Mining the past to determine the future: Comments

In forecasting, data mining is frequently perceived as a distinct technological discipline without immediate relevance to the challenges of time series prediction. However, Hand (2009) postulates that when the large cross-sectional datasets of data mining and the high-frequency time series of forecasting converge, common problems and opportunities are created for the two disciplines. This commentary attempts to establish the relationship between data mining and forecasting via the dataset properties of aggregate and disaggregate modelling, in order to identify areas where research in data mining may contribute to current forecasting challenges, and vice versa. To forecasting, data mining offers insights on how to handle large, sparse datasets with many binary variables, in feature and instance selection. Furthermore data mining and related disciplines may stimulate research into how to overcome selectivity bias using reject inference on observational datasets and, through the use of experimental time series data, how to extend the utility and costs of errors beyond measuring performance, and how to find suitable time series benchmarks to evaluate computer intensive algorithms. Equally, data mining can profit from forecasting’s expertise in handling nonstationary data to counter the out-of-date-data problem, and how to develop empirical evidence beyond the fine tuning of algorithms, leading to a number of potential synergies and stimulating research in both data mining and forecasting.     

Developmental network structure and support: gendered consequences for work–family strain and work–parenting strain in the Australian mining industry

Developmental networks enhance career success through the support received by the protégé via the network structure. This paper extends developmental network research by exploring the extent to which strain is associated with developmental network structure and support in the Australian mining industry, a highly volatile and unique context. Our research tests the popular notion of ‘the more support you get, the better’ which is in need of further exploration in the developmental networks literature particularly in specific work contexts with strain (vs. success variables) as outcomes. Results indicate that bigger, broader networks with more career, psychosocial and role modelling support are not always beneficial for protégés in this context. A smaller network with a broader range of developers is associated with reduced work–parenting strain, but not work–family strain. Increased career support reduced work–family strain, but this was not the case for increased psychosocial support and role modelling support. Further, gender moderated the relationship between psychosocial support and work–family strain possibly due to token group effects.     

How product representation influences the understanding of supply chain process models

ABSTRACT

Process models are increasingly being used to analyse business processes within supply chains. Although products are an essential part of supply chain semantics, product representation in supply chain process models is insufficient. This research proposes a novel product representation, namely, ‘labelled flows,’ which directly assigns product names to flows instead of implicitly representing products through other visual constructs. Using a laboratory experiment, we find that labelled flows improve domain understanding with respect to product comprehension and product modelling performance. Our contribution to modelling research is a novel product representation within supply chain process models, which enhances domain understanding.     

Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains

Anchored in the resource‐based view theory, the objective of this research is to empirically analyse the behavioural factors affecting the green supply chain management (GCSM) performance in a fast‐growing emerging economy by taking an empirical data set of 101 responses from personnel in the mining sector. Behavioural factors in green supply chains are still a critical challenge—not yet a well‐explored academic subject—when the focus is on the mining industry of emerging economies like India; the lack of studies in this field could be a factor preventing the Indian mining industry becoming more green. In terms of methodology, original survey data were processed through AMOS 4.0, adopted for assessing the causal connection among the six constructs, that is, top management support, teamwork, workplace culture, resistance to change, green innovation, and green motivation. We further explore the input from the human side of GCSM by highlighting that top management support and green motivation are the most crucial behavioural factors that influence GCSM in the Indian mining sector. The study will be helpful for mining companies because it will enable them to identify the areas that require their attention for enhancing GCSM performance related to behavioural aspects.     

Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time‐to‐event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real‐life data examples that focus on the survival of end‐stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed.     

