ETEC510:Organizational Knowledge Sharing Practices

How can I capture tacit knowledge?

We often think of knowledge as something that can be recorded in words, visualized, and taught. This is called explicit knowledge.

Explicit Knowledge: The knowledge that has been recorded and structured into an organizational knowledge asset. Others can find it, reuse it, and collaborate on the knowledge. Explicit knowledge includes documents, code, manuals, websites, videos, presentations, procedures, etc.

However, this isn’t always the case. Tacit knowledge is a class of knowledge that’s difficult to communicate.

Tacit Knowledge: This is the information and knowledge you keep in your head, and you can spew at will. It’s what we know that we don’t know. In other words, you don’t know what you know, until someone asks. For example: “What are the 3 sales strategies you would tell people to follow?”

How to Create a Knowledge Sharing Culture

Tacit knowledge is a particular challenge for the knowledge management process. Teams strive to make it accessible among co-workers and would like to prevent knowledge loss during employee turnover. Unfortunately, tacit knowledge almost always goes with the employee.

Tacit knowledge is essential to competitive advantage because it’s difficult for competitors to copy. It’s the reason some teams pump out innovation after innovation while other teams struggle.

Here are three ways you can capture that tacit knowledge before it’s too late:

1. Create a culture of knowledge sharing

Communicate the need and value of a collaborative culture. We get it; sometimes you don’t like to share your best ideas; it’s nice to have an ace up your sleeve. And sometimes it’s nice to consider yourself an expert. But, consider this: knowledge is not power if it is not shared.

What is ICT (Information and Communications Technology)?

To overcome these pitfalls, encourage teamwork. By having employees work in teams, your organization may be able to increase its employee’s perception of their team members. Working closely with each other will give the employees the opportunity to see how valuable their knowledge can be. This, in turn, may encourage the employee to want to help out their team members when they see that there is a knowledge gap, error, or false truth present. Help your employees understand that they will be gaining much more than they are giving.

2. Create incentives based on quality

Provide incentives (both monetary and other types) to employees who participate in knowledge sharing. An air of caution, though — volume does not equal value! So don’t just provide incentives based on the amount contributed; otherwise your knowledge base may end up being overloaded with non-value adding contributions. Recruit knowledge experts or knowledge managers who are responsible for verifying submitted information and ensuring each submission adds value to the business. Having this system of checks and balances will also help cut down the possibility of errors and false truths.

3. Create opportunities to share

Daily scrums, weekly one-on-one’s, monthly roundtables, or quarterly town halls — these are the perfect opportunities to elicit tacit knowledge. Ask questions, interview each other with the intent to learn, and convert that tacit knowledge into accessible, reusable information. Remember to phrase questions properly, listen, avoid arguments, focus on the expert’s approach, and look beyond the facts. Be cautious not to interrogate, interrupt, put the expert on the defensive, or pretend to understand when you actually don’t. Go in with the mindset of wanting to “know” how the experts know what they know.

What is the relationship between ICT and knowledge management?

Knowledge management (KM) is a process that transforms individual knowledge into organizational knowledge.Knowledgeis information that is meaningful in cognitive formssuch as understanding, awareness and ability .It is typically acquired by experience, information consumption, experimentation and thought processes such as imagination and critical thinking. communication and information technology(ICT) are technologies which facilitate the management to share knowledge and information .thus ,ICT have a prominent role on knowledge management initiatives. One of the key issues in KM is the role of communication and information technology. this is important from two points. the first is that ICT has played a central role in the primary literature of knowledge management. Second, ICT plays a prominent role in many early knowledge management initiatives. In thisarticle,the importance and role of information technology in knowledge management in organizations has been investigated and analyzed.

what is information and communication technology | what is ict | information technology management

Keywords: Information and Communication, Knowledge Management Processes Organizational The objectivist and practice-based perspective

what is ict?

Information and communications technology (ICT) is an extensional term for information technology (IT) that stresses the role of unified communications^[1] and the integration of telecommunications (telephone lines and wireless signals) and computers, as well as necessary enterprise software, middleware, storage and audiovisual, that enable users to access, store, transmit, understand and manipulate information.

The term ICT is also used to refer to the convergence of audiovisual and telephone networks with computer networks through a single cabling or link system. There are large economic incentives to merge the telephone network with the computer network system using a single unified system of cabling, signal distribution, and management. ICT is an umbrella term that includes any communication device, encompassing radio, television, cell phones, computer and network hardware, satellite systems and so on, as well as the various services and appliances with them such as video conferencing and distance learning.^[2] ICT also includes analog technology, such as paper communication, and any mode that transmits communication.^[3]

ICT is a broad subject and the concepts are evolving.^[4] It covers any product that will store, retrieve, manipulate, transmit, or receive information electronically in a digital form (e.g., personal computers including smartphones, digital television, email, or robots). Theoretical differences between interpersonal-communication technologies and mass-communication technologies have been identified by the philosopher Piyush Mathur.^[5] Skills Framework for the Information Age is one of many models for describing and managing competencies for ICT professionals for the 21st century.^[6]

Etymology[edit]

The phrase "information and communication technologies" has been used by academic researchers since the 1980s.^[7] The abbreviation "ICT" became popular after it was used in a report to the UK government by Dennis Stevenson in 1997,^[8] and then in the revised National Curriculum for England, Wales and Northern Ireland in 2000. However, in 2012, the Royal Society recommended that the use of the term "ICT" should be discontinued in British schools "as it has attracted too many negative connotations".^[9] From 2014, the National Curriculum has used the word computing, which reflects the addition of computer programming into the curriculum.^[10]

Variations of the phrase have spread worldwide. The United Nations has created a "United Nations Information and Communication Technologies Task Force" and an internal "Office of Information and Communications Technology".^[11]

Knowledge Management Interview Questions and Answers 2019 Part-1 | Knowledge Management

Monetisation[edit]

The money spent on IT worldwide has been estimated as US$3.8 trillion ^[12] in 2017 and has been growing at less than 5% per year since 2009. The estimate 2018 growth of the entire ICT is 5%. The biggest growth of 16% is expected in the area of new technologies (IoT, Robotics, AR/VR, and AI).^[13]

The 2014 IT budget of the US federal government was nearly $82 billion.^[14] IT costs, as a percentage of corporate revenue, have grown 50% since 2002, putting a strain on IT budgets. When looking at current companies' IT budgets, 75% are recurrent costs, used to "keep the lights on" in the IT department, and 25% are the cost of new initiatives for technology development.^[15]

The average IT budget has the following breakdown:^[15]

31% personnel costs (internal)
29% software costs (external/purchasing category)
26% hardware costs (external/purchasing category)
14% costs of external service providers (external/services).

The estimate of money to be spent in 2022 is just over US$6 trillion.^[16]

Technological capacity[edit]

The world's technological capacity to store information grew from 2.6 (optimally compressed) exabytes in 1986 to 15.8 in 1993, over 54.5 in 2000, and to 295 (optimally compressed) exabytes in 2007, and some 5 zetta bytes in 2014.^[17]^[18] This is the informational equivalent to 1.25 stacks of CD-ROM from the earth to the moon in 2007, and the equivalent of 4,500 stacks of printed books from the earth to the sun in 2014. The world's technological capacity to receive information through one-way broadcast networks was 432 exabytes of (optimally compressed) information in 1986, 715 (optimally compressed) exabytes in 1993, 1.2 (optimally compressed) zettabytes in 2000, and 1.9 zettabytes in 2007.^[17] The world's effective capacity to exchange information through two-way telecommunication networks was 281 petabytes of (optimally compressed) information in 1986, 471 petabytes in 1993, 2.2 (optimally compressed) exabytes in 2000, 65 (optimally compressed) exabytes in 2007,^[17] and some 100 exabytes in 2014.^[19] The world's technological capacity to compute information with humanly guided general-purpose computers grew from 3.0 × 10^8 MIPS in 1986, to 6.4 x 10^12 MIPS in 2007.^[17]

ICT sector in the OECD[edit]

The following is a list of OECD countries by share of ICT sector in total value added in 2013.^[20]

Rank	Country	ICT sector in %
1	South Korea	10.7
2	Japan	7.02
3	Ireland	6.99
4	Sweden	6.82
5	Hungary	6.09
6	United States	5.89
7	India	5.87
8	Czech Republic	5.74
9	Finland	5.60
10	United Kingdom	5.53
11	Estonia	5.33
12	Slovakia	4.87
13	Germany	4.84
14	Luxembourg	4.54
15	Switzerland	4.63
16	France	4.33
17	Slovenia	4.26
18	Denmark	4.06
19	Spain	4.00
20	Canada	3.86
21	Italy	3.72
22	Belgium	3.72
23	Austria	3.56
24	Portugal	3.43
25	Poland	3.33
26	Norway	3.32
27	Greece	3.31
28	Iceland	2.87
29	Mexico	2.77

Knowledge Management - Interview with Eric Tsui - LUT

ICT Development Index[edit]

The ICT Development Index ranks and compares the level of ICT use and access across the various countries around the world.^[21] In 2014 ITU (International Telecommunications Union) released the latest rankings of the IDI, with Denmark attaining the top spot, followed by South Korea. The top 30 countries in the rankings include most high-income countries where the quality of life is higher than average, which includes countries from Europe and other regions such as "Australia, Bahrain, Canada, Japan, Macao (China), New Zealand, Singapore, and the United States; almost all countries surveyed improved their IDI ranking this year."^[22]

The WSIS process and ICT development goals[edit]

On 21 December 2001, the United Nations General Assembly approved Resolution 56/183, endorsing the holding of the World Summit on the Information Society (WSIS) to discuss the opportunities and challenges facing today's information society.^[23] According to this resolution, the General Assembly related the Summit to the United Nations Millennium Declaration's goal of implementing ICT to achieve Millennium Development Goals. It also emphasized a multi-stakeholder approach to achieve these goals, using all stakeholders including civil society and the private sector, in addition to governments.

