The measurement factor of employee participation for Knowledge Management System in engineering consulting firms

AbstractKnowledge Management Systems (KMS) is a fundamental tool in the implementation process of Knowledge Management (KM), which is used to manage knowledge of information technology systems in an organization. However, a well-developed KMS should not only take advantage of information technology function, but also require the development of a complete measurement system for KMS. The purpose of this research is to develop an employee participation framework to describe the cause-effect relationship of KMS. Three case studies were interviewed for building measurement factors of KMS used by engineering consulting firms. Two questionnaires were sent to managers of engineering consulting firms for establishing main factors and sub factors in the measurement system in which four primary objectives were identified as “Activity in forum”, “Statistic of knowledge system”, “Participation of KM activity”, and “Number of documents”. The cause-effect relationship among four objectives was shown by path analysis.


Introduction
Ever since scholars first introduced the concept of Knowledge Management (KM) (Nonaka, Takeuchi 2007), applications of KM in facilitating and managing innovation and organizational learning have become an important issue (Choi, Lee 2003;Goh 2006;Ling, Gui 2009;Yu et al. 2009;Tserng et al. 2010;Lin, Lee 2012;Wu et al. 2012). Knowledge management includes the processes of creating, securing, capturing, coordinating, combining, retrieving, and distributing knowledge (O'Dell, Grayson 1998;Group 1999;Bhatt 2001;Tserng et al. 2010). Having processed previous information and knowledge, engineers and experts can reuse and share data to reduce the time and cost involved in solving a current problem. Knowledge management is particularly attractive to the construction industry because of its competitive environment and low profit margins (Tserng, Lin 2004;Carrillo, Chinowsky 2006;Tserng et al. 2010;Lin, Lee 2012;Wu et al. 2012).
In the construction industry, general contractors and engineering consultant firms continually plan, implement, and use KM for translating tacit knowledge into explicit knowledge and enhancing the performance and capabilities of their resources (Carrillo, Chinowsky 2006; Lin of information technology but also of cultural change, rewards, and executive teams (Chang, Li 2007;Du Plessis 2008;Yu et al. 2009;Ding et al. 2011).
No matter how much effort upper management invests in a KMS, implementing the highest levels of KM still requires employee participation. Managers should have a preliminary understanding that employee participation would facilitate KM activities (Tseng 2008a;Tserng et al. 2010). However, limited research has been conducted on employee participation in KMS using a systematic approach. Hence, this research paper aims to establish a methodology for measuring the degree of employee participation.
The objective of this research is to explore the measurement factors of KMS from the employee point of view. We developed an employee participation framework to describe the cause-effect relationships within KMS. The framework identifies principal factors and analyzes the cause-effect relationship for employee participation in KMS. Our methodology involved using three case studies to build measurement factors of KMS, and two questionnaires to establish objective factors and subfactors in measurement factors of engineering consulting firms. Discussion of cause-effect relationships among the objective factors was shown by path analysis.

