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Open AccessOpen Access JUSTC Management 13 December 2022

Preinstalled application policies of smart device firms

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0012
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  • Author Bio:

    Ningxin Lei received her master’s degree in Management Science from the University of Science and Technology of China in 2022. Her research mainly focuses on operations management and marketing interface

    Mingjun Li received her Ph.D. degree in Management Science from the University of Science and Technology of China (USTC) in 2021. She is currently a postdoctoral fellow at the School of Management, USTC. Her major research interests focus on operations management

  • Corresponding author: E-mail: mjlee@mail.ustc.edu.cn
  • Received Date: 15 January 2022
  • Accepted Date: 26 March 2022
    • Available Online: 13 December 2022
    Abstract

  • Abstract

    Recent technological advancements in smart devices have paved the way for a booming mobile commerce industry. As smart device vendors launch products with a rich variety of business applications, it is critical for all stakeholders to understand the attitudes of different vendors toward preinstalled applications in the smart device industry. We address this issue by exploring an analytical model for preinstalled application policies. Specifically, we study how to choose an optimal policy in a market with hypercritical consumers who have disutility from preinstalled applications, and expert consumers who have removal knowledge. The results show that, as marginal preinstallation income increases, firms tend to force more consumer segments to use preinstalled applications. By comparing monopolistic and competitive situations, we find that the advantages of the policy change are different, and competitive firms prefer to adopt more stringent policies than monopolistic firms when the marginal preinstallation income is smaller. The initiative of expert consumers introduced new findings to the research. The increase in such consumers may lead to an increase in the profits of monopolistic firms when they adopt a preinstallation policy with a low removal threshold, but this has no impact on the profits of competitive firms. Additionally, an increase in such consumers will lead competitive firms to choose to bundle applications when the marginal preinstallation income is smaller and the impact on monopolistic firms’ policy decisions is morecomplex.

    Graphical abstract

    Preinstalled applications and higher uninstall thresholds are not always beneficial for smart device firms.

    Abstract

    Recent technological advancements in smart devices have paved the way for a booming mobile commerce industry. As smart device vendors launch products with a rich variety of business applications, it is critical for all stakeholders to understand the attitudes of different vendors toward preinstalled applications in the smart device industry. We address this issue by exploring an analytical model for preinstalled application policies. Specifically, we study how to choose an optimal policy in a market with hypercritical consumers who have disutility from preinstalled applications, and expert consumers who have removal knowledge. The results show that, as marginal preinstallation income increases, firms tend to force more consumer segments to use preinstalled applications. By comparing monopolistic and competitive situations, we find that the advantages of the policy change are different, and competitive firms prefer to adopt more stringent policies than monopolistic firms when the marginal preinstallation income is smaller. The initiative of expert consumers introduced new findings to the research. The increase in such consumers may lead to an increase in the profits of monopolistic firms when they adopt a preinstallation policy with a low removal threshold, but this has no impact on the profits of competitive firms. Additionally, an increase in such consumers will lead competitive firms to choose to bundle applications when the marginal preinstallation income is smaller and the impact on monopolistic firms’ policy decisions is morecomplex.

    • Public Summary

    • Preinstalled applications and higher uninstall thresholds are not always beneficial for smart device firms.
    • Compared to the monopoly situation, the preinstallation strategy of the firm in a competitive situation is more sensitive to marginal preinstallation revenue.
    • The removal of applications by expert consumers may not always hurt the firm’s interests; they may also increase profits for the firm in a monopoly.

