Catch them young:
How attitude towards future team knowledge sharing relates to soft and hard skill development among college students.
Femke Meeuwes 10144684
Master’s thesis Corporate Communication Master track Communication Science
University of Amsterdam Graduate School of Communication
Dr. L. Helfer 14-11-2017
Abstract
In today’s rapidly developing work environment, employees are expected to keep learning to keep up with innovations and technological development. Organizations speak of a high demand for employees who have developed both hard (e.g. technological) and soft (e.g. political) skills, but experience difficulty finding employees who have developed both. In this study is examined if students’ preference to work in a knowledge sharing environment is positively related to the development of skills supportive to knowledge sharing behavior (political and technological). A cross-sectional study is conducted among current college students (N = 154). Result indicate a positive relationship between students’ attitude towards knowledge sharing and the valuation of the development of political and technological skill during education. This relationship was not dependent on individual differences in study track, nor in extraversion level. In a comparison between Natural/Computer Science students (N = 79) and Social Science/Humanities students (N = 75), no difference was found in the valuation of the development of political skill. Social Science/Humanities students did assign relative low value to the development of technological skill. Concluded is that students can be encouraged to develop skills when they experience them to be important for a future work environment. Students need to be made aware of the importance of knowledge sharing, and practice the behavior at universities to enhance development of skills important to succeed in today’s workplace.
Keywords: lifelong learning, knowledge sharing, skill development, soft skills, hard skills, political skill, technological skill, college students, utility value, expectancy-value theory, study track, identity-based motivation, extraversion, career development.
The term of lifelong learning, which entails employees learning throughout their lives (Chao & Yap, 2009), has become a “mantra”, mostly referred to while discussing the future of society and the role of professionals in this future (Webster-Wright, 2009, p. 702). Operating in the new “learning society” (Tynjälä, 2008; Field, 2005) employees are expected to
continuously gain new knowledge and skills to keep up with rapid technological development and changing work environments (Tynjälä, 2008; Chao & Yap, 2009). In sum, organizations are speaking of an increasing demand for a combination of both hard and soft skills for every employee (Shanks, Sinha & Thomas, 2016; Manpower Group, 2017). However, especially in the IT sector, companies face difficulty finding and recruiting employees who have developed both hard and soft skills, and experience a gap between the skills required and those acquired by graduates (Dutch top-CIO’s take the lead, 2016; Tynjälä, 2008; Cranmer, 2006). To
provide possible solutions for this organizational people’s dilemma (Dutch top-CIO’s take the lead, 2016), it is crucial to examine how students can be encouraged to develop both soft and hard skills during their education. Examining this skill development to provide advice to organizations and educators how to prepare the new generation for their future work field, is of societal relevance.
The skill development of individuals can be enhanced through knowledge sharing (Yang, 2010). Knowledge sharing leads to organizational learning as it is "the process where individuals exchange their knowledge and jointly create new knowledge” (Van den Hooff & de Ridder, 2004, p. 118). Also, individuals can develop specific skills that are supportive to knowledge sharing behavior. Both hard (e.g. technological) and soft (e.g. interpersonal) skills support knowledge sharing and can make it occur more often and more efficiently (Siemsen, Roth & Balasubramanian, 2008; O’Neill & Adya, 2007; Wagner & Bolloju, 2004). Many previous studies provided insights concerning different factors influencing the attitude towards knowledge sharing behavior, both internal, such as motivation (e.g. Kwok & Gao,
2005; Lin, 2007) and personality (e.g. Mooradian, Renzl & Matzler, 2006), and external, such as organizational culture (e.g. Bock, Zmud, Kim & Lee, 2005). However, to our knowledge, the relationship between students’ attitude towards knowledge sharing behavior and their valuation of skill development supportive to knowledge sharing has not been examined yet.
It is scientifically relevant to examine this relationship, as it will add to the literature on organizational behavior and knowledge sharing (De Vries et al., 2006), and to literature on the relation between future work environment and current skill development (Husman & Lens, 1999; Vasquez & Buehler, 2007; Wang & Degol, 2013). The current study will distinguish itself from former studies on knowledge sharing attitude by focussing on the perspective of students, and on the relation with two specific skills that are currently high in demand, political and technological skill.
For students to actively start developing skills, the perception of their development needs is an important antecedent (Noe & Wilk, 1993). Awareness of these development needs and agreement with the assessment that the student needs to develop, are two important aspects of the student’s perception of these needs (Noe & Wilk, 1993). Also, the perceived utility of the skills, for instance in a future job, can lead to development (Noe & Wilk, 1993; Crossan, Lane & White, 1999). The future image of working in a knowledge sharing team could therefore make students assign value to the development of political and technological skills necessary to perform in such a knowledge sharing work environment.
Technological skill within a person’s field of expertise is considered an important hard skill for every employee (Deepa & Seth, 2013; Eshet-Alkalai, 2004). The current and still unfolding IT revolution (Hornstein, Krusell & Violante, 2005) leads to an increased
importance of technology, expanding the boundaries of the IT department to other fields of expertise, such as Sales (Hunter & Perreault, 2007) and Marketing (Davenport & Philips, 2016). Therefore, the development of skills to support knowledge sharing seems highly
relevant, not solely for students who study technology or other beta-related studies (Daggett, 2010).
