Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experience

A next click recommender system for web-based service analytics with context-aware LSTMs

Software companies that offer web-based services instead of local installations can record the user’s interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs. We present a technique for a next click recommender system. Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks. We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context. The technique was evaluated with a real-life data set from a financial software provider. We used a hidden Markov model (HMM) as the baseline. The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements

Designing a Method for Resource-specific Next Activity Prediction

Predictive business process monitoring (PBPM) techniques aim at predicting future process behavior. A trend in PBPM is to use deep learning (DL), more concretely deep neural networks, to capture the entire process information within one predictive model. In most DL-based PBPM techniques, one single model is trained to predict the future behavior of a running process instance. However, especially resource information influences the efficiency and effectiveness of a process as paths through the process model can strongly depend on the resource executing the activities. Thus, a one-model-fits-all approach might not result in high predictive performance across all resources, such as humans or machines. Therefore, we design a novel DL-based method for resource-specific next activity predictions. In our preliminary evaluation, we present promising results based on two real-life event logs. Ultimately, we discuss our future research plans

The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations

Artificial intelligence (AI) offers promising tools to support the job-seeking process by providing automatic and user-centered job recommendations. However, job seekers often hesitate to trust AI-based recommendations in this context given the far-reaching consequences of the importance of the decision for a job on their future career and life. This hesitation is largely driven by a lack of explainability, as underlying algorithms are complex and not clear to the user. Prior research suggests that anthropomorphization (i.e., the attribution of human traits) can increase the acceptance of technology. Therefore, we adapted this concept for AI-based recommender systems and conducted a survey-based study with 120 participants. We find that that using an anthropomorphic design in a recommender system for open positions increases job seekers\u27 acceptance of the underlying system. However, algorithm aversion rises if detailed information on the algorithmic origin is being disclosed

A Method for Predicting Workarounds in Business Processes

Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative effects on an organization’s performance. Process managers are supposed to identify workarounds early, promoting their positive while reducing their negative consequences. While related research has touched upon detecting workarounds in event logs that include data on completed processes, little is known on how to predict workarounds in a running business process. We set out to design a workaround prediction method using a deep learning approach. The IT artifact enables process managers to proactively intervene if workarounds are about to emerge in a business process, reducing their adverse effects while supporting organizational learning and process innovation