Agile or Traditional Project Organization: A Quantitative Assessment of Decision Criteria among Firms in the Dach Region

The so-called agile approach is increasingly popular in the world of project management as a response to more dynamic and competitive environments. This study follows the question: What are the decisive criteria that result in the use of agile process models in practice? Therefore, a broad range of decision criteria is investigated, representing different reasons for firms to decide in favour or against the usage of agile process models, namely: Time-saving, increased efficiency, availability of qualified personnel, uniform terminology, project comparability and functions as a knowledge base. Most existing research on agile project management is of qualitative nature; this study uses a quantitative approach to assessing 51 firms and nine different industries within the DACH region. The collected data was analysed in a binary logistic regression model. Results reveal that time-saving positively predicts the use of agile process models, while high ratings in function as a knowledge base and project comparability predict the absence of agile process models. Therefore, practitioners are suggested to educate project and portfolio managers in the creation of hybrid environments and integration of agile process models in traditional project portfolios. Implications for Central European audience : The results of this study provide valuable insights into the selection of project management approaches across different industries in the DACH region. industry and x 8 = firm size, are added to the model, with industry designed as a categorical variable with the dimensions IT and “Other”. The analysis was carried out with the Statistical Package for Social Sciences (SPSS Version 27). The Wald statistics and the p-values are considered to assess whether the suggested causalities result in significant correlations in the model. For this study, a p-value of p < 0.05 is considered significant. The Hosmer-Lemeshow test is applied to determine the overall quality of the model (goodness-of-fit), providing information to what degree the estimated model fits the data sample, dividing the observations into groups and comparing the predicted observations with the actual observations for each group. Based on this comparison, the Chi-square test is then carried out. The result showed significance (p<0.05), indicating that there is a significant deviation between the model and the data. The odds ratio (Exp(ß)) is considered to assess the effect strength of the respective predictor variables. The odds ratio gives the change in the probability of the event Y=1, if one independent variable is increased by one unit, given that all the other variables of the model are held constant. For negative odds ratios, the reciprocal value is calculated.