Political skill can be considered a key interpersonal “soft” skill, when it comes to knowledge sharing (Siemsen, et al., 2008; O’Neill & Adya, 2007). In an emergent approach to knowledge sharing, the behavior is considered a result of rich social interaction between co-workers (Van den Hooff & Huysman, 2009). In that way, political skill can make an
important contribution to the knowledge sharing process (Fang, Chi, Chen & Baron, 2015; Jing & Hui-Hui, 2014; Dietl, Meurs & Blickle, 2017).
A possible relationship between attitude towards team knowledge sharing and the valuation of skills (e.g. Eccles & Wigfield, 2002) might be dependent on individual
differences in choice of study and personality. Personality trait extraversion has been shown to in influence occupational choice (Lapan, Shaughnessy & Boggs, 1996), and both
knowledge sharing (De Vries, Van den Hooff & De Ridder, 2006) and political skill
development (Perrewé, Zellars, Ferris, Rossi, Kacmar, & Ralston, 2004). Therefore a possible moderating role of extraversion will be examined. The aim of this study is to provide insight on the awareness of the importance of team knowledge sharing and valuation of skills among college students, by answering the following research question (for a visualisation of the hypotheses see figure 1),
RQ: Does students’ attitude towards knowledge sharing in a future work environment influence their valuation of the development of technological and political skill during education, and are differences dependent on personality and study track?
Figure 1. Conceptual Model
Theoretical Framework
Attitude towards knowledge sharing and utility value of supportive skills Students’ future orientation can positively influence their motivation and goalsetting
concerning the development of certain skills. For example, a high school student can become highly motivated on her mathematics because she wants to become a brilliant engineer (e.g. Husman & Lens, 1999). Imagining future success can enhance people’s motivation to achieve it (Vansquez & Buehler, 2007). Wang and Degol (2013) showed that female and male
participants valued mathematics differently because of different occupational values. Men have a higher preference to work with objects, machines and tools, while women prefer more social interactive jobs (Wang & Degol, 2013). There seems to be a relationship between the desired future work environment and a preference for developing related skills during education. This relationship can be explained with Eccles’ expectancy-value model (1983). According to the model, choices are influenced by the relative value and probability of success (expectancy) assigned to certain tasks or activities.
Students can assign value to current skill development due to both short- (e.g. to keep up with friends) and long term future goals (e.g. career path) (Eccles & Wigfield, 2002). This utility value is determined by how well the activity fits current and future goals. Assigning value to the possession of certain skills therefore improves motivation and persistence to develop those skills (Eccles & Wigfield, 2002). Compared to other components of task-value, such as intrinsic value due to personal enjoyment in performing the activity, or attainment value due to the perceived fit between the activity and ones’ identity, utility value is solely based on the usefulness of the activity (Oyserman & Destin, 2010). For instance, the activity can have positive value to a person because it is assumed to help achieve career goals, even if the person is not interested in the activity itself (Eccles & Wigfield, 2002). Therefore, students who have a positive attitude towards knowledge sharing in a future environment, might assign higher value to the skills that are useful for knowledge sharing behavior.
This attitude towards team knowledge sharing is expected to variate per individual student. Although knowledge sharing improves team performances (Wang & Wang, 2012; Wang & Noe, 2010), it can be considered undesirable for an individual employee for strategic reasons (Gibbs, Rozaidi, & Eisenberg, 2013). Knowledge sharing in organizations is based on an “ideology of openness” (Gibbs et al., 2013, p. 103). Employees can try to avoid this
information can reduce power or status (Gibbs et al., 2013). Individuals can be afraid others will steal their opportunities due to the knowledge they shared (Olson & Olson, 2000). Students could have a more negative attitude towards team knowledge sharing when they have an individualistic orientation (Ramamoorthy & Flood, 2004), a preference to work from home (Keifer, 2014), or a preference to work alone (French, Walker & Shore, 2011;
Gottschall & Garcia-Bayonas, 2008). For instance, Math students indicated that they would rather work alone (63%), compared to Education (34,5%) and Business students (47,3%) (Gottschall, Garcia-Bayonas, 2008). All seem to indicate a lower need for social interaction with co-workers, which is an important aspect of the knowledge sharing process (Titi Amayah, 2013).
Van den Hooff and Huysman (2009) discuss two approaches to knowledge sharing, an emergent and engineering approach. Within the emergent approach learning is treated as a social phenomenon and the relationship between individuals and their social interaction are central. Within the engineering approach, managerial interventions to facilitate knowledge sharing processes, for instance by managing and controlling technical infrastructure and tools, is prominent (Van den Hooff & Huysman, 2009). Both approaches seem to complement each other, the engineering approach also creates opportunities for emerging knowledge sharing practices (Van den Hooff & Huysman, 2009). Also, both approaches are assumed to require different skills from employees.
