This paper uses a market segmentation approach to examine the effects of digital disruption in the book industry. A Bayesian efficient experimental design is employed in order to develop a stated preference discrete choice experiment that incorporates an array of book formats and attributes. The experiment was conducted on a panel of Australian readers. The results were analysed using a latent class model, leading to the identification of three distinct classes of book readers. The largest class of readers is comprised of ‘technological adopters’ who demonstrate a similar willingness to read on both traditional printed book formats and newer digital ones. Members of this class rely on critical review scores to make purchasing decisions and are the youngest of the three classes. Second is a class of ‘popular readers.’ This group consists of price-sensitive readers who clearly favour reading popular fiction on traditional paper-based book formats only. The third group are ‘avid readers’ who exhibit the highest willingness to pay for books and show a desire to read books of all genres. The identification of discrete segments of readers, along with their associated price elasticities and willingness to pay figures, can assist book industry stakeholders (such as book publishers and sellers) with the development of effective strategies to guide them through the various stages of digital book formats technology adoption life cycle. Such results can also be used to help steer cultural policy makers during a period of rapid technological change and uncertainty.
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Notable exceptions to the ‘micro-focussed’ nature of this literature include Hjorth-Andersen (2000), who presents a macro model of the Danish book market in order to show the theoretical interactions between readers, publishers and technological change in the industry. Canoy et al. (2006), on the other hand, present a cross-country overview of book market structures and related government policies.
A mixed logit model also allows for preferences to vary across individuals by specifying taste parameters that are randomly distributed across individuals.
A ‘no-choice’ alternative is also included in all choice tasks should the respondent not find any of the options presented to them appealing.
List et al. (2006) note that despite their hypothetical nature, carefully designed SP choice experiments for private goods (such as books) present respondents with choices similar to other common purchasing decisions (such as a trip to the grocery store). The authors therefore find little evidence to suggest hypothetical bias (or overbidding) in their valuation estimates.
In order to further justify the selection of attributes, respondents were asked ex post to state which of the four attributes presented to them influenced their choices the most. Unsurprisingly, 44% of respondents stated that genre was the most important attribute. Price was deemed most important by 28% of respondents, while 17% focused on critical consensus. Level of Australian cultural content was the most important attribute for 11% of respondents, suggesting that the cultural nature of the book in question plays an important role in the purchasing decisions of many readers.
The inclusion of more specific sub-genres (for example, crime fiction or romance sagas in the case of genre fiction) would undoubtedly enable a richer picture of patterns of book choice to emerge from the results of the experiment. However, having too many levels in a single attribute has the potential to cognitively overburden respondents, leading to potentially biased results. Therefore, it was deemed sensible to proceed with the ‘wider’ levels of the genre attribute.
As well as signals of quality, other studies of consumer behaviour in the presence of imperfect information have centred on the role of advertising (Ackerberg 2003), first impressions (Agnew et al. 2016), product labelling (Foreman and Shea 1999; Jin and Leslie 2003), learning from others (McFadden and Train 1996), social influence (Salganik et al. 2006) and signals of prices and advertising outlays (Caves and Greene 1996).
This aggregated professional review score can be thought of in the same light as the likes of the ‘Tomatometer’ score found at www.rottentomatoes.com (which measures critical sentiment towards movies) or the ‘Metascore’ available from www.metacritic.com (which also covers movies, along with music, TV and video games).
The recognition that the cultural nature of books is a highly valued amongst readers is not a new one. See, for example, Throsby et al. (2017).
Specifically, respondents were told that books with no Australian cultural content contain no uniquely Australian ideas, symbols and ways of life and therefore do not contribute to building a collective Australian identity. Books with a low level of Australian cultural content contain some references to uniquely Australian ideas, symbols and ways of life and therefore contribute in a small way to building a collective Australian identity. Books with a high level of Australian cultural content are primarily centred on the communication of uniquely Australian ideas, symbols and ways of life and therefore contribute greatly to building a collective Australian identity.
As a point of reference, Nielsen BookScan Australia report that the average selling price of a book (across all formats and genres) in 2016 was AU$19.40.
Efficiency in this context refers to the estimation of reliable parameter estimates with small standard errors.
Eligible respondents were remunerated by the market research company for their participation in the experiment.
The actual number of complete responses was 250; however, eight were dropped due to the failure of a variety of quality checks that were implemented in order to maintain the integrity of the data. Such checks included both attention and timing filters.
The experimental design software, NGENE, generates an ‘S estimate’, which is the minimum sample size required for the estimation of significant parameters. The S estimate for this particular experimental design was 102. Therefore, the actual sample size of 242 is deemed more than sufficient for the purposes of this experiment.
The instructional information was displayed on screen for respondents to read before the choice tasks were undertaken and was also available as a ‘pop-up’ window during every choice task. A copy of the instructional information sheet is available on request from the author.
That is to say, readers could conceivably have a higher probability of choosing a book format that they have chosen in the past, giving rise to the possibility that there may be some inertia in format choice that could bias results.
Such segmentation variables are commonly referred to in the literature as ‘membership functions.’
In addition to evaluations of \(\rho ^2\) across models, a log-likelihood ratio test was also conducted on the MNL model, confirming that the addition of the chosen attributes brought with them a statistically significant improvement in model fit over a base MNL model containing only the four alternatives on offer.
Changing the reference level of this attribute to either ‘no Australian cultural content’ or ‘a high degree of Australian cultural content’ did not significantly alter the results. A reference level of ‘a low degree of Australian cultural content’ was therefore chosen to permit a clearer interpretation of the results, where movements to books at the extreme ends of the attribute level scale are subject to a less ambiguous interpretation. The framing of this attribute can be seen as an extension to the work of Van Rees et al. (1999) who also use a latent class model to examine patterns of ‘highbrow’ and ‘lowbrow’ reading.
Socio-demographic variables such as income and marital status are often found to influence demand for other goods with cultural and experiential traits (such as visits to the opera and theatre). However, due to the relatively low cost of books, coupled with their general appeal it perhaps is not surprising that such membership functions were found to be insignificant in this experiment.
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The author is grateful to William H. Greene, David Throsby and Jordi McKenzie for guidance with the design and estimation. The author would also like to thank participants of (i) 5th International Choice Modelling Conference, Cape Town (April 2017), and (ii) 14th WEAI International Conference, Newcastle (January 2018), for helpful comments. The author is responsible for any errors.
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Crosby, P. Don’t judge a book by its cover: examining digital disruption in the book industry using a stated preference approach. J Cult Econ 43, 607–637 (2019). https://doi.org/10.1007/s10824-019-09363-2