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Publications


Open list in Research Information System

2023

More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency

K. Brennig, O. Müller, in: Proceedings of the 56th Hawaii International Conference on System Sciences, 2023

Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality.


Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time

D. Leffrang, K. Bösch, O. Müller, in: Proceedings of the 56th Hawaii International Conference on System Sciences, 2023

Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.


2022

Shifting ML value creation mechanisms: A process model of ML value creation

A. Shollo, K. Hopf, T. Thiess, O. Müller, The Journal of Strategic Information Systems (2022), 31(3), 101734

DOI


Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

J. Kucklick, O. Müller, ACM Transactions on Management Information Systems (2022)

Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.


Visual Interpretability of Image-based Real Estate Appraisal

J. Kucklick, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022

Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.


Using Geolocated Text to Quantify Location in Real Estate Appraisal

T. Heuwinkel, J. Kucklick, O. Müller, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022

Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.


Towards a model- and data-focused taxonomy of XAI systems

J. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022

Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.


Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing

B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, O. Müller, in: Business Process Management, Springer International Publishing, 2022, pp. 251–267

Existing process mining methods are primarily designed for processes that have reached a high degree of digitalization and standardization. In contrast, the literature has only begun to discuss how process mining can be applied to knowledge-intensive processes—such as product innovation processes—that involve creative activities, require organizational flexibility, depend on single actors’ decision autonomy, and target process-external goals such as customer satisfaction. Due to these differences, existing Process Mining methods cannot be applied out-of-the-box to analyze knowledge-intensive processes. In this paper, we employ Action Design Research (ADR) to design and evaluate a process mining approach for knowledge-intensive processes. More specifically, we draw on the two processes of product innovation and engineer-to-order in manufacturing contexts. We collected data from 27 interviews and conducted 49 workshops to evaluate our IT artifact at different stages in the ADR process. From a theoretical perspective, we contribute five design principles and a conceptual artifact that prescribe how process mining ought to be designed for knowledge-intensive processes in manufacturing. From a managerial perspective, we demonstrate how enacting these principles enables their application in practice.


Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift

M. Caron, in: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), IEEE, 2022

In recent years, many cases of deep neural networks failing dramatically when faced with adversarial or real-world examples have been reported. Such failures, which are quite hard to detect, are often related to a generalization problem known as shortcut learning. Yet, with state-of-the-art transformer models now being ubiquitous in financial text mining, one cannot help but wonder how reliable the results conveyed in the ever-growing literature genuinely are. Against this background, we expose, in this work, how vulnerable contemporary financial text mining approaches are to shortcut learning. Focussing on the common learning task of financial sentiment classification, we assess, using two entity-based sampling strategies and our publicly-available dataset, the discrepancies between i.i.d. and o.o.d. performance estimates of four transformer models. Our results reveal that o.o.d. performance estimates are consistently weaker than those of their i.i.d. counterparts, with the error rate increasing by as much as 29.7%, thus, demonstrating how this issue can, when overlooked, lead to misleading evaluations. Moreover, we show how additional preprocessing steps, such as entity removal and vocabulary filtering, can help reduce the effects of shortcut learning by filtering out entity-related linguistic cues.


Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models

M. Caron, F.S. Bäumer, O. Müller, in: 55th Annual Hawaii International Conference on System Sciences (HICSS 2022), 2022

Our world is more connected than ever before. Sadly, however, this highly connected world has made it easier to bully, insult, and propagate hate speech on the cyberspace. Even though researchers and companies alike have started investigating this real-world problem, the question remains as to why users are increasingly being exposed to hate and discrimination online. In fact, the noticeable and persistent increase in harmful language on social media platforms indicates that the situation is, actually, only getting worse. Hence, in this work, we show that contemporary ML methods can help tackle this challenge in an accurate and cost-effective manner. Our experiments demonstrate that a universal approach combining transfer learning methods and state-of-the-art Transformer architectures can trigger the efficient development of toxic language detection models. Consequently, with this universal approach, we provide platform providers with a simplistic approach capable of enabling the automated moderation of user-generated content, and as a result, hope to contribute to making the web a safer place.


Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation

M. Caron, C. Bartelheimer, O. Müller, in: Proceeding of the 28th Americas Conference on Information Systems (AMCIS), 2022

Now accounting for more than 80% of a firm's worth, brands have become essential assets for modern organizations. However, methods and techniques for the monetary valuation of brands are still under-researched. Hence, the objective of this study is to evaluate the utility of explanatory statistical models and machine learning approaches for explaining and predicting brand value. Drawing upon the case of the most valuable English football brands during the 2016/17 to 2020/21 seasons, we demonstrate how to operationalize Aaker's (1991) theoretical brand equity framework to collect meaningful qualitative and quantitative feature sets. Our explanatory models can explain up to 77% of the variation in brand valuations across all clubs and seasons, while our predictive approach can predict out-of-sample observations with a mean absolute percentage error (MAPE) of 14%. Future research can build upon our results to develop domain-specific brand valuation methods while enabling managers to make better-informed investment decisions.


2021


A Comparison of Multi-View Learning Strategies for Satellite Image-based Real Estate Appraisal

J. Kucklick, O. Müller, in: The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services, 2021



PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data

O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), 2021

Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.


To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock

M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information Systems (ICIS 2021), 2021

In early 2021, the finance world was taken by storm by the dramatic price surge of the GameStop Corp. stock. This rise is being, at least in part, attributed to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit group. In this work, we set out to address if user activity on the WSB subreddit is associated with the trading volume of the GME stock. Leveraging a unique dataset containing more than 4.9 million WSB posts and comments, we assert that user activity is associated with the trading volume of the GameStop stock. We further show that posts have a significantly higher predictive power than comments and are especially helpful for predicting unusually high trading volume. Lastly, as recent events have shown, we believe that these findings have implications for retail and institutional investors, trading platforms, and policymakers, as these can have disruptive potential.


Designing a Risk Assessment Tool for Artificial Intelligence Systems

P.R. Nagbøl, O. Müller, O. Krancher, in: The Next Wave of Sociotechnical Design, Springer International Publishing, 2021

DOI


2020

Data mining for small shops: Empowering brick-and-mortar stores through BI functionalities of a loyalty program1

M.R. Kamm, J. Kucklick, J. Schneider, J. vom Brocke, Information Systems Management (2020), 38(4), pp. 270-286

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.


Location, location, location: Satellite image-based real-estate appraisal

J. Kucklick, O. Müller, in: Symposium on Statistical Challenges in Electronic Commerce Research (SCECR), 2020


Design Principles for Explainable Sales Win-Propensity Prediction Systems

T. Thiess, O. Müller, L. Tonelli, in: International Conference on Wirtschaftsinformatik, 2020

DOI


Business Intelligence & Analytics Cost Accounting: An Action Design Research Approach

R. Grytz, A. Krohn-Grimberghe, O. Müller, in: European Conference on Information Systems, 2020

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.


Extending Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores

J. Kucklick, M.R. Kamm, J. Schneider, J. vom Brocke, in: Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020

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.


Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility

M. Caron, O. Müller, in: 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020, pp. 4383-4391

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.


2019

Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture

T. Schmiedel, O. Müller, J. vom Brocke, Organizational Research Methods (2019), pp. 941--968

DOI


Conceptualizing smart service systems

D. Beverungen, O. Müller, M. Matzner, J. Mendling, J. vom Brocke, Electronic Markets (2019), pp. 1--12

DOI


The Effect of Marker-less Augmented Reality on Task and Learning Performance

P. Sommerauer, O. Müller, L. Maxim, N. Østman, in: International Conference on Wirtschaftsinformatik, 2019

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.



2018

ECM implementations in practice: objectives, processes, and technologies

R. Jaakonmäki, A. Simons, O. Müller, J. vom Brocke, Journal of Enterprise Information Management (2018)(5), pp. 704--723

DOI


The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics

O. Müller, M. Fay, J. vom Brocke, Journal of Management Information Systems (2018)(2), pp. 488--509

DOI


Hospital-wide Process-oriented Organization of Care: The Case of Turku University Central Hospital

R. Suomi, O. Müller, J. vom Brocke, Journal of Information Technology Theory and Application (2018), 19(4), pp. 3

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.


Augmented Reality in Informal Learning Environments: Investigating Short-term and Long-term Effects

P. Sommerauer, O. Müller, in: Hawaii International Conference on System Sciences, 2018

DOI


Towards Design Principles for Data-Driven Decision Making–An Action Design Research Project in the Maritime Industry

T. Thiess, O. Müller, in: European Conference on Information Systems, 2018

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.


Augmented reality for teaching and learning - A literature review on theoretical and empirical foundations

P. Sommerauer, O. Müller, in: European Conference on Information Systems, 2018

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.


