Options analysis and knowledge management: Implications for theory and practice

Information Sciences 181 (2011) 3861–3877

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Information Sciences journal homepage: www.elsevier.com/locate/ins

Options analysis and knowledge management: Implications for theory and practice Mu-Yen Chen a, Chia-Chen Chen b,⇑ a b

Department of Information Management, National Taichung Institute of Technology, Taichung 404, Taiwan Department of Information Management, Tunghai University, Taichung 407, Taiwan

a r t i c l e

i n f o

Article history: Received 12 January 2005 Received in revised form 22 April 2011 Accepted 30 April 2011 Available online 14 May 2011 Keywords: Knowledge management Performance measurement and evaluation Real options Black–Scholes model

a b s t r a c t Real options can be a powerful tool for quantifying the value of strategic and operational flexibility associated with uncertain IT investments. They also constitute a new way of thinking as to how knowledge management (KM) can be implemented and managed to maximize the upside potential while minimizing any downside risk. This paper explored how practitioners can incorporate options analysis into contemporary knowledge management. Options analysis is recognizing real options and then determines how they add value. The main issue here is to manage knowledge so that the theoretical option value is realized in practice. This paper developed a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at any given point in time as follows: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. The higher the efficiency of the KM process, the higher the KMPI, thereby is enabling firms to become increasingly knowledgeable. To prove the contribution of the KMPI, a questionnaire survey was conducted among 121 firms listed on the Taiwan Stock Exchange Corporation (TSEC). This paper makes five important contributions: (1) it shows that if the KM process improves, the KMPI is enhanced. This finding is based on a survey of the questionnaires. As a result, the KMPI can effectively and efficiently represent the KM process; (2) it shows that when the KMPI increases, a company’s performance will be enhanced in five different areas This indicates a significant relationship between the KMPI and the company’s performance; (3) it also shows that the higher the KMPI, the higher the performance of the organization. This is especially valuable to any organization where knowledge is not being used optimally; (4) it provides an option analysis that may be helpful to managers for making the right decision in an uncertain environment; and (5) it presents the first application of the Black–Scholes model to use an actual business situation involving KM as its test bed. The results proved that the option pricing model can act as a measurement guideline for the entire knowledge of the whole company. Ó 2011 Elsevier Inc. All rights reserved.

1. Introduction Large organizations are becoming increasingly aware of the importance of knowledge to their company’s efficiency and competitiveness. As a result, knowledge management (KM) is quickly becoming a buzz word. Although there are a number of viewpoints and approaches when it comes to KM, they all centre on the notion that knowledge is a valuable asset that must

⇑ Corresponding author. Address: Department of Information Management, Tunghai University, No. 181, Section 3, Taichung-Port Road, Taichung City 40704, Taiwan. Tel.: +886 4 23504886; fax: +886 4 23504930. E-mail address: [email protected] (C.-C. Chen). 0020-0255/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2011.04.046

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be managed. The essence of KM is to design strategies that provide the most suitable knowledge to the right people, at the right time, and in the right format. However, as the prominence of KM grows, Milton et al. [32] considered that the systematic documentation, distribution, and reuse of knowledge are both difficult and time consuming tasks [32]. Recent surveys indicated that issues such as ‘measuring the value of KM’ and ‘evaluating KM performance’ are very important to managers in Asia [1,12,27], the United States [19,28,39], and the United Kingdom [38,42]. Brynjolfsson et al. [8] found the increasing role of KM to increase business competitiveness, and the large interest by managers to measure and evaluate both the performance and the benefits of KM not surprising [8]. Moreover, Chang and Wang [9] integrated the fuzzy theory with multi-criteria decision making to measure the possibility of successful KM [9]. In addition, many scholars are interested in the performance of KM in the e-learning environment. Chen and Hsiang [10] investigated the success factors critical for corporations embarking on knowledge community-based e-learning [10]. Qin et al. [37] and Vertommen et al. [46] presented a technical approach to improve knowledge sharing for personal learning. Monclar et al. [33] used spatial–temporal information to improve social networking and knowledge dissemination [33]. It is evident that both the implementation and application of KM has moved into e-learning and the online community. In addition, it seems that knowledge assets tend to be more easily created, accumulated, shared, and utilized among individuals through the use of information technology (IT) and the knowledge management system (KMS). Indeed, many KM practitioners have used IT to practice KM through the KMS. However, it has been pointed out by several researchers that while the level of investment in IT is correlated to corporate revenues, it is not correlated to either productivity or profitability [15,22,35]. In addition, the knowledge assets of an organization depend upon the quality of their knowledge and their intangible assets in general [20,21]. Thus, managers have found it difficult to demonstrate tangible returns for the resources they expended on the planning, development, implementation and operation of KM. It is evident that the fundamental issue of measuring and evaluating the investment in and the performance of KM remains unresolved. This leaves us with an important research question: how do most firms that initiated a KM system develop an appropriate metric to measure the effectiveness of their initiative? It is obvious that there is a need for a metric to justify a KM program within an organization. Given that most KM benefits are intangible, one method of measuring is the balanced scorecard (BSC). It includes both financial and other perspectives such as customer, internal business, as well as growth and learning perspectives. However, linking KM initiatives to performance is not sufficient. A more rigorous metric is required to assess the performance of KM, one that has the ability to explain and suggest future strategic actions for the firm to take in order to improve its KM performance. Therefore the objective of this paper is to propose a new metric for evaluating the performance of knowledge management. This paper aims to propose option pricing models in such a way that they will become part of management practice when evaluating KM solutions. The main contribution of this paper is the description of an actual case study that demonstrates the use of an option valuation method for analyzing the KM process. The remainder of this paper is organized as follows. An overview of the related research on KM evaluation is provided in Section 2. The process and the department responsible for organizational performance are described in Section 3. The methodology for the evaluation of KM is provided in Section 4, while Section 5 describes how the option models can serve as evaluation tools for the KM manager. The case study is presented in Section 6. Finally, conclusions are drawn in Section 7. 2. Preliminary KPMG reported that the reasons for the creation of knowledge management initiatives, cited by most companies, are to facilitate better decision making, increase profits and reduce costs. It is very important to evaluate KM. However, it is rather difficult to do because KM suffers from the same challenges as many other management issues, in that it assumes that knowledge is a ‘thing’, which is amenable to being ‘managed’ by a ‘manager’. We must first determine which KM process is key to achieving a competitive advantage, and second, which measurement method is the most appropriate to appraise the performance of the KM program chosen? In this section, we discuss the literature on the KM performance measurement methods. Moreover, an empirical result shows that KM lacks a compelling quantitative metric to assess outcomes [13]. Therefore, the Black–Sholes pricing model can be used as an effective quantitative metric to solve this problem. 2.1. Evaluation methods of KM performance KM performance measurement methods are broad categories of research issues. Alavi and Leidner [3] said that the method development is so diverse due to the background of the researchers, their particular expertise and the problem domains [3]. Chen and Chen [13] classified KM evaluation methods into the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis, and organization-orientated analysis, together with their measurement matrices for different research and problem domains [13]. The evolution of the KM process over the past decade is shown in Fig. 1. This literature survey began in January 2005. It was based on a search for ‘knowledge management’ in the keyword index and article abstract within the ISI, Elsevier SDOS, IEEE Xplore, EBSCO, Ingenta and Wiley InterScience online databases, for the period from 1995 to 2004. A total of 3699 articles were found. After topic filtering, there remained 108 articles, from 80 journals, related to the keyword ‘knowledge

