nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2022‒12‒05
forty-nine papers chosen by

  1. Cryptocurrency Market in Kenya: A Review of Awareness and Participation by the youths By Kamau, Charles Guandaru
  2. Crypto trading and Bitcoin prices: evidence from a new database of retail adoption By Raphael Auer; Giulio Cornelli; Sebastian Doerr; Jon Frost; Leonardo Gambacorta
  3. Distinguishable Cash, Bosonic Bitcoin, and Fermionic Non-fungible Token By Zae Young Kim; Jeong-Hyuck Park
  4. Bitcoin flash crash on May 19, 2021: What did really happen on Binance? By Baumgartner, Tim; Güttler, André
  5. What makes Punks worthy? Valuation of Non-Fungible Tokens based on the CryptoPunks collection using the hedonic pricing method. By Ewelina Plachimowicz; Piotr Wójcik
  6. The global geography of digital platforms: towards platforms international locational determinants By Victo José da Silva Neto; Tulio Chiarini; Leonardo Costa Ribeiro; Igor Santos Tupy
  7. Social media or online shopping websites: Will/How platforms influence eWOM effectiveness By Ni, Yue; Cheng, Qiqi
  8. Input control and its signalling effects for complementors' intention to join digital platforms By Adam, Martin; Croitor, Evgheni; Werner, Dominick; Benlian, Alexander; Wiener, Martin
  9. The Art NFTs and Their Marketplaces By Lanqing Du; Michelle Kim; Jinwook Lee
  10. The Use of Technology to Counter Frauds and Scams for the Benefit of Society: A Detailed Study By Sharma, Devashish
  11. Household welfare in the digital age: Assessing the effect of mobile money on household consumption volatility in developing countries By Ablam Estel Apeti
  12. Understanding the Maker Protocol By Jason Chen; Kathy Fogel; Kose John
  13. Violence and financial decisions: evidence from mobile money in Afghanistan By Blumenstock, Joshua; Callen, Mike; Ghani, Tarek; González, Roberto
  14. Testing sociological theories with digital trace data from online markets By Przepiorka, Wojtek
  15. Miners' Reward Elasticity and Stability of Competing Proof-of-Work Cryptocurrencies By Kawaguchi, Kohei; Noda, Shunya
  16. Endogenous Network Effects By Dewenter, Ralf; Löw, Franziska
  17. The economics of content moderation: Theory and experimental evidence from hate speech on Twitter By Jiménez-Durán, Rafael
  18. DeFi vs TradFi: Valuation Using Multiples and Discounted Cash Flow By Teng Andrea Xu; Jiahua Xu; Kristof Lommers
  19. Trends in Competition among Digital Platforms for Shared Mobility: Insights from a Worldwide Census and Prospects for Research By Virginie Boutueil; Luc Nemett; Thomas Quillerier
  20. Mobile Internet Access and the Desire to Emigrate By Aksoy, Cevat Giray; Adema, Joop; Poutvaara, Panu
  21. Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces By Susan Athey; Dean Karlan; Emil Palikot; Yuan Yuan
  22. The Influence of Payment Method: Do Consumers Pay More with Mobile Payment? By Yizhao Jiang
  23. An Empirical Model of Mobile App Competition By Kawaguchi, Kohei; Kuroda, Toshifumi; Sato, Susumu
  24. The influence of the quantity of mistakes in online reviews on the reactions of internet users By Egwen Kervizic; Jean-François Lemoine
  25. Self-Preferencing, Quality Provision, and Welfare in Mobile Application Markets By Xuan Teng
  26. FinTech Lending under Austerity By Alperovych, Yan; Divakaruni, Anantha; Le Grand, François
  27. Evaluating the Competitiveness of Government Mobile Apps: An Assessment of Their Impact on Society By Driskell, David
  28. Evaluating Impact of Social Media Posts by Executives on Stock Prices By Anubhav Sarkar; Swagata Chakraborty; Sohom Ghosh; Sudip Kumar Naskar
  29. Consumer agency within C to C platforms : a comparison of online sales platforms and dating platforms By Audrey Bonnemaizon; Alain Debenedetti; Ibtihal Lakehal
  30. Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum By Chengxiang Jin; Jiajun Zhou; Jie Jin; Jiajing Wu; Qi Xuan
  31. Content Licensing with Endogenous Homing By Lu, Qiuyu
  32. Discreet Personalized Pricing By Benjamin R. Shiller
  33. Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index By Katarzyna Kryńska; Robert Ślepaczuk
  34. Heterogeneous Position Effects and the Power of Rankings By Rafael P. Greminger
  35. Regulating big tech: From competition policy to sector regulation? By Budzinski, Oliver; Mendelsohn, Juliane
  36. Network structures of a centralized and a decentralized market. A direct comparison. By Sylvain Mignot; Annick Vignes
  37. Coo-petencia Oligopólica y Rentismo Digital en el Mercado Tecnológico Global By Carina Borrasterp; Ignacio Juncos
  38. E-commerce et pouvoir des plateformes : quels enjeux environnementaux et éthiques ? By Maria Mercanti-Guérin
  39. Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning By Tristan Lim
  40. Balances Are on the Rise—So Who Is Taking on More Credit Card Debt? By Andrew F. Haughwout; Donghoon Lee; Daniel Mangrum; Joelle Scally; Wilbert Van der Klaauw
  41. The cost of banking crises: Does the policy framework matter? By Grégory Levieuge; Yannick Lucotte; Florian Pradines-Jobet
  42. From in-person to online: the new shape of the VC industry By Alekseeva, Liudmila; Fontana, Silvia Dalla; Genc, Caroline; Ranjbar, Hedieh Rashidi
  43. Is running a marathon like running a business? Identifying occupational differences in overconfidence using long-distance running data. By Hayk Amirkhanyan; Michał Krawczyk; Maciej Wilamowski
  44. Influence of the assistant's voice on user reactions By Nicolas Kusz; Jean-François Lemoine
  46. Fake News in Social Networks By Aymanns, Christoph; Foerster, Jakob; Georg, Co-Pierre; Weber, Matthias
  47. Online Channels Sales Premia in Times of COVID-19: First Evidence from Germany By Joachim Wagner
  48. Crowdsourcing innovation challenges: How participants react when their ideas are rejected By Cyrielle Vellera; Elodie Jouny-Rivier; Aurélie Hemonnet-Goujot
  49. Stock Trading Volume Prediction with Dual-Process Meta-Learning By Ruibo Chen; Wei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun

  1. By: Kamau, Charles Guandaru (Technical University of Mombasa)
    Abstract: This study examines Kenyan youths' level of knowledge and involvement in the cryptocurrency industry. Digital currencies known as cryptocurrencies use a peer-to-peer technology to speed up internet transactions. The idea of crypto currencies started out slowly in the 1980s but has since developed significantly. The study collected secondary data and conducted online surveys. In this study, panel data from four different crypto currencies' values, transaction fees, and volume over a six-year period were studied. The findings indicate a connection between the number of cryptocurrency transactions, their prices, and their transaction costs. The research also demonstrates how much the youth in Kenya are aware of and using cryptocurrencies. This paper also highlights some factors that may be considered when engaging in crypto business. It also highlights some of the principal properties of cryptos. The study concludes that there is a need for both local and international regulation of the cryptocurrency market so as to boost investor confidence and improve security.
