Bayesian network revealing pathways to workplace innovation and career satisfaction in the public service

Abstract

This paper examined the innovation process in the Australian Public Service (APS) using a Bayesian network (BN) founded on an empirically derived structural equation model.The focus of the BN was to examine the impact of leadership style and organisational culture on workplace innovation and career satisfaction in the APS. Using scenario analysis, the best combination of managerial actions for enhancing APS career satisfaction was determined. The results emphasise the benefit of encouraging management to adopt a transformational leadership style and instilling innovative culture in their organisation. In addition, innovative culture was a key driver of workplace innovation, which served to improve the career satisfaction of APS employees.Implications are discussed to propose practical strategies for organisations wish to encourage innovation among employees.

The impact of gamification on consumer loyalty, electronic word‐of mouth sharing and purchase behavior

“…The second limitation is the cross‐sectional design which precludes us from drawing conclusions as to the causal interference of the variables under investigation. Future scholars are advised to utilize alternative designs (i.e., longitudinal) and methods such as machine learning and artificial intelligence techniques (Abubakar, 2018), Bayesian networks (Wipulanusat, Panuwatwanich, Stewart, Arnold, & Wang, 2020), fuzzy sets techniques (Huarng & Roig‐Tierno, 2016) and mixed methodologies (Kaya et al, 2019; Wipulanusat et al, 2020). The third limitation is that participants were Arabs characterized by oriental culture, thus, expanding the purview of fit research to non‐Arab contexts is important, as people in different cultures may hold different views on the theme of interest.…”

Effect of Cross-Departmental Collaboration on Performance: Evidence from the Federal Highway Administration

“…SEM is a confirmatory technique that examines casual relationships in a conceptual model, and it is appropriate to explain established theoretical relationships from pre-existing knowledge [47]. In contrast, BN is an exploratory technique to provide theoretical explanations by learning the quantitative probabilities from the data [47,48]. This novel approach combines a theoretical construction based on an empirically validated structural model with a graphical interaction's BN.…”

“…The most straightforward method to learn the parameter is the counting algorithm. In addition, the counting algorithm should be applied in all possible circumstances because it is acknowledged as a true Bayesian learning algorithm [48]. Thus, this study adopted the counting algorithm to calculate the CPT from sample data.…”

“…The total aggregation method was applied by averaging responses to questionnaire items in each construct to form a single variable as a node. By determining the occurrence frequency, the numerical value of each node was discretized to transform the scale into three states: [1-2.5] as low, [2.5-4] as medium, and [4,5] as high [48]. This study used the commercially available software package Netica to develop the BN.…”

Technological Innovation Research

“…The results indicate that both innovation novelty (Lei et al 2020;Li 2018;Yang et al 2018;Wipulanusat et al 2020) and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition positively both affect firms' innovation performance.…”