Travel and tourism competitiveness index and the tourism sector development

Abstract

The objective of this study is to test whether Travel and Tourism Competitiveness Index (TTCI) enhances tourism sector development in terms of tourist arrivals, tourism receipts, and the change in both tourist arrivals and tourism receipts. The results found that TTCI main index (Level 1) is positively associated with tourist arrivals but not tourism receipts and the change in tourist arrivals and tourism receipts. Further analysis (Level 2) reveals that while the T&T policy and enabling conditions index is positively associated with tourist arrivals, the infrastructure index is positively associated with tourism receipts. None of the Level 2 indices drives the change in tourist arrivals and tourism receipts. Among results obtained from Level 3 sub-indices, while price competitiveness, air transportation infrastructure, and cultural resources indices have a positive association with tourist arrivals, only ground and port infrastructure have a positive association with tourism receipts. Moreover, while safety and security, human resources and labor market, and air transportation infrastructure sub-indices drive a positive change in tourist arrivals, ICT readiness and natural resources drive a negative change in tourist arrivals and none of the sub-indices drives a change in tourism receipts. The study suggests several practical implications for tourism firms and policymakers.

Keywords

Travel and tourism competitiveness index tourism sector development tourist arrivals tourism receipts ICT

Introduction

Destination management is one of the most popular topics in the tourism sector since it helps managers to design more attractive destinations which impact practices of stakeholders (in)directly in the tourism sector (Constantoglou, 2020; del Mar Gálvez-Rodríguez et al., 2020; Rita and António, 2020). Hence, researchers and managers have elucidated two questions “what is destination competitiveness?” and “how is destination competitiveness measured?” by focusing on destination management theory (Crouch and Ritchie, 1999; Gelter et al., 2021). Destination competitiveness is defined as mainly outperforming competitor destinations by meeting tourists’ wants and needs (Croes, 2011; Crouch and Ritchie, 1999; Dwyer and Kim, 2003). Hence, the efforts for enhancing destination competitiveness inspired many studies in the literature focusing on components of destination competitiveness (Abreu-Novais et al., 2016; Cronjé and Du Plessis, 2020; Kubickova and Martin, 2020).

Destination competitiveness deals with the attractiveness of destinations for tourists. To measure the attractiveness of the destination, researchers have proposed models including data related to arrivals’ perception and numbers, destinations’ resources quality offered and/or prepared to arrivals, and comparison of these data with other destinations’ numbers (Crouch, 2010; Dwyer and Kim, 2003; Gomezelj and Mihalič, 2008; Gu et al., 2019; Kumar and Dhir, 2020; Mendola and Volo, 2017; Neto et al., 2020). One of the globally accepted models was developed by World Economic Forum and called as Travel and Tourism Competitiveness Index (TTCI).

TTCI is the unique and comprehensive index and used to compare countries’ or regions’ competitiveness in the travel and tourism industry (Dwyer et al., 2014; Ivanov and Webster, 2013; Mazanec and Ring, 2011). The TTCI aims to assess the policies and factors which help the tourism and travel sector development across countries (Perles-Ribes, 2014). It has three categories: travel and tourism regularity (Level 1), travel and tourism business environment and infrastructure (Level 2), and travel and tourism human, cultural and natural resources (Level 3) (Wu et al., 2012). This index is used by policymakers and considered by tourism investors; therefore, researchers have investigated its composition, relations in the composition, and its link with other travel and tourism aspects (Gursoy et al., 2009; Kayar and Kozak, 2010; Kendall and Gursoy, 2007; Petrović et al., 2017; Wu et al., 2012; Zhang et al., 2011). However, policymakers and tourism investors have limited insights into how TTCI influences tourism development in the countries and which components of this index are more relevant in promoting tourism development (Andrades and Dimanche, 2017; Kubickova and Martin, 2020). In this respect, the purpose of the study is to assess the relevancy of the Travel and Tourism Competitiveness Index (TTCI) in enhancing tourism sector development. While doing this, we followed the data hierarchy proposed by TTCI and categorized the indices as Level 1, Level 2, and Level 3. This enabled authors to analyze and show the association of every level of TTCI with tourism sector development. Unlike some prior studies which are based on a self-constructed competitiveness index (Assaf and Josiassen, 2012)¹, the study uses standard TTCI established by the WEF (2020) following several prior studies (Das and DiRienzo, 2009; Fernández et al., 2020; Krstic et al., 2016; Webster & Ivanov, 2014). Hence, the objective of the paper is to assess the association of the TTCI with tourism development proxied by international tourist arrivals, international tourism receipts, and the change in tourist arrivals and tourism receipts. To suggest refined guidelines for policymakers, we realized the analyses under the above-highlighted three levels.

The rest of the paper presents a literature review including destination competitiveness, measuring destination competitiveness, and the link between destination competitiveness and tourism development. Second, we explain the research methodology utilized. Then, we outline our results by focusing on each TTCI level. Last, we highlight the conclusions of the study, suggest policymaking implications, set the limitations, and offer prospective research avenues.

Literature review

Destination competitiveness

The meaning of destination competitiveness relies on understanding the meaning of each word in the destination competitiveness from the tourism perspective. At the first glance, a destination is a geographic place where people visit. However, in both epistemology and ontology, it is a very dynamic concept since it holds three interrelated concepts—tourist, place, and organizations to manage this place—in its body. Therefore, since those three concepts are the ingredient of several disciplines such as management, marketing, sociology, geography, politics, and technology, the destination in tourism theory should be elaborated from two perspectives as complementary approaches—business-related practices, and socio-cultural structures (Framke, 2002). As a result, this study considers a tourist destination definition by the UNWTO (2007, p. 1) as follows:

A physical space in which a visitor spends at least one overnight. It includes tourism products such as support services and attractions and tourism resources within one daýs return travel time. It has physical and administrative boundaries defining its management, images, and perceptions defining its market competitiveness. Local tourism destinations incorporate various stakeholders often including a host community and can nest and network to form larger destinations.

As seen in the destination definition, one of the main components is business-related practice and its link with other components. For any business, competition is a central concept that helps an organization do better business practices than others do. Hence, a destination should organize its resources and capabilities better to create more attractiveness for the tourist than other destinations do. This brings how a destination competes with other destinations. To address this issue, the investigation of the link between destination and competition emerges. In this respect, to what extent does a destination manage its business-related practice with the socio-cultural structure to attract more tourists than other destinations generate destination competitiveness as a dynamic and measurable concept. This concept benefits from several disciplines like the economy, politics, technology, sociology, marketing, and strategic management (Cimbaljević et al., 2019; d'Hauteserre, 2000; Knežević Cvelbar et al., 2016; Kubickova and Martin, 2020; Novais et al., 2018). As a result, destination competitiveness makes its own stakeholders, including tourists, destination management organizations, investors, workers, researchers, and other business-related organizations or agencies. The relation between the mentioned stakeholders and destination competitiveness has produced significant literature (Abreu-Novais et al., 2016; Cronjé and Du Plessis, 2020; Segui-Amortegui et al., 2019; Teixeira and Ferreira, 2018) for public administrators and practitioners to create a more attractive tourist destination. Therefore, how we can measure destination competitiveness has remained a main question in the academy and business environment for researchers and practitioners respectively.

Measuring destination competitiveness

Measurement of destination competitiveness deals with what strengths and weaknesses the destination has, and how the destination captures opportunities to improve the attractiveness of a destination, and what shields the destination can build to eliminate or minimize the threat influencing the attractiveness of the destination. In this respect, one group of studies highlights drivers or attributes of destination competitiveness. For example, Dwyer and Kim (see 2003, p. 400) emphasized a set of indicators for destination competitiveness by highlighting five subcategories—endowed resources, supportive factors, destination management, situational conditions, demand factors, and market performance indicators. Crouch (2011) demonstrated the importance of determinant five attributes—core resources and attractors, destination management, qualifying and amplifying determinants, destination policy, planning and development, and supporting factors and resources by utilizing the analytic hierarchy process method. In addition to these, plenty of study tests destination competitiveness in different country concepts (e.g., Albayrak et al., 2018; Michael et al., 2019; Natalia et al., 2019).

The second group of studies elaborates destination competitiveness by specifying one or two factors. Goffi, Cucculelli, and Masiero (2019) evaluate Brazil’s competitiveness via sustainability factors in destination competitiveness by employing the regression model. They showed that sustainability is an important factor in destination competitiveness. Kubickova and Martin (2020) test the link between tourism area lifecycle, governance, and destination competitiveness. Natalia et al. (2019) address destination competitiveness for accessible tourism, which is one of the tourism subfields.

The last group of studies uses TTCI to assess destination competitiveness. It has been employed for evaluating different regional destinations’ competitiveness, including Mediterranean destinations (Kendall and Gursoy, 2007), Middle Eastern destinations (Gursoy et al., 2009), European Union countries (Kayar and Kozak, 2010), and the Asia Pacific destinations (Leung and Baloglu, 2013). Rodríguez-Díaz and Pulido-Fernández (2020) utilized a global analysis to identify sustainability’s association with the destination competitiveness by considering TTCI.