    Date: 2022–07–29
  2. By: Raphael Auer; Giulio Cornelli; Sebastian Doerr; Jon Frost; Leonardo Gambacorta
    Abstract: Prices for cryptocurrencies have undergone multiple boom-bust cycles, together with ongoing entry by retail investors. To investigate the drivers of crypto adoption, we assemble a novel database (made available with this paper) on retail use of crypto exchange apps at daily frequency for 95 countries over 2015–22. We show that a rising Bitcoin price is followed by the entry of new users. About 40% of these new users are men under 35, commonly identified as the most "risk-seeking" segment of the population. To establish a causal effect of prices on adoption, we exploit two exogenous shocks: the crackdown of Chinese authorities on crypto mining in mid2021 and the social unrest in Kazakhstan in early 2022. During both episodes price changes have a significant effect on the entry of new users. Results from a PVAR model corroborate these findings. Overall, back of the envelope calculations suggest that around three-quarters of users have lost money on their Bitcoin investments.
    Keywords: bitcoin, cryptocurrencies, cryptoassets, regulation, decentralised finance, DeFi, retail investment.
    JEL: E42 E51 E58 F31 G28 L50 O32
    Date: 2022–11
  3. By: Zae Young Kim; Jeong-Hyuck Park
    Abstract: Modern technology has brought novel types of wealth. In contrast to hard cashes, digital currencies do not have a physical form. They exist in electronic forms only. Yet, it has not been clear what impacts their ongoing growth will make, if any, on wealth distribution. Here we propose to identify all forms of contemporary wealth into two classes: 'distinguishable' or 'identical'. Traditional tangible moneys are all distinguishable. Financial assets and cryptocurrencies, such as bank deposits and Bitcoin, are boson-like, while non-fungible tokens are fermion-like. We derive their ownership-based distributions in a unified manner. Each class follows essentially the Poisson or the geometric distribution. We contrast their distinct features such as Gini coefficients. Further, aggregating different kinds of wealth corresponds to a weighted convolution where the number of banks matters and Bitcoin follows Bose-Einstein distribution.
    Date: 2022–11
  4. By: Baumgartner, Tim; Güttler, André
    Abstract: Bitcoin plunged by 30% on May 19, 2021. We examine the outage the largest crypto exchange Binance experienced during the crash, when it halted trading for retail clients and stopped providing transaction data. We find evidence that Binance back-filled these missing transactions with data that does not conform to Benford's Law. The Bitcoin futures price difference between Binance and other exchanges was seven times larger during the crash period compared to a prior reference period. Data manipulation is a plausible explanation for our findings. These actions are in line with Binance aiming to limit losses for its futures-related insurance fund.
    Keywords: Benford's law,Binance,Bitcoin,cryptocurrency,crypto exchange,derivatives,extreme volatility,fraud,market crash,trading outage
    JEL: G10 G12 G14 K22
    Date: 2022
  5. By: Ewelina Plachimowicz (University of Warsaw, Faculty of Economic Sciences); Piotr Wójcik (University of Warsaw, Faculty of Economic Sciences)
    Abstract: This article focuses on an attempt to value Non-Fungible Tokens from the CryptoPunks collection. Based on the data from January 2021 to July 2021, a hedonic pricing model was built, based on the transaction history and characteristics of a given NFT, as well as external markets variables - cryptocurrency prices (Bitcoin and Ethereum), natural gas prices and the popularity of the collection in social media (Twitter). According to the literature, we decided to build three regression models: Ordinary Least Squares model, XGBoost algorithm and bidirectional Long Short-Term Memory Model. Based on the results, we were able to prove that such complex issues as NFT valuation require more advanced methods than the classical regression model. In addition, we proved that one of the most important categories of variables in the case of NFT valuation is the history of token sales and its characteristics, indicating a particular rarity. Moreover, we have shown that the cryptocurrency and natural gas market is not an important factor in the NFT valuation. Finally, we proved that the increase in the popularity of tokens in social media translates into an increase in NFT prices, and this is an important element when trying to valuate tokens.
    Keywords: NFT, Non-Fungible Token, pricing, valuation, luxury goods, cryptocurrencies
    JEL: Z11 G14 G12
    Date: 2022
  6. By: Victo José da Silva Neto (Radboud University); Tulio Chiarini (IPEA); Leonardo Costa Ribeiro (Cedeplar/UFMG); Igor Santos Tupy (UFV)
    Abstract: Digital platforms have positioned themselves at the center of global flows of capital, knowledge, and work. Their ability to influence and organize these flows makes it imperative to understand the locational decisions of platform companies. This paper explores new evidence on the digital platform economy geography. Our objective is threefold. First, we propose a novel methodology using data science and artificial intelligence tools to identify platform companies. Second, with a set of over three thousand companies, we introduce worldwide maps where it is possible to see the countries and cities that host platform companies. Third, we present platform companies’ locational choice using econometric models.While we observe a geographic concentration of platform companies in the U.S. and China, we also see that digital platform companies are spreading to all geographical directions, including tax havens, reinforcing the hypothesis that "platforming" is a worldwide phenomenon.
