizes that cultural differences will influence learning motivation (Hui, 1988; Kemmelmeier et al., 2003). 2.2. Learning motivation and academic achievement Among the diverse studies and theories in the field of education, most research has been guided by the selfdetermination theory (SDT) of Deci and Ryan (1985) (Komarraju, Karau, & Schmeck, 2009; Pintrich & De Groot, 1990;

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Ryan & Deci, 2000; Vallerand & Bissonnette, 1992). In SDT, the most basic distinction occurs between intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something because it leads to a separable outcome (Ryan & Deci, 2000). Intrinsic motivation refers to the fact that one engages in an activity for the activity itself. When intrinsically motivated, a person is moved to act for the fun or interest of participating in learning. They are influenced by “the satisfaction, enjoyment, and pleasure of learning itself” (Deci, 1975; Deci & Ryan, 1985). On the other hand, extrinsic motivation refers to doing an activity to achieve a particular outcome. When extrinsically motivated, a person is moved to act “for external prods, pressures, or rewards; they are influenced by competition, evaluation, approval, or other factors external to the individual” (Vallerand & Bissonnette, 1992; Vallerand et al., 1992). In both developmental and educational practices, intrinsic motivation remains an important construct, reflecting the natural human propensity to learn. As students move to higher year levels in school, however, they are less likely to be intrinsically motivated. Extrinsic motivation is also argued to be an extremely important construct (Ryan & Deci, 2000). Thus, a balance between intrinsic and extrinsic motivations is necessary. Previous studies related to learning motivation are ongoing and are slightly differentiated according to the subscales of learning motivations (e.g., Komarraju et al., 2009; Ryan & Connell, 1989; Vallerand et al., 1992). For example, Ryan and Connell (1989) suggested a total of four subscales of learning motivations (intrinsic and extrinsic motivations), such as external regulation, introjected regulation, and identified regulation. In this vein, Vallerand et al. (1992) extended the scales proposed to a total of seven subscales based on Ryan and Connell (1989). They suggested three subscales of intrinsic motivations (knowledge, accomplishment, and simulation) and added a motivation scale. Thus, the Academic Motivations Scale developed by Ryan and Connell operationalizes self-determination theory by measuring intrinsic (3 subscales), extrinsic (3 subscales), and amotivation in academic contexts. In addition, Amabile, Hill, Hennessey, and Tighe (1994) assessed individual differences in intrinsic and extrinsic motivations using the Work Preference Inventory. Here, intrinsic and extrinsic motivations are each subdivided into two subscales (enjoyment and challenge; outward and compensation). Likewise, most of the follow-up studies have drawn upon and used the scales related to learning motivation from previous studies (e.g., Komarraju et al., 2009; Mills & Blankstein, 2000). In addition, a handful of studies have examined academic achievement as a significant construct (e.g., Amabile et al., 1994; Komarraju et al., 2009; Pintrich & De Groot, 1990) because it is the outcome of education. Academic achievement is defined as the extent to which a student has perceived and achieved his or her educational goals (Pintrich & De Groot, 1990; Ryan & Deci, 2000; Sandler, 2000; Zimmerman, Bandura, & Martinez-Pons, 1992). There are two representative methodologies suggested in studies related to academic achievement. The first methodology is quantitative and is based on students' grade point average (GPA). The other methodology extracts abstract factors using measures of knowledge acquisition, accomplishment, and achievement to name just a few measures (Zimmerman et al., 1992). For example, Nota, Soresi, and Zimmerman (2004) investigated sample students' foreign language, mathematics, and technical subjects scores in student's academic achievement. In addition, Amabile et al. (1994) used the scores of both the mid-term and final exam to assess individual differences in intrinsic and extrinsic motivational orientations. Notably, research related to measuring the extent of perceived academic achievement is complicated by the difficulty of acquiring students' GPA scores due to their privacy rights. Thus, for example, most studies have measured academic achievement using abstract constructs related to perceived cognitive, affective, and psychomotor learning (e.g., Cheng & Chan, 2003; Rovai, Wighting, Baker, & Grooms, 2009). This study measured the academic achievement of both Korean and US students. We measured academic achievement because it was difficult to obtain students' GPA scores for comparison and examining the samples. 2.3. Career decision self-efficacy and career preparation behavior Career decision self-efficacy is derived from the self-efficacy theory of Bandura (1997). The definition of self-efficacy is “the conviction that one can successfully execute the behavior required to produce the outcomes”(p. 193). Self-efficacy theory has been used to explain that self-efficacy leads to individuals' specific behaviors as a direct/mediated role and a function (Zimmerman, 2000). Bandura has explained self-efficacy within the context of social learning theory, and selfefficacy determines whether a person takes a job, what types of jobs the person takes, and how long the person continues the job within all aspects of human development (Betz, Hammond, & Multon, 2005), and a person who has low self-efficacy tends to avoid the behaviors, while a person who has high self-efficacy tends to successfully conduct the behaviors (Luzzo, 1993; Watson, Brand, Stead, & Ellis, 2001). Career decision self-efficacy based on Bandura's theory is defined as “an individual's confidence in her or his ability to effectively engage in career decision-making tasks and activities” (Luzzo, Hitchings, Retish, & Shoemaker, 1999). Studies on career decision self-efficacy started with the first study combining career development and self-efficacy by Hackett and Betz (1981). They proposed that career decision self-efficacy can influence performance tasks or decision-making when selfefficacy related to ability to perform tasks is applied to the career field. In order to verify these relationships, many studies have tried to develop scales which can measure career decision self-efficacy, and doing so developed the Career Decision Self-Efficacy Scale (CDSE) by Taylor and Betz (1983). CDSE can evaluate their self-confidence in tasks in career decision making based on the career maturity scale proposed by Crites (1971), and consists of five subscales: self-appraisal, occupational information, goal selection, planning and problem-solving, and each subscale has ten items. It has been reported that CDSE is a predictor of the relationship between career decision and career preparation behavior. However, in later studies including Taylor and Betz's (1983) study, CDSE was not abstracted by five subscales, and it was

