Embarking on the Digital Transformation Journey Toward a Data-Driven Organization: Empirical Insights Into Transformation Starting Points

2023 | European Conference On Information Systems | Citations: 0

Authors: Fischer, Hannes; Wiener, Martin; Strahringer, Susanne

Abstract: Organizations are increasingly initiating and expanding their digital transform ... Expand

Abstract: Organizations are increasingly initiating and expanding their digital transformation initiatives to become more data-driven. Given that such digital transformations are highly complex and idiosyncratic endeavors, organizations striving to become a data-driven organization (DDO) have many options on where to start. To better understand such transformation starting points, we analyzed 13 companies of various industries and sizes currently undertaking DDO transformations. On this empirical basis, we find that top managers embark on transformation journeys by either focusing on the transformation foundations (asset-driven, workforce-driven), or on the outcome (exploitative use case-driven, explorative use case-driven), with each starting point showing different DDO dimensions being addressed first. Our data suggest that the selection of a specific starting point is mainly based on managers' understandings of a DDO and the motivation to become such an organization. Finally, our work points to advantages and disadvantages of the respective starting points. Contributions and practical implications are discussed. Collapse

Topics: digital transformation organizational transformation decision making database system data management

Methods: field study case study qualitative interview maturity model triangulation

Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Zhao, Xiaohang; Fang, Xiao; He, Jing; Huang, Lihua

Abstract: Industry assignment, which assigns firms to industries according to a predefine ... Expand

Abstract: Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structurebased knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the timespecificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods. Collapse

Topics: innovation management electronic finance industry 4.0 database system

Methods: support vector machine deep learning doc2vec experiment computational algorithm

Business Intelligence in the Database Marketing – A Case Study of a German Insurance Company

2021 | HICSS | Citations: 0

Authors: Ernst, Claus-Peter; Geiger, Frauke

Abstract: We perform a case study in order to analyze the BI practices of the database mar ... Expand

Abstract: We perform a case study in order to analyze the BI practices of the database marketing departments of insurance companies and hone in on the challenges they face. More specifically, we take a look at the database marketing department of one of the biggest German insurance companies by executing three guided expert interviews and enhancing the information gathered through a document analysis. Among our findings regarding the department’s BI process are that the department collects customer data solely from internal sources and that the collected data is mainly analyzed through SQL queries. The department’s greatest challenges involve including internal and external data sources that are currently not used in their analyses, gathering and understanding all the data that the company already has — since it is stored in different database tables and in heterogenous formats across the company — and measuring the success of their BI activities. Collapse

Topics: marketing management database system electronic mail logistics management structured query language

Methods: qualitative interview case study qualitative content analysis company material regression analysis method

Towards a Typology of Ecosystem Roles in the Era of Digital Innovation – An Inductive Empirical Analysis

2020 | International Conference on Information Systems | Citations: 0

Authors: Biedebach, Marius; Hanelt, André

Abstract: Digital technologies are increasingly responsible for creating new digital envir ... Expand

Abstract: Digital technologies are increasingly responsible for creating new digital environments and reshaping existing contexts. As a consequence of this pervasive digitalization, we witness the emergence of ecosystems, encompassing a variety of dispersed actors. Although research is dealing with the characterization of ecosystem roles in general, there is still a lack when it comes to understanding ecosystem role types that accurately describe the composition of ecosystems in the era of digital innovation. Therefore, we investigated the automotive ecosystem in a time period of 2012-2017. By drawing on an inductive empirical analysis, we derive a typology of ecosystem roles. In addition, we illustrate these roles by applying a network visualization. Thereby, we reveal the presence of five distinct role types including Followers, Dominators, Leaders, Intermediaries and Local Champs. Our study contributes to information system research on ecosystems by providing insights into the composition of ecosystems in the era of digital innovation. Collapse

Topics: innovation management digital innovation value creation information technology capability mergers and acquisition

Methods: cluster analysis hierarchical clustering SDC Platinum computational algorithm network analysis

Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU

2020 | Business & Information Systems Engineering | Citations: 0

Authors: Ferreiro-Ferreiro, Ana Maria; García-Rodríguez, José Antonio; Souto, Luis A.; Vázquez, Carlos

Abstract: In the insurance sector, Asset Liability Management refers to the joint manageme ... Expand

Abstract: In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, arXiv:1808.00866). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282–298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting. Collapse

Topics: biometric parallel computing computer hardware accounting application programming interface

Methods: computational algorithm Monte Carlo simulation optimization model design artifact testing experiment

Product Categories as Judgment Devices: The Moral Awakening of the Investment Industry

2019 | Organization Science | Citations: 8

Authors: Arjaliès, Diane-Laure; Durand, Rodolphe

Abstract: Product categories are more than classification devices that organize markets; w ... Expand

Abstract: Product categories are more than classification devices that organize markets; when reflecting market actors' purposes, they are also judgment devices. Taking stock of the literature on product categories and drawing on the distinction between the faculties of knowing and judging, we elaborate a framework that accounts for how and why market actors include or exclude normative attributes in a product category definition. Based on a field study of the development of socially responsible investment (SRI) funds in France, we describe the phases and conditions of a judgment framework for category definition for both established and nascent categories. We discuss implications for research on product categories and the workings of markets more broadly. Collapse

Topics: evaluation criteria database system blog Twitter climate change

Methods: qualitative interview theory development field study qualitative coding longitudinal research

