Knowledge-sharing rewards in enterprise social networks: effects of learner types and impact of digitisation

ABSTRACT

The era of total digitisation demands companies to seek innovative approaches in managing digital assets. This paper presents and studies an analytical model of an enterprise social network (ESN) employed by organisations in facilitating knowledge sharing and learning. Exploring the design of reward systems that motivate workers to share knowledge, we focus on how the dominant type of learners (avid or impressionable), the network structure of the ESN, and digitisation affect the reward systems. This research provides valuable insights for practitioners to implement appropriate reward systems to promote knowledge sharing and learning in ESNs.     

National differences in entrepreneurial networking

The paper reports findings of a study into the personal contact networks of Greek entrepreneurs, and compares these to results already published for other countries (Canada, Japan, Italy, Northern Ireland, Sweden, the UK, and the USA). Findings show that generic behaviour across borders cannot be assumed, although similarities exist in the under-representation of women as network members, the average age of network contacts, duration of relationships, and average monthly meetings. A clear example has also been provided of a culture where business networks are very deeply embedded in social structures, and where little or no evidence of an instrumentalist neo-classical model can be found. From a theoretical perspective, adaptation of Hofstede's four-dimensional model provides a generally robust interpretive framework for the results.     

原始性创新人格形成的影响因素实证研究

在阅读大量国内外文献的基础上，对原始性创新人格影响因素进行了总结和补充，通过建立和训练BP神经网络确定了影响因素综合权值，结合DEMATEL算法，对1901-2012年诺贝尔物理学奖获得者原始性创新人格形成影响因素进行了重要性分析。按照中心度计算结果，对原始性创新人格形成影响因素进行了排序，根据原因度结果，判定哪些属于原因类影响因素，探寻诺贝尔物理学奖获得者原始性创新人格形成规律。最后，提出了有益于塑造原始性创新人格的对策。     

Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

Forecasting the NN5 time series with hybrid models

We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set.     

ARIMA融合神经网络的人民币汇率预测模型研究

本文在深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型特点的基础上,建立了ARIMA融合NN的人民币汇率时间序列预测模型。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,即将汇率时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARI-MA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终合成为整个序列的预测结果。通过对三种人民币汇率序列的仿真实验表明,融合模型的预测准确率显著高于包括随机游走模型在内的单一模型的预测准确率,从而证实了融合模型用于汇率预测的有效性。这一结果也表明,人民币汇率市场并不符合有效市场假设,可以通过模型对汇率未来走势做出较准确预测。     

第三产业产值比重预测的BP神经网络模型

宏观经济政策的制定要参照各次产业的发展水平,所以对产业发展水平进行评估和预测就显得十分必要。通过运用改进BP算法神经网络的建模方法对我国第三产业产值比重进行分析,最后建立了单隐层的BP神经网络模型。结果表明基于改进BP算法的神经网络模型预测精度高,收敛速度快,具有良好的泛化能力,模型的通用性和实用性强。     

基于广义回归神经网络的铁路货运量预测

针对BP神经网络预测存在局部极小缺陷和收敛速度慢的问题,提出基于广义回归神经网络(GRNN)的预测模型。基于我国1999—2008年铁路货运量的历史统计数据,应用GRNN模型和混沌BP神经网络模型对铁路货运量进行预测。通过两种预测模型的计算结果比较说明,GRNN模型具有良好的收敛性和较高的精度,而且模型结构简单、计算速度快,具有良好的实用性。     

基于模糊神经网络的商品价格预测模型

为了方便地预测大型商品的价格,建立了一个模糊神经网络预测模型。该模型能够通过分析影响商品价格的各种参数及历史数据来评定一个新商品的大致价格水平。同时,在比较了FNN与ANN的基础上得出了FNN的优势。     

Understanding novelty creation in exploration networks—Structural and relational embeddedness jointly considered

In exploration networks the key-organisational question is not how to organise a division of labour but instead how to create novelty. The aim of this paper is to develop an understanding of how such novelty in exploration networks is created. Based on an empirical analysis of the multimedia and biotechnology industries in the Netherlands, this paper shows that exploration networks face a trade-off between diversity and selection. Moreover, the findings indicate that depending on the type of exploration task, exploration networks need to make a combination of density and tie strength in such a way that diversity and selection are aligned. The paper concludes, among others, that the views of Burt, Coleman and Granovetter should not be seen as contradictory, but rather as proponents of complementary views.