Within the emergent approach, knowledge sharing is considered a product of social relationships, and can be stimulated by rich social interaction between co-workers (Van den Hooff & Huysman, 2009). Often, employees tend to share their knowledge through informal interactions (Swap, Leonard, & Mimi Shields, 2001; Taminiau, Smit, & De Lange, 2009) with the intention to help colleagues handle things better or more efficiently (Titi Amayah, 2013). It is especially tacit knowledge, often referred to as ‘know-how’, that is transferred through
informal knowledge sharing, mostly through face-to-face methods (Smith, 2001). For the sharing of tacit knowledge, the organization can support and facilitate (Van den Hooff & Huysman, 2009), although employees’ own initiative remains essential (Taminiau et al., 2009). Skills supportive to this type of knowledge sharing would have to be the social capabilities of individuals to build relationships and interact with others. In this approach, it seems imperative that employees built connections and convince these connections of the importance of the knowledge they share. Political skill seems highly supportive to this process, as it implies “the ability to effectively understand others, and to use such knowledge to influence others to acts in ways that enhance one’s personal and/or organizational
objectives” (Ahearn, Ferris, Hochwarter, Douglas & Ammeter, 2004, p. 311).
When examining the transfer of knowledge in a communicative view, expertise is seen as a construct build in social interaction, as opposed to solely the property of the “expert” (Treem, 2012). Especially in knowledge-intensive organizations, specific work out-puts of employees tend to be invisible, and the judgment of employees’ expertise have to be made through communication and interaction between employees (Treem, 2012). With specific behavior and communication cues, an employee can make known to be an expert to other employees (Treem, 2012). Politically skilled individuals are ‘skilled’ in performing this behavior, since they have more interpersonal influence (Ferris et al., 2005; Currie & Kerrie, 2003), know how to build and use their social network (Ferris et al., 2005; Fang et al., 2015), are good at building strong social relationships (Chiu, Hsu, & Wang, 2006; Hansen et al., 2005), and “appear to others as possessing high levels of integrity, authenticity, sincerity and genuineness”, making them trustworthy (Mooradian et al., 2006; Kharabsheh, 2007).
Succesful knowledge sharing is dependent on behaviors that influence judgments of colleagues. Politically skilled individuals have higher knowledge sharing ability (Siemsen, et al., 2008), and are more willing to share knowledge, which are both an important drive to
share knowledge (O’Neill & Adya, 2007). Although political skill can be regarded as partly dispositional and the behavior may come more natural for certain personalities, it can be trained through formal and informal development experiences (Ferris, Anthony, Kolodinsky, Gilmore, & Harvey, 2002; Ferris, Treadway, Kolodinsky, Hochwarter, & Kacmar, 2005).
Based on the previous discussed expectancy-value theory (Eccles & Wigfield, 2002), students’ orientation towards a knowledge sharing environment is expected to influence perceived importance of the development of skills supportive to knowledge sharing during education. Therefore the student will assign higher utility value to the development of political skill, which will be tested with the following hypothesis,
H1: Students’ attitude towards team knowledge sharing in a future work environment has a positive influence on the utility value assigned to the development of political skill for future career success.
Considering the engineering approach to knowledge sharing, organizations can facilitate knowledge sharing through their IT infrastructure (Van den Hooff & Huysman, 2009).
Nowadays, knowledge sharing in organizations does not only occur through social interaction, but through Information Technology as well (Wagner & Bolloju, 2004). Sales departments implement Customer Relationship Management and Sales Automation tools to increase effectiveness and efficiency (Hunter & Perreault, 2007), in Marketing many activities are automated by algorithms (Davenport & Philips, 2016) and Supply Chain organizations use digital platforms to manage their activities (Rai, Patnayakuni, & Seth, 2006). Knowledge sharing occurs through IT applications such as online forums, blogs and wikis (Wagner & Bolloju, 2004; Davison, Ou, & Martinsons, 2013). Knowledge is captured as ‘data’, which are mostly numeric information, or observations of work activities that can be quantified (Smith,
2001). Organizations try to keep up with competitors with the use of data-driven analytics; measuring and analyzing information retrieved by Information Technology tools (McAfee & Brynjolfsson, 2012). With organizations becoming more and more ‘data-driven’ (determined by the collection and analysis of data) (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011), skills in cleaning and organizing large data-sets, statistical analysis and data
visualisation skills are gaining importance (McAfee & Brynjolfsson, 2012). Also, effective use of IT tools is claimed to be essential (Smith, 2001). Therefore, technological skills are considered supportive to knowledge sharing behavior. It is expected that students with a higher preference to work in a knowledge sharing team will assign higher utility value to the development of technological skills,
H2: Students’ attitude towards team knowledge sharing in a future work environment has a positive influence on the utility value assigned to the development of
technological skills for future career success.
Differences between study tracks
The valuation of development of both technological and political skill, might be different for Natural/Computer Science students and Social Science/Humanities students. In a current study IT students listed interpersonal skills as second lowest in importance to achieve short-term career goals (McKenzie, Coldwell-Neilson & Palmer, 2017). Studies like this show that IT students might be lacking the knowledge of the importance of political skill (interpersonal) (McKenzie et al., 2017). IT professionals are not always prepared during their education for the requirement of interpersonal skills and are learning them “the hard way” at work (Kumar & Hsiao, 2007, p. 19). Next to a large body of literature focussing on the importance of soft
skills for IT professionals and IT students (e.g. Aasheim et al., 2012; Wynekoop & Walz, 2000; Ahmed, Capretz & Campbell, 2012; Boyatzis, Rochford, & Cavanagh, 2017), the increasing demand for technological skills among students from other fields of expertise is hardly discussed. Nonetheless, some researchers emphasize the importance of technology in the classroom to prepare students for their future jobs in a “technology-driven” world (e.g. Daggett, 2010, p. 1).
