%0 Research article
%T Analysis of decision-making processes for strategic technology investments in Swedish large-scale forestry
%A Jonsson, Rikard
%A Woxblom, Lotta
%A Björheden, Rolf
%A Nordström, Eva-Maria
%A Blagojevic, Bosko
%A Lindroos, Ola
%D 2022
%J Silva Fennica
%V 56
%N 3
%R doi:10.14214/sf.10755
%U https://silvafennica.fi/article/10755
%X <p>Technological development gives forest companies opportunities to maintain competitiveness in the highly cost-sensitive market for forest products. However, no previous studies have examined the technological development decisions made by forest companies or the support tools used when making them. We therefore aimed to describe and analyze 1) the processes used when making such decisions, 2) the associated decision situations, and 3) the use of and need for decision support tools in these processes, with a harwarder concept as case. Semi-structured interviews were conducted with respondents from six forestry organizations. Two theoretical frameworks were used to analyze the interviews, one for unstructured decision processes and one for decision situations. The respondents’ descriptions of their decision processes were consistent with those observed in other industries, and it was shown that decision-making could potentially be improved by investing more resources into diagnosing the problem at hand. The main objective in decision-making was to maximize economic criteria while satisfying threshold requirements relating to criteria such as operator well-being, soil rutting, and wood value. When facing large uncertainties, interviewees preferred to gather data through operational trials and/or scientific studies. If confronted with large uncertainties that could not be reduced, they proceeded with development only if the potential gains exceeded the estimated uncertainties, and implemented innovations in a stepwise manner. These results indicate a need for greater use of existing decision-support tools such as problem-structuring methods to enable more precise diagnoses, simulations to better understand new innovations, and optimization to better evaluate their theoretical large-scale potential.</p>