Firm growth in the Finnish software industry a longitudinal study of growth aspirations and growth outcomes

This master's thesis uses motivational theories and a quantitative, longitudinal approach to explore the relationship between growth aspirations and firm growth outcomes in the Finnish Software Industry. Specifically, it employs cross-lagged analyses to examine how past growth aspirations and o...

Täydet tiedot

Bibliografiset tiedot
Päätekijä: Huiso, May
Muut tekijät: Kauppakorkeakoulu, School of Business and Economics, Taloustieteet, Business and Economics, Jyväskylän yliopisto, University of Jyväskylä
Aineistotyyppi: Pro gradu
Kieli:eng
Julkaistu: 2023
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/87173
Kuvaus
Yhteenveto:This master's thesis uses motivational theories and a quantitative, longitudinal approach to explore the relationship between growth aspirations and firm growth outcomes in the Finnish Software Industry. Specifically, it employs cross-lagged analyses to examine how past growth aspirations and outcomes influence current growth aspirations and outcomes, focusing on revenue and personnel count. The research findings support established theories, such as the Theory of Planned Behavior and the Behavioral Theory of the Firm, showing a two-way relationship between growth aspirations and firm growth. Specifically, past growth aspirations influence current aspirations and firm growth outcomes, while past growth outcomes influence current aspirations. However, the influence of past growth outcomes on growth aspirations and firm growth is complex and can be influenced by various factors at the firm and environmental levels. This study contributes to entrepreneurial growth research by providing empirical evidence on the interplay between aspirations and outcomes over time. It highlights the importance of aligning growth aspirations with realistic goals and leveraging past experiences for sustainable growth. Future studies should consider incorporating firm and industry-level factors and expanding the sample size to enhance the robustness of the results.