Journal of Information and Organizational Sciences (JIOS)
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The Mediating Role of Work-Family Conflict in the Relationship between Work Overload Perception and Job Satisfaction
No full textThis study aims to investigate whether work-family conflict has a mediating role in the relationship between work overload perception and job satisfaction. In this framework, data were obtained from 250 accounting sector employees working in Erzincan province of Turkey by survey method. The obtained data were analyzed using SPSS and AMOS programs. As a result, it was found that work overload perception has a significant and negative impact on job satisfaction, and a significant and positive impact on work-family conflict, and work-family conflict and job satisfaction variables are significantly and negatively correlated. Also, it was determined that there is a partial mediating role of work-family conflict in the relationship between work overload perception and job satisfaction
Evaluating Compromise in Social Choice Functions
No full textWe investigate the notion of compromise in the strict preferential voting setting. We introduce divergence as an inverse measure of compromise in a collection of strict preferential votes. Classical functions of social choice theory are analyzed with respect to divergence. New social welfare functions and new social choice functions with the objective of compromise are defined directly from optimization of divergence and later analyzed with respect to the common desiderata of social choice theory. For a very natural function, a simple divergence minimizer, we prove it satisfies the properties of anonymity, neutrality, consistence, and continuity. Consequently, according to Young’s theorem of characterization it follows that this function is a scoring point function. Its scoring point vector is also given. Finally, we discuss the parameter p in the divergence measure which was introduced to address vagueness and fuzziness of compromise and to control for a variety of intended levels of compromise
The Relationship of Intergenerational Perceptions of Work Ethics and Workplace Deviation Behaviors in Academic Staff
No full textThe aim of the research is to examine the work ethics perceptions of academics from different generations and the relationship between these perceptions and workplace deviation behaviors. In line with the purpose, the questionnaire prepared by using 5 questions with demographic variables, academic ethical values scale and workplace deviation behavior scale were applied to 472 academicians working at state universities in Ankara and the nearby provinces. The results revealed moderately negative relationship between academic ethical values and one of its sub-dimensions which is the values for the institution and workplace deviation behaviors. Moreover, weak and strong negative relationships were found between academic ethical values and other sub-factors of workplace deviation behaviors. Intergenerational differences were found between academic ethical values and academic ethical values towards the teaching process and serious workplace deviation towards the organization, and between academic ethical values towards colleagues and deviant behaviors towards the organization
Early Cocoa Blackpod Pathogen Prediction with Machine Learning Ensemble Algorithm based on Climatic Parameters
No full textMachine learning has been useful for prediction in the various sectors of the economy. The research work proposed an ensemble SA-CCT machine learning algorithm that gives early and accurate prediction of blackpod disease to farmers and agricultural extension officers in South-West, Nigeria. Since data mining put into consideration the types of pattern in a given dataset, the study considered the pattern in climatic dataset retrieved from Nigeria Meteorological agency (NIMET). The proposed model uses climatic parameters (Rainfall and Temperature) to predict the outbreak of blackpod disease. The ensemble SA-CCT model was formulated by hybridizing a linear algorithm Seasonal Auto Regressive Integrated Moving Average (SARIMA) and a nonlinear algorithm Compact Classification Tree (CCT), the implementation was done with python programming. The proposed SA-CCT model gives the following results after evaluation. Precision: 0.9429, Recall 0.9167, Mean Square Error: 0.2357, Accuracy: 0.944
A Mobile Based Pharmacy Store Location-aware System
No full textThis paper presents a formulated mobile-based location-awareness model that was implemented into a Location-awareness System (L-aS hereafter) for finding the pharmacy location where prescribed drugs and their prices are available for sale. The scenario that inspired the model formulation was formalized using the unified modeling language. The model was implemented within the android studio integrated development environment with the L-aS database created through SQL lite database. The system was tested using user experience based testing technique. Based on core system performance testing, the system demonstrated a normal response time, resource utilization (i.e. storage and memory usage), and data use potentials of 414.6ms, 4.964mb and 1.9116 kb/secs, and 3.0296mb respectively. Therefore, the system performed well under ordinary conditions as an android application running on small memory devices. The study concluded that the developed mobile based pharmacy store location-aware system was useful to provide information to purchase prescribed drugs especially in perplexed situation(s)
Venezuelan Scientific Journals in Scopus: Production and Impact 2000-2020
No full textThe historical behavior of Venezuelan journals indexed in Scopus from 2000 to 2020, their visibility and impact were studied. The data were obtained from the Scimago Journals and Country Rank portal and were a number of journals, published papers, impact factor (SJR) and quartile of location. The historical behavior of each variable was analyzed using sequential time series models to establish whether the behavior has been random or whether it follows a defined trend. It was found that both the number of journals and the number of published papers follow definite trends and SJR behaves randomly. The decrease in the number of journals may be influenced by the critical socioeconomic situation of the country; however, the number of papers has increased over time and the SJR has remained in a narrow range, meaning that most journals fall between the Q3 and Q4 quartile
Exploring Students’ Perspective of a Platform for Digital Competence Acquisition in Schools
No full textThis study aims to evaluate the ways that the actual usage of a platform for digital competence acquisition, evaluation and certification contributes to satisfaction and perceived success of students in primary and secondary schools. A cross-sectional survey was implemented online to collect 1725 students’ answers in six European countries. The analysis of collected data was carried out by employing Pearson correlation, Partial least squares structural equation modelling (PLS-SEM) and Importance-performance map analysis (IPMA). Findings indicated that the usage of such a platform has greater effects on the impacts than on students’ satisfaction. Detailed analysis of correlations revealed that students’ decision on whether they will use the platform in the future greatly depends on how it contributes to the success of their learning processes. Results also suggest that teachers are seen as an inevitable part of such a process and are mandatory to achieve the full potential of the platform
Development of Activity Recognition Model using LSTM-RNN Deep Learning Algorithm
No full text This study analyses numerous human activities and also classifies the activities based on their trait of motion using wearable sensors data. As a part of the Human Activity Recognition Framework's development, the LSTM-RNN algorithm was implemented. We have considered ten types of motions for recognition and based on the duration of motions have classified those motions into repetitive and non-repetitive motions. The dataset utilized to evaluate the model's performance was recordings from Opportunity.The best trained model achieved an overall accuracy of 94% and The findings of the study stated that the LSTM-RNN model achieved greater accuracy of 91% pertaining to motions that are not repeating that means motions that are performed for shortperiods of time in comparison to the motions having long dependencies which achieved accuracy of 80%. The determination of performance has been done in terms of score of accuracy, score of precision and f1 score. In addition to this, a disparity analysis of thepresented model with another devised model has also been done
Office Documents Classification under Limited Sample: A Case of Table Detection Inside Court Files
No full text Deep convolutional neural networks (CNNs) became an industry standard in image processing. However, in order to keep their high efficiency, a large annotated sample is required in the case of supervised learning. In this paper we apply the techniques specific for relatively small sample to a court files dataset. Specifically, we propose transfer learning and semisupervised learning to classify scanned page as having a table or not. We use four CNNs architectures established in the literature and find that transfer learning improves the classification performance, compared to the fully supervised learning. This result is especially evident in the scenarios where only a part of convolutioanl layers are transferred. The gains from semisupervised learning are ambiguous, as the results vary over CNNs architectures. Overall, our results show that office documents classification can achieve high accuracy when transferring initial convolutional layers is applied