Analysis on the impact of technological innovation on tourism development: The case of Yangtze River Delta Urban Agglomeration in China

Abstract

Despite the recognition of the relationship between technological innovation and tourism development, there is a dearth of rigorous empirical specifications to examine the effect of technological innovation on the latter. With 27 cities of the Yangtze River Delta Urban Agglomeration (YRDUA) in China as an empirical case, this study explores whether technological innovation can promote tourism development by using a series of panel regression models. The empirical results indicate that technological innovation has a positive effect on the development of tourism in the YRDUA. With respect to different regions, types of cities, and stages, there are differences in the positive impact of technological innovation on tourism development. Additionally, the impact of different types of technological innovations on tourism development is also diverse.

Keywords

Technological innovation tourism development panel regression model Yangtze River Delta Urban Agglomeration

Introduction

Technological innovation is widely acknowledged as one of the crucial factors driving industrial improvement and economic development across the world (Sigala, 2018). In the Fourteenth Five-Year Plan, unveiled by the Chinese government in March 2021, technological innovation has been regarded as an important strategy for fostering new competitive advantages. In other words, the increasing dependence on technological innovation is recognized far and widely for its role in China’s economic development. The tourism industry, as a strategic pillar, is not an exception for the applications of technologies (Gössling, 2020), especially in the context of the Corona Virus Disease 2019 (COVID-19). Against this background of high-quality development, the tourism industry needs to not only improve the development efficiency but also foster new drivers of growth in China (Shi et al., 2021; Pan et al., 2021). On the one hand, modern technologies, such as information systems, can directly improve the management efficiency of tourism sectors (Alrawadieh et al., 2020). On the other hand, quite a few technical products, such as virtual reality (VR) and augmented reality (AR), are conducive to attracting more tourists and thus fostering new drivers of development (Jeong and Shin, 2019). Based on the above analysis, technological innovation can play a multi-faceted role in the high-quality development of the tourism industry in China (Fennell, 2020). Therefore, there is an urgent need to examine the effect of technological innovation on tourism development.

Due to the important role of technological innovation in the development of tourism, more and more scholars focus on the link between specific technologies and tourism development since the early 1990s (Stipanuk, 1993). Scholars conducted research studies on the relationship between technological innovation and the tourism development from the perspectives of both the supplier and consumer (Law et al., 2010). From the perspective of consumer, scholars pay more attention to the feelings, happiness, and convenience of tourists, brought by modern technologies (Ponsignon and Derbaix, 2020; Jeong and Shin, 2019; Lei et al., 2019). For example, Aebli (2019) found that gamified features can help tourists accomplish motivational goals and strengthen their interactions with tourism sectors. From the perspective of supplier, the impact of technological innovation on the improvement in the efficiency of management, the promotion of tourism products, and the improvement of tourism brands have become hot topics (Fukui and Ohe, 2019; Li et al., 2019a; Coghlan, 2020). For instance, Li et al. (2019a) concluded that social media marketing can exert a positive effect on casual-dining restaurant performance. On the whole, the majority of researchers pay more attention to the impacts of specific technologies, such as information and communications technology (ICT), AI technology, and smartphone technology, on the development of tourism, all the whilst little attention is paid to the effects regarding the ability of technological innovation as a whole on tourism development. However, this is not conducive to the comprehensive understanding of the role that the whole ability of technological innovation plays in the tourism development. Additionally, formulating favorable policies of tourism technological innovation needs to investigate the link among the two from holistic perspective. Therefore, it is imperative that the empirical relationship between technological innovation and tourism development needs to be comprehensively examined.

In order to make up for the abovementioned deficiencies, with 27 cities of the Yangtze River Delta Urban Agglomeration in China as an empirical case, this study adopts the panel regression models in order to examine the impact of technological innovation on tourism development. Furthermore, we go one step further by testing the heterogeneous effects of technological innovation. Our research makes twofold contributions. Theoretically, it complements the extant studies by offering empirical evidence on the positive effect of technological innovation on tourism development. Practically, our findings are most relevant to policymakers who focus on improving performance and expanding the market scale. Moreover, this study can provide guidance to promote the high-quality development of the tourism industry across other urban agglomerations in China.

The rest of this study is structured as follows. Section 2 reviews the related literature. The empirical strategy is represented in Section 3, in which introduces the empirical area, empirical model, the definition of variables, data source and processing, and descriptive analysis. The empirical results are reported in Section 4, in which also features the baseline result, heterogeneity effect, and the robustness test. Moreover, the instrumental variable (IV) approach is used to deal with endogeneity issue. Section 5 discusses our findings in relation to the extant literature. Last, Section 6 draws conclusions, provides management recommendations, and summarizes the drawbacks.

Literature review

Impact of technological innovation on tourism development

The significant impact of modern technology on tourism development has been long discussed amongst scholars (Buhalis and Law, 2008). Great progress has been made in this field of research over roughly 30 years (Xiang, 2018). According to the classification put forward by Law et al. (2020), the effects of technological innovation on tourism can be divided into effects felt by the supplier and effects felt by the consumers.

With respect to the supplier, technology is widely applied to product design, promotion, distribution, exchange, and communication (Gössling, 2021; Sigala, 2018; Fennell, 2020). On the one hand, technological innovation enables suppliers to improve service efficiency (Sun et al., 2020), reduce operation costs (Alrawadieh et al., 2020), raise brand awareness (Sun et al., 2020), enable access to collaboration (Han et al., 2021), attract tourism investment (Kim and Hall, 2020), and thus promoting the performance of tourism enterprises. On the other hand, technological progress can improve employees’ performance (Srivastava and Dhar, 2016), increase tourism royalties and recurrence (Smith et al., 2015), receive more support from governments (Zhang and Dong, 2021), and finally strengthen tourism enterprises’ development resilience. Especially under the impact of COVID-19, technological innovation plays an increasingly important role in the recovery of tourism. Li et al. (2021) believe that the application of VR and AR enables the avoidance of direct social interaction with tourists. What’s more, Nanni and Ulqinaku (2020) indicate that VR can be employed to provide virtual tours in order to avoid crowds. Moreover, some scholars contend that quite a few new technologies can drive the sustainable development of tourism by reducing conventional energy consumption, decreasing resource intensity of production processes, and promoting environmental protection (Nagle and Vidon, 2020; Chaoqun, 2011).

