Student Networking

Student Networking Behavior, Culture, and Grade Performance: An Empirical Study andPedagogical RecommendationsAuthor(s): Alvin Hwang, Eric H. Kessler, Anne Marie FrancescoSource: Academy of Management Learning & Education, Vol. 3, No. 2 (Jun., 2004), pp. 139-150Published by: Academy of ManagementStable URL: http://www.jstor.org/stable/40214244 .Accessed: 04/03/2011 17:33

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unlessyou have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and youmay use content in the JSTOR archive only for your personal, non-commercial use.

Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at .http://www.jstor.org/action/showPublisher?publisherCode=aom. .

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

Academy of Management is collaborating with JSTOR to digitize, preserve and extend access to Academy ofManagement Learning & Education.

http://www.jstor.org

http://www.jstor.org/action/showPublisher?publisherCode=aom

http://www.jstor.org/stable/40214244?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp

http://www.jstor.org/action/showPublisher?publisherCode=aom

® Academy of Management Learning and Education, 2004, Vol. 3, No. 2, 139-150.

Student Networking Behavior, Culture, and Grade

Performance: An Empirical Study and Pedagogical

Recommendations ALVIN HWANG

ERIC H. KESSLER Pace University

ANNE MARIE FRANCESCO Hong Kong Baptist University

We examine how culture influences student networking behaviors and these behaviors' consequent impact on grade performance. We tested research hypotheses integrated through a path model with data from three countries, two in the Far East and one in the West. Regardless of country origin, individualistic rather than collectivistic orientation predicted two forms of networking behaviors - one targeting professors (vertical networking behavior) and another targeting fellow students (horizontal networking behavior). Both networking behaviors had a positive impact on grade performance. In addition, mean differences in vertical and horizontal networking were detected among the three countries. Pedagogical implications are discussed in light of the results.

Today management education is a global phenom- enon, partly due to the growth in international business, the increase in people working and studying in geographically more diverse settings, and the subsequent development of globally oriented academic programs, faculty, and curricula (Pierce, 1999; Webb, Mayer, Pioche, & Allen, 1999). Notwithstanding, the role of culture in pedagogy has not been adequately considered, particularly in many Western approaches to management education (Adler, Doktor, & Redding, 1986; Bailey, Chen, & Dou, 1997). Given its implications for managing the increasingly complex learning dynamics of culturally diverse students, this is an important issue. Although a substantial amount of literature addresses intraclassroom learning needs, little research addressing cultural implications of extraclassroom behaviors such as stu- dents1 peer- and professor-oriented networking activities has been done. In our study we seek to address some of these concerns, specifically the relationship between students' culture, networking behavior, and their grade performance.

Networking is the age-old practice of establish- ing effective relationships with key people - both inside and outside the organization (Luthans, 1988) - who have the potential to assist in one's work or career (Forrett & Dougherty, 2001). Network- ing behaviors involve the building and nurturing of personal and professional links to create a sys- tem of information, contacts, and support. Re- searchers have demonstrated that successful managers spend more of their time networking than average managers, and that this may be the most important contributor to their success (Luthans, 1988). This insight can be traced to classic political thought and is also attributable to the fact that networking behaviors can facilitate learning and knowledge acquisition (Leeman & Why mark, 2001; Sonnenberg, 1990). Successful managers actively network with a wide variety of people to get the information they need, including horizontally targeted actions (e.g., with peers) and vertically targeted actions (e.g., with the boss; Kotter, 1982). Net- working among managers is also related to the

139

HO Academy of Management Learning and Education June

trend of taking more personal responsibility for performance achievement, continuous learning, and career development (Poell, Chivers, Van der Krogt, & Wildemeersch, 2000). As a result, self- initiated networking behaviors have become an important avenue to meet a variety of needs (Arthur, Inkson, & Pringle, 1999; Ibarra, 1993).

Although researchers have highlighted the expanding role of networking in the business world, it is equally important in the learning environment, such as in universities, which are themselves overlapping systems of individuals interacting within a shared academic context. For example, in selecting and completing courses, students are largely responsible for navigating through this environment. To do this, they interact with their professors and classmates through a variety of channels for information to meet their needs. Students' formal learning through class lec- tures and activities can be contrasted with learning that occurs by way of networking, which is less formal and includes hallway and library conver- sations, study groups, and outside interactions with professors (Poell et al., 2000).

In this study, we examine two primary patterns of students' learning-oriented networking behaviors. First, we consider networking behavior focused on getting information from other students. This includes meeting classmates outside of class to discuss unclear answers, confusing questions, and other areas of student difficulties. The second pattern we examine is networking behavior aimed

at getting information from the professor, including checking views with the professor outside of class, privately seeking out the professor for solutions to questions, and taking time to meet the professor. Because interpersonal behavior such as networking may be strongly affected by culture (cf ., Hofstede, 1980; Triandis, 1995), we also examine possible cultural influences and their impact on learning performance. In summary, we initiated this study to address the following research questions:

1. How does culture predict learning-oriented networking behaviors?

2. How do these learning-oriented networking behaviors impact performance?

MODEL AND HYPOTHESES

To address these research questions, we propose a path model examining relationships among ele- ments of culture, networking behaviors, and performance (see Figure 1). With regard to culture, we focus on individualism- collectivism (IC), given that it has been extensively researched and shown to explain behavioral differences in many areas (cf., Hofstede, 1980; Triandis, 1995). We examine how IC predicts two forms of student learning- oriented networking behavior: (a) horizontal, student-targeted networking, and (b) vertical, professor-targeted networking. We then consider the impact of these networking behaviors on student grades.

