Factors affecting motivation in the public sector under the context of self-determination theory and public service motivation : the case of the Hellenic Agricultural Insurance Organization (H.A.I.O.)

Purpose: The main aim of this study is to develop and test a conceptual framework that explores the factors that motivate employees in public services. The study is based on two theories, the Self Determination Theory (SDT), and the Public Service Motivation (PSM). Design/methodology/approach: The empirical testing of the proposed conceptual framework was conducted using a structured questionnaire that was distributed to 390 employees of a Greek public sector organization, namely the Hellenic Agricultural Insurance Organization (Η.Α.Ι.Ο.). The population of the study consists of 489 public servants. Exploratory factor analysis, confirmatory factor analysis and the structural equation modeling (SEM) technique were used to test the research hypotheses. Findings: Results highlight, among others, the importance of work autonomy and job satisfaction and their impact on employees’ intrinsic motivation. Moreover, empirical results emphasize the need for building a supportive work environment that contributes on employee satisfaction, autonomy and relatedness. Results also confirm the existence of a positive relationship among intrinsic motivation, public service motivation and job performance. Practical implications: The findings suggest that in cases where external incentives are significantly limited (e.g., in the public sector), it is crucial to consider the importance of promoting and supporting intrinsic motivation and support public service incentives. Originality/value: Self-determination theory seems to be poorly explored in the Greek public sector, and moreover, in economies and countries with similar characteristics. Therefore, it seems crucial to increase the knowledge about the real value of self-determination theory as a work motivation approach, especially in the public sector. Finally, research results may help supervisors and business leaders to implement appropriate systems and practices that promote a supportive environment in the workplace.peer-reviewe

Causal interactions between Frontalθ – Parieto-Occipitalα2 predict performance on a mental arithmetic task

Many neuroimaging studies have demonstrated the different functional contributions of spatially distinct brain areas to working memory (WM) subsystems in cognitive tasks that demand both local information processing and interregional coordination. In WM cognitive task paradigms employing electroencephalography (EEG), brain rhythms such as θ and α have been linked to specific functional roles over given brain areas, but their functional coupling has not been extensively studied. Here we analyzed an arithmetic task with five cognitive workload levels (CWLs) and demonstrated functional/effective coupling between the two WM subsystems: the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontalθ/Fθ) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α2 dominant brain rhythm (Parieto-Occipitalα2/POα2). We focused on important differences between and within WM subsystems in relation to behavioral performance. A repertoire of brain connectivity estimators was employed to elucidate the distinct roles of amplitude, phase within and between frequencies, and the hierarchical role of functionally specialized brain areas related to the task. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at Fθ and POα2, b) the intra- and inter-frequency phase interactions between Fθ and POα2, and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power or phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between Fθ and POα2 revealed frontal brain region(s) as the leader, while the strength differentiated between correct and wrong responses in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral Fθ and right POa2 could serve as an indicator of mental calculation failure. Overall, our study highlights the significant role of coordinated activity between Fθ and POα2 via their causal interactions and the timing for arithmetic performance

A Novel, Fast, Reliable, and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude—Latency Estimation Based on Proximity Graphs and Network Analysis

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm

Here, there, in between, beyond…: Identity negotiation and sense of belonging among Southern Europeans in the UK and Germany

Whilst most of the research on intra-EU mobility has mainly focused on the reasons behind young Southern Europeans leaving their home countries, and secondly on their experiences within the new context, little is known about their sense of belonging and identities. This article aims to fill this gap by exploring Italian and Spanish migrants\u2019 social identity repositioning and the cultural change characterising their existential trajectories. Drawing on 69 semi-structured interviews with Italians and Spaniards living in London and Berlin, this article shows that the sense of belonging to one or more political communities and boundary work are related to individual experiences and can change due to structural eventualities such as the Brexit referendum. While identification with the host society is rare, attachment to the home country is quite common as a result of people\u2019s everyday experiences. Cultural changes and European/cosmopolitan identification are linked to exposure to new environments and interaction with new cultures, mostly concerning those with previous mobility experience, as well as to a sentiment of non-acceptance in the UK. However, such categories are not rigid, but many times self-identification and attachments are rather blurred also due to the uncertainty around the duration of the mobility project. This makes individual factors (gender, age, family status, employment, education) that are often considered as determinants of identification patterns all but relevant

Mining time-resolved functional brain graphs to an EEG-based chronnectomic brain aged index (CBAI)

The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM). The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI) extracted from resting-state data (N = 94 subjects) with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index), the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM) classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically, our results revealed a very high prediction of age for eyes-open (R2 = 0.60; y = 0.79x + 8.03) and lower for eyes-closed (R2 = 0.48; y = 0.71x + 10.91) while we succeeded to correctly classify young vs. middle-age group with 97.8% accuracy in eyes-open and 87.2% for eyes-closed. Our results were reproduced also in a second dataset for further external validation of the whole analysis. The proposed methodology proved valuable for the characterization of the intrinsic properties of dynamic functional connectivity through the age untangling developmental differences using EEG resting-state recordings

Stable reliability diagrams for probabilistic classifiers

A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting approach to plotting reliability diagrams has been hampered by a lack of stability under unavoidable, ad hoc implementation decisions. Here, we introduce the CORP approach, which generates provably statistically consistent, optimally binned, and reproducible reliability diagrams in an automated way. CORP is based on nonparametric isotonic regression and implemented via the pool-adjacent-violators (PAV) algorithm—essentially, the CORP reliability diagram shows the graph of the PAV-(re)calibrated forecast probabilities. The CORP approach allows for uncertainty quantification via either resampling techniques or asymptotic theory, furnishes a numerical measure of miscalibration, and provides a CORP-based Brier-score decomposition that generalizes to any proper scoring rule. We anticipate that judicious uses of the PAV algorithm yield improved tools for diagnostics and inference for a very wide range of statistical and machine learning methods

A multiplex connectivity map of valence-arousal emotional model

high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users’ emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants’ ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes

The added value of implementing the Planet Game scenario with Collage and Gridcole

This paper discusses the suitability and the added value of Collage and Gridcole when contrasted with other solutions participating in the ICALT 2006 workshop titled “Comparing educational modelling languages on a case study.” In this workshop each proposed solution was challenged to implement a Computer-Supported Collaborative Learning situation (CSCL) posed by the workshop’s organizers. Collage is a pattern-based authoring tool for the creation of CSCL scripts compliant with IMS Learning Design (IMS LD). These IMS LD scripts can be enacted by the Gridcole tailorable CSCL system. The analysis presented in the paper is organized as a case study which considers the data recorded in the workshop discussion as well the information reported in the workshop contributions. The results of this analysis show how Collage and Gridcole succeed in implementing the scenario and also point out some significant advantages in terms of design reusability and generality, user-friendliness, and enactment flexibility