Cookies är idag ett utbrett fenomen som nyttjas vid digital insamling av information. Informationen som samlas in är ofta av personlig karaktär och används bland annat för att individanpassa användarupplevelser på webbsidor. Ovissheten kring digital insamling av personlig information skapar en oro som idag är mycket omdebatterad. Detta arbete utgörs av en kvantitativ studie med syfte att undersöka möjliga samband mellan universitetsstudenters medvetenhet, attityd och beteende gällande digital insamling av information. Undersökningen består av en onlinebaserad enkät. Det insamlade materialet har analyserats utifrån det teoretiska ramverket Communication Privacy Management (CPM) med hjälp av analysmetoden Partial Least Squares (PLS) samt IBM Statistical Package for Social Sciences (SPSS) för att finna relevanta samband. Resultat påvisade att majoriteten universitetsstudenter känner till fenomenet cookies, men färre känner till dess användningsområden. Det konstaterades även att medvetenhet kring cookies har en påverkan på både beteende och attityd. Det fastställdes även att oavsett om universitetsstudenter har en negativ attityd förändras inte dess beteende. / Cookies are a widespread phenomenon and the main technique for digital collection of information. The collected information is often of personal nature and is used, among other things, to personalize user experiences on web pages. The uncertainty regarding digital collection of personal information creates privacy concerns that is significantly debated today. This essay consists of a quantitative study with the aim to investigate possible relations between university students awareness, attitude and behaviour regarding digital collection of information. The survey consist of an online-based poll. The gathered material has been analyzed by the theoretical framework Communication Communication Management (CPM) with the analysis method Partial Least Squares (PLS) and the IBM Statistical Package for Social Sciences (SPSS) to find relevant relations. Results showed that the majority of university students are familiar with the phenomenon of cookies, but few are aware of its area of use. It was also found that awareness of cookies has an influence on both behaviour and attitude. It can be established that regardless of whether university students have a negative or positive attitude regarding cookies, it does not affect their behaviour.

Recently computational topology had an important development in data analysis giving birth to the field of Topological Data Analysis. Persistent homology appears as a fundamental tool based on the topology of data that can be represented as points in metric space. In this work, we apply techniques of Topological Data Analysis, more precisely, we use persistent homology to calculate topological features more persistent in data. In this sense, the persistence diagrams are processed as feature vectors for applying Machine Learning algorithms. In order to classification, we used the following classifiers: Partial Least Squares-Discriminant Analysis, Support Vector Machine, and Naive Bayes. For regression, we used Support Vector Regression and KNeighbors. Finally, we will give a certain statistical approach to analyze the accuracy of each classifier and regressor. / Recentemente a topologia computacional teve um importante desenvolvimento na análise de dados dando origem ao campo da Análise Topológica de Dados. A homologia persistente aparece como uma ferramenta fundamental baseada na topologia de dados que possam ser representados como pontos num espaço métrico. Neste trabalho, aplicamos técnicas da Análise Topológica de Dados, mais precisamente, usamos homologia persistente para calcular características topológicas mais persistentes em dados. Nesse sentido, os diagramas de persistencia são processados como vetores de características para posteriormente aplicar algoritmos de Aprendizado de Máquina. Para classificação, foram utilizados os seguintes classificadores: Análise de Discriminantes de Minimos Quadrados Parciais, Máquina de Vetores de Suporte, e Naive Bayes. Para a regressão, usamos a Regressão de Vetores de Suporte e KNeighbors. Finalmente, daremos uma certa abordagem estatística para analisar a precisão de cada classificador e regressor.

