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!!!!!!!!!!!!!!!E-reader ownership in the US !!!!!!!!!!!!!!!!!!!!!!!!!!!!
Collaboration and Decision Technologies MT 8312
Professor Murtaza Haider Peter (Yi Nan) Zhang
500597806
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!Table of Content !!!!
Table of Content 2
Background 3
Methodology 4
Demographic Influences 4
Technological Factors 9
Conclusion & Recommendations 11
Reference 12
Appendix 12
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Background !People engage in many leisurely activities, either for relaxation or for intellectual stimulation, and book-reading is one such activities that can fulfil both needs. Whether you are buried in the thrilling Sherlock Holmes or the heart-wrenching Anna Karenina, books alway provide that special moment where you are directed away from the streets of the mundane, and stirred to the halls of the enlightened.
Human had gone a long way from the Homeric oral-tradition to the Gutenberg printing-press , and the advent of digital books made possible a future where millions of books can be stored in a single hard-drive. Along with digital books also came a wave of electronic readers (e-readers) that are hand-held devices specifically designed for the use of reading digitals books.
E-readers took the market by storm, reaching 25 million units shipped globally in 2011 (The Wall Street Journal, 2013), and 45.6 million ownership in the US adult demography in 2012 (eMarketer, 2012). However, despite of the increments in ownership, the demand for new e-readers decreased by 28% from 2011 to 2012 (IDC, 2012), and is forecasted to drop further (Figure 1 & 2).
Two challenges arise concurrent with the gloomy forecast. First, users who already own an e-reader do not see the urgency to buy a new one, due to retarded advancement in the core e-ink technology (which has slower response rate and problem with producing colour). The second challenge is regarding competing platforms such as tablet, phablet, and mobile. The Pew Research Center (2012) found that the readership of e-books increase from 2011 (16%) to 2012 (23%), but people are more likely to read on tablets rather than an e-reader.
Since technological breakthroughs are lagging for e-readers, therefore, manufactures cannot provide added value in terms of hardware functionality, and have to rely on targeting valuable costumers. The purpose of this report is to use survey information collected from The Pew Research Center to identify factors that affect e-reader ownership, such as the impacts of co-ownership of tablet, or family income.
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Figure 1: E-reader Shipments 2010-2013 & 2014 Forecast
Figure 2: US Adult E-reader Users and Penetration, 2010-2015
Source: IHS iSuppli Research 2013 Source: eMarketer, June 2012
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Methodology !The data taken from The Pew Research Center contains 1054 respondents, 504 via cellphones and the rest through in-person interviews. Few data points not relevant towards this report are excluded, and the following are included in the statistical survey and modelling of this report: e-reader ownership; cellphone/smartphone ownership; personal computer ownership; tablet ownership; number of books read in the past 12 months; out of the books read, how many are printed/audio/e-books; age; sex; family income; race; employment status; educational achievement.
Chi-square, logit-regression, and step-wise regression were used in this report, and the software used is R-commander. !Demographic Influences !Respondents median age is 55, and the data is recoded into young adult(18-25), adult(26-35), middle aged(36-50), and seniors (>50) for logit regression purposes. Figure 3 shows that e-reader ownership is almost the same across, with middle aged respondents owning 14 percentage points more e-readers. Statistically this difference is insignificant.
Female cohorts own 2% points more e-readers as their male counterparts. However this difference is statistically insignificant, the p-value for Pearson’s Chi-Squared test is 0.03251, indicating that this ownership difference is likely due to chance.
A full-time employee is 11% points more likely than a part-time employed or unemployed person to own a e-reader. All else being equal, this difference is not statistically significant (Figure 4).
Moving forward, much liked with employment status is a person’s educational attainment, we would assume that a less educated person would be less likely to attracted to reading, thus owning a e-reader.
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Percent ereader_use
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Employed full-time Employed part-time Not employed Don't know/Refused (VOL.)employ
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Figure 3: Impact of age difference on e-reader ownership Figure 4: Impact of employment status on e-reader ownership
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Figure 5 represents e-reader ownership among different educational groups. Due to graphical space limitation, the order is listed here from the left to right: less than high school; high school incomplete; high school graduate; some college, no degree; two year associate degree from college or university; four year college or university degree/Bachelor’s degree; some postgraduate or professional schooling; postgraduate or professional degree, including master’s, doctorate, medical or law degree.