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Qualitative

1995-1999 2000-2004

20 OrganizationalOrientated

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15 10

Quantitative

5 0 Internal Performance

Project-Orientated

External Performance Fig. 1. KM development trend analysis.

management performance evaluation’. We then divided this data into eight categories of KM performance evaluation methodologies. Our goal was to examine the changes in the research trend of KM performance evaluation. We divided the time period into two phases in order to be able to compare the first five years (1995–1999) with the next five years (2000–2004). The main findings can be described as follows: (1) KM performance evaluation is becoming increasingly important. Articles on the performance evaluation of KM have been published in the literature for the past five years, showing that the research topics on KM have changed from KM creation, transformation, and implementation to the evaluation of KM performance. (2) Quantitative analysis is the primary methodology used to evaluate KM performance, and in the last five years most research articles on KM performance have applied the quantitative analysis. (3) Firms are now highlighting the KM performance of competitors through benchmarking or best practices, rather than internally auditing KM performance via BSC. Fig. 1 shows that the number of articles outlining the external performance approach has increased substantially. These results infer that in the future, firms will carefully consider their own KM performance, as well as that of their competitors. (4) Firms are beginning to focus more on measuring the performance of the management of an individual KM project than on that of the entire organization. Fig. 1 shows that the number of articles using this project-orientated approach have grown considerably. This seems to indicate that measurement and control of a KM project is becoming a major focus. A major challenge for KM research lies in designing models and theories to evaluate the performance and values of KM. Thus, our literature survey critically reviewed those studies where option pricing was used as the basis for KM performance analysis for evaluating its merits in a real word business setting. The characteristics of the option pricing model are very suitable for the future trend of KM performance evaluation. The details will be discussed in Section 4.1. 2.2. Evaluation of general KM methods As mentioned earlier, from a theoretical perspective we classified the KM performance evaluation based on eight approaches. However, from a practical point of view, investigating general KM evaluation methods has some drawbacks. Here we used a case study methodology to evaluate the performance of general KM evaluation methods. The test was carried out as follows. We selected a high-technology company as the test case. We then designed a questionnaire and interviewed the end-users. We used fuzzy linguistic analysis to adjust the fuzzy weight value for each KM process, including knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. Then, we calculated the triangular fuzzy number for each measure. Table 1 shows an example of the KM performance measurement from each of the four perspectives. The results show the KM performance from each perspective. However, it does not tell us very much because there are no differentiable measures in Table 1. Thus, we used our proposed option pricing model to estimate the KM performance from each perspective. The details will be discussed in Sections 4.2 and 4.3. 2.3. Option valuation approach For many years scholars who specialized in management issues have argued that uncertain investments should be viewed in new technology through a ‘‘real options’’ lens. The pioneer work of DosSantos [17] employs Margrabe’s exchange option model [30] for valuing an IS project that uses a novel technology for testing. He argues that the option model would

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M.-Y. Chen, C.-C. Chen / Information Sciences 181 (2011) 3861–3877 Table 1 The value of four perspectives in KM. KM process

Objective

Measure

Value

Creation

Continuously training and development The innovation abilities for users The average seniority for users

0.62 0.60 0.62

0.61

Conversion

Users’ experiences Users’ professional skills User’s satisfaction

0.60 0.62 0.64

0.62

Circulation

The incentive systems for users The sharing culture among users The standardization of documents

0.56 0.52 0.54

0.54

Completion

Ensure the KM project provides business value The quantities/qualities of knowledge database The numbers of patents

0.58 0.50 0.52

0.53

be better than NPV to evaluate new IT projects. Similarly, Kambil et al. [24] used the Cox–Rubinstein binomial option pricing model [14] to determine whether a pilot project should be undertaken. McGrath [31] noted the similarity between options on physical assets and the kinds of options created by technology positioning investments [31]. A technology positioning investment is an initial expenditure on a technology (e.g., for R&D) that creates the opportunity, but not the obligation to earn the benefits associated with further development and deployment of the technology. This is similar to the structure of a real option, which confers the right, but not the obligation to make the profit associated from some physical asset. The options associated with new IT platforms have similarities to those created by R&D and can be seen as a specific instance of the general case of technology positioning investments. For a software platform, usually several options are relevant. In an analogy to Kester’s ‘‘growth options’’ for firms [25], Taudes [43] investigated options for evaluating ‘‘software growth options’’ to value software platforms and benefits [43]. In addition to the examples described above, option models have been applied to investments in other applications. Kumar [26] proposed the use of the options theory for systematical analysis, understanding, and possibly quantifying the flexibility resulting from the use of decision-supporting systems [26]. Panayi and Trigeorgis [36,45] analyzed an actual case for the IT infrastructure decision by a telecommunications authority. The result proved that options valuation can be justified to make such strategic investment decisions even if the NPV suggests otherwise. Benaroch and Kauffman [5] investigated the problem of investment timing using the option model for a practical case study to deal with the development of point-of-sale (POS) debit service [5]. In a follow-up paper, Benaroch and Kauffman [6] used sensitivity analysis to probe the Black–Scholes option valuation for IT investment opportunities [6]. Taudes et al. [44] also compared the NPV to the Black–Scholes option valuation method for employing SAP R/2 or to switch to SAP R/3 [44]. These results opened the door to follow-up IT projects related to EDI, workflows, logistics and supply chain management.