Differences in the valuation of the development of technological and political skill between both groups seems rather unfortunate, since these skills are considered very important for employees from different fields of expertise (e.g. Davenport & Philips, 2016; Kumar & Hsiao, 2007). Still, differences are expected when looking at the different nature of the skill-sets. Both skills require different learning and thinking styles adopted by the
students, making the development of the skills easier or harder. Kolb and colleagues (2001) show that employees with technical jobs tend to use a “Converging” learning style, which entails watching and doing, to acquire their technological skills. Soft skills, such as political skill, are more easily acquired using an “Accommodating” learning style, which entails feeling and doing (Kolb, Boyatzis & Mainemelis, 2001).Developing hard skills requires a more abstract thinking style, while the development of soft skills requires concrete experience (Kolb et al., 2001).
Students who have specialized in one skill-set, might be less motivated to learn skills from the other set once these skills do not fit their self-concept (Oyserman & Destin, 2010). Identity-based Motivation Theory explains that individuals prefer identity-congruent to identity-incongruent actions (Oyserman & Destin, 2010). While learning new skills that do not match the employees’ perceived identity, facing difficulty can suggest that the behavior is pointless and “not for people like them”, leading to avoidance of the behavior (Oyserman & Destin, 2010). This idea is supported by the finding of Chamorro‐Premuzic and colleagues
(2010) that students from ‘soft degrees’ (e.g. humanities and social science) rate more importance and are more self-conscious about their own improvement in soft skills than students in the ‘hard degrees’ (e.g. exact and natural science).
In this study a comparison will be made between Natural/Computer Science students (e.g. biological, physical, engineering, math and computer science) and a group of both Social Science (e.g. sociology) and Humanities (e.g. communication) because of the expectation for the first group to assign higher value to technology, and the second to assign higher value to political skill. Due to the expected relative low value to political skill and a relative high value to technological skill among Natural/Computer Science students, the difference in valuation of both is expected to be larger than for the Social Science/Humanities group,
H3: The difference in assigned utility value to the development of political and technological skill is larger for Natural/Computer Science students than for Social Science/Humanities students.
The strength of the relationship between knowledge sharing attitude and political skill development (H1) might be dependent on study track. For students of Social
Science/Humanities, who are expected to be more aware and assign higher value to the development of political skill (Chamorro‐ Premuzic, Arteche, Bremner, Greven, & Furnham, 2010), there might be a stronger link between team knowledge sharing and the development of political skill. On the other hand, IT students might be more aware of current technological developments with knowledge sharing through Information Technology tools (Wagner & Bolloju, 2004), as technology often is subject to their studies. At the same time, they might consider political skill, networking and influencing other people, as less important to achieve knowledge sharing goals.
H4: The relationship between the attitude towards team knowledge sharing in a future work environment and the assigned utility value to the development of political skill is stronger for Social/Science/Humanities students than for Natural/Computer Science students.
The influence of extraversion
Besides students’ study track, the personality trait extraversion could influence their preference for a future work environment, the development of political skill and the relationship between both. Extraversion concerns the extent to which a person experiences vitality and is willing to engage in social situations and feels comfortable in such situations. Introverts tend to act distant on social events, feel unpopular and unenthusiastic when they are the centre of attention (De Vries, Ashton & Lee, 2009). When testing for extraversion,
occupational samples from math and science areas (e.g. mathematicians, computer programmers) tend to be more introverted than samples from different occupational areas (e.g. broadcasters, social workers, lawyers) (Lapan et al., 1996). There seems to be a relation between choice of study and occupation and level of extraversion. Multiple studies show a connection between extraversion and knowledge sharing behavior in organizations (De Vries et al., 2006; Ferguson, Paulin & Bergeron, 2010; Amayah, 2011; Mooradian et al., 2006) extraverts are expected to feel more comfortable in a knowledge sharing environment (e.g. Ferguson et al., 2010). For instance, collaborative experiences in software development education tend to be disliked by introverts (Layman, 2006).
Especially among students, extraversion can influence work environment preference. Extraversion is a trait, and therefore considered dispositional, but change in personality is possible across the life course (Branje, Van Lieshout & Gerris, 2007). For individuals before
their 30’s, their level of extraversion is less likely to change due to environmental factors, as young adults actively try to change their environment to fit their personality (Kandler, 2012). Individuals older than 30 try to “stabilize and protect the quality of their lives and ongoing relationships”, and therefore tend to change their personality to fit their environment (e.g. Kandler, 2012, p. 5). Students, as young adults will try to change their environment to fit their personality, and their level of extraversion will influence their preference for team knowledge sharing in a future work environment.
Besides future work environment preference, students’ personality can influence the development of political skill during education. Extraversion has been shown to predict not only interest but also self-efficacy in programs concerning the development of interpersonal skills (Hartman & Betz, 2007), and to be directly related to political skill (Perrewé et al., 2004; Blickle & Schnitzler, 2010). Extraverts are assumed to experience less difficulty when developing political skill, which increases the feeling that the development of political skill matches their self-concept (Oyserman & Destin, 2010). Due to this identity-fit, extraverts are expected to perceive political skill development as more important,
H5: Extraversion has a positive influence on the utility value assigned to the development of political skill for future career success.