FIGURE 1 Hypothesized Hong Kong, Singapore, and U.S. Network Learning Model. STDALONE = Stand Alone;

WINALL = Win Above All; GRPPREF = Group Preference; SACRIF = Sacrifice; INDIVTHK = Individual Thinking; HORIZONTAL NT = Horizontal Networking; VERTICAL NT = Vertical Networking; GRADE =

Previous Semester Grades.

y/B STDALONE ^S^^

yyy^ 1 \ ^*^. horizontal nt

V /X X^ GRPPREF ,S jf y( GRADE

^^SS. ^ / 2 VERTICAL NT /

^cj INDIVTHK ̂̂ ^^^^^

2004 Hwang, Kesslei, and Francesco 141

Individualism-Collectivism and Student Learning Individualism- collectivism is a key concept in the study of cultural differences. Researchers in management, education, and psychology have found IC to be useful in explaining a wide range of behaviors including those that could affect personal networking (cf., Earley, 1989; Greenfield, Raeff, & Quiroz, 1996; Hofstede, 1980; Triandis, 1995; Wag- ner, 1995). Markus and Kitayama (1991) argued that an independent construal of self has generally been found in Western cultures, such as the United States, where societal norms promote values of autonomy, uniqueness, self-sufficiency, and self- actualization. By contrast, in non- Western societies, the interdependent construal of the self is dom- inant, and personal relationships are more highly valued. Triandis' (1995) definitive work suggests that IC has four important attributes:

1. Definition of self varies, with emphasis on independence and personal aspects for individualists versus interdependence and group aspects for collectivists.

2. Goal priority varies such that personal goals are more important for individualists, whereas group goals take precedence for collectivists.

3. Determinants for social behavior vary such that individualistic behavior is dominated by self-focused attitudes, personal rights, and contracts, whereas collectivistic behavior is guided by norms, obligations, and duties.

4. The nature of relationships varies such that individualists rationally consider the exchange, whereas collectivists emphasize the communality of the relationship, even when this represents a disadvantage.

Apart from the important works of Markus and Kitayama (1991), and Triandis (1995), a range of perspectives on both conceptualizations and measures of IC exists - a reflection of IC's complex nature (cf., Hui, 1988; Triandis, 1995; Wagner, 1995). For example, while researchers such as Triandis (1995) and Chen, Yu, and Miner (1997) advocate vertical and horizontal IC orientations, there are others, such as Wagner (1995) and Earley (1993) who have not adopted this perspective because the added horizontal and vertical dimensions seem to be very similar to Hofstede's (1980) power distance dimension. In order to reveal possible underlying IC dimensions across a wide range of different IC conceptualizations, Wagner (1995) developed a composite measure on which he conducted an exploratory factor analysis to produce three individualistic factors, Stand Alone, Win Above All, and Individual Thinking, and two collectivistic factors, Group Preference and Sacrifice. Stand Alone focuses on individual independence and self-reliance. Win Above All reflects an all-consuming in-

clination to win in competitive situations. Group Preference shows a preference to work with others in groups. Sacrifice recognizes that individuals have to make personal sacrifices in group situations, and Individual Thinking reflects a need for individual beliefs to be sublimated in group situations. These five dimensions were used to test for IC differences across the three countries in this study.

Our next question was how a person's IC orientation might influence personal networking. An important finding from Earley's (1989) work showed that persons with a more individualistic orientation were more likely to engage in social loafing behaviors when placed in interpersonal situations in a learning environment. A later cross-cultural study by Earley (1993) produced distinct performance results. While collectivists worked better in in-groups, that is, those where members shared similar traits and background characteristics, individualists performed better when they were working alone. Further, collectivists see themselves as most effective when working with an in-group as reflected in higher group- and self- efficacy scores. This was in contrast to individualists who had higher self-efficacy expectations when they were working alone. These findings support Olson's (1971) and Wagner's (1995) argument that individualists' self-interest motivation made them less cooperative in interactive work. The results of these studies suggest that collectivists rather than individualists are more likely to find networking useful in informal student groups where learning and grades are unifying goals. In fact, within the learning environment, Earley (1994) found that individualists' self-efficacy and performance were more influenced by self -focused training whereas collectivists' self-efficacy and performance were more affected by interpersonally focused training. Apart from performance differences, Bailey et al. (1997) argued that collectivists, possibly as a result of their interdependent view of self, preferred failure feedback, that is, they wanted to know about their mistakes for group improvement, whereas individualists, in contrast, were more interested in success feedback, possibly reflecting a desire to enhance personal repu- tation and self-image.

IC has also been a subject of discussion for those in elementary and secondary education. In the education systems of individualist societies, children are handled on an individual basis, whereas in collectivist societies, teachers deal with children on a group basis (Hofstede, 2001). Sometimes these two orientations may conflict. For example, one study showed Hispanic students in the United

142 Academy of Management Learning and Education June

States formed groups within the classroom for social interaction rather than work on the task, even though this was against their teacher's wishes (Greenfield et aL, 1996). Although the (individualistic) teacher considered the social interaction as problematic or even as a form of cheating on personal assignments, the (collectivistic) students did not view it this way, but in fact considered such social interactions as an important and expected way to behave.