Destination competitiveness and tourism development

The studies highlighted in the preceding section help tourism managers and policymakers to improve destination competitiveness from a comprehensive perspective. However, practitioners and researchers have limited insights into how destination competitiveness enhances tourism development. Andrades and Dimanche (2017) focused on the relationship between destination competitiveness and tourism development in Russia. For this purpose, they used TTCI and Crouch and Ritchie’s tourism destination competitiveness model (see Crouch and Ritchie, 1999). They showed that tourism development in Russia is limited due to the destination image, infrastructure development, workforce training and education, quality management, and sustainable management.

As highlighted above literature review, most prior studies focused on measuring destination competitiveness rather than relating it to tourism development except the Russian study (Andrades and Dimanche, 2017). Therefore, our study aims to investigate the link between TTCI and tourism development from a global perspective. Moreover, we investigate the link between TTCI and tourism development by considering the hierarchy among TTCI sub-components and individual sub-indices. First, we measure overall TTCI’s association with tourism development, and then we are deepening investigation downward among indices and sub-indices to prescribe synthesized implications to tourism practitioners and policymakers. Hence, our study seeks an answer to the following research question (RQ):

RQ: Is TTCI relevant in predicting tourism development (Level 1 investigation) across the world? If yes, which of four indices (Level 2) and fourteen sub-indices (Level 3) are relevant in fostering tourism development?

Research methodology

Multiple appropriate univariate and multivariate methods are incorporated in this study. Initially, the data preprocessing step is performed. The summary of the included variables and the bivariate correlation among them are reported using descriptive statistics and Pearson’s correlation coefficients. In the baseline analysis, the formulation of the research models is summarized based on per level (Level 1–3) of the included indices as the independent variables. In the baseline research analysis methodology, the panel data analysis with the fixed-effects (FE) method is used to answer the research question.

Sample

The data screening phase is performed before further analysis. Initially, the descriptive statistical analysis showed that the included variables did not show any heavy skewness with the possible extreme values, thus, the variables were not subject to any winsorization process. Furthermore, the outlier detection process is performed using the multivariate outlier detection methodology with the minimum covariance determinant (MCD) estimator which can robustify the Mahalanobis distance (Verardi and Dehon, 2010). The MCD analysis results indicate no multivariate outliers; thus, no observation is eliminated from the sample.

The initial sample of the research is based on the country-year level where there are 142 countries listed for 2015, 2017, and 2019 in each year. In the selection of these periods, the availability of TTCI data issued by WEF (2020) played a role. The total sample size is 397 country-year records. In the sample, IntTA has 1 (0.25%) missing observation which is significantly less than or near 5% which is considered to be inconsequential (Schafer, 1999) while there is no missing observation among the rest of the research variables. However, IntTACH and IntTRCH have 141 country-year missing observations due to missing values in the calculating rate of change. In addition, less than 10% of missing values do not yield biased statistical analysis (Bennett, 2001). Thus, the raw observation without any imputation is subject to further analysis. Hence, the final sample consists of 397 country-year observations out of which 134 (33.75%) observations exist in 2015, 130 (32.75%) in 2017, and 133 (33.50%) in 2019. Table A1 in Appendix shows the list of countries included in the sample along with the mean values of Levels 1 and 2 TTCI indices.

Table A1.

Mean value of the dependent and level 1, and Level 2 indices for countries.