    Keywords: Platformization; Platform capitalism; Natural language processing; Zero-Inflated Negative Binomial regression model; Orbis
    JEL: F01 L86 O33
    Date: 2022–11
  7. By: Ni, Yue; Cheng, Qiqi
    Abstract: This article examines how the message topics of electronic word of mouth (eWOM) (feasibility vs. desirability) and platforms with different psychological distances contribute to the eWOM effect. Within the framework of construal level theory, we focus on the moderating role of the platforms (Social media vs. Online shopping websites) on the suggested eWOM message-Perceived credibility-Perceive diagnosticity-eWOM effect relationship. The results from 317 participants (M = 29.4, SD = 8.95, Range = [17, 70], 220 females) show that for social media eWOM readers, the effect of feasibility orientation eWOM is greater than desirability eWOM, and for online shopping websites eWOM readers, there is no difference between the two types of eWOM messages. The results support the moderating effect of platforms on the relationship. The effect of feasibility eWOM is greater for readers on social media than for readers on online shopping websites, and the effect of desirability eWOM is similar for online shopping websites and social media readers. The findings provide managerial implications for an eWOM marketing strategy and theoretical implications for studying the platforms effect, construal level theory, and Elaboration Likelihood Model of information adoption.
    Date: 2022–10–05
  8. By: Adam, Martin; Croitor, Evgheni; Werner, Dominick; Benlian, Alexander; Wiener, Martin
    Date: 2022
  9. By: Lanqing Du; Michelle Kim; Jinwook Lee
    Abstract: Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier for ownership, powered by blockchain technology. Technically speaking, anything digital could be minted and sold as an NFT, which provides proof of ownership and authenticity of a digital file. For this reason, it helps us distinguish between the originals and their copies, making it possible to trade them. This paper focuses on art NFTs that change how artists can sell their products. It also changes how the art trade market works since NFT technology cuts out the middleman. Recently, the utility of NFTs has become an essential issue in the NFT ecosystem, which refers to the owners' usefulness, profitability, and benefits. Using recent major art NFT marketplace datasets, we summarize and interpret the current market trends and patterns in a way that brings insight into the future art market. Numerical examples are presented.
    Date: 2022–10
  10. By: Sharma, Devashish
    Abstract: With the advent of the digital age, humanity has gained a number of benefits, such as the internet, mobile technology, and crypto currencies. Nevertheless, this has also allowed criminal strategies to evolve and spread as a result of this development. As a result, fraud attempts have increased from analog to digital as a result of the move from analog to electronic methods. When scams and fraud are present in society, there is a reduction in the ability of the government to provide its citizens with the services they need and to support them at the same time. Fraud results in a loss of dollars which could have been invested in healthcare, education, economic development, or public safety if these dollars had not been lost to fraud. The term "Digital Fraud or Digital Scams" refers to the use of a computer or a mobile device, along with the various means of communication available on the internet, by criminals who intend to deceive or harm a company, or an individual, in order to gain financial gain through digital fraud. Organizations need to update outdated analog technology that has been repurposed for digital applications in order to remain competitive against scams and frauds. As we move into a new digital world where we live in today, companies and governments will need to take a fresh look at their authentication procedures.
    Keywords: Fraud, Internet, Authentication Procedures, Analog technology, repurpose digital application, Scam
    JEL: O1 O32 O33
    Date: 2022–10–16
  11. By: Ablam Estel Apeti (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)
    Abstract: Based on a sample of 76 developing countries over 1990-2019, we assess the effect of adopting mobile money on consumption volatility using entropy balancing. We reveal that countries with mobile money exhibit lower consumption volatility. After checking the robustness of this result, we show that the key drivers of mobile money's stabilizing effect are financial inclusion and migrant remittances. Heterogeneity tests conducted indicate the sensitivity of the result to time and type of mobile money and to some structural factors, including trade openness, inflation, rural population, the rule of law, and level of development.
    Keywords: Mobile money,entropy balancing,consumption volatility,developing countries
    Date: 2022
  12. By: Jason Chen; Kathy Fogel; Kose John
    Abstract: This paper discusses a decentralized finance (DeFi) application called MakerDAO. The Maker Protocol, built on the Ethereum blockchain, enables users to create and hold currency. Current elements of the Maker Protocol are the Dai stable coin, Maker Vaults, and Voting. MakerDAO governs the Maker Protocol by deciding on key parameters (e.g., stability fees, collateral types and rates, etc.) through the voting power of Maker (MKR) holders. The Maker Protocol is one of the largest decentralized applications (DApps) on the Ethereum blockchain and is the first decentralized finance (DeFi) application to earn significant adoption. The objective of this paper is to analyze and discuss the significance, uses, and functions of this DeFi application.
    Date: 2022–10
  13. By: Blumenstock, Joshua; Callen, Mike; Ghani, Tarek; González, Roberto
    Abstract: We provide evidence that violence reduces the adoption and use of mobile money in three separate empirical settings in Afghanistan. First, we spatially merge nationwide administrative data on 96,000 violent events with the universe of mobile money transactions and find that users exposed to nearby violence reduce their mobile money account balances and conduct fewer transactions. Second, using high-frequency panel survey data from a field experiment, we find that subjects expecting violence are half as likely to respond to a randomized mobile money supply shock as those not expecting violence. Finally, analyzing financial survey data from nineteen of Afghanistan’s 34 provinces, we find that individuals expecting violence hold more cash. Collectively, our evidence suggests that violence can impede the growth of formal financial systems.
    Keywords: violence; financial development; mobile money
    JEL: O17 O33 D14
    Date: 2022
  14. By: Przepiorka, Wojtek
    Abstract: Today’s online market platforms allow millions of small businesses and people to trade in goods and services such as books, electronics, food, labour, transportation, care and accommodation. Most of these platforms use reputation systems to mitigate the trust problems that can arise when strangers from far and near trade with each other. By engaging in social and economic exchange via online markets, people produce digital trace data that can be used to research sociologically relevant phenomena. Starting out from Coleman’s foundational considerations of the role of trust in social and economic exchange, this chapter gives an overview of how sociologists and researchers from neighbouring disciplines have approached phenomena such as trust building, reputation formation, social preferences, discrimination, social inequality, etc. using online market data. Throughout, the chapter points to ongoing debates, open question and promising future research directions.