Taken together, the body of research suggests that IC orientation does have an important impact on behavior, and that this also should be true with student networking behaviors. Based on this evidence, we predict that collectivists' traits of interdependent orientation and view of self, preference for interpersonal situations, and propensity for mu- tually beneficial interaction are more consistent with networking behavior than those of their individualist counterparts. Our argument is also sup- ported by research indicating that networking relationships are facilitated by engagement in open conversation, a goal of collaboration, and beliefs that giving will beget receiving and not loss (Lee- man & Why mark, 2001) - activities that are all consistent with a collectivistic orientation. Thus, we suggest that: Hypothesis 1: Collectivism will be positively re-

lated to student networking behavior.

Student Learning Networks and Grade Performance

We have argued that networking behavior, by cre- ating systems of information, contacts, and support, is related to learning (Leeman & Whymark, 2001; Sonnenberg, 1990) and work-related success (Luthans, 1988). We also believe a relationship between networking and success exists in the educational context. For example, because a person's access to the "right people" helps him or her achieve important goals (Brass, 1984), student learning-related networking activities such as getting together with other students outside of class (vs. studying alone) and actively interacting with faculty members (Light, 2001) should also help ob- tain the desired information. The importance of student networking is germane to the learning environment because class time is a limited re- source, and consequently, students can benefit by pursuing elaboration on course material outside of class. In support of this thesis, Treisman (1992) found that a differentiating factor between high- versus low-performing students in a college calculus class was the time they spent with peers out-

side of class in learning-oriented interactions. The more successful students (here, of Chinese origin) would get together in the evenings to go over the homework, check each other's answers, and learn from each other. The less successful students (here, non- Asian) would work alone and spend little time and effort networking with their peers outside of class.

The functionality of students' networking behavior is also consistent with learning network theory (LNT). According to LNT (Poell et al., 2000), networking represents liberal learning-oriented behavior insofar as individuals create their own sets of learning activities and self-directed learning programs. This networking can take the form of horizontal learning engagements, through interactions among peers that occur informally outside of class, or vertical networking engagements through interactions with those at different hierarchical levels (e.g., professor, tutor, teaching assistant) again for the purpose of obtaining knowledge. Networking may also benefit learning insofar as face-to-face interactions create rich information channels (Daft & Lengel, 1984) that facilitate informal but important higher order learning of complex, abstract, and experience-driven information. Such learning may not take place if students rely only on the textbook within a large class environment. Stu- dents who initiate networking engagements may consequently have an advantage over nonpartici- pants.

All in all, we see student-initiated networking behaviors targeting both fellow students and professors as vehicles for gaining unique information and knowledge, thereby offering the potential for grade performance advantages. Thus we make the following prediction: Hypothesis 2: Students' networking behaviors tar-

geting both other students and their professors will be positively related to their grade performance.

METHODOLOGY

Subjects

The sample for the study came from three countries: the United States (n = 253), Hong Kong (n = 266), and Singapore (n = 131). All the respondents were undergraduate business students with an age range between 18 and 44 years. The mean age was 20.8 years, with a standard deviation of 2.87 years. Both mode and median ages were at 20 years. Close to 63% of the participants in the sample were female.


Measures

Scales from Wagner's (1995) five factors were used to measure IC. Cronbach alpha coefficients for the five measures across the international sample were between 0.77 and 0.84. Two measures, horizontal networking behaviors (extent that students targeted other students outside of class) and vertical networking behaviors (extent that students targeted professors outside of class), adopted from Hwang, Ang, and Francesco (2002), had reliabili- ties of .92 and 89, respectively. The IC, horizontal networking, and vertical networking scales were all anchored on a 7-point scale, ranging from strongly agree to strongly disagree.

Grade performance was measured by asking students to list their classes and grades in the prior semester. Grades from the three different grading systems were then normalized by country. Because students were enrolled in different numbers of courses, we used the four highest normalized grade scores of each student as indicators of performance. Choosing more courses would have dra- matically reduced the sample size. This normalized grade measure had a reliability of .92. All scale items, individual construct reliability coefficients, and factor loadings are given in Table 1. All questionnaires were written in English, as this was the medium of instruction in all the universities where subjects were enrolled. Also, all participants in the three universities had been well im- mersed in the English language medium in grade schools, and had either completed the U.S. SAT tests or the local equivalent of the British General Certificate of Education (Advanced Level) in En- glish before entry into university. Thus, we decided that translation into other languages was not nec- essary for this study.

Analyses Because cross-cultural researchers have argued that the IC orientation of people can vary even within a single culture (Triandis, 1995), our first step was to carry out an exploratory factor analysis to determine the replicability of Wagner's (1995) factor structure. The next major step was to simul- taneously test the stated hypotheses for significant variations across countries. Although the overall sample of 650 cases was adequate to test hypothesized relationships among the eight constructs based upon 32 response items (response-to-item ratio of 20), the number of cases at the individual country level was too low to permit this. The response-to-item ratios of 7.5 for Hong Kong, 6.2 for the United States, and 3.6 for Singapore were not

adequate for the recommended range of at least five to ten times the number of respondents for every item in the structural models (Bentler & Chou, 1987). Consequently, we tested the hypothesized relationships using Lisrel path models through traditional variables that were composed from the factors (Joreskorg & Sorbom, 1989). As a check on the reliability of the composed variables for path models, we performed Cronbach alpha reliability tests. The variables had acceptable coefficients with most variables in the .80s and .90s, along with a few at more moderate .72 and above levels.

