1,721,108 research outputs found
ROUTINES IN PATIENT CARE: ESSAYS ON THE DESIGN AND USE OF THE INFORMATION TECHNOLOGY ARTIFACT
No full textPrédictions that information technology (IT) will become a dominant driver in patient care delivery continue to proliferate. While IT's potential benefits in healthcare are manifold, past research has shown that the digitalization of medicine remains more of a promise than a reality. Given the current limitations of IT in healthcare, in this dissertation, I argue that we need prescriptive design knowledge on how IT artifacts ought to be to function in patient care delivery.
Investigating two dominant IT artifacts in healthcare, electronic health record (EHR) systems and mobile médical apps, this dissertation unpacks the 'black box' of IT artifacts and sheds light on the design and the effective use of IT artifacts in routine patient care. The dissertation comprises three interrelated research streams, each taking a specific angle to study the aforementioned aspects. Research stream 1 provides a conceptual framework on the interdependencies between routines in patient care and EHR systems and devises two stratégies to objectively assess and improve the effective use of EHR systems. Rather than studying design and use of EHR systems separate from each other, our framework suggests combine the two. Research stream 2 investigates routines at the individual level and describes the affordances of mobile apps to accommodate différences in EHR system use among individual physicians. Research stream 3 centers around the patient and studies the design of médical apps to provide a way for patients to self-diagnose their acute symptoms and to enhance the monitoring of an illness. This research stream présents design principles that effective médical apps should possess in order to engage the patient in the delivery of care. The theoretical contributions can be classified as mid-range theories and inform design practice by being specific about both users (i.e. patients and physicians) and IT artifacts (i.e. EHR systems and médical apps).
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Les projections selon lesquelles les technologies de l'information (TI) vont devenir un facteur déterminant dans les soins dispensés aux patients ne cessent d'accroître. Alors que les bénéfices potentiels des TI dans la santé sont multiples, les recherches montrent que la digitalisation de la médecine s'avère être une promesse plus qu'une réalité. Etant donné les limitations actuelles des TI dans la santé, au travers cette dissertation, je soutiens que nous avons besoin d'une connaissance prescriptive sur la manière de conceptualiser des artefacts informatiques pour assister dans les soins dispensés aux patients.
En examinant deux artefacts informatiques dans la santé, que sont les systèmes de dossiers médicaux électroniques (DME) et les applications mobiles médicales, cette dissertation ouvre la «boîte noire» des artefacts informatiques et fait la lumière sur la conception et sur une utilisation efficace des artefacts informatiques dans les routines de soins des patients. Cette dissertation comprend trois axes de recherches interconnectés, chacun abordant un aspect spécifique des thématiques susmentionnées. Le premier axe de recherche fournit un framework sur les interdépendances entre les routines de soins des patients et les systèmes de DME en élaborant deux stratégies pour évaluer et améliorer de manière objective l'efficacité de l'utilisation des systèmes DME. Au lieu d'étudier la conception et l'utilisation des systèmes DME séparément, notre framework suggère de combiner les deux. Le deuxième axe de recherche analyse les routines au niveau individuel; il décrit les «affordances» des applications mobiles et leurs adaptations aux différences dans l'utilisation des systèmes DME d'un médecin à l'autre. Le troisième axe de recherche se concentre sur les patients et envisage la conception d'applications mobiles médicales leur offrant la possibilité d'auto-diagnostiquer la criticité de leurs symptômes et d'améliorer le contrôle de leur maladie. Cet axe de recherche présente des principes de conception que les applications mobiles médicales devraient implémenter afin d'impliquer les patients dans la dispense des soins. Les contributions théoriques de ce travail peuvent être perçues en tant que théories «mid- range»; étant spécifiques à la fois aux utilisateurs (c.-à-d. patients et médecins) et aux artefacts informatiques (c.-à-d. systèmes DME et applications mobiles médicales), elles permettent aussi d'orienter les pratiques de conception
DESIGN, IMPLEMENT, REPEAT: ESSAYS ON BUSINESS MODEL MANAGEMENT IN OFFLINE-BORN ORGANIZATIONS
No full textIt is commonly acknowledged that business model innovation carries enormous opportunities for incumbent organizations, especially when driven by digital transformation. New revenue models, highly efficient value creation mechanisms, and unprecedented interaction with the customer are only few of the numerous benefits that managers expect to see. However, less is known and discussed about the challenges of organizations that were established before the diffusion of the Internet - these organizations are sometimes known as “offline -born” - which attempt to tackle business model innovation. Lack of digital expertise, a conservative mindset, resource constraints, and fear of cannibalization of long-established business models are hurdles that can prevent incumbents from embracing this journey of change. In this context, we contribute to the business model domain with two research streams having a common denominator: offline-born organizations performing business model innovation.
