domain: shs stat

text:
Lianlian Song
email: songll@nuaa.edu.cn

Yang Shi

Geoffrey Kwok

Fai Tso

Commercial Audience Retention of Television Programs: Measurement and Prediction

Keywords: commercial audience retention, commercial avoidance, regression model, media planning

Program audience ratings are typically used as a reference in placing and pricing television advertisements. However, the discrepancy in audience size between a program and its commercials impairs the reliability of the program ratings. This study proposes a new metric, commercial audience retention (CAR), to measure a program's capability of retaining its audience when a commercial break occurs and develops a model to predict a program's CAR. The CAR metric and prediction model are tested and validated using a sufficient dataset with consumer TV viewing and program broadcast records for 1 year. We find that some factors that influence program ratings or commercial avoidance have no significant effect or have different effects on CAR. Our empirical results may be of value for advertisers and TV stations in purchasing and pricing of commercial airtime. A real-world application of the CAR metric for an advertising company is offered as an illustration.

Introduction

Despite the rapid emergence of new media, the traditional television, as a free public program provider, is still the major advertising platform. As reported by Statista, global TV advertising expenditure exceeded 175 billion dollars in 2016 (Statista 2017). Program audience ratings are widely used as a reference in placing advertisements (ads) and negotiating advertising prices between TV stations and advertisers [START_REF] Givon | Using partially observed Markov processes to select optimal termination time of TV shows[END_REF][START_REF] Danaher | Forecasting television ratings[END_REF]. However, the discrepancy in audience size between programs and their commercials due to commercial avoidance impairs the reliability of the ratings [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF]. As many as 50% of consumers have admitted avoiding ads by switching channels (zapping), talking with others (multitasking), going to the bathroom or making a cup of coffee (physical zapping), and fast-forwarding through commercials (zipping) [START_REF] Smit | Segmentation based on affinity for advertising[END_REF]. Commercial avoidance is a serious concern, because it eliminates consumers' exposure to ads and thus reduces advertising effectiveness (Tse & Lee 2001). [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF] find that America's Next Top Model draw high program audience ratings but considerably lower commercial audience ratings; the average difference was 15%. This example shows that a program may have high program audience ratings but it may lose a large proportion of its audience during commercials. The discrepancy between program audience ratings and commercial ratings is well established (Tse & Lee 2001;[START_REF] Rojas-Mendez | Avoiding television advertising: some explanations from time allocation theory[END_REF], and it is arguably unfair to advertisers if they have to pay on the basis of a large program audience while their ads garner a much smaller audience (Wilbur 2008;Wilbur et al. 2013). Therefore, advertisements should be placed and priced based not only on the program's audience ratings, but also on its commercial audience retention (CAR) capability-that is, its ability to retain viewers during commercial breaks.

Although any program can suffer from the loss of a part of its audience during commercials (Teixeira et al. 2010), money for advertising would be better spent if the chosen program could retain more of its audience during commercials. As a consequence, a program's CAR is a valuable metric that can help advertisers decide whether to purchase the program's commercial airtime. In accordance with previous studies of commercial avoidance, a program's CAR may be related to various elements. Danaher reports that the drop in audience during commercials varies with program genre and length [START_REF] Danaher | What happens to television ratings during commercial breaks[END_REF].

If the program is attractive enough, consumers may tolerate the embedded commercials to avoid missing even a second of the program content that immediately follows [START_REF] Lu | Television audience satisfaction: antecedents and consequences[END_REF][START_REF] Dennis | An episode-by-episode examination: what drives television-viewer behavior: digging down into audience satisfaction with television dramas[END_REF]. [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF] find that Dramas suffer less from commercial avoidance than programs in other genres. Accordingly, we have identified a critical issue: Different genres of programs may suffer from different levels of CARs, depending on a bundle of variables. This highlights the need for a valid measurement of a program's CAR, as well as a sound method to predict it.

The purpose of this research is threefold. We first propose the CAR metric and develop a specific formula to calculate it. Second, based on a comprehensive literature review and empirical results of multivariate regressions, we investigate the covariates that have significant impacts on CAR and construct a model to predict CAR. Third, we examine the performance of CAR and apply it to a real-world case to illustrate how the CAR can be used by advertising firms to increase advertising effectiveness.

The dataset used for empirical analysis is the people meter data provided by Television Broadcasts Limited (TVB) in Hong Kong. The dataset contains two data files that cover the whole of 2014. One file records the audience viewing behavior, such as which channel a consumer is watching during a certain time period, and the other provides the detailed program schedule. Combining the two data files generates accurate details of when the consumer started and stopped watching a specific program including commercial breaks. This rich dataset, which covers more than 2,000 programs, is adequate for the purposes of this study.

To sum up, in this study, we propose a new metric, CAR, to capture a program's capability of retaining its audience during commercial breaks. The CAR metric is directly defined and measured for each entire program. This differentiates our study from previous studies, which mainly focus on examining TV viewers' commercial avoidance behavior at commercial-break level. In addition, we have developed a systematic way to predict the CAR. During the process of selecting valid predictors, we find that several variables that are predictive for program ratings and/or commercial avoidance (e.g. lead-in and genre match) have no significant effect on CAR, which further contributes to our understanding of commercial avoidance behavior. Since all predictors included in the CAR model are available when advertisers purchase commercial airtime, advertisers can predict CARs and use them in combination with the expected program ratings to assess reasonable prices for ad slots and, in turn, make better purchasing decisions.

The CAR metric

The use of program audience ratings to price and schedule ads has been challenged (Steinberg & Hampp 2007), because a program's audience rating is not consistent with its commercial rating. We thus propose the CAR metric to capture the number of viewers retained during a program's commercial breaks. The CAR can be used for ad pricing and scheduling as complementary to the program ratings.

Without loss of generalizability, we measure a program's CAR by the ratio of its rating retained during commercial breaks to the program rating: Since many TV stations allow advertisers to pay different rates for in-program commercial breaks and between-program commercial breaks, we also measure the CAR metric for these separately-i.e., in-program CAR and between-program CAR. Similar to the ratio presented in Equation ( 1), both CARs are also ratios of the program rating retained during in-program commercial breaks and the program audience rating. The denominator is the same; the numerator for in-program/between-program CAR is the average audience rating over all minutes of in-program/between-program commercial breaks of the program. If the program is a TV series (e.g., Drama and Reality Shows) that contains several episodes, its CAR is first calculated at each episode and then averaged over all episodes.

Next, we provide an example to illustrate how we calculate a program's CAR. Suppose the population comprises 100 persons. Figure 1 shows the audience rating (100%) of each minute during a half-hour broadcasting period. This period starts with commercials in the first 5 minutes, followed by a program from the 6 th minute to the 25 th minute, and ends with commercials in the last 4 minutes. During the program's broadcast time, an in-program commercial break occurs from the 14 th minute to the 16 th minute. Two between-program commercial breaks are shown in the figure. According to our definition, the second betweenprogram commercial break, from the 26 th to the 29 th minute, belongs to this program.