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    • References(27)
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    [2]
    Han Q, Cho D. Characterizing the technological evolution of smartphones: Insights from performance benchmarks. In: Proceedings of the 18th Annual International Conference on Electronic Commerce: E-Commerce in Smart Connected World. NY, USA: Association for Computing Machinery, 2016: 1–8.
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    [4]
    Kim S H, Choe Y, Lee Y. How heavy is your smartphone? Imaginary weight perception of smartphone users and its impact on product evaluation. ACR North American Advances, 2016, 44: 512–513.
    [5]
    Elahi H, Wang G, Chen J. Pleasure or pain? An evaluation of the costs and utilities of bloatware applications in Android smartphones. Journal of Network and Computer Applications, 2020, 157: 102578. doi: 10.1016/j.jnca.2020.102578
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    Cavusoglu H, Cavusoglu H, Geng X. Bloatware and jailbreaking: Strategic impacts of consumer-initiated modification of technology products. Information Systems Research, 2020, 31 (1): 240–257. doi: 10.1287/isre.2019.0883
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    [21]
    Shugan S M, Moon J, Shi Q, et al. Product line bundling: Why airlines bundle high-end while hotels bundle low-end. Marketing Science, 2017, 36 (1): 124–139. doi: 10.1287/mksc.2016.1004
    [22]
    Cui Y, Duenyas I, Sahin O. Unbundling of ancillary service: How does price discrimination of main service matter? Manufacturing & Service Operations Management, 2018, 20 (3): 455–466. doi: 10.1287/msom.2017.0646
    [23]
    Jedidi K, Jagpal S, Manchanda P. Measuring heterogeneous reservation prices for product bundles. Marketing Science, 2003, 22 (1): 107–130. doi: 10.1287/mksc.22.1.107.12850
    [24]
    Prasad A, Venkatesh R, Mahajan V. Product bundling or reserved product pricing? Price discrimination with myopic and strategic consumers. International Journal of Research in Marketing, 2015, 32 (1): 1–8. doi: 10.1016/j.ijresmar.2014.06.004
    [25]
    Johnson M D, Herrmann A, Bauer H H. The effects of price bundling on consumer evaluations of product offerings. International Journal of Research in Marketing, 1999, 16 (2): 129–142. doi: 10.1016/S0167-8116(99)00004-X
    [26]
    Chen T, Yang F, Guo X. Retailer-driven bundling when valuation discount exists. Journal of the Operational Research Society, 2020, 71 (12): 2027–2041. doi: 10.1080/01605682.2019.1650620
    [27]
    Shulman J D, Geng X. Add-on pricing by asymmetric firms. Management Science, 2013, 59 (4): 899–917. doi: 10.1287/mnsc.1120.1603
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    Figure  1.  Timeline.

    Figure  2.  Numerical demonstration of Proposition 4.1.