Additionally, although the effects of common method variance are less prevalent than often thought (Crampton & Wagner, 1994; Forrett & Dougherty, 2001), we followed Podsakoff and Or- gan (1986) and conducted multiple checks for potential common method variance problems:

1. Performing unrotated factor analysis to check whether the results break into multiple factors as was hypothesized.

2. Performing scale-trimming analysis by pre- senting two lists not identified by construct labels (one of the dependent and another of the independent variables) to neutral parties, and asking them if any item on the first list had essentially the same meaning as any item on the second list.

If the results break into factors close to what was predicted a priori, and the neutral parties do not perceive duplication across the lists, we concluded that there was not a serious common method variance problem in the data. No common variance problems were identified by either test.

RESULTS

The first analytical step, an exploratory factor analysis of the IC scales across the three countries, revealed a five-factor structure that was similar to that in Wagner's 1995 study (see Table 1). The total variance explained by these five factors was 54.8%. These five factors were IC1 Stand Alone (Stdalone), IC2 Win Above All (Winall), IC3 Group Preference (Grppref), IC4 Sacrifice (Sacrif), and IC5 Individual Thinking (Indivthk). The two networking factors were also stable, and together they explained 72.2% of the variance. The exploratory factor analysis results are shown in Table 1. Means and standard deviations of the variables appear in Table 2.

The next step was to develop models that simul- taneously tested the hypotheses in each country and across countries. We used Lisrel path models that were built upon traditional variables. We then tested the hypothesized relationships in the Lisrel


TABLE 1 Factor Loadings of Variables

IC Factors (n = 630; r2 = 0.548) Individualism-Collectivism Factor Loadings

Stand Alone (alpha = 0.79) 12 3 4 5 11. Only those who depend on themselves get ahead in life .15 .04 .67 .01 .07 12. To be superior a person must stand alone .25 -.06 .61 -.08 .11 13. If you want something done right, you've got to do it yourself .17 .11 .65 -.05 .03 14. What happens to me is my own doing .08 .11 .58 -.04 -.08 15. In the long run, the only person you can count on is yourself .20 .07 .69 -.12 .03

Win Above All (alpha = .83) 16. Winning is everything .75 -.04 .28 -.07 .02 17. I feel that winning is important in both work and games .69 .05 .14 -.07 -.05 18. Success is the most important thing in life .64 .03 .21 -.01 .09 19. It annoys me when other people perform better than I do .62 -.04 .12 -.06 .12

110. Doing your best isn't enough; it is important to win .76 .02 .13 -.05 .09 Group Preference (alpha = .84)

111. I prefer to work with others in a group rather than working alone -.03 .01 -.05 .86 .10 112. Given the choice, I would rather do a job where I can work -.14 -.12 -.10 .70 -.10

alone rather than doing a job where I have to work with others in a group (reverse-scored)

113. Working with a group is better than working alone -.05 .09 -.09 .86 .08 Sacrifice in Group (alpha = .83)

114. People should be made aware that if they are going to be part of .02 .70 .04 -.06 -.04 a group, then they are sometimes going to have to do things they don't want to do

115. People who belong to a group should realize that they're not .03 .78 .06 -.03 -.08 always going to get what they personally want

116. People in a group should realize that they sometimes are going -.06 .86 .10 .01 -.13 to have to make sacrifices for the sake of the group as a whole

117. People in a group should be willing to make sacrifices for the .02 .68 .07 .05 -.10 sake of the group's well-being

Individual Thinking (alpha = .77) 118. A group is more productive when its members do what they .03 -.08 .06 .01 .77

want to do rather than what the group wants them to do 119. A group is most efficient when its members do what they think .07 -.08 .04 .00 .71

is best rather than doing what the group wants them to do 120. A group is more productive when its members follow their own .13 -.16 .00 .06 .67

interests and concerns

Network Factors (n = 644; r2 = .72.2) Network Factor Loadings

Horizontal Networking Behavior (alpha = .92) 1 2 Nl. I meet my classmates after class to check on unclear answers .84 .11 N2. I meet my classmates after class to check on answers to .84 .11

questions the instructor did not answer adequately N3. I meet my classmates after class to check on answers to .92 .13

confusing questions N4. I meet my classmates after class to discuss areas I do not .82 .21

understand Vertical Networking Behavior (alpha = .89)

N5. I check my views with my instructor after class .12 .72 N6. I privately seek out my instructor for solutions to questions .15 .83 N7. I raise questions with my instructor after class .18 .85 N8. I take time to meet my instructor after class .07 .88

Normalized Best Grades (n = 566; r2 = .75; alpha = .92) Normalized Grade Factor Loadings

51. Top Best Grade .76 52. Second Best Grade .88 53. Third Best Grade .94 54. Fourth Best Grade .87

Nofe. Maximum likelihood extraction and varimax rotation.


TABLE 2 Means and Standard Deviations of Variables

Mean (HK) Mean (Sg) Mean (U.S.) Variables (n = 266) (n = 131) (n = 250) SD (HK) SD (Sg) SD (U.S.)