Emotional contagion through online newspapers

K. Bösch, O. Müller, J. Schneider, in: European Conference on Information Systems, 2018

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.


2017

An open-data approach for quantifying the potential of taxi ridesharing

B. Barann, D. Beverungen, O. Müller, Decision Support Systems (2017), pp. 86--95

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.


Beyond crowd judgments: Data-driven estimation of market value in association football

O. Müller, A. Simons, M. Weinmann, European Journal of Operational Research (2017)(2), pp. 611--624

DOI


Conceptualizing smart service systems

D. Beverungen, O. Müller, M. Matzner, J. Mendling, J. vom Brocke, Electronic Markets (2017), pp. 7-18

DOI


The Impact of Content, Context, and Creator on User Engagement in Social Media Marketing

R. Jaakonmäki, O. Müller, J. vom Brocke, in: Hawaii International Conference on System Sciences, 2017

DOI


2016

Utilizing big data analytics for information systems research: Challenges, promises and guidelines

O. Müller, I. Junglas, J. vom Brocke, S. Debortoli, European Journal of Information Systems (2016)(4), pp. 289--302

DOI


The Role of Gender in Business Process Management Competence Supply

E. Gorbacheva, A. Stein, T. Schmiedel, O. Müller, Business and Information Systems Engineering (2016)(3), pp. 213--231

DOI


Text Mining for Information Systems Researchers: An Annotated Tutorial

S. Debortoli, O. Müller, I. Junglas, J. vom Brocke, Communications of the Association for Information Systems (2016), pp. 555--582

DOI


Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis

O. Müller, T. Schmiedel, E. Gorbacheva, J. vom Brocke, Enterprise Information Systems (2016)(1), pp. 50--80

DOI


Using text analytics to derive customer service management benefits from unstructured data

O. Müller, S. Debortoli, I. Junglas, J. vom Brocke, MIS Quarterly Executive (2016)(4), pp. 243--258

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.


Disentangling the Relationship Between the Adoption of In-Memory Computing and Firm Performance

M. Fay, O. Müller, J. vom Brocke, in: European Conference on Information Systems, 2016

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.


Identifying and quantifying cultural factors that matter to the IT workforce: An approach based on automated content analysis

T. Schmiedel, O. Müller, S. Debortoli, J. vom Brocke, in: European Conference on Information Systems, 2016

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.


2015

Potenzialbeurteilung neuer Technologien im Prozesscontrolling

J. vom Brocke, O. Müller, S. Debortoli, N. Reuter, Controlling (2015), 26(2), pp. 83-89

DOI


A gender perspective on business process management competences offered on professional online social networks

E. Gorbacheva, A. Stein, T. Schmiedel, O. Müller, in: European Conference on Information Systems, 2015

DOI


Real-time Business Process Intelligence. Comparison of different architectural approaches using the example of the order-to-cash process.

A. Korotina, O. Müller, S. Debortoli, in: International Conference on Wirtschaftsinformatik, 2015, pp. 1710-1724

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.


2014

New frontiers in business process management

T. Kohlborn, O. Müller, J. Pöppelbuss, M. Röglinger, Business Process Management Journal (2014)(4), pp. 3--6

DOI


Comparing business intelligence and big data skills: A text mining study using job advertisements

S. Debortoli, O. Müller, J. vom Brocke, Business and Information Systems Engineering (2014)(5), pp. 289--300

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How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti

J. vom Brocke, S. Debortoli, N. Reuter, O. Müller, Communications of the Association for Information Systems (2014), pp. 151--167

DOI


Interview with Michael Rosemann on ambidextrous business process management

T. Kohlborn, O. Müller, J. Poeppelbuss, M. Roeglinger, Business Process Management Journal (2014), pp. 634-638

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How In-memory Technology Can Create Business Value: Insights from the Hilti Case

J. vom Brocke, S. Debortoli, O. Müller, N. Reuter, Communications of the Association for Information Systems (2014), 34(1), pp. 7

DOI


Identifying and characterizing topics in enterprise content management: a latent semantic analysis of vendor case studies

A. Herbst, A. Simons, J. vom Brocke, O. Müller, S. Debortoli, S. Vakulenko, in: European Conference on Information Systems, 2014


Identifying the role of information systems in achieving energy-related environmental sustainability using text mining