3. The process and the department for organizational performance Simply put, performance management includes activities to ensure that goals are consistently met in an effective and efficient manner. Organization management can focus on the performance of the whole organization, processes, a department to build a product or service, employees, etc. Information in this section emphasizes the process and issues in that part of the research involved in the performance of the organization as a whole. 3.1. Knowledge management process While authors differ in their use of terminology in describing the KM process, the aggregate of their works can be described as a simple KM process as shown in Fig. 2. The conclusion was generalized from a collection of related KM research and defined the ‘‘4C’’ process of KM activities: creation, conversion, circulation and completion. The first component of the KM process is knowledge creation. It relates to knowledge addition and the correction of existing knowledge. Nonaka and Takeuchi [34] suggest four modes of knowledge creation: socialization, externalization, internalization and combination. This model deals with a variety of knowledge, whether tacit or explicit, it is accelerated by encouraging synergistic interrelations of individuals from diverse backgrounds [34]. Knowledge conversion is the second component. It relates to individual and organizational memory. While, organizational memory reflects the shared interpretation of social interactions, individual memory depends on the individual’s experiences and observations. Walsh and Ungson [47] presented that knowledge conversion in firms plays an important role in eliminating obstacles and inefficiencies and, at the same time, in improving management performance [47]. However, if knowledge created through management activities for years is not conversed systematically, it cannot be beneficial to future decision-making needs.

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Fig. 2. KM process [2,4,11,13,16,27,29,34,48].

The third component of the KM process is knowledge circulation. It is the didactic exchange of knowledge between source and receiver. Transfer occurs at various levels: transfer of knowledge between individuals, from individuals to explicit sources, from individuals to groups, between groups, across groups, and from the groups to the organization. In conclusion, it requires integration of knowledge from multiple sources to obtain improved performance. Knowledge completion, the fourth component of the KM process means that the source of competitive advantage resides in the knowledge itself. Here, a big challenge is how to integrate internal knowledge which is gained from outside. In addition, knowledge completion creates new knowledge. In this way, it provides a basis for active knowledge creation. 3.2. Department performance Recently the notion of Organizational Learning (OL) has become very prominent. Managers see OL as a powerful tool to improve the performance of their organization. Thus, it is not only the scholars who are interested in the phenomenon of OL doing research on organizations but also the practitioners who have to deal with the subject of OL. Organizations can accumulate and manage knowledge the same way. In recent time, management studies have come to view organizations from a new perspective: the systems perspective [40]. Senge [41] defined it as follows ‘‘a system is a collection of subsystems integrated to accomplish an overall goal. Systems have input, processes, outputs, and outcomes, with ongoing feedback among the various subsystems.’’ If one part of the system is removed, the nature of the system will be changed. Therefore, from a systematic perspective, managing a successful business requires on-going leadership and management of the subsystems, especially the human resource, production, marketing, financial, and R&D departments. 4. Applying the real option model Real options are a means of capturing the flexibility of management to address uncertainties as they are revealed. Capital budgeting fails to account for this flexibility or to integrate this flexibility with strategic planning. Fichman [18] proposed the flexibility of options available to management include: defer, abandon, shut down and restart, expand, contract, and switch use [18]. The key valuation concept of the options theory is that an option can be priced based on the construction of a portfolio of a specific number of shares with an underlying asset against which shares can be borrowed at a riskless rate in order to replicate the return of the option in a risk-neutral world.

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Real options methodologies can take the best features of discounted present value (DPV) and discounted cash flow (DCF) without their failures. The traditional approach to project evaluation and investment decisions uses DPV or DCF methods. These methods explicitly assume that the project will meet the expected cash flow with no intervention in the process by management. All the uncertainty is conducted in the discount rate. This process is static. At most, the expected value of the cash flow is incorporated into the analysis. The real options method can make a significant difference in the valuation. It expands the notion of the manager’s flexibility and strategic interaction in skewing the results of the traditional DPV analysis which, as with financial options, allows gaining on the upside, and minimizes the downside potential, thus increasing the valuation. For example, the Black–Scholes method of option valuation is suitable to assess project investments and knowledge management. 4.1. Fundamental real option model Embedded KM options can take many forms, including the options to: (1) Time-to-build investments, (2) to change the (after) scale of investment, (3) to abandon investments, (4) to defer initiation of investment, (5) to create growth opportunities based on an initial investment, and (6) to switch assets created by the investment to another use (see Table 2). Acquiring an appreciation for these option types and how each one adds value will make them easier to recognize them in practice. Consequently, the key to understanding the KM performance evaluation in which option pricing is worthwhile, is by using basic elements of the real option valuation. For example: (1) Time-to-build options. KM, in practice, is divided into four processes (creation, conversion, circulation, and completion) for the purpose of resource planning and setting milestones for tracking. Therefore, knowledge management can be viewed as a time-to-build option. To create value using time-to-build options requires each process to be made contingent upon a reassessment of the costs and benefits of completing each of the four periods. In addition, business requirements or opportunities often change in ways that increase or decrease the importance of what a given process delivers, and so the managers will be better able to recognize and avoid investing in processes that no longer have a worthwhile payoff. (2) After scale options. KM always is implemented by a project-orientated management. The project manager will design and control the entire package of KM policies and activities. In addition, the project manager will also decide if and when and by how much to increase, decrease, suspend, or abandon the KM investments in the project. However, this management discretion has a value, which is not incorporated into the DPV. The real options methodology goes beyond this view of valuation and more closely matches the manner in which a firm operates. Therefore, knowledge management can be viewed as an after scale option. (3) Abandon and defer options. KM infrastructure investments often are made without any immediate expectation of payback. However, they can be converted investment opportunities into the option’s underlying asset. Some examples of these investments include the Intranet and Internet environment, data warehousing and data mining technologies, and web service. A defer option exists when a decision-making on whether or not to invest can be delayed for some period without compromising the potential benefits. When uncertainty is high but can be resolved over time, deferral options can be surprisingly valuable. Otherwise, an abandon option will be used to exit from loss-making investments.

