Browsing by Issue Date, starting with "2023-01-27"
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- The impact of performance communication on process efficiency : a RPA implementation projectPublication . Morfeld, Franz-Jakob; Wagner, LauraHow can you use an effective and reproducible process to incentivise executives of one of Portugal's largest companies to prioritise a repetitive task? This question led to our main goal of the project, which is documented in the following and supported by fundamental literature research, how to design and implement effective performance-based communication through the technological mapping of Robotic Process Automation. The problem of the Master Thesis was brought to the author's attention by the research partnership of the Portuguese Postal Service CTT with the Catolica Lisbon School of Business and Economics and was attempted to be solved in an implementation project with the help of the already existing RPA capabilities of CTT. In a first step, the processes affected by the problem were documented and subsequently restructured with the help of the CTT project management framework in order to integrate them into the architecture of the organisation using the RPA technology. In order to solve the problem of incentivising higher prioritisation of the task, the nudging method of communication was identified. By applying this, personalised ranking-based notifications were sent to solve the problem. In summary, the underlying work should provide the reader with documentation to apply the described process in a replicable way to comparable problems.
- The influence of artificial intelligence and organizational levels on meaning at workPublication . Gebben, Aimée Jacomijn; Almeida, Filipa deAs AI can change essential tasks, it can affect occupational boundaries and consequential team and organizational structures, thereby changing work's social relations, structures and meaning (Craig et al., 2019). Experiencing meaning at work has many positive effects in both work and life in general, and it is therefore, essential to optimize as much as possible. This research aims to find out whether AI does influence meaning at work, how it influences meaning at work and if this influence of AI differs over organizational levels. The results of this research indicate that AI negatively influences meaning at work significantly. Although AI negatively influences all components (positive meaning, meaning-making and greater good motivations) of meaning at work, the influence differentiates over those components, where positive meaning is influenced the most and greater good motivations the least. Other than that, it is found that AI's influence differs over organizational levels, where the strategic (higher) organizational level was significantly affected more than operational (lower) level. These findings imply that AI influences certain aspects of work more than others. Theoretical and managerial implications are discussed.
- How can companies in the clothing and textile industry manage tensions in the implementation of circular business models? A paradox perspectivePublication . Åsberg, Isabella Margareta Amorina; Leglise, LaureThis study aims to examine tensions in the implementation of circular business models in the clothing and textile industry and the responding strategies employed by companies to manage these tensions. Adopting a qualitative approach, and drawing on paradox theory, I conducted a comparative case study on five Swedish companies in the clothing and textile industry that actively strive to incorporate sustainability in their business strategy. The findings suggest that all companies experienced tensions whilst learning, performing, and organizing were more present than belonging. Additionally, all companies emphasized acceptance and resolution strategies. The results potentially show that applying paradoxical thinking within organizations can help to reduce tensions related to complex sustainability challenges and facilitate to manage the implementation of circular business models in the clothing and textile industry.
- Algorithmic aversion in artificial intelligence co-leadership and the impact of metaphors and comparisonsPublication . Leal, Carolina Maria Sequeira; Mendonça, CristinaThe importance of Artificial Intelligence (AI) has been increasing at a fast pace. Besides transforming our lives, these technologies are crucial to improving business operations by conducting tasks faster, better and with lower costs, and by helping in the decisionmaking process that is an intellectually demanding task. However, despite the advantages AI can offer, people still disbelieve the ability of algorithms, often prefer to decide for themselves and refuse to rely on algorithms after seeing them err. This phenomenon is called algorithm aversion and goes against the best interest of companies that need to gain competitive advantage in a very competitive market. In this way, this dissertation intends to study the potential use of metaphors and comparisons as strategies to reduce algorithm aversion. For this to be done, an experimental study with three experimental groups was conducted. The effect of a languagebased metaphor, a visual metaphor and an explicit comparison between human and artificial neurons was studied by relying on a specific type of AI called Artificial Neural Network (ANN) to see if people would prefer this technology that seems to function in a similar way to humans, over a general AI in a leadership position. The results of the study did not corroborate the hypothesis that the metaphors were going to reduce algorithm aversion, as the only difference found in the leadership acceptance was between the general AI group and the human one in which the new leader was a normal person.
- The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business contextPublication . Fortuin, Eva Kim; Almeida, Filipa deWith the intention of overcoming human decision making biases, organizations are increasingly using AI as decision support. However, to unlock the full potential of AI-based advice to improve decision making, users must be willing to rely on it in the first place. To better understand people’s readiness to accept advice from AI, two experimental studies were conducted in the scope of this research. Study 1 examined whether people rely more on advice coming from AI or a human. People showed algorithm appreciation in both tasks – the performance evaluation of an employee and the closing price prediction of a stock. The effect was fully mediated by people’s trust in the source and varied across different levels of confidence in one’s own decision. Study 2 examined whether people also choose AI advice over human advice when presented with both options and whether they choose equally for themselves and for others. In this setting, algorithm appreciation persisted only for the stock price prediction task, irrespectively of who the decision was made for. Furthermore, several influencing factors were identified that point to domains where AI is most likely to be accepted and ways in which its benefits can be maximized. The results from these studies have clear implications for organizations that turn to Big Data and AI-generated advice to improve decision making, suggesting that AI might be a good addition to their daily operations.
