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Paradox of choice and sharing personal information


The purpose of this study is to investigate the relationship between a firm’s strategy and consumers’ decisions in the presence of the paradox of choice and sharing personal information. The paradox of choice implies that having too many choices does not necessarily ensure happiness and sometimes having less is more. A new model is constructed introducing a factor of information sharing into the model of a previous study that embedded the paradox of choice only (Kinjo and Ebina in AI Soc 30(2):291–297, 2015). A key feature of the model is its disutility function. It is demonstrated that if the sign of the cross derivative of the function is positive (negative) at the optimum, there is a positive (negative) correlation between the degree of sharing personal information chosen by the consumers and the number of products offered by the firm in its recommendation systems. It is also numerically indicated that the profit function of the firm becomes convex or concave depending on the shape of the disutility function. These results suggest that firms should carefully investigate the shape of the disutility function, under the paradox of choice and sharing personal information.

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  1. 1.

    The phenomena have been referred to, variously, as the too-much-choice effect (Iyengar and Lepper 2000); choice overload (Chernev 2003); information overload (Van Zandt 2004); and hyperchoice; A large amount of similar research has been conducted. For example, research on information overload is scattered in the fields of computer and information science, marketing, law, psychology, and economics (Van Zandt 2004, p. 544). Hence, references are cited with respect to artificial intelligence, information systems, economics, and marketing to meet the purpose explained below.

  2. 2.

    Other fields are economics (Acquisti et al. 2016); management (Rust et al. 2002; Hann et al. 2008; Smith et al. 2011); psychology (Alge et al. 2006; Norberg and Horne 2007); law (Solove 2006); and information systems (Belanger and Crossler 2011; Pavlou 2011; Li 2012).

  3. 3.

    An e-commerce company such as Amazon offers advertisements in its recommendation systems (e.g., (20/08/2021).

  4. 4.

    By formal approaches (e.g., mathematical and/or algorithmic approaches), many studies have addressed various related problems in AI (e.g. Ebina and Kinjo 2019; Naudé and Dimitri 2020), such as autonomous vehicles (e.g. Ebina and Kinjo 2021) and recommendation systems (e.g. Ikegami et al. 2020).

  5. 5.

    There are mainly two previous studies having similar motivations to this study, but not in the consideration of the paradox of choice. Focusing on consumers’ utility, Rust et al. (2002) considered a situation in which personal information is traded in a market among firms and consumers have to pay costs to protect their information. The study analyzes consumers’ and firms’ behavior. A point of commonality with this model is that consumers have an ideal point with respect to the degree of sharing personal information \({m}^{*}\in(\mathrm{0,1})\) in the setting explained in the next section). Unlike this study, consumers purchase personal information from firms. As a result, if firms can freely trade consumers’ personal information in the market, the level of consumers’ utility decreases.

    Using a game-theoretic approach, Hann et al. (2008) analyzed firms’ and consumers’ behavior under a setting in which two types of heterogeneous consumers determining concealment or reflection of their personal information existed and many firms competed in the market. The study states that previous analytical research has mostly ignored the harm that marketing imposes on consumers. Advertising and direct marketing (e.g., direct mail, telephone, and fax, and electronically) imposes inconvenience and other harms on consumers, which are introduced into the model. The previous study showed that if a consumer opens their information, they can get useful information about the product but also receive useless information or solicitation. Further, from the social viewpoint, solicitation presented by the firms is excessive to the social optimum.

  6. 6.

    We consider a one-dimensional model of consumer preference with respect to \(m\) for mathematical tractability. By using a generalized projection pursuit regression or a logistic regression, several potential dimensions (e.g., age, sex, address) can be projected into one variable (Lingjaerde and Liestøl 1998).

  7. 7.

    One can extend this simultaneous setting to sequential one: after consumers choose their degree of sharing personal information, the platform observes it and chooses the number of products to offer. Under sequential settings, the platform can access more information about consumers, for example, purchasing or searching history. We show that if the optimal values of m and n are independent, the fundamental property does not change in the new setting. Hence, the simultaneous setting is analyzed in the current study.

  8. 8.

    Several studies in marketing and advertising introduced a benefit/cost function of sharing consumers’ personal information (e.g. Hann et al. 2008).

  9. 9.

    We do not treat the privacy paradox, which assumes that the decision-making about personal information has irrational aspects (Kokolakis 2017). This phenomenon indicates that while people claim to be concerned about their privacy, they undertake little to protect their personal data in social media. By setting \(\tilde{m }>{m}^{*}\) in our model, one can easily derive equilibrium behaviors under the privacy paradox.

  10. 10.

    For example, see the following website: (20/08/2021).

  11. 11.

    Note that the disutility function is called cost function in the previous study.

  12. 12.

    Some people might hesitate to disclose their personal information because it attracts their unintended information. We have also mentioned this point in Remark 2. Thanks to an anonymous referee for bringing our attention to this viewpoint.

  13. 13.

    Following the previous study, the positive (negative) cross derivative is defined as strategic complement (substitute). Therefore, this proposition states that if the disutility function is complemented (substitute) at the optimum, there is a positive (negative, respectively) correlation between the degree of sharing personal information and the number of products offered to the consumer.

  14. 14.

    Setting \(\left(a-P\right)=1\) and \(b=-1\) in Eq. (1) presented by Rust et al. (2002), the utility function without considering the calculation cost, that is \(c\left(n\right)=0\), corresponds to their setting. Rust et al. (2002) mainly focused on a price charged per unit of privacy, whereas the focus of this study is on the degree of sharing personal information and the number of products offered by a firm.

  15. 15.

    Note that the strategic complementarity also holds when \(a=0\), because \({X}_{12}\left({m}^{*},{n}^{*}\right)<0\) holds.


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Ebina acknowledges the financial support from JSPS KAKENHI Grant-in-Aid for Scientific Research (C) JP18K01627 and (C) JP21K01468. Kinjo acknowledges financial support from JSPS KAKENHI Grant-in-Aid for Scientific Research (C) JP20K02004.


The first author acknowledges the financial support from JSPS KAKENHI Grant-in-Aid for Scientific Research (C) JP18K01627 and (C) JP21K01468. The second author acknowledges financial support from JSPS KAKENHI Grant-in-Aid for Scientific Research (C) JP20K02004.

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The authorship of the paper is as follows: TE (corresponding author): methodology, formal analysis, writing original draft, project administration, writing review and editing, visualization, and funding acquisition. KK: conceptualization, formal analysis, administration, funding acquisition.

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Correspondence to Takeshi Ebina.

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Ebina, T., Kinjo, K. Paradox of choice and sharing personal information. AI & Soc (2021).

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  • Paradox of choice
  • Choice overload
  • Burden of information
  • Information sharing