Organizational Learning in the Context of an Agri-Food Chain

This paper reports on the outcomes of the first stage of a longitudinal study that focused on the transformational change process being undertaken within the Supply Chain and Operations Area of a major Australian food manufacturing company. Organizational learning is an essential prerequisite for any successful change process and an organization's ability to learn is dependent on the existence of an environment within the organization that nurtures learning and the presence of key enablers that facilitate the learning process. An organization's capacity to learn can be enhanced through its ability to form and sustain collaborative relationships with its chain partners. The results show that an environment that supports organizational learning is being developed through consultative leadership and the empowerment of individuals within a culture that supports innovation and cross-functional teamwork but demands responsibility and accountability. The impact of these changes within the Supply Chain and Operations Area is evident in the significant improvement in the Area's productivity and efficiency levels over the past twelve months. The company's endeavours to engage its major supply chain partners in the learning process have been limited by the turmoil within the company. However the company has involved its supply chain partners in a series of mutually beneficial projects that have improved communication and built trust thereby laying the foundations for more collaborative chain relationships.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.