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Quantitative Guidance-Decision Synergies (SF19/MOS/ONKAL)

Project Description

Decision making lies at the core of management. It permeates and dictates the full spectrum of managerial activities extending from complex tasks such as risk management and scenario planning to more fundamental tasks such as adoption of models and toolkits constitute the lower end. There is persistent evidence in the literature pointing out the reluctance of decision makers to accept/use the quantitative guidance and advice provided to them. Even in cases where decision makers are willing to integrate such advice, there may be instances of severe discounting and excessive adjustments. Furthermore, there exists well-documented evidence of ‘algorithm aversion’, where people erroneously avoid algorithms (quantitative advice from models) and favour advice from human experts. Given the trust dynamics underlying decision-makers’ use of advice, this project aims to contribute to the current understanding on decision-making, focussing particularly on decision makers’ assessments and use of quantitative guidance (especially in the form of forecasts derived from quantitative models) versus advice from experts with varying credibility. In doing so, issues related to human-computer interaction, algorithm aversion, and trust in forecasts and advice provided by computerized/automated systems will be examined via behavioural experiments.

Eligibility and How to Apply:

Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/…) will not be considered.

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.


Recent publications by supervisors relevant to this project:

Thomson M.E., A.C. Pollock, D. Önkal, and M.S. Gönül (in press) “Combining Forecasts: Performance and Coherence”, International Journal of Forecasting.

Goodwin, P., M.S. Gönül, D. Önkal, (in press). “When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions”, European Journal of Operational Research., DOI:10.1016/j.ejor.2018.09.033

Goodwin P., D. Önkal, M.S. Gönül, M.Thomson and E. Öz, (2017) “Evaluating expert advice in forecasting: Users’ reactions to presumed vs experienced credibility”, International Journal of Forecasting, 33, 280-297, DOI:10.1016/j.ijforecast.2015.12.009

Kocabıyıkoğlu, A., I. Göğüş and M.S. Gönül, (2016) "Decision Making and the Price-Setting Newsvendor: Experimental Evidence" Decision Sciences, 47(1), 157-186. DOI: 10.1111/deci.12158

Kocabıyıkoğlu, A., I. Göğüş and M.S. Gönül, (2015) "Revenue management vs. newsvendor decisions: Does behavioral response mirror normative equivalence?", Production and Operations Management, 24(5), 750-761, DOI: 10.1111/poms.12297.

Önkal, D., K.Z. Sayım and M.S. Gönül, (2013) “Scenarios as channels of forecast advice”, Technological Forecasting & Social Change, 80(4), 772-788.

Goodwin P., M.S. Gönül and D. Önkal, (2013) “Antecedents and effects of trust in forecasting advice”, International Journal of Forecasting, 29(2), 354-366.

Thomson M.E., A.C. Pollock, M.S. Gönül and D. Önkal, (2013) “Effects of trend strength and direction on performance and consistency in judgmental exchange rate forecasting”, International Journal of Forecasting, 29(2), 337-353.

Önkal, D., P. Goodwin, M.E. Thomson, M.S. Gönül, A.C. Pollock (2009) “The Relative
Influence of Advice from Human Experts and Statistical Methods on Forecast
Adjustments”, Journal of Behavioral Decision Making, 22(4), 390-409.

Gönül, M.S., D. Önkal, and P. Goodwin. (2009) “Expectations, Use and Judgmental Adjustment of External Financial and Economic Forecasts: An Empirical Investigation”, Journal of Forecasting, 28, 19-37.

Önkal, D., M.S. Gönül and M. Lawrence. (2008) “Judgmental Adjustments of Previously-Adjusted Forecasts”, Decision Sciences, 39(2), 213-238.

Gönül, M.S., D. Önkal and M. Lawrence. (2006) "The Effects of Structural Characteristics of Explanations on Use of a DSS", Decision Support Systems, 42(3), 1481-1493.

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