Keywords
Knowledge Creation, Digital Innovation, Digital Economy, Systematic Literature Review, IT Projects, Information Technology, Transactive Memory System, Trust
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Soon Seng T, Dorasamy M, Razak R et al. Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.12688/f1000research.70646.2)
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Systematic Review
Revised
Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review
[version 2; peer review: 1 approved, 1 approved with reservations]
PUBLISHED 19 Nov 2021
OPEN PEER REVIEW
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Abstract
Corresponding authors:
Tung Soon Seng, Magiswary Dorasamy
Competing interests:
No competing interests were disclosed.
Grant information:
The author(s) declared that no grants were involved in supporting this work.
Copyright:
© 2021 Soon Seng T et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Soon Seng T, Dorasamy M, Razak R et al. Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.12688/f1000research.70646.2)
First published: 12 Oct 2021, 10:1040 (https://doi.org/10.12688/f1000research.70646.1)
Latest published: 02 Dec 2021, 10:1040 (https://doi.org/10.12688/f1000research.70646.3)
Revised Amendments from Version 1
We trust that the revised paper has addressed all the concerns raised by the peer reviewers.
Introduction:
- Paragraphs in introduction are now improved.
- Relationship between IT and DI projects has been explained.
- Word ‘IT’ has been updated to ‘IT project’ accordingly.
Results:
-Further explanation for Figures 5 and 7 is now added to indicate KC-IT research gaps.
- Further explanation for Figure 6 depicted the objective of the study to understand the current view of the KC-IT literature in terms of sub categories.
- Explanation on Figure 8 is now provided
Discussion:
-The descriptions in the discussion follows the sequence of SLR questions. For example, it begins with answering the research gap in KC-IT in connection to DI. Next, the description highlighted TMS and trust affecting KC-IT. Third, it explains current view of KC-IT literature in terms of the KC process, method and factor. Lastly, it discusses the underlying theories used by the literature.
- Key findings in Table 4 and 5 are now improved.
Theory of KC-IT DI:
-The requirement for KC-IT-DI is the linkages between them. We have highlighted this in findings.
Limitations:
- Limitation of past studies are added.
See the authors' detailed response to the review by Mohammad Jabbari
See the authors' detailed response to the review by Ab Razak Che Hussin
There is a newer version
of this article available.
of this article available.
Introduction
Knowledge is an important asset for promoting organisational change in the 21st century.1 Knowledge comprises a complicated blend of individual experiences, beliefs, relevant information and personal perspective.2 Moreover, knowledge is a driver of worldwide competitiveness in Industry 4.0 (I4.0). Knowledge helps businesses integrate machinery and processes, as complemented by cutting-edge technology.3
Knowledge creation (KC) is an on-going process to acquire new context, views and knowledge and thus transcends the limits of the old to a new self.4 In this study, the theory of organisational knowledge creation (TOKC) from Nonaka and Takeuchi was adopted as the primary theoretical base given its prevalence as the most significant theoretical model in KC studies.5,6 TOKC explained the organisational KC process through the four modes of conversion including Socialisation, Externalisation, Combination and Internalisation (SECI) of the concepts and embodying knowledge to create product value.4 The Current KC paradigm has shifted to encompass wider areas such as energy, education and high technology.7 A New KC model integrates the SECI process with grey knowledge (half tacit and half explicit knowledge) in high technology projects. The model promotes time as a new dimension in cross-cultural IT industries.8
IT project interactivity and pervasiveness are shifting the conversation around the value of KC and digital innovation (DI) for organisational performance.9 KC provides valuable and productive outputs to enhance IT projects, it has become a source of global competitiveness in I4.0. DI refers to the application of emerging technologies in a broad variety of innovation.10 Organisations in the digital economy require digital technology to support business innovation. IT project workers today need new skills because they perform in dynamic environments that frequently require new abilities. In this context, DI is essential as technology evolves. IT project requires DI to upgrade old processes, leverage emerging technology, build new service channels, and execute new business models.
From the individual perspective, people may benefit from a transactive memory system (TMS) as it enables KC to generate expert knowledge within a community or organisation.11 Past KC literature stressed trust as an important feature for the externalisation of tacit knowledge.4 However, hardly any empirical evidence on TMS and trust on KC was provided. Given the above context, this work seeks to answer the call from Pagona et al.12 and Holmström13 to dive further into the intricacies of DI. We aim to highlight the research gaps in KC in IT project research and it is an important component for DI. A total of 57 papers were found relevant to this study.