    Date: 2022–08–18
  15. By: Kawaguchi, Kohei; Noda, Shunya
    Abstract: Proof-of-Work cryptocurrencies, such as Bitcoin and its forks, hire miners (freelance contributors) to maintain the system by algorithmically setting the reward. Therefore, the nature of miners' labor supply is essential for the cryptocurrency's stability. We develop a short-run supply-side model of the multicurrency mining market and estimate miners' labor supply elasticity by exploiting the discontinuity created by an event called halving. The stability of Bitcoin hinges on external factors lowering the labor supply elasticity, such as the interaction with competing currencies. Upgrading algorithm can stabilize Bitcoin regardless of external factors and improve the mining market's energy consumption rate by 2.9%.
    Date: 2022–08–26
  16. By: Dewenter, Ralf (Helmut Schmidt University, Hamburg); Löw, Franziska (Helmut Schmidt University, Hamburg)
    Abstract: In contrast to traditional business models, two-sided platforms internalize indirect network effects that exist between different groups of platform participants. The strength of the network effects has a decisive influence on the success of the platform and its market position. Markets with particularly strong network effects are also often characterized by a high degree of concentration. However, the strength of the network effects is not exogenously given but can be influenced by targeted investment. This paper analyses how platforms can affect network effects by investing in appropriate infrastructure, data, or artificial intelligence. We derive optimal quantities, prices, profits, and investments depending on different types of investments.
    Keywords: two-sided markets; indirect network effects; endogenous network effects; optimal investment strategy
    JEL: D21 D42 L10
    Date: 2022–08–09
  17. By: Jiménez-Durán, Rafael
    Abstract: Social media platforms ban users and remove posts to moderate their content. This "speech policing" remains controversial because little is known about its consequences and the costs and benefits for different individuals. I conduct two pre-registered field experiments on Twitter to examine the effect of moderating hate speech on user behavior and welfare. Randomly reporting posts for violating the rules against hateful conduct increases the likelihood that Twitter removes them. Reporting does not affect the activity on the platform of the posts' authors or their likelihood of reposting hate, but it does increase the activity of those attacked by the posts. These results are consistent with a model in which content moderation is a quality decision for platforms that increases user engagement and hence advertising revenue. The second experiment shows that changing users' perceived content removal does not change their willingness to pause using social media, a measure of consumer surplus. My results imply that content moderation does not necessarily moderate users, but it can marginally increase advertising revenue. It can be consistent with both profit and welfare maximization as long as out-of-platform externalities are small.
    Keywords: social media,moderation,report,hate speech,experiment,welfare
    JEL: C93 D12 D85 D90 I31 J15 L82 L86 Z13
    Date: 2022
  18. By: Teng Andrea Xu; Jiahua Xu; Kristof Lommers
    Abstract: As of August 2022, blockchain-based assets boast a combined market capitalisation exceeding one trillion USD, among which the most prominent are the decentralised autonomous organisation (DAO) tokens associated with decentralised finance (DeFi) protocols. In this work, we seek to value DeFi tokens using the canonical multiples and Discount Cash Flow (DCF) approaches. We examine a subset of DeFi services including decentralised exchanges (DEXs), protocol for loanable funds (PLFs), and yield aggregators. We apply the same analysis to some publicly traded firms and compare them with DeFi tokens of the analogous category. Interestingly, despite the crypto bear market lasting for more than one year as of August 2022, both approaches evidence overvaluation in DeFi.
    Date: 2022–10
  19. By: Virginie Boutueil (LVMT - Laboratoire Ville, Mobilité, Transport - ENPC - École des Ponts ParisTech - Université Gustave Eiffel); Luc Nemett (LVMT - Laboratoire Ville, Mobilité, Transport - ENPC - École des Ponts ParisTech - Université Gustave Eiffel); Thomas Quillerier (LVMT - Laboratoire Ville, Mobilité, Transport - ENPC - École des Ponts ParisTech - Université Gustave Eiffel)
    Abstract: Mobility systems in metropolitan areas in both the Global North and the Global South have entered an era of rapid change since the early 2010s under the influence of mobile information and communication technologies (ICTs). Mobile ICT-based shared mobility platforms have been filling some of the gaps in transport supply left by historical modes of transport (i.e., private cars, public transit, and for-hire services). Shared mobility digital platforms are a subcategory of mobility applications that give individual customers direct and full access to one or several shared mobility services. Based on a worldwide systematic census, this paper documents the diversity of services provided by such platforms, then analyzes the trends in geographic distribution and competition among platforms across the world's metropolises. It proposes a new classification of shared mobility services. Since innovations in shared mobility are also taking a leading place in the Global South, future research avenues in this field are discussed in an effort to break away from the prior focus of the scientific literature on the Global North. The census brings out four original findings. First, the rise of shared mobility digital platforms is a worldwide metropolitan phenomenon transcending the traditional distinction between the Global North and the Global South. Second, emerging countries have become clusters for innovation and competition among platforms. Third, three types of shared mobility digital platforms are identified based on geographic reach (local, regional, or global). Fourth, shared mobility digital platforms providing for-hire services are the most widespread in the world.
    Date: 2021
  20. By: Aksoy, Cevat Giray; Adema, Joop; Poutvaara, Panu
    Abstract: In this paper, we present theory and global evidence on how mobile internet access affects desire and plans to emigrate. Our theory predicts that mobile internet access increases desire and plans to emigrate. Our empirical analysis combines survey data on 617,402 individuals from 2,120 subnational districts in 112 countries with data on worldwide 3G mobile internet rollout from 2008 to 2018. We show that an increase in mobile internet access increases the desire and plans to emigrate. Instrumenting 3G rollout with pre-existing 2G infrastructure suggests that the effects are causal. The effect on the desire to emigrate is particularly strong in high-income countries and for above-median-income individuals in lower-middle-income countries. In line with our theory, an important mechanism appears to be that access to the mobile internet lowers the cost of acquiring information on potential destinations. In addition to this, increased internet access reduces perceived material well-being and trust in government. Using municipal-level data from Spain, we also document that 3G rollout increased actual emigration flows.
