Purpose
The purpose of this paper is to examine the linkages between leadership and satisfaction models with neural networks to epistemologically explore both the theoretical and practical basis of these paradigms to analyze the effect employee readiness has on job satisfaction. A review of the literature indicates an absence of a paradigmatic precursor to the satisfaction-performance dyadic. Revisiting theoretical frameworks builds a reconceptualized prism that amalgamates leadership and job satisfaction constituents to form a theoretical scaffold and linkage between employee readiness and job satisfaction.
Design/methodology/approach
Reviewing the literature explores a theoretical existence of a readiness model preceding the satisfaction-performance paradigm that measures how the amalgam of readiness variables affects job satisfaction. This conceived theory uses a unidirectional model that extends the linear progression and institutes a backwards propagation linkage to the satisfaction-performance linkage using the following unidirectional correlation: readiness-satisfaction→ satisfaction-performance. Using a neural network approach, a total of 160 companies are integrated into a simulation using leadership, satisfaction and readiness variables, with an emphasize on high relationship, to ascertain the effect of readiness on job satisfaction.
Findings
While there are studies that interchangeably link satisfaction and performance, revisiting the literature provides theoretical insight that validates the formation of a preceding construct that converges leadership and satisfaction constituencies to form a dyadic relationship between readiness and satisfaction. Research has tirelessly attempted to discover variable correlation between job performance and job satisfaction. However, these attempts are met with contradictory results. To truly link employee readiness to the job satisfaction/job performance dyad, a neural network is created, which deduces that random probabilities confirm the continuous exactitude of a positive correlation between readiness and job satisfaction. This, in turn, confirms an existent theoretical precursor to the satisfaction-performance paradigm. The implications of not linking job readiness to satisfaction and performance can potentially leave managers amiss when triangulating performance decline. Reclassifying the satisfaction-performance dyadic corroborates Judge et al.’s (2001) theory that reinventions of this impression should be researched, and Graen and Uhl-Bien’s (1991) conclusive remarks that an evaluation beyond “trait-like” individual differences of leaders is necessary to recognize the leadership paradigm loop, which is inclusive of the leader, the follower and the dyadic relationship.
Originality/value
This research paper is useful for practitioners and academics to refer as the comparative and intersecting explanation of leadership and job satisfaction models, as it peripherally conveys a legitimate view of a preceding relational construct that will add value to the relevance of employee readiness as it affects job satisfaction. In addition, the neural network approach is a sound and unique method to algorithmically validate the correlation between job satisfaction models and leadership. Through codifying, the environmental variables comprised Herzberg et al.’s (1959) motivation and hygiene factors that are directly related to a leader-member exchange function, an evidentiary linkage validates the literature works of Hersey and Blanchard (2001) and directly links it to job satisfaction precursors.
ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS
