87,715 research outputs found
Using webcrawling of publicly available websites to assess E-commerce relationships
We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation
Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"
Affected by urbanization, centralization and the decrease of overall
population, Japan has been making efforts to revitalize the rural areas across
the country. One particular effort is to increase tourism to these rural areas
via regional branding, using local farm products as tourist attractions across
Japan. Particularly, a program subsidized by the government called Michinoeki,
which stands for 'roadside station', was created 20 years ago and it strives to
provide a safe and comfortable space for cultural interaction between road
travelers and the local community, as well as offering refreshment, and
relevant information to travelers. However, despite its importance in the
revitalization of the Japanese economy, studies with newer technologies and
methodologies are lacking. Using sales data from establishments in the Kyushu
area of Japan, we used Support Vector to classify content from Twitter into
relevant topics and studied their causal relationship to the sales for each
establishment using LiNGAM, a linear non-gaussian acyclic model built for
causal structure analysis, to perform an improved market analysis considering
more than just correlation. Under the hypotheses stated by the LiNGAM model, we
discovered a positive causal relationship between the number of tweets
mentioning those establishments, specially mentioning deserts, a need for
better access and traf^ic options, and a potentially untapped customer base in
motorcycle biker groups
Penentuan Produk yang Diminati Pasar Menggunakan Algoritma K-Means
Grouping to get class similarity and dividing into several classes is one of the processes of data mining. The accuracy of the grouping is an important factor to determine the product's interest in the market. The purpose of this study is to determine which products are classified as the most desirable, desirable and lessdesirable markets, so that petrified in decision making. This research uses the CRISP-DM (Cross Industry Standard Process for Data Mining) method is a methodology from minimum data that is used to analyze problems in business processes or research units. K-Means algorithm is used for grouping products that are of interest to the market. K-Means algorithm partitioned class similarity based on predetermined parameters, by calculating the centroid distance in a class. This research resulted in a product determination information system that is of interest to the market. From the test results using six parameters, namely, the number of transactions, sales volume, product categories, product diversity, average sales and number of stocks with transaction data of 1,235 transactions. Obtained the three best clusters, performance testing has been done using the Elbow method with the most SSE difference of 28,00782.
Keywords: Data mining, K-Means, clustering, products market demand.Pengelompokan untuk mendapatkan kesamaan kelas serta membagi menjadi beberapa kelas adalah salah satu proses dari data mining. Keakuratan pengelompokan menjadi faktor penting untuk menentukan produk tersebut diminati pasar. Tujuan dari penelitian ini adalah menentukan produk yang tergolong paling diminati, diminati dan kurang diminati pasar, sehingga membatu dalam pengambilan keputusan. Penelitian ini menggunakan metode CRISP-DM@(Cross Industry Standard Process for Data Mining) adalah metodologi dari data minig yang digunakan untuk melakukan analisis masalah pada proses bisnis atau unit penelitian. Algoritma K-Means digunakan untuk pengelompokan produk yang diminati pasar. Algoritma K-Means mempartisi kesamaan kelas berdasarkan parameter yang telah ditentukan, dengan menghitung jarak centroid pada suatu kelas. Penelitian ini mengahasilkan sebuah sistem informasi penentuan produk yang diminati pasar. Dari hasil pengujian menggunakan enam parameter yaitu, jumlah transaksi, volume penjualan, kategori produk, keragaman produk, rata-rata penjualan dan jumlah stok dengan data transaksi sebanyak 1.235 transaksi. Diperoleh tiga cluster yang terbaik, telah dilakukan uji performa menggunakan metode Elbow dengan selisih nilai SSE terbanyak sebesar 28.00782.
