@inproceedings{17140, author = {{Thiess, Tiemo and Müller, Oliver and Tonelli, Lorenzo}}, booktitle = {{International Conference on Wirtschaftsinformatik}}, title = {{{Design Principles for Explainable Sales Win-Propensity Prediction Systems}}}, doi = {{https://doi.org/10.30844/wi_2020_c8-thiess}}, year = {{2020}}, } @inproceedings{17095, abstract = {{In order to sustain their competitive advantage, data driven organizations must continue investing in business intelligence and analytics (BI&A) while mitigating inherent cost increases. Research shows that examining outlays by individual BI&A artifact (e.g. reports, analytics) is necessary, but introduction in practice is cumbersome and adoption is slow. BI&A service-oriented cost allocation (BIASOCA) represents an improvement to this situation. This approach enables to render the BI&A cost pool accountable and improves cost transparency, which leads to a higher BI&A penetration of economically viable applications in organizations. Against this background, this paper aims at designing and implementing BIASOCA in a medium-sized company. To record organizational impact and increase customer acceptance, this study is carried out as action design research (ADR). Our findings indicate improvements in BI&A management from working with consumers to locate cost savings and drivers. After invoicing, consumers’ BI&A awareness increased, releasing resources while also making a better understanding of BIASOCA necessary. We detail how to implement BIASOCA in a real-life setting and the challenges attendant in so doing. Our research contributes to theory and practice with a set of design principles highlighting, besides the accuracy of cost accounting, the importance of collaboration, model comprehensibility and strategic alignment.}}, author = {{Grytz, Raphael and Krohn-Grimberghe, Artus and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, title = {{{Business Intelligence & Analytics Cost Accounting: An Action Design Research Approach}}}, year = {{2020}}, } @inproceedings{35660, abstract = {{Effective customer loyalty programs are essential for every company. Small and medium sized brick-and- mortar stores, such as bakeries, butcher and flower shops, often share a common overarching loyalty program, organized by a third-party provider. Furthermore, these small shops have limited resources and often cannot afford complex BI tools. Out of these reasons we investigated how traditional brick-and- mortar stores can benefit from an expansion of service functionalities of a loyalty card provider. To answer this question, we cooperated with a cross-industry customer loyalty program in a polycentric region. The loyalty program was transformed from simple card-based solution to a mobile app for customers and a web- application for shop owners. The new solution offers additional BI services for performing data analytics and strengthening the position of brick-and-mortar stores. Participating shops can work together in order to increase sales and align marketing campaigns. Therefore, shopping data from 12 years, 55 shops, and 19,000 customers was analyzed.}}, author = {{Kucklick, Jan-Peter and Kamm, Michael Reiner and Schneider, Johannes and vom Brocke, Jan}}, booktitle = {{Proceedings of the 53rd Hawaii International Conference on System Sciences}}, keywords = {{brick-and-mortar stores, business intelligence, case study, loyalty program}}, title = {{{Extending Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores}}}, year = {{2020}}, } @article{35662, abstract = {{While the analysis and usage of data are increasing in importance, the application of sophisticated BI solutions in small stores is limited by available technical capabilities and financial resources. This study investigates how brick-and-mortar stores can benefit from an expansion of service functionalities of a cross-industry loyalty card provider. Digitalizing the loyalty program created new opportunities, while the analysis of shopping data of 13 years, 19,000 customers, and 55 shops empowered data-based decision support.}}, author = {{Kamm, Michael Reiner and Kucklick, Jan-Peter and Schneider, Johannes and vom Brocke, Jan}}, issn = {{1058-0530}}, journal = {{Information Systems Management}}, keywords = {{Customer loyalty, case study, brick-and-mortar stores, business intelligence, loyalty programs}}, number = {{4}}, pages = {{270--286}}, publisher = {{Informa UK Limited}}, title = {{{Data mining for small shops: Empowering brick-and-mortar stores through BI functionalities of a loyalty program1}}}, doi = {{10.1080/10580530.2020.1855486}}, volume = {{38}}, year = {{2020}}, } @inproceedings{21563, abstract = {{Historically, the field of financial forecasting almost exclusively relied on so-called hard information – i.e., numerical data with well-defined and unambiguous meaning. Over the last few decades, however, researchers and practitioners alike have, following the advances in natural