This study’s research questions are as follows:
1. Is there a research gap in KC-IT in connection to DI?
2. Is there a research gap in TMS and trust affecting KC-IT?
3. What is the current view of KC-IT literature in terms of the KC process, method and factor?
4. What are the underlying theories used by the literature?
The research objectives of this work are as follows:
Review method
This work offers a systematic literature overview to identify research gaps and limitations in KC-IT on DI. Key aspects in the KC-IT toward attaining DI were investigated using TOKC as a theoretical basis. The systematic literature review was conducted according to the five stages proposed by Tranfield et al.14:
a. Planning the review;
b. Identifying and evaluating studies;
c. Extracting and synthesising data;
d. Reporting descriptive findings; and
e. Utilising the findings to inform research and practice.
Institutional Review Board Statement
Institutional Review Board Statement: Research Ethical Committee (REC) of Multimedia University (EA1382021). The study was conducted according to the guidelines and approved by the Research Ethical Committee (REC) of MULTIMEDIA UNIVERSITY.
Stage 1: Planning the review
This paper provides a comprehensive overview of existing work with emphasis on established and emerging critical factors. TMS and trust as KC factors in IT were investigated. Figure 1 shows this study’s scope.
Figure 1. Scope of the review.
The strategy for the selection of databases and methods are based on Moher et al.15 Methods include searching keywords around terms for KC (the concept) and IT projects (the context) in online databases, including AISeL, IEEE, Emerald, SSCI, Scopus and ProQuest.
Stage 2: Identifying and evaluating studies
The study’s keywords cover context and content. The search found 24,293 KC papers, but only 527 had keywords for IT projects (Table 1). Per the criteria, only 57 papers actually addressed KC in IT projects. These papers were classified using Mitchell and Boyle’s16 three major KC dimensions. The KC process refers to the investigations of the measurements or practices performed within KC. The KC factors refers to variables that contribute causally to KC, and the KC method focuses on employing tools or solutions to improve KC.
Table 1. Number and percentage of papers on KC.
| Detail | No. of papers | Percentage over total KC papers |
|---|---|---|
| Total papers on KC related to IT projects | 527 | 2.1% |
| Selected papers (KC+IT, DI) | 57 | 0.23% |
| Total papers on KC | 24,293 |
Inclusion and exclusion criteria
The inclusion and exclusion criteria for the paper search are presented in Figure 2.
Figure 2. Inclusion and exclusion criteria.
Keywords
We focused on two main research areas: (1) KC, (2) IT projects, and (3) DI. For the first area, we included terms such as ‘knowledge creation’ and ‘KC’ (abbreviations). The next key terms used were ‘project’, ‘IT project’, ‘IT projects’ and ‘digital innovation’. Each of these keywords was searched with the keyword ‘Knowledge creation’ individually. The search was subsequently extended by adding more keywords. Table 2 presents the keyword sets used for this research.
Table 2. Keyword combination sets.
Search strategy
We sifted through papers that discussed KC in IT projects for DI. Our strategy was to identify papers through major online databases. We searched six online databases that encompass a vast range of KC as well as IT project-related research and are popular databases for social science study.
1. Association of Information Systems Electronic Library (AISeL)
2. Emerald
3. ProQuest
4. Scopus
5. IEEE
6. Science Direct
A detailed of search strategy is presented in Figure 3.
Figure 3. Detail of search strategy.
Stage 3: Extracting and synthesising data
We extracted papers from the aforementioned sources on the basis of the following extraction process (Figure 4).
Figure 4. Flow of the extraction process.
Figure 4 recaps our basis for selecting papers to review. The extraction process was adopted from Moher et al.15 As indicated regarding the main databases and other options that were utilised, only KC papers linked to IT projects and/or DI were selected for further review. The following subsection presents a report of the papers that were relevant according to our selection criteria.
Stages 3, 4 and 5 of Tranfield et al.14 will be presented in the form of findings and the discussion.
Result
Table 3 presents the outcomes from the inclusion conditions and the extraction process mentioned above. A total of 527 papers were identified, 8 papers were removed due to duplicate records and 519 papers were screened. Out of 519 papers, 462 were excluded as irrelevant to the study context. Finally, 57 papers were chosen for analysis. In this part, we further categorised the papers to indicate their respective types.
Table 3. Number of papers by country.
Figure 5 provides a bar chart to highlight the research gap according to the keyword search of KC, KC + Project, KC + IT Project and DI. The KC papers amounted to 24,293. The fractions of the total KC papers can be seen as 55.9% (13,573) on KC in projects, 2.15% (57) on KC in IT projects and 0.02% (5) papers were related to KC in IT project for DI. Figures 5 and 7 were able to meet the study's objective and indicate KC-IT research gaps.
Figure 5. KC papers by categories.
