From One Segment to a Segment of One - The Evolution of Market Segmentation Theory

The purpose of this study was to deepen the understanding of market segmentation theory by studying the evolution of the concept and by identifying the antecedents and consequences of the theory. The research method was influenced by content analysis and meta-analysis. The evolution of market segmentation theory was studied as a reflection of evolution of marketing theory. According to this study, the theory of market segmentation has its roots in microeconomics and it has been influenced by different disciplines, such as motivation research and buyer behaviour theory. Furthermore, this study suggests that the evolution of market segmentation theory can be divided into four major eras: the era of foundations, development and blossoming, stillness and stagnation, and the era of re-emergence. Market segmentation theory emerged in the mid-1950’s and flourished during the period between mid-1950’s and the late 1970’s. During the 1980’s the theory lost its interest in the scientific community and no significant contributions were made. Now, towards the dawn of the new millennium, new approaches have emerged and market segmentation has gained new attention.

Spontaanin puheen prosodinen jaksottelu

The common focus of the studies brought together in this work is the prosodic segmentation of spontaneous speech. The theoretically most central aspect is the introduction and further development of the IJ-model of intonational chunking. The study consists of a general introduction and five detailed studies that approach prosodic chunking from different perspectives. The data consist of recordings of face-to-face interaction in several spoken varieties of Finnish and Finland Swedish; the methodology is usage-based and qualitative. The term “speech prosody” refers primarily to the melodic and rhythmic characteristics of speech. Both speaking and understanding speech require the ability to segment the flow of speech into suitably sized prosodic chunks. In order to be usage-based, a study of spontaneous speech consequently needs to be based on material that is segmented into prosodic chunks of various sizes. The segmentation is seen to form a hierarchy of chunking. The prosodic models that have so far been developed and employed in Finland have been based on sentences read aloud, which has made it difficult to apply these models in the analysis of spontaneous speech. The prosodic segmentation of spontaneous speech has not previously been studied in detail in Finland. This research focuses mainly on the following three questions: (1) What are the factors that need to be considered when developing a model of prosodic segmentation of speech, so that the model can be employed regardless of the language or dialect under analysis? (2) What are the characteristics of a prosodic chunk, and what are the similarities in the ways chunks of different languages and varieties manifest themselves that will make it possible to analyze different data according to the same criteria? (3) How does the IJ-model of intonational chunking introduced as a solution to question (1) function in practice in the study of different varieties of Finnish and Finland Swedish? The boundaries of the prosodic chunks were manually marked in the material according to context-specific acoustic and auditory criteria. On the basis of the data analyzed, the IJ-model was further elaborated and implemented, thus allowing comparisons between different language varieties. On the basis of the empirical comparisons, a prosodic typology is presented for the dialects of Swedish in Finland. The general contention is that the principles of the IJ-model can readily be used as a methodological tool for prosodic analysis irrespective of language varieties.

Kiinan kielen automaattinen käsittely ja kieliteknologia

Tutkielma käsittelee kiinan kielen automaattista käsittelyä ja kieliteknologiaa. Kieliteknologian osa-alueista keskitytään kiinan kielelle tyypilliseen sanarajatunnistus- eli segmentointiongelmaan, joka kumpuaa kiinan kielen kirjoitusjärjestelmän erityispiirteistä. Tutkielma on aihepiiriä esittelevä pilottitutkimus, jonka tarkoitettu lukijaryhmä on kiinan kieliteknologisesta tutkimuksesta kiinnostuneet opiskelijat ja tutkijat. Lähdemateriaali koostuu englannin- ja kiinankielisestä kirjallisuudesta, lähinnä konferenssiartikkeleista. Tutkielma esittelee kiinan kirjoitusjärjestelmää automaattisen käsittelyn näkökulmasta, käsittelee perinteisten ja yksinkertaistettujen merkkien eroja, merkkikoodauksia sekä erilaisia lähestymistapoja käyttäviä syöttöjärjestelmiä. Kirjoitusjärjestelmän esittely tarjoaa esitietoja kielen rakenteen ymmärtämiseksi sekä rakentaa pohjaa sanarajatunnistusta käsitteleviä osuuksia varten. Sanarajatunnistus- eli segmentointiongelma johtuu kiinan kirjoitusjärjestelmästä, jossa sanojen välejä ei merkitä välilyönneillä. Kielen kieliteknologista käsittelyä varten sanojen rajat tulee kuitenkin selvittää. Sanarajatunnistusjärjestelmät ovat tietokoneohjelmia, jotka etsivät ja merkitsevät nämä rajat automaattisesti. Tehtävä ei kuitenkaan ole yksinkertainen kielen monitulkintaisuuksien ja ns. tuntemattomien sanojen vuoksi. Joissain tilanteissa ei ole olemassa yksiselitteisen oikeaa segmentointia. Tutkielmassa esitellään kaksi segmentointijärjestelmää, keskittyen erityisesti niiden toiminnan kuvaukseen lukijalle ymmärrettävässä muodossa. Tärkeää on menetelmien ymmärtäminen, ei tekniset yksityiskohdat. Lopuksi paneudutaan segmentointijärjestelmien evaluaation ongelmiin. Sanarajatunnistusta suorittavien ohjelmien vertailu on usein hankalaa, koska monissa tapauksissa järjestelmät eivät tuota yhteismitallisia tuloksia. Tutkielmassa esitellään yritys saada aikaan yhteismitallisia evaluaatiomenetelmiä segmentointiohjelmien Chinese Word Segmentation Bakeoff -kilpailujen muodossa. Tutkielmassa todetaan sanarajatunnistusongelman olevan tärkeä tutkimuskohde. Ratkaisemattomia ongelmia on kuitenkin edelleen, tärkeimpänä evaluaatio. Avainsanat – Nyckelord – Keywords kiinan kieli, sanarajatunnistus, segmentointi,kirjoitusmerkit, merkkikoodaukset, kiinan syöttötavat

3 - 5-vuotiaiden lukemaan oppiminen : montessorivälineet kielellisen tietoisuuden ja varhaisen lukitaidon edistäjinä

Aims. The beginning point of this research was confusion between studies claiming, that children mature Metalinguistic to read at 6-7 of age, and the fact, that in Montessori playschools children easily start writing and reading at age 3 to 5. Aim was also find out how conception of slow Metalinguistic development has started, and if there is some evidence of phoneme awareness of reading of young children in the field of research of reading. Aim was also seek evidence of the sensitive period of reading as Montessori described it. The research also wanted to turn up, if phoneme awareness only develops in children, who work with graphemes and with reading, or could it be found in children, who do not. The mean was to research how the Montessori reading material supports child’s Metalinguistic development, when child begins learning to read. The research plans to represent knowledge about how young children learn to write and read. Methods. Research performed in ordinary kindergarten and in Montessori playschool in Espoo. In kindergarten observed six children, age 3-4, at eight grapheme-rhyme sessions from January to April 2007, and conducting a test based on Chaney’s (1992) study of phoneme awareness of young children. In Montessori kindergarten were observed 17 children about their phoneme awareness and reading competition from January 2007 to March 2008. Their developments in reading were also measured three times from 1.9.07 to 20.3.08 with classification constructed for this study, loosely based on Chall’s (1983) reading stages. The Montessori reading material was analyzed about the influence they have to a child’s Metalinguistic development. This was done based to theory and its concepts from the field of research of reading; phoneme awareness, morphological, syntactical and semantic consciousness. Results and conclusions. Research proved that children 3-5 have naturally developed phoneme awareness. In kindergarten and in Montessori playschool children between 2 and 4 could do phoneme synthesis, and in the latter they also could do phoneme segmentation of words. Montessori reading material guided children gradually, except to read, also to observe and absorb Metalinguistic knowledge. Children learned to write and read. At the last evaluating day almost 50 % of children write and read clauses or stories, and 82 % could read at least words. Children can develop Metalinguistic awareness, while using the Montessori materials for learning to write and read. To reach literacy is easy for children because of their phoneme awareness.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Mining Sequential Data – in Search of Segmental Structures

Segmentation is a data mining technique yielding simplified representations of sequences of ordered points. A sequence is divided into some number of homogeneous blocks, and all points within a segment are described by a single value. The focus in this thesis is on piecewise-constant segments, where the most likely description for each segment and the most likely segmentation into some number of blocks can be computed efficiently. Representing sequences as segmentations is useful in, e.g., storage and indexing tasks in sequence databases, and segmentation can be used as a tool in learning about the structure of a given sequence. The discussion in this thesis begins with basic questions related to segmentation analysis, such as choosing the number of segments, and evaluating the obtained segmentations. Standard model selection techniques are shown to perform well for the sequence segmentation task. Segmentation evaluation is proposed with respect to a known segmentation structure. Applying segmentation on certain features of a sequence is shown to yield segmentations that are significantly close to the known underlying structure. Two extensions to the basic segmentation framework are introduced: unimodal segmentation and basis segmentation. The former is concerned with segmentations where the segment descriptions first increase and then decrease, and the latter with the interplay between different dimensions and segments in the sequence. These problems are formally defined and algorithms for solving them are provided and analyzed. Practical applications for segmentation techniques include time series and data stream analysis, text analysis, and biological sequence analysis. In this thesis segmentation applications are demonstrated in analyzing genomic sequences.