language understanding, started recognizing the benefits of integrating soft information into financial modelling. In line with the above, this paper examines whether contemporary attention-based sequence-to-sequence models, known as Transformers, can help improve stock return volatility prediction when applied to corporate annual reports. Using a publicly available benchmark dataset, we show, in an empirical analysis, that out-of-the-box Transformer models have the ability to outmatch current state-of-the-art results and, more importantly, that our proposed feature-based Transformer approach can outperform a robust numerical baseline. To the best of our knowledge, this is the first empirical study focusing on stock return volatility prediction (1) to ever experiment with state-of-the-art Transformer architectures and (2) to demonstrate that a model based solely on soft information can surpass its numerical counterpart. Furthermore, we show that by including an additional numerical feature into our best text-only model, we can push the performance of our model even further, suggesting that soft and hard information contain different predictive signals.}}, author = {{Caron, Matthew and Müller, Oliver}}, booktitle = {{2020 IEEE International Conference on Big Data (Big Data)}}, location = {{Online}}, pages = {{4383--4391}}, title = {{{Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility}}}, doi = {{10.1109/BigData50022.2020.9378134}}, year = {{2020}}, } @inproceedings{17096, abstract = {{Augmented Reality (AR) technologies have evolved rapidly over the last years, particularly with regard to user interfaces, input devices, and cameras used in mobile devices for object and gesture recognition. While early AR systems relied on pre-defined trigger images or QR code markers, modern AR applications leverage machine learning techniques to identify objects in their physical environments. So far, only few empirical studies have investigated AR's potential for supporting learning and task assistance using such marker-less AR. In order to address this research gap, we implemented an AR application (app)with the aim to analyze the effectiveness of marker-less AR applied in a mundane setting which can be used for on-the-job training and more formal educational settings. The results of our laboratory experiment show that while participants working with AR needed significantly more time to fulfill the given task, the participants who were supported by AR learned significantly more.}}, author = {{Sommerauer, Peter and Müller, Oliver and Maxim, Leonard and Østman, Nils}}, booktitle = {{International Conference on Wirtschaftsinformatik}}, title = {{{The Effect of Marker-less Augmented Reality on Task and Learning Performance}}}, year = {{2019}}, } @article{17094, abstract = {{In reaction to the productivity challenges that hospitals around the world have faced, some hospitals have begun to move towards a process-oriented organization of care in order to enhance productivity. Existing research on process-oriented organization emphasizes severe challenges along the implementation process. However, the literature contains only a small number of documented cases of hospital-wide process-oriented reorganization. Against this background, in this case study, we explain how hospitals can successfully implement organization-wide process orientation. To do so, we conducted an exploratory single case study with semi-structured, face-to-face interviews and document analyses as our primary data-collection methods. We developed a theoretical framework of antecedents, interventions, enablers, barriers, and consequences that explain the trajectory of this successful hospital-reorganization project. We contribute a substantive theory on which other researchers can build and can extend in future studies. Further, in analyzing our unique case, we identify factors that the extant literature has not yet discussed, such as the blackboxing of diagnosis and treatment activities as an enabler. In line with existing literature, we also found that, even in this case, inflexible healthcare IT represented a barrier that hindered the case study in implementing process orientation.}}, author = {{Suomi, Reima and Müller, Oliver and vom Brocke, Jan}}, issn = {{1532-3416}}, journal = {{Journal of Information Technology Theory and Application}}, number = {{4}}, pages = {{3}}, title = {{{Hospital-wide Process-oriented Organization of Care: The Case of Turku University Central Hospital}}}, volume = {{19}}, year = {{2018}}, } @inproceedings{17099, abstract = {{Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results.