Domestic and export marketing of Auracaria timber

This article examines the need for a marketing approach to support the expansion of trade in Australian forest Products. Opportunities available for trade in hoop pine ( Araucaria cunninghamii), a Queensland species of timber, are examined. Markets within China and Japan are found to have substantial potential end product uses for the plantation timber.

Before the first export decision : internationalisation readiness in the pre-export phase

Although the internationalisation process of the firm has been well researched since the 1970s, the behaviour of firms prior to internationalisation has not received commensurate research attention.This paper argues that a focus on firms’ pre-internationalisation activities will not only offer an additional important perspective to the study of firm internationalisation but it will also address a significant research gap in studies that are theoretically based on the so-called stages models. During the pre-internationalisation phase, a firm is exposed to stimuli factors that may trigger an impulse for foreign market expansion. Decision makers’ perceptions of stimuli, their attitudinal commitment towards internationalisation, the firms’ resources and capabilities, as well as the mediating effect of lateral rigidity comprise a learning process that leads a firm towards readiness to initiate an internationalisation decision. This paper advances the concept of internationalisation readiness and proposes a method for developing an Internationalisation Readiness Index.

Measuring export readiness using a multiple-item index

Since the 1970s the internationalisation process of firms has attracted wide research interest. One of the dominant explanations of firm internationalisation resulting from this research activity is the Uppsala stages model. In this paper, a pre-internationalisation phase is incorporated into the traditional Uppsala model to address the question: What are the antecedents of this model? Four concepts are proposed as the key components that define the experiential learning process underlying a firm’s pre-export phase: export stimuli, attitudinal/psychological commitment, resources and lateral rigidity. Through a survey of 290 Australian exporting and non-exporting small-medium sized firms, data relating to the four pre-internationalisation concepts is collected and an Export Readiness Index (ERI) is constructed through factor analysis. Using logistic regression, the ERI is tested as a tool for analysing export readiness among Australian SMEs.

Export decisions within Taiwanese electrical and electronic SMEs : the role of management characteristics and attitudes

In this globalized environment, Taiwanese firms have been very successful in achieving growth via international market expansion. In particular, the Taiwanese electronics industry has shown a dynamism lacking in comparable industries around the world. However, in recent years there has been a move by many of the larger Taiwanese manufacturing firms to outsource their manufacturing to low-cost producers such as China in order to remain competitive. Conversely, most Taiwanese small- to medium-sized enterprises (SMEs) have retained their production facilities in Taiwan. These SMEs seek to expand their sales beyond the domestic market by employing an export strategy, making a significant socioeconomic contribution to the domestic and regional economies. This paper highlights the key dimensions such as enhancing factors (benefits/advantages), inhibiting factors (barriers/costs), and managerial factors (characteristics/commitment) that play an important role in the internationalization of SMEs located within the Taiwanese electronics industry. A logistic regression model is used to predict the probability of a firm being an exporter.

Handling incomplete data in survival analysis with multiple covariates

This paper studies the missing covariate problem which is often encountered in survival analysis. Three covariate imputation methods are employed in the study, and the effectiveness of each method is evaluated within the hazard prediction framework. Data from a typical engineering asset is used in the case study. Covariate values in some time steps are deliberately discarded to generate an incomplete covariate set. It is found that although the mean imputation method is simpler than others for solving missing covariate problems, the results calculated by it can differ largely from the real values of the missing covariates. This study also shows that in general, results obtained from the regression method are more accurate than those of the mean imputation method but at the cost of a higher computational expensive. Gaussian Mixture Model (GMM) method is found to be the most effective method within these three in terms of both computation efficiency and predication accuracy.