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The purpose of this paper is to address gaps in the knowledge about human resource (HR) professional involvement in strategic decision-making in China compared with that in Australia.

First, the authors compare the strategic involvement of Chinese and Australian HR professionals. Second, based on the upper echelon theory, the authors compare the impact of chief executive officer (CEO) and top management team (TMT) between both countries on HR involvement in strategic decision-making. Data were collected from matched pairs of HR and TMT executives in China (n = 168) and in Australia (n = 102).

Results indicate a difference, despite of no statistical significance, in HR involvement in strategic decision-making between Chinese and Australian samples. TMT behavioural integration was positively related to HR involvement in strategic decision-making in a collectivistic culture (i.e. in China), but not in an individualistic culture (i.e. in Australia). However, CEO support for HRM was positively related to HR involvement in strategic decision-making in Australia, whereas it is not related in China.

The paper conducts a comparative study and practical, and research implications are discussed at the end.

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。

A major source of failure for new ventures is the entrepreneurs℉ misunderstanding of the product-market fit. Recently, researchers have suggested that to get a better understanding of the product-market fit, entrepreneurs should “get out of the building” and interview many customers. This approach, while advantageous, is not without drawbacks. This article presents a conceptual model that incorporates the characteristics of “getting out of the building” to conduct customer interviews, and the biases that can arise to influence the entrepreneurs℉ misjudgment of the product-market fit. We provide recommendations to overcome these biases.