STDALONE 4.49 4.20 4.50 1.05 1.29 1.31 WINALL 4.00 3.06 3.93 1.14 1.11 1.40 GRPPREF 3.97 4.64 4.10 1.19 1.30 1.39 SACRIF 5.32 5.79 5.51 0.69 0.71 1.01 INDIVTHK 3.80 3.08 3.03 1.23 1.15 1.33 HORIZONTAL NETWORKING BEHAVIOR 3.48 3.25 2.83 1.16 1.06 1.29 VERTICAL NETWORKING BEHAVIOR 2.76 2.36 2.76 1.10 0.88 1.29 NORMGRD (0.01) (0.02) (0.06) 0.92 0.89 0.95

path model across the three countries using a stacked model approach (Joreskorg & Sorbom, 1989). Age and gender were controlled for in the stacked model. The results (Figure 2) showed that the overall model fit the three countries well and did not vary significantly across countries (GFI = .91; NFI = .92; CFI = .94; Chi-Square (df = 88) = 281.27, p < 0.01).

Results indicated that all five IC variables were significantly related to each other in the model although in varying degrees. The correlations among IC variables were not surprising, as all of them measured some aspect of the individualism- collectivism dimension. The resulting phi coefficients ranged from .13 between Stdalone and Winall to .74 between Grppref and Indivthk. Al- though all were related to each other, only Stdalone predicted the networking variables, horizontal networking (Horizontal Nt; gamma = .68) and vertical networking (Vertical Nt; gamma = .75).

The direction of these relationships ran counter to the prediction of Hypothesis 1. Second, both horizontal networking behavior (beta = .32) and vertical networking behavior (beta = .54) predicted grade performance. All results were significant at the t > 1.96 level and support Hypothesis 2.

Although Lisrel fit indices indicated no significant overall model relationship differences across the three countries in the stacked model, we were nevertheless curious whether mean value differences existed across countries for the two network variables. Such differences could indicate future research directions and possibly shed light on the meaning of informal networks in each country. We therefore did a one-way ANOVA test on the two network variables with subsequent post hoc tests of between country differences (see Tables 3A and 3B). Although scores on the networking variables were not high in any country (ranging between 2.36 and 3.48), the post hoc ANOVA test results did show

FIGURE 2 Hong Kong, Singapore, and U.S. Network Learning Model. Goodness of Fit Index = 0.91; Normed Fit

Index = .92; Chi-Square (88 df) = 281.27, (p < 0.01). All bolded lines t > 1.96 (Tau-Equivalence Stacked Model).

yyj STDALONE >^^ 6Q

^// r-13 \ ^"^^ HORIZONTAL NT .

/f(l\ J[ WINALL

I \ \ .32

V /X'57/^ GRPPREF \ GRADE

NCX^^I SACRIF 1 V / .54

\SSs/^ 71 I VERTICALNT * ^« INDIVTHK


TABLE 3A Post Hoc Comparison of Network Means by Country

95% Confidence Interval

Mean Difference Std. Lower Upper

Dependent Variable (I) Location (J) Location (I-J) Error Sig. Bound Bound

HORIZONTAL NETWORKING BEHAVIOR Hong Kong Singapore 0.23 0.13 0.17 -0.07 0.53 Hong Kong U.S. 0.65 0.11 0.00 0.40 0.90 Singapore U.S. 0.42 0.13 0.00 0.12 0.72

VERTICAL NETWORKING BEHAVIOR Hong Kong Singapore 0.40 0.12 0.00 0.12 0.69 Hong Kong U.S. 0.00 0.10 1.00 -0.23 0.24 Singapore U.S. -0.40 0.12 0.00 -0.69 -0.11

TABLE 3B Post Hoc Comparison of Network Means Within Country

95% Confidence Interval

Mean Difference Lower Upper

Country (I-J) Std. Error Sig. Bound Bound

HORIZONTAL NETWORKING VERTICAL NETWORKING (I-J) Std. Error t value df Sig (2-tailed) BEHAVIOR (I) BEHAVIOR (J)

Hong Kong (n = 266) 3.48 2.76 .72 .08 8.64 265 0.00 Singapore (n = 131) 3.25 2.36 .89 .12 7.39 130 0.00 U.S. (n = 249) 2.83 2.76 .07 .09 .77 248 .443

some significant differences. First, horizontal networking behavior in the U.S. sample (M = 2.36) was significantly lower (F (2, 643) = 19.37) than in Sin- gapore (M = 3.25) or Hong Kong (M = 3.48). On the other hand, an examination of vertical networking behavior showed that the Singapore sample (M = 2.36) was significantly lower (F (2, 643) = 6.43) than Hong Kong (M = 2.76) or the United States (M = 2.76). Another analysis of differences in networking behaviors within the country showed that horizontal networking behaviors were significantly higher than vertical networking behaviors in Singapore (Mean Diff = .88; t > 7.39) and Hong Kong (Mean Diff = .71; t > 8.65). There was no significant difference between these two types of networking behaviors in the United States.

DISCUSSION AND PEDAGOGICAL RECOMMENDATIONS

The stability of the five IC and two networking factors across the three countries, together with the lack of country differences in the overall model, indicated some degree of across-country congru- ence in the way IC was influencing networking behaviors and grade performance. Unexpectedly, the Stand Alone dimension of IC was the only clear

predictor of both types of networking behaviors. These results indicated that people who value this aspect of individualism more often seek out others - both professors and students - for information.

