scholarly journals Systematic Review of Multiple Contents Synchronization in Interactive Television Scenario

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Ricardo Mendes Costa Segundo ◽  
Celso Alberto Saibel Santos
Keyword(s):  
Classification Scheme ◽  
Interactive Television ◽  
General Information ◽  
Future Research ◽  
Full Potential ◽  
Interactive Tv ◽  
Mapping Study ◽  
Research Topics ◽  
Television Content ◽  
High Level

Context. Interactive TV has not reached yet its full potential. How to make the use of interactivity in television content viable and attractive is something in evolution that can be seen with the popularization of new approaches as the use of second screen as interactive platform. Objective. This study aims at surveying existing research on Multiple Contents TV Synchronization in order to synthesize their results, classify works with common points, and identify needs for future research. Method. This paper reports the results of a systematic literature review and mapping study on TV Multiple Contents Synchronization published until middle 2013. As result, a set of 68 papers was generated and analyzed considering general information such as sources and time of publication; covered research topics; and synchronization aspects such as methods, channels, and precision. Results. Based on the obtained data, the paper provides a high level overview of the analyzed works; a detailed exploration of each used and proposed technique and its applications; and a discussion and proposal of a scenario overview and classification scheme based on the extracted data.

Advancing customer experience practice and strategy in Thailand

2019 ◽  
Vol 31 (2) ◽  
pp. 327-343 ◽  
Author(s):  
Yan Chi Tiffany Tivasuradej ◽  
Nam Pham
Keyword(s):  
Design Methodology ◽  
Customer Experience ◽  
Future Research ◽  
Full Potential ◽  
Research Topics ◽  
Content Type ◽  
The Past ◽  
Current State ◽  
Managerial Implications

Purpose The purpose of this paper is to provide a broad preliminary overview and critical viewpoint on the current state of customer experience innovation and strategy in Thailand. Design/methodology/approach This paper outlines and critically analyses the key trends based on 15 prime instances of customer experience innovation from the past ten years in Thailand across three industries: retail, fuel service and insurance. Findings Customer experience in Thailand is still in its nascent stage. This is because firms are yet to realise their full potential as critical brand differentiators. Many Thai firms also miss collaboration opportunities with external partners when innovating customer experiences. This is despite the overwhelming contributions from local SMEs to breakthrough innovations and creativity. Consequently, many customer experience innovations in Thailand are yet to be truly memorable and unique. Originality/value This is the first paper that critically examines the trends in customer experience across the retail, fuel service and insurance. It is also the only paper that outlines strategic implications of customer experience strategies and innovations to date for Thailand. Both future research topics and managerial implications for Thai professionals are discussed in the paper.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Conclusions and Future Directions for the Study of Accurate Personality Judgment

2020 ◽  
Author(s):  
Tera D. Letzring
Keyword(s):  
Standardized Tests ◽  
Personality Trait ◽  
Training Programs ◽  
Future Research ◽  
Research Topics ◽  
Theoretical Frameworks ◽  
Future Directions ◽  
Personality Judgment ◽  
Exciting Time ◽  
And Training

This chapter identifies several well-established findings and overarching themes within personality trait accuracy research, and highlights especially promising directions for future research. Topics include (1) theoretical frameworks for accuracy, (2) moderators of accuracy and the context or situation in which judgments are made, (3) the important consequences of accuracy, (4) interventions and training programs to increase judgmental ability and judgability, (5) the generalizability of previous findings, and (6) standardized tests of the accuracy of judging personality traits. The chapter ends by stating that it is an exciting time to be a researcher studying the accuracy of personality trait judgments.

scholarly journals Opportunities and Challenges of Green-Lean: An Integrated System for Sustainable Construction

2020 ◽  
Vol 12 (11) ◽  
pp. 4460 ◽  
Author(s):  
Mohammadsoroush Tafazzoli ◽  
Ehsan Mousavi ◽  
Sharareh Kermanshachi
Keyword(s):  
Cost Savings ◽  
Integrated Approach ◽  
Integrated System ◽  
Waste Minimization ◽  
Sustainable Construction ◽  
Future Research ◽  
Safety Improvement ◽  
The Individual ◽  
High Level ◽  
Industry Professionals