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!!!!!!!!!!!!!There is a clear increase of e-reader ownership as one’s educational attainment improves, and a Chi-squared test showed that these differences are highly statistically significant (p=5.826e-07). The jump in e-reader ownership is most significant between having less than high school education and high school education, and between having a associate degree and a bachelor’s degree (circled in green). All other increments of ownership have a constant 4% points rate, with the exception of bachelor’s degree to graduate degree (2% point increase).
One caveat lies in putting education as a dependent variable in the logit model along side with other dependent variables. In the model:
!GLM <- glm(ereader_use ~ educ2 + cell_use + ebook + income + pc_use + tablet_use)
!Education suddenly becomes insignificant compared to, for instance, income and tablet use. The explanation is such that by itself, educational attainment is a excellent indicator for e-reader ownership, however, the explanatory power is not strong enough compared to other variables, and that collinearity exist among the dependent variables.
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Less than high school (Grades 1-8 or no formal schooling) High school incomplete (Grades 9-11 or Grade 12 with NO diploma) High school graduate (Grade 12 with diploma or GED certificate) Some college, no degree (includes some community college) Two year associate degree from a college or university Four year college or university degree/Bachelor's degree (e.g., BS, BA, AB) Some postgraduate or professional schooling, no postgraduate degree Postgraduate or professional degree, including master's, doctorate, medical or law degree (e.g., MA, MS, PhD, MD, JD) Don't know/Refused
educ2
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Yes
Figure 5: Impact of education on e-reader ownership
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To test these two hypotheses, I conducted two tests. In order to test the that comparative explanatory power leads education to be statistically non-significant, I ran the model: !
GLM <- glm(ereader_use ~ age_factor + cell_use + ebook + income + pc_use + tablet_use + educ2) !
Then, I ran a stepwise regression model to see whether adding or removing “education” will improve the model’s Akaike information criterion (AIC). !! !
Start: AIC=2547.2
ereader_use ~ age_factor + cell_use + ebook + income + pc_use +
tablet_use + educ2
Df Deviance AIC
-‐ educ2 8 2524.9 2544.7
<none> 2511.4 2547.2
-‐ age_factor 3 2529.0 2558.8
-‐ cell_use 2 2534.6 2566.4
-‐ pc_use 1 2550.6 2584.4
-‐ income 4 2566.2 2593.9
-‐ tablet_use 1 2670.0 2703.8
-‐ ebook 1 2701.2 2734.9 !Just as anticipated, having education in conjunction with other variables does not improve the strength of the logit model (red arrow in Figure 6).
The second test relates to collinearity between education and income, since these two should be highly correlated. I used variance inflation factor (VIF) to determine collinearity between all the dependent variables in the regression model above.
!> vif(GLM)
GVIF Df GVIF^(1/(2*Df)) educ2 1.493909 8 1.025405 cell_use 1.011221 2 1.002793 ebook 1.058642 1 1.028903 income 1.370517 4 1.040185 pc_use 1.125100 1 1.060708 tablet_use 1.067638 1 1.033265 !
Figure 6: Stepwise Regression for logit model
Figure 7: VIF test for collinearity
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The result showed that there doesn’t appear to be collinearity between education and income (Figure 7). Therefore, I have to conclude that the explanatory power of education in the full logit model is not sufficient. I removed education in the final logit model. !Minor variations exist between difference racial groups, but the difference is not statistically meaningful (Figure 8).
!!!!!!!!!!!!!!!!!!!!!!!!Family income has the most significant impact on e-reader ownership (Figure 9). The e-reader ownership gap between the highest income bracket compared to the lowest income bracket is 35% points (Chi-squared p= 2.2e-16).
Family with under $30k income is the least likely group to own an e-reader. Family with $50k-$75K is 124% more likely to have an e-reader, and family with above $75k income has 142% likelihood of owning an e-reader compared to the lowest income families.
