Analyzing M-Service Quality Dimensions Using Multivariate Statistical Techniques

This paper continues previous work of the authors concerning the identification and statistical analysis of the quality dimensions in mobile services (m-services). In this work, the structure of mservice quality into dimensions and criteria, which these dimensions are further analyzed into, is examined and grounded through an empirical analysis. The use of multivariate statistical techniques is decomposed into two stages: in the first stage, Factor Analysis in order to explore the relationship between the examined items (quality criteria) and the constructs (dimensions) proposed through the study of the relevant literature. In the second stage, Cluster Analysis and Principal Component Analysis are employed in order to explore intra-construct relationships. The contribution of this paper lies on the fact that a mix of multivariate statistical techniques is all integrated in a single framework, so that information about the structure of m-service quality criteria and constructs is obtained. The findings of the study confirm the theoretical background and provide valuable managerial insights

Determining the factor structure of an integrated innovation model

This paper reports on elemental factor analyses of the innovativeness study in the Turkish manufacturing industry, drawing on a sample of 184 manufacturing firms. Factor structures are constructed in order to empirically test a framework identifying the relationships among innovativeness, performance and determinants of innovation. After several independent principal component analyses, factor structures of innovations, firm performance, organization culture, intellectual capital, manufacturing strategy, innovation barriers, and monitoring strategies are presented

Academic quality measurement: A multivariate approach

This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).Quality Measurement, Postgraduate Programme, Nonparametric Model.

Fundamental concepts in management research and ensuring research quality : focusing on case study method

This paper discusses fundamental concepts in management research and ensuring research quality. It was presented at the European Academy of Management annual conference in 2008

Virtual Astronomy, Information Technology, and the New Scientific Methodology

All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The Virtual Observatory concept is the astronomical community's response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broather impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century

Human resource management practices and organizational performance. The mediator role of immaterial satisfaction in Italian Social Cooperatives

The paper deals with the mediating role of immaterial satisfaction between substantive human resources (HR) features and organizational performance. We address this relationship in the Italian social service sector using a survey dataset that includes 4134 workers and 320 not-for-profit social cooperatives. The obtained results show that human resource management (HRM) practices influence immaterial satisfaction and, satisfaction positively impacts on firm performance. However, the impact of the different HRM practices is not the same. In this sense, worker involvement and workload pressure have a positive impact on firm performance; but task autonomy or collaborative teamwork do not have impact on organizational performance

What Types of Predictive Analytics are Being Used in Talent Management Organizations?

[Excerpt] Talent management organizations are increasingly deriving insights from data to make better decisions. Their use of data analytics is advancing from descriptive to predictive and prescriptive analytics. Descriptive analytics is the most basic form, providing the hindsight view of what happened and laying the foundation for turning data into information. More advanced uses are predictive (advanced forecasts and the ability to model future results) and prescriptive (“the top-tier of analytics that leverage machine learning techniques … to both interpret data and recommend actions”) analytics (1). Appendix A illustrates these differences. This report summarizes our most relevant findings about how both academic researchers and HR practitioners are successfully using data analytics to inform decision-making in workforce issues, with a focus on executive assessment and selection