Strategy models for enabling offshore outsourcing: Russian short-cycle-time software development

Economic factors are driving software development projects onto globally dispersed models, as offshore outsourcing becomes more common. Software development companies in developing economies compete for lucrative, job-creating offshore contracts on the basis of industry maturity, labor skills, technology infrastructure, and government support. Diffusion of technology is a key aspect of each of these determinants of competitiveness. This paper analyzes the development of strategies for the diffusion of short-cycle-time software development into and within Russia. Short-cycle-time development is sometimes called agile development or Internet-speed development and uses a number of techniques to move software quickly into production. These techniques are spreading rapidly among software developers worldwide. The benefits of these techniques are well known and provide a credible explanation for why this rapid diffusion is occurring. This paper explains how these techniques are spreading in a borderless fashion. Using the Kline model of innovation diffusion and the Greiner model of evolution and growth of organizations, we analyze the enablers and barriers to diffusion of short-cycle-time software development techniques in Russia. This analysis reveals a complex interaction of political, economic, and technical elements enabling and inhibiting the development of knowledge that supports the innovation diffusion necessary for companies to compete for offshore contracts. © 2005 Wiley Periodicals, Inc.     

Design for innovation – A methodology to engineer the innovation diffusion into the development process

In its hyper-inflated usage, innovation simply means “something new”, and is applied to any technical novelty. In its true meaning, innovating means designing something that will not only work under a technical point of view, but will also make business sense. “Design for Innovation” means considering that design cannot simply focus on a narrow meaning of “product use”, because this could severely limit the diffusion of innovative products. The paper proposes an original model for representing what we call “beyond-use situations” and the influences among the actors involved in the innovation diffusion process.Taking inspiration from social influence network models and from the Multi-issue Actor Strategy Analysis Model (MASAM), the paper presents an operational methodology to assess the influence of different actors on the decision to adopt a new product. In turn, such methodology should support design teams to conceive novel solutions more likely to become factual innovations. The paper also describes a computer-implementable technique, loosely derived from Quality Function Deployment, to practically apply the proposed methodology. An industrial case study from the medical-care sector illustrates its logic and operational steps.     

A knowledge-focused perspective on the diffusion and adoption of complex information technologies: the BPR example

This paper presents a knowledge‐focused perspective for the development of a model to explain the diffusion and adoption of complex integrating technologies. Business process re‐engineering (BPR) is used as the example to illustrate the model. However, while BPR is used to illustrate our argument, the model that is developed is relevant to understanding the innovation processes surrounding any complex IT‐based innovation. It is argued that the strength of this diffusion model is that it focuses not on the spread of particular technological artifacts (whether it is BPR or any other IT‐based innovation), but on the spread of the ideas and knowledge underpinning the technology. In particular, the model draws attention to the ways in which technology suppliers commodify knowledge and present ‘packaged’ solutions. This creates problems for potential users who need to unpack this knowledge and integrate it with existing organizational knowledge. The diffusion and adoption of innovations is thus seen as a process of integrating knowledge across disparate communities. Such knowledge integration, however, is difficult. This can help to explain the apparent contradiction between the limited success rate of BPR and its widespread diffusion among western firms.     

图像扩散去噪模型的分析与改进

总结与分析了已有图像扩散去噪模型的优缺点。在理论上明确解释了张量型扩散模型的物理意义,通过分析P-M扩散模型的局部扩散行为,提出一个新的扩散系数,进一步给出一个改进的张量型扩散模型。从主观与客观两个方面比较各种扩散去噪模型的效果都不容易,因为需要合适耦合各个模型的参数及数值离散方法等,为此给出了扩散模型统一的数值实现算法,可用来比较各个模型的去噪效果。数值模拟实验的结果表明,改进的扩散模型在有效去除噪声的同时,能很好地对图像中的边缘、角点、纹理等特征进行保护,去噪后的图像有较好的视觉效果。     

ICT adoption in Cameroon SME: application of Bass diffusion model*

ABSTRACT

In almost all low-income countries, small- and medium-sized enterprises (SMEs) are a primary engine for the country’s economic development. While many information and communications technology (ICT) diffusion studies exist, only few focus on low-income countries and even fewer employ Bass-based analysis to examine ICT diffusion in these countries. This study applies the Bass diffusion model to understand SME adoption of ICT in Cameroon, a low-income country. The Bass model was employed because of its predictive capacity. We find that diffusion of ICT among SMEs in the context of a low-income economy is largely driven by forces of imitation rather than forces of innovation. Contributing to practice, this study finds that SMEs with greater sizes, multiple plants, and whose owners have higher education have a greater tendency to adopt ICT early. The theoretical contribution of the paper is applying the well-recognized Bass model from marketing to the IT/IS field and applying it within a low-income country environment by evaluating diffusion of ICTs among SMEs in Cameroon.     

