2,545 research outputs found
Ontology Driven Web Extraction from Semi-structured and Unstructured Data for B2B Market Analysis
The Market Blended Insight project1 has the objective of improving the UK business to business marketing performance using the semantic web technologies. In this project, we are implementing an ontology driven web extraction and translation framework to supplement our backend triple store of UK companies, people and geographical information. It deals with both the semi-structured data and the unstructured text on the web, to annotate and then translate the extracted data according to the backend schema
FEASIBILITY OF B2C CUSTOMER RELATIONSHIP ANALYTICS IN THE B2B INDUSTRIAL CONTEXT
Abstract The purpose of the paper is to evaluate the feasibility of business-to-consumer (B2C) customer relationship analytics in the industrial business-to-business (B2B) context, in particular spare part sales. The contribution of the paper is twofold; the article identifies analytics approaches with value potential for B2B decision-making, and illustrates their value in use. The identified analytics approaches, customer segmentation, market basket analysis and target customer selection, are common in the B2C marketing and e-commerce. However, in the industrial B2B marketing, the application of these approaches is not yet common.. The different kinds of analytics under examination in this paper use machine learning (ML) techniques. The examination takes into account the applicability and usefulness of the techniques as well as implementation challenges. The research suggests that the identified analytics may serve different business purposes and may be relatively straightforward to implement. This requires careful examination of the desired purposes of use in a particular business context. However, the continuous and real-time use of such analyses remains a challenge for further examination also in information systems research. Keywords: Business analytics, B2B decision-making, Machine learning, Data mining, Artificial intelligence, CR
Semantic business process management: a vision towards using semantic web services for business process management
Business process management (BPM) is the approach to manage the execution of IT-supported business operations from a business expert's view rather than from a technical perspective. However, the degree of mechanization in BPM is still very limited, creating inertia in the necessary evolution and dynamics of business processes, and BPM does not provide a truly unified view on the process space of an organization. We trace back the problem of mechanization of BPM to an ontological one, i.e. the lack of machine-accessible semantics, and argue that the modeling constructs of semantic Web services frameworks, especially WSMO, are a natural fit to creating such a representation. As a consequence, we propose to combine SWS and BPM and create one consolidated technology, which we call semantic business process management (SBPM
4th Industry Revolution Digital Marketing Adoption Challenges in SMEs and its Effect on Customer Responsiveness
4th industrial revolution of cyber-physical technologies (4IR) intersecting digital technologies and entrepreneurship serves as an external stimulus in fostering a new method of venture creation transforming customersâ purchasing and consuming behavior. Large corporations are leading in leveraging on 4IR digital marketing for their marketing strategy but SMEs are lacking behind. This study explores the conundrum using an exploratory sequential mixed method. Semi-structured interviews were carried out on a sample selected using non-probability purposive sampling and determined through attaining thematic saturation of discursive patterns. Scale development for quantitative instruments performed using SPSS statistical software. A quantitative study was carried out on a sample size of 153 SME respondents. Analysis was undertaken by paired-sample T-test. Kuskal-Wallis H test and Spearmanâs rho correlation test due to the nonparametric nature of data distribution. The outcome reveals that although SMEs are increasingly reliant on DM for their marketing strategy, most of these SMEs are only willing to invest in building low-level DM capability citing a lack of financial budget, inadequate technology infrastructure to support such setup, cyber security issues and lack of DM knowledge. Financial budget and technology infrastructure are considered the most critical concerns by SMEs with low and moderate DM adoption. However, these concerns are less pronounced in SMEs with high DM adoption. Finally, the weak but significant correlation between SMEsâ DM adoption and customer responsiveness infers the significant role of 4IR technology as an enabler of digital marketing strategy that also depends on other critical contributing factors such as price and qualit
The need to use Data Mining techniques in E-business
The number of Internet users rose from 400 million in 2000 to just over 2 billion in early 2011. This means that approximately one third of the world's population uses the internet. Taking these conditions into consideration, the way how businesses are designed need to be changed Many companies that, over the last century could not even dream that could have a certain volume of activity or they could face competition with industry giants, have succeeded in giving to enjoy great success. For example: Amazon.com, founded in 1995, had in 1999 a turnover of at least 13 times higher than other prestigious names in the U.S., such as Barnes & Noble and Borders Books & Music. E-business is the key to make life easier for the people. Knowledge of e-business environment is essential for doing business in this century. More must be understood and new technologies applied to extract knowledge from data.data mining, clustering, regresion, asociation rule, e-business
Personal data broker instead of blockchain for studentsâ data privacy assurance
Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is
showcased in recent cases seen in media of bad use of studentsâ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Peer ReviewedPostprint (author's final draft
Interdependencies and Collaborative Action for Platform Leadership: A Comparative Analysis of Two Leading Chinese Multi-Sided Digital Platforms
Asia continues to lead e-commerce growth worldwide, with multi-sided platforms like Alibaba.com, 360buy.com and Taobao.com leading the race. Despite their rising prominence, few studies articulate how these multi-sided platforms in Asia service and collaborate with it sides. It is important to learn how platforms encounter and adapt to changing suppliers-platform-customers interactions, to better understand the implications of e-commerce necessary to compete and lead in this digitally enabled landscape. Furthermore, scholars suggest research to discern between modernization of Asia and Westernization. To close these gaps, the authors conduct a case study of two of Chinaâs leading multi-sided digital platformsâA.com and M.com. The researchers cross-examine the development of the two firms since their establishing, focusing on collaborative strategies with their sides and within their business units, through interdependencies and collective action conceptual perspectives. The contributions of this paper are two-fold. Firstly, we introduce a framework that identifies four types (I-IV) of multi-sided platform collaborations. This framework prescribes guidelines to identify and manage different types of collaborative action for strategic planning and operations between platform partners. Secondly, we consolidate four lessons learnt from our dataâteach, consolidate, co-opete and ultimately leadâa set of actionable guidelines for platform leadership in the marketplace
Link Before You Share: Managing Privacy Policies through Blockchain
With the advent of numerous online content providers, utilities and
applications, each with their own specific version of privacy policies and its
associated overhead, it is becoming increasingly difficult for concerned users
to manage and track the confidential information that they share with the
providers. Users consent to providers to gather and share their Personally
Identifiable Information (PII). We have developed a novel framework to
automatically track details about how a users' PII data is stored, used and
shared by the provider. We have integrated our Data Privacy ontology with the
properties of blockchain, to develop an automated access control and audit
mechanism that enforces users' data privacy policies when sharing their data
across third parties. We have also validated this framework by implementing a
working system LinkShare. In this paper, we describe our framework on detail
along with the LinkShare system. Our approach can be adopted by Big Data users
to automatically apply their privacy policy on data operations and track the
flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on
Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE
International Conference on Big Data (IEEE BigData 2017) December 14, 2017,
Boston, MA, US
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