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White Black or African-American Asian or Pacific Islander Mixed race Native American/American Indian Other (SPECIFY) Don't know/Refused (VOL.)race
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Figure 8: Impact of race on e-reader ownership
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Under $30K $30K-LT $50K $50K-LT $75K $75K+ Undesignatedincome
Percent ereader_use
NoYes
Figure 9: Impact of family income on e-reader ownership
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Technological Factors !It has been hypothesized that other e-reading platforms, such as the tablet, are competing with e-readers. However, another argument is that by disseminating the idea of e-reading to a wider audience, the attractiveness of the e-readers can be strengthened. In this section of the report, we look at how the use of other platforms affect e-reader ownership. !Even though only 7% of respondents do not have a cellphone, the odds of a person with a cellphone is 364% more likely to have a e-reader compared to a person without a cellphone (Figure 10). In addition, e-reader ownership among smartphone owners are 23% points higher than owners with regular phones (Figure11).
!!!!!!!!!!!!!!!!Similar trends exist between PC ownership, tablet ownership, and e-reader ownership. Figure 12 indicates that tablet owners are 37% points higher than non-tablet owners to have an e-reader, and all else being equal, 257% more likely to have an e-reader. There are 40% points difference between PC owners and non-PC owners e-reader ownership, and all else being equal, a PC owner is 166% more likely than a non-PC owner to have an e-reader (Figure 12).
!Lastly, people who had read an e-book is 294% more likely than people who hadn’t read an e-book to own a e-reader, and the ownership difference is 50% points (Figure 13). Moreover, books read in a given year also give rise to more e-reader ownership (Figure 14).
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Cell, smartphone Cell, not a smartphone No cellsmartphone
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NoYes
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No Yes othercell_use
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Figure 10: Impact of cellphone ownership on e-reader ownership
Figure 11: Impact of smartphone ownership on e-reader ownership
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0_1 2_5 6_15 16_upbooks_read_factor
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Figure 13: Impact of PC ownership on e-reader ownershipFigure 12: Impact of tablet ownership on e-reader ownership
Figure 14: Impact of e-book readership on e-reader ownership
Figure 15: Impact of number of books read on e-reader ownership
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Conclusion & Recommendations !Family income is the the only demographical indicator to the likelihood of a person owning an e-reader, therefore, marketing strategies should be focused on identifying, engaging, and retaining high income families regardless of education attainment, race, gender, age, and employment status.
Tablet, PC, and cellphone ownership are highly indicative of e-reader ownership. However, this fact does not say anything about whether people will likely to upgrade their e-reader or purchase multiple ones. Nevertheless, in order to develop new market for the e-readers, companies can either advertise on these media, or target customers who have these devices.
In terms of software and book catalog offering, e-reader companies should not only encourage the purchase of books, but also completion of books, as 50% of avid readers have e-readers compared to 17% of readers who finish none to one book a year. E-reader companies should also rest assured that 75% of people who do read e-books have e-readers, and by widening the demographic of people who read e-books, either through promotion via tablet or smartphones, the e-reader companies can regain their previous shipment units.
In conclusion, given the limited functionality and technology barriers of e-readers, tablets and other platforms are more attractive for the value-conscious users. However, creating new market and maintaining purchasing platform royalty (e.g. AppleStore, Amazon) remain paramount.
