Available online at www.sciencedirect.com Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 10th Triple Helix Conference 2012 Relationship among Soft Skills, Hard Skills, and of Knowledge Workers in the Knowledge Economy Era Achmad Fajar Hendarman a,*, Jann Hidajat Tjakraatmadja b a,b School of Business and Management - ITB,Ganesha 10, Bandung 40132, Indonesia Abstract For the last two hundred years, neo-classical economics has recognized only two factors of production: labor and capital. But, nowadays information and knowledge are replacing capital and energy as the primary wealth-creating assets. Technological developments have transformed wealth-creating work from physically-based to knowledge based. Technology, knowledge as well as innovation are now the key factors of production. Knowledge economy is economy based on creating, evaluating, and trading knowledge. In a knowledge economy, labour costs become progressively less important and traditional economic concepts such as scarcity of resources and economies of scale of economy cease to apply. The most valuable asset of the 21st-century institution, either business or non-business, will be its knowledge workers and their productivity. Knowledge workers productivity is the biggest of the 21st century management challenges. In the developed countries, it is their first survival requirement. Making knowledge workers productive requires changes in attitude, not only on the part of the individual knowledge worker but also on the part of the whole organization. (Drucker, 1999, in http://www.knowledgeworkerperformance.com). Creating knowledge relates to the education system, which creates knowledge workers and innovation system, for example soft skills (worker behaviour), Positive Psychological Capital (Luthan et. al, 2007), and hard skill such as ICT literacy. The innovation system, which contributes to innovativeness, in any country, consists of institutions, rules, and procedures that affect how the system acquires, creates, disseminates, and uses knowledge. The objectives of this research are to develop a conceptual model which describse the relationship among the soft skills of knowledge workers, hard skills of knowledge workers, innovativeness of knowledge workers in the context of knowledge economy in Indonesia as a developing country. Multiple Regression Analysis was used as a method for this research. The conceptual model was designed with independent and dependent variables. The independent variables are hard skills, information seeking hard skills, concept thinking soft skills, and self efficacy - positive psychological capital. The dependent variables are: technical innovativeness (product and services) and non-technical innovativeness (organization and marketing). Only one company as the subject of this research which becomes the limitation of the study. The research findings are that only information seeking soft skill that positively influnced technical innovativeness and only hard skills that positively influenced non-technical innovativeness. 2012 The Authors. Published by Elsevier B.V. 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Institut Teknologi Bandung Selection and/or peer-review under responsibility of Institut Teknologi Bandung Open access under CC BY-NC-ND license. Keyword: Soft Skills Knowledge Workers; Hard Skills Knowledge Workers; Knowledge Workers; Knowledge Economy (KE). *Corresponding Author. Tel.: +62-22-253-1923; fax: +62-22-250-4249 1877-0428 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Institut Teknologi Bandung Open access under CC BY-NC-ND license. doi:10.1016/j.sbspro.2012.09.439
36 Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 1. Introduction 1.1 Background For the last two hundred years, neo-classical economics has recognised only two factors of production: labour and capital. But, nowadays information and knowledge are replacing capital and energy as the primary wealth-creating assets. Technological developments have transformed wealth-creating work from physicallybased to knowledge- based. Technology, knowledge as well as innovation are now the key factors of production. The Organisation for Economic Co-Operation and Development (OECD) in 1996 stated that Knowledge is now recognised as the driver of productivity and economic growth, leading to a new focus on the role of information, technology and learning in economic performance. The term knowledge-based economy stems from fully recognized places of knowledge and technologies in modern OECD countries. Knowledge Economy is an economy which is based on creating, evaluating, and trading knowledge. In knowledge economy, labour costs become progressively less important and traditional economic concepts such as scarcity of resources and economies of scale cease to apply. Creating knowledge relates to education system and innovation system, which needs skills (both hard and soft skills). Education is the fundamental enabler of the knowledge economy. Well-educated and skilled people are essential for creating, sharing, disseminating, and using knowledge effectively. The knowledge economy of the twenty-first century demands a set of new competencies, which includes not only ICT skills but also such soft skills as problem solving, analytical skills, group learning, working in a team-based environment, and effective communication. Therefore, it is necessary to research Indonesia s knowledge economy in relation with its human capital. 1.2 Research Questions This Research should answer the questions such as: 1. Are there any relationship among soft skills, hard skills and innovativeness knowledge workers? 2. What policies implication can be made for academic, business or government? 1.3 Objectives The objectives of this research are to develop a conceptual model which describes the relationship among soft skills, hard skills, and innovativeness of knowledge workers in the context of knowledge economy in Indonesia as a developing country and to give recommendation to the academics, businesses and government in Indonesia. 1.4 Original Contribution and Benefit The original contribution of this research is that it develops a conceptual model which describes the relationship among soft skills, hard skills, and innovativeness of knowledge workers through empirical findings in a relevant industry. On the other hand, the benefit of this research are as follows: a. For the research community, its contributions are in the area of human behavior, positive psychological capital, and knowledge economy. b. For the businesses and government, this model can be used as a reference of knowledge in defining strategy and program in human behaviour development and in defining policy in the context of knowledge economy.
Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 37 2. State of The Art The innovation system in any country consists of institutions, rules, and procedures that affect how the system acquires, creates, disseminates, and uses knowledge. Innovation in a developing country concerns not only the domestic development of frontier-based knowledgebut also the application and use of new and existing knowledge in the local context. Innovation requires a favorable climate to entrepreneurs, among of which are free from bureaucracy, regulatory, and other obstacles (The World Bank Institute, 2005). Soft skills are personal attributes that enhance an individual's interactions and his/her job performance. Unlike hard skills, which are about a person's skills set and ability to perform a certain type of task or activity, soft skills are interpersonal and broadly applicable. There has been so much research in soft skills, such as the one conducted by Spencer and Spencer (1993) focusing on positive psychology, as one of soft skills, which shows that human behaviour contains a positive psychological capital. Research in positive psychological capital (Psy-Cap) has been developed completing human capital and social capital (Luthans et al., 2004; Luthans and Youssef, 2004). Leadership is important in organizations because it consists of people who have human capital, social capital, and positive psychological capital. Also, there was research that explored the relationship between Psy-Cap (Luthans et al., 2004; Luthans and Youssef, 2004) and authentic leadership (Avolio et al., 2004; Luthans and Avolio, 2003). Psy-Cap is defined as "an individual's positive psychological state of development that is characterized by: (1) having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resiliency) to attain success" (Luthans, Youssef, & Avolio, 2007 in Larson and Luthans, 2006). There is a significant positive relationship -- regarding the four individual facets (states of hope, optimism, self efficacy, and resiliency) -- between performance and satisfaction. The composite factor may be a better predictor of performance and satisfaction than the four individual facets (Luthans, Avolio, Avey, dan Norman, 2006). Tan et. al (2008) have assessed the relative efficiency of 12 selected Asia Pacific countries in their development of Knowledge Economy (KE). The performances of the selected countries are evaluated using data envelopment analysis (DEA). The DEA scores indicate that four of the emerging countries (India, Indonesia, Thailand and Mainland China) are relatively inefficient in KE development compared to the other eight which are equally efficient. The main reason for their backwardness is due to the outflow of their human capital to the developed countries. Young (2008) argued that knowledge economy had effect when distinctive knowledge on how (know how) to produce competitive products and services became vital. 3. Methodology Workers behavior, knowledgeable workers, and innovativeness were measured using a questionnaire. The judgment sampling and stratified random sampling method were used in this research. The company, as the subject of this research, was chosen from the industries which implement design and innovation to produce the goods or services, i.e. House Ware Porcelain Company, with make to order based production system In the context of human behavior, soft skills are researched based on Spencer and Spencer (1993) model and Pys-Cap (Luthan et. al., 2007; Hendarman and Tjakraatmadja, 2007). In addition to the knowledge economy, various variables were included (Van Oort, 2009) such as: 1. Knowledge workers hard skills with indicators: ICT literacy and management knowledge 2. with indicators: technical and non-technical innovativeness. Multiple Regression Analysis was used as a method for this research, using SPSS 13.0 for windows software. Fig. 1 shows the proposed conceptual model that was tested.
38 Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 Hard Skills Soft Skills Self Efficacy Technical Information Seeking Non-Technical Conceptual Thinking Fig. 1 Conceptual Model Proposed Questionnaires were spread to the employees including administration staff, middle level management and high level management staff members. The minimum respondents educational level is senior high school level. This group of respondents was chosen as a representative for knowledge workers. The valid questionnaires, with 32 respondents, are the full answer variables which are input to SPSS 13.0. Statistically, using multiple regression analysis, the number of data (32 data) is not bad (sufficient) but more data is better. Table 1, 2, and 3 describe the questionnaire and respondent data.
Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 39 Table 1 Number of Questionnaires Questionnaire Number Percentage Spread 60 100% Collected 45 75% Valid 32 50.33% Table 2 Educational Level * Educational Level Frequency Percent Valid Percent Cumulative Percent Valid Senior High School 8 25.0 25.0 25.0 D1 1 3.1 3.1 28.1 D3 4 12.5 12.5 40.6 S1 15 46.9 46.9 87.5 S2 4 12.5 12.5 100.0 Total 32 100.0 100.0 *) S1 (bachelor degree) is the majority respondent with 46.9%, D stand for Diploma and S2 mean master degree level. Table 3 Job Level* Job Level Frequency Percent Valid Percent Cumulative Percent Valid Top Level management 4 12.5 12.5 12.5 Middle Level management 16 50.0 50.0 62.5 Staff 12 37.5 37.5 100.0 Total 32 100.0 100.0 *) Middle level management (supervisor, assistant manager, and division head) and high level management (managers). Middle level is the majority respondent wit 50%
40 Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 4. Findings and Interpretation These research findings are as follows: 1) all variables are valid for each construct with the factor loading scores is above 0.6 or 0.7; 2) all variables are reliable, with each Cronbah Alpha s score is above 0.7; 3) only information seeking soft skill that positively influenced technical innovativeness, with Sig 0.036, R 2 =0.207; and 4) only hard skills that is positively significant to influence non-technical innovativeness, with Sig 0.041, R 2 =0.187. Table 1 Construct Validity Variable Factor Loading Hard Skills Microsoft office abilities 0.862 Internet abilities 0.865 Management knowledge 0.696 Self Efficacy Confidence of finishing the job 0.967 Confidence of getting best result 0.967 Information Seeking Soft Skill Degree of Information seeking 1 Conceptual Thinking Soft Skill Degree of Conceptual Thinking 1 Technical New product 0.871 Old product improvement 0.900 New service 0.824 Old service improvement 0.887 New product for the market 0.908 New product but not new for the market 0.796 Non-Technical Cooperation with other institution 0.909 Marketing system improvement 0.909 Table 2 Reliability Factor Cronbach s Alpha Hard Skills 0.932 Self Efficacy 0.930 Technical 0.932 Non-Technical 0.788
Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 41 Model 1 Model Summary Adjusted Std. Error of R R Square R Square the Estimate.432 a.187.066 1.24434 a. Predictors: (Constant), SoftSkillConceptualThinking, SoftSkillInformationSeeking, PsyCapSE, HardSkills Model 1 (Constant) HardSkills PsyCapSE SoftSkillInformation Seeking SoftSkillConceptual Thinking Coefficients a Unstandardized Coefficients a. Dependent Variable: Technical Standardized Coefficients B Std. Error Beta t Sig. 2.253 2.175 1.036.309 -.082.248 -.063 -.330.744 -.126.371 -.062 -.341.736.430.200.397 2.149.041.138.246.105.562.579 Fig. 2 Technical (Dependent Variable) Multiple Regression Model Summary and Independent Variable Significance Model 1 Model Summary Adjusted Std. Error of R R Square R Square the Estimate.455 a.207.089 1.07205 a. Predictors: (Constant), SoftSkillConceptualThinking, SoftSkillInformationSeeking, PsyCapSE, HardSkills Model 1 (Constant) HardSkills PsyCapSE SoftSkillInformation Seeking SoftSkillConceptual Thinking Coefficients a Unstandardized Coefficients a. Dependent Variable: NonTechnical Standardized Coefficients B Std. Error Beta t Sig. -2.442 1.874-1.303.204.471.214.413 2.202.036.461.319.260 1.443.161.022.172.023.127.900.160.212.140.756.456 Fig. 3 Non-Technical (Dependent Variable) Multiple Regression Model Summary and Independent Variable Significance
42 Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 Hard Skills Soft Skills Self Efficacy (+) Sig 0.036, R 2 =0.207 Technical (+) Sig 0.041, R 2 =0.187 Information Seeking Non-Technical Conceptual Thinking Fig. 4 Conceptual Model Findings From the conceptual model findings, we can discuss that both Self Efficacy and Conceptual Thinking do not influence the Technical and Non Technical because needs a creativity which needs an out of the box thinking, positive psychology, and self efficacy. Technical needs Information Seeking Soft Skill because in this research Technical means the ability to create a new product or some services in which new data and information are very important. On the other hand, Non-Technical -- which consists of the ability to cooperate with other institutions and the ability to improve marketing or strategy -- needs ability to use Microsoft Office software, internet, and management knowledge. 5. Conclusions In Knowledge Economy era, to have an innovative employee is important. An innovative employee who has technical innovativeness needs soft skills. In this research, information seeking soft skill positively influenced technical innovativeness, which is needed to create a new product or some services. On the other hand, hard skills, among of which are the ability to use Microsoft Office software, internet, and management knowledge,
Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 43 positively influenced non-technical innovativeness, which consists of the ability to cooperate with other institutions and the ability to improve marketing or strategy. 6. Policy Implications Leydesdorff (2012) elaborated a three-dimensional space of Triple Helix interaction. Academics and Government will create Knowledge Infrastructure, Government and Businesses will create Political Economy, and Academics and Businesses will create Innovation. Furthermore, this three-dimensional space of Triple Helix interaction will create Knowledge Economy (see Fig. 4). Tjakraatmadja et. al. (2012) described the role of Academics, Businesses, and Government in an innovation. They argued that academics should provide knowledge. Furthermore, knowledge can be shared to the Businesses and Government through regions (geographically) based on Leydesdorff (2012) and through the knowledge hubs that required knowledge infrastructures. In the context of economy and political economy, Government should provide incentives to support innovativeness, such as: tax holiday and easy permit for Business purposes. Business sphere should provide funding and facilities to develop knowledge and skills (hard and soft) so that information can support innovations. In Knowledge Economy era, especially in Indonesia, information seeking soft skill is important to be delivered in educational systems not only in higher educational level but also in the lower education level. Furthermore, hard skills which consist of the ability to use Microsoft Office software, internet, and management knowledge are supposed to be delivered starting from the elementary school level. Companies should develop their employees skills by delivering information seeking soft skills and hard skills, at least the skills to operate the personal computer and internet. Last but not least, companies should improve the management and business knowledgeof their staff members in all levels. Fig. 4 Knowledge-based Economy (KE) Generated by Three-Dimensional Space of Triple Helix Interaction 7. Directions for further research Some directions for further research are as follows: 1. conducting research on exploring other variables of soft skills and hard skills 2. conducting similar research, but on creative industries, 3. conducting similar research, but with more samples and more companies, and 4. conducting similar research in which its data processing uses Structural Equation Modelling or Partial Least Square.
44 Achmad Fajar Hendarman and Jann Hidajat Tjakraatmadja / Procedia - Social and Behavioral Sciences 52 ( 2012 ) 35 44 References Drucker. 1999. http://www.knowledgeworkerperformance.com/peter-drucker-knowledge-worker-productivity.aspx OECD. 1996. http://www.oecd.org/dataoecd/51/8/1913021.pdf. The World Bank Institute. 2005. http://info.worldbank.org/etools/docs/library/145261/india_ke_overview.pdf. Jensen, Susan M., Luthans, Fred. 2006. Relationship between Entrepreneurs' Positive psychological capital and Their Authentic Leadership. Journal of Managerial Issues Vol. XVIII Number 2 summer 2006: 254-273. Luthans, Fred; Avolio, Bruce J.; Avey, James B.; Norman, Steven M. 2006. Positive Positive psychological capital: Measurment and Relationship with Performance and Satisfaction. Gallup Leadership Institute-Department of Management, University of Nebraska- Lincoln; Department of Management, Central Washington University; Department of Management,Mesa State College Larson, M., Luthans, Fred. 2006. Potential Added Value of Positive psychological capital in Predicting Work Attitudes. Journal of Leadership & Organizational Studies. Flint: 2006.Vol.13, Iss. 1; pg. 45. Tan, H. B., Hooy, C. W., Islam, S. M. N., & Manzoni, A. 2008. Relative efficiency measures for the knowledge economies in the asia pacific region. Journal of Modeling in Management, 3(2), 111-111-124. doi:10.1108/17465660810890108 Young, T. 2008. Defining and building the "knowledge economy". Knowledge Management Review, 11(2), 8-8-9. Retrieved from http://search.proquest.com/docview/217472634?accountid=31562 Spencer, Spencer. 1993. Competenece at Work. McGraw Hill. Hendarman, A. Fajar, Tjakraatmadja, Jann Hidajat. 2007. Relationship Among Employees Phsycologycal Capital, Servant Leadership, and Employees Performance. International Colloquium on Business and Management, Bangkok, Thailand. Van Oort, F.,G., Oud, J. H., L., & Raspe, O. 2009. The urban knowledge economy and employment growth: A spatial structural equation modeling approach. The Annals of Regional Science, 43(4), 859-859-877. doi:10.1007/s00168-009-0299-2 Leydesdorff, Loet. 2012. The Triple Helix of University-Industry-Government Relations. University of Amsterdam. Netherlands. http://www.leydesdorff.net/th12/th12.pdf Tjakraatmadja, Jann Hidajat; Wicaksono, Agung; and Martini, Lenny. 2011. Institut Teknologi Bandung (ITB) as Potential Knowledge Hub to Create Value from Academia, Business and Government Linkages. Beyond The Knowledge Trap. World Scientific Publishing.