Table 2. Research trends and related...

Table 2.

Research trends and related lines for future research

Area	Topic	Suggested lines for future research	References
Artificial intelligence	Leveraging advanced AI and transfer learning models	Investigate multi-modal methods for transfer learning, specifically by leveraging visual information (images and videos) to improve performance in tasks involving textual UGC	Hartmann et al. (2023) Qian et al. (2025)
		Enhance model precision by integrating advanced techniques such as retrieval-augmented generation (RAG)
		Conduct comparative studies between innovative transfer learning/LLM approaches and traditional research methods to assess how well integrating them might capture the authentic lived experience
	Addressing model challenges and biases	Enhance model capabilities to address complexities in unstructured text, such as sarcasm, spam and fake reviews	Qian et al. (2025) Wang and Liu (2023)
		Apply multi-label emotion detection algorithms to address the current limitation where models often assume a single emotion per user review/post
		Apply new methodologies and algorithms (e.g. Naive Bayes or Random Forests) to capture complex, non-linear relationships in customer experience dynamics more effectively than do traditional linear models
	Data source diversification and integration	Use data from multiple UGC platforms to mitigate platform bias and enhance the generalisability of findings	Bigne et al. (2023) Shahhosseini and Khalili Nasr (2024)
	Data source diversification and integration	Incorporate customer demographics, more settings and different consumer patterns when investigating customer satisfaction and service experience	Bigne et al. (2023) Shahhosseini and Khalili Nasr (2024)
	Authorship and ethics	Examine how AI-assisted modification of UGC alters perceptions of authorship, accountability and the ethical legitimacy of modifying consumer-created content	Pocchiari et al. (2025)
UGC effects	Exploring new variables	Identify which specific emotional factors stimulate particular planning behaviour following exposure to visual UGC formats	Nguyen et al. (2023)
	Comparing UGC across different sources and endorsers	Compare the effects of human UGC with non-human endorsers, such as virtual influencers, avatars on Instagram and AI as endorsers, in offline advertising, online social media and the metaverse	Hariningsih et al. (2025)
	Refining methodological approaches	Collect behavioural purchasing data instead of relying only on self-reported data	Wei et al. (2023)
Platform mechanisms	Platform governance and moderation	Misrepresentation and missing content: investigate how consumers determine whether a body of content is systematically missing content due to platform interference	Baier et al. (2025) Hochstein et al. (2025)
		Investigate how consumers make judgements about fake versus real information, and paid versus organic reviews
		Investigate consumer perceptions of human versus algorithmic platform content moderation and curation
		Examine whether transparency about UGC management (e.g. moderation, company reuse) influences users’ trust in the content, with user-perceived control over their own UGC acting as a moderator
	Content formats	Study the behavioural side of Q&A systems, particularly how consumers incorporate Q&A into their purchase decision-making processes	Khern-am-nuai et al. (2024)
UGC vs FGC	Comparative influence and interaction mechanisms	Investigate the causal relationship and interactions between UGC and FGC in influencing consumer behaviours	Li et al. (2024) Barquero Cabrero et al. (2023)
		Examine the effects of FGC, UGC and co-created content on brand economic value and company revenues
		Assess the differential audience effects of brand campaigns integrating UGC vs FGC vs influencer content
	Expand platforms and contexts	Investigate the effectiveness of UGC and FGC on less-studied social media platforms, such as TikTok, Snapchat, LinkedIn and WeChat	Crapa et al. (2024) Li et al. (2024)
	Expand platforms and contexts	Analyse empirical evidence of UGC and FGC effects across industries and in less-studied industries (e.g. durable goods)	Crapa et al. (2024) Li et al. (2024)
	Company intervention in consumer-led spaces	Examine how company involvement in consumer-led UGC environments is perceived by consumers and potential consequences: legitimate stewardship vs appropriation of consumer space	Pocchiari et al. (2025)
	Company intervention in consumer-led spaces	Analyse whether consumers apply different ethical standards to company interventions in online reviews depending on whether the intervention is relational or commercially oriented	Pocchiari et al. (2025)
Multimodality	Combination of multimedia formats	Explore how rich content, such as live streams and short-format videos, for example, TikToks and Instagram reels, coexist with leaner mixtures (text and/or images) and how consumers derive value from these distinctive forms	Singh and Pandey (2024) Kübler et al. (2024)
	Content alignment	Examine how consumers resolve conflict when there is misalignment between photos/videos and text (valence incoherence)	Ceylan et al. (2024) Kübler et al. (2024)
	Content alignment	Investigate how images in fake reviews work in terms of helpfulness and trust	Ceylan et al. (2024) Kübler et al. (2024)
	Capturing emotions through new technologies	Complement sentiment analysis of textual UGC with analysis of hidden emotions within UGC videos, through auto-emotion-detection AI technologies	Bigne et al. (2024) Jia et al. (2023)
	Capturing emotions through new technologies	Use of neuromarketing techniques to assess consumers’ emotional responses to the emotions expressed in both textual and video UGC	Bigne et al. (2024) Jia et al. (2023)
	Simultaneous dimensions	Study complex relationships between all three content dimensions: format, source and platform	Singh and Pandey (2024)
Destination image	Destination image dissonance	Examine the interrelationships among the destination image projected by DMOs, image as portrayed by users through UGC and tourists’ perceptions of the image in visuals-based social media platforms	Torres-Pruñonosa et al. (2024)
	Destination image processing	Examine the hypothesis that most encounters with idealised destination UGC photographs in everyday life are processed via the aesthetics-only route, generating immediate positive aesthetic experiences rather than critical cognitive evaluations	Ortanderl and Bausch (2023)
	Cross-cultural studies	Assess the role of cultural concepts (religion, psychographic features) in destination image formation	Lee and Park (2023)

