A decision to recall products by firms can lead to negative consequences such as erosion of shareholder wealth and loss of customer goodwill. Further, the way a recall is managed can lead to more negative consequences than the recall decision itself. Therefore the manner in which firms manage such decisions can help mitigate these negative consequences. This thesis examines two such decisions: recall restitution and time to recall. A firm’s decisions on restitution offered to affected customers and time to recall may evoke conflicting reactions from shareholders and customers, where serving the interests of one stakeholder affects the interests of the other. While higher restitutions and faster recalls improve customer goodwill, they lead to erosion of shareholder wealth. This finding is used to hypothesize the influence of organizational characteristics (position of the firm in the value chain, firm’s internal operations, and firm’s recall experience), and key crisis factors (ambiguity and severity) on these decisions. This thesis uses data on toy recalls issued in the U.S. from 1988 to 2011. The results show that firms tend to favor shareholders by offering lower restitutions to affected customers when they are situated farther from the customer in the supply chain, when they have more experience with recalls, when the crisis is severe, and when the cause of the crisis is ambiguous. When the recall is due to the internal operations of the firm, restitution offered to affected customers is lower only when the severity of the recall is high. Firms issue recalls quickly when the crisis is severe in order to reduce customer hazards and avoid negative publicity. Severe recalls, however, may be delayed when firms are experienced in recall management, and when such recalls are caused by the internal operations of the firm. The findings of this thesis highlight one of the dilemmas that firms face in a crisis decision making situation and help foster an understanding of the conditions under which firms manage shareholder versus customer reactions in order to mitigate the negative consequences of recall management decisions.

A decision to recall products by firms can lead to negative consequences such as erosion of shareholder wealth and loss of customer goodwill. Further, the way a recall is managed can lead to more negative consequences than the recall decision itself. Therefore the manner in which firms manage such decisions can help mitigate these negative consequences. This thesis examines two such decisions: recall restitution and time to recall. A firm’s decisions on restitution offered to affected customers and time to recall may evoke conflicting reactions from shareholders and customers, where serving the interests of one stakeholder affects the interests of the other. While higher restitutions and faster recalls improve customer goodwill, they lead to erosion of shareholder wealth. This finding is used to hypothesize the influence of organizational characteristics (position of the firm in the value chain, firm’s internal operations, and firm’s recall experience), and key crisis factors (ambiguity and severity) on these decisions. This thesis uses data on toy recalls issued in the U.S. from 1988 to 2011. The results show that firms tend to favor shareholders by offering lower restitutions to affected customers when they are situated farther from the customer in the supply chain, when they have more experience with recalls, when the crisis is severe, and when the cause of the crisis is ambiguous. When the recall is due to the internal operations of the firm, restitution offered to affected customers is lower only when the severity of the recall is high. Firms issue recalls quickly when the crisis is severe in order to reduce customer hazards and avoid negative publicity. Severe recalls, however, may be delayed when firms are experienced in recall management, and when such recalls are caused by the internal operations of the firm. The findings of this thesis highlight one of the dilemmas that firms face in a crisis decision making situation and help foster an understanding of the conditions under which firms manage shareholder versus customer reactions in order to mitigate the negative consequences of recall management decisions.