Table 2 Six types of real options. Option

Object

Applying to KM

How KM value is created

Time-to-build

KM process

After scale

KM project

It creates value by providing the chance to alter or terminate KM before each process of funding, based on updated information about costs and benefits Resources allocated to KM project can be contracted, expanded, scaled up or down

Abandon

KM infrastructure investments

KM investments can be terminated midstream, and the remaining resources are easily redeployed

Defer

KM infrastructure investments

Growth

KM embedded technologies

A decision on whether investment can be deferred for some process without imperiling the potential benefits An initial baseline technology opens the door to pursue a variety of potential follow-up opportunity

As each process is completed the ambiguities about the net payoffs from subsequent process are resolved; only each process with positive payoffs are pursued The managers can increase the scale of a KM project, if circumstances are favorable; or can reduce the scale if circumstances are unfavorable As an investment unfolds actual costs and benefits become more clear, and losses can be curtailed by terminating it The firm avoids investing from what is destined to be a losing proposition

Switch

Knowledge investments

Knowledge can be viewed as a product and gain tangible or intangible profits. Besides, one knowledge can be exchanged for another

Over time, the relative value of follow-on technology becomes more apparent and only investments with positive pay offs are pursued The usage of the knowledge concept is similar to option pricing. Depending on the time remaining to exercise. Besides, knowledge assets are developed for one purpose, which can be redeployed in order to serve another purpose

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(4) Growth options. KM embedded technologies are often difficult for forecasting their value payoffs in the face of unpredictability, implementation difficulty, and maintenance costs. Some examples of these technologies include search engines, enterprise information portals, and automated workflow systems. An embedded growth option exists when an initial baseline investment opens the opportunity to pursue a variety of potential benefits. Unlike the other options described so far which produce value mainly by limiting the extent of potential losses in the event of unfavorable conditions, growth options add value by increasing potential gains in the event of favorable conditions. IT implementations by their very nature tend to have a variety of embedded growth opportunities. (5) Switch options. Knowledge investments are an acknowledgement that knowledge is a core part of a company providing it with a competitive edge in the market place Therefore, knowledge can be viewed as a product and as such gain tangible or intangible profits. Nevertheless, knowledge has its ‘‘product life cycle’’ from newborn through mature and abandoned phases. The knowledge usage concept is similar to option pricing, and is related to the time remaining to exercise. Besides, knowledge assets developed for one purpose can be redeployed to serve another purpose. Therefore, knowledge management can be viewed as a flexible option. 4.2. Assumption about Black–Scholes option pricing model Since the parentage of the real option model is the financial option pricing model, it is useful to start with a description of the latter. The most well-known of the option pricing models is the Black–Scholes option pricing model [7]. It prices European call or puts options on a stock that does not pay a dividend or make any other distributions. The formula presents that the underlying stock price follows a geometric Brownian motion with constant volatility. 4.2.1. Call and put options on financial assets A call option gives the buyer of that option the right, but not the obligation, to buy the asset on which it is written at an exercise price at maturity of the option contract. The price of the option is called option premium. A put option gives the buyer the right, but not the obligation, to sell the asset at the exercise price at maturity. An investor buys a call option when he expects the asset to be increased in value beyond the exercise price. An investor buys a put when he expects the asset to be declined in value below the exercise price. 4.2.2. The definition of the Black–Scholes option pricing model

Option pricing model prices ¼ Intrinsic value þ Time value:

ð1Þ

Eq. (1) can be explained by the fact that perfect financial markets are arbitrage-free in the sense that no investor can make a profit without taking some risks or expending some capital. Such profit could be gained if an option is priced differently than a portfolio consisting of the underlying asset and a risk-less security with the amounts continuously adjusted so that the value of the portfolio replicates the value of the option. In Eq. (1), the value of a company or an asset based on an underlying perception of the value is called intrinsic value. For call options, this is the difference between the underlying stock price and the strike price; and further, time value represents the portion of the option premium that is attributed to the amount of time remaining until the expiration of the option contract. Basically, time value is the value the option has in addition to its intrinsic value. 4.2.3. Applying the Black–Scholes option pricing model In the Black–Scholes model, Hull [23] described the value of a call option is its discounted expected terminal value, E [CT]. The current value of a call option is given by C = E[CT](1 + r)t, where (1 + r)t is the present value factor for risk-neutral investors. A risk-neutral investor is indifferent between an investment with a certain rate of return and an investment with an uncertain rate if the return expected value matches that of the investment’s rate of return. Given that CT = max [0, ST K], and assuming that ST is log-normally distributed, it can be shown that:

Black—Scholes option pricing model : C ¼ S  Uðd1 Þ  Kð1 þ rÞt Uðd2 Þ;

ð2Þ

where

ln KS þ ðr þ 0:5r2 Þt ffiffi p ; r t S 2 pffiffi ln þ ðr  0:5r Þt p ffiffi d2 ¼ K ¼ d1  r t : r t d1 ¼

As shown in Eq. (2), the Black–Scholes option pricing model contains fewer parameters making it easier to determine. In addition to the ‘‘ease of use’’ issue, applying option-pricing concepts is attractive because of the conceptual clarity it brings to the analysis. Many knowledge management initiatives indicate that the high potential variance of expected revenues from KM would be the key element in making the right decision. In this sense, option pricing seem to be right. The parameters of Black–Scholes option pricing model are assumed to be applied to KM.