Therefore, the program audience rating is the average of the minute ratings from the 6 th minute (with audience rating 69%) to the 13 th minute (with audience rating 77%) and the 17 th minute (with audience rating 86%) to the 25 th minute (with audience rating 85%), which is equal to 79.59%. The program rating retained during commercial breaks of this program is the average of the minute rating from the 14 th minute (with audience rating 70%) to the 16 th minute (with audience rating 64%) and the 26 th minute (with audience rating 75%) to the 29 th minute (with audience rating 65%), which is equal to 68.42%. Therefore, the CAR of this program is 0.86, which equals the ratio of the program rating retained during commercial breaks (68.42%) to the program rating (79.59%).

In addition, if we only include the minutes during the in-program or between-program commercial breaks, we can obtain the program rating retained during in-program and between-program breaks, which are 66.3% and 70%, respectively. Dividing these ratings by the program rating, we can get the in-program and between-program CARs: 0.83 and 0.88, respectively. Separate measures for the in-program CAR and between-program CAR could guide advertisers' choices of commercial airtime during and outside of a program. The CAR metric measures a program's capability for program audience retention during commercial breaks, which also, conversely, reflects the program's probability of losing part of its audience during commercials.

--Insert Figure 1 about here --

Literature review

Discrepancy between program audience ratings and commercial audience ratings

Several prior studies examine the discrepancy in audience ratings between a program and its embedded commercials. Using a second-by-second people meter dataset for New Zealand during 1 week, [START_REF] Danaher | What happens to television ratings during commercial breaks[END_REF] examines changes in audience ratings during commercial breaks. He measures audience loss during a commercial break by calculating the ratio of the number of seconds spent viewing during the ad break to the number of seconds of potential viewing and finds considerable variation in the audience loss, which is related to the program genre, program rating, and commercial-specific characteristics. Van Meurs (1998) examines changes in audience ratings during commercial breaks by measuring the decline in ratings during a specific commercial break due to channel switching and the increase in ratings for a specific commercial break that can be attributed to new arrivals. Van Meurs finds that both commercial break characteristics, such as the length of the break, and program characteristics, such as genre, have significant impacts on commercial audience ratings. In this study, we propose a metric, CAR to measure the loss of audience during commercial breaks of a program. CAR of a program is defined as the proportion of a program's rating retained during its commercial breaks. A model for prediction of a program's CAR and a systematic way to select the predictive variables are offered. Compared with Danaher and Van Meurs whose studies are at commercial-break level and focus more on investigating influential factors, CAR is defined and measured for each entire program-the common target unit for selling ad slots-and we focus on how to predict CAR, because our main goal is to develop a metric for practical use.

Based on set-top box live tuning data for a full TV season in the US, [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF] use an adjustment factor (a log-transformed value) to measure the gap between the potential program audiences and the potential commercial audiences and examine the variation across programs via a hierarchical model with two layers. Their study shows that popular programs may have low commercial ratings due to commercial avoidance, and the avoidance rate varies across specific programs. Our study is closely related to that of Schweidel and Kent, but differs in two aspects. First, while we examine the CAR directly for each entire program, Schweidel and Kent measure program ratings and commercial ratings at half-hour level, but capture program-level effects using two program-specific random factors; and we count only program viewers when measuring the viewership retained during a program's commercial breaks, while they include all viewers to measure the commercial audience size. Second, we aim to provide a way to predict a program's capability of retaining its audience during commercials in order to assist advertisers in purchasing ad slots. The variables we consider in our prediction model are general and available before the program is broadcast, while Schweidel and Kent focus on demonstrating the degree of commercial avoidance; therefore their models are not suitable for predicting a program's CAR, since the program-specific random effect term is not identifiable for a new program (i.e., before it is broadcast).

We summarize the differences between the abovementioned three studies and ours in terms of research purposes, modelling approach, operationalization of dependent variable (DV), and unit of analysis in Table 1. More detailed comparisons of the four measures are provided in the online appendix. While the three prior studies focus on the overall audience size during a commercial break or within half an hour, we measure a program's audiences retained during its commercial breaks, excluding non-program viewers when calculating commercial audience size since the commercial viewing behavior of non-program viewers is hardly affected by the program and thus is irrelevant to a program's CAR. Measuring a program's audience retention ability (CAR) is important because it can reflect the extent to which the commercial audience is affected by the program. Knowing CAR of a program can help advertisers make better advertising purchasing decisions as currently commercial airtime is sold and priced on program basis.

--Insert Table 1 about here -

Factors that affect TV program viewing and/or commercial avoidance

To the best of our knowledge, no prior literature examines the predictors of CAR since this is the first study to propose the CAR metric. However, there is substantial research on the factors that impact consumers' TV program choice and commercial avoidance behavior.

Below, we comprehensively review these factors.

Channel. Many studies of TV program ratings or consumers' program choices include the channel as an explanatory variable, and results show that consumers' viewing utilities vary significantly across channels when other factors, such as program genre and broadcast time, are controlled for (e.g., Reddy et al.1998;Danaher & Gagger 2011;[START_REF] Deng | TV viewing and advertising targeting[END_REF]. With regard to commercial avoidance, Wilbur et al. (2013) and [START_REF] Deng | TV viewing and advertising targeting[END_REF] find considerable heterogeneity in consumers' switching behavior during commercial breaks on different channels.

Day-part. Day-part is another important predictor of TV program ratings (e.g., Horen 1980;[START_REF] Rust | An audience row model of television viewing choice[END_REF][START_REF] Kelton | Optimal television schedules in alternative competitive environments[END_REF]. Usually, program ratings are higher in prime time. Zufryden et al. (1993) and Van Meurs (1998) find that the commercial avoidance rate is also higher in prime time, while [START_REF] Mcsherry | The current scope of channel switching[END_REF] shows that channel switching is the least prevalent during prime time. [START_REF] Siddarth | To zap or not to zap: a study of the determinants of channel switching during commercials[END_REF] argue that programs during prime time are usually more attractive and can help retain consumers during commercials; however, consumers may switch more because of fiercer competition from other channels during prime time. Some other studies examine the impact of day-part by considering viewer behavior during specific hours of the day (e.g., [START_REF] Gensch | Models of competitive television ratings[END_REF]Wilbur et al. 2013;Wolf & Donato 2019).

Prior research also suggests other important time-based predictors of program ratings: day of the week, month, season, and year (e.g., [START_REF] Danaher | Forecasting television ratings[END_REF]Danaher & Gagger 2012;Wilbur et al. 2013). In addition, Van Meurs (1998) identifies an increase in commercial avoidance on weekends, and [START_REF] Smit | The differential effects of position, ad and reader characteristics on readers' processing of newspaper ads[END_REF] find that viewers respond differently to ads across days of the week. Other time-based variables, such as month, season, and year, are not examined in terms of commercial avoidance in the literature.