N. Reuter, S. Vakulenko, J. vom Brocke, S. Debortoli, O. Müller, in: European Conference on Information Systems, 2014


Enriching iTunes App Store categories via topic modeling

S. Vakulenko, O. Müller, J.v. Brocke, in: International Conference on Information Systems, 2014

Mobile application development is an emerging lucrative and fast growing market. With the steady growth of the number of apps in the repositories the providers will inevitably face the need to fine-grain the existing hierarchy of categories used to organize the apps. In this paper we present a method to bootstrap the categorization process via topic modeling. We apply Latent Dirichlet Allocation (LDA) to the textual descriptions of iTunes apps in order to identify recurrent topics in the collection. We evaluate and discuss the results obtained from training the model on a set of almost 600,000 English-language app descriptions. Our results demonstrate that automated categorization via LDA-based topic modeling is a promising approach, that can help to structure, analyze and manage the content of app repositories. The topics produced complement the original iTunes categories, concretize and extend them by providing insights into the underlying category content.


Handbuch für offene gesellschaftliche Innovation

C. Raffl, J. Lucke, O. Müller, H. Zimmermann, J. Vom Brocke, in: TOGI-Schriftenreihe, ePubli GmbH, 2014


2013

Bridging the Gap Between Manufacturing and Service Through IT-Based Boundary Objects

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuß, IEEE Transactions on Engineering Management (2013)(3), pp. 468--482

DOI


Designing Interaction Routines in Service Networks: A Modularity and Social Construction-Based Approach

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuß, Scandinavian Journal of Information Systems (2013)(1), pp. 37--68


Designing interaction routines in service networks: A modularity and social construction-based approach

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuss, Scandinavian Journal of Information Systems (2013)(1), pp. 17--47


Bridging the gap between manufacturing and service through IT-based boundary objects

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuss, IEEE Transactions on Engineering Management (2013)(3), pp. 468--482

DOI


Reflection, abstraction and theorizing in design and development research

S. Gregor, O. Müller, S. Seidel, in: European Conference on Information Systems, 2013


MUSE: Implementation of a Design Theory for Systems that Support Convergent and Divergent Thinking

O. Müller, S. Debortoli, S. Seidel, in: International Conference on Design Science Research in Information Systems, Springer-Verlag, 2013, pp. 438 - 445

DOI


2012

Beyond process monitoring: A proof-of-concept of event-driven business activity management

C. Janiesch, M. Matzner, O. Müller, Business Process Management Journal (2012)(4), pp. 625--643

Purpose – The purpose of this paper is to show how to employ complex event processing (CEP) for the observation and management of business processes. It proposes a conceptual architecture of BPM event producer, processor, and consumer and describes technical implications for the application with standard software in a perfect order scenario. Design/methodology/approach – The authors discuss business process analytics as the technological background. The capabilities of CEP in a BPM context are outlined an architecture design is proposed. A sophisticated proof-of-concept demonstrates its applicability. Findings – The results overcome the separation and data latency issues of process controlling, monitoring, and simulation. Distinct analyses of past, present, and future blur into a holistic real-time approach. The authors highlight the necessity for configurable event producer in BPM engines, process event support in CEP engines, a common process event format, connectors to visualizers, notifiers and return channels to the BPM engine. Research limitations/implications – Further research will thoroughly evaluate the approach in a variety of business settings. New concepts and standards for the architecture's building blocks will be needed to improve maintainability and operability. Practical implications – Managers learn how CEP can yield insights into business processes' operations. The paper illustrates a path to overcome inflexibility, latency, and missing feedback mechanisms of current process modeling and control solutions. Software vendors might be interested in the conceptualization and the described needs for further development. Originality/value – So far, there is no commercial CEP-based BPM solution which facilitates a round trip from insight to action as outlines. As major software vendors have begun developing solutions (BPM/BPA solutions), this paper will stimulate a debate between research and practice on suitable design and technology.


Open Societal Innovation: The Alemannic Definition

J. von Lucke, J. Herzberg, U. Kluge, J. vom Brocke, O. Müller, H. Zimmermann, SSRN Electronic Journal (2012)

DOI


A review of event formats as enablers of event-driven BPM

J. Becker, M. Matzner, O. Müller, M. Walter, in: Lecture Notes in Business Information Processing, 2012

Event-driven Business Process Management (EdBPM) is based upon exchanging and processing business events. As yet, no commonly adopted event format for communicating business events between distributed event producers and consumers has emerged. This paper is an effort to review the status quo of event formats against the requirements of EdBPM. We particularly discuss BPAF, CBE, and XES as promising candidates and identify prospects for development.


