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Managerial Factors That Influence the Success Of Knowledge Management Systems: A Systematic Literature Review

Abstract: The purpose of this research is to remove the ambiguity that clouds the analysis of knowledge management systems. This is because of an overall lack of consensus on how knowledge management systems adapt to the new ‘knowledge explosion’ embraced by the booming ‘Big Data’ hype. In this paper, a refreshing synthesis of literature will uncover benefits and identify gaps in current knowledge. These findings will also be of benefit to researchers and industries as it allows for the holistic analysis of a KMS.

This systematic literature review collected 54 papers for qualitative analysis. This analysis led to a synthesis of factors evident in the research and how they could be combined and collected as key categories. Once each factor was categorized; the future directions of research was analysed and documented. The primary factors discussed include: 1) formal processes, 2) company culture, 3) top-down support, 4) motivation, 5) clear goals and 6) quality of KMS. This research has created a baseline for the further evaluation of knowledge management systems’ in the real world.

Keywords: Knowledge management, Knowledge management systems, Success factors, Knowledge, Systematic literature review, Factors.

Acknowledgements: This research has been conducted with the support of the Australian Government Research Training Program Scholarship, ARC Discovery Project DP180101051, UOW Matching Scholarship 2018.

1.     Introduction

The method of dealing with a company’s knowledge is traditionally defined as Knowledge Management (KM). This KM process is the fundamental practice of creating, capturing and transferring knowledge within a company (Davenport and Prusak, 1998, O’dell and Grayson, 1998, Alavi and Leidner, 2001). Knowledge is described as information in a specific context (Sviokla, 1996) and it is viewed as an intangible resource (Alavi and Leidner, 2001). This resource is seen as a principal source of competitive advantage (Eisenhardt and Martin, 2000, Grant, 1996, Romer, 1990). There is also mounting evidence that organizations are dependent on the development and deployment of new knowledge for economic performance (Blundell et al., 1999, Furman et al., 2002, Roberts, 2000). Development refers to the process of creating new knowledge through internal or external sources to promote the growth of employee expertise. Deployment, on the other hand, is the crucial capability of sharing of knowledge amongst employees (Eisenhardt and Martin, 2000, Grant, 1996). These capabilities are being reinvigorated through the use of Knowledge Management Systems (KMS). KMSs are a class of information systems that are dedicated to organising a company’s knowledge and supporting the knowledge sharing process (Alavi and Leidner, 2001).

The evaluation of knowledge sharing technologies have been plagued with ambiguity and lacks a common foundation. These KMSs are advertised with grandiose promises of complete knowledge sharing and seamless transfer of expertise (Powell and Grodal, 2005). The ambitions of these KMSs are checked by the tacit side of knowledge sharing. This being the inability to quantify certain knowledge, that in turn, creates difficulties during their evaluation. In fact, managers across industries readily admit that they are unable to measure the benefits of these tools within their organizations (Li, 2012). This is affected by the limited understanding of influencing factors on KMSs. The ambiguity of these systems in practice is the motivation of this research. What is needed is a foundation of influential factors that really affect KMSs in any industry. This paper provides this through a synthesis of modern research papers and the creation of a platform for future research.

KMSs have enjoyed a sustained increase in investments that speaks to their importance and how vital they are to organizations (Haggie and Kingston, 2003, Hanafizadeh et al., 2014). These systems also empower employees, giving them the ability to be connected across regions by providing and strengthening interpersonal communication (O’dell and Grayson, 1998). This is accomplished by allowing knowledge seekers to explore and communicate with knowledge sources in a connected virtual environment (Hasan and Handzic, 2003). The benefits of KMSs are the reason for the growth in popularity in the research areas of knowledge sharing (Blundell et al., 1999, Furman et al., 2002, Roberts, 2000) and KMSs (Powell and Grodal, 2005, Wuchty et al., 2007). However, there is an overall lack of consensus in how KMSs adapt to new ‘knowledge explosion’ embraced by the booming ‘Big Data’ hype. In this paper, a refreshing synthesis of literature will uncover discovered benefits and identify gaps in current knowledge. These findings will also be of benefit to researchers and industries as it allows for the holistic analysis of a KMS.

