引文分析学的知识流动理论探析

从观念联系与知识流动的角度来探讨引文分析理论,除去那些复杂的文献引用动机或引用的社会影响因素外,可以把文献引用的过程抽象为知识流动的过程。从知识论关于知识的发展模式来看,文献引用的过程都是在前人知识的基础上知识进化的过程,是知识的选择、遗传和变异的过程,也是知识的生产、传播和应用的过程。因此,引文分析的过程就是对知识流动过程和知识活动系统的分析。     

大数据分析能力与制造企业服务创新绩效:一个链式中介模型

围绕动态能力理论,挖掘制造企业服务创新绩效提升的前因变量,探究大数据分析能力对制造企业服务创新绩效的影响及其作用机制,并利用285家制造企业数据检验研究假设.基于层级回归分析和Bootstrap方法的实证结果表明,大数据分析能力不仅正向影响制造企业服务创新绩效,也正向影响资源拼凑与组织敏捷性;资源拼凑与组织敏捷性均有助于制造企业服务创新绩效的提升;资源拼凑与组织敏捷性在大数据分析能力和制造企业服务创新绩效的关系中起部分中介作用,且为链式中介.研究结论的实践启示为:制造企业需重视对大数据分析的投资,通过提升大数据分析能力实现高资源拼凑、高组织敏捷性和服务创新高绩效;同时需重视采取资源拼凑战略行为,通过有意识地将现有资源进行组拼以率先实现对新服务需求的满足、创造出优于竞争对手的服务创新成果.     

几个重要复积分定理的条件的简化

本文证明了在简单曲线上处处解析的函数在此曲线上存在原函数,由此证明了强可积的条件和Cauchy积分定理、复合闭路定理的条件可在一定范围内得到简化,并给出了相应的积分计算公式与留数计算公式．     

A probabilistic approach to information management of order fulfilment reliability with the help of perfect-order analytics

Supplier reliability and order fulfilment performance are usually assessed using a perfect-order calculation. Information management of perfect-order estimation is frequently reduced to expert estimates and to the multiplication of probabilities of failure-free performance of some logistics operations. Moreover, perfect-order estimation is calculated without consideration of supply chain structure, possible combinations of failures, and operational policies (e.g., safety stock levels and alternative transportation routes). As a result, the existing methods frequently provide different estimates for the same statistics and cannot be consistently used in the allocation of companies’ resources to improve the order fulfilment process. This paper considers different variants of probabilistic assessment of a perfect order and proposes an approach to assess the impact of changes in parameter probabilities and number of parameters on the value of a perfect order. The proposed models are based on an analytical approach using discrete distributions of random variables. We illustrate the applicability of our approach to several numerical examples to confirm the adequacy of the proposed method. Our approach can be immediately applied in practice to assess supply and order fulfilment process reliability and to evaluate the effectiveness of various operational policies (safety stock levels or modes of transportation) to achieve some planned values of a perfect order in the supply chain.     

Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor

Analyzing and extracting insights from user-generated data has become a topic of interest among businesses and research groups because such data contains valuable information, e.g., consumers’ opinions, ratings, and recommendations of products and services. However, the true value of social media data is rarely discovered due to overloaded information. Existing literature in analyzing online hotel reviews mainly focuses on a single data resource, lexicon, and analysis method and rarely provides marketing insights and decision-making information to improve business’ service and quality of products. We propose an integrated framework which includes a data crawler, data preprocessing, sentiment-sensitive tree construction, convolution tree kernel classification, aspect extraction and category detection, and visual analytics to gain insights into hotel ratings and reviews. The empirical findings show that our proposed approach outperforms baseline algorithms as well as well-known sentiment classification methods, and achieves high precision (0.95) and recall (0.96). The visual analytics results reveal that Business travelers tend to give lower ratings, while Couples tend to give higher ratings. In general, users tend to rate lowest in July and highest in December. The Business travelers more frequently use negative keywords, such as “rude,” “terrible,” “horrible,” “broken,” and “dirty,” to express their dissatisfied emotions toward their hotel stays in July.     

