Document Type

Theses, Ph.D


This item is available under a Creative Commons License for non-commercial use only


Business and Management.

Publication Details

Sucessfully submitted for the award of Doctor of Philosophy (Ph.D) to the Dublin Institute of Technology, 2008.


This research seeks to establish why ABC adoption rates are low given the claimed benefits of the system. The view is taken that there are likely to be two sets of interacting variables influencing ABC adoption, contingent variables and the company’s ability or willingness to address implementation barriers. The contingency approach is a recent and important development in ABC research. From the perspective that there is no one universally appropriate MAS system, but that the appropriateness of any system is dependent on the factors facing the firm, it can be argued that ABC system adoption and success will depend upon specific contingent factors such as product diversity, cost structure, firm size, competition, and business unit culture. A contingency model of ABC adoption has been developed in order to examine and investigate the reasons why the take up or adoption of ABC systems remains low. This model seeks to incorporate contingency theory relating to a set of variables which will be identified from the literature as likely to be influential in ABC adoption. The view is taken that such contingency variables will not of themselves explain ABC adoption rates, rather such contingency factors may be viewed as rendering ABC suitable or otherwise for adoption by companies but that there are also implementation issues which influence adoption. The implementation factors can be classified based upon a review of the literature into three main types Behavioural, Systems and Technical. This study seeks to establish which of these three sets of factors constitutes the dominant barriers to ABC implementation. Based upon the contingency model, companies are classified into groups, each group having a different “profile” with regard to the individually established contingent variables. Thus, one such group will have a “good match” with the contingent variables and another will have a “poor match”, e.g. if “size” is found to be a contingent variable, one group will comprise the larger firms, and another group will comprise the smaller firms, with a number of intermediate groups. The grouping is based on all established contingent variables. Each such group is subdividing into ABC adoption or non-adoption, and the reasons for non-adoption establish for each such group. A mail questionnaire survey was considered an appropriate method for this study. The survey undertaken comprised all firms listed in Business and Finance (2004) Irelands Top 1000 Companies (the total number of companies included in the list were only 925 companies). 218 questionnaires were returned, generates a 23.6% response rate. The quantitative data were processed using a SPSS program, leading to appropriate descriptive and inferential statistical analysis, including frequencies, means, standard deviations, chi-square, t-test, Mann-Whitney and ANOVA tests. Cluster analysis was used to profile the companies according to the individually significant contingent factors. Seven contingent variables were identified from the literature, six of which were found to be statistically significantly associated with ABC adoption. Companies were “clustered” using these variables into three groups, and reasons for non-adoption were identified. Based upon an analysis of the given reasons for non-adoption, Technical Issues were dominant amongst these companies in the cluster which profile most closely matches the contingent factors. The findings suggest that in the adoption of ABC, two distinct sets of variables are at work. The ‘Contingent Variables’ which likely render it appropriate or useful for the company to adopt ABC, and the company’s ability, or willingness to address the ‘Barriers’ and difficulties associated with ABC adoption. The results show a strong significant association between contingent variables and the adoption of ABC. The results suggest that the contingent variables alone may not of themselves adequately explain the actual take up of ABC systems. Moreover, it suggests that two companies which have similar profiles with regard to contingent variables (with higher overheads, more product diversity etc.) may yet reach different decision with regards to ABC adoption, due to their differing abilities or willingness to address and overcome the issues relating to ABC implementation, the results completely support this suggestion. The results also show that ‘Technical Issues’ are the most common factor militating against ABC adoption within companies who are rejecting and actively considering its adoption within the cluster whose profile most closely matches the prime factors.

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