The KC + IT Project papers are divided into three sub categories: KC Process, KC Method and KC Factor. The number of units is indicated in the parentheses, and a pie chart is presented in Figure 6 to reflect the percentages. Figure 6 depicted the objective of the study to understand the current view of the KC-IT literature in terms of sub categories. Figure 4 reveals that 41.8% of the research papers are sub categorised under the KC Factor and 36.4% under the KC Method. Meanwhile, 21.8% papers were related to the KC Process.
Figure 6. Percentages of KC in IT project papers under three sub categories.
The papers are divided into two main categories of conceptual and empirical papers. A total of 23 conceptual papers (40.4%) and 34 empirical papers (59.6%) were identified. Conceptual papers lack actual test findings. On the contrary, empirical papers consist of evidence-based research and inputs for testing and findings. Figure 7 presents the percentages of papers by categories.
Figure 7. Percentages by paper type.
Figure 8. Proposed theoretical framework.
A total of 50 countries were involved in empirical research (Table 3). Iran has the highest count of empirical research (4 papers), followed by Australia, Brazil, China, South Africa and United States with 3 papers each.
The complete summary of all the 57 papers is shown in Tables 5, 6 and 8 and according to 3 categories: the KC Method (20 papers), KC Factor (23 papers) and KC Process (12 papers).
Discussion
Research gap in KC in IT projects for digital innovation (KC-IT-DI)
Only two papers, those by Ordieres-Meré et al.17 and Van den Berg,18 were pertinent to KC in IT projects for DI. The key findings in Table 5 reveal that 0.9% papers are related to KC in IT projects for DI (Table 5), but no empirical evidence is presented. The first paper was written by Ordieres-Meré et al. and stated that Industry 4.0 is considered to have a strong association with economic, environmental and social.17 The second paper was written by Van den Berg who developed a paradigm for DI skills encompassing ‘meta-knowledge’ which is the information necessary to drive soft skills.18 The rest of the papers include the work of Park et al. who presented novel concepts for organising work.19 Kyakulumbye et al. found that relevance and usability are crucial for evaluating systems.20 Shimamoto analysed the strategy for Japanese chemical industry R&D from 1980 to 2010.21 These three papers are not related to KC in IT project for DI. Furthermore, research on KC in IT project for DI is lacking.
TMS and trust affecting KC-IT-DI
TMS and trust were found to be important factors to KC-IT. However, the key findings in Table 4 shows two journals that identify TMS as positively related to KC.22,23 Four journals examine the trust relationship with KC but did not associate their frameworks with DI. This situation is a new research gap for us.24-27 We proposed that this research gap should be filled according to the theoretical framework (Figure 7).
Table 4. Search result.
Table 4 shows the details of the search results by keywords and units of analysis.
Table 5. Summary of KC method papers in IT projects.
Table 5 shows the details of 20 KC method papers by the theory used, respondent group and key findings.
| Author | Theory used | Respondent group | Method user | Key findings | |
|---|---|---|---|---|---|
| 1 | 31Mir & Rahaman (2003) | Theory of Organizational Knowledge Creation | Organization workers | Inter-team collaboration | Workers’ experiences and opinions are seen as a vital sources of new knowledge by the firm. |
| 2 | 32Kamimaeda, Izumi & Hasida (2007) | Discourse Semantic Authoring | Organization workers | Group discussion | Participants’ knowledge contributions were evaluated primarily on the substance of their arguments rather than the quantity of comments they made. |
| 3 | 10Balestrin, Vargas & Fayard (2008) | Theory of Organizational Knowledge Creation | Firm managers | Firm network | Knowledge creation process can be developed by a network’s inter-relational structure. |
| 4 | 33Ha, Okigbo & Igboaka (2008) | Theory of Organizational Knowledge Creation | Farmers | Broadband internet and computer | Customised information and socialising functions are critical to gaining support in a knowledge creation. |
| 5 | 16Mitchell & Boyle (2010) | Knowledge creation measurement methods | - | - | Three major dimensions of KC classifications: Process, Method and Factor. |
| 6 | 34Wu, Senoo & Magnier-Watanabe (2010) | Theory of Organizational Knowledge Creation | - | - | An ontological shift SECI model was suggested as a tool for diagnosing organisations in knowledge creation. |
| 7 | 35Song, Uhm & Yoon (2011) | Theory of Organizational Knowledge Creation | IT firms manager | Expert review | Discovered new methodical approach of scale development. |
| 8 | 36Zurita & Baloian (2012) | Theory of Organizational Knowledge Creation | Mobile device users | Software application | Geo-referencing software aids in the conversion of tacit into explicit knowledge. |
| 9 | 37Durst, Edvardsson & Bruns (2013) | Theory of Organizational Knowledge Creation | Small and medium enterprise firms | Network activities | To produce knowledge, SMEs employ knowledge sources prioritise friendly enterprises in the same industry. |
