Preinstalled application policies of smart device firms

Ningxin Lei; Jie Wu; Mingjun Li; Xiang Ji

doi:10.52396/JUSTC-2022-0012

JUSTC > 2022 > 52(11): 5-1-5-13. > DOI: 10.52396/JUSTC-2022-0012 CSTR: 32290.14.JUSTC-2022-0012

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Open Access JUSTC Management Article 12 December 2022

Preinstalled application policies of smart device firms

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

Cite this: JUSTC, 2022, 52(11): 5

https://doi.org/10.52396/JUSTC-2022-0012

CSTR: 32290.14.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:
Mingjun Li, E-mail: mjlee@mail.ustc.edu.cn
Received Date: January 14, 2022
Accepted Date: March 25, 2022
Available Online: December 12, 2022

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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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1. Introduction

Since the early 1930s, the first polyethylene product (low-density polyethylene) was generated by free radical polymerization^[1]. More importantly, in the 1950s, Nata et al.^[2] and Ziegler et al.^[3] used coordination polymerization catalysts to achieve olefin polymerization under mild conditions, thereby promoting the vigorous development of the polyolefin industry. Over seventy years later, polyolefins were widely used in food packaging, healthcare, machinery, automotive manufacturing, and other applications due to their excellent chemical corrosion resistance and mechanical properties^[4].

The transition metal catalysts required for coordinating polymerization to prepare polyolefins have attracted widespread attention from both industry and academia with the continuous development of the polyolefin industry^[5]. Among them, late transition metal catalysts such as nickel and palladium have been widely studied in the copolymerization of olefins and polar monomers due to their high tolerance to polar groups and low hydrophilicity^[6]. For example, these nickel/palladium catalysts include [N,N] catalysts^[7], [N,O] catalysts^[8,9], [P,O] catalysts^[10–12], etc. Among them, α-diimine nickel/palladium catalysts (Fig. 1a) was first reported by Johnson et al.^[13] in 1995 and is known for its unique chain walking mechanism; these catalysts can be utilized to synthesize polyolefins with branched chain structures and diverse polymer topologies.

Figure 1. Representative diimide catalysts (a–e) and novel structures used in this work (f).

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Since then, domestic and foreign scholars have extensively designed and researched this type of highly distinctive [N,N]-type catalyst by designing and synthesizing a series of novel α-diimine nickel/palladium catalysts with varying degrees of electronic effects and steric hindrance^[14–17]. For example, one important progress was the introduction of a diisopropylphenyl substituent, which significantly increased the axial steric hindrance of the metal center of the catalysts, thereby suppressing β-H elimination and improving the stability of the catalyst, allowing it to maintain high activity at 100 °C (Fig. 1b)^[18]. Dai et al.^[19] also studied the electronic effect of metal center coordination based on this large steric hindrance α-diimine palladium catalyst, further regulating the polymerization performance of the catalyst. In addition, Brookhart, Daugulis, and Coates et al.^[20] synthesized a “sandwich” type with increased axial steric hindrance using an 8-(p-tolyl) naphthalene-1-amine α-diimine nickel catalyst (Fig. 1c), which can prepare polyethylene with a high branching density (up to 150 branches per 1000 carbons) in ethylene polymerization.

By further adjusting the electronic effects and steric hindrance of the complex center, Wang et al.^[21] synthesized a chiral α-diimine nickel catalyst with paraphenylethyl compounds that exhibited high ethylene polymerization activity (up to 2.85 × 10⁶ g·mol⁻¹·h⁻¹·bar⁻¹) and produced polyethylene with a branching density of up to 117 branches per 1000 carbons at high temperatures. In addition, Coates et al.^[22–24] introduced similar chiral structures into α-diimine nickel catalysts and used them for the polymerization of propylene and 1-butene to improve the regularity of the polymer. The steric hindrance of these nickel catalysts was significantly different from that of classical 2,6-diisopropyl nickel catalysts and 2,6-diisopropylphenyl nickel catalysts, with only half of the steric hindrance present. On the basis of these works, we designed new nickel catalysts with asymmetric steric hindrance and studied the polymerization performance and physical properties of the generated polyethylene in detail.

2. Results and discussion

2.1 Synthesis and characterization

According to Scheme 1, aniline derivatives were prepared from precursors and p-anisidine by Friedel-Crafts alkylation. Subsequently, acetic acid was used as a catalyst, and ligand L1−L3 in high yield was easily prepared by the condensation of two equivalent aniline derivatives and one equivalent of 1,2-acenaphthenedione (Scheme 1). The ligands were reacted with equimolar amounts of (DME)NiBr₂ to prepare the corresponding α-diimine nickel complex Ni1–Ni3, which was a reddish-brown solid in almost quantitative yield. These complexes were characterized by elemental analysis, ¹H NMR and ¹³C NMR. The purity and identity of the two ortho-phenyl-substituted nickel complexes were determined by elemental analysis and XRD.