Area	Topic	Suggested lines for future research	References
Artificial intelligence	Leveraging advanced AI and transfer learning models	Investigate multi-modal methods for transfer learning, specifically by leveraging visual information (images and videos) to improve performance in tasks involving textual UGC	Hartmann et al. (2023)Qian et al. (2025)
		Enhance model precision by integrating advanced techniques such as retrieval-augmented generation (RAG)
		Conduct comparative studies between innovative transfer learning/LLM approaches and traditional research methods to assess how well integrating them might capture the authentic lived experience
	Addressing model challenges and biases	Enhance model capabilities to address complexities in unstructured text, such as sarcasm, spam and fake reviews	Qian et al. (2025)Wang and Liu (2023)
		Apply multi-label emotion detection algorithms to address the current limitation where models often assume a single emotion per user review/post
		Apply new methodologies and algorithms (e.g. Naive Bayes or Random Forests) to capture complex, non-linear relationships in customer experience dynamics more effectively than do traditional linear models
	Data source diversification and integration	Use data from multiple UGC platforms to mitigate platform bias and enhance the generalisability of findings	Bigne et al. (2023)Shahhosseini and Khalili Nasr (2024)
	Data source diversification and integration	Incorporate customer demographics, more settings and different consumer patterns when investigating customer satisfaction and service experience	Bigne et al. (2023)Shahhosseini and Khalili Nasr (2024)
	Authorship and ethics	Examine how AI-assisted modification of UGC alters perceptions of authorship, accountability and the ethical legitimacy of modifying consumer-created content	Pocchiari et al. (2025)
UGC effects	Exploring new variables	Identify which specific emotional factors stimulate particular planning behaviour following exposure to visual UGC formats	Nguyen et al. (2023)
	Comparing UGC across different sources and endorsers	Compare the effects of human UGC with non-human endorsers, such as virtual influencers, avatars on Instagram and AI as endorsers, in offline advertising, online social media and the metaverse	Hariningsih et al. (2025)
	Refining methodological approaches	Collect behavioural purchasing data instead of relying only on self-reported data	Wei et al. (2023)
Platform mechanisms	Platform governance and moderation	Misrepresentation and missing content: investigate how consumers determine whether a body of content is systematically missing content due to platform interference	Baier et al. (2025)Hochstein et al. (2025)
		Investigate how consumers make judgements about fake versus real information, and paid versus organic reviews
		Investigate consumer perceptions of human versus algorithmic platform content moderation and curation
		Examine whether transparency about UGC management (e.g. moderation, company reuse) influences users’ trust in the content, with user-perceived control over their own UGC acting as a moderator
	Content formats	Study the behavioural side of Q&A systems, particularly how consumers incorporate Q&A into their purchase decision-making processes	Khern-am-nuai et al. (2024)
UGC vs FGC	Comparative influence and interaction mechanisms	Investigate the causal relationship and interactions between UGC and FGC in influencing consumer behaviours	Li et al. (2024)Barquero Cabrero et al. (2023)
		Examine the effects of FGC, UGC and co-created content on brand economic value and company revenues
		Assess the differential audience effects of brand campaigns integrating UGC vs FGC vs influencer content
	Expand platforms and contexts	Investigate the effectiveness of UGC and FGC on less-studied social media platforms, such as TikTok, Snapchat, LinkedIn and WeChat	Crapa et al. (2024)Li et al. (2024)
	Expand platforms and contexts	Analyse empirical evidence of UGC and FGC effects across industries and in less-studied industries (e.g. durable goods)	Crapa et al. (2024)Li et al. (2024)
	Company intervention in consumer-led spaces	Examine how company involvement in consumer-led UGC environments is perceived by consumers and potential consequences: legitimate stewardship vs appropriation of consumer space	Pocchiari et al. (2025)
	Company intervention in consumer-led spaces	Analyse whether consumers apply different ethical standards to company interventions in online reviews depending on whether the intervention is relational or commercially oriented	Pocchiari et al. (2025)
Multimodality	Combination of multimedia formats	Explore how rich content, such as live streams and short-format videos, for example, TikToks and Instagram reels, coexist with leaner mixtures (text and/or images) and how consumers derive value from these distinctive forms	Singh and Pandey (2024)Kübler et al. (2024)
	Content alignment	Examine how consumers resolve conflict when there is misalignment between photos/videos and text (valence incoherence)	Ceylan et al. (2024)Kübler et al. (2024)
	Content alignment	Investigate how images in fake reviews work in terms of helpfulness and trust	Ceylan et al. (2024)Kübler et al. (2024)
	Capturing emotions through new technologies	Complement sentiment analysis of textual UGC with analysis of hidden emotions within UGC videos, through auto-emotion-detection AI technologies	Bigne et al. (2024)Jia et al. (2023)
	Capturing emotions through new technologies	Use of neuromarketing techniques to assess consumers’ emotional responses to the emotions expressed in both textual and video UGC	Bigne et al. (2024)Jia et al. (2023)
	Simultaneous dimensions	Study complex relationships between all three content dimensions: format, source and platform	Singh and Pandey (2024)
Destination image	Destination image dissonance	Examine the interrelationships among the destination image projected by DMOs, image as portrayed by users through UGC and tourists’ perceptions of the image in visuals-based social media platforms	Torres-Pruñonosa et al. (2024)
	Destination image processing	Examine the hypothesis that most encounters with idealised destination UGC photographs in everyday life are processed via the aesthetics-only route, generating immediate positive aesthetic experiences rather than critical cognitive evaluations	Ortanderl and Bausch (2023)
	Cross-cultural studies	Assess the role of cultural concepts (religion, psychographic features) in destination image formation	Lee and Park (2023)

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