The evolution of knowledge navigator model: The construction and application of KNM 2.0

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The Evolution of Knowledge Navigator Model: The Construction and Application of KNM 2.0 Ping Jung Hsieh , Chinho Lin , Shofang Chang PII: DOI: Reference:

S0957-4174(20)30035-X https://doi.org/10.1016/j.eswa.2020.113209 ESWA 113209

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Expert Systems With Applications

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14 June 2019 14 January 2020 15 January 2020

Please cite this article as: Ping Jung Hsieh , Chinho Lin , Shofang Chang , The Evolution of Knowledge Navigator Model: The Construction and Application of KNM 2.0, Expert Systems With Applications (2020), doi: https://doi.org/10.1016/j.eswa.2020.113209

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Highlights  Knowledge Navigator Model proposed in 2009 has been practically promoted in Taiwan. 

This study described the evolution of KNM to become KNM 2.0.



KNM 2.0 covers the contemporary topics such as big data and KM performance.



Qualitative and quantitative research methods were conducted to construct KNM 2.0.



The results of a 139 cases survey revealed the applicability of KNM 2.0.

1

The Evolution of Knowledge Navigator Model: The Construction and Application of KNM 2.0

Ping Jung Hsieh a,* Chinho Lin b Shofang Chang c

a Graduate Institute of Human Resource and Knowledge Management, National Kaohsiung Normal University, Kaohsiung, Taiwan. Email address: [email protected]; [email protected] b Department of Industrial and Information Management & Institute of Information Management, National Cheng Kung University, Tainan, Taiwan. Email address: [email protected] c Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan. Email: [email protected]

* Corresponding author: Ping Jung Hsieh

Abstract The knowledge management (KM) maturity model provides a framework against which both old and new KM initiatives can be assessed to determine whether they are capable of generating new knowledge. Knowledge Navigator Model (KNM) proposed in 2009 (Hsieh et al., 2009) has been promoted in Taiwan to assist organizations to evaluate their KM status, and also aided to be a diffusion platform for government, academic and practice to exchange their KM experience. However, for the past 10 years, the industrial environment has been constantly changing that has brought the developing of KM practice.

This study described the

evolution of KNM to become KNM 2.0 in terms of construction and application. 2

Qualitative and quantitative research methods were conducted to construct the proposed three modules of KNM 2.0.

A 139 cases survey was employed and the

results reveal the applicability of KNM 2.0. Our research contributes to the body of concepts on KM maturity models that could evaluate the readiness of service-oriented knowledge economy, big data and smart factory, and strategic KM performance.

The proposed KNM 2.0, with these

features which are novel and rapidly expanding fields, has been developed as an online Knowledge Management Evaluation (KM evaluation) website available for the practice to use to better understand their overall value brought by contemporary KM. Moving forward, by using KNM 2.0 to continually collect the data from the industries in Taiwan, it is expected to obtain more information about the practical KM that keeps developing with the trends, and also keep playing the role to distribute the contemporary concepts of KM. The KM experience could be referenced and the methodology could be applied to other countries. Keywords: KM maturity model, Knowledge Navigator Model, Big data, KM performance 1. Introduction Knowledge, in this era of knowledge economy, is recognized as a critical asset for organizations to gain competitive advantage and to maintain long-term success (Whelan & Carcary, 2011; Al Ariss et al., 2014; Akhavan et al., 2015; Beamond et al., 2016; Nissen, 2019).

At present, applying knowledge management (KM) to

management activities is quite common for many businesses (Nonaka, 1991; Hsieh et al., 2019).

The multifarious term 'Knowledge Management' is still widely used

throughout theory and practice.

Regardless of whether an organization is just getting

started, conducting the first implementations of KM pilot projects, or preparing to revitalize or leverage successful KM approaches and tools to other areas in the enterprise, it should have a road map with milestones and checkpoints to guide its efforts (Hubert & Lemons, 2010; Akhavan & Philsoophian, 2018). The KM maturity model provides a rubric against which both old and new KM initiatives can be assessed to determine whether they are capable of generating new knowledge (Arling & Chun, 2011). This model determines the appropriate starting point, steps for implementation, and resource requirements for driving long-term corporate development (Hsieh et al., 2009). 3

The related KM maturity models have been developed and also been applied in many research and practical fields (KPMG, 2000; Infosys, 2000; Tiwana, 2002; APQC, 2003; Siemens, 2004; Hsieh et al., 2009). Knowledge Navigator Model (KNM) was proposed in 2009 (Hsieh et al., 2009) that defined the KM maturity level into five stages:

knowledge chaotic stage (Level I), knowledge conscientious stage(Level II),

KM stage(Level III), KM advanced stage(Level IV), and KM integration stage(Level V).

The evaluation framework of KNM consists of three aspects: 3 target

management objects (TMOs), 68 KM activities, and 16 KM key areas (KAs).

KNM

had not yet considered the readiness of service-oriented knowledge economy, big data and smart factory, and strategic KM performance. Furthermore, there was only one version of questionnaire for all organizations, no matter their scope or operating in production or service industries. In order to investigate the KM implementing trend in Taiwan, the Knowledge Management Evaluation (KM evaluation) website (https://km.ekm.org.tw/KMPP2016/Web/VoteIntroduction.aspx) was built up based on the model of KNM by Taiwan IDB (Industrial Development Bureau, Ministry of Economic Affairs) to collect the KM implementation status from the industries in year 2008 and forward. (Table 1).

At the end of year 2017, this website has collected 1219 cases

The data in Table 1 shows that, from 2008 to 2017, it was getting more

and more companies that concern their development of KM, and would use this website to evaluate their KM maturity level and obtain the feedbacks on improvement. [Insert Table 1 about here] Furthermore, by using the promotion of KNM as a KM teaching material and evaluation tool, IDB had hold many events to distribute the concepts of KM and For example, IDB held “Alliance of Chief

introduce consulting source to industries.

Knowledge Officer (Alliance of CKO)” that started in 2011, until 2018, it gathers 168 CKO participants and hold 31 best practices visitings in well-known companies in Taiwan.

In addition, IDB has conducted 8 KM contests for academic and practice

to share their KM projects and compete for “The Best Knowledge Management Award”.

To accelerate the distribution of KM in industries, IDB has spent over USD

10 million on consulting and financial subsidy to many companies to implement KM. From these events and supportive sources, IDB could gather academic and practice to share and devote their knowledge, experience and effort in promoting KM in Taiwan. Therefore, the emergent of KNM in year 2008 had become a communication medium 4

to distribute the concepts of KM, to be a KM evaluation tool and the collective results being viewed as an index to realize the KM status in Taiwan, and also a diffusion platform for government, academic and practice to exchange their knowledge. However, for the past 10 years, the industrial environment has been constantly changing. In the field of KM,

as organizations become increasingly aware that

knowledge is among their most valuable strategic assets, they realize that the foundation of strategic success relies on the effective management of an organization's knowledge assets and for this to be successful there needs to be an effective way of assessing performance (Turner & Jackson-Cox, 2002). KM and particularly its performance measurement dimension has become the most important economic task for most organizations (Minonne & Turner, 2009; Zaim et al., 2019). In addition, increasing role of services in the economy has been recognized in many industries (Dreyer et al., 2019). Over the past two decades, service productivity and service innovation have evolved into crucial priorities within service research (Aspara1 et al., 2018).

The structure of most developed economies has shifted away

from manufacturing and towards service industries which cause service sector’s growing share of the GDP in most developed and developing economies (Wang et al., 2016; Desyllas et al., 2018).

Service firms seeking to survive in turbulent times

should focus upon adopting effective knowledge management systems (Chaston, 2012). With continued growth of the service-oriented knowledge economy, knowledge-intensive service industries have become a trend nowadays for industrial development. Enterprise activities such as service innovation and service value-added require the domain professional knowledge and experiences. Therefore, effective KM must be achieved to rapidly accumulate knowledge assets and increase the efficiency of knowledge-intensive service industries (Chen et al., 2012). Strongly rooted in the Internet of Things (IoT) and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the smart factory and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures (Ardito et al., 2019). The 4th industrial revolution will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency (Preuveneers & Ilie-Zudor, 2017; Fernandes et al., 2019). 5

Therefore, a KM maturity model that

could evaluate the readiness of service-oriented knowledge economy, smart factory, and strategic KM performance is necessary for organizations to use to understand their overall value brought by KM, but is missing in current academic organizations. This study described the evolution of KNM to become KNM 2.0 in terms of construction and application that pertains the considerations of the above-mentioned issues. Of all the East Asian Newly Industrialized Countries (NICs) Taiwan has one of the most inspiring stories. As a latecomer, Taiwan has built its advanced manufacturing and innovative capabilities on knowledge intensive industries such as semiconductors, software, and consumer electronics.

