In the field of motivation, incentives are seen as a means of motivating people. Incentives are usually applied in the form of a scheme, such as piece-rate and fixed-rate monetary rewards. Since the field of knowledge management involves a certain measure of motivation, a number of organizations have used incentives to encourage their employees to share knowledge. Research to date concerning the role of incentives in knowledge sharing seems to contradict one another. Furthermore, when an incentive is sufficiently large, some individuals are inspired to increase their performance to reflect the incentive received (London & Oldham, 1976). Along with this negative disposition, intrinsically motivated individuals would experience a deterioration of such motivation due to the introduction of incentives, thus jeopardizing the whole knowledge sharing initiative (Deci, Koestner, & Ryan, 1999; Jordan, 1986). Some research (Bock & Kim, 2002; O’Dell & Grayson, 1998) has suggested a trigger effect that comes from implementing incentives. Empirical evidence concerning the long-term effects of incentives in the field of knowledge sharing is also lacking (Fossum, 1979; O’Dell & Grayson). This research seeks to consolidate the many different views of past research, investigating areas that are lacking. Is it possible to consolidate the different views of incentives in knowledge sharing? Are there differences between having fixed-rate, piece-rate, or no incentive schemes in knowledge sharing initiatives? Do incentives exhibit a triggering effect in motivating individuals to share their knowledge? Would the removal of incentives after the trigger period affect a knowledge sharing initiative? Will the continual increase of incentives remain effective in the long term for knowledge sharing initiatives? These research questions will be answered as the article progresses.
Multirate Filtering Techniques
Multirate filtering is one of the best approaches for solving complex filtering problems when a single filter operating at a fixed sampling rate is of a very high order. With a multirate filter, the number of arithmetic operations per second is considerably reduced. The multirate technique is used in filters for sampling rate conversion where the input and output rates are different, and also in constructing filters with equal input and output rates. For multirate filters, FIR (finite impulse response) or IIR (infinite impulse response) transfer functions can be used. An FIR filter easily achieves a strictly linear phase response, but requires a larger number of operations per output sample when compared with an equal magnitude response IIR filter. Multirate techniques significantly improve the efficiency of FIR filters that makes them very desirable in practice.
Figure 1 depicts an overview of different multirate filtering techniques.
An overview of multirate filtering techniques
Polyphase realization is used to provide an efficient implementation of multirate filters. A polyphase structure is obtained when an Nth order filter transfer function is decomposed into M polyphase components, M<N. For FIR filters, polyphase decomposition is obtained simply by inspection of the transfer function (Crochiere, & Rabiner, 1983; Fliege, 1994; Harris, 2004; Mitra, 2006; Proakis & Manolakis 1996; Vaidyanathan, 1993). For multirate IIR filters, several approaches to polyphase decomposition have been developed (Bellanger, Bonnerot & Coudreuse, 1976; Crochiere, & Rabiner 1983; Drews & Gaszi, 1986; Krukowski & Kale, 2003; Renfors & Saramäki, 1987; Russel, 2000).
Key Terms in this Chapter
Knowledge Sharing: It is the voluntary process of transferring or disseminating knowledge from one person to another person or group in an organization.
Fixed-Rate Incentive: It is a scheme that pays individuals predetermined amounts of money for each unit produced.
Piece-Rate Incentive: It is a scheme that pays a predetermined sum to individuals for their participation in a task.
Incentive or Rewards: They are events or objects external to the individual that can incite action.
Knowledge Management Systems: These are a class of information systems developed to support and enhance the organizational processes of knowledge creation, storage and retrieval, transfer, and application.
Explicit Knowledge: It is knowledge that has been captured and codified into manuals, procedures, and rules, and is easy to disseminate.
Implicit Knowledge: It is knowledge that can be expressed in verbal, symbolic, or written form but has yet to be expressed.
Relevant Knowledge: It is knowledge that is correct and complete, related to the course work of students, answers the question posed by another student, and was not previously shared before by another student.
Irrelevant Knowledge: Knowledge that is erroneous and incomplete, unrelated to the course work, does not answer the question of the knowledge seeker, and was previously shared by another student.