Is data-driven decision-making driven only by data? When cognition meets data

Zaitsava, Maryia; Marku, Elona; Guardo, Maria Chiara Di

doi:10.1016/j.emj.2022.01.003

Cited by 15 publications

(4 citation statements)

References 97 publications

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“…Second, a parallel-competitive view is consistent with growing evidence from experimental and neurological studies (Alós-Ferrer & Strack, 2014;Healey et al, 2015;Howarth et al, 2019;Kuo et al, 2009;Lieberman, 2007). Third, the adoption of a parallel-competitive view corresponds with recent trends in management and organization studies, where the focus is about to slightly shift from a dominance of defaultinterventionist accounts towards a parallel-competitive view (Adinolfi, 2021;Adinolfi & Loia, 2022;Hodgkinson & Sadler-Smith, 2018;Keller and Sadler-Smith 2019;Luoma & Martela, 2021;Zaitsava et al, 2022). This is also in line with the conclusion from Cecchini (2021) that recent empirical findings strongly favor an independent rather than a hierarchical dual-process theory in the moral domain.…”

Section: A Dual-process View Of Moral Judgmentsupporting

confidence: 74%

Analysis and Intuition Effectiveness in Moral Problems

Julmi

2023

J Bus Ethics

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There has been a longstanding controversy in research as to whether moral judgment is the result of an analytical or an intuitive process. Today, researchers increasingly recognize that moral judgments can be the result of both intuition and analysis, and that the two paths can lead to different results. This raises the question as to which of the two processes leads to a better moral judgment. The article develops a typology of moral problems depending on their moral uncertainty and moral equivocality and links the derived types with analysis and intuition effectiveness. The typology considers four types of moral problems: compliance problems (low uncertainty, low equivocality), professional ethics problems (high uncertainty, low equivocality), conformity problems (low uncertainty, high equivocality) and ethical dilemmas (high uncertainty, high equivocality). The article argues that compliance and professional ethics problems are best solved analytically, whereas intuition is assumed to be more suitable for conformity problems and ethical dilemmas. Moreover, the article shows that it is not sufficient to contrast intuition with analysis alone. The higher the moral uncertainty, the more important the interplay of intuition and analysis becomes in making moral judgments.

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Section: A Dual-process View Of Moral Judgmentsupporting

confidence: 74%

Analysis and Intuition Effectiveness in Moral Problems

Julmi

2023

J Bus Ethics

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“…We suggest that some other sub-dimensions of Veracity and Velocity might arise in different contexts. Lastly, BD has been seen as a meaningful technology used in the decision-making process (Aversa et al, 2018;Galliers et al, 2020;Zaitsava et al, 2022). The present work could not capture insights on decision support systems; however, future research might focus on how the quality of BD affects decision making, what is the role of cognition in this context, and how the characteristics of BD engage in an interplay with cognitive individual or collective decision-making processes.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 98%

A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study

et al. 2022

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The creation of knowledge from Big Data is increasingly drawing the attention of scholars and practitioners in management research. Valuable knowledge first requires identifying the Big Data features connected to knowledge insights creation and the mechanism beyond this creation. This paper examines Big Data dimensions and insights creations at a fine-grained level by adopting the knowledge creation lens. Specifically, what is the mechanism of creating knowledge from Big Data? How to transform raw Big Data into knowledge? We adopted a qualitative case study to explore the large-scale multinational pilot launched in three European cities. The pilot amalgamated a large amount of data feeds from different sensors and open data and created various insights to inform cities' strategies. By employing an inductive content analysis with abductive procedures and coupling it with participatory observations, we were able to ground findings on the multi-level empirical and theoretical base and build a framework that embraces all discovered complexities and fine-grained features of Big Data dimensions and guides knowledge creation from Big Data. Our research offers a more in-depth understanding of the mechanism of knowledge creation in the BD context. First, we opened up BD's black box by disentangling the knowledge creation mechanism while transforming raw BD into BD insights. Second, our study offered empirical evidence of the growth mechanism working on Volume and Variety dimensions. The uniqueness of this study lies in the fine-grained perspective of BD characteristics and the underlying mechanism of insights creation.

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“…Employers have to rethink their employees' learning strategies and "strengthen the formation process of employee learning" (Chen et al, 2023(Chen et al, , p. 2186 as well. Moreover, even executives must understand the algorithm-based systems (Zaitsava et al, 2022) to boost their confidence in the data and AI algorithms (Cabiddu et al, 2022;Zaitsava et al, 2022).…”

Section: Practical Implicationsmentioning

confidence: 99%

“…DDDM is defined as making business decisions based on a wide range of data rather than purely intuition (Dodman et al, 2021; Lepri et al, 2017; Provost & Fawcett, 2013; Troisi et al, 2019). It allows the enhancement of “human decision‐making using algorithm‐based systems and algorithmically generated insights” (Zaitsava et al, 2022, p. 657). Internal and external data, which act as catalysts to guide actions, anticipate complexities, and enable changes, should be incorporated into the decision‐making process.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Strategic organizational changes: Adopting data‐driven decisions

Bilkštytė‐Skanė,

Akstinaite

2023

Strategic Change

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The paper examines the adoption of a data‐driven decision‐making (DDDM) process in organizations from purposefully selected European Union (EU) countries. It determines what organizational changes are required to adopt this process in organizations. This study uses a mixed‐method approach to identify organizational changes required for DDDM adoption. The responses from quantitative research in 10 EU countries (1091 respondents) and qualitative research with 20 C‐level managers are analyzed. The study offers the following organizational changes needed to implement DDDM in organizations: culture and mindset changes, digitalization, process improvements, new competencies, re‐organization, and legal requirements. This research contributes to a better understanding of the usage and adoption of DDDM globally and suggests specific organizational changes required to adopt this process.

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Is data-driven decision-making driven only by data? When cognition meets data

Cited by 15 publications

References 97 publications

Analysis and Intuition Effectiveness in Moral Problems

Analysis and Intuition Effectiveness in Moral Problems

A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study

Strategic organizational changes: Adopting data‐driven decisions

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