}}, author = {{Thiess, Tiemo and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, title = {{{Towards Design Principles for Data-Driven Decision Making–An Action Design Research Project in the Maritime Industry}}}, year = {{2018}}, } @inproceedings{17098, abstract = {{Augmented Reality (AR) based teaching and learning has evolved rapidly over the past years. Re-searchers have shown that AR has the potential to deliver persuasive learning experiences in for-mal teaching (e.g., in classrooms) and in informal learning environments (e.g., museums). Howev-er, comparatively little extant research is firmly grounded in learning theories and applies rigor-ous empirical methods to evaluate the effect of AR on learning performance. In order to build a cumulative body of knowledge on AR-based instructional design and its effectiveness, it is neces-sary to consolidate both the theoretical foundations of and empirical evidence for using AR for teaching and learning. Against this background we conducted a focused systematic literature re-view on theoretical and empirical foundations of AR in education. We identify theory-based de-sign elements and empirical measures for developing and applying AR teaching and learning ap-plications and consolidate them in a design framework.}}, author = {{Sommerauer, Peter and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, title = {{{Augmented reality for teaching and learning - A literature review on theoretical and empirical foundations}}}, year = {{2018}}, } @inproceedings{17097, abstract = {{Emotions spread through online and offline social networks and subsequently influence individuals’ decisions and behaviours. Empirical studies on emotional contagion are almost non-existent in infor-mation systems research, leaving a gap in understanding how individuals are affected by emotions ex-pressed in online sources. Online newspaper articles and the associated readers’ comments provide a rich and mostly unfiltered data source that is utilized in this work to identify emotional contagion effects between newspaper publishers and its readers. By applying lexicon-based sentiment analysis and multi-level linear regression models to 1,151 online newspaper articles and 28,948 associated readers' com-ments, we model the relationships between sentiments in newspaper articles and comments. The results provide empirical support for emotional contagion effects between emotions expressed in online news-paper articles and emotions expressed in readers' comments. Linguistic, psychological and methodo-logical limitations are considered and discussed.}}, author = {{Bösch, Kevin and Müller, Oliver and Schneider, Johannes}}, booktitle = {{European Conference on Information Systems}}, title = {{{Emotional contagion through online newspapers}}}, year = {{2018}}, } @inproceedings{17103, abstract = {{Recent growth in data volume, variety, and velocity led to an increased demand for high-performance data processing and analytics solutions. In-memory computing (IMC) enables organizations to boost their information processing capacity, and is widely acknowledged to be one of the leading strategic technologies in the field of enterprise systems. The majority of technology vendors now have IMC technologies in their portfolio, and the interest of companies in adopting such solutions in order to benefit from big data is increasing. Although there is first research on the business value of IMC in the form of case studies, there is a lack of large-scale quantitative evidence on the positive effect of such solutions on firm performance. Based on a unique panel data set of IMC adoption information and financial firm performance data for a sample of companies from the Fortune 500 list this study aims at explaining the relationship between the adoption of IMC solutions and firm performance. In this research-in-progress paper we discuss the theoretical background of our work, describe the proposed research design, and develop five hypotheses for later testing. Our work aims at contributing to the research streams on IT business value and business analytics by helping to better understand the nature of the interdependencies between IMC adoption and firm performance. }}, author = {{Fay, Maria and Müller, Oliver and vom Brocke, Jan}}, booktitle = {{European Conference on Information Systems}}, title = {{{Disentangling the Relationship Between the Adoption of In-Memory Computing and Firm Performance}}}, year = {{2016}}, } @inproceedings{17102, abstract = {{Organizational culture represents a key success factor in highly competitive environments, such as, the IT sector. Thus, IT companies need to understand what makes up a culture that fosters employee performance. While existing research typically uses self-report questionnaires to study the relation of culture and the success of companies, the validity of this approach is often discussed and researchers call for new ways of studying culture. Therefore, our research goal is to present an alternative ap-proach to culture analysis for examining which cultural factors matter to the IT workforce. Our study builds on 112,610 online reviews of Fortune 500 IT companies collected from Glassdoor, an online platform on which current and former employees can anonymously review companies and their man-agement. We perform an automated content analysis to identify cultural factors that employees em-phasize in their reviews. Through a regression analysis on numerical employee satisfaction ratings, we find that a culture of learning and performance orientation contributes to employee motivation, while a culture of assertiveness and gender inegalitarianism has a strong negative influence on em-ployees’ satisfaction in the IT workforce. Future research can apply our approach as an alternative method to quantifying culture and its impact on other variables.