Performance of TV programs: a robust MCDM approach

2020 ◽  
Vol 27 (3) ◽  
pp. 1188-1209 ◽  
Author(s):  
Liz Hassad de Andrade ◽  
Jorge Junio Moreira Antunes ◽  
Peter Wanke
Keyword(s):  
Principal Component ◽  
Performance Criteria ◽  
Data Set ◽  
Long Tail ◽  
Content Type ◽  
Relational Analysis ◽  
Complex Decision ◽  
Grey Relational ◽  
First Time ◽  
Practical Implications

PurposeThe aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.Design/methodology/approachThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.FindingsBy comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.Practical implicationsTo determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).Originality/valueThis work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

Grey relational analysis between Turkey’s macroeconomic indicators and domestic savings

2017 ◽  
Vol 7 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Engin Duran ◽  
Burcu Uzgur Duran ◽  
Diyar Akay ◽  
Fatih Emre Boran
Keyword(s):  
Grey Relational Analysis ◽  
Data Set ◽  
Content Type ◽  
Relational Analysis ◽  
Domestic Savings ◽  
Macroeconomic Indicators ◽  
Private Savings ◽  
Grey Relational ◽  
High Degree ◽  
The Relationship

Purpose It is of great importance for economy policy makers to comprehend the relationship between macroeconomic indicators and domestic savings, and to find out which indicator is more determinative on the dynamics of domestic savings. The purpose of this paper is to analyze the degree of relationship between Turkey’s domestic savings and selected macroeconomic indicators. Design/methodology/approach To examine the relationship, grey relational analysis (GRA) is applied together with the entropy method to determine the weight of the indicators according to the information level they provide. The analysis covers the data of the period from 1990 to 2014. In practice, however, the data set is used by dividing into two separate periods including before and after the 2001 crisis. Findings The results indicate that the unemployment rate and the gross domestic product (GDP) per capita growth stand out with a relatively high degree of relationship for the period before 2001. When examining the post-2001 period, current balance ratio and GDP growth are ascertained as indicators which have a high degree of relationship with domestic savings. Practical implications These indicators have different aspects affecting both public and private savings. Therefore, it may be beneficial to concentrate on these indicators when designing a policy in order to increase the domestic saving rate. Originality/value There are many econometric models used for investigating Turkey’s macroeconomic indicators and domestic savings causality. But before now, any study which investigates relationship between macroeconomic indicators and domestic savings by GRA could not be encountered. Using one of the newest developed theories (the grey systems theory) for this subject is the significance of this research.

Assessment of performance using MPSS based DEA

2015 ◽  
Vol 22 (4) ◽  
pp. 624-642 ◽  
Author(s):  
Subhadip Sarkar
Keyword(s):  
Principal Component ◽  
Original Data ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Cost Strategy ◽  
A New Technique ◽  
Most Productive Scale Size ◽  
Practical Implications

Purpose – Identification of the best school among other competitors is done using a new technique called most productive scale size based data envelopment analysis (DEA). The paper aims to discuss this issue. Design/methodology/approach – A non-central principal component analysis is used here to create a new plane according to the constant return to scale. This plane contains only ultimate performers. Findings – The new method has a complete discord with the results of CCR DEA. However, after incorporating the ultimate performers in the original data set this difference was eliminated. Practical implications – The proposed frontier provides a way to identify those DMUs which follow cost strategy proposed by Porter. Originality/value – A case study of six schools is incorporated here to identify the superior school and also to visualize gaps in their performances.

Antecedents of mobile advertising value: a precedence analysis using the hybrid RIDIT-GRA approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anshuman Sharma ◽  
Vivek Kumar Pathak ◽  
Mohammad Qutubuddin Siddiqui
Keyword(s):  
Communication Technologies ◽  
Shopping Mall ◽  
Mobile Advertising ◽  
Mobile Platforms ◽  
Content Type ◽  
Relational Analysis ◽  
Priority Ranking ◽  
Intercept Survey ◽  
Grey Relational ◽  
Practical Implications

Purpose Massive transformations in mobile communication technologies have forced marketers to recognize and emphasize the factors that influence consumers’ perception of advertising value. This paper aims to explore and rank the various antecedents of advertising value as perceived by consumers to offer meaningful conclusions to marketers on mobile platforms. Design/methodology/approach Responses were collected from 483 consumers using a shopping mall intercept survey and analyzed using SPSS to confirm reliability, validity and data reduction. The Relative to an Identified Distribution (RIDIT) analysis and Grey Relational Analysis (GRA) methods were then applied to prioritize the scale items of the antecedents of mobile advertising value. Findings Five antecedents of advertising value were found: credibility, entertainment, informativeness, irritation and message relevance. A priority ranking was allotted to the antecedents’ scale items using the RIDIT analysis and was verified via GRA results with a correlation of 98% between the rankings of the two independent methodologies. Practical implications The findings provide a roadmap to determine which antecedents of mobile advertising value have a higher or lower impact on consumers’ overall perceptions of the advertisements they are exposed to on mobile platforms. Originality/value This study aims to use first-hand data to prioritize the underlying antecedents of mobile advertising value, which has rarely been done to the best of the authors’ knowledge. It also used two different approaches in a single study to rank the dimensions, thus producing more valid results.

