Angelos Markos

Modeling users' acceptance of mobile services

The success of mobile services adoption hinges on their ability to cover user needs and attract consumer interest. The extant literature focuses on understanding the factors that might affect consumers’ actual adoption of such services through their effect on behavioral intention; these studies are mostly based on behavioral intention theories, such as Technology Acceptance Model, Diffusion of Innovation and Unified Theory of Acceptance and Use of Technology. In this work, new theoretical constructs are combined with existing evidence in order to extend the Technology Acceptance Model (TAM) as it was initially established by Davis and later further enriched by other researchers. The proposed model includes behavioral intention, perceived usefulness, perceived ease of use, trust, innovativeness, relationship drivers, and functionality. Within this approach, relationship drivers introduce a marketing perspective to the original models of technology adoption by building emotional connections between the users and the mobile services. The hypothesized model is empirically tested using data collected from a survey on m-commerce consumers. Structural Equation Modelling (SEM) was used to evaluate the causal model and Confirmatory Factor Analysis (CFA) was performed to examine the reliability and validity of the measurement model. It is briefly concluded that behavioral intention is directly affected by perceived usefulness, innovativeness and relationship drivers; the findings provide interesting insights and useful hints to practitioners and researchers.

The CHIC Analysis Software v1.0

In this paper we describe CHIC (Correspondence & HIerarchical Cluster) Analysis, a specialized software package for Correspondence Analysis-CA (Simple and Multiple) and Hierarchical Cluster Analysis (Benzecri’s chi-square distance, Ward’s linkage criterion). The implementation of CA is in line with both the French approach and the Gifi System of data analysis. CHIC Analysis combines the graphical interface features of CodeGear Delphi with the computational power of MatLab. The software was implemented as an attempt to contribute to the effectiveness and reliability of CA. For this purpose, it offers a variety of aids to the results’ interpretation and tools for the construction of special data tables. A modified version of the CA algorithm is implemented in the multivariate case. Special emphasis has been put on the graphical options for biplots, maps and dendrograms.

Collaborative filtering through SVD-based and hierarchical nonlinear PCA

In this paper, we describe and compare two distinct algorithms aiming at the low-rank approximation of a user-item ratings matrix in the context of Collaborative Filtering (CF). The first one implements standard Principal Component Analysis (PCA) of an association matrix formed from the original data. The second algorithm is based on h-NLPCA, a nonlinear generalization of standard PCA, which utilizes an autoassociative network, and constrains the nonlinear components to have the same hierarchical order as the linear components in standard PCA. We examine the impact of the aforementioned approaches on the quality of the generated predictions through a series of experiments. Experimental results show that the latter approach outperforms the standard PCA approach for most values of the retained dimensions.

Short-Term Results of Autologous Blood Injection for Treatment of Habitual TMJ Luxation

Abstract Many surgical and nonsurgical methods for the treatment of temporomandibular joint (TMJ) hypermobility have been published. The purpose of this study was to evaluate the results after autologous blood injection in and around the TMJ for the treatment of habitual luxation. Twenty-five patients were diagnosed as having habitual TMJ luxation and treated with autologous blood injection into the upper joint space and around the joint capsule (group A). A control group of 15 patients with the same diagnosis were subjected to physiotherapy with muscular exercise (group B). Patients in group A were reevaluated 3 months after treatment and those in group B were reevaluated after 3 months of physiotherapy. A statistically significant reduction in maximal mouth opening and TMJ sounds was noted only in group A, whereas the reduction for group B was minimal. These measurements show that intraauricular and periauricular autologous blood injection results in remission of signs and symptoms of TMJ luxation in the short term.

An Optimal Scaling Approach to Collaborative Filtering Using Categorical Principal Component Analysis and Neighborhood Formation

Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. The most common and accurate approaches to CF are based on latent factor models. Latent factor models can tackle two fundamental problems of CF, data sparsity and scalability and have received considerable attention in recent literature. In this work, we present an optimal scaling approach to address both of these problems using Categorical Principal Component Analysis for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. The optimal scaling approach has the advantage that it can be easily extended to the case when there are missing data and restrictions for ordinal and numerical variables can be easily imposed. We considered different measurement levels for the user ratings on items, starting with a multiple nominal and consecutively applying nominal, ordinal and numeric levels. Experiments were executed on the MovieLens dataset, aiming to evaluate the aforementioned options in terms of accuracy. Results indicated that a combined approach (multiple nominal measurement level, ‘‘passive’’ missing data strategy) clearly outperformed the other tested options.

