The DOI matches its Crossref record; 40 of the 43 references it cites were found in a registry or an index.
Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis
Abstract
While the growth of the mobile apps market has created significant market opportunities and economic incentives for mobile app developers to innovate, it has also inevitably invited other developers to create rip-offs. Practitioners and developers of original apps claim that copycats steal the original app’s idea and potential demand, and have called for app platforms to take action against such copycats. Surprisingly, however, there has been little rigorous research analyzing whether and how copycats affect an original app’s demand. The primary deterrent to such research is the lack of an objective way to identify whether an app is a copycat or an original. Using a combination of machine learning techniques such as natural language processing, latent semantic analysis, network-based clustering, and image analysis, we propose a method to identify apps as original or copycat and detect two types of copycats: deceptive and nondeceptive. Based on the detection results, we conduct an econometric analysis to determine the impact of copycat apps on the demand for the original apps on a sample of 10,100 action game apps by 5,141 developers that were released in the iOS App Store over five years. Our results indicate that the effect of a specific copycat on an original app’s demand is determined by the quality and level of deceptiveness of the copycat. High-quality nondeceptive copycats negatively affect demand for the originals. By contrast, low-quality, deceptive copycats positively affect demand for the originals. Results indicate that in aggregate the impact of copycats on the demand of original mobile apps is statistically insignificant. Our study contributes to the growing literature on mobile app consumption by presenting a method to identify copycats and providing evidence of the impact of copycats on an original app’s demand. The online appendix is available at https://doi.org/10.1287/isre.2017.0735 .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Evolutionary Theory of Economic Change.
Loasby, B. J., Nelson, R. R., & Winter, S. G. (1983) · The Economic Journal · doi:10.2307/2232409 · Cited by 21,426
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,743
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Introduction to modern information retrieval
Dillon, M. (1983) · Information Processing & Management · doi:10.1016/0306-4573(83)90062-6 · Cited by 5,981
BRISK: Binary Robust invariant scalable keypoints
Leutenegger, S., Chli, M., & Siegwart, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126542 · Cited by 3,689
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563
An efficient algorithm for large-scale detection of protein families
Enright, A. J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.7.1575 · Cited by 3,536
Nelson, P. J. (1974) · Journal of Political Economy · doi:10.1086/260231 · Cited by 3,414
Inside the Turk: Understanding Mechanical Turk as a Participant Pool
Paolacci, G., & Chandler, J. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414531598 · Cited by 2,136
Brynjolfsson, E., Hu, Y. J., & Smith, M. D. (2003) · Management Science · doi:10.1287/mnsc.49.11.1580.20580 · Cited by 1,164
Fast and effective text mining using linear-time document clustering
Larsen, B., & Aone, C. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312186 · Cited by 869
The Effect of File Sharing on Record Sales: An Empirical Analysis
Oberholzer‐Gee, F., & Strumpf, K. (2007) · Journal of Political Economy · doi:10.1086/511995 · Cited by 831
Crowdsourcing graphical perception
Heer, J., & Bostock, M. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753357 · Cited by 819
Product differentiation advantages of pioneering brands
Schmalensee, R. (1980) · American Economic Review · Cited by 723
Ghose, A., Ipeirotis, P. G., & Li, B. (2012) · Marketing Science · doi:10.1287/mksc.1110.0700 · Cited by 649
Self-citationA Survey of Text Clustering Algorithms
Aggarwal, C. C., & Zhai, C. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4_4 · Cited by 645
Chris Forman, A., Ghose, A., & Avi Goldfarb, B. (2008) · Management Science · doi:10.1287/mnsc.1080.0932 · Cited by 539
Investment Strategy and Growth in a New Market
Spence, A. M. (1979) · The Bell Journal of Economics · doi:10.2307/3003316 · Cited by 473
Measuring Prices and Price Competition Online: Amazon.com and BarnesandNoble.com
Chevalier, J. A., & Goolsbee, A. (2003) · Quantitative Marketing and Economics · doi:10.1023/a:1024634613982 · Cited by 459
An Evolutionary Theory of Economic Change
Nelson, R. R., & Winter, S. G. (1997) · doi:10.1093/oso/9780198781806.003.0008 · Cited by 437
Ghose, A., Smith, M. D., & Telang, R. (2006) · Information Systems Research · doi:10.1287/isre.1050.0072 · Cited by 288
The Effects of Free Sample Promotions on Incremental Brand Sales
Bawa, K., & Shoemaker, R. E. (2004) · Marketing Science · doi:10.1287/mksc.1030.0052 · Cited by 279
Is the First to Market the First to Fail? Empirical Evidence for Industrial Goods Businesses