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overlapped among subscales. For this reason, it was suggested that the CDSE scale required modification (Betz, Klein, & Taylor 1996; Luzzo, 1993). Thus, Betz et al. (1996) developed a revised version of the CDSE, Career Decision Self-Efficacy Scale-Short Form (CDSE-SF), in order to solve this problem. CDSE-SF has also five subscales, but reduced ten items into five items in each subscale, and consists of twenty-five items. After CDSE-SF was verified by high reliability and validity, the new scale, CDSE-SF, has been cited by various later studies such as the study of relationship between gender and age (Nilsson, Schmidt, & Meek, 2002), the study of perception differences according nationality and race (Betz et al., 2005; Chaney, Hammond, Betz, & Multon 2007), the study of the relationship with academic achievement (Watson et al., 2001), and the study of the relationship with career preparation behavior (Luzzo et al., 1999). Meanwhile, though career preparation behavior is a significant construct to identify reasonable career decision, there are few studies about career preparation behavior (Sagen, Dallam, & Laverty 2000; Skorikov, 2007). The reason is that existing studies related to career decisions have only focused on personal perceptional or emotional perspectives, and have focused less on behavioral perspectives (Blau, 1993; Ryn & Vinokur, 1992; Van Hooft, Born, Taris, & Van der Flier, 2005). Studying career preparation behavior can allow researchers to evaluate how much time a person spends on a reasonable career decision, and how a person specifically devotes behavioral efforts to achieve goals (Niu, 2010). In line with Niu's research, studies related to developing scales which can measure career preparation behavior have progressed, and researchers who study this area have the same opinion about information searching and collecting related to career, preparation of skills needed in careers, and practical efforts for goal achievement (Blau, 1993; Ryn & Vinokur, 1992; Sagen et al., 2000; Skorikov, 2007; Van Hooft et al., 2005). In particular, Blau (1993) divided career preparation behavior into the preparatory step and the active step. The preparatory step includes searching and collecting information about careers through newspaper, magazine, and the Internet, and talking about careers with relatives and professors. In addition, the outcome in the preparatory step differs according to individuals' time, effort, and money. On the other hand, the active step includes formal career preparation behaviors such as making and sending a curriculum vitae, or direct interviewing and counseling with a director or an employee in the prospective career field. Based on these theoretical approaches, Blau (1993) measured career preparation behavior with three subscales: preparatory job search behavior, active job search behavior, and general effort job search. In addition, Sagen et al. (2000) measured career preparation behavior with subscales such as advanced skills, career cooperative education, internship, mentoring, and work experience, and Skorikov (2007) also verified the positive relationship between career preparation behavior and career decision self-efficacy with career indecision, career planning, and career confidence. Furthermore, the studies of Ryn and Vinokur (1992) and Van Hooft et al. (2005) have a single dimension unlike other studies having multiple dimensions. For instance, they have measured multiple items (i.e., reading the newspaper, talking to friends, family, or other people for job leads, sending out a resume, and going on a job interview) with a single dimension, career preparation behavior. All of the career preparation behavior scales mentioned above have been cited by various later studies because they have been verified with a high level of reliability and validity. 2.4. Hypothesis development 2.4.1. HI, VI, HC and VC and learning motivation Many cultural theorists have established that culture influences learning motivation because they agree that culture regulates and controls an individual's attitude and behavior (Kemmelmeier et al., 2003), and learning motivation is an attitude or belief created by personal perspective (Zimmerman, 1985). Walker (2010) studied the relationship between learning motivation and HI, VI, HC and VC, and proposed that individualism (i.e., HI and VI) is deeply related to internal motivation (i.e., fun, interesting, and enjoyable). Mills and Blankstein (2000) proposed that a group valuing self-oriented individualism pursues internal motivation (i.e., challenge and tasks performance), while a group valuing other-oriented collectivism pursues external motivation. In addition, Kim, Guo, Wang, and Agrusa (2007) studied learning motivation according to college students' nationality (i.e., Korea, China and Taiwan). Based on the results of the study, while Chinese students tend to have external motivation, Korean and Taiwanese students tend to have internal motivation. For this reason, it is proposed that these different cultural backgrounds can influence learning motivation. Finally, Hartung et al. (2010) studied the relationship between HI, HC, VI, VC and college students' career motivation, and extended applications of field of study and career field by identifying that VI and VC are related to external motivation, while HI and HC are related to internal motivation. These research findings have confirmed that culture strongly impacts motivation and performances in the context of educational settings. Based on the results of previous studies, this study proposes Hypotheses 1 and 2 as follows. H1. There are differences between American and Korean students in terms of cultural dimensions and learning motivations. H2. HI, VI, HC and VC have a significant influence on learning motivation. 2.4.2. Learning motivation and academic achievement Previous studies have identified that there is a significant relationship between learning motivation and academic achievement. For instance, Amabile et al. (1994) proposed that internal motivation consisting of challenge and enjoyment influence academic achievement more than external motivation. Komarraju et al. (2009) analyzed the relationship between learning motivation (i.e., internal motivation, external motivation and amotivation) and GPA, which was equated to

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academic achievement. The study proposed that the accomplishment factor, one of the internal motivations, positively influences academic achievement, while external motivation and a motivation are not statistically significant. In the same vein, Pintrich and De Groot (1990) proposed that there is a positive relationship between internal motivation and overall academic achievement (i.e., homework, quizzes and tests, and essays and reports). These results of previous studies allow researchers to verify that learning motivation directly influences academic achievement as an antecedent. Based on the results, this study proposes Hypothesis 3 as follows: H3. Learning motivation has a significant influence on academic achievement.

