Consumer neuroscience: usability of brain imaging methods in marketing research

Consumer neuroscience (neuromarketing) is an emerging field of marketing research which uses brain imaging techniques to study neural conditions and processes that underlie consumption. The purpose of this study was to map this fairly new and growing field in Finland by studying the opinions of both Finnish consumers and marketing professionals towards it and comparing the opinions to the current consumer neuroscience literature, and based on that evaluate the usability of brain imaging techniques as a marketing research method. Mixed methods research design was chosen for this study. Quantitative data was collected from 232 consumers and 28 marketing professionals by means of online surveys. Both respondent groups had either neutral opinions or lacked knowledge about the four themes chosen for this study: benefits, limitations and challenges, ethical issues and future prospects of consumer neuroscience. Qualitative interview data was collected from 2 individuals from Finnish neuromarketing companies to deepen insights gained from quantitative research. The four interview themes were the same as in the surveys and the interviewees’ answers were mostly in line with the current literature, although more optimistic about the future of the field. The interviews also exposed a gap between academic consumer neuroscience research and practical level applications. The results of this study suggest that there are still many unresolved challenges and relevant populations either have neutral opinions or lack information about consumer neuroscience. The practical level applications are, however, already being successfully used and this new field of marketing research is growing both globally and in Finland.

Consumer neuroscience: usability of brain imaging methods in marketing research

Consumer neuroscience (neuromarketing) is an emerging field of marketing research which uses brain imaging techniques to study neural conditions and processes that underlie consumption. The purpose of this study was to map this fairly new and growing field in Finland by studying the opinions of both Finnish consumers and marketing professionals towards it and comparing the opinions to the current consumer neuroscience literature, and based on that evaluate the usability of brain imaging techniques as a marketing research method. Mixed methods research design was chosen for this study. Quantitative data was collected from 232 consumers and 28 marketing professionals by means of online surveys. Both respondent groups had either neutral opinions or lacked knowledge about the four themes chosen for this study: benefits, limitations and challenges, ethical issues and future prospects of consumer neuroscience. Qualitative interview data was collected from 2 individuals from Finnish neuromarketing companies to deepen insights gained from quantitative research. The four interview themes were the same as in the surveys and the interviewees’ answers were mostly in line with the current literature, although more optimistic about the future of the field. The interviews also exposed a gap between academic consumer neuroscience research and practical level applications. The results of this study suggest that there are still many unresolved challenges and relevant populations either have neutral opinions or lack information about consumer neuroscience. The practical level applications are, however, already being successfully used and this new field of marketing research is growing both globally and in Finland.

Comparison of thinning methods in a Scots pine stand on drained peatland : a simulation study

Tiivistelmä: Harvennusmenetelmien vertailu ojitetun turvemaan männikössä. Simulointitutkimus

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Agile Methods in Small Software Projects

Agile software development methods are attempting to provide an answer to the software development industry's need of lighter weight, more agile processes that offer the possibility to react to changes during the software development process. The objective of this thesis is to analyze and experiment the possibility of using agile methods or practices also in small software projects, even in projects containing only one developer. In the practical part of the thesis a small software project was executed with some agile methods and practices that in the theoretical part of the thesis were found possible to be applied to the project. In the project a Bluetooth proxy application that is run in the S60 smartphone platform and PC was developed further to contain some new features. As a result it was found that certain agile practices can be useful even in the very small projects. The selection of the suitable practices depends on the project and the size of the project team.

Level Set Methods in Image Processing

This thesis gives an overview of the use of the level set methods in the field of image science. The similar fast marching method is discussed for comparison, also the narrow band and the particle level set methods are introduced. The level set method is a numerical scheme for representing, deforming and recovering structures in an arbitrary dimensions. It approximates and tracks the moving interfaces, dynamic curves and surfaces. The level set method does not define how and why some boundary is advancing the way it is but simply represents and tracks the boundary. The principal idea of the level set method is to represent the N dimensional boundary in the N+l dimensions. This gives the generality to represent even the complex boundaries. The level set methods can be powerful tools to represent dynamic boundaries, but they can require lot of computing power. Specially the basic level set method have considerable computational burden. This burden can be alleviated with more sophisticated versions of the level set algorithm like the narrow band level set method or with the programmable hardware implementation. Also the parallel approach can be used in suitable applications. It is concluded that these methods can be used in a quite broad range of image applications, like computer vision and graphics, scientific visualization and also to solve problems in computational physics. Level set methods and methods derived and inspired by it will be in the front line of image processing also in the future.

Elicitation of Customer Requirements with Group Methods in Software Engineering

Vaatimusmäärittely on tärkeä vaihe ohjelmistotuotannossa, koska virheelliset ja puutteelliset asiakasvaatimukset vaikuttavat huomattavasti asiakkaan tyytymättömyyteen ohjelmistotuotteessa. Ohjelmistoinsinöörit käyttävät useita erilaisia menetelmiä ja tekniikoita asiakasvaatimusten kartoittamiseen. Erilaisia tekniikoita asiakasvaatimusten keräämiseen on olemassa valtava määrä.Diplomityön tavoitteena oli parantaa asiakasvaatimusten keräämisprosessia ohjelmistoprojekteissa. Asiakasvaatimusten kartoittamiseen käytettävien tekniikoiden arvioinnin perusteella kehitettiin parannettu asiakasvaatimusten keräämisprosessi. Kehitetyn prosessin testaamiseksi ja parantamiseksi järjestettiin ryhmätyöistuntoja liittyen todellisiin ohjelmistokehitysprojekteihin. Tuloksena vaatimusten kerääminen eri sidosryhmiltä nopeutui ja tehostui. Prosessi auttoi muodostamaan yleisen kuvan kehitettävästä ohjelmistosta, prosessin avulla löydettiin paljon ideoita ja prosessi tehosti ideoiden analysointia ja priorisointia. Prosessin suurin kehityskohde oli fasilitaattorin ja osallistujien valmistautumisessa ryhmätyöistuntoihin etukäteen.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.