nep-ict New Economics Papers
on Information and Communication Technologies
Issue of 2018‒11‒05
six papers chosen by
Walter Frisch
Universität Wien

  3. Real estate digitalization and the underlying modes of operation By Daniel Piazolo
  4. More Amazon Effects: Online Competition and Pricing Behaviors By Alberto Cavallo
  5. Central bank digital currencies: An assessment of their adoption in Latin America By Noelia Camara; Enestor Dos Santos; Francisco Grippa; Javier Sebastian; Fernando Soto; Cristina Varela
  6. Reference Price Shifts and Customer Antagonism: Evidence from Reviews for Online Auctions By Gesche, Tobias

  1. By: Zeynep Ozsut Bogar (Pamukkale University); Askiner Gungor (Pamukkale University)
    Abstract: Electronic waste or e-waste is one of the fastest growing waste types all over the world due to technological developments, especially affecting the IT based products. When an electrical or electronic product is replaced with a new one at any stage in its life cycle it becomes a potential e-waste. Estimation of e-waste potential is important and valuable to manage e-waste related issues such as e-waste collection, recycling and recovery facility location and capacity decisions, reverse and/or closed-loop supply chain networks design, recycling based operational decisions, and etc. In this study, the effort is specifically dedicated to estimate the waste computers based on the sales data by proposing a logistic model for the case of Turkey. Since consumer behaviors and adoption levels keep on changing with the technological developments, the model provides different scenario based solutions based on various levels of lifespans (base, upper and lower) and carrying capacities (upper, lower). Boundaries of potential quantity of waste computers are determined and a general view is given for this valuable e-waste stream. The study may provide benefit to develop e-waste management systems and more effective practice for estimation of other e-waste types in Turkey.
    Keywords: E-waste, WEEE, Logistic model, Estimation
    JEL: Q53
    Date: 2018–07
  2. By: Amit Sharma (University School of Management Studies, GGS Indraprastha University)
    Abstract: Digital buying behavior is now the most used method by Indian consumer for shopping. Indian consumer who was reluctant and skeptical of online purchases until half a decade ago has taken a major leap towards digital buying. India, being second most populous country in the world is definitely a huge market even if ninety percent of us may not buy products online. This ten percent digital consumers are enough to give big business to companies selling their products online and the selling platforms. Since this explosive digital buying behavior is very recent for India as a digital market, there is urgent need to understand the digital consumer behavior of Indian customer. In this context the present study was conducted to see how consumer with different types of personality and demographic differences differ on their frequency of online purchase, types of products purchased, type of websites preferred to purchase online, preferred mode of payment, attraction of online offers, and reasons to prefer online purchase over traditional go market behavior. The study was conducted on a sample of 160 respondents from various regions of India. The results of this research reveals that digital consumer behavior in India is affected by demographic factors like Gender, Age, Marital Status and Personality factors like Agreeableness, Conscientiousness and Open to Change. The paper discuss the implications of these differences with respect to Indian digital market.
    Keywords: Frequency of online purchase, Reasons to prefer online purchase, Age, Gender, Marital Status, Agreeableness, Conscientiousness, Open to Change.
    JEL: M10
    Date: 2018–07
  3. By: Daniel Piazolo
    Abstract: Data are the new currency of our time. Various digital technologies drive the successful business models of today, but will also determine the real estate industry in the future. The paper examines the relevant digital technologies and various real estate tech startups. The underlying modes of operation can be condensed to four types:1.) Increasing transparency2.) Raising efficiency3.) Enhancing flexibility4.) Enabling new opportunities, new contents, and new insights.Based on these modes of operation it is possible to describe the successful companies, the driving forces behind real estate digitalization and thus our industry in the near future.
    Keywords: BIM; Digital Asset Management; Digital Modes of Operation; Digital Transformation; Structural Change
    JEL: R3
    Date: 2018–01–01
  4. By: Alberto Cavallo
    Abstract: I study how online competition, with its algorithmic pricing technologies and the transparency of the Internet, can change the pricing behavior of large retailers and affect aggregate inflation dynamics. In particular, I show that online competition has raised both the frequency of price changes and the degree of uniform pricing across locations in the U.S. over the past 10 years. These changes make retail prices more sensitive to aggregate ``nationwide" shocks, increasing the pass-through of both gas prices and nominal exchange rate fluctuations.
    JEL: E31 E5
    Date: 2018–10
  5. By: Noelia Camara; Enestor Dos Santos; Francisco Grippa; Javier Sebastian; Fernando Soto; Cristina Varela
    Abstract: This document focuses on identifying factors affecting the implementation of a Central Bank digital currency (CBDC) in Latin American countries. The adoption of a CBDC (non-universal) for the interbank and wholesale payment system would lead to a relatively minor level of disruption in the economy. In this case, the implementation costs is an important issue.
    Keywords: Working Paper , Digital Regulation , Central Banks , Digital economy , Financial Markets , Financial Inclusion , Latin America
    JEL: O33 E43 E58
    Date: 2018–10
  6. By: Gesche, Tobias
    Abstract: Using data from a large-scale sales campaign on eBay, I show that successful auction customers punish the seller through unfavorable public feedback when they later learn discover a cheaper fixed-price offer. The probability of receiving such feedback is four times bigger for auctions than for fixed -price sales of the same item from the same seller. Remarkably, this probability is increasing in the auction price, even though auction customers actively shaped this price themselves. In line with an explanation based on ex-post reference price shifts, this price effect is concentrated in a period during which reference prices were particularly salient because customers information about them, but not about idiosyncratic transaction features (e.g. quality), could change. Consistent with the reference price explanation, the difference in unfavorable feedback between auctions and fixed-price sales is also concentrated in this period and drops to a quarter of its initial size afterwards.
    Keywords: customer antagonism,pricing,reference prices,online reputation,eBay
    JEL: D44 D91 M31
    Date: 2018

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