Journal of critical reviews 1219
HOW DOES ORGANIZATIONAL FACTORS INFLUENCE THE ASSIMILATION OF KNOWLEDGE MANAGEMENT AND E-LEARNING FUNCTIONS AT THE STATE
UNIVERSITY?
1
Femilia Zahra*,
2Ni Made Suwitri Parwati ,
3Muhammad Ilham Pakawaru,
4Deddy Wachyudi,
5Zakiyah Zahara
Tadulako University, Palu, Indonesia Corresponding E-mail: [email protected]
Received: 21.03.2020 Revised: 22.04.2020 Accepted: 23.05.2020 Abstract
This study aims to examine the organizational factors’s effect to the knowledge transfer and their consequences for the assimilation of knowledge management and e-Learning functions. This research was conducted by distributing questionnaires to 150 lecturers from various faculties at the University of Tadulako. Questionnaires were distributed to the lecturer through google form media which connected to the Whatsapp application. There were 132 questionnaires returned and could be processed for this study, so the total response rate was 88%. The results showed that organizational factors such as Information Technology (IT), Trust Culture (TC), Flexible Structure and Design (FSD) can support the success of knowledge transfer at state universities, especially Tadulako University. This study also found that knowledge transfer plays an important role on the assimilation of knowledge management and e-learning functions. These results indicate that individual intention to transfer their knowledge and experience will support the assimilation of knowledge management and e-learning functions.
Keywords: Information Technology, Learning strategy, Flexible Structure and Design, Trust Culture, Knowledge Transfer, Assimilation of Knowledge Management and E-Learning
© 2020 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) DOI: http://dx.doi.org/10.31838/jcr.07.12.212
INTRODUCTION
Today, most of universities in Indonesia have adopted a new model in the learning and teaching process, which is to become a research-based university. Universities need to foster the student’s interest in conducting research and generate their research skills to achieve the goal. Four research skills include critical thinking, problem solving, analysis and dissemination (Zahra, 2009). These skills can be developed by using a type of learning that emphasizes student activeness (Bower, 2015). This type of learning is called student-centered learning. This type of learning provides more opportunities for students to be more active in learning by using learning tools.
The presence of information technology can help the implementation of the learning process. Information technology allows the students to collaborate in the learning process with many users. The learning process collaboration that many users can access is called online collaboration tools at the same time as the concept of. The usefulness of online communication enables students to participate effectively in the learning process (Mubarok, Romansyah, Ismail & Yunanto, 2010). In this study the use of information technology in the learning process was related to the concept of e-learning. In addition to involving the knowledge transfer process related to the knowledge management concept, online collaborative tools are also an alternative to E-Learning media systems.
E-learning is a learning system using IT in a teaching and learning process. E-learning framework has three essential components that are framework of learning management, e- content and e-services. Though knowledge management is used in large amounts and on time to identify, coordinate, and deliver the information. Knowledge management has an important impact on the development and execution of strategies (Grant, 1996; Conner and Prahalad , 1996; Zander and Kogut, 1995).
The effectiveness of knowledge management assists the organization in making decisions, creating and enhancing
innovation and improving performance and sustained competitiveness (Nonaka and Takeuchi, 1995; Davenport and Prusak, 1998). Information management generates expertise that is built through the process of constantly generating new knowledge, and translating knowledge and integrating new knowledge into the organization's established knowledge context (Kusunoki et al., 1998). The explicit knowledge is instilled by sharing knowledge within the organization through information systems and technology (IT) (Stewart, 1998). But the formation and transmission of tacit knowledge is still a "black box"
(Spender, 1996); it is still unclear how tacit knowledge is transmitted and used, as well as its impact on creativity and organizational efficiency.
