Abstract

PurposeThe purpose of this study is to validate multiplicative cycle that exists between the job readiness and satisfaction model explored by Matthewset al. (2018), the satisfaction and performance paradigmatic nuances analyzed by Judgeet 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/approachAn 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.FindingsThe 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/valueBased 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.

Highlights

  • Researchers have extensively studied instrumental modes of learning transference through intrinsic and extrinsic correlates (Deci and Ryan, 1985; Lepper et al, 2005), organizational learning culture (Egan et al, 2004), learner feedback (Islam, 2019). Noe (1986) defined the motivation to transfer learning as “the learner’s intended efforts to utilize skills and knowledge learned in training setting to a real-world work situation” (p. 743)

  • This study aims to test if a direct link exists between Matthews et al (2018) readinesssatisfaction dyad, Park et al (2017) satisfaction-performance dyad, and Vroom (1964) performance-achievement dyad

  • This study shows that an elevated presence of motivation to transfer learning serves as a prerequisite to maintain an active cyclical and recursive effect between all hypothesized dyads

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Summary

Introduction

Researchers have extensively studied instrumental modes of learning transference through intrinsic and extrinsic correlates (Deci and Ryan, 1985; Lepper et al, 2005), organizational learning culture (Egan et al, 2004), learner feedback (Islam, 2019). Noe (1986) defined the motivation to transfer learning as “the learner’s intended efforts to utilize skills and knowledge learned in training setting to a real-world work situation” (p. 743). The need to achieve presents an inherent factor that drives expectancy and instrumentality used to influence effort and performance calculation and perceived outcomes, increasing job satisfaction (Vroom, 1964). While the literature contains exhaustive studies on correlative relationships between readiness and satisfaction (Hersey and Blanchard, 1982; Matthews et al, 2018), satisfaction and performance (Gu and Chi, 2009; Judge et al, 2001; Park et al, 2017) and performance and achievement (Vroom, 1964), no study can be found that captures the analysis of each dyad separately interrelatedly links all coupled taxonomies to examine the nuanced effects of the motivation to transfer learning. Several researchers addressed leadership theories that denoted the latitudinal empowerment (i.e. influence in decisionmaking, open communications, confidence) and independence given by subordinate-oriented boss (Hersey and Blanchard, 1982; Tannenbaum and Schmidt, 1975; Harris et al, 2011)

Motivation to Transfer Learning
Findings
Discussion and conclusion
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