With respect to the

improvement of national competitiveness, starting at 2001, the Taiwanese government was aggressively fostering a knowledge-based economy for achieving sustainable development (Hsieh et al, 2009).

In response, Taiwanese companies have been

striving to introduce business initiatives such as KM to leverage and extract value from their intellectual or knowledge assets.

For the past 17 years, KM is evolving

into a strategically important area for many organizations, especially those hi-tech companies in Taiwan. Thus, it is worthwhile to observe the deployment and practice of KM in Taiwan, with the results of this observation serving as a significant reference for other countries. The paper proceeds as follows. The next section reviews prior literature with an emphasis on KM maturity models, big data and KM, and KM performance. Section 3 presents the research methodology and the proposed KNM 2.0. Section 4 demonstrates the applicability of the proposed KNM 2.0 with a 139 case survey. Section 5 has some conclusions remarks.

The last section makes discussion and

reveals managerial implications. 2. Research background 2.1.KM maturity model The process toward maturity is a continuous changing phenomenon that could be observed in an entity's life cycle.

The maturity model describes the evolution and

development of an entity which can be a living individual, a culture, an organization, or just a system or a new management initiative.

Each entity develops through a

series of stages over time until it reaches the highest level.

Maturity models in an

organization describe steps of growth, qualification or a nature of thing over time (Khatibian et al., 2010).

A KM maturity model provides a template against which 6

organizations can map their progress towards a mature KM environment(Arling & Chun, 2011).

A KM maturity model identifies the barriers which need to be

overcome, determines the resource requirements and makes reasonable adjustments for improving the next maturity level (Lin et al., 2012; Philsoophian et al., 2016; Akhavan & Philsoophian, 2018). KM maturity is the level of capabilities that impact the KM processes in an organization.

Level of maturity shows organization’s status

quo on KM. The well-known KM maturity models are KPMG’s “Knowledge Management Framework Assessment Exercise: Knowledge Journey” (KPMG, 2000); “KMM Model” (Infosys, 2000); Infosys’s Tiwana’s “The 10-Step KM Roadmap” (Tiwana, 2002); APQC’s “Road Map to Knowledge Management Results: Stages of Implementation” (APQC, 2003); Siemens’ “Knowledge Management Maturity Model (KMMM)” (Siemens, 2004); “Knowledge Navigator Model (KNM)” (Hsieh et al., 2009).

A comprehensive KM maturity model could be used (Hsieh et al., 2009)

 to assist in the clarification and promotion of KM concepts.  to assist the understanding of the current situation for companies implementing KM.

 from the perspective of industries, to assist in comprehending the KM implementation in the whole industries, and provide the best practice model. KM maturity models have been applied in many research and practical fields. Johansson et al., (Johansson et al., 2011) applied knowledge maturity as a means to support decision making during product-service systems development projects in the aerospace sector. Lin et al., (Lin et al., 2012) explored the barriers to knowledge flow at different knowledge management maturity stages. Capaldo et al., (Capaldo et al., 2017) studied the relationship between knowledge maturity and the scientific value of innovations.

Marques et al., (Marques et al., 2019) indicated a significant

relationship between the organizational commitment to the knowledge transfer and to the knowledge management maturity.

Many studies practically used KM maturity,

such as Kruger et al., (Kruger et al., 2010) interested in gaining insight into KM maturity that occurred in the extremely diversified environment of South Africa. Serenko et al., (Serenko et al., 2016) investigate the level of KM maturity of credit unions.

Centobelli et al., (Centobelli et al., 2017) used KM maturity models to

identify the stages of the process of adoption of KMSs for SMEs.

Based on the

concept of KM maturity model, some researchers have developed models such as 7

customer knowledge management maturity (CKMM) (Afrazeh et al., 2010) and knowledge sharing maturity model (Arif et al., 2017). 2.2.

Big data and KM

With increasing technological advancements, manufacturing intelligence has become a crucial issue for maintaining competitive advantages. Industry 4.0, first introduced in 2011 at the Hannover Messe trade fair in Germany, was the subject of an Industry 4.0 working group established by the German federal government and is one of the large-scale projects to achieve manufacturing intelligence and smart production (Sniderman et al., 2019). Others include the Advanced Manufacturing Partnership 2.0 (AMP2.0) from the United States, Industry 4.1J of Japan and Made in China 2025 (Chien et al., 2017), have made intelligent manufacturing as an orientation supported by their nations with priority (Xu & Hua, intelligent

2017).

information

Industry 4.0 can make a factory smart by applying

processing

approaches,

future-oriented techniques, and more (Yan et al., 2017).

communication

systems,

Under the background of

Industry 4.0 and cyber-physical systems, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data (Xu & Hua,

2017; Wang et al., 2018).

Industrial big data analytics is believed to be a

promising and indispensable enabler for the implementation of smart factories. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability (Yan et al., 2017).

Several

diversified technologies such as IoT, computational intelligence, machine type communication, big data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications (Mishra et al., 2015; Ardito et al., 2019). Orenga-Roglá and Chalmeta (Orenga-Roglá & Chalmeta, 2017) mentioned that KMS 2.0 are KMS that use Web 2.0 and big data technologies and are focused on facilitating collaboration in order to enhance knowledge (Shimazu and Koike 2007). KMS 2.0 are based on the participation of people, who generate new knowledge and are not limited to just consuming it, i.e. users are active contributors (Razmerita et al. 2009).

As data are generated from different sources and formats (video, text 8

document, audio, image, etc.), they are analyzed by using their corresponding machine learning algorithms. For instance, data coming from social media channels may be analyzed by using text mining, sentiment analysis, natural language processing (NLP), and so on, to manage and categories human information.

Other

supervised and unsupervised prediction models, such as decision trees, logistic regression, artificial neural networks, clustering, etc, are commonly used to analyze big data to support human decisions-making (Orenga-Roglá & Chalmeta, 2017). Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improves the decision-making ability (Sumbal et al., 2017). 2.3.KM performance As organizations become increasingly aware that knowledge is among their most valuable strategic assets, they will be forced to re-evaluate the way in which they engage with the source of that knowledge to underpin their sustainable development(Kim et al., 2014; Chen & Fong, 2015). This will create a fundamental change to established practice; a change that results in a paradigm shift from the traditional operational approach to a more strategic involvement in knowledge management (Minonne & Turner, 2009).

The foundation of strategic success relies

on the effective management of an organization's knowledge assets and for this to be successful there needs to be an effective way of assessing performance (Turner & Jackson-Cox, 2002; Chin et al., 2010). An effective evaluation of KM performance will allow managers to understand the deficiencies in their management practices, identify the key elements impacting organizational development, and provide theoretical evidences for continuous improvement (Lyu et al., 2016). In doing so, they should resist the temptation to focus only on what is easily measurable, which generally is the efficiency dimension of activities and costs (Pfeffer, 1997; Minonne & Turner, 2009). Rather, they should focus on measuring outcomes that meet real organizational needs such as innovation, technological development and employee attitudes, experience, learning, tenure and turnover, which are more likely to represent KM effectiveness rather than efficiency (Minonne & Turner, 2009). Although its importance is unanimously accepted by business practitioners and academic researchers, it lacks consensus on the standardized method to measure the performance of KM in organizations (Lyu et al., 2016). 9

Tiwana (2002) proposed a

10-Step KM Roadmap that demonstrated the linkage between business strategy and KM.