Program genre. Some prior studies have confirmed that program genre affects program ratings [START_REF] Lehmann | Television show preference: application of a choice model[END_REF][START_REF] Gensch | Evaluation of television program content for the purpose of promotional segmentation[END_REF][START_REF] Headen | The duplication of viewing law and television media schedule evaluation[END_REF]. Other studies have also shown that program genre is an important factor that affects consumers' program viewing decisions (e.g., [START_REF] Reddy | SPOT: scheduling programs optimally for television[END_REF]Yoo & Kim 2002;[START_REF] Deng | TV viewing and advertising targeting[END_REF]. For example, Soap Operas have higher ratings than Information/Reality programs [START_REF] Henry | Predicting program shares in new time slots[END_REF]. Program genre is also found to have a significant impact on consumers' commercial avoidance behavior (e.g., Danaher 1995; Van Meurs 1998;[START_REF] Cho | Effects of violent television programmes on advertising effectiveness among young children: Findings from a field experiment of kindergarten samples in South Korea[END_REF][START_REF] Kent | Advertisements in DVR time: the shelf life of recorded television commercials in drama, reality, and sports programs[END_REF]. Audiences for programs in different genres may have different levels of commercial avoidance. Previous research shows that Sports, Soap Operas, Series, and Game Shows experience an increase in channel switching (Billet Consultancy Limited 1992), while News programs tend to tie the audience to the channel [START_REF] Capocasa | Understanding audiences of TV commercial breaks: What people do, how they react, how much they recall[END_REF][START_REF] Horsley | A quantified study on what people do during commercial breaks[END_REF]).

In addition, [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF] find that Dramas suffer less from commercial avoidance than Reality programs.

Program length

The impact of program length on program ratings is examined extensively in prior research [START_REF] Henry | Predicting program shares in new time slots[END_REF][START_REF] Danaher | Forecasting television ratings[END_REF][START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF]. Program length is treated as a continuous variable when it is nearly continuously distributed [START_REF] Henry | Predicting program shares in new time slots[END_REF][START_REF] Danaher | Forecasting television ratings[END_REF][START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF], but it is treated as a discrete variable when the value of program length clusters within some discrete intervals or when some programs have very different lengths compared with the remaining programs (Danaher & Mawhinney 2001;Yoo & Kim 2002). The significant impact of program length on program ratings has been documented in previous research (Danaher & Mawhinney 2001;Yoo & Kim 2002). However, signs of the impact are not consistent; they are sometimes positive (e.g., [START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF]Yoo & Kim 2002) and sometimes negative (e.g., [START_REF] Henry | Predicting program shares in new time slots[END_REF]. [START_REF] Danaher | What happens to television ratings during commercial breaks[END_REF] further shows that the drop in audience ratings during commercial breaks varies by program length.

Lead-in.

Watching TV is a passive leisure activity, and consumers exhibit strong state dependence: Viewers of a program on a given channel are more likely to watch the next program on the same channel (e.g., Goettler & Shachar 2001;Yang et al. 2006;[START_REF] Anand | Advertising, the match maker[END_REF]. To forecast program ratings, many prior studies capture the state dependence effect by adding a variable, lead-in, based on the rating of the program immediately prior to the current program, and demonstrate that lead-in has a significant impact on the program's rating (e.g., [START_REF] Kelton | Optimal television schedules in alternative competitive environments[END_REF]Danaher & Mawhinney 2001;[START_REF] Jardine | Retaining the primetime television audience[END_REF].

However, no prior study investigates the direct impact of lead-in on commercial audience ratings for the following program.

Genre match. This variable, genre match, is usually considered together with lead-in [START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF]. Consumers have intrinsic preferences for a certain program genre (Webster & Phalen 1997;[START_REF] Ruggiero | Uses and gratifications theory in the 21st century[END_REF], and if they like the current program's genre and the next program is the same genre, they are more likely to watch the next program (e.g., [START_REF] Henry | Predicting program shares in new time slots[END_REF][START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF][START_REF] Jardine | Retaining the primetime television audience[END_REF]. Similar to lead-in, no prior study examines the impact of genre match on commercial avoidance.

Break length. Van Meurs (1998) has shown the significant effect of break length on commercial avoidance. Longer breaks trigger consumer impatience with commercials and allow enough time to do other things (Swaminathan & Kent 2013). As a result, longer commercial breaks are linked to higher levels of commercial avoidance [START_REF] Danaher | What happens to television ratings during commercial breaks[END_REF][START_REF] Dix | Predictors of commercial zapping during live prime-time television[END_REF]. Since break length is break-specific, previous studies do not consider it as a factor that impacts program ratings.

Number of commercial breaks. Taking a program as a unit of observation, the number of commercial breaks embedded in the program measures the frequency of commercial breaks during the program. Van Meurs (1998) has shown that commercial break frequency has a significant effect on channel switching. Zhou (2004) suggests that less frequent breaks help retain more viewers. We might expect that the more breaks a program has, the more likely it is to experience loss of audience. However, previous studies offer little evidence on the potential impact of the number of commercial breaks on commercial avoidance.

Reruns. If a program is not being broadcast for the first time, it is a rerun. [START_REF] Danaher | Forecasting television ratings[END_REF] and [START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF] demonstrate that reruns have lower ratings. To the best of our knowledge, no prior study examines the effect of reruns on viewers' channelswitching behavior.

The aforementioned variables affect either program ratings or commercial avoidance or both. We summarize all factors and differentiate each factor's impact on program ratings and commercial avoidance in Table 2. Wilbur et al. (2013) find that consumer demographics and advertisement characteristics also play important roles in commercial avoidance. The attractiveness of other (competing) channels during commercial breaks also affects consumers' channel-switching decisions (e.g., Van Meurs 1998;[START_REF] Danaher | Forecasting television ratings[END_REF][START_REF] Deng | TV viewing and advertising targeting[END_REF]. However, these variables are not measured at program level, so they are not suitable for predicting a program's CAR.

Prior research also shows that program promotion can attract more viewers to a program [START_REF] Schweidel | Predictors of the gap between program and commercial audiences: an investigation using live tuning data[END_REF][START_REF] Anand | Advertising, the match maker[END_REF] and render them less likely to skip commercials by increasing their tolerance levels (Jolna & Cusick 2015). Due to data limitations, however, we do not include program promotion in our model. Moreover, prior studies find that consumers are more likely to switch channels during between-program commercial breaks (i.e., commercial breaks between two different programs) than in-program commercial breaks (i.e., commercial breaks within one program) [START_REF] Anand | Advertising, the match maker[END_REF]Yao et al. 2017). Accordingly, we separately measure a program's capability to retain consumers during these two types of commercial breaks in the following analysis.

--Insert Table 2 about here --

Data

Our research on CAR is based on people meter data. This system is used worldwide to record consumers' TV-watching behaviors. In addition to channel switching, it records consumers' other actions, such as talking with others (multitasking) and going to the bathroom or making a cup of coffee (physical zapping). The reliability of people meter data was confirmed through a "coincidental survey" conducted by [START_REF] Danaher | A coincidental survey of people meter panelists: comparing what people say with what they do[END_REF]. In their study, 92% of panelists pushed the correct buttons, and thus people meter data are considered to be consistent with individuals' actual behavior. Although this system has been used for many years, neither the recording technology nor the sampling method has changed; structure of the people meter data has also remained unchanged [START_REF] Song | A dynamic multiple-variety choice adaption model[END_REF]. In practice, the system is widely used in many countries, including Hong Kong, Mainland China, the US, the UK, etc. [START_REF] Song | A dynamic multiple-variety choice adaption model[END_REF]Nielsen 2018;Bulgrin 2019). In academic research, people meter data are still used to examine TV-watching behavior, including by Lu and Lo (2010); [START_REF] Danaher | Using a nested logit model to forecast television ratings[END_REF]; and [START_REF] Song | Converting people-meter data from per-minute to per-second analysis: A statistical model offers a closer look at TV ad avoidance and effectiveness[END_REF]. We believe, therefore, that the people meter data are reliable.