Analysing the tweets to examine the behavioural response of Indian citizens over the approval of national education policy 2020

Indian education regulating body introduces the National Education Policy 2020 (NEP 2020) with the objective of improving the education infrastructure. In this study, we analyse the tweets (related to NEP) intending to look for the behavioural responses of the Indian People over the approval of NEP. It interestingly points out that some of the Indian states are showing majorly negative emotions for the acceptance of policies in the new education system in the initial period of the NEP. This opposition is demonstrated in the form of agitation and starting opposing hashtags on social networks. The outcomes of this study will help the education policymakers in getting an insight into the response of Indian citizens in context to the purposed policy. They can take preventive, perfective, and corrective measures to improve and provide more clarification to the people of the regions where the negative emotions are alarming.     

What can moment-by-moment learning curves tell about students’ self-regulated learning?

Many students in primary education learn arithmetic using adaptive learning technologies (ALTs) on tablets every day. Driven by developments in the emerging field of learning analytics, these technologies adjust problems based on learners' performance. Yet, until now it is largely unclear how students regulate their learning with ALTs. Hence, we explored how learners regulate their effort, accuracy and learning with an ALT using moment-by-moment learning curves. The results indicated that moment-by-moment learning curves did reflect students’ accuracy and learning, but no associations with effort were found. Immediate drops were associated with high prior knowledge and suboptimal learning. Immediate peaks were associated with robust learning and pointed to effective student regulation. Close multiple spikes showed moderate learning and lower initial levels of accuracy but, with system support, these students seemed able to regulate their learning. Separated multiple spikes indicated reduced learning and accuracy and potentially signal the inability of students to regulate their learning. In this light, moment-by-moment learning curves seem to be valuable indicators of accuracy regulation during learning with ALTs and could potentially be used in interventions to support SRL with personalized visualizations.     

The use of model based recursive partitioning as an analytic tool in child welfare

Child welfare agencies are tasked with investigating allegations of child maltreatment and intervening when necessary. Researchers are turning to the field of predictive analytics to optimize data analysis and data-driven decision making. To demonstrate the utility of statistical algorithms that preceded the current predictive analytics, we used Model Based (MOB) recursive partitioning, a variant of regression analysis known as decision trees, on a dataset of cases and controls with a binary outcome of serious maltreatment (defined as hospitalization or death). We ran two models, one which split a robust set of variables significantly correlated with the outcome on the partitioning of a proxy variable for environmental poverty, and one which ran the same variable set partitioned on a variable representing confirmed prior maltreatment. Both models found that what most differentiated children was spending greater than 2% of the timeframe of interest in foster care, and that for some children, lack of Medicaid eligibility almost doubled or tripled the odds of serious maltreatment. We find that decision trees such as MOB can augment risk assessment tools and other data analyses, informing data-driven program and policy decision making. We caution that decision trees, as with any other predictive tool, must be evaluated for inherent biases that may be contained in the proxy variables and the results interpreted carefully. Predictive analytics, as a class, should be used to augment, but not replace, critical thinking in child welfare decision making.     

Evolution of game-play in the Australian Football League from 2001 to 2015

This study investigated the evolution of game-play manifested via team performance indicator characteristics in the Australian Football League (AFL) from the 2001 to 2015 seasons. The mean values for 18 performance indicators were collated for every AFL team over 15-seasons. A multivariate analysis was used to uncover temporal trends in the dataset. Compared to the 2004 season, the 2005 to 2010 seasons were characterised by large growth in the counts of handballs (d = 0.83; 90% CI = 0.22–1.43), disposals (d = 1.24; 90% CI = 0.59–1.87), uncontested possessions (d = 1.37; 90% CI = 0.71–2.01), clangers (d = 2.14; 90% CI = 1.39–2.86) and marks (d = 1.43; 90% CI = 0.76–2.07). Contrastingly, the effective disposal percentage declined rapidly during the same period. The number of inside 50 m counts remained stable throughout the 15-season period. The ordination plot of league-wide performance indicator characteristics illustrated a distinct cluster from the 2001 to 2004 seasons, an abrupt shift from the 2005 to 2009 seasons, and an emergent (re)stabilisation from the 2010 to 2015 seasons. The results demonstrate the synchronous league-wide evolution of game-play in the AFL from the 2001 to 2015 seasons. Amongst other constituents, this evolution likely reflects the introduction of modernised coaching strategies, rule changes and changing perceptions of rule interpretations.