Knowledge Contribution as a Factor in Project Selection

Shuang Geng, Management Science Department, Management School, Shenzhen University, Shenzhen, China

Kong Bieng Chuah, Systems Engineering and Engineering Management Department, College of Science and Engineering, City University of Hong Kong, Hong Kong

Kris M. Y. Law, Department of Industrial and Systems Engineering, Faculty of Engineering, Hong Kong Polytechnic University, Hong Kong

Che Keung Cheung, Systems Engineering and Engineering Management Department, College of Science and Engineering, City University of Hong Kong, Hong Kong

Y. C. Chau, Systems Engineering and Engineering Management Department, College of Science and Engineering, City University of Hong Kong, Hong Kong

Cao Rui, Crest View Technology Investment Ltd., Shenzhen, China


Project selection is a crucial decision-making process in many organizations. By adopting a project-based learning perspective, this study sets out to develop a framework to integrate organizational knowledge development with project selection. We utilize various knowledge management models to create a structured evaluation metric to measure project contribution to organizational knowledge. A project selection model, which involves project knowledge contribution as one of the evaluation perspectives, is proposed. Results of a focus group study effectively validate the proposed evaluation metric. The article concludes with an empirical implementation of the model in an electronic component manufacturing company.

KEYWORDS: project selection; multicriteria decision making; project-based learning

Project Management Journal, Vol. 49, No. 1, 25–41
© 2018 by the Project Management Institute
Published online at


Project selection is a process aimed at evaluating an individual project or groups of projects and choosing to implement a set of them, so that the objectives of the parent organization are achieved (Meredith & Mantel, 2006). In functionally structured organizations with various operations and business units, this decision-making process is often complex. The group of decision makers is usually composed of various functional experts of the organization. In addition to the decision makers providing their respective insights about projects from various perspectives, the project evaluation should be aligned with the organization's strategies and objectives in both the short and long term. In organizations that view knowledge as their core competitive advantage—such as high-tech companies—the sustainable and continuing advancement of knowledge is a key to success. A suitable project evaluation and selection process can help these organizations to align technology development with the business strategy. While there are many decision-making models for project selection, the literature suggests that few consider or include the potential knowledge contribution of a project in the evaluation process. A decision-making model that includes the consideration of the knowledge dimension of projects is required to assist a company's knowledge development process.

A project is the result of the application and exchange of knowledge (Von Krogh, Roos, & Kleine, 1998). The idea of “learning by doing” led to project-based learning in school classrooms as an effective educational approach several decades ago (Barron et al., 1998). In an organization projects are mainly used to deliver short-term goals isolated from its history, stripped of its contemporary social and spatial context, and independent of the future (Grabher, 2004). The processes of creating and accumulating knowledge are seen to arise at the interface between projects and organizations, networks, and institutions in and through which projects operate. Ahern, Leavy, and Byrne (2014) adopted a problem-solving perspective and analyzed the role of knowledge formation and learning in managing complex projects. The purpose of this study is to integrate organizational learning and knowledge development with a project selection process by proposing a framework to evaluate project knowledge contribution and developing a project selection model based on the Analytic Hierarchy Process (AHP). The evaluation of projects in terms of knowledge contribution resonates with the earlier work of Law and Chuah (2004), which developed a learning approach for project-based teams.

The article first reviews the project selection problem as a multicriteria decision-making process. It then reviews the project-based learning and knowledge perspective of project evaluation. The AHP multicriteria decision-making method is also discussed; following this, a project evaluation metric for its knowledge contribution is presented. The proposed project selection model based on a novel evaluation metric is discussed in the following section. A focus group study that validates the evaluation metric is reported, and an empirical study that illustrates and tests the project selection model is presented next. As a conclusion, the contribution of this study, its limitations, and possible extensions are discussed.

Project Selection as a Multicriteria Decision-Making Process

An organization often prioritizes projects based on a list of selection criteria that takes into consideration the perspectives of its operation and development. These selection criteria seek to constrain the level of uncertainty when evaluating possible project alternatives (Ghosh & Jintanapakanont, 2004). The decision makers are subject to “bounded rationality” (Simon, 1997), which limits their ability to interpret large amounts of data required in the project selection and results in decisions that are not always rational. The emergence of project selection models allows managers to focus on relevant perspectives in a systematic way during the decision-making process.

Meredith and Mantel (2006) divided project selection models into two basic types: numeric models and nonnumeric models. The nonnumeric models try to evaluate projects based on management's subjective judgment about how a project meets the organization's various needs, such as operations, competition, product line extension, social responsibility, and so forth. The evaluation process relies less on numeric analysis and more on management belief and judgment. The numeric models try to quantify both the financial and nonfinancial value of the project. Some organizations use mainly financial models in their project evaluation; in the contemporary social and business environment, however, short-term financial performance alone can no longer guarantee the long-term success of an organization. The consideration of multiple perspectives in project evaluation helps to avoid sub-optimization (Linstone, 1999). As reported by Swanson (2011), management now not only considers return on investment (ROI) but also strategic contribution, resource limitations, and non-numeric factors, such as regulatory mandates and operating necessities. This explains the popularity of multicriteria project evaluation models, which help decision makers to balance the multiple objectives of organizations.

Various perspectives have been included in the evaluation of projects to meet or align with different organizational objectives, project types, and social contexts. These perspectives include strategic importance, competitive advantage, innovation, business fit, reasonableness, relevance, financial benefit, risk, environment, and social and political impacts (Oral, Kettani, & Lang, 1991; Brenner, 1994; Buchanan, Henig, & Henig, 1998; Henriksen & Traynor, 1999; Mikkola, 2001; Reisinger, Cravens, & Tell 2003; Bitman & Sharif, 2008; Hsu, 2005). The impact of the project on organizational learning and knowledge development, however, has often been neglected despite the fact that project-based learning is widely adopted and studied (Thomas, 2010). Left on its own, innovation resulting from projects is often short term and, though relevant to the creation of new knowledge, not automatically complementary to organizational-level learning. Therefore, we propose including knowledge contribution as one of the project evaluation criteria in the multicriteria decision-making process.