| 10 | 38Esterhuizen et al. (2013) | Theory of Organizational Knowledge Creation | - | - | Knowledge creation is a critical facilitator in the development of innovation capacity. |
| 11 | 39Suorsa (2015) | Play theory | - | - | The way of being in knowledge creation interaction may be explained by playfulness, which is absolute present in the event and immersion in the dialogue. |
| 12 | 40Brix (2017) | Theory of Organizational Knowledge Creation, Organizational learning theory | IT project members | Inter-team collaboration | A paradigm for organisational learning and knowledge development that is integrative. |
| 13 | 41Elsa & Runar (2018) | Theory of Organizational Knowledge Creation | Small and medium enterprise managers | Open discussion with customers, suppliers, and research institutions | Team expertise and teamwork are crucial components to generates new knowledge. |
| 14 | 29Faccin & Balestrin (2018) | Theory of Organizational Knowledge Creation | Research & Development (R&D) engineers | Collaborative practice | Atheoretical framework to examine variables of collaborative practice in R&D projects. |
| 15 | 42Li, Liu & Zhou (2018) | Theory of Organizational Knowledge Creation | - | - | A new KC model to integrate SECI process with grey knowledge (half tacit and half explicit knowledge) in high technology projects. |
| 16 | 43Salehi et al. (2018) | Theory of Organizational Knowledge Creation | Medical practitioners | Conference and clinical unit | Themes for KC included scientific debate, exchanging clinical experiences, attending conferences, and creating interpersonal relationships. |
| 17 | 8Chin et al. (2020) | Theory of Organizational Knowledge Creation | - | - | Introduce Polychronic KC to promote time as the new dimension in cross-cultural IT industries. |
| 18 | 44Choi & Gu (2020) | Theory of Organizational Knowledge Creation | Factory managers | Online meeting | Knowledge produced from knowledge providers regardless of physical proximity. |
| 19 | 45Wang & Li (2020) | Evolutionary game theory | Enterprise community | Community of practice | Using an effective competitive mechanism to promote KC. |
| 20 | 46Pokrovskaia et al. (2021) | Theory of Organizational Knowledge Creation | Universities | Online course | Online course for students are crossed with digital instruments ensuring the socio-psychological aspects of the learning process. |
Table 6. Summary of papers on KC factors in IT projects.
Table 6 shows the details of 23 KC factor papers by the theory used, respondent group and key findings.
| Author | Theory used | Respondent group | Key findings | |
|---|---|---|---|---|
| 1 | 47Miyashita (2003) | Theory of Organizational Knowledge Creation | Manufacturing firm employees | Organizational effectiveness is linked to knowledge creation and information technology. |
| 2 | 45Merx-Chermin & Nijhof (2005) | Innovative organisations | - | - |
| 3 | 26Teerajetgul & Charoenngam (2006) | Theory of Organizational Knowledge Creation | Project teams | IT support significant affects knowledge creation combination and internalization mode. Collaboration has a strong impact on socialization and externalization. |
| 4 | 22Dunaway & Sabherwal (2012) | Transactive Memory System, Knowledge Management Theory, Theory of Organizational Knowledge Creation | Organization workers | Team transactive memory systems improve the knowledge creation process, which has an impact on team performance. |
| 5 | 49Siadat et al. (2012) | Social capital theory, Organizational culture theory | Universities | Organizational culture and social capital significantly influenced knowledge creation. |
| 6 | 24Castro & Sánchez (2013) | Theory of Organizational Knowledge Creation, Concept of Ba | - | New types of leadership and contextual factors such as goodwill, trust, cohesion, commitment, ethic of contribution, high care, atmosphere, wise leadership, love and friendship in the knowledge creation and transfer process. |
| 7 | 25Sankowska (2013) | Theory of Organizational Knowledge Creation | Firm employees | There is positive association between organizational trust and knowledge creation. |
| 8 | 50Thang, Quang & Nguyen (2013) | Resource-based view, Theory of Organisational Knowledge Creation | Firm employees | Knowledge creation processes were affected by a combination of leadership, teamwork, corporate culture, and human resource management. |
| 9 | 51Lee, Park & Kim (2014) | Theory of Organizational Knowledge Creation | R&D workers | Organizational identity and human capital of workers had positive effects on their knowledge creation. |
| 10 | 52Begoña Lloria & Peris-Ortiz (2014) | Knowledge Creation Enablers | Firm employees | Knowledge creation enables such as intention, autonomy, redundancy, variety and trust and commitment have a positive and significant relation with knowledge creation. |
| 11 | 53Nair, Ramalingam & Ashvini (2015) | Knowledge Creation Enablers | Automobile industry workers | All four factors expected have positive impact on knowledge creation. |
| 12 | 54Mikhaylov (2016) | Theory of Organizational Knowledge Creation | Universities | Cultural curiosity influences intrinsic motivation to engage in cultural knowledge creation and sharing. |