Knowledge management and knowledge management systems
There are several challenges for the implementation of knowledge management in construction, such as short supply times, organizational culture, lack of standard work processes, and insufficient funding. More specifically, these barriers manifest themselves in a lack of managerial support, employee resistance to knowledge sharing, poor information technology infrastructure, lack of a real-time integrated database, and inadequate cash flow (Carrillo, Chinowsky 2006). The control points of KMS in the stages of execution, evaluation, and reinforcement of KM include the system usability and effectiveness, the number of knowledge objects, progress reports, user satisfaction, knowledge object application count, usage count, appraisals, review mechanisms, portal maintenance, and process tuning (Chang, Li 2007).
Therefore, construction is a project-based industry, which makes the continuation of knowledge difficult (Tserng, Lin 2004;Lin et al. 2006;Tserng et al. 2010). It also makes the management of knowledge and the extension of experience essential. How to transform implicit knowledge into explicit knowledge is, therefore, a principal subject. Engineering consulting firms in charge of planning, designing, and supervising construction projects are knowledge-intensive businesses (Mortazavi 2010;Ding et al. 2011). Most of the knowledge assets are mentally retained in the minds of senior engineers or in masses of documents. Traditionally, the gathering of knowledge is through reading documents or through oral communication between employees. However, loss of knowledge by the resignation of senior engineers can result in loss of the competitive edge (Yu et al. 2009Ding et al. 2011;Lin, Lee 2012). Thus, it becomes critical to increase the productivity of engineers and to reduce the impact of staff attrition. The KM activities include the processes of creating, capturing, storing, sharing, and reusing knowledge. These processes are viewed by engineering consulting firms as a fundamental basis for competition. Previous research on KM had only focused on the performance evaluation of KMS (Teh, Yong 2011;Lin, Lee 2012;Yang et al. 2012).
There are three critical factors influencing KMS performance: its strategy, planning, and implementation. The strategy of Knowledge Management is affected by the degree of both internal and external analysis. The planning of Knowledge Management is affected by goal setting, employee training, and technique requiring. The implementation of Knowledge Management is affected by employee commitment and the measurement system. Moreover, developing a complete measurement system for the KMS becomes an essential issue (Lin, Tseng 2005;Tseng 2008b). This research focuses on the critical factors involved in the implementation of KM and establishes a measurement system framework for employee participation.

Research design and methodology
The literature describes several critical factors affecting KMS performance, however, not all of these are relevant to the construction industry. Hence, for this research, we statistically analyzed case studies in order to examine which of the KMS factors are present in engineering consulting firms.
A case study methodology was adopted in order to gain a detailed insight into senior manager participation in KMS. This research selected three large engineering consulting firms to gain a perspective on how KM is used in the industry. For each firm, the person with overall responsibility for the KMS and employee participation measurement factors were gleaned through interview. The interviews were transcribed and returned to engineering consulting firms in order ensure the accuracy of transcript. Even if the studies on KMS in general are remarkable, their specific application in engineering consulting firms is not identical to that in other industries (Tserng, Lin 2004;Tseng 2008a;Tserng et al. 2010). In order to establish a precise and quantitative process for building measurement factors, we used two questionnaires.
An independent-sample T-test was used in the questionnaire analysis to identify both significant and non-significant factors. This is a method of quantitative analysis used to compare mean values of two samples to determine the likelihood that the samples are from similar populations, but with different mean values. When two samples are taken from the same population, it is very unlikely that the means of the two samples will be identical. The T-test compares the mean scores of two groups on a given variable (Peterson 1994;Henson 2001;Zhang 2006;Brown et al. 2007). The analysis was used to describe the variability of the significant factors within the population. On the other hand, the path analysis method is used for verifying the theoretical model (Wright 1921(Wright , 1934Pett et al. 2003;Brown et al. 2007;Huang, Hsueh 2007;Lin, Kuo 2007).
The first questionnaire was explored by correlation analysis and was used to check the linear relationship between factors, which constituted the basic assumptions of path analysis (Wright 1921(Wright , 1934Demian, Fruchter 2006;Huang, Hsueh 2007). Thus, the degree of correlation is first confirmed through correlation analysis, and the cause-effect relationship is then confirmed through path analysis in the second questionnaire.
As a result, the first questionnaire is directed from the perspective of engineering consulting firms, to establish the factors affecting construction industry. Through statistical analysis, significant factors were filtered and screened. The purpose of the second questionnaire is to explore the correlation between these common factors. Further, the use of method validation and path analysis allowed exploration into the cause and effect relationship between the statistical probabilities (Brown et al. 2007). This article employs a quantitative approach to establish measurement factors for employee participation in KMS. Engineering consulting firms may refer to this study when implementing KMS, so as to improve KMS application efficiency and to avoid implementation failure.