With regard to the consumer, scholars devote enough effort to exploring the impacts of up-to-date technologies on obtaining tourism information, purchasing tickets, booking accommodations, participating in tourism activities, and sharing tourism experiences (Leoni and Cristofaro, 2021; MacKay and Vogt, 2012; Gössling, 2020; Voda et al., 2019). On the one hand, technologies such as smart tourism apps can help tourists receive valuable information (MacKay and Vogt, 2012), find attractive destinations (Huang et al., 2017), design route mappings (Adeola and Evans, 2019a; Ernawati et al., 2018), book tickets and accommodations (Dickinson et al., 2012), formulate personalized schedules (Gretzel et al., 2015), and thereby generating demand for tourism (Yoo et al., 2017). On the other hand, as Anastasiadou and Vettese (2019) pointed out, information technologies allow tourists to become “prosumers,” with coproduction turning into part of the memorable experience. Moreover, sharing one’s own opinions with companions based on the applications of interactive technologies can lead to an overall better experience (Ponsignon and Derbaix, 2020). There is no doubt that a memorable experience enables tourists to generate more trust, support, and interest toward their destinations, which is conducive to expanding the market scale to a certain extent (Gajdošík, 2019; Kaushik et al., 2015). With the tremendous improvement of technology, quite a few scholars emphasize that information security and privacy protection should not be overlooked in the growing use and reliance on information technologies (Kaushik et al., 2015; Wang et al., 2019).

On the one hand, technological innovation can improve efficiency, reduce cost, promote communication, raise brand awareness, and support the decision-making process and thus generating more revenue and strengthening supplier competitiveness. On the other hand, technological innovation can help tourists make favorable travel plans, gain convenient, authentic, and memorable experiences, beneficial for stimulating travel demand and expanding market scale. Therefore, technological innovation has a positive effect on the development of tourism, to a certain extent. Based on these analyses, this study puts forward the following hypothesis.

H1: Technological innovation has a positive influence on tourism development.

Empirical strategy

Empirical area

Yangtze River Delta Urban Agglomeration, with an area of 0.226 million km² and 0.146 billion of the total population, is one of the urban agglomerations with the highest degree of openness and the strongest ability of technological innovation in China (Wu et al., 2021). Administratively, YRDUA consists of Shanghai City (municipality), Jiangsu Province, Zhejiang Province, and Anhui Province. Outline of the Yangtze River Delta Regional Integration Development Plan, issued by the State Council in 2019, pointed out that 27 cities are central region of YRDUA¹. YRDUA is rich in tourism resources. To be more specific, there are 45 scenic spots at AAAAA(5A) level such as the Classical Gardens of Suzhou, West Lake, and Xixi Wetland (Ruan et al., 2019). Additionally, tourism income increased tremendously in YRDUA, by approximately 8.2 times, from 0.4 trillion RMB in 2005 to 8.18 trillion RMB in 2019, with an average annual growth rate of 51.32%. Therefore, YRDUA has become the representative demonstration area of tourism development in China. Given the relatively stronger ability of technological innovation, technological innovation has become an important factor driving tourism economic development in YRDUA(Shi et al., 2021). Especially against the background of COVID-19, such as culture and tourism system of city brain in Hangzhou and big data platform of rural tourism in Nanjing, play an indispensable role in providing virtual tourism service, consolidating a relationship with tourists, and promoting tourism brand awareness. The YRDUA is one of the six main urban agglomerations in the world (Hou et al., 2021), with a similar link between technologies and tourism development. Based on the abovementioned introductions, the selection of case study has the better typicality and representativeness to a certain degree.

Empirical model

The main object of our study is to explore the effect of technological innovation on the development of tourism. This study conducts empirical research by adopting the panel regression model as presented by equation (1)

ln t o u r_{i t} = β_{0} + β_{1} ln t e c h_{i t} + θ ln X_{i t} + μ_{i} + ε_{i t}

(1)

where tour_it represents the level of tourism development; tech_it is the ability of technological innovation; X_it denotes the control variable; β₀ represents constant term; β₁ is the key coefficient of technological innovation; θ is the coefficient of control variable; μ_i is the individual’s fixed effect; ε_it is a random error term.

Variables selection

Dependent variable

Tourism development (tour). Referencing to Zhang et al. (2021), this study adopts per capita tourism income as the measurement of tourism development. Tourism income equals domestic tourism income plus inbound tourism income. Additionally, the ratio of tourist numbers to total population size is regarded as the proxy variable of tourism development in robustness examination (Kim et al., 2006; Adeola and Evans, 2019b). Similar to tourism income, domestic tourist number plus inbound tourism number is tourism number. Tourism income is the economic output of tourism development. Tourism number is the number of tourists, and can also be used to mirror the level of tourism development (Tu and Zhang, 2020). The relevant data on tourism income and tourism number can be directly collected by statistical yearbooks.

Core independent variable

Technological innovation (tech). Technological innovation can promote the speed and efficiency of tourism development to a certain extent. Referencing to Lin and Zhao (2020) and Zhao et al. (2021), this study adopts the sum of three types of the patent granted to represent the ability of technological innovation. Based on the classification put forward by the China Statistical Yearbook, the patent is classified into three types, namely, the patent of invention, the patent of utility model, and the patent of design.

Control variables

A set of control variables are introduced to exactly reflect the impact of technological innovation on tourism development in this study.

(1) Government intervention (govern). Government intervention is a double-edged sword for tourism growth (Nguyen, 2021). In particular, when the tourism industry undergoes stagnancy, appropriate intervention can optimize resource allocation and create better circumstances for development (Deng et al., 2019). However, intemperate intervention may reduce the efficiency of tourism resource allocation. This study adopts the ratio of fiscal expenditure to cities’ gross domestic product (GDP) to mirror the degree of government intervention (Lin and Zhao, 2020).

