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Research was conducted to empirically demonstrate the relationships between personal meaning, calling and organizational commitment in the context of spiritual leadership. Wong's Personal Meaning Profile was used to establish the various sources of personal meaning and identify those that predict calling. The results showed significant positive correlations between self‐transcendent personal meaning and calling. Further, calling was also positively correlated with organizational commitment and contrasted with work‐as‐job as a predictor of commitment. The study suggests that not all sources of personal meaning are predictive of calling, and that calling mediates the relationship between self‐transcendent personal meaning and organizational commitment. Theoretical and practical implications are discussed.

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.

Traditionally, the distribution activities of a firm have been regarded solely as a cost of doing business. Because of this orientation, the analytical techniques applied to the solutions of problems in this area have tended to be optimising models aimed at reducing total cost subject to various constraints. Typically, service levels were incorporated either as an absolute level requirement so that the specific demand restricted the optimising technique or as an opportunity cost of lost sales with this cost included as a part of the total objective function to be minimised.