2.     Methodology of Literature review

Using Jesson et al’s (2011) well established principles for a systematic review, our method is broken into 6 parts, these being 1) mapping of the field through a scoping review, 2) a comprehensive search for papers, 3) an assessment of quality of the papers, 4) an extraction of relevant data, 5) a synthesis of data found, and finally, 6) a full write up of research for presentation.

First, a research plan was developed to create and evaluate research questions of interest. This was the grounds for the paper which focused on the keywords, reasons to include papers and a set of criteria for the exclusion of non-relevant papers. The driving aim of this paper is to discover and re-examine the influential factors that affect KMS from real world case studies.

The initial keywords used in this discovery phase were “technology”, “systems”, “factors” and “issues” to form the full search of: technology OR systems AND factors OR issues. “Technology” and “systems” were used interchangeable to find any information technology system. “Factors” and “issues” were used in the same way to find any problematic system. This initial search produced an unsatisfactory outcome in terms of sheer quantity of publications. The work of Serenko and Bontis (2017) were used to narrow the search parameters but to still ensure only top level knowledge management journals were selected. These journals included the Journal of Knowledge Management, the Journal of Intellectual Capital, The learning organisation, Knowledge Management Research & Practice, Knowledge and Process management: The journal of Corporate Transformation, VINE: The Journal of Information and Knowledge management Systems and the Journal of Information and Knowledge Management. This resulted in 1598 identified papers.

To produce a list of manageable papers further inclusion and exclusion criteria were applied. The inclusion criteria were: publications from 2014-2018 to provide the most up to date research; empirical research papers, to gain first hand data; peer reviewed, to have a guarantee of quality; English language, to limit misunderstandings and a focus on knowledge management, to keep the data relevant. The exclusion criteria were: papers published prior to 2014, grey literature such as reports or non-academic papers. Once these criteria were added, 58 were investigated in greater detail.

Each paper was evaluated on the suitability by the inclusion and exclusion criteria. Only four papers were discarded due to unusable or unrelated research. This left 54 articles for the next phase.

Data from each paper was extracted. The main findings were used to produce a comprehensive table that allows the reader to gain an overview of the research analysed. This table includes data on the authors, publications, date, methods, main findings and a list of factors the paper discusses (See Appendix Table. 3).

The next stage was of the synthesis. This stage focused on factors evident in the research and how they could be combined and collected as key categories. Once each factor was categorized; the future directions of research was analysed and documented.

3.     Findings

The following is a composition of the factors discovered and their parent category. Each of the papers discussed can be found in the appendix. This figure illustrates the connections each factor has with its category (see Table 1).

Category Factors Papers
Formal processes Formal processes; Dynamic processes; Daily interactions with KMS; Relationship with customer; Unorganised; 1-6, 7, 9, 12, 13, 15-28, 30-35, 37-43, 46, 47, 49, 52, 53.
Company culture Company Culture; Geographic issues; The value of Knowledge; 1, 2, 6-11, 15, 21-27, 29, 30, 32, 34, 36, 40, 41, 43, 46-48, 50-54.
Top down support Top down support; Knowledge hierarchies; 1, 2, 5, 6, 9-14, 17-19, 21, 27-29, 31, 37, 41, 44, 45, 47, 51.
Motivation Motivation; Incentives; Trust; Knowledge protection; Intellectual property; 4, 8, 9, 11, 12, 14, 17-19, 22, 30-34, 37, 39, 41, 43, 47-51, 53.
Clear strategy Clear strategy; Clear goals; Performance measures of KMS; Change management; Inter-organisation knowledge sharing; 1-6, 13, 15, 16, 18, 19, 21, 23, 24, 28-34, 41-45, 47, 48, 50, 51, 53, 54.
Quality of KMS Quality of KMS; Features of KMS; Extracting knowledge; Integrating technology with strategy; Social media; 1, 5, 6, 7, 10, 12, 13, 15, 16, 19, 23, 25, 30-32, 34-37, 40-42, 44-48, 54.