The KM performance index (KMPI), developed by Lee et al. (2005), includes

five components (knowledge creation, accumulation, sharing, utilization, and internalization) and three financial indicators (stock price, price earnings ratio, and research and development expenditure) to assess the efficiency of the knowledge circulation process within organizations. Incorporating the total quality management and business excellence models, Yin and Fai (2014) derived an integrated knowledge management (IKM) model to evaluate how KM performance contributes to organizational goals. Lee and Wong (2015) designed a survey instrument for KM performance measurement in small and medium companies.

Among many

approaches for KM performance evaluation, BSC appears to be a promising one as KM naturally fits in the learning and growth category of the BSC framework (Lyu et al., 2016).

Many studies demonstrated the applicability of BSC for KM performance

evaluation. (De Gooijer, 2000; Lin, 2015; Lyu et al., 2016). 3. Research methodology and the proposed KNM 2.0 The KNM 2.0 comprises three modules: evaluation, calculation and result modules.

The function of evaluation module is to collect users’ preferences, the

calculation module is to count the evaluation scores and obtain the KM maturity levels, while the result module is used to show the analysis results to users (Fig. 1). [Insert Fig.1 about here] 3.1. The evaluation module of KNM 2.0 3.1.1. Research methodology Qualitative research methods, including a literature review and focus groups, are used to construct the evaluation module of KNM 2.0. Three focus groups were hold, and total included eight KM scholars, six KM policy makers in government, ten KM consultants in consultant companies, and twenty-four CKO or senior managers of eighteen companies who are responsible for implementing KM in their enterprises. These ten consultants engage in KM consulting for an average of 9 years. They have assisted many international companies in promoting KM, including banks, electronic companies, semiconductor companies, and so on. The eighteen companies served by these twenty-four CKO or senior managers have implemented KM for an average of more than 8 years. Most of their parent companies are in Taiwan, but they have markets around the world.

Of these companies, six of them have won “The Best

Knowledge Management Award” in the past few years. 10

These participants of these

three focus groups are well qualified KM experts with fully experience in inspecting the KM development in Taiwan for several years.

The qualitative research to

construct the evaluation module for KNM 2.0 took 2 months to complete.

The

results conducted the following conclusion: (1)

KNM 2.0 could keep five maturity levels as KNM used, but the evaluation items (KM activities), and KM key areas (KAs) should be more concise.

The

proper numbers of the evaluation items (KM activities) is 38~40, and the proper numbers of KM key areas (KAs) is 9. (2)

The KM performance should be considered when organizations evaluate their KM maturity.

Therefore, KNM 2.0 should be extended to include four target

management objects (culture, process, technology, KM performance). (3)

KNM 2.0 should be able to evaluate the readiness of the application of big data and smart factory.

(4)

KNM 2.0 should be applicable to production/service industries and large/ small-medium companies.

3.1.2. The framework of the evaluation module From the research results of literature review and focus groups, the study propose that the framework of the evaluation module consists of: 5 maturity levels, 4 target management objects (TMOs), 38~40 KM activities, and 9 KM key areas (KAs). The 4 TMOs are culture, process, technology and KM performance which are based on the fine theories of previous studies and confirmed by focus group as key success factors in implementing KM. 2.

The contents of the 4 TMOs are shown in Fig.

The 38~40 KM activities are the practices performed directly or indirectly to

promote KM. Through field observations, these activities could be measured by users to obtain the frequency and extent of operations. Each KM activity belongs to a corresponding target management object, a maturity level, a key area, and these KM activities are the evaluation items in the KNM 2.0 system. A key area (KA) refers to a cluster of related KM activities that have the same management goal, but some of the KM activities belong to culture issues and some are issues related to process, technology or KM performance.

Since these KM

activities interact directly or indirectly with each other, when collectively implement, they could possibly results in synergy or offset. Hence, when an organization is to achieve a certain KA (as a management goal), the integrated performance results of these KM activities pertaining in this KA could be considered as the achievement 11

result of this KA. 

9 KAs are depicted as below (Table 2).

KA1_KM Strategy: A method by which an organization can comprehensively link knowledge resources and strategic needs.



KA2_KM Promotion: Units, regulations, and mechanisms that enhance and promote KM implementation.



KA3_Knowledge Sharing: Interpersonal interactions (such as discussion, debate, and joint problem solving), through which one unit (such as a person, group, department, organization) is affected by implicit and explicit knowledge of other units.



KA4_Data and Knowledge Acquire: A unit (such as a person, group, department, organization) obtains internal and external diverse information and knowledge through physical or virtual methods.



KA5_Knowledge Store: Store the outcomes, causal relationships, and exceptions observed in work execution and decision making activities.



KA6_Knowledge and Intelligent Application: Use big data or artificial intelligence concepts or tools to collect or analyze data and apply the results to business management decisions, problem solving, and product or process improvements.



KA7_Knowledge Creation and Innovation: Creation refers to the process of continuous self-transcendence, transcending old thinking into new visions, acquiring new contexts, new perspectives on the world, and new knowledge. Innovation refers to the creation, identification and implementation of new ideas, new processes, new products or services or the adoption of new ideas, practices or objects by relevant units.



KA8_Knowledge Protection: Protecting implicit and explicit knowledge from alteration, exploitation and deterioration.



KA9_Knowledge

Learning:

To

obtain

knowledge,

skill,

attitude,

or

comprehension to improve action. [Insert Fig.2 and Table 2 about here] 3.2. The calculation module of KNM 2.0 The calculation module is used to analyze the data collected from the evaluation module, and then obtain the maturity of culture, process, technology, and overall construct. To develop the algorithm of the calculation module, the focus groups were hold to decide the maturity level for each evaluation items, and their weights 12

contributing to the maturity of Culture, Process, Technology, and overall construct. Three steps were performed as below to construct the calculation module (Fig. 1). After that, a survey with 139 case companies was conducted, and the data were analyzed by using cluster analysis to obtain the score intervals to decide the maturity level. Step1. Count the sum of evaluation items for each target management object to obtain the sum of the weighted scores for maturity of Culture, Process, Technology, and overall construct The notations used in the calculation module are shown in Table 3. The focus groups decided that the maturity of Culture, Process, Technology, and overall construct should be evaluated based on the KM implementations and their KM performance.

Hence, the sum of the weighted scores for maturity of Culture,

Process, Technology, and overall construct are

∑𝑐𝑖𝑐 =1, 𝐶𝑖𝑐 + ∑𝑓𝑖𝑓 =1 𝐹(𝑐)𝑖𝑓

(Equ.1)

∑𝑝𝑖𝑝 =1, 𝑃𝑖𝑝 + ∑𝑓𝑖𝑓 =1 𝐹(𝑝)𝑖𝑓

(Equ.2)

∑𝑡𝑖𝑡 =1 𝑇𝑖𝑡 + ∑𝑓𝑖 =1 𝐹(𝑡)𝑖𝑓

(Equ.3)

𝑓

∑𝑐𝑖𝑐=1 𝐶(𝑜)𝑖 + ∑𝑝𝑖𝑝=1 𝑃(𝑜)𝑖 + ∑𝑡𝑖𝑡 =1 𝑇(𝑜)𝑖𝑡 + ∑𝑓𝑖𝑓 =1 𝐹(𝑜)𝑖𝑓 𝑐 𝑝

(Equ.4)

respectively. The notations used in Equ.1, 2, 3, 4 are shown in Table 4. The algorithms of Equ. 1,2,3,4 are similar.

Take 𝐶𝑖𝑐 in Equ. 1 as an example.

For the evaluation items in

the TMO_C (Culture), every item presents different importance to the maturity of Culture.

These importances were assigned by the focus group as “𝑚𝑐𝑖𝑐 , the maturity

level of 𝑖𝑐 th item in TMO_C” and “𝑤𝑐𝑖𝑐 , the weight of 𝑖𝑐 th item in TMO_C contributing to the maturity of Culture”.

Therefore, 𝐶𝑖𝑐 = (𝑠𝑐𝑖𝑐 × 𝑚𝑐𝑖𝑐 × 𝑤𝑐𝑖𝑐 ).

There are two weighted scores contributing to the maturity of Culture, one is 𝐶𝑖𝑐 , and the other is 𝐹(𝑐)𝑖𝑓 .

