To investigate the impact of smart city construction (SCC) on destination tourism efficiency (TE), this study analyzed the impact paths and mechanisms of SCC on destination TE from 3 aspects: technological innovation, informatization, and industrial structure upgrading. Based on panel data of Chinese prefecture-level cities from 2004–2019, we combined a stochastic frontier analysis model with a difference-in-differences model to conduct an empirical analysis to assess the policy effects of SCC on destination TE and validate the impact paths and mechanisms. The results show that the tourism of 153 sample cities show a low TE level overall, an increasing trend year by year, a distribution pattern of high in the east, and low in the middle and west. SCC can significantly improve destination TE, and the impact of SCC on destination TE has spatially heterogeneous.
AignerDLovellCAKSchmidtP (1977) Formulation and estimation of stochastic frontier production function models. Journal of Econometrics6(1): 21–37.
2.
AlbinoVBerardiUDangelicoRM (2015) Smart cities: definitions, dimensions, performance, and initiatives. Journal of Urban Technology22(1): 3–21.
3.
Almeida-GarcíaFDomígunez-AzcueJMercadé-MeléP, et al. (2020) Can a destination really change its image? The roles of information sources, motivations, and visits. Tourism Management Perspectives34: 100662.
4.
AndersonRIFishMXiaY, et al. (1999) Measuring efficiency in the hotel industry: a stochastic frontier approach. International Journal of Hospitality Management18(1): 45–57.
5.
ArkhangelskyDAtheySHirshbergDA, et al. (2021) Synthetic difference in differences. The American Economic Review111(12): 4088–4118.
6.
AssafAGTsionasM (2018) The estimation and decomposition of tourism productivity. Tourism Management65: 131–142.
7.
AssafAGTsionasMKockF, et al. (2021) A Bayesian non-parametric stochastic frontier model. Annals of Tourism Research87: 103116.
8.
AzisNAminMChanS, et al. (2020) How smart tourism technologies affect tourist destination loyalty. Journal of Hospitality and Tourism Technology11: 603–625.
9.
BanoSLiuLKhanA (2022) Dynamic influence of aging, industrial innovations, and ICT on tourism development and renewable energy consumption in BRICS economies. Renewable Energy192: 431–442.
10.
BatteseGECoelliTJ (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis3(1-2): 153–169.
11.
BatteseGECoelliTJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics20: 325–332.
12.
BjørnerT (2021) The advantages of and barriers to being smart in a smart city: the perceptions of project managers within a smart city cluster project in greater copenhagen. Cities114: 103187.
13.
BoesKBuhalisDInversiniA (2016) Smart tourism destinations: ecosystems for tourism destination competitiveness. International Journal of Tourism Cities2(2): 108–124.
14.
Boto-GarcíaDBaños-PinoJFDel ValleE, et al. (2023) Vacation rental market regulation and accommodation supply growth. Tourism Economics29(6): 1551–1576.
15.
CaragliuADel BoCFNijkampP (2011) Smart cities in europe. Journal of Urban Technology18: 65–82.
16.
CardDKruegerA (1994) Minimum wages and employment: a case study of the fast food industry in New Jersey and Pennsylvania. The American Economic Review84(4): 772–793.
17.
CharnesACooperWWRhodesE (1979) Measuring the efficiency of decision-making units. European Journal of Operational Research3(4): 339.
18.
ChenP (2022) The impact of smart city pilots on corporate total factor productivity. Environmental Science and Pollution Research International29: 83155–83168.
19.
ChoiKKangHJKimC (2021) Evaluating the efficiency of Korean festival tourism and its determinants on efficiency change: parametric and non-parametric approaches. Tourism Management86(1): 104348.
20.
DengXLiangLWuF, et al. (2022) A review of the balance of regional development in China from the perspective of development geography. Journal of Geographical Sciences32: 3–22.
21.
EichelbergerSPetersMPikkemaatB, et al. (2020) Entrepreneurial ecosystems in smart cities for tourism development: from stakeholder perceptions to regional tourism policy implications. Journal of Hospitality and Tourism Management45: 319–329.
22.
FanXYuBChuZ, et al. (2020) A stochastic frontier analysis of the efficiency of municipal solid waste collection services in China. Science of The Total Environment743: 140707.
23.
García-MilonAJuaneda-AyensaEOlarte-PascualC, et al. (2020) Towards the smart tourism destination: key factors in information source use on the tourist shopping journey. Tourism Management Perspectives36: 100730.