Case study
To obtain a representative sample in the case study, all engineering consulting firms that were chosen had to have an already implemented KMS. In each company, three managers in charge of implementing KM were interviewed, which provided the basis of the case studies on how companies establish KMS. The interviewees followed three main themes: (1) the strategy behind the use of KM at their firm (Demian, Fruchter 2006;Yu et al. 2009); (2) the framework of their implemented KMS (Lee, Hong 2002); and (3) the system of measurement in their KMS (Lin et al. 2006;Tseng 2008b;Yu et al. 2009;Lin, Lee 2012). All of the case study firms are categorized as large engineering consulting firms of more than 1000 employees (Carrillo, Chinowsky 2006). Moreover, these companies had invested heavily in information technology before implementing Knowledge Management Systems (Lee, Hong 2002). The scope of the projects in the case study is illustrated in Table 1, and the summary of the findings of the KM and KMS in the engineering consulting firms are shown in Table 2.

Engineering consulting firm A (ECF-A)
ECF-A has more than 1300 engineers. Knowledge Management has been implemented since 2001. The ECF-A Table 1. The scope of projects undertaken by engineering consulting firms in case study

No.
The scope of projects ECF-A Electric power, water conservation, urban construction, industry and agricultural construction, environment, civil engineering, transportation, building, machinery, and electricity.

Defined knowledge to include CAD files and Engineering
Reports. This reference to knowledge focused on explicit knowledge. The main strategy in ECF-A was to establish document management. Therefore, the company stores all records when executing KM for analyzing explicit knowledge out of tacit knowledge on the basis data collected. Data mining is the next project in the postimplementation of KM. ECF-A has four levels in its KMS: "information acquiring", "information value adding", "building the knowledge framework", and "integrating the wisdom". At the information acquiring level, information is input into the system, and the output material is the enterprise or work process. Information acquired by ECF-A included engineering reports, CAD files, ISO standards, and external data. All materials followed ISO standards and were stored in knowledge bases. At the information value adding level, ECF-A introduced several techniques to assist information value adding to help engineers find information without leaving an operating job. These techniques were document scanning, image processing, database, and full text retrieval. These techniques not only processed all material information, but also established tagging and indexing in the database. This level translated non-structural information into structural information. At the same time, this level added safety information, information usability, as well as user authority management.
In building the knowledge framework, the next step was to supply a useful query interface, a reusable function, and a friendly viewer. This level assists engineers in transforming information to knowledge easily and efficiently, as well as in setting up the suitable authority.
In integrating the wisdom, the engineering consulting firm requires that its engineers coordinate when solving problems. When engineers are required to express problems systematically and solve them in the same manner, knowledge becomes wisdom. The function of the KMS is to include coordination, discussion forums, knowledge recommendations, and e-learning in ECF-A.

The engineering consulting firm B (ECF-B)
ECF-B employs more than 1000 engineers. Knowledge Management was implemented in the year 2000. ECF-B defined knowledge to include personal experience, technique reports, handbooks, and program source code. However, this definition of knowledge makes it difficult to draw a distinguishing line between tacit knowledge and explicit knowledge. Developing a knowledge map is the next step in the post-implementation of KM; this involves establishing connections among documents, allowing employees to find information in the KMS more easily. In order to evaluate employee participation after implementing the KMS, ECF-B demanded to establish factors of measurement for employee participation in the KMS. The structure of the KMS used by ECF-B included "the specialized knowledge base" and "the knowledge community".
The specialized knowledge base had a welldesigned classification structure, which included a technically-based and content-based structure wherein explicit knowledge could be stored and reused. The classification structure also reduced the repeated effort of sharing knowledge across different organizations.
The knowledge community supplied a mechanism for knowledge communication and collaboration in tacit knowledge. The functionalities of the knowledge community for users included a submit and reply topic and a search and sort by classification. The functions of the manager included setting and maintaining the knowledge published as well as classifying, deleting, and expert grouping it.

