In complex operational environments, hybrid decision-making frameworks offer a means to integrate human expertise - characterized by contextual sensitivity, adaptability, and experiential knowledge - with the objective, standardized precision of machine-based systems.MethodThis study develops a decision-support structure by comparing supervised Machine Learning (ML) models; random forest (RF), support vector machine (SVM) and a bidirectional encoder representation from transformer-based (KB/BERT) model - using hierarchical and flat approaches against a manual classification process, involving more than 200 train delay codes across 10 days. ML models are trained on same-day delay data and evaluated against the outcomes from a multi-actor decision process.ResultsHierarchical models outperform flat ones, achieving near-human assessors on basic level coding (Level 1 and 2), though with greater variability (mean F1-scores (50-91 per cent)), compared to manual classification (mean F1-scores (87-98 per cent)) at the most granular level (Level 3) of prediction. "Simpler" models also outperform the more complex KB/BERT.Practical ImplicationsWe discuss the functionality and accuracy of ML-based hybrid decision-support systems (HDSS), noting the need for trade-offs between precision and accuracy. ML models demonstrate potential to complement - not replace - human expertise, particularly with uncertainty estimation tools that mitigate classification risks and support decision-making. We conclude with implications for data representation in the design of HDSS within socio-techno-economic contexts.

This paper serves to deepen the understanding of how inconsistencies between feeling, thinking, and doing are managed by decision-makers in emergency settings. We use a practice approach and investigate the emergency physicians in an Emergency Department (ED), by means of 200 h of observations, 12 in-depth interviews, and organizational documentation. Data are analyzed using an abductive template-based approach. The configuration of three different decision-making modes, namely an experiential-based mode, an ostensive-based mode, and an action-based mode, provide an eight-fold typology of emergency physicians' decision-making praxis. “Weak” signals are the starting point for clinical assessment, and inconsistencies among the modes are strategically used and surprisingly often associated with positive treatment outcomes. The praxis perspective used in this article bridges literature on choice and interpretation—processes usually separated in organizational and decision-making literature. Inconsistency between the modes allow physicians to create an action space where decision-making is about more than providing the “right” answer. Making use of the eight-fold typology helps physicians identify “blind spots”, improve practice in both mundane and medically rare cases, as well as aid in revision of existing routines. This awareness also provides for high-quality care, an increased acceptance of inconsistencies by the public, with a potential to reduce litigation issues. © 2022

Background: Software engineering research aims to establish software development practice on a scientific basis. However, the evidence of the efficacy of technology is insufficient to ensure its uptake in industry. In the absence of a theoretical frame of reference, we mainly rely on best practices and expert judgment from industry-academia collaboration and software process improvement research to improve the acceptance of the proposed technology. Objective: To identify acceptance models and theories and discuss their applicability in the research of acceptance behavior related to software development.Method: We analyzed literature reviews within an interdisciplinary team to identify models and theories relevant to software engineering research. We further discuss acceptance behavior from the human information processing perspective of automatic and affect-driven processes (“fast” system 1 thinking) and rational and rule-governed processes (“slow” system 2 thinking). Results: We identified 30 potentially relevant models and theories. Several of them have been used in researching acceptance behavior in contexts related to software development, but few have been validated in such contexts. They use constructs that capture aspects of (automatic) system 1 and (rational) system 2 oriented processes. However, their operationalizations focus on system 2-oriented processes indicating a rational view of behavior, thus overlooking important psychological processes underpinning behavior. Conclusions: Software engineering research may use acceptance behavior models and theories more extensively to understand and predict practice adoption in the industry. Such theoretical foundations will help improve the impact of software engineering research. However, more consideration should be given to their validation, overlap, construct operationalization, and employed data collection mechanisms when using these models and theories.