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4.2.4. Input variables In order to use the Black–Scholes equation for finding the value of an option the relevant variables must be collected. These variables are the following:  C (The theoretical call premium). The Black–Scholes model is used to calculate a theoretical call price using the five key determinants of an option’s price: stock price, strike price, volatility, time to expiration, and short-term (risk free) interest rate. From a KM perspective it refers to the final KM performance value when the KM project has been completed.  S (Present value of the underlying asset). S denotes the current price of the underlying asset, or the present value of future cash inflows. These expected cash flows can be estimated by prognosis or by using a simulation model. In a KM project it can be taken as the actual costs.  K (The exercise price). The value of the investment is equivalent to the exercise price of a financial option. In reality this value might not be constant or may not be known in the beginning of a project. However, in practice it is not considered unreasonable to assume it to be certain. In a KM project it can be taken as the value of the expected revenues.  r (Volatility). Here volatility refers to the variability of the return of the underlying asset. Volatility is a function of market-priced risk as well as private risk. There are several different approaches one can use for creating or judging an estimate of volatility. First, one can take an educated guess. Assets to which a higher hurdle rate will be assigned because of a higher than average systematic risk are likely to present higher volatility. From here we can adjust for the higher r that individual companies usually have from the market and for an even higher r that an individual project has, compared to the company as a whole. Another way to estimate volatility would be to gather some data. For some businesses we can estimate their volatility using historical data of their return on investment and compare it to that of the same or related industries. Therefore, from a KM perspective we can use r to represent the uncertain factors. It is viewed as the KM investment forecast of the volatility that is expected to prevail until the maturity date of the project.  t (Time to maturity). As in the case of the financial options, this is the time available to exercise the option before that time expires. In some cases this can be a fixed time period derived, for example, from the ownership of a patent. After the expiration of the patent the firm loses this particular competitive advantage over the other firms. Therefore, in a KM project, it is seen as the time left to the end of the project.  r (Risk-free rate of return). This is the return on risk-free treasury instruments. The difference between the real options approach and the traditional valuation tools is that the short-term rate is used even for long-lived projects. In the real options approach, the risk-free rate is the return to the hedge position over a short time interval, while in a KM project, it is seen as a fixed constant.  U() (The cumulative standard normal distribution).

In order to understand the model itself, we divide it into two parts. The first part, SU(d1), derives the expected benefit from acquiring a stock outright. This benefit is found by multiplying the stock price [S] by the change in the call premium based on the change in the underlying stock price [U(d1)]. The second part of the model, K(1 + r)tU(d2), provides the present value of paying the exercise price on the expiration day. From a KM perspective, U(d1) is seen as the ratio representing the relationship between KM investment and its output. At the same time, U(d2) is used to express the probability of success or failure of the KM. 4.3. Construction of a KM performance index In this section, the Black–Scholes model is used to estimate the knowledge creation process as an example. In Eqs. (3)–(5), the parameters of the Black–Scholes model is designed to calculate the appropriate value that can represent the total KM Performance Index. In Eq. (6), we can determine which KM process or perspective must be improved by KM Performance

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M.-Y. Chen, C.-C. Chen / Information Sciences 181 (2011) 3861–3877 Table 3 KM Performance Index of Black–Scholes model. KM process

Intrinsic value (S–K)

Creation Conversion Circulation Completion

Time value

Black–Scholes option value

S

K

r (%)

r (%)

t

2500 2400 2000 2200

3500 3200 3000 4200

12 12 12 12

6 6 6 6

3 3 3 3

73.115 100.937 32.786 2.469

Index. As shown in Table 3, the knowledge completion process is the weakest in whole KM activities (KM Performance Index = 2.469). Therefore, the manager will enhance related objectives in this perspective according to the above statement. (1) Calculate the investment costs of KM (S)

SKM ¼

t X

C KM ¼ ðequipment cos t þ labor cos t þ time cost þ operation cos tÞ:

ð3Þ

1

(2) Calculate the expected revenues of KM (K)

K KM ¼

t X

RKM ¼ ðphysical rev enues þ inv isible rev enuesÞ:

ð4Þ

1

(3) Calculate the uncertain factors (r)

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðSi  SKM Þ2 t ; n i¼1

Si ¼ SKM ðtÞ  SKM ðt  1Þ:

ð5Þ

(4) Calculate the KM Performance Index

BSKM v alue ¼ KM Performance Index;

ð6Þ t

KM Performance Index ¼ C ¼ S  Uðd1 Þ  Kð1 þ rÞ Uðd2 Þ: 5. Methodology 5.1. Test of KM Performance Index The KM process influences the efficient work-processing and performance of management. We claim that the KM Performance Index can measure the quality of organizational knowledge, and that it is related directly and/or indirectly to the performance of the firms’ knowledge management. Therefore, we hypothesize that the firm with a good quality organizational performance will increase their KM Performance Index, and that a firm with a larger KM Performance Index will improve its knowledge management performance. Thus, our research hypotheses are: Hypothesis 1. The KM Performance Index value has a significant relationship with the Knowledge Management Process.

Hypothesis 2. The KM Performance Index value has a significant relationship with human resource performance. Hypothesis 3. The KM Performance Index value has a significant relationship with production management performance. Hypothesis 4. The KM Performance Index value has a significant relationship with marketing management performance. Hypothesis 5. The KM Performance Index value has a significant relationship with financial management performance. Hypothesis 6. The KM Performance Index value has a significant relationship with R&D Management Performance. Hypothesis 7. The higher the KM Performance Index value the better the Organizational Performance will be.

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Fig. 3. Research architecture.

Based on these above research hypotheses, the research architecture is shown in Fig. 3. In order to acquire the effect for each firm estimating the knowledge management performance in the proposed KM Performance Index, we designed questionnaires and interviewed end-users. Our goal is to prove that the option pricing model can act as a measurement guideline to KM Performance Index and these seven hypotheses. 5.2. Survey instrument development In this research, we designed two parts of a single questionnaire (as shown in Appendix A). The first part (Part I) aims to explore the fruitful results of using KM Performance Index to access the KM process. For that reason, our exploratory instrument involves 19 items, with the four variables – knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. The perceived overall performance of these variables, and the perceived success of knowledge management as a criterion, were developed using a five-point Likert-type scale, with anchors ranging from ‘‘strongly disagree’’ to ‘‘strongly agree’’. For each question, respondents were asked to circle the response which best described their level of agreement. The purpose of the second part (Part II) is to explore the fruitful results of using KM Performance Index to estimate the department’s performance. For that reason, our exploratory instrument involves 20 items, with the five variables – human management performance, production management performance, marketing management performance, financial management performance, and R&D management performance. The perceived overall performance of these variables, and the perceived success of knowledge management as a criterion, was developed using a five-point Likert-type scale, with anchors ranging from ‘‘strongly disagree’’ to ‘‘strongly agree.’’ For each question, respondents were asked to circle the response which best described their level of agreement in comparison with other firms in the same industry. After careful examination of the results from the surveys and the interviews, the statements were further adjusted to make their wording as precise as possible. 5.3. Data collection A cross-sectional field survey was conducted at companies in the TSEC market in Taiwan. A directory of firms compiled by a securities brokerage company was used as the sampling frame. This directory consists of firms with at least one of the following two criteria: (1) They were members of the TSEC markets. (2) Each company already had a KM system implemented, and each respondent had experience involving KM projects or was using a KM system. For Part I, the respondents were all a CKO (Chief Knowledge Officer) and had completed a self-administered, 19-item questionnaire. For Part II, the respondents were all high-level management team members and had completed a self-administered, 20-item questionnaire. For each question, the respondents were asked to circle the response which best described their level of agreement with the statements. Of the 200 surveys, 121 properly completed questionnaires were returned,