    Date: 2022–05–30
  21. By: Susan Athey; Dean Karlan; Emil Palikot; Yuan Yuan
    Abstract: Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.
    JEL: D0 D40 J0 O1
    Date: 2022–11
  22. By: Yizhao Jiang
    Abstract: The introduction of new payment methods has resulted in one of the most significant changes in the way we consume goods and services. In this paper, I present results of a field and a laboratory experiment designed to determine the effect of payment method (cash vs. mobile payment) on spending, and a meta-analysis of previous literature about payment method effect. In the field experiment, I collected cashier receipts from Chinese supermarkets. Compared to cash payment, mobile payments significantly increased the amount purchased and the average amount spent on each item. This effect was found to be particularly large for high price elasticity goods. In the laboratory experiment, participants were randomly assigned to one of four groups that varied with respect to the kind of payment and the kind of incentives, eliminating the potential endogeneity problem from the field experiment. I found that compared to cash, mobile payments lead to a significantly higher willingness to pay (WTP) for consumption. In contrast to others, I found that pain of paying does not moderate the payment method effect; however, other psychological factors were found to work as potential mechanisms for affecting WTP.
    Date: 2022–10
  23. By: Kawaguchi, Kohei; Kuroda, Toshifumi; Sato, Susumu
    Abstract: This paper proposes an empirical model of mobile app competition, in which consumers decide downloads and usage time, and apps compete in price and advertising intensity. We estimate the model using data from Google Play in Japan from 2015 to 2017. We demonstrate merger simulation and the analysis of the vertical relation with Google Play. We find that a reduction of the fee imposed by Google Play can increase the price for game apps by inducing the shift of revenue source from advertising to downloads, highlighting the importance of considering two-sidedness and mixed business models.
    Date: 2022–09–28
  24. By: Egwen Kervizic (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne); Jean-François Lemoine (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne, ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers)
    Date: 2022–10–13
  25. By: Xuan Teng
    Abstract: Platforms may give preferential treatment to their own products in search results. Whether and how to regulate this self-preferencing behavior is an intensely debated antitrust issue. This paper identifies self-preferencing and quantifies its equilibrium welfare effects in Apple App Store. I start by examining the effect of a change in the platform’s search algorithm that dropped several Apple’s apps from top positions. I find that the search algorithm change leads to significantly higher installations and update frequencies of independent apps that compete with Apple’s apps in the same categories. Then I develop an empirical model of consumer search and update competition allowing for potential self-preferencing. The model is estimated with aggregate data on consumer search and purchase, search ranking, and app characteristics. Estimation results point to self-preferencing: Apple’s apps are more likely to be ranked higher than independent apps conditional on app quality, price, ratings, and title match with search terms. Based on counterfactual simulations, I find that eliminating the identified self-preferencing modestly increases the quality of independent apps on average. Furthermore, the elimination improves consumer surplus by $2.2 million and profits of independent developers by $1.6 million per month.
    Keywords: search algorithm, consumer search, endogenous product characteristics, mobile application
    JEL: D12 D43 D83 L13 L41 L86
    Date: 2022
  26. By: Alperovych, Yan; Divakaruni, Anantha; Le Grand, François
    Abstract: We document public welfare spending as an important growth driver of FinTech lending. Examining the massive austerity-led cuts to local welfare spending initiated by the UK government in 2010, we show that the gradual uneven rollback of the local welfare state since then is strongly associated with a rise in demand for peer-to-peer (P2P) consumer loans among affected areas, primarily in areas facing more banking and digital exclusion. P2P loans issued in austerity-affected areas are more expensive compared to those issued in unaffected areas, consistent with the P2P platform’s risk pricing sensitivity to higher default rates in affected areas. Overall, our findings show that P2P lending, as an alternative means to household finance, can help smooth cuts in welfare transfers particularly among households in economically deprived areas.
    Date: 2022–07–21
  27. By: Driskell, David
    Abstract: The strategy of mobile app implementation must be adjusted as the app progresses through different stages of maturity. The assessment of the quality of mobile apps used by governments is difficult as they progress through the stages of maturity of the service. A multi-item scale is proposed in this study to assess the quality of mobile apps provided by governments that involve transactions. An extensive review of research conducted by academic scholars and practitioners identified factors that influence the quality of mobile apps developed by the government. We conducted a survey of fully operational mobile apps using a questionnaire based on an analysis of reviews and interviews with users. The data was analysed quantitatively in order to develop a scale based on the responses received. Citizens can use this scale to evaluate the perceived quality of government mobile applications. According to the analysis of the data, seven constructs can be used to assess the quality of government apps on the demand side, including user friendliness, transaction transparency, loading speeds, flexibility, complete information, trust and safety, and efficiency.
    Keywords: Society and government mobile apps, impact of mobile apps, competitiveness and mobile apps, mobile apps quality assessment, society and mobile apps
    JEL: O30 O32 O33
    Date: 2022–07–05
  28. By: Anubhav Sarkar; Swagata Chakraborty; Sohom Ghosh; Sudip Kumar Naskar
    Abstract: Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.
    Date: 2022–10
  29. By: Audrey Bonnemaizon (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Alain Debenedetti (IRG - Institut de Recherche en Gestion - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12); Ibtihal Lakehal (Université Gustave Eiffel)
    Abstract: Les recherches sur le vécu des plateformes d'échange entre consommateurs présentent, le plus souvent d'une manière critique, l'aspect coercitif des plateformes existantes. Elles tendent ainsi à négliger l'agence du consommateur, c'est-à-dire sa capacité à agir au-delà de ce que les fonctionnalités de ces dispositifs digitaux lui imposent. Cette recherche qualitative exploratoire investigue dans quelle mesure et comment les consommateurs exercent leur agence au prisme de l'appropriation des fonctionnalités mises à disposition par deux types de plateformes C to C aujourd'hui très largement utilisées : les plateformes de vente et les plateformes de rencontre en ligne. Elle montre comment les consommateurs « fabriquent » de l'intime dans le contexte des plateformes de vente et dans quelle mesure ils « fabriquent » du marchand dans le contexte des plateformes de rencontre. Cette recherche exploratoire pose finalement la question du rôle de la cohérence entre l'objet des plateformes et des fonctionnalités et ses conséquences sur l'agence du consommateur.
