Cross-disciplinary knowledge: desperate call from business enterprises in coming smart working era.
Lee, Jungwoo
JEL Classification: D21, E24, I21, M53, O15.
Introduction
Advancement of science and technology, in effect, underlies
today's industrialized society with so many business enterprises
based on these advancements, especially since the Industrial Revolution.
The Industrial Revolution was the period when science and technology was
actually begun to put into use by applying it to practices and
businesses, with these applications bringing about tremendous changes in
our society (Leydesdorff 2012). Based on such changes, capitalism has
evolved into the world of business corporations of mass production and
business management. Hence, the source of wealth behind contemporary
society is based on the development and use of science and technology by
business enterprises.
In this century, there have been many studies concerning the
processes through which science and technology advances or of where and
how they exert influences (Pinch, Bijker 1984; Bijker et al. 1987). In
this context, science, technology society (STS) emerged as an academic
discipline, though the emergence of STS is relatively recent as compared
to the history of science and technology itself (Osborne et al. 2003;
Borup et al. 2006). STS is a cross-disciplinary discipline which is
based on the perception that science and technology have the same
historical root as humanities and social sciences and that the
interaction and convergence of these areas are getting more important
when we are facing the current reality of increasing specialization and
detachment (Gregory 2007).
However, an analysis of STS-related studies shows that STS can be
characterized as a discipline combining different research trends rather
than as a clearly-defined independent field of science (Yager 2000;
Simmons et al. 2005; Zeidler et al. 2005; Wajcman 2006). Such trends in
research of STS are also reflected in education, and many studies have
been conducted on the organization and improvement of curricula in
education under the name of STS. In particular, since the 1980s, STS has
been a globally-recognized discipline in secondary education (Yager
2000, 2007; Sjoberg 2001; Aikenhead 2003; Brickhouse, Kittleson 2006;
Mansour 2009). STS studies in educational contexts investigate how the
development of STS curricula mainly in middle and high schools has
changed the way teachers and students perceive science and technology.
In this context, several studies have made progressive attempts to
develop and refine instruments to measure perceptions concerning science
and technology (Sjoberg 2001; Lee, Erdogan 2007; Turner 2008). In
addition, general public's perceptions of science and technology
are being discussed and studied from policy makers' view point
(Davison et al. 1997; Cajas 1999; Michael 2002; Miller 2004; European
Commission 2005, 2010; Delgado et al. 2011; Prpic 2011; Retzbach et al.
2011).
However, although business enterprises are at the center of
contemporary society, built upon advanced science and technology and are
the main employers of students who come out of educational institutions,
there have not been many studies of how business executives perceive the
importance of STS and other types of cross-disciplinary knowledge. In
other words, students' and teachers' perceptions of science,
technology and society are also important in an educational context
(Stukalina 2012), but it is critical for us to understand how business
executives perceive STS in their operational context (Prpic 2011;
Retzbach et al. 2011; King 2012; Stukalina 2012), as businesses are the
pillars of modern society. Graduates taught through these
cross-disciplinary curricula in higher educational institutions are the
major source of human resources in these business enterprises (Skare
2011).
In this regard, a survey of business executives was conducted in
this study to understand how business owners and managers perceive the
value and priorities of cross-disciplinary education (i.e. STS) in
connection with their business operations and executions. A
questionnaire was newly developed by a focus group of professionals to
examine how different knowledge domains are perceived in different
aspects of business operations, such as staff training or recruitment,
and related decision criteria used in their business processes.
Previously developed items available in the STS literature (e.g. VOSTS)
are not suitable for the purpose of this study, as those items measure
very general perceptions of science and technology, such as definitions
of science and technology and R&D, the effects of science and
technology on each part of our society, and the images of scientists and
technologists (Aikenhead et al. 1989). In this study, a questionnaire is
specifically designed and developed to measure and assess how executives
evaluate these cross-disciplinary knowledge related to their task and
job. Furthermore, the questionnaire asks about the priorities in STS
curricula design as well as the actually preferred composition of
curricula.
1. Research procedure
One of the most frequently used measuring tools in STS is the VOSTS
(Views On Science-Technology Society), which was developed in the
1980's (Aikenhead et al. 1989). VOSTS assesses what students or
teachers think of technology itself as well as its social implications.
VOSTS consists of eight dimensions: definitions of science and
technology, society's influence on science and technology, the
influence science and technology have on the society, the effect of
science education in school on the society, the characteristics of a
scientist, the social composition of scientific knowledge, the social
composition of technology, and the epistemological stance concerning
science and technology. Also, rather than resorting to a quantitative
Likert-type scale, the VOST asks open-ended questions geared towards
qualitative analysis.
In reality, VOSTS was developed based on surveys and interviews of
Canadian high school students and may not be the most accurate match for
discovering what business executives think of science and technology in
terms of their business operations. For example, in VOSTS, regarding the
question of what science is, the answers choices are: a field of
studies, experimentation, knowledge, or an entire organization for
acquiring knowledge, all of which are mostly irrelevant when it comes to
business operations. As this study seeks to examine how business leaders
view the importance of science and technology as it is related to the
management of their businesses, such feedback on simple recognition
falls short of providing accurate information for the purpose of this
study.
1.1. Instrument development
The survey instrument for this study was developed in three stages
as shown in Figure 1. (1) Three executives were recruited for discussing
and identifying a rough list of related topics for and of their own
business operations, related to STS. At the beginning of the session,
the researcher briefed the participants on the goal of the session and
triggered their discussion around issues involving science and
technology in their businesses. The goal of this session was to identify
relatively large-grained topics concerning science and technology and
the use of science and technology in business operations. (2) At the
second stage of instrument development, other three experts were
recruited for the actual item development: two experts are academics in
education and communications while third is a business executive with
more than ten years of experience in electronics business. Topics
identified in the first session were delivered and briefed at the
beginning of a three-hour focus group session in this session. Forty
questions were delivered at the end. (3) Five experts were, again,
recruited separately for questionnaire refinement including a pretest.