O objetivo deste estudo é investigar, de forma comparativa entre as categorias alimento e limpeza doméstica, os antecedentes da intenção do consumidor comprar marcas próprias, bem como analisar os efeitos da diferença nos níveis de risco percebido entre essas duas categorias de produtos na formação da intenção de compra. Para o alcance desse objetivo construiu-se um modelo com base em revisões da literatura sobre marcas próprias, bem como sobre risco percebido, imagem da loja, imagem da marca própria e atitudes, construtos identificados como os principais preditores da intenção de compra. O marco teórico permitiu a construção de hipóteses acerca dos principais relacionamentos existentes entre esses construtos no contexto de marcas próprias. A seguir foi realizada uma pesquisa empírica com o objetivo de testar tais hipóteses, utilizando um questionário auto-administrado elaborado com escalas já validadas na literatura. Esse levantamento de campo (survey) coletou opiniões de uma amostra não probabilística de 1.938 clientes de supermercados, composta por pessoas de ambos os gêneros e provenientes de 26 Estados do Brasil e do Distrito Federal, com predominância do Estado de São Paulo. Os resultados obtidos por meio de modelagem de equações estruturais utilizando a ferramenta Smart PLS demonstram que há relacionamentos significativos entre o risco percebido e a imagem da marca própria; entre a imagem da loja e imagem da marca própria; entre a imagem da marca própria e a atitude; e entre a atitude e a intenção de compra, validando as hipóteses de trabalho. O estudo também conclui que o risco percebido e a imagem da loja são fortes preditores da imagem da marca própria e da atitude a qual, por sua vez, é uma forte preditora da intenção de compra. Por fim, os resultados sugerem que a imagem da loja exerce maior influência na imagem da marca própria quando se trata da categoria alimentos, identificada como de menor risco percebido. Já na categoria limpeza doméstica, a cadeia mais relevante de antecedentes à intenção de compra por parte dos consumidores passa pelas relações entre risco percebido, imagem da marca e atitude frente a marcas próprias. Essas conclusões sugerem diferenças de intensidade, senão em natureza, nas relações entre os antecedentes psicológicos à intenção de compra do consumidor quando são consideradas categorias que se diferenciam no risco percebido pelo cliente, contribuindo para uma melhor compreensão teórica do fenômeno e sugerindo implicações gerenciais na adoção de estratégias de marketing por parte dos varejistas detentores de marcas próprias. / This study aims to investigate the antecedents of consumer´s intention to purchase private brands and analyze the effects of different perceived risk levels in intention formation by comparing food and house cleaning product categories. To attain to such goal a model was developed based on literature reviews about private brands, as well as perceived risk, store image, brand image, and attitudes, constructs identified as the main predictors for purchase intention. The theoretical framework allowed the construction of hypotheses about the key relationships among the constructs in the context of private brands. A survey was then designed and executed in order to test the hypotheses. A self-administered questionnaire was build with validated scales found in marketing literature. The empirical study collected opinions from a non-probabilistic sample of 1,938 private brands supermarket shoppers, comprising persons of both genders and from all 26 Brazilian states and the Federal District, predominating residents in the state of São Paulo. Results obtained by Structural Equation Modeling using Smart PLS suggest there are significant relationships between perceived risk and private brand image; between store image and brand image; between private brand image and attitude; and between attitude and purchase intention, validating the proposed hypotheses. The study also concluded that perceived risk and store image are strong predictors for private brand image and attitude which, in turn, is a strong predictor for purchase intention. Finally, results suggest that store image has more influence on private brand image when food products are the focused category as consumers present lower perceived risk toward it. For house cleaning products, the most relevant antecedents for consumer´s purchase intention form a chain of relations from perceived risk, brand image and attitude towards private labels. These findings suggest differences in intensity, if not in nature, on the relations among consumer´s psychological antecedents for purchase intention when categories that differ on perceived risk are considered. Such findings contribute to a better theoretical understanding of the phenomenon and suggest managerial implications for marketing strategies to retailers who hold private brands.