The evidence that burglaries cluster spatio-temporally is strong. However, research is unclear on whether clustered burglaries (repeats/near-repeats) should be treated as qualitatively different crimes compared to spatio-temporally unrelated burglaries (non-repeats). This study, therefore, investigated if there were differences in modus operandi-signatures (MOs, the habits and methods employed by criminals) between near-repeat and non-repeat burglaries across 10 Swedish cities, as well as whether MO-signatures can aid in predicting if a burglary is classified as a nearrepeat or a non-repeat crime. Data consisted of 5744 residential burglaries, with 137 MO features characterizing each case. Descriptive data of repeats/non-repeats is provided together with Wilcoxon tests of MO-differences between crime pairs, while logistic regressions were used to train models to predict if a crime scene was classified as a near-repeat or a non-repeat crime. Near-repeat crimes were rather stylized, showing heterogeneity in MOs across cities, but showing homogeneity within cities at the same time, as there were significant differences between near-repeat and non-repeat burglaries, including subgroups of features, such as differences in mode of entering, target selection, types of goods stolen, as well the traces that were left at the crime scene. Furthermore, using logistic regression models, it was possible to predict near-repeat and non-repeat crimes with a mean F1-score of 0.8155 (0.0866) based on the MO. Potential policy implications are discussed in terms of how data-driven procedures can facilitate analysis of spatio-temporal phenomena based on the MO-signatures of offenders, as well as how law enforcement agencies can provide differentiated advice and response when there is suspicion that a crime is part of a series as opposed to an isolated event. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Från förordet: Årets rapport kartlägger det svenska entreprenörskapets utveckling under en turbulent tid präglad av pandemi, restriktioner och nedstängningar av hela samhällen vilket du kan läsa om i kapitel två. Trots pandemin ökar svenskt entreprenörskap under 2021 till nio procent (TEA) vilket är den högsta siffran som redovisats i GEM-projektet för Sveriges del sedan 2010. En minst sagt glädjande siffra.

Det är emellertid inte en anledning att luta sig tillbaka. Världen står inför stora utmaningar i form av komponentbrist, störningar i leveranskedjor samt en oroande geopolitisk situation, inte minst i och med Rysslands invasion av Ukraina. Därtill kan tilläggas att Sverige dras med olösta och kända problem som till exempel en dysfunktionell arbetsmarknad präglad av kompetensbrist kombinerat med hög arbetslöshet och låg självförsörjningsgrad bland stora delar av befolkningen.

I årets rapport görs även nedslag i entreprenörernas relation till miljömässig- och social hållbarhet. Svenska entreprenörer uppvisar på intet sätt det största engagemanget för bland annat miljömässig hållbarhet globalt sett. Men det finns anledning att tro att det åtminstone delvis kan bero på att företagare i Sverige ställer högre krav inom sin verksamhet, speciellt jämfört med företagare i utvecklingsländer.

This study examines management teams' work in making fast strategic decisions under extreme time pressure. Focusing on affective reactions as behavioural responses to business disruptions due to unforeseen events, we elaborate the strategy-as-practice perspective by drawing upon qualitative and quantitative datasets collected from 39 sites in a corporate setting over three consecutive phases during a four-year period. The data show two types of patterns: intensity-focused and type-focused affective reactions in management teams' use of management tools as part of mental shortcuts when making fast decisions. These patterns are contingent on whether the teams functioned in contexts that had previous experience of management of similar unforeseen events. Affective reactions in the use of tool-based mental shortcuts unveil a mechanism of practices that explains middle management teams' strategic actions during business disruption due to unforeseen events. While research predominantly suggests that affect is "bad" for management teams in crisis-related contexts, we find that this view is misleading. Affective reactions not only hinder but also aid crucial information exchanges between middle management teams and corporate levels while making strategic decisions under extreme time pressure. Therefore, we propose a reconceptualized view of managing fast strategic decision making and discuss the implications for theory and practice.

We studied how emergency operators' interpretation of callers' anger, fear, and sadness influenced emergency assessments in 146 authentic emergency calls. All emergency calls need to be decided on quickly and operators discriminated emotional expressions, by separating fear from other emotions across help need levels. Using path analysis and structural equation modeling, caller's fear, as opposed to expressions of anger and sadness, showed both direct and indirect effects on the intention to provide help. Based on the findings, emergency operators are argued to actively incorporate callers' expressions into emergency decisions, rather than peripherally processing emotional expression. Such findings allow for a discussion on the interpersonal effects of emotional expressions and, more practically, how fast help will be able to arrive to the location of the emergency. © 2018 Ubiquity Press Ltd. All rights reserved.

This paper explores and problematizes decision-making in a high-consequence industry which is characterized by the stage-gate process. We showcase that decision-makers do not differentiate between the types of knowledge used for modelling decisions and calculation of risks. This makes them susceptible to incorporating cognitive distortions—biases—into the stage-gate process. We conclude by amending the risk for certain kinds of biases with a knowledge maturity framework in order to outline conditions for pragmatic decision making.