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on Information and Communication Technologies |
| By: | Marinescu, Ioana E. (University of Pennsylvania); Klein, Nadav (University of Chicago); Chamberlain, Andrew (Glassdoor, Inc.); Smart, Morgan (Glassdoor, Inc.) |
| Abstract: | Online reviews are a powerful means of propagating the reputations of products, services, and even employers. However, existing research suggests that online reviews often suffer from selection bias – people with extreme opinions are more motivated to share them than people with moderate opinions, resulting in biased distributions of reviews. Providing incentives for reviewing has the potential to reduce this selection bias, because incentives can mitigate the motivational deficit of people who hold moderate opinions. Using data from one of the leading employer review companies, Glassdoor, we show that voluntary reviews have a different distribution from incentivized reviews. The likely bias in the distribution of voluntary reviews can affect workers' choice of employers, because it changes the ranking of industries by average employee satisfaction. Because observational data from Glassdoor are not able to provide a measure of the true distribution of employer reviews, we complement our investigation with a randomized controlled experiment on MTurk. We find that when participants' decision to review their employer is voluntary, the resulting distribution of reviews differs from the distribution of forced reviews. Moreover, providing relatively high monetary rewards or a pro-social cue as incentives for reviewing reduces this bias. We conclude that while voluntary employer reviews often suffer from selection bias, incentives can significantly reduce bias and help workers make more informed employer choices. |
| Keywords: | employer reviews, bias, incentives |
| JEL: | J2 J28 L14 L86 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp11367&r=ict |
| By: | Omarini, Anna |
| Abstract: | One of the main changes in the industry is becoming digitalization which is witnessing a profound transformation to the banking system. Digitalization offers new opportunities for banks to place the customer at the center of the development process. New technologies seem to be and stay in the market to disrupt the retail financial service value chain, as well as introducing new players into the competitive arena. Incumbents and new comers have innovative levers to adopt. The forces shaping these changes have led the industry to reconsider the role of banking and finance, more as an “enabler” than a provider of products and services. The article aims at defining digital transformation in the banking industry, outlining what banks and FinTech companies are both developing in the market, and also pointing out that it is not going to be the technology itself that will be the disruptor of the banking industry, but rather how firm deploys the technology that will cause the disruptio |
| Keywords: | Digitalization; Digital Transformation; FinTech; Retail Banking; Business Model; Incumbents; Innovation. |
| JEL: | G20 G21 G23 G29 O30 |
| Date: | 2017–06–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:85228&r=ict |
| By: | Alberto Abadie |
| Abstract: | Significance tests are probably the most common form of inference in empirical economics, and significance is often interpreted as providing greater informational content than non-significance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are typical and even prevalent in economics, where data sets are large (and becoming larger) and where there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. In consequence, we advocate a visible reporting and discussion of non-significant results in empirical practice. |
| JEL: | C01 C12 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24403&r=ict |