Ce travail concerne les analyses visant à étudier les données où les individus sont structurés en différents groupes (données multi-groupes). La thèse aborde la question des données multi-groupes ayant une structure en un seul tableau, plusieurs tableaux, trois voies et deux blocs (régression). Cette thèse présente plusieurs méthodes d'analyse de données multi-groupes dans le cadre de l'analyse factorielle. Notre travail comporte trois parties. La première partie traite de l'analyse de données multi-groupes (un bloc de variables divisé en sous-groupes d'individus). Le but est soit descriptif (analyse intra-groupes) ou prédictif (analyse discriminante ou analyse inter-groupe). Nous commençons par une description exhaustive des méthodes multi-groupes. En outre, nous proposons deux méthodes : l'Analyse Procrustéenne duale et l'Analyse en Composantes Communes et Poids Spécifiques duale. Nous exposons également de nouvelles propriétés et algorithmes pour l'Analyse en Composantes Principales multi-groupes. La deuxième partie concerne l'analyse multi-blocs et multi-groupes et l'analyse trois voies et multi-groupes. Nous présentons les méthodes existantes. Par ailleurs, nous proposons deux méthodes, l'ACP multi-blocs et multi-groupes et l'ACP multi-blocs et multi-groupes pondérée, vues comme des extensions d'Analyse en Composantes Principales multi-groupes. L'analyse en deux blocs et multi-groupes est prise en compte dans la troisième partie. Tout d'abord, nous présentons des méthodes appropriées pour trouver la relation entre un ensemble de données explicatives et un ensemble de données à expliquer, les deux tableaux présentant une structure de groupe entre les individus. Par la suite, nous proposons quatre méthodes pouvant être vues comme des extensions de la régression PLS au cas multi-groupes, et parmi eux, nous en sélectionnons une et la développons dans une stratégie de régression. Les méthodes proposées sont illustrées sur la base de plusieurs jeux de données réels dans le domaine de la biologie. Toutes les stratégies d'analyse sont programmées sur le logiciel libre R. / This work deals with multi-group analysis, to study multi-group data where individuals are a priori structured into different groups. The thesis tackles the issue of multi-group data in a multivariate, multi-block, three-way and two-block (regression) setting. It presents several methods of multi-group data analysis in the framework of factorial analysis. It includes three sections. The first section concerns the case of multivariate multi-group data. The aim is either descriptive (within-group analysis) or predictive (discriminant analysis, between-group analysis). We start with a comprehensive review of multi-group methods. Furthermore, we propose two methods namely Dual Generalized Procrustes Analysis and Dual Common Component and Specific Weights Analysis. We also exhibit new properties and algorithms for multi-group Principal Component Analysis. The second section deals with multiblock multi-group and three-way multi-group data analysis. We give a general review of multiblock multi-group methods. In addition, we propose two methods, namely multiblock and multi-group PCA and Weighted-multiblock and multi-group PCA, as extensions of multi-group Principal Component Analysis. The two-block multi-group analysis is taken into account in the third section. Firstly, we give a presentation of appropriate methods to investigate the relationship between an explanatory dataset and a dependent dataset where there is a group structure among individuals. Thereafter, we propose four methods, namely multi-group PLS, in the PLS approach, and among them we select one and develop it into a regression strategy. The proposed methods are illustrated on the basis of several real datasets in the field of biology. All the strategies of analysis are implemented within the framework of R.