Although the two concepts of lean and sustainable construction have been developed due to different incentives, and they do not pursue the same exact goals, there exists considerable commonality between them. This paper discusses the potentials for integrating the two approaches and their practices and how the resulting synergy from combining the two methods can potentially lead to higher levels of fulfilling the individual goals of each of them. Some limitations and challenges to implementing the integrated approach are also discussed. Based on a comprehensive review of existing papers related to sustainable and lean construction topics, the commonality between the two approaches is discussed and grouped in five categories of (1) cost savings, (2) waste minimization, (3) Jobsite safety improvement, (4) reduced energy consumption, and (5) customers’ satisfaction improvement. The challenges of this integration are similarly identified and discussed in the four main categories of (1) additional initial costs to the project, (2) difficulty of providing specialized expertise, (3) contractors’ unwillingness to adopt the additional requirements, and (4) challenges to establish a high level of teamwork. Industry professionals were then interviewed to rank the elements in each of the two categories of opportunities and challenges. The results of the study highlight how future research can pursue the development of a new Green-Lean approach by investing in the communalities and meeting the challenges of this integration.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

Accounting for natural disasters from a historical perspective: A literature review and research agenda

2021 ◽  
Vol 26 (2) ◽  
pp. 179-204
Author(s):  
Massimo Sargiacomo ◽  
Stefania Servalli ◽  
Serena Potito ◽  
Antonio D’Andreamatteo ◽  
Antonio Gitto
Keyword(s):  
Natural Disasters ◽  
Economic History ◽  
Research Agenda ◽  
Historical Research ◽  
Future Research ◽  
Research Topics ◽  
Open Research ◽  
Emerging Themes ◽  
Published Research ◽  
Selection Of

This study offers an analysis of published historical research on accounting for natural disasters. Drawing on the insights provided by an examination of 35 accounting/business/economic history and generalist journals, 11 articles have been selected and analysed. The analysis conducted on the scattered literature identified the emerging themes, disasters investigated, periods of time explored and main contributions of published research. The analysis is extended by the examination of some key conferences of interdisciplinary history associations, and of the eventual journals/issues where the papers presented were published. The investigation has also been complemented by a brief selection of books showing historical analyses of diverse disasters, typologies and periods of investigation. The stimuli provided by the study have helped to portray the main features of an open research agenda, highlighting possible future research topics and suggesting ancient and recent disasters’ loci to be investigated worldwide.

scholarly journals Social Media Use Among Young People in China: A Systematic Literature Review

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110164
Author(s):  
Lian Tang ◽  
Siti Zobidah Omar ◽  
Jusang Bolong ◽  
Julia Wirza Mohd Zawawi
Keyword(s):  
Social Media ◽  
Young People ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Media Use ◽  
Academic Research ◽  
Future Research ◽  
Research Topics ◽  
Social Media Use ◽  
Systematic Analysis

The widespread use of social media has promoted extensive academic research on this channel. The present study conducts a systematic analysis of extant research on social media use among young people in China. This systematic literature review aims to identify and bridge gaps in topics, theories, variables, and conceptual frameworks in studies of social media usage among young people in China. The study aims to develop a cause–effect framework that shows the causal relationships among research structures. The PRISMA method is used to review 20 articles drawn from the Scopus and Google Scholar databases. From the analysis, 10 major research topics, eight theories or models, and a complete framework of causal relations emerge. It is recommended that future research on social media should include a greater diversity of types of social media, investigate a wider range of research topics, and adopt different theories or models. Researchers should also implement a more complete and detailed systematic method for reviewing literature on social media research in China.

Probabilistic Rule Learning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun
Keyword(s):  
Probability Theory ◽  
Rule Learning ◽  
Background Knowledge ◽  
Learning Systems ◽  
Future Research ◽  
Research Directions ◽  
System Architectures ◽  
Probabilistic Rule ◽  
Future Research Directions ◽  
High Level

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

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