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on Information and Communication Technologies |
| By: | Jikhan Jeong |
| Abstract: | Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumersâ prior reviews can be useful for inexperienced ones. However, the one-sided review system (e.g., Amazon.com) only provides the opportunity for consumers to write a review as a buyer and contains no feedback from the sellerâs side, so the information displayed about individual buyers is limited. This study analyzes consumersâ digital footprints (DFs) to identify and predict unobserved consumer preferences from online product reviews. It makes use of Python coding along with high-performance computing to extract reviewersâ DFs for a specific product group (programmable thermostats) from a dataset of 141 million Amazon reviews. It identifies consumersâ sentiment toward product content dimensions (PCDs) extracted from review text by applying topic modeling and domain expert annotations. However, some questionable reviews (posted by âsuspicious one-time reviewersâ and âalways-the-same rating reviewersâ) are excluded. This paper obtains three main results: First, I find that the factors that affect consumer ratings are: (a) userâ DFs (e.g., length of the product review, average rating across all categories, volume of prior reviews overall and in sub-categories), (b) reviewersâ attitudes toward eight product content dimensions (smart connectivity, easiness, energy saving, functionality, support, price value, privacy, and the Amazon effect), and (c) other prior reviewers DFs (e.g., length of the review summary.) All the heteroskedastic ordered probit models with DF and sentiment variables show a better model fit than the base model. This paper is the first to identify the effect of service quality of the online platform (Amazon.com) on ratings. Second, extreme gradient boosting (XGBoost) is found to obtain the highest F1 score for predicting the ratings of potential consumers before they make a purchase or write a review. All the models containing DF and sentiment variables show a higher prediction performance than the base model. Classifications with a lower range of labels (three-class or binary classifications) show better prediction performance than the five-star rating classification. However, the performance for the minority class is low. Third, a convolutional neural network (CNN) on top of Bidirectional Encoder Representations from Transformers (BERT) embedding shows the highest F1 score for classifying consumersâ sentiment toward a specific PCD. Overall, this approach developed in this paper is applicable, scalable, and interpretable for distinguishing important drivers of consumer reviews for different goods in a specific industry and can be used by industry to identify and predict unobserved consumer preferences and sentiment associated with product content dimensions. |
| JEL: | D80 M21 M31 C45 |
| Date: | 2020–11–10 |
| URL: | http://d.repec.org/n?u=RePEc:jmp:jm2020:pje208&r=all |
| By: | Marsden, Christopher T; Brown, Ian; Veale, Michael |
| Abstract: | Forthcoming in Martin Moore & Damian Tambini (eds.) Dealing with Digital Dominance (OUP 2021) This chapter elaborates on challenges and emerging best practices for state regulation of electoral disinformation throughout the electoral cycle. It is based on research for three studies during 2018-20: into election cybersecurity for the Commonwealth (Brown et al. 2020); on the use of Artificial Intelligence (AI) to regulate disinformation for the European Parliament (Marsden & Meyer 2019a; Meyer et al. 2020); and for UNESCO, the United Nations body responsible for education (Kalina et al. 2020). The research covers more than half the world’s nations, and substantially more than half that population, and in 2019 the two largest democratic elections in history: India’s general election and the European Parliamentary elections. |
| Date: | 2020–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:lawarx:aerw9&r=all |
| By: | Zhijun Chen; Chongwoo Choe; Jiajia Cong; Noriaki Matsushima |
| Abstract: | Recent years have seen growing cases of data-driven tech mergers such as Google/Fitbit, in which a dominant digital platform acquires a relatively small firm possessing a large volume of consumer data. The digital platform can consolidate the consumer data with its existing data set from other services and use it for personalization in related markets. We develop a theoretical model to examine the impact of such mergers across the two markets that are related through a consumption synergy. The merger links the markets for data collection and data application, through which the digital platform can leverage its market power and hurt competitors in both markets. Personalization can lead to exploitation of some consumers in the market for data application. But insofar as competitors remain active, the merger increases total consumer surplus in both markets by intensifying competition. When the consumption synergy is large enough, the merger can result in monopolization of both markets, leading to further consumer harm when stand-alone competitors exit in the long run. Thus, there is a trade-off where potential dynamic costs can outweigh static benefits. We also discuss policy implications by considering various merger remedies. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:dpr:wpaper:1108&r=all |