The people meter data used in this study are provided by Television Broadcasts Limited (TVB), which is the main provider of free TV service in Hong Kong. The data contain both audience viewing records and TV program schedules for all channels in 2014. Audience viewing data are collected by the people meter system operated by Nielsen Media Research.

The panel contains approximately 2,790 people aged 8 or above, who are spread in a representative manner across the entire market. Each people meter has several buttons, with each button assigned to one member of a household. Household members are required to press their assigned buttons every time they start watching TV and press the button again when they stop. Combined with the information on program schedules, the people meter data tell us who was watching which channel during what time period, which enables us to examine audience viewing behavior for each TV program and commercial break.

Data screen

In this study, we focus on five TV channels: Asia Television Limited Home (ATVH); Asia Television Limited World (ATVW); TVB's Chinese Jade channel (TVBJ); TVB's Chinese Jade 2 (J2); and TVB's Chinese Pearl (Pearl). These five channels account for nearly 90% of all viewing across Hong Kong. They are free for local residents and totally supported by advertising.

The number of programs broadcast in 2014 is sufficient for the purpose of adequate sample size. Broadcasting content with the same name is considered to be the same program.

A program may be broadcast for several days or even several months-for example, Hong Kong News-but contain different content every day. We treat content on different days as different episodes, and calculate the program's CAR the same as we do for drama series. We do not include programs that have no embedded ads in our sample, since we cannot measure their CARs. Programs that are less than 5 minutes long are also excluded. After the screening, we have 2,067 unique programs. According to the TV station's program genre definitions, we consider the following eight genres: Drama, Movie, Public Affairs, News,

Children & Animations, Sports, RTHK (programs that comment on major events), and Special (special festival activities). These genres account for more than 80% of programs aired during the sample period. The remaining genres are categorized as "Other." The distribution of programs by genre across the five channels is shown in Table 3.

--Insert Table 3 about here --

CAR presentation

In all of the following analyses, we calculate the CAR based on ratings for individual viewers who are 8 years or older. Our study is a blend of viewing by adults and children. We do not distinguish between CARs for different types of viewers, because our study focuses on construction of the CAR metric and the method for predicting CAR, which could be applicable in a general context. In this section, we use summary statistics to show the variation in programs' CARs across channels, program genres, and day-parts based on the TVB data from Hong Kong. Table 4 shows the average CARs of programs on each of the five channels for the overall CAR, in-program CAR, and between-program CAR.

As can be seen in Table 4, TVBJ has the highest overall CAR of 0.79, which means that 21% of the program audience is lost during commercials. J2 has a comparatively lower overall CAR of 0.44, indicating that 56% of the program audience is lost during commercials.

Channels with higher overall CARs also have higher in-program CARs and between-program

CARs. In-program CARs are higher than between-program CARs.

--Insert Table 4 about here ----Insert Figure 2 about here ----Insert Figure 3 about here --In Figure 2, we report average CARs by program genre: Public Affairs, News, and RTHK programs retain more commercial audience than programs in other genres. In accordance with the time partition of TV stations, we divide the 24 hours of a day into 6 dayparts: 23:00-02:59; 03:00-06:59; 07:00-10:59; 11:00-14:59; 15:00-18:59; and 19:00-22:59.

Of these, 19:00-22:59 is the prime time period. Average CARs by day-parts are reported in Figure 3. As can be seen, the period from 19:00 to 22:59 may be the "golden time" for program ratings, but not for retaining the program audience during commercials. In contrast, the period from 15:00 to 18:59 has the highest CARs on most of the channels. During this period, programs retain a larger proportion of their audience during commercials.

The channel, program genre, and day-parts may not fully explain the variations in programs' CARs. However, comprehensive consideration of all possible variables may explain systematically why some programs suffer more from commercial avoidance and why others face less of that avoidance.

Prediction of CAR

Multivariate regression analysis

We use multivariate regression models to predict CAR based on the total sample of 2,067 unique programs. Our selection of potential explanatory variables is based on the comprehensive literature review. Similar to the variable classification of Danaher and Gagger (2011) In the group of program-specific variables, break length is the average length of commercial breaks that belong to the program, and the number of commercial breaks is the total number of commercial breaks of the program. These two variables are measured at program level and are thus program-specific. Since the distribution of program length in our sample is not continuous but clusters in three intervals, we classify all programs as short (less than 21 minutes), normal (between 21 and 60 minutes), or long (longer than 60 minutes), accounting for around 30%, 40%, and 30% of the full sample, respectively. We use two dummy variables to represent the short and normal programs. If a program has more than one episode, then program length is the average length across episodes. According to the genre classification, program genre is represented by eight dummy variables, and the "Other" program genre is the baseline. The lead-in is treated as a numerical variable, which is measured by the ratings of the program broadcast immediately prior to the current program.

In order to be consistent with the practice whereby advertisers must use the expected lead-in provided by the TV station to predict CARs when purchasing ad slots, we use the expected value rather than the actual value of the lead-in in our model. Genre match is coded as 1 if the current and preceding shows on a given channel are the same genre, and 0 otherwise.

Channel is represented by four dummy variables, with TVBJ as the baseline.

The group of time-based variables includes day-part, weekday or weekend, and season.

Five dummy variables are used to capture the effect of day-parts on CARs, and the baseline is prime time (19:00-22:59). Most of the programs are broadcast on either weekdays or weekends. The dummy variable weekday is equal to 1 if the program is broadcast on weekdays and 0 otherwise. The dummy season reports which season the program is broadcast in, and the baseline is winter.

The multivariate regression model is as follows:

, ( 2
)
where CAR is given in Equation (1); X is a column vector of all program-specific variables including break length, number of commercial breaks, program length, program genre, leadin, genre match, channel, and reruns; Y is a column vector of all time-based variables including day-part, weekday or not, and season; c is the intercept; and is the random error term. The column vector collects all coefficients corresponding to the variables in X, and the column vector collects all coefficients corresponding to the variables in Y. All variables are available when advertisers make ad-purchasing decisions and are included in the multivariate regression model at once. 5. It can be seen that all VIFs are below 10. According to the collinearity diagnostic criteria of [START_REF] Hair | Multivariate data analysis[END_REF], there is no collinearity problem between independent variables when the VIF is less than 10 (the tolerance of independent variables is greater than 0.1). Hence our models do not suffer from multicollinearity.