Extending BPMN for business activity monitoring

J.P. Friedenstab, C. Janiesch, M. Matzner, O. Müller, in: Hawaii International Conference on System Sciences, 2012

Real-time access to key performance indicators is necessary to ensure timeliness and effectiveness of operational business processes. The concept of Business Activity Monitoring (BAM) refers to the observation, analysis, and presentation of real-time information about business activities across systems' and companies' borders. Designing and maintaining BAM applications is challenging, as the involved concepts (e.g., business processes, audit logs, performance measures) --though being strongly interrelated-- are developed by different communities of practice. Also, they reside on different levels of abstraction, and are handled by different IT systems. Hence, we developed a conceptual modeling language which extends the widely accepted Business Process Modeling Notation (BPMN) by BAM-relevant concepts. The main results presented in this paper are: (1) a meta-model which formally describes the conceptual aspects of the developed BPMN extension (abstract syntax); (2) graphical symbols as an exemplary representation of this abstract syntax (concrete syntax); (3) a demo scenario that illustrates the application of the language in a fictitious scenario.


Business activity management for service networks in cloud environments

C. Janiesch, R. Fischer, M. Matzner, O. Müller, in: Workshop on Middleware for Service Oriented Computing, 2012, pp. 1 - 6

DOI


Product-Service System Approaches

D. Beverungen, M. Matzner, O. Müller, J. Becker, in: Handbook of Service Description, 2012, pp. 19--44

DOI


2011

Flexible Informationssystem-Architekturen für hybride Wertschöpfungsnetzwerke (Flexnet) : Forschungsprojekt im Rahmen der BMBF-Fördermaßnahme Integration von Produktion und Dienstleistung

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelfuß, Arbeitsberichte des Instituts für Wirtschaftsinformatik, 2011

DOI


Pricing of Value Bundles: A Multi-Perspective Decision Support Approach

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, Enterprise Modelling and Information Systems Architectures (2011)(2), pp. 54--69


Leaving the Beaten Tracks in Creative Work – A Design Theory for Systems that Support Convergent and Divergent Thinking

F. Müller-Wienbergen, O. Müller, S. Seidel, J. Becker, Journal of the Association for Information Systems (2011)(11), pp. 714--740

DOI


Information Needs in Service Systems --- A Framework for Integrating Service and Manufacturing Business Processes

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, in: Proceedings of the 44th Annual Hawaii International Conference on System Sciences, 2011

DOI


Design Science in Service Research: A Framework-Based Review of IT Artifacts in Germany

J. Becker, D. Beverungen, M. Matzner, O. Müller, J. Pöppelbuß, in: International Conference on Design Science Research in Information Systems and Technology, 2011, pp. 366--375

DOI


A blueprint for event-driven business activity management

C. Janiesch, M. Matzner, O. Müller, in: Lecture Notes in Computer Science, 2011

DOI


Slipstream: architecture options for real-time process analytics

C. Janiesch, M. Matzner, O. Müller, R. Vollmer, J. Becker, in: Proceedings of the ACM Symposium on Applied Computing, 2011, pp. 295 - 300

DOI


Integrierte Informationslogistik in der hybriden Wertschöpfung

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuß, in: Mit Dienstleistungen die Zukunft gestalten: Impulse aus Forschung und Praxis. Beiträge der 8. Dienstleistungstagung des BMBF, 2011, pp. 247--255


2010

Enabling Individualized Recommendations and Dynamic Pricing of Value-Added Services through Willingness-to-Pay Data

K. Backhaus, J. Becker, D. Beverungen, M. Frohs, O. Müller, M. Weddeling, R. Knackstedt, M. Steiner, Electronic Markets (2010)(2), pp. 131--146

DOI


Incorporating Willingness-to-Pay Data into Online Recommendations for Value-Added Services

K. Backhaus, J. Becker, D. Beverungen, M. Frohs, O. Müller, M. Weddeling, in: European Conference on Information Systems, 2010


Total Cost of Service Life --- Decision Support for Selecting and Orchestrating Services

J. Becker, D. Beverungen, M. Matzner, O. Müller, in: Proceedings of the IADIS Internatioal Conference e-Society 2010, 2010, pp. 322--329


Total Costs of Service Life: The Need of Decision Support in Selecting, Comparing and Orchestrating Services

J. Becker, D. Beverungen, M. Matzner, O. Müller, in: Exploring Service Sciences, 2010, pp. 282--288

DOI


An ontology-based natural language service discovery engine--design and experimental evaluation