Although we had hypothesized that collectivism rather than individualism would be positively related to networking, the contrary results found here make sense if we consider the deeper meaning of the Stand Alone individualism factor - one that has factor items reflecting a sense of self- reliance, a dimension that has been shown to pow- erfully shape the self-concept (Bailey et al., 1997). In particular, item statements such as "To be superior a person must stand alone" and "What happens to me is my own doing" indicate this self- reliance focus in Stand Alone, and this is consistent with Triandis' (2002) view that individualists are prone to be more self-reliant than collectivists. This self reliance, which maps closely with a high internal locus of control, could be the reason for the relationship between Stand Alone and networking because highly individualistic people have to depend on themselves rather than others to network for their own informational needs. Results from this study show that networking is important,


but individualists, particularly those who are self- reliant, are more likely to take advantage of it.

When we examined the networking means across countries, U.S. students were more reluctant to network with peers compared to their counterparts in Singapore and Hong Kong. In addition, participants in both Asian samples preferred horizontal networking to vertical networking, and their horizontal networking means were significantly higher than those of participants in the U.S. sample. These results are interesting and, to some extent, may reveal a possible power distance influence (Hofstede, 1980) in networking because vertical networking activities require a degree of com- fort in interacting with those higher up while horizontal networking activities do not have such a requirement. The results may also offer some in- direct support for our Hypothesis 1 insofar as students in traditionally collectivistic societies (Hofstede, 1980; Trompenaars, 1993) showed a com- paratively greater preference for horizontal networking behaviors. It is important to note, though, that this study did not replicate the IC value differences between traditionally collectivistic societies of Hong Kong and Singapore and the traditionally individualistic United States (Table 2). However, this disparity is consistent with the no- tion that the IC dimension is more complex than originally conceived (Wagner, 1995) and agrees with recent research showing that traditional IC values may be changing Qavidan & House, 2001), perhaps due to export of Western values in education and media.

A further examination of Table 2 indicates that both networking variables had relatively low mean values across countries (between 2.36 and 3.48 on a 7-point scale). This was disconcerting because learning-oriented networking behaviors were found to be functional. However, it was not entirely surprising in that educational researchers have already raised "fear of embarrassment" as an important issue, even for students seeking information in a learning environment (Fassinger, 1995). This fear of embarrassment was also found in the management research literature where "personal considerations," such as impression management and political issues, have been shown to negatively influence the pure feedback-seeking process to achieve performance goals (Leary & Kowalski, 1990).

Both networking variables positively predicted grade performance, with vertical networking behavior a stronger predictor than horizontal networking behavior. This suggests that networking with fellow students and professors was beneficial in the management education context, although

networking with professors may have a slight edge. The stronger vertical networking to grade performance may reflect the more accurate information that a professor could give to students, which in turn leads to better grades. Overall, the results showed both horizontal and vertical networking activities to be beneficial to students, and where resources permit, different networking channels should be used to maximize learning outcomes. Having diverse network targets (e.g., both students and professors) are potentially more beneficial because they provide access to a wider range of information (Baker, 2000) and are more likely to yield suitable exchange partners (Hart, 1996; Rangan, 2000). It might also be the case that the functionality of networking for learning outcomes generalizes to other types of organizational contexts and participants, and this too can be pur- sued in future empirical studies.

The overall applicability of the networking model in all three countries indicates that both horizontal and vertical networking behaviors had consistent impact on learning outcomes. Thus, networking can help to achieve learning performance across different cultural environments. As such, the study represents an extension of Light's (2001) research, which focused only on American samples. One of the most important pedagogical implications from this study is that student self-initiated learning-oriented networking is a good thing, at least to the extent that higher grades are preferred to lower ones. The results also raise the important question of how academic institutions should facilitate learning-oriented networking, as- suming that developing and leveraging sets of relationships is a skill or competency that can be built and encouraged (Arthur et al., 1999). This challenge is especially important given the com- bination of the (a), relatively low frequency of networking behavior observed in this study and (b), significant positive relationships between networking and performance.

First and foremost, educational institutions could provide a supportive infrastructure in which networking activities can take place. This might require allocating common rooms or other places for students to meet and conduct informal discus- sions or networking. Instead of asking students to use the library where they are expected to be quiet, alternatives such as seats along corridors and out- door areas could be provided so that many different locations would be available for students to congregate for informal networking activities. Space can be designated for student use when it is not used for formal classes. This will allow students access to areas for engagement with similar


others. Some other creative ways to encourage student-to-student networking include coordinating the courses of students residing in the same dorms or scheduling large classes just before mealtime because classmates often continue the discussion of the course issues in these social settings (Light, 2001).

Professors can also facilitate student-targeted networking by encouraging students to participate in these learning activities (Forrett & Dougherty, 2001). For instance, professors can help students to deal with issues that may inhibit network partici- pation such as lack of self-esteem or confidence, difficulty in asking others for help, wanting to reach goals without help from others, concerns about reciprocity obligations, or concerns about sharing information (Hwang et al., 2002). Professors may address these issues by developing an appropriate mind-set and positive cooperative orientation and by focusing on appropriate skill-building seminars and workshops. Additionally, given that networking behaviors are facilitated by open- space design, use of teams and participatory processes (Baker, 2000), professors can significantly improve related learning by "opening up" classroom layouts (e.g., by using nontraditional seating arrangements) and assigning or encouraging students to form interactive outside-of-class groups (Light, 2001).

