A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Big data innovation and diffusion in projects teams: Towards a conflict prevention culture

Despite the enormous literature on how team conflicts can be managed and resolved, this study diverges, by examining factors that facilitate conflict prevention culture in project teams, especially when introducing Big Data Technology. Relying on findings from relevant literatures and focus group discussions, 28 attributes for embedding conflict prevention culture were identified and put together in questionnaire survey. Series of statistical tests including reliability analysis and exploratory factor-analysis. The results identified five critical success factors for entrenching the culture of conflict prevention in project teams introducing big data driving innovations. The five-factor solution include “building effective relationship”, “effective project communications”, “project team efficacy”, “pro-active conflict management approach” and “effectual project documentation”. Result of this study presents a Conceptual framework for effective management of human resource in relation to conflict prevention among project teams, as an effective strategy for facilitating seamless adoption and diffusion of big data innovation in organisations

The Effect of Artificial Intelligence (AI) on Human Capital Management in Indonesia

Human resources in an organization is the capital for the organization and its performance is the main indicator for the organization to achieve its goals. In the current digital era, the shift in human resources is demonstrated by the application of Artificial Intelligence (AI) to organizations in Indonesia, both in companies and in several government organizations. A company has a responsibility to its stakeholders to do the job and get the desired profit. However, no research specifically discusses the analysis of the influence of AI (Machine Learning Algorithm, Deep Learning and  Big Data) on Human Capital Management in Indonesia. This study aims to determine the effect of artificial Intelligence (AI) on Human Capital Management in Indonesia. This sample of this study  is 85 respondents of organizational leaders and human resource managers (HR) in Indonesia. It can be concluded that adoption of Deep Learning and  Big Data has a significant  positive impact on Human Capital Management

Big Data Management Using Scientific Workflows

Humanity is rapidly approaching a new era, where every sphere of activity will be informed by the ever-increasing amount of data. Making use of big data has the potential to improve numerous avenues of human activity, including scientific research, healthcare, energy, education, transportation, environmental science, and urban planning, just to name a few. However, making such progress requires managing terabytes and even petabytes of data, generated by billions of devices, products, and events, often in real time, in different protocols, formats and types. The volume, velocity, and variety of big data, known as the 3 Vs , present formidable challenges, unmet by the traditional data management approaches. Traditionally, many data analyses have been performed using scientific workflows, tools for formalizing and structuring complex computational processes. While scientific workflows have been used extensively in structuring complex scientific data analysis processes, little work has been done to enable scientific workflows to cope with the three big data challenges on the one hand, and to leverage the dynamic resource provisioning capability of cloud computing to analyze big data on the other hand. In this dissertation, to facilitate efficient composition, verification, and execution of distributed large-scale scientific workflows, we first propose a formal approach to scientific workflow verification, including a workflow model, and the notion of a well-typed workflow. Our approach translates a scientific workflow into an equivalent typed lambda expression, and typechecks the workflow. We then propose a typetheoretic approach to the shimming problem in scientific workflows, which occurs when connecting related but incompatible components. We reduce the shimming problem to a runtime coercion problem in the theory of type systems, and propose a fully automated and transparent solution. Our technique algorithmically inserts invisible shims into the workflow specification, thereby resolving the shimming problem for any well-typed workflow. Next, we identify a set of important challenges for running big data workflows in the cloud. We then propose a generic, implementation-independent system architecture that addresses many of these challenges. Finally, we develop a cloud-enabled big data workflow management system, called DATAVIEW, that delivers a specific implementation of our proposed architecture. To further validate our proposed architecture, we conduct a case study in which we design and run a big data workflow from the automotive domain using the Amazon EC2 cloud environment

The Frictionless Data Package : data containerization for addressing big data challenges [poster]

Presented at AGU Ocean Sciences, 11 - 16 February 2018, Portland, ORAt the Biological and Chemical Oceanography Data Management Office (BCO-DMO) Big Data challenges have been steadily increasing. The sizes of data submissions have grown as instrumentation improves. Complex data types can sometimes be stored across different repositories . This signals a paradigm shift where data and information that is meant to be tightly-coupled and has traditionally been stored under the same roof is now distributed across repositories and data stores. For domain-specific repositories like BCO-DMO, a new mechanism for assembling data, metadata and supporting documentation is needed. Traditionally, data repositories have relied on a human's involvement throughout discovery and access workflows. This human could assess fitness for purpose by reading loosely coupled, unstructured information from web pages and documentation. Distributed storage was something that could be communicated in text that a human could read and understand. However, as machines play larger roles in the process of discovery and access of data, distributed resources must be described and packaged in ways that fit into machine automated workflows of discovery and access for assessing fitness for purpose by the end-user. Once machines have recommended a data resource as relevant to an investigator's needs, the data should be easy to integrate into that investigator's toolkits for analysis and visualization. BCO-DMO is exploring the idea of data containerization, or packaging data and related information for easier transport, interpretation, and use. Data containerization reduces not only the friction data repositories experience trying to describe complex data resources, but also for end-users trying to access data with their own toolkits. In researching the landscape of data containerization, the Frictionlessdata Data Package (http://frictionlessdata.io/) provides a number of valuable advantages over similar solutions. This presentation will focus on these advantages and how the Frictionlessdata Data Package addresses a number of real-world use cases faced for data discovery, access, analysis and visualization in the age of Big Data.NSF #1435578, NSF #163971

PENGARUH KEPUASAN KERJA DAN KOMITMEN ORGANISASI TERHADAP TURNOVER INTENTION PADA KARYAWAN PT LIPPO GENERAL INSURANCE JAKARTA