1. Synthetic route to nickel complexes Ni1−Ni3.

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Single crystals of complexes Ni1 and Ni2 were prepared by treating toluene solution with n-pentane at 0 °C on the top layer (Figs. 1 and 2, Tables S1 and S2). X-ray crystallographic analysis showed that the nickel dibromide complexes Ni1 and Ni2 (Figs. 2 and 3) crystallized symmetrically at two nickel centers with a twisted tetrahedral geometry at the center of the bromine-bridged dimer, where each nickel atom is connected to two bridged bromine atoms and an α-diimine ligand. The two nickel atoms in complexes Ni1 and Ni2 are connected by a quaternary ring.

Figure 2. Crystal structure of Ni1. The selected bond lengths (Å) and angles (°) were as follows: Br2–Ni1 = 2.3442(13), Br1–Ni1 = 2.3577(13), Ni1–N1 = 2.043(5), Ni1–N2 = 0.055(6), Br2–Ni1–Br1 = 110.57(5), N1–Ni1–N2 = 83.0(2), N1–Ni1–Br2 = 97.94(17), N1–Ni1–Br1 = 134.85(18), and N2–Ni1–Br1 = 99.18(16).

DownLoad: Full-Size Img PowerPoint

Figure 3. Crystal structure of Ni2. The selected bond lengths (Å) and angles (°) are as follows: Br1–Ni1 = 2.3451(9), Ni1–Br2 = 2.3256(9), Ni1–N2 = 2.020(4), Ni1–N1 = 2.020(4), Br2–Ni1–Br1 = 122.07(4), N2–Ni1–Br1 = 115.23(10), N2–Ni1–Br2 = 107.94(10), N1–Ni1–Br1 = 101.99(10), N1–Ni1–Br2 = 120.38(10), and N1–Ni1–N2 = 82.98(15).

DownLoad: Full-Size Img PowerPoint

2.2 Ethylene polymerization

Under fixed aggregation conditions and with the assistance of the AlEt₂Cl catalyst, the effect of the Ni1‒Ni3 catalysts on ethylene polymerization was investigated by altering the temperature (0–90 °C) while maintaining a [Al]/[Ni] ratio of 500 (Table 1). As shown in Table 1, Ni1 exhibited a high activity of up to 6.51 × 10⁶ g·mol⁻¹·h⁻¹ and produced polyethylene with a high molecular weight of up to 38.2 × 10⁴ g·mol⁻¹ (Table 1, Entries 1–4). For Ni1, as the polymerization temperature increased, the polymerization activity, molecular weight and melting point changed. The polymerization activity reached its maximum value at 30 °C, while the molecular weight of the polymer decreased with increasing temperature (from 38.2 × 10⁴ g·mol⁻¹ to 16.6 × 10⁴ g·mol⁻¹), the melting point of the polyethylene decreased with increasing polymerization temperature (from 128.2 to 114.0 °C), and the branching density increased to 41 branches per 1000 carbons. Compared with Ni1, Ni2 was obtained by replacing 2-phenylpropan-2-yl with 1-phenylethyl, and the steric hindrance of the nickel metal center decreased due to the reduction in the number of methyl groups. Compared with those of Ni1, the polymerization activity of Ni2 and the molecular weight of the prepared polyethylene slightly decreased, while the branching density increased to 61 branches per 1000 carbons (Table 1, Entries 5–8). Compared to Ni1 and Ni2, Ni3 obtained by completely removing the substituent at this position significantly reduced the ethylene polymerization activity, especially at high temperatures (90 °C), where it almost became inactive. The molecular weight of the polymer was reduced by more than half compared to that of Ni1 and Ni2 (with a maximum value of only 16.2 × 10⁴ g·mol⁻¹). However, due to the sharp decrease in the steric hindrance of Ni3, chain transfer and chain walking accelerated, and the branching density of polyethylene increased to 94 branches per 1000 carbons (Table 1, Entries 9–12). Furthermore, we investigated the temperature-dependent polymerization activity of the Ni1 and Ni2 catalysts at 90 °C (Fig. 4), and the results showed that the Ni1 and Ni2 catalysts could maintain their polymerization activity for up to 40 min at 90 °C, with Ni1 exhibiting the best performance.

Table 1. Ethylene homopolymerization catalyzed by nickel catalyst Ni1−Ni3^a.