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Sampling

Received

Response ratio (%)

Semi-conductor industry Electronics industry Banking industry Chemical industry Department store industry Construction material industry Garment and textile industry Food industry

50 50 30 20 15 15 10 10

31 38 17 9 7 8 6 5

62.00 76.00 56.67 45.00 46.67 53.33 60.00 50.00

Total

200

121

60.50

Table 5 Details of the questionnaire survey. Industry

Semi-con

Survey Respondents

50 50 30 20 15 15 Questionnaire Part I – CKO/Questionnaire Part II – High-level Manager Team Members 6 5 4 2 2 3 40 38 36 34 38 34

Pos. Avg. exp. Avg. age

Elect.

Banking

Chem.

Dep. store

Con. material

G&T

Food

10

10

1 33

1 36

resulting in a response rate of 60.50%. The average age of the respondents was approximately 36 years of age, and they had approximately 3-years of working experience in KM. The male-to-female ratio was approximately 2.1–1. Fifty-eight percent had completed a college or university degree, while 20% had obtained a postgraduate degree. The profiles of the respondents are shown in Tables 4 and 5. In our pre-test, we sampled the top ten enterprises in KM implementation. The respondents were all CKOs or high-level managers. Reliability was evaluated by assessing the internal consistency of the items representing each factor, using Cronbach’s a. In Part I, the 19-item instrument had a reliability of 0.86, exceeding the minimum standard of 0.80 suggested for basic research. Each of the 19 items had a corrected item-to-total correlation of above 0.612. In Part II, the 20-item instrument had a reliability of 0.90, exceeding the minimum standard of 0.80 suggested for basic research. Each of the 20 items had a corrected item-to-total correlation of above 0.640. After the questionnaire survey was completed, we wanted to investigate if implementing the KM process improved the performance of the department. To this end, multiple semi-structured individual interviews were conducted with key informants from the participating companies (see Appendix B). We expected to be able to identify important themes and evidence crucial to this research from the data collected in these individual interviews. During the interviews, the participants were asked questions about their experience in dealing with KM, including KM performance evaluation. 6. Results 6.1. Results with survey instrument A preliminary factor analysis which validates the measures was used in the relationship of the KM Performance Index calculation with the KM process. Exploratory factor analysis was adapted to use the orthogonal rotation method. Four factors had a Cronbach’s alpha value greater than 0.7, indicating that internal consistency is guaranteed for each of the four factors. Table 6 shows the factor structure of the variables, where reliability and convergent validity were significant because Cronbach’s alpha was greater than or equal to 0.70, and all convergent validity was greater than 0.50. In addition, Table 7 indicating the factor analysis validating the measures used in the relationship of the KM Performance Index calculation with department performance. Five factors had a Cronbach’s alpha value greater than 0.7, indicating that internal consistency is guaranteed for each. Moreover, all convergent validity was greater than 0.50. This indicates that the reliability and convergent validity were all significant. 6.2. Results from the individual interviews After the questionnaire survey, we selected the top ten firms in KM implementation from the participating firms for an interview. Each interview lasted from 1 to 3 h. All interviews conducted for this research were audio taped, and the recorded data was then transcribed, line by line, into MS Word format on a PC. All the collected data was then analytically coded. In the first stage, the initial coding, the data was examined line by line. Once that was completed, focused coding was carried out. Here the codes were sorted and categorized based on the themes of the theoretical framework. During this process some

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Table 6 Factor structure of the KM process (N = 121). Factor

Eigenvalue

Cronbach’s

Item

Factor loadings

Item-to-total correlation

Continuously promote knowledge through training and development Ensure executive support and encourage KM projects The innovation capability of the users The average seniority of the users

0.82

0.64

0.74 0.83 0.66

0.61 0.65 0.56

Users’ experience

0.78

0.69

Users’ professional skills Users’ information management ability The investment of new products or services The investment of employees

0.69 0.66 0.78 0.81

0.58 0.52 0.62 0.67

The average age of users

0.71

0.59

The educational level of users The incentive systems for users The sharing-culture among users

0.74 0.64 0.76

0.57 0.51 0.61

Ensure the KM project provides business value

0.63

0.51

The quantities/qualities of the knowledge database The numbers of patents To Sell appropriate KM products and service to third parties Improve business brand Improve business market value

0.81 0.59 0.64

0.69 0.54 0.51

0.52 0.57

0.51 0.53

a Knowledge creation

Knowledge conversion

Knowledge circulation

Knowledge completion

3.68

4.13

3.41

2.95

0.81

0.84

0.78

0.74

Table 7 Factor structure of department performance (N = 121). Factor

Item

Cronbach’s

a Human resource performance

Production management performance

The level of experience of employees in their jobs The level of aspiration accepting new information and knowledge for employees The ability of learning new information and knowledge for employees The comprehension ability of skills for employees’ job

0.83

The efficiency of learning new manufacturing technology

0.88

Financial management performance

The ability of understanding markets and the needs of customers quickly The efficiency of developing new products based on market demand The comprehension ability of salesmen’s know-how toward their market The ability of responding or reflecting to the feedback from the market

0.86

The analysis of earning capacity

0.82

The ability The ability The ability market The ability

to promote a new product into the market of developing new originality of products of amending existing products and being available in the of improving technology efficiently