The strength of the expected relationship between team knowledge sharing preference and political skill development (H1) might be dependent on individual differences in extraversion. Transferring the idea of future career goals to the acquirement of skills can be considered an important aspect of ‘career development’. Career development is affected by personal, behavioral and environmental factors (Lent, Lopez, Lopez, & Sheu, 2008). Self-efficacy, outcome expectations and personal goals play an important role (Lent et al., 2008).
Extraversion has been shown to have a positive influence on career self-efficacy and career goals, which both lead to higher career exploration (Wang, Jome, Haase & Bruch, 2006). Extraverts feel more confident in the process of career exploration and perform more career information seeking behavior (Reed, Bruch & Haase, 2004). Also, extraverts are more likely to have a proactive personality, and proactive personalities engage more in career
development (Major, Turner & Fletcher, 2006). Based on these findings it is expected that for extraverted individuals the recognition of the importance of knowledge sharing in a future work environment will lead to a higher development of political skills than for introverted individuals. Extraverted students will be more proactive and recognize the importance of their own development to succeed in such an environment.
H6: The relationship between the attitude towards team knowledge sharing in a future work environment and the assigned utility value to the development of political skill is stronger for extravert students than for introvert students.
Method Procedure
A cross-sectional quantitative study was conducted through an online survey to collect the data. Respondents were contacted through personal messages on Facebook and Whatsapp with the recruitment text and an URL to the survey (see appendix A). A similar message was posted on Facebook group pages and was forwarded by several professors to the mailing lists of Computer Science masters. Additionally, 18 students were asked in person to fill in the survey through a short bitlink at the University of Amsterdam. Respondents were recruited in the period of two weeks from October 16th till October 28th, 2017.
Respondents
In two weeks the survey had 191 responses. After removing 22 students for not completing the survey and 15 respondents who answered they were not students, a final sample of 154 students, 95 women (61.7%) and 59 men (38.3%) remained. The age of the students ranged from 18 till 42, with an average age of 24 (SD = 3.53), one participant did not fill in her age. The sample consisted of 79 Natural/Computer Science students (51.3%) and 75 Social Science/Humanities students (48,7%) (49 Social Science, 26 Humanities).
Measures
The survey started with an introduction, guaranteeing anonymity and explaining the aim of the research. First, the dependent variables, the development of technological skill and political skill, were measured. Further, the respondent’s level of extraversion and attitude towards team knowledge sharing were measured. Also, respondents were required to select their study track or Other. The ‘other’ option was given to prevent a lower response by forcing respondent to choose or wrong responses by students who were not sure. The 16 students that chose this option could eventually all be assigned to one of the study tracks by the researcher based on their current study. The full survey is added in appendix B.
Technological skill. Based on existing scales of technical competency (Anwar, Rolle and Memon, 2005; Meier, Williams & Humphreys, 2000), a general scale with four items to measure the development of technological skill, concerning all three study tracks, was developed. The question to measure utility value was formulated: “For my future career success, during my education I’m making sure I develop…”. Some items from the existing scales were reformulated to be applicable for all tracks, such as “technical skills concerning
technology trends and their applications”, and “demonstrate technical competency in my field of expertise” and some items were added to clarify what we consider technological skills, such as “hard skills (examples: computer programming, web design, data, and other quantifiable skills). All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis revealed that the four items to measure technological skill formed a single uni-dimensional scale. There was only one component with an
eigenvalue above 1 (eigenvalue 2.69) which was explained by the four items for 67.13 per cent. The scree plot showed a clear point of inflection after this component. All items correlated positively with the component and factor loadings ranged from 0.79 till 0.85. Reliability of the scale is good, Cronbach’s alpha = .83. High scores on the items indicate higher development of technological skill for future career success (M = 3.70, SD = 0.88).
Political skill. The 15 items of the political skill inventory of Ferris and colleagues (2005) were used with a slight variation of the introductory text to fit with the goals of this study: “For my future career success, during education I’m making sure I…”. The political skill construct consists of four components, interpersonal influence, social astuteness, apparent sincerity and networking ability. The components were measured with statements like, “become good at getting people to like me”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis with Varimax rotation revealed that the 15 items do not form a single uni-dimensional scale. There were three components with an eigenvalue above 1. The scree plot does not show one clear point of inflection (see table 1 in Appendix C for factor loadings). There are three components separated instead of four due to high factor loadings of interpersonal influence and apparent sincerity on the same component. In sum,
interpersonal influence, social astuteness, networking ability and apparent sincerity are computed as one variable ‘political skill’, in line with existing literature (Ferris et al., 2005). The reliability of this scale is good, Cronbach’s alpha = .89. High scores on the items indicate higher development of political skill during education (M = 3.78, SD = 0.62).
Extraversion. Ten items from the HEXACO personality questionnaire (De Vries et al., 2009) were used to measure the five facets of extraversion; sociability, social self-esteem, social boldness and liveliness (Ashton & Lee, 2016). Respondents were asked to what extent the statements described the way they felt and reply to statements like “I feel reasonably satisfied with myself overall”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis with Varimax rotation revealed three components with an eigenvalue above 1. The items to measure liveliness and social self-esteem both load on the same component, and although one item of social self-esteem has the highest factor loading on the ‘sociability’ component, it loads respectively high on the social self-esteem component with 0.43 (see table 2 in Appendix C for all factor loadings). In line with existing literature, the items to measure extraversion are computed as one scale (De Vries et al., 2009). Reliability of the complete extraversion scale is good, Cronbach’s alpha = .81. High scores on the items indicate higher extraversion (M = 4.08, SD = 0.73).
