Masterarbeit, 2017
142 Seiten, Note: 1,5
1 Introduction
1.1 Company-specific problem description
1.2 Framework and content design of this work
2 Theoretical framework
2.1 SHEL-Model
2.1.1 Related concepts and studies
2.1.2 Evaluation
2.2 Model of SA in dynamic decision making
2.2.1 Measurement of SA
2.2.2 Related concepts and studies
2.2.3 Implications for system design
2.2.4 Evaluation
2.3 Interfaces between the SHEL-Model and the Model of SA
3 Method
3.1 Sample
3.2 Design and Procedure
3.2.1 Diagnosis
3.2.2 Scheduling
3.2.3 Data collection
4 Results
4.1 Data editing/ preparation
4.1.1 Transcription
4.1.2 Data analysis
4.2 Presentation of results
4.2.1 Quantitative results
4.2.2 Qualitative results
4.3 Answering the research questions
4.4 Derived actions
5 Discussion
5.1 Interpretation of results
5.2 Methodical aspects
5.2.1 Sample
5.2.2 Design and procedure
5.2.3 Data editing/ preparation
5.3 Recommendations
6 Conclusion
The primary objective of this thesis is to identify human errors in socio-technical systems and to evaluate the influence and causality of human factors on dynamic decision-making processes. The research focuses on an industrial company to diagnose system failures related to configuration software and internal information flow, aiming to derive optimization strategies.
1 Introduction
“Work organizations exist to do work – which involves people using technological artifacts (whether hard or soft) to carry out sets of tasks related to specified overall purposes. Accordingly, a conceptual reframing was proposed in which work organizations were envisaged as socio-technical systems rather than simply as social systems (Trist, 1950, in Trist, 1981, p. 10).
This statement describes socio-technical systems (STS), which will be focused on in this paper within the scope of human factors research regarding human error, decision making and organizational performance. Human factors “involves the study of factors and development of tools that facilitate the achievement of these goals” (Wickens, Lee, Lui & Becker, 2004, p. 2). The key role is the diagnosis and solution of system failures as well as the comprehension of its elements and the related system design (Wickens et al., 2004). In order to optimize organizational performance in STS, relationships between humans and circumstances of the working environment should be improved by taking human sciences and system engineering into account (Edwards, 1985, in Hawkins, 1993, p. 20). The former focusses strengths and limitations of the human component (ibid.). Human nature includes physical, psychological and social characteristics (Badke-Schaub, Hofinger & Lauche, 2012). According to Hawkins (1993), “Human Factors attempts to research and explain the nature of human behavior and human performance, using human sciences. Armed with this knowledge it tries to predict how a person will react and respond in a given set of circumstances.” (p. 26).
Considering human error in STS, Wickens & Hollands (2000) emphasize that system breakdowns have their roots either in human error or poor system design. For the detection of error causes, people are seen as key roles in organizations, as only humans is able to identify dangers and prevent error (ibid.). Hawkins (1993) suggests, prevention of failures could be realized by a workplace design, which is adapted to strengths and limitations of all interacting components in STS.
1 Introduction: Introduces the concept of socio-technical systems and sets the research scope on human factors, decision-making, and organizational performance within the specific company context.
2 Theoretical framework: Details the SHEL-Model and Endsley’s model of Situation Awareness as a combined theoretical foundation to analyze system interactions and decision-making errors.
3 Method: Describes the qualitative research approach, including participant sampling from sales and order processing departments and the procedure of diagnosing and collecting critical incident data.
4 Results: Presents the qualitative and quantitative analysis of identified critical incidents, categorized by SHEL components, and details the derivation of recommended system improvements.
5 Discussion: Critically evaluates the research results, discusses methodical strengths and weaknesses, and interprets findings regarding human factors and system interface design.
6 Conclusion: Summarizes the key theoretical and empirical findings, confirming that system-related improvements and interface optimizations significantly contribute to error reduction and enhanced organizational performance.
Socio-technical systems (STS), SHEL-Model, Situation Awareness (SA), dynamic decision making, human error, system design, performance, automation, scope of action, out of the loop performance problem, usability, qualitative analysis, critical incidents, information flow, team coordination.
The research fundamentally addresses human error in socio-technical systems, specifically examining how human factors and system interfaces influence the accuracy and efficiency of dynamic decision-making processes within an industrial environment.
The work explores system ergonomics, software usability, team communication, and the interaction between individual cognitive performance and automated technical systems.
The central goal is to determine which SHEL-categories or interfaces require improvement to reduce errors in configuration software and how insights from situation awareness models can be applied to optimize decision-making processes.
The study utilizes a qualitative methodology, conducting expert and staff interviews to identify critical incidents, which are then analyzed using the SHEL-Model framework and deductive/inductive coding techniques.
The main body integrates a theoretical review of the SHEL-Model and Situation Awareness, followed by empirical analysis that maps operational problems in configuration software and departmental information flow to specific system design and human factor deficiencies.
Key terms include socio-technical systems, SHEL-Model, Situation Awareness, dynamic decision-making, configuration software, and human error mitigation.
The research concludes that the LS-interface is critical; maladjustments here, such as lack of transparency and poor automation, lead to the "out of the loop" performance problem, directly hindering the operator's perception, comprehension, and projection of the situation.
The author recommends implementing "dynamic defaults," "dynamic checklists," intuitive user interface design, and integrated online help systems to increase system transparency and reduce the cognitive burden on operators.
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