	Country	IntTA (in thousands)	IntTR (in millions)	T&T_CompMainIndex_L1	EnablingEnvIndex_L2	T&TPolCondIndex_L2	InfrustructureIndex_L2	-2
1	AGO	455.50	1057.20	2.67	3.34	3.43	2.15	1.74
2	ALB	3761.33	1634.29	3.39	4.82	3.93	3.03	1.76
3	ARE	13,326.56	16,216.93	4.45	5.78	4.36	5.37	2.30
4	ARG	6005.92	4695.95	4.03	4.68	3.79	3.33	4.33
5	ARM	1256.96	838.04	3.55	5.14	4.08	3.11	1.87
6	AUS	7547.33	33,952.49	5.07	5.63	4.47	5.11	5.08
7	AUT	26,997.47	19,594.70	4.88	5.86	4.72	5.35	3.58
8	AZE	2168.48	2562.10	3.66	5.29	4.24	3.16	1.95
9	BDI	190.67	2.21	2.64	3.52	3.47	2.00	1.57
10	BEL	8118.29	12,620.26	4.53	5.64	4.36	4.99	3.14
11	BEN	268.00	138.67	2.93	3.95	3.65	2.16	1.97
12	BFA	180.50	127.48	2.72	3.46	3.59	2.02	1.82
13	BGD	132.67	204.40	2.96	3.94	3.50	2.43	1.97
14	BGR	7626.50	3750.22	4.13	5.19	4.61	3.88	2.85
15	BHR	2213.67	1963.38	3.88	5.39	4.11	4.51	1.52
16	BIH	800.35	743.44	3.20	4.61	3.81	2.74	1.64
17	BOL	938.00	682.46	3.37	4.07	3.93	2.56	2.94
18	BRA	6235.98	6119.07	4.44	4.48	3.97	3.58	5.73
19	BRB	548.16	960.40	3.99	5.21	4.17	4.91	1.68
20	BRN	258.96	177.37	3.78	5.26	4.45	3.70	1.72
21	BTN	135.56	67.28	3.52	4.66	4.36	2.78	2.30
22	BWA	1749.03	565.61	3.47	4.39	4.23	2.86	2.42
23	CAN	18,453.68	18,180.71	4.98	5.60	4.37	5.57	4.38
24	CHE	9801.69	16,505.33	4.98	6.16	4.64	5.75	3.39
25	CHL	4834.81	2753.89	4.07	5.13	4.68	3.63	2.83
26	CHN	57,770.53	66,130.25	4.71	4.99	4.08	3.83	5.97
27	CIV	1176.67	234.88	3.11	3.83	3.57	2.72	2.32
28	CMR	845.33	441.14	2.91	3.71	3.48	2.19	2.25
29	COD	271.00	3.05	2.66	3.24	2.91	1.81	2.67
30	COL	3097.71	4225.80	3.85	4.19	4.43	2.99	3.80
31	CPV	563.43	414.77	3.52	4.52	4.32	3.74	1.51
32	CRI	2682.69	3217.80	4.20	4.97	4.67	3.74	3.41
33	CYP	2905.62	2844.30	4.16	5.32	4.51	4.59	2.23
34	CZE	10,986.75	6676.85	4.26	5.49	4.60	4.48	2.45
35	DEU	34,655.92	39,300.58	5.30	5.87	4.60	5.53	5.20
36	DNK	10,241.33	7194.29	4.46	5.88	4.45	4.71	2.81
37	DOM	5492.39	6119.40	3.63	4.31	4.33	3.70	2.19
38	DZA	2297.91	221.73	3.05	4.40	3.47	2.24	2.08
39	ECU	1576.47	1604.13	3.89	4.51	4.50	3.33	3.21
40	EGY	8823.08	6629.00	3.68	4.31	4.43	3.15	2.81
41	ESP	70,350.28	62,382.53	5.39	5.41	4.77	5.64	5.75
42	EST	3035.52	1485.56	4.21	5.74	4.80	4.28	2.04
43	ETH	826.00	483.17	3.05	3.79	3.86	2.25	2.30
44	FIN	2866.51	3333.10	4.46	6.07	4.62	4.63	2.53
45	FRA	85,365.20	54,428.88	5.32	5.51	4.54	5.47	5.77
46	GAB	187.33	9.00	2.99	4.13	3.59	2.28	1.98
47	GBR	34,418.77	45,180.12	5.17	5.74	4.26	5.46	5.22
48	GEO	2805.54	2119.99	3.75	5.28	4.43	3.40	1.90
49	GHA	933.36	860.78	3.07	4.24	3.60	2.41	2.01
50	GIN	58.00	8.90	2.75	3.46	3.43	2.02	2.08
51	GMB	156.07	103.77	3.18	4.14	4.21	2.65	1.72
52	GRC	22,904.41	16,113.27	4.47	5.17	4.62	4.71	3.38
53	GTM	1484.86	1542.03	3.47	4.13	4.43	2.80	2.52
54	HKG	26,743.88	36,129.15	4.79	6.14	4.49	5.39	3.12
55	HND	864.78	657.80	3.45	3.93	4.62	2.94	2.31
56	HRV	13,077.08	9774.55	4.41	5.11	4.53	4.53	3.49
57	HTI	443.40	513.73	2.75	3.41	4.03	2.20	1.38
58	HUN	13,592.00	5595.26	4.13	5.27	4.74	4.04	2.48
59	IDN	10,718.96	10,803.66	4.16	4.59	4.78	3.40	3.86
60	IND	10,179.20	22,258.27	4.21	4.08	4.18	3.71	4.87
61	IRL	9375.33	4961.87	4.53	5.63	4.74	4.95	2.81
62	IRN	4957.67	2830.00	3.43	4.39	4.05	2.70	2.58
63	ISL	1440.19	1901.72	4.51	5.92	4.71	4.97	2.45
64	ISR	3124.79	5950.47	3.83	5.32	3.65	4.17	2.18
65	ITA	52,229.57	42,531.47	5.02	5.10	4.27	5.08	5.63
66	JAM	2161.46	2428.03	3.69	4.43	4.36	3.90	2.05
67	JOR	3849.99	4273.52	3.60	5.02	4.39	3.27	1.73
68	JPN	19,597.50	24,722.48	5.19	5.84	4.63	5.08	5.22
69	KAZ	4681.67	1552.72	3.58	5.31	4.03	2.96	2.02
70	KEN	1303.79	840.88	3.60	3.89	4.41	3.00	3.10
71	KGZ	3565.00	461.27	3.14	4.62	3.88	2.13	1.93
72	KHM	4862.52	3141.93	3.32	4.12	4.28	2.58	2.30
73	KOR	12,914.32	14,423.50	4.57	5.57	4.39	4.73	3.60
74	KWT	230.67	369.79	3.34	5.10	3.44	3.28	1.52
75	LAO	2510.03	595.91	3.33	4.39	4.07	2.84	2.01
76	LBN	1549.70	6779.24	3.37	4.36	4.13	3.29	1.68
77	LBR		45.58	2.61	3.62	3.44	1.93	1.45
78	LKA	1729.79	2873.53	3.78	4.68	4.32	3.39	2.73
79	LSO	320.00	34.42	2.89	3.77	4.12	2.12	1.55
80	LTU	2202.17	1314.06	3.92	5.53	4.53	3.73	1.90
81	LUX	1026.60	4563.77	4.48	5.97	4.78	4.90	2.25
82	LVA	1836.33	902.52	4.01	5.45	4.63	4.04	1.91
83	MAR	10,524.04	6714.96	3.84	4.73	4.32	3.48	2.83
84	MDA	111.73	249.65	3.18	4.86	3.90	2.54	1.41
85	MDG	220.35	447.50	2.99	3.58	3.97	2.28	2.12
86	MEX	31,844.93	17,672.97	4.53	4.46	4.38	3.90	5.37
87	MKD	505.27	286.80	3.45	5.02	3.86	3.17	1.75
88	MLI	164.67	207.49	2.82	3.30	3.73	2.23	2.01
89	MLT	1879.79	1499.70	4.26	5.48	4.73	4.72	2.09
90	MMR	2044.00	281.00	2.72	3.42	3.31	2.07	2.06
91	MNE	1587.18	942.43	3.78	5.06	4.15	4.00	1.90
92	MNG	424.44	276.83	3.36	4.86	3.86	2.44	2.30
93	MOZ	1628.47	194.77	2.88	3.25	3.99	2.26	2.01
94	MRT	30.00	26.48	2.65	3.36	3.71	1.93	1.62
95	MUS	1162.07	1500.14	3.94	5.18	4.53	4.27	1.79
96	MYS	25,795.06	19,138.83	4.47	5.35	4.64	4.50	3.40
97	NAM	1354.33	324.74	3.65	4.31	4.23	3.61	2.45
98	NGA	1248.00	1165.46	2.81	3.29	3.53	2.27	2.16
99	NIC	1467.72	595.43	3.44	4.19	4.53	2.76	2.27
100	NLD	15,237.67	14,501.23	4.70	5.87	4.55	5.32	3.07
101	NOR	5448.93	5342.24	4.58	5.93	4.63	4.79	2.98
102	NPL	758.98	515.51	3.30	4.14	4.15	2.23	2.68
103	NZL	3074.33	9038.46	4.69	5.80	4.89	4.81	3.25
104	OMN	1940.00	1503.25	3.85	5.29	4.11	3.85	2.15
105	PAK	965.76	318.86	2.97	3.55	3.64	2.62	2.07
106	PAN	1870.29	3934.99	4.28	4.77	4.82	4.54	2.99
107	PER	3550.56	3346.24	4.03	4.43	4.36	3.34	3.99
108	PHL	5554.30	5651.04	3.66	4.34	4.46	3.09	2.77
109	POL	16,928.67	11,146.00	4.14	5.26	4.52	3.79	2.99
110	PRI	3199.70	3333.50	3.91	4.73	4.06	4.64	2.22
111	PRT	13,213.79	14,036.05	4.76	5.49	4.70	4.96	3.88
112	PRY	1128.06	452.96	3.16	4.31	4.05	2.42	1.86
113	QAT	2599.04	4820.88	4.10	5.76	4.26	4.67	1.71
114	ROU	2236.38	1942.94	3.85	5.07	4.36	3.37	2.59
115	RUS	28,030.97	9799.35	4.18	5.04	4.04	4.03	3.63
116	RWA	927.67	349.84	3.31	4.44	4.21	2.62	1.97
117	SAU	15,827.85	9945.76	3.83	5.29	3.84	4.03	2.16
118	SEN	1144.87	419.78	3.18	4.00	3.73	2.72	2.28
119	SGP	12,619.31	18,502.35	4.82	5.92	4.98	5.66	2.73
120	SLE	52.92	41.06	2.75	3.63	3.60	2.02	1.75
121	SLV	1413.49	770.34	3.30	4.01	4.48	2.92	1.81
122	SRB	1183.72	1148.95	3.45	4.95	3.91	3.13	1.82
123	SUR	249.10	84.15	3.28	4.42	3.67	3.01	2.01
124	SVK	6890.33	2614.12	3.90	5.21	4.42	3.53	2.45
125	SVN	2850.46	2654.31	4.23	5.32	4.61	4.29	2.72
126	SWE	9673.86	12,318.66	4.52	5.84	4.45	4.75	3.05
127	SWZ	944.50	21.55	3.16	3.81	4.29	2.95	1.60
128	SYC	290.07	413.15	3.96	4.93	4.26	4.84	1.83
129	TCD	102.33	25.22	2.49	2.90	3.42	1.83	1.83
130	THA	30,650.82	48,036.62	4.38	4.74	4.48	4.55	3.75
131	TJK	422.38	4.31	3.23	4.63	3.94	2.31	2.06
132	TUN	6226.57	1625.24	3.54	4.58	4.40	3.18	2.01
133	TUR	38,291.96	25,697.00	4.15	4.55	4.24	4.44	3.38
134	TZA	1147.33	2150.07	3.41	3.67	4.37	2.45	3.15
135	UGA	1303.91	1089.96	3.17	3.66	4.12	2.30	2.59
136	UKR	13,328.96	1171.50	3.61	4.77	4.26	3.26	2.14
137	URY	3043.67	2084.80	3.68	5.19	4.21	3.17	2.14
138	USA	74,740.03	196,133.30	5.16	5.61	4.42	5.71	4.92
139	VNM	9479.37	7914.25	3.77	4.74	4.01	2.96	3.36
140	ZAF	9575.18	8763.31	4.02	4.34	4.07	3.81	3.86
141	ZMB	976.53	489.23	3.19	3.94	3.99	2.35	2.46
142	ZWE	1944.58	868.50	3.10	3.58	3.90	2.41	2.51

Variables and data

Ianioglo and Rissanen (2020) classify the factors that influence tourism development into two as demand and supply factors of which the demand factors are concerning the tourists and the supply factors are concerning goods and services, human resources, institutional, regulatory, and regional factors, and market infrastructure. Hence, our study assesses the association between the supply side (i.e., proxied by the TTCI index) and the demand side proxied by the tourist arrivals and tourism receipts.

The dependent variables, namely international tourist arrivals (IntTA) and international tourism receipts in US$ (IntTR) were adopted from prior studies for measuring tourism sector development in terms of both volume and value (respectively) (Bazargani and Kiliç, 2021; Joshi et al., 2017; Ozturk and Van Niekerk, 2014; Uyar et al., 2021). However, these two indicators were log-transformed (i.e., natural logarithm) (LN_IntTA and LN_IntTR) to reduce skewness and enhance normality (Joshi et al., 2017). Furthermore, we also measured the change in these dependent variables namely the change in tourist arrivals and tourism receipts (IntTACH and IntTRCH respectively) to assess whether TTCI drives a change in IntTA and IntTR (Ozturk and Van Niekerk, 2014). The change models are incorporated to address the endogeneity concerns such as omitted variables bias and strengthen the causality between TTCI and tourism development (Du et al., 2016; Du et al., 2020). The test variables (TTCI) are leveled at three categories adopting the categorization of the WEF (2020); accordingly, we leveled indices as Level 1, Level 2, and Level 3. In Level 1, we took the overall composite Travel and Tourism Competitiveness Index (T&T_CompMainIndex_L1) and tested its association with tourism sector development. Subsequently, in Level 2, we took four indices, namely Enabling environment index (EnablingEnvIndex_L2), T&T policy and enabling conditions index (T&TPolCondIndex_L2), Infrastructure index (InfrustructureIndex_L2), and Natural and cultural resources index (NatCultResIndex_L2) and investigated their association with the tourism sector development.

