Determinants of loyalty in destination branding: perspective from supply side

Figures & data

Table 1. Different tourisms associations.

#	Name of associations	Number of members	Percent
01	Tanzania Association of Tour Operator (TATO)	256	46.3
02	Tanzania Society of Travel Agents (TASOTA)	25	4.5
03	Tanzania Air Operator Association (TAOA)	30	5.4
04	Hotel Association of Tanzania (HAT)	67	12.1
05	Intra-African Travel and Tourism Association (IATTA)	7	1.3
06	Tanzania Tours Guides Association (TPHA)	67	12.1
07	Zanzibar Association of Tourism Investors (ZATI)	100	18.1
08	Tanzania Association of Cultural Tourism Organizers (TACTO)	1	0.2
TOTAL		553	100

Sources: official websites of the associations (2016).

Table 2. Proportional sample size from each association.

#	Name of associations	Proportional sample size	Percent
01	Tanzania Association of Tour Operator (TATO)	130	46.4
02	Tanzania Society of Travel Agents (TASOTA)	13	4.6
03	Tanzania Air Operator Association (TAOA)	15	5.4
04	Hotel Association of Tanzania (HAT)	34	12.1
05	Intra-African Travel and Tourism Association (IATTA)	3	1.1
06	Tanzania Tours Guides Association (TPHA)	34	12.1
07	Zanzibar Association of Tourism Investors (ZATI)	51	18.2
08	Tanzania Association of Cultural Tourism Organizers (TACTO)	–	–
TOTAL		280	100

Table 3. Operationalization of variables.

#	Variable categories	Constructed statements for the measurement of variables	References
1	Destination Identification (DI)	This destination I owned it, when someone criticizes it I always feel embarrassed	Zouganeli et al. (2012), Bregoli (2012), Zenker et al. (2017)
2	Local Residents’ Propensity to Invest in tourism (LRPIT)	I hope that my destination can be developed further for tourism	Presenza et al. (2014)
		I am willing to support the development of tourism in the region with a financial contribution to tourism promotion
		I am willing to support further development of the destination personally by investing in tourism activity
	Ability of Involvement by Local Authorities (AILA)	Overall, local authorities should promote and encourage community participation in tourism planning	Presenza et al. (2014)
		Overall I feel involved and listened in the process of tourism programming for the destination
		Local authorities are able to strike a fair balance between protecting the needs and interests of the residents with the need to increase tourist visitation
3	Brand Commitment (BC)	This destination deserves my pledge I have been always bond to it and not others.	Zouganeli et al. (2012), Bregoli (2012), Zenker et al. (2017)
		This destination has been inspiring me to put in a great deal of effort and resource to ensure its success
		This destination has been encouraging me to be ready and happy to put my interests aside for its betterment.

Table 4. Discriminant validity.

	AVE	MSV	MaxR(H)	LRPIT	AILA
LRPIT	0.54	0.003	0.790	0.732
AILA	0.50	0.003	0.750	0.056	0.706

Note: the bolded diagonal parameters are the square root of AVE.

Table 5. Standardized regression weight, cronbach alpha coefficient (α), composite reliability (CR), average variance extracted (AVE), and goodness of it indexes for LRE construct.

Variables	Standardized regression weight	Cronbach alpha coefficient (α)	Composite reliability (CR)	Average variance extracted (AVE)
lrpit1 <--- LRPIT	0.667	0.773	0.775	0.54
lrpit2 <--- LRPIT	0.811
lrpit3 <--- LRPIT	0.712
aila1 <--- AILA	0.714	0.749	0.750	0.50
aila2 <--- AILA	0.719
aila3 <--- AILA	0.687

Model fit Indicators: GFI = 0.983, AGFI = 0.955, NFI = 0.965 IFI = 0.982, TLI = 0.966, CFI = 0.982, x²/df = 2.019, RMSEA = 0.058.

Figure 1. Structural equation model.

Table 6. The goodness of fit index for the structural model.

Categories of model fit	The goodness of fit index	Recommended value	Actual value	Comment
Absolute	GFI	Close to 1	0.929	Good
	AGFI	Close to 1	0.891	Satisfactory
	NFI	Close to 1	0.875	Satisfactory
Relative	IFI	Close to 1	0.913	Good
	TLI	Close to 1	0.887	Satisfactory
	CFI	Close to 1	0.912	Good
Parsimony	x^2/df	1 ≤ x^2/df ≤ 3	2.974	Good
	RMSEA	0 ≤ RMSEA ≤ 0.1	0.099	Good

Table 7. Regression analysis output.

Hypotheses	Variables			Estimate	S.E	C.R	P
H1	DEI	<---	AILA	.469	.074	6.345	***
H2	DEI	<---	LRPIT	.728	.092	7.882	***
H3	BRC	<---	DEI	.829	.091	9.140	***

***implies p-value is < 0.001.

Table 8. SOBEL test output.

Variables	Coeff	s.e	T	Sig(two)
LRE→SDB	.4584	.0504	9.0913	.0000
LRE→DEI	.3915	.0209	8.7371	.0000
DEI→SDB	.9515	.1283	7.4182	.0000
LRE→DEI→LDB	.0859	.0684	1.2566	.0032

.0000 implies p-value is < 0.001.

Zouganeli, S., Trihas, N., Antonaki, M., & Kladou, S. (2012). Aspects of sustainability in the destination branding process: A bottom-up approach. Journal of Hospitality Marketing & Management, 21(7), 739–757. https://doi.org/10.1080/19368623.2012.624299

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Bregoli, I. (2012). Effects of destination managemnet organization (DMO) coordination on destination brand identity: A mixed method study on the city of Edinburg. Journal of Travel Research, 52(2), 212–224. https://doi.org/10.1177/0047287512461566

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Zenker, S., Braun, E., & Petersen, S. (2017). Branding the destination versus the place: The effects of brand complexity and identification for residents and visitors. Tourism Management, 58, 15–27. https://doi.org/10.1016/j.tourman.2016.10.008

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Presenza, A., Micera, R., Splendiani, S., & Chiappa, G. D. &. (2014). Stakeholder e-involvment and participatory tourism planning: Analysis of an Italian case study. International Journal of Knowledge-Based Development, 5(3), 311–328. https://doi.org/10.1504/IJKBD.2014.065320

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