4.1. The concept of competence sets
The study on Competence Set Analysis began with Yu (1989), as a derivative of HD theory. Its mathematic foundation was built by Yu & Zhang (1989, 1990, 1993).
The competence set (CS) for a given decision problem is defined as a collection of ideas, knowledge, skills, resources and efforts for its effective solution. Such a set, like HD, implicitly contains potential domain, actual domain, reachable domain, and activation probability as discussed in Chapter 3. Anything or anyone, including a
product or service that can release the pain, frustration and charge, has competence.
Everyone, and every corporation, has its competence sets.
One will set up the goal and evaluate what competence sets (including people, techniques and resource) are needed, and whether the existing competence sets can assist him/her to effectively solve the problem when a spontaneous or triggered creativity causes an event input in the competence sets transformation process (Yu &
Lai, 2004). If the existing competence sets are inadequate to solve the problem effectively, one will start thinking about what competence sets are necessary, but not yet obtained, and how these competence sets should be effectively obtained. The competence sets transformation process of a corporation or producer is also the same.
To analyze the competence sets of individuals or corporations, we can decompose the CS as follow:
1 2 3
( , , ,..., n)
t t t t t
CS
=CS CS CS CS
(6)where
CS denotes the kth item of the CS at time t. Note that CS will be
tk dynamically changed as time (t) goes by.4.2. Decision blinds and decision traps
Because of HDs and being unaware of the decision parameters and their changing nature, people would easily have decision blinds or even get into decision traps. Recall in Example 1, we might not be able to know or misunderstand the complete word or situation because we are trapped by our perception frame (the lighted area). The dotted area outside the frame is our blind.
At time t, let us denote the truly needed CS for solving problem E successfully by CSt
(E), and its perception by decision makers, by CS
t*(E). Then CS
t(E)\CS
t*(E)
would be the decision blinds, the set of all the competences required but not seen by the decision makers at time t. See the illustration of Figure 3. Note that the larger the decision blind is, the more likely decision makers might make dangerous mistakes.Refer to Example 1. Suppose that the perception frame of Figure 1(e) is CSt
(E). Then
the shaded areas of Figure 1(a) to 1(d) are the decision blinds. Note that as the blinds are progressively reduced, the picture becomes progressively clearer, and eventually the fuzziness disappears.Figure 3: Decision blinds
Usually, CSt
(E) and CS
t*(E) can be changed with time. Suppose that CS
t*(E) is
fixed or trapped in a certain domain and CSt(E)\CS
t*(E) is large, then we tend to make
mistake in decision and we are in a decision trap. Decision trap (i.e. CSt*(E) is fixed,
independent of t) can lead to dangerous mistake, especially when CSt(E) changes
rapidly with time and CSt(E)\CS
t*(E) becomes very large.
Note that CSt*
(E) being fixed or trapped in a certain domain is equivalent to the
corresponding actual domain (ADt) and reachable domain (RDt) being fixed or trapped in a certain domain. This can occur when we are in a very highly charged state of mind or when we are over confident, which makes us respond quickly, and unwittingly and habitually commit the behavior of decision traps. Recall in Chapter 1, suppose that YouTube was in highly charged state of serious financial difficulty, it might fall into a decision trap, and sell the services to less interested customers with much less value created.By changing our actual domains (ADs), we can change and expand our reachable domains (RDs). We can reduce decision blinds and/or avoid decision traps by systematically changing the ADs. For illustration, assume that CS(E) and RDs are given, as depicted in Figure 4. Then as we move the AD from A to B, then to C, our
decision blind reduces progressively from CS(E)\RD(A) to CS(E)\(RD(A
)
∪RD(B))
then CS(E)\(RD(A)
∪RD(B)
∪RD(C))
.Figure 4: Decision blind reduces as we move our AD from A to B then to C For challenging decision problem, we can treat the decision parameters as different points for ADs. Systematically moving over the parameters and pondering their possible RDs can expand our RDs for dealing with the challenging problems. As a consequence, CS*
(E) is expanded and our decision blinds, CS(E)\CS
*(E), reduced.
In additions, the HD tools (Yu 1990, 1995, 2002, 2009) can work with the individual decision parameters as to reduce the decision blinds and avoid decision traps. They can expand and enrich our actual domains and reachable domains and look into the depth of the potential domains, they can also expand and enrich our perception on the decision problem and its related parameters.
4.3. Acquirement, expansion and transformation of competence sets
When confronting a decision problem, a corporation needs to evaluate its current competence sets to see if they are adequate for solving the problem. If the current competence sets are inadequate, then competence sets transformation or expansion must be processed. Competence sets transformation can be shown in the following equations:
1 T ( , )
t t t t
CS
+ =CS E
(7)where Et denotes the events, decision problems, or environment that the corporation or individual is confronted. It may be a challenge or information that a corporation faces, or a motivation that promotes a corporation to change. After transforming by the function Tt, the original competence set CSt is expanded into a new one, CSt+1.
