Towards Responsible Data Analytics: A Process Approach

The big data movement has been characterised by highly enthusiastic promotion, and caution has been in short supply. New data analytic techniques are beginning to be applied to the operational activities of government agencies and corporations. If projects are conducted in much the same carefree manner as research experiments, they will inevitably have negative impacts on the organisations conducting them, and on their employees, other organisations and other individuals. The limited literature on process management for data analytics has not yet got to grips with the risks involved. This paper presents an adapted business process model that embeds quality assurance, and enables organisations to filter out irresponsible applications

Die De-Organisation von Organisation?

Menschliche Entscheiderinnen und Entscheider in Organisationen werden heutzutage zunehmend durch daten-intensive Algorithmen ersetzt. Algorithmen steuern Autos, Flugzeuge und Fahrstuhlsysteme, schreiben Nachrichten und Bücher, und verschlagworten Fotos auf digitalen Plattformen. Dieses sog. „algorithmische Entscheiden“ scheint folglich signifikante Veränderungen in Organisationen zu verursachen. Nichtsdestotrotz spielt dieses Phänomen im soziologischen Diskurs bislang kaum eine Rolle. Lediglich außerhalb der Soziologie findet sich mittlerweile eine Vielzahl an Arbeiten, die sich mit den sozialen Folgen algorithmischen Entscheidens auseinandersetzen. Allerdings wird in eben diesen Studien weder die Organisation als Untersuchungsgegenstand beachtet, noch wird der Begriff der „Entscheidung“ näher definiert. Beide Phänomene werden als gegeben hingenommen und nicht weiter reflektiert. In der Folge wissen wir überraschend wenig darüber, welche Implikationen das Ersetzen menschlicher Entscheiderinnen und Entscheider durch Algorithmen hat. Der vorliegende Beitrag adressiert diesen Umstand in zwei Hinsichten. Er präsentiert zum einen ein strikt soziologisches Verständnis von „Entscheidungen“, welches es erlaubt, das Phänomen des algorithmischen Entscheidens und dessen weitreichende Implikationen theoretisch zu erfassen. Zum anderen geht der Beitrag kurz auf einige Folgen algorithmischer Entscheidungen am Beispiel selbst-lernender Algorithmen ein. Dabei wird diskutiert, ob und inwieweit Organisationen ihre Entscheidungen überhaupt noch selbst treffen, wenn solche Algorithmen zum Einsatz kommen. Außerdem wird darauf eingegangen, inwiefern Organisationen als kollektiven Personen die Verantwortung für algorithmische Entscheidungen zugerechnet werden kann

Assessing factors of behavioral intention to use Big Data Analytics (BDA) in banking and insurance sector: proposition of an integrated model

Banking and insurance sectors have long been largely data-driven by nature. However, with the rise in the predominance of data flooding from several sources resulting from the introduction of new customers and markets, with the help of Big Data Analytics, value can be extracted more effectively, and analysis of this type of unstructured data combined with a wide range of datasets can be used to efficiently and precisely extract commercial value. The aim of this paper is to develop a conceptual framework to explain the intention of information technology practitioners in banks and insurance companies to use Big Data Analytics by exploiting the Technology Acceptance Model (TAM) joined by the Task-Technology-Fit paradigm, information quality, security, trust, and the moderating effect of managerial commitment by top management on the relationship between users’ perception and their intention to use, in order to conceptualize and test an integrated framework for analyzing and measuring attitudes toward the usage of Big Data Analytics. This paper contributes by proposing the model to assess the factors that influence users’ intention towards the use of Big Data Analytics, by asserting users’ perception towards the technology, trust factor, security and the effect of managerial commitment. Although the model we developed in this paper is conceptual and still needs to be tested empirically, it will serve as a basic framework for further research that is designed to evaluate factors affecting IT practitioners’ attitudes towards the adoption of Big Data Analytics within the finance sector.   Keywords: Big Data Analytics, TAM, TTF, Security, Trust, Managerial commitment, Bank, Insurance  JEL Classification: O32 Paper type: Theoretical ResearchLes secteurs de la banque et de l'assurance sont depuis longtemps largement axés sur les données par nature. Cependant, avec l'augmentation de la prédominance de l'inondation de données provenant de plusieurs sources résultant de l'introduction de nouveaux clients et marchés, avec l'aide du Big Data Analytics, la valeur peut être obtenue plus efficacement, et l'analyse de ce type de données non structurées combinées à un large éventail d'ensembles de données peut être utilisée pour extraire efficacement et précisément la valeur commerciale. L'objectif de cet article est de développer un cadre conceptuel pour expliquer l'intention des praticiens des technologies de l'information dans les banques et les compagnies d'assurance d'utiliser le Big Data Analytics en exploitant le Modèle d'Acceptation de la Technologie (TAM) associé au paradigme Adéquation Tache-Technologie, la qualité de l'information, la sécurité, la confiance et l'effet modérateur de l'engagement du management sur la relation entre la perception des utilisateurs et leur intention d'utilisation, afin de conceptualiser et de tester un cadre intégré pour analyser et mesurer les attitudes envers l'utilisation du Big Data Analytics. Cet article contribue en proposant un modèle pour évaluer les facteurs qui influencent l'intention des utilisateurs vers l'utilisation du Big Data Analytics, en affirmant la perception des utilisateurs envers la technologie, le facteur de confiance, la sécurité et l'effet de l'engagement managérial. Bien que le modèle que nous avons développé dans cet article soit conceptuel et nécessite encore d'être testé empiriquement, il servira de cadre de base pour des recherches ultérieures conçues pour évaluer les facteurs affectant les attitudes des informaticiens envers l'adoption du Big Data Analytics dans le secteur financier.   Keywords: Big Data Analytics, TAM, TTF, Security, Trust, Managerial commitment, Bank, Insurance  JEL Classification: O32 Paper type: Theoretical Researc