The engineering consulting firm C (ECF-C)
ECF-C employs over 5000 engineers. Knowledge Management was implemented in 2005. ECF-C defined knowledge to include patents, engineering technology, and reputation. This company solely stored knowledge into the knowledge base as its significant strategy in research; moreover, there is a chance for the raw data and the unprocessed information to become knowledge for storage because ECF-C considered knowledge to be applicable across all cases whilst raw data and unprocessed information to be more case specific.
The framework of its KMS included five components termed as "Knowledge Base", "Knowledge Experts", "Knowledge Community", "E-learning system", and "Links".
The Knowledge Base includes Knowledge Documents, Cyclical Information, and Historical Information. The Knowledge Documents component classified documents into four categories: profession, industry, project management, and corporate management. Cyclical information includes current information such as news, financial information, announcements, monthly reports, journal papers, projects, and catalogues. Historical information includes final reports, enterprise information, company information, collection reports of best practice, and practical experience reports.
The Knowledge Experts contains classified information and builds the experts contact list and profiles. The contact list assists engineers in finding the appropriate internal, external, and retired experts related to the company.
The Knowledge Community gathers engineers with similar interests to collectively share their engineering experiences. The original knowledge community focused on value engineering. The community found a solution and was able to reduce the costs of the project through brainstorming.
The E-learning system is integrated into the KMS used by ECF-C. Teaching material was found to be capable of referencing and querying within the E-learning system, which facilitated the reliability and regularity of knowledge transfer.
Links refers to the component used to connect the internal knowledge within the company's external knowledge bases that comprise government organizations, academic organizations, and A/E/C industry.

The measurement factors of the case study
Following the case study, this research summarizes the interviews and literature and then constructs 37 measurement factors of employee participation in the KMS. Eleven measurement factors were found in ECF-A, 25 in ECF-B, and 19 in ECF-C. The main strategy of KM in ECF-A is document management in the KMS; hence, there were fewer observed measurement factors. ECF-B had established a knowledge base and a knowledge community, and therefore, held the largest number of measurement factors. Although ECF-C's KMS framework was larger than ECF-B's, it only stored knowledge into its Knowledge Base without raw data and added information.
Thus, ECF-C ended up with fewer factors than ECF-B. A detailed description of factors is shown in Table 3.

Questionnaires' survey
This research used two questionnaires for evaluating and analyzing the discovered measurement factors. These factors in the pilot survey and final survey had used a Likert-Type Scale. The scale ranged from 1 to 5 (with "1" being "not significant", "2" being "fairly significant", "3" being "normal significant", "4" being "significant", and "5" being "very significant") (Zhang 2006). To measure employee participation in KMS, all samples of these questionnaires focused on managers in engineering consulting firms.