To help anchor and expand ICT to every habitable part of the world, "2015 is the deadline for achievements of the UN Millennium Development Goals (MDGs), which global leaders agreed upon in the year 2000."^[24]

In education[edit]

Today's society shows the ever-growing computer-centric lifestyle, which includes the rapid influx of computers in the modern classroom.

There is evidence that, to be effective in education, ICT must be fully integrated into the pedagogy. Specifically, when teaching literacy and math, using ICT in combination with Writing to Learn ^[25]^[26] produces better results than traditional methods alone or ICT alone.^[27] The United Nations Educational, Scientific and Cultural Organisation (UNESCO), a division of the United Nations, has made integrating ICT into education part of its efforts to ensure equity and access to education. The following, taken directly from a UNESCO publication on educational ICT, explains the organization's position on the initiative.

Information and Communication Technology can contribute to universal access to education, equity in education, the delivery of quality learning and teaching, teachers' professional development and more efficient education management, governance, and administration. UNESCO takes a holistic and comprehensive approach to promote ICT in education. Access, inclusion, and quality are among the main challenges they can address. The Organization's Intersectoral Platform for ICT in education focuses on these issues through the joint work of three of its sectors: Communication & Information, Education and Science.^[28]

Introduction to Knowledge Management: KM Essentials

OLPC Laptops at school in Rwanda2

Despite the power of computers to enhance and reform teaching and learning practices, improper implementation is a widespread issue beyond the reach of increased funding and technological advances with little evidence that teachers and tutors are properly integrating ICT into everyday learning. Intrinsic barriers such as a belief in more traditional teaching practices and individual attitudes towards computers in education as well as the teachers own comfort with computers and their ability to use them all as result in varying effectiveness in the integration of ICT in the classroom. ^[29]

Mobile learning for refugees[edit]

School environments play an important role in facilitating language learning. However, language and literacy barriers are obstacles preventing refugees from accessing and attending school, especially outside camp settings.^[30]

Mobile-assisted language learning apps are key tools for language learning. Mobile solutions can provide support for refugees’ language and literacy challenges in three main areas: literacy development, foreign language learning and translations. Mobile technology is relevant because communicative practice is a key asset for refugees and immigrants as they immerse themselves in a new language and a new society. Well-designed mobile language learning activities connect refugees with mainstream cultures, helping them learn in authentic contexts.^[30]

Developing countries[edit]

Africa[edit]

Representatives meet for a policy forum on M-Learning at UNESCO's Mobile Learning Week in March 2017

ICT has been employed as an educational enhancement in Sub-Saharan Africa since the 1960s. Beginning with television and radio, it extended the reach of education from the classroom to the living room, and to geographical areas that had been beyond the reach of the traditional classroom. As the technology evolved and became more widely used, efforts in Sub-Saharan Africa were also expanded. In the 1990s a massive effort to push computer hardware and software into schools was undertaken, with the goal of familiarizing both students and teachers with computers in the classroom. Since then, multiple projects have endeavoured to continue the expansion of ICT's reach in the region, including the One Laptop Per Child (OLPC) project, which by 2015 had distributed over 2.4 million laptops to nearly 2 million students and teachers.^[31]

What is tacit and explicit knowledge creation - Innovation and Marketing

The inclusion of ICT in the classroom often referred to as M-Learning, has expanded the reach of educators and improved their ability to track student progress in Sub-Saharan Africa. In particular, the mobile phone has been most important in this effort. Mobile phone use is widespread, and mobile networks cover a wider area than internet networks in the region. The devices are familiar to student, teacher, and parent, and allow increased communication and access to educational materials. In addition to benefits for students, M-learning also offers the opportunity for better teacher training, which leads to a more consistent curriculum across the educational service area. In 2011, UNESCO started a yearly symposium called Mobile Learning Week with the purpose of gathering stakeholders to discuss the M-learning initiative.^[31]

Implementation is not without its challenges. While mobile phone and internet use are increasing much more rapidly in Sub-Saharan Africa than in other developing countries, the progress is still slow compared to the rest of the developed world, with smartphone penetration only expected to reach 20% by 2017.^[31] Additionally, there are gender, social, and geo-political barriers to educational access, and the severity of these barriers vary greatly by country. Overall, 29.6 million children in Sub-Saharan Africa were not in school in the year 2012, owing not just to the geographical divide, but also to political instability, the importance of social origins, social structure, and gender inequality. Once in school, students also face barriers to quality education, such as teacher competency, training and preparedness, access to educational materials, and lack of information management.^[31]

Modern ICT In modern society ICT is ever-present, with over three billion people having access to the Internet.^[32] With approximately 8 out of 10 Internet users owning a smartphone, information and data are increasing by leaps and bounds.^[33] This rapid growth, especially in developing countries, has led ICT to become a keystone of everyday life, in which life without some facet of technology renders most of clerical, work and routine tasks dysfunctional.

The most recent authoritative data, released in 2014, shows "that Internet use continues to grow steadily, at 6.6% globally in 2014 (3.3% in developed countries, 8.7% in the developing world); the number of Internet users in developing countries has doubled in five years (2009-2014), with two-thirds of all people online now living in the developing world."^[22]

Importance of Tacit Knowledge in Education | Richard Brock | TEDxCambridgeUniversity

However, hurdles are still large. "Of the 4.3 billion people not yet using the Internet, 90% live in developing countries. In the world's 42 Least Connected Countries (LCCs), which are home to 2.5 billion people, access to ICTs remains largely out of reach, particularly for these countries' large rural populations."^[34] ICT has yet to penetrate the remote areas of some countries, with many developing countries dearth of any type of Internet. This also includes the availability of telephone lines, particularly the availability of cellular coverage, and other forms of electronic transmission of data. The latest "Measuring the Information Society Report" cautiously stated that the increase in the aforementioned cellular data coverage is ostensible, as "many users have multiple subscriptions, with global growth figures sometimes translating into little real improvement in the level of connectivity of those at the very bottom of the pyramid; an estimated 450 million people worldwide live in places which are still out of reach of mobile cellular service."^[32]

Favourably, the gap between the access to the Internet and mobile coverage has decreased substantially in the last fifteen years, in which "2015 [was] the deadline for achievements of the UN Millennium Development Goals (MDGs), which global leaders agreed upon in the year 2000, and the new data show ICT progress and highlight remaining gaps."^[24] ICT continues to take on a new form, with nanotechnology set to usher in a new wave of ICT electronics and gadgets. ICT newest editions into the modern electronic world include smartwatches, such as the Apple Watch, smart wristbands such as the Nike+ FuelBand, and smart TVs such as Google TV. With desktops soon becoming part of a bygone era, and laptops becoming the preferred method of computing, ICT continues to insinuate and alter itself in the ever-changing globe.

Information communication technologies play a role in facilitating accelerated pluralism in new social movements today. The internet according to Bruce Bimber is "accelerating the process of issue group formation and action"^[35] and coined the term accelerated pluralism to explain this new phenomena. ICTs are tools for "enabling social movement leaders and empowering dictators"^[36] in effect promoting societal change. ICTs can be used to garner grassroots support for a cause due to the internet allowing for political discourse and direct interventions with state policy^[37] as well as change the way complaints from the populace are handled by governments. Furthermore, ICTs in a household are associated with women rejecting justifications for intimate partner violence. According to a study published in 2017, this is likely because “[a]ccess to ICTs exposes women to different ways of life and different notions about women’s role in society and the household, especially in culturally conservative regions where traditional gender expectations contrast observed alternatives."^[38]

Models of access to ICT[edit]

Scholar Mark Warschauer defines a “models of access” framework for analyzing ICT accessibility. In the second chapter of his book, Technology and Social Inclusion: Rethinking the Digital Divide, he describes three models of access to ICTs: devices, conduits, and literacy.^[39] Devices and conduits are the most common descriptors for access to ICTs, but they are insufficient for meaningful access to ICTs without third model of access, literacy.^[39] Combined, these three models roughly incorporate all twelve of the criteria of “Real Access” to ICT use, conceptualized by a non-profit organization called Bridges.org in 2005:^[40]

Physical access to technology
Appropriateness of technology
Affordability of technology and technology use
Human capacity and training
Locally relevant content, applications, and services
Integration into daily routines
Socio-cultural factors
Trust in technology
Local economic environment
Macro-economic environment
Legal and regulatory framework
Political will and public support

Toxic culture of education: Joshua Katz at TEDxUniversityofAkron

Devices[edit]

The most straightforward model of access for ICT in Warschauer’s theory is devices.^[39] In this model, access is defined most simply as the ownership of a device such as a phone or computer.^[39] Warschauer identifies many flaws with this model, including its inability to account for additional costs of ownership such as software, access to telecommunications, knowledge gaps surrounding computer use, and the role of government regulation in some countries.^[39] Therefore, Warschauer argues that considering only devices understates the magnitude of digital inequality. For example, the Pew Research Center notes that 96% of Americans own a smartphone,^[41] although most scholars in this field would contend that comprehensive access to ICT in the United States is likely much lower than that.

