Table 1. Future research avenues Themes... | Emerald Publishing

Table 1.

Future research avenues

Themes	SM-related research proposition	Potential ML/AI technique (evaluation metrics)	IC* and DS*	Hospitality context	Hospitality-Specific issues	Benefits	Future research directions(FRD) and limitations	Ethical risk and mitigation
Atmospheric and sensory cues	Employing sensory cues optimised by AI/ML in hospitality enhances the customer experience and limits the interference of other cues	Reinforcement learning and unsupervised learning	IC data privacy, integration with existing systems	Hotels, restaurants, and event venues can use AI/ML to analyse guest feedback and sensor data to adjust sensory cues to create a pleasant atmosphere	Inconsistent guest experience across locations, difficulty in personalising ambiance	Create tailored atmospheres for different guest segments, improve brand perception	FRD – Evaluating the long-term impact on guest preferences	Risk – unintended behavioural reinforcement
		Using these to predict optimal sensory configurations; IoT for real-time adjustments	DS guest feedback, sensor data				Limitations – Accuracy of AI models, potential for guest discomfort	Mitigation – Transparent model goals, and use of explainable AI (XAI)
(In)-Congruence	Using AI-driven technologies to dynamically adjust SM strategies to ensure real-time congruency for the target audience while maintaining authenticity and brand integrity	NLP, deep learning, generative AI and reinforcement learning: AI can categorise congruent sensory cues with brand identity. Deep learning algorithms could analyse brand elements and recommend, e.g., scents that complement a store’s visuals, promoting brand consistency	IC cost of technology, training data requirements DS brand guidelines, customer data	AI can tailor the environment of hotel lobbies, conference rooms, and dining areas to match the brand’s identity and guest preferences	Maintaining brand identity across diverse locations, ensuring design elements complement each other	Enhance brand image to deliver consistent and dynamic customer experience	FRD – Investigating the role of human oversight in AI-driven design for hospitality settings Limitations – Potential for manipulation, bias in algorithms	Risk – Misinterpretation, cultural bias Mitigation – Human-in-the-loop review

Multisensory cues	Explore and examine the impact of multi-sensory cues in digital environments, aided by AI, to ensure a holistic consumer experience that parallels physical settings	AI-driven simulations and VR/AR technologies; using these solutions to create and test multisensory environments prior creating physical ones	IC technical feasibility, customer acceptance of VR/AR technology DS customer preferences, product data	Digital twins of hotel rooms or virtual tours of resorts can be used to simulate and test multisensory experiences before actual implementation	Limited ability to showcase new experiences before physical implementation	Enhance customer engagement, ability to virtually test new hotel features or services	FRD - Developing accessibility guidelines for VR/AR experiences in hospitality settings	Risk – Inequitable access/exclusion of disabled users
							Limitations – Technical limitations, accessibility concerns	Mitigation – Follow inclusive design principles; provide alternative sensory modes (audio descriptions, haptics, etc.)
Perceptions	Use emerging technologies, such as AI-driven analytics and biometric measurements, to enhance and accurately measure consumer perception of SM	Deep learning; can analyse vast datasets, including sensory cues and consumer behaviour, to understand consumer perceptions. Biometric measurements allow for tracking reactions. Hotels can employ broad NLP or generative AI and deep learning to personalise and enhance guest experiences by understanding and responding to sensory preferences and feedback	IC data security, ethical considerations DS customer feedback data, biometric data	Hotels can employ AI solutions to personalise and enhance guest experiences by understanding and responding to their sensory preferences and feedback	Difficulty in gauging guest satisfaction and preferences in real-time	Deeper understanding of guest preferences, ability to personalise service in real-time	FRD – Developing ethical frameworks for using biometric data in AI-powered customer service applications within the hospitality industry Limitations – Potential for customer discomfort, privacy concerns	Risk - Hidden bias in clustering/ segmentation; overfitting to consumer data; loss of spontaneity Mitigation – Bias audits and fairness metrics; maintain variation in recommendations; allow manual override or “randomise” option