For this session, academics were recruited from different field:
journalism, biology, information technology, public administration, and
theology, balancing different perspectives.
[FIGURE 1 OMITTED]
In the first stage, eight topics emerged and were confirmed by the
panel of three business executives (Table 1) as critical when they make
operational decisions for their businesses. The topics include both
specific and general topics. Specific topics include an identification
of necessary courses while general topics include personal acceptance
and tolerance levels of unfamiliar knowledge.
In the second stage, three experts from different field of
expertise (education, communication and business) were recruited for a
focus group. The goal of this focus group was briefed at the beginning
of the session, this being the development of actual questions for the
topics identified in the first session. Eight topics were briefed at the
beginning of the session, and using their own notebook, they were
allowed to consult previous studies on the Internet for reference. After
four hours of deliberation, forty specific questions were developed with
measurement scales and other details.
Again, five experts were recruited separately from the panels in
previous sessions. The goal of the third stage was to refine the
questions developed in the second session. These experts were recruited
from among the members of Science Technology Society Forum at Yonsei
University, Seoul, Korea. The five from different departments:
journalism, biology, information technology, public administration, and
theology, thus balancing different perspectives. Two of the recruits had
more than five years of business experience before academia. The focus
of this session was on the accurate wording of questions and on the
order of the questions. They were not allowed to add or exclude topic
categories or questions. After the correcting and re-wording of forty
questions, the final survey instrument was constructed with demographic
questions.
1.2. Data collection
For data collection, business executives were recruited from
various executive training programs at Yonsei University in Seoul,
Korea. Initially, program coordinators were contacted by email and phone
to solicit their participation. There were nine executive programs
ongoing at the point of contact. Four agreed to participate in the
study. Printed questionnaire were delivered on the designated day of the
class. Briefing of the purpose and methods of the study was done before
handing out the questionnaires. Out of two hundred and six executives
enrolled in these four programs, one hundred and forty nine
questionnaires were collected and returned. This was due to the high
absence ratio at the beginning of the class. Four were removed due to
incompleteness and finally one hundred and forty five returned
questionnaires were used for the analysis. SPSS was used for the
analysis.
1.3. Demographics
The demographics of the respondents are presented in Table 2. CEOs
representing corporations made up the largest proportion, and executives
made up over 60% of the respondents. As for the types of businesses,
communication and services together accounted for over 40%, the highest
proportion. Sixty two percent of the companies represented had over 100
employees, and 20% of the companies had less than 20 employees. As for
college degrees, 69 of the respondents were in engineering, and 76 in
humanities and social science, showing a balanced spread of different
fields. Their professional areas also showed diversity with finance and
manufacturing relatively less represented than other areas.
2. Analysis
A survey of business executives were conducted in this study to
understand how they assess the importance of and need for
cross-disciplinary education and training (i.e. STS) in connection with
their business operations. A new questionnaire was developed and
administered. One hundred and forty five data points were collected.
Analyses were conducted using SPSS. For each topic of interest
identified, the mean values for each question was compared against each
other, testing the statistical significance of differences. For some
interesting topics, more detailed analyses were conducted.
2.1. Effect of cross-disciplinary knowledge on business operations
First, the importance of science and technology for business
management was evaluated at 3.75 (75%) on a five-point scale, meaning
that these executives value science and technology knowledge relatively
highly in their work. They also value highly the contribution of science
and technology knowledge on business problem-solving ability (3.77,
75%). Interestingly, cross-disciplinary knowledge seems to be valued
lightly higher than science and technology knowledge itself when it
comes to the application to actual business problems (compare P2 and P3
in Table 3).
At a glance, P3 and P4 scored higher than P1 and P2, showing a
tendency to view cross-disciplinary ability to combine technology with
other areas of knowledge to be more important than the influence of
technology or technological knowledge alone (P3). An understanding of
the socio-cultural changes arising from technological advancement was
awarded higher scores than an understanding of technology per se (P4).
To verify these results statistically, pair-wise t-tests were conducted
on the mean difference of each variable. The results of these tests are
shown in Table 4. It was found that the means of P3 and P4 are
statistically significantly higher than means of P1 and P2. Thus, it can
be concluded that business executives value cross-disciplinary knowledge
more than they do technological knowledge per se.
Next, the responses were grouped according to the respondents'
undergraduate major and an independent sample t-test was conducted for
P1 to P4 (Table 5). The purpose of this test is to determine if the
group may have different views on different types of knowledge.
Interestingly, the results showed that those with degrees related to
science and technology placed somewhat higher value on science and
technology than those with humanities and social science degrees.
However, no significant differences were found in terms of the valuing
cross-disciplinary knowledge in terms of their business operation. The
reason behind technology majors valuing technology more than those with
humanities backgrounds seems to be a natural partiality stemming from
their education. There is no statistically significant difference
between the two groups regarding their assessment of cross-disciplinary
knowledge; this may be due to the fact that beyond their educational
training, they must have embodied the importance of cross-disciplinary
skills in their business practice.
2.2. Acceptance and tolerance of cross-disciplinary knowledge
The next set of questions evaluated the respondents'
acceptance and tolerance of knowledge from domains different from their
own (Table 6). As technologies develop further and becomes integrated
more deeply in our modern society, the acquisition of new information is
becoming more critical. These questions are included based on the idea
that especially in managing business, application of knowledge
encompassing different fields in a convergent manner are more important
that in-depth knowledge itself in a specific domain. P5 asks about their
acceptance and tolerance to knowledge in humanities and social sciences.
