Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making

Inga Toma; Dursun Delen; Gregory Moscato

doi:doi:10.11648/j.ijdsa.20200601.12

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Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making

Inga Toma, Dursun Delen, Gregory Moscato

Published in International Journal of Data Science and Analysis (Volume 6, Issue 1)

Received: 29 December 2019 Accepted: 8 January 2020 Published: 4 February 2020

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Abstract

Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.

Published in	International Journal of Data Science and Analysis (Volume 6, Issue 1)
DOI	10.11648/j.ijdsa.20200601.12
Page(s)	12-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Profit and Loss Forecasting, Algorithms, Advice-Taking, Business Analytics, Data-Driven Decision Making, Prospect Theory

References

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Inga Toma, Dursun Delen, Gregory Moscato. (2020). Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. International Journal of Data Science and Analysis, 6(1), 12-19. https://doi.org/10.11648/j.ijdsa.20200601.12

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ACS Style

Inga Toma; Dursun Delen; Gregory Moscato. Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. Int. J. Data Sci. Anal. 2020, 6(1), 12-19. doi: 10.11648/j.ijdsa.20200601.12

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AMA Style

Inga Toma, Dursun Delen, Gregory Moscato. Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. Int J Data Sci Anal. 2020;6(1):12-19. doi: 10.11648/j.ijdsa.20200601.12

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@article{10.11648/j.ijdsa.20200601.12,
  author = {Inga Toma and Dursun Delen and Gregory Moscato},
  title = {Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {12-19},
  doi = {10.11648/j.ijdsa.20200601.12},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.12},
  abstract = {Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.},
 year = {2020}
}

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TY  - JOUR
T1  - Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making
AU  - Inga Toma
AU  - Dursun Delen
AU  - Gregory Moscato
Y1  - 2020/02/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200601.12
DO  - 10.11648/j.ijdsa.20200601.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 12
EP  - 19
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200601.12
AB  - Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.
VL  - 6
IS  - 1
ER  -

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Author Information

Inga Toma

Business Administration, International University of Monaco, Principality of Monaco, Monaco
Dursun Delen

Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, OK, US
Gregory Moscato

Business Administration, International University of Monaco, Principality of Monaco, Monaco

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APA Style

Inga Toma, Dursun Delen, Gregory Moscato. (2020). Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. International Journal of Data Science and Analysis, 6(1), 12-19. https://doi.org/10.11648/j.ijdsa.20200601.12

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ACS Style

Inga Toma; Dursun Delen; Gregory Moscato. Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. Int. J. Data Sci. Anal. 2020, 6(1), 12-19. doi: 10.11648/j.ijdsa.20200601.12

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AMA Style

Inga Toma, Dursun Delen, Gregory Moscato. Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making. Int J Data Sci Anal. 2020;6(1):12-19. doi: 10.11648/j.ijdsa.20200601.12

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@article{10.11648/j.ijdsa.20200601.12,
  author = {Inga Toma and Dursun Delen and Gregory Moscato},
  title = {Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {12-19},
  doi = {10.11648/j.ijdsa.20200601.12},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.12},
  abstract = {Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making
AU  - Inga Toma
AU  - Dursun Delen
AU  - Gregory Moscato
Y1  - 2020/02/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200601.12
DO  - 10.11648/j.ijdsa.20200601.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 12
EP  - 19
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200601.12
AB  - Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.
VL  - 6
IS  - 1
ER  -

Copy | Download