24.
GoharMMuzammalMUr RahmanA (2018) Smart TSS: defining transportation system behavior using big data analytics in smart cities. Sustainable Cities and Society41: 114–119.
25.
GretzelUWerthnerHKooC, et al. (2015) Conceptual foundations for understanding smart tourism ecosystems. Computers in Human Behavior50: 558–563.
26.
GretzelUKooC (2021) Smart tourism cities: a duality of place where technology supports the convergence of touristic and residential experiences. Asia Pacific Journal of Tourism Research26(4): 352–364.
27.
GuoQZhongJ (2022) The effect of urban innovation performance of smart city construction policies: evaluate by using a multiple period difference-in-differences model. Technological Forecasting and Social Change184: 122003.
28.
GuoLLiPZhangJ, et al. (2022) Do socio-economic factors matter? A comprehensive evaluation of tourism eco-efficiency determinants in China based on the Geographical Detector Model. Journal of Environmental Management320: 115812.
29.
GuptaSModgilSLeeC-K, et al. (2022) Artificial intelligence enabled robots for stay experience in the hospitality industry in a smart city. Industrial Management & Data Systems122: 2331–2350.
30.
ImbensGWAngristJD (1994) Identification and estimation of local average treatment effects. Econometrica62(2): 467.
31.
IvusOBolandM (2015) The employment and wage impact of broadband deployment in Canada. Canadian Journal of Economics/Revue Canadienne D’économique48(5): 1803–1830.
32.
JacobsonLSLaLondeRJSullivanDG (1993) Earnings losses of displaced workers. The American Economic Review83(4): 685–709.
33.
JavedARShahzadFRehmanS, et al. (2022) Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects. Cities129: 103794.
34.
JeongMShinHH (2019) Tourists’ experiences with smart tourism technology at smart destinations and their behavior intentions. Journal of Travel Research59(8): 004728751988303.
35.
JiangQMcCabeS (2021) Information technology and destination performance: examining the role of dynamic capabilities. Annals of Tourism Research91: 103292.
36.
KimYRWilliamsAMParkS, et al. (2021) Spatial spillovers of agglomeration economies and productivity in the tourism industry: the case of the UK. Tourism Management82: 104201.
37.
KooCKimJAltR (2023) Editorial - spatial is special: special issue on exploration for spatial approach in smart tourism cities. Information Processing & Management60(4): 103401.
38.
KumbhakarSCLovellCAK (2000) Stochastic Frontier Analysis. Cambridge, UK: Cambridge University Press.
39.
KwakYHLeeJ (2021) Toward sustainable smart city: lessons from 20 Years of Korean programs. IEEE Transactions on Engineering Management70(2): 1–15.
40.
FerraraELChongADuryeaS (2012) Soap operas and fertility: evidence from Brazil. American Economic Journal: Applied Economics4(4): 1–31.
41.
LeeM, J (2016) Matching, Regression Discontinuity, Difference in Differences, and beyond. Oxford, UK: Oxford University Press.
42.
LeeCWangC (2022) Financial development, technological innovation and energy security: evidence from Chinese provincial experience. Energy Economics112: 106161.
43.
LiZLiuH (2022) How tourism industry agglomeration improves tourism economic efficiency?Tourism Economics28(7): 1724–1748.
44.
LiYWangYLiJ, et al. (2023) Global value chain embedding mode and carbon emission efficiency: evidence from China's manufacturing industry. Technological Forecasting and Social Change194: 122661.
45.
LiangXMaLChongC, et al. (2020) Development of smart energy towns in China: concept and practices. Renewable and Sustainable Energy Reviews119: 109507.
46.
LiuHGaoCTsaiH (2023) Spatial spillover and determinants of tourism efficiency: a low carbon emission perspective. Tourism Economics135481662311670.
47.
LiuAWuDC (2019) Tourism productivity and economic growth. Annals of Tourism Research76: 253–265.
48.
LiuJZhangJFuZ (2017) Tourism eco-efficiency of Chinese coastal cities – analysis based on the DEA-Tobit model. Ocean & Coastal Management148: 164–170.
49.
LuoJWangZWuM (2021) Effect of place-based policies on the digital economy: evidence from the smart city program in China. Journal of Asian Economics77: 101402.
50.
MaitiA (2023) Impact of COVID-19 on individual income in tourism and hospitality industry in India: a difference-in-differences approach. Tourism Economics29(7): 1790–1811.