--Insert Table 5 about here --Significant Effects of Program-specific Variables. Table 5 shows that both break length and number of commercial breaks have significant and negative effects on CAR, in-program CAR, and between-program CAR. This indicates that fewer program viewers are retained during commercial breaks when the program has longer breaks or more breaks. On the one hand, consumers have more opportunities to switch channels when there is more commercial airtime. On the other hand, consumers may form expectations about the commercial airtime from past viewing experiences, and they are more likely to switch when they expect higher break frequency or longer break duration.

Program length has a significant and positive effect on CARs. A valuable finding is that shorter programs have higher retention of program audience during commercial breaks, since the coefficient values of short programs are larger than normal and long programs in the three models. That is, programs shorter than 21 minutes have the best commercial audience retention, and suffer from the least commercial avoidance.

The estimates of program genres provide some interesting insights regarding the most attractive genre. Consistent across the three models, Children & Animations and News are the top two program genres in terms of retaining program audience during commercial breaks, while Sports and Special programs retain the smallest proportion of their audiences.

The lead-in variable is usually found to be significantly related to program ratings in prior studies [START_REF] Napoli | The unpredictable audience: an exploratory analysis of forecasting error for new prime-time network television programs[END_REF]. Our results show that the lead-in also has a significant influence on CARs in Model 1 and Model 3. In addition, nearly all of the channel dummies are significant in affecting various CARs across the three models. Compared with the baseline channel of TVBJ, the other four channels have negative effects, which indicates their weaker capability of retaining their commercial audiences than TVBJ. The variable of rerun is estimated to negatively and significantly affect between-program CARs, which indicates that reruns have weaker capability of retaining their commercial audiences during betweenprogram breaks than programs broadcast for the first time.

Significant Effects of Time-based Variables.

Considering the results of the three models, generally, audience retention ability of programs broadcast during the baseline prime time period of 19:00-22:59 is higher than that of programs broadcast during periods 23:00-02:59, 03:00-06:59, and 11:00-14:59, but lower than that of period 15:00-18:59. This result shows that the prime time period may attract the largest program audience, but not the largest commercial audience. One possible reason could be that program content during prime time is more competitive among channels, so consumers frequently search for better programs during commercial breaks [START_REF] Siddarth | To zap or not to zap: a study of the determinants of channel switching during commercials[END_REF]. In contrast, although the daypart of 15:00-18:59 is not the golden time for increasing program ratings, it retains larger audiences during commercials than the prime time period. Regarding seasons, winter has the greatest effect on programs' CARs compared with the other three seasons. It is possible that winter is more suitable for staying home to watch TV.

As the above analysis shows, CARs are not constant across programs, but rather are dependent on program-specific and time-based covariates. As a result, the CARs, which measure the extent to which program ratings overestimate commercial ratings, differ from program to program.

Selection of covariates for CAR prediction

The objective of this research is not only to develop a CAR metric but also to provide a useful and accurate model for predicting CARs. In the above analysis, we have tested possible effective variables on CARs using the full sample. However, a variable that is significant in a model does not guarantee that it will improve predictions [START_REF] Danaher | Forecasting television ratings[END_REF]. Therefore, rather than using all of the variables reported in Table 5, we select variables that can improve the predictive power of the model.

In particular, we first drop the variables that have no significant effects on CARs. In predicting overall CARs (M1), we drop genre match and reruns. In predicting in-program CARs (M2), we drop lead-in, genre match and reruns. In predicting between-program CARs, genre match is dropped. Then, based on the remaining variables that have significant effects on CARs, we input groups of variables sequentially into the model and check whether adding them improves or worsens the predictions. In marketing and economic literature, studies on longitudinal or panel data usually use the first 80% or 70% of sample data to train the model and the remaining 20% or 30% data to test the model's prediction performance (e.g., [START_REF] Danaher | Forecasting television ratings[END_REF][START_REF] Guitart | Competitive advertising strategies for programmatic television[END_REF][START_REF] Song | Forecasting week-to-week television ratings using reduced-form and structural dynamic models[END_REF]. Following this convention, we use the first 10 months (i.e., from January to October) of 2014 to be the calibration period and the remaining 2 months (i.e., November and December) to be the validation period. We select predictors of CAR based on the model performance in the validation period, in which there are 413 unique programs distributed across five channels: 97 on channel ATVH, 61 on channel ATVW, 118 on channel TVBJ, 57 on channel J2, and 80 on channel Pearl.

Prediction errors are assessed using two measures, mean absolute deviation (MAD = average of |actual -predicted|) and mean absolute percentage error (MAPE = average of |actual-predicted|/actual). MAD measures the discrepancy between the predicted data and the actual data. MAPE, which expresses accuracy as a percentage, measures the predictive accuracy of a method/model in statistics. These two measures have been used extensively to gauge the predictive accuracy of TV ratings [START_REF] Napoli | The unpredictable audience: an exploratory analysis of forecasting error for new prime-time network television programs[END_REF]Nikolopoulos et al. 2007;[START_REF] Danaher | Forecasting television ratings[END_REF].

Table 6 reports prediction errors when we include different variables in the three proposed models. Only variables that are found to be significant in the above multivariate analysis are included. It should be noted that in progressing from the first group of variables to the weekday column of Table 6, we add covariates to the model cumulatively, so that the column headed "+season" means that the covariates included are the break length, number of breaks, program genre, channel, and weekday plus the season dummy variable. We measure the prediction errors for all three models. Results show that the prediction error decreases when season, program length, and reruns are added, but increases when lead-in is added.

Although the lead-in variable is significant in the fitted models of M1 and M3, it does not improve the CAR predictions. A possible reason is that in the prediction model, we use the expected value of lead-in, which itself is subject to prediction errors. Therefore, we remove the variable of lead-in from the CAR prediction models. The previous literature identifies a total of 11 potential predictors for CAR. The predictive power of each variable X could be measured as the difference in prediction error (such as MAD and MAPE) between the model without variable X and the full model with all 11 variables. A larger difference suggests a stronger impact of variable X on the accuracy of model prediction so that variable X is considered to have larger predictive power. With respect to the prediction power for the overall CAR, variables can be ranked in the following order: the first group includes variables such as number of breaks, break length, program genre and channel, followed by the second group including daypart and season, and the third group including weekday and program length. Variables rerun and genre match have no significant influence on the prediction results while lead-in even worsens the prediction performance. Note that a variable's prediction power may be different for the in-program CAR and between-program CAR. The same approach can be used to measure prediction power of different variables in these models.

--Insert Table 6 about here --

Accuracy of CAR prediction

To further check the robustness of the CAR prediction model with the above-selected covariates, we examine its prediction accuracy across different calibration and validation samples. Adopting an approach similar to Wang and Wang (2017), we split the whole sample into 6 different combinations of the calibration and validation periods, and analyze each combination separately. This can increase the randomness of the data split and avoid data selection bias. In particular, we choose each of the following 2-month periods to be a validation period: January-February, March-April, May-June, July-August, September-October, and November-December, and the remaining 10-month period is the corresponding calibration period. On average, there are more than 1,600 programs in the calibration sample and more than 400 programs in the validation sample. MAD and MAPE are again used to measure calibration and validation accuracy. The numbers of programs in the calibration and validation periods and the prediction errors are shown in Table 7.