Pilot survey and factor analysis
In the pilot survey, 104 questionnaires were sent, with 57 responses. There were 4 potential respondents who were unable to fill out this questionnaire. The effective response rate was 50.96%. The pilot survey questionnaire that was designed for this research consisted of two parts, including (1) a manager profile, and (2) the 37 factors for employee participation in KMS. By independent samples of the T-test, this research removed five non-significant factors: factors 3, 5, 6, 11, and 12. The other 32 significant factors remained after factor analysis.
Factor analysis was found to be a useful statistical method for describing the variability among measurement factors. The Kaiser-Meyer-Olkin (KMO) test and Barlett's test were used to measure whether the sampling adequacy analysis was a factor for comparing the magnitudes of the observed correlation coefficients to the magnitudes of partial correlation coefficients. A value greater than 0.5 was the threshold for a satisfactory factor analysis to proceed (Pett et al. 2003;Zhang 2006;Yuan et al. 2010). The results of this research are shown in Table 4. Figure 1 illustrates the scree plot of the factor analysis of employee participation. The four principal components were extracted by specifying a minimum initial eigenvalue of 2. The pilot survey established 32 factors and extracted 4 principal factors for Employee Participation in the KMS. Each of the four principal factors included eight sub-factors. As a result, this research used Cronbach's Alpha internal consistency coefficients test, which is the most commonly used for Likert scale surveys (Henson 2001). The analysis showed that Cronbach's Alpha for the four principal components were well above 0.5 (including 0.806, 0.827, 0.723, and 0.676), therefore the reliability of all principal components could be assumed (Peterson 1994). As shown in Table 5, the variance of the four principal extracted components were greater than 0.6 and they cumulatively explained a 61.64% total variance. Table 6 shows the rotated component matrix in which absolute values less than 0.5 were suppressed.
After examining the effect of each component on employee participation, the four principal components were renamed respectively as "Objective 1: Activity in forum"; "Objective 2: Statistic of knowledge system"; "Objective 3: Participation of KM activity"; and "Objective 4: Number of documents". The four principal components are the renamed factors of four objectives shown in Table 7.
Objective 1 was designated "Activity in forum" because its factors included records of submissions, replies, additional answers, and submitted topics with a final solution in forum. Moreover, submitting and replying in the SOS system was similar to activity in a forum. The activity in forum was an integrated workflow with personalized alerts and on-line information sharing (Chang, Li 2007) .
Objective 2 was named "Statistic of knowledge system", which focused on the usage in the system and is important for knowledge reinforcement (Kreng, Tsai 2003;Chang, Li 2007). All records tracked employee participation, which included the records of uploading and sharing record documents, login frequency, reading on-line documents, working and non-working time, pages printing, and category records.
Objective 3 was labeled "Participation of KM activity", which helps employees to articulate, capture, and share experts' experiences (Butler, Murphy 2007). All records represented real activity in the KMS, including uploading and sharing personal documents, publications' recommend times and scores, regular activity and host records in the knowledge community, and training classes.
Objective 4, "Number of documents", was an important control point in KM evaluation (Chang, Li 2007). For instance, these records held patent applications, contacts of publicly recognized managers, published reports, and books searched or borrowed.

Final survey
In the final survey, this research sent 104 questionnaires, to which there were 54 responses. The effective response  Table 8 illustrates a reliability analysis of the various objective factors and sub-factors, and shows that the reliability of the objective factors is higher than 0.7, that of the sub factors is higher than 0.6, and the overall reliability of the scale is over 0.8. The results show a high consistency and reliability of the final survey.
In order to understand the relationship between four objective factors, this study used a Pearson correlation analysis (Table 9). A higher positive correlation was found in the objective factors of "Activity in forum" versus "Participation of KM activity"; "Activity in forum" versus "Number of documents"; and "Participation of KM activity" versus "Number of documents", whose significant values were less than 0.01, and had correlation values of 0.603**, 0.447**, and 0.689**, respectively. The positive correlation in the objective factors of "Activity in forum" versus "Statistic of knowledge system", and "Statistic of knowledge system" versus "Number of documents", whose significant values were less than 0.05, had correlation values of 0.295* and 0.285*, respectively. An insignificant value was "Statistic of knowledge system" versus "Participation of KM activity", which had a correlation value of 0.028.   No. 28 The framework in the KMS processes data into information and knowledge (Davenport, Prusak 2000). Therefore, this research assumes that employee participation in the KMS also requires access to both the data and knowledge (Davenport et al. 1996;Davenport, Prusak 2000;Bhatt 2001;Sambamurthy, Subramani 2005;Tseng 2008a). The results in the final survey were the analysis of the four objective factors; their relationships in knowledge management are shown in Figure 2.
The objective factor, "Number of documents", which collected records about the number of documents  . 28 No. 8 No. 37 No. 24 No. 25 No. 19 No. 10 No. 4 0.806 2 No. 36 No. 20 No. 7 No. 32 No. 34 No. 35 No. 29 No. 17 0.827 3 No. 21 No. 9 No. 13 No. 33 No. 31 No. 27 No. 14 No. 22 0.723 4 No. 15 No. 30 No. 16 No. 1 No. 23 No. 18 No. 2 No. 26 0.676 from consulting firms in the KMS, not only translated raw data and information, but was also developed by referring to the results of other objective factors such as "Activity in forum", "Statistic of knowledge system", and "Participation of KM activity". The participation of employees in the use of the KMS is an implicit behavior, and is thus not easy to quantify. Through the study of factors, we found four major quantifiable components, which are the Number of documents, Activity in forum, Statistic of knowledge system, and Participation in KM activity. From the interviews and the results of this study, these four components matched the transformation framework from data, to information, to knowledge. In future, this framework may help engineering consulting firms to understand if employees participate in the implementation of KMS. Furthermore, the study reveals the indirect beneficial effect of KMS on existing procedures.
The other objective factors, "Activity in forum", "Statistic of knowledge system", and "Participation of KM activity", were found to be fundamental factors in employee participation in KMS implementation. "Statistic of knowledge system" and "Participation of KM activity" represent tacit knowledge. Tacit knowledge translates into explicit knowledge in the "Activity in forum". Hence, these factors both directly and indirectly translate tacit knowledge into explicit knowledge.
The theoretical model of these four objective factors is shown in Figure 3.

