44,530 research outputs found

    Cross-cultural Knowledge Management

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    The success of international companies in providing high quality products and outstanding services is subject, on the one hand, to the increasing dynamic of the economic environment and on the other hand to the adoption of worldwide quality standards and procedures. As market place is becoming more and more global, products and services offered worldwide by international companies must face the multi-cultural environment challenges. These challenges manifest themselves not only at customer relationship level but also deep inside companies, at employee level. Important support in facing all these challenges has been provided at cognitive level by management system models and at technological level by information cutting edge technologies Business Intelligence & Knowledge Management Business Intelligence is already delivering its promised outcomes at internal business environment and, with the explosive deployment of public data bases, expand its analytical power at national, regional and international level. Quantitative measures of economic environment, wherever available, may be captured and integrated in companies’ routine analysis. As for qualitative data, some effort is still to be done in order to integrate measures of social, political, legal, natural and technological environment in companies’ strategic analysis. An increased difficulty is found in treating cultural differences, common knowledge making the most hidden part of any foreign environment. Managing cultural knowledge is crucial to success in cultivating and maintaining long-term business relationships in multicultural environments. Knowledge Management provides the long needed technological support for cross-cultural management in the tedious task of improving knowledge sharing in multi-national companies and using knowledge effectively in international joint ventures. The paper is approaching the conceptual frameworks of knowledge management and proposes an unified model of knowledge oriented enterprise and a structural model of a global knowledge management system.Global Business, Intercultural Competencies, Business Intelligence, Multicultural Knowledge Management, Business Knowledge Frameworks, Knowledge Capital

    Global Solutions vs. Local Solutions for the AI Safety Problem

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    There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progres

    Ethical Implications of Predictive Risk Intelligence

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    open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

    Computational entrepreneurship: from economic complexities to interdisciplinary research

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    The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new academic discipline of computational entrepreneurship, which centers on: (i) an exponentially growing (and less expensive) computing power, to the extent that almost everybody in a modern society can own and use that; (ii) omnipresent high-speed Internet connectivity, wired or wireless, representing our modern day’s economic connectomics; (iii) growing concern of exploiting “serendipity” for a strategic commercial advantage; and (iv) growing capabilities of lay people in performing calculations for their informed decisions in taking fast-moving entrepreneurial opportunities. Computational entrepreneurship has slowly become a new mode of operation for business ventures and will likely bring the academic discipline of entrepreneurship back to mainstream economics

    New Trends in Development of Services in the Modern Economy

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    The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks. The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living. However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors. Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment

    Trading Data for Discounts: An Exploration of Unstructured Data Through Machine Learning in Wearable Technology

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    The development of computing sensor devices with the capability of tracking an individual’s activity changed the way we live and move. The data collected and generated from wearable technology provides implications to the user for leading a healthy, more active lifestyle; however, the potential data uses extend beyond the user. Significant opportunity exists in the insurance industry as it relates to discounting premiums. The purpose of this research was to provide insight as to whether insurance companies should consider offering discount on premiums for policyholders who use wearable technology to track their personal fitness by identifying and suggesting potential groups of consumers to target these discounts toward. Using the platform R, researchers collected and analyzed tweets about four leading wearable technology companies including Fitbit, Jawbone, Misfit, and Withings. Both unsupervised and supervised learning techniques were pursued during the study in the form of topic modeling and artificial intelligence. Through detailed analysis, researchers determined that companies may want to consider reducing premiums for wearable technology users who use the devices for weight loss, as it would benefit both policyholders and insurance companies

    Platform Business Models – A Case Study of the Technology Industry

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    The opportunity to create a value exchange environment is uniquely offered by the platform businesses. A novel approach of co-creating value is writing the rules in the platform business world. This paper analyzes the platform business models within the technology industry based on a multiple case study. As the main driver of business performance in this environment is technology, companies are using it to develop new products or to provide technology as a service. Thus, the main objective is to debate on the actual business needs in terms of business model innovation and to investigate how platform business models are developed through strategic acquisitions to achieve competitive advantage. The cases analysis suggests that technology acquisitions made around the core business may contribute to business model innovation. In addition, new partnerships with the external environment may facilitate mutual value creation exchanges and the platform may evolve through adding extra features from its external partners. We contribute to the advancement of business model research by putting platform business model study into the competitive context of the technology industry, with findings on how platforms are used in the digital era to innovate the core business model. From a practitioner’s perspective, this study may help companies to understand the importance of investing in other technology companies and to identify the opportunities of business model innovation through strategic partnerships. The limitation of this study is that the main data used for the multiple case study was derived from secondary sources and it provided insights about each company’s platform business model from a macro perspective
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