Robinson, W. T., & Min, S. (2002) · Journal of Marketing Research · doi:10.1509/jmkr.39.1.120.18938 · Cited by 229
Competing with Free: The Impact of Movie Broadcasts on DVD Sales and Internet Piracy
Smith, M. D., & Telang, R. (2008) · MIS Quarterly · doi:10.2307/20650294 · Cited by 229
Inferring App Demand from Publicly Available Data1
Garg, R., & Telang, R. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.4.12 · Cited by 227
How Many Topics? Stability Analysis for Topic Models
Greene, D., O’Callaghan, D., & Cunningham, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44848-9_32 · Cited by 226
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis
Tang, J., Meng, Z., Nguyen, X., Mei, Q., & Zhang, M. (2014) · Cited by 218
Innovators and imitators: Organizational reference groups and adoption of organizational routines
Massini, S., Lewin, A. Y., & Greve, H. R. (2005) · Research Policy · doi:10.1016/j.respol.2005.07.004 · Cited by 200
Persuasion or Information? The Economics of Prescription Drug Advertising
Leffler, K. B. (1981) · The Journal of Law and Economics · doi:10.1086/466973 · Cited by 164
Varadarajan, R., Yadav, M. S., & Shankar, V. (2007) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-007-0080-y · Cited by 124
Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
Landauer, T. K., Laham, D., & Foltz, P. W. (1997) · Cited by 114
Adoption of Information Technology Under Network Effects
Lee, D., & Mendelson, H. (2007) · Information Systems Research · doi:10.1287/isre.1070.0138 · Cited by 68
Explaining fashion cycles: Imitators chasing innovators in product space
Caulkins, J. P., Hartl, R. F., Kort, P. M., & Feichtinger, G. (2006) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2006.05.008 · Cited by 56
Commercialization of innovations: an overarching framework and research agenda
Datta, A., Reed, R., & Jessup, L. (2013) · American Journal of Business · doi:10.1108/ajb-08-2012-0048 · Cited by 54
How Many Topics? Stability Analysis for Topic ModelsPreprint
Greene, D., O’Callaghan, D., & Cunningham, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.4606 · Cited by 24
The Dual Impact of Movie Piracy on Box-Office Revenue: Cannibalization and PromotionPreprint
Ma, L., Montgomery, A. L., & Smith, M. D. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2736946 · Cited by 21
Efficient Exact Similarity Searches Using Multiple Token Orderings
Kim, J., & Lee, H. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.79 · Cited by 15
Andersson, O., & Marquez, S. R. (2016) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 14
Innovation Without Patents? Evidence from the Smartphone App MarketsPreprint
Kraaijenzank, S. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2529669 · Cited by 2
Flagged first, then by citations
3 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 12
- Bilton N, 2014, New York Times
- Brustein J, 2014, Bloomberg Business
- Davidson J, 2014, TechnoBuffalo
- Dredge S, 2012, The Guardian
- Ferguson J, 1974, Advertising and Competition: Theory, Measurement and Fact
- Juan L, 2009, Internat. J. Image Processing, 3, 143
- Khalaf S, 2014, Flurry
- Koekkoek H, 2013, Distimo
- Lacus S, 2011, Political Anal., 20, 1
- Sareen H, 2013, Huffington Post
- Schmalensee R, 1982, Amer. Econom. Rev., 72, 349
- Tuerck D, 1978, Issues in Advertising: The Economics of Persuasion
Cited by
Sorted by citations
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“…co-occurrences of products in shopping baskets (Gabel et al. 2019), detecting copycat mobile apps using a machine learning copycat-detection method (Wang et al. 2018), and aiding social media content engineering by employing natural language processing algorithms that discover the associations…”
MethodNatural language processing (NLP) in management research: A literature review
Kang, Y., Cai, Z., Tan, C., Huang, Q., & Liu, H. (2020) · Journal of Management Analytics · doi:10.1080/23270012.2020.1756939 · Cited by 625
Artificial intelligence in E-Commerce: a bibliometric study and literature review
Bawack, R. E., Wamba, S. F., Carillo, K., & Akter, S. (2022) · Electronic Markets · doi:10.1007/s12525-022-00537-z · Cited by 506
Burström, T., Parida, V., Lahti, T., & Wincent, J. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.01.016 · Cited by 424
Personalized mobile marketing strategies
Tong, S., Luo, X., & Xu, B. (2019) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00693-3 · Cited by 405
Metadata
- Authors
- Wang, Q., Li, B., & Singh, P. V.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Information Systems Research
- Published
- 2018-06
- Volume
- 29
- Issue
- 2
- Pages
- 273-291
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W3121821896
- Cited by
- 162
- References
- 43
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 19 · 2023: 24 · 2022: 21 · 2021: 19 · 2020: 30 · 2019: 9 · 2018: 3
- FWCI
- 25.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Business, Management and Accounting › Marketing › Copyright and Intellectual Property
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- iOS App Store, app developers, app quality, market competition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