Finally, in Level 3, we took the following 14 sub-indices; Business environment subindex (BusinessEnvironment_L3), Safety and security subindex (SafetySecurity_L3), Health and hygiene subindex (HealthHygiene_L3), Human resources and labor market subindex (HumanResLabMarket_L3), ICT² readiness subindex (ICTreadiness_L3), Prioritization of Travel and Tourism subindex (PrioritizTravelTourism_L3), International Openness subindex (InternationalOpenness_L3), Price competitiveness subindex (PriceCompetitiveness_L3), Environmental sustainability subindex (EnvirSustainability_L3), Air transport infrastructure subindex (AirTranspInfrastructure_L3), Ground and port infrastructure subindex (GroundPortInfrastructure_L3), Tourist service infrastructure subindex (TouristServInfrastructure_L3), Natural resources subindex (NaturalResources_L3), and Cultural resources and business travel subindex (CulturalResBusinessTrav_L3). All indices in Levels 1, 2, and 3 are scaled from 1 (worst) to 7 (best). Please see Figure 1 for the hierarchy of indices based on Levels 1, 2, and 3.

Figure 1.

The hierarchy of indices concerning levels 1, 2, and 3 is drawn by the authors based on the data retrieved from the WEF (2020).

Out of TTCI, in line with prior studies (Bazargani and Kiliç, 2021; Detotto et al., 2021; Webster & Ivanov, 2014), we incorporated several control variables into the study such as the natural logarithm of gross domestic product per capita (GDP), trade (as % of GDP), the natural logarithm of population, World Governance Indicators (WGIs)³, and geographic regions (Region). The data for the control variables were retrieved from the World Bank (2021a and 2021b)⁴.

Empirical methodology

The empirical methodology section includes the formulation of the proposed models. For the investigation of the proposed models, panel data analysis is used due to the time-variant association characteristic of the independent variables and the dependent variables as well as the structure of the sample being in the country-year panel data format of the sample. According to Baltagi (2001), it reduces the risk of multicollinearity and estimation bias. To decide the panel data analysis with the correct estimator, various post estimations were used initially. First, the results of the F-test show that panel data analysis with fixed-effects (FE) is the most appropriate approach compared to the ordinary linear regression analysis. Second, the results of the Breusch–Pagan Lagrange Multiplier (LM) test reveal that panel data analysis with random-effects (RE) is the most appropriate compared to the ordinary linear regression analysis. Finally, the results of Hausman’s test (Hausman, 1978) show that panel data analysis with FE panel regression is the most appropriate model compared to the RE regression analysis. As a result of these post estimation analyses, the panel data analysis with FE panel regression analysis is chosen as the most appropriate multivariate analysis approach to test the proposed hypothesis.

The formulation of the proposed models is described in equation (1). The proposed models incorporate three levels

y_{i t} = α + β_{1} X_{1 i t} + β_{2} X_{2 i t} + ϑ_{i} + ε_{i t}

(1)

The term “ $y_{i t}$ ” represents the dependent variables: LN_IntTA, LN_IntTR, IntTACH, and IntTRCH for Level 1, 2, and 3 analyses. Besides, the term “ $X_{1 i t}$ ” represents the independent testing variables. The list of the independent testing variables per level is listed below.

Level 1: T&T_CompMainIndex_L1.

Level 2: EnablingEnvIndex_L2, T&TPolCondIndex_L2, InfrustructureIndex_L2, and NatCultResIndex_L2.

Level 3

I. BusinessEnvironment_L3, SafetySecurity_L3, HealthHygiene_L3, HumanResLabMarket_L3, ICTreadiness_L3,

II. PrioritizTravelTourism_L3, InternationalOpenness_L3, PriceCompetitiveness_L3, EnvirSustainability_L3,

III. AirTranspInfrastructure_L3, GroundPortInfrastructure_L3, TouristServInfrastructure_L3

IV. NaturalResources_L3, and CulturalResBusinessTrav_L3.

Furthermore, the term “ $X_{2 i t}$ ” represents the independent control variables which include GDP, Trade, Population, WGI, and Region. In the research models, Region is incorporated as the categorical control variable including East Asia and Pacific (45), Europe and Central Asia (132), Latin America and Caribbean (59), Middle East and North Africa (45), North America (6), South Asia (17), and Sub-Saharan Africa (93) regions with the indicated country-year observations in the parenthesis.

In the functional relationship, the index “i” shows the Country as the panel variable and the index “t” represents the years as the time variable while the term “ $ϑ_{i} + ε_{i t}$ ” indicates the error term where “ $ϑ_{i}$ ” is the country-specific error term while “ $ε_{i t}$ ” is the regular error term.

In the further analysis, the heteroscedasticity-consistent standard errors using Huber–White standard errors (Huber, 1967; White, 1980) are reported to alleviate any possible risk of heteroskedasticity within-panel serial correlation in the idiosyncratic error term (” $ε_{i t}$ “) (Wooldridge, 2013).

According to Wooldridge (2010), the fixed-effects panel data regression analysis alleviates the possible risk of omitted variable bias by estimating the amount of changes within countries as the panel variable across years as the time variable. Therefore, any possible risk of omitted variable bias is addressed by incorporating panel data analysis with the fixed-effects estimator.

Findings

Descriptive statistics

The summary of the descriptive statistics of the included independent variables per level and the dependent variables is provided in Table 1. The results reveal that the mean is IntTA is approximately 8515 (in Thousands), IntTR is 8870 (in Millions), IntTACH is 0.63, and IntTRCH is 0.54. Moreover, the mean value of T&T_CompMainIndex_L1 is 3.82 with a relatively small variability ranging from 2.43 to 5.44. Regarding the level 2 independent testing variables, the average of EnablingEnvIndex_L2 is 4.76, T&TPolCondIndex_L2 is 4.22, InfrustructureIndex_L2 is 3.59, and NatCultResIndex_L2 is 2.72 with relatively small variabilities around the mean values. Finally, there are 14 independent testing variables in Level 3. The mean values range between 2.27 (CulturalResBusinessTrav_L3) and 5.28 (SafetySecurity_L3) with considerable small variations around the mean values. The descriptive statistics concerning control variables are also reported in Table 1.

Table 1.

Descriptive statistics.

Dependent variables, level 1, level 2, level 3 indices, and control variables
Variable	Obs	Mean	Std. Dev	Min	Max
IntTA	396	8,515,440	14,700,000	23,798	86,900,000
IntTR (in US$)	397	8,870,000,000	21,000,000,000	98,224	211,000,000,000
IntTACH	256	0.63	7.33	−0.71	117.24
IntTRCH	256	0.54	4.05	−0.71	60.19
T&T_CompMainIndex_L1	397	3.82	0.69	2.43	5.44
EnablingEnvIndex_L2	397	4.76	0.79	2.74	6.22
T&TPolCondIndex_L2	397	4.22	0.42	2.80	5.15
InfrustructureIndex_L2	397	3.59	1.09	1.75	5.79
NatCultResIndex_L2	397	2.72	1.08	1.30	6.10
BusinessEnvironment_L3	397	4.51	0.67	2.67	6.16
SafetySecurity_L3	397	5.28	0.83	2.59	6.70
HealthHygiene_L3	397	5.10	1.25	1.72	6.99
HumanResLabMarket_L3	397	4.53	0.63	2.30	5.85
ICTreadiness_L3	397	4.39	1.24	1.31	6.62
PrioritizTravelTourism_L3	397	4.56	0.82	1.88	6.18
InternationalOpenness_L3	397	3.22	0.91	1.29	5.53
PriceCompetitiveness_L3	397	4.91	0.75	2.57	6.73
EnvirSustainability_L3	397	4.21	0.57	2.82	5.98
AirTranspInfrastructure_L3	397	3.07	1.20	1.30	6.76
GroundPortInfrastructure_L3	397	3.57	1.11	1.78	6.45
TouristServInfrastructure_L3	397	4.13	1.31	1.65	6.83
NaturalResources_L3	397	3.17	0.98	1.46	6.13
CulturalResBusinessTrav_L3	397	2.27	1.41	1.01	6.96
GDP (natural logarithm)	397	8.86	1.42	5.57	11.65
Trade	397	90.33	58.94	25.85	376.83
Population (natural logarithm)	397	16.33	1.64	11.44	21.06
WGI	397	0.13	0.85	−1.66	1.86
Region	Freq	Percent
East Asia and Pacific	45	11.34
Europe and Central Asia	132	33.25
Latin America and Caribbean	59	14.86
The Middle East and North Africa	45	11.34
North America	6	1.51
South Asia	17	4.28
Sub-Saharan Africa	93	23.43
Total	397	100

Correlation analysis

To investigate the bivariate association between each pair of the included variables, Pearson’s correlation coefficients are provided in Table 2 where the correlation coefficients of the dependent variables, Level 1, and Level 2 indices are presented. The results show that T&T_CompMainIndex_L1 (Level 1), EnablingEnvIndex_L2 (Level 2), T&TPolCondIndex_L2 (Level 2), InfrustructureIndex_L2 (Level 2), and NatCultResIndex_L2 (Level 2) have a significant positive linear correlation with LN_IntTA and LN_IntTR (p < 0.05). Also, T&T_CompMainIndex_L1 (Level 1), EnablingEnvIndex_L2 (Level 2), T&TPolCondIndex_L2 (Level 2), and InfrustructureIndex_L2 (Level 2) have a significant negative linear correlation with IntTRCH (p < 0.05). However, the testing variables from Level 1 and Level 2 do not have a significant correlation with IntTACH.

