Performance of TV programs: a robust MCDM approach

Abstract

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.

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“…Putting into other words, information entropy enters the TOPSIS model for each ijk triplet as a cornerstone for measuring performance. According to Aye et al (2018) and Andrade et al (2020), performance is often assessed with multi-criteria decision making (MCDM) methods like TOPSIS, which develop cardinal or scale metrics on positive and negative ideal solutions that are obtained through linear combinations of the original criteria. Putting into simpler wording, these combinations are formed by the best achieved values for the positive criteria and the worst achieved values for negative criteria, in a way that best and worst (i.e.…”

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“…Whereas the negative ideal solution maximizes the cost criteria and minimizes the benefit criteria, the positive ideal solution maximizes the benefit criteria and minimizes the cost criteria [ 59 ]. As shown by [ 60 ]; the TOPSIS technique is built upon an evaluation matrix consisting of m alternatives and n criteria with the intersection of each option and criteria given as xij. First, one obtains a matrix ( x ij ) mxn , which is first normalized from a regulated matrix R∗=( r ij ) as demonstrated in Eq.…”

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“…Moreover, TOPSIS has the advantage of utilizing the computational procedure regardless of problem size (Tansel, 2012; Velasquez and Hester, 2013). Lastly, TOPSIS also develops cardinal or scale metrics on positive and negative ideal solutions that are obtained through linear combinations of the original criteria (Aye et al , 2018; Andrade et al , 2020), while it is also one of several multi-criteria models resembling consecrated nonparametric and parametric efficiency measurement methods, such as DEA and stochastic frontier analysis, respectively (Bogetoft and Otto, 2011) [3].…”