Project-Based Learning

Most of the early literature on project-based learning is in the education area, but project-based learning and retaining of knowledge in organizations is drawing more and more attention from researchers. The contextual view of projects emphasizes that the essential processes of creating and accumulating knowledge accrue at the interface between projects and the organizations, networks, and institutions in and through which projects operate (Scarbrough et al., 2004). Different types of logic that create and accumulation knowledge drive different learning processes and organizational practices (Grabher, 2004). The individual learning and one-off venture can be transferred to repeatable solutions through repeated cycles of interaction within the organization and between the organization and the environment. This is referred to as the ‘cumulative learning mode.’ The reconfiguration of existing relationships and solutions that minimize the scope for repeatable solutions is recognized as the ‘disruptive learning mode.’ In both modes, explicit knowledge contained in manuals and procedures is accumulated. The tacit/ know-how skills of individuals are also developed through learning-by-doing and reflections; therefore, project-based learning can improve the knowledge and competencies residing with the company's employees, which is defined as human capital by Magrassi (2002). The organizational capital is described as the collective know-how, even beyond the capabilities of individual employees (Sullivan, 2000) and can also develop through project-based learning. A framework developed by Chuah and Law (2006), called “Project-Based Action Learning,” has proved to be effective in enabling personal learning in the project process and, over time, leads to organizational learning. The literature in project-based learning lays the foundation of this study by confirming the contribution of project-based learning to organizational learning. The project contribution to organizational knowledge development, therefore, should be considered and evaluated as well in the project selection process.

The Knowledge Perspective of Project Evaluation

Recent research has focused on the capturing and reuse of knowledge for effective knowledge governance in organizations in order to tackle the limitations caused by the temporal nature and organizational factors of a project (Bresnen, Edelman, Newell, Scarbrough, & Swan, 2003; Grabher, 2004; Pemsel, Wiewiora, Müller, Aubry, & Brown, 2014) and knowledge management concepts are very often applied. Knowledge management is described as the practice of adding actionable value to information by capturing tacit knowledge and converting it to explicit knowledge; by filtering, storing, retrieving, and testing new knowledge (Nemati, Steiger, Iyer, & Herschel, 2002). This has become increasingly important, as many organizations are relying more on “knowledge workers” and investing more resources in human capital development. Learning through projects provides such an approach to training more “knowledge workers” within an organization.

Although research has reported how knowledge could be “harvested” during the course of a project (Schindler & Eppler, 2003), the evaluation of a project's contribution to organizational knowledge has been neglected. We believe this is important, especially in project-based organizations and knowledge intensive organizations. In this article, we discuss the dimensions of project knowledge contribution based on several knowledge management models (namely, Nonaka, 1994; Watkins & Marsick, 1993, 2003; Botha, Kourie, & Snyman, 2014) and develop a project selection model that integrates the knowledge perspective into the project selection process using the Analytic Hierarchy Process (Saaty, 1980).

The Analytic Hierarchy Process

Many multicriteria decision-making techniques have been developed to help decision makers evaluate and prioritize projects. Some of the most popular methods are: the outranking approach, such as ELECTRE (Roy & Vincke, 1981); multi-attribute utility theory (Keeney & Raiffa, 1993); the Analytic Hierarchy Process (Saaty, 1990); and the Bayesian analysis (Newman, 1971). The existing methods assume that the decision alternatives are independent from each other, and the criteria of the alternatives are also independent from each other (Cho, 2003). In some organizations, project selection seems to be the result of a political process and sometimes involves questionable ethics, complete with winners and losers (Baker & Menon, 1995). In others, the organization is so rigid in its approach to decision making that it attempts to reduce all decisions to algorithmic proceedings in which predetermined programs make choices so that humans have minimal involvement and responsibility. In this study, we consider these decision-making techniques as supportive tools for decision makers in making rational comparisons between projects. It is still up to the decision makers to make the final project selection decision.

The AHP method was developed by Saaty (1990). AHP is a comprehensive multicriteria framework capable of dealing with tangible and intangible criteria and amenable to group decision making without needing a consensus to combine group decisions (Saaty, 2003). It has found its widest application in multicriteria decision making, planning and resource allocation, and conflict resolution (Vargas, 1990). The AHP method assumes a unidirectional hierarchical relationship among decision levels. The top element of hierarchy is the overall goal for the decision model. The application of AHP requires users to make repeated, level-by-level pairwise comparisons to obtain the priority weights of all the criteria and subcriteria. Users also need to make pairwise comparisons between project alternatives to obtain project weights for each criterion. The combination of criteria priority weights and project weights for each criterion will give the project a final score for project ranking and selection. Using a hierarchical structure to represent the perspectives and criteria of each perspective can provide opportunities for fine-tuning decision-making mechanisms (Bitman & Sharif, 2008). Based on the hierarchical structure of our proposed evaluation metric, AHP is an appropriate tool here.

In our proposed project selection model, the evaluation criteria at the same level of the evaluation hierarchy are assumed to be independent from each other (Cho, 2003). The priority weights of the lowest level criteria are used as variables representing the importance of these criteria. The project priority weights for each of these criteria are used as variables representing the value of the project for each of these criteria. The project's final score consists of multiplying each priority of a project alternative by the priority of its corresponding criterion and adding up all the criteria to obtain the overall priority of that project's alternative (Saaty, 2003). More details about the evaluation criteria are presented in the section about the hierarchical presentation of project selection criteria.

Framework Development

The above discussion about project selection as a multicriteria decision-making process and project-based learning set the theoretical foundation of this study. The importance of project-based learning and its contribution to organizational learning indicate the necessity of considering project knowledge contribution in the project selection process. To characterize the project knowledge contribution, we developed an evaluation metric based on several knowledge management models (Nonaka, 1994; Botha et al., 2014; Watkins & Marsick, 1993, 2003). By combining the proposed evaluation metric with existing project selection criteria, a hierarchical multiperspective project selection criterion is obtained. To illustrate the implementation of this multiperspective project selection criterion, we also developed a project selection model that uses the AHP method. Then we carried out a focus group study to validate the proposed evaluation metric. Empirical implementation of the proposed model helps to test the model and demonstrate its implementation. The following section will provide details about the proposed evaluation metric.

The Evaluation Metric for Project Knowledge Contribution

Evaluating the suitability of a project from the knowledge perspective requires the insight of related subject experts and identifying the dimensions of its knowledge contribution. Here, we use the term “knowledge contribution” to denote the impacts and outcomes of project-based learning. The decomposition of the “knowledge contribution” should be based on the knowledge taxonomy of an organization. Among the various taxonomies of knowledge, a widely cited classification of knowledge is based on Nonaka's dimensions of tacit and explicit knowledge (Nonaka, 1994). The tacit dimension of knowledge is rooted in action, experience, and involvement in a specific context. The explicit dimension of knowledge is articulated, codified, and communicated in symbolic form and/ or natural language. In addition to the tacit and explicit types of knowledge, embedded knowledge refers to the knowledge that is locked in processes, products, culture, routines, artifacts, or structures (Gamble & Blackwell, 2001). Based on these taxonomies of knowledge, many knowledge models have been developed.