Team knowledge sharing. The knowledge sharing scale of De Vries, Van Den Hooff and De Ridder (2006) was slightly reformulated to capture the concept of attitude towards team knowledge sharing in a future work environment, and was measured with the question: “When I start working after graduating I would like to work in an environment where…”, with statements such as “my colleagues regularly inform me and each other of what they are
working on”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis revealed that the ten items formed a single uni-dimensional scale. There was only one component with an eigenvalue above 1 (eigenvalue 2.30) which was explained by the four items for 57.44 per cent. The scree plot showed a clear point of inflection after this component. All items correlated positively with the component and factor loadings ranged from 0.67 till 0.79. The scale is reasonably reliable, Cronbach’s alpha = .75. High scores on the items indicate higher preference for team knowledge sharing in a future work environment (M = 3.89, SD = 0.65).
Method
Procedure
A cross-sectional quantitative study was conducted through an online survey to collect the data. Respondents were contacted through personal messages on Facebook and Whatsapp with the recruitment text as added in appendix A. The message was forwarded by several professors to the mailing lists of thee Computer Science masters. Additionally, students were asked in person to fill in the survey through a short bitlink at the University of Amsterdam. Respondents were recruited in the period of two weeks from October 16th till October 28th, 2017.
Respondents
The exact response rate of the survey can’t be stated due to snowball sampling, as respondents were asked to share the URL with classmates and friends and it is not clear how many
respondents were exposed to this requests. In two weeks the survey had 191 responses. After removing 22 students for not completing the survey and 15 respondents who answered they were not students, a final sample of 154 students, 95 women (61.7%) and 59 men (38.3%) remained. The age of the students ranged from 18 till 42, with an average age of 24 (SD = 3.53), one participant did not fill in her age. The sample consisted of 79 Natural/Computer Science students (51.3%) and 75 Social Science/Humanities students (48,7%) (49 Social Science, 26 Humanities).
Measures
The survey started with an introduction, to guarantee anonymity and explain the aim of the research. First, the dependent variables, the development of technological skill and political skill, were measured. Further, the respondent’s level of extraversion and attitude towards team knowledge sharing were measured. Also, respondents were required to select their study track, Natural/Computer Science (examples: mathematics, biomedical sciences, information), Social Sciences (examples: sociology, political science, health), Humanities (examples: literature, history, communication) or Other. The ‘other’ option was given to prevent a lower response by forcing respondent to choose or wrong responses by students who were not sure. The 16 students that chose this option could eventually all be assigned to one of the study tracks by the researcher based on their current study. The full survey is added in appendix B.
Technological skill. Based on existing scales of technical competency (Anwar, Rolle and Memon, 2005; Meier, Williams & Humphreys, 2000), a general scale with four items to
measure the development of technological skill, concerning all three study tracks, was developed. The question to measure utility value was formulated: “For my future career success, during my education I’m making sure I develop…”. Some items from the existing scales were reformulated to be applicable for all tracks, such as “technical skills concerning technology trends and their applications”, and “demonstrate technical competency in my field of expertise” and some items were added to clarify what we consider technological skills, such as “hard skills (examples: computer programming, web design, accounting, legal, data and other quantifiable skills). All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis revealed that the four items to measure technological skill formed a single uni-dimensional scale. There was only one component with an
eigenvalue above 1 (eigenvalue 2.69) which was explained by the four items for 67.13 per cent. The scree plot showed a clear point of inflection after this component. All items correlated positively with the component and factor loadings ranged from 0.79 till 0.85. Reliability of the scale is good, Cronbach’s alpha = .83. High scores on the items indicate higher development of technological skill for future career success (M = 3.70, SD = 0.88).
Political skill. The 15 items of the political skill inventory of Ferris and colleagues (2005) were used with a slight variation of the introductory text to fit with the goals of this study: “For my future career success, during education I’m making sure I…”. The political skill construct consists of four components; interpersonal influence, apparent sincerity, social astuteness and networking ability. The components were measured with statements such as: “…good intuition or savvy about how to present myself to others”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis with Varimax rotation revealed that the 15 items do not form a single uni-dimensional scale. There were three components with an eigenvalue above 1. The scree plot does not show one clear point of inflection (see table 1 in Appendix C for factor loadings). Although interpersonal influence and apparent sincerity both load on the same component, in line with existing literature they will be treated as separate aspects of the political skill concept, and the scale will be used as one (Ferris et al., 2005). The reliability of the complete political skill scale is good, Cronbach’s alpha = .89. High scores on the items indicate higher development of political skill during education (M = 3.78, SD = 0.62).
Extraversion. Ten items from the HEXACO personality questionnaire (De Vries, Ashton & Lee, 2009) were used to measure the five facets of extraversion; sociability, social self-esteem, social boldness and liveliness (Ashton & Lee, 2016). Respondents were asked to what extent the statements described the way they felt and informed there are no right or wrong answers to reduce social desirability bias. Statements were for instance “I feel reasonably satisfied with myself overall” and “I rarely express my opinion in group meetings”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis with Varimax rotation revealed three components with an eigenvalue above 1. The items to measure liveliness and social self-esteem both load on the same component, and although one item of social self-esteem has the highest factor loading on the ‘sociability’ component, it loads respectively high on the social self-esteem component with 0.43 (see table 2 in Appendix C for all factor loadings). In line with existing literature, the extraversion scale will be used as one scale (De Vries, Ashton & Lee, 2009). Reliability of the complete extraversion scale is good, Cronbach’s alpha = .81. High scores on the items indicate higher extraversion (M = 4.08, SD = 0.73).