The mean score was 3.2569 (65%). P6 asks about the acceptance and
tolerance of knowledge related to science and technology. The mean was
3.1862 (64%), which is slightly lower than that of the other group but
of no statistical significance.
An independent sample t-test was performed to compare the means of
the two groups: science technology majors (ST) and humanities and social
science majors (HS). Regarding the question on the tolerance level of ST
people for knowledge in different area, the ST group evaluates
themselves as being more tolerant (3.46, 69.2%) while the HS group
believe ST majors were not as tolerant of knowledge in other area (3.07,
61.4%). These two group means were significantly different (t = 2.732).
With regards to the question on the tolerance level of HS people, the
two groups of respondent seem to agree at a similar level (3.16 and
3.11, respectively with no statistical significance).
2.3. Relative importance of different knowledge fields in the area
of career development
The next set of questions was related to important human resource
management questions: which fields of knowledge these business
executives viewed as more important when promoting their employees.
Regarding the detailed questions in this part of the survey, the experts
in the second phase of instrument development took a long time to come
to an agreement. Rather than making a coarse division of knowledge into
technology and humanities, suggestions were made by panel members to use
a slightly more detailed but meaningful typology of the type that is
commonly used in business. It took a considerable amount of time to
decide whether this was conducive for the purpose of this study. Each
classification in the suggested typology was discussed in detail, after
which the following seven subareas of knowledge were included for these
questions: science and technology (U1), humanities and social sciences
(U2), creativity and art (U3), economy and policy (U4), business
management (U5), social ethics (U6), and organizational communications
(U7).
The means of responses are presented in Table 7. Organizational
communication was assessed as highest (4.64/92.8%) while science and
technology was lowest (3.63/72.6%). Interestingly, score increases from
U1 to U7 incrementally. To verify the differences in the means of these
seven areas statistically, an independent sample t-test was conducted
for each adjacent pair. Statistically significant differences in means
were found in every pair except two: between U3 and U4 (creative art and
economic policy), and between U5 and U6 (business management and social
ethics).
Most of all, communication skills seems to be most critical for
being promoted, with ethics and management-related knowledge following.
An understanding of economy/ policy and creativity/art comes next, while
domain knowledge comes last. This is consistent with previous findings
in human resource research (Allred et al. 1996), in which
cross-disciplinary knowledge and collaborative leadership along with
good personal traits of flexibility, integrity and trustworthiness were
critically emphasized to become a good manager.
2.4. Relative importance of different knowledge orientations in
recruiting
Next four questions dealt with the importance of knowledge areas
referenced in recruiting new employees. R1 asks about knowledge of their
own academic area while R2 asks about knowledge about the business of
the company for which they want to work. R3 ask about the importance of
knowledge related to STS while R4 explores the importance of general
cross-disciplinary knowledge (Table 8). The seven knowledge area used in
the previous section regarding the promotion decision were not used here
because decision criteria for recruiting are not as complicated as
promotion cases. From the perspective of business operations, it seems
natural for executives to value practical knowledge more than academic
knowledge. Together with the analyses of the seven knowledge area
mentioned above, it can be concluded that business executives value
cross-disciplinary knowledge and application capability much more than
in-depth academic knowledge in a particular area.
Therefore, it can be expected the cross-disciplinary knowledge
would be valued somewhat higher than STS, as STS seems to be more
specific. A comparison of R3 and R4 met this expectation. We found
statistically a significant difference between R3 (3.67, 73.4%) and R4
(4.13, 82.6%). General cross-disciplinary knowledge seems to be valued
much higher than specific STS-related knowledge (Coll, Zegwaard 2006).
This can be ascribed to the fact that the range of cross-disciplinary
knowledge is seen as more comprehensive than STS-related knowledge.
2.5. Need for cross-disciplinary education and training
The next set of questions was geared towards assessing the need for
cross-disciplinary training. The first question asks about the need for
human and social science education for science and technology people in
business (N1), while the second asks about the need for science and
technology education for human and social science people in business
(N2). Compared to the questions in other sections, the means of N1 and
N2 were found to be relatively high with no statistically significant
differences (4.10/82% for N1 and 4.03/80.6% for N2), signifying the
importance of cross-disciplinary knowledge in business operations. Table
9 presents the t-test results.
N3 explores whether cross-disciplinary education becomes more and
more important as an employees are promoted to a higher rank in
business. The mean of N3 was 4.52 (90.5%), and was found to be
significantly higher than N1 and N2. This is consistent with findings in
other areas (Chen et al. 2005), in which cross-disciplinary knowledge
relates to corporate entrepreneurship.
2.6. Status of cross-disciplinary education and training
Next, the survey inquired into the current status of cross- and
inter-disciplinary education and training programs in place. The
questions along with the means and standard deviations are reported in
Table 10. First, of all education areas, the proportion of science and
technology education was scaled at 20%, 40%, 60%, 80%, and 100% (E1).
The mean for E1 was 2.7448 (roughly 54.89% of all education and
training). This was slightly higher than the expected value.
Question E2 and E3 examine the proportions of cross-disciplinary
training. E2 asks about relative frequencies of science and technology
education given to humanities and social science majors while E3 asks
about the relative frequency of education related to humanities and
social science given to science and technology majors. The answer was
scaled into five levels: never, once or twice a year, once or twice a
quarter, once or twice per month, and constantly at work. The means for
these questions were 2.81 and 2.86, respectively, which may lead to the
conclusion that cross-disciplinary training is given at least once but
less than twice every quarter.
An interesting and serendipitous finding is worth mentioning here.