Sensations	Using AI to decode and understand consumers’ subconscious temporal dimensions and sensory experiences, identify effective SM strategies, and deliver immersive sensations that shape consumer engagement and well-being	Predictive analytics using real-time data (biometrics, online behaviour); can anticipate customer emotions and adjust sensory cues (lighting, music) accordingly. This personalises the consumption experience based on mood	IC accuracy of sentiment analysis, potential for manipulation DS customer feedback data, sensor data	Hotels and resorts can use AI to monitor and enhance sensory experiences, ensuring long-term guest satisfaction and loyalty	Limited ability to anticipate and address guest needs proactively	Increased customer engagement and improved guest satisfaction by anticipating needs	FRD – Research into the ethical implications of using AI to influence customer emotions in hospitality settings limitations – Limited understanding of subconscious emotional states	Risk – Privacy, emotional manipulation Mitigation – Data protection impact assessments (DPIA), consent, opt-out options

Overload	Employing AI to dynamically adjust the intensity and congruency of sensory cues in various settings to avoid sensory overload while enhancing the customer experience	ML can personalise the visiting experience by adjusting sensory cues (lighting, music) based on individual preferences and time spent. May reduce sensory fatigue for customers. Can be measured using heart rate and skin conductance, to gauge their level of sensory stimulation and adjust the intensity of lighting or music accordingly (IoT)	IC individual privacy concerns, managing customer expectation DS customer preferences, sensor data	Hotels and large event venues can use AI to monitor and adjust sensory inputs in real-time to avoid overwhelming guests, ensuring a pleasant experience throughout their stay	Guest feeling overwhelmed by excessive stimuli in public areas or rooms	Reduced sensory overload and improved well-being for guests	FRD – Developing consumer education initiatives regarding AI use in hospitality Limitations – Potential for customer resistance to AI-controlled environments	Risk – Hyper-personalisation, sensory manipulation or fatigue Mitigation – Introduce sensitivity thresholds; ensure opt-in; monitor for adverse outcomes; explain customisation logic

Themes	SM-related research proposition	Potential ML/AI technique (evaluation metrics)	IC* and DS*	Hospitality context	Hospitality-Specific issues	Benefits	Future research directions(FRD) and limitations	Ethical risk and mitigation
Atmospheric and sensory cues	Employing sensory cues optimised by AI/ML in hospitality enhances the customer experience and limits the interference of other cues	Reinforcement learning and unsupervised learning	IC data privacy, integration with existing systems	Hotels, restaurants, and event venues can use AI/ML to analyse guest feedback and sensor data to adjust sensory cues to create a pleasant atmosphere	Inconsistent guest experience across locations, difficulty in personalising ambiance	Create tailored atmospheres for different guest segments, improve brand perception	FRD – Evaluating the long-term impact on guest preferences	Risk – unintended behavioural reinforcement
		Using these to predict optimal sensory configurations; IoT for real-time adjustments	DS guest feedback, sensor data				Limitations – Accuracy of AI models, potential for guest discomfort	Mitigation – Transparent model goals, and use of explainable AI (XAI)
(In)-Congruence	Using AI-driven technologies to dynamically adjust SM strategies to ensure real-time congruency for the target audience while maintaining authenticity and brand integrity	NLP, deep learning, generative AI and reinforcement learning: AI can categorise congruent sensory cues with brand identity. Deep learning algorithms could analyse brand elements and recommend, e.g., scents that complement a store’s visuals, promoting brand consistency	IC cost of technology, training data requirementsDS brand guidelines, customer data	AI can tailor the environment of hotel lobbies, conference rooms, and dining areas to match the brand’s identity and guest preferences	Maintaining brand identity across diverse locations, ensuring design elements complement each other	Enhance brand image to deliver consistent and dynamic customer experience	FRD – Investigating the role of human oversight in AI-driven design for hospitality settingsLimitations – Potential for manipulation, bias in algorithms	Risk – Misinterpretation, cultural biasMitigation – Human-in-the-loop review

