Search results

1 – 3 of 3
To view the access options for this content please click here
Article

Abbas J. Ali and Dietrich L. Schaupp

Investigates managerial values as predictors of managerial decisionstyles. A multiple regression analysis indicated that existential valuesrelated positively to a…

Abstract

Investigates managerial values as predictors of managerial decision styles. A multiple regression analysis indicated that existential values related positively to a consultative decision‐making style while tribalistic values related significantly to a pseudo‐consultative decision‐making style. Furthermore, identifies value dimensions attributed to each decision‐making style.

Details

International Journal of Manpower, vol. 13 no. 3
Type: Research Article
ISSN: 0143-7720

Keywords

To view the access options for this content please click here
Article

John R. Farnsworth and Brian H. Kleiner

Concentrates on ethics, leadership and responsibility in the business world in the USA today. Looks at how distrust in the workplace has increased for workers, which…

Abstract

Concentrates on ethics, leadership and responsibility in the business world in the USA today. Looks at how distrust in the workplace has increased for workers, which breeds dissatisfaction. Comments on ethics courses and pinpoints some of them and the universities involved. Employs tables to aid in explanation and methods. Sums up that educational institutions cannot accomplish the mission alone and students can have a two‐way experience with regard to experiences prior to enrolment.

Details

Management Research News, vol. 26 no. 2/3/4
Type: Research Article
ISSN: 0140-9174

Keywords

To view the access options for this content please click here
Article

Soraya Sedkaoui

The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently…

Abstract

Purpose

The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in the dynamic processes. Working with big data and applying a series of data analysis techniques require strong multidisciplinary skills and knowledge of statistics, econometrics, computer science, data mining, law and business ethics, etc. Higher education institutions (HEIs) are concerned by this phenomenon which is also changing learning needs and require a reorientation toward the development of novel approaches and advancements in their programs. The purpose of this paper is to introduce and define big data analytics as having an immense potential for generating value for businesses. In this context, one prominent strategy is to integrate big data analytics in educational programs to enrich student’ understanding of the role of big data, especially those who want to explore their entrepreneurial ways and improve their effectiveness. So, the main purpose of this article consists, on the one hand, in why HEIs must carefully think about how to provide powerful learning tools and open a new entrepreneurship area in this field, and, why, on the other hand, future entrepreneurs (students) have to use data analytics and how they can integrate, operationally, analytics methods to extract value and enhance their professional capabilities.

Design/methodology/approach

The author has established an expert viewpoint to discuss the notion of data analytics, identify new ways and re-think what really is new, for both entrepreneurs and HEIs, in the area of big data. This study provides insights into how students can improve their skills and develop new business models through the use of IT tools and by providing the ability to analyze data. This can be possible by bringing the tool of analytics into the higher educational learning system. New analytics methods have to help find new ways to process data and make more intelligent decisions. A brief overview of data analytics and its roles in the context of entrepreneurship and the rise of data entrepreneur is then presented. The paper also outlines the role of educational programs in helping address big data challenges and transform possibilities into opportunities. The key factors of implementing an efficient big data analytics in learning programs, to better orientate and guide students’ project idea, are also explored. The paper concludes with suggestions for further research and limitations of the study.

Findings

The findings in this paper suggest that analytics can be of crucial importance for student entrepreneurial practice if correctly aligned with their business processes and learning needs and can also lead to significant improvement in their performance and quality of the decisions they make. The added value of big data is the ability to identify useful data and turn it into usable information by identifying patterns and exploiting new algorithms, tools and new project solutions. So, the move toward the introduction of big data and analytics tools in higher education addresses how this new opportunity can be operationalized.

Research limitations/implications

There are some limitations to this research paper. The research findings have significant implications for HEIs in the field of analytics (mathematics and computer science), and thus, it is not generalizable with any further context. Also, the viewpoint is centered on the data analytics process as a value generator for entrepreneurial opportunities.

Originality/value

This research can be considered as a contribution with literature about educational quality, entrepreneurship and big data analytics. This study describes that new analytics thinking and computational skills are needed for the newer generation of entrepreneurs to handle the challenges of big data. But, preparing them to capture, analyze, store and manage the large amounts of data available today – so they can see value in data – is not just about implementing and using new technologies. This is also, about, a dynamic, operational and modern educational learning process from which a student can extract the maximum benefit. In another words: How to make new opportunities from these data? Which data to select for the analysis? and How to efficiently apply analytical techniques to generate value?

Details

International Journal of Innovation Science, vol. 10 no. 2
Type: Research Article
ISSN: 1757-2223

Keywords

1 – 3 of 3