Financial performance assessment using Grey relational analysis (GRA)

2019 ◽  
Vol 9 (4) ◽  
pp. 502-516 ◽  
Author(s):  
Anandarao Suvvari ◽  
Raja Sethu Durai S. ◽  
Phanindra Goyari
Keyword(s):  
Performance Analysis ◽  
Financial Performance ◽  
Life Insurance ◽  
Grey Relational Analysis ◽  
Statistical Distribution ◽  
Insurance Companies ◽  
Content Type ◽  
Relational Analysis ◽  
Grey Relational ◽  
Practical Implications

Purpose Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis. Design/methodology/approach Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance. Findings The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies. Practical implications This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference. Originality/value This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.

Grey relational analysis coupled with principal component analysis for optimization of the cyclic parameters of a solar-driven organic Rankine cycle

2017 ◽  
Vol 7 (2) ◽  
pp. 218-235 ◽  
Author(s):  
Deepak Tiwari ◽  
Ahmad Faizan Sherwani ◽  
Mohammad Asjad ◽  
Akhilesh Arora
Keyword(s):  
Grey Relational Analysis ◽  
Organic Rankine Cycle ◽  
Principal Component ◽  
Fuel Mixture ◽  
Dew Point ◽  
Inlet Temperature ◽  
Content Type ◽  
Point Temperature ◽  
Relational Analysis ◽  
Grey Relational

Purpose The purpose of this paper is to investigate the effect of four controllable parameters (fuel mixture, evaporation bubble point temperature, expander inlet temperature and condensation dew point temperature) of a solar-driven organic Rankine cycle (ORC) on the first-law efficiency, the exergetic efficiency, the exergy destruction and the volume flow ratio (expander outlet/expander inlet). Design/methodology/approach Nine experiments as per Taguchi’s standard L9 orthogonal array were performed on the solar-driven ORC. Subsequently, multi-response optimization was performed using grey relational and principal component analyses. Findings The results revealed that the grey relational analysis along with the principal component analysis is a simple as well as effective method for solving the multi-response optimization problem and it provides the optimal combination of the solar-driven ORC parameters. Further, the analysis of variance was also employed to identify the most significant parameter based on the percentage of contribution of each cyclic parameter. Confirmation tests were performed to check the validity of the results which revealed good agreement between predicted and experimental values of the response variables at optimum combination of the input parameters. The optimal combination of process parameters is the set with A3 (the best fuel mixture in the context of optimal performance is 0.9 percent butane and 0.1 percent pentane by weight), B2 (evaporation bubble point temperature=358 K), C1 (condensation dew point temperature=300 K) and D3 (expander inlet temperature=370 K). Research limitations/implications In this research, the Taguchi-based grey relational analysis coupled with the principal components analysis has been successfully carried out, whereas for any optimized solution, it is required to have a real-time scenario that may be taken into consideration by the application of different soft computing techniques like genetic algorithm, simulated annealing, etc. The results generated are purely based on theoretical modeling, and, for further research, experimental analyses are required to consolidate the generated results. Originality/value This piece of research work will be helpful to users of solar energy, academicians, researchers and other concerned persons, in understanding the importance, severity and benefits obtained by the application, implementation and optimization of the cyclic parameters of the solar-driven ORC.

Effect of soybean roasting on soymilk sensory properties

2018 ◽  
Vol 120 (12) ◽  
pp. 2832-2842 ◽  
Author(s):  
Willard Navicha ◽  
Yufei Hua ◽  
Kingsley George Masamba ◽  
Xiangzhen Kong ◽  
Caimeng Zhang
Keyword(s):  
Sensory Analysis ◽  
Principal Component ◽  
Sweet Taste ◽  
Sensory Properties ◽  
Small Scale ◽  
Consumer Acceptability ◽  
Content Type ◽  
First Time ◽  
Practical Implications