| By: | Amrita Chatterjee (Assistant Professor, Madras School of Economics); Nitigya Anand (Associate Solution Advisor, Deloitte & Touche Assurance and Enterprise Risk Services India Pvt. Ltd.) |
| Abstract: | There have been enough evidences to accept that Financial Inclusion (FI) and Information and Communication Technology (ICT) play positive role in economic growth, even though there are some exceptions. Moreover, we cannot deny the fact that ICT like mobile phone and internet penetration can strengthen the inclusiveness of formal banking sector. The present study has first examined whether ICT development can be an important determinant of Financial Inclusion by using a fixed effect panel data model. The results show that ICT is indeed an important determinant of FI. The same panel data of 41 countries was then used to test whether the growth process of the countries are influenced by Financial Inclusion and ICT diffusion in a dynamic Panel Data Model. Further the paper has investigated the role of FI powered by a better ICT penetration in fostering the growth of the nations using system GMM method by incorporating interactions between FI and ICT indicators. The results suggest that both FI and ICT individually and together through their close interaction can improve current year’s growth. However, we need education, awareness and technical assistance to get sustained growth. |
| Keywords: | Financial Inclusion, Growth, Information and Communication Technology, Dynamic Panel data model, System GMM estimator |
| JEL: | L86 L96 C23 O0 G2 |
| URL: | http://d.repec.org/n?u=RePEc:mad:wpaper:2017-165&r=all |
| By: | Cao, Ruiqing; Koning, Rembrand (Harvard Business School); Nanda, Ramana |
| Abstract: | Using data from a prominent online platform for launching new digital products, we document that the composition of the platform's `beta testers' on the day a new product is launched has a systematic and persistent impact on success. Specifically, we use word embedding methods to classify products launched on this platform as more or less focused on the needs of female customers, and show that female-focused products launched on a typical day—when nine-in-ten users on the platform are men—experience 40% less growth and are 5 percentage points less likely to have an any users a year after launch. Using exogenous variation driven by the platform's daily newsletter, we find that that the product gender gap shrinks on days when women are more likely to engage with the platform. Conversely, entrepreneurs who happen to launch a female-focused product on an especially male-dominated day reduce their product development efforts by roughly 30% and are 4 percentage points less likely to raise venture funding. Overall, our findings suggest that sample bias can systematically corrupt signals of a startup's market potential, bias entrepreneurial strategy, and so lead to a dearth of innovations aimed at consumers who are underrepresented among early-users. |
| Date: | 2020–11–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:g6wjn&r=all |
| By: | Consoli, Davide; Fusillo, Fabrizio; Orsatti, Gianluca; Quatraro, Francesco (University of Turin) |
| Abstract: | Scholars and policy makers frame the debate on labour market polarisation by emphasising the role of key drivers such as international trade and of technological change. The present paper explores these themes from a different perspective, and inquires whether de-routinisation has harmed local innovation capacity. Our empirical study builds on the literature on learning-bydoing and incremental innovation, and focuses on advanced manufacturing technologies (AMTs) in US Metropolitan Statistical Areas over the period 1990-2012. Results provide support to the hypothesis that de-routinisation is associated with a generalized decline of local innovation performance, especially in AMTs. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:uto:dipeco:202025&r=all |
| By: | Loretta J. Mester |
| Abstract: | Since the beginning of this conference series, the discussions have consistently been very topical, and the agenda for the next two days does not disappoint on that score. The conference will cover many of the hot issues confronting practitioners, academics, and policymakers as financial system innovation proceeds at a rapid pace. Today I will discuss the implications of digitalization for financial inclusion and some steps that need to be taken to ensure that digitalization helps to foster inclusion rather than promote exclusion. The views I will present today are my own and not necessarily those of the Federal Reserve System or my colleagues on the Federal Open Market Committee. |
| Date: | 2020–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcsp:89015&r=all |