0.78 0.69 0.72 0.73 0.77 0.67 0.69 0.72

The analysis of business efficiency The analysis of debt-paying The analysis of trend-moving R&D management performance

0.56 0.71 0.63 0.67

The advanced level of manufacturing technology in the same trade The ability of adjusting production management for specific customers The ability of manufacturing new products with a high-speed schedule Marketing management performance

Item-to-total correlation

0.69 0.72 0.57 0.67

0.89

0.81 0.78 0.78 0.72

of the codes were considered irrelevant or less productive to the research and were discarded. The remaining codes were then reexamined. Their concepts were then further elaborated on for future analysis. In the process of data coding, a second coder was asked to code the transcripts of three interviews in order to compare the codes developed by two different individuals with similar expertise in the research focus. This was done to eliminate any negative effect of potential individual

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bias or subjectivity. The reliability of data coding with multiple coders was then examined by evaluating the index of Cohen’s Kappa for inter-rater agreement. A Kappa index ranging from zero (no agreement whatsoever) to one (perfect agreement), was used to evaluate ‘‘the extent of agreement between coders while controlling for chance’’. The Kappa value calculated for this study was 0.75, suggesting adequate reliability. Thus, the respondents agreed that the KMPI was suitable and useful for the evaluation of the department’s performance. It should be noted here that the KM process can actually improve the department performance in many aspects, including the amount of sales, profit, increased productivity, and others. 6.3. Results of the Hypotheses Table 8 shows the correlation between the KM Performance Index and two variables – the KM process and the department performance. Hypotheses 1–6 were all proved at the 0.01 significance level. This shows the value of the KM Performance Index by indicating the importance of the correlation between the KM Performance Index and two variables. Moreover, as theorized, the KM process significantly affects the KM Performance Index because of a high correlation coeffi-

Table 8 Correlation with the KM Performance Index value.

***

Performance measures

Components

Correlation with KMPI

KM process

Knowledge Knowledge Knowledge Knowledge

0.59*** 0.47*** 0.56*** 0.49***

Department performance

Human resource performance Production management performance Marketing management performance Financial management performance R&D management performance

creation conversion circulation completion

0.46*** 0.37*** 0.24*** 0.21*** 0.52***

P < 0.01.

Table 9 Organizational performance with KM Performance Index value. Sample

KMPI

OP

Sample

KMPI

OP

Sample

KMPI

OP

Sample

KMPI

OP

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 X29 X30

28.93 17.73 19.50 35.57 18.12 12.84 40.87 29.24 37.60 15.44 23.53 17.38 35.85 32.58 10.40 19.28 20.22 14.24 38.81 21.07 18.31 15.41 30.30 8.62 13.78 25.44 33.44 42.09 39.75 29.48

0.67 0.41 0.45 0.83 0.42 0.36 0.95 0.68 0.87 0.39 0.59 0.43 0.90 0.81 0.23 0.43 0.45 0.43 0.90 0.72 0.43 0.36 0.70 0.22 0.31 0.57 0.74 0.94 0.92 0.69

X31 X32 X33 X34 X35 X36 X37 X38 X39 X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52 X53 X54 X55 X56 X57 X58 X59 X60

41.73 22.29 23.62 17.68 22.60 22.48 25.47 12.48 17.10 20.91 25.97 22.42 18.57 36.39 16.06 31.94 11.09 12.73 5.37 34.74 10.71 18.82 32.91 25.87 28.43 21.65 5.61 7.76 20.08 33.12

0.97 0.52 0.55 0.41 0.59 0.59 0.67 0.27 0.43 0.52 0.65 0.56 0.42 0.91 0.42 0.84 0.29 0.33 0.12 0.81 0.25 0.44 0.77 0.60 0.71 0.54 0.14 0.19 0.50 0.83

X61 X62 X63 X64 X65 X66 X67 X68 X69 X70 X71 X72 X73 X74 X75 X76 X77 X78 X79 X80 X81 X82 X83 X84 X85 X86 X87 X88 X89 X90

41.91 33.28 6.94 29.50 34.75 29.04 24.28 31.55 11.97 39.75 23.06 11.63 21.88 3.87 5.66 31.64 13.69 19.77 32.08 8.65 29.37 9.57 42.53 23.92 41.91 4.06 22.12 22.38 12.13 16.96

0.97 0.77 0.16 0.69 0.81 0.68 0.56 0.73 0.28 0.92 0.58 0.21 0.55 0.10 0.14 0.70 0.30 0.44 0.71 0.21 0.68 0.22 0.99 0.56 0.97 0.09 0.49 0.50 0.27 0.39

X91 X92 X93 X94 X95 X96 X97 X98 X99 X100 X101 X102 X103 X104 X105 X106 X107 X108 X109 X110 X111 X112 X113 X114 X115 X116 X117 X118 X119 X120 X121

26.76 7.82 23.12 20.77 33.48 9.80 8.43 23.55 21.94 23.04 33.71 31.65 13.09 10.34 13.18 11.37 8.01 10.65 7.06 10.01 16.64 22.84 41.57 24.89 4.99 32.06 10.90 24.65 25.87 24.71 11.71

0.62 0.18 0.54 0.48 0.78 0.26 0.22 0.62 0.58 0.58 0.84 0.79 0.33 0.26 0.33 0.29 0.21 0.28 0.18 0.22 0.39 0.53 0.97 0.58 0.12 0.80 0.41 0.62 0.65 0.62 0.52