That is, the maturity of Culture is determined by the status of the

KM activities, and the performance or outcome of practicing the KM activities. Therefore, the sum of the weighted scores for maturity of Culture is ∑𝑐𝑖𝑐=1, 𝐶𝑖𝑐 + 13

∑𝑓𝑖 =1 𝐹(𝑐)𝑖𝑓 . 𝑓

[Insert Table 3 & 4 about here] Step 2.Compare the sum of the weighted scores for maturity of Culture, Process, Technology, and overall construct with the score intervals to decide the maturity level of Culture, Process, Technology, and overall construct After deriving the sum of the weighted scores for Culture, Process, Technology, and overall construct, the score intervals to decide the maturity level were obtained by using cluster analysis and followed by the focus groups to confirm.

Cluster analysis

is the task of grouping a set of objects in such a way that objects in the same group are as homogeneous as possible (with respect to the clustering variables) to each other than to those in other groups (Sharma, 1996).

There are two main types of analytical

clustering techniques: hierarchical and non-hierarchical.

In this study, KM maturity

is presented in five levels, thus non-hierarchical clustering is an appropriate method to be applied. Step3. Count the sum of evaluation items to obtain the weighted scores for 9 KAs Each KA consists of 1~8 KM activities (evaluations items), and each KM activity pertains to one of four TMOs: Culture, Process, Technology and KM performance. The weighted score for each KA is the total weighted scores of the knowledge activities included in this KA. Take knowledge sharing (KA3) as an example, there are six KM activities included as below. 

“C1.Members have knowledge sharing culture, and with the positive attitude.” belongs to TMO_Culture.



“P5.The regulations or processes to encourage knowledge sharing within organization.” “P6.The regulations or processes to share knowledge with external organizations.” “P18.The regulations or processes to encourage members to participate CoPs.”belongs to TMO_Process.



“T2.The related system for CoPs or collaboration work.” belongs to TMO_Technology.



“F6.Perform many knowledge sharing activities.” belongs to TMO_KM performance. Take (𝑠𝑐1 × 𝑚𝑐1 ) as an example (Equ. 5), for the evaluation items belonging to

the TMO_Culture in Knowledge share (KA3), every item presents different importance to the maturity of Culture. focus group as “𝑚𝑐𝑖𝑐 ”.

These importances were assigned by the

Hence, the weighted score for knowledge sharing (KA3) 14

is as below. The notations used in the calculation module are shown in Table 3.

(𝑠𝑐1 × 𝑚𝑐1) + (𝑠𝑝5 × 𝑚𝑝5 ) + (𝑠𝑝6 × 𝑚𝑝6 ) + (𝑠𝑝18 × 𝑚𝑝18 ) + (𝑠𝑡2 × 𝑚𝑡2) + (𝑠𝑓6 × 𝑚𝑓6 )

(Equ.5)

3.3. The result module of KNM 2.0 The result module displays the following analysis results to users: (1) For Culture/Process/Technology/ overall construct 

The

sum

of

the

weighted

scores

for

the

maturity

of

Culture/Process/Technology/ overall construct, and their achievement percentage in comparison to the full score. 

The maturity levels for Culture/Process/Technology/ overall construct

(2) For 9 KAs 

The sum of the weighted scores for 9 KAs, and their achievement rate in comparison to the full score.

(3) The comparison result with other companies 

The level pie chart illustrating the level distribution across organizations participating in the system.



The maturity level radar chart illustrating the achievement rate of the weighted scores

for

Culture/Process/Technology/overall construct

comparison to the full score.

in

From the maturity level radar chart, user could

understand the achievement status across organizations participating in the system. 

The KAs radar chart illustrating the achievement percentage of the weighted scores for 9 KAs in comparison to the full score.

From the KAs radar chart,

user could understand the achievement status across organizations participating in the system. (4) The expert advice exhibits the qualitative suggestions for users to have a guideline to improve their KM implementation.

These qualitative suggestions displayed

automatically by the system based on the calculation results of several programmed algorithms.

These qualitative suggestions and algorithms were

developed by the focus group and constructed in a knowledge base. The qualitative suggestions were amended from the evaluation items such as “the company should enhance the creative and innovation culture”.

The algorithms

consist of rules and conditions such as the comparison among the preference 15

scores of evolution items, and the comparison among the levels of Culture/Process/Technology/overall construct (Table 5). There are two kinds of expert advice, one is “Immediate improvement items”, and the other is “Advanced improvement items”.

The calculation results of these algorithms

will decide which expert advice being displayed in the column of immediate or advanced improvement items.

Take overall maturity Level III as an example, the

rules are as below. 

Rule #1, for any construct maturity level in V, all items of this construct should achieve “strongly agree”.

If not, the items without “strongly agree”

will be shown in “Immediate improvement items”. 

Rule #2, for any construct maturity level in IV, the items of this construct with Level I~IV should achieve “strongly agree”.

If not, the items without

“strongly agree” will be shown in “Immediate improvement items”. However, to encourage the respondent companies to make more effort on promoting KM to therefore upgrade their maturity level. The items of this construct with Level V will be advised to achieve “strongly agree”.

Hence,

those items without “strongly agree” will be shown in “Advanced improvement items”. 

Rule #3, for any construct maturity level in I, II, III, the items of this construct with Level I~III should achieve “strongly agree”.

If not, the items

without “strongly agree” will be shown in “Immediate improvement items”. However, to encourage the respondent companies to make more effort on promoting KM to therefore upgrade their maturity level. The items of this construct with Level IV and V will be advised to achieve “strongly agree”. Hence, those items without “strongly agree” will be shown in “Advanced improvement items”. [Insert Table 5 about here] 4. A 139 cases survey A survey with 139 case companies was conducted to demonstrate the applicability of KNM 2.0, and practically obtain the initial version of the score intervals.

These

case companies have been recognized as experienced in implementing KM for 6 months to 15 years, more than 25 of them were also the case companies in previous study KNM.

These cases companies were evaluated by “self-evaluation” and 16

“assessment team-evaluation”. The assessment team consisted of KM consultants, KM scholars, CKO or senior managers in practice, and government officers. The consistency between self-evaluation results and assessment team results were analyzed by using Wilcoxon signed-rank test (Wilcoxon, 1945) and Cohen’s kappa (Neuendorf, 2002; Williams & Plouffe, 2007). There are four research stages to process the survey: preparatory, data collection, data analysis, and result display stage. 4.1. Preparatory stage In this stage, three training workshops were hold to let the assessment team and the cases companies be aware of the details of KNM 2.0. In addition, the possible questions and difficulties that could be encountered in answering KNM 2.0 questionnaires or in the interviews of assessment teams’ visiting case companies have been collated and printed in a Q&A handbook for participants' reference. 4.2. Data collection stage Self-evaluation part: All of the 139 companies performed the self-evaluation on the web-based KNM 2.0 system.

Due to KNM 2.0 involving in various functional departments, the case

companies were suggested to fill the evaluation items by teams instead of a single person.

It took three weeks to collect the preference scores of self-evaluation from

the 139 companies. Assessment team part: Each case company was evaluated by two members of the assessment team. Two sets data as below were collected. 

The assessment team directly advised the maturity levels for Culture, Process, Technology and overall construct.



The assessment team filled in the scores for evaluation items of TMO_culture, process, technology, and KM performance on the web-based KNM 2.0 system. In order to understand their KM implementation of these 139 case companies, the

assessment team reviewed KM related documents, interviewed with KM program managers and staffs, and talked to the staffs at different hierarchy levels in different functional departments. These activities were performed by phones and in person visiting, and were required to be taped. After these review and visiting, the assessment team directly advises the maturity levels of Culture, Process, Technology and overall construct by using their experience and proficient. In addition, the assessment team

17

used the web-based KNM 2.0 system to fill in their preference on the evaluation items. The assessment team part took six months to finish. 4.3. Data analysis stage In this stage, the data collected from the previous stage would be analyzed and then obtain: (1) The score intervals to differentiate maturity levels of Culture, Process, Technology and overall construct The data collected from 139 companies performing the self-evaluation on the web-based KNM 2.0 system were analyzed by using cluster analysis.