51.
MarchesaniF (2022) Exploring the relationship between digital services advance and smart tourism in cities: empirical evidence from Italy. Current Issues in Tourism26(24).
52.
NamKDuttCSChathothP, et al. (2019) Blockchain technology for smart city and smart tourism: latest trends and challenges. Asia Pacific Journal of Tourism Research26(4): 454–468.
53.
NurmatovRFernandezXLCoto MillánPP (2021) The change of the Spanish tourist model: from the sun and sand to the security and sand. Tourism Economics27(8): 1650–1668.
54.
ÖnderIWöberKZekanB (2017) Towards a sustainable urban tourism development in Europe. Tourism Economics23(2): 243–259.
55.
Pérez-GranjaUInchausti-SintesF (2023) On the analysis of efficiency in the hotel sector: does tourism specialization matter?Tourism Economics29(1): 92–115.
56.
Puertas MedinaRMMartín MartínJMGuaita MartínezJM, et al. (2022) Analysis of the role of innovation and efficiency in coastal destinations affected by tourism seasonality. Journal of Innovation & Knowledge7(1): 100163.
57.
RuanWZhangS (2021) Can tourism information flow enhance regional tourism economic linkages?Journal of Hospitality and Tourism Management49: 614–623.
58.
ShinHKimJJeongM (2023) Memorable tourism experience at smart tourism destinations: do travelers' residential tourism clusters matter?Tourism Management Perspectives46: 101103.
59.
ShuYDengNWuY, et al. (2023) Urban governance and sustainable development: the effect of smart city on carbon emission in China. Technological Forecasting and Social Change193: 122643.
60.
SilvaBNKhanMHanK (2018) Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society38: 697–713.
61.
SimarLWilsonPW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics136(1): 31–64.
62.
SteadADWheatP (2020) The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data. European Journal of Operational Research280(1): 59–77.
63.
Sunp.HuangSYapG (2022) The impact of holiday tourism development on tourism total factor productivity: evidence from China. Tourism Management Perspectives43: 100993.
64.
ToneK (2001) A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research130(3): 498–509.
65.
ToneKTsutsuiM (2010) An epsilon-based measure of efficiency in DEA – a third pole of technical efficiency. European Journal of Operational Research207(3): 1554–1563.
66.
TripathyAKTripathyPKRayNK, et al. (2018) iTour: the future of smart tourism: an IoT framework for the independent mobility of tourists in smart cities. IEEE Consumer Electronics Magazine7(3): 32–37.
67.
UmTChungN (2019) Does smart tourism technology matter? Lessons from three smart tourism cities in South Korea. Asia Pacific Journal of Tourism Research26(4): 396–414.
68.
VelosoBMLealFMalheiroB, et al. (2019) On-line guest profiling and hotel recommendation. Electronic Commerce Research and Applications34: 100832.
69.
WingCSimonKBello-GomezRA (2018) Designing difference in difference studies: best practices for public health policy research. Annual Review of Public Health39(1): 453–469.
70.
WuYCLinSW (2022) Efficiency evaluation of Asia's cultural tourism using a dynamic DEA approach. Socio-Economic Planning Sciences84: 101426.
71.
XinBQuY (2019) Effects of smart city policies on green total factor productivity: evidence from a quasi-natural experiment in China. International Journal of Environmental Research and Public Health16(13): 2396.
72.
YanJLiZ (2022) The integrated development of tourism and cultural and creative industries based on smart city cloud service information system. Mobile Information Systems2022: 1–10.
73.
YangYJiangLZhangZ (2021) Tourists on shared bikes: can bike-sharing boost attraction demand?Tourism Management86: 104328.
74.
ZengDTimYYuJ, et al. (2020) Actualizing big data analytics for smart cities: a cascading affordance study. International Journal of Information Management54: 102156.
75.
ZhaJHeLLiuY, et al. (2019) Evaluation on development efficiency of low-carbon tourism economy: a case study of Hubei Province, China. Socio-Economic Planning Sciences66: 47–57.
76.
ZhangDTuJZhouL, et al. (2021) Higher tourism specialization, better hotel industry efficiency?International Journal of Hospitality Management87: 102509.
77.
ZhangYZhangYZhangH, et al. (2022) Evaluation on new first-tier smart cities in China based on entropy method and TOPSIS. Ecological Indicators145: 109616.
78.
ZhouCShiCWangS, et al. (2018) Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models. Ecological Indicators86: 67–80.
