scholarly journals Identifying New Product And Service Export Opportunities For South Africa Using A Decision Support Model

2014 ◽  
Vol 13 (6) ◽  
pp. 1403 ◽  
Author(s):  
Wilma Viviers ◽  
Ludo Cuyvers ◽  
Ermie Steenkamp ◽  
Sonja Grater ◽  
Marianne Matthee ◽  
...  

In the face of slow economic growth and development, and the perennial problems of unemployment, poverty and inequality, the South African government and business community have long recognised the importance of growing and diversifying the countrys tangible goods and services export sectors. One of the challenges in designing and implementing effective export promotion strategies is identifying the right markets, given South Africas ever-fluid skills, capacity and trading relationships. The Decision Support Model (DSM) is an export market selection tool that makes use of a sophisticated filtering process to sift through an extensive range of product-/service- and country-related data to reveal those product-/service-country combinations (export opportunities) that are the most realistic and sustainable. The DSM, which has been applied for Belgium, Thailand and South Africa, not only brings greater precision to the export market selection process, but also unveils opportunities that may not have been contemplated before thus supporting the quest for export diversification. This paper examines the role of the DSM for products and the DSM for services, respectively, and illustrates how, using the results from the application of these models, they herald the start of a new era in export market selection and promotion in South Africa.

2020 ◽  
Vol 18 (6) ◽  
pp. 1927-1950
Author(s):  
Eric Kwame Simpeh ◽  
John Julian Smallwood

Purpose The purpose of this paper is to examine the predictable effect of economic and non-economic factors regarded as the most important to stimulate stakeholders’ behavioural intentions to adopt green building. Design/methodology/approach The primary data was collected from 106 green building accredited professionals in both the public and private sectors registered with the Green Building Council of South Africa. The data analysis techniques adopted include descriptive and inferential statistics, namely, factor analysis and logistic regression model (LRM). Findings The LRM results revealed five predictors and two control variables made a unique statistically significant contribution to the model. The strongest predictor to enhance the intention to adopt green building was a financial benefit (FB), recording an odds ratio of 9.1, which indicates that the likelihood to adopt is approximately 9.1 times more if FBs is evident. Practical implications It is anticipated that the most significant facilitators/enablers identified by built environment stakeholders will create an enabling environment to enhance the adoption of green building. Originality/value This research has contributed to the existing knowledge by developing a decision support model. The decision support model provides predictive indicators for clients, consultants and contractors to harness their resources and identify significant parameters to improve their decision-making in adopting green building.


2013 ◽  
Vol 22 (2) ◽  
pp. 367-374 ◽  
Author(s):  
A. Fuchsia Howard ◽  
Kirsten Smillie ◽  
Vivian Chan ◽  
Sandra Cook ◽  
Arminee Kazanjian

Author(s):  
Marjana Cubranic-Dobrodolac ◽  
Libor Svadlenka ◽  
Goran Z. Markovic ◽  
Momcilo Dobrodolac

Sign in / Sign up

Export Citation Format

Share Document