Botha, Kouric, and Sayman (2014) divided business knowledge into individual, organizational, and structural knowledge. Individual knowledge resides only in the minds of employees. Organizational knowledge results from learning that occurs in a group at the division level. Structural knowledge is embedded in the culture and makeup of the organization through processes, manuals, business rules, and codes of conduct and ethics. Project-based learning can influence business knowledge development as well as organizational culture. This is supported by Watkins and Marsick (1993, 2003), who defined three levels of organizational learning: individual level, team or group level, and organizational level. From the studies of Botha et al. (2014) and Watkins and Marsick (1993, 2003), we identified three dimensions or categories for the aforementioned “knowledge contribution” perspective: individual learning, organizational level learning, and organizational culture change. Each of these three categories is further decomposed into multiple sub-categories as more specific evaluation criteria.

The first category is individual learning. As noted by Senge (1990), the knowledge and skills of its workforce and the knowledge platform upon which these skills are based, govern the performance of a company. The fulfillment of project objectives requires project members to apply their knowledge and skills. From the problem-solving perspective, complex projects also require continuous learning over the life cycle by the project team as a community of learners (Ahern et al., 2014). The knowledge creation is a continuous and dynamic interaction between tacit and explicit knowledge. According to Nonaka and Takeuchi's (1995) knowledge conversion model, there are four modes of knowledge conversion: socialization, externalization, combination, and internalization. The content of knowledge created by each mode is naturally different. The socialization is a process of sharing experiences thereby creating tacit knowledge that can be called “sympathized knowledge,” such as shared mental models and technical skills. Externalization is a process of articulating tacit knowledge into explicit concepts thereby creating “conceptual knowledge.” Combination refers to systemizing concepts into a knowledge system and creating “systemic knowledge.” Internalization means embodying explicit knowledge into tacit knowledge and is closely related to “learning by doing.” It produces “operational knowledge” about project management, production process, new product usage, and policy implementation. These four types of knowledge content are adopted as criteria in the evaluation of individual learning by assuming that individual learning in projects involves the four modes of knowledge conversion.

The second category is organizational level learning. It is distinct from organizational learning, which is a broader concept and research area. In the project evaluation process, a preliminary project requirement analysis is conducted so that decision makers can decide if the organization has the capability of accomplishing the project. Knowledge competency is an important aspect in this consideration if the project relies on the capabilities of knowledge workers. Selecting projects with challenging requirements for project member knowledge capabilities will provide motivation for project-based learning. The knowledge management process model of Botha et al. (2014) grouped learning activities into three categories: knowledge creation and sensing, knowledge organizing and capture, and knowledge sharing and dissemination. This model focuses on learning at the organizational or team level. Therefore, we identified the following four types of organizational level learning based on the types of team or group learning activities: “organizational knowledge creation,” “knowledge sharing and dissemination,” “specificity of project earning goals,” and “knowledge transferability.” The first two elements are important knowledge management activities in many knowledge management models (Botha et al., 2014; Evans, Dalkir, & Bidian, 2014). The “specificity of project learning goals” refers to the planning of learning activities in the course of a project. This is based on the assumption that setting specific learning goals could enhance the effectiveness of project-based learning. “Knowledge transferability” (Argote & Ingram, 2000) describes the reusability of lessons or knowledge learned in the project in future business operations or projects. This is supported by the contextual view of project management, which considers the project as a source of expertise and reputation for an organization.

The third category is organizational culture change. Organizational culture reflects the values and beliefs, which are integral parts of what one chooses to see and absorb (Davenport & Prusak, 1998). Organizational culture was also emphasized by Bukowitz and Williams (1999) and Gamble and Blackwell (2001) as an enabler for the transfer and creation of knowledge. Dimovski and Reimann (1994) defined organizational learning as a process of information acquisition, information interpretation, and the resulting behavioral and cognitive changes. Projects are interwoven with organizational and social contexts and require key information from employees, previous business decisions, top managers, and external resources. Project members need to employ various means and ways for interpreting the information. Learning happens when information acquisition and interpretation lead to behavioral and cognitive changes. Communication among employees within and across structural units of an organization, affirmed by Keyton (2005), plays an important role in managing and changing organizational culture; therefore, the project has an impact on organizational learning culture. In order to measure the project's impact on organizational learning culture, the Dimensions of Learning Organization Questionnaire (DLOQ), developed by Watkins and Marsick (1993, 2003), is adopted in this study. Several empirical studies have validated the DLOQ and suggested that the DLOQ has acceptable reliability estimates (Ellinger, Ellinger, Yang, & Howton, 2002; Watkins & Marsick, 2003; Yang, 2003). The seven dimensions of learning culture are adopted as the evaluation criteria of organizational culture change: continuous learning, inquiry and dialog, team learning, embedded system, system connection, empowerment, and provide leadership.

In a nutshell, the proposed evaluation metric for project knowledge contribution is composed of two levels of criteria and has a hierarchical structure. The first level contains three criteria and the second level contains a total of fifteen subcriteria. A summary of the perspectives, criteria, and sub-criteria is provided in Table 1.

The Hierarchical Presentation of Project Selection Criteria

A literature search for multiple perspective-based project ranking models identified a set of evaluation perspectives and criteria. Although these models differ from each other to some extent due to specialization and the application context, we attempt to consolidate these perspectives into six major categories. The other perspectives and their subcriteria, in addition to project knowledge contribution, are also listed in Table 1. Figure 1 provides a graphic presentation of the hierarchical relationships between perspectives and their subcriteria.

The cognitive fit theory explains how the fit between the method that is used to represent the data and the nature of the decision task affects the quality of the resulting decision (Vessey, 1991). Different data representations can emphasize different aspects of the data while assisting decision makers in creating a mental model to analyze the data. It is common for decision makers to rate one perspective—such as profitability—as the overarching standard for project evaluation even though they have organizational learning in mind. The “bounded rationality” is used to illustrate that decision makers have limitations in interpreting large amounts of data. Therefore, we use this hierarchical project evaluation metric with various perspectives to combine our proposed evaluation metric with perspectives in the literature to support the project selection decision making. The “knowledge contribution” perspective and its subcriteria help with the mental modeling of decision makers in embodying and structuring the benefit of project-based learning. Using a hierarchical project evaluation metric helps decision makers to abstract the key information from multiple perspectives and avoids the overemphasis of one focus area.

In order to provide guidance on implementing this evaluation metric, we developed a project selection process model composed of the decision-maker actions and process inputs and outputs. The process model is described in the next section.