Conduits[edit]

A conduit requires a connection to a supply line, which for ICT could be a telephone line or Internet line. Accessing the supply requires investment in the proper infrastructure from a commercial company or local government and recurring payments from the user once the line is set up. For this reason, conduits usually divide people based on their geographic locations. As a Pew Research Center poll reports, rural Americans are 12% less likely to have broadband access than other Americans, thereby making them less likely to own the devices.^[42] Additionally, these costs can be prohibitive to lower-income families accessing ICTs. These difficulties have led to a shift toward mobile technology; fewer people are purchasing broadband connection and are instead relying on their smartphones for Internet access, which can be found for free at public places such as libraries.^[43] Indeed, smartphones are on the rise, with 37% of Americans using smartphones as their primary medium for internet access^[43] and 96% of Americans owning a smartphone.^[41]

Literacy[edit]

Youth and adults with ICT skills, 2017

In 1981, Sylvia Scribner and Michael Cole studied a tribe in Liberia, the Vai people, that has its own local language. Since about half of those literate in Vai have never had formal schooling, Scribner and Cole were able to test more than 1,000 subjects to measure the mental capabilities of literates over non-literates.^[44] This research, which they laid out in their book The Psychology of Literacy,^[44] allowed them to study whether the literacy divide exists at the individual level. Warschauer applied their literacy research to ICT literacy as part of his model of ICT access.

Scribner and Cole found no generalizable cognitive benefits from Vai literacy; instead, individual differences on cognitive tasks were due to other factors, like schooling or living environment.^[44] The results suggested that there is “no single construct of literacy that divides people into two cognitive camps; [...] rather, there are gradations and types of literacies, with a range of benefits closely related to the specific functions of literacy practices.”^[39] Furthermore, literacy and social development are intertwined, and the literacy divide does not exist on the individual level.

Warschauer draws on Scribner and Cole’s research to argue that ICT literacy functions similarly to literacy acquisition, as they both require resources rather than a narrow cognitive skill. Conclusions about literacy serve as the basis for a theory of the digital divide and ICT access, as detailed below:

The Observer: Tacit vs. Explicit Knowledge: Why one of them is indefinable yet so valuable.

There is not just one type of ICT access, but many types. The meaning and value of access varies in particular social contexts. Access exists in gradations rather than in a bipolar opposition. Computer and Internet use brings no automatic benefit outside of its particular functions. ICT use is a social practice, involving access to physical artifacts, content, skills, and social support. And acquisition of ICT access is a matter not only of education but also of power.^[39]

Therefore, Warschauer concludes that access to ICT cannot rest on devices or conduits alone; it must also engage physical, digital, human, and social resources.^[39] Each of these categories of resources have iterative relations with ICT use. If ICT is used well, it can promote these resources, but if it is used poorly, it can contribute to a cycle of underdevelopment and exclusion.^[44]

The primary role of ICT is to store and share knowledge through collaborative and teamwork software as well as virtual teams.

What is the role ICT can play in Tacit Knowledge Saring?

I want to do research on the role of ICT to boost the capacity of undergraduate Engineering students in Public Universities.

I want to be clear about what really ICT can do?

Do really Engineering Education can be learnt through Tacit Knowledge sharing among Undergraduate student in the absence of instructors?

What is the role ICT can play in Tacit Knowledge Sharing?

Perhaps following can be approached:

Deploy IoT end point devices to collect related tacit data e.g. universities engineering students' blogs, social networking websites, Q&A forums, smartphones' group whatsapp etc. (ensure you received approval or clearance from universities' research ethical committee etc.)
Pooled above data collected into data lake
Adopt big data analytics to ETLT, analyze (via different analyses techniques) & provide insight / knowledge from those big data in data lake
Use appropriate visualization software to format & view those insight / knowledge
Deposit those tacit knowledge into repository / database for knowledge sharing or even further research etc.

Best Ways to Transfer Tacit Knowledge

How ICT tools ease teachers load? And how can ICT enable full coverage of the school curriculum?

Most schools use ICT tools to ease the load on teachers overburdened teachers, enable full coverage of the school curriculum and make learning fun. Do you agree?

ICT permits teachers to generate and adjust resources rapidly and effortlessly. ICT tools permit access to an extensive scope of data in numerous set-ups, and interactive whiteboards (IWBs) have become indispensable tools in the teaching space.

Many studies have shown the effectiveness of ICT and its tool in the educational process, as well as explaining each strategy, method, and role of the teacher and the learner in it so that there is no overload on the edges of the learning process, but are all tools starting from web1.0, web 2.0 and web 3.0 Has the teacher been trained on it? In order to be used properly, especially the great diversity of these tools that are widespread in light of the Coronavirus (Covid-19) pandemic

By using ICT tools and software, a teacher can teach a unit of syllabus within one hour But in the traditional teaching method, it will take a minimum of one week.

There are three main factors involved in ICT education

1. Hardware accessories these things are explained in this video https://youtu.be/9kIiFDFdQ_c

2. Effective Content development with pedagogical aspects. These things are explained in this video https://youtu.be/FASRXEC4g7o

3. using OER (Open Educational Resources.) these things are explained here https://vinganam.blogspot.com/p/free-web-resources-effective-teaching.html

Role of Technology in Knowledge Management

By creating and using of Interactive multimedia self-learning package with proper pedagogical aspects and instructional designing. the goal can be reached sample for Interactive multimedia self-learning package is here https://vinganam.blogspot.com/p/interactive-self-learning-packages.html

There are many disagreements with the use of ICT, in teaching, and there are many who celebrate its use as a mechanism for rationalizing qualified labor in universities, that is, technologies do what teachers have traditionally done. In many ways, surprisingly, it works. There is no shortage of people who assume the criterion that teachers are becoming partially unnecessary.

The use of technologies allows serving large groups and assuming them with standardized evaluations.

The impact of technologies on good health, students and teachers, is multidimensionally approached, therefore, contradictory analytical positions are not lacking or will be lacking in this horizon. In any case, you have to see the positive side and also take into account its drawbacks. What we can be sure of is that technology will have, every time, a greater presence in education and in life in general, which is why stronger evaluation mechanisms are required, to obtain accurate recommendations and ensure a consistent use with a better quality of life for all.

More information on this matter can be found at:

https://www.researchgate.net/profile/Reinaldo-Requeiro/research

Yes, I fully agree with the idea that ICT tools ease teachers' workload. This is because ICT tools make it easy for teachers to cover many topics within a limited duration. They also help teachers to make their lessons concrete and easy for students to understand, etc.

ICT is a veritable tool in modern days classroom. Teachers are saddled with many responsibilities among which is how to get the content knowledge across to learners in the best possible ways. ICT has lessen the burdens of teachers especially in discovering new ideas coupled with existing knowledge which can be unraveled through the use of modern technology. Others include how best to present the required knowledge in the best possible ways which learners can recall at the expected time and applied where possible through modern technology. Other scholars' ideas which can improve the quality of teachers inputs can also be assessed through ICT such as through the web or any online mode which was not as easy as it is now in the past....

How to build a knowledge management system (PKMS) and why it will help you be smarter

Most schools want to use ICT tools to ease the load on overburdened teachers but they just add to the knowledge teachers have to learn because the skills needed for everyday use of technologie and the skills needed to use technologie to teach are very different.

I don't think full coverage of the school curriculum is a reason to use ITC. ITC is a tool not a teacher making sure you see all the theorie. Programs are usually developed to master a precise skill not the whole curriculum.

Teachers (not school) try to use ITC to make learning more fun but sometime fail because they haven't mastered the program they are using.

ICT is the best thing to ever happen in the teaching-learning process. Contemporary teachers with progressive minds and passion for excellent teaching embrace ICT with both hands. Teachers are now able to execute their day-to-day tasks with joys rather than pains. They are offered opportunities to employ individualised teaching of learners, co-operative teaching of learners and collaborative teaching of learners. ICT permits individual learners to compete among themselves rather than having them getting involved into unhealthy competition situations with other learners. The emphasis is mastery learning through the use of ICT-enabled criterion-referenced assessment and intensive and extensive learning through exposure to ICT-enabled co-operative and collaborative learning episodes. Learners are masters of their own destiny (they dictate their learning pace) since they take the centre stage during the learning process, while teachers remain as guides and learner facilitators. However, it should be realised that teachers need thorough ICT training on e-pedagogies in teachers colleges and universities if ever they are to discharge their duties with chilling efficiency and effectiveness.

ICT is the best tool for delivering quality education to students in very a interesting and interactive way. ICT can be a great help for easing out teachers load. Teachers can reach out to students anytime anywhere. Once standard e- materials are designed for particular subject and class, the same can be shared with students multiple time for continuous learning. Using audio visual teacher explain a topic, an experiment in an interactive way. Teachers can update their knowledge from numerous web source and thus make themselves ready for answering any queries of students.

No one denies the importance of ICT in simplifying and facilitating teachers' duties and responsibilities of enhancing effective teaching. Nonetheless, most teachers particularly in developing countries are found to be technophobic.

I agree with Robson, everyone acknowledge the importance of ITC in the teaching and learning processes. However, in most developing countries the basics of ITC do not exist, electricity itself is a perennial problem. ITC as a tool works well if the notion is introduced at an early stage of learning, however most trained teachers will be partly exposed to ITC at tertiary education level and/or not at all.

Tacit Knowledge | Abhinav Iyer | TEDxGEMSNewMillenniumSchool

What is the role of ICT in knowledge management?

Knowledge Management (KM) has become the key factor for the success of all organizations. ICTs are technologies which facilitate the management to share knowledge and information. Thus, ICTs have a prominent role on Knowledge Management initiatives. In the current business environment, the implementation of Knowledge Management projects has become easier with the help of technological tools. The value of Knowledge Management is more when made available to the right people at the right time. Thus, knowledge sharing is facilitated through information and communication technologies including computers, telephones, e-mail, databases, data-mining systems, search engines, video-conferencing equipment and many more. The purpose of this study is to identify the significant role of information and communication technologies (ICTs) in Knowledge Management (KM) initiatives that lead to organizational effectiveness. This paper moves towards an understanding of the overall importance of ICTs to knowledge management that paves way to achieve organizational effectiveness. Finally, an integrated model linking ICTs, Knowledge Management processes and organizational effectiveness is done and thereby the relationship between ICTs and KM processes is conceptualized.

Keywords

Information and Communication Technologies Knowledge Management Processes Organizational Effectiveness

How ICT can be used to disseminate knowledge throughout the Organisation?