When we compared the means of E1 in the two groups (the science and
technology major group and humanities and social science major group),
we found statistically significant differences. The science and
technology major group think education and training related to science
and technology accounts for about 63.48% (3.17) of all training, whereas
the humanities and social science major group think the proportion is
somewhat lower, at 47.11% (2.36), which represents cognitive dissonance
between these two groups for a seemingly objective question (Table 11).
This can be interpreted as the humanities and social science major
group's thirst for education and training related to science and
technology being greater than that felt by their counterparts.
Alternatively, they may not realize some of the training sessions are
related to science and technology. Further studies are necessary to
investigate the cognitive bias or dissonance causing this difference.
2.7. Suggested positioning of cross-disciplinary education and
training
The next set of questions asked about the suggested positioning of
cross-disciplinary courses of education and training (from L1 to L4).
respondents were asked to prioritize the proper positioning appropriate
for cross-disciplinary education and training: (1) as undergraduate
common courses taught across disciplines; (2) as an undergraduate major;
(3) as a professional graduate school; and (4) as corporate re-training
program after employed. Table 12 presents the frequency count for the
responses. The response frequency counting undergraduate major as most
appropriate was found to be 14 out of a possible 145 (9.7%), while this
as least appropriate was counted 67 (46.2%), which implies that
cross-disciplinary education and training as an undergraduate major is
not preferred at all by these business executives.
As shown in Table 12, executives suggest corporate training a the
most appropriate position for the cross-disciplinary program (50 out of
145) and a graduate major as the second most appropriate (42 out of
145). Undergraduate common courses received 39 out of 145 possible votes
as most appropriate. Compared to the others, the undergraduate major
enticed only 14 votes out of a possible 145. The larger discrepancy
between the undergraduate major and the other three options suggests
that rigorous training in a single paradigmatic discipline is a
necessary condition for goo cross-disciplinary training. Executives seem
to view undergraduate majors as a corner-stone before anything can be
built.
It seems that, though they think this type of education is critical
for their business operations, this type of training cannot be achieved
before students choose their own area of expertise. Cross-disciplinary
education and training can only be done after securing specialized
knowledge; moreover, this specialization can only be built with new,
consilient and applied knowledge (Gudas 2009). This is more evident when
examining the fourth row of Table 12, which shows the least frequent
choices. Those who chose the undergraduate common course as the least
ideal were highest in terms of frequency (67), while the frequencies of
the other three were only 42, 11, and 25.
Especially in engineering, cross-disciplinary education and
training was strongly emphasized pointing out engineering demands
practical application of integrated cross-disciplinary knowledge in the
field. Hence progressively more cross-disciplinary elements were added
to the undergraduate curriculum and were emphasized, and enforced (Adams
et al. 2011; Litzinger et al. 2011). However, the executives, here may
think otherwise. It is suggested here that the path for proper
cross-disciplinary education starts from appropriate disciplinary
training at the undergraduate level (Borrego, Newswander 2008). As this
finding somewhat contradicts the current trend in curricular development
in engineering schools, it might need to be investigated further in
comparative studies.
2.8. Assessing the priorities of cross-disciplinary courses
The last set of questions concerned the importance of actual course
offerings of a cross-disciplinary nature to be offered in this type of
education and training program. A list of courses was built from an
Internet search and from expert input at the second phase of survey
development. After refinement, the expert panel formulated a list of
twelve courses most commonly offered in this type of program across the
globe, as listed in Table 13. Here, the respondents were asked to rate
the importance of each subject using a five-point Likert scale. Means
for each course are presented in Table 13, with the highest at the top
and lowest at the bottom. A matched samples t-test comparison was
conducted for sets of adjacent pairs to test whether the differences
were statistically significant.
Three out of twelve courses marked a mean score higher than four:
science technology communications (4.34), technology management (4.10),
and science technology entrepreneurship (4.02). This finding can be
interpreted to mean that business executives highly value flexible
communicative competence, which may be obtained by employees trained in
a cross-disciplinary manner. Executives gave prominently higher marks to
science technology communications. It can thus be inferred that they
also value highly business-related applications of this
cross-disciplinary knowledge as they gave relative high marks to
technology management and related entrepreneurship.
Between scores of 3.5 and 4, six courses were positioned: science
technology society (3.95), science technology policy (3.91), science
technology ethics (3.90), cyber ethics (3.88), technology market
analysis (3.84), and science technology literature (3.81). These
secondgroup courses are mostly related to social issues. Three courses
were rated below 3.5 with statistically different mean scores compared
to the second group, which was related to social issues of science and
technology. These courses were science technology art (3.42), science
technology philosophy (3.34), and history of science and technology
(3.24). The last group deals issues related to humanities of science and
technology.
In sum, twelve courses were grouped into three categories based on
the mean values of the importance rating. These three categories of
courses are termed as follows: (1) Science Technology Enterprise (STE);
(2) Science Technology Society (STS); and (3) Science Technology
Humanities (STH) as presented in Figure 2. The first group (STE)
received highest importance rating from the business executives. STE
courses deals directly with business-related issues using knowledge of
science and technology, such as entrepreneurship, organizational
communication, and management. The second group of courses (STS) mostly
deals with social approaches to science and technology including policy
and market analyses. Ethical issues are also dealt with in these courses
as well as literary explications related to science and technology. The
third group (STH) seems to deal with issues indigenous to science and
technology, such as philosophy, history and arts. Philosophy
investigates the supporting internal logics of science and technology
while history documents developments in the areas of science and
technology (Fig. 2).
