BI Open (Norwegian Business School)
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Predictive Modeling for Urban Bike-Sharing Systems: A Comparative Analysis of LSTM and GCN-Based Models
Get PDFMasteroppgave(MSc) in Master of Science in Business Analytics, Handelshøyskolen BI, 2024The emergence of bike-sharing systems (BSS) marks a significant shift in ur-ban mobility, offering sustainable solutions to alleviate traffic congestion, reduce emissions, and enhance transportation access. By 2023, over 800 cities worldwide have adopted these systems, highlighting their rapid expansion and challenges. This thesis focuses on the Oslo City Bike system, emphasizing the importance of accurate demand forecasting for service excellence and user satisfaction.
Advanced methodologies predicted hourly station-level bike demand in Oslo, incorporating diverse spatial and temporal attributes. Four predictive mod-els—Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), GCN-LSTM, and A3T-GCN—were compared. The LSTM model achieved superior prediction accuracy, especially with comprehensive external features and ex-tended training periods. Despite the inferior performance of GCN-based models, valuable insights were gained regarding network connection optimization and geographical considerations.
Key trade-offs include balancing model accuracy and complexity, and the impact of extended training periods. The GCN-LSTM model captured spatial-temporal dynamics without external features, offering a flexible demand forecast-ing approach. Challenges for GCN-RNN models, such as sensitivity to features and computational constraints, were also highlighted.
Future research should focus on automated edge construction, integrating attention mechanisms with GCN and LSTM, and expanding the model’s ap-plication to the entire network. These advancements could enhance forecasting precision and deepen understanding of bike-sharing usage drivers.
In conclusion, this thesis underscores the potential of LSTM and GCN-LSTM models for urban bike-sharing systems, highlighting opportunities for refinement and increased contextual awareness. The findings offer practical implications for improving operational efficiency and customer satisfaction in urban mobility systems
Acting on the Norwegian Transparency act: interpretation and implementation
Get PDFThe chapter delves into the ramifications of Norway’s Transparency Act (Åpenhetslo-ven, 2021), which was enacted on 1stof July, 2022, compelling businesses to fosterhuman rights and fair working conditions in their supply chains through enhancedtransparency. It scrutinizes the interpretation and operationalization of The Actwithin two distinct companies, employingthe Knowledge Transfer as Translation(KTT) theory–traditionally applied to knowledge transfer within corporate culture–to navigate The Act’s conversion into corporate actions. This exploration uncovers theobstacles and divergent compliance strategies among the firms, showing that TheAct’s indeterminate language and the specific resources and individuals within eachcompany lead to varied corporate reactions. Despite The Act’s objective to improvesupply chain transparency, the lack of clear norms or a unified understanding of thelegislation at this early stage results in inconsistent applications. The study also positsthat KTT offers a valuable framework for examining the enactment of not only ab-stract cultural issues but also tangible legal mandates, suggesting its broader applica-bility in legal interpretation and corporate action alignment.Acting on the Norwegian Transparency act: interpretation and implementationpublishedVersio
Tipping privacy: The detrimental impact of observation on non-tip responses
Get PDFDigital point-of-sale platforms disrupted the norm of privacy-while-tipping. Previous research indirectly suggests that firms can increase—or at least not decrease—tips by reducing tipping privacy. The effects of tipping privacy on non-tip responses, defined as customer responses subsequent to the tip selection, including repatronage and word-of-mouth, remain unexamined. Related voluntary payment contexts (e.g., donations) suggest consumers sometimes prefer public observability and other times prefer privacy. We examine how and why tipping privacy affects non-tip responses. A field study and four controlled experiments find that diminished tipping privacy reduces non-tip responses because customers feel less generous and in control. Allowing customers to change initial tip amounts mitigates these detrimental effects. Providing insight into the inconsistent effects of privacy on tips, we find that diminished perceived control increases tip amounts, while diminished perceived generosity reduces tips. Managers adopting privacy-reducing technologies and service scripts should consider the damaging effects on non-tip responses.Tipping privacy: The detrimental impact of observation on non-tip responsespublishedVersio
Communication in the Digital Realm: Examining ChatGPT's Impact on the Leader-Subordinate Trust Relationship
Get PDFMasteroppgave(MSc) in Master of Science in Leadership and Organizational Psychology - Handelshøyskolen BI, 2024Since its initial public release in 2022, ChatGPT has experienced substantial growth, bringing about significant organizational advantages such as heightened proficiency, increased effectiveness, and reduced complexity. With its immense potential and groundbreaking capabilities, it is transforming industries and empowering people to accomplish tasks more effectively. It ushers a new area in human civilization, making it an indispensable tool in today’s digital landscape. However, the use of AI in leadership may bring about pitfalls. The present study put an emphasis on the application of ChatGPT in sensitive interpersonal situations, focusing on sexual harassment. It further delves into exploring potential impacts of perceived trustworthiness and authentic leadership on the dynamics of the leader-subordinate trust relationship.
Our first three hypotheses suggested that using ChatGPT to prohibit sexual harassment negatively affects the perceived trustworthiness of the leader. This trust was measured based on three factors proposed by Mayer et al., (1995); benevolence, ability, and integrity. Our last three hypotheses examined the same effect on two components of authentic leadership: internalized moral perspective (in the leader and in an email), and relational transparency.
Our statistical analysis, using one-way ANOVA, supported all six hypotheses. The results indicated that when a leader uses ChatGPT to construct a message about a personal matter, their subordinates may perceive them as less trustworthy and less authentic, potentially damaging the leader-subordinate trust relationship.
