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Knowledge creation in IT projects to accelerate digital innovation: two decade systematic literature review [version 2; peer review: 2 approved with reservations]

The interactivity and ubiquity of digital technologies are exerting a significant impact on the knowledge creation in information technology (KC-IT) projects. According to the literature, the critical relevance of KC-IT is highly associated with digital innovation (DI) for organisational success. However, DI is not yet a fully-fledged research subject but is an evolving corpus of theory and practise that draws from a variety of social science fields. Given the preceding setting, this study explores the interaction of KC-IT with DI. This work provides a systemic literature review (SLR) to examine the literature in KC-IT and its connection to DI. A SLR of 527 papers from 2001 to 2021 was performed across six online databases. The review encompasses quantitative and qualitative studies on KC-IT factors, processes and methods. Three major gaps were found in the SLR. Firstly, only 57 (0.23%) papers were found to examine the association between KC and IT projects. These works were analysed for theories, type of papers, KC-IT factors, processes and methods. Secondly, the convergence reviews indicate that scarce research has examined TMS and trust in KC-IT as factors. Thirdly, only 0.02% (5) core papers appeared in the search relevant to KC in IT projects to accelerate DI. The majority of the papers examined were not linked to DI. A significant gap also exists in these areas. These findings warrant the attention of the research community.


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 Holmström 13 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.

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.  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.
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's 16 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.

Inclusion and exclusion criteria
The inclusion and exclusion criteria for the paper search are presented in Figure 2.

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.

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.   Figure 3.

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 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. 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. 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  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.

Result
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

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      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. 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.  13 55 Wang, Zhang & Li

Author
Knowledge-based view R&D workers Competence trust has a positive effect on knowledge creation. Goodwill trust has U-shape relationship with knowledge creation.
Resource-based theory, Theory of organizational knowledge Organization workers Organic structure and organizational culture has a positive influence on knowledge 20 60 Goswami & Agrawal (2020) Theory of Organizational Knowledge Creation IT companies Shared goals and hope have a direct impact on knowledge sharing and creation.
22 62 Yoon et al.   61 Tajedini & Tandiseh  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. 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.

Author
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][25][26][27] We proposed that this research gap should be filled according to the theoretical framework (Figure 7).
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.
KC Factor: This dimension included 23 papers. We further classified the papers into three sub-dimensions of KC factors as suggested by Thani and Mirkamali 28 (Table 11).  Table 13.
However, hardly any research mentioned TOKC in KC-IT-DI papers. Therefore, this scarcity is a research gap.  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 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  57 • Talent management processes. 58 • Organic structure and organizational culture 59 and Information culture. 61 • Library, laboratory, infrastructure 28 and Social media. 56 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. 18 Van den Berg (2019)

Teaching Innovation
Universities Digital innovation skills including 'metaknowledge' which refers to the information required to drive creativity, innovative, problem-solving, critically, communication, and collaboration. 19 Park et al.
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. 20 Kyakulumbye, 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. 21

Shimamoto (2011)
Japanese chemical companies' R&D strategy changed from commercialization to diversification, and then transformed to specialized strategy. 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 Sabherwal 22 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.

Mohammad Jabbari
School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia 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 a relevant study and can provide potential contributions, I see major issues in the study that needs to be addressed.
First: a good SLR "should strive to identify thematic gaps and theoretical biases, propose some future research directions, including alternative theoretical underpinnings, and not just stop at the summarizing/synthesizing stage." (Rowe, 2014, pg. 250 1 ). This study claims that they have proposed a theoretical framework (Figure 7) that could potentially suggest future research directions. However, it is not clear how the framework was developed based on the findings of the SLR. I suggest that the authors include a section and discuss their framework development based on the results.

Result:
Further explanation is needed for each of Figures 5, 6, and 7 in terms of how they can be interpreted to the objectives of this study.
○ Figure 8 may not be relevant here because it suddenly appears and there is no explanation about it. It may be moved at the end of the paper, or it may not be relevant in the SLR paper.

Discussion:
The descriptions in the discussion should follow the sequence of SLR questions so that they are easy to understand.

○
The key findings in each table can be taken from the table and explained in the paragraph after each table. This will improve the readability of the paper.

Theory for KC-IT-DI:
It may be necessary to clarify KC-IT-DI requirements that do not exist in current SLRs.

Limitations in current research and recommendations for future investigation:
The section title is not about limitations in current research. It should be a limitation of the previous study because the current study refers to the research conducted by the authors of this paper.
expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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.

○
There are differences between IT and IT projects and therefore please be sure to use them consistently in questions, objectives, and throughout the paper.
○ Author Response to Comments -Paragraphs in introduction is now improved.
-Relationship between IT and DI projects has been explained.
-Word 'IT' has been updated to 'IT project' accordingly.

Review Method
The 6 databases covered in the SLR are good. ○ Keywords, steps and extraction process are well executed.
○ Author Response to Comments -Thank you for these comments.

Result
Further explanation is needed for each of Figures 5, 6, and 7 in terms of how they can be interpreted to the objectives of this study. ○ Figure 8 may not be relevant here because it suddenly appears and there is no explanation about it. It may be moved at the end of the paper, or it may not be relevant in the SLR paper.
○ Author Response to Comments -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.
- Figure 8 placement was recommended by the editorial board hence no relocation was made.

Discussion
The descriptions in the discussion should follow the sequence of SLR questions so that they are easy to understand.

○
The key findings in each table can be taken from the table and explained in the paragraph after each table. This will improve the readability of the paper. Lastly, discusses the underlying theories used by the literature.
-Key findings in Table 4 and 5 are now improved.

Theory of KC-IT DI
It may be necessary to clarify KC-IT-DI requirements that do not exist in current SLRs.
○ Figure 8 seems relevant to be placed here with further explanation of it.
○ Author Response to Comments -The requirement for KC-IT-DI is the linkages between them. We have highlighted this in findings.
-Explanation on Figure 8 is now provided.

Limitations
The section title is not about limitations in current research. It should be a limitation of the previous study because the current study refers to the research conducted by the authors of this paper.
○ Author Response to Comments -Limitation of past studies are added.

Conclusion
Good.
○ Author Response to Comments -Thank you for this comment.
Competing Interests: No competing interests were disclosed.