Using reduced-processing training to improve decision efficiency among perfectionists

Liu, Rebecca Y. M.; Morrison, Ben; Wiggins, Mark W.; Perry, Nathan

doi:10.1007/s10111-021-00672-1

Cited by 3 publications

(4 citation statements)

References 57 publications

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“…Other highly ranked clusters of items refer largely to system usability (e.g. ease of understanding, amount of information provided and degree of intuition in use), which underlines a need to consider the relative cognitive compatibility between users and DSSs in investigations of system acceptance, which has been noted here and elsewhere (Hollnagel & Woods, 2005;Inagaki, 2008;Liu et al, 2021;Lyons, 2013;Rushby, 2002). The fact that user-related factors, such as relative experience with technology and/or DSSs, are perceived to be relatively less important among workers, emphasises the need to consider a human-centred approach to the design of DSSs, and potentially the need to develop systems that are 'forgiving' of user errors that may arise from a lack of experience, overconfidence, or similar.…”

Section: Important Factors In Dss Acceptancementioning

confidence: 74%

“…Taking these factors into account, we argue that developers should look to design systems that afford the user an optimal degree of control in the input of data and provide enough transparency for users to understand how their actions are influencing the system’s decisions. Additionally, ideally users should be able to interact with the inputs in a way that matches their natural information processing strategies (Liu et al, 2021; Morrison et al, 2010). However, despite advances in the field of explainable and ‘white-box’ AI (XAI; Loyola-Gonzalez, 2019), the extent to which users can understand DSS processes, and to which the user should have control in system interactions remain challenging questions for developers.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, their capacity to reduce the complexity of a task for the user, for instance, by comparing the critical features associated with multiple options (Allen & Abate, 1999). Secondly, DSSs can guide inexperienced users in a manner that protects the integrity of the system (Liu et al, 2021). As a result, some DSS interfaces can fulfil a training function for less experienced workers (Perry et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

Morrison,

Bergin,

Kelson

et al. 2023

Journal of Cognitive Engineering and Decision Making

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A richer approach to studying Decision Support System (DSS) interactions is required to understand and predict the nature of actual use in the workplace. We used questionnaire and interview techniques to examine workers’ experiences relating to DSS use in naturalistic settings. We aimed to: 1) Reveal what workers perceive to be the most important factors when deciding whether to accept support from a DSS and 2) Elicit patterns that emerge from DSS users’ recounted experiences using the systems, which may impact their future use. Current and prospective DSS users ( N = 93) from numerous industries responded to a questionnaire relating to the factors they perceive to influence their use of DSSs. Subsequently, a retrospective interview protocol was employed to investigate the experiences of a subset of DSS users ( N = 10). The questionnaire results underscore a range of factors considered to be very important to the acceptance of DSSs (i.e. decision quality; decision importance; decision risk; historical accuracy; decision accountability; and system comprehension). Further, a series of interconnected themes relating to workers’ use of DSSs were identified from the interview transcripts using thematic analysis. We discuss how these issues may impact workers’ intentions to use DSSs in the workplace, and advocate for the use of naturalistic decision-making techniques to study technology acceptance.

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Section: Important Factors In Dss Acceptancementioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

Morrison,

Bergin,

Kelson

et al. 2023

Journal of Cognitive Engineering and Decision Making

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“…Consumers are paradoxically rational and irrational, consistent and inconsistent, and aware and unaware at the same time (Cojanu, 2017). Consumers use strategies to navigate through alternatives to reach a decision: a compensatory strategy consisting of an equallyweighted strategy (Von Gunten & Scherer, 2019), a different weighted strategy (Vidhate & Kulkarni, 2018), a non-compensatory strategy consisting of satisficing (Zhou, Zhang, Li, & Liang, 2018), elimination by aspects (Liu, Morrison, Wiggins, &Perry, 2021), andlexicographic (Von Gunten &Scherer, 2019).…”

Section: Introductionmentioning

confidence: 99%

Why Do Consumers Use Ride-Hailing? Evidence from China and Indonesia

Gunarso

2022

Binus Business Review

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Consumers’ attitudes and preferences in developing countries differ from those in developed countries. The research compared the motivation to use ride-hailing in developing and developed countries to understand consumers better. The research applied a qualitative survey with 52 drivers, followed by a quantitative survey with 741 passengers. It was finalized by one quantitative survey of 818 respondents to answer the research questions. The passengers in both quantitative surveys were from Indonesia and China who were Chinese, Indonesian, and Expatriates in China. Indicators in Partial Least Square-Structural Equation Modelling (PLS-SEM) were used to capture ride-hailing phenomena closer to actual business situations and made more robust measurements for future testing of consumers’ decisions in sharing economy setting. The results show that the habit factor influences the decision to use ride-hailing among Indonesian, Chinese, and Expatriate respondents. Meanwhile, the financial factor only influences Indonesian respondents. Safety factor affects Expatriate respondents, and utility and convenience factors influence the decision of Chinese respondents. Innovation and social factors are significant when the significance level is reduced to the exploratory level. In conclusion, managers of sharing economy companies in developing and developed countries can use Importance-Performance Mapping Analysis (IPMA) result in the research to identify important but weak factors that can be improved.

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The futility of decision making research

Weiss¹,

Shanteau²

2021

Studies in History and Philosophy of Science Part A

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Using reduced-processing training to improve decision efficiency among perfectionists

Cited by 3 publications

References 57 publications

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

Why Do Consumers Use Ride-Hailing? Evidence from China and Indonesia

The futility of decision making research

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