}}, author = {{Schmiedel, Theresa and Müller, Oliver and Debortoli, Stefan and vom Brocke, Jan}}, booktitle = {{European Conference on Information Systems}}, title = {{{Identifying and quantifying cultural factors that matter to the IT workforce: An approach based on automated content analysis}}}, year = {{2016}}, } @article{4691, abstract = {{Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others.}}, author = {{Debortoli, Stefan and Müller, Oliver and Junglas, Iris and vom Brocke, Jan}}, isbn = {{9781615679119}}, issn = {{1529-3181}}, journal = {{Communications of the Association for Information Systems}}, keywords = {{Latent dirichlet allocation, Online customer reviews, Text mining, Topic modeling, User satisfaction}}, pages = {{555--582}}, title = {{{Text Mining for Information Systems Researchers: An Annotated Tutorial}}}, doi = {{10.17705/1CAIS.03907}}, year = {{2016}}, } @inproceedings{17105, author = {{Gorbacheva, Elena and Stein, Armin and Schmiedel, Theresa and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, isbn = {{978-3-00-050284-2}}, title = {{{A gender perspective on business process management competences offered on professional online social networks}}}, doi = {{10.18151/7217329}}, year = {{2015}}, } @article{17108, author = {{vom Brocke, Jan and Müller, Oliver and Debortoli, Stefan and Reuter, Nadine}}, issn = {{0935-0381}}, journal = {{Controlling}}, number = {{2}}, pages = {{83--89}}, publisher = {{Verlag Franz Vahlen}}, title = {{{Potenzialbeurteilung neuer Technologien im Prozesscontrolling}}}, doi = {{10.15358/0935-0381_2014_2_83}}, volume = {{26}}, year = {{2015}}, } @inproceedings{17106, abstract = {{Business operations are becoming more and more integrated with the real-time intelligence. Core business activities are being carried out through OLTP systems that provide limited monitoring capabilities of the running process instances. The article shows how to turn the gap between the classic transactional system and the process-centric approach into an organization that provides more accurate and faster decisions on the strategic and operational management levels. This study aims at determination of what kind of information can be retrieved during the process execution and it tries to identify the need for the real-time process intelligence on the example of the order-to-cash process. Furthermore, we compare two architectural approaches of the real-time process intelligence monitoring system. The proposed frameworks retrieve process data from the ERP system in order to record crucial performance indicators on a real-time basis with the use of the in-memory technology.}}, author = {{Korotina, Anastasiia and Müller, Oliver and Debortoli, Stefan}}, booktitle = {{International Conference on Wirtschaftsinformatik}}, pages = {{1710--1724}}, title = {{{Real-time Business Process Intelligence. Comparison of different architectural approaches using the example of the order-to-cash process.}}}, year = {{2015}}, } @article{17107, author = {{Kohlborn, Thomas and Müller, Oliver and Poeppelbuss, Jens and Roeglinger, Maximilian}}, issn = {{1463-7154}}, journal = {{Business Process Management Journal}}, pages = {{634--638}}, publisher = {{Emerald Group Publishing Limited}}, title = {{{Interview with Michael Rosemann on ambidextrous business process management}}}, doi = {{10.1108/bpmj-02-2014-0012}}, year = {{2014}}, } @inbook{17109, author = {{Raffl, Celina and Lucke, Jörn and Müller, Oliver and Zimmermann, Hans-Dieter and Vom Brocke, Jan}}, booktitle = {{TOGI-Schriftenreihe}}, publisher = {{ePubli GmbH}}, title = {{{Handbuch für offene gesellschaftliche Innovation}}}, volume = {{11}}, year = {{2014}}, } @inproceedings{17116, author = {{Herbst, Andrea and Simons, Alexander and vom Brocke, Jan and Müller, Oliver and Debortoli, Stefan and Vakulenko, Svitlana}}, booktitle = {{European Conference on Information Systems}}, title = {{{Identifying and characterizing topics in enterprise content management: a latent semantic analysis of vendor case studies}}}, year = {{2014}}, } @inproceedings{17117, author = {{Reuter, Nadine and Vakulenko, Svitlana and vom Brocke, Jan and Debortoli, Stefan and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, title = {{{Identifying the role of information systems in achieving energy-related environmental sustainability using text mining}}}, year = {{2014}}, }