La recherche développée dans le cadre de cette thèse aborde différents aspects relevant de l’analyse statistique de données. Dans un premier temps, une analyse de trois indices d’associations entre deux tableaux de données est développée. Par la suite, des stratégies d’analyse liées à la standardisation de tableaux de données avec des applications en analyse en composantes principales (ACP) et en régression, notamment la régression PLS sont présentées. La première stratégie consiste à proposer une standardisation continuum des variables. Une standardisation plus générale est aussi abordée consistant à réduire de manière graduelle non seulement les variances des variables mais également les corrélations entre ces variables. De là, une approche continuum de régression a été élaborée regroupant l’analyse des redondances et la régression PLS. Par ailleurs, cette dernière standardisation a inspiré une démarche de régression biaisée dans le cadre de régression linéaire multiple. Les propriétés d’une telle démarche sont étudiées et les résultats sont comparés à ceux de la régression Ridge. Dans le cadre de l’analyse de plusieurs tableaux de données, une extension de la méthode ComDim pour la situation de K+1 tableaux est développée. Les propriétés de cette méthode, appelée P-ComDim, sont étudiées et comparées à celles de Multiblock PLS. Enfin, la situation où il s’agit d’évaluer l’effet de plusieurs facteurs sur des données multivariées est considérée et une nouvelle stratégie d’analyse est proposée. / The research developed in this thesis deals with several statistical aspects for analyzing datasets. Firstly, investigations of the properties of several association indices commonly used by practitioners are undergone. Secondly, different strategies related to the standardization of the datasets with application to principal component analysis (PCA) and regression, especially PLS-regression were developed. The first strategy consists of a continuum standardization of the variables. The interest of such standardization in PCA and PLS-regression is emphasized.A more general standardization is also discussed which consists in reducing gradually not only the variances of the variables but also their correlations. Thereafter, a continuum approach was developed combining Redundancy Analysis and PLS-regression. Moreover, this new standardization inspired a biased regression model in multiple linear regression. Properties related to this approach are studied and the results are compared on the basis of case studies with those of Ridge regression. In the context of the analysis of several datasets in an exploratory perspective, the method called ComDim, has certainly raised interest among practitioners. An extension of this method for the analysis of K+1 datasets was developed. Properties related to this method, called P-ComDim, are studied and compared to Multiblock PLS. Finally, for the analysis of datasets depending on several factors, a new approach based on PLS regression is proposed.

<p> </p><p>The today quarry planning at Klinthagen is rough, which provides an opportunity to introduce new exciting methods to improve the quarry gain and efficiency. Nordkalk AB, active at Klinthagen, wishes to start a new quarry at a nearby location. To exploit future quarries in an efficient manner and ensure production quality, multivariate statistics may help gather important information.</p><p>In this thesis the possibilities of the multivariate statistical approaches of Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were evaluated on the Klinthagen bore data. PCA data were spatially interpolated by Kriging, which also was evaluated and compared to IDW interpolation.</p><p>Principal component analysis supplied an overview of the variables relations, but also visualised the problems involved when linking geophysical data to geochemical data and the inaccuracy introduced by lacking data quality.</p><p>The PLS regression further emphasised the geochemical-geophysical problems, but also showed good precision when applied to strictly geochemical data.</p><p>Spatial interpolation by Kriging did not result in significantly better approximations than the less complex control interpolation by IDW.</p><p>In order to improve the information content of the data when modelled by PCA, a more discrete sampling method would be advisable. The data quality may cause trouble, though with sample technique of today it was considered to be of less consequence.</p><p>Faced with a single geophysical component to be predicted from chemical variables further geophysical data need to complement existing data to achieve satisfying PLS models.</p><p>The stratified rock composure caused trouble when spatially interpolated. Further investigations should be performed to develop more suitable interpolation techniques.</p>