The Project Selection Process Model

Process models were shown to be an effective tool to assist in project selection (Lin & Hsieh, 2004; Bitman & Sharif, 2008; Amiri, 2010). In this study, we developed a project selection process model that uses AHP to integrate the proposed hierarchical evaluation metric (as shown in Figure 1).

The process model encapsulates a set of decision-making actions (Rose, 2013) and action inputs and outputs as shown in Figure 2. The rectangles represent the artifacts/actions that build up the model; these rectangles are indexed with numbers for explanation purposes. The inputs and outputs of these actions are represented by ovals and are indexed alphabetically. The arrows denote information flow between actions and their inputs and/or outputs. A step-by-step explanation follows.


Table 1: Project evaluation perspectives, criteria, and subcriteria.

The project selection model starts with two tasks: (1) perspectives and criteria selection and (2) stakeholder identification. There are three inputs for the selection of perspectives and criteria: perspectives and criteria highlighted in the literature, expert knowledge and experience, and the knowledge accumulation perspective. The shortlisted perspectives and criteria form a hierarchical structure of the evaluation metric (oval d) analogous to the evaluation metric in Figure 1. The stakeholder identification step helps to identify a group of decision makers for project evaluation and selection. By adopting the AHP method, pairwise comparisons are made between perspectives and between criteria in sub-levels. The perspective weights and criteria weights are calculated based on the comparison matrices. This process corresponds to rectangle 2.1. Pairwise comparisons between projects in terms of each criterion are also performed at the same time (rectangle 2.2). Project weights in terms of each criterion are calculated based on the comparison matrices. Let Equation 1


Figure 1: The hierarchical structure of project selection criteria.


Figure 2: The project selection process model.

Wv = {W1v, W2v,…, Wuv} (Equation 1)

denote the priority weightings of project V in terms of u criteria. The overall score of the project is calculated by Equation 2:


With Wci denoting the global weighting of the ith criteria. In this way, each decision maker produces a final project score. Then the decision makers are supposed to talk about their own ranking results and discuss the differences that may change the selection results. The project weights in terms of each criterion also serve as evidence for each decision maker to explain his or her preference. The group of decision makers makes the final decision on project selection after the discussion. For organizations with multiple projects to be selected and implemented, the project selection is just the start of the sustainable management process. The post-project evaluation could test the fitness of the evaluation criteria and help to adjust the evaluation metric. Decision makers also need to note that the selection of perspectives and criteria is flexible in dealing with different types of projects and different organizational development needs.

In order to seek insight from industry practitioners and validate the proposed project evaluation metric, we conducted a focus group study. The proposed project selection model is tested through an empirical study and details are provided in the following sections.

Focus Group Study

In order to understand the perceptions of industrial practitioners on project knowledge contribution, a focus group study was conducted. A small group of participants, consisting of representatives from different departments in an organization, took part in the focus group study. We selected the focus group study participants based on these four principles:

1. The participants should come from the same industrial sector.

2. The participants are from different functional groups or departments in the industrial sector.

3. The expertise areas of the participants can be related to some important stakeholder roles in the most project selection scenarios.

4. The participants should have adequate experience and knowledge in order to judge the features and potential value of projects.

Four participants from a high-tech electronic manufacturing company and one expert in the organizational learning field were invited to join our study. The four participants are experts in different functional areas, including quality engineering, human capital development, industrial engineering, and senior management. For a knowledge-intensive organization or organization with long-term learning goals, it is normal practice to invite an external expert in organizational learning as a management consultant to assist in various knowledge management activities. For this, we invited an expert in organizational learning to participate in our study. The group of participants was then asked to act as a decision-making group with a shared understanding of organizational objectives. According to the expertise of the five participants, we assigned different stakeholder roles for each participant. Participants were asked to serve in the stakeholder role and provide judgment. Since our participants have rich experience in the manufacturing industry, the stakeholder roles are some of the most typical functional roles in the manufacturing industry. The stakeholder roles are “quality manager,” “human capital manager,” “industrial engineering manager,” “senior manager,” and “organizational learning consultant.” Table 2 shows the assignment of stakeholder roles to participants and the expected expertise of each role.

A valid project evaluation metric for project selection should be aligned with the organization's strategies and objectives and be capable of dealing with different project selection scenarios. The validity of any evaluation metric is dependent on its fitness with the organizational management philosophy. In the focus group study, we asked decision makers to take the project knowledge contribution in the pairwise comparison between perspectives into consideration.

Stakeholder Role in This Study Job Function of Participant Expertise Area
Quality manager Quality engineering Quality management, quality control, quality assurance
Human capital manager Human capital development Human resource management, employee development
Industrial engineering manager Industrial engineering Product engineering, product transfer, production line improvement
Senior manager Senior management Company strategy development and implementation, customer relationship management
Organizational learning consultant Organizational learning consultant Individual learning, project-based learning, organizational learning
Table 2: Stakeholder roles assignment.

The focus group study was conducted using the following steps:

1. Introducing the background of this research;

2. Assigning stakeholder roles to participants;

3. Explaining the hierarchical project evaluation metric (see Figure 1), the project selection process model, and the AHP method; and

4. Asking participants to perform pairwise comparisons of the seven perspectives.

The participants were given adequate time in each step to understand their roles and provide input. After the above steps, the pairwise comparison results and participants' comments were collected. The results and discussion are presented in the next section.

Results from the Focus Group Study

Weightings of the perspectives were computed based on the pairwise comparison results of the five stakeholders. The seven perspectives are: economic, technology, operation, knowledge accumulation, strategy, customer and partner, and resource. Table 3 shows the weightings of perspectives provided by each stakeholder. The average weighting of all the inputs was 0.143, and weightings that were significantly larger than the average are boldface.

We assume that the higher weighting of a perspective provided by a stakeholder, the more important the perspective is among the seven perspectives. The boldfaced weightings in Table 10 show that the focus areas of the stakeholders are distributed among different perspectives. This is in line with our earlier statement that decision makers from different function areas tend to emphasize different project perspectives.

As shown in Table 3, the knowledge contribution perspective received the highest weighting (0.282) from the human capital manager and the second highest weighting (0.191) from the industrial engineering manager. The strategy perspective received the highest score from the quality manager (0.524) and organizational learning consultant (0.413), which agreed with the claim that project selection criteria should be aligned with organizational strategies and policies. The average weights of the perspectives also highlight the dominant importance of strategy, technology, and customer and partner. This does not mean, however, that knowledge contribution does not influence the decision-making results, as it receives relatively the same weight as resource and operation. The high weight given by the human capital manager implies that project-based learning can assist with employee development. The high weighting provided by the industrial engineering manager implies that the project knowledge is considered to be a source of expertise for engineers. From these results, we also found that the technology perspective received similar weight as the knowledge accumulation perspective. An interpretation of this result is that technology competency as the advantage of a company is inseparable with its emphasis on organizational learning and knowledge management. While the “knowledge contribution” was ranked fifth in the overall score and does not play a dominant role compared with “strategy” in the environment of a manufacturing company, we believe this is determined by the specific situation. The focus study results have shown that the “knowledge contribution” perspective needs to be included in the project selection consideration.