Path analysis and discussion
In statistics, path analysis is used to describe the directed dependencies between a set of variables. This includes using models equivalent to any form of multiple regression analysis and factor analysis. In order to calculate validly the relationship between any two components in the diagram, scholars proposed a simple set of path tracing rules (Wright 1934) for calculating the correlation between two variables. The rules for path tracing are: the investigator traces backward up an arrow and then forward along the next, or forwards from one variable to the other, but never forward and then back; the investigator can then pass through each variable only once in a given chain of paths.
Because correlation analysis does not verify a cause-effect relationship (Huang, Hsueh 2007;Cheng et al. 2010), we applied the path analysis method for discovering a cause-effect relationship for the four principal factors. The path analysis method was a simplified type of structural equation modeling that used a series of regression analyses for verifying the theoretical model (Huang, Hsueh 2007;Lin, Kuo 2007). This research separated the path analysis into two equations. The first equation analyzed the objective factors for  First Equation: Y 1 = b 1 X 1 + b 2 X 2 + b 3 X 3 + ε 1 ; (1) Second Equation: Y 2 = b 4 X 1 + b 5 X 3 + ε 2 , where: Y 1 -number of documents; Y 2 -activity in forum; X 1 -statistic of knowledge system; X 2 -activity in forum; X 3 -participation of KM activity; ε n -n = 1~2, error term.
The cause-effect relationship and path coefficients within the path analysis are shown in Figure 4 (Wright 1921(Wright , 1934. The results in the first equation indicated three paths to the number of documents, including the statistic of knowledge system, the activity in forum, and the participation of KM activity. The paths of the statistic of knowledge system and the participation of KM activity were direct and significantly influenced the number of documents; their path coefficients were 0.286** and 0.727**. The second equation indicated two paths to the activity in forum factor, which including statistic of knowledge system and the participation of KM activity. These two paths were both direct and had a significant influence on the activity in forum, and their path coefficients were 0.278** and 0.595**. These two equations proved that the statistic of knowledge system and the participation of KM activity both have two direct cause-effect relationships with the number of documents and the activity in forum.
The significant relations presented above are consistent with most of the foregoing conditions in this research. However, the objectives of "Activity in forum" and "Number of documents" do not have a significant cause-effect relationship. All three case studies indicated that tacit knowledge and explicit knowledge were not only hidden in the discussion forum, but were also difficult to distinguish from one another. Because the factors under the objectives of "Activity in forum" included both explicit and tacit knowledge, the results of the analysis are not a significant cause-effect relation. Both tacit and explicit knowledge were compared with the principal factor of "Number of documents" and were not found to indicate a cause-effect relationship in this research.
The content inside "Activity in forum" includes known and unknown topics, wrong and correct replies, and positive and negative discussion in which complicated tacit knowledge can be converted into explicit knowledge. Future research might discover how to distinguish tacit and explicit knowledge embedded within the activity in forum.