The 54 papers analysed are presented below by their parent category (see Table 1). The Papers are ID referenced with the complete list of papers in Appendix Table 3 (Download original paper). This table is further broken down into its components to simplify the synthesis in the discussion of findings.

4.     Discussion of Findings

The following is a synthesis of the research findings presented in the collected papers. This provides a valuable snap shot of the research, and their underlying case studies where used, previously published and more importantly, where this research could lead to in the future. Each of the 6 found categories are analysed, first on their content then on the gaps in research generated from this work. For a full list of papers, refer to Appendix Table 3.

4.1.  Formal Processes

Formal processes are guidelines and procedures that structure the knowledge transfer process. This is the combination of the concepts; guidance, communication, training and regulation (Norese and Salassa, 2017, Perez-Soltero et al., 2016).

Common amongst the most successful formal approaches was the use of the consistent terminology in a company process (Hirose Nishihara, 2018, Wiedenhofer et al., 2017). This approach can be supported by the use of a ‘process owner’ to enforce guidelines and processes (Pohjola and Puusa, 2016, Cavicchi and Vagnoni, 2018). This would require that the person with this responsibility had enough authority to ensure that the operations will be executed as designed. Conversely, consistency and process ownership can be derailed by geographical, technological and organisational obstacles (Aubert, 2018). In fact, organisational barriers are more detrimental than the technological (Bolisani and Scarso, 2016). A lack of clear authority can disintegrate a KM initiative and prevented it from fully attaining its goals (Pohjola and Puusa, 2016).

To address these concepts practically, favourable organisational conditions need to be created. This increases the success rate of KM initiatives and allows a company to tackle each challenge that a competitive company will face. There has been an increased understanding in the literature of the importance of these formal processes. What is not understood, is the cognitive change of a positive informal process to the formal. This would need to start with a clear way of identifying a positive informal process and then focus on transformation of this process into a formal activity. How this effects the process itself and how this new formal process can be transferred to another area of a company, would provide considerable insights for the KM discipline. Another area of interest would be to determine if the value of an informal process is lost in the transformation to the formal.

4.2.  Company culture

Organisational culture is a set of norms and values that are embedded in an employee’s sense making process (Zheng et al., 2010). Multiple authors stress the importance of a positive knowledge sharing culture (Mojibi et al., 2017). In fact, Biloslavo et al. (2018) calls it the antecedent to knowledge management effectiveness. This importance is derived from the promotion of knowledge sharing (Chang et al., 2017), lessening of communication barriers (van Dijk et al., 2016) and its effect on fostering teamwork (Ismail Al‐Alawi et al., 2007).

In some resistant company cultures, a breakthrough is needed before a KMS can even be implemented (Wing Chu, 2016). These breakthroughs are the result of a cultivation of positive cultural attributes (Biloslavo et al., 2018).

If culture could also be described as a combination of individual ideals towards a central goal, Pohjola and Puusa’s (2016) explored what happens when that central goal changes. They focused on an open source community of practice that disbanded after outside interests began investing in the group. This led to a shift in individual ideals and the central goal of a homogenous group became unsustainable.

 To summarise, a company culture is an organic process that needs to be monitored and fostered to continue to have a positive effect on a KM strategy. What is missing from the research, is an empirical comparison of successful cultural activities. In the papers reviewed, there were documented changes in the company culture, but they lacked a means of measuring that change (Pohjola and Puusa, 2016).

4.3.  Top down support

Top down support are the visible contributions from management that show their enthusiasm and commitment to the sharing of knowledge within a company. For a successful implementation of a KM strategy, top-level management involvement is imperative (Akhavan et al., 2006). Top down support is provided by formal or informal management positions (Wing Chu, 2016). In the absence of designated leaders, a collective will nominate leaders based on seniority, founder status or level of involvement (Pohjola and Puusa, 2016). Strong leadership qualities are needed (Danilova, 2018), because a lack of support can led to an implementation of KM being abandoned (Abukhader, 2016).

