Managing service productivity using data envelopment analysis

This chapter provides the theoretical foundation and background on data envelopment analysis (DEA) method. We first introduce the basic DEA models. The balance of this chapter focuses on evidences showing DEA has been extensively applied for measuring efficiency and productivity of services including financial services (banking, insurance, securities, and fund management), professional services, health services, education services, environmental and public services, energy services, logistics, tourism, information technology, telecommunications, transport, distribution, audio-visual, media, entertainment, cultural and other business services. Finally, we provide information on the use of Performance Improvement Management Software (PIM-DEA). A free limited version of this software and downloading procedure is also included in this chapter.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Balancing expectations in engineering education:enhancing student experiences through empirical research - a comparative analysis

This paper draws upon the findings of an empirical study comparing the expectations and concerns of engineering students with students enrolled on business and management programs. It argues that whilst the two groups of students have very similar expectations, motivations and concerns before their start their studies, once at university, engineering students are twice as likely to drop-out than are their compatriots in business studies. Drawing upon the study findings, recommendations are made as to what might be done to counteract this. The conclusion argues that there is a need for more in-depth research to be conducted in this area in order to identify the reasons behind the different attrition rates and to further enhance engineering undergraduate experience.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Institutional entrepreneurship:a literature review and analysis of the maturing consulting field

With few exceptions (e.g. Fincham & Clark, 2002; Lounsbury, 2002, 2007; Montgomery & Oliver, 2007), we know little about how emerging professions, such as management consulting, professionalize and establish their services as a taken-for-granted element of social life. This is surprising given that professionals have long been recognized as “institutional agents” (DiMaggio & Powell, 1983; Scott, 2008) (see Chapter 17) and professionalization projects have been closely associated with institutionalization (DiMaggio, 1991). Therefore, in this chapter we take a closer look at a specific type of entrepreneurship in PSFs; drawing on the concept of “institutional entrepreneurship” (DiMaggio, 1988; Garud, Hardy, & Maguire, 2007; Hardy & Maguire, 2008) we describe some generic strategies by which proto-professions can enhance their “institutional capital” (Oliver, 1997), that is, their capacity to extract institutionally contingent resources such as legitimacy, reputation, or client relationships from their environment.

Text messaging forensics : Txt 4n6: idiolect free authorship analysis?

Current debate within forensic authorship analysis has tended to polarise those who argue that analysis methods should reflect a strong cognitive theory of idiolect and others who see less of a need to look behind the stylistic variation of the texts they are examining. This chapter examines theories of idiolect and asks how useful or necessary they are to the practice of forensic authorship analysis. Taking a specific text messaging case the chapter demonstrates that methodologically rigorous, theoretically informed authorship analysis need not appeal to cognitive theories of idiolect in order to be valid. By considering text messaging forensics, lessons will be drawn which can contribute to wider debates on the role of theories of idiolect in forensic casework.

Analysis of constructivist, network-based discourses:concepts, prospects, and illustrations

The authors propose a new approach to discourse analysis which is based on meta data from social networking behavior of learners who are submerged in a socially constructivist e-learning environment. It is shown that traditional data modeling techniques can be combined with social network analysis - an approach that promises to yield new insights into the largely uncharted domain of network-based discourse analysis. The chapter is treated as a non-technical introduction and is illustrated with real examples, visual representations, and empirical findings. Within the setting of a constructivist statistics course, the chapter provides an illustration of what network-based discourse analysis is about (mainly from a methodological point of view), how it is implemented in practice, and why it is relevant for researchers and educators.