2.4.3. Academic achievement, career decision self-efficacy, and career preparation behavior Previous studies have supported the claim that academic achievement is a leading variable which influences career decision self-efficacy and career preparation behavior. For instance, Zimmerman (1985) proposed that groups having high academic achievement tend to have a more active attitude toward information searching and goal setting for making career decision than groups having low academic achievement. Sandler (2000) also supports the positive causal relationship between academic achievement and career decision self-efficacy with an integrated model of study persistence. In regard to the relationship between academic achievement and career preparation behavior, Luzzo (1993) found a strong causal relationship between two factors. In Healy and Reilly's (1989) study of career counseling for college students, it is reported that students prefer careers in similar fields to their majors. In addition, Sagen et al. (2000) reported that students who have high academic achievement acquire advanced skills, and tend to have a more active attitude toward career preparation behavior by attending various organizations to get information than students who have low academic achievement. Based on the results of these previous studies, this study proposes Hypotheses 4 and 5 as follows: H4. Academic achievement has a significant influence on career decision self-efficacy. H5. Academic achievement has a significant influence on career preparation behavior.

2.4.4. Career decision self-efficacy and career preparation behavior Skorikov (2007) studied factors affecting career preparation behavior, and verified that career decision self-efficacy, along with self-esteem and social adaptation is a leading variable of career preparation behavior. Chuang and Dellmann-Jenkins (2010) supported the relationship between career decision self-efficacy and career preparation behavior with a result that students who have high career decision self-efficacy tend to have high immersion in career preparation behavior. In addition, Van Hooft et al. (2005) proposed that career decision self-efficacy is a better leading variable of career preparation behavior than demographic variables, and Ryn and Vinokur (1992) argued that career decision self-efficacy is a better predictor of career preparation behavior than subject norm or attitude. Based on the results of these previous studies, this study proposes Hypothesis 6 as follows: H6. Career decision self-efficacy has a significant influence on career preparation behavior (Fig. 1).

3. Method 3.1. Measures The questionnaire was developed through the adaptation of items from previous literature related to this study. First of all, 16 items on culture background including individualism (HI and VI) and collectivism (HC and VC) were borrowed from Singelis and Triandis (1995) and Triandis and Gelfans, 1998, and slightly modified for this study. The items of culture background were assessed on a seven-point scale ranging from “Strongly disagree” (1) to “Strongly agree” (7). Learning motivation included 20 items taken from Amabile et al. (1994) with regard to the factors of enjoyment (EN), challenge (CH), outward (OU) and compensation (CO) based on the WPI (work preference inventory) scale. Three items on academic achievement (AA) were also adopted from Cheng and Chan (2003) and Rovai et al. (2009), and slightly modified for this study. The items were measured on a seven-point Likert-type scale where “Strongly disagree” ¼(1) and “Strongly agree”¼(7). Furthermore, career decision self-efficacy (CDSE-SF) was measured by 25 items taken from the Career Decision Self-Efficacy Scale-Short Form (CDSE-SE) on a seven-point Likert-type scale ranging from “not at all” (1) to “extremely” (7) (Betz et al., 1996; Chaney et al., 2007), pertaining to the five sub-dimensions of self-appraisal (SA), occupational information (OI), goal selection (GS), planning (PL), and problem solving (PS). As a consequence of the study model, seven items on career preparation behavior (CPB) were borrowed from Blau (1993) and Ryn and Vinokur (1992), with questions such as “How much time have you spent preparing for your career for past six months?” The items were measured on a sevenpoint Likert-type scale where “no time at all” ¼(1) and “very much time”¼(7). The survey instrument also included questions on demographic characteristics such as gender, grade level, and age.

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HI

Career Decision Self-Efficacy

H4

VI

H1 H2

Learning Motivation

H3

Academic Achievement

HC

H6

H5 Career Preparation Behavior

VC

Fig. 1. Proposed research model.

3.2. Data collection Respondents were recruited from a single university in the Midwest of the U.S. and a single university in the southern region of South Korea, respectively. The survey questionnaires were collected during the period from March to April in 2011. As a result, a total of 188 questionnaires (about 64% of 300 questionnaires) and 234 questionnaires (about 87% of 300 questionnaires) were used to examine the hypotheses of this study, excluding four incomplete responses for American respondents and twenty-eight incomplete responses for Korean respondents, respectively. 3.3. Data analysis To reach the study objectives, four empirical analyses were conducted. Initially, a descriptive analysis was used to investigate demographic information among both American and Korean respondents. Secondly, the reliability and validity of dimensions was examined by an exploratory factor analysis (EFA). Thirdly, a t-test was used to identify the differences between American and Korean respondents' perceptions on each dimension. Finally, a multiple regression analysis was performed to test cause-and-effect relationships among the salient dimensions in the model. 3.4. Demographic characteristics Demographic characteristics were identified as follows. Regarding gender, about 51% of American respondents were male, while about 72% of Korean respondents were female. In terms of grade level, it was observed that among American respondents, about 37%, 37% and 26% indicated that they were sophomores, juniors, and seniors, respectively, while among Korean respondents, about 42%, 35% and 23% revealed that they were sophomores, juniors, and seniors, respectively. Furthermore, the average age was about 22 years old among both American and Korean respondents. 4. Results 4.1. Descriptive statistics of the dimensions Table 1 shows the findings of a descriptive analysis on the dimensions used in this study. The primary finding showed that there were significant mean differences between American and Korean respondents in twelve out of fifteen dimensions used in this study. In terms of culture background (i.e., individualism and collectivism), it was found that there was a higher mean value of HI (mean¼5.24) among American respondents, but a higher mean value of VI (mean ¼5.35) and VC (mean¼5.35) among Korean respondents. There was no significant difference for HC (−1.70, p¼.089). This seems to reveal that the perception of individualism and collectivism in an educational setting varies depending on ethnic group (i.e., American or Korean students). Rather, it is assumed that American students may behave individually and fairly in a horizontally oriented educational structure, while Korean students who prefer a hierarchical and authoritarian learning system within an academic group may demonstrate characteristics of both individualism and collectivism in a vertically oriented education structure. This may be consistent with the results of the previous related research (Chiou, 2001; Kemmelmeier et al., 2003; Singelis & Triandis, 1995; Triandis & Gelfans, 1998). Moreover, it was observed that there were significant mean differences between American and Korean students in learning motivation. This indicates that American students may perceive the dimensions of CO (mean¼5.11) and OU (mean¼5.02) as more significant external learning motivations as compared to Korean students, whereas Korean respondents regarded the dimension of EN (mean ¼4.92) as a

