Table 1 Axial coding – qualitative...

Table 1

Axial coding – qualitative validation (SUM of references, average weight percentages (%)) of codes and sentiment analysis

Axial code	Ref¹ (SUM)	Open codes	X_k	X_l	Boolean operators (keywords)	Av. weight percentage² (%)	Importance category³	Sentiment⁴
Transport enhancement	124	Widespread of SAE 4–5 vehicles	1	39	“self-driving” OR “autonomous” OR “driverless” OR “robotic”	0.0186	Category 2	(+)
		Alternative propulsion	2	72	“alternative” OR “hybrid” OR “electric” OR “hydrogen”	0.0343	Category 2	(+)
		Maglev	3	2	“maglev” OR “magnetic levitation”	0.0010		(+)
		Hyperloop	5	2	“hyperloop”	0.0010	Category 1 Category 1	(+)
		Drones (air taxi)	6	9	“drone” OR “robotaxi” “flying taxi”	0.0043	Category 1	(+)
Holographic solutions	61	Client management and information gathering	6	61	“holographic” OR “hologram”	0.0290	Category 2	(+/−)
Climate anxiety	75	Destination choice and availability	7	75	“climate OR “climatic” AND “problem” OR “climate change” OR/AND “endangered” OR/AND “jeopardized” AND “destination” AND “attraction”	0.0357	Category 3	(−)
Artificial intelligence	146	Embodiment of humanity	8	8	“solve” AND “health” AND “problem” AND “disease” OR “human advancement”	0.0038	Category 1	(+)
		Anthropomorphism in robotics	9	42	“anthropomorphism” OR “human-like” AND “robot”	0.0200	Category 2	(+/−)
		VR/AR/MR-based service availability	10	96	“virtual reality” OR/AND “augmented reality” OR/AND “mixed reality”	0.0457	Category 3	(+)
Perception of health and safety risks from altered climatic conditions	75	–	11	75	“health” AND “risk” AND “danger” AND “climate change”	0.0357	Category 3	(−)
Age group isolation possibility	73	–	12	73	“separation” OR “isolation” OR “specialized” AND “tour” OR “travel for the elderly”	0.0348	Category 2	(+)
Perceived risk over health disparity/need for accessibility	16	–	13	16	“need” AND “accessibility” OR “health” AND “risk”	0.0076	Category 1	(−)
Previous travel experience	27	–	14	27	“travel” AND “experience”	0.0129	Category 2	(+)
Family background	390	–	15	390	“family” OR “husband” OR “wife” OR “partner” OR “child/ren” OR “daughter” OR “son” “granddaughter” OR “grandson” OR “relatives” OR “marriage”	0.1857	Category 3	(+)
Current health status	45	–	16	45	“health” AND “status” OR “problem” OR “issue” OR “disease” OR “illness”	0.0214	Category 2	(−)
Psychological readiness	7	–	17	7	“ready for” AND “try out” OR “experience” OR “use”	0.0033	Category 1	(−)
Sci-fi as mass tourism	154	AI-based tourism experience	18	31	“artificial intelligence OR “AI” OR/AND “tourism” OR/AND “travel”	0.0148	Category 2	(+/−)
Sci-fi as mass tourism	154	Space tourism	19	123	“space” OR “moon” OR “spacecraft” OR “Mars” OR “colonization”	0.0586	Category 3	(+)
Nostalgia as a niche	52	Back to the homeland	20	34	“homeland” OR “childhood” OR “home” OR “my country”	0.0162	Category 2	(−)
Nostalgia as a niche	52	Digital/AI-detoxication	21	18	“AI” AND “refuse” OR “get rid of” OR “detoxication”	0.0086	Category 1	(−)
Hedonism	24	Overtourism	22	21	“overtourism” OR “crowdy” OR “hordes of tourists”	0.0100	Category 1	(−)
Hedonism	24	“Couch” tourism	23	3	“couch” OR “stay at home tourism”	0.0014	Category 1	(+)
Seniority as a twilight of life	37	–	24	37	“old” AND “too” OR “last years” OR “last travel”	0.0176	Category 2	(−)
Progress in healthcare	30	–	25	30	“modern” OR “developed” OR “superb” OR “progressed” AND “healthcare”	0.0143	Category 2	(+)
Technological anxiety	26	–	26	26	“anxiety” AND “stress” AND “technology” OR “fear” OR “fright”	0.0124	Category 1	(−)
AI-dependence	48	–	27	48	“artificial intelligence” AND “depend” OR “influence” OR “	0.0229	Category 3	(−)
Enhanced tourism experience	145	–	28	145	“tourism” AND “experience” AND “unforgettable” OR “good”	0.0690	Category 3	(+)

