Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees’ flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task—an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, self-reported flow was positively associated with both perceived and objective task performance, while PNS activation was not related to either performance measure. Lastly, we found no relationship between PNS activation (measured by HF-HRV) and self-reported flow, contributing to an important debate in the NeuroIS literature on whether physiological evidence constitutes an alternative or a complement to self-reports. Overall, our findings indicate that frequent interruptions are not harmful per se. Rather, considering content relevance is critical for a more comprehensive understanding of the effects on self-reported flow, its dimensions, and the underlying physiology.

Purpose Numerous design methods are available to facilitate digital innovation processes in user interface design. Nonetheless, little guidance exists on their appropriate selection within the design process based on specific situations. Consequently, design novices with limited design knowledge face challenges when determining suitable methods. Thus, this paper aims to support design novices by guiding the situational selection of design methods. Design/methodology/approach Our research approach includes two phases: i) we adopted a taxonomy development method to identify dimensions of design methods by reviewing 292 potential design methods and interviewing 15 experts; ii) we conducted focus groups with 25 design novices and applied fuzzy-set qualitative comparative analysis to describe the relations between the taxonomy's dimensions. Findings We developed a novel taxonomy that presents a comprehensive overview of design conditions and their associated design methods in innovation processes. Thus, the taxonomy enables design novices to navigate the complexities of design methods needed to design digital innovation. We also identify configurations of these conditions that support the situational selections of design methods in digital innovation processes of user interface design. Originality/value The study’s contribution to the literature lies in the identification of both similarities and differences among design methods, as well as the investigation of sufficient condition configurations within the digital innovation processes of user interface design. The taxonomy helps design novices to navigate the design space by providing an overview of design conditions and the associations between methods and these conditions. By using the developed taxonomy, design novices can narrow down their options when selecting design methods for their specific situations.

Quality management plays a vital role in manufacturing organizations to ensure effective and efficient production processes. To achieve this, organizations implement various data-driven techniques and tools to monitor and manage the quality of their production processes. One essential tool is the control chart, which tracks the performance of a specific quality characteristic over time by taking samples. However, manual sample-taking for a large number of quality characteristics can be time-consuming and costly. To address this challenge, organizations seek to enhance the efficiency of the sample-taking process while accurately detecting production process performance. Recently, machine learning (ML) models have been proposed to predict various quality characteristics, thereby reducing the need for manual measurements. However, existing control chart system designs have been found to be inadequate for integrating ML-predicted quality characteristics. To address this gap, this research aims to design an analytical control chart system with quality characteristics predicted by ML models. Our technical evaluation indicates significant improvements in the efficiency of the quality management process while feedback from a focus group demonstrates the effectiveness of our proposed solution.