Facilitating students' networking with their professors poses a different challenge - the demand on professors' time. This is because the time re- quired to meet student's learning needs outside of class may come at the expense of other duties, especially research. Rather than considering just this time aspect, perhaps we should look to the professor's attitude. A willingness to meet students informally and engage in networking behaviors would facilitate the learning process. If that "pro- student" learning attitude were present, professors might also find other creative ways to increase their availability, say by eating lunch in the student union. Such an attitude toward learning activities should be encouraged and given due insti- tutional recognition so that efforts to network are not impeded by school or professional norms.

An additional means for improving networking activities may be through distance-learning mech- anisms, particularly for individualists (Anakwe, Kessler, & Christensen, 1999). For example, elec- tronic mail and blackboards, virtual discussion rooms, and similar communication systems could be adopted into professors' pedagogical toolkits to expand their out-of-class roles. With the move toward greater learning flexibility, professors should be encouraged to become more accessible

through these technological means. Another interesting finding of our study was the important role of underlying self-reliant characteristics in the Stand Alone variable that influenced networking behaviors. These characteristics were the underlying source of influence to seek out both fellow students and professors as information resources for learning and ultimately grade performance. It seems that students with Stand Alone values saw others as important sources of information for self- enhancement and were more likely to tap these sources to increase their grade performance. In light of this finding and considering the pedagogical implications of the Stand Alone aspect of individualism, we suggest a few ways that educational institutions and professors could incorporate this important orientation into the learning environment:

1. Schools could address this value at its source as a recruiting tool by emphasizing personal initiative and networking skills in admissions criteria, and later by sponsoring university- level training on effective networking, perhaps through previously discussed professor inter- ventions in classes and workshops.

2. Institutions could also encourage departments and professors to incorporate course-level training on networking, perhaps through ded- icated modules and assignments.

3. Complementary approaches could address issues such as developing personal initiative (e.g., via enhancing internal locus of control and self-esteem beliefs), building interpersonal skills such as communication and conflict resolution, expanding cross-cultural training, and making students aware of the informal networking benefits and available resources.

Although our findings here are interesting and consistent with previous research, it is important to note their limitations. Specifically, because the sample was drawn from three sets of undergraduate students, each from only one university in one country, it is possible that the participants were not representative of students in their respective countries or of students in general. Also, the performance measures obtained here, that is, previous semester grades, were based on self-reports with the possibility that students did not report their actual grades accurately, either intentionally or unintentionally. We normalized the grades of the three institutions for the sake of comparability across countries. However, it is possible that the assumed normal distributions did not in fact rep- resent the actual performance level of the students in our samples. Additionally, there were many variables not measured here that might also impact networking and grade performance (e.g.,


power distance, total hours studied, aspiration level, prior academic performance). Based on these limitations, future research could perhaps make use of different approaches to performance measurement so that the problem of possible non- equivalent data could be avoided. Another sugges- tion is to expand the model to control or formally test other relationships and interactions.

In all, our contribution here has been to provide initial evidence of some underlying cultural influences on student networking behavior and their consequent impact on learning outcomes. Our findings suggest that educators and trainers need to consider different networking strategies arid their facilitation to maximize the learning of their students.

REFERENCES Adler, N. J., Doktor, R.# & Redding, G. S. 1986. From the Atlantic

to the Pacific century: Cross-cultural management re- viewed. Journal of Management 12: 295-318.

Anakwe, U., Kessler, E. H., & Christensen, E. W. 1999. Distance learning and cultural diversity: Potential users' perspective. International Journal of Organizational Analysis, 7(3): 224-243.

Arthur, M. B., Inkson, K., & Pringle, J. K. 1999. The new careers: Individual action and economic change. London: Sage.

Bailey, J. R., Chen, C. C, & Dou, S. G. 1997. Conceptions of self and performance-related feedback in the U.S., Japan and China. Journal of International Business Studies, 28: 605- 625.

Baker, W. E. 2000. Achieving success through social capital: Tapping the hidden resources in your personal and business networks, San Francisco, CA: Jossey-Bass.

Bentler, P. M., & Chou, C. 1987. Practical issues in structural modeling. Sociological Methods and Research, 16: 78-117.

Brass, D. J. 1984. Being in the right place: A structural analysis of individual influence in an organization. Administrative Science Quarterly, 29: 518-539.

Chen, C. C, Yu, K. C, & Miner, J. B. 1997. Motivation to manage: A study of women in Chinese state-owned enterprises. Jour- nal of Applied Behavioral Science, 33(2): 160-173.

Crampton, S. M., & Wagner, J. A. 1994. Percept-percept inflation in micro-organizational research. Journal of Applied Psy- chology, 79: 67-76.

Daft, R. L, & Lengel, R. H. 1984. Information richness: A new approach to managerial behavior and organizational design. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior, (6: 191-233). Greenwich, CT: JAI Press.

Earley, P. C. 1989. Social loafing and collectivism: A comparison of the United States and the People's Republic of China. Administrative Science Quarterly, 34: 565-581.