Table 2.

Pearson’s correlation coefficients between dependent variables, level 1, level 2 indices, and control variables.

	Variables	1	2	3	4	5	6	7	8	9	10	11	12	13
1	LN_IntTA	1
2	LN_IntTR	0.879^*	1
3	IntTACH	−0.063	−0.208^*	1
4	IntTRCH	−0.099	−0.176^*	−0.01	1
5	T&T_CompMainIndex_L1	0.799^*	0.835^*	−0.057	−0.127^*	1
6	EnablingEnvIndex_L2	0.599^*	0.652^*	−0.015	−0.137^*	0.843^*	1
7	T&TPolCondIndex_L2	0.547^*	0.596^*	−0.067	−0.195^*	0.722^*	0.648^*	1
8	InfrustructureIndex_L2	0.697^*	0.764^*	−0.067	−0.123^*	0.937^*	0.654^*	0.655^*	1
9	NatCultResIndex_L2	0.699^*	0.666^*	−0.04	−0.026	0.726^*	0.315^*	0.324^*	0.516^*	1
10	GDP	0.594^*	0.686^*	−0.09	−0.125^*	0.847^*	0.894^*	0.574^*	0.896^*	0.393^*	1
11	Trade	0.108^*	0.129^*	−0.04	−0.052	0.255^*	0.441^*	0.345^*	0.373^*	−0.181^*	0.374^*	1
12	Population	0.411^*	0.332^*	−0.013	0.06	0.095	−0.242^*	−0.170^*	−0.096	0.597^*	−0.208^*	−0.462^*	1
13	WGI	0.497^*	0.583^*	−0.088	−0.125^*	0.813^*	0.863^*	0.652^*	0.846^*	0.345^*	0.841^*	0.415^*	−0.278^*	1

*p < 0.05.

Furthermore, the results in Table 3 include the correlation coefficients between the dependent variables and the independent testing variables of Level 3. The results show that the 13 testing variables have a significant positive correlation with LN_IntTA and LN_IntTR while only PriceCompetitiveness_L3 has a significant negative correlation with them. Moreover, while HealthHygiene_L3, ICTreadiness_L3, PrioritizTravelTourism_L3, InternationalOpenness_L3, and TouristServInfrastructure_L3 have a significant negative correlation with IntTRCH, none of the testing variables from Level 3 has a significant correlation with IntTACH.

Table 3.

Pearson’s correlation coefficients between dependent variables, Level 3 (Standard) Indices, and control variables.

	Variable	1	2	3	4	5	6	7	8	9	10	11
1	LN_IntTA	1
2	LN_IntTR	0.879^*	1
3	IntTACH	−0.063	−0.208*	1
4	IntTRCH	−0.099	−0.176^*	−0.01	1
5	BusinessEnvironment_L3	0.407^*	0.456^*	−0.019	−0.061	1
6	SafetySecurity_L3	0.256^*	0.272^*	0.033	−0.066	0.569^*	1
7	HealthHygiene_L3	0.552^*	0.595^*	0.026	−0.163^*	0.391^*	0.460^*	1
8	HumanResLabMarket_L3	0.619^*	0.639^*	0.036	−0.076	0.728^*	0.592^*	0.700^*	1
9	ICTreadiness_L3	0.637*	0.714*	−0.107	−0.158^*	0.678^*	0.599^*	0.812^*	0.829^*	1
10	PrioritizTravelTourism_L3	0.518^*	0.613^*	−0.037	−0.208^*	0.527^*	0.397^*	0.484^*	0.616^*	0.581^*	1
11	InternationalOpenness_L3	0.534^*	0.595^*	−0.046	−0.161^*	0.400^*	0.325^*	0.502^*	0.600^*	0.596^*	0.591^*	1
12	PriceCompetitiveness_L3	−0.125*	−0.193^*	−0.039	0.014	−0.234^*	−0.233^*	−0.213^*	−0.329^*	−0.271^*	−0.267^*	−0.297^*
13	EnvirSustainability_L3	0.205^*	0.208^*	−0.025	−0.035	0.434^*	0.524^*	0.323^*	0.457^*	0.514^*	0.320^*	0.424^*
14	AirTranspInfrastructure_L3	0.688*	0.740^*	−0.045	−0.099	0.658^{^*}	0.438^*	0.572^*	0.729^*	0.760^*	0.580^*	0.564^*
15	GroundPortInfrastructure_L3	0.560^*	0.622^*	−0.047	−0.106	0.704^*	0.540^*	0.618^*	0.729^*	0.765^*	0.567^*	0.581^*
16	TouristServInfrastructure_L3	0.637^*	0.707^*	−0.089	−0.129^*	0.525^*	0.463^*	0.716^*	0.704^*	0.811^*	0.687^*	0.632^*
17	NaturalResources_L3	0.497^*	0.476^*	−0.028	0.049	0.039	−0.015	0.077	0.265^*	0.187^*	0.284^*	0.385^*
18	CulturalResBusinessTrav_L3	0.726^*	0.690^*	−0.042	−0.073	0.194^*	0.121^*	0.405^*	0.419^*	0.443^*	0.279^*	0.416^*
19	GDP	0.594^*	0.686^*	−0.09	−0.125^*	0.649^*	0.545^*	0.786^*	0.769^*	0.927^*	0.557^*	0.574^*
20	Trade	0.108^*	0.129^*	−0.04	−0.052	0.409^*	0.415^*	0.317^*	0.351^*	0.401^*	0.281^*	0.286^*
21	Population	0.411^*	0.332^*	−0.013	0.06	−0.186^*	−0.380^*	−0.167^*	−0.109^*	−0.183^*	−0.204^*	−0.034
22	WGI	0.497*	0.583*	−0.088	−0.125^*	0.743^*	0.675^*	0.626^*	0.797^*	0.838^*	0.614^*	0.669^*
	Variable	12	13	14	15	16	17	18	19	20	21	22
12	PriceCompetitiveness_L3	1
13	EnvirSustainability_L3	−0.398^*	1
14	AirTranspInfrastructure_L3	−0.336^*	0.376^*	1
15	GroundPortInfrastructure_L3	−0.407^*	0.489^*	0.733^*	1
16	TouristServInfrastructure_L3	−0.422*	0.494^*	0.743^*	0.706^*	1
17	NaturalResources_L3	−0.188^*	0.118^*	0.421^*	0.117^*	0.317^*	1
18	CulturalResBusinessTrav_L3	−0.236^*	0.110^*	0.640^*	0.451^*	0.456^*	0.622^*	1
19	GDP	−0.375^*	0.491^*	0.792^*	0.783^*	0.850^*	0.213^*	0.456^*	1
20	Trade	−0.098	0.296^*	0.235^*	0.488^*	0.303^*	−0.218^*	−0.126^*	0.374^*	1
21	Population	0.133^*	−0.335^*	0.096	−0.135^*	−0.214^*	0.475^*	0.585^*	−0.208^*	−0.462^*	1
22	WGI	−0.504^*	0.661^*	0.726^*	0.792^*	0.780^*	0.216^*	0.379^*	0.841^*	0.415^*	−0.278^*	1

^*p < 0.05.

The risk of multicollinearity is addressed using the Variance Inflation Factors (VIFs) approach. The results of VIFs indicate that there is no risk of multicollinearity since the VIFs of the independent variables are significantly less than the cut-off value of 10 (Hair, et al., 2010).

Empirical findings

Level 1: Based on the post estimation tests, FE panel regression analysis is employed (Table 4) to test the association between T&T_CompMainIndex_L1 and four tourism development proxies. Accordingly, the results indicate that T&T_CompMainIndex_L1 has a significant positive association with LN_IntTA (p < 0.01). However, the coefficient of T&T_CompMainIndex_L1 is insignificant with respect to LN_IntTR, IntTACH, and IntTRCH. Hence, the composite TTCI is positively associated with tourist arrivals but not tourism receipts and the change in tourist arrivals and tourism receipts.

Table 4.

Fixed-effects linear panel data analysis (level 1: Main index).