(2) Industrial structure (indus). A favorable industrial structure can not only stimulate tourism industry agglomeration but also the beneficial for the integration of tourism and other industrial sectors (Li et al., 2019b). In particular, the prosperity of consumer-oriented service industries can improve the quality of service in the tourism sector. The process regarding upgrading the industrial structure is directly manifested in the continuous augment of tertiary industry added value in China’s GDP (Ma, 2020). The ratio of the added value from tertiary industries to cities’ GDP is applied to reflect the industrial structure (Lin and Zhao, 2020).

(3) Transportation accessibility (trans). The improvement of transportation accessibility can change the depth and width of tourism economic connection; moreover, favorable accessibility and mobility brought by transportation can expand tourists’ demand to a certain extent (Campa et al., 2016). This study adopts the road mileage to reflect cities’ transportation accessibility.

(4) Investment level (invest). Investment is the prerequisite for the economic development of tourism. Furthermore, the enhancement of investment can optimize tourism infrastructures and public services, and thus promoting tourism competitiveness of cities (Alam and Paramati, 2017). The ratio of fixed-asset investment to a city’s GDP is used to represent the level of regional investment (Tu and Zhang, 2020).

(5) Openness degree (ope). Improving the degree of openness may promote the economic development of tourism by attracting more inbound tourists and foreign investors; the proportion of total import and export in GDP is employed to represent the degree of openness in this study (Deng et al., 2020).

(6) Market scale (mar). The prosperity of the consumer market can improve the scale of tourism consumption and expand the tourism market. The market scale is reflected by total retail sales in this research (Chen et al., 2020).

Data sources and processing

Annual data on the cities of YRDUA are matched with data received from different sources. The city-level panel data of the number of patent licenses were collected from China Statistical Yearbook on Science and Technology (2006∼2020). Annual data on tourism income and tourist number were received from the Shanghai City Statistical Yearbook (2006∼2020), Jiangsu Province Statistical Yearbook (2006∼2020), Zhejiang Province Statistical Yearbook (2006∼2020), and the Anhui Province Statistical Yearbook (2006∼2020). Annual data on other control variables of 27 cities in the YRDUA covering the period between 2005∼2019 were taken from the China City Statistical Yearbook (2006∼2020). Part of the data was taken from the Statistics Bulletin of National Economic and Social Development (2005∼2019) of every city.

Descriptive analysis

Taking year 2005 as example, the scatter plot was intuitively drawn to reflect the relationship between technological innovation and tourism development (Figure 1), indicating that there is a positive correlation between technological innovation and tourism development. Nevertheless, there is still an econometric model to examine the relationship between technological innovation and the development of tourism. Moreover, to eliminate heteroscedasticity, which may deactivate the empirical results, all variables are regarded logarithmically in this study. The variance inflation factors (VIF) of all variables are less than 10, and the correlation coefficients among explanatory variables are below 0.8 (Tables 1 and 2), indicating that the multicollinearity among variables does not affect the results. The descriptive statistics results are shown in Table 1, with the mean value, standard deviation, minimum value, maximum value, skewness, and kurtosis.

Figure 1.

The relationship between technological innovation and tourism development in 2005.

Table 1.

Statistical description of each variable.

Type	Variable	Mean	Std. Dev	Min	Max	Skewness	Kurtosis	VIF
Dependent variable	lntour	8.973	1.120	−5.828	1.932	−0.438	2.690	—
Independent variable	lntech	8.556	1.747	2.833	11.518	−0.799	3.214	6.43
Control variable	lngover	−2.143	0.354	−2.900	−1.287	0.379	2.543	1.66
	lnindus	3.728	0.195	3.152	4.286	−0.029	3.168	2.42
	lntrans	9.057	0.629	6.511	9.945	−1.526	5.578	2.92
	lninvest	10.473	0.731	7.851	12.074	−0.752	3.497	3.20
	lnope	−1.092	0.965	−3.434	1.932	0.014	2.953	1.66
	lnmar	−1.064	0.222	−1.859	0.515	−0.396	4.024	1.97

Table 2.

Correlation matrix.

Variable	lntour	lntech	lngover	lnindus	lntrans	lninvest	lnope	Lnmar
lntour	—	—	—	—	—	—	—	—
lntech	0.668^***	—	—	—	—	—	—	—
lngover	0.218^***	−0.073	—	—	—	—	—	—
lnindus	0.672^***	0.610^***	0.108^**	—	—	—	—	—
lntrans	0.039^***	0.503^***	0.136^**	0.280^***	—	—	—	—
lninvest	0.790^***	0.636^***	0.256^***	0.430^***	0.045	—	—	—
lnope	0.435^***	0.481^***	−0.349^***	0.379^***	−0.179^***	0.270^***	—	—
lnmar	0.377^***	0.400^***	0.226^***	0.621^***	0.400^***	0.153^***	0.066	—

Note: ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

Empirical results

Panel stationarity test

In order to avoid spurious regression, which may result in biased and meaningless results, it is necessary to conduct the panel unit root test to examine the stationary character of time-series data. The panel stationarity test is composed of two kinds of models, namely, the homogeneous unit root test (e.g., the Levin-Lin-Chu (LLC test); the Hadri Z test) and the heterogeneity unit root test (e.g., the Im, Pesaran, and Shin [IPS test]; Fisher-Augmented Dickey-Fuller [Fisher-ADF test]; and Fisher-Phillips-Perron [Fisher-PP test]). To guarantee the reliability and accuracy of results, this study adopts five methods to test the stationarity of all variables. Given the complexity regarding unit root test theory, and the differences of applicable objects, as shown in Table 3, a part of variables cannot pass all types of unit root test. Referring to Zhao and Zhang (2017), if more than half of the results indicate that the null hypothesis can be strongly rejected, the panel regression model will be conducted in this study. Three variables (indus, ope, and mar) do not meet some tests, but more than half of the results regarding unit root test can reject the null hypothesis. This reveals that the panel data is stable.

