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Purpose
Mobile crowd sensing (MCS) is a new paradigm enabled by Internet of Things (IoT) in which sensor-rich ubiquitous devices collect and share the data over a large geography. Human behaviour attributes (perception, comprehension and projection) play a key role in the decision-making process for sharing and processing the data. This study aims to understand how situation awareness plays an important role in MCS in an IoT ecosystem.
Design/methodology/approach
A systematic literature review was conducted by following a rigorous search protocol that identified a total of 470 peer-reviewed research papers. These papers were further filtered and finally 31 relevant papers were selected.
Findings
The major issues and concerns arising due to human participation in the MCS system were identified. Further, probable strategies were explored to deal with the challenges resulting due to certain human behaviour attributes.
Practical implications
This study provides the recommendations to address the major challenges related to the MCS system, which in turn may enhance the adoption of emerging smart technology-driven services.
Originality/value
The study is original and is based on the existing literature and its interpretation.
The purpose of this paper is to investigate how the Kirkpatrick Learning Evaluation Model (1950) can be augmented to make it more credible and successful evaluation parameter in the changing times. Since the advent of Information Technology industry the rigid structures of organizations are replaced by the Flat/Matrix structures, removing the bars of time and place. This paper is an attempt to include three gaps identified in the Kirkpatrick Model, Training motivation, Organization citizenship behaviour and the Assessment of both the individual and the Organization simultaneously. Through co-relation and regression analysis these gaps were tested on the data obtained from 461 employees. The data support the various relationships to be included in Kirkpatrick Model and it identifies that for an effective training program it's essential to perform both pre-training and post-training analysis using the four parameters of Kirkpatrick Model viz. Reaction (changed to Motivation), Learning, Behaviour (or Performance) and Results.
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