The factor, top down support, is compared throughout industries and across disciplines. What is not investigated or summarised is what effective top down support looks like in the work place. Plagued by the same issues that the study of tacit knowledge has, the level of support needs to be supported by explicit measurements. Where this factor can be further investigated is the internal comparison of leadership support. What most papers focus on is the inter-organisational comparisons of support levels. An interesting new direction would be an internal comparison of departments and how department management could provide support for the knowledge sharing process. In research terms, an empirical study could be conducted to measure differing levels of support and cross examining them with other departments in the same company to reduce influencing factors.

4.4.  Motivation

Motivation refers to the enthusiasm employees have to share their hard-won knowledge. It is driven from a set of incentives that can either be intrinsic or extrinsic rewards, which encourage an employee to share knowledge with their peers. Intrinsic incentives are psychological or internal rewards. These could include satisfaction from act of sharing knowledge, prestige attained or team building. Extrinsic, on the other hand, is the tangible rewards a company can offer. These are bonuses, promotions or recognition. Either intrinsic or extrinsic, there is a contentious divide in in the literature for the effect of incentives (Šajeva, 2014). Zhang et al (2010) highlights the fact that there is no conclusive evidence of the influence of a reward system. While other research gives evidence to a significant relationship between the amount of knowledge shared and a reward system (Ismail Al‐Alawi et al., 2007, Alam et al., 2009). Wing Chu (2016) focuses on the introduction of basic KM practices in a school environment and shows how the benefits of KM need to be explained for the whole and the individual. What these discussed studies demonstrate is that, it is the level of motivation to share knowledge that is paramount, but they are undecided on the exact method.

Similar to the other areas of research, motivation needs to be measured empirically. The studies used in this review emphasise that extrinsic rewards are more effective at cultivating motivation over intrinsic. A promising research direction would be a formal metrics of intrinsic and extrinsic motivation and how these levels could be manipulated in the work place to derive the optimal level of reward.

4.5.  Clear strategy

Szulanski (1996) found evidence that the resistance to share knowledge can be traced to a lack of direction and clarity. Without the establishment of clear goals, even concentrated efforts on knowledge sharing, a KM initiative can fail (Ciborra and Patriotta, 1998). This clarity represents the unity of the goals of each key actor. Conflicting (Pohjola and Puusa, 2016) and vague goals have been seen to cause the collapse KM groups (Pezzillo Iacono et al., 2014). Clarity can also be disrupted by the shift in the valuation of individual pieces of knowledge and thus, change the behaviours of key actors (Pohjola and Puusa, 2016).

A needed tool for a clear strategy is the use of benchmarking. Pal and Jasial (2015) found that this method fostered confidence in KM processes and its benefits. By explaining the strategy and more importantly its direction, increases trust and creates positive knowledge sharing conditions (Perez-Soltero et al., 2016). This was evident in an educational KM initiative that measured the shift in trust between teachers and how it affected the perceived usefulness of the KMS as a whole (Wing Chu, 2016). While it is important for the employee to understand the goals of KM, Danilova (2018) found that it is equally important that process owners to understand business goals and strategies. This is top level management not only being actively involved but can see the value of the KMS.

4.6.  Quality of KMS

A quality KMS provides for the needs of its users and in turn allows the organisation to remain in a process of continuous improvement. McCracken and Edwards (2017) demonstrated this with their medical KMS as it provided a holistic view of patient care and contributed heavily to patient satisfaction. In another study, Perez-Soltero et al. (2016) describes the KMS of their study as the ‘cornerstone’ of problem solving. What this leads too is the old adage of ‘if you build it, they will come’. (Xu, 2016). Now in contrast to this evidence, Serenko et al. (2017) shows that IT investment and deployment levels are not all that is needed. The measure of a quality system can be defined by its ability to access KM resources smoothly (Xu, 2016), provide clear benefits for the users (ease of use, reliability, etc) (Perez-Soltero et al., 2016) and most importantly fulfil its potential by being in line with its parent KM strategy (Serenko et al., 2017).

KMSs have been evaluated in multiple contexts and often include customised software to fit the needs of the company and their relevant KM strategies. A common theme is the limited understanding of the KM process by key actors (Burnett and Williams, 2017). What is missing is a single system, tested across multiple companies. A single system that operates a KM process could be used in multiple companies to measure required capabilities and the most successful features. Although this would require an enormous amount of cooperation between companies and researchers, the benefits for this type of study would be substantial. Both the research and the KMS require due care and control (Uden and He, 2017).