--Insert Table 7 here --As can be seen in Table 7, the two sets of errors of calibration and validation periods are very close, which indicates little degradation in predictive performance in the validation period. This suggests that the covariates we choose are able to capture the variation in programs' CARs. Average MAPE errors over the validation periods of Model 1, Model 2, and Model 3 are 10.8%, 10.3%, and 10.0%, respectively. Since this study is the first to propose the CAR metric and develop the CAR prediction model, it is difficult to find prior CAR predictions for comparison. However, many previous studies have reported the MAPEs for predicting TV program ratings; these include 13% [START_REF] Cattin | A model for forecasting the audience of TV programs[END_REF]); 45% (Danaher & Mawhinney 2001); 18.2% [START_REF] Henry | Predicting program shares in new time slots[END_REF]; and 21.4% [START_REF] Napoli | The unpredictable audience: an exploratory analysis of forecasting error for new prime-time network television programs[END_REF], all of which are less favorable than our MAPE errors.

The MAPE potentially enables comparison across studies. However, one limitation of this measure is that it is sensitive to the size of the actual values, because the MAPE increases as the actual value declines. For specific CARs, we believe that advertisers and broadcasters tend to measure differences between actual and predicted values in absolute rather than relative terms. This means that the MAD is a more practical measure of predictive accuracy in the TV industry (Nikolopoulos et al. 2007). Accordingly, we next focus on the MADs of the CAR prediction errors shown in Table 7. The average MADs over the six validation periods of Model 1, Model 2, and Model 3 are 0.042, 0.040, and 0.038, respectively, which indicates that the average difference between actual and predicted CARs (including overall CAR, in-program CAR, and between-program CAR) is approximately 0.04.

As can be seen in Table 7, in most validation periods, in-program CARs and betweenprogram CARs are predicted more accurately than overall CARs. These findings are consistent with our model fitting results, and demonstrate that if the type of commercial break can be chosen before purchasing, in-program CARs and between-program CARs are preferable to overall CARs for use.

For a more detailed understanding of our CAR predictions, we provide the actual and predicted CARs (based on Model 1) of the programs in the validation period of January and February for two channels, ATVH and TVBJ, in Figures 4 and5, respectively. As shown, the CARs vary across programs. Only three programs retain all of their program audiences, with CARs equal to 1; more than 90% of programs lose some portion of their audiences during commercials. For ATVH, the majority of programs have actual CARs approaching 0.6, which means that at most 60% of the program audience remains to watch the commercials. TVBJ, in contrast, retains 80% of program audiences for commercials. Both Figures 4 and5 reflect the well-predicted CARs using our metric and selected covariates. Compared with the prediction errors reported in prior literature on TV program ratings (e.g., as noted above, with MAPEs of 13% in [START_REF] Cattin | A model for forecasting the audience of TV programs[END_REF]; 45% in Danaher and Mawhinney (2001); 18.2% in [START_REF] Henry | Predicting program shares in new time slots[END_REF]; and 21.4% in [START_REF] Napoli | The unpredictable audience: an exploratory analysis of forecasting error for new prime-time network television programs[END_REF]), the gap between the actual and predicted CARs in our study is small, with a MAPE of 11.7% for programs on ATVH and 7.4% for programs on TVBJ, which indicates the good performance of our CAR prediction model.

--Insert Figure 4 about here ----Insert Figure 5 about here -

Fundamental implications

In the Hong Kong TV industry, around 80% of ad slots for a year are sold upfront at the end of the preceding year, and the remaining 20% are sold in the scatter market close to the program broadcast time and usually at higher unit prices. The actual ratings of commercials depend on the actual number of viewers during commercial airtime. Since the actual ratings are not available at the time of purchasing ad slots, advertisers usually make purchases based on the program schedules and expected program ratings provided by the TV station.

Although measures of the program audience reflect the average popularity of the program, they do not provide information about the extent of audience declines during commercial breaks. Some popular programs with high ratings may experience dramatic audience loss during commercials. Other programs, with small audiences, may also experience large commercial avoidance. The CAR measures a program's capability of retaining its commercial audiences, and can help advertisers identify the extent to which a program would experience commercial avoidance.

We have started applying CARs in an advertising company. Since all of the effective variables in our CAR prediction model are available when advertisers purchase the ad slots, the CAR can be predicted and used complementarily with the expected program ratings to guide the pricing of ad slots. Figure 6 gives an example to demonstrate the use of CARs. We predict the CARs of 20 programs that are available for embedded ads. The horizontal axis represents program ratings, ranging from 0 to 1, and the vertical axis denotes predicted CARs, with the interval between 0 and 1. Note that since predicting program ratings is not the focus of this paper, we use actual program ratings in the horizontal axis for our ex post analysis. In practice, the horizontal axis shows the expected program ratings provided by the TV station.

Figure 6 is drawn as a quadrantal diagram. The programs depicted in the upper-left quadrant are expected to have higher CARs (more than 0.5) and experience less ad avoidance, even though they may have lower program ratings (less than 0.5). That is, the audience ratings for these programs are low-less than 0.5-but they can retain more than half (at least half and at most 100%) of the program audience during commercial breaks. As such, these programs represent alternative choices for advertisers. Conversely, programs in the lowerright quadrant have higher program ratings-more than 0.5-but are expected to retain less than half of the program audience during commercials (i.e., their CARs are less than 0.5). To reiterate, these programs may lose more than half of their program audience during commercials, even though their program ratings are higher (at least 0.5). In such cases, advertisers need to be more cautious when relying on program ratings, because they may end up paying the price for high program ratings but actually obtain lower ad audience ratings. A large proportion of the program audience will, for instance, switch channels during commercial breaks. Advertisers should consider potential bargains before purchasing commercial airtime in these programs. This result further confirms the value of our research in this study: Not all programs with higher program ratings have higher abilities to retain their audience during commercials. Now we consider the remaining two quadrants of Figure 6. Programs in the lower-left quadrant have low audience ratings (less than 0.5) and low abilities to retain the audience during commercials (CARs less than 0.5). These programs retain less than half of their program audience during commercial breaks. Therefore, ad slots in these programs are worth less. In contrast, the programs depicted in the upper-right quadrant are the best choices for many advertisers, since they are expected to have high audience ratings as well as high commercial ratings. These programs' audience ratings are more than 0.5, and they retain more than half of their program audiences during commercials (CARs larger than 0.5).

In Figure 6, we use the cutoff point 0.5 for both program ratings and CARs as an example to illustrate the use of CARs. In practice, the cutoff point can be chosen by advertisers according to their target ratings and the prices they can afford. For instance, if the advertiser sets 0.7 to be the cutoff point for program ratings and 0.8 to be the cutoff point for CARs, programs in the upper-right quadrant may obtain commercial ratings of at least 0.56 (0.7*0.8). These programs have high value. Similarly, we are able to obtain the quadrantal diagrams of program ratings vs. in-program CARs and program ratings vs. between-program CARs. Since some programs' capabilities to retain audiences differ for in-program commercial breaks and between-program commercial breaks, their locations vary in the diagrams based on in-program CARs and between-program CARs. Advertisers can use these diagrams if they are allowed to choose the location of commercial breaks with respect to a given program.