Low-dimensional tracking of association structures in categorical data

In modern applications, such as text mining and signal processing, large amounts of categorical data are produced at a high rate and are characterized by association structures changing over time. Multiple correspondence analysis (MCA) is a well established dimension reduction method to explore the associations within a set of categorical variables. A critical step of the MCA algorithm is a singular value decomposition (SVD) or an eigenvalue decomposition (EVD) of a suitably transformed matrix. The high computational and memory requirements of ordinary SVD and EVD make their application impractical on massive or sequential data sets. Several enhanced SVD/EVD approaches have been recently introduced in an effort to overcome these issues. The aim of the present contribution is twofold: (1) to extend MCA to a split-apply-combine framework, that leads to an exact and parallel MCA implementation; (2) to allow for incremental updates (downdates) of existing MCA solutions, which lead to an approximate yet highly accurate solution. For this purpose, two incremental EVD and SVD approaches with desirable properties are revised and embedded in the context of MCA.

Measuring ocean literacy in pre-service teachers: psychometric properties of the Greek version of the Survey of Ocean Literacy and Experience (SOLE)

The aim of the present study was to respond to the increasing demand for comprehensive tools for the measurement of ocean literacy, by investigating the psychometric characteristics of a Greek version of the Survey of Ocean Literacy and Experience (SOLE), an instrument that assesses conceptual understanding of general ocean sciences content, focusing on the knowledge component. Four hundred twenty-one pre-service primary school teachers participated in a cross-sectional study. The dichotomous Rasch model was used to examine the measurement properties of the SOLE, namely, person-item targeting and separation, reliability, dimensionality and differential item functioning (DIF). Steps were taken to improve the instrument, where any of these attributes were outside acceptable ranges. Results suggested that a modified SOLE showed an adequate fit to the Rasch model, is unidimensional, free of DIF, and is particularly well-suited to the population under study. Our findings suggest that the SOLE constitutes a valuable tool which can be applied to a different cultural context and population. The proposed use of the instrument could contribute to the assessment of the quality of marine education in school-based and non-formal education contexts and to the cross-cultural comparison of ocean literacy, which are prerequisites for the improvement of ocean literacy.

Development, construct validation and measurement invariance of the Greek cyber-bullying/victimization experiences questionnaire (CBVEQ-G)

This study investigated the construct validity of a newly developed Greek questionnaire assessing cyber-bullying (CB) and cyber-victimization (CV), the Cyber-Bullying and Victimization Experiences Questionnaire-Greek (CBVEQ-G), constructed due to dearth of relevant measures. Analyses were performed on data collected from 1097 Greek adolescents. The structure of the CBVEQ-G was modeled by means of confirmatory factor analysis, and its convergent validity was tested against theoretically related measures. Results supported the validity and reliability of a correlated two-factor (CB, CV) model, while the correlated four-factor model was marginally supported. Measurement invariance across gender and grade level was established, while significantly positive correlations were found between cyber and traditional bullying/victimization, and between CB and antisocial personality traits. A correlated two-factor (cyber-bullying and cyber-victimization) model was supported.Measurement invariance across gender and grade level was established.Cyber and traditional bullying/victimization were positively correlated.Cyber-bullying and antisocial personality traits were positively correlated.

Adoption and Sophistication of Clinical Information Systems in Greek Public Hospitals: Results from a National Web-based Survey

Objectives: The objective of this study was to assess the current level of Clinical Information Systems (CIS) adoption and sophistication in Greek public hospitals through a national web-based survey. To do so, a comprehensive measurement instrument that integrates the existing theoretical and empirical literature knowledge on CIS adoption in hospitals was developed.

On the Performance of SVD-Based Algorithms for Collaborative Filtering

In this paper, we describe and compare threeCollaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implementations are based on three standard techniques for fitting a factor model to the data: Standard Singular Value Decomposition (sSVD), Principal Component Analysis (PCA) and Correspondence Analysis (CA). CA and PCA can be described as SVDs of appropriately transformed matrices,which is a key concept in this study. For each algorithm we implement two similar CF versions. The first one involves a direct rating prediction scheme based on the reduced user-item ratings matrix, while the second incorporates an additional neighborhood formation step. Next, we examine the impact of the aforementioned approaches on the quality of the generated predictions through a series of experiments. The experimental results showed that the approaches including the neighborhood formation step in most cases appear to be less accurate thanthe direct ones. Finally, CA-CF outperformed the SVD-CFand PCA-CF in terms of accuracy for small numbers ofretained dimensions, but SVD-CF displayed the overall highest accuracy.