Table I. Summary of the key literature... | Emerald Publishing

Table I.

Summary of the key literature for temporal dynamics in web personalisation and web adaptation

Source	Approach	Application area	Focus of interest	Timescale	Key finding(s)
Ho and Tam (2005)	Matching stated preferences	E-commerce	Stage of decision making	Mid- to short-term	Personalisation is effective when users form their consideration sets but not after the decision has been made
Hauser et al. (2009)	Matching user cognitive styles based on click-stream	Website (conversion)	Psychological fit	Short-term (long-term)	Adapting to match cognitive styles boosts conversion considerably
Ho et al. (2011)	Matching simulated preferences/click-stream	E-commerce	Timing of showing personalised content within the visiting period	Short-term	The effectiveness of personalisation is a question of optimising the early presentation of recommendations and the quality of the recommendations
Hong et al. (2012)	Collaborative filtering coupled with short- and long-term profiles	E-commerce	Stage of lifecycle	Long-term	Incorporating lifecycles into recommendations boosts performance
Lambrecht and Tucker (2013)	Dynamic re-targeting	Banner advertising	Stage of decision making	Mid- to long-term	Dynamic re-targeting is only effective if it fits a user’s stage of decision making: high-level information is effective in the early stages; detailed information is effective in later stages. preferences narrow
Urban et al. (2013)	Matching user cognitive styles based on click-stream	Banner advertising	Psychological fit and stage of decision making	Short-term	Improved banner effectiveness goes beyond traditional targeting
Hauser et al. (2014)	Matching user cognitive styles based on click-stream	Website (conversion)	Psychological fit and stage of the visit	Short-term (Long-term)	Morphing to match a user’s cognitive style increases conversion
Li et al. (2014)	Content-based filtering coupled with short- and long-term profiles	Online news	Variance between long-term and short-term profiles	Mixed	Combining long-term and short-term profiles increases personalisation effectiveness
Ding et al. (2015)	Real-time backward learning with dynamic learning	E-commerce	Learning user real-time intent based on browsing behaviour and tested the effectiveness of marketing and web stimuli	Short-term	Intent-based website transformation decreases shopping cart abandonment and increases conversion

Source	Approach	Application area	Focus of interest	Timescale	Key finding(s)
Ho and Tam (2005)	Matching stated preferences	E-commerce	Stage of decision making	Mid- to short-term	Personalisation is effective when users form their consideration sets but not after the decision has been made
Hauser et al. (2009)	Matching user cognitive styles based on click-stream	Website (conversion)	Psychological fit	Short-term (long-term)	Adapting to match cognitive styles boosts conversion considerably
Ho et al. (2011)	Matching simulated preferences/click-stream	E-commerce	Timing of showing personalised content within the visiting period	Short-term	The effectiveness of personalisation is a question of optimising the early presentation of recommendations and the quality of the recommendations
Hong et al. (2012)	Collaborative filtering coupled with short- and long-term profiles	E-commerce	Stage of lifecycle	Long-term	Incorporating lifecycles into recommendations boosts performance
Lambrecht and Tucker (2013)	Dynamic re-targeting	Banner advertising	Stage of decision making	Mid- to long-term	Dynamic re-targeting is only effective if it fits a user’s stage of decision making: high-level information is effective in the early stages; detailed information is effective in later stages. preferences narrow
Urban et al. (2013)	Matching user cognitive styles based on click-stream	Banner advertising	Psychological fit and stage of decision making	Short-term	Improved banner effectiveness goes beyond traditional targeting
Hauser et al. (2014)	Matching user cognitive styles based on click-stream	Website (conversion)	Psychological fit and stage of the visit	Short-term (Long-term)	Morphing to match a user’s cognitive style increases conversion
Li et al. (2014)	Content-based filtering coupled with short- and long-term profiles	Online news	Variance between long-term and short-term profiles	Mixed	Combining long-term and short-term profiles increases personalisation effectiveness
Ding et al. (2015)	Real-time backward learning with dynamic learning	E-commerce	Learning user real-time intent based on browsing behaviour and tested the effectiveness of marketing and web stimuli	Short-term	Intent-based website transformation decreases shopping cart abandonment and increases conversion