5.     Promising research directions

The analysis of the literature collected above has highlighted some promising research directions. While not exhaustive, they do represent interesting lines of research to further expand this discipline (see Table 2).

Area Promising research directions
Formal processes Determine how valuable informal processes can be converted into formal processes. How are these formal processes communicated? How does the level of communication effect these processes?
Company culture Insights into how does the company culture changes over time and how does this effect KM effectiveness. In positive knowledge sharing cultures what are the remaining obstacles?
Top down support A comparison of internal departments with differing levels of top down support. Practical insights into what does effective top down support look like in the work place.
Motivation Insights into how intrinsic or extrinsic rewards effect the level of motivation an employee has to share knowledge. How can a company sustain a high level of motivation?
Clear strategy Further investigation into how a KM strategy can be benchmarked. Insights into effective methods of communication of strategy to the employee.
Quality of KMS Insights into the operation and performance of different KMS in the same context. What are the critical success features for a KMS?

6.     Conclusions

Knowledge management systems (KMS) provide a wealth of opportunity and challenges. This paper focuses on re-examining the most influential factors affecting the implementation of a KMS. Through the synthesis of this review on the literature and case studies published in the past 5 years, this paper has outlined what was well and not well understood in this area and provides a platform for future areas of study. This was achieved through the use of an exhaustive systematic literature review. By using the systematic method described by Jesson (2011), 54 papers were qualitatively analysed. The discovered factors were then grouped into categories. They include: 1) formal processes, 2) company culture, 3) top-down support, 4) motivation, 5) clear goals and 6) quality of KMS. This paper was limited by the journal-based search parameters and its multi-context analysis. This traded off provide a manageable pool of papers for analysis. For more confidence in these results, the search criteria could be expanded. The authors suggest taking this research and applying it directly to generate more primary studies to check the validity of these factors. In conclusion, this research has created a baseline for the further evaluation of KMS’s in the real world, through the use of a SLR.

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TAHERI, L., SHAFAZAND, M. Y., PA, N. C., ABDULLAH, R. & ABDULLAH, S. 2017. A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation. Knowledge and Process Management, 24, 257-268.

UDEN, L. & HE, W. 2017. How the Internet of Things can help knowledge management: a case study from the automotive domain. Journal of Knowledge Management, 21, 57-70.

VALE, J., RIBEIRO, J. A. & BRANCO, M. C. 2017. Intellectual capital management and power mobilisation in a seaport. Journal of Knowledge Management, 21, 1183-1201.

VALMOHAMMADI, C. & GHASSEMI, A. 2016. Identification and prioritization of the barriers of knowledge management implementation using fuzzy analytical network process: A case study of the Iranian context. VINE Journal of Information and Knowledge Management Systems, 46, 319-337.

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8.     Appendix

8.1.  Appendix Table 1

Category Factors Papers
Formal processes Formal processes; Dynamic processes; Daily interactions with KMS; Relationship with customer; Unorganised; 1-6, 7, 9, 12, 13, 15-28, 30-35, 37-43, 46, 47, 49, 52, 53.
Company culture Company Culture; Geographic issues; The value of Knowledge; 1, 2, 6-11, 15, 21-27, 29, 30, 32, 34, 36, 40, 41, 43, 46-48, 50-54.
Top down support Top down support; Knowledge hierarchies; 1, 2, 5, 6, 9-14, 17-19, 21, 27-29, 31, 37, 41, 44, 45, 47, 51.
Motivation Motivation; Incentives; Trust; Knowledge protection; Intellectual property; 4, 8, 9, 11, 12, 14, 17-19, 22, 30-34, 37, 39, 41, 43, 47-51, 53.
Clear strategy Clear strategy; Clear goals; Performance measures of KMS; Change management; Inter-organisation knowledge sharing; 1-6, 13, 15, 16, 18, 19, 21, 23, 24, 28-34, 41-45, 47, 48, 50, 51, 53, 54.
Quality of KMS Quality of KMS; Features of KMS; Extracting knowledge; Integrating technology with strategy; Social media; 1, 5, 6, 7, 10, 12, 13, 15, 16, 19, 23, 25, 30-32, 34-37, 40-42, 44-48, 54.