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cient (between 0.47 and 0.59). Besides, five departments also significantly affected the KM Performance Index. This was because human management performance, production management performance, and R&D management performance had a medium or high correlation coefficient with the KM Performance Index. In addition, the other departments were also significant at the 0.01 levels, even though they had a low correlation coefficient. The empirical results are shown in Table 9, they represented the quality of the organizational performance that was used in a wide variety of decision-making in the firm. In our assumption, the organizational performance is equal to the KM process value added by the department performance. The final results show that if the KM Performance Index value increases, then the organizational performance will significantly increase as well. For example, sample X31 has a high KM Performance Index value of 41.73; at the same time the organizational performance is 0.97. On the contrary, sample X2 has a very low KM Performance Index value of 17.73; and the organizational performance is only 0.41. Therefore, hypothesis 7 was proven, and the value of the KM Performance Index indicates the importance of the correlation between the KM Performance Index and the organizational performance. Thus, if the knowledge management performance is good, the quality of the organizational performance improves significantly. 7. Conclusions In this paper, we summarized the arguments that the option pricing model can be applied to the KM performance valuation. From a list of approaches, divided into eight categories we choose the KM performance approach, through which we defined the 4C process of the KM process: creation, conversion, circulation and completion. In the next stage, we investigated which process would lead to the enhancement of knowledge performance in a firm; hence we integrated the department performance into five different departments with the KM process. Based on our assumption, it seems that the whole organizational performance will depend on the KM process and the department performance. Finally, we illustrated how the Black–Scholes model can be applied in the case of an actual KM performance option, where significant uncertainties that are not appropriately dealt with in traditional financial analysis were presented. The results proved that the option pricing model can act as a measurement guideline to KM performance. This paper proposed a new metric for assessing KM performance. Based on the above argument we claimed that the KMPI can measure the quality of organizational knowledge. The complexity and multifaceted nature of organizational knowledge and KM have resulted in the need to develop a new metric for accessing KM performance. To deal with this, we proposed a concept of KM process and department performance to devise the function of the KMPI. This paper makes five important contributions: (1) it shows that if four components of the KM process increase, the KMPI will be enhanced based on a survey of the questionnaires; hence, the KM Performance Index can represent the KM process effectively and efficiently; and (2) it shows that if the KMPI increases, the department performance will be enhanced in five different departments; hence, the KM Performance Index and the department performance have a significant relationship; and (3) it also shows that if the KMPI increases, then the organizational performance increases significantly as well, and it improves the organization where knowledge is not optimally used. Thus, the KMPI can recognize the value of the organizational performance, allowing managers to identify each pertinent KM process and department performance; and (4) it furnishes the option analysis that might be useful for managers to make right decisions in uncertain conditions; and (5) it presents the first application of the Black–Scholes model that uses a real world business situation involving KM as its test bed. The results proved that the option pricing model can act as a measurement guideline for the performance of the entire organization. Acknowledgements The authors thank the support of National Scientific Council (NSC) of the Republic of China (ROC) to this work under Grant No. NSC-99-2410-H-025-011. The authors also gratefully acknowledge the Editor and anonymous reviewers for their valuable comments and constructive suggestions. Appendix A. Questionnaire Dear Participants, For my project as part of my KM research, I am carrying out a survey for my research. This aims of this survey is to evaluate the KM performance for organization performance. Your responses will be treated confidentially and used only for the purpose of the research. A.1. Part I: Evaluation on Using KM Performance Index to access the KM process Directions: Please indicate your answer by circling the appropriate number. The higher the level, the more important the evaluation of knowledge management process. (Note: 1 ? Strongly Disagree 2 ? Disagree 3 ? Average 4 ? Agree 5 ? Strongly Agree)

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1

2

3

4

5

I. Knowledge creation Continuously promote knowledge through training and development Ensure executives support and encourage KM projects The innovation capability of the users The average seniority of the users

h h h h

h h h h

h h h h

h h h h

h h h h

II. Knowledge conversion Users’ experience Users’ professional skills Users’ information management ability The investment of new products or services The investment of employees

h h h h h

h h h h h

h h h h h

h h h h h

h h h h h

III. Knowledge circulation The average age of users The education level of users The incentive systems for users The sharing culture among users

h h h h

h h h h

h h h h

h h h h

h h h h

IV. Knowledge completion Ensure the KM project provides business value The quantities/qualities of the knowledge database The numbers of patents Sell appropriate KM products and services to third parties Improve business brand Improve business market value

h h h h h h

h h h h h h

h h h h h h

h h h h h h

h h h h h h

A.2. Part II: Evaluation on using KM Performance Index to access the department performance Directions: Please indicate your answer by circling the appropriate number. The higher the level, the more important the evaluation of department performance. (Note: 1 ? Strongly Disagree 2 ? Disagree 3 ? Average 4 ? Agree 5 ? Strongly Agree) 1

2

3

4

5

I. Human resource performance The level of experience of employees in their jobs The level of aspiration accepting new information for employees The ability of learning new information for employees The comprehension ability of skills for employees’ job

h h h h

h h h h

h h h h

h h h h

h h h h

II. Production management performance The efficiency of learning new manufacturing technology The advanced level of manufacturing technology in the same trade The ability of adjusting production management for customers The ability of manufacturing products with a high-speed schedule

h h h h

h h h h

h h h h

h h h h

h h h h

III. Marketing management performance The ability of understanding markets and needs of customers quickly The efficiency of developing new products based on market demand The comprehension ability of salesmen’s know-how toward market The ability of responding to the feedback from the market

h h h h

h h h h

h h h h

h h h h

h h h h

IV. Financial management performance The analysis of earning capacity The analysis of business efficiency The analysis of debt-paying The analysis of trend-moving

h h h h

h h h h

h h h h

h h h h

h h h h

(continued on next page)

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Part II: Evaluation on using KM Performance Index to access the department performance (continued)

V. R&D Management performance The ability of promoting product to become available in the market The ability of developing new originality of products The ability of amending existing products available in the market The ability of improving technology efficiently

1

2

3

4

5

h h h h

h h h h

h h h h

h h h h

h h h h

Appendix B. Semi-structured interviews This interview aims at gaining information about how you evaluate the KM Performance Index for Department Performance in your company. Due to privacy considerations, all the information you provide will remain confidential. We will not mention your real name, your company name nor any other identifiers in our research analysis and findings. I will go through a series of fairly open questions with you. Please respond to these questions in any way you like. This session will be recorded. If you have any objections to this, please let me know. 1. I would like to start with a fairly general question regarding your educational and administration background? Please, briefly introduce your educational and administration background, such as your academic training, your major, positions held, and how many years of administrative experience do you have in your present company? 2. What is your opinion on our proposed method for evaluating your department’s performance? Do you find it suitable and practical? 3. Do you have any suggestion for improving the evaluation process? 4. What are the differences between previous performance evaluations methods and our proposed method? 5. How do you think the KM processes that were carried out can improve your department’s performance, such as number of sales, profit, increased productivity, etc.? Is the KM Performance Index suitable and useful for department performance evaluation?

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