Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

Media-Based Sentiment Indices as an Alternative Measure of Consumer Confidence

In this paper, we consider the feasibility of constructing online sentiment indices, using large amounts of media data, as an alternative to the conventional survey method used to create the consumer confidence index in South Africa. A clustering framework is adopted to provide an indication of possible candidate sentiment indices constructed from a combination of different text sources and dictionaries that best mimic the traditional survey-based consumer confidence index from the South African Bureau for Economic Research (BER). The results conclude that it is possible to create an index using sentiment analysis using online editorial data that does resemble the BER’s consumer confidence index. The different media-based sentiment indices (MSI) show a significant level of correlation and co-movement with the BER’s CCI. Impulse responses and cross-correlation functions indicate that the MSI could potentially lead the survey-based method up to two quarters. Furthermore, Granger-causality tests show that the media-based indices are good predictors of future consumer confidence index values. The results provide motivation for further study on the use of sentiment-based techniques and online media data sources to track consumer confidence within an emerging market such as South Africa.     

Does corruption slow down innovation? Evidence from a cointegrated panel of U.S. states

I investigate the long-run relationship between corruption and innovative activity using annual data from 48 contiguous U.S. states between 1977 and 2006. Using U.S. data allows me to work with a panel long enough to exploit time series properties of the data. I use two different measures of innovative activity: one measuring the quantity and the other measuring the quality of the patents granted. I also use two different measures of corruption: one based on the number of corruption convictions, the other based on number of corruption stories covered in Associated Press news wires. Following Pedroni (1999, 2000), I estimate the cointegrating relationship between corruption and innovative activity with Fully Modified Ordinary Least Squares (FMOLS). The results indicate that corruption indeed slows down innovation in the long-run.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

In Data We Trust? An Analysis of Indonesian Socioeconomic Survey Data

What if a popular dataset that has generated a large amount of literature has been misunderstood and has led to misleading inferences? This paper examines household expenditure data from the Indonesian National Socio-economic Survey (Susenas), which started more than 50 years ago. Appropriate use of Susenas data for policy analysis requires an understanding that the survey’s expenditure variable does not measure true out-of-pocket expenses, because it includes subsidies received by households when obtaining goods and services. We also highlight an abrupt change in the survey instrument that occurred in 2015, when the reference period for certain items was extended. For health items, this generated a change in the expenditure series that can be misinterpreted as being the result of a social health insurance reform introduced in 2014 to lower the health care burden on households. Accordingly, we propose a way to account for this artificial expenditure movement in Susenas.     

Determinants of total efficiency in water utilities in Mexico

By using panel data analysis across 368 water utilities in Mexico over the period 2010–2014, we find that small firms are more efficient than medium and large providers and that increasing production is associated with less efficiency. Billing, income collection and profitability are relevant factors to improve efficiency. Medium and large firms require to measure consumption by service with meters in order to increase efficiency, while small firms do not require it.     

Intraday Lead-Lag Relationships Between the Futures-, Options and Stock Market

In rational, efficiently functioning and complete markets, returns on derivative and underlying securities should be perfectly contemporaneously correlated. Due to market imperfections, one of these markets may reflect information faster. The use of high-frequency data and the choice for a small unit time interval to measure these lead-lag relations comes at the cost of some or many missing observations, causing traditional estimators to either under- or overestimate covariances and correlations. We use a new estimator to estimate lead-lag relationships between the cash AEX index, options and futures. We find that futures returns lead both options and cash index returns by approximately 10 minutes. The relationship between options and the cash market is not completely unidirectional.     

Targeting Conservation Investments in Heterogeneous Landscapes: A Distance-Function Approach and Application to Watershed Management

To achieve an environmental objective at least cost, decision makers must integrate information about spatially variable biophysical and economic conditions. Although the biophysical attributes that contribute to an environmental outcome are often known, the way in which these attributes interact to produce the outcome is often unknown. We introduce a nonparametric conservation targeting approach that relies on distance functions to cost-efficiently allocate conservation funds. We apply the approach empirically to the case of conservation contracting for water-quality objectives. The contract portfolios derived from the application have many desirable properties, including intuitive appeal and robust performance across plausible parametric scoring measures.