ICT adoption and tourism consumption among rural residents in China

Abstract

Little is known about the tourism consumption of the rural population in developing and emerging countries. This note fills the gap by investigating how information and communication technology (ICT) adoption influences tourism participation decisions and expenditure of rural residents in China. We estimate the data of 11,000 samples from the China Labor-force Dynamic Survey data, using both recursive bivariate probit and treatment effects models. Findings reveal that ICT adoption increases the probability of tourism participation among rural Chinese residents by 18.6% but decreases the expenditure of tourism participants by 442 Yuan/capita on average. We also show that ICT adoption empowers rural women for tourism consumption. Our findings highlight the importance of improving the rural ICT infrastructure and promoting ICT adoption among rural residents to boost rural tourism consumption and social welfare.

Keywords

expenditure gendered differences ICT adoption rural residents tourism participation

Introduction

Tourism participation improves people’s quality of life and subjective well-being (Liu et al., 2012). However, for many people, participation in tourism is often restricted by wealth, information, and time (Alegre et al., 2013; Liu et al., 2012; Massidda et al., 2020; Minnaert, 2014). Minnaert (2014) showed that travel inexperience and associated uncertainty play a crucial role in making travel decisions. Adopting information and communication technologies (ICTs) such as computers and smartphones can facilitate tourism participation. ICT adoption allows people to widely browse tourism information via “apps” such as TripAdvisor, Booking.com, Agoda.com, and Travelzoo and helps them reduce transaction costs (e.g. information search costs and negotiation costs for the cheapest accommodations) and uncertainties associated with tourism planning.

A growing number of studies have examined the effects of ICT adoption on various aspects of social development. In general, they found that ICT adoption has a positive impact on productivity and income growth (Leng et al., 2020; Ma et al., 2020; Omulo and Kumeh, 2020), employment (Kılıçaslan and Töngür, 2019), environmental quality (Avom et al., 2020), financial access (Samargandi et al., 2019), and subjective well-being (Chan, 2015; Nie et al., 2020). Based on firm-level data from the Turkish manufacturing sector, Kılıçaslan and Töngür (2019) showed that ICT adoption enhances employment. The study by Leng et al. (2020) reveals that ICT adoption significantly increases rural income diversification and low-income rural households benefit more from its adoption. Still, some other studies provide substantial evidence that ICT facilitates the tourism industry’s development and management (Adeola and Evans, 2020; Pierdicca et al., 2019). Nevertheless, the association between ICT adoption and tourism consumption of the rural population in developing and emerging economies has been overlooked in the literature.

This research note adds to the literature by examining the association between ICT adoption and tourism participation and expenditure among rural residents in China. Both recursive bivariate probit (RBP) model and treatment effects (TE) model are utilized to address the selection bias issue associated with self-selected ICT adoption and estimate the data of 11,000 rural samples collected in the China Labor-force Dynamics Survey. Rural people are an interesting example because tourism participation is usually a luxury activity for them. We also account for gendered differences in ICT adoption effects. The findings of this study have important implications for policymakers in China and other countries in their efforts to improve regional ICT infrastructure and encourage tourism consumption.

The rest of this article is organized as follows. In the next section, we discuss the models and data. The third section presents and interprets the empirical results, while the final section concludes the article.

Models and data

Models

The decision to adopt the ICT is not random because rural residents voluntarily select whether to do so, depending on personal and socioeconomic factors such as age, education, marital status, and employment status (Leng et al., 2020; Nie et al., 2020). The fact leads to a selection bias issue, which should be addressed for unbiased and consistent estimates. Following Filippini et al. (2018) and Ma and Abdulai (2017), we use a two-stage RBP model to estimate the association between ICT adoption and tourism participation and a two-stage TE model to evaluate the correlation between ICT adoption and the expenditure of tourism participants.

The first stage of both the RBP model and TE model describes the probability of ICT adoption based on the following probit (treatment) model:

ICT adoption: N_{i}^{*} = β_{1 i} Z_{i} + δ_{i} I V_{i} + ε_{i} with N_{i} = \{\begin{matrix} 1 if N_{i}^{*} > 0 \\ 0 otherwise \end{matrix}

where $N_{i}^{*}$ is an unobserved latent variable which represents the probability that farmer i adopts the ICT, which is determined by the observed binary variable, N_i. In particular, $N_{i} =$ 1 if a farmer has chosen to adopt ICTs and $N_{i} =$ 0 otherwise; $Z_{i}$ is a vector of explanatory variables; $β_{1 i}$ and $δ_{i}$ are parameters to be estimated; $ε_{i}$ is a random error term; $I V_{i}$ represents an instrumental variable (IV), which refers to telephone access (see Table 1). The validity of the IV is tested by a Pearson correlation coefficient analysis (see Online Appendix A).