- Characterization of environmental and clinical antibiotic-resistant Escherichia coli isolatesPublication . Filipe, Daniel Fernandes Magalhães; Rodrigues, Célia Maria Manaia; Ferreira, Ana Catarina MorouçoThe rapid spread of antibiotic resistance among human pathogenic bacteria has been a global public health problem, expanding in recent decades. Antibiotic resistance genes found in clinical pathogens are not the only cause for concern, as it is recognized that commensal and environmental bacteria, as well as their mobile genetic elements, can function as reservoirs and vectors of resistance, and can spread genes through horizontal transfer and selection. The conjugation process is the horizontal gene transfer mechanism considered to be the most relevant in resistance spread. It is thought that the spread of resistance genes can be enhanced in the presence of various substances such as metal salts or antibiotics. Knowing the conditions under which mobilization takes place is crucial to control resistance spread. This study aimed to compare Escherichia coli isolates from clinical, wastewater and surface water samples (n=52), in order to assess if resistance profiles were source-dependent. A second objective was to understand if the rate and profile of antibiotic resistance gene transfer differed depending on the conjugation temperature. This work involved phenotypic and genotypic characterization and detection of resistance genes, as well as conjugation assays between a carbapenem resistant strain and a reference receptor. Specifically, susceptibility to different classes of antibiotics was determined by the disk diffusion method, PCR detection of antibiotic resistance genes and types of replicons present in plasmids was performed. In the conjugation assays, different temperatures were tested (25, 28, 35 and 40ºC), the transconjugants were selected in the presence of azide and ceftazidime, confirmed by genotyping and characterized for the presence of specific genetic determinants. Genotypic and phenotypic characterization of isolates identified as E. coli did not reveal significant differences (p>0.05) between environmental and clinical isolates, with the exception of IncF-type replicon plasmids, which were significantly (p0.05) between the rates observed at different temperatures. The transmission of the IncN plasmid holding the blaKPC gene was detected in all transconjugants analysed. However, the transmission of the FIBtype replicon was higher at conjugation temperatures of 35 and 40ºC than at 25 and 28ºC.
- Predicting and explaining Airbnb prices in Lisbon : machine learning approachPublication . Nunes, Madalena Ribeiro dos Santos Pais; Guedes, AnaAirbnb is an online platform that provides listing and arrangement for short-term local home renting services. Since its establishment in 2008, it has offered 7 million homes and rooms in more than 81,000 cities throughout 191 countries. Airbnb price prediction is a valuable and important task both for guests and hosts. Overall, for practical applications, these models can give a host an optimal price they should charge for their new listing. On the consumer side, this will help travellers determine whether the listing price they see is fair. Much research has been done in this field; however, the longitude and latitude of Airbnb listings are often disregarded. This project focuses on Airbnb price prediction using the most recent (Sep 2021) Airbnb data in Lisbon. Using Google Maps API, the original dataset was enriched with information on the number of ATMs, metro stations, bars and discos within a maximum radius of 1 km. Also, using the geodesic distance, the distance to the airport and the nearest attraction were computed for each listing. A Linear Regression and a Gradient Boosting algorithm were compared based on the original Airbnb dataset and the extended dataset to examine the impact of new features that have been identified. According to the results, all models perform better when the new features are included. The best results are achieved with the Gradient Boosting with the extended data, with an MAE of 0. 3102 and an adjusted R-squared of 0.4633.
- Customer relationship management as a cloud service empirical inductive analysis of cloud CRM implementation using salesforce as an examplePublication . Füsti-Molnár, Robin Benjamin; Almeida, Ana Filipa Martinho deNowadays, companies must adapt to rapidly changing market situations. Technologies are con stantly evolving and impacting the cutting-edge Customer Relationship Management (CRM) systems. By implementing modern cloud CRM systems like Salesforce, companies can adapt to changing customer needs, which can help gather and effectively use data about their custom ers and stay competitive in the marketplace. This thesis examines how companies have mi grated their CRM systems to the cloud. It is based on the following three research questions: Which benefits and opportunities arise from the implementation of Salesforce? Which obsta cles and risks are companies facing during the change process? Which actions should be taken to achieve a successful implementation? In answering them, it explains which benefits, oppor tunities, downsides, and obstacles are faced by clients and consultants by the conversion to Salesforce compared to the on-premises solutions used to date. In addition, recommendations for action that have positively influenced the implementation and are thus considered to point the way for other implementation approaches are identified. Based on the results of in-depth interviews, this thesis inductively derives empirical results. In addition, existing information and theories on cloud-CRM implementation are drawn upon to analyze and elaborate on the insights gained in the interviews.
- Eliminação da dupla tributação económica e seguros unit linkedPublication . Mina, Catarina Vilhena; Gama, João Taborda da
- Application of explainable AI in Machine Learning models to identify the main determinants of Bitcoin pricePublication . Morais, Ana Sofia Rosa; Guedes, AnaSince 2019, Bitcoin has become one of the most popular assets in the world. However, this decentralised cryptocurrency is typically characterised by high volatility and, in that sense, creates some concerns mainly to regulatory authorities and other decision-makers, such as governments and legislators. Furthermore, there are multiple approaches and results in the literature regarding the most relevant determinants to predict the Bitcoin price, the complexity of the Machine Learning (ML) model used to predict the Bitcoin price, and the trade-off between interpretability and the model’s performance. As a starting point, the simple model called Generalized Least Squares with Autocorrelation covariance structure (GLSAR) was found to be unrealistic to predict something as complex as the Bitcoin price. Alternatively, two more complex black box models were tested: a Long Short Term Memory neural network (LSTM) and a simple Deep Neural Network (DNN). LSTM achieved the highest 𝑅 2 score of 81.63% with DNN obtaining a 𝑅 2 score of 81.27%. Explainability techniques were applied on DNN and the results indicate that 71% of the twenty-one most significant variables are transaction-based, although future analysis can be done for occasional events. Moreover, the three most important features are the S&P500, the Bitcoin price in the previous day and how difficult it is to mine a Bitcoin block.
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