8.2.  Appendix Table 2

Area Promising research directions
Formal processes Determine how valuable informal processes can be converted into formal processes. How are these formal processes communicated? How does the level of communication effect these processes?
Company culture Insights into how does the company culture changes over time and how does this effect KM effectiveness. In positive knowledge sharing cultures what are the remaining obstacles?
Top down support A comparison of internal departments with differing levels of top down support. Practical insights into what does effective top down support look like in the work place.
Motivation Insights into how intrinsic or extrinsic rewards effect the level of motivation an employee has to share knowledge. How can a company sustain a high level of motivation?
Clear strategy Further investigation into how a KM strategy can be benchmarked. Insights into effective methods of communication of strategy to the employee.
Quality of KMS Insights into the operation and performance of different KMS in the same context. What are the critical success features for a KMS?
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Projects

Knowledge Capable

Knowledge Capable is a repository of tutorials, guides and examples of knowledge management in the real world. I used it for a central database to add everything I knew about KM.

If you have a spare minute or are looking to implement KM in your company have a look!

www.knowledgecapable.com

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Papers

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

ABSTRACT

Digital learning communities have become a focal point of organizational development. The education industry has begun to follow suit by using the same technologies to enhance the learning process through a deeper process of participation. These technological tools complement sound learning design to bring a wealth of benefits to students. These benefits are not without peril. New technological tools shift common issues of education into online environments. This article reviews recent implementations of digital communities and highlights their influencing factors. The factors are then connected to existing factors in knowledge management literature. The key factors found are A) Student interaction with the community, B) Interaction vs grades and C) Student experiences.

1. INTRODUCTION

Inter-student learning can be empowered through the application of technology [1]. Technologies are used to create knowledge sharing communities that are unified by their common cause [2]. Participants are not categorised by nationality, location or time. It is the shared endeavour that binds the group. Student-focused communities have reaped numerous benefits while being exposed to a variety of weaknesses.  Research on knowledge sharing communities, in the context of organisational learning, has provided extensive insights about these issues [3]. This paper provides linkages to knowledge management literature by comparing issues collected from a baseline of education case studies. This leads us to the research question: What issues exist in both knowledge management literature and education case studies?

The wide spread availability of technology has attributed to its growing use in the education industry. Traditional face-to-face instruction is now being supplemented or completely replaced in the educational experience [4]. These technologies can support any aspect of the teaching experience from content delivery to project presentations. A key aspect of these technologies is the ability of students to interact with each other to discuss content and compare notes.

When examining the effectiveness of a given technology there are a range of performance indicators. These indicators can be summarised into two categories. The first category is the explicit returns on the technology, that being the grades achieved by the students and how they compare with traditional learning design [5]. This is normally measured in the grades achieved, student pass rates and/or student dropout rates. The other category is the tacit side, which is the difficult process of measuring how much knowledge retained by the student and their experience in the course [6]. This is measured by student motivation, feedback, interest and satisfaction with the course.

The aim of this study is to collect influencing factors of digital learning communities and find comparisons in the knowledge management literature. This study is to benefit the application of technologies that promote inter-student learning by comparing common factors with those in industry.

2. METHODOLOGY

To compare the underlining factors of digital communities with existing knowledge management literature, we needed a baseline from previous education research. To find this baseline, a systematic literature review was used to focus the research and define its limits [7]. The necessary thorough planning is a guarantee to follow a clear direction on how to proceed through the literature search [8]. This was achieved by collecting a pool of relevant case studies in the area and cross checking their underlining factors to discover trends and key features. This method can be broken down into its three stages: 1) search, 2) selection and 3) systematic analysis.