Table 3.

Unit root tests of variables.

Variable	LLC test	IPS test	Fisher-ADF test	PP test	Hadri Z test
lntour	−8.010^***	−1.622^*	229.774^***	233.319^***	41.722^***
lntech	−10.083^***	−4.126^***	129.378^***	180.080^***	13.097^***
lngover	−6.533^***	−1.665^**	102.393^***	104.633^***	12.753^***
lnindus	−1.308^*	7.077	13.90	147.677^***	10.658^***
lntrans	2.085	−10.066^***	982.279^***	414.966^***	12.585^***
lninvest	−8.769^***	−3.028^***	166.910^***	201.012^***	14.645^***
lnope	−1.458^*	−1.300	124.006^***	38.113	12.859^***
lnmar	−1.651^**	4.748	15.718	174.848^**	12.399^***

Note: ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

Full sample estimation

Table 4 shows the results of different models in columns (1)–(4). The Breusch-Pagan Lagrange multiplier (LM) test has a value of 1544.30 and is statistically significant (p < 0.01), showing that the RE (Random Effect) model strongly rejects the null hypothesis of the absence of individual random effect and confirms that the RE model is superior to the pooled OLS model. Lastly, the Hausman test strongly rejects the null hypothesis of no difference between the FE (Fixed Effect) model and the RE model, indicating that the FE model outperforms the RE model. In addition, the individual FE model cannot solve the issue that time variation may lead to omitted variables, and thus resulting in estimation bias. Therefore, the two-way fixed-effect model is applied to control both the time effect and the individual effect. Taking into consideration the above advantages, the research mainly explains the estimated results of the two-way fixed-effect model.

Table 4.

Full sample estimation results.

Variable	Pooled OLS model	Random effect model	Fixed effect model	Two-way fixed-effect model
Variable	(1)	(2)	(3)	(4)
lntech	0.180^*** (5.02)	0.186^*** (7.24)	0.204^*** (7.44)	0.133^*** (4.20)
lngover	0.437^*** (4.86)	0.103 (1.01)	0.014 (0.13)	−0.011 (0.07)
lnindus	1.688^*** (8.56)	0.964^*** (7.29)	0.976^*** (7.06)	−0.109 (−0.54)
lntrans	−0.428^*** (−6.37)	0.018 (0.33)	0.064 (1.11)	0.109 (1.19)
lninvest	0.657^*** (10.86)	0.716^*** (13.79)	0.699^*** (13.01)	0.445^*** (4.13)
lnope	0.084^** (2.17)	0.164^*** (5.46)	0.175^*** (5.50)	0.157^*** (3.77)
lnmar	0.387^** (2.48)	0.356^** (2.58)	0.340^** (2.43)	0.285^* (2.00)
cons	−0.432 (−0.37)	−3.114^*** (−3.36)	−3.738^*** (−3.95)	2.581 (−1.70)
F test	236.23^***	—	730.580^***	—
Breusch-Pagan LM test	—	1544.30^***	—	—
Hausman test	—	—	48.970^***	—
R-squared	0.803	0.742	0.720	0.679
Observations	405	405	405	405

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

The model results, shown in column (4), provide evidence that there is a positive and statistically significant (p < 0.01%) association between technological innovation and tourism development. A 1% increase in technological innovation leads to a 0.133% rise in the development of tourism. As Buhalis and Law (2008) emphasized, “technological innovation and tourism development have been going hand in hand for years”. Technological innovation plays an indispensable role in the tourism growth of YRDUA, where technological factors are highly concentrated. The main impacts of technological progress on tourism growth are summarized as follows. Firstly, information technologies can undoubtedly improve business practices and promote management efficiency by establishing the platform that all tourism actors can actively participate in co-creating; secondly, transportation technologies can shorten the distance in time between the destinations and the departure locations of tourists, which would expand the tourism market demand by promoting the attractiveness of tourist destinations; thirdly, modern technologies, such as artificial intelligence, fifth-generation, and near-field communication, can increase tourists’ experience by allowing them to engage in the tourism scene.

For the effects of control variables on the development of tourism, three control variables (invest, ope, and mar) exercise a positive effect on tourism development. Firstly, the level of investment is positive and statistically significant associated with the growth of tourism. A one percent increase in investment contributes to a 0.445% increase in the development of tourism. The increase of investment is conducive to exploiting more tourist attractions, improving tourism infrastructures, and maintaining the sustainable mobility of the YRDUA to a certain extent. Secondly, the estimated coefficient of openness degree to the tourism development is positive and significant at the level of 1%. One percent increase in openness degree raises the tourism development by 0.157%. The improvement of openness degree can not only expand the inbound tourism market but also attract more foreign investment. Thirdly, the result indicates that the expansion of markets scale will stimulate tourism growth. More specially, one percent expansion in the market scale increases tourism growth by 0.285% at the level of 10%, which is also in line with the prosperity of the domestic tourism market in the YRDUA.

Heterogeneity analysis

The effect of technological innovation on tourism development in different regions. To our knowledge, the development of tourism and technological innovation presents an apparent pattern of spatial imbalance in the YRDUA (Shi et al., 2021). Therefore, this study explores the regional heterogeneous effect of technological innovation on tourism development. According to the relationship of administrative subordination, our samples are divided into four sub-samples, namely, Shanghai City, Jiangsu Province, Zhejiang Province, and Anhui Province. Shanghai, the economic center of China and the world-famous tourist destination, has abundant tourism resources, and favorable tourism infrastructures. Zhejiang and Jiangsu are all the strong provinces of tourism development in China. The cities, located in abovementioned two provinces, can attract more tourists by improving the convenience of transportation, and enhancing the diversity of tourism resources. However, given that the tourism industry developed later compared with other three regions, there is lack of accommodation facilities and high-grade tourism attractions in cities of Anhui Province. Therefore, the level of tourism development is relatively poor. The pooled OLS model is employed to examine the effect of technological innovation on tourism development. Additionally, this study adopts the Hausman test to judge which one of the FE and RE models is the more superior selection for conducting panel regression models. Results, shown in Table 5, reveal that the FE model outperforms the RE model in the estimation of the other three provinces.