Then the focus

group was hold to confirm the results of cluster analysis and the score intervals to differentiate maturity levels for Culture, Process, Technology and overall construct. (2) The consistency between the self-evaluation and assessment team results on the maturity levels for Culture, Process, Technology and overall construct The scores collected from self-evaluation of the 139 companies would be calculated by using Equs.1, 2, 3, and 4 to obtain the sum of the weighted scores for the maturity of Culture, Process, Technology and overall construct.

These data were

compared with the score intervals to decide their maturity levels.

Meanwhile, the

maturity levels from assessment team are given directly from assessment team through personal observations and experiences without using the questionnaire.

The

consistency analyses were used to calculate the agreement between self-evaluation and assessment team (Table 6). Psychologists commonly measure various characteristics by having a rater assign scores to observed people, objects, or events. When using such a measurement technique, it is desirable to measure the extent to which two or more raters agree when rating the same set of things.

A number of different measures can be used to

express the extent of agreement achieved among coders regarding the assignment of themes to categories (Nasir, 2005). One of the more robust and relatively conservative measures of inter-rater reliability is Cohen's kappa, introduced by Cohen (Cohen, 1960), which is a statistical coefficient that represents the degree of accuracy and reliability in a statistical classification. It measures the agreement between two raters who each classify items into mutually exclusive categories. An index with a value between 1 (perfect consensus between raters) and 0 (agreement is no better than chance), and the guidelines to interpret the Cohen's kappa results are as below (Neuendorf, 2002; Williams & Plouffe, 2007). 18

    

0.01 – 0.20 slight agreement 0.21 – 0.40 fair agreement 0.41 – 0.60 moderate agreement 0.61 – 0.80 substantial agreement 0.81 – 1.00 almost perfect or perfect agreement In this study, the results of inter-rater reliability analysis show that Cohen’s

kappa is from 0.79 to 0.86 which are within the acceptable range for studies of this nature. [Insert Table 6 about here] (3) The consistency between the self-evaluation and assessment team results on the sum of the weighted scores for the maturity of Culture, Process, Technology and overall construct The scores collected from both of self-evaluation and assessment team for the 139 companies would be calculated by using Equs.1, 2, 3, and 4 to obtain the sum of the weighted scores for the maturity of Culture, Process, Technology and overall construct. The Wilcoxon matched-pairs signed ranks is a non-parametric statistical hypothesis test used to compare two matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ. This test is based on the differences in distances, and no need for normally distributed parameters. It examines whether the differences are symmetrically distributed around the median zero (Wilcoxon, 1945).

It can be used as an alternative to the paired Student's

t-test (also known as "t-test for matched pairs" or "t-test for dependent samples") when the population cannot be assumed to be normally distributed.

The Wilcoxon

signed rank test pools all differences, ranks them and applies a negative sign to all the ranks where the difference between the two observations is negative. This is called the signed rank. The Wilcoxon signed rank test has the null hypothesis that both samples are from the same population. The Wilcoxon test creates a pooled ranking of all observed differences between the two dependent measurements. It uses the standard normal distributed z-value to test of significance. In this study, the Wilcoxon matched-pairs signed ranks were used to compare the scores consistency between the results of self-evaluation and assessment team.

The

statistically significant results of Wilcoxon matched-pairs signed ranks means the existing of difference between the results of self-evaluation and assessment team. Therefore, the Wilcoxon's test of construct KM process reveals a significant less than .05, which means the distributions are not identical (Table 6). 19

These

differences may come from the evaluator's incomprehension of the questionnaire items, or from the observation is not close enough to the fact.

Since the KM process

covers many KM activities, these activities are distributed across 9 key areas.

From

the view of case companies, if the assessment scores of KM process are collected from only one respondent's point of view instead of a group of respondents with various managerial backgrounds, the score may be biased.

On the other side, from

the perspective of assessment team, if the members of the assessment team are not familiar with the KM practices of case companies, it will also lead to the less accurate judgment.

These reasons probably lead to the statistically significant of Wilcoxon's

test results between the self-evaluation and assessment team for KM process. (4) The consistency between the self-evaluation and assessment team results on the sum of the weighted scores for 9 KAs The scores collected from both of self-evaluation and assessment team for the 139 companies would be calculated by using Equ.5 (as an example) to obtain the sum of the weighted scores for 9 KAs.

Wilcoxon matched-pairs signed ranks were used

to compare the consistency between the results of self-evaluation and assessment team on the sum of the weighted scores for 9 KAs.

After analysis, the results of Wilcoxon

matched-pairs signed ranks test show that there is statistically difference between self-evaluation and assessment team results on KAs_Data and knowledge acquire (Table 7).

That means the respondents of case companies and the assessment team

are not consistent in understanding the content of evaluation items, or have no consistent cognition in observing the objects and giving the corresponding preferences. The possible reason is that KAs_Data and knowledge acquire contains the evaluation items such as “Use data analysis to obtain knowledge about customers”.

Due to

“data analysis” is a new practice issue for many organizations, the organizations could not have sufficient concepts to understand their practices or experiences to evaluate their ability and related issues regarding data analysis. [Insert Table 7 about here] 4.4. Result display stage In this stage, this study uses a case company as an example to display the following results.

This company, a large size company in production industry, has

been implementing KM for 8 years.

From Fig 3, it shows this company has achieved

Level IV on the construct of Culture and Technology, and has the largest achievement rate 73.6% on Culture. It also shows this company has the largest achievement rate 20

73.8% on KAs_ knowledge and intelligent application.

The level pie for the

construct of Culture (Fig 4) shows there are 45.45% of companies participating in the system obtaining Level IV on the construct Culture. From the construct and KAs radar chart (Fig 5 and 6), this company could realize the lowest, average and their status of achievement rate of the weighted scores for the constructs and KAs.

In the

section of the expert advice (Fig 7), there are 11 suggests in “immediate improvement items” for this company. [Insert Fig. 3,4,5, 6 and 7 about here] 5. Conclusions remarks Organizations are constantly seeking innovative ways to exploit the benefits of their knowledge assets (Lyu et al., 2016). Knowledge management, which has been introduced to an organization, can only grow and step forward slowly and steadily after establishments and resources are put into in various fields such as developing a knowledge-oriented culture, introducing a technology-based KM system, and establishing relevant regulations and processes (Hsieh et al., 2019). The path to KM maturity is continually gradual, and this path and related issues will be adjusted with the latest management trends, so a KM maturity model that is up-to-date is essential. This study has managed to fill in the gaps between the need existing in the current KM practice and the literature.

The implications of the proposed KNM 2.0 model

are on the level of individual company, production/service industries, and the government (Fig.8). For an individual company, the evaluation results can be the reference used to realize its implementation of KM.

The pie chart and radar chart

demonstrating the comparison among organizations can reveal the relatively position of an individual company in production/service industries.

As for the government,

the evaluation results collected from the companies in production/service industries can be statistically analyzed to obtain the whole understanding of the implementation of KM in Taiwan. The foundation of KNM is cross-references to well-known KM maturity models in the previous studies (Hsieh et al., 2009), and KNM 2.0 evolve based on the practically implementing experiences of KNM.

Both KNM (before 2018) (Hsieh et

al., 2009) and KNM 2.0 (after 2018 and forward) are the research background for the online KM evaluation website which are officially used by the government IDB in Taiwan.

Comparing to the first version of KNM (Hsieh et al., 2009), KNM 2.0 21

remains to deliver a comprehensive framework with five maturity levels for navigating KM journey, but is extended to be a state-of-the-art instrument, the merits of KNM 2.0 include the following. 

To concise the evaluation framework, KNM 2.0 consist of 9 KAs and 38~40 KM activities, which involve the broadest views of KM practices while being presented in a concise structure.



To enhance the importance of KM performance, KNM 2.0 add KM performance as one of the 4 TMOs. In addition, from Equ 1~4, which presents that the sum of the weighted scores for the maturity of Culture, Process, Technology, and overall construct are contributed not only the scores from a certain construct but also from KM performance.



To evaluate the readiness of smart factory, KNM 2.0 has the related evaluation items such as big data, data mining, artificial intelligence technology, and so on. In addition, KNM 2.0 add “knowledge and intelligent application” as one of the 9 KAs.