ABSTRACT MUH TEDDY ADITYA P 2016; The Influence of Job Satisfaction and Organizational Commitment On Turnover Intention Employees PT Lippo General Insurance Jakarta. Skripsi: Jakarta, Human Resource Management Concentration, Management Study Program, Department of Management, Faculty of Economics, State University of Jakarta. The purpose of this study were: 1) To know how the overview level of job satisfaction, organizational commitment and turnover intention employees of PT Lippo General Insurance Jakarta. 2) To know the influence of job satisfaction on turnover intention employees of PT Lippo General Insurance Jakarta. 3) To know the influence of organizational commitment on turnover intention employees of PT Lippo General Insurance Jakarta. 4) To know the influence of job satisfaction and organizational commitment on turnover intention employees of PT Lippo General Insurance Jakarta. 5) To find out how big the contribution of job satisfaction and organizational commitment on turnover intention employees of PT Lippo General Insurance Jakarta. The analysis in this research are descriptive analysis and verification analysis. This research conducted on 66 employees of PT Lippo General Insurance Jakarta, while the technique of data collection is done by distributing questionnaires, which are then processed using SPSS 21.0 version. The results showed that: 1) The level of job satisfaction and organizational commitment employee is at a low level, while the turnover intention seems at high level. 2) Job satisfaction has significant and negative influence on turnover intention. 3) Organizational commitment has significant and negative influence on turnover intention. 4) Job satisfaction and organizational commitment significantly influence on turnover intention. Keywords: Job Satisfaction, Organizational Commitment, Turnover Intentio

Algebraic Structures of Neutrosophic Triplets, Neutrosophic Duplets, or Neutrosophic Multisets

Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the neutrosophic triplets were founded, which have a similar form (x, neut(x), anti(x)), that satisfy several axioms, for each element x in a given set.This collective book presents original research papers by many neutrosophic researchers from around the world, that report on the state-of-the-art and recent advancements of neutrosophic triplets, neutrosophic duplets, neutrosophic multisets and their algebraic structures – that have been defined recently in 2016 but have gained interest from world researchers. Connections between classical algebraic structures and neutrosophic triplet / duplet / multiset structures are also studied. And numerous neutrosophic applications in various fields, such as: multi-criteria decision making, image segmentation, medical diagnosis, fault diagnosis, clustering data, neutrosophic probability, human resource management, strategic planning, forecasting model, multi-granulation, supplier selection problems, typhoon disaster evaluation, skin lesson detection, mining algorithm for big data analysis, etc

e-HRM in a Cloud Environment Implementation and its Adoption: A Literature Review

[EN] As the digitization of HR processes in companies continues to increase, at the same time, the underlying technical basis is also developing at a rapid pace. Electronic human resources (e-HRM) solutions are used to map a variety of HR processes. However, the introduction of such systems has various consequences, which are not only technical but also imply organizational and functional changes within the organization. Additionally, the cloud environment contributes to enhancing e-HRM capabilities and introduces new factors in its adoption. A systematic review of the available literature on the different dimensions of electronic resources management was conducted to assess the current state of research in this field. This review includes topics such as the evolution of e-HRM, its practical application, use of technology, implementation as well as HR analytics. 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Bibliometric review on human resources management and big data analytics

Purpose; This study aims to provide an in-depth understanding of big data analytics (BDA) in human resource management (HRM). The emergence of digital technology and the availability of large volume, high velocity and a great variety of data has forced the HRM to adopt the BDA in managing the workforce. Design/methodology/approach; This paper evaluates the past, present and future trends of HRM through the bibliometric analysis of citation, co-citation and co-word analysis. Findings; Findings from the analysis present significant research clusters that imply the knowledge structure and mapping of research streams in HRM. Challenges in BDA application and firm performances appear in all three bibliometric analyses, indicating this subject’s past, current and future trends in HRM. Practical implications; Implications on the HRM landscape include fostering a data-driven culture in the workplace to reap the potential benefits of BDA. Firms must strategically adapt BDA as a change management initiative to transform the traditional way of managing the workforce toward adapting BDA as analytical tool in HRM decision-making. Originality/value; This study presents past, present and future trends in BDA knowledge structure in human resources management

Penguatan Strategi Pemasaran UMKM di masa Pandemi Covid-19: Studi Kasus Pelaku Usaha Tenun di Kabupaten Kulon Progo

The national economy experienced a slump at the beginning of 2020. The Covid-19 pandemic has reduced economic activity which has an impact on a decline in economic growth. SMEs (Small and Medium Enterprises) as the backbone of the national economy during the 2018 monetary crisis experienced a sharp decline in growth due to the Covid-19 pandemic. The revival of SMEs has a big role in the revival of the national economy. This study aims to analyze the right marketing strategy for woven fabric SMEs in Kulon Progo Regency during the Covid-19 pandemic. This study uses qualitative descriptive analysis of 32 business actors who were selected as informants with certain criteria. The type of data collected is primary data. The data analysis method used in this study is SWOT which consists of Strengths, Weaknesses, Opportunities, and Threats. The results of the analysis show that the most appropriate marketing strategy for woven fabric SMEs in Kulon Progo Regency during the Covid-19 pandemic is a turn-around strategy through increasing human resource capabilities in the use of information technology media; increasing digital marketing through the use of social media such as WhatsApp, Facebook, Instagram, Twitter and website in promoting products; utilizing and improving communication links to form partnerships between business actors in various fields; and improving management for business units