Empirical Study with Two PAL Projects

In this section, the proposed evaluation metric and project selection model were tested in an electronic component manufacturing company in southern China. We selected case projects from this company because it started the journey to becoming a learning organization more than a decade ago. Project-based action learning, developed by Law and Chuah (2004), has been implemented in the case company. The project-based action learning framework requires that each project-based action learning team have a project and pre-set learning goals with the organization and be committed to providing the necessary organizational learning infrastructure, guidance, and facilitation (Law, 2007; Law & Chuah, 2015). The project-based action learning framework implementation proceeds in a wave-like manner. In each round of PAL implementation, projects are proposed in order to address a wide variety of problems from different parts of the entire organization.

Quality Manager Human Capital Manager Industrial Engineering Manager Senior Manager Organizational Learning Consultant Overall Average
Economic 0.099 0.041 0.015 0.016 0.126 0.059
Technology 0.042 0.260 0.466 0.054 0.059 0.176
Operation 0.091 0.269 0.145 0.030 0.139 0.135
Knowledge contribution 0.033 0.282 0.191 0.095 0.030 0.126
Strategy 0.524 0.075 0.036 0.147 0.413 0.239
Customer and partner 0.141 0.037 0.036 0.372 0.108 0.139
Resource 0.071 0.034 0.112 0.285 0.126 0.126
Table 3: Weightings of perspectives provided by each stakeholder.

We used two projects proposed for PAL implementation to demonstrate the proposed model for project selection. This will provide guidance for future implementation of the proposed model. We named the two projects ‘project A’ and ‘project B.’ Project A was proposed to reduce production material cost and improve the testing capacity for a specific product. Project B was proposed to reduce the scrap rate of another specific product. The two projects are of similar scale, both focusing on production process; there is no obvious advantage of one project over the other by simple comparison and judgment. The steps for using the proposed model to compare the two projects from multiple perspectives are described as follows:

Step 1. Perspective and criteria selection (1.1)

These two projects are both focused on the production process of specific products, with one attempt to reduce the production cost and the other attempt to improve production quality. The implementation of each project is relevant to all seven perspectives listed in Table 1; therefore, we use all seven perspectives in the illustration. The granularity of evaluation or the specificity of the criteria to be used is determined by the project scale and availability of project information. There is only one layer of criteria for most perspectives except for the knowledge contribution. Since the two projects are proposed for PAL implementation, the learning goals and skills domain are already planned in the proposal. This provides adequate information for evaluation in terms of the knowledge contribution; therefore, we adopted both criteria and subcriteria of the knowledge contribution perspective.

Step 2. Identify stakeholder groups (1.2)

The selection of PAL projects in the case company is determined by a senior manager who led the PAL-based OL implementation. The senior manager possesses expertise in both operation processes and PAL implementation. With solid knowledge of the organization's development needs, resource constraints, and project goals, the senior manager will supervise the formation of project teams and act as PAL facilitator in the project process.

Step 3. Pairwise comparison (2.1, 2.2)

To elicit preferences of perspectives and criteria, the decision maker needs to make a series of pairwise comparisons. In AHP, pairwise comparisons of the elements in each level are conducted with respect to their relative importance toward their upper level criteria or control criteria. Saaty (1990) suggested a ratio scale of 1 to 9, with a score of 1 representing the indifference between the two elements and 9 representing the overwhelming dominance of one element over the other element. The reverse comparison score between the two elements is the reciprocal value. In our example, we used a set of comparison matrices to record the input of the decision maker.

The decision maker also needs to compare the two projects with respect to criteria at the lowest level or the most specific level in the hierarchy shown in Figure 1. The project team composition, project task schedule, and project goal descriptions were used as references for the judgment of decision makers.

Step 4. Criteria priority weighting (oval-f)

There are 11 comparison matrices for criteria and perspectives: one for the perspectives with respect to the overall evaluation goal; seven for the first-level criteria with respect to their own perspective of immediate upper level; three for the second-level criteria with respect to criteria of their immediate upper level: individual learning, organizational level learning, and organizational culture change. Three examples of the pairwise comparison matrices (by the engineering department manager) are shown in Tables 4, 5, and 6.

Economic Technology Operation Knowledge Contribution Strategy Customer and Partner Resource Priorities
Economic 1 1/9 1/7 1/7 1/7 1/7 1/7 0.016
Technology 9 1 7 1/5 1/7 1/7 1/7 0.054
Operation 7 1/7 1 1/7 1/7 1/5 1/7 0.030
Knowledge contribution 7 5 7 1 1/4 1/5 1/7 0.095
Strategy 7 7 7 4 1 1/7 1/5 0.147
Customer and partner 7 7 5 5 7 1 3 0.372
Resource 7 7 7 7 5 1/3 1 0.285
Table 4: Pairwise comparison matrix of perspectives with respect to the overall evaluation goal.
Individual Learning Organizational Level Learning Organizational Culture Change Priorities
Individual learning 1 2 2 0.493
Organizational level learning 1/2 1 2 0.311
Organizational culture change 1/2 1/2 1 0.196
Table 5: Pairwise comparison matrix of criteria with respect to the knowledge contribution.

In Table 1, each of the perspectives listed in the left column are compared with the rest perspectives in the column separately, based on which one is more important with respect to the goal of overall project evaluation. The priority for each perspective is obtained by dividing the geometric mean of its row by the sum of the geometric means of all the rows. The criteria priorities listed in Table 6 are weighted by the priority of their parent criteria (organizational level learning) and the priority of the parent perspective of their parent criteria (knowledge contribution) to obtain their global priority.

Step 5. Project score calculation (3)

The two projects are also compared with respect to each criterion and 31 comparison matrices were created. An example of the comparison matrix is shown in Tables 7 and 8. The priorities for each matrix are obtained as those from the matrix of comparisons for the criteria.

The knowledge contribution perspective has two levels of criteria. The project priorities for the subcriteria (second-level criteria) are combined with the weights of criteria (first-level criteria) to obtain the project priorities for the three first-level criteria. Table 9 presents the project priorities for the subcriteria and the obtained project weights for the three criteria.