Effective Means to Capture Tacit Knowledge

Information and Communication Technology (ICT) is an electronic means of capturing processing, storing and disseminating information. In education, information and communication technology is seen to encompass the use of a wide range of technologies including computers, the internet and interactive video. Information Communication Technology (ICT) plays a critical role in the dissemination of information around the world. Information and Communication Technologies (ICTs) are defined as a diverse set of technological tools and resources used to communicate and to create, disseminate, store, and manage information. The purpose of the study was to establish the role of ICT in Dissemination of Information in Secondary Schools in Kenya: A Literature Based Review. The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes. A critical review of empirical literature was conducted to establish the role of ICT in information dissemination in schools. It was established that Information Communication Technology (ICT) plays a critical role in the dissemination of information in schools. Result findings from literature based review indicated that ICT facilitated efficient and economic storage, retrieval and dissemination of accurate information which improved the administration processes in schools. It was also established that through the integration of ICT for example instance internet, emails, video conferencing new venture of communication dissemination in schools has been established. Due to the recent development in ICT, school administrators use ICT for improved communication as a process of transforming thoughts, sharing and imparting of information among different schools, and knowledge experts locally and all over the world which in return enhances the administration of their schools. It was found that ICT is relevant in facilitating effective and quick decision making, aiding analyzing data quickly and accurately, facilitating information gathering & dissemination, enhancing monitoring of educational facilities & resources, facilitating adequate data storage, improving coordination of tasks and activities, making administrators more productive, enhancing effective communication and knowledge sharing, facilitating fast educational planning process, improving skill acquisition & development and enhancing data processing. The study concluded that ICT has a significant role in the dissemination of information in schools. It is also concluded that that ICT has played a big role in the communication and dissemination of information in schools. The study recommended that the government through the ministry of education should lay out ICT infrastructure in schools to facilitate the use of ICT in the managements of schools. Through the laying down of ICT infrastructure such as the internet, secondary schools in Kenya will be able to receive, retrieve and disseminate information effectively.

Key words: Information Communication Technology, Secondary Schools, Kenya

Author Biography

Maryanne Karanja, Jomo Kenyatta University of Agriculture and Technology, Kenya

Post graduate student

References

Adebayo, S. S. (2013, September). The impact of application of Information and Communications Technology (ICT) in the administration of polytechnics in ogun state, Nigeria. International Journal of Students Research in Technology & Management, 1, 505-515.

Adomi, B. & Kpangban, E. (2010). Application of ICTS in Nigeria Secondary Schools. Available http://www.the free library.com/application. Accessed on 13th June 2018.

Ayoade, O. B. (2015). Access And Use Of Information And Communication Technology For Administrative Purposes By Institutional Administrators In Colleges Of Education In Nigeria: An Example Of Emmanuel Allayande College Of Education, Oyo.

Hennessy, S., Onguko, B., Harrison, D., Angâ€™ondi, E. K., Namalefe, S., Naseem, A., & Wamakote, L. (2010). Developing the use of information and communication technology to enhance teaching and learning in East African schools: Review of the literature. Centre for Commonwealth Education & Aga Khan University Institute for Educational Developmentâ€“Eastern Africa Research Report, 1.

Juma, K. S., Raihan, M. A., & Clement, C. K. (2016). Role of ICT in Higher Educational Administration in Uganda. World Journal of Educational Research, 3(1), 1.

Kokt, D., & Koelane, T. (2013). Reflecting on Information and Communication Technology (ICT) in marketing from a marketerâ€™ s and student perspective. African Journal of Business Management, 7(31), 3098-3108. http://dx.doi.org/10.5897/AJBM2013.7054

Makewa, L., Meremo, J., Role, E., & Role, J. (2013). ICT in secondary school administration in rural southern Kenya: An educator's eye on its importance and use. International Journal of Education and Development using Information and Communication Technology, 9(2), 48.

Mavellas, S., Wellington, M., & Samuel, F. (2015). Assessment Of The Availability And Utilization Of Icts For Teaching And Learning In Secondary Schools-Case Of A High School In Kwekwe, Zimbabwe. International Journal of Scientific & Technology Research, 4(8), 282-288.

Mueen,U. Asadullah, S., Raed, A.& Jamshed, M. (2013). Measuring Efficiency of Tier Level data Centers, Middle-East Journal of Scientific Research, 15(2): 200-207.

Mutisya, A. M., & Mwania, J. M. (2017). The Extend of ICT Integration in the Management of Public Secondary Schools in Kitui County, Kenya.

Ndibalema, P. (2014). Teachersâ€™ Attitudes towards the use of information communication technology (ICT) as a pedagogical tool in secondary schools in Tanzania: the case of Kondoa district. International Journal of Education and Research, 2(2), 1-16.

Ngugi, P. (2012). An investigation into the extent of the use of ICT in education management in public secondary schools in Naivasha District, Kenya (Doctoral dissertation).

Oboegbulem, A., & Ugwu, R. N. (2013). The Place of ICT (Information and Communication Technology) in the Administration of Secondary Schools in South Eastern States of Nigeria, 3(4), 231-238. Retrieved from http://www.eric.ed.gov/?q=information+research&ft=on&ff1=dtyIn_2013&ff2=subInformation+T echnology&id=ED542971\nhttp://files.eric.ed.gov/fulltext/ED542971.pdf

Ololube, N. P., Ubogu, A. E., & Ossai, A. G. (2006, September). ICT and distance education in Nigeria: a review of literature and accounts. In Proceedings of 2nd International Open and Distance learning (IODL) Symposium (pp. 643-655).

Saud, M. S., Shuaibu, B., Mohrsquo, L., Buntat, Y., & Yarsquo, A. (2011). Applicability of information and communication technologies (ICTs) in the administration of technical and vocational education and training (TVET) in a knowledge-based society. International Journal of Physical Sciences, 6(28), 6513-6520.

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Ufuophu, E & Agobami, O. (2012). Usage of information and communication technologies and job motivation among newspaper workers in Nigeria. Journal of Communication and Media Research. 4 (1), 45-59

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Wanjala, A. S. (2015). Teachersâ€™perceptions on the Use Of Information Communication Technology In The Administration Of Public Secondary Schools In Kimilili District, Bungoma County, Kenya.

Ziraba, A. (2012). The role of information and communication technology in the management of selected secondary schools in Central Uganda.

What Is the European Mentality?

what is Tacit Knowledge Sharing?

Tacit knowledge or implicit knowledge—as opposed to formal, codified or explicit knowledge—is knowledge that is difficult to express or extract, and thus more difficult to transfer to others by means of writing it down or verbalizing it. This can include personal wisdom, experience, insight, and intuition.^[1]

For example, knowing that London is in the United Kingdom is a piece of explicit knowledge; it can be written down, transmitted, and understood by a recipient. In contrast, the ability to speak a language, ride a bicycle, knead dough, play a musical instrument, or design and use complex equipment requires all sorts of knowledge which is not always known explicitly, even by expert practitioners, and which is difficult or impossible to explicitly transfer to other people.

Overview[edit]

Origin[edit]

The term tacit knowing is attributed to Michael Polanyi's Personal Knowledge (1958).^[2] In his later work, The Tacit Dimension (1966), Polanyi made the assertion that "we can know more than we can tell."^[3] He states not only that there is knowledge that cannot be adequately articulated by verbal means, but also that all knowledge is rooted in tacit knowledge. While this concept made most of its impact on philosophy of science, education and knowledge management—all fields involving humans—it was also, for Polanyi, a means to show humankind's evolutionary continuity with animals. Polanyi describes that many animals are creative, some even have Mental representations, but can only possess tacit knowledge.^[4] This excludes humans, however, who developed the capability of articulation and therefore can transmit partially explicit knowledge. This relatively modest difference then turns into a big practical advantage, but there is no unexplained evolutionary gap.

Definition[edit]

Tacit knowledge can be defined as skills, ideas and experiences that are possessed by people but are not codified and may not necessarily be easily expressed.^[5] With tacit knowledge, people are not often aware of the knowledge they possess or how it can be valuable to others. Effective transfer of tacit knowledge generally requires extensive personal contact, regular interaction,^[6] and trust. This kind of knowledge can only be revealed through practice in a particular context and transmitted through Social networks.^[7] To some extent it is "captured" when the knowledge holder joins a network or a community of practice.^[6]

Some examples of daily activities and tacit knowledge are: riding a bike, playing the piano, driving a car, hitting a nail with a hammer,^[8] putting together pieces of a complex jigsaw puzzle, and interpreting a complex statistical equation.^[5]

In the field of knowledge management, the concept of tacit knowledge refers to knowledge that cannot be fully codified. Therefore, an individual can acquire tacit knowledge without language. Apprentices, for example, work with their mentors and learn craftsmanship not through language but by observation, imitation, and practice.