Varje studie av en verklig process eller ett verkligt system är baserat på mätdata. Förr var den tillgängliga datamängden vid undersökningar ytterst begränsad, men med dagens teknik är mätdata betydligt mer lättillgängligt. Från att tidigare enbart haft få och ofta osammanhängande mätningar för någon enstaka variabel, till att ha många och så gott som kontinuerliga mätningar på ett större antal variabler. Detta förändrar möjligheterna att förstå och beskriva processer avsevärt. Multivariat analys används ofta när stora datamängder med många variabler utvärderas. I det här projektet har de multivariata analysmetoderna PCA (principalkomponentanalys) och PLS (partial least squares projection to latent structures) använts på data över avloppsvatten insamlat på Hammarby Sjöstadsverk. På reningsverken ställs idag allt hårdare krav från samhället för att de ska minska sin miljöpåverkan. Med bland annat bättre processkunskaper kan systemen övervakas och styras så att resursförbrukningen minskas utan att försämra reningsgraden. Vissa variabler är lätta att mäta direkt i vattnet medan andra kräver mer omfattande laboratorieanalyser. Några parametrar i den senare kategorin som är viktiga för reningsgraden är avloppsvattnets innehåll av fosfor och kväve, vilka bland annat kräver resurser i form av kemikalier till fosforfällning och energi till luftning av det biologiska reningssteget. Halterna av dessa ämnen i inkommande vatten varierar under dygnet och är svåra att övervaka. Syftet med den här studien var att undersöka om det är möjligt att utifrån lättmätbara variabler erhålla information om de mer svårmätbara variablerna i avloppsvattnet genom att utnyttja multivariata analysmetoder för att skapa modeller över variablerna. Modellerna kallas ofta för mjukvarusensorer (soft sensors) eftersom de inte utgörs av fysiska sensorer. Mätningar på avloppsvattnet i Linje 1 gjordes under tidsperioden 11 – 15 mars 2013 på flera ställen i processen. Därefter skapades flera multivariata modeller för att försöka förklara de svårmätbara variablerna. Resultatet visar att det går att erhålla information om variablerna med PLS-modeller som bygger på mer lättillgänglig data. De framtagna modellerna fungerade bäst för att förklara inkommande kväve, men för att verkligen säkerställa modellernas riktighet bör ytterligare validering ske. / Studies of real processes are based on measured data. In the past, the amount of available data was very limited. However, with modern technology, the information which is possible to obtain from measurements is more available, which considerably alters the possibility to understand and describe processes. Multivariate analysis is often used when large datasets which contains many variables are evaluated. In this thesis, the multivariate analysis methods PCA (principal component analysis) and PLS (partial least squares projection to latent structures) has been applied to wastewater data collected at Hammarby Sjöstadsverk WWTP (wastewater treatment plant). Wastewater treatment plants are required to monitor and control their systems in order to reduce their environmental impact. With improved knowledge of the processes involved, the impact can be significantly decreased without affecting the plant efficiency. Several variables are easy to measure directly in the water, while other require extensive laboratory analysis. Some of the parameters from the latter category are the contents of phosphorus and nitrogen in the water, both of which are important for the wastewater treatment results. The concentrations of these substances in the inlet water vary during the day and are difficult to monitor properly. The purpose of this study was to investigate whether it is possible, from the more easily measured variables, to obtain information on those which require more extensive analysis. This was done by using multivariate analysis to create models attempting to explain the variation in these variables. The models are commonly referred to as soft sensors, since they don’t actually make use of any physical sensors to measure the relevant variable. Data were collected during the period of March 11 to March 15, 2013 in the wastewater at different stages of the treatment process and a number of multivariate models were created. The result shows that it is possible to obtain information about the variables with PLS models based on easy-to-measure variables. The best created model was the one explaining the concentration of nitrogen in the inlet water.

The today quarry planning at Klinthagen is rough, which provides an opportunity to introduce new exciting methods to improve the quarry gain and efficiency. Nordkalk AB, active at Klinthagen, wishes to start a new quarry at a nearby location. To exploit future quarries in an efficient manner and ensure production quality, multivariate statistics may help gather important information. In this thesis the possibilities of the multivariate statistical approaches of Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were evaluated on the Klinthagen bore data. PCA data were spatially interpolated by Kriging, which also was evaluated and compared to IDW interpolation. Principal component analysis supplied an overview of the variables relations, but also visualised the problems involved when linking geophysical data to geochemical data and the inaccuracy introduced by lacking data quality. The PLS regression further emphasised the geochemical-geophysical problems, but also showed good precision when applied to strictly geochemical data. Spatial interpolation by Kriging did not result in significantly better approximations than the less complex control interpolation by IDW. In order to improve the information content of the data when modelled by PCA, a more discrete sampling method would be advisable. The data quality may cause trouble, though with sample technique of today it was considered to be of less consequence. Faced with a single geophysical component to be predicted from chemical variables further geophysical data need to complement existing data to achieve satisfying PLS models. The stratified rock composure caused trouble when spatially interpolated. Further investigations should be performed to develop more suitable interpolation techniques.