‧A
‧B ‧C
RD(A)
RD(B) RD(C)
CS(E)
RD: Reachable
By adding new functions or capabilities on the original products or services, corporations can expand their old competence sets into new ones. For simplicity, let us drop the notation of time (t). Suppose
CS denotes the original competence sets of
k products/services provided by corporations, andCS denotes the competence sets
k* after transformation and expansion. The transformation of competence sets can be presented as follow:where Δ denotes new functions or capabilities. It could be some single item/ k function, or some comprehensive competence set. After transforming by the function Tk, the original competence set
CS is expanded into a new one,
kCS .
k*For instance, a traditional grocery store added information technology, operation management capability, and modern equipment to its own competence sets, and transformed into a convenience store, which can serve more customers and release more people’s pain and charge. Also, Google originally specialized in information searching technology, but after it merged with YouTube and DoubleClick, its competence sets further expanded, and become a complex web portal with more equipped functions.
For a corporation, when the goal is set, the next step is to process competence sets transformation, and allow equation (8) to be possible. Competence sets expansion and transformation can be processed in the following two methods:
(i) Internal adjustment or development: Improve or transform the original competence sets to achieve the goal or solve problems by adjusting corporation resource, time or management procedure or method.
(ii) Integrate with external competences: Expand or transform the original competence sets to assist the corporation to solve problems through borrowing, proper application or sharing external resource or competence sets. As an example, China Mengniu Dairy Company Limited (蒙牛乳業) cooperated with television media, telecommunication operators, and network operators to improve the sales performance of its dairy products through the spread of “Super Girl” and the power of the audience, which successfully achieved its goal of improving the sales performance (see Section 6.1 for details). In addition, through outsourcing, strategic alliance or merging, the competence sets can be also expanded by obtaining external ones.
Equations (6), (7), and (8) described the decomposition, transformation and expansion concept of competence sets. To allow the study to focus on the corporate cases, we will not explore the mathematical model and proof of competence sets analysis in detail here.
4.4. Research Issues of Competence Set Analysis
Competence set analysis has two inherent domains: competence domain and problem domain. Like HD, each domain has its actual domain and potential domain, as depicted in Figure 5.
Figure 5: Two Domains of Competence Set Analysis From these two domains, there are two main research directions:
(i) Given a problem or set of problems, what is the needed competence set? and how
to acquire it?
For example, how to produce and deliver a quality product or service to satisfy customers’ needs is a main problem of supply chain management. To successfully solve this problem, each participant in a supply chain including suppliers, manufacturers, distributors, and retailers must provide the chain with its own set of competence, so that the collected set of competence can effectively achieve the goal of satisfying customers’ needs.
How to expand the existent competence set to the needed competence set in most effective and efficient way? A mathematical foundation for such competence analysis is provided by Yu & Zhang (1990). Under some suitable assumptions, the problem can be formulated and solved by decision tree, graph theory, spanning trees, spanning tables and mathematical programming (Feng & Yu 1998; Huang et al. 2004; Li&Yu
(ii) To create value effectively
Competence Domain
potential competence domain
Problem Domain potential problem domain (i) To acquire needed CS
efficiently and effectively
1994; Li et al. 2000; Lin 2006; Shi & Yu 1996; Yu & Zhang 1989). Most earlier researches have focused only on the deterministic situation. However, one could remove this assumption to include uncertainty, fuzziness, and unknowns. In the recent studies, some heuristic methods, such as genetic algorithm (GA), hybrid genetic algorithm (hGA), multicriteria genetic algorithm, multi-objective evolutionary algorithm (MOEA), data mining technology, and forest learning technique have also been incorporated into the analysis of competence set expansion (Chen 2002; Hu et al.
2003; Huang et al. 2004; Lin 2006; Opricovic&Tzeng 2003, 2009).
(ii) Given a competence set, how to locate a set of problems to solve as to maximize
the value of the competence?
Given a competence set, what is the best set of problems that can be solved by the competence set as to maximize its value? If someone has already acquired a particular competence set, what are the problems he/she should focus to solve as to maximize its value? For instance, if we get a doctoral degree from certain university, which symbolize we have a certain set of competence, how do we maximize the value of this degree? Think of the opportunities in actual domains and potential domains. There are lots of studies of competence sets analysis working in this direction. For example, Chen (2001) established several indices that have impact on consumers decision making and provided a framework for helping firms in expanding the benefits of their products to fully address the consumer’s needs. Hu et al. (2002, 2004) generate learning sequences for decision makers through competence sets expansion to help them make better decisions. Chang & Chen (2009) develop an analytical model of competence sets to assist drivers in routing decisions. Chiang-Lin et al. (2007) studied the change of value when competence set are changed in linear patterns so that the corporations can create value by taking loss at the ordering time and making profit at the delivery time.
Competence sets expansion and transformation play a vital role in the corporation innovation process. The follow-up cases explored in the study all have a close relationship with the aforementioned two directions. We will inspect how each corporation case obtained its required competence sets to solve problems, and will also analyze how these corporations utilize their competence sets and create values.