Debolina Halder Adhya, Eesa M. Al Bastaki, Sara Suleymanova, Nasiruddeen Muhammad and Arunprasad Purushothaman
The COVID-19 pandemic has compelled higher education institutions (HEI) in the United Arab Emirates (UAE) and globally to shift to a new pedagogy that is sustainable and resilient…
Abstract
Purpose
The COVID-19 pandemic has compelled higher education institutions (HEI) in the United Arab Emirates (UAE) and globally to shift to a new pedagogy that is sustainable and resilient to crises and disruptions. It necessitated the integration of technologies as part of pedagogical innovation and modification of higher education practices – advancing toward a more holistic integration of physical and digital tools and methods to enable more flexible, creative, collaborative and participatory learning. In terms of pedagogy, an open approach to learning is essential, combining in-person teaching with technological tools and online learning.
Design/methodology/approach
This paper examines theoretical and empirical literature to define the potential benefits of utilizing open educational practices (OEP) in higher education, including better access, furthering equity and enhancing teaching, learning and assessment.
Findings
It proposes a comprehensive framework built on a continuum of open pedagogy (OP) that comprises “Emphasis”, “Essentials” and “Evolution”. Based on this framework, a set of recommendations for using OEP for successful knowledge building is provided.
Originality/value
The research determined the significance of increased OEP involvement for sustainable learning possibilities and the UAE’s initiatives in developing educators to support innovative pedagogies and technology-enabled teaching-learning standards. The study suggests placing more emphasis on faculty and student scaffolding while using OP for better learning experiences and outcomes, as well as more institutional support and the need for policy development to transform the UAE into a global hub for sustainable education.
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Keywords
– The purpose of this paper is to identify and measure the organizational learning outcomes in a sample of knowledge-intensive firms like software companies.
Abstract
Purpose
The purpose of this paper is to identify and measure the organizational learning outcomes in a sample of knowledge-intensive firms like software companies.
Design/methodology/approach
The sample chosen for this study comprised software professionals; the software companies were chosen based on the listing in the National Association of Software and Services Companies annual report with financial turnover as a base for classification. The learning outcomes discussed in this study are grounded on the dimensions of the building blocks of learning organization, which are classified as learning dynamics, organization transformation, knowledge management, people empowerment and technology application.
Findings
Statistical analysis revealed significant differences in learning outcomes based on the organization’s age. The organizational learning orientation for medium- and very large-scale companies are on the higher side with reduced organization hierarchy; better technology-based learning; structured knowledge management practices; learning-centric talent acquisition, talent management and total rewards. Small-scale companies fared well in organization transformation dimension and large-scale companies constantly nurture the congenial learning environment.
Practical implications
The tool can help knowledge-intensive firms to analyze the extent to which organizational practices aligned with learning initiatives are visibly seen in terms of learning outcomes. Thus, the learning culture can be articulated and associated with the growing needs of an organization.
Originality/value
Organizational learning initiatives can be enhanced and reinforced through customized organization practices by observing the measures of learning dimensions.
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Keywords
Mahesh Babu Purushothaman, Jeff Seadon and Dave Moore
This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.
Abstract
Purpose
This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.
Design/methodology/approach
A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships.
Findings
The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days.
Research limitations/implications
The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations.
Practical implications
Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes.
Originality/value
To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.