Dental Service Utilization, Dental Health Production and Equity in Dental Care: the Finnish Experience

This thesis is grounded on four articles. Article I generally examines the factors affecting dental service utilization. Article II studies the factors associated with sector-specific utilization among young adults entitled to age-based subsidized dental care. Article III explores the determinants of dental ill-health as measured by the occurrence of caries and the relationship between dental ill-health and dental care use. Article IV measures and explains income-related inequality in utilization. Data employed were from the 1996 Finnish Health Care Survey (I, II, IV) and the 1997 follow-up study included in the longitudinal study of the Northern Finland 1966 Birth Cohort (III). Utilization is considered as a multi-stage decision-making process and measured as the number of visits to the dentist. Modified count data models and concentration and horizontal equity indices were applied. Dentist s recall appeared very efficient at stimulating individuals to seek care. Dental pain, recall, and the low number of missing teeth positively affected utilization. Public subvention for dental care did not seem to statistically increase utilization. Among young adults, a perception of insufficient public service availability and recall were positively associated with the choice of a private dentist, whereas income and dentist density were positively associated with the number of visits to private dentists. Among cohort females, factors increasing caries were body mass index and intake of alcohol, sugar, and soft drinks and those reducing caries were birth weight and adolescent school achievement. Among cohort males, caries was positively related to the metropolitan residence and negatively related to healthy diet and education. Smoking increased caries, whereas regular teeth brushing, regular dental attendance and dental care use decreased caries. We found equity in young adults utilization but pro-rich inequity in the total number of visits to all dentists and in the probability of visiting a dentist for the whole sample. We observed inequity in the total number of visits to the dentist and in the probability of visiting a dentist, being pro-poor for public care but pro-rich for private care. The findings suggest that to enhance equal access to and use of dental care across population and income groups, attention should focus on supply factors and incentives to encourage people to contact dentists more often. Lowering co-payments and service fees and improving public availability would likely increase service use in both sectors. To attain favorable oral health, appropriate policies aimed at improving dental health education and reducing the detrimental effects of common risk factors on dental health should be strengthened. Providing equal access with respect to need for all people ought to take account of the segmentation of the service system, with its two parallel delivery systems and different supplier incentives to patients and dentists.

Does relationship marketing improve customer relationship satisfaction and loyalty?

Purpose - This study investigates the relationship marketing (RM) strategy of a retail bank and examines whether - after its implementation - customer relationships were strengthened through perceived improvements in the banking relationship and consequent loyalty towards the bank. Design/methodology/approach - A survey was conducted on two profitability segments, of which the more profitable segment had been directly exposed to a customer oriented RM strategy, whereas the less profitable segment had been subjected to more sales oriented marketing communications. Findings - No significant differences were found between the segments on customers’ evaluations of the service relationship or their loyalty toward the bank. Furthermore regression analysis revealed that relationship satisfaction was less important as a determinant of loyalty in the more profitable segment. Research limitations/implications - This study was conducted as a case study of one specific branch of a bank group in Finland, which limits the external validity of its results. It was not possible to ascertain if, or to what extent, customers of the more profitable segment had received the intended RM treatment. Other limitations are also discussed. Practical implications - Customer orientation is desirable within retail banking and more studies are needed on the differential drivers of loyalty across customer profitability segments. By identifying the aspects of a banking relationship that are more highly valued among more profitable customers than among less profitable customers, bank managers would be able to more effectively devise appropriate strategies for different segments. Originality/value - The study contributes to the RM literature and marketing of financial services by providing empirical evidence of the effects of RM activities on customer relationship perceptions in different profitability segments.

Panel Cointegration of Chinese A and B Shares

This paper uses panel unit root and cointegration methods to test the stationarity of the premium on domestic investors’ A shares over foreign investors’ B shares and cointegration between the A and B share prices on the Chinese stock exchanges. We find that the A share price premium is nonstationary until 2001, when the A and B share markets were partially merged, and that the A and B share prices are cointegrated in the panel.Cointegration is more likely to be found for firms in the service sector and for firms that issued B shares recently.