The resource-based view of strategy: application to the agricultural industry

IP a The paper examines the application of the Resource-Based View of strategy (RBV) to the Australian floral industry. Despite the RBV's successful application to research in a number of discipline areas and the formalisation of its relationship with Competitive Advantage (CA) 15 years ago, the empirical support for the benefit of the RBV and development of research constructs has been inadequate. This has been partly due to the difficulty of identifying and separating the contribution of resources. The RBV literature is now consistent in the criteria required of a resource for CA and identifies a range of empirical research objectives (e.g. the need for contextual constructs), data evaluation focuses (e.g. measuring the impact of management, process, regional and scale affects) and results objectives (such as identifying the causal structure of resources). Research was conducted in the Australian floral industry to produce supporting generalisable data and constructs for the RBV. This industry is well bounded with several strongly differentiating resources and operates in a global market environment, which is necessary for these research objectives. Six hypotheses were examined; (1) the use of resources as the input of the CA, (2) the impact of the development process on resources, (3) the impact of management control on the development of resources (4) the impact on capability of management, process, region and scale, (5) the impact of resource development maturity on the approach to resource development and (6) the possibility of evaluating individual resources according to various criteria. The data was collected using selected participant interviews, with validation of conclusions by industry experts. It was analysed using content analysis, comparative analysis and cognitive mapping. The research determined that organisations in the Australian floral industry possessed important resources including geography, skills, technology, R&D, supply chains and production costs. These contributed to four CA creating production outputs; quality, capacity, reliability and customer convenience. The research findings supported hypotheses 1, 3, 4, 5 and 6. The lack of support for the two remaining hypotheses, relating to the process of resource development, may be explained by the low resource development maturity of the industry which masks the impact of the resource development process. The results also determined that one resource could contribute to a number of CAs and that resources not meeting all of the normal RBV CA criteria could still provide a CA in an industry where few resources met all criteria. It was postulated that these resources’ contribution to competitive was not durable.

Evaluation of deterministic and complex analytical hierarchy process methods for agricultural land suitability analysis in a changing climate

Land suitability analysis is employed to evaluate the appropriateness of land for a particular purpose whilst integrating both qualitative and quantitative inputs, which can be continuous in nature. However, in agricultural modelling there is often a disregard of this contiguous aspect. Therefore, some parametric procedures for suitability analysis compartmentalise units into defined membership classes. This imposition of crisp boundaries neglects the continuous formations found throughout nature and overlooks differences and inherent uncertainties found in the modelling. This research will compare two approaches to suitability analysis over three differing methods. The primary approach will use an Analytical Hierarchy Process (AHP), while the other approach will use a Fuzzy AHP over two methods; Fitted Fuzzy AHP and Nested Fuzzy AHP. Secondary to this, each method will be assessed into how it behaves in a climate change scenario to understand and highlight the role of uncertainties in model conceptualisation and structure. Outputs and comparisons between each method, in relation to area, proportion of membership classes and spatial representation, showed that fuzzy modelling techniques detailed a more robust and continuous output. In particular the Nested Fuzzy AHP was concluded to be more pertinent, as it incorporated complex modelling techniques, as well as the initial AHP framework. Through this comparison and assessment of model behaviour, an evaluation of each methods predictive capacity and relevance for decision-making purposes in agricultural applications is gained.