Table 5.

Effect of technological innovation on tourism development in different regions.

Variable	Shanghai City	Jiangsu Province	Zhejiang Province	Anhui Province
Variable	(1)	(2)	(3)	(4)
lntech	0.653^*** (4.57)	0.046^** (2.14)	0.134^* (1.92)	0.303^*** (6.51)
lngover	−0.266 (−0.75)	0.891^*** (8.69)	0.429 (1.18)	−0.087 (−0.38)
lnindus	6.854^** (2.37)	0.076 (0.90)	2.107^*** (3.64)	1.268^*** (4.42)
lntrans	0.931^** (2.61)	1.451^*** (6.28)	0.036 (0.23)	0.087 (0.92)
lninvest	−0.513 (−1.08)	0.031 (0.50)	0.668^*** (4.58)	0.632^*** (6.96)
lnope	2.503^* (2.18)	0.047 (0.83)	0.130^** (2.75)	0.038 (0.39)
lnmar	−1.851^* (−2.11)	−0.053 (−0.51)	−0.383 (−0.79)	−0.135 (−0.55)
cons	−14.947 (−1.44)	−6.583^*** (−8.59)	−6.537^* (−1.93)	−5.881^*** (−3.55)
F test	58.82^***	1118.26^***	198.98^***	260.38^***
Hausman test	—	83.29^***	54.73^***	51.21^***
R-squared	0.966	0.814	0.838	0.621
Model	—	FE	FE	FE
Observations	15	135	135	120

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

Results indicate that the estimated coefficients of technological innovation to tourism development, as shown in columns (1)–(4), are positive and statistically significant. This indicates that technological progress can effectively promote tourism development to a different extent; in four regions, a one percent increase in technological innovation contributes to 0.653%, 0.046%, 0.134%, and 0.303% on tourism development, respectively. Significantly, the spillover effect of technological innovation found in Shanghai City is slightly stronger than in the other three provinces. The stronger ability of technological innovation can not only improve value-added of tourism industry by attracting more tourists but also promote operation efficiency of tourism sectors such as hotels and travel agencies. For instance, smart ticket cloud systems and intelligent interpretation systems have been early used to the destination management in Shanghai. Compared with Jiangsu Province and Zhejiang Province, the positive effect of technological innovation is stronger in cities where belong to Anhui Province. As can be seen in Table 5, the positive effect of the government intervention, the transportation accessibility, or investment level are all relatively weaker, indicating the driving factors of tourism development are more simplex in Anhui Province. Therefore, the positive effect of technological progress may be more apparent in cities of Anhui Province. With the improvement of attention, technological innovation will exert a stronger positive effect on tourism development in these cities of Jiangsu and Zhejiang Province.

The effect of technological innovation on tourism development in different types of cities. Referring to the Notice on Adjusting the Standard of City Size Division, issued by the State Council of China, this study divides 27 cities into three classes based on the number of urban populations from the Sixth Population Census (Qi et al., 2016), namely, big city, medium city, and small city². Results in Table 6 reveal that the FE model outperforms the RE model in the estimation of three types of cities. The estimated coefficients of technological innovation to tourism development, as shown in columns (1) to (3) of Table 6, are positive and statistically significant. A one percent increase in technological innovation contributes to 0.117%, 0.177%, and 0.235% on tourism development of three types of cities, respectively. In addition, the estimated coefficients of technological innovation found in small city are bigger than that of other two types of cities. This may result from the fact that some small cities such as Anqing, Xuancheng, and Tongling more depend on tourism industry; therefore, the government may pour relatively more attention into the technological application in the process of tourism development.

Table 6.

Effect of technological innovation on tourism development in different types of cities.

Variable	Big city	Medium city	Small city
Variable	(1)	(2)	(3)
lntech	0.117^*** (3.69)	0.177^* (1.66)	0.235^*** (5.37)
lngover	−0.014 (−0.11)	0.182 (0.66)	0.001 (0.01)
lnindus	1.078^*** (6.39)	1.262^*** (2.73)	1.924^*** (6.06)
lntrans	0.448^*** (4.998)	0.127 (0.68)	−0.208^** (−2.19)
lninvest	0.699^*** (11.30)	0.580^*** (3.66)	0.935^*** (19.68)
lnope	0.053 (1.27)	0.274^*** (3.78)	0.830^*** (9.15)
lnmar	0.220 (1.31)	0.426 (1.31)	−0.812^** (−2.28)
cons	−7.295^*** (−6.08)	−3.395 (−1.16)	−7.380^*** (−4.20)
F test	456.69^***	138.23^***	302.78^***
Hausman test	18.06^***	31.60^**	78.08^***
Model	FE	FE	FE
R-squared	0.764	0.704	0.766
Observations	180	120	105

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

The effect of technological innovation on tourism development in different stages. According to the principle of equal parts division, the period of 2005∼2019 is classified into three stages, in order to explore the impact of technological innovation on tourism development. The three stages are set as 2005∼2009, 2010∼2014, and 2015∼2019. The results, given in Table 7, reveal that the FE model outperforms the RE model in all three stages. Columns (1) to (3) of Table 7 indicate that the estimated coefficients of technological innovation are all positive and statistically significant, revealing that the technological innovation exerts a positive influence on the tourism development across the three sub-periods. To be more specific, a 1% increase in technological innovation is associated with an increase of 0.145%, 0.205%, and 0.223% in tourism development of three sub-periods, respectively. Technological innovation plays a more crucial role in tourism development as time goes on. The increasingly strong impact of technological innovation implies that, with the goal regarding high-quality development of tourism put forward, governments at all levels in the YRDUA pour more attention into technological innovation, application, and promotion in the tourism industry. For example, the technology of virtual reality and augmented reality can be popularized in West Lake, Hangzhou to promote a greater experience and expand awareness.

