Analysis of The Best Strategy for Great Education Policy Using Prescriptive Analytics (Indonesian School Experience)

This paper discusses an analytical prescriptive approach to analyzing education policy. This study uses an appropriate literature review method so that the results of the study are in the form of an overview based on a critical analysis of the subject matter of the benefits of prescriptive analytics in the field of Islamic education management. The main issues discussed in this study include a prescriptive analytics approach as an educational policy analysis approach, a prescriptive approach model in education policy analysis, and the advantages and disadvantages of a prescriptive approach in education policy analysis. This study finds that a project manager must undertake prescriptive analytics today and in the Industry 4.0 era. However, managers have a weakness in skills in this area because the future of data analysis is prescriptive, despite the impact of the obligation to change management. Research advises education managers to realize that prescriptive analytics is not always correct. Prescriptive analytics products can only be applied to one educational institution. However, the domino effect of prescriptive analytics has many benefits for educational institutions, both tangible and intangible

A systematic review on business analytics

Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study explores different perspectives on the definition of business analytics and its relation with business intelligence Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed. Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Web of Science and Scopus. Based on the bibliometric information of the papers selected, we did a bibliometric analysis. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information. Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills. Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.Peer Reviewe

Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future

Embracing digitalization in HR: theory and practice of HR Analytics

New socio-economic reality and abrupt technological advances gave an impetus to the rapid development of the business analytics field. While functions like marketing and finance have been fast to embrace analytics, HR has been lagging behind for a long time. Yet, recently the new phenomenon of HR Analytics has emerged holding a great promise to improve decision-making on people matters, boost productivity and profitability of organizations and elevate the role of the HR function. Despite these impressive benefits, the adoption of the practice by companies has been slow and the topic has not drawn much attention from the research community. However, given all the gains resulting from the use of the innovative practice, HR Analytics represents an important topic to explore, as a better understanding of the practice and the ways it can be applied within an organizational context can enable companies to move forward with their analytics initiatives in HR. Thus, this thesis aims to elevate and amplify knowledge of the HR Analytics phenomenon and its practical application. In particular, it focuses on exploring enablers, moderators, elements and outcomes of the practice to obtain a holistic view on the phenomenon. The multiple-case design is used to study HR Analytics in the context of three case companies. The case companies are represented by large international organizations at different levels of HR Analytics maturity. The study relies on the data collected from multiple sources including exploratory interviews, case interviews, documents derived from the case companies and open sources. The findings of this study indicate that even though the application of HR Analytics is contingent upon the context, within which it is applied, there are general aspects pertaining to the practice that are common across the case companies. The study, for instance, shows that three groups of moderating factors – knowledge, skills, and attitudes of HR professionals, technological and organizational – identified through the literature review affect the application of HR Analytics across all the three cases. These common grounds in HR Analytics application represent a great learning opportunity for researchers and organizations alike. Considering general paucity of knowledge related to the HR Analytics phenomenon, there are many directions, in which the future research on the topic could go. In regard to the present study, the future research can focus on exploring key aspects related to HR Analytics practice in more detail separately or as a group in line with the framework developed within this thesis

Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A Causal Comparative Analysis of Leveraging the Business Analytical Capabilities and the Value and Competitive Advantages of Mid-level Professionals Within Higher Education

The purpose of this quantitative causal-comparative study is an empirical examination of the differences in business intelligence capability and the value and competitive advantage of mid-level higher education academia professionals from community colleges, four-year public, and four-year private institutions within the United States. Institutions of higher education have an overabundant amount of student data that is often inaccessible and underutilized. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Management Information Systems/Decision Support Systems theory, using two-way ANOVA analysis, this research examined factors to understand the mastery of readiness for mid-level professionals in higher education institutions to embrace digital technologies and resources to develop a culture of digital transformation. This study applied the Business Analytics Capability Assessment survey responses from 176 mid-level higher education professionals, from community colleges, four-year private, and four-year public institutions, to understand how higher education professionals use Business Intelligence Analytics (BIA) and Big Data (BD) to improve the organization, operational business decisions, and data management strategies to provide actionable insights. This study found no significance between the type of institution that has business intelligence capability and the value and competitive advantage. A significant difference with a medium effect was identified between the Business Analytics Capability and the Value and Competitive Advantage for mid-level professionals who do and do not utilize BIA and BD resources. Therefore, this study calls for future research to understand how successful institutions have implemented BIA and BD tools and how higher education is shaped on a macro level