During the last three decades, a number of studies based on national environmental monitoring data have found increased concentrations of total organic carbon (TOC) in surface waters in much of the northern hemisphere including Sweden. There are many hypothesis of what has been the main cause of this trend, including changes in land use, decreased atmospheric deposition of acidifying compounds and climate change. Different hypothesis may have different implications for quantifying pre-industrial levels and for future predictions of TOC concentrations, which in turn will have different implications for water classification according to the European Water Framework Directive, water management and drinking water treatment. To analyse the long term effects of industrialisation and climate change on TOC in surface waters there is a need for long term time series of data. Since environmental monitoring data in Sweden only extends back to the mid-1980s, other techniques must be used in order to reconstruct data. In this study, sediment cores from 17 lakes along a climatic and deposition gradient in Sweden were collected and analysed with visible near infrared spectroscopy (VNIRS), an analytical technique that makes it possible to reconstruct historic surface water concentrations of TOC to pre-industrial conditions. A previous study with VNIRS showed that TOC concentrations declined in response to sulfate deposition until peak sulfur deposition in 1980, and thereafter increased as a result of sharp reductions of sulfate emissions. It was noted that the rate of increase of TOC after 1980 was faster than the rate of decrease due to sulfate deposition before 1980. The purpose of this study was therefore to explore the hypothesis that increasing TOC concentrations have not only been due to recovery from acidification, but also due to changes in climate. It was possible to analyse the long term effects of industrialisation and climate change on surface water TOC by analysing the reconstructed TOC data together with climate data from the beginning of the 1900s, modelled data of atmospheric sulfate deposition and environmental monitoring data, with uni- and multivariate analysis methods. It was found that the recent increase in TOC concentrations could be explained by both decreases in acidifying atmospheric deposition and increased precipitation, while temperature may have a decreasing effect on TOC. It was also found that the rate of increase of TOC-concentrations has been faster in the colder northern parts of Sweden and slower in the warmer south. The results imply that TOC concentrations will continue to rise to unpreceded levels and should be of concern for drinking water treatment plants that will need to adapt their treatment processes in the future. / Under de senaste tre årtiondena har ett flertal studier baserade på data från nationella miljöövervakningsprogram rapporterat ökande koncentrationer av organiskt kol (TOC) i ytvatten på norra halvklotet inklusive Sverige. Det finns många hypoteser om vad som ligger bakom trenden, till exempel förändringar i markanvändning, minskad atmosfärisk deposition av försurande ämnen och klimatförändringar. Olika förklaringar till vad som ligger bakom den ökande trenden ger konsekvenser vid kvantifiering av förindustriella nivåer och för förutsägelser om framtida koncentrationer, vilket i sin tur ger konsekvenser för vattenklassificering enligt Ramvattendirektivet, vattenförvaltning och dricksvattenberedning. För att kunna analysera de långsiktiga effekterna av industrialisering och klimatförändringar på TOC i ytvatten behövs långa tidsserier av data. Då den svenska miljöövervakningen endast sträcker sig tillbaka till mitten av 1980-talet måste andra tekniker användas för att rekonstruera data. I den här studien har sedimentproppar från 17 sjöar längs en klimat- och depositionsgradient analyserats med visible near infrared spektroskopi (VNIRS), en analysteknik som gör det möjligt att rekonstruera TOC-koncentrationer i ytvatten till förindustriell tid. En tidigare studie med VNIRS visade att TOC-koncentrationer sjönk till följd av försurande nedfall fram till 1980 då nedfallet kraftigt minskade, varefter koncentrationer av TOC började öka. Det noterades i studien att ökningen av TOC efter 1980 varit snabbare än vad minskningen var före 1980 på grund av försurande nedfall. Syftet med den här studien var därför att undersöka hypotesen att den senaste tidens ökning av TOC inte bara berott på minskat nedfall av försurande ämnen, utan även på grund av klimatförändringar. Det var möjligt att undersöka de långsiktiga effekterna av industrialisering och klimatförändringar på TOC i ytvatten genom att analysera rekonstruerad TOC data, klimatdata från början av 1900-talet, modellerad sulfatdepositionsdata och miljöövervakningsdata med uni- och multivariata analysmetoder. Resultaten visade att den senaste tidens ökning av TOC kunde förklaras med både en minskande deposition av försurande ämnen och en ökad nederbörd, medan ökande temperaturer kan ha haft en minskande effekt på TOC. Resultaten visade även att förändringshastigheten av TOC-koncentrationer varit snabbare i de norra, kalla delarna av Sverige och långsammare i de varmare södra. Resultaten indikerar att koncentrationer av TOC kommer att öka till nivåer som aldrig tidigare skådats, vilket är något vattenreningsverk kommer att behöva anpassa sina reningsmetoder till i framtiden.