Earley, P. C. 1993. East meets West meets Mideast: Further explorations of collectivistic and individualistic work groups. Academy of Management Journal, 36: 319-348.

Earley, P. C. 1994. Self or group? Cultural effects of training on

self-efficacy and performance. Administrative Science Quarterly, 39: 89-117.

Fassinger, P. A. 1995. Understanding classroom interaction: Stu- dents' and professors' contribution to students' silence. Journal of Higher Education, 66: 82-96.

Forrett, M. L, & Dougherty, T. W. 2001. Correlates of networking behavior for managerial and professional employees. Group and Organization Management, 26(3): 283-311.

Greenfield, P. M., Raeff, C, & Quiroz, B. 1996. Cultural values in learning and education. In B. Williams, (Ed.), Closing the achievement gap: A vision for changing beliefs and prac- tices: 37-55. Alexandria, VA: Association for Supervision and Curriculum Development.

Hart, R. 1996. Effective networking for prof essional success: Mak- ing the most of your personal contacts. Brussels, Belgium: Sterling Books.

Hofstede, G. 1980. Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage.

Hofstede, G. 2001. Culture's consequences: Comparing values, behaviors, institutions, and organizations across nations, (2nd Ed.). Thousand Oaks, CA: Sage.

Hui, C. H. 1988. Measurement of individualism-collectivism. Journal of Research in Personality, 22(1): 17-36.

Hwang, A., Ang, S., & Francesco, A. M. 2002. The silent Chinese: The influence of face and Kiasuism on student feedback seeking behaviors. Journal of Management Education, 26(1): 76-98.

Ibarra, H. 1993. Personal networks of women and minorities in management: A conceptual framework. Academy of Man- agement Review, 18: 56-87.

Javidan, M., & House, R. J. 2001. Cultural acumen for the global manager: Lessons from project GLOBE. Organizational Dy- namics, Spring: 289-305.

Joreskorg, K. G., & Sorbom, D. 1989. Lisrel 7: A guide to the program and applications (2nd Ed.). Chicago, IL: SPSS Inc.

Kotter, J. 1982. The general manager. New York: The Free Press.

Leary, M. R., & Kowalski, R. M. 1990. Impression management: A literature review and two-component model. Psychological Bulletin, 107: 34-47.

Leeman, R., & Why mark, J. 2001. Networking for knowledge and business improvement: A bumpy ride for networking? Man- agement Services, 45(8): 14-17.

Light, R. J. 2001. Making the most of college: Students speak their minds. Boston, MA: Harvard University Press.

Luthans, F. 1988. Successful vs. effective real managers. Acad- emy of Management Executive, 2(2): 127-132.

Markus, H. R., & Kitayama, S. 1991. Culture and the self: Impli- cations for cognition, emotion, and motivation. Psychologi- cal Review, 98: 224-253.

Olson, M. 1971. The logic of collective action. Cambridge, MA: Harvard University Press.

Pierce, J. A. 1999. Faculty survey on business education reform. Academy of Management Executive, 13(2): 105-109.

Podsakoff, P. M., & Organ, D. W. 1986. Self reports in organizational research: Problems and prospects. Journal of Man- agement, 12: 531-544.

Poell, R. F., Chivers, G. E., Van der Krogt, F. J., & Wildemeersch,


D. A. 2000. Learning-network theory. Management Learn- ing. 31(1): 25-49.

Rangan, S. 2000. The problem of search and deliberation in economic action: When social networks really matter. Academy of Management Review, 25(4): 813-828.

Sonnenberg, F. K. 1990. The professional (and personal) profits of networking. Training and Development Journal, 44: 55-60.

Treisman, U. 1992. Studying students studying calculus: A look at the lives of minority mathematics students in college. The College Mathematics Journal, 23(5): 362-372.

Triandis, H. C. 1995. Individualism and collectivism. Boulder, CO: Westview Press.

Triandis, H. C. 2002. Generic individualism and collectivism. In M. J. Gannon, & K. L. Newman (Eds.), The Blackwell hand- book of cross-cultural management: 16-45. Oxford, En- gland: Blackwell.

Trompenaars, F. 1993. Riding the waves of culture: Understand- ing diversity in global business. London: The Economist Books.

Wagner, J. A., III. 1995. Studies of individualism-collectivism: Effects on cooperation in groups. Academy of Management Journal, 38(1): 152-172.

Webb, M. S., Mayer, K. R., Pioche, V., & Allen, L. C. 1999. Inter- nationalization of American business education. Manage- ment International Review, 39(4): 379-397.

Alvin Hwang is an associate professor at Pace University. He received his PhD from UCLA and has research interests in human resources, learning performance, cross-cultural differences, competitiveness and leadership. He received the 2000 Academy of Management Best Paper in Management Development Award and also 2000 and 2003 Outstanding Reviewer Awards.

Eric H. Kessler (PhD, Rutgers University) is an associate professor of management and director of the Lubin Leaders and Scholars Program at Pace University. His research, widely published in leading management and technology journals, focuses on decision making in organizations particularly as applied to innovation, learning, and emerging technology. Anne Marie Francesco is a faculty member in the Department of Management, School of Business, Hong Kong Baptist University. She has a PhD in industrial/organizational psychology from The Ohio State University. Her current research interests include cross-cultural management and organizational behavior, life balance, and culture and feedback processes.

Documents

Student Networking