| 13 | 55Wang, Zhang & Li (2017) | Knowledge-based view | R&D workers | Competence trust has a positive effect on knowledge creation. Goodwill trust has U-shape relationship with knowledge creation. |
| 14 | 56Papa et al. (2018) | Theory of Organizational Knowledge Creation | Small and medium enterprise firms | Social media promote knowledge creation through socialization, externalization, and combination. |
| 15 | 28Thani & Mirkamali (2018) | Theory of Organizational Knowledge Creation | Universities | Personal, institutional, and support factors were found to influence knowledge creation. |
| 16 | 57Cauwelier, Ribiere & Bennet (2019) | Team psychological safety | Engineering teams | Team safety and team learning positively impact team knowledge creation. |
| 17 | 23Çetin (2019) | Knowledge creation capability, Transactive memory system | Firm employees | Transactive memory systems have effects on knowledge creation capability. |
| 18 | 58Mohammed, Baig, & Gururajan (2019) | Talent management processes | Universities | There is a direct influence between talent management processes and knowledge creation |
| 19 | 59Stojanović-Aleksić, Nielsen & Bošković (2019) | Resource-based theory, Theory of organizational knowledge | Organization workers | Organic structure and organizational culture has a positive influence on knowledge |
| 20 | 60Goswami & Agrawal (2020) | Theory of Organizational Knowledge Creation | IT companies | Shared goals and hope have a direct impact on knowledge sharing and creation. |
| 21 | 61Tajedini & Tandiseh (2020) | Information culture theory | Universities | Culture of information increase organization’s knowledge creation. |
| 22 | 62Yoon et al. (2020) | Systems model of creativity | Public service organization | Creativity and knowledge creation have a positive association. |
| 23 | 27Tootell et al. (2021) | Organizational justice theory, Relationship marketing theory | University, industrial workers | Knowledge creation are fostered by shared value and trust. |
Table 7. Summary of KC factor papers in IT projects with variables.
Table 7 shows the details of KC Factors by independent variables, dependent variables and whether the papers mentioned TMS and Trust.
| Author | Independent variable | Dependent variable | Transactive memory system | Trust | |
|---|---|---|---|---|---|
| 1 | 47Miyashita (2003) | Knowledge creation, Information technology | Organization effectiveness, Organization management | ||
| 2 | 48Merx-Chermin & Nijhof (2005) | Strategic alignment, structure, climate, systems, leadership | Knowledge creation process, innovation, Learning | ||
| 3 | 26Teerajetgul & Charoenngam (2006) | Vision, Incentive, Collaboration, Trust, IT support, Individual competency | Knowledge creation process | ||
| 4 | 22Dunaway & Sabherwal (2012) | Transactive Memory System, IT support for KM | Knowledge creation, Knowledge sharing, Knowledge application, Team performance | √ | |
| 5 | 49Siadat et al. (2012) | Organizational culture, Social capital | Knowledge creation | ||
| 6 | 24Castro & Sánchez (Z013) | Goodwill, trust, cohesion, commitment, ethic of contribution, high care, atmosphere, wise leadership, love and friendship. | Knowledge creation | √ | |
| 7 | 25Sankowska (2013) | Organizational trust | Knowledge transfer, Knowledge creation, innovativeness | √ | |
| 8 | 50Thang, Quang & Nguyen (2013) | Leadership, teamwork, corporate culture, and human resource management. | knowledge creation | ||
| 9 | 51Lee, Park & Kim (2014) | Organizational identity, Mobility direction, Human capital | Knowledge creation | ||
| 10 | 52Begoña Lloria & Peris-Ortiz (2014) | Intention, Autonomy, Fluctuation, Redundancy, Requisite Variety, Trust, Commitment, Creative Chaos | Knowledge creation | ||
| 11 | 53Nair, Ramalingam & Ashvini (2015) | Organizational communication, Feedback promotion, Policy formulation, Information sharing | Knowledge creation, Organisational performance | ||
| 12 | 54Mikhaylov (2016) | Cultural curiosity | Intrinsic motivation, Knowledge creation | ||
| 13 | 55Wang, Zhang & Li (2017) | Competence trust, Goodwill trust | Knowledge creation | √ | |
| 14 | 56Papa et al. (2018) | Social media | Knowledge creation process, Innovation | ||
| 15 | 28Thani & Mirkamali (2018) | Personal factors, Institutional factors, Support factors | Knowledge creation | ||
| 16 | 57Cauwelier, Ribiere & Bennet (2019) | Team safety, Team learning | Knowledge creation | ||
| 17 | 23Çetin (2019) | Transactive memory system, Collective mind, innovative culture | Knowledge Creation Capabilities | √ | |
| 18 | 58Mohammed, Baig & Gururajan (2019) | Talent retention, development, attraction | Knowledge creation | ||
| 19 | 59Stojanović-Aleksić, Nielsen & Bošković (2019) | Organic Structure, Organizational Culture | Knowledge creation, Knowledge sharing | ||
| 20 | 60Goswami & Agrawal (2020) | Shared goal, Hope | Knowledge creation, Knowledge sharing | ||
| 21 | 61Tajedini & Tandiseh (2020) | Information culture | Knowledge creation | ||
| 22 | 62Yoon et al. (2020) | Creativity | Knowledge creation | ||
| 23 | 27Tootell et al. (2021) | Opportunistic behaviour, Trust, Shared value | Knowledge creation | √ |
Table 8. Summary of KC process papers in IT projects.