UNDERSTANDING THE ACCEPTANCE OF SMART HOME-BASED INSURANCES

2019 | European Conference On Information Systems | Citations: 0

Authors: Eggert, Mathias

Abstract: The distribution of smart home technology (SHT) in private households grows stea ... Expand

Abstract: The distribution of smart home technology (SHT) in private households grows steadily and enables disruptive business models. Besides the obvious advantage of more comfortability through the usage of SHT, the probability of residence-related risks such as fire, water leakage or burglary might decrease. In turn, insurance companies benefit from less insurance risks and more accurate actuarial models. Insurance companies already offer smart home devices combined with insurance products and assistance services, so called smart home-based insurances (SHBI). The research work at hand provides a theoretical model that partially explains the acceptance of SHBI. Its research contribution is threefold. First, an explanatory model for the acceptance of SHBI is presented. Second, the results of a survey with 127 participants provide the data for the hypotheses evaluation. Third, the analysis results foster discussions about ongoing research in the field of SHBI acceptance. Collapse

Topics: usage intention privacy smart home technology adoption perceived usefulness

Methods: survey descriptive statistic partial least squares regression scale validation structural equation modeling

Theories: technology acceptance model diffusion of innovations theory

Anomaly-Based Duplicate Detection: A Probabilistic Approach

2019 | DESRIST | Citations: 0

Authors: Obermeier, Andreas

Abstract: The importance of identifying records in databases that refer to the same real-w ... Expand

Abstract: The importance of identifying records in databases that refer to the same real-world entity (“duplicate detection”) has been recognized in both research and practice. However, existing supervised approaches for duplicate detection need training data with labeled instances of duplicates and non-duplicates, which is often costly and time-consuming to generate. On the contrary, unsupervised approaches can forego such training data but may suffer from limiting assumptions (e.g., monotonicity) and providing less reliable results. To address the issue of generating high-quality results using easy to acquire duplicate-free training data only, we propose a probabilistic approach for anomaly-based duplicate detection. Duplicates exhibit specific characteristics which differ significantly from the characteristics of non-duplicates and therefore represent anomalies. Based on the grade of anomaly compared to duplicate-free training data, our approach assigns the probability of being a duplicate to each analyzed pair of records while avoiding limiting assumptions (of existing approaches). We demonstrate the practical applicability and effectiveness of our approach in a real-world setting by analyzing customer master data of a German insurer. The evaluation shows that the results provided by the approach are reliable and useful for decision support and can outperform even fully supervised state-of-the-art approaches for duplicate detection. Collapse

Topics: Python record linkage knowledge base database system decision making

Methods: design artifact support vector machine machine learning literature study design science

Theories: probability theory decision theory

A Mixed Methods Analysis of the Adoption and Diffusion of Chatbot Technology in the German Insurance Sector

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Rodríguez Cardona, Davinia; Werth, Oliver; Schönborn, Svenja; Breitner, Michael H.

Abstract: In recent years, gradual improvements in information, computing, communication ... Expand

Abstract: In recent years, gradual improvements in information, computing, communication and connectivity technologies have enabled new technical possibilities for the adoption of Chatbots across diverse sectors. In the case of the insurance sector, the implementation of service innovations based on Chatbot technology can contribute, among other benefits, to improve the efficiency across the insurance value chain, reduce costs and generate customer loyalty and trust (Barrett et al., 2015;Ross et al., 2016). However, despite the advantages, the adoption success of Chatbot Technology depends on the understanding of the ambivalent perceptions, attitudes, and beliefs of the main social actors (i.e. practitioners and potential users) towards the customer interface. Using a mixed methods design based on an interpretive paradigm and within the frameworks of acceptance and diffusion research, we identified the "relative advantages" and "IS infrastructure" as the most critical ambivalent socio-technical factors for the adoption and diffusion of Chatbot technology in Germany. Collapse

Topics: artificial intelligence innovation management privacy behavioral intention Facebook

Methods: chatbot qualitative interview survey mixed method descriptive statistic

Theories: theory of everything diffusion of innovations theory

Event-Driven Duplicate Detection: A Probability-based Approach

2018 | European Conference On Information Systems | Citations: 0

Authors: Heinrich, Dr Bernd; Klier, Mathias; Obermeier, Andreas Alexander; Schiller, Alexander

Abstract: The importance of probability-based approaches for duplicate detection has been ... Expand

Abstract: The importance of probability-based approaches for duplicate detection has been recognized in both research and practice. However, existing approaches do not aim to consider the underlying real-world events resulting in duplicates (e.g., that a relocation may lead to the storage of two records for the same customer, once before and after the relocation). Duplicates resulting from real-world events exhibit specific characteristics. For instance, duplicates resulting from relocations tend to have significantly different attribute values for all address-related attributes. Hence, existing approaches focusing on high similarity with respect to attribute values are hardly able to identify possible duplicates resulting from such real-world events. To address this issue, we propose an approach for event-driven duplicate detection based on probability theory. Our approach assigns the probability of being a duplicate resulting from real-world events to each analysed pair of records while avoiding limiting assumptions (of existing approaches). We demonstrate the practical applicability and effectiveness of our approach in a realworld setting by analysing customer master data of a German insurer. The evaluation shows that the results provided by the approach are reliable and useful for decision support and can outperform wellknown state-of-the-art approaches for duplicate detection. Collapse

Topics: decision support database system knowledge base data quality Python

Methods: design artifact machine learning literature study

Theories: probability theory