Entry	Catalyst	T(°C)	Yield^b (g)	Activity^b (10⁶)	M_n^c(10⁴)	PDI^c	Branch^d	T_m^e(°C)
1	Ni1	0	2.65	3.18	38.2	1.8	7	128.2
2	Ni1	30	5.43	6.51	33.6	2.1	21	118.0
3	Ni1	60	2.52	3.02	21.9	2.0	40	114.4
4	Ni1	90	2.01	2.41	16.6	2.1	41	114.0
5	Ni2	0	2.35	2.82	37.6	1.8	15	120.2
6	Ni2	30	4.11	4.93	29.7	1.9	26	117.0
7	Ni2	60	2.76	3.31	17.3	2.1	46	113.6
8	Ni2	90	0.90	1.08	14.8	2.1	61	80.9
9	Ni3	0	1.10	1.32	16.2	2.3	36	115.1
10	Ni3	30	1.94	2.33	12.8	2.6	51	106.1
11	Ni3	60	0.80	0.96	11.9	3.1	72	69.1
12	Ni3	90	0.02	0.02	11.5	3.2	94	–
^a 1 μmol of catalyst in CH₂Cl₂ (2 mL), [Al]/[Ni] = 500. V_n_-heptane = 20 mL, t_{polymerization} = 10 min, P_ethylene = 8 atm. ^b Activity is in units of 10⁶ g·mol⁻¹·h⁻¹. ^c Determined by Gel Permeation Chromatography (GPC) in 1,2,4-trichlorobenzene at 150 °C. ^d Branches per 1000 carbons, determined by ¹H NMR. ^e Determined by differential scanning calorimetry.

| Show Table

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Figure 4. The polymerization performance of catalysts. (a) Polymer weight comparisons of generated polyethylene at 0 °C, 30 °C, 60 °C and 90 °C. (b) Molecular weight comparisons of generated polyethylene at 0 °C, 30 °C, 60 °C and 90 °C. (c) Branching density comparisons of generated polyethylene at 0 °C, 30 °C, 60 °C and 90 °C. (d) Time-dependent studies (polymer yields vs polymerization time) at 90 °C.

DownLoad: Full-Size Img PowerPoint

2.3 Mechanical properties of synthesized polyethylene

Subsequently, as shown in Fig. 5, we investigated the tensile properties of polyethylene prepared with these nickel catalysts and found that the stress distribution of these polyethylene materials was between 15 and 30 MPa and that the strain distribution was between 400% and 800%. These polyethylene materials exhibit excellent tensile properties, which may be attributed to their high molecular weight (M_n greater than 17.3 × 10⁴ g·mol⁻¹). For the polyethylene samples prepared from Ni1 and Ni2, the sample with the highest stress was obtained at 30 °C, while the polyethylene sample obtained at 0 °C had the lowest stress, which may be related to its branching density.

Figure 5. Tensile curve of polyethylene prepared with nickel catalysts.

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3. Conclusions

We synthesized a series of novel α-diimine nickel complexes with asymmetric steric hindrance and studied their catalytic performance for ethylene polymerization. The polymerization activity of these catalysts can reach up to 6.51 × 10⁶ g·mol⁻¹·h⁻¹ and can be used to prepare high-molecular-weight polyethylene with adjustable branching density distributions between 7 and 94 branches per 1000 carbons. More importantly, the polyethylene prepared from these α-diimine nickel catalysts exhibited excellent tensile properties.

Supporting information

The supporting information for this article can be found online at https://doi.org/10.52396/JUSTC-2024-0001. The supporting information includes 34 figures. Experimental details, figures, and characterization data for the nickel catalysts and polymers are provided in the supporting information.

Conflict of Interest

The authors declare that they have no conflict of interest.

① https://www.techradar.com/uk/news/phone-and-communications/mobile-phones/microsoft-and-samsung-sign-cross-licensing-patent-deal-1030063

② http://finance.cnr.cn/315/gz/20150704/t20150704_519073134.shtml

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.

References (27)

References

[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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Figure 2. Numerical demonstration of Proposition 4.1.

References

[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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[5]	YU Rui, WAN Caixia, CHEN Xin, LI Liangbin. A novel amidoxime modified polyethylene nanofibrous membrane with high uranium adsorption capacity[J]. JUSTC, 2021, 51(11): 831-839. DOI: 10.52396/JUST-2021-0088
[6]	YANG Feng, CHANG Yuxin, BI Chen. Financing the overconfident newsvendor under the information asymmetry[J]. JUSTC, 2021, 51(9): 671-689. DOI: 10.52396/JUST-2021-0105
[7]	TIAN Bo, CHEN Denggen, GUO Shuyong, XIA Ping. Spontaneous symmetric breaking in two-lane totally asymmetric simple exclusion process with narrow entrances[J]. JUSTC, 2020, 50(9): 1258. DOI: 10.3969/j.issn.0253-2778.2020.09.005
[8]	LI Na, HU Xiaoqian, BAI Ruke. Preparation and characterization of polymer nanosheets by radical polymerization[J]. JUSTC, 2018, 48(3): 191-198. DOI: 10.3969/j.issn.0253-2778.2018.03.003
[9]	FERBER T., SHWARTZ B. (for the Belle/BelleⅡ collaboration). Perspectives of a precise measurement of the charge asymmetry in muon pair production at Belle Ⅱ[J]. JUSTC, 2016, 46(6): 476-480. DOI: 10.3969/j.issn.0253-2778.2016.06.005
[10]	ZHANG Xiang, XU Yunhe, Loh Teck-Peng. Bioinspired cationic polyene cyclization[J]. JUSTC, 2013, 43(11): 877-888. DOI: 10.3969/j.issn.0253-2778.2013.11.003

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