Table 7.

Effect of technological innovation on tourism development in different stages.

Variable	2005∼2009	2010∼2014	2015∼2019
Variable	(1)	(2)	(3)
lntech	0.145^** (2.48)	0.205^*** (4.76)	0.223^* (1.85)
lngover	0.502 (1.08)	0.832^*** (4.22)	0.045 (0.27)
lnindus	0.281 (0.57)	−0.034 (−0.08)	1.544^*** (6.53)
lntrans	0.144 (1.22)	−0.022 (−0.16)	−0.203^* (−1.81)
lninvest	0.387^** (2.03)	0.698^*** (7.38)	0.136^*** (4.06)
lnope	−0.275^** (−2.06)	0.177^** (2.15)	1.106^*** (10.58)
lnmar	−1.584^** (−2.57)	0.248^* (1.89)	−0.707^*** (−4.40)
cons	−0.001 (−0.01)	0.140 (0.51)	−7.286^** (2.22)
F test	26.04^***	95.31^***	635.10^***
Hausman test	20.79^***	16.03^**	14.96^**
Model	FE	FE	FE
R-squared	0.643	0.448	0.341
Observations	135	135	135

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

The effects of different types of technological innovation on tourism development. In the light of the classification standard of patent, this study adopts three types of patents (the patent of invention, the patent of utility model, and the patent of design) as three kinds of technological innovations. Then, the panel regression models are applied to examine the effect regarding different types of technological innovation on tourism development. Results show that, except for the patent of invention, the FE model outperforms the RE model for the other two types of patents. Columns 1 and 2 of Table 8 reveal that the estimated coefficients regarding the patent of invention and the patent of utility model are all positive and significant at the level of 1%. Simultaneously, the estimated coefficient regarding the patent of design is positive and significant at the level of 10%. The estimated coefficient regarding the patent of utility model is bigger than that of the other two types of patents, indicating that it has a stronger positive influence on tourism development. On the one hand, the approval procedure regarding the patent of utility model is faster; therefore, it can be quickly applied to tourism management and marketing. On the other hand, the cost regarding the patent of utility model is less, and thus covering a wider range. More importantly, the patent of utility model has higher practical value in tourism development compared with other two types of patents. For instance, the intelligent travel equipment can provide voice broadcast, GPS positioning, and communication, and thereby helping tourists gain a favorable experience. Additionally, the tourism management system can not only improve the service level of guides but also safeguard the personal security of tourists. Furthermore, the patent of invention and the patent of design are applied mostly in the minority of large tourism enterprises in YRDUA. Therefore, the positive effect regarding the patent of utility model is stronger in tourism development of YRDUA.

Table 8.

Effect of different types of technological innovation on tourism development.

Variable	The patent of invention	The patent of utility model	The patent of design
Variable	(1)	(2)	(3)
lntech	0.172^*** (8.06)	0.260^*** (10.76)	0.035^* (1.86)
lngover	0.175^* (1.75)	−0.001 (−0.01)	0.129 (1.11)
lnindus	0.587^** (4.29)	0.787^*** (6.19)	0.856^*** (5.75)
lntrans	0.024 (0.44)	0.058 (1.08)	0.165^*** (2.73)
lninvest	0.688^*** (13.42)	0.592^*** (11.66)	0.935^*** (19.68)
lnope	0.145^*** (4.93)	0.177^*** (6.01)	0.136^*** (4.06)
lnmar	0.091 (0.65)	0.248^* (1.899)	0.393^*** (2.63)
cons	−1.049 (−1.05)	−2.272^** (−2.50)	−4.928^*** (−4.95)
F test	—	841.41^***	635.10^***
Hausman test	6.96	13.12^*	26.56^***
Model	RE	FE	FE
R-squared	0.742	0.708	0.740
Observations	405	405	405

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

Robustness test and endogeneity issue

Robustness test. The ratio of tourist numbers to the total population size is applied as a proxy of the development of tourism in the estimation to examine whether the above regression results are robust. Additionally, this study uses the method that control variable is eliminated one by one in the robustness check in Table 9. The results of the Hausman tests indicate that the FE model outperforms the RE model in the estimation of columns (3) to (6). Technological innovation, presented in columns (1)–(6), exercises positive and statistically significant effects on tourism development, which is in line with the results of Table 4. In other words, the regression results are robust. Additionally, the result of the Hausman test shows that the FE model is the preferred choice in the estimation of column (7). When the ratio of tourist numbers to the total population size is regarded as the dependent variable, the estimated coefficient of technological innovation to tourism development is 0.135, positive and statistically significant at the level of 1%. A one percent increase in technological innovation contributes to a 0.135% increase in tourism development. Consequently, we can further confirm that the results in Table 4 are reliable.

Table 9.

Results of robustness tests.

Dependent variable: Tourism income (Tourism development)							Dependent variable: The ratio of tourist numbers to the total population size
Explanatory variable	Eliminate lngover	Eliminate lnindus	Eliminate lntrans	Eliminate lninvest	Eliminate lnope	Eliminate lnmar
Explanatory variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
lntech	0.187^*** (7.31)	0.162^*** (6.00)	0.212^*** (8.03)	0.454^*** (19.39)	0.173^*** (6.23)	0.205^*** (7.47)	0.135^*** (3.05)
lngover	—	0.038 (0.36)	0.034 (0.31)	0.531^*** (4.29)	0.128 (1.14)	0.044 (0.41)	0.154 (0.87)
lnindus	0.952^*** (7.00)	—	0.970^*** (7.02)	1.569^*** (9.98)	0.883^*** (6.20)	1.151^*** (9.70)	0.967^*** (4.33)
lntrans	0.027 (0.49)	0.008 (0.15)	—	0.027 (0.39)	0.067 (1.11)	0.099^* (1.74)	−0.074 (−0.79)
lninvest	0.739^*** (15.69)	0.830^*** (15.89)	0.696^*** (12.96)	—	0.724^*** (13.02)	0.697^*** (12.90)	0.681^*** (7.85)
lnope	0.166^*** (5.58)	0.147^*** (4.63)	0.175^*** (5.50)	0.210^*** (5.50)	—	0.172^*** (5.38)	0.449^*** (8.73)
lnmar	0.375^*** (2.74)	0.870^*** (7.02)	0.377^*** (2.78)	0.325^* (1.93)	0.314^** (2.17)	—	0.545^** (2.41)
cons	−3.600^*** (−4.57)	−0.026 (−0.03)	−3.082^*** (−4.16)	0.692^*** (0.65)	−3.394^*** (−3.46)	−5.005^*** (−6.29)	−8.023^*** (−5.25)
Hausman test	5.17	8.54	10.91^*	28.67^***	20.94^***	10.75^*	13.73^*
Model	RE	RE	FE	FE	FE	FE	FE
R-squared	0.734	0.723	0.732	0.584	0.718	0.714	0.380
Observations	405	405	405	405	405	405	405