基于信息扩散视角的虚拟社区用户影响力研究

节点排序研究领域中,少有研究考虑群聚效应下的群体规范对传播效率的影响,这可能导致用户影响力度量的准确性下降。针对这一问题,从信息扩散角度出发,借鉴创新扩散理论与Bass扩散模型,提出一种适用于虚拟社区网络的用户局部影响力度量模型CSA-LL（Cohesive Subgroup Analysis Based Local Leadership）：基于凝聚子群挖掘与分析,定义子群内部信息扩散效率,并结合用户全局影响力,计算模型输出值作为节点排序的依据。爬取近期的豆瓣社区数据进行网络构建,使用AISAS模型等方法验证了该模型输出的用户比PageRank算法和Hits算法结果具有更强的营销能力。使用LT模型进一步验证了模型的有效性和子群信息扩散效率对用户传播能力存在正向影响。再使用多个虚拟社区网络数据集和IC模型,分别验证了模型鲁棒性与结论稳健性。     

微博网络信息传播研究综述

微博作为一种新型的社交网络平台, 以其使用方式便捷、传播迅速、交互性强等特点, 在信息分发和舆论传播方面发挥了非常重要的作用。简要介绍了微博的相关机制, 分析了微博信息的传播特征以及影响信息传播的三大因素, 对目前研究所使用的信息传播模型进行了分类, 重点综述了基于传播过程的模型、基于影响力的模型以及基于转发因素的模型, 并对这三类模型的优缺点作了比较分析, 提出了该领域的研究方向和目标。     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.     

基于行为预测的微博网络信息传播建模

研究微博网络中的信息传播及扩散机制在市场营销、舆情管控等方面具有重要意义。当前的传播模型大多忽视了用户间的个体差异。为解决这一问题, 提取了影响转发行为的四类特征, 利用机器学习中的逻辑回归模型分析预测个体转发行为, 并在此基础上融入用户个体差异, 建立了一种基于行为预测的信息传播模型。实验表明, 该模型能较好地模拟真实网络中的信息传播过程。     

Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.     

传播结果的同源分析

传播结果的同源分析旨在识别出哪些历史传播结果是由同一组传播源头产生的。使用伯努利混合模型对传播结果的同源分析问题进行建模求解,同一组源头产生的传播结果对应一个伯努利分量,而伯努利分量的参数反映源头的影响在各点的传播到达概率;基于该模型和观测数据构建对数似然函数,并使用EM算法求解其中的伯努利参数,确定同源分析的结果。大量实验结果证明,对比于传统聚类算法,提出算法同源聚类的准确度更高,能够更高效准确地对传播结果数据进行同源分析。     

Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust

The growing presence of online travel communities is leading to great developments in the travel industry. Grounded in the innovation diffusion theory (IDT) and the technology acceptance model (TAM), this paper seek to develop and empirically test a comprehensive framework to examine the antecedents of customers' intention to participate in online travel community. Using SEM to analyse the data collected from a sample of 495 members, the results indicate that innovation diffusion theory and TAM with trust provide an appropriate model for explaining consumers' intention to participate; this intention in turn has a positive influence on intention to purchase and positive WOM. Furthermore, religiosity plays an important role in understanding consumers' behavioural intention. The results offer important implications for online service provider and are likely to stimulate further research in the area of online travel community.     

两强竞争市场中的企业广告策略分析

将Dawid和Feichtinger的混沌型的广告扩散型扩展到两强竞争的市场环境下，为检验模型的可用性开发了一个计算机仿真系统，仿真的结果表明在两强竞争下的企业最好的广告策略是增加广告投入直到达到“双赢状态”，即市场占有率的振荡即将发生的临界状态，分析应用结果表明提出的混沌型模型和开发的仿真系统为市场竞争环境下的企业广告策略的分析和制定提供了一个有效的工具。     

知识状态神经推理的知识追踪模型

针对知识追踪研究忽略知识点之间的拓扑关系,从而限制了知识点状态的表示并最终影响预测效果的问题,提出一种知识状态神经推理知识追踪模型。首先建立知识点关系图和知识点状态图;其次利用扩散模型得到关系图和状态图的投影并完成融合;然后利用逆扩散过程得到融合了知识点关系的整体知识状态表示;最后预测学习者的表现。在模型有效性方面,在若干数据集上对比四个相关模型,所提模型均取得了一定的优势。在可解释性方面,展示了知识状态进化过程与真实答题结果之间的对应关系。在实际应用方面,以该模型为基础的智慧学习环境已分别应用于一门人工智能课程和英语语法课程,并取得了优于对比模型的结果。     

Numerical Solutions to Some Optimal Control Problems Arising from Innovation Diffusion

In this paper we propose a numerical approach for the solution of some optimalcontrol problems arising in the field of marketing decision models. Inparticular, we account for a specific innovation diffusion model. A numericalapproach may be useful to investigate some features of state variables andparameters of interest. The discrete problem is solved by the SimulatedAnnealing method and the resulting numerical scheme is applied to some testcases.     