Table 8 shows the details of 12 KC process papers by the theory used, respondent group and key findings.
| Author | Theory used | Respondent group | Key findings | |
|---|---|---|---|---|
| 1 | 63Kippenberger (1997) | Theory of Organizational Knowledge Creation | Organization workers | Organizational knowledge creation making accessible and amplifying knowledge developed by people, as well as crystallising and linking it with an organization’s knowledge system. |
| 2 | 64Eliufoo (2008) | Theory of Organizational Knowledge Creation | Construction firms manager | Social characteristics are critical for organisations to improve knowledge. |
| 3 | 65Spraggon & Bodolica (2008) | Theory of Organizational Knowledge Creation | IT firms manager | Discovered virtual socialization mode in IT software firms. |
| 4 | 66Matysiewicz et al. (2013) | Theory of Organizational Knowledge Creation | Scientific networks participants | Participants are more engaged, that partnerships are more established, and there are more prospects for publishing and research. |
| 5 | 67Naicker, Govender & Naidoo (2014) | Theory of Organizational Knowledge Creation | Electrical and Electronics engineers | Engineers use socialization and externalization modes of knowledge conversion, but internalization is important in knowledge creation and transfer. |
| 6 | 68Marsina et al. (2015) | Theory of Organizational Knowledge Creation | IT firms manager | There is lack of IT adoption in Slovakia enterprises for their project activities. |
| 7 | 69Shongwe (2015) | Organisational learning theory, Learning Organisation, Theory of Organisational Knowledge Creation, Knowledge-integration theory, Communities of practice theory | Software engineers | Engineers can produce knowledge from a variety of sources, including presentations, from the lectures, the Internet, older students, and professional developers. |
| 8 | 70Yao, Han & Li (2015) | Theory of Organizational Knowledge Creation | Aerospace firm managers | Integrate Chinese philosophy I-Ching into dynamics of knowledge creation. |
| 9 | 71Moraes et al. (2016) | Theory of Organizational Knowledge Creation | Electrical and Electronics engineers | The new process of group socialization is used to foster a network of internal connections in order to generate knowledge. |
| 10 | 72Chatterjee, Pereira & Sarkar (2018) | Theory of Organizational Knowledge Creation | IT firms manager | Learning transfer system inventory foster organizational knowledge creation. |
| 11 | 73Rusland, Jaafar & Sumintono (2020) | Theory of Organizational Knowledge Creation | Navy officers | Externalization and combination modes of knowledge conversion are more difficult to incorporate among the navy officers than socialization and internalization. |
| 12 | 74Konno & Schillaci (2021) | Theory of Organizational Knowledge Creation | Entrepreneurs | Adding entrepreneurial activities to the SECI model as experimental processes. |
The proposed theoretical framework suggests that TMS and trust are important factors for influencing the KC. The KC will enable DI to create new products and services.
KC-IT project literature in three categories
KC-IT literature can be classified into three categories (see Tables 9 and 10) of the KC process, method and factor. The papers are presented in the following table by three categories as suggested by Mitchell and Boyle.16 The benefit of viewing KC-IT literature in three categories include a better understanding of the current landscape of KC-IT.