To

consider

the

applicability

for

production/service

industry

and

large/small-medium companies, KNM 2.0 has 4 versions of questionnaires to meet the need of different sectors and scales.

The comparison among

organizations in the result module is calculated and displayed based the data set collected separately in 4 different versions. [Insert Fig. 8 about here] The qualitative data collected to construct KNM 2.0 are representative and collective contributions from the participants who are KM professions with fully experiences in observing the KM development in Taiwan for several years, and with various academic or practical backgrounds.

Owing to these advantages, KNM 2.0

has keeping drawing the attention of academic, practitioners and government, it will be continually moving forward and publicly used to investigate the development of a nationwide KM in Taiwan. The algorithm-based calculation framework developed in this study is to accurately estimate the scores collected from respondents’ preference and provide the users with the results of KM maturity level. The applicability of the calculation framework was statically approved in the study.

From the quantity results of the 139

case companies, the consistency analysis reveals that the respondent's understanding of the questionnaire items, the awareness of the new KM tools or concepts, and the 22

accuracy of the observations will affect the rightness of the respondents’ preferences. Therefore, in order to improve the respondents' preference being closer to the fact, the KM evaluation website will be added more supplementary explanations and small real-world cases to the questionnaire items to remind the respondents of KM circumstances.

In addition, to alleviate the bias from a single respondent, KM

evaluation website will be revised to be more friendly for simultaneously collecting inputs from multiple respondents of a company.

The proposed model KNM 2.0 is

useful for the case study in the current paper, and is practically useful for a general scope. 6. Discussion and managerial implications There is a general recognition among academics that KM is a cross-functional and multifaceted discipline (Nissen, 2019).

For many companies that intend to deploy

KM may be confused by a variety of activities and efforts that go under the name of KM.

It is imperative for companies confronting with new business initiatives to look

to management instruments for guidance (Lee & Choi, 2003).

In the era of

knowledge economy, the first version KNM proposed in 2009 and the proposed KNM 2.0 undertake different missions.

The first version KNM has contributed as a

framework for KM researchers to investigate KM deployment, and for KM practitioners from government and industries to get more systematical understanding of KM and lead the KM journey along management targets and development stages (Hsieh et al., 2009).

The application of KNM from 2009 to 2017 has practically

promoted the development of KM initiatives in different industries and organization scales in Taiwan. To sustain competitive advantage, organizations are facing internal and external pressures that enforce organizations to introduce various emerging managerial initiatives or tools (Rocha et al., 2007). However, how these managerial initiatives or tools could interact with each other to achieve synergy and how to recognize the potential gains from the integrated practice of these concepts have become concerned issues to enterprises.

The scholars and practitioners all observe KM’s relevant

activities from different perspectives and attempt to study the correlation between KM, organizational strategies, and organizational performance (Bosua, & Venkitachalam, 2013; Floyde et al., 2013). Hence, after 10 years’ implementation of KM in Taiwan, organizations have been paying more attention to the KM performances and their values benefiting to other business initiatives (Minonne & Turner, 2009; Zaim et al., 23

2019). As businesses turn to new technologies to increase their competitiveness, there is a recognition that there needs to be a convergence and close alignment between KM and data analytics, cloud computing and digital channels (Xhafa, 2016; Fernandes et al., 2019). Therefore, the proposed KNM 2.0 continually inherit the task of KNM to distribute the KM concepts, to be an interactive platform for KM proponents, and also collect the implementing status from industries with the advanced consideration to satisfy different needs in the production/service industry and large/small-medium companies.

Furthermore, KNM 2.0 undertake the mission to assist KM practitioners

to acknowledge the KM maturity resulted not only from KM activities but also from their overall KM performance. Plus, KNM 2.0 is able to raise the awareness of enterprises regarding the importance of big data, data mining, artificial intelligence technology, and as a tool to evaluate the readiness of these business initiatives. Further research of KNM 2.0 may focus on the analysis of data collected from the KNM 2.0 system to reveal the difference of maturity status across the production/service industry and large/small-medium companies.

In addition, the

relationship among KM culture, process, technology, and KM performance is worthy to be observed, and the interplay between Knowledge and intelligent application is as well. The results of the above-mentioned researches should feedback to continually upgrade the KNM 2.0, and hopefully these longitudinal research results could demonstrate the evolution and contribution of KM in Taiwan, and can be a reference to other countries. Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Credit Author Statement Ping Jung Hsieh conceived the study, designed the model and the computational framework and analyzed the data, and was in charge of overall direction and planning. Chinho Lin contributed to revise the model, interpreted the results, and made conclusions. Shofang Chang contributed to contact with the case companies, sample preparation, collection and data analysis.

24

All authors provided critical feedback to complete the research, wrote and revised the manuscript.

Acknowledgement This work was supported by Industrial Development Bureau, Ministry of Economic Affairs (IDB) in Taiwan.

We special thanks participating KM academics,

government officers in IDB, the consultants in China Productivity Center (CPC) and other consulting companies, and the representatives in case companies for being the domain experts of the study and their constructive contribution, which led to noticeably improved exposition of the research.

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Whelan, E., & Carcary, M. (2011). Integrating talent and knowledge management: Where are the benefits?. Journal of Knowledge Management, 15(4), 675-687. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics, 1, 80-83. Williams, B.C., & Plouffe C.R. (2007). Assessing the evolution of sales knowledge: A 20-year content analysis. Industrial Marketing Management, 36(4), 408–419. Xhafa, F. (2016). Advanced knowledge discovery techniques from big data and cloud computing. Enterprise Information Systems, 10 (9), 945–946. Xu, X.Y., & Hua, Q.S.(2017). Industrial big data analysis in smart factory: Current status and research strategies, IEEE Access, 5,17543-17551. Yin Rebecca Yiu, M. & Fai Pun, K. (2014). Measuring knowledge management performance in industrial enterprises: An exploratory study based on an integrated model. The Learning Organization, 21(5), 310–332. Yan, J.H., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance, IEEE ACCESS, 5, 23484-23491. Zaim, H., Muhammed, S., & Tarim, M. (2019). Relationship between knowledge management processes and performance: critical role of knowledge utilization in organizations. Knowledge Management Research and Practice, 17 (1), 24-38.

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Calculation module

Result module

Count the evaluation scores and obtain the KM maturity levels

Display the evaluation results to users

Evaluation module

Function

Research methodology

Core content

Collect users’ preferences on evaluation items  literature review  focus groups

 focus groups  A survey of 139 case companies  Cluster analysis, Wilcoxon signed-rank test, Cohen’s kappa

 5 maturity levels  4 TMOs (Culture, Process, Technology, and KM performance)  38~40 evaluation items and 9 KAs  4 questionnaires (production /service/ large/ small-medium)  Website: https://km.ekm.org.tw/ KMPP2016/Web/VoteI ntroduction_EN.aspx

 Step 1.Count the sum of evaluation items for each TMO to obtain the weighted scores for maturity of Culture, Process, Technology, and overall construct  Step 2. Compare the sum of the weighted scores for maturity of Culture, Process, Technology, and overall construct with the score ranges to decide the maturity level of Culture, Process, Technology, and overall construct  Step 3.Count the sum of evaluation items to obtain the weighted scores for 9 KAs

 focus groups

 Maturity levels for Culture, Process, Technology and Overall construct  Scores and achievement rate for Culture, Process, Technology and Overall construct  Scores and achievement rate for 9 KAs  Pie chart and radar chart to show the comparison among organizations  Expert advice_Immediate improvement items/ Advanced improvement items

Fig.1 Three modules of the proposed KNM 2.0 model

31

Level I.

Level II.

Level III.

Level IV.

Level V.

Knowledge chaotic stage

Knowledge conscientious stage

Knowledge Management stage

KM advanced stage

KM integration stage

A practical definition of KM is explored within an organization and consideration of its applicability is made. Organizational processes are partly described as KM tasks and, by virtue of ideas from individual "KM pioneers", pilot projects on KM typically emerge.

The goal of this level is to provide evidence of the business value of KM by formally conducting KM programs and capturing lessons learned that can be transferred and used to help the organization better implement KM on a larger and expanding scale.

