Business & Information Systems Engineering最新文献

Data platforms enable actors to exchange personal and business data. While data is relevant for any digital platform, data platforms exclusively revolve around data artifacts. This paper argues that the specific characteristics of data artifacts challenge the authors’ understanding of platform openness. Specifically, it is argued that data artifacts are editable, interactive and distributable, which means that the consequences of opening up a data platform extend far beyond the focal platform and its context. From this, the study infers that the scope of platform openness extends beyond the data platform on which data artifacts originate. At the same time, the very nature of data artifacts afford new mechanisms to realize and reduce the risks of openness. New avenues are suggested to study platform openness in the realm of data platforms. These avenues include (1) exploring and incorporating novel consequences of platform openness in a data platform setting, (2) examining new arenas for defining openness beyond a focal platform’s confines, and (3) theorizing the implications of new mechanisms for realizing openness while maintaining apparent control over data artifacts.

Internet of Things (IoT) solutions are still far from using their enormous potential, partly because misconceptions lead employees to avoid using IoT solutions and stick to established working routines. To shed light on the non-rational perspective of users, which allows for inference on the emergence of cognitive misconceptions, 489 respondents' perceptions of benefits and costs of IoT solutions were analyzed. Using the perspective of “status quo bias”, the qualitative analysis reveals that the perceptions of experienced and inexperienced users partly overlap on benefits such as the reduction of errors and relief of personnel. However, the perceptions also diverge in part, as inexperienced users consider IoT solutions to be gimmicky, fostering mistrust. In addition, inexperienced users overestimate learning phases for interacting with IoT solutions, leading to loss aversion and consequently to cognitive misperceptions. Hence, the study examines the gap between experienced and inexperienced users as a neglected aspect in IoT adoption. Further, identifying relevant drivers for the implementation of IoT solutions at the individual level helps to extend the hitherto technical view of IoT solutions towards a multi-layer approach that includes a holistic, behavioral perspective.

Car manufacturers and suppliers in the Automotive industry increasingly face the issue of optimization of highly complex supply chains that need to accommodate each customer's precise demands, requiring a vast array of parts and information to be available at the right place and at the right time. This involves data sharing between organizations, which is hindered by various issues, such as fear of data misappropriation by the data receiver or the involuntary disclosure of business secrets. The paper proposes design principles for a novel type of Inter-Organizational Information System, which addresses these challenges through the technical implementation of data sovereignty. The study reports on an Action Design Research study in the Automotive industry between a car manufacturer and a 1st-tier supplier. It contributes (a) design requirements, (b) design features, (c) an instantiation, and (d) design principles for this type of data sovereign inter-organizational information system.

Human resources are considered a strategic asset for organizations and play a key role in the execution of business processes. Hence, organizations should provide an environment that enables them to operate in an effective and efficient manner. To shape such an environment, an improved understanding and monitoring of the real-life involvement of human resources in processes and the teams in which they operate would be beneficial. Using event data from information systems, process mining can play a role in this respect. Over the years, several human resource mining methods have been developed, i.e., process mining methods that convey insights related to the human resources in a process using an event log. However, there is a lack of a holistic understanding of the breadth of these methods. Against this backdrop, the paper uses a systematic literature review to develop a framework providing an overview of human resource mining use cases. These use cases are classified according to two dimensions: the level of analysis (individual versus multiple human resources) and the focus of analysis (organization-focused versus human-focused). The authors illustrate the versatility of process mining for providing insights into human resources and highlight opportunities for further enriching and extending this area of research to analyze, among other things, how teams of resources can perform better.

The transition from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent power supply and demand. Although demand-side energy flexibility, offered particularly by industrial companies, is seen as a promising and necessary approach to address these challenges and realize benefits for companies, its implementation is not yet common practice. Often facing highly complex process landscapes and operational systems, process mining provides significant potential to increase transparency of actual process flows and to discover or reflect existing dependencies and interrelationships of activities, instances or resources. It facilitates the implementation of energy flexibility measures and enables the realization of monetary benefits associated with flexible process operation. This paper contributes to the successful integration of energy flexibility into process operations by presenting a design science research artifact called PM4Flex. This is a prescriptive process monitoring approach that uses linear programming to generate recommendations for pending process flows optimized under fluctuating power prices by utilizing established energy flexibility measures. Thereby, event logs and corresponding company- as well as process-specific constraints are considered. PM4Flex is demonstrated and evaluated based on its implementation as a software prototype, its application to exemplary data from two real-world processes exhibiting power cost savings of up to 75% compared to the original execution, and based on semi-structured expert interviews. PM4Flex provides new design knowledge at the interface of prescriptive process monitoring and the energy domain providing decision support to optimize industrial energy procurement costs.

