Knowledge Contribution as a Factor in Project Selection

Geng, Shuang; Chuah, Kong Bieng; Law, Kris M. Y.; Cheung, Carmencita; Chau, Y. C.; Cao, Rui

doi:10.1177/875697281804900103

Cited by 11 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is a major difference between innovative projects and other types of projects. According to research by Shuang and his colleagues [ 42 ], knowledge is the major perspective of product development project selection. Other perspectives include economics, technology, operation, strategy, customer, partner, and resources.…”

Section: Methodsmentioning

confidence: 99%

Bridging the knowledge gap between technology and business: An innovation strategy perspective

2022

View full text Add to dashboard Cite

Decision-makers (DMs) are not sufficiently exposed to concepts such as efficiency and risk in innovative activities from the perspective of organizational strategy. The challenges become even greater when these DMs lack expertise in technology and deal with uncertain circumstances. In this sense, exchanging expert knowledge between DMs and technical teams will strengthen the link between technology planning and strategic management. The purpose of this study is to bridge the knowledge gap between these two groups. It introduces a framework to translate the organization’s strategy into technological decisions at an acceptable innovation risk level. This framework considers aspects such as knowledge, type of innovation, and innovation process. This study focuses on determining whether activities should be accepted or rejected by examining the uncertainty and efficiency of innovation. It also introduces a novel perspective on the hybrid "success-failure" uncertainty of innovation, and a new measure called "efficiency probability," which DMs and technology developers can use to intuitively engage in the innovation process. This paper seeks to propose a practical strategy map for new product development under uncertain conditions. To achieve this goal, the Fuzzy Front-End (FFE) concept, fuzzy data envelopment analysis (FDEA) model, and adjustable possibilistic programming (APP) approach are applied. The results of this study indicate that innovative activities typically have low efficiency and high uncertainty. Therefore, the decision to implement or abandon them requires reviewing and balancing the goals and strategic approach of the organization with technological and business features.

show abstract

Section: Methodsmentioning

confidence: 99%

Bridging the knowledge gap between technology and business: An innovation strategy perspective

2022

View full text Add to dashboard Cite

show abstract

“…KM systems and processes in project management are widely represented in the recent works. S. Geng et al [3] proposed to develop a framework to integrate organizational knowledge development with project selection. D. Shrikant [4] examined the use of knowledge in society and explained how projects can benefit from it by the creating a rational order through storyboarding.…”

Section: Related Workmentioning

confidence: 99%

Model for Assessing the Level of Knowledge Convergence in Multinational Projects

Sharovara¹,

Dorosh²,

Trunova³

et al. 2022

IJC

View full text Add to dashboard Cite

In modern conditions, knowledge management acquires a new meaning and becomes one of the decisive factors for success in the project implementation. Knowledge transfer is significantly complicated in international projects. This requires an in-depth analysis of different participants` project management systems, identifying their differences and determining the ability to converge (convergence) through knowledge transfer. The paper proposes the model for assessing the convergence level of project management systems, which includes a fuzzy assessment of the factors influencing the ability of the system to transfer knowledge, as well as assessing the rate of convergence (approximation) in projects. The results of the study shows that the proposed methods allow identifying “bottlenecks” of knowledge transfer processes in multinational projects and determining a strategy to increase the level of knowledge systems convergence at the project initial stage. Evaluation of the accuracy and reliability of the proposed methods prove the adequacy of their applications for forecasting new project convergence level.

show abstract

“…The specification of the knowledge contribution results from the knowledge taxonomy used in a company, and it can be considered from the following perspectives: (i) individual learning, (ii) organizational level learning, and (iii) organizational culture change (Botha et al, 2014;Geng et al, 2018). The first perspective is related to knowledge and skills of team members who are involved in meeting project objectives.…”

Section: Acquiring Knowledge Related To New Product Developmentmentioning

confidence: 99%

“…Challenging requirements occurring in a project can provide motivation for project-based learning by team members. According to the third perspective, projects significantly impact organizational culture change, as they require communication among employees within and across structural units of an organization (Geng et al, 2018).…”

Section: Acquiring Knowledge Related To New Product Developmentmentioning

confidence: 99%

The Analytic Hierarchy Process for New Product Screening Decisions

Relich

2022

ECKM

View full text Add to dashboard Cite

The selection of the most promising new product development projects is one of the most important decisions in a company. This decision affects the cost of failed projects, company’s profitability, and its survival. The selection of new product development projects with the greatest potential requires the evaluation criteria that reflect the adjustment of a new product to customer requirements, company’s strategy, manufacturing, and knowledge management issues. There are specified sub-criteria to identify the impact of a new product on a single area of the company’s activity, for example, on the knowledge management area. The criteria and sub-criteria are evaluated by managers, engineers (including knowledge engineers), and IT specialists, who work in departments such as research and development, sales and marketing, manufacturing, IT, and top management. Company professionals have the most updated information of the ongoing processes related to new product development. This paper is concerned with using the analytic hierarchy process (AHP) methodology to a new product screening problem, paying attention to the knowledge management perspective. So far, this perspective is neglected in the context of the decision problem of new product screening. This research develops the field of knowledge acquisition from experts towards selecting and evaluating criteria related to the potential of a new product. Knowledge acquisition refers to issues related to a new product, customer requirements, and uncertainties of project performance. Using criteria related to various areas of the company’s activity, the decision maker can identify factors significantly impacting performance of new product projects, and compare these projects with each other. Moreover, the AHP approach prioritizes criteria and sub-criteria, and as a result, it can identify areas of the company’s activity that could be improved.

show abstract

Knowledge Contribution as a Factor in Project Selection

Cited by 11 publications

References 38 publications

Bridging the knowledge gap between technology and business: An innovation strategy perspective

Bridging the knowledge gap between technology and business: An innovation strategy perspective

Model for Assessing the Level of Knowledge Convergence in Multinational Projects

The Analytic Hierarchy Process for New Product Screening Decisions

Contact Info

Product

Resources

About