Le supply chain management fait l'objet d'une attention particulière de la part des entreprises et des chercheurs notamment en sciences de gestion. Il s'articule autour de la mutualisation de ressources et des compétences de chaque membre de la supply chain et la synergie qui subsiste entre eux afin de créer collectivement une valeur supérieure à la somme des valeurs créées séparément par chacun. Cette alliance, qui nécessite la coopération collective dans la création et le partage équitable de la valeur, est caractérisée par des comportements opportunistes qui entraînent des défaillances comme l'effet Bullwhip. Ainsi, notre objectif est d'analyser ces comportements afin de comprendre leur dynamique au sein de la supply chain. A cet égard, nous nous appuyons sur la perspective des dilemmes sociaux pour proposer deux mécanismes : un mécanisme motivationnel (partage de l'identité de la supply chain) et un mécanisme structurel (investissement dans les ressources relationnelles) pour à la fois réduire et décourager l'opportunisme. Afin d'appuyer notre raisonnement, ces mécanismes sont articulés pour créer un capital social qui, en facilitant le supply chain management, permet d'améliorer la performance de la supply chain. Cela a donné lieu à la conception d'un modèle intégrateur de la dynamique sociale de la supply chain. Celui-ci a été validé auprès d'un échantillon de 130 répondants (Responsables supply chain, directeurs logistiques, etc.). Les données collectées ont été analysées tout d'abord par une analyse univariée, suivie d'une analyse en composantes principales afin d'épurer nos échelle de mesure. En second lieu, nous avons fait appel aux modèles d'équations structurelles PLS-PM (régressions aux moindres carrées partiels), pour estimer d'une part, la validité convergente ainsi que la validité discriminante des échelles de mesures, et d'autre part, valider nos hypothèses de recherche relatives aux antécédents et aux conséquences du capital social de la supply chain. Les résultats d'analyse nous ont permis de valider toutes les hypothèses selon lesquelles les deux mécanismes de résolution du dilemme social permettent de développer un capital social au sein de la supply chain qui, en agissant comme un lubrificateur des relations inter-organisationnelle, permet d'améliorer la performance de la supply chain. / Supply chain management is subject of particular interest to professionals and researchers especially in management science. It revolves around the sharing of resources and expertise between supply chain members, and the synergy that exists between them to collectively create value that is more than the sum of the values created separately by each member. This alliance, which requires collective cooperation in the creation and the equitable sharing of the value, is characterized by opportunistic behaviors that lead to failure, as illustrated by the Bullwhip effect. Thus, our objective is to analyze these behaviors in an attempt to understand their dynamics within the supply chain. In this regard, we rely on the perspective of social dilemmas to propose two mechanisms: a motivational mechanism (sharing the identity of the supply chain) and a structural mechanism (investment in relational resources) to discourage and reduce opportunism. To support our reasoning, these mechanisms are articulated to create social capital, facilitating supply chain management, in turn improving supply chain performance. This leads to the design of an integrative model of supply chain social dynamics. This was validated with a sample of 130 respondents (supply chain heads, logistics managers, etc.). The collected data were analyzed first by univariate analysis followed by principal component analysis to refine our scale. Second, we used PLS-PM (partial least squares regression) structural equation models to estimate, on one hand, the convergent and the discriminant validity of the measurement scales validity, and on the other hand, validate our research hypotheses on the antecedents and the consequences of social capital in the supply chain. The results of these analyses have allowed us to validate all the assumptions that the two mechanisms for resolving social dilemma help develop social capital within the supply chain, and act to lubricate inter-organizational relations, allowing improved supply chain performance.