@article{4681, author = {{Müller, Oliver and Fay, M. and vom Brocke, J.}}, issn = {{1557928X}}, journal = {{Journal of Management Information Systems}}, number = {{2}}, pages = {{488----509}}, title = {{{The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics}}}, doi = {{10.1080/07421222.2018.1451955}}, year = {{2018}}, } @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{4683, author = {{Jaakonmäki, Roope and Müller, Oliver and vom Brocke, Jan}}, booktitle = {{Hawaii International Conference on System Sciences}}, isbn = {{978-0-9981331-0-2}}, title = {{{The Impact of Content, Context, and Creator on User Engagement in Social Media Marketing}}}, doi = {{10.24251/HICSS.2017.136}}, year = {{2017}}, } @article{4687, author = {{Müller, Oliver and Simons, Alexander and Weinmann, Markus}}, issn = {{03772217}}, journal = {{European Journal of Operational Research}}, keywords = {{Crowdsourcing, Football, Market value, OR in Sports, Soccer}}, number = {{2}}, pages = {{611----624}}, title = {{{Beyond crowd judgments: Data-driven estimation of market value in association football}}}, doi = {{10.1016/j.ejor.2017.05.005}}, year = {{2017}}, } @article{17158, author = {{Beverungen, Daniel and Müller, Oliver and Matzner, Martin and Mendling, Jan and vom Brocke, Jan}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, pages = {{7--18}}, title = {{{Conceptualizing smart service systems}}}, doi = {{10.1007/s12525-017-0270-5}}, year = {{2017}}, } @article{2856, abstract = {{Taxi ridesharing1 (TRS) is an advanced form of urban transportation that matches separate ride requests with similar spatio-temporal characteristics to a jointly used taxi. As collaborative consumption, TRS saves customers money, enables taxi companies to economize use of their resources, and lowers greenhouse gas emissions. We develop a one-to-one TRS approach that matches rides with similar start and end points. We evaluate our approach by analyzing an open dataset of > 5 million taxi trajectories in New York City. Our empirical analysis reveals that the proposed approach matches up to 48.34% of all taxi rides, saving 2,892,036 km of travel distance, 231,362.89 l of gas, and 532,134.64 kg of CO2 emissions per week. Compared to many-to-many TRS approaches, our approach is competitive, simpler to implement and operate, and poses less rigid assumptions on data availability and customer acceptance.}}, author = {{Barann, Benjamin and Beverungen, Daniel and Müller, Oliver}}, journal = {{Decision Support Systems}}, keywords = {{Taxi ridesharing Collaborative consumption Transportation Open data Sustainability Shared mobility}}, number = {{July 2017}}, pages = {{86--95}}, publisher = {{Elsevier}}, title = {{{An open-data approach for quantifying the potential of taxi ridesharing}}}, doi = {{10.1016/j.dss.2017.05.008}}, volume = {{99}}, year = {{2017}}, } @article{4689, author = {{Müller, Oliver and Junglas, Iris and vom Brocke, Jan and Debortoli, Stefan}}, isbn = {{0960-085X}}, issn = {{14769344}}, journal = {{European Journal of Information Systems}}, keywords = {{analytics, big data, data source, information systems research, methodology}}, number = {{4}}, pages = {{289----302}}, title = {{{Utilizing big data analytics for information systems research: Challenges, promises and guidelines}}}, doi = {{10.1057/ejis.2016.2}}, year = {{2016}}, } @article{4690, author = {{Gorbacheva, Elena and Stein, Armin and Schmiedel, Theresa and Müller, Oliver}}, issn = {{18670202}}, journal = {{Business and Information Systems Engineering}}, keywords = {{BPM workforce, Business process management, Competences, Gender diversity, Latent semantic analysis, Skills, Text mining}}, number = {{3}}, pages = {{213----231}}, title = {{{The Role of Gender in Business Process Management Competence Supply}}}, doi = {{10.1007/s12599-016-0428-2}}, year = {{2016}}, } @article{4692, author = {{Müller, Oliver and Schmiedel, Theresa and Gorbacheva, Elena and vom Brocke, Jan}}, issn = {{17517583}}, journal = {{Enterprise Information Systems}}, keywords = {{abilities, business process management, competences, knowledge, latent semantic analysis, professionals, skills, typology}}, number = {{1}}, pages = {{50----80}}, title = {{{Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis}}}, doi = {{10.1080/17517575.2014.923514}}, year = {{2016}}, } @article{4693, abstract = {{Deriving value from structured data is now commonplace. The value of unstructured textual data, however, remains mostly untapped and often unrecognized. This article describes the text analytics journeys of three organizations in the customer service management area. Based on their experiences, we provide four lessons that can guide other organizations as they embark on their text analytics journeys.}}, author = {{Müller, Oliver and Debortoli, S. and Junglas, I. and vom Brocke, J.}}, issn = {{15401979}}, journal = {{MIS Quarterly Executive}}, number = {{4}}, pages = {{243----258}}, title = {{{Using text analytics to derive customer service management benefits from unstructured data}}}, year = {{2016}}, } @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{4694, author = {{Kohlborn, T and Müller, Oliver and Pöppelbuss, J and Röglinger, M}}, issn = {{1463-7154}}, journal = {{Business Process Management Journal}}, number = {{4}}, pages = {{3----6}}, title = {{{New frontiers in business process management}}}, doi = {{10.1108/BPMJ-02-2014-0015}}, year = {{2014}}, }