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Firm Policies and Critical Data Sources
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-0971-0589
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Description
Abstract [en]

In the dynamic fluctuating economic landscape, firm policies are the guiding principles that steer market conditions and firms' behaviour. In the past, these policies were formulated based on limited data and a heavy reliance on expert opinions. However, a new era is dawning, characterised by vast amounts of data processing using advanced statistical and computer science methodologies. Data-driven decision-making uses these methodologies to consolidate and process data into actionable information, leading to firm policies. The critical data sources are the data sources on which the policies are based. The data-driven decision-making allows the data to speak for itself, relying less on expert opinions for policymaking. However, it also necessitates a higher requirement of validation. This thesis investigates five different cases of firm policy and critical data sources. Each one of them will present one aspect of this broad topic. The first paper investigates selecting auxiliary variables to estimate firm characteristics, aiming to reduce bias and improve accuracy. Simple variables outperform complex ones, and complete data enhances accuracy. The second paper introduces a methodology for quantitative inductive research on ordinal survey data by new- and traditional- penalising methods. The new methods outperformed the old and could find a significant variable, while the older models could not. The third paper examines how macro-factors impacted various Small and Medium Enterprises (SME) sectors in the European Union’s member states from 2005 to 2019. The research offers valuable insights for policymakers and business leaders, aiding in tailored policy interventions and support mechanisms to address regional disparities and economic conditions. The fourth and fifth papers investigated Short-Time Work (STW), a primary policy tool during the COVID-19 pandemic. These studies used Swedish firm-level data to assess the STW policy. In the fourth paper, STW was associated with a reduction in employee numbers and a slightly increased productivity level compared to non-STW firms. In the fifth paper, STW did not increase the survival of SMEs. In conclusion, the ever-evolving economic landscape necessitates data-driven decision-making for informed actions and policymaking. This thesis is by no means a complete investigation, and further research is needed on this topic.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2024.
Series
Dalarna Doctoral Dissertations ; 37
Keywords [en]
Firm policies, Critical data, Large data, Data-driven decision-making, Quantitative inductive methods
National Category
Economics Business Administration Computer and Information Sciences
Identifiers
URN: urn:nbn:se:du-48605ISBN: 978-91-88679-72-7 (print)OAI: oai:DiVA.org:du-48605DiVA, id: diva2:1861312
Public defence
2024-09-27, room B301, campus Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2024-06-18 Created: 2024-05-27 Last updated: 2024-06-18Bibliographically approved
List of papers
1. Improved Accuracy in Firm Modelling and Prediction: A Variable Suggestion
Open this publication in new window or tab >>Improved Accuracy in Firm Modelling and Prediction: A Variable Suggestion
(English)Manuscript (preprint) (Other academic)
National Category
Business Administration
Identifiers
urn:nbn:se:du-48773 (URN)
Available from: 2024-06-18 Created: 2024-06-18 Last updated: 2024-06-18Bibliographically approved
2. An Inductive Approach to Quantitative Methodology—Application of Novel Penalising Models in a Case Study of Target Debt Level in Swedish Listed Companies
Open this publication in new window or tab >>An Inductive Approach to Quantitative Methodology—Application of Novel Penalising Models in a Case Study of Target Debt Level in Swedish Listed Companies
2024 (English)In: Journal of Risk and Financial Management, E-ISSN 1911-8074, Vol. 17, no 5, article id 207Article in journal (Refereed) Published
Abstract [en]

 This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of the study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The purpose of the case is to select explanatory variables correlated with the target debt level in Swedish-listed companies. 

The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression trees (CART)-based imputations by multiple imputations chained equations (MICE) to address this problem.

The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel Element Linked Multinomial-Ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one; the quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable to affect the target debt level.

National Category
Economics
Identifiers
urn:nbn:se:du-48603 (URN)10.3390/jrfm17050207 (DOI)
Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-06-18Bibliographically approved
3. Unravelling the Dynamics: Which Macro-factors are Shaping the SME Sector Landscape Within the European Union?
Open this publication in new window or tab >>Unravelling the Dynamics: Which Macro-factors are Shaping the SME Sector Landscape Within the European Union?
(English)Manuscript (preprint) (Other academic)
National Category
Economics
Identifiers
urn:nbn:se:du-48600 (URN)
Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-06-03Bibliographically approved
4. Short-Time Work as a Response to the COVID-19 Crisis: A Study on SME Firm-Level Data in Sweden
Open this publication in new window or tab >>Short-Time Work as a Response to the COVID-19 Crisis: A Study on SME Firm-Level Data in Sweden
(English)Manuscript (preprint) (Other academic)
National Category
Economics
Identifiers
urn:nbn:se:du-48601 (URN)
Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-06-03Bibliographically approved
5. The Effect of Short-Time Work on SME Firm Survival during the COVID-19 Pandemic
Open this publication in new window or tab >>The Effect of Short-Time Work on SME Firm Survival during the COVID-19 Pandemic
(English)Manuscript (preprint) (Other academic)
National Category
Business Administration
Identifiers
urn:nbn:se:du-48602 (URN)
Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-06-03Bibliographically approved

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Grek, Åsa

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