An Intercountry Analysis on the Factors and Barriers to Internet Accessibility

The world of the Internet is shaping the future of many industries around the world, from the airline industry to the production of music. The Internet is changing how we communicate and exchange ideas and thoughts. Many countries are now eager to setup master plans and policies to develop their Internet infrastructure. This paper examines the essential factors that are affecting the development and usage of the Internet in the developed and the developing world and shed light on the need to view the development and usage of the Internet on a more holistic view than atomistic view. The results show that urbanization, electrical usage, and the usage of mobile cellular have a positive relationship with Internet usage. In case of the least developed countries, GNI per capita has a positive relation of Internet usage

Information effect in Social Commerce: A case of TicketMonster

Social Commerce sites are in vogue enough to be recognized as a new trend in online shopping arena. Social Commerce can be defined as the electronic commerce triggered by social media. It has been growing very rapidly with enormous discount rate, quality services and precise information. This research analyzes effects of posted numeric information on daily sales volumes. Hetrosckedasticity arises with the real transaction data that was acquired from TicketMonster which is one of the biggest Social Commerce sites in Korea. Therefore, GLS model was applied to have results that original price, discounted price, minimum quantity to have discounted price, and maximum units of sales are statistically significant. Minimum quantity of sales to meet the requirement to have discounted prices has threshold effect on the purchase of consumers like the ways they have group buying on the Internet. However, additional studies are required to identify if this correlated information can be results of reasonable estimates by the vendors and the intermediary or play a role of signal to attract sales. More research opportunities are addressed on services types, consumer groups and information richness

Is the Leaderboard Information Useful to Investors? : The Leaderboard Effect in P2P Lending

P2P (Online Peer-to-Peer) lending provides an open marketplace where borrowers make requests for loans by lenders who subsequently decide whether to bid or not following an examination of the relevant information posted by borrowers. In this P2P lending context, the leaderboard, where popular loan requests are displayed at the web’s front page, provides information for lenders to use when evaluating the requests. We empirically examine the effects of leaderboard information regarding the most popular existing loan requests. Our results show that the leaderboard information works ex ante in attracting additional bids to get loan requests successfully financed. However, it does not work ex post in improving the performance so that it has less potential for default

Genetic Improvement @ ICSE 2020

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the International Conference on Software En- gineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, jus- tifyability, exploitability) and GI benchmarks. We also con- trast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face to face interac- tion. Finally we speculate on how the Coronavirus Covid-19 Pandemic will a ect research next year and into the future

IMPLEMENTATION OF DIGITAL SIGNAL PROCESSING WITH HIGH DATA RATE SENSORS FOR DATA COMPRESSION

The onboard telemetry system of Korea Space Launch Vehicle-II (KSLV-II) acquires acoustic, vibration, and piezoelectric pressure sensors that require high data rate over several kilo samples per second, so the compression method is needed to expand link margin of telemetry system. This paper implements third-octave and FFT signal processing algorithms to reduce sensor data with high compression ratio depends on data acquisition requirements. The developed signal processing hardware module is composed of analog signal conditioning block and digital signal processing block on FPGA, and the digital block is fully implemented with dedicated hardware using HDL. For digital hardware implementation, multistage structure with ANSI standard octave filter bank is used for third-octave processing, and pipelined architecture is used for FFT. The performance of data acquisition and signal processing is evaluated and compared to the commercial data acquisition equipment.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Is Bitcoin a Viable E-Business? : Empirical Analysis of the Digital Currency’s Speculative Nature

Ever since its creation by the presumed pseudonymous Satoshi Nakamoto, Bitcoin has garnered significant attention as an innovative online payment system. The purpose of this paper is to discover the degree to which the participation of a Bitcoin user is dependent on the speculative opportunities in the Bitcoin market and, accordingly, to test Bitcoin’s competence against traditional currency. Using a panel data set from one of the largest Bitcoin traders in Asia, we find that $1 increase in arbitrage between market prices is associated with 0.1 more log-ins of users. However, the paper also suggests that such a speculative nature might not be strong enough to dominate user behaviors entirely. The findings report that the actual reason for Bitcoin’s incompetence as a form of currency against the conventional tools of trade may be attributable to its low level of network effects

Building a Korean Fashion Platform in Taobao, China's Biggest Online Commerce: The Case of Fashion e-Commerce Company Accommate

In 2008, a Korean online fashion distributor, Accommate, is founded in China and it tapped into the Chinese online market with the alliance of Taobao, online C2C(consumer-to-consumer) commerce platform of Alibaba. Taobao offered free listings to attract more sellers in order to compete e-bay and introduced keyword search features which Accommate made use of as an early adopter and partner. As Taobao became Chinas market leader, Accommate secured the partner position to distribute Korean apparels and strengthened its partnership with Alibaba. Soon Alibaba introduced a new online brand mall called Taobao Mall or Tmall to complement the C2C platform. Accommate extended it business with Alibaba by taking charge of the Korean product sections in the online brand mall of Tmall. From the perspective of scales, China represented for Accommate a dynamic and fast-growing market, though it is so competitive. Moreover, it is a tough job to manage the adequate level of inventory for clothes which have as short lifecycle as 45 days. A number of Korean predecessors failed to manage cash flow issues resulted from the inventory. Accommate came to encounter the management issues of both meeting the changing requirement of Chinese customers and managing the inventory level at the same time. Considering these changes and challenges, we may well think what the strategic and operational issues regarding Accommate are and how the company should grow in this fast growing electronic marketplace. This case study will help readers think about the emerging electronic market in China and form an opinion on the associated management issues and analyses

Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization

Can Government Regulate an Evil Online Game?: An Empirical Analysis of Regulation Policy Effects Using Vector Autoregression

The objective of this study is to examine the effects of regulation policy on online gambling, an increasingly popular type of entertainment in the online game industry. Prior information systems (IS) studies on online game focus primarily on user behavior. However, there is a growing need to investigate the effects of regulation policy on dynamic changes of games or service providers instead of ad hoc heuristic approaches on individual behavior. Going beyond the approaches of previous studies, this study empirically tests the regulation policy effect with three theoretical perspectives: social influence, prior experience and perceived switching cost. A vector autoregression (VAR) methodology is used to forecast game usage and to model several patterns of the co-movement of online games. Evidence is also provided of strong Granger-causal interdependencies within games and service providers. This study provides one of the first empirical evidences that examine the effects of regulation policies on online game. In research methodology aspect, this study also introduces an exposition of VAR methodology in IS research. Therefore, it provides advanced knowledge on gambling behavior and helps develop suitable regulation policy to protect users of online gambling as well as to satisfy policy makers

Attention Economy in Online Daily Deals: Demand Estimation using Structural Models

Online daily deal sites such as Groupon, which have grown explosively in recent years, allow merchants to gain the attention of online consumers and thereby increase their customer base. In this paper, we use structural models to estimate demand for a large daily deals site. We find that for a voucher with an average market share, a one per cent extension in the sale period is associated with a decrease in voucher demand by 0.22%. Moreover, a one per cent increase in the price is associated with a decrease in demand by 2.43%. Furthermore, the counter-factual experiment results show that, ceteris paribus, a one-day extension of the sale time period will decrease the product’s weekly market share by 8.9%. This suggests the presence of attention economy in online daily deals such that limited-time offers are likely to draw the consumers’ attention and consequently lead to additional sales