Knowledge management has an significant impact on the development and execution of strategies (Grant, 1996; Conner and Prahalad , 1996; Zander and Kogut, 1995). The effectiveness of knowledge management leads to better judgment in decision- making, can create and enhance innovation, performance and sustainable competitiveness (Nonaka and Takeuchi, 1995;
Davenport and Prusak, 1998). Management of information generates insight based on the method of constantly generating new knowledge and transmitting knowledge and translating new knowledge into the organization's established knowledge sense (Kusunoki et al., 1998). The creation of explicit knowledge is done through the sharing of that knowledge within the organization through information systems and technology (IT) (Stewart, 1998). But the formation and transmission of tacit knowledge is still a "black box" (Spender, 1996); it is still unclear how tacit knowledge is transmitted and used, as well as its impact on creativity and organizational efficiency.
Organizational factors such as structure, culture, processes and strategies, and information technology influence the effectiveness of organizational knowledge transfer (Ives et al . , 2003 & Spender, 1996). Although preceding studies examine the relationship between knowledge management and innovation (Calantone et al., 2002; Hurley and Hult, 1998), Yet the
Journal of critical reviews 1220 relationship between knowledge transfer and organizational
success, and also the relationship between creativity and organizational efficiency, has not been clear.
This research seeks to explore the organizational barriers to the transfer of information and how the transfer of knowledge influences the assimilation of knowledge management and e- learning. Organizational factors are factors that promote knowledge transfer in organizations, including IT, learning policy, confidence culture, and scalable structure and design.
REVIEW OF LITERATURE
The concept of knowledge cannot be interpreted philosophically, since it is still being discussed. Awareness is not just intelligence.
Davenport (1998) defines knowledge as a mixture of experiences, values, contextual information, expert views and basic intuitions that create an environment and framework for assessing and gathering new information experiences.
Knowledge may be divided into two categories: explicit and tacit.
Tacit is hard to describe and difficult to convey in words, texts or pictures, since tacit requires thoughts , ideas or ideas. Although definition of information that can be captured in real form such as words, records, sounds, or images is explicit.
Explicit knowledge is understood implicitly, and can be expressed more clearly and formally. Explicit information is readily codified, registered, exchanged, and transferred. Explicit knowledge may be of many kinds, including declarative knowledge (something knowledge, such as concepts, definitions or descriptors), Procedural knowledge (knowledge of how something occurs or is done), causal knowledge (knowing that it occurs).
Tiwana (2000) defines knowledge management as the process of managing knowledge in organization so that it is beneficial for its users in their activities and also useful for the organization.
According to Newman (1991) knowledge management is a set of processes related to the process of creating, disseminating and using knowledge. Becerra-Fernandez (2004) said that knowledge management is an activity to create, capture, disseminate and use knowledge so that it can be used to achieve organizational goals. In addition, Davidson and Voss (2003) state that knowledge management is an organizational process that includes identification of existing knowledge assets that reflect what is known and unknown by the organization. the conclusion of some previous definitions of knowledge management is that knowledge management is an activity to manage knowledge so that it can provide more value to the organization.
E-learning is an educational resource that involves self- motivation, connectivity, performance, and technology (Davidson and Voss, 2003). E-learning is more effective, as it removes the flow of distance and round trip. Distance is eliminated because the e-learning content is designed with media accessible from the computer Terminals and available through the Internet or the network. In this process , students must keep themselves motivated because there are limitations in e-learning to social interaction.
The assimilation of e-learning technology will make the knowledge management phase more effective as shown below:
- Socialization: Measuring abilities and competencies that help distinguish individuals within the company with special interests, talents, and experience.
- Externalization: knowledge is captured by systems which seek to teach others the knowledge. This stage would boost the knowledge-capture cycle.
- Combination: understanding results and learning processes to make knowledge more effective and efficient.
- Internalization: measurement of skills and competencies that help to identify people who are unfamiliar and solve their
problem by providing knowledge through online training. E- learning will ensure that someone learns knowledge through the use of scoring systems. Alternative methods of learning are provided where necessary.
- Cognition: supports the knowledge of individuals within the organization by providing its desired training to complete assignments.
