Increasing competition in the present dynamic business environment pressurizes companies to innovate perpetually. The ability to establish and successfully manage a permanent innovation process depends on the quality of decisions made at its consecutive stages. At the same time, management control systems provide managers with the informational basis for decision-making. Thus, the purpose of this paper was to measure the impact of different forms of management control on decision-making quality throughout the innovation process. The analysis was based on a survey conducted amongst the representatives of 64 Polish industrial companies in the third and fourth quarters of 2019 and the first quarter of 2020. Analytical tools included principal component analysis (PCA), used to aggregate data, and multivariate multiple regression models, used to determine the relationships between variables. The findings demonstrate that the relationship between mechanistic and organic forms of management control and decision-making quality may not be analyzed in isolation from the stage of the innovation process. The direction and strength of the relationships between these variables differs at consecutive stages of the innovation process in a statistically significant way.
PL
Współczesna rzeczywistość gospodarcza charakteryzuje się nasilającą się walką konkurencyjną, która wymusza na przedsiębiorstwach podejmowanie ciągłych działań innowacyjnych. Możliwość ustanowienia nieprzerwanego procesu innowacyjnego uzależniona jest od jakości decyzji podejmowanych na jego kolejnych etapach. Jednocześnie systemy controllingowe dostarczają menedżerom informacji niezbędnych do podejmowania decyzji. W tym kontekście celem niniejszego artykułu było zmierzenie wpływu implementacji różnych typów controllingu na jakość decyzji podejmowanych w ramach procesu innowacyjnego. Analizę empiryczną oparto na badaniu ankietowym przeprowadzonym wśród przedstawicieli 64 polskich przedsiębiorstw giełdowych w trzecim i czwartym kwartale 2019 roku oraz pierwszym kwartale roku 2020. Dane zagregowano przy wykorzystaniu analizy głównych składowych (PCA), a kierunek i siłę zależności pomiędzy zmiennymi określono z użyciem regresji wielorakiej. Uzyskane wyniki wskazują, iż zależność pomiędzy wprowadzeniem organicznych i mechanistycznych typów controllingu a jakością decyzji nie może być analizowana w oderwaniu od etapu procesu innowacyjnego. Kierunek i siła zależności pomiędzy tymi zmiennymi różni się w sposób statystycznie istotny na kolejnych etapach tego procesu.
The paper attempts to fulfil the research gap concerning the mutual relation between company innovation and its corporate social responsibility practices, by determining the conditions in which the innovation/CSR relation appears and develops. The research was based on systematic literature studies performed using SALSA and backwards-snowballing methods. The data was examined with the use of the meta-synthesis approach. The authors’ model explaining the studied relation was proposed. The research suggested that the impact of innovation on the CSR practices depended on the type of innovation and degree of novelty involved; while the way CSR affected innovation depended on such CSR features as: type of reaction, degree of development, and field of activity. The relation was also moderated by a series of six exogenous factors: external factors, industry, company characteristics, attitude, performance, and R&D.
Despite the research conducted in this field of innovation the attempt to model the effects of communicating innovation through announcements on market value changes has not been undertaken yet. Thus the purpose of the present research was to model the relationship between communicating innovation and market value of service companies. Summary of the existing evidence relied on such methods of systematic literature studies as SALSA, one step forward and backward snowballing, meta-synthesis and mapping review procedure. In order to represent the relationship between innovation and market value in services a conceptual model is proposed. It encompasses seven innovation-level and seven firm-level predictors. It covers also interaction and second-order effects.The research was burdened with several limitations. Namely it was limited to papers published in English and included scientific articles and conference proceedings only. Further quantitative research aiming at testing the model empirically seems beneficial from the point of view of model development.
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