In the search phase, popular and relevant international databases were examined to provide a reliable cross-section of the literature. The databases chosen were Emerald Insight, Science Direct and IEEE Xplore. These databases provided a range of scientific journals and allowed for a large target pool. With the databases chosen, search keywords were needed that summarized the focus of this paper and those that would narrow the hunt for useful case studies. To describe how a digital community might exist in the literature the following keywords were used: “communities”, “informal” “learning”, “education” and “technology”. Initial searches using knowledge management terminology returned limited results as the education industry rarely used terms such as ‘knowledge management system’, ‘communities of practice’ or ‘organizational learning’. The final terms chosen were found to be the most basic and direct terms that adequately described what we were looking for.

The selection phase required a smaller assortment criterion to compare the studies. Firstly, the data range was limited to only include the most recent studies available from 2014 to the present. The next criteria were primary case studies that dealt with digital communities directly. This was found to be the easiest way to get first hand data on the issues involved. The final criteria were the context of the education industry. This was used to eliminate any study with a company emphasis and narrow the field to just studies that focused on students. The last filters of this phase were year “2014-2018”, type “case study” and industry “education”. “Case study” is not a reference to it being labelled a case study but the structure of the research done. The third and final phase of the methodology was the systematic analysis of the literature. Each chosen case study was analysed for its issues and results. These issues were collected into a detailed factor matrix. From this matrix, issues were grouped into overarching themes. This was done to simplify the comparison of underlining issues with the knowledge management literature.

3. RESULTS

The initial search of the international databases identified 21417 articles. Give the vast return, the second phase of the literature discovery was implemented. The year filter “2014-2018” was used to cut this number down to 6910 articles. Following this the type “case study” lowered this number again to 249 and from these, 11 were found in the industry “education”. A more detailed breakdown of the origin of the articles can be found in table 2 and a list of the found articles can be found in table 3. These articles were analysed based on their relevance to the topic and, more importantly, what factors they discovered in their respective cases. These factors were placed into a factor matrix to visualise correlations. These commonalities were grouped into the following headings: 1) student interaction with the community, the 2) interaction vs student grades, the 3) student experience.

3. a) Student interaction with the community

Each case study compares these interactions with traditional face-to-face courses. Overall, interactions with a digital community generally showed a higher level of motivation, attention and engagement for students [13]. Students involved with interactive environments found learning outcomes, learning experience and overall structure were clearer than in traditional class [17]. When used in combination with face-to-face learning, higher levels of attendance were reported [9] and it led to advanced levels of preparation before classes [17]. An additional benefit for instructors, was that the most challenging concepts were the highest discussed points within the community [9].

Morillas et al found that there was a different level of positivity shown towards a given technology based on the course offered [13]. This was evident when comparing the student experience of different disciplines when given the same technologies to use. Students also need time to adjust to any major shift from traditional learning practices [17]. Personal student issues also come into play when interacting with in a new community. Weaker communication skills showed an increase in the difficulty to immerse themselves in some communities [11]. This could be the result of a reported lower level of content dissemination and guidance from lectures in digital communities. In fact, many students found the traditional lectures were more effective at covering a wide spectrum of academic content [17].  Rambocas et al recommended that the reasonability was on the instructors to make sure that new methods are explained effectively to students and their benefits are clearly

3. b) Interaction vs. Grades

Conde et al used a system that rewarded higher participation with higher grades without penalization for low participation [9]. In their study they had an overwhelmingly positive 72.8% relationship of higher interaction levels of student achieved better grades. Gewerc et al may have found limited evidence of a positive relationship but concluded that the students with the lowest levels of participation often corresponded to the students with the lowest marks [11]. In contrast, Cheng et al found that while supplementary benefits were seen in the experimental group, there was no difference in the grades achieved [10]. This is supported by Rambocas et al who found no statistical difference in student performance [17]. Another measure that was used was the rate of students passing the course. When determining this, Rajab K.D. [19] and Encalada [16] both found no significant statistical difference. On the other hand, Rambocas et al found that the student development was significantly higher in the technology enabled classes [17] and there was a higher interest in completing courses using digital communities [18].

3. c) Student experiences

The numerous case studies evaluated in this paper demonstrate clear evidence for the benefit on the student experience. Students from Rambocas et al’s study found the experience novel, enjoyed the community like environment and the opportunity to learn from classmates [17].  This is referring to the additional learning vector of another student in the course not just the lecturer. Higher levels of satisfaction [19] and interest [18] were also reported.