Team knowledge sharing. The knowledge sharing scale of De Vries, Van Den Hooff and De Ridder (2006) was slightly reformulated to capture the concept of attitude towards team knowledge sharing in a future work environment, and was measured with the question: “When I start working after graduating I would like to work in an environment where…”, with statements such as “my colleagues regularly inform me and each other of what they are working on”. All items were measured on a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A principal component analysis revealed that the ten items formed a single uni-dimensional scale. There was only one component with an eigenvalue above 1 (eigenvalue 2.30) which was explained by the four items for 57.44 per cent. The scree plot showed a clear point of inflection after this component. All items correlated positively with the component and factor loadings ranged from 0.67 till 0.79. The scale is reasonably reliable, Cronbach’s alpha = .75. High scores on the items indicate higher preference for team knowledge sharing in a future work environment (M = 3.89, SD = 0.65).
Results
Regression model 1 (see table 3), to test hypothesis 1, with attitude towards team knowledge sharing in a future work environment as independent variable (IV) and the
development of political skill as dependent variable (DV) is significant, F(1, 152) = 13.16, p < .001. The regression model can therefore be used to predict the development of political skill, but the strength of the prediction is weak: 8.0 per cent of the variation in development of apparent sincerity can be predicted on the basis of knowledge sharing preference (R2 = .08).
Knowledge sharing preference, b* = 0.28, t = 3.627, p < .001, 95% CI [.12, .41], has a
significant weak association with political skill development. For each additional point on the scale of attitude towards knowledge sharing, which runs from 1 (totally disagree) to 5 (totally agree), the average development of political skill during education increases with 0.27. We find support for H1: students’ attitude towards team knowledge sharing in a future work environment is positively associated with the development of political skill during their education.
Concerning the relation between knowledge sharing attitude (IV) and technological skill development (DV), a second regression model is used. The model is significant, F(1, 152) = 11.57, p = .001. It can therefore be used to predict the development of technological skill, but the strength of the prediction is weak: 7.1 per cent of the variation in development of technological skill can be predicted on the basis of knowledge sharing preference (R2 = .07). Attitude towards knowledge sharing, b* = .27, t = 3.402, p = .001, 95% CI [.15, .57], has a
significant weak association with technological skill development. For each additional point on the scale of knowledge sharing preference, which runs from 1 (totally disagree) to 5
(totally agree), the average development of technological skill during education increases with 0.36. With this finding, we find support for H2: students’ attitude towards team knowledge sharing in a future work environment is positively associated with the development of technological skills during their education.
For multiple comparisons between the two study tracks, several independent samples t-tests and paired samples t-tests on a selection of the sample are executed (see table 4 and 5). As presented in the tables, there was no significant difference between the two study tracks in development of political skill. Natural/Computer Science students did rate a significant higher development of technological skill than Social Science/Humanities. Social
Science/Humanities students rated significant higher development of political skill than technological skill and Natural/Computer Science students rated significant higher
development of technological skill than political skill (table 5). Natural/Computer Science students were significantly more introverted than Social Science/Humanities students, effect size is small (d = .37). The difference in assigned utility value to the development of political and technological skill is significantly larger for Natural/Computer Science students than for Social Science/Humanities students, the effect size is large (d = 1.34). We find support for H3.
To test an interaction effect of knowledge sharing attitude (IV) and study track as moderating variable (MV) on political skill development (DV), regression model 3 (see table 3) is significant and can be used to predict political skill development, F(3, 150) = 6.45, p <.0001. The interaction effect is not significant, b* = 0.34, t = 0.719, p = 0.473, 95% CI [-.18, .40]. We reject H4, the relationship between attitude towards team knowledge sharing in a future environment and the development of political skill during education is not stronger for Social Science/Humanities students than for Natural/Computer Science students.
Regression model 4 (see table 3) with extraversion as independent variable and political skill development as dependent variable is significant, F(1, 152) = 31.38, p <.001.
The regression model can therefore be used to predict the development of political skill, but the strength of the prediction is weak: 17.1 per cent of the variation in development of
political skill can be predicted on the basis of extraversion (R2 = .17). Extraversion, b* = 0.41, t = 5.602, p < .001, 95% CI [.25, .53], has a significant weak association with political skill development. For each additional point on the scale of extraversion, which runs from 1 (totally disagree) to 5 (totally agree), the average development of political skill during education increases with 0.39. We find support for H5, extraversion is positively associated with a higher development of political skill during education.
Regression model 5 (see table 3), the interaction effect of team knowledge sharing (IV) attitude and extraversion (MV) on political skill development (DV) is significant F(3, 150) = 14.42, p <.0001. The regression model can therefore be used to predict the
development of political skill, but the interaction effect of extraversion and knowledge sharing is not significant, b* = 0.68, t = 1.13, p = 0.259, 95% CI [-.08, .30]. H6 is therefore rejected, the relationship between attitude towards team knowledge sharing in a future environment on the development of political skill during education is not stronger for extraverted students than for introverted students.