The key to acquiring tacit knowledge is experience. Without some form of shared experience, it is extremely difficult for people to share each other's thinking processes.^[9]

IMCCRT-2020-1293 Title: TACIT KNOWLEDGE: ROLE OF SOCIAL MEDIA FOR SUSTAINABLE GROWTH

Embodied knowledge[edit]

Tacit knowledge has been described as 'know-how', as opposed to 'know-what' (facts).^[1] This distinction between know-how and know-what is considered to date back to a 1945 paper by Gilbert Ryle given to the Aristotelian Society in London.^[10] In his paper, Ryle argues against the (intellectualist) position that all knowledge is knowledge of Propositions ('know-what'), and therefore the view that some knowledge can only be defined as 'know-how'. Ryle's argument has, in some contexts, come to be called "anti-intellectualist". There are further distinctions: "know-why" (science), or "know-who" (networking).^{[citation needed]}

Tacit knowledge involves learning and skill but not in a way that can be written down. On this account, knowing-how or embodied knowledge is characteristic of the expert, who acts, makes judgments, and so forth without explicitly reflecting on the principles or rules involved. The expert works without having a theory of his or her work; he or she just performs skillfully without deliberation or focused attention.^[7] Embodied knowledge represents a learned capability of a human body's nervous and endocrine systems.^[11]

Differences from explicit knowledge[edit]

Although it is possible to distinguish conceptually between explicit and tacit knowledge, they are not separate and discrete in practice.^[9] The interaction between these two modes of knowing is vital for the creation of new knowledge.^[12]

Tacit knowledge can be distinguished from explicit knowledge in three major areas:^[2]

Codifiability and mechanism of transferring knowledge: Explicit knowledge can be codified (for example, 'can you write it down' or 'put it into words' or 'draw a picture'), and easily transferred without the knowing subject. In contrast, tacit knowledge is intuitive and unarticulated knowledge that cannot be communicated, understood or used without the 'knowing subject'. Unlike the transfer of explicit knowledge, the transfer of tacit knowledge requires close interaction and the buildup of shared understanding and trust among them.
Main methods for the acquisition and accumulation: Explicit knowledge can be generated through logical deduction and acquired through practical experience in the relevant context. In contrast, tacit knowledge can only be acquired through practical experience in the relevant context.
Potential of aggregation and modes of appropriation: Explicit knowledge can be aggregated at a single location, stored in objective forms, and appropriated without the participation of the knowing subject. Tacit knowledge, in contrast, is personal and contextual; it is distributed across knowing subjects, and cannot easily be aggregated. The realization of its full potential requires the close involvement and cooperation of the knowing subject.

The process of transforming tacit knowledge into explicit or specifiable knowledge is known as codification, articulation, or specification. The tacit aspects of knowledge are those that cannot be codified, but can only be transmitted via training or gained through personal experience. There is a view against the distinction, where it is believed that all propositional knowledge (knowledge that) is ultimately reducible to practical knowledge (knowledge how).^[13]

Nonaka-Takeuchi model[edit]

Ikujiro Nonaka proposed a model of knowledge creation that explains how tacit knowledge can be converted to explicit knowledge, both of which can be converted into organisational knowledge.^[14] While introduced by Nonaka in 1990,^[15] the model was further developed by/with Hirotaka Takeuchi and is thus known as the Nonaka-Takeuchi model.^[14]^[16] In this model, tacit knowledge is presented variously as uncodifiable ("tacit aspects of knowledge are those that cannot be codified") and codifiable ("transforming tacit knowledge into explicit knowledge is known as codification"). This ambiguity is common in the knowledge management literature.

Assuming that knowledge is created through the interaction between tacit and explicit knowledge, the Nonaka-Takeuchi model postulates four different modes of knowledge conversion:^[14]

Capturing tacit Knowledge: Methods and techniques

from tacit knowledge to tacit knowledge, or socialization;
from tacit knowledge to explicit knowledge, or externalization;
from explicit knowledge to explicit knowledge, or combination; and
from explicit knowledge to tacit knowledge, or internalization.

Nonaka's view may be contrasted with Polanyi's original view of "tacit knowing." Polanyi believed that while declarative knowledge may be needed for acquiring skills, it is unnecessary for using those skills once the novice becomes an expert. Indeed, it does seem to be the case that, as Polanyi argued, when people acquire a skill, they acquire a corresponding understanding that defies articulation.^[7]

Examples[edit]

One of the most convincing examples of tacit knowledge is facial recognition: one knows a person's face, and can recognize it among a thousand, indeed a million. Yet, people usually cannot tell how they recognize that face, so most of this cannot be put into words. When one sees a face, they are not conscious about their knowledge of the individual features (eye, nose, mouth), but rather see and recognize the face as a whole.^[17]
Another example of tacit knowledge is the notion of language itself: it is not possible to learn a language just by being taught the rules of grammar—a native-speaker picks it up at a young age, almost entirely unaware of the formal grammar which they may be taught later.
Other examples are how to ride a bike, how tight to make a bandage, or knowing whether a senior surgeon feels an intern may be ready to learn the intricacies of surgery; this can only be learned through personal experimentation.
Harry M. Collins showed that Western laboratories long had difficulties in successfully replicating an experiment that a team led by Vladimir Braginsky at Moscow State University had been conducting for 20 years (the experiment was measuring the quality, Q, factors of sapphire). Western scientists became suspicious of the Russian results and it was only when Russian and Western scientists conducted the measurements collaboratively that the trust was reestablished. Collins argues that laboratory visits enhance the possibility for the transfer of tacit knowledge.^[18]^[19]
The Bessemer steel process is another example: Henry Bessemer sold a patent for his advanced steelmaking process and was subsequently sued by the purchasers after they could not get it to work. In the end, Bessemer set up his own steel company because he knew how to do it, even though he could not convey it to his patent users.^[20]
When Matsushita (now Panasonic) started developing its automatic home bread-making machine in 1985, an early problem was how to mechanize the dough-kneading process, a process that takes a master baker years of practice to perfect. To learn this tacit knowledge, a member of the software development team, Ikuko Tanaka, decided to volunteer herself as an apprentice to the head baker of the Osaka International Hotel, who was reputed to produce the area's best bread. After a period of imitation and practice, one day she observed that the baker was not only stretching, but also twisting the dough in a particular fashion ("twisting stretch"), which turned out to be his secret for making tasty bread. The Matsushita home bakery team drew together eleven members from completely different specializations and cultures: product planning, mechanical engineering, control systems, and software development. The "twisting stretch" motion was finally materialized in a prototype, after a year of iterative experimentation by the engineers and team members working closely together, combining their explicit knowledge. For example, the engineers added ribs to the inside of the dough case in order to hold the dough better as it is being churned. Another team member suggested a method (later patented) to add yeast at a later stage in the process, thereby preventing the yeast from over-fermenting in high temperatures.^[14]^: 284

How tacit knowledge is shared?

Abstract: Knowledge sharing that takes place among team members is a process of great relevance that builds ties and relationships which in turn results in positive organizational and team outcomes. However, as it is not usually formally included in the job descriptions and is not a formal part of organizations’ and team activities, it is considered to be an organization citizenship behavior. Our paper emphasizes significance of tacit and explicit knowledge sharing to team performance in the context of scientific cooperation. Positive relationship between tacit knowledge sharing and explicit knowledge sharing with team performance was found using linear regression. Furthermore, high levels of knowledge sharing and team performance were identified among scientists.

Keywords: Team Performance, Knowledge sharing, Tacit knowledge, Explicit knowledge, Scientific cooperation

The value of knowledge sharing impact of tacit and explicit knowledge sharing on team performance of scientists

7 Ways to Make a Conversation With Anyone | Malavika Varadan | TEDxBITSPilaniDubai

1. Introduction

A new perspective of a “knowledge-based view of the firm” (Dyer and Nobeoka，2002), that knowledge is the most important organizational resource has emerged. Scholars suggest that the key role of the firm is in creating, storing, and applying knowledge (Kogut & Zander, 1992; Conner& Prahalad, 1996; Grant, 1996) Knowledge sharing is an additional activity that firm should focus on. As concept knowledge sharing has been recognized as significant tool for increasing knowledge (Alavi & Leidner, 2000). Plenty of research aimed at investigating various factors contributing to knowledge sharing in recent years, mainly because when knowledge is being shared the collective memory of the organization is being enhanced, which leads to better organizational performance. By sharing knowledge employees contribute to the knowledge base, innovativeness and ultimately competitive advantage of their organization (Jackson et al., 2006) and the success of a project (Adenfelt, 2010). Furthermore, knowledge sharing that takes place among team members is a process of great relevance that builds ties and relationships which in turn enhance team performance. However, as it is not usually formally included in the job descriptions and is not a formal part of organizations’ and team activities, it is considered to be an organization citizenship behavior.

Nevertheless, its role is crucial for teams, projects’ and organizations’ success. Team performance as an outcome of knowledge sharing has to some extent been investigated in the prior literature however not in the context of scientific cooperation, and not as an outcome of sharing of explicit and tacit knowledge. For that reason, we divide knowledge sharing into tacit and explicit to investigate whether team performance of scientists is contingent on both explicit and tacit knowledge sharing. Additionally, we will evaluate the current level of knowledge sharing on the projects in question. Even among scientists, who are assumed to share their knowledge freely as they are engaged in knowledge intensive activities that require close cooperation, certain barriers can exist, sometimes reflecting the surrounding environment which does not foster positive characteristic and natural principles of science. Sometimes in the dynamic working environment characterized by geographic dispersion, electronic dependence, dynamic structure and national diversity of its members (Gibson and Gibbs, 2006) difficulties might occur. In such working environments due to the cultural diversity, language obstacles, task organization, lack of face-to-face interaction and geographical dispersion there is a lack of shared identity, sense of belonging and trust in others (Au and Marks, 2012). Consequently, misunderstanding and conflict between team members might occur (Richards and Bilgin, 2012), all which can hinder knowledge sharing. Still, work of scientists on various projects implies close cooperation and knowledge sharing, and so it can present a benchmark on knowledge sharing for other project and organizational teams. In order to conduct a deeper investigation of factors that influence knowledge sharing we should firstly determine its importance to the team performance in the context of our projects.

To author’s knowledge there has been no systematical analysis of nationally financed projects in Croatia up to this point. Furthermore, there have been no studies conducted in the context of knowledge sharing. The general objective of this study is to evaluate the current state of knowledge sharing and team performance in joint research projects between Croatia and other countries and at the same time determine the influence of explicit knowledge sharing and tacit knowledge sharing on team performance. Moreover, most of the prior studies do not distinguish between sharing of tacit and explicit knowledge, and did not investigate projects characterized by a dynamic labor environment.