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Table 1 Descriptive statistics of the constructs between U.S. and Korea. Variables

Horizontal collectivism Vertical individualism Vertical collectivism Horizontal individualism Compensation Challenge Outward Enjoyment Academic achievement Occupational information Planning Goal selection Self-appraisal Problem solving Career preparation behavior n

U.S.

Korea

M

SD

M

SD

5.20 4.53 5.12 5.24 5.11 4.74 5.02 4.58 5.29 5.52 5.29 5.14 5.42 5.17 4.72

1.01 1.14 1.08 .98 1.23 1.09 1.08 .91 1.14 .95 1.01 .95 .87 1.12 1.21

5.36 5.35 5.35 4.94 4.38 4.62 4.73 4.92 4.69 4.68 4.52 4.84 4.85 4.96 4.37

.91 .92 .97 .91 1.34 1.06 1.08 .92 1.01 .98 .95 1.08 .87 1.02 .94

t-Value

p-Value

−1.70 −8.11 −2.26 3.15 5.79 1.16 2.79 −3.80 5.62 8.88 7.95 3.04 6.69 1.94 3.37

.089 .000nnn .024n .002nn .000nnn .250 .006nn .000nnn .000nnn .000nnn .000nnn .002nn .000nnn .054 .001nnn

p o.05. p o .01. p o .001.

nn

nnn

more prominent dimension of internal motivation as compared to American students. Lastly, the dimensions of AA (mean¼5.29), SA (mean¼5.42), and CPB (mean¼4.72) were found to have higher scores among American respondents than among Korean respondents. Thus, Hypothesis 1 is supported. Table 2 shows the reliability and validity of the underlying dimensions used in this study. Initially, an exploratory factor analysis (EFA) was employed on the method of principle component analysis (PCA) with VARIMAX rotation in order to reduce and summarize data while losing as little information as possible. As a result, four items of learning motivation (e.g., “I enjoy learning and become so absorbed that I forget about everything else”) were deleted based on the cut-off of.40 (Kline, 1994). In using the same method as described above, two items of career decision self-efficacy (e.g., “I can determine steps to take if I am having academic trouble with my major”) were deleted by considering the cut-off of.40. In terms of the reliability of the salient dimensions, Cronbach's alpha was employed to identify the internal consistency of items within each dimension and assess the quality of the instruments. The result showed that all dimensions used in this study were very reliable in light of Cronbach's alpha values ranging from .85 to .95 (4.70), except for enjoyment (¼.65) (Nunnally, 1978). Table 3 represents the discriminant validity and convergent validity of the salient dimensions. In general, the withinsubdimension correlations should be higher than the between-subdimension correlations with regard to discriminant validity in a multiple regression model (Churchill, 1979), while convergent validity should be supported by higher correlations among the subdimensions of each construct in a model with multiple constructs. As shown in Table 3, it was found that correlations within dimensions (e.g., HC, HI, VC, and VI) were higher than correlations between dimensions (e.g., OI, PL, GS, SA, and PS), indicating that the entire bundle of determinants (e.g., culture background, career decision selfefficacy, etc.) may predict the consequence dimensions (e.g., academic achievement) without a multicollinearity problem in this study model. Given the fact that the between-subdimension correlations were found to range from .35 to .75, convergent validity was confirmed in this model. Consequently, the findings of the correlations among salient dimensions may support evidence of both convergent and discriminant validity in this study (Churchill, 1979; Hinkin, 1998). 4.2. Research model testing A multiple regression analysis was conducted to test causal effects among the salient dimensions (see Table 4). First, in order to examine Hypothesis 2, culture background (individualism and collectivism) as an independent construct was employed to predict the four dimensions of learning motivation. As a result, it was indicated that VI (β¼.235, t¼2.997, p o.01) and VC (β¼.252, t¼2.898, po.01) had significant and positive influences on only one dimension (OU: outward) out of the four dimensions among American respondents in learning motivation. On the other hand, among Korean respondents, VI (β¼.331, t¼4.775, p o.001) and HI (β¼−.156, t¼−2.360, p o.05) had a significant effect on CO; furthermore, both HC (β¼.319, t¼4.838, p o.001) and HI (β¼.264, t¼4.336, p o.001) had significant and positive impacts on CH. In addition, VI (β¼.403, t¼6.032, po.001) had a significant and positive impact on OU. HC (β¼.191, t¼2.821, po .01), VC (β¼.205, t¼3.157, p o.01), and HI (β¼.280, t ¼4.459, p o.001) had significant and positive impacts on EN. Thus, Hypothesis 2 was supported. Based on this finding, it seems that a horizontally-oriented group may prefer intrinsic motivation such as challenge and enjoyment in a fair education system, whereas a vertically-oriented group may focus more on extrinsic motivations such as outward and compensation in a hierarchical education system. This is consistent with the suggestion of previous related research (Hartung et al., 2010).

