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Integrating the pathway model of meaningful work and the intrinsic motivation principle of creativity, the authors investigate why, when and how paternalistic leadership relates to employee creativity in the Chinese organizational context. The authors suggest that the meaning of work (MOW) mediates the relationship between paternalistic leadership and employee creativity. The authors further identify perspective taking as a moderator in the mediated relationship for the path from MOW to creativity.

The authors relied on a sample of 340 employee-supervisor dyads collected from multiple organizations located in Eastern China to test the study hypotheses.

Results indicated that MOW mediated the positive relationships between the benevolence and morality dimensions of paternalistic leadership and employee creativity, and the negative relationship between the authoritarianism dimension of paternalistic leadership and employee creativity. Further, the indirect relationships between the three dimensions of paternalistic leadership (i.e. authoritarianism, benevolence and morality) and employee creativity through MOW were more pronounced when perspective taking was higher rather than lower.

Through a meaning-based perspective, the authors demonstrate that a culture-specific managerial philosophy (i.e. paternalistic leadership) shapes Chinese employees' perceptions of meaningful work and their subsequent creative performance. This paper complements the dominant focus on Western leadership in the creativity literature and denotes that paternalistic leadership matters for employee creativity in Chinese organizations.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.