- Feedback: Evaluations give feedback on how well someone learned and how well they applied what they learned to organizational performance
The Relationship between Organizational factors and Knowledge Transfer.
Information technology (IT) is an organizational component that has been proposed as an important tool for information management by many researchers and practitioners (Sher and Lee, 2004; Bharadwaj, 2000; Duffy 2000). Davenport et al.
(1998), as an organizational factor for knowledge transfer, reported a positive relationship between information technology systems. Several studies have concluded that information technology not only increases organizational efficiency but also accelerates knowledge transfer by accessing information to facilitate collaboration and cooperation between members of the company (Lee and Hong 2002; Alavi and Leidner, 2001).
Additionally, assimilating IT systems such as portals, data search, workforce search, customer relationship management, and e- learning can improve the ability to transfer and transfer organizational knowledge. But IT systems are merely instruments. This is not the only option. Digital technology also requires person readiness to share knowledge and information (Wong and Aspinall, 2003; Rhodes et al., 2008). The first hypothesis we propose is as follows from the above discussion:
H1: Information technology has significantly beneficial influence on knowledge transfer.
The ability to learn from others and cultural openness within organizations have a significant impact on how knowledge is transmitted in organizational learning (Senge, 1990). Bukowitz and Williams (1999) suggest a knowledge management process system that demonstrates the level of learning techniques, including "win," "use," "learn" and "contribute." Learning process and adding expertise are the most demanding and critical steps to creativity and overall organizational success. This study proposes a second hypothesis according to the organizational learning and learning strategies reference in organizations:
H2: The Learning Strategies have significant and constructive impact on the knowledge transfer.
Improving knowledge transfer can be accomplished through open lines of communication, social networks, and trust (McEvily et al., 2003). Relational factors such as skill-based trust may influence factors of knowledge such as tacit knowledge (Levin and Cross, 2004; Rhodes et al . , 2008). These highlight the importance of better understanding of the role of relational variables, such as trust and emotions as the key to success or obstacles to knowledge transfer effectiveness. Furthermore, they propose that certain features of social relationships such as trust enable organizational social structure to be more (or less) efficient in information development and transition.
Trust plays an important role in the transfer and sharing of knowledge with others by individual style. Organizational control in knowledge management influences the behavior of individuals (Turner and Makhija, 2006). Fair Reward or incentive system motivates people to easily share knowledge. It also enhances individual awareness of the culture of confidence in organizations. Previous research appear to support the proposition that if an company has a culture of confidence and good cooperation then it would be easier to pass creativity,
Journal of critical reviews 1221 learning, and expertise. (Knapp, 1998; Prahalad, and Conner,
1996). We propose the third hypothesis as set out in the above discussion:
H3: The Trust Culture has an important and positive effect on knowledge transfer
An organization's design structure can be a significant determinant of whether an organization can incorporate internal information successfully (Grant, 1996b). When an organization faces a dynamic environment, the use of several structures may be necessary to support the organization's knowledge management (Nonaka and Takeuchi, 1995). For example, certain divisions of the company might need to change the team structure more frequently than others. They need social networks, channels of trust and collaboration to sustainably shape and change the organizational structure. Functional cross teams should make it easier for workers to create information maps so they can quickly locate correct knowledge (Greengard, 1998). In this versatile climate, individuals who show the ability to adjust readiness may be more fitting. They may be more willing to share information and knowledge so they can achieve goals more quickly (Rhodes et al . , 2008). Therefore it can be inferred that this kind of versatile team arrangement or partnership will facilitate greater transfer of information. Based on the above discussion, We propose the fourth hypothesis as follow:
H4: The flexible structure and design have a significant and positive effect on the transfer of knowledge.
The Relationship between knowledge transfer and Integration of knowledge Management and e-learning E-learning technology evolved separately from the technology of knowledge management. A recent study on the assimilation of these two technologies has been carried out (Brusilovsky and Vassileva 2003). Ras et al., (2005) discusses many ways of connecting information management and the disciplines of e- learning. One of several ways is to improve learning through contributions to Knowledge Management systems. Another approach is to extend e-learning as a Knowledge Management Software operational growth (Ras et al. 2005).