2. Literature Review

On the individual level knowledge sharing is considered to be a significant process resulting in positive organizational outcomes, such as superior innovation capability, work-environment creativity (Schepers and van den Berg, 2007), team performance cohesion, knowledge integration and decision satisfaction (Mesmer-Magnus et al., 2009).

What is TACIT KNOWLEDGE? What does TACIT KNOWLEDGE mean? TACIT KNOWLEDGE meaning & explanation

As a result in the extensive research on knowledge management individual knowledge sharing has justifiably held an important position and therefore has been a subject of many studies. In those studies knowledge is usually divided into two types: explicit and tacit. Explicit knowledge usually refers to the type of knowledge that can easily be communicated with words, codified and subsequently shared. Explicit knowledge is easy to capture and usually comes in a somewhat tangible form, generally as documents, PPTs, manuals. Sharing of explicit knowledge is usually being facilitated by information technology. Tacit knowledge or know-how (Kogut and Zander, 1992; Grant, 1996;) on the other hand is related to an individual’s experience and thoughts (Alavi and Leidner,2001)and is subject to social interaction (Käser and Miles, 2002; Nonaka, 1994) and friendship (Osterloh and Frey, 2000). Team members’ sharing of tacit knowledge is reinforced in situations in which they interact face-to face in the context of project work. (Howells, Jeremy, 1996). Geographical proximity of team members, common language and mutual trust all affect the level of tacit knowledge utilization on projects, which can in turn affect team performance. (Koskinen, Pihlanto and Vanharanta, 2003). According to the SECI model, illustrating knowledge creation, developed by Nonaka and Takeuchi, a nonstop interaction between individuals occurs in which knowledge is being continuously converted from tacit to explicit and from explicit to tacit. SECI process is comprised of four knowledge creation modes: socialization (tacit to tacit), combination (explicit to explicit), externalization (tacit to explicit), and internalization (explicit to tacit). As tacit knowledge is internal, and embedded in people, human interactions are essential for its transfer. So in the socialization process tacit knowledge in the form of experience or skills can be transferred between individuals.

Externalization, on the other hand is a process of making tacit knowledge explicit. For example, organizations will try to capture what the employees know through creating platforms where they can interact and share knowledge, usually internal forums for communities of practice where they can exchange knowledge. Through synthesizing the body of knowledge, to some extent, but not fully will the process of externalization be successful. Early knowledge management practice and research have been mostly focused on managing explicit knowledge in forms of documents, forms, procedures and etc. creating huge repositories of knowledge and relying on IT to facilitate knowledge sharing processes, and enhance the collective memory of an organization However the assumption that when technology for knowledge sharing is implemented that employees will share knowledge is showed to be false, and often failed to make tacit knowledge explicit due to the cognitive nature of tacit knowledge (Pawlowski and Robey, 2004). Sharing of knowledge does not only depend on the technology factor but on many others. Furthermore, technology itself often fails to capture the most important component of knowledge, the tacit one. Our efforts are aimed at examining both sharing of tacit and explicit knowledge. We posit that sharing of information and codified knowledge facilitated by information technology, especially on the projects which are to some extent virtual, as well as tacit knowledge, ingrained in daily routines and embedded in people through the process of socialization are relevant for team performance. Based on this proposition we build our research model.

3. Research Model and Hypotheses

In the establishment of our research model we tested the influence of both tacit and explicit knowledge sharing on team performance. We also posit that scientists intensively share both tacit and explicit knowledge, consequently resulting in high team performance. Prior research has widely demonstrated positive effects of knowledge sharing on team performance (Argote and Ingram 2000; Cummings 2004; Hansen 2002; Choi et al., 2010).

Crafty's Theory of Learning - Tacit Knowledge and Language

3.1 Knowledge Sharing and Team Performance

3.1.1 Explicit Knowledge Sharing

Explicit knowledge sharing encompassing various formal and systematically stored, articulated and disseminated information (Becerra-Fernandez and Sabherwal, 2001) is beneficial for workers, teams and organizations. However often redundancy of information is present, as these systems accumulate wide knowledge, still with proper filtering mechanisms used to distinguish relevant from irrelevant knowledge, team members and workers can easily get required information. Organizations have built systems for managing explicit knowledge, knowledge platforms document repositories, search engines and intranets (Hansen and Haas 2001) making information widely available and easily accessible.). In their study…. indirect influence of knowledge sharing through knowledge application was found, but no direct effect of knowledge sharing on team performance (Choi et al.,2010). Through the means of IT support collaboration and communication contributing to the building of TMS in teams is fostered. (Choi et al., 2010).

As a result team members can decrease the time of search and creation by quickly accessing and using collective knowledge made explicit, which in turn will enhance their individual efficiency and consequently team performance.

Many studies have shown relationship between effective explicit knowledge sharing, or information sharing and team performance (Greenhalgh and Chapman, 1998; Schittekatte and Van Hiel, 1996). According to the meta-analysis on information sharing conducted by (Mesmer-Magnus et al., 2009) two characteristics of sharing are relevant. Meta-analytic results from 72 studies show the positive link of information sharing to team performance, cohesion, decision satisfaction, and knowledge integration.. By using distinctive knowledge from their members in order to gain advantage, they will enlarge the knowledge fund which would enhance team task performance. Secondly, the concept of openness in explicit knowledge sharing could provide more opportunities to share unique knowledge and contributing to the trusting climate which would improve team socio-emotional outcomes and in turn team task performance (Beal, Cohen, Burke, McLendon, 2003).

3.1.2 Tacit Knowledge Sharing

Via sharing of tacit knowledge which is embedded in people, individuals provide their valuable knowledge and tap into what others know. Tacit knowledge has a crucial role in the organizational performance improvement (Small and Sage, 2006; Reychav and Weisberg, 2009). Through the process of socialization knowledge can be transferred from one person to another. Expertise, skills or experience which are difficult to capture and codify can be shared through creating mentoring programs or various workshops which will create shared mental modes that would ease the coordination and collaboration process resulting in better utilization of knowledge and higher team performance (Marks et al., 2000).

Sharing Tacit Knowledge - Nancy Dixon tells the story about Xerox Copy Repair Technicians

Transactive memory of “who knows what” has had a positive relationship with team performance due to improved coordination (Wegner et al., 1987). Transactive memory system (TMS) refers to a shared mental mode of the collective indicating which individuals know certain things and which individuals know who knows certain things (Jarvenpaa and Majchrzak, 2008) and it encompasses encoding, storage, and retrieval of knowledge from different spheres (Wegner et al., 1987) Through building a transactive memory system specialization of knowledge together with trust in knowledge of others and knowledge coordination according to the task structure are achieved (Wegner et al.,1987). Elements of knowledge sharing, such us feedback, communication influence the development of TMS (Hollingshead 1998a, 1998b; Moreland et al., 1996) which in turn enhances team performance (Liang et al., 1995; Lewis and Kyle, 2004; Kanawattanachai and Yoo, 2002; Faraj and Sproull, 2000).

Having an insight in the knowledge of others, a best practice can be recognized and implemented by other individuals. Also, the shortening of a learning curve for younger scientists would lead to higher efficiency and improved team performance. In the environment where people are trusting, open, interacting and share their tacit knowledge without the fear of losing their own unique value, can result in higher collective performance (Käser and Miles, 2002) and building of a strong team identity and positive team characteristics that can lead to a superior team performance and in turn generate positive outcomes, such as innovation or financial performance, either in the context of projects or organizational context. Tacit knowledge is considered an important source of competitive advantage for individuals, teams and organizations as it is specific to the context, personal and thus hard to imitate (Berman, Down, Hill, 2002).

Taking all the ample evidence into consideration on the existing relationship between knowledge sharing and team performance, we posit that in the context of scientific project cooperation significance is present.

Therefore we hypothesize:

H1 Sharing of tacit knowledge positively influences team performance

H2 Sharing of explicit knowledge positively influences team performance

3.1.3 Level of Knowledge Sharing and Team Performance

Three Eras of Knowledge Management - Nancy Dixon

Team performance has exhibited its value in the context of innovation, competitive advantage, quality etc., and has received much attention in the prior research (Cohen and Bailey 1997).For project work teams are usually constructed and play an important role in knowledge-based organizations as they are often utilized to work on complex tasks (Cummings 2004; Rico et al. 2008). As knowledge has been recognized as a driver of innovation and a strategic asset, and in knowledge intensive groups and companies it has taken a central role in team performance. Knowledge workers on such projects are participating in intensive knowledge tasks, solving complex problems, have a high education and therefore through their collaborative efforts enhance team performance and drive innovation and other positive organizational outcomes. Knowledge workers tend to demonstrate flexibility, initiative, and higher job performance (Stewart and Barry, 1997; Davenport and Prusak, 1998). In a study of knowledge acquisition variables on financial and non-financial team performance, there was a positive relationship found, especially with a communication understanding component (Politis, 2003). In the context of scientific work knowledge sharing can hardly be put in the domain of organizational citizenship behavior. As for scientists tacit knowledge sharing is a matter of daily practice, in which they engage in knowledge intensive activities which require collaboration. Conducting experiments in the laboratory, use of equipment, close cooperation, joint publications and presentations of the project result, all encompass both sharing of tacit and explicit knowledge and are necessary for project completion. As scientists are characterized by a strong passion for practice and as knowledge sharing in the case of science projects cannot be considered organizational citizenship behavior. Free flow of knowledge and openness are basic principles of scientific cooperation, as science can be advanced through complete transparency and sharing.

Based on this we hypothesize:

H3 Tacit knowledge sharing level between scientists is high

H4 Explicit knowledge sharing between scientists is high

H5 Team performance is high

4. Research methodology

In our investigation we applied a survey strategy for data collection by which we examined a sample of 277 members of project teams working on international research projects. Sample consists of knowledge-intensive projects aimed to solve complex problems through innovative solutions for which knowledge sharing is highly relevant. We focus on investigating international science and technology project team members’ interaction and knowledge sharing. During the projects, the scientists from different countries worked together (from several months to several years), and were involved in knowledge intensive activities aimed to result in relevant scientific findings.

