Decoding digital transformational outsourcing: The role of service providers’ capabilities

The business process outsourcing industry has got disrupted, first by the significant shift in value creation activities from the clients to the service providers, and second by pervasive digital penetration, resulting in the emergence of Digital Transformational Outsourcing (DTO). Service providers now play a more significant role, making their capabilities important. In the new context, service providers require a uniquely different set of dynamic capabilities to handle end-to-end business functions on behalf of their clients while delivering digital value propositions.We study 26 of the largest global business process outsourcing providers to conceptualise and identify six dynamic capabilities of service providers salient in the new context, i.e., consultative, orchestration, insights, network management, knowledge access, and standardisation. Interviews conducted with industry experts provided evidence in support of the identified dynamic capabilities. A novel firm capability dataset was created using secondary data, and using fuzzy-set Qualitative Comparative Analysis (fsQCA), we identify configurations for high and low performance and find them to vary by the firm's broad/narrow scope.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).     

论大众竞技运动与精英竞技运动的协调发展

体育与竞技运动是两个不同的概念 ,两者都有普及与提高的问题 ,精英竞技运动的发展不是建立在大众体育和学校体育的基础之上的 ,其发展的基础是竞技运动在全社会的普及。传统的认识存在许多缺陷 ,导致在实践中产生很多偏差。讨论了大众竞技运动与精英竞技运动的异同 ,分析了西方国家管理与发展竞技运动的模式、中国发展竞技运动成功的经验及存在的问题 ,说明了应如何理解所谓大众竞技运动与精英竞技运动协调发展的问题 ,最后从 5个方面探讨了大众竞技运动与精英竞技运动协调发展的基本对策。     

正交变换在积分中的应用

利用正交变换,在重积分、曲面积分中处理积分的变数替换。     

浅析师生互动式营销案例教学法

现代经济社会所要求的人才培养的目标是具有创新思维、创新能力的新一代创造型人才,而以学生为中心的教学模式无疑是达到这一目标的有效途径。本文通过对各类企业典型案例的深入分析,使学生既能牢固地掌握所学的基础理论知识,又能针对具体企业、具体问题作出自己的判断,提出具有个性化的分析诊断以及营销管理对策,这对于培养具有创新观念的新型人才意义深远。     

知识经济背景下科技期刊的变革与编辑的转型

论述知识经济时代科技期刊及其生存空间的变革，为适应这种变革，科技期刊编辑应尽快转型，包括思想观念的转型，知识结构的转型，编辑职能和工作方式的转型，以及编辑社会角色的转型。     

谈我国体育体系的根本性大变革

党的十六届三中全会的“决定”中提出，我国体育要脱开同卫生事业的历史性联合，转入文化范畴，这就表明体育的本质功能和根本任务的根本变化，也标志着体育体系的大革变，有其重要意义，其间当然存在许多理论和实践问题，需要我们认真探讨。     

哪些研究问题需要用分层线性模型回答

采用常规的回归分析或方差分析处理行为科学与社会科学研究中经常遇到的嵌套数据，不能满足独立观察的假定，得到的标准误差较小，导致I型错误扩大化，同时，也不利于对不同层次变量不同作用的探讨，分层线性则明确区分数据层次，通过对个体水平变量和组别水平变量的分层综合分析，避免了上述弊病，因而可对个体水平的变量进行更准确的预测和更合理的解释，通过实例介绍了将研究问题与分层线性模型有机结合的方法。     

民国时期社会风俗现代转型的二重性特征

民国时期新旧递嬗、方生方死的持续过渡型社会形态,铸就了此期风俗文化现代转型鲜明的二重性特征:在中西新旧风俗文化的相互关系上,新旧纷陈的杂糅性、过渡性与中西会通、迎新纳异的兼容性、开放性并存;在移风易俗的力度、广度上,较前而言的深入性、广泛性与此期仍甚显著的迟滞性、不平衡性并存;在风俗变迁的社会效应上,推动社会文化发展的进步性与尚奢、崇洋之风日益滋长的负面性兼而有之。