[FIGURE 2 OMITTED]
Results, discussion and conclusion
This study examined and analyzed business executives'
understanding of the need for cross-disciplinary education and training
related to science and technology in business enterprises for coming
knowledge based smart working era. Survey instruments previously
developed to measure perceptions of science and technology seemed
inappropriate in business contexts as most of previous surveys were
concerned about the education and training of young students and
teachers. Thus, a new questionnaire was developed via phased refinements
by experts in the field. Eight topical areas deemed critical in making
operational decisions for their businesses were identified by a panel of
three business executives during the first phase of instrument
development. Specific topics included identifications of necessary
courses while general topics included personal acceptance and tolerance
levels of unfamiliar knowledge. Forty questions were developed for these
eight topical areas, reviewed, and pretested by another group of experts
before the actual questionnaire administration. For the actual survey,
business executives were recruited from various executive training
programs. One hundred and forty five data points were collected and used
for in-depth analysis.
Executives value science and technology very highly and understand
that it helps their employees solve business problems. Also, in terms of
subareas of knowledge, executives value organizational communications
very highly, as it integrates several areas of cross-disciplinary
knowledge, followed by social ethics, business management, economy and
policy, creativity and arts, humanities and social sciences, and science
and technology, in this order. When recruiting new hires, it seems that
they also emphasize cross-disciplinary knowledge beyond the specifics of
majors in the college. They understand the critical need for
cross-disciplinary training when promoted to a higher level of
management, and they conduct these types of training events at least
once every quarter. Most executives demand that their recruits have
college-level cross-disciplinary education and training, though they are
willing to offer post-hire in-house training on these issues. Executives
view cross-disciplinary education and training in terms of three
subareas: Science Technology Enterprise (STE), Science Technology
Society (STS), and Science Technology Humanities (STH) in this order of
importance.
Analysis results suggest that business executives maintain clear
understanding that science and technology are critical elements for
today's businesses and indispensable for our lives and society.
Technologies have been undergone tremendous specialization leading
people to specialize in focused domain knowledge in industrialized and
manufacturing-centric business operations. However, in today's
complex environment with different technologies converging with each
other creating novel ideas and pushing envelopes for new applications of
old ideas, cross-disciplinary integration of knowledge is realistically
a necessity for practitioners to survive, executives confirm.
Findings of this study provide a good basis for the development of
future cross-disciplinary education and training programs in coming
smart working era. However, this study has its limitations. The
questionnaire newly developed in this study may need further validation
via empirical replications in a variety of context with larger samples.
Though it went through a rigorous three-step refinements process during
several expert panels, the process was mostly qualitative. Also, the
coverage of issues may not be comprehensive enough to reveal all of the
relevant aspects of science and technology as viewed from a business
operations perspective. Only replications and refinement via qualitative
and quantitative research would advance our understanding in this area.
Caption: Fig. 1. Instrument development
Caption: Fig. 2. Grouping of cross-disciplinary courses
doi: 10.3846/20294913.2013.880080
Acknowledgements
Author would like to thank the members of the Science Technology
Society Forum at Yonsei University in Seoul, Korea, for their
encouragement and guidance throughout this research as well as their
assistance in data collection. This study was initiated as a part of the
series of forum activities funded by the Korea Foundation for the
Advancement of Science and Creativity. Primary part of this study was
partially supported by the National Research Foundation of Korea Grant
funded by the Korean Government (NRF-2012S1A3A2033474). Appreciation
also goes to my research assistant Hyejung Lee for her dedication to and
appreciation of rigorous academic endeavors.
References
Adams, R.; Evangelou, D.; English, L.; De Figueiredo, A. D.;
Mousoulides, N.; Pawley, A. L.; Schifellite, C.; Stevens, R.; Svinicki,
M.; Trenor, J. M.; Wilson, D. M. 2011. Multiple perspectives on engaging
future engineers, Journal of Engineering Education 100(1): 48-88.
http://dx.doi.org/10.1002/j.2168-9830.2011.tb00004.x
Aikenhead, G. S. 2003. STS education: a rose by any other name. A
vision for science education: responding to the work of Peter J.
Fensham. R. Cross. London and New York: RoutledgeFalmer, 59-75.
Aikenhead, G. S.; Ryan, A. G.; Fleming, R. W. 1989. Views on
science-technology society. Department of Curriculum Studies, College of
Education, University of Saskatchewan. 116 p.
Allred, B. B.; Snow, C. C.; Miles, R. E. 1996. Characteristics of
managerial careers in the 21st century, Academy of Management Executive
10(4): 17-26.
Bijker, W. E.; Hughes, T. P.; Pinch, T. J. (Eds.). 1987. The social
construction of technological systems: new directions in the sociology
and history of technology. Cambridge: MA, MIT Press. 405 p.
Borrego, M.; Newswander, L. K. 2008. Characteristics of successful
cross-disciplinary engineering education collaborations, Journal of
Engineering Education 97(2): 123-134.
http://dx.doi.org/10.1002/j.2168-9830.2008.tb00962.x
Borup, M.; Brown, N.; Konrad, K.; Van Lente, H. 2006. The sociology
of expectations in science and technology, Technology Analysis &
Strategic Management 18(3-4): 285-298.
http://dx.doi.org/10.1080/09537320600777002
Brickhouse, N. W.; Kittleson, J. M. 2006. Visions of curriculum,
community and science, Educational Theory 56(2): 191-204.
http://dx.doi.org/10.1111/j.1741-5446.2006.00011.x
Cajas, F. 1999. Public understanding of science: using technology
to enhance school science in everyday life, International Journal of
Science Education 21(7): 765-773.
http://dx.doi.org/10.1080/095006999290426
Chen, J.; Zhu, Z.; Anquan, W. 2005. A system model for corporate
entrepreneurship, International Journal of Manpower 26(6): 529-543.
http://dx.doi.org/10.1108/01437720510625449
Coll, R. K.; Zegwaard, K. E. 2006. Perceptions of desirable
graduate competencies for science and technology new graduates, Research
in Science & Technological Education 24(1): 29-58.
http://dx.doi.org/10.1080/02635140500485340
Davison, A.; Barns, I.; Schibeci, R. 1997. Problematic publics: a
critical review of surveys of public attitudes to biotechnology,
Science, Technology and Human Values 22(3): 317-348.
http://dx.doi.org/10.1177/016224399702200303
Delgado, A.; Lein Kjolberg, K.; Wickson, F. 2011. Public engagement
coming of age: from theory to practice in STS encounters with
nanotechnology, Public Understanding of Science 20(6): 826-845.
http://dx.doi.org/10.1177/0963662510363054
European Commission 2005. Europeans, science and technology.