--Insert Figure 6 about here -

Conclusions and future work

In this research, we incorporate a comprehensive data file with consumer TV viewing records throughout the whole year of 2014, which covers a longer data frame than what has typically been employed in prior research on program ratings or commercial avoidance.

Using detailed records of more than 2,000 unique programs, we construct a multivariate regression model to predict CARs of different programs. Our results show that most programs have CARs that are significantly different from the expected values by traditional wisdom. Some variables that have been confirmed to be effective on program audience ratings or commercial avoidance have no significant influence on CARs. As such, advertisers who engage in traditional advertising may be able to find bargains, such as greater delivery per ad dollar spent, through careful analysis and CAR predictions for target programs. Our results highlight the importance of the CAR metric and its accurate prediction. This research makes important contributions to the growing body of research on program ratings and the corresponding study of commercial avoidance. Theoretically, we have proposed a new metric, CAR, to capture a program's capability to retain its audience during commercials. While the literature on commercial avoidance mainly focuses on the analysis at commercial-break level, the CAR integrates program ratings and commercial ratings in one definition. This helps us to enrich our understanding of commercial avoidance behavior from a new perspective; for instance, the program-level CAR provides more information on channel switching behaviour during commercial breakes within a program.

Methodologically, we have developed a systematic way to measure and predict the CAR. Our prediction method is applicable to any TV station that broadcasts programs with embedded commercials. Practically, the CAR metric provides advertisers with insights into audience sizes of their commercials relative to the program. This may help advertisers to improve media planning precision and to better negotiate advertising rates. In addition, TV stations may also benefit by understanding programs' capabilities for retaining commercial audiences.

For TV markets in which ratings are guaranteed (e.g., in the US), advertisers may be disappointed by the high price paid for program ratings given the low commercial ratings obtained, and as a result compel TV stations to provide costly "make goods" to compensate them. In this situation, if TV stations can also predict CARs and use the predicted results as a basis for selling ad slots, they can reduce the possibility of disappointing advertisers as well as being compelled to issue "make goods."

Our CAR metric is generalizable to other countries. First, the CAR metric is proposed to measure a program's ability to retain audiences with regard to commercial avoidance, which is a widely confirmed and common television audience behavior in many countries, such as the US, the UK, and Australia (Danaher & Gagger 2012;Swaminathan & Kent 2013;Bulgrin 2019). Although the degree of commercial avoidance may vary across countries, channels, and programs, the definition and measurement of CAR is applicable to any program in any country as long as there are embedded commercials. Second, all variables we consider in our CAR prediction model are program-based and time-based (rather than audience-specific) variables. A large mass of literature has discussed the influence of these variables on program ratings and/or commercial avoidance, indicating that data of these variables are available from TV stations in many other countries, such as the US, the UK, and Mainland China (Danaher & Gagger 2012;Swaminathan & Kent 2013;[START_REF] Song | Converting people-meter data from per-minute to per-second analysis: A statistical model offers a closer look at TV ad avoidance and effectiveness[END_REF], and not only in Hong Kong. Third, our data were collected by AC Nielson's people meter system. This system has a long history and is still used in many countries, including the US, the UK, Australia, and Mainland China (Nielsen 2018;Bulgrin 2019;[START_REF] Song | Converting people-meter data from per-minute to per-second analysis: A statistical model offers a closer look at TV ad avoidance and effectiveness[END_REF]. All data from this system are structured similarly, which further guarantees the generalizability of our CAR model to other areas or countries.

Along the lines of investigating the degree of viewers' commercial avoidance at program level, more work can be done in further research. First, due to the rapid development of new media, such as the internet and smartphones, people tend to perform multiple tasks while watching TV. However, our people meter system is not able to record viewers' multitasking behavior, which may lead to overestimated CARs. Although the inability to capture multi-tasking behavior is common to most of the extant literature on program viewing and commercial avoidance, we believe that when such data are available, there will be great value in incorporating people's use of other media in calculating the CAR in future research. Doing so may yield different estimated coefficients and predictors of CARs, but our definition of CAR, as well as the prediction procedure, will still be applicable.

Second, our study uses a regression model to measure and predict CARs in a general model without considering the heterogeneity of TV viewers. Future research may consider employing the maximum utility theory and constructing a random utility model to examine the commercial avoidance behavior of the individual consumer while capturing viewer heterogeneity using viewer demographics or random specifications. Moreover, as advertisers face the challenge of dividing their budget among different advertising channels, a hierarchical model could be developed to gain insights into how CARs vary across demographic groups.

Third, a similar modeling approach could be used to study how program appeal and ad content affect CARs. Advertisers and TV stations could identify specific items relevant to program elements and commercial content to maximize programs' CARs, which is also valuable for choosing ads' positions during a commercial break. Note that the measurement and prediction method of CAR can be easily applied to different viewer segments. In this study, we analyze CAR for all viewers. If an advertiser is only interested in one segment (say females between 25 to 35), the advertiser can choose the segment viewers as the sample and follow the same procedure to conduct prediction as what we have done for the sample of all viewers in this study. Moreover, in the prediction model of CAR, we consider the main effects of 11 variables but ignore their interaction effects because including all relevant interactions would make the model over complicated, which shall go beyond our research purpose. The prediction power of the current model is already high enough for practical use.

However, future research can include interaction terms to test the interaction effects among a set of factors, whose findings may add to the extant consumer TV viewing and zapping behavior literature. 1,0 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Program rating CAR

  CAR of a program=program rating retained during breaks of a program / program rating (1)As shown in Equation (1), the program's rating is the basis for the metric. The audience rating measures audience size as a percentage of the population, and can be calculated for each minute of a program. The program rating is the average rating over all minutes of a program (excluding embedded commercial breaks). The program rating retained during breaks of a program is calculated as the average rating contributed by program viewers (who have watched at least one minute of the program) over all minutes of commercial breaks that belong to the program. Consumers who have not watched the program but tune in during the commercial breaks are excluded when calculating the rating retained during breaks because they are not program viewers and thus are irrelevant to a program's audience retention ability. Generally, each program has one between-program commercial break broadcast before and one broadcast after it. According to previous literature[START_REF] Danaher | What happens to television ratings during commercial breaks[END_REF]) and our interactions with experts in this area, between-program commercial slots are sold based on the rating of the program prior to the commercial break. Therefore, we define a program's commercial breaks as including all in-program commercial breaks and the between-program commercial break that follows the program.

  for predicting program ratings, we divide all explanatory variables of the CAR into program-specific variables and time-based variables. Program-specific variables include break length, number of commercial breaks, program length, program genre, lead-in, genre match, channel, and rerun; time-based variables include day-part, weekday or weekend, and season.

