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Consumer confusion from price competition and excessive product attributes under the curse of dimensionality

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Abstract

The purpose of our study is to (i) investigate the effects of the number of products, product attributes, and prices on consumer confusion, (ii) conduct a numerical analysis to check the robustness of the results, and (iii) present an example of the cell phone market in Japan. Following an ideal point model and embedding the number of products and product attributes, we clarify how these factors affect consumer confusion and purchase probability. We show that as the number of product attributes increases, the choice probability of each product becomes equal, implying that consumer confusion occurs. This result is robust to the introduction of prices as strategic variables.

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Notes

  1. Even when assumption 1 or 2 is not assumed, our main result holds. We illustrate this outcome in Sect. 3 using numerical analysis.

  2. This assumption is the standard in the ideal point model. For example, see Freeman et al. (2012). In this study, all consumers purchase the product, and we do not consider non-purchasing behavior because the non-purchasing assumption is not necessary.

  3. See Sibson (1969). As for Kullback–Leibler divergence, \({\text{KL}}(p,q) = \mathop \sum \limits_{n = 1} p(x_{n} )\log \frac{{p(x_{n} )}}{{q(x_{n} )}}\), because the values between \({\text{KL}}(p,q)\) and \({\text{KL}}(q,p)\) can be different, the analysis is complicated (Kullback and Leibler 1951). However, using the IR, comparing the values is simply necessary. Therefore, we adopt the IR.

  4. As pointed out by a referee, we recognize that our formulation is simplified to apply to real-world situations. However, this setting is restricted not only to a relationship between the younger and older generations. Our setting is useful as a first step and can be applicable to two types of consumers, such as informed and uninformed ones, male and female, and so on. We conjecture that our approach is useful for a case of more than two types. If one segments a market based on consumers’ preferences and divide the market into some groups, our method becomes applicable to various situations (Moskowitz and Rabino 1994; Honkanen et al. 2004). This issue remains for future research.

  5. Many researchers in the fields of economics, marketing science, management, and applied mathematics, have been studied how to set a price for a firm to a product (e.g., Lee and Staelin 1997; Aguirre et al. 2010; Safari et al. 2015; Zhang et al. 2016).

  6. Using the example of the iPhone 4 in the United States, Freeman, Spenner, and Bird discussed the behavior of “not to choose the better alternative” and noted the importance of “simplicity” for corporate strategies (Chen et al. 2009).

  7. See “Second Comparative Report on Featurephone, iPhone, Android User’s Usage” written in Japanese and released by the Mobile Content Forum (October, 2013), http://www.mcf.or.jp/en/index.html. This phenomenon is also seen in other countries. See “World’s cell phone market” (August, 2017), https://www.strategyanalytics.com/strategy-analytics/news/strategy-analytics-press-releases/strategy-analytics-press-release/2017/08/16/, “China’s cell phone market” (June, 2016), https://www.counterpointresearch.com/oppo-becomes-the-leading-smartphone-brand-in-china-in-june-2016/.

  8. See “DENSHISYOSEKI KONTENTSU SHIJOTYOUSA” written in Japanese and released by ICT Reseach & Consulting (October, 2014), http://ictr.co.jp/report/20141015000069.html.

  9. Consider the example presented by Turnbull et al. (2000) of a consumer confusion phenomenon in the cell phone market attributable to similarity among products. They stated that consumer confusion is likely to occur in highly turbulent industries, which are characterized by rapid technological change and evolving competition. They showed that consumer confusion exists in the cell phone market and firms should develop a strong brand image to avoid such confusion.

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Acknowledgements

The authors are grateful to the session participants at the 6th International Conference on Intelligent Decision Technologies. Ebina acknowledges a Grant-in-Aid for Young Scientists (B) from the Japanese Ministry of Education, Science, Sports, and Culture (15K17047). Kinjo acknowledges financial support from a Grant-in-Aid for Young Scientists (16K17203) from the Japan Society for the Promotion of Science.

Author information

Authors and Affiliations

  1. School of Commerce, Meiji University, 1-1, Kanda-surugadai, Chiyoda-ku, Tokyo, Japan

    Takeshi Ebina

  2. Faculty of Economics, Okinawa International University, 2-6-1 Ginowan, Ginowan City, Okinawa, Japan

    Keita Kinjo

Authors
  1. Takeshi Ebina
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  2. Keita Kinjo
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Correspondence to Takeshi Ebina.

Additional information

A part of this paper in Sects. 1, 2 and 3.1, which are respectively the introduction, a description of our model, and a part of the numerical analysis, is included in Ebina and Kinjo (2014), the proceedings of Smart Digital Futures 2014 under the title, “Too Many Attributes and Consumer’s Dysfunction.

Appendix: case study

Appendix: case study

For example, in our results, consider the case of Japan’s cell phone market. NTT DoCoMo, Inc., the predominant cell phone operator in Japan, provides various smartphones, such as the REGZA Phone (Toshiba), Xperia acro (Sony), Medias (NEC), and Galaxy (Samsung). These smartphones have many attributes, such as color, size, mail functions, presence or non-presence of camera, carrier, and operating system. In addition to these physical attributes, customers must also choose a contract type, including fee structure (fixed fee plus variable fee) and time span.Footnote 6 According to 2011 data, none of these products monopolizes the market, and each product has approximately the same market share (Table 1).Footnote 7

Table 1 Share of NTT Docomo’s top ten among smartphones in Japan
Full size table

The similar phenomena are observed in several markets in Japan. One example of this is electronic book market. The number of electronic book store comprises genre of specialty, the number of contents offered, usage method, supported OS, discount, free contents, and so on and so forth. A share of each electronic book store in Japan is roughly as follows; Kobo presented by Rakuten kobo 15.5%, Kindle store by Amazon 15.3, honto 8.7% by 2Dfacto established by NTT DOCOMO and Dai Nippon Printing, iBookstore 8.0% by Apple, Line Manga 6.8% by LINE, ReaderStore 8.0% by Sony, GooglePlay books 6.6% by Google, eBookJapan 6.1%, BookLive! 5.6%, BPOOKWALKER 4.2%, and others 15.3%.Footnote 8 In addition, this phenomenon is seen in Internet service provider market. The attributes comprises line services (upstream/downstream), setup cost, e-mail address, packet capacity, cashback, the number of account registration, and so on. In particular, since many firms set up and practice their campaign, this campaign causes consumers to be complicated to choose among the goods with many attributes, and consumers need to choose which one is the best for them. Concretely, the share of top three firms is about the same roughly as follows: NTT communications 18.0%, KDDI 16.5%, Softbank 13.4%(Nikkei Publishing 2015).Footnote 9

One may think that the factors determining the probability of each product purchased are both consumer confusion and the diversity of consumer preference. In Subsection 3.1, this study investigates not only the case in which consumers with identical preferences exist but also the case, in Subsection 3.2, in which some consumers have identical preferences but others have different preferences. In both cases, we find the same result—that the probability of each product’s purchase is identical. We indicate that consumer confusion (having no preference for attributes) becomes a reason for our result to hold.

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Ebina, T., Kinjo, K. Consumer confusion from price competition and excessive product attributes under the curse of dimensionality. AI & Soc 34, 615–624 (2019). https://doi.org/10.1007/s00146-017-0771-y

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