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Table 2 Reliability of measures from exploratory factor analysis. Measures

Factor loadings

Communalities Alpha if item deleted

Horizontal collectivism (eigenvalue ¼ 4.64, variance ¼ 28.97, coefficient alpha ¼.78) I feel good when I cooperate with others To me, pleasure is spending time with others The well-being of my coworkers is important to me If a coworker gets a prize, I would feel proud

.785 .758 .756 .668

.624 .644 .656 .499

.734 .722 .698 .757

Vertical individualism (eigenvalue¼ 2.09, variance¼ 13.03, coefficient alpha ¼.78) Winning is everything When another person does better than I do, I get tense and aroused Competition is the law of nature It is important that I do my job better than others

.864 .776 .714 .600

.762 .625 .542 .562

.671 .725 .729 .762

Vertical collectivism (eigenvalue¼ 1.85, variance ¼ 11.53, coefficient alpha ¼.80) Family members should stick together, no matter what sacrifices are required It is my duty to take care of my family, even when I have to sacrifice what I want Parents and children must stay together as much as possible It is important to me that I respect the decisions made by my peers

.843 .793 .744 .581

.755 .696 .607 .570

.694 .721 .778 .776

Horizontal individualism (eigenvalue ¼ 1.44, variance¼ 8.97, coefficient alpha ¼ .71) I rely on myself most of the time; I rarely rely on others I'd rather depend on myself than others I often do “my own thing” My personal identity, independent of others, is very important to me

.847 .809 .612 .489

.745 .701 .521 .491

.591 .577 .663 .708

Compensation (eigenvalue ¼4.04, variance ¼25.28, coefficient alpha ¼.87) I am well aware of the GPA goals I have for myself I am well aware of the goals I have for getting good grades I always think about grades and awards When I study, I'm concerned about exactly what grades or awards I can earn

.846 .823 .823 .804

.752 .714 .711 .689

.830 .845 .831 .848

Challenge (eigenvalue ¼ 3.55, variance ¼ 22.21, coefficient alpha ¼.81) I enjoy trying to solve complex problems The more difficult the problem, the more I enjoy trying to solve it Curiosity is the driving force behind much of what I learn I enjoy tackling problems that are completely new to me

.863 .850 .747 .629

.760 .741 .615 .562

.754 .719 .797 .774

Outward (eigenvalue ¼1.60, variance¼ 10.01, coefficient alpha ¼ .77) I am concerned about what other people think of my learning outcomes (i.e., grades) I'm concerned about how other people are going to react to my ideas I hope other people would recognize me as a good student I am strongly motivated by the recognition I can earn from other people

.853 .831 .643 .580

.777 .717 .518 .557

.620 .685 .781 .740

Enjoyment (eigenvalue ¼1.17, variance ¼ 7.31, coefficient alpha ¼ .65) What matters most to me is enjoying what I learn No matter what my grade earned in a class, I am satisfied if I feel I gained new learning outcomes (i.e., knowledge) I'm more comfortable when I can set my own goals I prefer to figure things out for myself

.761 .719

.647 .585

.533 .568

.555 .472

.502 .519

.652 .569

Academic achievement (eigenvalue ¼ 2.51, variance ¼83.49, coefficient alpha ¼ .90) I've obtained the knowledge and skills in a logical and systematical process through classes I've extended my knowledge through classes I feel a sense of learning achievement through classes

.935 .904 .901

.875 .818 .812

.821 .873 .879

Occupational information (eigenvalue ¼8.60, variance¼ 37.41, coefficient alpha ¼.86) I can find out about the average yearly earnings of people in an occupation I can find out the employment trends for an occupation over the next 10 years I can find information about graduate or professional schools I can talk with a person already employed in the field I am interested in I can find information in the library about occupations I am interested in

.776 .741 .739 .719 .698

.693 .641 .668 .644 .545

.820 .823 .821 .829 .847

Planning (eigenvalue ¼ 1.93, variance ¼ 8.38, coefficient alpha ¼.85) I can prepare a good resume I can identify employers, firms, institutions relevant to my career possibilities I can successfully manage the job interview process I can determine the steps I need to take to successfully attain my chosen career I can make a plan of my goals for the next 5 years

.795 .731 .715 .617 .522

.738 .707 .615 .619 .494

.808 .800 .825 .808 .845

Goal selection (eigenvalue ¼1.83, variance¼ 7.95, coefficient alpha¼ .82) I can select one career from a list of potential careers I am considering I can select one occupation from a list of potential occupations I am considering I can choose a career that will fit my preferred lifestyle I can choose a career that will fit my interests

.832 .780 .688 .654

.765 .715 .705 .641

.754 .768 .765 .778

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Table 2 (continued ) Measures

Factor loadings

Communalities Alpha if item deleted

I can make a career decision and then not worry about whether it was right or wrong

.570

.466

.872

Self-appraisal (eigenvalue¼ 1.52, variance¼ 6.59, coefficient alpha ¼ .83) I can define the type of lifestyle I would like to live I can figure out what I am and am not ready to sacrifice to achieve my career goals I can decide what I value most in an occupation I can determine what my ideal job would be I can accurately assess my abilities

.742 .731 .726 .683 .423

.665 .631 .644 .646 .533

.797 .789 .787 .779 .816

Problem solving (eigenvalue¼ 1.20, variance¼ 5.23, coefficient alpha ¼ .83) I can change occupations if I am not satisfied with the one I enter I can change careers if I did not like my first choice I can identify some reasonable career alternatives if I am unable to get my first choice

