Causative effects of motivation to transfer learning among relational dyads: the test of a model

• Brian Matthews ^[1] ; Jamie Daigle ^[1] ; Joy Cooper ^[1]
  1. [1] Texas A&M University

     Texas A&M University

     Estados Unidos

     
• Localización: European journal of management and business economics, ISSN-e 2444-8494, ISSN 2444-8451, Vol. 29, Nº. 3 (https://www.emerald.com/insight/publication/issn/2444-8494/vol/29/iss/3), 2020, págs. 297-314
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Purpose – The purpose of this study is to validate multiplicative cycle that exists between the job readiness and satisfaction model explored by Matthews et al. (2018), the satisfaction and performance paradigmatic nuances analyzed by Judge et al. (2001) and Gu and Chi (2009), in addition to the expectancy model theorized by Vroom (1964). The motivation to transfer learning serves as a conveyable variable transmitted within a learning continuum that sustains cyclical outputs.

    Design/methodology/approach – An archetype to explore the connection between the three hypothesized theories is created through a neural network program. Exploring this connection develops deeper understandings of the derivatives of employee motivation as it pertains to its effect on readiness, satisfaction, performance and achievement dyads. A detailed analysis of the literature leads to the hypothesis that the motivation to transfer learning creates a multiplicative effect among hypothesized relationships.

    Findings – The neural network program scaffolds the proposed general belief that positive effects of transfer motives cause a cyclical effect that continues to perpetuate among hypothesized dyads. Conversely, if this motivation decreases or ceases among one or more dyads, the cyclical effect will retract and, eventually stop.

    Originality/value – Based on the neurologic outcome, one central theme emerged: managers must offer opportunities to acquire knowledge through assistive mechanisms (i.e. training) by providing external stability through controlled channels that activates the motivation to transfer learning into new opportunities. The transference of this knowledge produces reconstructive growth opportunities through continuous learning thus increasing performance