一种图像放大的偏微分方程综合模型

针对传统图像放大处理过程中基于线性插值方法通常导致边缘模糊问题,分析了各向同性扩散模型和各向异性扩散模型在图像处理中的优缺点,提出了一种线性扩散和P-M方程自适应结合的图像放大综合模型。该模型对图像非平滑区域采用各向异性扩散模型处理,而平滑区域则采用各向同性扩散模型处理。实验结果表明,该综合模型在保持图像边缘锐度的同时提高了图像的清晰度,能够有效提高放大图像的主观视觉质量和客观SNR及PSNR。     

International Diffusion of Digital Mobile Technology: A Coupled-Hazard State-Based Approach

The convergence of the Internet, electronic commerce, and wireless technology has created the basis for the rapid global diffusion of mobile commerce. We believe that one approach to understand mobile commerce diffusion is to study the diffusion of digital mobile devices required in mobile commerce activities. Although prior research in technology diffusion has identified a set of variables that affect the entire diffusion process, our knowledge about the factors that dominate at different states of a diffusion process is still incomplete. This research puts forward a new theoretical perspective to enable managers to better understand the states of technology diffusion in the context of digital mobile phones. Our empirical methods involve a coupled-hazard analysis of an interdependent event model to test the effects of country characteristics, the digital and the analog mobile phone industry characteristics, and the regulatory policies on various states of digital mobile phone diffusion across countries. We conduct non-parametric and parametric survival analysis of the model. The results illustrate a broader set of factors that drive the diffusion speed from the early to the partial diffusion state than from the introduction to the early diffusion state.     

热扩散和整体变分模型自适应调整的图像放大模型

近年来基于偏微分方程的图像处理因其具有更强的局部自适应特性和高度的灵活性而成为继小波图像处理之后新的研究热点。本文在对热扩散模型和TV（Total Variation）模型分析的基础上,针对二模型在图像平滑区域及边缘或纹理区域扩散所表现出的不同特性和优势,提出一种基于图像局部特性的自适应混合图像放大模型,该模型以双线性插值后的放大图像为初始值,根据像素点梯度特性在平滑区域和边缘或纹理区域自适应的调整扩散模型,使得在平滑区域进行热传导的同性扩散,而在边缘和纹理区域处则进行沿与梯度方向相垂直的方向扩散。该模型很好的抑制了图像插值放大后所带来的块状效应,以及放大后边缘或纹理区域的假边缘现象。理论上证明了所提出模型解的存在性,同时大量的仿真实验验证了所提出模型的有效性。     

Predicting personal information system adoption using an integrated diffusion model

The purpose of this study is to develop a research model that integrates the relationship between innovation characteristics (innovation diffusion theory) and technology characteristics (task-technology fit model). With the research model, the current study investigates the adoption of the “personal information system,” a concept that we develop in the context of mobile technologies. In this paper, we performed the first quantitative test of the model by integrating the innovation diffusion and task-technology fit models. Further, we proposed a theoretical definition of “personal information systems” by highlighting the differences in the types of tasks across individual users’ needs. To test our research model, 202 college students were surveyed. Partial least squares (PLS) structural equation modeling was employed to analyze our data, and these analyses provided empirical support for the proposed hypotheses. Quality, compatibility, cost, and relative advantage were found to be important indicators of the intention to adopt due to their impact on the intended communication, information, transaction, and entertainment tasks, while compatibility, relative advantage, and complexity had a direct impact on the intention to adopt.     

基于动态扩散卷积交互图神经网络的网络流量预测

现有的网络流量预测模型存在着泛化能力弱和预测准确率低等问题,为了解决此问题,提出了一种结合动态扩散卷积模块和卷积交互模块的预测模型。动态扩散卷积模块可以提取网络流量中复杂的空间特征和动态特性,而卷积交互模块则能捕获到流量中的时间特征,两者的有机结合可以有效预测网络中的流量。通过与其他网络流量预测模型在美国能源科学网（ESnet）流量数据上进行对比实验,验证了提出的动态扩散卷积交互图神经网络模型（DDCIGNN）的有效性。实验结果表明,DDCIGNN模型的均方根误差（RMSE）在最好的情况下优化了大约13.0％,说明该模型能够进行更有效的网络流量预测。