.901 .895 .617

.853 .835 .615

.671 .711 .874

.755

.571

.778

.732

.536

.780

.723 .702 .687 .653

.523 .492 .472 .426

.776 .782 .785 .797

.551

.404

.808

Career preparation behavior (eigenvalue ¼3.32, variance ¼47.49, coefficient alpha ¼ .81) I have tried to find the information about job market trends, or salary ranges from the Internet, books, or brochures I have tried to find information about my potential job and career from other sources rather than the Internet, books, or brochures I have watched job-related TV programs or participated in career fairs I have contacted career centers, research centers, or other career staffs at MU I have taken advice about my potential jobs from current employees in the field I have tried to find information about vocational education or training institutions/programs from the Internet, books, or brochures I have talked with friends, relatives, parents, or professors about my potential jobs or future careers

Table 3 Correlation matrix of the constructs. HC HC VI VC HI CO CH OU EN AA OI PL GS SA PS CPB

1

M SD

5.29 .96

.24n .45n .26n .10n .32n .16n .24n .27n .17n .15n .18n .15n .19n .15n

VI

VC

HI

CO

CH

OU

EN

AA

OI

PL

GS

SA

PS

CPB

1 .29n .26n .12n .15n .26n .20n .02 .03 −.01 .05 .01 .09 .10

1

4.99 1.10

5.25 1.03

.21n .11n .23n .17n .24n .16n .13n .11n .14n .10n .16n .16n

1 .10n .31n .10n .25n .22n .28n .25n .21n .30n .19n .18n 5.08 .95

1 .03 .45n −.14n .31n .23n .24n .12n .20n .05 .19n

1

4.71 1.34

4.68 1.07

.04 .49n .28n .22n .27n .20n .23n .23n .26n

1 −.07 .14n .19n .16n .14n .12n .12n .17n 4.86 1.08

1 .14n .12n .12n .10n .20n .17n .10n 4.77 .93

1 .32n .31n .24n .36n .29n .26n 4.96 1.11

1 .56n .46n .48n .35n .39n 5.06 1.05

1 .51n .62n .38n .45nn 4.86 1.04

1 .54n .26n .36n 4.98 1.03

1 .37n .25n 5.11 .91

1 .19n 5.05 1.06

1 4.53 1.07

Note: HC¼ horizontal collectivism; VI¼vertical individualism; VC¼ vertical collectivism; HI ¼horizontal individualism; CO¼ compensation; CH ¼ challenge; OU ¼outward; EN¼ enjoyment; AA ¼academic achievement; OI¼ occupational information; PL¼ planning; GS¼ goal selection; SA¼ self-appraisal; PS ¼problem solving; CPB ¼career preparation behavior; M ¼ means; SD ¼standard deviations. n p o.05.

Table 5 also indicates the results of Hypothesis 3. Among Korean respondents, CO (β¼.355, t¼4.829, p o.001) and CH (β¼.243, t¼3.167, p o.01) had significant and positive impacts on AA, while among Korean respondents, EN (β¼.245, t¼3.291, p o.001), CO (β¼.182, t¼2.584, p o.01), and CH (β¼.147, t¼ 1.988, p o.05) had significant impacts on AA. Thus, Hypothesis 3 was supported. Consistent with the previous studies (Amabile et al., 1994; Pintrich & De Groot, 1990), the results show that intrinsic motivation has a highly positive impact on AA as compared to extrinsic motivation. As shown in Table 6, a multiple regression analysis was conducted to test Hypothesis 4 regarding the relationships between AA and the five dimensions of career decision self-efficacy. Among American respondents, AA had a significant and positive

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Table 4 The comparison of regression analysis between American and Korean students. DV

IV

U.S. β

Korea 2

2

t-Value

p-Value

R (adj. R )

β

t-Value

p-Value

R2 (adj. R2)

CO

HC VI VC HI

.160 .130 .157 .081

1.878 1.646 1.798 1.000

.062 .102 .074 .319

.145 (.127)

−.091 .331 −.020 −.156

−1.279 4.775 −.301 −2.360

.202 .000nnn .764 .019n

.102 (.086)

CH

HC VI VC HI

.151 .130 .069 .155

1.749 1.630 .782 1.894

.082 .105 .435 .060

.131 (.112)

.319 −.065 .104 .264

4.838 −1.018 1.650 4.336

.000nnn .310 .100 .000nnn

.226 (.212)

OU

HC VI VC HI

.059 .235 .252 −.079

.694 2.997 2.898 −.981

.489 .003nn .004nn .328

.152 (.134)

.034 .403 −.035 −.062

.498 6.032 −.540 −.967

.619 .000nnn .590 .335

.160 (.145)

EN

HC VI VC HI

.090 .137 .019 .152

1.016 1.679 .214 1.824

.311 .095 .830 .070

.086 (.066)

.191 −.105 .205 .280

2.821 −1.599 3.157 4.459

.005nn .111 .002nn .000nnn

.194 (.180)

Note: DV ¼dependent variable; IV ¼independent variable; CO¼ compensation; CH ¼ challenge; OU ¼ outward; EN¼ enjoyment; HC ¼horizontal collectivism; VI¼vertical individualism; VC ¼vertical collectivism; HI ¼horizontal individualism. n p o .05. nn p o .01. nnn po .001.

Table 5 The regression analysis for learning motivation on academic achievement. DV

AA

IV

CO CH OU EN

Note: DV ¼ dependent EN ¼enjoyment. n p o .05. nn p o .01. nnn po .001.

U.S.