    Keywords: consumer agency,selling platforms,dating applications,technological devices,Agence du consommateur,plateformes de vente,plateformes de rencontre en ligne,fonctionnalités
    Date: 2022–11–17
  30. By: Chengxiang Jin; Jiajun Zhou; Jie Jin; Jiajing Wu; Qi Xuan
    Abstract: With the development of Web 3.0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency. Recently, a large number of criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and phishing scams, which severely endanger decentralized finance. Existing graph-based abnormal behavior detection methods on blockchain usually focus on constructing homogeneous transaction graphs without distinguishing the heterogeneity of nodes and edges, resulting in partial loss of transaction pattern information. Although existing heterogeneous modeling methods can depict richer information through metapaths, the extracted metapaths generally neglect temporal dependencies between entities and do not reflect real behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation (TMFAug) as a plug-and-play module to capture the real metapath-based transaction patterns during Ponzi scheme detection on Ethereum. The proposed module can be adaptively combined with existing graph-based Ponzi detection methods. Extensive experimental results show that our TMFAug can help existing Ponzi detection methods achieve significant performance improvements on the Ethereum dataset, indicating the effectiveness of heterogeneous temporal information for Ponzi scheme detection.
    Date: 2022–10
  31. By: Lu, Qiuyu
    Abstract: This paper examines the licensing strategy of a monopoly content provider that supplies horizontally differentiated content through downstream distributors to consumers who can potentially purchase from both distributors. When consumers' additional gain from the second purchase is high, the mismatch cost is low, and the quality of the extra content is high, some consumers purchase from both firms, which is called multi-homing. Apart from that, all consumers purchase from either distributor. When some consumers multi-home, the content provider always licenses to only one distributor. When all consumers single-home, the content provider either licenses to one distributor or shares the licensing.
    Keywords: Multi-homing, Licensing, Exclusive Dealing, Digital Content, Online Platform
    JEL: D43 L13 L42
    Date: 2022–11–09
  32. By: Benjamin R. Shiller
    Abstract: Emerging tracking data allow precise predictions of individuals’ reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized pricing as dynamic pricing. Specifically, a firm can sometimes tailor the “posted” price for the arriving consumer but privately commits to change price infrequently. Note such pricing may unintentionally arise through algorithmic pricing. I examine outcomes in four contexts: one empirical and three hypothetical distributions of consumer valuations. I find that this strategy is most intense and raises profits most for medium popularity products. Furthermore, improvements in the precision of individual-level demand estimates raise the range of popularities this strategy can be profitably applied to. I conclude that this is an auspicious strategy for online platforms, if not already secretly in use.
    Keywords: personalized pricing, algorithmic pricing, price discrimination, targeted pricing, behavioural pricing, dynamic pricing, sticky pricing
    JEL: L81 D40 L10
    Date: 2022
  33. By: Katarzyna Kryńska (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance.
    Keywords: machine learning, deep learning, recurrent neural networks, LSTM, algorithmic trading, ensemble investment strategy, intra-day trading, S&P 500 Index, Bitcoin
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2022
  34. By: Rafael P. Greminger
    Abstract: Most online retailers, search intermediaries, and platforms present products on product lists. By changing the ordering of products on these lists (the "ranking"), these online outlets can aim to improve consumer welfare or increase revenues. This paper studies to what degree these objectives differ from each other. First, I show that rankings can increase both revenues and consumer welfare by increasing the overall purchase probability through heterogeneous position effects. Second, I provide empirical evidence for this heterogeneity and quantify revenue and consumer welfare effects across different rankings. For the latter, I develop an estimation procedure for the search and discovery model of Greminger (2022) that yields a smooth likelihood function by construction. Comparing different counterfactual rankings shows that rankings targeting revenues often also increase consumer welfare. Moreover, these revenue-based rankings reduce consumer welfare only to a limited extent relative to a consumer-welfare-maximizing ranking.
    Date: 2022–10
  35. By: Budzinski, Oliver; Mendelsohn, Juliane
    Abstract: The European Commission has proposed a new regulatory tool for the governance of digital markets. The Digital Markets Act (DMA) intents to limit the market behavior of socalled gatekeeper companies to ensure contestable and fair digital markets. We review the provisions of the DMA both from a legal and from an economic perspective. Notwithstanding a number of benefits, we identify several issues with the current proposal. When looking at the core provisions of the proposal from an economic perspective, several issues of contention arise: many of the provisions seem to be quite narrow in scope and it seems difficult to extrapolate more general rules from them; the economic harm of some of the provisions is both uncertain and in principle debatable; the alleged distinction between different types of obligations cannot be verified; and, in addition, Art. 5-7 DMA seem to contain three distinct regulatory instruments; last but not least, while the DMA seeks to control existing gatekeepers, the 'tipping' of markets and the rise of further gatekeepers is not guaranteed by the proposed regulation, this in turn leads to a larger critical analysis of the gatekeeper as the DMA's norm addressee. While the goals and nature of the DMA have gained in clarity throughout the legislative process, its scope remains somewhat obtuse. On the one hand it seems set on regulating gatekeepers as they exist today, on the other, also wants to bring about systemic change in the digital single market. How it expects to achieve the latter is not entirely clear. In this light and by critically looking at the nature of ex ante and ex post measures in broader competition policy, we conclude that a reform of the competition policy regime would better suit the overalls aims of reining in big tech in future.