The classification of police reports according to the typification of the criminal act described in them is not an easy task. The reports are written in natural language and often present missing, imprecise, or even inconsistent information, or lack sufficient details to make a clear decision. Focusing on property crimes, the aim of this work is to assist judges in this classification process by automatically extracting information from police reports and producing a list of possible classifications of crimes accompanied by a degree of confidence in each of them. The work follows the design science research methodology, developing a tool as an artifact. The proposal uses information extraction techniques to obtain the data from the reports, guided by an ontology developed for the Spanish legal system on property crimes. Probabilistic inference mechanisms are used to select the set of articles of the law that could apply to a given case, even when the evidence does not allow an unambiguous identification. The proposal has been empirically validated in a real environment with judges and prosecutors. The results show that the proposal is feasible and usable, and could be effective in assisting judges to classify property crime reports.

The Internet of Things (IoT) is one of the driving forces behind Industry 4.0 and has the potential to improve the entire value chain, especially in the context of industrial manufacturing. However, results derived from IoT data are only viable if a high level of data quality is maintained. Thereby, completeness is especially critical, as incomplete data is one of the most common and costly data quality defects in the IoT context. Nevertheless, existing approaches for assessing the completeness of IoT data are limited in their applicability because they assume a known number of real-world entities or that the real-world entities appear in regular patterns. Thus, they cannot handle the uncertainty regarding the number of real-world entities typically present in the IoT context. Against this background, the paper proposes a novel, probability-based metric that addresses these issues and provides interpretable metric values representing the probability that an IoT database is complete. This probability is assessed based on the detection of outliers regarding the deviation between the estimated number of real-world entities and the number of digital entities. The evaluation with IoT data from a German car manufacturer demonstrates that the provided metric values are useful and informative and can discriminate well between complete and incomplete IoT data. The metric has the potential to reduce the cost, time, and effort associated with incomplete IoT data, providing tangible benefits in real-world applications.

A remarkable peculiarity of videoconferencing (VC) applications – the self-view – a.k.a. digital mirror, is examined as a potential reason behind the voiced exhaustion among users. This work draws on technostress research and objective self-awareness theory and proposes the communication role (sender vs. receiver) as an interaction variable. We report the results of two studies among European employees (n1 = 176, n2 = 253) with a one-year time lag. A higher frequency of self-view in a VC when receiving a message, i.e., listening to others, indirectly increases negative affect (study 1 & 2) and exhaustion (study 2) via the increased state of public self-awareness. Self-viewing in the role of message sender, e.g., as an online presenter, also increases public self-awareness, but its overall effects are less harmful. As for individual differences, users predisposed to public self-consciousness were more concerned with how other VC participants perceived them. Gender effects were insignificant.

Business process management (BPM) is changing in the digital age. As a result, organizations are confronted with new logics that their business processes adhere to: processes are designed to allow for easy adaptability, infrastructure becomes progressively more flexible, and process participants make their own decisions in ambiguous situations. In this context, business process change becomes increasingly important. Digital ventures – key phenomena in the digital age – heavily rely on digital technology and, hence, have the potential to change quickly. Consequently, their business processes need to change at the same speed. While the literature on BPM proposes different types of business process change and acknowledges that digital technology can enable such developments, it remains to be explored which specific characteristics of digital technology facilitate business process change. The study investigates this by drawing on a multiple case study with seven digital ventures. It finds four patterns of business process changes in digital ventures, illustrating digital technology’s impact on business processes. The study compares the patterns with existing types of business process change from the literature and discusses differences and similarities, trying to advance the understanding of business process dynamics in the digital age.