Korea

β

t-Value

p-Value

R2 (adj. R2)

β

t-Value

p-Value

R2 (adj. R2)

.355 .243 −.113 −.013

4.829 3.167 −1.555 −.168

.000nnn .002nn .122 .866

.180 (.162)

.182 .147 .100 .245

2.584 1.988 1.424 3.291

.010nn .048n .156 .001nnn

.165 (.150)

variable;

IV¼independent

variable;

AA ¼academic

achievement;

CO¼compensation;

CH ¼challenge;

OU ¼outward;

impact on PA (β¼.302, t¼ 4.311, po.001), SA (β¼.276, t¼3.923, po.001), and PL (β¼.213, t¼2.964, po.01) in the order of effect sizes, while among Korean respondents, AA had a statistically significant and positive influence on OI (β¼.331, t¼ 5.282, po.001), SA (β¼.324, t¼5.212, po.001), GS (β¼.267, t¼4.181, po.001), PL (β¼.265, t¼4.158, po.001), and PS (β¼.245, t¼3.843, po.001) in the order of effect sizes. Thus, Hypothesis 4 was supported (see Table 6). In terms of the casual relationships between AA and CPB (Hypothesis 5), AA had a statistically significant and positive impact on CPB among both American respondents (β¼.238, t¼3.337, po.001) and Korean respondents (β¼.209, t¼3.254, po.001) (see Table 7), which supports Hypothesis 5. These findings are also consistent with previous research (Healy & Reilly, 1989; Luzzo, 1993). Table 8 indicates the result of Hypothesis 6. In order to examine the relationship of career decision self-efficacy and career preparation behavior (CPB), for the American respondents, of five independent variables, only PL has a significant relationship with CPB (β¼.374, t¼ 4.089, po.001), whereas for the Korean respondents, PL (β¼.306, t ¼4.064, po.001), OI (β¼.278, t¼4.123, p o.001), GS (β¼.233, t¼ 3.418, po.001), and SA (β¼−.210, t¼−2.883, po.01) had statistically significant and positive impacts on CPB. Thus, Hypothesis 6 was supported.

5. Discussion The present study focused primarily on identifying the relationships between salient predictors (i.e., learning motivation, academic achievement, and career decision self-efficacy) of career preparation behavior between Korean and American college students within the context of hospitality and tourism. Given the empirical results of this study, several theoretical

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Table 6 The regression analysis for academic achievement on career decision self-efficacy. DV

OI PL GS SA PS

IV

AA AA AA AA AA

U.S.

Korea 2

2

β

t-Value

p-Value

R (adj. R )

β

t-Value

p-Value

R2 (adj. R2)

.135 .213 .136 .276 .302

1.856 2.964 1.877 3.923 4.311

.065 .003nn .062 .000nnn .000nnn

.018 .045 .019 .076 .091

.331 .265 .267 .324 .245

5.282 4.158 4.181 5.212 3.843

.000nnn .000nnn .000nnn .000nnn .000nnn

.109 .070 .071 .105 .060

(.013) (.040) (.013) (.071) (.086)

(.106) (.066) (.067) (.101) (.056)

Note: DV ¼dependent variable; IV¼ independent variable; OI¼ occupational information; PL¼ planning; GS¼ goal selection; SA¼ self-appraisal; PS ¼ problem solving; AA ¼academic achievement. np o.05. nn p o .01. nnn p o .001.

Table 7 The regression analysis for academic achievement on career preparation behavior. DV

CPB

IV

AA

U.S.

Korea 2

2

β

t-Value

p-Value

R (adj. R )

β

t-Value

p-Value

R2 (adj. R2)

.238

3.337

.001nnn

.057 (.051)

.209

3.254

.001nnn

.044 (.040)

Note: DV ¼ dependent variable; IV ¼independent variable; CPB ¼ career preparation behavior; AA¼ academic achievement. npo .05, nnn p o .001.

nn

p o .01.

and practical implications are suggested as follows. At first glance, the study suggests an integrated model that focuses on the linkage between student's learning and career decision-making in the hospitality and tourism college education. Compared to the significant relationships of previous models in relation to the concepts of students' learning behavior for their career preparation, learning motivation, and academic achievement (Healy & Reilly, 1989; Luzzo, 1993; Sagen et al., 2000; Sandler, 2000), the unique contribution of the present study is to develop the model of students learning variables directly associated with career behavior. More specifically, the results show that the relationship between students learning and career behavior vary depending on the magnitude of academic achievement, career decision-making, self-efficacy, and career preparation behavior (supported by Hypotheses 4 and 5). That is, those who have a high level of academic achievement may have progressive attitudes and remarkable ability for the specific career preparation in order to achieve their desired job goals without any switching behavior. This implies the importance of identifying the relationship between students learning and career in an appropriate career decision-making process. The research extends the research framework with respect to students' learning and career decision behavior. Despite the importance of students' career choice attributes when deciding their desired career goal (Richardson, 2009; White, 2006), there has been a paucity of research endeavors in understanding various antecedents on student career preparation behavior in hospitality and tourism education. This pertains to a limited approach of identifying the relationship between academic learning and career behavior (Chuang & Dellmann-Jenkins, 2010; Kim et al., 2007; O'Mahony, McWilliams, & Whitelaw, 2001; Walker, 2010). Thus, the results of this study facilitate future research that focus on more diverse learningrelated concepts in student career related research. The study also examined the significant relationships of culture value, career decision self-efficacy and career preparation behavior in a mediating role of learning motivation and academic achievement. This might enable us to understand the more systematic process of college student's career decision-making in light of reflecting not only their cultural background but also their learning motivation and academic achievement. In regard to the cultural differences, the results of the significant relationships between culture value (individualism and collectivism) and learning motivation provide some meaningful implication in hospitality and tourism programs. As shown in Table 2, the results show that horizontal collectivism (HC) and horizontal individualism (HI) are significantly correlated with the factors of challenge and enjoyment, while vertical individualism (VI) and vertical collectivism (VC) are significantly correlated with the factors of compensation and outward among both American and Korean students. More specifically, horizontally-oriented students who want to learn something individually and equally within a group would be high in intrinsic motivation than other students. On the other hand, vertically-oriented students who prefer a hierarchy and authority learning system within a group would be high in extrinsic motivation (e.g., self-sacrifice) than other students. With this recognition, the four types of culture value (HC, HI, VC, and VI) need to primarily be considered to stimulate students' learning motivation in each ethnic group. The study suggests the concept of learning motivation needs to be regarded as a significant predictor of academic achievement. Table 5 shows that a group who prefers the learning motivation factors of compensation and challenge rather than others is more likely to obtain higher academic achievement than other groups among American students, while

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Table 8 The regression analysis for career decision self-efficacy on career preparation behavior. DV

CPB

IV

OI PL GS SA PS

U.S.