Axial code	Ref¹ (SUM)	Open codes	X_k	X_l	Boolean operators (keywords)	Av. weight percentage² (%)	Importance category³	Sentiment⁴
Transport enhancement	124	Widespread of SAE 4–5 vehicles	1	39	“self-driving” OR “autonomous” OR “driverless” OR “robotic”	0.0186	Category 2	(+)
		Alternative propulsion	2	72	“alternative” OR “hybrid” OR “electric” OR “hydrogen”	0.0343	Category 2	(+)
		Maglev	3	2	“maglev” OR “magnetic levitation”	0.0010		(+)
		Hyperloop	5	2	“hyperloop”	0.0010	Category 1Category 1	(+)
		Drones (air taxi)	6	9	“drone” OR “robotaxi” “flying taxi”	0.0043	Category 1	(+)
Holographic solutions	61	Client management and information gathering	6	61	“holographic” OR “hologram”	0.0290	Category 2	(+/−)
Climate anxiety	75	Destination choice and availability	7	75	“climate OR “climatic” AND “problem” OR “climate change” OR/AND “endangered” OR/AND “jeopardized” AND “destination” AND “attraction”	0.0357	Category 3	(−)
Artificial intelligence	146	Embodiment of humanity	8	8	“solve” AND “health” AND “problem” AND “disease” OR “human advancement”	0.0038	Category 1	(+)
		Anthropomorphism in robotics	9	42	“anthropomorphism” OR “human-like” AND “robot”	0.0200	Category 2	(+/−)
		VR/AR/MR-based service availability	10	96	“virtual reality” OR/AND “augmented reality” OR/AND “mixed reality”	0.0457	Category 3	(+)
Perception of health and safety risks from altered climatic conditions	75	–	11	75	“health” AND “risk” AND “danger” AND “climate change”	0.0357	Category 3	(−)
Age group isolation possibility	73	–	12	73	“separation” OR “isolation” OR “specialized” AND “tour” OR “travel for the elderly”	0.0348	Category 2	(+)
Perceived risk over health disparity/need for accessibility	16	–	13	16	“need” AND “accessibility” OR “health” AND “risk”	0.0076	Category 1	(−)
Previous travel experience	27	–	14	27	“travel” AND “experience”	0.0129	Category 2	(+)
Family background	390	–	15	390	“family” OR “husband” OR “wife” OR “partner” OR “child/ren” OR “daughter” OR “son” “granddaughter” OR “grandson” OR “relatives” OR “marriage”	0.1857	Category 3	(+)
Current health status	45	–	16	45	“health” AND “status” OR “problem” OR “issue” OR “disease” OR “illness”	0.0214	Category 2	(−)
Psychological readiness	7	–	17	7	“ready for” AND “try out” OR “experience” OR “use”	0.0033	Category 1	(−)
Sci-fi as mass tourism	154	AI-based tourism experience	18	31	“artificial intelligence OR “AI” OR/AND “tourism” OR/AND “travel”	0.0148	Category 2	(+/−)
Sci-fi as mass tourism	154	Space tourism	19	123	“space” OR “moon” OR “spacecraft” OR “Mars” OR “colonization”	0.0586	Category 3	(+)
Nostalgia as a niche	52	Back to the homeland	20	34	“homeland” OR “childhood” OR “home” OR “my country”	0.0162	Category 2	(−)
Nostalgia as a niche	52	Digital/AI-detoxication	21	18	“AI” AND “refuse” OR “get rid of” OR “detoxication”	0.0086	Category 1	(−)
Hedonism	24	Overtourism	22	21	“overtourism” OR “crowdy” OR “hordes of tourists”	0.0100	Category 1	(−)
Hedonism	24	“Couch” tourism	23	3	“couch” OR “stay at home tourism”	0.0014	Category 1	(+)
Seniority as a twilight of life	37	–	24	37	“old” AND “too” OR “last years” OR “last travel”	0.0176	Category 2	(−)
Progress in healthcare	30	–	25	30	“modern” OR “developed” OR “superb” OR “progressed” AND “healthcare”	0.0143	Category 2	(+)
Technological anxiety	26	–	26	26	“anxiety” AND “stress” AND “technology” OR “fear” OR “fright”	0.0124	Category 1	(−)
AI-dependence	48	–	27	48	“artificial intelligence” AND “depend” OR “influence” OR “	0.0229	Category 3	(−)
Enhanced tourism experience	145	–	28	145	“tourism” AND “experience” AND “unforgettable” OR “good”	0.0690	Category 3	(+)

Note(s)

¹Frequency of occurrence of the axial code in the documents analyzed

²Average of weight percentage (WP) of open codes. The evaluation value is $x_{l}^{k};$ where k is the number of open codes [1–28] and l is the number of occurrences of open codes. To determine weight percentage (⁠ $X^{j}$ ⁠) of a code, the $x_{i}^{j}$ values are divided by the total words of documents analyzed (210.000), multiplied by 100 (eq. 1). $X^{j} = ⌊ \frac{x_{l}^{k}}{\sum m} ⌋ x 100$

³Categorization based on the AWP values

⁴Qualitative classification (Sentiment analysis) of the context of an open code based on the tone of voice

Source(s): Authors’ own editing

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