    [1]
    Vrhovec S L R. Safe use of mobile devices in the cyberspace. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatija, Croatia: IEEE, 2016: 1397–1401.
    [2]
    Han Q, Cho D. Characterizing the technological evolution of smartphones: Insights from performance benchmarks. In: Proceedings of the 18th Annual International Conference on Electronic Commerce: E-Commerce in Smart Connected World. NY, USA: Association for Computing Machinery, 2016: 1–8.
    [3]
    Kaufman L, Weed B. Too much of a good thing? Identifying and resolving bloat in the user interface. ACM SIGCHI Bulletin, 1998, 30 (4): 46–47. doi: 10.1145/310307.310370
    [4]
    Kim S H, Choe Y, Lee Y. How heavy is your smartphone? Imaginary weight perception of smartphone users and its impact on product evaluation. ACR North American Advances, 2016, 44: 512–513.
    [5]
    Elahi H, Wang G, Chen J. Pleasure or pain? An evaluation of the costs and utilities of bloatware applications in Android smartphones. Journal of Network and Computer Applications, 2020, 157: 102578. doi: 10.1016/j.jnca.2020.102578
    [6]
    McDaniel P. Bloatware comes to the smartphone. IEEE Security & Privacy, 2012, 10 (4): 85–87. doi: 10.1109/MSP.2012.92
    [7]
    Cavusoglu H, Cavusoglu H, Geng X. Bloatware and jailbreaking: Strategic impacts of consumer-initiated modification of technology products. Information Systems Research, 2020, 31 (1): 240–257. doi: 10.1287/isre.2019.0883
    [8]
    Kotzias P, Caballero J, Bilge L. How did that get in my phone? Unwanted app distribution on android devices. In: IEEE Symposium on Security and Privacy (SP). San Francisco, USA: IEEE, 2021: 53–69.
    [9]
    Alam I, Khan M A, Naeem M, et al. There is no such thing as free lunch: An investigation of bloatware effects on smart devices. Journal of Information Communication Technologies and Robotic and Applications, 2017 (8): 20–30.
    [10]
    Elahi H, Wang G, Li X. Smartphone bloatware: An overlooked privacy problem. In: Wang G, Atiquzzaman M, Yan Z, Choo K K, editors. Security, Privacy, and Anonymity in Computation, Communication, and Storage. Cham: Springer, 2017: 169–185.
    [11]
    Suarez-Tangil G, Tapiador J E, Peris-Lopez P, et al. Evolution, detection and analysis of malware for smart devices. IEEE Communications Surveys & Tutorials, 2014, 16 (2): 961–987. doi: 10.1109/SURV.2013.101613.00077
    [12]
    Deneckere R J, McAfee R P. Damaged goods. Journal of Economics & Management Strategy, 1996, 5 (2): 149–174. doi: 10.1111/j.1430-9134.1996.00149.x
    [13]
    Wei X, Nault B R. Monopoly versioning of information goods when consumers have group tastes. Production and Operations Management, 2014, 23 (6): 1067–1081. doi: 10.1111/poms.12180
    [14]
    Bhargava H K, Choudhary V. Research note: When is versioning optimal for information goods? Management Science, 2008, 54 (5): 1029–1035. doi: 10.1287/mnsc.1070.0773
    [15]
    Chellappa R K, Shivendu S. Mechanism design for “free” but “no free disposal” services: The economics of personalization under privacy concerns. Management Science, 2010, 56 (10): 1766–1780. doi: 10.1287/mnsc.1100.1210
    [16]
    Chellappa R K, Mehra A. Cost drivers of versioning: Pricing and product line strategies for information goods. Management Science, 2018, 64 (5): 2164–2180. doi: 10.1287/mnsc.2016.2698
    [17]
    Thompson D V, Hamilton R W, Rust R T. Feature fatigue: When product capabilities become too much of a good thing. Journal of Marketing Research, 2005, 42 (4): 431–442. doi: 10.1509/jmkr.2005.42.4.431
    [18]
    Jain S. Time inconsistency and product design: A strategic analysis of feature creep. Marketing Science, 2019, 38 (5): 835–851. doi: 10.1287/mksc.2019.1170
    [19]
    Bhargava H K, Feng J. America online’s Internet access service: How to deter unwanted customers. Electronic Commerce Research and Applications, 2005, 4 (1): 35–48. doi: 10.1016/j.elerap.2004.10.008
    [20]
    Geng X, Stinchcombe M B, Whinston A B. Bundling information goods of decreasing value. Management Science, 2005, 51 (4): 662–667. doi: 10.1287/mnsc.1040.0344
    [21]
    Shugan S M, Moon J, Shi Q, et al. Product line bundling: Why airlines bundle high-end while hotels bundle low-end. Marketing Science, 2017, 36 (1): 124–139. doi: 10.1287/mksc.2016.1004
    [22]
    Cui Y, Duenyas I, Sahin O. Unbundling of ancillary service: How does price discrimination of main service matter? Manufacturing & Service Operations Management, 2018, 20 (3): 455–466. doi: 10.1287/msom.2017.0646
    [23]
    Jedidi K, Jagpal S, Manchanda P. Measuring heterogeneous reservation prices for product bundles. Marketing Science, 2003, 22 (1): 107–130. doi: 10.1287/mksc.22.1.107.12850
    [24]
    Prasad A, Venkatesh R, Mahajan V. Product bundling or reserved product pricing? Price discrimination with myopic and strategic consumers. International Journal of Research in Marketing, 2015, 32 (1): 1–8. doi: 10.1016/j.ijresmar.2014.06.004
    [25]
    Johnson M D, Herrmann A, Bauer H H. The effects of price bundling on consumer evaluations of product offerings. International Journal of Research in Marketing, 1999, 16 (2): 129–142. doi: 10.1016/S0167-8116(99)00004-X
    [26]
    Chen T, Yang F, Guo X. Retailer-driven bundling when valuation discount exists. Journal of the Operational Research Society, 2020, 71 (12): 2027–2041. doi: 10.1080/01605682.2019.1650620
    [27]
    Shulman J D, Geng X. Add-on pricing by asymmetric firms. Management Science, 2013, 59 (4): 899–917. doi: 10.1287/mnsc.1120.1603
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