Korea 2

2

β

t-Value

p-Value

R (adj. R )

β

t-Value

p-Value

R2 (adj. R2)

.071 .374 .125 −.079 −.025

.859 4.089 1.398 −.853 −.321

.392 .000nnn .164 .395 .749

.191 (.169)

.278 .306 .233 −.210 −.013

4.123 4.064 3.418 −2.883 −.216

.000nnn .000nnn .001nnn .004nn .829

.299 (.283)

Note: DV¼ dependent variable; IV¼ independent variable; CPB ¼career preparation behavior; OI¼ occupational information; PL¼ planning; GS ¼ goal selection; SA¼ self-appraisal; PS ¼ problem solving. np o .05. nn p o .01. nnn po .001.

Korean students are more likely to obtain higher academic achievement if they pay more attention to the learning motivation of enjoyment, compensation and challenge. This may be consistent with the result suggested by Ryan and Deci (2000) in terms of the importance of students' intrinsic motivation from a pedagogical perspective, except for the result of the insignificant relationship between challenge and academic achievement among American students. Thus, it appears that it should be essential for American students to develop course and curriculum contents, enabling them to improve their self-problem solving ability and meet their learning goal in classrooms. The result reveals that the concept of academic achievement should be utilized as a predictor of salient outcomes such as career decision self-efficacy and career preparation behavior. This indicates the importance of the relationship between academic achievement and career decision-making/behavior in hospitality and tourism education settings. As mentioned in Table 6, specifically, it appears that students who have a high level of academic achievement may attempt to solve learning problems in the best of their ability among American students, whereas Koran students may try to seek occupational information related to their fields in the high level of academic achievement. This indicates a critical culture difference of college students between the two countries. It has been observed that Korean students are more likely to consider their academic achievements to job seeking leverage. Indeed, most Korean students' main reason for entering college is more utilitarian (e.g., preparation for prosperous job) than academic interests. This difference would imply more tailored implications to designing and developing student consulting programs and materials. Furthermore, the result of the positive relationship between academic achievement and career preparation behavior implies that American students may do a variety of information-seeking activity by themselves. That is, they tend to prepare the second and/or third options for their career path in order to make provisions against unforeseen situations when they fail to obtain their first desired career goal in the future. In the case of Korean students, they focus on the second and/or third options for their career path by fitting current market trends such as salary, job positions, and promotion opportunities in an effective goal setting. Consequently, American students would be more future-oriented and flexible than Korean students who are known for having a reality-oriented learning and preparation type in education. This leads to insightful implications for American students regarding the necessity of setting an individual desired career goal in their realityoriented perception (e.g., information-seeking for current field information/trends, job openings, etc.). Lastly, understanding the concept of career decision self-efficacy makes it possible for hospitality and tourism college students to do optimal career preparation behavior. Given the fact that students' career preparation behavior varies depending heavily on the factor of planning in academic achievement (see Table 6) among both American and Korean students, it appears that college students in hospitality and tourism might focus more on doing résumé writing and interview preparation in a thoughtful manner. Especially, Korean students who are aware of their field information regarding current trends, salary, promotion opportunities, etc. are more likely to do career preparation behavior in a progressive way. In contrast, it seems that educational system planning (e.g., self-regulated planning) should be developed to encourage American students to set their desired career goal in a variety of learning motivation and job-related information-seeking in the sense that the planning of career decision self-efficacy is significantly and primarily related to career preparation behavior. Meanwhile, it would be expected that the concept of career decision self-efficacy determined by individual beliefs, confidence, and ability could be a negative component on students' actual career preparation behavior (e.g., progressive information-seeking). For example, students' overconfidence and/or excessive beliefs in their desired career achievement result in a lower level of career preparation behavior, and consequently they may fail to reach their career goals in the future. In this sense, academic advisors and/or consultants need to let students be aware of maintaining their career preparation behavior, not depending too heavily on excessive self-confidence and self-ability.

5.1. Limitations and future research directions Despite the fact that this study suggests the insightful implications, several research limitations should be suggested. First of all, a single university in each country (America and Korea) was selected as objects. This indicates that the

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application of more diverse university samples should be needed in hospitality and tourism college programs, overcoming the limitation of generalization. Secondly, future research should extend the model in favor of students' learning and career due to the limited application of learning related concepts used in this study, excluding some other prominent concepts such as attitudes, career identity, and career maturity. Thirdly, other career-related concepts should also be considered as predictors of career decision-making in a negative impact, such as career barriers, career anxiety and so on. This is because academic advisors and/or consultant need to handle negative factors affecting the career decision-making of hospitality and tourism college students, helping them overcome types of career barriers/anxiety (e.g., lack of career field information) and make a right decision in their career planning process. Lastly, there would be limitations to focus mainly on the causal effects of salient factors on career preparation behavior in this study. Thus, individual and demographic characteristics (e.g., gender, grade, and age) should be reflected for future research in terms of students' perceptions on learning and career in hospitality and tourism education settings.

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Kyuhwan Choi, Ph.D., is a Professor and Chair of International Tourism at Dong-A University, Busan, Korea. He is the Vice Dean of Graduate School of Business, and the Director of The Tourism Sciences Society of Korea (TOSOK). His research specialties include consumer behavior and tourism marketing.

Dae-Young Kim, Ph.D., is an Associate Professor of Hospitality Management at University of Missouri, USA where he teaches hospitality marketing and destination management. His research interests include cognitive psychology in tourism, information technology, advertising effect, and convention and meetings.