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Navigating the Learning Landscape: Social Cognition and Task-Technology Fit as Predictors for MOOCs Continuance Intention by Sales Professionals.

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Title: Navigating the Learning Landscape: Social Cognition and Task-Technology Fit as Predictors for MOOCs Continuance Intention by Sales Professionals.
Authors: Kamble, Aakash1, Upadhyay, Nitin2, Abhang, Nayna3
Source: International Review of Research in Open & Distributed Learning. Feb2024, Vol. 25 Issue 1, p24-44. 21p.
Subject Terms: *Massive open online courses, *Autodidacticism, *Self-managed learning (Personnel management), Social perception, Social cognitive theory
Abstract: Massive open online courses (MOOCs) have gained popularity among sales professionals who use them for self-directed learning and upskilling. However, research related to their intentions to continue learning is scarce. Drawing from the social cognition theory, this research aimed to address this gap by investigating the role of task-technology fit, self-development, and social recognition in sales professionals' continued use of MOOCs. The study hinged on empirical research and used a survey to collect data from 366 sales professionals. The results suggest that task-technology fit, self-development, and social recognition play a significant role in sales professionals' continued use of MOOCs. The study has practical implications for organizations promoting employee learning and development. The findings provide valuable information for MOOC designers and providers to develop more effective courses that meet the needs of sales professionals. [ABSTRACT FROM AUTHOR]
Copyright of International Review of Research in Open & Distributed Learning is the property of Governors of Athabasca University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22International+Review+of+Research+in+Open+%26+Distributed+Learning%22">International Review of Research in Open & Distributed Learning</searchLink>. Feb2024, Vol. 25 Issue 1, p24-44. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Massive+open+online+courses%22">Massive open online courses</searchLink><br />*<searchLink fieldCode="DE" term="%22Autodidacticism%22">Autodidacticism</searchLink><br />*<searchLink fieldCode="DE" term="%22Self-managed+learning+%28Personnel+management%29%22">Self-managed learning (Personnel management)</searchLink><br /><searchLink fieldCode="DE" term="%22Social+perception%22">Social perception</searchLink><br /><searchLink fieldCode="DE" term="%22Social+cognitive+theory%22">Social cognitive theory</searchLink>
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  Data: Massive open online courses (MOOCs) have gained popularity among sales professionals who use them for self-directed learning and upskilling. However, research related to their intentions to continue learning is scarce. Drawing from the social cognition theory, this research aimed to address this gap by investigating the role of task-technology fit, self-development, and social recognition in sales professionals' continued use of MOOCs. The study hinged on empirical research and used a survey to collect data from 366 sales professionals. The results suggest that task-technology fit, self-development, and social recognition play a significant role in sales professionals' continued use of MOOCs. The study has practical implications for organizations promoting employee learning and development. The findings provide valuable information for MOOC designers and providers to develop more effective courses that meet the needs of sales professionals. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Review of Research in Open & Distributed Learning is the property of Governors of Athabasca University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Feb2024
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