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

Endogeneity issue. Although the analysis of the heterogeneous effect or the two-way fixed effect can partly solve the endogeneity issue to a certain extent, it may not avoid reverse causality and omitted variable issues. The instrumental variable is widely used to solve the endogeneity issue in econometrics. The enrollment of higher education can create the late-blooming advantages for the cultivation of scientific manpower, and thus promoting the ability of technological innovation (Shi et al., 2022). Furthermore, Pearson’s correlation coefficient is 0.641 (p < 0.01), revealing that there is a correlation between technological innovation and the enrollment of higher education. However, there is neither close nor direct connection between the enrollment of higher education and tourism development. Therefore, the enrollment of higher education is basically regarded as an instrumental variable in this study.

Additionally, the lag effect of technological innovation can exert an important influence on tourism development. Therefore, the lagged term of technological innovation was seen as another instrumental variable in this study. The Five-Year Plan is an important part of economic and social development framework in China’s cities. Meanwhile, the positive effect of technological innovation may be not apparent due to the too long lag period. Finally, the five-period lag of technological innovation to the ten-period lag of technological innovation was examined one by one. However, there are the issues of unidentifiability, weak identification, or endogeneity in the six-period lag, seven-period lag, eight-period lag, nine-period lag, and the ten-period lag of technological innovation. The five-period lag of technological innovation was finally seen as another instrumental variable.

The relevant tests are shown in Table 10. Firstly, the F value of the weak instrumental variable test shown in column (1) is greater than the standard value of 10, revealing that there is not the issue of weak instrumental variable. Secondly, the Anderson canon. corr. LM statistic has a value of 98.102 and is statistically significant (p < 0.01), showing that this model rejects the null hypothesis of unidentifiability. Thirdly, the Kleibergen-Paap. rk. LM statistic is significant at the 1% significance, revealing that there is not the issue regarding a lack of identifiability. Fourthly, the p-values of the Sargan test and Basmann test are greater than the standard value of 0.05, reflecting that this model cannot reject the null hypothesis that all instrumental variables are exogenous. Meanwhile, the abovementioned tests can also reveal that there is not the issue of over-identification. These tests can further prove that the five-period lag of technological innovation and the enrollment of higher education are effective and suitable.

Table 10.

Regression results of the instrumental variable.

Variable	First stage	Second stage
Variable	(1)	(2)
lntech	—	0.195^** (2.10)
lntech_t-5	0.395^*** (10.81)	—
lnedu	0.209^*** (5.65)	—
Control variable	Control	Control
cons	0.036 (0.02)	1.841 (1.03)
F test	249.418^***	—
Anderson canon. corr. LM statistic	98.102^***	—
F-value of weak instrumental variables	68.208^***	—
Kleibergen-Paap. rk. LM statistic	53.616^***	—
p-value of Sargan test	0.985	—
p-value of Basmann test	0.986	—
R-squared	0.880	0.710
Observations	270	270

Note: The t-statistics for the coefficients are reported in parentheses; ^*, ^**, and ^*** represent 10%, 5%, and 1% levels, respectively.

2SLS (two-stage least square) is employed to further examine the impact of technological innovation on tourism development (Table 10). The results of the second stages given in column (2) reveal that the impact of technological innovation on the development of tourism is positive and significant at the 5% level, which is in accordance with the results of the two-way fixed-effect model. The analysis further confirms that technological innovation has a positive effect on tourism development in YRDUA.

Discussion

Tourism industry is changing from simple resource orientation to the situation of coordinated and integrated development. To be more specific, the goal of tourism development is not only to expand the industrial scale but also to improve the quality and efficiency of development based on technological innovation. Nevertheless, there is scant work on the empirical specifications to identify the impact of technological innovation on tourism development. The practical background and theoretical gap have led us to pay attention to examining the relationship between technological innovation and tourism development. Taking 27 cities of the YRDUA as the case studies, this study adopts the panel regression model to explore whether technological innovation can promote the development of tourism and to investigate the heterogeneous effect of technological innovation on tourism development.

By empirical examination, this study further confirms that technological innovation has a positive and significant influence on tourism growth in the YRDUA (Shi et al., 2021). A growing body of literature can confirm that technological innovation can promote tourism development by optimizing the allocation efficiency of tourism factors (Srivastava and Dhar, 2016; Alemayehu and Kumbhakar, 2021), boosting tourists’ experience feeling (Ponsignon and Derbaix, 2020; Fennell, 2020; Marques and Borba, 2017), promoting brand awareness (Smith et al., 2015; Sun et al., 2020), expanding tourism market (Kim and Hall, 2020; Anastasiadou and Vettese, 2019), and facilitating tourism education (Qiu et al., 2020; Bilotta et al., 2020). Hence, it is crucial to notice that technological innovation has already become one of the most important driving forces in the economic development of tourism.

