analysis methodology
Recently Published Documents

The purpose of this study was focused on exploring the relationship among the fans’ preferences, fans’ para-social interaction, and fans’ word-of-mouth. A survey consisted of 21 items based on the literature review and developed by this study. An online survey was distributed to the users of YouTube in Taiwan. A total of 606 valid samples was collected by survey. The instrument passed the reliability and validity test. Further, the data process applied the PLS (partial least squares) regression analysis methodology. The result shows that the ‘attractive’ impacted ‘para-social interaction’, ‘e-word-of-mouth’, and ‘preferences of fans’ positively. In addition, the para-social interaction plays an important role as a mediator between influencer’s attractiveness, w-word-of-mouth, and preferences of fans. Some suggestions were provided for social media influence’ related studies as reference.

This research delves into the television programs of political info- tainment (politainment) broadcast in 2018 in Spain and analyzes in a particular way the behavior of the infoshow ‘El Intermedio’, as well as the engagement of its social audience on the social network Twitter. For this, a content analysis methodology has been used on the tweets published by the program, the com- ments received and the reactions made by its social audience. The results of the investigation indicate that, as a whole, politainment programs, or those that include spectacular politics, and that are broadcast in the night time slot, have a greater number of followers on their Twitter profiles than those that are broad- cast in the morning, they also concentrate a higher accumulated monthly linear audience. Regarding the analysis of the tweets and comments of ‘El Intermedio’, the colloquial language and the critical and humorous intentionality are pre- dominant while the engagement obtained is scarce as it does not include only viralizing elements that reinforce its messages.

The properties of piezoelectric materials due to the effect of electrical, mechanical, thermal, radiation, and chemical parameters are systematized. On the basis of Maxwell's relations (obtained from expressions for thermodynamic functions) and the application of the system analysis methodology, it made it possible to develop an analytical model of the relationship between the parameters and properties of piezoelectrics in the form of a system of equations. The results of the metrological analysis of an analytical model, which made it possible to identify the sources of additional errors in the measurement of parameters, to derive formulas for their calculation, which in turn contributes to an increase in the accuracy of measurements of the piezoelectrics parameters and products based on them, are presented.

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

This paper is a systematic review of studies that used multicriteria decision analysis (MCDA) to address plastic waste management. A literature search for scientific articles in online databases (Web of Science and Scopus) enabled us to identify 20 relevant papers from 2008 to 2021, spanning case studies in three continents. These studies focus on: plastics as a resource (material), plastics as a product (reverse logistics), and plastics as a problem (pollution). Content analysis methodology was used, with the focus being on how the authors used MCDA for managing plastic waste, which has relevance for researchers and practitioners. Alternative solutions were found for the selection of disposal methods for almost all types of plastic categorized in this review. The most popular method was AHP, followed by TOPSIS, outranking methods, MAUT/MAVT and simple weighted sums, with some studies including more than one method. The choice of criteria spanned operational (mostly), but also environmental and economic aspects to evaluate the alternatives. Less frequently, one finds criteria related to social, managerial, and political aspects. The weighting of the criteria was performed mainly by consulting experts, followed by decision makers. Representatives of the affected population or other stakeholders have been consulted only on a few occasions. The authors of the studies consider their application of MCDA was successful, highlighting mainly the importance of being able to encompass different dimensions in the evaluation of the alternatives and the transparency of the process. In most cases, a winning alternative emerged clearly, which sometimes was a combination of multiple strategies. We also report other recommendations of these authors concerning marine and terrestrial plastic waste management.

Sample Preparation and Geochemical Analysis Methodology; Table S1: Major oxide percentages for metaigneous rocks of the Wedowee-Emuckfaw-Dahlonega basin; Table S2: Measured isotope ratios and normalized U-Pb ages calculated without 204Pb Correction; Table S3: Lu-Hf isotope analyses; Table S4: Latitude-longitude (WGS84), geologic unit, and age information for samples analyzed as part of this project.

This chapter uses the normative research methodology to review the literature for business research and analysis to search for a set of tools that can provide a comprehensive analysis methodology. The investigation of competitive advantage follows the dynamic capability of the subjective approach to the resource-based view (RBV). This view needs to establish the current position of the company's resources in the context of the surrounding environment to develop a clear understanding of the current competitive status of the organisation. The best practice of suggestions from the literature will be gathered into a proposal for a systematic research and analysis methodology governed by an information policy. A formal information project is suggested to collect and store the required information and to use a formalized set of visualization tools to provide an evolving holistic picture of the information leading to the architectural blueprint.

Information security has become one of the most important responsibilities of all organizations due to increasing cyber threats. Attackers take advantage of systems vulnerabilities; therefore, system administrators should be aware of potential threats to take necessary actions to protect their organizations and stakeholders. At this point, a risk assessment is needed to discover possible threats for vulnerable systems of the organization and to implement strategies for the business goals. This study proposes a hybrid risk management framework using both qualitative and quantitative methods to analyze risk within organizations and reduce them with practical countermeasures. Based on this framework, case studies have been carried out considering three hypothetical companies identifying possible information security risks, and these risks have been reduced to an acceptable level by applying the proposed risk analysis methodology.

This chapter aims to reveal the growing importance of cultural tourism, reflected in the cultural heritage of cities and its concrete tourist experience in a digital age society. One of the stipulated goals was to understand the existing relationship between tourist and cultural appropriation with the mediation of the digital. The chapter focuses on a city located in the central region of Portugal and followed a quantitative and qualitative analysis methodology. The digital era has been fostering a fundamental capital in the promotion of the existing resources in cities to attract visitors and to reveal the tourist experiences developed in the visited tourist destinations. The main conclusion is the interactions that take place between heritage, tourist experience and ICT implying connections that tourists spontaneously comment through online resources. When making their comments they end up demarcating their tourist experience classifying it according to their expectations in relation to the heritage resources they visit.

The current uncertain, dynamic scenario calls for a systemic perspective when referring to organizational complexity and behavior. Our research contributes to the analysis of organizational complexity through multidimensional behavioral mapping. Our method uses machine learning tools to detect the interconnections between the different behaviors of a person in his/her operating context. First, the research project dealt with prototyping a model to read the organizational behavior, the related detection tool, and a data analysis methodology. It used machine learning tools and ended with a data visualization phase. We set our model to read the organizational behavior by comparing the literature benchmark theories with our field experience. The model was organized around 4 areas and 16 behaviors. These were the basis for singling out the indicators and the questionnaire items. The data analysis methodology aimed at detecting the interconnections between behaviors. We designed it by joining univariate analysis with a multivariate technique based on the application of machine learning tools. This led to a high-resolution network map through three specific steps: (a) creating a multidimensional topology based on a Kohonen Map (a type of unsupervised learning artificial neural network) to geometrically represent behavioral relationships; (b) implementing k-means clustering for identifying which areas of the map have behavior similarity or affinity factors; and (c) locating people and the various identified clusters within the map. The research highlighted the validity of machine learning tools to detect the multidimensionality of organizational behavior. Therefore, we could delineate the networking of the observed elements and visualize an otherwise unattainable complexity through multimedia and interactive reporting. Application in the field of research consisted of the design and development of a prototype integrated with our LMS platform via a plugin. Field experimentation confirmed the effectiveness of the method for creating professional growth and development paths. Furthermore, this experimentation allowed us to obtain significant data by applying our model to several sectors, namely pharmaceutical, TLC, banking, automotive, machinery, and services.