PurposeThe purpose of this paper is to evaluate the changes in descriptive sensory properties and overall consumer acceptability of soymilk prepared from roasted soybeans.Design/methodology/approachIn total, 12 purposively selected post graduate students majoring in Food Science conducted descriptive sensory analysis after being trained for 18 h in sensory analysis, while 75 untrained students conducted consumer acceptability test of soymilk prepared by roasting soybeans at a temperature of 110°C for 20, 40, 60, 80 and 100 min and at 120°C for 20 min.FindingsResults have revealed that roasting soybeans improved sensory properties by significantly (p<0.05) decreasing the objectionable green, beany flavours and increasing sweet taste, viscosity and roasted flavour. Furthermore, results from the principal component analysis revealed that aroma and sweet taste were the most critical sensory attributes. In addition, it was found out that soymilk samples prepared by roasting soybeans at 110°C for 40 and 60 min and at 120°C for 20 min were significantly more acceptable than the control soymilk.Research limitations/implicationsThe participants in this study were from one locality and predominantly soybean consuming community and therefore there is need to conduct the study in a different locality in order to validate the study findings.Practical implicationsThe study can assist small scale processors that might not have access to lipoxygenase-free soybeans and other technologies for improving the quality of soymilk.Social implicationsThe study can be used as a guide for connecting the food processers with the external world of consumption.Originality/valueFor the first time, the study findings have demonstrated that controlled soybean roasting can be a useful strategy for improving soymilk sensory properties and consumer acceptability. The findings in this study can be usefully used in the quality control of soy bean-based products.

Identifying Industry 4.0 IoT enablers by integrated PCA-ISM-DEMATEL approach

2019 ◽  
Vol 57 (8) ◽  
pp. 1784-1817 ◽  
Author(s):  
Shubhangini Rajput ◽  
Surya Prakash Singh
Keyword(s):  
Industry 4.0 ◽  
Design Methodology ◽  
Principal Component ◽  
Integrated Approach ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Effective Implementation ◽  
Network Capability ◽  
First Time ◽  
Practical Implications

Purpose The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0. Design/methodology/approach IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum). Findings The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0. Practical implications The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0. Originality/value The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

Software solution of medical grey relational method based on SAS environment

2016 ◽  
Vol 6 (3) ◽  
pp. 309-321 ◽  
Author(s):  
Jin-Xiu Zhu ◽  
Xue-Rui Tan ◽  
Nan Lu ◽  
Shao-Xing Chen ◽  
Xiao-Jun Chen
Keyword(s):  
Original Data ◽  
Reference Sequence ◽  
The Novel ◽  
Data Set ◽  
Content Type ◽  
Relational Analysis ◽  
Typical Distribution ◽  
Grey Relational Coefficient ◽  
Grey Relational ◽  
Novel Algorithm

Purpose The purpose of this paper is to construct a new algorithm of program procedure for medical grey relational method based on SAS software. Design/methodology/approach Based on the SAS environment, the authors construct a new algorithm of program procedure through the following methods: the construction data set, confirmation of the comparison sequence and reference sequence, the original data transformation, calculation of the grey relational coefficient of reference sequence and comparison sequence and calculating the correlation. Findings The results show that the novel algorithm of program procedure for medical grey relational method based on SAS software satisfies the properties properly. It also fully confirmed the biggest advantage of the grey relational analysis is that its requirements are not too high for the amount of data, and it does not need to follow the typical distribution. Originality/value The paper succeeds in constructing a novel algorithm of program procedures for medical grey relational method and providing a valuable tool for solving similar problems.

A “Learning from Action” workshop in Japan

2019 ◽  
Vol 40 (2) ◽  
pp. 77-83
Author(s):  
Robert Douglas Hinshelwood ◽  
Luca Mingarelli ◽  
Simona Masnata
Keyword(s):  
Far East ◽  
Opportunity To Learn ◽  
Content Type ◽  
Work Activities ◽  
Defensive Role ◽  
Feedback Data ◽  
Experiential Workshop ◽  
First Time ◽  
Practical Implications

Purpose Many people in severe mentally disturbed states do not use language or other symbolic media well or coherently. Therefore, the non-verbal medium needs to be understood by workers with such people. The “Learning from Action” experiential workshop was developed in order to provide an opportunity to learn about hidden messages in the relationships and roles occurring in activities. In August 2017, a workshop was run for the first time in Japan. The purpose of this paper is to report the experience and dynamics observed by the three consultants, who are here the authors of this paper. Design/methodology/approach After the workshop all the staff and members, including interpreters, were invited to give feedback. Findings Analysis of the feedback data showed certain important dynamics, concerning especially dependence, cultural defences and the defensive role of activity in a multicultural context. Research limitations/implications This is an initial experience to be followed up by later feedback and further workshops. Practical implications Workers awareness of non-verbal communication within the roles of work activities is a training possibility. It faces various resistances including the mental health assumptions of meaninglessness of any communication outside the verbal. Originality/value This is a method of training not widely used even in European countries, and is the first in a country in the far east.

Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters

2016 ◽  
Vol 16 (2) ◽  
pp. 185-202 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Ali Shamsoddini ◽  
S. Travis Waller
Keyword(s):  
Supply Chain ◽  
Production Rate ◽  
Learning Method ◽  
Data Set ◽  
Content Type ◽  
Field Database ◽  
The Stability ◽  
Stable Learning ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method. Design/methodology/approach Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks. Findings MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches. Practical implications The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry. Originality/value Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.

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