The project with the largest total score should be selected. In Table 10, there are 19 criteria for all the perspectives and the weights of the projects for the criteria are provided in the third column.

Step 6. Make project selection decision (4)

As shown in Table 10, the difference between project A and project B is 0.05. The score of project A is 10.5% (0.05/0.475 = 10.5%) higher than score of project B. This result points to the selection of project A.

The case problem helps to illustrate the steps in the project selection process model. In order to evaluate the merit of adopting the knowledge contribution perspective in the model, the researcher recalculated the project priorities using the pairwise comparison results of only the other six perspectives. The comparison ratios between the other six perspectives and project comparison results with respect to the criteria of the six perspectives remained the same. Table 11 presents the project priority scores.

According to the results in Table 11, project A has a slight advantage (0.033) over project B. However, the difference is reduced to 6.8% (0.033/0.484 = 6.8%). Decision makers will hesitate to choose between the two options as their evaluation performances are too close to each other. Therefore, adding the knowledge contribution perspective provides the decision makers with a broader view of the project value obtained.

This study sets out to integrate knowledge management with the project selection process. The key findings include:

1. An evaluation metric for project contribution to organizational knowledge development. Results of the focus group study have validated the proposed evaluation metric and shown that the “knowledge contribution” perspective should be included in the multicriteria decision-making process of project selection.

2. A project selection process model for the implementation of the hierarchical project selection criteria. Results of empirical implementation have demonstrated the process model and confirmed that including the knowledge contribution perspective can effectively assist with decision makers in prioritizing the projects that best fit with an organization's development needs.

Organizational Knowledge Creation Knowledge Sharing and Diffusion Specificity of Project Learning Goal Knowledge Transferability Priorities
Organizational knowledge creation 1 3 1 1/2 0.243
Knowledge sharing and diffusion 1/3 1 1/2 1/4 0.099
Specificity of project learning goal 1 2 1 1/2 0.219
Knowledge transferability 2 4 2 1 0.439
Table 6: Pairwise comparison matrix of criteria with respect to organizational level learning.
Project A Project B Priorities
Project A 1 1/2 0.333
Project B 2 1 0.667
Table 7: Pairwise comparison matrix for the projects with respect to quality.
Project A Project B Priorities
Project A 1 2 0.667
Project B 1/2 1 0.333
Table 8: Pairwise comparison matrix for the projects with respect to specificity of the project learning goal.


Project selection is an important decision-making process for organizations. Project selection as a multicriteria decision-making process needs to balance between multiple perspectives to prioritize the projects that create the most value for the organization. This article presents an approach to integrating the knowledge perspective with the project selection process in order to take advantage of project-based learning to enhance the organization's knowledge competency. Based on several knowledge management models (Nonaka, 1994; Watkins & Marsick, 1993, 1996; Botha et al., 2014, we identify the elements of project knowledge contribution and created an evaluation metric for project contribution to organizational knowledge. This expands the literature on project selection and knowledge governance in organizations that rely on projects to operate and develop (Bresnen et al., 2003; Grabher, 2004; Pemsel et al., 2014). The results of the focus group study and empirical study confirm the advantage of using a multicriteria decision-making approach for project selection. This study also provides important guidance to industrial practitioners in integrating project-based learning considerations in the decision-making process.

Although the evaluation metric and project selection model showed promising results, there are limitations to this study. For organizations that have dominant evaluation criteria—such as projects required to conform to the environmental protection policy—the multicriteria decision-making method is not applicable. Depending on the specific implementation settings, the constructs of the proposed evaluation metric should be validated first by stakeholders prior to implementation. The use of the feedback loop for testing the evaluation metric and making adjustments could be demonstrated in future research with a longer term of observation.


We wish to wholeheartedly thank the case company and focus group participants for their collaboration and support. We respect their wish to remain unnamed.

Criteria Subcriteria Weight Project A Weight Project B Weight Project A Score Project B Score
Individual learning Sympathized knowledge 0.250 0.750 0.250 0.604 0.396
Conceptual knowledge 0.250 0.500 0.500
Systemic knowledge 0.250 0.500 0.500
Operational knowledge 0.250 0.667 0.333
Organizational level learning Organizational knowledge creation 0.243 0.333 0.667 0.606 0.394
Knowledge sharing and dissemination 0.099 0.500 0.500
Specificity of project-learning goals 0.219 0.667 0.333
Knowledge transferability 0.439 0.750 0.250
Organizational culture change Continuous learning 0.321 0.500 0.500 0.547 0.453
Inquiry and dialog 0.108 0.667 0.333
Team learning 0.241 0.500 0.500
Embedded system 0.041 0.500 0.500
System connection 0.173 0.667 0.333
Empowerment 0.049 0.500 0.500
Provide leadership 0.067 0.500 0.500
Table 9: The project priorities with respect to the subcriteria and criteria of knowledge contribution.
Weights Criteria Weight Project A Weight Project B Weight Project A Scores Project B Scores
Economic 0.016 Sales increase 0.800 0.667 0.333 0.008 0.004
0.016 Cost reduction 0.200 0.750 0.250 0.002 0.001
Technology 0.054 Improvement 0.333 0.667 0.333 0.012 0.006
0.054 Adoption of new technology 0.667 0.333 0.667 0.012 0.024
Operation 0.030 Process efficiency 0.167 0.500 0.500 0.003 0.003
0.030 Process effectiveness 0.167 0.333 0.667 0.002 0.003
0.030 Quality 0.667 0.333 0.667 0.007 0.013
Knowledge contribution 0.095 Individual learning 0.493 0.604 0.396 0.028 0.018
0.095 Organizational level learning 0.311 0.606 0.394 0.018 0.012
0.095 Organizational culture change 0.196 0.547 0.453 0.010 0.008
Strategy 0.147 Strategic importance 0.750 0.500 0.500 0.055 0.055
0.147 Benefit measurability 0.250 0.500 0.500 0.018 0.018
Customer and partner 0.372 Internal customer 0.152 0.333 0.667 0.019 0.038
0.372 External customer 0.476 0.667 0.333 0.118 0.059
0.372 Employee 0.116 0.500 0.500 0.022 0.022
0.372 Supplier 0.256 0.500 0.500 0.048 0.048
Resource 0.285 Estimated duration 0.200 0.500 0.500 0.029 0.029
0.285 Fund requirement 0.600 0.500 0.500 0.086 0.086
0.285 Personnel 0.200 0.500 0.500 0.029 0.029
Total scores 0.525 0.475
Table 10: Project weight with respect to criteria and project scores.
Weights Criteria Weight Project A Weight Project B Weight Project A Scores Project B Scores
Economic 0.019 Sales increase 0.800 0.667 0.333 0.010 0.005
0.019 Cost reduction 0.200 0.750 0.250 0.003 0.001
Technology 0.077 Improvement 0.333 0.667 0.333 0.017 0.009
0.077 Adoption of new technology 0.667 0.333 0.667 0.017 0.034
Operation 0.041 Process efficiency 0.167 0.500 0.500 0.003 0.003
0.041 Process effectiveness 0.167 0.333 0.667 0.002 0.005
0.041 Quality 0.667 0.333 0.667 0.009 0.018
Strategy 0.149 Strategic importance 0.750 0.500 0.500 0.056 0.056
0.149 Benefit measurability 0.250 0.500 0.500 0.019 0.019
Customer and partner 0.422 Internal customer 0.152 0.333 0.667 0.021 0.043
0.422 External customer 0.476 0.667 0.333 0.134 0.067
0.422 Employee 0.116 0.500 0.500 0.024 0.024
0.422 Supplier 0.256 0.500 0.500 0.054 0.054
Resource 0.293 Estimated duration 0.200 0.500 0.500 0.029 0.029
0.293 Fund requirement 0.600 0.500 0.500 0.088 0.088
0.293 Personnel 0.200 0.500 0.500 0.029 0.029
Total scores 0.517 0.484
Table 11: Project scores after removing the knowledge contribution perspective.


