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In complex environments, the use of technology to enhance the capability of people is commonplace. In rapidly changing and often unpredictable environments, it is not enough that these human-automated “teams” perform well when events go as expected. Instead, the human operators and automated aids must be flexible, capable of responding to rare or unanticipated events. The purpose of this chapter is to discuss the Framework of Automation Use (Dzindolet, Beck, Pierce, & Dawe, 2001) as it relates to adaptive automation. Specifically, our objectives are to: (1) examine a number of factors that determine how people can effectively integrate their activities with their machine partners in fluid environments and (2) consider the implications of these findings for future research.

Purpose – The purpose of this chapter is to conduct a critical literature review that examines the origins and development of research on service robots in organizations, as well as the key emotional and cognitive issues between service employees, customers, and robots. This review provides a foundation for future research that leverages the emotional connection between service robots and humans.

Design/Methodology/Approach – A critical literature review that examines robotics, artificial intelligence, emotions, approach/avoid behavior, and cognitive biases is conducted.

Findings – This research provides six key themes that emerge from the current state of research in the field of service robotics with 14 accompanying research questions forming the basis of a research agenda. The themes presented are as follows: Theme 1: Employees have a forgotten “dual role”; Theme 2: The influence of groups is neglected; Theme 3: Opposing emotions lead to uncertain outcomes; Theme 4: We know how robots influence engagement, but not experience; Theme 5: Trust is necessary but poorly understood; and Theme 6: Bias is contagious: if the human mind is irrational…so too are robot minds.

Practical Implications – Practically, this research provides guidance for researchers and practitioners alike regarding the current state of research, gaps, and future directions. Importantly for practitioners, it sheds light on themes in the use of AI and robotics in services, highlighting opportunities to consider the dual role of the employee, examines how incorporating a service robot influences all levels of the organization, addresses motivational conflicts for employees and customers, explores how service robots influence the whole customer experience and how trust is formed, and how we are (often inadvertently) creating biased robots.

This study aims to identify variability in aviation operators in order to gain greater understanding of the changes in aviation professional groups. Research has commonly addressed human factors and automation in broad categories according to a group’s function (e.g., pilots, air traffic controllers [ATCOs], engineers). Accordingly, pilots and Air Traffic Controls (ATCOs) have been treated as homogeneous groups with a set of characteristics. Currently, critical themes of human performance in light of systems’ developments place the emphasis on quality training for improved situational awareness (SA), decision-making and cognitive load.

As key solutions centre on the increased understanding and preparedness of operators through quality training, the authors deploy an iterative mixed methodology to reveal generational changes of pilots and ATCOs. In total, 46 participants were included in the qualitative instrument and 70 in the quantitative one. Preceding their triangulation, the qualitative data were analysed using NVivo and the quantitative analysis was aided through descriptive statistics.

The results show that there is a generational gap between old and new generations of operators. Although positive views on advanced systems are being expressed, concerns about cognitive capabilities in the new systems, training and skills gaps, workload and role implications are presented.

The practical implications of this study extend to different profiles of operators that collaborate either directly or indirectly and that are critical to aviation safety. Specific implications are targeted on automation complacency, bias and managing information load, and training aspects where quality training can be aided by better understanding the occupational transitions under advanced systems.

In this paper, the authors aimed to understand the changing nature of the operators’ profession within the advanced technological context, and the perceptions and performance-shaping factors of pilots and ATCOs to define the generational changes.

Office work has grown explosively in this century. Once a small occupational category, office work now includes about 40 percent of the American work force. Yet office work continues to be “the familiar unknown”: we worry about its growing size, we are concerned about its productivity, and we design systems to improve it; but our real knowledge of what goes on in the office is very shallow. This article discusses only a few of the many subtle facets of office work that vendors and users must understand to meet the needs of this attractive, but difficult market.

Decision making is inherently stressful since the decision maker must choose between potentially conflicting alternatives with unique hazards and uncertain outcomes. Whereas decision aids such as decision support systems (DSS) can be beneficial in stressful scenarios, decision makers sometimes misuse them during decision making, leading to suboptimal outcomes. The purpose of this paper is to investigate the relationship between stress, decision making and decision aid use.

The authors conduct an extensive multi-disciplinary review of decision making and DSS use through the lens of stress and examine how stress, as perceived by decision makers, impacts their use or misuse of DSS even when such aids can improve decision quality. Research questions examine underlying sources of stress in managerial decision making that influence decision quality, relationships between a decision maker’s perception of stress, DSS use/misuse, and decision quality, and implications for research and practice on DSS design and capabilities.

The study presents a conceptual model that provides an integrative behavioral view of the impact of a decision maker’s perceived stress on their use of a DSS and the quality of their decisions. The authors identify critical knowledge gaps and propose a research agenda to improve decision quality and use of DSS by considering a decision maker’s perceived stress.

This study provides a previously unexplored view of DSS use and misuse as shaped by the decision and job stress experienced by decision makers. Through the application of four theories, the review and its findings highlight key design principles that can mitigate the negative effects of stressors on DSS use.

Behavioral solutions to our cognitive biases have long been studied in the literature. However, there is still ample evidence of behavioral biases in decision-making, with only limited improvement in the medium/long term even when debiasing methods are applied. The purpose of this paper is to describe how financial investors could benefit from a proactive management of emotions combined with a set of learning and decision-making heuristics to make more efficient investments in the long run.

First, the authors offer a classification of the appropriate quantitative and qualitative methodologies to use in different ecological environments. Then, the authors offer a list of detailed heuristics to be implemented as behavioral principles intended to induce more long-lasting changes than the original rules offered by the adaptive toolbox literature. Finally, the authors provide guidelines on how to embed artificial intelligence and cognitive diversity within the investment decision architecture.