The first research stream addresses the process of business model management, analyzing phases that go beyond business model design. Specifically, we shed light on how incumbents analyze, design, evaluate, implement, and control their business models. We observe this process in practice, complementing the predominantly conceptual literature. Our main contributions include the activities performed in each process phase and two approaches to business model management: on the one hand, a deterministic and waterfall approach, characterized by a high level of certainty and confidence by the management team and, on the other hand, a discovery-driven approach, in which numerous design and evaluation iterations are performed before business model implementation.
The second research stream studies the design of business models for connected products. Phenomena like internet of things and smart cities require a complex network of actors in which organizations, individuals, and objects exchange value. Existing business model representations are not fully capable of describing such networks, having rather generic elements and components. Therefore, we take a first step towards new means of representation, proposing a taxonomy of design elements to represent business models for cyber-physical systems, the combination of physical and computational processes atthefoundationofconnectedproductsT. hemaincontributionofthisresearchisaspecificsetofactors’ roles, the value they exchange and perceive, as well as their dominance in the network
Data Products: Foundation, Design and Management
No full textData products have emerged as a new paradigm shift in the way data is managed and used in enterprises. Despite the growing interest, research had discussed it in fragmented streams, such as data science, data platforms, data markets and data mesh/fabric, resulting into a vague formulation of the data product concept. Additionally, data products have been examined from a predominantly technical perspective, emphasizing on ‘how-to-build’ aspect and overlooking key antecedents to product building, such as ‘why-to-build’, ‘what-to-build’ and for/by ‘who-to build’. In other words, research is lacking a socio-technical lens that blends the organizational, strategic and economic aspects to fully understand and exploit data products. Considering the gaps, the objective of the thesis is to establish a socio-technical understanding of data products, offer methodological guidance to design and apply data products and explore mechanisms to effectively manage data products for higher value. Hence, this thesis comprises three interconnected research streams. The first stream investigates the state-of-the-art on data products, examining the way they are elaborated in research and adopted in practice, contributing towards a revised definition, characteristics and types of data products. The second stream tackles the lack of methodological guidance to design data products. Leveraging design science method, we establish Data Product Canvas – a visual and versatile tool that helps cross-functional teams collaboratively design new data products – built upon three underlying themes: desirability, feasibility and viability. Additionally, we apply the Canvas in a naturalistic setting, i.e., a Fortune-500 technology company, to design real-life data products and outline recommendations to utilize the Canvas to drive their data democratization initiative. The third research stream explores how data products are managed in companies. We highlight three data product work systems within enterprises created to either selffunction or be interdependent on one another to produce different types of insights. Moreover, we contribute by illuminating on five emerging mechanisms that manage consumer-provider interaction – data contract, data catalog & marketplace, data product owner, data product manager and data product lifecycle – to unlock value co-creation from data products. Overall, our thesis advances the data product discourse by elaborating that data products are not merely technical objects that manipulate data, but comprehensive, reusable artifacts shaped by the organizational, strategic and economic aspects of a firm – thereby materializing the socio-technical lens on data products. For practitioners, we establish concrete drivers and motivations for data products, as well as propose a practitioner-friendly design tool to support cross-functional teams in designing the most appropriate data products for their organization
Essays on External Data Sourcing
No full textIn the age of digital transformation, enterprises are becoming increasingly aware of the value of external data, which originates beyond their four walls. Despite the growing number of datasets and their potential value, external data is sourced in an ad-hoc manner without clear guidelines. This leads to inconsistent sourcing decisions, characterized by a lack of clarity on the object of sourcing and the underlying data sourcing practices. Existing studies showcase scenarios of enterprises using external data, which are fraught with obstacles. A crucial challenge confronting companies that intend to use external data is to identify suitable datasets supporting specific business scenarios and to prepare them for use. In the context of a specific external data type – open data in our case – researchers have developed several data assessment techniques. Unfortunately, these techniques are limited in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. The emerging field of data sourcing also displays a notable absence of comprehensive research, prompting a clarion call for action in Information Systems (IS) research to address this gap. Considering the abovementioned research opportunities, this thesis – through three interrelated research streams – provides foundations for, analyzes, and improves data sourcing practices in the enterprise context. The first stream lays the foundations for the topic and investigates the company-wide sourcing and managing of external data. The second stream reflects on sourcing practices concerning open data, as one of the most prominent external data types, and challenges the widespread perception that open data is easily accessible and readily available. Focusing on one of the most pressing topics facing present-day companies, the third stream provides a foundation for the academic conceptualization of data sourcing in the context of sustainability.