Web数据挖掘中XML技术的应用研究

随着网络技术的发展,网上的信息资源越来越丰富,怎样对Web上海量的数据信息进行深层次的应用成了当今数据库技术的研究热点。文章针对Web上数据的特点和目前Web数据挖掘存在的挑战,结合XML语言的技术特点,主要探讨了XML技术在Web数据挖掘中的重要应用。     

Process innovation laboratory: a new approach to business process innovation based on enterprise information systems

Most organizations today are required not only to operate effective business processes but also to allow for changing business conditions at an increasing rate. Today nearly every business relies on their enterprise information systems (EIS) for process integration and future generations of EIS will increasingly be driven by business process models. Consequently business process modelling and improvement is becoming a serious challenge. The aim of this paper is to establish a conceptual framework for business process innovation (BPI) in the supply chain based on advanced EIS. The challenge is thus to create a new methodology for developing and exploring process models and applications. The paper outlines the process innovation laboratory (Π-Lab) as a new approach to BPI. The Π-Lab is a comprehensive framework and a collaborative workspace for experimenting with process models. The Π-Lab facilitates innovation by using an integrated action learning approach to process modelling in a controlled environment. The study is based on design science and the paper also discusses the implications to EIS research and practice.     

我国煤矿安全事故分析及对策探究

随着矿井开采深度的增加,煤炭开采条件越来越复杂,管理更加困难,加之我国煤层赋存条件的复杂性,形成了与国民经济飞速发展所需要的大量煤炭资源相矛盾的突出状况,这给今后我国煤矿安全生产带来了极大的挑战。文章通过分析我国煤矿安全事故的特征及相关的事故原因,提出了相关的对策,以期望对指导我国煤矿安全生产有所启示。     

Training and business performance: the Spanish case

This paper examines the relationship between training policies and business performance. Our research seeks to enlarge the empirical bibliography about the impact training has on firms and tries to challenge the criticism previous works with similar characteristics received. With this purpose in mind, we have used a theoretical model based on the hypothesis of a ‘cascade-type relationship’ between four types of organizational performance. The results obtained from a sample of Spanish firms show that training policy (based on the human capital theory and the resource-based theory) has a significant impact on firm performance.     

The links between active cooperation and eco-innovation orientation of firms: A multi-analysis study

The environmental orientation of companies is key for firms to gain a competitive advantage against peers. However, the high level of novelty and uncertainty involved with eco-innovations requires additional knowledge and capabilities that go beyond the firm and that can be achieved through cooperation. Thus, it is crucial to analyse how cooperation affects the elements that drive eco-innovation. This study tests the impact of cooperation on the environmental orientation of companies while innovating using structural equation modelling with partial least squares and multigroup analysis and a fuzzy-set qualitative comparative analysis for a sample of Spanish companies. Results suggest that companies that do not cooperate are less eco-innovation-oriented and show lower dependence on external information sources, although their impact on the orientation to product and process innovation is higher. This work leads to some theoretical conclusions and implications for researchers and practitioners.     

Evolutionäre Algorithmen im Marketing

Evolutionary algorithms (EA) are search heuristics, which simulate the basic principles of biological evolution and by reenacting the evolutionary mutation and selection processes, find optimum solutions in a predefined environment. In this article we examine the current state of research on EA in fields related to marketing and discuss potential applications in marketing research. Based on a bibliographic network and factor analysis, we identify nine focus areas in current research, most of which are in the operations research area. In particular, interactive EA hitherto have hardly ever been used in marketing research. In consumer surveys they can be linked to elements of classical market research in order to design products that provide consumers with maximum benefit from their viewpoint. Thanks to their model-free approach, evolutionary algorithms offer an alternative to conjoint analyses.     