    Keywords: big tech,digital economy,digital ecosystems,GAFAM,competition policy,antitrust,Digital Markets Act (DMA),sector-specific regulation,law and economics
    JEL: K21 K23 K24 L40 L50 L81 L86
    Date: 2022
  36. By: Sylvain Mignot; Annick Vignes (CAMS - Centre d'Analyse et de Mathématique sociales - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)
    Abstract: A fundamental assumption in economics is that rational individuals act in their own self interest. One implication is that, when trading, buyers are supposed to seek for the lowest price and sellers for the highest one and social interactions are not considered. It is now largely accepted that social relationships affect the efficiency of a market structure (centralized or decentralized) . The objectives of the current study is to examine the network structures of a very specific market : the Boulogne-sur-mer fish market. On this market two market design (auctions and bilateral exchanges) coexist, each beeing used by the same buyers and sellers, exchanging similar goods. For each sub-market we examine (1) the global network structure, (2) the local network structure, and (3) we identify the traders characteristics that best explain the network structures. The objective is to identify the the influence of trust nad the influence of reputation in the individual choices of trading partners. Structural measures are used to characterize networks structures. Exponential random graph models are used to evaluate how trader characteristics explain purchasing patterns, and how the influence of these characteristics vary with the market mechanism. We bring into the light that, when the transaction links on the auction market reflects the economic constraints of the partners, the relationships on the bilateral market depends on something more. Clearly, the prices of the bilateral transactions are the consequences of economics and non economics determinants. At first glance, the stable co-existence of two market structures looks like a paradox. Our results help to understand the distinctive characteristics and functioning of each sub-market. This discussion contributes to the debate about the efficiency of market structures.
    Keywords: social networks,market structures,trust,reputation,fish markets
    Date: 2022–11–08
  37. By: Carina Borrasterp (Universidad Nacional de Córdoba/CONICET); Ignacio Juncos (Universidad Nacional de Córdoba)
    Abstract: El objetivo del trabajo es analizar la dinámica de competencia y rentabilización del capital de algunas de las empresas líderes del mercado tecnológico global, identificadas como las “gigantes tecnológicas” (GT) occidentales y orientales: Google, Amazon, Facebook, Apple y Microsoft para el primer caso (el denominado grupo “GAFAM”) y Alibaba, Tencent y Huawei como las mayores exponentes del caso oriental (ATH). Nos nutrimos de los debates teóricos planteados por corrientes y lecturas del capitalismo que ubican al conocimiento y la concentración del capital basada en el dominio tecnológico como pilares de los sistemas de producción contemporáneos, bajo distintas conceptualizaciones complementarias como capitalismo de plataformas, capitalismo cognitivo, capitalismo intelectual monopolista y otras. A partir del análisisempírico realizado, observamos que las empresas GAFAM-ATH disputan el liderazgo del mercado tecnológico global sobre la base de un esquema predominantemente rentista e hiper-concentrado de competencia entre firmas gigantes, y proponemos la noción de oligopolios tecnológicos para caracterizar dicha dinámica.
    Keywords: Coo-petencia, Oligopolio tecnológico, Rentismo digital, Mercado tecnológico global.
    JEL: L1 O10 O14
    Date: 2022–11
  38. By: Maria Mercanti-Guérin (UP1 EMS - Université Paris 1 Panthéon-Sorbonne - École de Management de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne)
    Abstract: E-Commerce et RSE sont-ils compatibles ? L'e-commerce est-il injustement accusé et en pleine transformation écologique ? Des fédérations comme la FEVAD souhaitent relativiser les mauvais chiffres environnementaux du commerce en ligne. La FEVAD souligne qu'en France, la vente sur Internet de produits non alimentaires génère en moyenne 400 g de CO2 par produit vendu contre 600 g pour le commerce physique. Quant à l'artificialisation des sols due au commerce en ligne, elle représenterait moins de 1% des flux moyens annuels. L'e-commerce ne représenterait que 0,5% du trafic routier dans des zones comme Paris, Berlin ou Londres. De grands acteurs comme Amazon mettent en avant leurs transitions écologiques. Le programme d'Amazon est d'atteindre zéro émission nette pour l'ensemble de ses activités d'ici 2040, utiliser 100 % d'énergies renouvelables d'ici 2025, déployer 100 000 véhicules de livraison électriques personnalisés d'ici 2030. Le sujet de la RSE se pose de façon, dans tous les cas, de plus en plus cruciale pour les acteurs du e-commerce. En France, les problématiques RSE concernant l'e-commerce donnent une nouvelle direction à ce levier perçu comme créateur de proximité et de maintien des commerces en centre-ville, d'emplois supplémentaires et de nouvelles pratiques écoresponsables.
    Keywords: e-commerce,RSE,plateformes,Amazon
    Date: 2022–10–07
  39. By: Tristan Lim
    Abstract: The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
    Date: 2022–09
  40. By: Andrew F. Haughwout; Donghoon Lee; Daniel Mangrum; Joelle Scally; Wilbert Van der Klaauw
    Abstract: Total household debt balances continued their upward climb in the third quarter of 2022 with an increase of $351 billion, the largest nominal quarterly increase since 2007. This rise was driven by a $282 billion increase in mortgage balances, according to the latest Quarterly Report on Household Debt & Credit from the New York Fed’s Center for Microeconomic Data. Mortgages, historically the largest form of household debt, now comprise 71 percent of outstanding household debt balances, up from 69 percent in the fourth quarter of 2019. An increase in credit card balances was also a boost to the total debt balances, with credit card balances up $38 billion from the previous quarter. On a year-over-year basis, this marked a 15 percent increase, the largest in more than twenty years. Here, we take a closer look at the variation in credit card trends for different demographics of borrowers using our Consumer Credit Panel (CCP), which is based on credit reports from Equifax.
    Keywords: consumer credit; panel; credit cards
    JEL: D14
    Date: 2022–11–15
  41. By: Grégory Levieuge (LEO - Laboratoire d'Économie d'Orleans [FRE2014] - UO - Université d'Orléans - UT - Université de Tours - CNRS - Centre National de la Recherche Scientifique); Yannick Lucotte (LEO - Laboratoire d'Économie d'Orleans [FRE2014] - UO - Université d'Orléans - UT - Université de Tours - CNRS - Centre National de la Recherche Scientifique, PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université); Florian Pradines-Jobet (PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)
    Keywords: Banking crises,Fiscal rules,Monetary policy,Exchange rate regime,Constrained discretion
    Date: 2021–02–28
  42. By: Alekseeva, Liudmila; Fontana, Silvia Dalla; Genc, Caroline; Ranjbar, Hedieh Rashidi
    Abstract: Geographical clustering is an essential feature of the venture capital (VC) industry as proximity helps VCs to acquire soft information about early-stage companies and to conduct post-investment activities. However, whether the VC investment model based on in-person interactions is still justified in the age of online communication technologies remains an open question. In this paper, we address this question by using an unexpected interruption in face-to-face meetings during the recent pandemic. We document that VCs respond to this change by breaking their traditional norm: they invest in more distant startups. We find that this evolution goes along with selection criteria and syndication process changes despite some persisting behaviors. Thus, our study helps to understand how VCs revisit their investment model and sheds light on the value of in-person interactions for the VC industry.