The outcomes of this thesis project enable the transition from ad-hoc acquisition to well- informed, professional data sourcing approaches in the enterprise context. The contributions of the first research stream are an external data sourcing taxonomy (Essay 1), which informs sourcing decisions in an enterprise context, and a reference process to source and manage external data (Essay 2), which is accompanied by explicit prescriptions in the form of design principles. The second research stream proposes a use case-driven assessment of open corporate registers (Essay 3) and, building on the subsequent findings, a method to screen, assess, and prepare open data for use in support of companies’ open data activities (Essay 4). Finally, the third research stream reveals and elaborates on three data sourcing practices developed by companies in response to institutional pressures in the sustainability context (Essay 5)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT
No full textBig Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC).
The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees
UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS – LEVERAGING MARKET RESEARCH TECHNIQUES AND EXAMPLES IN PRIVACY-AWARE DESIGN
No full textWith cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. IS research has suggested several theories and models to explain user adoption and intentions to use, among them the IS Success Model and the Technology Acceptance Model (TAM). Although these approaches contribute to theoretical understanding of the adoption and use of IS in mass-markets, they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box (i.e., they do not sufficiently address the system internal characteristics). We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. More specifically, conjoint analysis (CA), from market research, can add user preference measurements for designing high-utility IS. CA has gained popularity in IS research, however little guidance is provided for its application in the domain. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumer’s preferences for multiple factors combing functional, non-functional and economic aspects. The results include a “Framework for Conjoint Analysis Studies in IS” and methodological guidance for applying CA. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption. We contribute to both academia and practice. For academia, we contribute to a more nuanced conceptualization of the IT artifact (i.e., system) through a feature-oriented lens and a preference-based approach. We provide methodological guidelines that support researchers in studying user perceptions and preferences for design variations and extending that to adoption. Moreover, the empirical studies for privacy- aware design contribute to a better understanding of the domain specific applications of CA for IS design and evaluation with a nuanced assessment of user preferences for privacy-preserving features. For practice, we propose guidelines for integrating the voice of the customer for successful IS design.
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Les technologies cloud et mobiles ont fait émerger une nouvelle catégorie de produits informatiques qui s’adressent à des utilisateurs hétérogènes par le biais de systèmes d'information (SI) distribués. Les termes “SI de masse” sont employés pour désigner ces nouveaux systèmes. Une conception réussie de ceux-ci passe par une phase essentielle de compréhension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggère plusieurs théories et modèles tels que le “IS Success Model” et le “Technology Acceptance Model”. Bien que ces approches contribuent à la compréhension théorique de l'adoption et de l'utilisation des SI de masse, elles sont critiquées pour ne pas être en mesure de fournir des informations exploitables sur la conception de SI car elles considèrent l'artefact informatique comme une boîte noire. En d’autres termes, ces approches ne traitent pas suffisamment des caractéristiques internes du système. Nous soutenons que la recherche en SI doit adopter des techniques d'étude de marché afin de mieux intégrer les exigences du client (“Voice of Customer”) dans un scénario de marché de masse. Plus précisément, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au développement de système SI à forte valeur d'usage. Si l’AC a gagné en popularité au sein de la recherche en SI, des recommandations quant à son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des préférences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et économiques. Les résultats comprennent un “Cadre de référence pour les études d'analyse conjointe en SI” et des recommandations méthodologiques pour l'application de l’AC. Nous avons utilisé ces contributions pour concevoir un SI de masse particulièrement sensible au respect de la vie privée des utilisateurs et nous avons évalué l’impact de nos recherches sur l'adoption de ce système par ses utilisateurs. Ainsi, notre travail contribue tant à la théorie qu’à la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancée de l'artefact informatique (c'est-à-dire du système) à travers le prisme des fonctionnalités et par une approche basée sur les préférences utilisateurs. Par ailleurs, les chercheurs peuvent également s'appuyer sur nos directives méthodologiques pour étudier les perceptions et les préférences des utilisateurs pour différentes variations de conception et étendre cela à l'adoption. De plus, nos études empiriques sur la conception d’un SI de masse sensible au respect de la vie privée des utilisateurs contribuent à une meilleure compréhension de l’application des techniques CA dans ce domaine spécifique. Nos études incluent notamment une évaluation nuancée des préférences des utilisateurs sur des fonctionnalités de protection de la vie privée. Pour les praticiens, nous proposons des lignes directrices qui permettent d’intégrer les exigences des clients afin de concevoir un SI réussi
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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