Conclusions
There have been numerous studies dealing with the interpretation of tacit and explicit knowledge in the field of KM. This study differs from the routine by proposing a framework within which measurement factors correlate with one another to make the best use of KMS.
The results of the questionnaire to illustrate tacit and explicit knowledge are relatively subjective to different requirements. For example, the regular rules of project planning and design specification are explicit knowledge to senior engineers, but tacit knowledge, hidden in every ongoing project, to junior engineers. Another example, solving different project problems is explicit knowledge to senior staff, but to junior engineers, it takes actual experience or project knowledge translated by senior engineers. The tacit knowledge hidden in documents can be shared structurally in KMS. As a result, more discussion in this study goes around the engineering consulting firm's operation of measurement factors to examine the degree to which the employee participation in each feature of the KMS is in use.
This research summarizes the literature and case studies for building a measurement system and uses a quantifiable survey method in independent-samples T-tests, factor analysis, relevant reliability and validity tests, and path analysis for identifying employee participation in KMS within engineering consulting firms. Four objectives were verified and termed as "Activity in forum", "Statistic of knowledge system", "Participation of KM activity", and "Number of documents". The result of path analysis indicated four significant paths, including Statistic of knowledge system → Number of documents; Participation of KM activity → Number of documents; Statistic of knowledge system → Activity in forum; and Participation of KM activity → Activity in forum. KMS have been widely used in engineering consulting firms but managers are sometimes not able to understand the results and performance of the implemented KMS. Therefore, this study can help managers to understand systematically the different levels of employee participation reflected in our KMS implementation results. This study transformed 37 measurement factors into four principal components to indirectly illustrate the results and performance of KMS. We used the causeeffect relationship analysis to show that the components in KMS, "Statistic of knowledge system", "Activity in forum", and "Participation of KM activity", are quantitative indicators that reflect whether the KMS is useful. All indicators will be presented to "Number of documents" in the end.
Managers would be concerned with employee participation in the KMS implementation process, especially when the number of documents increases significantly. However, managerial focus is usually on profit post-KMS implementation, not whilst the system is ongoing. If the number of documents stays the same, then it is possible that the activities separate from the existing operation procedures in KMS. If the number of documents continues to increase, it could verify that KMS activities have indeed been integrated with existing operation procedures. This can be considered as a successfully implemented KMS.
The study found that best practice is to increase the number of documents in the KMS post-implementation, highlighting the need to create and increase documents. For instance, when experts and senior engineers face problems, the memorandum of understanding documents (MOU) should be produced. The contents of the MOU documents includes information such as the name of the expert, what the problem is, when it happens, why the problem occurs, and how it was solved. The documents of these discussions need to be retained in the KMS. Another example is the list of experts and the reasons why an original procedure is modified into a new procedure. Before implementing the KMS, these documents are held by different employees in different data formats at different places. These documents need to be unified, modified, and stored in the KMS, for the purpose of solving similar problems in the future.
There are two contributions from this research: (1) the engineering consulting firms that are planning to implement or that have already implemented a KMS could reference the results of this research for measurement factors for their employee participation; and (2) the causeeffect relationship could remind managers to rediscover the fundamental factors. If an engineering consulting firm is planning to implement a KMS, the proposed measurement factors and the results can be used as a tool before bringing in resources for KMS support. The designer of the KMS will be able to consider what information needs to be documented. Measuring the degree of employee participation in the consulting firm helps to reduce the cost of post-customization. In addition, the manager can determine whether the implementation of the KMS changes the operating procedures within the company by the statistical results from these factors. These statistical results can be used as indicators for KMS improvement.
Future research could use the measurement factors in this paper to compare different factors between general contractors and engineering consulting firms. In addition to employee participation, employee satisfaction with a KMS is another significant topic in the construction industry. Future research could focus on a feasibility study to evaluate and analyze the potential benefits associated with implementing a KMS as well as employee satisfaction to investigate barriers to the planning and implementation of KMS.