Big data analytics as a management tool: An overview, trends and challenges

Innovative digital technologies and ever-changing business environment have and will continue to transform businesses and industries around the world. This transformation will be even more evident in view of forthcoming technological breakthroughs, and advances in big data analytics, machine learning algorithms, cloud-computing solutions, artificial intelligence, internet of things, and the like. As we live in a data-driven world, technologies are altering work and work-related activities, and everyday activities and interactions. This paper is focused on big data and big data analytics (BDA), which are viewed in the paper from organisational perspective, as a means of improving firm performance and competitiveness. Based on a review of selected literature and researches, the paper aims to explore the extent to which big data analytics is utilized in companies, and to highlight the valuable role big data analytics may play in achieving better business outcomes. Furthermore, the paper briefly presents main challenges that accompany the adoption of big data analytics in companies

Mapping the Landscape of Behavioral Theories: Systematic Literature Review

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.ESRC, CHR

Individual differences in the encoding of contextual details following acute stress:An explorative study

Information processing under stressful circumstances depends on many experimental conditions, like the information valence or the point in time at which brain function is probed. This also holds true for memorizing contextual details (or 'memory contextualization'). Moreover, large interindividual differences appear to exist in (context-dependent) memory formation after stress, but it is mostly unknown which individual characteristics are essential. Various characteristics were explored from a theory-driven and data-driven perspective, in 120 healthy men. In the theory-driven model, we postulated that life adversity and trait anxiety shape the stress response, which impacts memory contextualization following acute stress. This was indeed largely supported by linear regression analyses, showing significant interactions depending on valence and time point after stress. Thus, during the acute phase of the stress response, reduced neutral memory contextualization was related to salivary cortisol level; moreover, certain individual characteristics correlated with memory contextualization of negatively valenced material: (a) life adversity, (b) alpha-amylase reactivity in those with low life adversity and (c) cortisol reactivity in those with low trait anxiety. Better neutral memory contextualization during the recovery phase of the stress response was associated with (a) cortisol in individuals with low life adversity and (b) alpha-amylase in individuals with high life adversity. The data-driven Random Forest-based variable selection also pointed to (early) life adversity-during the acute phase-and (moderate) alpha-amylase reactivity-during the recovery phase-as individual characteristics related to better memory contextualization. Newly identified characteristics sparked novel hypotheses about non-anxious personality traits, age, mood and states during retrieval of context-related information

The epistemological foundations of data science: a critical analysis

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally

Theory-driven or Process-driven Prediction? : Epistemological Challenges of Big Data Analytics

Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence of data processing while seemingly abstaining from being theoretically informed about the subject matter. This paper discusses how to deal with the shift from theory-driven to process-driven prediction through analyzing the BDA steps and identifying the epistemological challenges and various needs of theoretically informing BDA throughout data acquisition, preprocessing, analysis, and interpretation. We suggest a theory-driven guidance for the BDA process including acquisition, pre-processing, analytics and interpretation. That is, we propose—in association with these BDA process steps—a lightweight theory-driven approach in order to safeguard the analytics process from epistemological pitfalls. This study may serve as a guideline for researchers and practitioners to consider while conducting future big data analytics.Validerad;2017;Nivå 1;2017-12-12 (andbra)</p

Research Data Management Practices And Impacts on Long-term Data Sustainability: An Institutional Exploration

With the \u27data deluge\u27 leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to deposit research data into open online data repositories, which, in turn, is engendering a new set of practices to adapt formal mandates to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them \u27data articulations.\u27 This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 studies: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as articulation work in genomics; and (3) a comparative case study of genomics and social science researchers to identify factors associated with the institutionalization of research data management (RDM). The findings of this research offer an in-depth understanding of the data management and deposit practices of academic research faculty, and surfaced institutional factors associated with data deposit. Additionally, the studies led to a theoretical framework of data deposit to open research data repositories. The empirical insights into the impacts of institutionalization of RDM and data deposit on long-term data sustainability update our knowledge of the impacts of increasing guidelines for RDM. The work also contributes to the body of data management literature through the development of the data articulation framework which can be applied and further validated by future work. In terms of practice, the studies offer recommendations for data policymakers, data repositories, and researchers on defining strategies and initiatives to leverage data reuse and employ computational approaches to support data management and deposit