  The above regression model (Model 1) in Equation (2) predicts a program's overall CAR. Considering the difference between in-program commercial breaks and betweenprogram commercial breaks, we also construct Model 2 and Model 3 to separately predict inprogram CAR and between-program CAR. In Models 2 and 3, the dependent variables are inprogram CAR and between-program CAR, respectively, while the explanatory variables are the same as those in Model 1. We first verify which factors provide significant explanations for CAR. Considering the large number of explanatory variables, we use multivariate analysis. Multicollinearity between variables is identified using multicollinearity diagnostics, and the variance inflation factor (VIF) is presented to verify the multicollinearity issue.Table5reports the results of fitted multivariate regression models M1, M2, and M3. The three models fit the data satisfactorily. The VIFs are shown in the last column of Table

FigureFigure 4 :Figure 5 :

 45 Figure 2:CARs by program genre

Table 1 :

 1 Comparison between prior relevant studies and this study

Table 2 :

 2 Summary of the literature on TV program ratings and/or commercial avoidance

	Variable	Channel Day-	Season Weekday Program	Program	Lead-	Genre	Break	Number of	Reruns
			part			genre	length	in	match	length	commercial
	Literature										breaks
	Henry & Rinne 1984								
	Capocasa et al. 1985				
	Horsley 1986				
	Rust & Alpert 1984		
	Danaher 1995									
	Van Meurs 1998										
	Reddy et al. 1998					
	Meurs 1998				
	Webster & Phalen 1997								
	Kelton & Stone 1998							
	Ruggiero 2000								
	Danaher & Mawhinney									
	2001
	Danaher & Dagger 2011											
	Danaher & Dagger 2012											
	Wilbur et al. 2013				
	Smit et al. 2013	
	Schweidel & Kent 2010										
	Jardine et al. 2016								
	Dix & Phau 2017									
	Deng & Mela 2018					
	Wolf & Donato 2019		
	Kent et al. 2019					
	Our study											

Table 3 :

 3 Distribution of programs by genre across five channels

				Public		Children &
		Drama Movie	Affairs News	Animations Sports RTHK Special Others Total
	ATVH	121	123	23	4	49	19	41	30	73	483
	ATVW	25	87	12	8	55	25	23	24	47	306
	TVBJ	94	177	23	8	67	35	39	42	107	592
	J2	23	88	5	9	26	16	25	19	73	284
	Pearl	43	95	12	19	57	23	30	22	101	402
	Total	306	570	75	48	254	118	158	137	401 2067

Table 4 :

 4 CARs by commercial break type across five channels (sample size N=2,067)

		ATVH	ATVW	TVBJ	J2	Pearl
	In-program CAR	0.66	0.52	0.84	0.47	0.51
	Between-program CAR	0.51	0.41	0.76	0.42	0.42
	Overall CAR	0.57	0.45	0.79	0.44	0.48

Table 5 :

 5 Results of regression models for CARs M1

			M2	M3	VIF
		(Overall CAR)	(In-program CAR)	(Between-program CAR)
		Estimate (Sd)	Estimate (Sd)	Estimate (Sd)
	Program-based Variables
	Break length	-0.033 (0.003)	-0.036 (0.004)	-0.033 (0.003)	2.040
	Number of commercial
	breaks	-0.049 (0.006)	-0.048 (0.008)	-0.049 (0.006)	2.662
	Program length-short	0.082 (0.020)	0.089 (0.026)	0.075 (0.021)	1.303
	Program length-normal	0.047 (0.010)	0.024 (0.012)	0.070 (0.010)	1.432
	Drama	0.041(0.015)	0.032 (0.019)	0.041 (0.016)	2.185
	Movie	-0.005 (0.014)	-0.016 (0.019)	-0.013 (0.015)	2.556
	Public Affairs	0.079 (0.018)	0.068 (0.018)	0.066 (0.020)	1.537
	News	0.109 (0.025)	0.092 (0.024)	0.091 (0.026)	1.253
	Children & Animations	0.133 (0.016)	0.127 (0.032)	0.164 (0.017)	2.024
	Sports	-0.094 (0.021)	-0.121 (0.021)	-0.067 (0.022)	1.316
	RTHK	0. 043 (0.017)	0.046 (0.027)	0.039 (0.018)	1.660
	Special	-0.104 (0.019)	-0.089 (0.021)	-0.120 (0.020)	1.511
	Lead-in	0.031 (0.010)	0.013 (0.024)	0.029 (0.000)	1.465
	Genre match	-0.004(0.009)	-0.006 (0.000)	-0.001 (0.009)	1.257
	Channel-ATVH	-0.159 (0.022)	-0.189 (0.011)	-0.129 (0.024)	6.345
	Channel-ATVW

-0.270 (0.022) -0.252 (0.028) -0.288 (0.023)

	5.942
	Channel-J2

-0.280 (0.022) -0.297 (0.028) -0.263 (0.024)

	5.544
	Channel-Pearl

-0.089 (0.023) -0.115 (0.029) -0.064 (0.024)

					7.586
	Reruns	0.008 (0.010)	-0.013 (0.029)	-0.030 (0.011)	1.363
	Time-based Variables
	23:00-02:59	-0.045 (0.011)	0.028 (0.013)	-0.117 (0.012)	1.355
	03:00-06:59	-0.057 (0.016)	-0.061 (0.014)	-0.052 (0.017)	1.283
	07:00-10:59	-0.003 (0.015)	-0.018 (0.020)	-0.012 (0.016)	1.317
	11:00-14:59	-0.035 (0.013)	-0.016 (0.020)	-0.053 (0.014)	1.253
	15:00-18:59	0.074 (0.014)	0.083 (0.016)	0.066 (0.015)	1.588
	Weekday	0.035 (0.008)	0.032 (0.018)	0.038 (0.009)	1.157
	Season-Spring	-0.029 (0.010)	-0.031 (0.010)	-0.027 (0.011)	1.273

Table 6 :

 6 CAR prediction errors as groups of covariates are added to the model

	No. of programs			Variables in the model
		413	break length,
			number of breaks, genre, channel,	+ season	+day part	+lead-	+ program	+reruns
			weekday			in	length
	M1	No. of covariates	14	16	20	21	23	--
	( Overall
	CAR)	MAPE	0.211	0.165	0.120	0.121	0.109	--
		MAD	0.111	0.085	0.051	0.052	0.041	--
			14	16	19	--	21	--
		No. of covariates
	M2	MAPE	0.199	0.161	0.118	--	0.106	--
	(In-program
	CAR)	MAD	0.101	0.082	0.052	--	0.035	--
			14	16	20	21	23	24
	M3	No. of covariates
	(Between	MAPE	0.199	0.159	0.111	0.114	0.103	0.101
	-program
	CAR)	MAD	0.100	0.071	0.051	0.054	0.033	0.031

Table 7 :

 7 Accuracy of CAR prediction by validation periods Jan. & Feb. Mar. & Apr. May & June July & Aug.

	Sep. & Oct.	Nov. & Dec.	Average
			MAPE/MAD

This work was supported by the [National Natural Science Foundation of China] under Grant [No. 71602089, No. 71702201], [NSF of Jiangsu Province] under Grant [No. BK20160785], and [the Strategic Research Grant of Hong Kong] under Grant [No. 7005294 and 7005430].

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Disclosure statement

No potential conflict of interest was reported by the authors.