These findings hold significant implications for future leaders, signaling the necessity of exercising careful judgment when contemplating the use of tools like ChatGPT. They underscore the persistent importance of human engagement and deliberation within leadership roles, suggesting that certain responsibilities may not be suited for support from LLMs or other forms of machine learning algorithms.
Keywords: Artificial Intelligence, ChatGPT, communication, sexual harassment, trust, perceived trustworthiness, authentic leadership, Leader-Member Exchange theory, Stereotype Content Model
The Role of Governance and ESG on Financial Performance
Get PDFMasteroppgave(MSc) in Master of Science in Finance - Handelshøyskolen BI, 2024The global shift towards sustainability has intensified focus on robust ESG practices among companies and investors. Analyzing long-short portfolios of 225 Japanese and 498 US companies from 2004 to 2023 using the Fama-French five-factor and Carhart models, we found predominantly negative alphas. In the US, alphas show a large magnitude and statistically significant, indicating an ESG premium, whereas in Japan, they were insignificant. Our results shows that portfolios with lower ESG scores outperform those with higher scores. Additionally, comparing random forest and univariate sorts, we found that univariate sorts provide better insights into ESG scores in our setting
Comparative Analysis of Time Series Forecasting Libraries Across Multiple Sectors
Get PDFMasteroppgave(MSc) in Master of Science in Business Analytics, Handelshøyskolen BI, 2024This thesis presents a comparative analysis of various time-series forecasting models applied to datasets from retail sales, exchange rates, and bike-sharing systems. The study evaluates traditional statistical methods, machine learning techniques, and deep learning approaches, focusing on their accuracy, efficiency, and flexibility. The research highlights the strengths and limitations of each model, emphasizing the impact of incorporating exogenous variables.
Traditional models like SARIMA demonstrated robustness in capturing long-term trends and seasonal variations, particularly in retail sales data, but struggled with high volatility. Advanced machine learning models, such as those implemented in GluonTS, showed significant improvements with the inclusion of exogenous variables, reducing RMSE and MAE values notably. However, there were some compatibility issues that impacted the usability of some models.
The findings indicate that no single model universally outperforms others across all datasets. Instead, the effectiveness of forecasting models is highly dependent on the dataset’s specific characteristics and the inclusion of relevant exogenous variables. This research underscores the importance of model selection and customization based on the unique requirements of each forecasting task, contributing valuable insights into the field of time series analysis
Analyzing the Financial Performance of Impact Investing Funds in the Private Equity Landscape: A Comparative Analysis
Get PDFMasteroppgave(MSc) in Master of Science in Sustainable Finance, Handelshøyskolen BI, 2024This study explores the financial performance of impact investing funds compared to non-impact private equity funds. By compiling a global dataset, we aim to shed light on the profitability of impact investing in the private equity market. We find that irrespective of the measure of performance we use, impact funds generate lower returns to investors than non-impact funds. Although, given the small number of impact funds in the sample, the difference is not consistently significant. In addition, vintage year emerges as a critical factor, indicating that long-lived impact funds tend to outperform their short-lived counterparts. Also, impact funds with a higher fund number underperform, hinting that earlier funds capture the most returns. While our results suggest a potential relationship, the evidence is insufficient to draw definitive conclusions. However, we have sufficient evidence to conclude on some aspects of performance for the private equity landscape. Specifically, venture capital funds underperform buyout funds in our sample; larger fund sizes correlate with decreased financial performance, and North American funds underperform. These findings provide nuanced insights into the dynamics of private equity and impact investing, highlighting the need for further research to understand these complex relationships fully
Providing individualized services under complex conditions: A configurational analysis of street-level organizations
Get PDFIndividualized services are provided under complex conditions, as a variety of factors can affect the ability of a street-level organization to adapt its services to individual needs and circumstances. Especially challenging are tensions between the means of control and standardization following new public management (NPM) and post-NPM ideas of holistic and coordinated services. Through a fuzzy-set qualitative comparative analysis of Norwegian sector-spanning street-level organizations, we show three different configurations that can promote individualized services. These consist of variations of structural circumstances (size, service variety); organizational responses (goal coherence, cross working); and manager capacity (professional background, managerial orientation). Service individualization is not an outcome of the interaction between street-level workers and clients alone, but an outcome of street-level organizations and their managers' use of measures and competencies across service sectors, and of their capacity to develop a shared perception of goals and an organization that handles institutional complexity.Providing individualized services under complex conditions: A configurational analysis of street-level organizationspublishedVersio
The Performance of Serial Acquirers: A Review and Integrative Framework
Get PDFSerial acquirers take on multiple acquisitions as part of an acquisition program. Recently, serial acquirers have received scholarly attention from several streams of research. In this chapter, the authors review this research, focusing on the antecedents, processes, and performance of serial acquisitions. The authors develop a conceptual model that integrates the various streams of research. Based on this review, the authors argue that future research on serial acquirers should consider the complexity of integrating multiple acquisitions, by broadening the scope to include the organizational implications and long-term consequences when evaluating the performance of serial acquirersacceptedVersio
Probabilistic judgment aggregation with conditional independence constraints
Get PDFProbabilistic judgment aggregation is concerned with aggregating judgments about probabilities of logically related issues. It takes as input imprecise probabilistic judgments over the issues given by a group of agents and defines rules of aggregating the individual judgments into a collective opinion representative for the group. The process of aggregation can be subject to constraints, i.e., aggregation rules can be required to satisfy certain properties. We explore how probabilistic independence constraints can be represented and incorporated into the aggregation process.publishedVersio