Ahern, T., Leavy, B., & Byrne, P. J. (2014). Knowledge formation and learning in the management of projects: A problem solving perspective. International Journal of Project Management, 32(8), 1423–1431.

Amiri, M. P. (2010). Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(9), 6218–6224. doi:10.1016/j.eswa.2010.02.103

Arenius, M., Artto, K., Lahti, M., & Meklin, J. (2002). Project companies and the multi-project paradigm: A new management approach. The Frontiers of Project Management Research, Project Management Institute, EUA, 289–307.

Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169. doi:10.1006/obhd.2000.2893

Baker, B., & Menon, R. (1995). Politics and project performance: The fourth dimension of project management. PM Network, 9, 16–21.

Barron, B. J., Schwartz, D. L., Vye, N. J., Moore, A., Petrosino, A., Zech, L., & Bransford, J. D. (1998). Doing with understanding: Lessons from research on problem-and project-based learning. Journal of the Learning Sciences, 7(3–4), 271–311.

Botha, A., Kourie, D., & Snyman, R. (2014). Coping with continuous change in the business environment: Knowledge management and knowledge management technology. Amsterdam, The Netherlands: Elsevier.

Bitman, W. R., & Sharif, N. (2008). A conceptual framework for ranking R&D Projects. IEEE Transactions on Engineering Management, 55(2), 267–278. doi:10.1109/tem.2008.919725

Brenner, M. S. (1994). Practical R&D project prioritization. Research-Technology Management, 37(5), 38–42.

Bresnen, M., Edelman, L., Newell, S., Scarbrough, H., & Swan, J. (2003). Social practices and the management of knowledge in project environments. International Journal of Project Management, 21(3), 157–166.

Buchanan, J. T., Henig, E. J., & Henig, M. I. (1998). Objectivity and subjectivity in the decision making process. Annals of Operations Research, 80, 333–345.

Bukowitz, W. R., & Williams, R. L. (1999). The knowledge management field book. Upper Saddle River, NJ: Financial Times, Prentice Hall.

Cho, K. T. (2003). Multicriteria decision methods: An attempt to evaluate and unify. Mathematical & Computer Modelling, 37(9–10), 1099–1119.

Chuah, K. B., & Law, K. M. Y. (2006). PAL in action: A company's OL experience. Team Performance Management, 12(1/2), 55–60.

Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Brighton, MA: Harvard Business School Press.

Dimovski, V., & Reimann, B. C. (1994). Organizational learning and competitive advantage: A theoretical and empirical analysis (Doctoral dissertation, Cleveland State University).

Eilat, H., Golany, B., & Shtub, A. (2006). Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology. European Journal of Operational Research, 172(3), 1018–1039.

Ellinger, A. D., Ellinger, A. E., Yang, B., & Howton, S. W. (2002). The relationship between the learning organization concept and firms' financial performance: An empirical assessment. Human Resource Development Quarterly, 13(1), 5–21.

Evans, M., Dalkir, K., & Bidian, C. (2014). A holistic view of the knowledge life cycle: The Knowledge Management Cycle (KMC) model. The Electronic Journal of Knowledge Management, 12(2), 85–97.

Gamble, P. R., & Blackwell, J. (2001). Knowledge management: A state of the art guide. London, England: Kogan Page.

Ghosh, S., & Jintanapakanont, J. (2004). Identifying and assessing the critical risk factors in an underground rail project in Thailand: A factor analysis approach. International Journal of Project Management, 22(8), 633–643.

Grabher, G. (2004). Temporary architectures of learning: Knowledge governance in project ecologies. Organization Studies, 25(9), 1491–1491.

Henriksen, A. D., & Traynor, A. J. (1999). A practical R&D project-selection scoring tool. IEEE Transactions on Engineering Management, 46(2), 158–170.

Hsu, K. H. (2005). Using balanced scorecard and fuzzy data envelopment analysis for multinational R&D project performance assessment. Journal of American Academy of Business, 7(1), 189–196.

Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives. Cambridge, UK: Cambridge University Press.

Keyton, J. (2005). Communication and organizational culture: A key to understanding work experiences. Thousand Oaks, CA: Sage Publications.

Law, K. M., & Chuah, K. (2004). Project-based action learning as learning approach in learning organisation: The theory and framework. Team Performance Management: An International Journal, 10(7/8), 178–186. doi:10.1108/13527590410569904

Law, K. M. Y. (2007). The development and implementation of Project Action Learning framework (PAL): From theory to practice (Doctoral dissertation, City University of Hong Kong).

Law, K. M., & Chuah, K. B. (2015). Organizational learning as a continuous process, DELO. In PAL driven organizational learning: Theory and practices (pp. 7–29). Cham, Switzerland: Springer International Publishing.

Lin, C., & Hsieh, P. (2004). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38(3), 383–398. doi:10.1016/s0167-9236(03)00118-0

Linstone, H. A. (1999). Decision making for technology executives: Using multiple perspectives to improved performance. Boston, MA: Artech House.

Magrassi, P. (2002). A taxonomy of intellectual capital, Wikimedia Foundation, Inc.

Meredith, J. R., & Mantel, S. J. (2006). Project management: A managerial approach. Hoboken, NJ: John Wiley.