Most of the survey participants were employees of research institutes and universities in Croatia, which worked on international science and technology projects in the fields of semiconductor industry, information technology, electronics, photonics, petrochemicals, medical science, biochemistry, etc. Scientists from various research institutes in Zagreb, and faculty members of University of Zagreb that were members of project teams under our study were invited to participate in the survey. With the intention of avoiding the threat of common method, bias data were collected from two different sources, both project managers (53.8%) and project team members (46.9%). Approximately 45.9% of the team members in the ﬁnal sample were male. A majority of the group members (62.5%) were between the ages of 40 and 49 and over. The main means of communication during the project involved both face-to-face communication and information technology (82.7%). More than one third of members had over 20 years of work experience (36.5%), whereas 23.8 percent of project participants had only up to 3 years of work experience, and the third largest group (20.9%) has worked for 13 to 19 years at the time of the data collection. Finally, vast majority of participants (89.2%) hold a PhD degree.At the time of the research majority of projects had been completed, (55.6%), some were in the second year (18.1%) and some in the third (16.6%). Almost half of teams (49.8%) consisted of 5-10 members, while team with less than 5 members formed 30.0% of all groups. Respondents all being of Croatian nationality cooperated with scientists form different countries. A large proportion of respondents (33.2 %) were involved in projects, which encompassed team members of more than two nationalities, whereas 23.1 percent cooperated with Croatian scientists only. The majority research projects belong to Life Science (31.6%), Physical Science (28.7%) and Social Science (23.2%) fields. All the descriptive features of the projects and the respondents are exhibited in Table 1.

Table 1: Demographic and project characteristics

Knowledge Management Training Session

4.1 Measurement Development

All the variables were operationalized and measured with existing scales which were validated by other researchers. To normalize the data, we adjusted the scale wording so all items were measured with a 7 point Likert scale with anchors being 1 = strongly disagree, 2 = somewhat disagree, 3 = disagree, 4 = neutral, 5 = somewhat agree, 6 = agree, 7 = strongly agree. The constructs in our study were measured with items adopted from previous studies utilizing already developed scales.

Team Performance

Team performance was measured with the adjusted scale of Baruch and Lin (2012) who adopted the measurements for Team performance from Stewart and Barrick (2000). Both team behaviors and team outcomes make up team performance and were included in the original version of scale measuring. In our study we assessed quality of work, quantity of work, planning and allocation of resources, overall team performance. Items that were dropped from the original measurement included interpersonal skills, knowledge of tasks, initiative and commitment to the team.

Explicit and Tacit Knowledge Sharing

We assessed explicit and tacit knowledge sharing with items adopted from Wang and Wang (2012) who reported that they operationalized explicit and tacit knowledge sharing variable by combining items from multiple sources (Reychav & Weisberg, 2010; Liebowitz, 1999; Alavi & Leidner, 2001) in order to capture the essence of the constructs. Six-item scale used to measure explicit knowledge sharing was slightly modified to suit the research setting of project work and encompassed practices of sharing reports, training and development programs, IT systems and general encouragement to share knowledge among project team members.

Tacit knowledge sharing measurement scale was assessed with items from Wang and Wang (2012) who had constructed it by linking items from different studies (Bock, Zmud, Kim, & Lee, 2005; Holste & Fields, 2010; C.P. Lin,2007; H.F. Lin, 2007; Reychav & Weisberg, 2010). Again we adjusted the item wording to fit the context of project member interaction and knowledge sharing context. Used items refer to project member experience, know-where and know-who, expertize and lessons learned from failed projects.

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5. Data Analysis and Results

5.1 Descriptive Statistics and Reliability

Descriptive statistics for Explicit knowledge sharing, Tacit knowledge sharing and Team performance are shown in table 5-2. Explicit knowledge sharing measure ranged across the whole possible range of values [1.00, 7.00], with mean 5.46 (95% confidence interval: [5.35, 5.56]) and median 5.50. Tacit knowledge sharing measure had taken values between 1.57 and 7, with the mean 5.77 (95% confidence interval: [5.67, 5.88]) and median 5.86. Team performance measure had values in [1.75, 7.00] range, with mean 5.24 (95% confidence interval: [5.13, 5.34]) and median 5.25.

Normality tests (Kolmogorov – Smirnov and Shapiro – Wilk) were performed on data. The results are shown in Table 2. In those tests, null hypothesis is that data is normally distributed. So, if the obtained coefficients are not significant, null hypothesis that data fits the normal distribution well cannot be rejected. According to the results of both normality tests, explicit knowledge sharing measure, tacit knowledge sharing measure and team performance measure fit the normal distribution reasonably well. Normality has been further assessed using the Q-Q’ plots for explicit knowledge sharing (Figure 5-1), tacit knowledge sharing (Figure 5-2) and Team performance (Figure 5-3). For normally distributed data, Q-Q’ plot should be approximately linear.

All the plots support the claim that the data fits the normal distribution reasonably well for all three observed variables. Reliability of the data was assessed using the Cronbach alpha coefficients. Cronbach alpha coefficients over 0.7 are considered acceptable. Global Cronbach alpha coefficient of 0.825 was obtained, and Item-total statistics, including Cronbach alpha coefficients if item deleted were calculated. The results indicate that data reliability is at an acceptable level.

Table 2: Normality test results on Explicit knowledge sharing, Tacit knowledge sharing and Team performance data

	Kolmogorov-Smirnova	Shapiro-Wilk
Explicit knowledge	.117	.951
Tacit knowledge	.110	.924
Team performance	.112	.969

Figure 1: Normal Q-Q’ plot of explicit knowledge sharing

Figure 2: Normal Q-Q’ plot of tacit knowledge sharing

Figure 3: Normal Q-Q’ plot of team performance

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5.2. Statistical Analysis of the Model

Since the theoretical model for each outcome predicted linear relationship between multiple predictors and the outcome, possible moderation effects and multilayered network architecture of mediated relationships, multiple linear regression was chosen as a tool to assess the model. The choice of multiple regression was appropriate given the ratio between the number of data points and the number of model variables. Necessary assumptions for the linear regression were shown to hold.

Multiple regression equation

(5-1)

5.2.1 Effects of Explicit and Tacit Knowledge Sharing on Team Performance

According to the proposed model, there is a positive linear relationship between team performance as an outcome and explicit and tacit knowledge sharing as input variables. In order to assess independent effects of explicit and tacit knowledge sharing on team performance, correlation analysis and multiple regression were performed on data points available after the removal of the data points with empty fields. Before performing the regression, assumption of linearity was confirmed and normal distribution of data points was ascertained. After performing the regression, choice of the model was justified by performing the residual plot. Descriptive statistics for explicit knowledge sharing, tacit knowledge sharing and team performance were calculated and are shown in table 3.

Table 3 : Descriptive Statistics for team performance, explicit and tacit knowledge sharing

Explicit knowledge sharing measure ranged across the whole possible value range: 1.00 – 7.00, (M= 5.43, SD = .97) with median 5.50. Tacit knowledge sharing measure had taken values 1.57 – 7.00, (M = 5.76, SD = .91) with median = 5.86. Team performance measure had values in range: 1.75 -7.00, (M = 5.10, SD =.92) with median = 5.25.

Correlation and Regression Analysis

As all the assumptions for Pearson product moment correlation were fulfilled, correlation analysis was applied in order to predict the linear relationships between variables. Mutual correlation coefficients were calculated for all the variables and are shown in table 5-4. As expected, input variables explicit knowledge sharing and tacit knowledge sharing are moderately positively mutually correlated, as well as moderately positively correlated with the measured outcome variable team performance.

Correlation coefficient r (275) = 0.74 between explicit and tacit knowledge sharing was obtained. Correlation coefficient can obtain values from – 1 to 1 with values greater than 0 indicating positive association implying that as the value of one variable increases the value of the other variable increases correspondingly and values less than 0 indicating negative association. Correlations coefficients r (275) =0.56 and r (275) =0.54 for the correlation between explicit and tacit knowledge with team performance were recorded, respectively. According to the general guidelines the strength of association is from 0.5 – 1.0 is large, 0.3 – 0.5 is medium and 0.1 – 0.3 is small. All the correlations were statistically significant (p<0.001). Attained positive correlations between all the observed measures confirm the theoretical expectations and convergent validity, which was previously verified by the factor analysis described in section 5.1.

Table 4: Correlation coefficients between explicit knowledge sharing, tacit knowledge sharing and team performance

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Given that the Pearson product-moment correlation does not determine causality and does not differentiate between dependent and independent variables, regression analysis is utilized to test the predictive value of the model. In order to further elucidate and quantitatively express the influence of explicit and tacit knowledge on team performance, regression analysis of the effects of explicit knowledge and tacit knowledge on team performance was performed using multiple linear regression in SPSS. Since data fit the normal distribution reasonably well and given that all the assumptions were fulfilled, use of linear regression was justified. Table 5 shows quality of the structural model fit. Model containing both explicit and tacit knowledge as predictors had the highest adjusted R2 =0.341 among the candidate models and was therefore chosen. Predictive power of the model, expressed by the R2=0.345, indicates that 34.5 % of variance in team performance can be explained by tacit and explicit knowledge sharing.

Table 5: Team performance regression analysis

Next, a relative contribution of each independent variable to the total variance explained is determined. Moderately small percentage of variance in team performance can be explained by varying explicit and tacit knowledge sharing, suggesting that additional effects unaccounted for by the model are present. Results of the regression analysis are shown in Table 5. There is a linear relationship between team performance as an outcome and explicit and tacit knowledge as predictors, quantitatively described by the (5-3) equation.