	(1)	(2)	(3)	(4)
Independent variables	LN_IntTA	LN_IntTR	IntTACH	IntTRCH
T&T_CompMainIndex_L1	0.55^***	0.42	−3.39	1.22
	(3.51)	(1.58)	(-0.34)	(0.62)
GDP	0.68^***	0.66^***	2.59	1.89
	(5.14)	(2.91)	(0.25)	(0.94)
Trade	0.0028	−0.0047	−0.0096	0.0067
	(1.38)	(-1.33)	(-0.07)	(0.23)
Population	1.21^***	2.91^***	−41.6	−4.09
	(2.99)	(4.21)	(-1.28)	(-0.64)
WGI	−0.32^*	−0.12	−9.61	0.37
	(-1.75)	(-0.37)	(-0.77)	(0.15)
Constant	−13.3^**	−33.3^***	674.6	45.3
	(-2.08)	(-3.03)	(1.31)	(0.44)
Region	Included	Included	Included	Included
Country-year effects	Yes	Yes	Yes	Yes
N	396	397	256	256
R²	0.28	0.18	0.02	0.02
F-stat	19.59^***	11.10^***	2.51^**	2.49^**

t statistics in parentheses.

^*p < 0.10.

^**p < 0.05.

^***p < 0.01.

Level 2: Similarly, FE panel regression analysis is employed to examine the independent testing variables’ (of Level 2) association with four tourism development proxies (Table 5). The results reveal that T&TPolCondIndex_L2 (p < 0.01) has a significant positive relationship with LN_IntTA while InfrustructureIndex_L2 (p < 0.05) has a significant positive relationship with LN_IntTR. However, EnablingEnvIndex_L2 and NatCultResIndex_L2 are not significant predictors. Thus, T&T policy and enabling conditions index and infrastructure index are positively associated with tourist arrivals and tourism receipts, respectively.

Table 5.

Fixed-effects linear panel regression analysis (Level2).

	(1)	(2)	(3)	(4)
Independent variables	LN_IntTA	LN_IntTR	IntTACH	IntTRCH
EnablingEnvIndex_L2	0.18	−0.17	−2.18	1.21
	(1.33)	(-0.77)	(-0.30)	(0.83)
T&TPolCondIndex_L2	0.24^***	0.044	−1.49	0.40
	(2.70)	(0.29)	(-0.24)	(0.33)
InfrustructureIndex_L2	0.020	0.35^**	2.69	−0.50
	(0.21)	(2.23)	(0.36)	(-0.34)
NatCultResIndex_L2	0.068	0.18	−5.59	0.24
	(0.72)	(1.12)	(-0.85)	(0.19)
GDP	0.60^***	0.82^***	2.50	1.80
	(4.17)	(3.35)	(0.24)	(0.86)
Trade	0.0022	−0.0036	−0.014	0.0099
	(1.06)	(-1.01)	(-0.09)	(0.33)
Population	0.77	3.69^***	−51.6	−5.25
	(1.60)	(4.49)	(-1.22)	(-0.63)
WGI	−0.30	−0.12	−9.15	0.43
	(-1.60)	(-0.37)	(-0.72)	(0.17)
Constant	−5.42	−47.0^***	848.1	63.1
	(-0.68)	(-3.45)	(1.22)	(0.46)
Region	Included	Included	Included	Included
Country-year effects	Yes	Yes	Yes	Yes
N	396	397	256	256
R²	0.29	0.19	0.03	0.03
F-stat	12.61^***	7.41^***	2.42^**	2.37^**

t statistics in parentheses.

^*p < 0.10.

^**p < 0.05.

^***p < 0.01.

Level 3: The results of the panel regression analysis for Level 3 are examined and reported in Table 6. Similarly, the same four dependent variables are included in the analysis together with 14 testing variables as well as the control variables. Accordingly, PriceCompetitiveness_L3 (p < 0.01), AirTranspInfrastructure_L3 (p < 0.01), and CulturalResBusinessTrav_L3 (p < 0.10) have a significant and positive association with LN_IntTA. Moreover, GroundPortInfrastructure_L3 (p < 0.10) has a significant positive relationship with LN_IntTR. Furthermore, SafetySecurity_L3 (p < 0.10), HumanResLabMarket_L3 (p < 0.05), AirTranspInfrastructure_L3 (p < 0.05) have a significant and positive association with IntTACH while ICTreadiness_L3 (p < 0.01) and NaturalResources_L3 (p < 0.10) have a significant and negative association with IntTACH. Finally, none of the 14 testing variables have a significant relationship with IntTRCH.

Table 6.

Fixed-effects linear panel regression analysis (Level3).

	(1)	(2)	(3)	(4)
Independent variables	LN_IntTA	LN_IntTR	IntTACH	IntTRCH
BusinessEnvironment_L3	−0.045	−0.15	−10.7	−0.30
	(-0.48)	(-0.92)	(-1.60)	(-0.21)
SafetySecurity_L3	0.074	−0.078	5.42^*	−0.36
	(1.36)	(-0.82)	(1.77)	(-0.55)
HealthHygiene_L3	0.11	0.081	3.77	0.37
	(1.36)	(0.59)	(0.89)	(0.41)
HumanResLabMarket_L3	0.13	−0.099	18.9^**	1.06
	(1.53)	(-0.64)	(2.50)	(0.65)
ICTreadiness_L3	−0.015	0.050	−11.8^***	0.32
	(-0.25)	(0.47)	(-3.27)	(0.41)
PrioritizTravelTourism_L3	0.042	0.055	1.28	−0.94
	(0.56)	(0.42)	(0.24)	(-0.83)
InternationalOpenness_L3	0.066	−0.066	5.20	−0.29
	(1.30)	(-0.74)	(1.50)	(-0.39)
PriceCompetitiveness_L3	0.12^***	0.071	−1.10	0.54
	(2.85)	(0.97)	(-0.38)	(0.86)
EnvirSustainability_L3	−0.094	−0.048	−0.64	0.87
	(-1.57)	(-0.45)	(-0.14)	(0.89)
AirTranspInfrastructure_L3	0.29^***	0.19	9.26^**	0.66
	(3.26)	(1.22)	(2.01)	(0.67)
GroundPortInfrastructure_L3	−0.11	0.25^*	−9.09	0.91
	(-1.47)	(1.9)	(-1.58)	(0.74)
TouristServInfrastructure_L3	−0.044	0.026	0.016	−1.56
	(-0.89)	(0.29)	(0.00)	(-1.65)
NaturalResources_L3	−0.016	0.15	−7.00^*	0.41
	(-0.25)	(1.36)	(-1.70)	(0.46)
CulturalResBusinessTrav_L3	0.18^*	−0.0059	3.18	0.087
	(1.83)	(-0.03)	(0.48)	(0.06)
GDP	0.42^***	0.65^**	1.65	1.09
	(2.69)	(2.36)	(0.16)	(0.48)
Trade	−0.00038	−0.0057	−0.22	0.0031
	(-0.17)	(-1.46)	(-1.37)	(0.09)
Population	0.58	3.40^***	−41.8	−11.9
	(1.09)	(3.63)	(-0.90)	(-1.19)
WGI	−0.22	0.040	−11.1	1.14
	(-1.20)	(0.12)	(-0.90)	(0.43)
Constant	−0.88	−40.9^**	664.1	178.0
	(-0.10)	(-2.54)	(0.85)	(1.06)
Region	Included	Included	Included	Included
Country-year effects	Yes	Yes	Yes	Yes
N	396	397	256	256
R²	0.36	0.22	0.21	0.08
F-stat	7.25^***	3.65^***	2.54^**	2.47^**

t statistics in parentheses.

^*p < 0.10.

^**p < 0.05.

^***p < 0.01.

Discussion, conclusions, and implications

The objective of this study is to test whether TTCI is associated with tourism sector development. For this purpose, the data were gathered from the WEF (2020) concerning the TTCI and the World Bank (2021a; 2021b) concerning the control variables. The study adopts four proxies for measurement of the tourism sector development; international tourist arrivals, international tourism receipts, and the change in arrivals and receipts to explore test variables’ association with the volume and value generation for the sector. Moreover, TTCI indices were measured in three steps: Level 1 (main index), Level 2 (indices), and Level 3 (further sub-indices). Hence, the study aims to provide detailed policymaking implications helpful for developing new strategies for the advancement of the tourism sector in nations. It also aims to provide implications for practitioners for the enhancement of destination attractiveness.

The results find that TTCI main index (Level 1) is positively associated with tourist arrivals but not tourism receipts and the change in tourist arrivals and tourism receipts. Further analysis (Level 2) reveals that while the T&T policy and enabling conditions index is positively associated with tourist arrivals, the infrastructure index is positively associated with tourism receipts. None of the Level 2 indices drives the change in tourist arrivals and tourism receipts. Among results obtained from Level 3 sub-indices, while price competitiveness, air transportation infrastructure, and cultural resources indices have a positive association with tourist arrivals, only ground and port infrastructure have a positive association with tourism receipts. Moreover, while safety and security, human resources and labor market, and air transportation infrastructure sub-indices drive a positive change in tourist arrivals, ICT readiness and natural resources drive a negative change in tourist arrivals and none of the sub-indices drives a change in tourism receipts.