An advanced strategic-oriented plan and standardized approaches to the subject of KM are a feature of Level IV organizations. Managers are able to harness knowledge from all the touch points in the organization and realize the business benefits from it.

Organizations have no formal processes to use organizational knowledge effectively. Organizational knowledge is fragmented in isolated pockets, or stays in people’s heads. Individual may have knowledge but not knowing how to harness it in a structured manner to derive business benefits.

Culture

C1.

C3.

Culture and People –these address the ‘mindset’ and relate to attributes of assessing people and culture.

Technology

Process

KM performance Formal determination of KM-related actions and their outcomes within a particular setting.

P11

T2, T3, T4

T5

T6

P5,P10,

P4, P15, P17,

P2, P3, P6, P7,

P1

P18, P19, P20

P8, P9, P12, P13, P14, P16

F1

F6, F7

Questionn aires for small-med ium/ production industry

C2.

T1

Technology and infrastructure -these are the enablers that help people harness the maximum out of the KM initiative.

Process, policy and strategy – these facilitate and guide the efforts of the people to capture and use the knowledge in the organization to achieve business benefits.

A Level V organization has developed the abilities to adapt flexibly in order to meet new requirements in KM or any business initiative without dropping a maturity level. These abilities are presented in the integration and fusion of internal, external, existing, and up-to-date business-related knowledge regarding product, service, operational process, and management discipline.

Questionn aires for large/ production industry

F 2, F 5, F 8, F 9,

F3, F4

F 10, F 11

Fig. 2. 5 maturity levels, 4 target management objects and the corresponding evaluation items C1~3 for Culture, T1~6 32 for Technology, P1~20 for Process, and F1~11 for KM performance

Questionn aires for large/ service industry

Questionn aires for small-med ium / service industry

Fig. 3.The screen example of maturity levels, scores and achievement rate

Fig. 4.The screen example of pie chart

33

Fig. 5.The screen example of radar chart for Culture, Process, Technology and overall

Fig. 6.The screen example of radar chart for 9 KAs

34

Fig. 7. The screen example for expert advice

35

Government A large size company in production industry The proposed KNM 2.0 model

The official online Knowledge Management Evaluation website was based on the proposed KNM 2.0

Company members advise the preferences on evaluation items

Count the evaluation scores

The large size and small-medium size companies in production/service industries

Evaluation module

Data base of large size companies in service industry

(Fig. 2 and Table 2)

Data base of small-medium size companies in service industry

mCalculation Module

Data base of small-medium size companies in production industry

(Tables 3, 4, and Equs 1~5)

Display the evaluation results

Data base of large size companies in production industry

Result Module (Table 5)

Compare  Maturity levels for Culture, Process, Technology and Overall construct  Scores and achievement rate for Culture, Process, Technology and Overall construct  Scores and achievement rate for 9 KAs  Expert advices_ Immediate improvement items/ Advanced improvement items

 Pie chart and radar chart to show the comparison among organizations

36

 Statistical analysis of Maturity levels, scores and achievement rate for Culture, Process, Technology and Overall construct among industries  Statistical analysis of scores and achievement rate for 9 KAs among industries

Fig. 8. The implications of the proposed KNM 2.0 model (A large size company in production industry as an example)

37

Table 1. Increasing number of companies in different levels based on large size (L)/small-medium size (S-M) companies by using KM evaluation website to collect data from 2008-2017 S-M

L

S-M

L

S-M

L

S-M

L

S-M

L

Level I~II

29 15

45 29

58 31

15 29

34 42

Level III

48 15

76 32

85 43

126 60

172 82

19 10

23 19

30 29

246

272

389

Level IV~V Total companies

2 114

5

9

7

198

38

Table 2.

The evaluation items of 9 KAs in KNM 2.0 Knowledge Learning

Knowledge Protection

Knowledge Creation and Innovation

Knowledge and Intelligent Application Knowledge Store

Data and Knowledge Acquire

Knowledge Sharing KM Promotion

KM Strategy CULTURE (C) C1.Members have knowledge sharing culture, and with the positive attitude. C2.Members have creative and innovation culture. C3.Members have knowledge or skill learning culture. PROCESSES (P) P1.KM support the organization strategy. P2.KM performance link to the organization performance. P3.Pay attention to intellectual capital. P4.Manage employees knowledge, skill, and attitude. P5.The regulations or processes to encourage knowledge sharing within organization. P6.The regulations or processes to share knowledge with external organizations. P7.Use data analysis to obtain knowledge about customers. P8.Use data analysis to obtain knowledge about service and product design. P9.Use data analysis to obtain knowledge about service and production processes. P10.The regulations or processes to obtain knowledge. P11.The regulations or processes to store data, information and knowledge. P12.The regulations or processes to improve members’ creativity. P13.The regulations or processes to improve organization’s innovation. P14.The regulations or processes to protect knowledge. P15.The regulations or processes to knowledge learning. P16.The learning and training program link to employees’ performance. P17.Perform benchmarking or best practices. P18.The regulations or processes to encourage members to participate CoPs. P19.The regulations or processes to construct and maintain Yellow Page. P20.The regulations or processes to construct and maintain knowledge system. TECHNOLOGY (T) T1.The e-learning or related training system. T2.TheT related system for CoPs or collaboration work. T3.The integral information system to transfer and deposit information. T4.KM system connect to daily work. T5.KM system connect to other enterprise system. T6.Use data mining, text mining, big data or other artificial intelligence technology to acquire business intelligence. KM PERFORMANCE (F) F1.Members’ concept of implementing KM. F2.Members’ KM activities are embedded in ordinary operating processes. F3.The overall benefit from KM in terms of the improvement on the customer service, product, and partner relations, and thus obtain good reputation. F4.Hold expertise knowledge that cannot be emulated in a short time. F5.Understand and hold important knowledge pertain core processes. F6.Perform many knowledge sharing activities. F7.Members are aware of where to obtain knowledge. F8.Members are able to apply internal knowledge to accomplish task. F9.Members are able to apply external knowledge to accomplish task. F10.Knowledge documents are useful for employees to complete tasks or learning. F11.There always are creative ideas on products, services or the workflows.

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ●

● ●

●

● ● ● ● ● ● ● ●

39

Table 3. Notations 𝑠𝑐𝑖𝑐 𝑠𝑝𝑖𝑝 𝑠𝑡𝑖𝑡 𝑠𝑓𝑖𝑓 𝑚𝑐𝑖𝑐 𝑚𝑝𝑖𝑝 𝑚𝑡𝑖𝑡 𝑚𝑓𝑖𝑓 𝑤𝑐𝑖𝑐 𝑤𝑝𝑖𝑝 𝑤𝑡𝑖𝑡 𝑤(𝐶) 𝑓𝑖𝑓 𝑤(𝑃) 𝑓𝑖𝑓 𝑤(𝑡) 𝑓𝑖𝑓 𝑤(𝑜) 𝑐𝑖𝑐 𝑤(𝑜) 𝑝𝑖𝑝 𝑤(𝑜) 𝑡𝑖𝑡 𝑤(𝑂) 𝑓𝑖𝑓 𝑐 𝑝 𝑡 𝑓

The notations used in the calculation module of KNM 2.0 Descriptions The score of 𝑖𝑐 th item in TMO_C (Culture) The score of 𝑖𝑝 th item in TMO_P (Process) The score of 𝑖𝑡 th item in TMO_T (Technology) The score of 𝑖𝑓 th item in TMO_F (KM performance) The maturity level of 𝑖𝑐 th item in TMO_C The maturity level of 𝑖𝑝 th item in TMO_P The maturity level of 𝑖𝑡 th item in TMO_T The maturity level of 𝑖𝑓 th item in TMO_F The weight of 𝑖𝑐 th item in TMO_C contributing to the maturity of Culture The weight of 𝑖𝑝 th item in TMO_P contributing to the maturity of Process The weight of 𝑖𝑡 th item in TMO_T contributing to the maturity of Technology The weight of 𝑖𝑓 th item in TMO_F contributing to the maturity of Culture The weight of 𝑖𝑓 th item in TMO_F contributing to the maturity of Process The weight of 𝑖𝑓 th item in TMO_F contributing to the maturity of Technology The weight of 𝑖𝑐 th item in TMO_C contributing to the maturity of Overall The weight of 𝑖𝑝 th item in TMO_P contributing to the maturity of Overall The weight of 𝑖𝑡 th item in TMO_T contributing to the maturity of Overall The weight of 𝑖𝑓 th item in TMO_F contributing to the maturity of Overall The numbers of evaluation items in TMO_C The numbers of evaluation items in TMO_P The numbers of evaluation items in TMO_T The numbers of evaluation items in TMO_F

