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NEW TECHNOLOGIES LIKE GRID COMPUTING WHICH CAN CONNECT RESOURCES AT DIVERSE LOCATIONS ARE MORE AND MORE ADOPTED FROM ORGANIZATIONS. SUCH TECHNOLOGIES CAN BOTH TRIGGER LINKAGES BETWEEN ORGANIZATIONS AND DIFFERENT DEPARTMENTS IN ONE SINGLE ORGANIZATION. WE DEVELOP A MODEL WHICH ACCOUNTS BOTH FOR INTER- AND INTRA-ORGANIZATIONAL INFLUENCE FACTORS ON THE ADOPTION PROCESS AND EMPIRICALLY IDENTIFIES THE MOST SIGNIFICANT INFLUENCE FACTORS.
How prices can be set to allocate grid computing resources in a financial service institution
(2011)
GRID COMPUTING IS AN IT CONCEPT TO SHARE COMPUTING RESOURCES AMONG DEPARTMENTS AND USERS THAT REDUCES IT COSTS AND PROVIDES COMPUTING RESOURCES DYNAMICALLY WHEN THEY ARE NEEDED. RESOURCE MARKETS ARE AN EFFECTIVE MECHANISM TO REGULATE THE RESOURCE SHARING, BUT THE MOST OFTEN USED AUCTIONS ARE COMPLEX. WE HAVE DEVELOPED A STEPWISE APPROACH TO HELP FIRMS OFFERING INTERNAL GRID COMPUTING SERVICES TO SET TRANSPARENT BUT EFFECTIVE PAY-PER-USE PRICING SCHEMES AS AN ALTERNATIVE TO AUCTIONS.
IN TWO-SIDED MARKETS SUCH AS EXCHANGES, AN INTERMEDIARY BRINGS TOGETHER TWO DISTINCT CUSTOMER POPULATIONS, E.G., BUYERS AND SELLERS. THESE CUSTOMER POPULATIONS INTERACT VIA A PLATFORM PROVIDED BY THE INTERMEDIARY, AND TYPICALLY NETWORK EFFECTS ARE OBSERVABLE IN THESE MARKETS; IF THE NUMBER OF BUYERS IS HIGH, MORE SELLERS ARE ATTRACTED TO THE PLATFORM, AND VICE VERSA. IN SUCH MARKETS IT IS DIFFICULT TO MEASURE THE ECONOMIC SUCCESS OF IT INVESTMENTS. THIS ARTICLE PROPOSES A SOLUTION.
Chatbots become human(like): the influence of gender on cooperative interactions with chatbots
(2019)
CURRENT TECHNOLOGICAL ADVANCEMENTS OF CONVERSATIONAL AGENTS (CAs) PROMISE NEW POTENTIALS FOR HUMAN-COMPUTER COLLABORATIONS. YET, BOTH PRACTITIONERS AND RESEARCHERS FACE CHALLENGES IN DESIGNING THESE INFORMATION SYSTEMS, SUCH THAT CAs NOT ONLY INCREASE IN INTELLIGENCE BUT ALSO IN EFFECTIVENESS. THROUGH OUR RESEARCH ENDEAVOUR, WE PROVIDE NEW AND COUNTERINTUITIVE INSIGHTS THAT ARE CRUCIAL FOR THE EFFECTIVE DESIGN OF COOPERATIVE CAs.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
THE PROLIFERATION OF THE INTERNET HAS ENABLED PLATFORM INTERMEDIARIES TO CREATE TWO-SIDED MARKETS IN MANY INDUSTRIES. IN SUCH MARKETS, NETWORK EFFECTS OFTEN OCCUR WHICH CAN DIFFER FOR NEW AND EXISTING CUSTOMERS. THE AUTHORS DEVELOP AN INFLUX-OUTFLOW MODEL TO INVESTIGATE THE CONDITIONS UNDER WHICH THE ESTIMATION OF SAME-SIDE AND CROSS-SIDE NETWORK EFFECTS SHOULD DISTINGUISH BETWEEN ITS IMPACT ON THE NUMBER OF NEW CUSTOMERS (I.E., ACQUISITION) AND EXISTING CUSTOMERS (I.E., THEIR ACTIVITY).