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The study aims to refine the local university’s supply chain management curriculum to meet regional industry demands, thus boosting the local economy.

Mixed-methods action research combined with neural network modeling was employed to align educational offerings with the needs of the local supply chain management industry.

The research indicates that curriculum revisions, informed by industry leaders and modeled through neural networks, can significantly improve the relevance of graduates' skills to the SCM sector.

The study is specific to one region and industry, suggesting a need for broader application to verify the findings.

Adopting the recommended curricular changes can yield a workforce better prepared for the SCM industry, enhancing local business performance and economic health.

The study supports a role for higher education in promoting economic vitality and social welfare through targeted, responsive curriculum development.

This study introduces an innovative approach, integrating neural network analysis with action research, to guide curriculum development in higher education based on industry requirements.

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.

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.

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.

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.

本研究的目的、除了驗證弗魯姆 (Vroom) (1964) 建立的「期望模型」理論外，也去驗證存在於工作準備就緒與馬修斯等人 (Matthews et al.) (2018) 所探索的滿足感模型之間的倍增週期，及質治等人 (Judge et al.) (2001) 和古與池 (Gu & Chi) (2009) 所分析的滿足感及表現之範式細微差別。學習轉移的動機作為一個被傳送至學習漸變體內的可輸送變量而運作，而這個學習漸變體是會維持週期性的輸出的。

透過神經網絡程序，創造一個用以探索這三個假設性理論之間的關係的原型。探究這些關係，會使我們更能深入了解僱員動機的衍生品，因這涉及僱員動機對準備就緒、滿足感、表現及功績二元體的影響。仔細分析文獻帶出了一個假設，就是: 學習轉移的動機會在各假設的關係裏創造一個倍增的效果。

這個神經網絡程序續步闡釋了一個被倡議的普遍觀念，那就是轉移動機的正面影響會帶來一個在假設性的二元體中會繼續持續下去的週期性效應。相反地，如果這動機在一個或多個二元體中減弱或停止，這週期性效應將會撤回及最終停止。

基於神經病學的結果，一個核心主題浮現了， 就是：管理人員必須提供透過輔助機制 (就是說：培訓) 而獲取知識的機會。方法是給會引發把學習轉為新機會的動機之受控渠道、提供外在穩定性。這知識的轉移，透過不斷學習而創造重建的成長，表現因而得以提升。

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.

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.

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.

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.

This chapter suggests that self-employment or entrepreneurship can constitute an alternative to employment for neurodivergent people. While this is a suitable and successful option for many, we explore the experience of one entrepreneur who was, and remains, very reluctant in his entrepreneurial endeavors. We use Michael's firsthand autobiographical accounts to showcase his struggle before and during entrepreneurship and its impact on his physical and mental health. We conclude with some reflections on his experience and highlight what the literature on neurodivergent entrepreneurs can learn from it.