Table 9. KC process.
| Knowledge Creation (KC) Process |
|---|
| KC Process has 12 papers. |
| 1. Kippenberger elevated organisational KC, which made information available and amplified it.63 |
| 2. Eliufoo performed a case study looks at how construction firms can map and understand KC processes.64 |
| 3. Virtual socialising mode in IT software businesses.65 |
| 4. Matysiewicz et al. investigates the mechanisms of KC and how they affect network members.66 |
| 5. A new Socialization-Externalization-Combination-Internalization (SECI) model was developed to explore how engineers generate and disseminate knowledge.67 |
| 6. Marsina et al. found there Is lack of IT adoption in Slovakia enterprises.68 |
| 7. Shongwe found a lack of software engineers may create knowledge from a number of sources, including lectures, older students, and professionals.69 |
| 8. I-Ching and knowledge dynamics were combined by Yao, Han, and Li.70 |
| 9. Moraes et al. discovered which aspects impact organisational socialisation and knowledge acquisition during innovation.71 |
| 10. A theoretical framework built by Chatterjee, Pereira, and Sarkar was created using data from the SECI model and KC.72 |
| 11. The Royal Malaysian Navy looks into its members’ comments to learn about present-day processes of KC in the fleet.73 |
| 12. Konno and Schillaci introduced a paradigm linking knowledge generation to intellectual capital in society 5.0.74 |
Table 10. KC method.
| KC methods |
|---|
| This dimension consists twenty journals. |
| 1. Mir and Rahaman observed that the workforce provides useful new information for the company.31 |
| 2. Discourse Semantic Authoring (DSA) was suggested by Kamimaeda, Izumi, and Hasida as a technique to evaluate discussion participants’ contributions to knowledge development.32 |
| 3. Inter-relational network foster knowledge creation.10 |
| 4. Broadband internet technology is being utilised to distribute agricultural knowledge in Nigeria.33 |
| 5. Knowledge creation categories include process, method and factor.16 |
| 6. Wu et al. built a theoretical framework known as the Ontological SECI model.34 |
| 7. Song, Uhm and Yoon surveyed measurement instruments for assessing organisational knowledge production.35 |
| 8. Geo-referencing software helps explicit information become tacit.36 |
| 9. Durst et al. discovered that networking activities foster knowledge creation.37 |
| 10. Knowledge creation facilitates innovation capacity development.38 |
| 11. Playfulness from event and dialogue facilitate knowledge creation.39 |
| 12. Brix suggested that knowledge creation and organisational learning are integrated.40 |
| 13. To learn about oneself and develop one’s knowledge, team skills and collaboration are critical for producing new knowledge.41 |
| 14. Faccin and Balestrin built a theoretical framework to study factors of collaborative practise in R&D projects.29 |
| 15. Li et al. suggested a novel knowledge production model integrating SECI with both explicit and tacit knowledge in high-technology projects.42 |
| 16. Salehi et al. suggested conference and clinical unit for exchanging knowledge of clinical experiences.43 |
| 17. Chin et al. established a new model (Polychronic KC) to help promote time as the new dimension in global IT industry.8 |
| 18. Knowledge creation regardless of physical location.44 |
| 19. Wang and Li applied statistical simulation using evolutionary game theory.45 |
| 20. Digital gadgets assure the socio-psychological components of the learning process.46 |
KC Factor: This dimension included 23 papers. We further classified the papers into three sub-dimensions of KC factors as suggested by Thani and Mirkamali28 (Table 11). Table 12 presents the summary of the 5 papers obtained when we have searched for the keyword combination of KC IT Project for DI. However, only 2 papers were found to have some relation to KC-TI-DI
Table 11. Summary of KC factors by the three types of factors.
| Personal factor | Institutional factor | Support factor |
|---|---|---|
|
|
Table 12. Summary of five papers on KC in IT projects for digital innovation.
| Author | Theory used | Respondent group | Key findings |
|---|---|---|---|
| KC in IT Project for Digital Innovation (2 papers) | |||
| 17Ordieres-Meré et al. (2020) | Organization sustainability theory | Organization workers | Industry4.0 has a close relationship with the three elements of sustainability: economic, environmental and social sustainability. A relationships exists between knowledge creation and sustainability via Industry4.0 as the primary driver. |
| 18Van den Berg (2019) | Teaching Innovation | Universities | Digital innovation skills including ‘meta-knowledge’ which refers to the information required to drive creativity, innovative, problem-solving, critically, communication, and collaboration. |
| 19Park et al. (2015) | Knowledge creation process philosophy | Firms employees | A case study shows that the idea centre continues to evolve and members of production teams produce knowledge as a result of their activities and interactions. |
| 20Kyakulumbye, Pather & Jantjies (2019) | Personal constructs theory, Situation awareness theory | Universities | User friendliness and relevance are critical knowledge structures for system assessment. System performance and interface attractiveness promote ease of use. |
| 21Shimamoto (2011) | Japanese chemical companies’ R&D strategy changed from commercialization to diversification, and then transformed to specialized strategy. | ||
Theories for KC-IT-DI
A total of 25 different theories were employed in the 57 papers analysed. 34 papers have used the TOKC by Nonaka and Takeuchi as the kernel theory.4 The theories are listed in Table 13.
Table 13. Summary of theories used in papers.
However, hardly any research mentioned TOKC in KC-IT-DI papers. Therefore, this scarcity is a research gap.
Limitations in current research and recommendation for future investigations
Limited research is available in KC in IT projects for DI. Past studies have not succinctly explained how knowledge may be applied to improve DI. Therefore, the KC-IT-DI literature is in its infancy and may warrant additional research. DI is important to the nation.29 KC-IT offers additional benefits, including improving existing processes, introducing new business models and setting up new service channels.8 To modernise products and services, KC-IT should be closely associated with DI.30
Another limitation is the choice of keywords, which is determined by the study's emphasis. As a result, it is possible publishing bias. If the keywords are widened to cover non-specific fields of study, more articles may be acquired.
Future research should be carried out in the following areas:
1. More research focusing on KC-IT-DI will help researchers understand the significance of KC-IT in DI. Researchers may gain a better grasp of the issues afflicting the KC community.
2. TMS foster individuals to distribute and exchange tacit knowledge for their own advantage, as indicated by Dunaway and Sabherwal22 and Çetin.23 Therefore, exploring how TOKC plays its roles in TMS is recommended.
3. Examining new variables or dimensions in the KC-IT-DI relationship is a means of extrapolating novel aspects to boost KC and innovation in the IT industry in the context of volatility, uncertainty, complexity, and ambiguity.
Conclusion
Three main points are addressed in this study. Firstly, the SLR found gaps in KC-IT linkage to DI. Secondly, TMS and trust are essential to KC. Finally, KC-IT-DI research limitations were addressed. This work advances the understanding of IT project management by studying the underlying factors to comprehend KC’s role in IT projects. This article mentions previous contributions other than the current concerns. This research focused on KC for interdisciplinary study. The implications herein provide relevant research and education references for researchers and the public. This work will also help scholars by offering directions. The shortcoming of the current study highlights the challenges in KC-IT-DI research. Furthermore, this article revealed a gap in KC in relation to IT projects, and the community is asked to research further to fill this gap.
Data availability
Figshare. Data File.xlsx
DOI: https://doi.org/10.6084/m9.figshare.14870655.v1
This project contains the following data:
This dataset is analysed for theories, type of papers, Knowledge Creation and Information Technology (KC-IT) factors, process, and method.75
PRISMA flowchart
Figshare. PRISMA checklist
DOI: https://doi.org/10.6084/m9.figshare.16657309.v1.77
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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CITE
how to cite this article
Soon Seng T, Dorasamy M, Razak R et al. Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.12688/f1000research.70646.2)
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Hussin ARC. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.79472.r100712)
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Reviewer Report 22 Nov 2021
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This article has been improved based on the suggestions that have been given previously. The flow and content are
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HOW TO CITE THIS REPORT
Hussin ARC. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.79472.r100712)
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Jabbari M. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.74248.r96845)
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Reviewer Report 02 Nov 2021
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This study aims to investigate knowledge creation in information technology projects for digital innovation through a systematic literature review. The study identified three main research gaps and proposed a framework to fill the gap. While I think the study is
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Jabbari M. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.74248.r96845)
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Author Response 02 Dec 2021Soon Seng Tung, Multimedia University, Malaysia02 Dec 2021Author ResponseWe thank you for all the valuable comments. We have addressed the comments as below:
Introduction
The study lacks a strong background. For example, the background should clearly specify ... Continue reading
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Author Response 02 Dec 2021Soon Seng Tung, Multimedia University, Malaysia02 Dec 2021Author ResponseWe thank you for all the valuable comments. We have addressed the comments as below:
Introduction
The study lacks a strong background. For example, the background should clearly specify ... Continue reading
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How to cite this report:
Hussin ARC. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.74248.r97965)
The direct URL for this report is:
https://f1000research.com/articles/10-1040/v1#referee-response-97965
https://f1000research.com/articles/10-1040/v1#referee-response-97965
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Reviewer Report 01 Nov 2021
Approved with Reservations
VIEWS 29
Introduction:
- It may be necessary to explain a little more why KC is important in IT projects. After that, the relationship between IT and DI projects also needs to be properly explained for better understanding.
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HOW TO CITE THIS REPORT
Hussin ARC. Reviewer Report For: Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:1040 (https://doi.org/10.5256/f1000research.74248.r97965)
The direct URL for this report is:
https://f1000research.com/articles/10-1040/v1#referee-response-97965
https://f1000research.com/articles/10-1040/v1#referee-response-97965
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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