What can AI do for you?

Simply put, most organizations do not know how to approach the incorporation of AI into their businesses, and few are knowledgeable enough to understand which concepts are applicable to their business models. Doing nothing and waiting is not an option: Mahidar and Davenport (2018) argue that companies that try to play catch-up will ultimately lose to those who invested and began learning early. But how do we bridge the gap between skepticism and adoption? We propose a toolkit, inclusive of people, processes, and technologies, to help companies with discovery and readiness to start their AI journey. Our toolkit will deliver specific and actionable answers to the operative question: What can AI do for you

Knowledge Organization Research in the last two decades: 1988-2008

We apply an automatic topic mapping system to records of publications in knowledge organization published between 1988-2008. The data was collected from journals publishing articles in the KO field from Web of Science database (WoS). The results showed that while topics in the first decade (1988-1997) were more traditional, the second decade (1998-2008) was marked by a more technological orientation and by the appearance of more specialized topics driven by the pervasiveness of the Web environment

HR Shared Services (HRSS): Model and Trends

[Excerpt] The findings of this research project are based on interviews with 44 Human Resources (HR) leaders across 39 national and international companies within 15 industries ranging from manufacturing to consulting services. The interviews ranged from 45 minutes to one hour, and sought to understand models, best practices, and trends. The interview included questions about employee experience, technology, and the integration between HR Shared Services (HRSS) and the overall HR Organization. To provide background information and data, the HR leaders answered a short survey, giving details about the structure of their HRSS, locations, areas of HR that had work performed in the shared services organization, systems, and technology capabilities

The offshore services value chain : developing countries and the crisis

This paper analyzes the recent evolution and impact of the global economic crisis on the offshore services industry. Using a global value chains framework, the authors classify the offshore services sector in a comprehensive set of general and industry-specific activities that correspond to different segments and stages in the value-adding process for services. Through an analysis of the impact of the economic crisis on the industry, a small decline in demand was found; however this did not cause any structural changes in the market. The crisis has created two opposing effects: general contraction of demand by existing customers due to the recession; and, at the same time, a substitution effect by which new services are being moved from developed countries to emerging economies in search of cost reduction. The paper concludes that the offshore services industry will continue to offer growth opportunities for developing countries not only among existing market players, but also a range of new countries. The industry has the potential to become an important source for employment and economic growth around the globe.ICT Policy and Strategies,E-Business,Water and Industry,Housing&Human Habitats,Public Sector Corruption&Anticorruption Measures

Recruiter Perceptions of Applicant Fit: Commonalities and Differences

To date, normative selection models have focused primarily on matching individual knowledges, skills, and abilities to job requirements. However, it has increasingly been argued that people should also be selected for fit to broader organizational characteristics such as strategy, culture and values. Despite the apparent reasonableness of these claims, there has been little research on how employers actually go about the task of screening or selecting for broader organizational fit. Accordingly, the present study examined how organizational recruiters assess applicant fit. Fifty-four campus recruiters in four colleges provided examples of best-fitting and worst-fitting applicants from just-completed interview schedules, along with specific descriptions of what it was that made each applicant fit or not fit . Examination of interview transcripts suggested that despite the recent emphasis on unique organizational values, strategies, or cultures in discussions of fit, by far the most frequently-mentioned determinants of fit were either (1) job-related coursework or experience, or (2) generally (rather than uniquely) desirable personal characteristics such as articulateness, positive personal appearance, and good general communication skills. However, some systematic differences were detected in the extent to which particular characteristics were sought by recruiters in different colleges or by those recruiting for different types of vacancies. Findings are related to previous research, and implications for applicants, employers, and future researchers are offered

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

Extending the production dice game.

The production dice game is a powerful learning exercise focusing on the impact of variability and dependency on throughput and work-in-process inventory of flow lines. In this paper we will extend the basic dice game along the following lines. First, we allow that the operations take place concurrently as opposed to the more traditional way of playing the game sequentially. Second, we allow both starvation and blocking of the line. Third, we consider balanced lines with work stations characterized by different degrees of variability. Fourth, we use different sets of dice in order to represent a wide range of coefficients of variation of the production line. The game can be played manually in a classroom setting, but it is also modelled as an easy-to-use simulation tool.