Technological innovation is conducive to promoting tourism development in different provinces of the YRDUA, while the positive effect is stronger in Shanghai. Shanghai has a stronger ability for innovation and thus increasingly more technology products are widely applied in tourism destinations management. This result is in accordance with the conclusion of Tian et al. (2015). Additionally, the positive effect of technological innovation found in small city is bigger than that of other two types of cities, resulting from that small city may more dependent on tourism development, and attach more attention to the application of technological products in tourism development (Gong et al., 2016). Over time, the role that technological innovation plays in tourism development is increasingly important in the YRDUA, resulting from the fact that the expansion of tourism market demand drives tourism administrations to widely adopt information technology (Shi et al., 2021). Compared with the other two types of patents, the patent of utility model can exert a stronger positive effect on tourism development in the YRDUA. On the one hand, the patents of utility model not only have the faster approval procedure, and thus covering a wider range. On the other hand, the cost regarding the patents of utility model is less; therefore, it can be widely applied to tourist destinations management. Hence, the impact of the patent of utility model on tourism services and management is stronger (Song and Ma, 2010).

This study makes literature contributions as follows. Firstly, to our knowledge, this study is one of the first that uses empirical specifications to examine the effects of technological innovation on tourism development in the YRDUA. Our findings add to a growing body of evidence indicating that technological progress is conducive to promoting tourism development, to a certain extent. Secondly, an urban agglomeration is a special tourist destination, where technological innovation can not only promote tourism development of a single city but also strengthen tourism economic cooperation among different cities, based on the spatial spillover effect, as this research casts a new light on our understanding of how to promote the high-quality development of tourism in urban agglomerations, by the cooperation of technological innovation. Thirdly, this study contributes to theory and perspective via establishing the adopted dataset of the model contributions to still confined understandings of this topic in academic circles. Although the dataset is limited to the YRDUA, this analysis framework and methodology are generalizable and flexible across other urban agglomerations in the world.

Conclusions

The conclusions indicate that technological innovation can exert positive effects on tourism development in the YRDUA. In addition, this study finds that, from the perspectives of different regions, stages, and types of patents, there are tremendous differences in the effect of technological innovation on tourism development. The impact of technological innovation becomes stronger over time in the YRDUA. On the provincial level, the positive effect of technological innovation in Shanghai is stronger than in the other three provinces. Additionally, technological innovation exerts a bigger effect on the tourism development of small cities. Moreover, the patent of utility model can exert more intense positive effects on the development of tourism, due to their higher applicability.

Based on the abovementioned conclusions, quite a few implications are provided for authorities and enterprises concerned with the high-quality development of tourism. Firstly, cities located in the YRDUA should not only pay more attention to strengthening capital investment in technology innovation of tourism but also take the promotion and application of innovative technologies, such as smart technology, digital technology, information technology, mobile technology, and low-carbon technology among various cities. Furthermore, a favorable innovation environment should be provided for tourism enterprises, which have ambitions to make greater progress in the technological innovation of product distribution, market promotion, and product communication. Secondly, the cooperation mechanism of tourism innovation should be established by the information network interconnection, the knowledge-sharing platform, and the talent flow network, in order to achieve provincial coordinated and win–win tourism development in the YRDUA. To be more specific, Shanghai needs to make full use of its stronger ability for innovation to play the role of fugleman in promoting tourism technical progress among other cities. Some small cities should continue to consolidate the important role of technological innovation and to introduce new technologies, tourism talent, and managerial experience from metropolises, such as Shanghai, Nanjing, Hangzhou, and Hefei. Thirdly, cities should give full play to the patents of utility model in perfecting tourism service and promoting destinations management. For instance, intelligent explanation systems and multifunction tag systems can be widely used in scenic spots across the YRDUA. Simultaneously, the patents of invention and design, such as energy-saving systems, and artificial intelligence technology, should not be neglected by tourism enterprises and administrators.

This study is not without limitations that should not be overlooked but also pave the potential road for future study. Firstly, quite a few control variables such as the number of scenic spots, the number of hotels, and the number of travel agencies are not obtained at the city level, due to the inapplicability of data. Secondly, there is a dearth of the specific types of patents, such as digital innovations or green technology innovations. Therefore, future scholars can pick a specific component of technological innovation to analyze the relationship among the two. Thirdly, future research can also explore the effect of technological innovation on different types of tourism activities when the statistic framework of the relevant datasets is established.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Office for Philosophy and Social Sciences; 18BJY191, National Natural Science Foundation of China; 41771162.

ORCID iDs

Kai Wang

Mihai Voda

Note

Author biographies

Mr. Chang Gan is a doctor student at the Tourism College of Hunan Normal University, Changsha, China. He is currently studying on tourism economic development in urban agglomeration. Chang Gan has published several academic papers in English journals such as Journal of Hospitality and Tourism Management, Sustainability, and International Journal of Environmental Research and Public Health. Gan can be contacted at the following email: 1902188840@qq.com.

Kai Wang is a professor at the Tourism College of Hunan Normal University, Changsha, China. He obtained doctoral degree from East China Normal University, Shanghai, China. His research interests involve tourism geography and tourism management. He has published a number of academic papers in tourism journals, for example, Tourism Economics. Meanwhile, Professor Wang is reviewer for Tourism Economics and Asia Pacific Journal of Tourism Research. Dr. Wang’s contact details are as follows: Mail Address: No. 36 Lushan Road, Changsha, Hunan, China. E-mail: kingviry@163.com. Mobile phone: 13637411488.

Mr. Voda is an Associate Professor at the Department of Tourism Geography at Dimitrie Cantemir University from Targu-Mures. He holds a PhD in Geography from Babes-Bolyai University and an MA in Applied Informatics from the Technical University in Cluj-Napoca. His research interest is in applied informatics, with a particular focus on smartphone geo-applications analysis and Geomedia techniques. His primary fields of thematic interest are the geography and sustainability. Voda can be contacted at the following email: mmvoda@yahoo.com.

Mrs. Jun Ye is an Associate Professor at College of Geography and Tourism at Huanggang Normal University. Her research interests with regarding to rural tourism and tourism economic.

Mrs. Lijun Chen is an Associate Professor at College of Geography and Tourism at Huanggang Normal University. Her research interests with regarding to rural tourism and tourism economic development.

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