The following can be described for illustrating the assimilation of knowledge management and e-learning in knowledge transfer:
- If the management of Information is important. E-learning provides Knowledge management needs with technology and tools. This situation could be characterized as accepting. This situation can be defined as the adoption of Knowledge management e-learning;
- If the program is based on E-learning and e-learning.
Techniques and approaches to knowledge management are designed and used to improve the e-learning efficiency.
That can be defined as the adoption of e-learning knowledge management (Islam and Kunifuji 2011), (Sivakumar 2006);
- If knowledge management and e-learning are seen as two parallel operating disciplines, then the assimilation of knowledge management and e-learning (Maier and Schmidt 2007), (Schmidt 2005), (Ungaretti and Tillberg-Webb 2011), is the general and consistent implementation and use.
The learning target is the ultimate actuality of Information management and e-learning.The following features can be given for information management and e-learning integration (Kilby 2009):
- Can be reused: the learning material is modulated into small instructional units that are ideal for assembly and can be reorganized into various training sessions
- Interoperability: instructional units working with one another, independent of creator or learning management system
- Durability: training unit immune to advances in technology - Accessibility: learning content accessible anywhere, any
time-learning content found and reused across the network.
We propose the fifth following hypothesis, in accordance with the above discussion:
H5: The transfer of knowledge has an important and positive impact on the assimilation of knowledge management and e- learning.
METHODOLOGY
Questionnaires were distributed to 150 lecturers from various faculties in Tadulako University through Google Forms media connected to the WhatsApp application for each lecturer. There were 132 questionnaires returned and could be processed for this study, so the total response rate was 88%.
This study used several variables include information technology (IT), learning strategy (LS), trust culture (TC), flexible structure and design (FSD), knowledge transfer (KT) and integration functional of knowledge management and e-learning. The calculation of the variables is derived from the results of the questionnaire from average answers. Table 1 shows the factor loadings, explained variances, and reliability measures. The appendix includes an abbreviated copy of the research questionnaire used in this analysis for assessing the self-reported variables.
Information Technology (IT), Learning Strategy (LS), Trust Culture (TC), Flexible structure and design (FSD), Knowledge Transfer (KT) are operationalized using the items scale used in Rhodes et al. (2008). For integration functional of knowledge Management and e-learning, we used four items scale in Judrups (2015). The response scale for those contructs are 5-point Likert- type scale.
Journal of critical reviews 1222 RESULTS
The table 1 below shows each construct's loading factor, variances and measurements of reliability.
Table 1 validity and Reliability Measurement
Constructs Loading
Factors
Composite reliability coefficients AVE Information Technology
(IT) - IT1 - IT2 - IT3 - IT4 - IT5
0.795 0.878 0.845 0.817 0.6
0.900 0.647
Learning Strategy (LS) - LS1
- LS2 0.799
0.728
0.924 0.858
Trust Culture (TC) - TC1
- TC2 - TC3 - TC4 - TC5
0.600 0.706 0.7 0.7 0.729
0.892 0.623
Flexible structure and design (FSD)
- FSD1 - FSD2 - FSD3
0.867 0.7 0.745
0.891 0.732
Knowledge Transfer (KT)
- KT1 - KT2 - KT3 - KT4 - KT5 - KT6 - KT7
0.507 0.5 0.6 0.734 0.7 0.843 0.702
0.853 0.5
Integration functional of knowledge Management and e-learning
- KM &EL1 - KM &EL2 - KM &EL3 - KM &EL4
0.7 0.718 0.795 0.7
0.884 0.656
In this study, Table 1 shows factor loadings, composite reliability coefficients and average variances of latent variables extracted.