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Reference !Benisnger, G. (2013). The E-reader revolution: over just as it has begun? Retrieved from: http:// online.wsj.com/news/articlesSB10001424127887323874204578219834160573010 !eMarketer (2012). Ereader shipments on the rise: millions still buying single-purpose ereaders. Retrieved from: http://www.emarketer.com/Article/Ereader-Shipments- on-Rise/1009471
Appendix !Call: glm(formula = ereader_use ~ age_factor + cell_use + ebook + income + pc_use + tablet_use, family = binomial(logit), data = Sub, weights = weight) !Deviance Residuals: Min 1Q Median 3Q Max -‐4.997 -‐1.210 -‐0.623 1.324 5.260 !Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -‐4.7774 0.4333 -‐11.026 < 2e-‐16 *** age_factor[T.adult] -‐0.2850 0.1736 -‐1.642 0.100686 age_factor[T.middle aged] 0.3908 0.1571 2.487 0.012880 * age_factor[T.seniors] 0.1612 0.1488 1.083 0.278627 cell_use[T.Yes] 1.5352 0.3781 4.061 4.89e-‐05 *** cell_use[T.other] -‐8.5065 324.7440 -‐0.026 0.979102 ebook[T.Yes] 1.3728 0.1026 13.383 < 2e-‐16 *** income[T.$30K-‐LT $50K] 0.5567 0.1580 3.524 0.000426 *** income[T.$50K-‐LT $75K] 0.8094 0.1635 4.949 7.45e-‐07 *** income[T.$75K+] 0.8846 0.1404 6.303 2.93e-‐10 *** income[T.Undesignated] 0.4063 0.1887 2.153 0.031287 * pc_use[T.Yes] 0.9820 0.1839 5.340 9.31e-‐08 *** tablet_use[T.Yes] 1.2741 0.1024 12.447 < 2e-‐16 *** -‐-‐-‐ Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 !(Dispersion parameter for binomial family taken to be 1) ! Null deviance: 3469.9 on 773 degrees of freedom Residual deviance: 2524.9 on 761 degrees of freedom (231 observations deleted due to missingness) AIC: 2544.4 !Number of Fisher Scoring iterations: 11 !!> step_glm <-‐ step(GLM.1) Start: AIC=2544.37 ereader_use ~ age_factor + cell_use + ebook + income + pc_use + tablet_use !!age_factor[T.adult] -‐24.800 age_factor[T.middle aged] 47.818 age_factor[T.seniors] 17.494 cell_use[T.Yes] 364.221 cell_use[T.other] -‐99.980 ebook[T.Yes] 294.649 income[T.Undesignated] 50.130
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pc_use[T.Yes] 166.971 tablet_use[T.Yes] 257.551 !!Gadget$cell_use <-‐ Recode(Gadget$pial1a, 'c("Yes",NA)="Yes"; "No"="No"; else="other"', as.factor.result=TRUE) Gadget$pc_use <-‐ Recode(Gadget$pial1b, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) Gadget$ereader_use <-‐ Recode(Gadget$pial1c, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) Gadget$tablet_use <-‐ Recode(Gadget$pial1d, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) names(Gadget)[c(13)] <-‐ c("books_read") Gadget$pial3a <-‐ Recode(Gadget$pial3a, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) Gadget$pial3b <-‐ Recode(Gadget$pial3b, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) Gadget$pial3c <-‐ Recode(Gadget$pial3c, '"Yes"="Yes"; "No"="No"; else=NA', as.factor.result=TRUE) names(Gadget)[c(14,15,16)] <-‐ c("printed","audio","ebook") !!#subset Sub <-‐ subset(Gadget, select=c(age,audio,books_read,cell_use,ebook,educ2,employ,ereader_use, income,pc_use,race,sex,smartphone,tablet_use,weight)) attach(Sub) !!Sub$cell_use <-‐ factor(Sub$cell_use, levels=c('No','Yes','other')) Sub$smartphone <-‐ factor(Sub$smartphone, levels=c('Cell, not a smartphone','No cell','Cell, smartphone')) Sub$age_factor <-‐ recode(Sub$age, '18:25= "young adult"; 26:35="adult"; 36:50="middle aged"; 51:120="seniors"; else=NA', as.factor.result=TRUE) Sub$books_read_factor <-‐ recode(Sub$books_read, '0:1= "0_1"; 2:5="2_5"; 6:15="6_15"; 16:99="16_up"; else=NA', as.factor.result=TRUE) Sub$age_factor <-‐ factor(Sub$age_factor, levels=c('young adult','adult','middle aged', 'seniors')) Sub$books_read_factor <-‐ factor(Sub$books_read_factor, levels=c('0_1','2_5','6_15', '16_up')) !!#glm+stepwise regression !GLM.1 <-‐ glm(ereader_use ~ age_factor + cell_use + ebook + income + pc_use + tablet_use, family=binomial(logit),weights=weight, data=Sub) summary(GLM.1) step_glm <-‐ step(GLM.1) round(cbind((exp(coef(GLM.1))-‐1)*100),3) !
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