Brussel, European Commission. 292 p.
European Commission 2010. Science and Technology. Special
Eurobarometer. Brussel, Directorate-General for Communication.
Gregory, J. 2007. Science, technology, and society: a sociological
approach, Isis 98(4): 882-884. http://dx.doi.org/10.1086/529342
Gudas, S. 2009. Enterprise knowledge modelling: domains and
aspects, Technological and Economic Development of Economy 15(2):
281-293. http://dx.doi.org/10.3846/1392-8619.2009.15.281-293
King, C. J. 2012. Restructuring engineering education: why, how and
when?, Journal of Engineering Education 101(1): 1-5.
http://dx.doi.org/10.1002/j.2168-9830.2012.tb00038.x
Lee, M. K.; Erdogan, I. 2007. The effect of
Science-Technology-Society teaching on students' attitudes toward
science and certain aspects of creativity, International Journal of
Science Education 29(11): 1315-1327.
http://dx.doi.org/10.1080/09500690600972974
Leydesdorff, L. 2012. Sociological and communication-theoretical
perspectives on the commercialization of the sciences, Science &
Education: 1-17.
Litzinger, T. A.; Lattuca, L. R.; Hadgraft, R. G.; Newstetter, W.
C.; Alley, M.; Atman, C.; DiBiasio, D.; Finelli, C.; Diefes-Dux, H.;
Kolmos, A.; Riley, D.; Sheppard, S.; Weimer, M.; Yasuhara, K. 2011.
Engineering education and the development of expertise, Journal of
Engineering Education 100(1): 123-150.
http://dx.doi.org/10.1002/j.2168-9830.2011.tb00006.x
Mansour, N. 2009. Science-Technology-Society (STS): a new paradigm
in science education, Bulletin of Science Technology Society 29(4):
287-297. http://dx.doi.org/10.1177/0270467609336307
Michael, M. 2002. Comprehension, apprehension, prehension:
heterogeneity and the public understanding of science, Science,
Technology and Human Values 27(3): 357-378.
http://dx.doi.org/10.1177/016439027003002
Miller, J. D. 2004. Public understanding of, and attitudes toward,
scientific research: what we know and what we need to know, Public
Understanding of Science 13(3): 273-294.
http://dx.doi.org/10.1177/0963662504044908
Osborne, J.; Simon, S.; Collins, S. 2003. Attitudes towards
science: a review of the literature and its implications, International
Journal of Science Education 25(9): 1049-1079.
http://dx.doi.org/10.1080/0950069032000032199
Pinch, T. J.; Bijker, W. E. 1984. The social construction of facts
and artefacts: or how the sociology of science and the sociology of
technology might benefit each other, Social Studies of Science 14(3):
399-441. http://dx.doi.org/10.1177/030631284014003004
Prpic, K. 2011. Science, the public, and social elites: how the
general public, scientists, top politicians and managers perceive
science, Public Understanding of Science 20(6): 733-750.
http://dx.doi.org/10.1177/0963662510366363
Retzbach, A.; Marschall, J.; Rahnke, M.; Otto, L.; Maier, M. 2011.
Public understanding of science and the perception of nanotechnology:
the roles of interest in science, methodological knowledge,
epistemological beliefs, and beliefs about science, Journal of
Nanoparticle Research 13(12): 6231-6244.
http://dx.doi.org/10.1007/s11051-011-0582-x
Simmons, P. E.; Brunkhorst, H.; Lunetta, V.; Penick, J.; Peterson,
J.; Pietrucha, B.; Staver, J. 2005. Developing a research agenda in
science education, Journal of Science Education and Technology 14(2):
239-252. http://dx.doi.org/10.1007/s10956-005-4424-4
Sj0berg, S. 2001. Science and Technology in Education--Current
Challenges and Possible Solutions. Meeting of European Ministers of
Education and Research. Uppsala, Sweden.
Skare, M. 2011. How important is human capital for growth in
reforming economies?, Technological and Economic Development of Economy
17(4): 667-687. http://dx.doi.org/10.3846/20294913.2011.635221
Stukalina, Y. 2012. Addressing service quality issues in higher
education: the educational environment evaluation from the
students' perspective, Technological and Economic Development of
Economy 18(1): 84-98. http://dx.doi.org/10.3846/20294913.2012.658099
Turner, S. 2008. School science and its controversies; or, whatever
happened to scientific literacy?, Public Understanding of Science 17(1):
55-72. http://dx.doi.org/10.1177/0963662507075649
Wajcman, J. 2006. New connections: social studies of science and
technology and studies of work, Work Employment & Society 20(4):
773-786. http://dx.doi.org/10.1177/0950017006069814
Yager, R. E. 2000. A vision for what science education should be
like for the first 25 years of a new millennium, School Science and
Mathematics 100(6): 327-341.
http://dx.doi.org/10.1111/j.1949-8594.2000.tb17327.x
Yager, R. E. 2007. STS requires changes in teaching, Bulletin of
Science, Technology & Society 27(5): 386-390.
http://dx.doi.org/10.1177/0270467607305737
Zeidler, D. L.; Sadler, T. D.; Simmons, M. L.; Howes, E. V 2005.
