Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

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 households’ 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

Mining social network data for personalisation and privacy concerns: A case study of Facebook’s Beacon

This is the post-print version of the final published paper that is available from the link below.The popular success of online social networking sites (SNS) such as Facebook is a hugely tempting resource of data mining for businesses engaged in personalised marketing. The use of personal information, willingly shared between online friends' networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. However, the use of SNS data for personalised marketing has provoked outrage amongst SNS users and radically highlighted the issue of privacy concern. This paper inverts the traditional approach to personalisation by conceptualising the limits of data mining in social networks using privacy concern as the guide. A qualitative investigation of 95 blogs containing 568 comments was collected during the failed launch of Beacon, a third party marketing initiative by Facebook. Thematic analysis resulted in the development of taxonomy of privacy concerns which offers a concrete means for online businesses to better understand SNS business landscape - especially with regard to the limits of the use and acceptance of personalised marketing in social networks