Conclusion
With this study is demonstrated that the preference for team knowledge sharing in a future work environment is positively related to higher development of political and technological skill during education. This finding adds to literature on the influence of future career on current skill development (Husman & Lens, 1999; Vasquez & Buehler, 2007; Wang & Degol, 2013), and provides support for the idea that students assign utility value to certain skills due
to their future work environment preferences (Eccles & Wigfield, 2002). The results suggests that by enhancing the preference for future team knowledge sharing the political and
technological skill development of students can be improved.
The relationship between attitude towards future team knowledge sharing and utility value to political skill was not dependent on study track. The hypothesis on a possible interaction effect was refuted. Social Science/Humanities students do not relate future knowledge sharing to political skill development any more than Natural/Computer Science students. Natural/Computer Science seem to be aware of the importance of political skill in a knowledge sharing environment, and do not link the behavior solely to technological skills. The Natural/Computer Science students, like in previous research, tend to be more introverted than the Social Science/Humanities group (e.g. Lapan et al., 1996), but did not assign lower value to the development of political skill. The expectation for this group to perceive this interpersonal soft skill to be less important, due to a possible lower identity-fit, is rejected (Oyserman & Destin, 2010). These findings suggest that Natural/Computer Science students are quite aware of the high demand for interpersonal skills in their (probable) future
profession, unlike previous findings of other authors (e.g. Kumar & Hsiao, 2007; Conn, 2002; McKenzie et al., 2017).
The difference in political skill and technological skill valuation was larger for Natural/Computer Science, which was caused by a relative high valuation of technological skill development. Social Science/Humanities assigned relative low value to the development of technological skill. Which implies they might not be aware of the high demand for
technological skill in their future career (Daggett, 2010). The largest gap employers might experience in the skills required and acquired by newly hired might be the lack of
Although extraverts did show a higher preference to work in a knowledge sharing environment, which adds to a large body of literature on the relationship between knowledge sharing and extraversion (e.g. de Vries et al., 2006), and assign higher value to the
development of political skill (e.g. Perrewé et al., 2004), the relationship is not dependent on extraversion. The hypothesis on a possible interaction effect, due to higher career exploration among extraverts, was refuted. We find explanation for this outcome in the study of Nauta (2007), who finds a negative relation between extraversion and career exploration, which she explains by a possible focus of extraverts on others, rather than on themselves. Although it was expected of extraverts to respond more proactively to future work needs than introverts (Major, Turner & Fletcher, 2006), there was no difference between introverts and extraverts in the influence of future work environment on current skill development.
Discussion
Limitations and recommendations for future research
First limitation to this study, is that the results of can’t be optimally generalized to the overall student population in the Netherlands. Convenient sampling, especially in the Social
Science/Humanities group, might have limited the external validity of this study. Most of the Social Science and Humanities students are recruited from a personal network related to a student association, which might attract extraverted students and where the development of oratorical, and political skills such as networking, is stimulated (Rubin, Bommer & Baldwin, 2002. On the other hand, membership of a student association might be more common among Social Science/Humanities students than among Science students, which perhaps limits the negative influence on external validity.
A second limitation is that in this study variables are measured by self-report of the students. Not the actual capabilities nor activities students engage in to develop skills are measured. A choice has been made to focus on the value students claim to assign to the development of certain skills during education, and not on the actual skill development. Future research could focus on the actual skill development of students by teacher or fellow classmates reports, or perhaps observations and longitudinal research (e.g. Crowell & Kuhn, 2014; Papay, Taylor, Tyler, & Laski, 2016).
Longitudinal research would also allow to address the third limitation of this study. No causal effects could be demonstrated. There are strong theoretical foundations to expect that the image of a future work environment will motivate students to develop skills now (Eccles & Wigfield, 2002). However, it might be possible that the development of political skill during education leads to a higher preference to work in an environment where those skills can be used. In the present cross-sectional study causality cannot be addressed, the direction of the of the relationship between future team knowledge sharing and political and
technological skill development is therefore negotiable.
In sum, it is recommended for future research to examine the causality of the relationship found and to measure the actual skill development of students with different research methods. Also, it is advised to create a sample that is more generalizable to the actual student population. Further, it would be interesting to examine other types of value
assignment than solely utility value. Perhaps by focussing on personal enjoyment and intrinsic value as well, results might have been different (Wlodkowski & Ginsberg, 2017).
Practical implications
Based on the results of this study a few practical implications for both organizational
skill demands in the current work environment by letting students practice with knowledge sharing. New teaching methods should ask more initiative from the students and let them interact with each other and create knowledge together. Despite the increasing
multidisciplinary collaboration in their future organizations (e.g. Conboy, Coyle, Wang & Pikkarainen, 2011), it occurs rarely that students from different study tracks collaborate. Based on the clear result in this study that Social Science/Humanities assign relative low value to the development of technological skill, it might be very interesting to arrange more interaction between this group of students with Natural/Computer Science students and provide opportunities for knowledge sharing. Second, organizations need to be clear in their communication on skill requirements for the future, so that student can anticipate.
Organizations and universities need to cooperate to bridge the gaps between education and practice (e.g. Brunhaver, Korte, Barley & Sheppard, 2017).
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