The ground and port infrastructure’s positive association with international tourism receipts is in line with an African study (Adeola and Evans, 2020). The findings also partially confirm the result of Fernández et al. (2020) ⁵ that found air transport infrastructure and cultural resources are the prominent factors in fostering countries’ competitiveness. Although Adeola and Evans (2020) and Kumar and Kumar (2020) found a positive association between ICT and tourism development, Lee et al. (2021) found both positive and negative associations between the two variables across varying tourism quantiles. Hence, with our negative finding, the association between ICT and tourism development justifies further research. Moreover, even though the positive association between cultural resources and tourism development is in line with a European study, the negative association between natural resources and tourism development contradicts it (Romão, Guerreiro and Rodrigues, 2013)⁶. Previously, Ghaderi et al. (2017) found that while security is positively associated with the number of tourist arrivals in developed nations, it is negatively associated with the number of tourist arrivals in developing countries. Hence, we provide additional worldwide evidence on the association between safety and security and a change in the number of tourist arrivals.

The study suggests several practical implications for policymakers associated with the tourism sector development and for tourism sector professionals⁷; they are suggested to consider the results by segregating differential outputs for tourist arrivals, tourism receipts, and the change in these two metrics since some countries’ focus might be enhancing the tourism development through attracting more tourists some others’ focus might be generating greater tourism receipts. The results might be of interest to tourism and culture ministries, chambers of tourism, local administrations who wish to expand tourism volume and value within the economic development of the countries. The results suggest that TTCI could be relevant in the tourism development of nations, but it is essential to focus on relevant factors. The significance of the price competitiveness index in association with tourist arrivals but not tourism receipts may imply that ticket taxes and airport charges, hotel price index, fuel price levels play role in attracting more tourists but not generating greater receipts. Another major factor associated with tourist arrivals and also increase in tourist arrivals is air transport infrastructure which implies the quality of the infrastructure, available domestic and international seat kilometers, aircraft departures, airport density, and the number of operating airlines. On the other hand, ground and port infrastructure such as quality of roads, road density, railroad infrastructure and density, quality of port infrastructure are potentially significant drivers of tourism receipts which imply that they play a role in stimulating tourism revenues by facilitating tourists’ mobility within the country.

The countries who wish to increase tourist arrivals are suggested to increase safety and security precautions such as reducing business costs of crime, violence, and terrorism, enhancing the reliability of police services, and homicide rate. Besides, human resources and the labor market also spur an increase in tourist arrivals which is enabled by higher primary and secondary education enrollment rates, staff training, and well treatment of customers. Furthermore, the positive finding for cultural resources index in association with tourist arrivals imply that tangible and intangible cultural sites and resources, sports stadiums, and hosting international events and meetings play a role in attracting a greater number of tourists. We refrain from suggesting detailed implications due to the inconsistent results between our study and some prior studies particularly for the association between ICT and natural resources and tourism development. The contradicting findings on these associations suggest more future studies to explore in what way ICT could stimulate tourism development and what the motivation or discouragement is behind natural resources and tourism development. The latter investigation invites researchers to consider regulatory factors and tourism policies of the countries too since they might be encouraging or constraining tourists’ interests.

The findings suggest implications for tourism firms too; they can strive for the betterment of the significant factors associated with TTCI as long as the factors are within the scope of their authority. For example, although ensuring infrastructure quality is beyond their authority, they can set more competitive prices, enhance safety and security precautions in the establishments and amusement parks, and improve human resources. Concerning policymaking and infrastructure issues, the private sector can contribute to the agenda-setting of chambers of tourism and local administrations and help authorities recognize the sector’s and tourists' expectations.

The study has several limitations. The period of the study is limited to the available number of years in the data source (WEF, 2020). Moreover, all the data concerning TTCI is drawn from a single source; this may impose a limitation particularly for survey-based indicators embedded in the TTCI. However, some items were drawn by WEF (2020) from other sources such as purchasing power parity and fuel price levels were taken from the World Bank, and bilateral air service agreements were received from the World Trade Organization. Future studies might be designed to focus on the outcomes of some specific indices of TTCI (e.g., sustainable tourism index) such as on economic growth or environmental performance. Moreover, some studies might deepen the investigation and focus on insignificant or negative indices and explore underlying reasons behind those indices’ insignificant or negative associations with tourism sector development. In addition to quantitative studies, qualitative studies based on interviewing policymakers could reveal complementary findings and explain why and how.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ali Uyar

Cemil Kuzey

Mehmet Ali Koseoglu

Abdullah S Karaman

Notes

Appendix

Author iographies

Ali Uyar is currently a Professor of Accounting at La Rochelle Business School, Excelia Group, France. He holds a PhD in Accounting and Finance and prestigious international CMA certification granted by IMA (USA). His research interests are corporate governance, sustainability/CSR performance/reporting, and management accounting. His works have been published in prominent journals including Journal of Cleaner Production, Expert Systems with Applications, Journal of Intellectual Capital, Australian Accounting Review, Advances in Accounting, Managerial Auditing Journal, Transport Policy, Tourism Economics.

Cemil Kuzey is an Associate Professor at Murray State University, USA. Dr. Kuzey is primarily teaching Operation Research and Statistics topics for Social Sciences. His research interests are related to Operation Research, Data Mining, and Business Intelligence. His papers have been published in various prominent journals including Journal of Cleaner Production, Expert Systems with Applications, Decision Support Systems, International Journal of Public Administration, Managerial Auditing Journal.

Mehmet Ali Koseoglu is an Assistant Professor at College of Management, Metropolitan State University in Minnesota. His research interests are focused on strategic management, corporate social responsibility and bibliometric analysis in tourism and hospitality. He has published in major tourism and hospitality journals including Annals of tourism Research, Journal of Sustainable Tourism, International Journal of Hospitality Management, and International Journal of Contemporary Hospitality Management.

Abdullah S. Karaman is currently an Associate professor of Industrial Engineering at the American University of the Middle East, Kuwait. He received his Ph.D. degree in Industrial and Systems Engineering from Rutgers University, New Jersey, USA. His research and teaching activities have been in the areas of supply chain management, business analytics, simulation, sustainability reporting, and multi-criteria decision-making. His research has been supported by the Department of Homeland Security, USA. His works have been published in the European Journal of Operational Research, Journal of Cleaner Production, Transport Policy, Journal of Air Transport Management, Sustainability Accounting, Management and Policy Journal, among others.

References

Abreu-Novais

Ruhanen

Arcodia

(2016) Destination competitiveness: what we know, what we know but shouldn’t and what we don’t know but should. Current Issues in Tourism 19(6): 492–512.

Adeola

Evans

(2020) ICT, infrastructure, and tourism development in Africa. Tourism Economics 26(1): 97–114.

Albayrak

Caber

Rosario González-Rodríguez

, et al. (2018) Analysis of destination competitiveness by IPA and IPCA methods: the case of costa Brava, Spain against Antalya, Turkey. Tourism Management Perspectives 28: 53–61.

Andrades

Dimanche

(2017) Destination competitiveness and tourism development in Russia: issues and challenges. Tourism Management 62: 360–376.

Assaf

Josiassen

(2012) Identifying and ranking the determinants of tourism performance. Journal of Travel Research 51(4): 388–399.

Baltagi

(2001) Econometric Analysis of Panel Data. 2nd edition. Chicester: John Wiley & Sons.

Zadeh Bazargani

Kiliç

(2021) Tourism competitiveness and tourism sector performance: empirical insights from new data. Journal of Hospitality and Tourism Management 46: 73–82.

Bennett

(2001) How can i deal with missing data in my study? Australian and New Zealand Journal of Public Health 25(5): 464–469.

Cimbaljević

Stankov

Pavluković

(2019) Going beyond the traditional destination competitiveness–reflections on a smart destination in the current research. Current Issues in Tourism 22(20): 2472–2477.

10.

Constantoglou

(2020) Destination management in lesvos, greece. characteristics, preferences, images, satisfaction, and overall experience. Business Ethics and Leadership 4(3): 81–106.

11.

Croes

(2011) Measuring and explaining competitiveness in the context of small island destinations. Journal of Travel Research 50(4): 431–442.

12.

Cronjé

du Plessis

(2020) A review on tourism destination competitiveness. Journal of Hospitality and Tourism Management 45: 256–265.

13.

Crouch

(2010) Destination competitiveness: an analysis of determinant attributes. Journal of Travel Research 50(1): 27–45.

14.

Crouch

(2011) Destination competitiveness: an analysis of determinant attributes. Journal of Travel Research 50(1): 27–45.

15.

Crouch

Ritchie

JRB

(1999) Tourism, competitiveness, and societal prosperity. Journal of Business Research 44(3): 137–152.

16.

Das

DiRienzo

(2009) Global tourism competitiveness and freedom of the press. Journal of Travel Research 47(4): 470–479.

17.

del Mar Gálvez-Rodríguez

Alonso-Cañadas

Haro-de-Rosario

, et al. (2020) Exploring best practices for online engagement via facebook with local destination management organisations (DMOs) in Europe: a longitudinal analysis. Tourism Management Perspectives 34: 100636.

18.

Detotto

Giannoni

Goavec

(2021) Does good governance attract tourists? Tourism Management 82: 104155.