Beyond STS: a research-based framework for socioscientific issues
education, Science Education 89(3): 357-377.
http://dx.doi.org/10.1002/sce.20048
Jungwoo LEE
Yonsei University, Yonsei Ro 50, Sudaemun Gu, 120-749 Seoul,
Republic of Korea
Received 14 June 2012; accepted 19 May 2013
Corresponding author Jungwoo Lee E-mail: jlee@yonsei.ac.kr
Jungwoo LEE. He is a Professor of Systems, Technologies and
Information in the Graduate School of Information at Yonsei University,
Seoul, Korea. He teaches graduate level students and executives on
topics on and around the changing nature of work and business by
information and communication technologies. At Yonsei, he served as the
director of IT Strategy and Policy Research Institute, CEO IT program,
and the executive director for the Yonsei University Newspaper and
Broadcasting. His external services includes the Director of Smart Work
Forum, the President of the Data Governance Forum, the President of the
Korea Society for e-Business Studies, the Advisory Committee Chair for
the Federation of Korean Information Industries. Other advisory
capacities include the Government of Seocho, the Universal Content
Identifier Users Forum, the National Digital Library, and other public
organizations and private firms. He has published over 20 papers in
international journals, over 40 in Korean journals, and numerous book
chapters as well as articles in practitioner magazines. Aside from the
academic responsibilities, he had published own columns in the Digital
Times and the Segyeilbo, and served as a main anchor for a news program
at the M-Money Broadcasting Station specialized in economic analysis. He
holds a PhD and MS in computer information systems from Georgia State
University, MBA from Sogang University, BA in English Language and
Literature from Yonsei University. He has 7 years industrial experience
before entering the academics, and recently spent a sabbatical year at
the Samsung Economic Research Institute as a visiting scholar conducting
the industrial grade research.
Table 1. Topics identified
No. Category Questions
1 Effects of cross-disciplinary knowledge 4
(such as STS) on business operations
2 Acceptance and tolerance of 2
cross-disciplinary knowledge
3 Relative importance of different knowledge 7
fields in career development
4 Relative importance of different knowledge 4
fields in recruitment
5 Need for cross-disciplinary education 3
and training
6 Current status of cross-disciplinary 3
education and training
7 Suggested positioning of cross-disciplinary 5
education and training
8 Assessing priorities of different 12
cross-disciplinary courses
Total number of questions 40
Table 2. Demographics
Business Number of Position Number of
Type respondents respondents
(%) (%)
Communication 37(25) CEO 54(37)
Services 30(19) Senior Executive 27(19)
Manufacturing 28(19) Director 14(10)
Finance 15(10) Department Head 29(20)
Distribution 10(7) Manager 17(12)
Others 25(20) Others 4(3)
Total 145 Total 145
Business Number of Number of
Type employees respondents
(%)
Communication Over 100 90(62)
Services Under 20 29(20)
Manufacturing 21-50 19(13)
Finance 51-100 7(5)
Distribution
Others
Total Total 145
Professional Number of Age Number of
area respondents respondents
(%) (%)
Sales 31(21) 20s 23(16)
Human Resources 13(9) 30s 59(41)
Finance 3(2) 40s 60(41)
Marketing 10(7) 50s 2(1)
R&D 8(6) 60s 1(1)
Manufacturing 5(3)
Planning 19(13)
Others 56(39)
Total 145 Total 145
Professional Undergraduate Number of
area major respondents
(%)
Sales Engineering 69(48)
Human Resources Social Science 76(52)
Finance
Marketing
R&D
Manufacturing
Planning
Others
Total Total 145
Table 3. Effects of cross-disciplinary knowledge of business
operations
Questions Mean Std dev
P1 Importance of science & technology 3.7517 1.1337
knowledge on professional performance (75%)
P2 Effects of science and technology 3.7655 1.0001
knowledge on the ability to solve (75%)
business problems
P3 Effects of cross-disciplinary 3.9862 0.9353
knowledge on the ability to solve (79.7%)
business problems
P4 Importance of understanding 3.9310 0.8552
socio-cultural changes incurred (78.6%)
by technological advancement
Table 4. Mean difference test for knowledge assessment of
different areas
Mean Std dev Std err t-value Sig
P1-P2 -0.01379 0.70697 0.05871 -0.235 0.815
P2-P3 -0.22069 0.96802 0.08038 -2.745 0.007
P3-P4 0.05517 0.77082 0.06401 0.862 0.390
P4-P1 0.17931 1.10973 0.09216 1.946 0.054
Table 5. Group means for the area knowledge assessments
Group N Mean Std dev Std err
Importance of SE 69 4.0870 0.88682 0.10676
science & technology
(P1) HS 76 3.4474 1.24788 0.14314
Science & technolo- SE 69 4.0000 0.80440 0.09684
gy knowledge on
problem solving (P2) HS 76 3.5526 1.11229 0.12759
Cross-disciplinary SE 69 4.0290 0.89065 0.10722
knowledge on
problem solving (P3) HS 76 3.9474 0.97836 0.11223
Understanding SE 69 3.9420 0.83814 0.10090
social changes (P4) HS 76 3.9211 0.87580 0.10046
t-value Sig
Importance of
science & techno- 3.525 0.001
logy (P1)
Science & technology
knowledge on 2.751 0.007
problem solving (P2)
Cross-disciplinary
knowledge on 0.523 0.601
problem solving (P3)
Understanding 0.147 0.883
social changes (P4)
*ST: science and technology
majors, HS: humanities and
social science majors.
