Exploring the consumer decision journey and online shoping experience through an emotional prespective

Traditional consumer decision-making models have long used quantitative research to address a link between emotional and rational behavior. However, little qualitative research has been conducted in the area of online shopping as an end-to-end experience. This study aims to provide a detailed phenomenological account of consumers’ online shopping experience and extend Mckinsey & Companys’s consumer decision journey model from an emotional perspective. Six semi-structured interviews and a focus group of nine people are analyzed using Interpretive Phenomenology Analysis and five superordinate themes emerged from the results: emotional experience, empathy and encouragement, in relation to brand preference, emotional encounters in relation to consumer satisfaction and emotional exchange and relationship with a company or brand. A model interrelating these themes is then introduced to visually represent the emotional essence of a large online purchase. This study promises to be applicable as a descriptive, and perhaps, better predictive report for understanding the complex consumer decision-making process as it relates to online consumer behavior. Future research topics are also identified.

How to potentiate the business model of Mobitto for B2B?

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Hotel industry: An answer to the emerging trends on the global demand side of tourism

This dissertation studies essentially how Millennials are changing the hotel industry, in the sense that new trends are emerging with this generation and hotels need to respond accordingly, in order to survive within their competitive industry. Emphasis is also given to Asian travellers, as the enlargement of these countries’ middle class populations is predicted, therefore making Asian travellers a valuable target for the hotel industry. To successfully target this segment, hoteliers need also to consider the cultural differences and aspirations that come together with the Asian travellers, and appropriately adapt their offer to them. I will then redirect this study to the city of Lisbon, the Portuguese capital, to analyse if Lisbon’s four and five-star hotel managers are aware of the new market trends, and to understand how they are changing their hotels in order to make them more attractive to Millennials and Asian travellers. Using a sample of 12 hotels (four and five-stars ratings), I have concluded that, although there is a notable undergoing process of adaptation to these guests, there is a long way ahead in order for Lisbon’s hotels to entirely please and retain millennial guests.

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.