Team Performance Multiple Regression Equation

TP = 0.33 * Explicit + 0.30 * Tacit + 1.66 (5-2)

Both regression coefficients for explicit knowledge sharing and tacit knowledge sharing as independent predictors are highly statistically significant β= 0.33, p<0.001, 95% CI [0.20, 0.47] for explicit knowledge sharing and β = 0.30, p<0.001, 95% CI [0.16, 0.45] for tacit knowledge sharing respectively. Therefore, theoretical model has been justified by the regression results. Positive linear relationship between explicit knowledge was confirmed, where one unit increase in the measure of explicit knowledge sharing results in 0.33 (β = 0.33, t (274) = 4.96, p < .001) units of increase in team performance measure, given constant tacit knowledge sharing measure.

Additionally, positive linear relationship between tacit knowledge and team performance, where a unit increase in tacit knowledge sharing measure results in 0.30 units of increase of team performance measure (β = 0.33, t(274) = 4.22, p < .001 ), given constant explicit knowledge sharing measure, was detected. The results of the analysis are depicted in figure 5 -1.

Figure 4: Regression analysis of knowledge sharing constructs on team performance

5.3. Analysis of the Proposed Hypotheses

5.3.1. Sharing of Tacit Knowledge Positively Influences Team Performance

Null hypothesis is defined as H0:=Team performance is not positively influenced by sharing of tacit knowledge. Since the mean regression coefficient for the effect of tacit knowledge sharing on team performance is 0.30, with 95% confidence interval in range [0.16, 0.45], null hypothesis can be rejected at p<0.001. Therefore, we can conclude that sharing of tacit knowledge positively influences Team performance.

5.3.2. Sharing of Explicit Knowledge Positively Influences Team Performance

Null hypothesis is defined as H0:=Team performance is not positively influenced by sharing of explicit knowledge. Since the mean regression coefficient for the effect of explicit knowledge sharing on team performance is 0.33, with 95% confidence interval in range [0.20, 0.45], null hypothesis can be rejected at p<0.001. Therefore, we can conclude that sharing of explicit knowledge positively influences Team performance.

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5.3.3. Tacit Knowledge Sharing Level Between Scientists is High

Null hypothesis is defined as H0:=Tacit knowledge sharing level between scientists is not high (where not high would be defined as measure levels lower than 5). Since the mean of Tacit knowledge sharing is 5.77, with 95% confidence interval in range [5.67, 5.88], null hypothesis can be rejected at p<0.001.

Therefore, we can conclude that tacit knowledge sharing level between scientists is high.

5.3.4. Explicit Knowledge Sharing Between Scientists is High

Null hypothesis is defined as H0:=Explicit knowledge sharing level between scientists is not high (where not high would be defined as measure levels lower than 5). Since the mean of explicit knowledge sharing is 5.46, with 95% confidence interval in range [5.35, 5.56] null hypothesis can be rejected at p<0.001. Therefore, we can conclude that explicit knowledge sharing level between scientists is high.

5.3.5. Team Performance is High

Null hypothesis is defined as H0:=Team performance is not high (where not high would be defined as measure levels lower than 5). Since the mean of team performance is 5.24, with 95% confidence interval in range [5.13, 5.34] null hypothesis can be rejected at p<0.05. Therefore, we can conclude that team performance is high.

5.3.6. Sharing of Tacit Knowledge Positively Influences Team Performance

5.3.7. Sharing of Explicit Knowledge Positively Influences Team Performance

6. Discussion

Our findings are in line with what was found in prior studies, indicating that knowledge sharing exerts positive influence on team performance. In our study both tacit and explicit knowledge are significant for team performance. Teams that share knowledge freely and openly tend to be more effective. For that reason, research and academic institutions should emphasize the importance of knowledge sharing and apply management initiatives aimed at facilitating knowledge sharing. Effective knowledge sharing attained through proper management of various factors leads to a better team performance and other positive outcomes of the project. Principles of uniqueness and openness in regard to explicit knowledge sharing contribute to the team performance. (Nahapiet and Ghoshal, 1998). Furthermore, demonstrability, cooperation and discussion structure were found to enhance explicit knowledge sharing and informational interdependence, information distribution and team member heterogeneity were found to negatively affect knowledge sharing (Mortensen and Hinds, 2001).

Managers’ efforts should be aimed at generating knowledge sharing conditions through leadership (Zollo and Winter, 2002) and various organizational mechanisms. For instance, fair treatment of employees, empowerment and carefully applied incentives can all be implemented to create the conditions conducive to fostering a knowledge sharing environment. Furthermore, when creating management initiatives managers should take into consideration personal dispositions of teams and individuals, as personality and attitude play an important role in generating behavioral outcomes. In the socialization process effort is not being put in converting explicit into tacit knowledge. Information technology should therefore be aimed at facilitating socialization process and not in making tacit knowledge explicit. In recent years 2.0 technologies have emerged which are characterized by the richness of the media and might be more suitable for effective socialization process facilitated by IT. Through video tutorials, abundant internet learning resources and social networks that provide communities of practice a platform for intensive communication and idea exchange can all lead to improved team performance. As for governments financing policy, it should be adjusted to types of projects. In industries that possess a synthetic or symbolic knowledge base knowledge exchange in geographical closeness is highly significant because the interpretation of the knowledge has a tendency to vary between places, unlike in those industries with analytical knowledge base where knowledge is codified, more abstract and universal (Martin, Roman, and Jerker Moodysson, 2013). In order to increase the mobility of scientists and ensure there is sufficient face-to-face contact needed for effective knowledge exchange they should be doing so by keeping in mind the industry in question and nature of work.

Information technology || general knowledge || ict || management information systems

Additionally, team performance and sharing of both tacit and explicit knowledge between scientists are on high levels, indicating that scientists working on projects engage in sharing of explicit knowledge such as information, data, product samples, materials, equipment and instruments despite their diverse characteristics and challenging environment. Having a limited face-to-face time as well as tacit knowledge embedded in people and facilitated through the exchange of team members and other technical experts of global teams. Through face-to face communication, electronic networks and other information technology it is possible to exchange knowledge essential to the success of the project. When it comes to distributed research and development process (Ahuja et al., 2000) and teams whose members are geographically spread; knowledge flows are enabled by properly utilized electronic networks and other computer mediated communication tools. Actually, experts with diverse backgrounds and training integrating information are more likely to reach quality solutions on complex projects (Mesmer-Magnus and DeChurch, 2009). By gaining access to expertise, ideas and information which are not available locally to project team members, they can benefit greatly by enhancing their knowledge base which in turn will improve team performance and drive innovation.

7. Conclusion

In so far, knowledge sharing has been tied to team performance in many instances, still in the context of scientific cooperation this investigation has not been attempted. Furthermore, the division of tacit and explicit knowledge sharing is required, as they are quite different in nature, and as they might not contribute to the team performance equally. This study, aimed to explore knowledge sharing between the project team members and to explain influence sharing of tacit and explicit knowledge have on team performance. In the attempt to achieve this goal we analyzed knowledge sharing between scientists, drawing lessons from government funded research projects.

Through the development of a framework an understanding of the relationships between sharing of tacit and explicit knowledge sharing and team performance, in research project teams, had been achieved. In addition, we provided a first analysis of the current state of knowledge sharing on research projects in Croatia. The outcomes of this study shed light on knowledge sharing behavior between researchers, therefore, contributing towards the successful implementation of knowledge sharing initiatives as part of research project knowledge management as well as organizational knowledge management initiatives.

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What is the role ICT can play in Tacit Knowledge Sharing?

1. Create a culture of knowledge sharing

2. Create incentives based on quality

3. Create opportunities to share

Etymology[edit]

Monetisation[edit]

Technological capacity[edit]

ICT sector in the OECD[edit]

Knowledge Management - Interview with Eric Tsui - LUT

ICT Development Index[edit]

The WSIS process and ICT development goals[edit]

In education[edit]

Mobile learning for refugees[edit]

Developing countries[edit]

Africa[edit]

Models of access to ICT[edit]

Toxic culture of education: Joshua Katz at TEDxUniversityofAkron

Devices[edit]

Conduits[edit]

Literacy[edit]

What is the role ICT can play in Tacit Knowledge Saring?

Best Ways to Transfer Tacit Knowledge

How ICT tools ease teachers load? And how can ICT enable full coverage of the school curriculum?

Keywords

Author Biography

References

Overview[edit]

Origin[edit]

Definition[edit]

Embodied knowledge[edit]

Differences from explicit knowledge[edit]

Nonaka-Takeuchi model[edit]

Examples[edit]

1. Introduction

2. Literature Review

3. Research Model and Hypotheses

3.1 Knowledge Sharing and Team Performance

3.1.1 Explicit Knowledge Sharing

3.1.2 Tacit Knowledge Sharing

3.1.3 Level of Knowledge Sharing and Team Performance

4. Research methodology

Knowledge Management Training Session

4.1 Measurement Development

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5. Data Analysis and Results

5.1 Descriptive Statistics and Reliability

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5.2. Statistical Analysis of the Model

5.2.1 Effects of Explicit and Tacit Knowledge Sharing on Team Performance

5.3. Analysis of the Proposed Hypotheses

5.3.1. Sharing of Tacit Knowledge Positively Influences Team Performance

5.3.2. Sharing of Explicit Knowledge Positively Influences Team Performance

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5.3.3. Tacit Knowledge Sharing Level Between Scientists is High

5.3.4. Explicit Knowledge Sharing Between Scientists is High

5.3.5. Team Performance is High

5.3.6. Sharing of Tacit Knowledge Positively Influences Team Performance

5.3.7. Sharing of Explicit Knowledge Positively Influences Team Performance

6. Discussion

7. Conclusion

References

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