Mikkola, J. H. (2001). Portfolio management of R&D projects: Implications for innovation management. Technovation, 21(7), 423–435.

Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: An architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143–161. doi:10.1016/s0167-9236(01)00141-5

Newman, J. W. (1971). Management applications of decision theory. New York, NY: Harper and Row.

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37.

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford, UK: Oxford University Press.

Oral, M., Kettani, O., & Lang, P. (1991). A methodology for collective evaluation and selection of industrial R&D projects. Management Science, 37(7), 871–885.

Osawa, Y., & Murakami, M. (2002). Development and application of a new methodology of evaluating industrial R&D projects. R&D Management, 32(1), 79–85.

Pemsel, S., Wiewiora, A., Müller, R., Aubry, M., & Brown, K. (2014). A conceptualization of knowledge governance in project-based organizations. International Journal of Project Management, 32(8), 1411–1422.

Reisinger, H., Cravens, K. S., & Tell, N. (2003). Prioritizing performance measures within the balanced scorecard framework. Mir Management International Review, 43(4), 429–437.

Rose, K. H. (2013). A guide to the project management body of knowledge (PMBOK® guide) – Fifth edition. Project Management Journal, 44(3) (book review).

Roy, B., & Vincke, P. (1981). Multicriteria analysis: Survey and new directions. European Journal of Operational Research, 8(3), 207–218. doi:10.1016/0377-2217(81)90168-5

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary? European Journal of Operational Research, 145(1), 85–91.

Scarbrough, H., Swan, J., Laurent, S., Bresnen, M., Edelman, L., & Newell, S. (2004). Project-based learning and the role of learning boundaries. Organization Studies, 25(9), 1579–1600.

Schindler, M., & Eppler, M. J. (2003). Harvesting project knowledge: A review of project learning methods and success factors. International Journal of Project Management, 21(3), 219–228. doi:10.1016/s0263-7863(02)00096-0

Senge, P. M. (1990). The fifth discipline: The art and practice of learning organization. Performance + Instruction, 30(5), 58.

Simon, H. A. (1997). Models of bounded rationality: Empirically grounded reasons. Cambridge, MA; London, England: MIT Press.

Sullivan, P. H. (2000). Value driven intellectual capital: How to convert intangible corporate assets into market value. Hoboken, NJ: John Wiley & Sons, Inc.

Swanson, S. A. (2011). All things considered It's time for executives to break out of the ROI stranglehold and look beyond the bottom line when picking projects. PM Network, 25(2), 36.

Thomas, J. W. (2010). A review of research on project-based learning, 2000. San Rafael, CA: The Autodesk Foundation:

Vargas, L. G. (1990). An overview of the analytic hierarchy process and its applications. European Journal of Operational Research, 48(1), 2–8. doi:10.1016/0377-2217(90)90056-h

Vessey, I. (1991). Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219–240.

Von Krogh, G., Roos, J., & Kleine, D. (Eds.). (1998). Knowing in firms: Understanding, managing and measuring knowledge. Thousand Oaks, CA: Sage.

Watkins, K. E., & Marsick, V. J. (1993). Sculpting the learning organization: Lessons in the art and science of systemic change. San Francisco, CA: Jossey-Bass.

Watkins, K. E., & Marsick, V. J. (Eds.). (2003). Make learning count! Diagnosing the learning culture in organizations. Advances in Developing Human Resources, 5(2).

Yang, B. (2003). Identifying valid and reliable measures for dimensions of a learning culture. Advances in Developing Human Resources, 5(2), 152–162.

Shuang Geng received a BS degree in Engineering from the City University of Hong Kong, Hong Kong SAR, P. R. China, in 2013. She is a Research Assistant in the Management Science Department, Shen Zhen University, China and a PhD student in the Systems Engineering and Engineering Department, City University of Hong Kong. Her research interests include project management, knowledge discovery, and recommender system. Her work has appeared in The Learning Organization and Knowledge Management: An International Journal. She can be contacted at or

Dr. Kong Bieng Chuah is an Associate Professor in the Systems Engineering and Engineering Department, City University of Hong Kong. He is a core faculty member in the Engineering Doctorate, MSc Engineering Management, and BEng Industrial Engineering and Engineering Management programs. His current academic interests center on project management and project-based organizational learning. He is a consultant in project management and organizational learning and regularly conducts project management courses and workshops in Hong Kong and China's industrial organizations. In addition, Dr. Chuah is a mechanical engineer with expertise in engineering metrology and surface roughness characterization and advises on engineering measurement problems and calibration setups. He can be contacted at

Dr. Kris M. Y. Law is currently a lecturer at the Hong Kong Polytechnic University, visiting professor in different universities around the world, and she received her PhD from the City University of Hong Kong. She has published several books and articles in impactful international journals, including Industrial Management & Data Systems, Journal of Engineering and Technology Management, International Journal of Production Economics, Computer and Education, IEEE in Education, and International Journal of Production Research. Her current research interests are in the areas of organizational learning and development, technology innovation management, entrepreneurship, and education analytics. She can be contacted at kris.

Mr. Che Keung Cheung is currently working toward his Engineering Doctorate (EngD) degree in the Department of Systems Engineering and Engineering Management at the City University of Hong Kong. His research focuses on organizational learning at the shop-floor level among operators and workers in manufacturing companies by using a project-based action learning (PAL) strategy. He can be contacted at

Dr. Y. C. Chau graduated from Hong Kong Polytechnic University Mechanical Engineering, received his MBA degree from the University of South Australia, and his PhD from the Systems Engineering and Engineering Management Department, City University of Hong Kong. He has been working in a high-technology global company since 1988 and has solid experience in managing high technology product operations, especially in the areas of productivity and quality improvement. He has held lecturing positions at the City University of Hong Kong and Hong Kong Polytechnic University. His research interests include organizational learning and learning organization development and he has solid experience in implementing organizational learning and learning organization strategy and turning them into reality; he is also conducting research in Industry 4.0 Implementation. Dr. Chau is a member of the Hong Kong Institute of Engineers (HKIE), HK SAR; a senior member of the Institute of Industrial and System Engineers (IISE), USA; a member of the Institute of Engineering and Technology (IET), UK; a Chartered Engineer (CEng), UK EC, and he is also a certified First Class Corporate Trainer in the People's Republic of China. He can be contacted at

Dr. Cao Rui is the L&D Assistant Manager of a leading global education company. He is in charge of staff learning and development programs and projects in the China region, including the mainland, Hong Kong, and Macau. His research interests and publications have focused on the implementation and facilitation of organizational learning vehicles to achieve measurable organizational outcomes. He can be contacted at

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