The results show that these variables have good convergent validity, discriminating validity because for each indicator, nearly all constructs have loads greater than 0,6. Although there are several indicators that have a loading value of 0.5, this is still acceptable. These results indicate that the research instrument has fulfilled the element of convergent validity which indicates
that the instrument is able to collect data with the same pattern to measure the same construct. Furthermore, the results of this study have good reliability, since all constructs have extracted more than 0,7 average variances.
Analysis of structural models with WarpPLS 6.0 shows the results of full structural equation modeling as follows:
Figure 1. Output of WarpPLS 6.0 - Full Model Measurement of model fit shows output model fit with APC value
= 0.313, p <0.001, ARS = 0.321, p = 0.001, AARS = 0.308, p = 0.001, AVIF = 1.4550, (acceptable if <= 5, ideal <= 3.3) and AFVIF
= 2,074, (acceptable if <= 5, ideally <= 3.3). The WarpPLS
Journal of critical reviews 1223 provisions state that the value of ρ for APC and ARS must be less
than 0.05 (significant). AVIF and AFVIF values as indicators of multicollinearity must be smaller than 5. Referring to these provisions, it can be concluded that the model of this study is fit.
Tabel 2 Path Coefficients, ρ-Value, Effect Size -Full Model Path Coefficients ρ-value
IT → KT 0.144 0.044
LS → KT -0.245 0.002
TC → KT 0.414 <0.001
FSD → KT 0.211 0.006
KT→ KM&EL 0.550 <0.001 Effect Size Coefficients
IT → KT 0.054
LS → KT 0.120
TC → KT 0.284
FSD → KT 0.121
KT→ KM&EL 0.302
Figure 1 and Table 2 show the path coefficient and the ρ value of each direct relationship in the research model. Path of IT → KT shows the coefficient value 0.144 and significant with the value ρ
= 0.044. The results of this hypothesis test show a positive direction indicating information technology (IT) has a positive effect on knowledge transfer (KT) and the hypothesis is statistically supported with a p value <0.05.
Path of LS → KT shows the coefficient value -0.245 and significant with p value = 0.002. The results of this hypothesis test indicate a negative direction that indicates Learning strategy (LS) has a negative effect on knowledge transfer (KT). The results of this static test cannot support the second hypothesis, because it has a negative direction. These results indicate that certain learning strategies might reduce the ability of knowledge transfer.
Path of TC → KT shows a coefficient value of 0.414 and is significant with the value of ρ <0.001. The results of this hypothesis test show a positive direction that indicates trust culture (TC) has a positive effect on knowledge transfer (KT) and the hypothesis is statistically supported with a p value <0.05.
The FSD → KT path shows a coefficient value of 0.211 and is significant with the value of ρ = 0.006. The results of this hypothesis test show a positive direction indicating that Flexible structure and design (FSD) has a positive effect on knowledge transfer (KT) and the hypothesis is statistically supported with a p value <0.05.
The path of KT → KM&EL shows the coefficient value of 0.550 and is significant with the value of ρ <0.001. The results of this hypothesis test show a positive direction indicating the transfer of knowledge (KT) has a positive effect on assimilating the management and e-learning functional knowledge. This hypothesis is supported statistically with a value of p <0.05.
CONCLUSION
This study aims to examine organizational factors for knowledge transfer and its consequences for the assimilation of knowledge management and e-learning functions. The results showed that organizational factors such as Information Technology (IT), Trust Culture (TC), Flexible structure and design (FSD) can support the success of knowledge transfer at state universities, especially Tadulako University. However, one organizational factor, which is Learning Strategy (LS), shows the different results. This result ndicates that certain learning strategies might reduce the ability of knowledge transfer.
Other research found the impact of knowledge transfer on the assimilation of the functions of information management and e- learning. Individual purpose in information transfer and practice can help assimilate knowledge management and e-learning
functions. Organizational support is also very effective in enhancing the assimilation of knowledge management and e- learning while moving knowledge from one individual to another within the organization. There are different forms companies can use to enhance integration efficiency, such as presentations and training.
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