Students of Warin et al’s experimental class found the level of learning satisfaction was high but regretted the cost of a increased workload [14]. The students found that more effort was required to achieve the same results of traditional classes. Workload increases also put stress on inter-student relations in group work as concerns were raised of the performance of work by other team members [14]. Individual apprehensions also included a feeling of isolation brought on by a fear of public speaking and an alienation within the community [17]. This refers to the intimidating process of interacting with fellow students in a significantly more social way than is normally expected. Some students did not embrace the responsibility of constructing their own learning paths and were confused when presented with “so many different views on a single topic” [17].

4. DISCUSSION

The collected case studies provided compelling insight into the effect of digital communities on the grades achieved by the students. When compared with industry, companies use knowledge management technologies to gain a competitive edge over the competition [20]. The individual employee’s benefits are measured by personal achievements such as monetary awards, promotion or social recognition. A common issue concerning knowledge management technologies is the balance of effort and reward. From the education side, increased work load was a regrettable cost when compared to the grades achieved [14]. Industry evidence indicated that the rewards received were not proportional to their perceived contribution to a digital community. This is further exemplified by feelings of underappreciation for their efforts and how their rewards did not meet their expectation [21].

This issue is exacerbated as employees found that benefits of monetary awards, promotion or social recognition were seldom received [21]. This lack of difference in the benefits received for the individual can be seen in the comparison between traditional courses and those using digital communities [16, 19]. Immediate benefits, either through financial or higher grades, are rarely documented when using these forms of technologies. On the other hand, student and professional development are often rated significantly higher when using these technologies [10, 17, 21]. This implies a positive impact of these technologies but the perceived benefits are sometimes lacking for the individual.

Beyond the academic performance and interaction with digital communities, it is important to study the student experience of using these new technologies. This is paralleled in industry as accompanying factors are often not considered when implementing new strategies [22]. It is important to select the appropriate technology for the right course [17]. Although an unbalanced approach with a focus on technology over personal issues, has led to many failures and unsuccessful implementations of digital communities [23]. This issue also leads to inter-student tensions as they were concerned about their contributions compared to the performance of their group members [14]. This is a common issue in industry as a feeling of loss of personal power and job security builds mistrust in the work place  [24-26]. This is before you consider individual anxieties that are rooted in a feeling of isolation brought on by a fear of public speaking and an alienation within the community [17]. A step to counter this is measuring the popularity of discussed topics, as Conde et al found the most discussed topics were rated the most difficult by students [9].

5. CONCLUSION

In summary, by using education case studies that utilized technology for inter-student knowledge sharing, we have found a number of factors that impact the learning process. These factors were grouped into three areas. The first being how a student interacted with the community itself, which was measured in attendance, interaction and time. The second was the comparison of interaction and the effect this had on grades; this gave conflicting evidence to a positive correlation. The third group of factors was the student experience of the community. This focused on how a student rated the interaction and if they perceived any benefit of the community. All the factors outlined in this paper highlight the polarizing nature of using a technology-enabled community. The benefits are seen but the negative influencing factors still need to be addressed in each case study.

This paper is limited by the number of case studies collected and the differences in technologies used. Broadening the search parameters would allow for more confidence in the issues collected. Reliability could have been increased in the comparison of issues by providing a summary of case studies from the knowledge management literature. In that scenario, groups of case studies could have been compared directly, instead of using a wider collection of sources. This research presented in this paper could easily be expanded to include longitudinal case studies in the education sector by focusing on the recent developments in knowledge management and applying them to student communities.

In conclusion, this research highlighted the supporting influence knowledge management literature can have on the education industry. This was achieved by collecting common issues from various case studies and comparing them to existing knowledge management literature. Expanding this research has the potential to greatly benefit technology driven inter-student learning.

6. ACKNOWLEDGEMENTS

This research has been conducted with the support of the Australian Government Research Training Program Scholarship, ARC Discovery Project DP180101051, UOW Matching Scholarship 2018.

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