scispace - formally typeset
K

Kin Keung Lai

Researcher at Shenzhen University

Publications -  587
Citations -  15177

Kin Keung Lai is an academic researcher from Shenzhen University. The author has contributed to research in topics: Supply chain & Artificial neural network. The author has an hindex of 60, co-authored 547 publications receiving 13120 citations. Previous affiliations of Kin Keung Lai include City University of Hong Kong & North China Electric Power University.

Papers
More filters
Journal ArticleDOI

k-ZI: A GENERAL ZERO-INTELLIGENCE MODEL IN CONTINUOUS DOUBLE AUCTION

TL;DR: The interesting finding is that parameter "k" affects the allocation efficiency and the trader ratio in continuous double auction markets.
Journal ArticleDOI

Alternative approaches to constructing composite indicators: an application to construct a Sustainable Energy Index for APEC economies

TL;DR: This paper first uses a state-of-the-art MCDM method with mild weights restrictions to aggregate sub-indicators, without determining exact values of weights, to construct a Sustainable Energy Index for eighteen APEC economies.
Journal ArticleDOI

On the value of information sharing in the presence of information errors

TL;DR: It is suggested that transmission error and source error have significantly different impacts on the value of information sharing and the manufacturer's optimal strategy.
Journal ArticleDOI

Bricks or clicks: The impact of manufacturer ' s encroachment on both manufacturer-owned and traditional retail channels

TL;DR: In this paper, the authors investigate the performance of both manufacturer-owned channel and traditional retail channel when the manufacturer encroaches upon the traditional channel in different forms (brick-and-mortar and online form) under different market structures (Stackelberg and Bertrand).
Journal ArticleDOI

Factor analysis of financial time series using EEMD-ICA based approach

TL;DR: The empirical results show that EEMD-ICA based analysis approach is a vital technique for exploring the underlying factors of single financial time series.