Kata Kunci: Data mining, K-Means, clustering, produk diminati pasar
Marketing relations and communication infrastructure development in the banking sector based on big data mining
Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federationâ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authorsâ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe
Effective Tax Rates in Transition
The paper addresses the question of effective tax rates for Russian economic sectors in transition. It presents a detailed account of fiscal environment for 1995 and compares statutory obligations with reported tax liabilities. The paper finds that taxation did not contribute to recession, as some observors believed at the time. It extends research by questioning the role that inflation played distorting revenue structure. When the costs of intermediate inputs are adjusted for inflation, many sectors have negative residual revenue, which is indicative of recession. Yet, modeling tax changes to correct the situation does not produce positive results, for the tax share in the cost structure of many sectors is small and cannot compensate for inflationhttp://deepblue.lib.umich.edu/bitstream/2027.42/39762/3/wp378.pd
Is Cartelisation Profitable? A Case Study of the Rhenish Westphalian Coal Syndicate, 1893-1913
We examine the effect of one of the presumably most powerful cartels ever on the profitability of its members. More precisely, we consider the Rhenish-Westphalian Coal Syndicate, a coal cartel that operated in Imperial Germany in the late 19th and early 20th century, using a newly constructed dataset and two different methodological approaches. At first, we employ event study methodology to asses the reaction of the stock market to the foundation of the cartel and two major revisions of its original contract. Furthermore, we look at different performance measures calculated from accounting and financial data in a dynamic panel data framework. Overall, our results suggest that the investigated cartel had no significant effect on the profitabil-ity of its members. However, we also find that it was able to stabilise coal prices and powerful enough to ensure that on average, prices were set high enough to avert negative repercussions on company performance.Cartel, Economic history, Event study, Germany pre-1913
THE ECONOMIC AND ENVIRONMENTAL IMPACTS OF INCREASING THE IRISH CARBON TAX. ESRI RESEARCH SERIES NUMBER 79 OCTOBER 2018
This study investigates the economic and environmental impacts of increasing the
current carbon tax in Ireland from C20 per tonne of CO2 to C25, C30, C35 and C40. For
this purpose, an Energy Social Accounting Matrix (ESAM) is developed for Ireland with
33 activities, 39 commodities, and ten household groups based on disposable income.
The ESAM reproduces the structure of the Irish economy including production sectors,
households and the government and quantifies the nature of all existing economic
transactions among the diverse economic agents. Furthermore, the ESAM includes the
flows of energy and emissions, creating a framework that can examine how money as
well as energy and emissions flows between production sectors, households and the
government. In this way the carbon content of different products and different
householdsâ consumption is estimated.
The current carbon tax in Ireland stands at C20 per tonne of carbon and is levied to
incentivise households and producers to reduce their use of carbon-intensive goods. The
carbon tax is relatively low, however, and constitutes just 1.9 per cent of total taxes
levied on commodities in Ireland. Carbon tax accounts for only 7.6 per cent of total
excise duties levied on petrol and 14 per cent of all excise duties on diesel.
Our results reveal that increases in the carbon tax affect the prices of diesel and petrol
the most. A C5 increase will increase the prices of carbon commodities by on average 0.8
per cent, and a doubling of the carbon tax to C40 per tonne of CO2 will increase the
prices of carbon commodities by on average 3.4 per cent. The diesel price is expected
to increase the most due to an increase in the carbon tax, whereby a C25 tax would
result in a 1.7 per cent increase in diesel prices. A C40 tax would result in a 7 per cent
increase in diesel prices. Putting this into context, it can be noted that in 2018 alone
consumers have faced much greater fluctuations in diesel prices. Consumers are
accustomed to relatively large fluctuations in fuel prices and may not react to increases
in prices, assuming prices will fall again. This makes it extremely important to
communicate a clear commitment to an increasing carbon tax by the government
The Impact of Employment Web Sites' Traffic on Unemployment: A Cross Country Comparison
Although employment web sites have recently become the main source for re-
cruitment and selection process, the relation between those sites and unemploy-
ment rates is seldom addressed. Deriving data from 32 countries and 427 web
sites, this study explores the correlation between unemployment rates of
European countries and the attractiveness of country specific employment web
sites. It also compares the changes in unemployment rates and traffic on all
the aforementioned web sites. The results showed that there is a strong
correlation between web sites traffic and unemployment rates.Comment: 9 page
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