Improvements in decision skills involve changes that rarely succeed through a single event but through a succession of steps that must be habitualized. This paper argues that implementing a more conscious set of personal and group principles is necessary for long-lasting changes and provides guidelines on how to minimize systematic errors with adaptive heuristics. To maximize their positive effects, the principles outlined in this paper should be embedded in an architecture that fosters cognitively diverse teams. Moreover, when using artificial intelligence, the authors advise to maximize the interpretability/accuracy ratio in building decision support systems.

The paper proposes a theoretical reflection on the field of behavioral research and decision-making in finance, where the chief goal is to offer practical advices to investors. The literature on debiasing cognitive biases is limited to the detection and correction of immediate effects. The authors go beyond the traditional three building blocks developed by the behavioral finance literature (search rules, stopping rules and decision rules) and aim at helping investors who are interested in finding long-term solutions to their cognitive biases.

The case is presented as descriptive in nature and primarily involves exploratory research.

Ashraf, a young graduate from Bangalore, India, started a chain of lifestyle shops, his family business in Khartoum, Sudan. To modernize the shops, Ashraf approached a small finance bank for financial assistance. However, after submitting the required documents and with a good credit score, he was denied a loan. The bank officials had mentioned that the loan automation software did not approve the application. Hence, the bank personnel said that they could not do anything further. Disappointed, Ashraf sought the help of his professor, John, to understand why the software rejected his application. Professor John explained to Ashraf the advantages and disadvantages of automation. In the process, Ashraf understood the significance and compelling need to address “Algorithm Bias,” a situation in which specific attributes of an algorithm cause unfair outcomes. The case place students in Ashraf’s position to help them understand the advantages and issues of applying automation through artificial intelligence.

The case suits graduate-level courses like business analytics, financial analytics and business intelligence.

Through the case, the students will be able to: Understand the role of algorithms in business and society. Understand the causes, effects and methods of reducing algorithm bias. Demonstrate the ability to detect algorithm bias. Define policies to mitigate algorithm bias.

The purpose of this paper is to highlight the prominent cognitive biases that influence Lean practices in organisations that have a multi-cultural work environment which will aid the organisational managers and academics in enhancing the understanding of the human thought process and mitigate them suitably.

A multiple case study was conducted in organisations that were previously committed to Lean practices and had a multi-cultural work environment. This research was conducted on five companies based on 99 in-depth semi-structured interviews and seven process observations that sought to establish the system-wide cognitive biases present in a multi-cultural Lean environment.

The novel findings indicate that nine new biases influence Lean implementation and practices in a multi-cultural environment. This study also found strong connectivity between Lean practices and 45 previously identified biases that could affect positively or negatively the lean methodologies and their implementation. Biases were resilient enough that their influence on Lean in multi-cultural workplaces, even with transient populations, did not demonstrate cultural differentiation.

Like any qualitative research, constructivism and narrative analyses are subjected to understanding based on knowledge gained on the subject, and data may have been interpreted differently. Constructivist co-recreation of process scenarios based result limitations is therefore acknowledged. The interactive participation in exploring the knowledge sought after and interaction that could have a probable influence on the participant need to be acknowledged. However, the research design, multiple methods of data collection, generalisation based on data collection and analysis methods limit the effects of these and findings are reliable to a greater extent.

The results can provide an enhanced understanding of biases and insights into a new managerial approach to take remedial steps on biases’ influence on Lean practices that can result in improved productivity and well-being from a business process perspective. Understanding and mitigating the prominent biases can aid Lean manufacturing processes and support decision makers and line managers in improving lean methodologies’ effectiveness and productivity. The biases can be negated and used to implement decisions with ease. The influence of biases and the model could be used as a basis to counter implementation barriers.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes in a multi-cultural environment to identify the cognitive biases that influence Lean practices in organisations that were previously committed to Lean practices. The novel findings indicate that nine new biases and 45 previously identified biases influence Lean implementation and practices in a multi-cultural environment. The second novelty of this study shows the connection between cognitive biases, Lean implementation and practices in multi-cultural business processes.

The paper aims to fill this gap by positing a framework that considers the service automation decision as a matter of knowledge management: a choice between human resident and codified knowledge assets.

The paper is a conceptual paper, grounded in the knowledge-based view.

The paper uses the information processing theory, which argues that the level of uncertainty in a process should dictate the type of knowledge deployed, as the contingency for the automation choice, and customer interaction uncertainty as the driver of that contingency. From these ideas, propositions are generated relating customer interaction uncertainty and service automation. Further implications for artificial intelligence (AI) are also explored.

The framework illuminates and informs the strategic choices regarding service automation, including the use of AI in professional services, a timely and highly important topic. It offers a valuable model for practitioners and contributes to the academic literature by pointing the way for future directions for scholarly research.

This chapter focuses upon operators' automation usage decisions (AUDs), choices in which people have the option of relying on automation or employing a manual or less technologically advanced means of control. Misuse, the over-utilization of automation, and disuse, the under-utilization of automation, result from inappropriate AUDs. Three causes of misuse and disuse were identified. Operators may: (1) not recognize that both automated and manual alternatives are available (recognition errors), (2) inaccurately estimate the utilities of the automated and/or manual options (appraisal errors), or (3) knowingly select the alternative with the lowest expected outcome (action errors). A decision-making model was used to organize the literature, identify new areas of inquiry, and examine the effects of a number of variables known to affect AUDs. These included machine reliability and consistency, performance feedback, trust in automation, self-confidence in manual control, perceived utility, and the automation bias.