Multisensory cues	Explore and examine the impact of multi-sensory cues in digital environments, aided by AI, to ensure a holistic consumer experience that parallels physical settings	AI-driven simulations and VR/AR technologies; using these solutions to create and test multisensory environments prior creating physical ones	IC technical feasibility, customer acceptance of VR/AR technologyDS customer preferences, product data	Digital twins of hotel rooms or virtual tours of resorts can be used to simulate and test multisensory experiences before actual implementation	Limited ability to showcase new experiences before physical implementation	Enhance customer engagement, ability to virtually test new hotel features or services	FRD - Developing accessibility guidelines for VR/AR experiences in hospitality settings	Risk – Inequitable access/exclusion of disabled users
							Limitations – Technical limitations, accessibility concerns	Mitigation – Follow inclusive design principles; provide alternative sensory modes (audio descriptions, haptics, etc.)
Perceptions	Use emerging technologies, such as AI-driven analytics and biometric measurements, to enhance and accurately measure consumer perception of SM	Deep learning; can analyse vast datasets, including sensory cues and consumer behaviour, to understand consumer perceptions. Biometric measurements allow for tracking reactions. Hotels can employ broad NLP or generative AI and deep learning to personalise and enhance guest experiences by understanding and responding to sensory preferences and feedback	IC data security, ethical considerationsDS customer feedback data, biometric data	Hotels can employ AI solutions to personalise and enhance guest experiences by understanding and responding to their sensory preferences and feedback	Difficulty in gauging guest satisfaction and preferences in real-time	Deeper understanding of guest preferences, ability to personalise service in real-time	FRD – Developing ethical frameworks for using biometric data in AI-powered customer service applications within the hospitality industry Limitations –Potential for customer discomfort, privacy concerns	Risk - Hidden bias in clustering/ segmentation; overfitting to consumer data; loss of spontaneity Mitigation – Bias audits and fairness metrics; maintain variation in recommendations; allow manual override or “randomise” option

Sensations	Using AI to decode and understand consumers’ subconscious temporal dimensions and sensory experiences, identify effective SM strategies, and deliver immersive sensations that shape consumer engagement and well-being	Predictive analytics using real-time data (biometrics, online behaviour); can anticipate customer emotions and adjust sensory cues (lighting, music) accordingly. This personalises the consumption experience based on mood	IC accuracy of sentiment analysis, potential for manipulationDS customer feedback data, sensor data	Hotels and resorts can use AI to monitor and enhance sensory experiences, ensuring long-term guest satisfaction and loyalty	Limited ability to anticipate and address guest needs proactively	Increased customer engagement and improved guest satisfaction by anticipating needs	FRD – Research into the ethical implications of using AI to influence customer emotions in hospitality settings limitations –Limited understanding of subconscious emotional states	Risk – Privacy, emotional manipulationMitigation – Data protection impact assessments (DPIA), consent, opt-out options

Overload	Employing AI to dynamically adjust the intensity and congruency of sensory cues in various settings to avoid sensory overload while enhancing the customer experience	ML can personalise the visiting experience by adjusting sensory cues (lighting, music) based on individual preferences and time spent. May reduce sensory fatigue for customers. Can be measured using heart rate and skin conductance, to gauge their level of sensory stimulation and adjust the intensity of lighting or music accordingly (IoT)	IC individual privacy concerns, managing customer expectationDS customer preferences, sensor data	Hotels and large event venues can use AI to monitor and adjust sensory inputs in real-time to avoid overwhelming guests, ensuring a pleasant experience throughout their stay	Guest feeling overwhelmed by excessive stimuli in public areas or rooms	Reduced sensory overload and improved well-being for guests	FRD – Developing consumer education initiatives regarding AI use in hospitalityLimitations – Potential for customer resistance to AI-controlled environments	Risk – Hyper-personalisation, sensory manipulation or fatigueMitigation – Introduce sensitivity thresholds; ensure opt-in; monitor for adverse outcomes; explain customisation logic

Note(s):

IC* = Implementation/considerations; DS* = data source

Source(s): Developed by authors