Table 6. Acceptance and tolerance for knowledge in other domains
Group N Mean Std dev Std err t-value Sig
P5 All 145 3.2569
ST 69 3.4638 0.91683 0.11037 2.732 0.007
HS 76 3.0677 0.82746 0.09555
P6 All 145 3.1862
ST 69 3.2609 0.91799 0.11051 0.963 0.337
HS 76 3.1184 0.86359 0.09906
Table 7. Comparing the importance of seven knowledge areas for
deciding on promotion
Area Mean Diff Mean diff Std dev
Science and 3.6276 U1-U2 -0.23448 1.09933
technology (U1)
Humanities and social 3.8621 U2-U3 -0.14483 0.90507
sciences (U2)
Creativity and art (U3) 4.0069 U3-U4 0.04138 1.01294
Economy and policy (U4) 3.9655 U4-U5 -0.23448 0.68732
Business management (U5) 4.2000 U5-U6 -0.03448 0.88517
Social ethics (U6) 4.2345 U6-U7 -0.40690 0.81220
Organizational 4.6414
communications (U7)
Area Std err t-value Sig
Science and 0.09129 -2.568 0.011
technology (U1)
Humanities and social 0.07516 -1.927 0.056
sciences (U2)
Creativity and art (U3) 0.08412 0.492 0.624
Economy and policy (U4) 0.05708 -4.108 0.000
Business management (U5) 0.07351 -0.469 0.640
Social ethics (U6) 0.06745 -6.033 0.000
Organizational
communications (U7)
Table 8. Differences in means for general knowledge areas for
recruiting
Content Mean Diff Diff Std dev
R1 Knowledge of one's 3.7448 R1-R2 -0.26897 0.90719
own academic area
R2 Business specific 4.0138
knowledge
R3 STS 3.6690 R3-R4 -0.46207 0.88992
R4 Cross-disciplinary 4.1310
knowledge
Content Std err t-value Sig
R1 Knowledge of one's 0.07534 -3.570 0.000
own academic area
R2 Business specific
knowledge
R3 STS 0.07390 -6.252 0.000
R4 Cross-disciplinary
knowledge
Table 9. Need for cross-disciplinary training
Need for Mean Difference Diff
N1: Humanities & social
science education for science 4.1034 N1-N2 0.06897
& technology people
N2: Science & technology education 4.0345 N2-N3 -0.48966
for humanities and social
science people
N3: Cross-disciplinary education 4.5241 N3-N1 -0.42069
Need for Std dev Std err t-value
N1: Humanities & social
science education for science 0.66306 0.05506 1.252
& technology people
N2: Science & technology education 0.69838 0.05800 -8.443
for humanities and social
science people
N3: Cross-disciplinary education 0.70385 0.05845 -7.197
Need for Sig
N1: Humanities & social
science education for science 0.212
& technology people
N2: Science & technology education 0.000
for humanities and social
science people
N3: Cross-disciplinary education 0.000
as promoted higher
Table 10. Current status of cross-disciplinary training
Questions Mean Std dev
E1 Proportion of science & technology 2.74 1.110
education and training
E2 Science & technology training for 2.81 1.429
humanities & social science majors
E3 Humanities & social science training 2.86 1.422
for science & technology majors
Table 11. Two-group mean differences for training status
Group N Mean Std dev Std err t-value Sig
N1 ST 69 3.1739 1.01397 0.12207 4,755 0.000
HS 76 2.3553 1.05456 0.12097
Table 12. Frequency counts for the positioning of cross disciplinary
training
Priority Undergraduate Undergraduate Graduate Corporate
common (L1) major (L2) major (L3) training (L4)
1 39 (26.9) 14 (9.7) 42 (29.0) 50 (34.5)
2 19 (13.1) 26 (17.9) 54 (37.2) 46 (31.7)
3 45 (31.0) 38 (26.2) 38 (26.2) 24 (16.6)
4 42 (29.0) 67 (46.2) 11 (7.6) 25 (17.2)
* frequency (%)
Table 13. Importance ratings of twelve cross-disciplinary courses
Courses Mean Diff (var) Diff Std dev
Science & technology 4.3448 C12-C9 0.24828 0.86226
communications (C12)
Technology 4.0966 C9-C8 0.07586 0.80866
Management (C9)
Science & Technology 4.0207 C9-C1 0.06897 0.86326
Entrepreneurship (C8)
Science Technology 3.9517 C1-C6 0.03448 0.85321
Society (C1)
Science & Technology 3.9172 C6-C10 0.01379 1.02053
Policy (C6)
Science Technology 3.9034 C10-C11 0.01379 0.74523
Ethics (C10)
Cyber Ethics (C11) 3.8897 C11-C7 0.04828 1.20952
Technology Market 3.8414 C7-C5 0.02759 1.06030
Analysis (C7)
Science Tech 3.8138 C5-C4 0.39310 0.74811
Literature (C5)
Science Technology 3.4207 C4-C3 0.08276 0.72172
Art (C4)
Science Technology 3.3379 C3-C2 0.09655 0.81925
Philosophy (C3)
History of Science & 3.2414
Technology (C2)
Courses Std err t-value Sig
Science & technology 0.07161 3.467 0.001
communications (C12)
Technology 0.06716 1.130 0.261
Management (C9)
Science & Technology 0.07169 0.962 0.338
Entrepreneurship (C8)
Science Technology 0.07086 0.487 0.627
Society (C1)
Science & Technology 0.08475 0.163 0.871
Policy (C6)
Science Technology 0.06189 0.223 0.824
Ethics (C10)
Cyber Ethics (C11) 0.10044 0.481 0.632
Technology Market 0.08805 0.313 0.755
Analysis (C7)
Science Tech 0.06213 6.327 0.000
Literature (C5)
Science Technology 0.05994 1.381 0.169
Art (C4)
Science Technology 0.06803 1.419 0.158
Philosophy (C3)
History of Science &
Technology (C2)