19.

d’Hauteserre

A-M

(2000) Lessons in managed destination competitiveness: the case of foxwoods casino resort. Tourism Management 21(1): 23–32.

20.

Zeng

, et al. (2016) Religious atmosphere, law enforcement, and corporate social responsibility: evidence from China. Asia Pacific Journal of Management 33(1): 229–265.

21.

(2020) Does corporate social responsibility affect auditor-client contracting? Evidence from auditor selection and audit fees. Advances in Accounting 51: 100499.

22.

Dwyer

Kim

(2003) Destination competitiveness: determinants and indicators. Current Issues in Tourism 6(5): 369–414.

23.

Dwyer

Cvelbar

Mihalič

, et al. (2014) Integrated destination competitiveness model: testing its validity and data accessibility. Tourism Analysis 19(1): 1–17.

24.

Fernández

JAS

Azevedo

Martín

JMM

, et al. (2020) Determinants of tourism destination competitiveness in the countries most visited by international tourists: proposal of a synthetic index. Tourism Management Perspectives 33: 100582.

25.

Framke

(2002) The destination as a concept: a discussion of the business-related perspective versus the socio-cultural approach in tourism theory. Scandinavian Journal of Hospitality and Tourism 2(2): 92–108.

26.

Gelter

Lexhagen

Fuchs

(2021) A meta-narrative analysis of smart tourism destinations: implications for tourism destination management. Current Issues in Tourism 24(20), 2860–2874.

27.

Ghaderi

Saboori

Khoshkam

(2017) Does security matter in tourism demand? Current Issues in Tourism 20(6): 552–565.

28.

Goffi

Cucculelli

Masiero

(2019) Fostering tourism destination competitiveness in developing countries: the role of sustainability. Journal of Cleaner Production 209: 101–115.

29.

Gomezelj

Mihalič

(2008) Destination competitiveness-applying different models, the case of Slovenia. Tourism Management 29(2): 294–307.

30.

Ren

Jin

, et al. (2019) Tourism destination competitiveness evaluation in Sichuan province using TOPSIS model based on information entropy weights. Discrete & Continuous Dynamical Systems-S 12(4&5): 771.

31.

Gursoy

Baloglu

Chi

(2009) Destination competitiveness of middle Eastern countries: an examination of relative positioning. Anatolia 20(1): 151–163.

32.

Hair

Anderso

Jr Tatham

, et al. (2010) Multivariate Data Analysis with Readings. 7th edition. Englewood Cliffs, NJ: Prentice-Hall.

33.

Hausman

(1978) Specification tests in econometrics. Econometrica 46: 1251–1271.

34.

Huber

(1967) The behavior of maximum likelihood estimates under nonstandard conditions. In: Vol 1 of Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 221–233.

35.

Ianioglo

Rissanen

(2020) Global trends and tourism development in peripheral areas. Scandinavian Journal of Hospitality and Tourism 20(5): 520–539.

36.

Ivanov

Webster

(2013) Globalisation as a driver of destination competitiveness. Annals of Tourism Research 43: 628–633.

37.

Joshi

Poudyal

Larson

(2017) The influence of sociopolitical, natural, and cultural factors on international tourism growth: a cross-country panel analysis. Environment, Development and Sustainability 19(3): 825–838.

38.

Kayar

ÇH

Kozak

(2010) Measuring destination competitiveness: an application of the travel and tourism competitiveness index (2007). Journal of Hospitality Marketing & Management 19(3): 203–216.

39.

Kendall

Gursoy

(2007) A managerial approach to positioning and branding: eponymous or efficient. Tourism Analysis 12(5–6): 473–483.

40.

Knežević Cvelbar

Dwyer

Koman

, et al. (2016) Drivers of destination competitiveness in tourism: a global investigation. Journal of Travel Research 55(8): 1041–1050.

41.

Krstic

Jovanovic

Jankovic-Milic

, et al. (2016) Examination of travel and tourism competitiveness contribution to national economy competitiveness of sub-Saharan Africa countries. Development Southern Africa 33(4): 470–485.

42.

Kubickova

Martin

(2020) Exploring the relationship between government and destination competitiveness: the TALC model perspective. Tourism Management 78: 104040.

43.

Kumar

(2020) Relationship between ICT and international tourism demand: a study of major tourist destinations. Tourism Economics 26(6): 908–925.

44.

Kumar

Dhir

(2020) Associations between travel and tourism competitiveness and culture. Journal of Destination Marketing & Management 18: 100501.

45.

Lee

Chen

, et al. (2021) The Impacts of ICTs on Tourism Development: International Evidence Based on a Panel Quantile Approach. Information Technology & Tourism, 23(4), 509–547.

46.

Leung

Baloglu

(2013) Tourism competitiveness of Asia Pacific destinations. Tourism Analysis 18(4): 371–384.

47.

Mazanec

Ring

(2011) Tourism destination competitiveness: second thoughts on the world economic forum reports. Tourism Economics 17(4): 725–751.

48.

Mendola

Volo

(2017) Building composite indicators in tourism studies: measurements and applications in tourism destination competitiveness. Tourism Management 59: 541–553.

49.

Michael

Reisinger

Hayes

(2019) The UAE’s tourism competitiveness: a business perspective. Tourism Management Perspectives 30: 53–64.

50.

Natalia

Clara

Simon

, et al. (2019) Critical elements in accessible tourism for destination competitiveness and comparison: principal component analysis from Oceania and South America. Tourism Management 75: 169–185.

51.

Neto

Dimmock

Lohmann

, et al. (2020) Destination competitiveness: how does travel experience influence choice? Current Issues in Tourism 23(13): 1673–1687.

52.

Novais

Ruhanen

Arcodia

(2018) Destination competitiveness: a phenomenographic study. Tourism Management 64: 324–334.

53.

Ozturk

Van Niekerk

(2014) Volume or value: a policy decision for Turkey’s tourism industry. Journal of Destination Marketing & Management 3(4): 193–197.

54.

Perles-Ribes

(2014) Market share as a tourist destinations competitiveness measure: Sense and limitations. Cuadernos de Turismo(34): 423–425.

55.

Petrović

Milićević

Djeri

(2017) The information and communications technology as a factor of destination competitiveness in transition countries in European Union. Tourism Economics 23(6): 1353–1361.

56.

Rita

António

(2020) Promotion of inclusive tourism by national destination management organizations. Worldwide Hospitality and Tourism Themes 12(6): 701–714.

57.

Rodríguez-Díaz

Pulido-Fernández

(2020) Sustainability as a key factor in tourism competitiveness: a global analysis. Sustainability 12(1): 51.

58.

Romão

Guerreiro

Rodrigues

(2013) Regional tourism development: culture, nature, life cycle and attractiveness. Current Issues in Tourism 16(6): 517–534.

59.

Schafer

(1999) Multiple imputation: a primer. Statistical Methods in Medical Research 8(1): 3–15.

60.

Seguí-Amortegui

Clemente-Almendros

Medina

, et al. (2019) Sustainability and competitiveness in the tourism industry and tourist destinations: a bibliometric study. Sustainability 11(22): 6351.

61.

Teixeira

Ferreira

JJDM

(2018) A bibliometric study of regional competitiveness and tourism innovation. International Journal of Tourism Policy 8(3): 214–243.

62.

UNWTO (2007) Practical Guide to Tourism Destination Management. Madrid: World Tourism Organisation.

63.

Uyar

Kuzey

Kilic

, et al. (2021) Does firms’ CSR engagement support tourism sector development? Moderation effect of CSR committee and CEO duality. Tourism Economics: 13548166211024502.

64.

Verardi

Dehon

(2010) Multivariate outlier detection in stata. The Stata Journal: Promoting Communications on Statistics and Stata 10(2): 259–266.

65.

Webster

Ivanov

(2014) Transforming competitiveness into economic benefits: does tourism stimulate economic growth in more competitive destinations? Tourism Management 40: 137–140.

66.

WEF (World Economic Forum) (2020) Travel & Tourism Competitiveness Index. http://reports.weforum.org/travel-and-tourism-competitiveness-report-2017/ranking/ (accessed 19 April 2020).

67.

White

Jr (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48: 817–838.

68.

Wooldridge

(2010) Econometric Analysis of Cross Section and Panel Data. Cambridge, Massachusetts, London, England: MIT press.

69.

Wooldridge

(2013) Introductory Econometrics: A Modern Approach. 5th edition. Mason, OH: South-Western: Cengage.

70.

World Bank (2021a) World Development Indicators. https://databank.worldbank.org/reports.aspx?source=world-development-indicators (accessed 14 November 2021).

71.

World Bank (2021b) World Governance Indicators. https://info.worldbank.org/governance/wgi/(accessed 14 November 2021).

72.

W-W

Lan

Lee

Y-T

(2012) Critiquing the world economic forum’s concept of destination competitiveness: a further analysis. Tourism Management Perspectives 4: 198–206.

73.

Zhang

C-L

L-W

, et al. (2011) The evaluation of tourism destination competitiveness by TOPSIS & information entropy - a case in the Yangtze River Delta of China. Tourism Management 32(2): 443–451.