    Date: 2022–08–22
  43. By: Hayk Amirkhanyan (University of Warsaw, Faculty of Economic Sciences); Michał Krawczyk (University of Warsaw, Faculty of Economic Sciences); Maciej Wilamowski (University of Warsaw, Faculty of Economic Sciences)
    Abstract: It is often claimed that certain career choices, notably running a business, are associated with excessive confidence in own capabilities. Such a link could partly explain e.g., the surprisingly high number of unsuccessful start-ups. We verify these claims in a sample of marathon runners. We take starting too fast and then slowing down in a marathon race as a proxy for overconfidence. In a sample of over 50 thousand runners, we match marathon pacing data with job titles that are partly reported by the runners themselves and partly identified by us (using runners’ names, years of birth, and places of residence to find their personal web sites, social media profiles etc., whenever possible). We observe that job categories have a significant impact on slowing down (as a proxy for overconfidence), also when we control for observable demographic factors (such as age, gender, place of residence). In particular, entrepreneurs tend to be more overconfident than the general population.
    Keywords: overconfidence, slowdown, occupational differences, gender differences, selection into professions
    JEL: D01 L26 J24 J16
    Date: 2022
  44. By: Nicolas Kusz (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne); Jean-François Lemoine (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne, ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers)
    Abstract: Voice assistants (Google, Alexa...) are technologies that are entering in homes and people's life, more and more every day. Beyond their usefulness and functionalities, there is an obvious interface between man and machine that has not been taken into consideration by companies that have developed their own assistant yet : the voice used for the assistant. This paper aims to measure the impact of the voice of these assistants on users reactions. Thanks to 15 interviews of users, the results suggest that the type of voice of the assistant influences the social presence and trust in the assistant. Nevertheless, unlike the literature on virtual agents and chatbots, our study reveals that realism strongly influences the trust ; a synthetic voice that perfectly mimics a human, on the contrary, has a negative effect on the perception of the assistant.
    Abstract: Les assistants vocaux (Google, Alexa...) sont des technologies qui entrent chaque jour un peu plus dans les foyers et le quotidien des individus. Au-delà de leur utilité et des fonctionnalités qu'ils proposent, il existe une Interface évidente entre l'Homme et la Machine qui n'a pas encore été suffisamment prise en considération par les entreprises ayant développé leur propre assistant : la voix adossée à l'assistant. Cette recherche exploratoire vise à mesurer l'impact de la voix des assistants vocaux sur les réactions des consommateurs. A partir de 15 entretiens semi-directifs menés auprès des utilisateurs, nous mettons en évidence que le type de voix d'un assistant vocal influence la présence sociale et la confiance envers l'assistant. Toutefois, contrairement à la littérature portant sur les agents virtuels et les chatbots, notre étude révèle que le réalisme de la voix influence fortement la confiance ; une voix de synthèse qui imite à la perfection celle d'un humain peut au contraire desservir la perception de l'assistant vocal.
    Keywords: Trust,Social presence,Voice assistant,HCI,Synthetic voice,Assistant vocal,IHM,Voix de synthèse,Confiance,Présence sociale
    Date: 2022–05–18
  45. By: Jean-François Lemoine (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne, ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers); Thomas Sender (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne)
    Date: 2022–10–13
  46. By: Aymanns, Christoph; Foerster, Jakob; Georg, Co-Pierre; Weber, Matthias (University of St. Gallen)
    Abstract: We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of these findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model. This suggests that our model is suitable to analyze the spread of fake news in social networks.
    Date: 2022–07–21
  47. By: Joachim Wagner (Leuphana University Lüneburg and Kiel, Institute for the Word Economy)
    Abstract: Presence on the web tends to be important for firms. Empirical studies show that firms with a better performance along various dimensions, and firms that are more internationally active, tend to have a website. Furthermore, a website helped firms to survive in times of the COVID-19 pandemic. An open question that is not discussed in this literature is how the use of online channels for sales is related to various dimensions of firm performance. This note contributes to the literature by using a unique recently released set of firm level data from Germany to investigate for the first time the links between online channels sales and firm characteristics.
    Keywords: Online channels sales; firm performance; COVID-19; Germany 2021, Enterprise Survey Data Set
    JEL: D22 L25
    Date: 2022–11
  48. By: Cyrielle Vellera (TSM - Toulouse School of Management Research - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées); Elodie Jouny-Rivier (ESSCA School of Management, France, ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers); Aurélie Hemonnet-Goujot (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université)
    Abstract: Although crowdsourcing challenges as tools for generating high levels of innovation have received much attention, little research has investigated the impact on participants when their submissions are rejected. This research is aimed at gaining a better understanding of the consequences of rejection for participants' relationships with the brand engaged in the crowdsourcing activity. To investigate these issues, two quantitative studies were carried out with participants whose challenge proposals had not been selected. The results highlight positive effects on participant–brand relationships, especially on brand attachment, proselytism, brand commitment and brand loyalty. A confirmatory, interview-based qualitative study then identifies managerial perspectives and marketing strategies for brands and crowdsourcing platforms following the announcement of challenge results. This paper contributes to both the co-creation and crowdsourcing literature by extending academic knowledge and provides opportunities for further research.
    Keywords: Brand relationship,Crowdsourcing
    Date: 2022
  49. By: Ruibo Chen; Wei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun
    Abstract: Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the same set of parameters for different stocks. On the other hand, learning different models for each stock would face data sparsity or cold start problems for many stocks with small capitalization. To take advantage of the data scale and the various characteristics of individual stocks, we propose a dual-process meta-learning method that treats the prediction of each stock as one task under the meta-learning framework. Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters. Furthermore, we propose to mine the pattern of each stock in the form of a latent variable which is then used for learning the parameters for the prediction module. This makes the prediction procedure aware of the data pattern. Extensive experiments on volume predictions show that our method can improve the performance of various baseline models. Further analyses testify the effectiveness of our proposed meta-learning framework.
    Date: 2022–10

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.