La transformation de modèles consiste à transformer un modèle source en un modèle cible conformément à des méta-modèles source et cible. Nous distinguons deux types de transformations. La première est exogène où les méta-modèles source et cible représentent des formalismes différents et où tous les éléments du modèle source sont transformés. Quand elle concerne un même formalisme, la transformation est endogène. Ce type de transformation nécessite généralement deux étapes : l’identification des éléments du modèle source à transformer, puis la transformation de ces éléments. Dans le cadre de cette thèse, nous proposons trois principales contributions liées à ces problèmes de transformation. La première contribution est l’automatisation des transformations des modèles. Nous proposons de considérer le problème de transformation comme un problème d'optimisation combinatoire où un modèle cible peut être automatiquement généré à partir d'un nombre réduit d'exemples de transformations. Cette première contribution peut être appliquée aux transformations exogènes ou endogènes (après la détection des éléments à transformer). La deuxième contribution est liée à la transformation endogène où les éléments à transformer du modèle source doivent être détectés. Nous proposons une approche pour la détection des défauts de conception comme étape préalable au refactoring. Cette approche est inspirée du principe de la détection des virus par le système immunitaire humain, appelée sélection négative. L’idée consiste à utiliser de bonnes pratiques d’implémentation pour détecter les parties du code à risque. La troisième contribution vise à tester un mécanisme de transformation en utilisant une fonction oracle pour détecter les erreurs. Nous avons adapté le mécanisme de sélection négative qui consiste à considérer comme une erreur toute déviation entre les traces de transformation à évaluer et une base d’exemples contenant des traces de transformation de bonne qualité. La fonction oracle calcule cette dissimilarité et les erreurs sont ordonnées selon ce score. Les différentes contributions ont été évaluées sur d’importants projets et les résultats obtenus montrent leurs efficacités. / Model transformations take as input a source model and generate as output a target model. The source and target models conform to given meta-models. We distinguish between two transformation categories. Exogenous transformations are transformations between models expressed using different languages, and the whole source model is transformed. Endogenous transformations are transformations between models expressed in the same language. For endogenous transformations, two steps are needed: identifying the source model elements to transform and then applying the transformation on them. In this thesis, we propose three principal contributions. The first contribution aims to automate model transformations. The process is seen as an optimization problem where different transformation possibilities are evaluated and, for each possibility, a quality is associated depending on its conformity with a reference set of examples. This first contribution can be applied to exogenous as well as endogenous transformation (after determining the source model elements to transform). The second contribution is related precisely to the detection of elements concerned with endogenous transformations. In this context, we present a new technique for design defect detection. The detection is based on the notion that the more a code deviates from good practice, the more likely it is bad. Taking inspiration from artificial immune systems, we generate a set of detectors that characterize the ways in which a code can diverge from good practices. We then use these detectors to determine how far the code in the assessed systems deviates from normality. The third contribution concerns transformation mechanism testing. The proposed oracle function compares target test cases with a base of examples containing good quality transformation traces, and assigns a risk level based on the dissimilarity between the two. The traces help the tester understand the origin of an error. The three contributions are evaluated with real software projects and the obtained results confirm their efficiencies.

La transformation de modèles consiste à transformer un modèle source en un modèle cible conformément à des méta-modèles source et cible. Nous distinguons deux types de transformations. La première est exogène où les méta-modèles source et cible représentent des formalismes différents et où tous les éléments du modèle source sont transformés. Quand elle concerne un même formalisme, la transformation est endogène. Ce type de transformation nécessite généralement deux étapes : l’identification des éléments du modèle source à transformer, puis la transformation de ces éléments. Dans le cadre de cette thèse, nous proposons trois principales contributions liées à ces problèmes de transformation. La première contribution est l’automatisation des transformations des modèles. Nous proposons de considérer le problème de transformation comme un problème d'optimisation combinatoire où un modèle cible peut être automatiquement généré à partir d'un nombre réduit d'exemples de transformations. Cette première contribution peut être appliquée aux transformations exogènes ou endogènes (après la détection des éléments à transformer). La deuxième contribution est liée à la transformation endogène où les éléments à transformer du modèle source doivent être détectés. Nous proposons une approche pour la détection des défauts de conception comme étape préalable au refactoring. Cette approche est inspirée du principe de la détection des virus par le système immunitaire humain, appelée sélection négative. L’idée consiste à utiliser de bonnes pratiques d’implémentation pour détecter les parties du code à risque. La troisième contribution vise à tester un mécanisme de transformation en utilisant une fonction oracle pour détecter les erreurs. Nous avons adapté le mécanisme de sélection négative qui consiste à considérer comme une erreur toute déviation entre les traces de transformation à évaluer et une base d’exemples contenant des traces de transformation de bonne qualité. La fonction oracle calcule cette dissimilarité et les erreurs sont ordonnées selon ce score. Les différentes contributions ont été évaluées sur d’importants projets et les résultats obtenus montrent leurs efficacités. / Model transformations take as input a source model and generate as output a target model. The source and target models conform to given meta-models. We distinguish between two transformation categories. Exogenous transformations are transformations between models expressed using different languages, and the whole source model is transformed. Endogenous transformations are transformations between models expressed in the same language. For endogenous transformations, two steps are needed: identifying the source model elements to transform and then applying the transformation on them. In this thesis, we propose three principal contributions. The first contribution aims to automate model transformations. The process is seen as an optimization problem where different transformation possibilities are evaluated and, for each possibility, a quality is associated depending on its conformity with a reference set of examples. This first contribution can be applied to exogenous as well as endogenous transformation (after determining the source model elements to transform). The second contribution is related precisely to the detection of elements concerned with endogenous transformations. In this context, we present a new technique for design defect detection. The detection is based on the notion that the more a code deviates from good practice, the more likely it is bad. Taking inspiration from artificial immune systems, we generate a set of detectors that characterize the ways in which a code can diverge from good practices. We then use these detectors to determine how far the code in the assessed systems deviates from normality. The third contribution concerns transformation mechanism testing. The proposed oracle function compares target test cases with a base of examples containing good quality transformation traces, and assigns a risk level based on the dissimilarity between the two. The traces help the tester understand the origin of an error. The three contributions are evaluated with real software projects and the obtained results confirm their efficiencies.