40

Mark Users’ preferences on evaluation items The maturity level for each evaluation items decided by focus group The weights contributing to the maturity of culture, process, technology decided by focus group

The weights contributing to the maturity of overall construct decided by focus group The numbers of evaluation items in target management object decided by focus group

Table 4. The notations used in Equ.1, 2, 3, 4 Notations Descriptions

𝐶𝑖𝑐 𝐹(𝑐)𝑖𝑓 𝑃𝑖𝑝 𝐹(𝑝)𝑖𝑓 𝑇𝑖𝑡 𝐹(𝑡)𝑖𝑓 𝐶(𝑜)𝑖𝑐 𝑃(𝑜)𝑖𝑝 𝑇(𝑜)𝑖𝑡 𝐹(𝑜)𝑖𝑓

𝐶𝑖𝑐 = (𝑠𝑐𝑖𝑐 × 𝑚𝑐𝑖𝑐 × 𝑤𝑐𝑖𝑐 ) is the weighted score of the 𝑖𝑐 th item in TMO_ C contributing to maturity of Culture. 𝐹(𝑐)𝑖𝑓 = (𝑠𝑓𝑖𝑓 × 𝑚𝑓𝑖𝑓 × 𝑤(𝑐) 𝑓𝑖𝑓 ) is the weighted score of the 𝑖𝑓 th item in TMO_ F contributing to maturity of Culture. 𝑃𝑖𝑝 = (𝑠𝑝𝑖𝑝 × 𝑚𝑝𝑖𝑝 × 𝑤𝑝𝑖𝑝 ) is the weighted score of the 𝑖𝑝 th item in TMO_ P contributing to maturity of Process. 𝐹(𝑝)𝑖𝑓 = (𝑠𝑓𝑖𝑓 × 𝑚𝑓𝑖𝑓 × 𝑤(𝑝) 𝑓𝑖𝑓 )is the weighted score of the 𝑖𝑓 th item in TMO_ F contributing to maturity of Process. 𝑇𝑖𝑡 = (𝑠𝑡𝑖𝑡 × 𝑚𝑡𝑖𝑡 × 𝑤𝑡𝑖𝑡 ) is the weighted score of the 𝑖𝑡 th item in TMO_ T contributing to maturity of Technology. 𝐹(𝑡)𝑖𝑓 = (𝑠𝑓𝑖𝑓 × 𝑚𝑓𝑖𝑓 × 𝑤(𝑡) 𝑓𝑖𝑓 )is the weighted score of the 𝑖𝑓 th item in TMO_ F contributing to maturity of Technology. 𝐶(𝑜)𝑖𝑐 = (𝑠𝑐𝑖𝑐 × 𝑚𝑐𝑖𝑐 × 𝑤(𝑜) 𝑐𝑖𝑐 ) is the weighted score of the 𝑖𝑐 th item in TMO_ C contributing to maturity of Overall construct. 𝑃(𝑜)𝑖𝑝 = (𝑠𝑝𝑖𝑝 × 𝑚𝑝𝑖𝑝 × 𝑤(𝑜) 𝑝𝑖𝑝 ) is the weighted score of the 𝑖𝑝 th item in TMO_ P contributing to maturity of Overall construct. 𝑇(𝑜)𝑖𝑡 = (𝑠𝑡𝑖𝑡 × 𝑚𝑡𝑖𝑡 × 𝑤(𝑜) 𝑡𝑖𝑡 ) is the weighted score of the 𝑖𝑡 th item in TMO_ T contributing to maturity of Overall construct. 𝐹(𝑜)𝑖𝑓 = (𝑠𝑓𝑖𝑓 × 𝑚𝑓𝑖𝑓 × 𝑤(𝑜) 𝑓𝑖𝑓 ) is the weighted score of the 𝑖𝑓 th item in TMO_ F contributing to maturity of Overall construct.

41

Table 5.

The algorithms consisting of rules and conditions are used to automatically display the expert advice Overall Construct The items which preference is The qualitative suggestions maturity maturity will be shown in the section not “strongly agree” level level of “ ” Immediate improvement items

V IV

I,II,III,IV,V V I,II,III,IV V IV

III I,II,III V IV

II

III

I~II V IV

III

I II

I

All items of this construct All items of this construct The items of this construct with Level V The items of this construct with Level I~IV All items of this construct The items of this construct with Level V The items of this construct with Level I~IV The items of this construct with Level IV,V The items of this construct with Level I~III All items of this construct The items of this construct with Level V The items of this construct with Level I~IV The items of this construct with Level IV,V The items of this construct with Level I~III The items of this construct with Level III,IV,V The items of this construct with Level I,II All items of this construct The items of this construct with Level V The items of this construct with Level I~IV The items of this construct with Level IV,V The items of this construct with Level I~III The items of this construct with Level III,IV,V The items of this construct with Level I,II The items of this construct with Level II,III,IV,V The items of this construct with Level I

42

Advanced improvement items

v v v v v v v v v v v v v v v v v v v v v v v v v

Table 6. The numbers of participation companies in different levels, and the consistency test results Level I

Level II

Level III

Level IV

Level V

The consistency test for the sum of the weighted Scores (Wilcoxon Signed Ranks Test)

Self-evaluation

3

28

49

35

24

Consultant-evaluation

2

25

52

38

22

Z=0.83 p=0.203

Cohen’s kappa=0.79

Self-evaluation

3

19

40

51

26

Consultant-evaluation

1

30

49

36

23

Z=1.97 p=0.024*

Cohen’s kappa=0.86

Self-evaluation

3

25

48

38

25

Consultant-evaluation

2

24

47

41

25

Z=0.42 p=0.337

Cohen’s kappa=0.82

Self-evaluation

2

26

39

47

25

Z=0.19

1

31

50

34

23

p=0.424

Construct

Culture Process Technology Overall construct

Consultant-evaluation p<0.05: Significant

43

The consistency test for Maturity level (Inter-rater reliability analysis)

Cohen’s kappa=0.81

Table 7. The average achievement rates of 9 KAs for participation companies in different industries and scales, and the consistency test results Large /Production 28 cases

Small-medium /Production 52 cases

Large /Service 24 cases

Small-medium /Service 35 cases

The consistency test for the sum of the weighted Scores (Wilcoxon Signed Ranks Test)

Self-evaluation Consultant-evaluation

55%

60%

51%

45%

56%

58%

52%

43%

Z=1.52 p=0.064

Knowledge Promotion

Self-evaluation Consultant-evaluation

82% 81%

78% 76%

72% 71%

64% 60%

Z=0.07 p=0.472

Knowledge Sharing

Self-evaluation Consultant-evaluation

79%

82%

75%

72%

78%

82%

73%

70%

Z=1.16 p=0.123

Data and Knowledge Acquire

Self-evaluation Consultant-evaluation Self-evaluation Consultant-evaluation

81% 80% 84%

76% 72% 78%

69% 66% 72%

Knowledge Storage

79% 77% 85% 82%

83%

76%

71%

Z=0.20 p=0.42

Knowledge and Intelligent Application

Self-evaluation Consultant-evaluation

60%

66%

60% 55% 69% 66%

Z=0.60 p=0.274

Self-evaluation Consultant-evaluation

58% 68% 67%

56% 50%

Knowledge Creation and Innovation

58% 78% 76%

59% 56%

Z=-1.08 p=0.140

Knowledge Protection

Self-evaluation Consultant-evaluation

72%

61%

69%

71% 82% 78%

59% 78% 75%

66% 85% 82%

52% 50% 76% 72%

Z=0.59 p=0.277

9 Key Areas Knowledge Strategy

Knowledge Learning

Self-evaluation Consultant-evaluation

p<0.05: Significant

44

Z=1.82 p=0.034*

Z=0.61 p=0.270