烟草建设项目知识管理研究

烟草建设的大型化、复杂化,给项目管理的知识共享带来极大的挑战,如何应用现代管理手段,实现知识的收集、积累、挖掘、总结和共享,是当前项目管理工作亟待解决的问题。本文基于烟草建设项目的工程实践,对知识管理在烟草建设项目管理中的应用作了一定的研究,分析了知识管理的对象和实施机理,提出了知识管理的措施。     

基于盈利能力的智能化客户细分

在竞争激烈的商务环境下,企业保持竞争优势的关键因素是识别最有盈利能力的顾客。文章构建了一个多智能代理的系统,以满意度调查为基础的盈利能力顾客细分,应用商业智能工具DEA数据包络分析方法,SOM自组织映射神经网络和C4.5判定树来按顾客的盈利能力进行细分,并且给出了应用实例的分析以及相应的营销战略。     

A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining

As product development has recently emphasized user innovation, it should necessarily reflect customer-perceived value, as well as technological value itself. However, while previous studies for technology planning have focused on analyzing the potential of technology, they have not considered the customer-perceived value that technology can create in a new product. Therefore, this research suggests a new approach to assessing the level of technology and evaluating R&D projects, by investigating customer-perceived value on technology through the use of the structural equation model and opinion mining. For this, the assessment framework is developed in terms of technology, product quality, and customer satisfaction, respectively, by investigating a variety of databases on each factor. The relationship between technology level and customer satisfaction is analyzed, using structural equation model and opinion mining. Based on the results, a strategy for technology development is established through gap analysis and simulation, after selecting and evaluating technologies that need to be developed. The proposed approach is applied to the real case of a moving system, in particular an automobile door, and we obtained that an R&D project for hinge-related technology would be promising, enhancing the customer satisfaction. It can suggest a future direction for new technology development. This paper contributes to enhancing the efficiency of technology planning based on the customer-perceived value, enabling the launch of new R&D projects.     

How does line-manager support enhance worker wellbeing? A study in China

Abstract

Drawing on the job demands-resources and effort-reward imbalance models, this study investigates the mediating roles of intrinsic motivation and distributive justice in the relationships between line-manager support and employee wellbeing (job satisfaction, work engagement and physical health). Responses to a survey of 357 front-line workers in a Chinese manufacturer were analysed through structural equation modelling. We find, in this context, that line managers are a strong determinant of employee wellbeing when they foster the intrinsic motivation of employees and their perceptions of organisational justice. Such findings are helpful for our understanding of attitudes in the Chinese workforce and underline the value of analysing multiple mediating mechanisms and multiple wellbeing outcomes in human resource management-wellbeing research.     

基于GIS的遥感影像分类方法研究

在分析空间数据获取现状的基础上提出空间数据挖掘的必要性,对遥感影像分类技术和方法进行了研究,提出GIS平台和数据挖掘算法集成所挖掘的知识是其影像分类的重要知识源。最后通过实验对以上的研究和分析进行了验证。     

Effectiveness of Q-learning as a tool for calibrating agent-based supply network models

This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for ‘optimal’ values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the ‘optimal’ parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the ‘optimal’ parameter values in all model settings and scenarios considered.     

Analysing enterprise architecture maturity models: a learning perspective

In order to aid organisations in the adoption of enterprise architecture (EA) best practices, maturity models have been proposed in the literature. These models offer organisational roadmaps and assessment frameworks for increasing EA maturity. However, key questions concerning the implied meaning of the term maturity in the context of these models have been left unexplored by previous research. This research, aided by the field of organisational learning, offers new insights into the implied assumptions of current EA maturity models and offers initial concepts and constructs to guide the conceptualisation, construction and refinement of enterprise maturity models.     

上洋坑矿区构造特征及其对含煤地层的影响分析

文章通过对上洋坑矿区构造特征及其形成机制的分析与研究,初步探讨了滑覆断层、正断层以及岩浆岩对含煤地层的影响,对今后该矿区的勘探与开发具有一定的指导意义。     

Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods

ABSTRACT

A major challenge in the analysis of micro-level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We propose extending the simple spatially lagged explanatory (SLX) model to include two spatial weighting matrices at different spatial scales to reduce omitted-variable bias. The approach is suitable when actual interaction takes place on a smaller local level, while the omitted variables are spatially correlated at a larger regional level and correlated with the included characteristics. We provide an empirical motivation and use Monte Carlo simulation to illustrate the bias-reduction effects in certain settings.