J. Becker, O. Müller, M. Woditsch, in: European Conference on Information Systems, 2010


TCO-as-a-Service --- Servicebasierte Lebenszyklusrechnung für hybride Leistungsbündel

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, S. Müller, in: Vertriebsinformationssysteme --- Standardisierung, Individualisierung, Hybridisierung und Internetisierung, 2010, pp. 161--174

DOI


Preisfindung für hybride Leistungsbündel--Modellbasierte Integration von Ansätzen zur Entscheidungsunterstützung

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, in: Dienstleistungsmodellierung 2010, Physica-Verlag HD, 2010, pp. 144-166

DOI


Online-Produktkonfiguratoren – Status quo und Entwicklungsperspektiven

J. Becker, R. Knackstedt, O. Müller, A. Benölken, O. Schmitt, M. Thillainathan, A. Schulke, in: Vertriebsinformationssysteme, Springer-Verlag, 2010, pp. 85 - 104

DOI


2009

Model-Based Decision Support for the Customer-Specific Configuration of Value Bundles

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, Enterprise Modelling and Information Systems Architectures (2009)(1), pp. 26--38


Model-Based Decision Support for the Customer-Specific Configuration of Value Bundles

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, Enterprise Modelling and Information Systems Architectures (2009)(1), pp. 26--38

DOI


A Framework for Design Research in the Service Science Discipline

J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, J. Pöppelbuß, in: Americas Conference on Information Systems, 2009


Design Requirements to Support Information Flows for Providing Customer Solutions: A Case Study in the Mechanical Engineering Sector

J. Becker, D. Beverungen, M. Matzner, O. Müller, in: Proceedings of the First International Symposium on Services Science (ISSS'09), 2009, pp. 75--85


Documentation of flexible business processes-A healthcare case study

J. Becker, K. Bergener, O. Müller, F. Müller-Wienbergen, in: Americas Conference on Information Systems, 2009, pp. 93


A design research study on enhancing creativity-The case of developing product-service bundles

F. Müller-Wienbergen, S. Seidel, O. Müller, R. Knackstedt, J. Becker, in: European Conference on Information Systems, 2009


Comparing Architectural Styles for Service-Oriented Architectures – a REST vs. SOAP Case Study

J. Becker, M. Matzner, O. Müller, in: Information Systems Development, Springer, 2009, pp. 207-215

DOI


2008

Entwicklung Serviceorientierter Architekturen zur Integration von Produktion und Dienstleistung --- Eine Konzeptionsmethode und ihre Anwendung am Beispiel des Recyclings elektronischer Geräte

D. Beverungen, R. Knackstedt, O. Müller, Wirtschaftsinformatik (2008)(3), pp. 220--234


Entwicklung Serviceorientierter Architekturen zur Integration von Produktion und Dienstleistung – Eine Konzeptionsmethode und ihre Anwendung am Beispiel des Recyclings elektronischer Geräte

D. Beverungen, R. Knackstedt, O. Müller, WIRTSCHAFTSINFORMATIK (2008)(3), pp. 220--234

DOI


Modeling, Customer-Specific Configuration and Calculation of Value Bundles

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, in: Americas Conference on Information Systems, 2008


Konzeption einer Modellierungssprache zur tool-unterstützten Modellierung, Konfiguration und Bewertung hybrider Leistungsbündel

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, in: Proceedings of the GI-Tagung Modellierung, Workshop Dienstleistungsmodellierung, 2008, pp. 45-62


Towards a semantic data quality management-using ontologies to assess master data quality in retailing

J. Becker, M. Matzner, O. Müller, A. Winkelmann, in: Americas Conference on Information Systems, 2008, pp. 129


An ontology-based service discovery approach for the provisioning of product-service bundles

R. Knackstedt, D. Kuropka, O. Müller, A. Polyvyanyy, in: European Conference on Information Systems, 2008


Konzeption einer Modellierungssprache zur softwarewerkzeugunterstützten Modellierung, Konfiguration und Bewertung hybrider Leistungsbündel

J. Becker, D. Beverungen, R. Knackstedt, O. Müller, in: Dienstleistungsmodellierung --- Methoden, Werkzeuge und Branchenlösungen, 2008, pp. 53--70


Serviceorientierte Informationssystemarchitekturen zur Integration von Produktion und Dienstleistung am Beispiel des WEEE-Recycling

R. Knackstedt, O. Müller, in: Wertschöpfungsnetzwerke, Physica-Verlag HD, 2008, pp. 235-252

DOI


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