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• Case | HBS Case Collection | September 2018

  Apple Pay and Mobile Payments in Australia (A)

  Feng Zhu, Susan Athey, David Lane, Feng Zhu, Susan Athey and David Lane

  In summer 2016, four of Australia’s top five banks petitioned regulators for permission to bargain collectively with Apple over the terms under which they would support its digital wallet, Apple Pay. They argued that doing so would force concessions from Apple that would improve market competitiveness, consumer choice, price transparency, and transaction security for all mobile payments in Australia. The banks stood to benefit as well. If they succeeded in their primary aim of opening the contactless payment functionality of Apple’s devices to their own digital wallets—something Apple had thus far refused in any market it had entered—the banks could retain customer relationships that Apple Pay threatened to usurp and limit Apple’s free-riding on the contactless payment infrastructure the banks had just spent several years building.

  Keywords: Australia;

  Citation:

  Zhu, Feng, Susan Athey, and David Lane. "Apple Pay and Mobile Payments in Australia (A)." Harvard Business School Case 619-010, September 2018.  View Details

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• Case | HBS Case Collection | September 2018

  Apple Pay and Mobile Payments in Australia (A)

  Feng Zhu, Susan Athey, David Lane, Feng Zhu, Susan Athey and David Lane

  In summer 2016, four of Australia’s top five banks petitioned regulators for permission to bargain collectively with Apple over the terms under which they would support its digital wallet, Apple Pay. They argued that doing so would force concessions from Apple that would improve market competitiveness, consumer choice, price transparency, and transaction security for all mobile payments in Australia. The banks stood to benefit as well. If they succeeded in their primary aim of opening the contactless payment functionality of Apple’s devices to their own digital wallets—something Apple had thus far refused in any market it had entered—the banks could retain customer relationships that Apple Pay threatened to usurp and limit Apple’s free-riding on the contactless payment infrastructure the banks had just spent several years building.

  Keywords: Australia;

  Citation:

  Zhu, Feng, Susan Athey, and David Lane. "Apple Pay and Mobile Payments in Australia (A)." Harvard Business School Case 619-010, September 2018.  View Details

  CiteView DetailsEducators

  Related

• Article | MIS Quarterly | September 2018

  Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia

  Shane Greenstein and Feng Zhu

  Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective, unverifiable, or controversial information. Using data from Encyclopædia Britannica, authored by experts, and Wikipedia, an encyclopedia produced by an online community, we compare the slant and bias of pairs of articles on identical topics of U.S. politics. Our slant measure is less (more) than zero when an article leans towards Democratic (Republican) viewpoints, while bias is the absolute value of the slant. We find that Wikipedia articles are more slanted towards Democratic views than are Britannica articles, as well as more biased. The difference in bias between a pair of articles decreases with more revisions. The bias on a per word basis hardly differs between the sources because Wikipedia articles tend to be longer than Britannica articles. These results highlight the pros and cons of each knowledge production model, help identify the scope of the empirical generalization of prior studies comparing the information quality of the two production models, and offer implications for organizations managing crowd-based knowledge production.

  Keywords: online community; Collective Intelligence; wisdom of crowds; bias; Wikipedia; Britannica; knowledge production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias;

  Citation:

  Greenstein, Shane, and Feng Zhu. "Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia." MIS Quarterly 42, no. 3 (September 2018): 945–959.  View Details

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