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Title Challenging the Revenue Management Model in City Hotels Tony Kiely School of Hospitality Title Challenging the Revenue Management Model in City Hotels Tony Kiely School of Hospitality Management and Tourism Dublin Institute of Technology Cathal Brugha Street, Dublin.

Introduction l Yield Management Involves Profitably Managing Fixed Capacity l Pressure Exists to Maximise Introduction l Yield Management Involves Profitably Managing Fixed Capacity l Pressure Exists to Maximise Revenue l Future Uncertainty Complicates Matters l Increasing Need For Optimum Solutions

Mathematical Approaches to Decision Making. l The Management Science Model (Computerised Yield Management ) Mathematical Approaches to Decision Making. l The Management Science Model (Computerised Yield Management ) Should Minimise Guesswork in Capacity Decisions l Statistical Analysis Employed l Bayesian Assignment of Judgement

Mathematical Decision Inhibitors: l The Human Idiosyncratic Factor (Influence of Bias and Heuristics) l Mathematical Decision Inhibitors: l The Human Idiosyncratic Factor (Influence of Bias and Heuristics) l Time and Forecasting Pressures l Reluctance to Embrace Technology l A “Ready, - Fire, - Aim” Approach Rather than a “Ready, - Aim, - Fire”

The Management Science Model l Rational Decision Making l Handling Multiple Variables, thus Avoiding The Management Science Model l Rational Decision Making l Handling Multiple Variables, thus Avoiding Problems Associated with Information Overload l Offers the Perfect Solution to Decision Optimisation

Problems With The Rational Model l Preference for Historical Pricing Strategy Over Dynamic Pricing Problems With The Rational Model l Preference for Historical Pricing Strategy Over Dynamic Pricing Strategy l Perception of Loss of Control l The Seductive Influence of “Local Rationality”

Heuristics/Biases in Decision Making l “Availability” l “Anchoring and Adjustment” l “Representativeness” l “Sympathethic Heuristics/Biases in Decision Making l “Availability” l “Anchoring and Adjustment” l “Representativeness” l “Sympathethic Magical Theory” and “The Affect Heuristic”

The Availability Heuristic l Ignoring Diagnostic Information l Gravitation Towards Data which is “Vivid” The Availability Heuristic l Ignoring Diagnostic Information l Gravitation Towards Data which is “Vivid” l Avoidance of Bad Experiences (Avoidance of Data that is Vivid for all the wrong Reasons) l Preference for “Feelgood” Data.

Anchoring and Adjustment l Being Over-Influenced by Original Data and by Personal Experience l Anchoring and Adjustment l Being Over-Influenced by Original Data and by Personal Experience l Having Selective Perception of Solutions l Conflict Between Initial Intuition and a More Measured Rational Belief

The Representativeness Heuristic l “Gut Feeling” l Subconsciously “Filtering Out” Better Information l Falling The Representativeness Heuristic l “Gut Feeling” l Subconsciously “Filtering Out” Better Information l Falling into the “Status Quo” Trap (Association with Subconscious Impacts) l Falling into the “Evidence” Trap (Seeking out Confirmatory Evidence)

Sympathetic Magical Theory l l l Strongly Associated with “Emotion” Suggests that the Intuition Sympathetic Magical Theory l l l Strongly Associated with “Emotion” Suggests that the Intuition of the Emotion Driven Manager Differs from the Intuition of the Expert “Awareness” of Better Solutions often Leads to Rationalising the Irrational – Conscious Contradiction of Empirical Data – Avoidance of Disagreement and Risk – Modification of the Halo Effect

The Affect Heuristic l Feelings Correlate with Positive and Negative Attitudes to Decision Making The Affect Heuristic l Feelings Correlate with Positive and Negative Attitudes to Decision Making l Risk and Benefit Become Negatively Correlated l Time Pressures give Greater Weightings to Emotional Evaluation l Justification Follows (Ready, - Fire, -Aim)

The Conundrum l How to Maximise Revenue, while at the Same Time Offering a The Conundrum l How to Maximise Revenue, while at the Same Time Offering a Product that Satisfies Price Sensitive Customers l Are the Right Decisions made for the Right Reasons, by the Right People, in a Reasonable and Predictable Manner?

Findings l Strong Preference for Greater Human Involvement in the Decision Making Process Corresponding Findings l Strong Preference for Greater Human Involvement in the Decision Making Process Corresponding with l Lack of Support for Computerised Decision Making

Findings l Unwillingness to Validate Data Corresponding with l Willingness to Use Unvalidated Data Findings l Unwillingness to Validate Data Corresponding with l Willingness to Use Unvalidated Data

Findings l Data Overload Facilitated Selection of Particular Data Sets to Suit The Required Findings l Data Overload Facilitated Selection of Particular Data Sets to Suit The Required Decision Corresponds With l Decisions Supported by Emotional Factors and Gut Feeling

Conclusions. l The Management Science Model was Sidelined in Favour of Human Intervention Despite Conclusions. l The Management Science Model was Sidelined in Favour of Human Intervention Despite l An Underlying Belief that Technology Would Offer More Optimal Solutions l Internal Rationalisation Ensued

Concluding Questions l Is Yield Management Decision Behaviour Geared Towards – – – Avoidance Concluding Questions l Is Yield Management Decision Behaviour Geared Towards – – – Avoidance of Failure? Self Aggrandisement? Self Preservation?

Concluding Questions l If So! l Are Hospitality Organisations Non Rational with Respect to Concluding Questions l If So! l Are Hospitality Organisations Non Rational with Respect to Yield Management Decision Making? Are They Possibly Incubating Non Rational Tendencies With Respect to Decision Making? Could This Impact on Revenue Maximisation l l

Contact l Thanks for Listening l tony. m. kiely@dit. ie Contact l Thanks for Listening l tony. m. kiely@dit. ie