Table 1.

Variable definitions and descriptive statistics.

Variables	Definitions	Mean (SD)	Mean (SD)^b
ICT adoption	1 = ICT adopters	0.541 (0.498)	0.661 (0.473)
Tourism participation	1 = Tourism participants	0.220 (0.414)
Tourism expenditure	Annual tourism expenditure (1000 Yuan/capita)^a		0.722 (2.334)
Age	Age of the respondent (years)	44.914 (12.097)	42.237 (11.040)
Gender	1 = Male	0.470 (0.499)	0.465 (0.499)
Education	Educational levels of the respondent 2	7.109 (3.847)	7.953 (3.551)
Marital status	1 = Married	0.935 (0.247)	0.926 (0.261)
Employment	1 = Employed	0.768 (0.422)	0.804 (0.397)
Household size	Number of people residing in a household	6.142 (3.004)	5.019 (1.838)
Farm size	Farm size per capita (µ; 1 µ = 1/15 hectare)	1.813 (10.875)	2.296 (21.228)
Income	Household income (1000 Yuan/capita)	8.966 (7.964)	10.857 (8.499)
Distance	Distance from the village a respondent resides to the county (km)	2.983 (0.883)	2.920 (0.875)
Eastern	1 = Eastern	0.430 (0.495)	0.540 (0.499)
Central	1 = Central	0.235 (0.424)	0.202 (0.402)
Western	1 = Western	0.338 (0.473)	0.258 (0.438)
Telephone access	The time that the village has the first telephone (1 = no telephone access; 2 = between 1990 and 2015; 3 = between 1979 and 1989; 4 = before 1978)	2.466 (0.921)	2.623 (0.891)
Observations		11,000	2419

Note: ICT: information and communication technology.

^a Yuan is the Chinese currency.

^b The sample in the last column refers to tourism participants only.

The second stage of the RBP model analyzes the association between ICT adoption and tourism participation, while the second stage of the TE model estimates the correlation between ICT adoption and the expenditure of tourism participants. The two outcome equations are specified as follows:

Tourism participation: F_{i}^{*} = φ_{i} N_{i} + β_{2 i} Z_{i} + μ_{i} with F_{i} = \{\begin{matrix} 1 if F_{i}^{*} > 0 \\ 0 otherwise \end{matrix}

Tourism expenditure : E_{i} = γ_{i} N_{i} + β_{3 i} Z_{i} + σ_{i} for F_{i} = 1

where $F_{i}^{*}$ is an unobserved latent variable, which is determined by the observed binary variable, F_i ( $F_{i} = 1$ for tourism participants and $F_{i} = 0$ for nonparticipants); E_i represents the expenditure of tourism participants; $N_{i}$ refers to farmers’ ICT adoption status; $Z_{i}$ is defined as above; $φ_{i},$ $β_{2 i}$ , $γ_{i}$ , and $β_{3 i}$ are parameters to be estimated; $μ_{i}$ and $σ_{i}$ are error terms.

Data

This research note uses open-access data collected by Sun Yat-sen University (Guangzhou, China) for the China Labor-force Dynamics Survey (CLDS) project in 2016. The survey covers 29 mainland provinces (excluding Tibet and Hainan), 401 villages, 14,226 households, and 21,086 individuals (4714 urban samples, 16,322 rural samples, and 50 samples without indicating their rural/urban status). Thus, the samples are nationally representative. Because we are interested in rural residents’ tourism consumption behavior, we only keep rural samples in data cleaning. Then, we drop samples with missing information. Finally, 11,000 samples of rural residents were used in the RBP model, and 2419 samples of rural tourism participants were used in the TE model.

The definitions and descriptive statistics of the variables used in the RBP model and the TE model are presented in Table 1. The table shows that more than half of the respondents adopted ICTs in 2015, while only 22% participated in tourism. The average expenditure of tour participants was 722 Yuan/capita.

Results

Table 2 presents the empirical results. The RBP model estimates the equations (1) and (2) simultaneously, while the TE model estimates the equations (1) and (3) simultaneously. After controlling for the selection bias issue of the ICT adoption by employing both the RBP and TE models, our results reveal that ICT adoption boosts rural tourism consumption. On average, ICT adoption increases the probability of rural residents’ tourism participation by 18.6%, and tourism participants who adopted ICTs spend 442 Yuan/capita less on tourism activities than their counterparts who did not adopt it. ICT adoption facilitates information search and interactions between rural residents and travel agencies, reduces uncertainties of traveling, and enables rural residents to book accommodation and transportation at lower prices. Thus, ICT adoption stimulates farmers’ decision to participate in tourism activities and meanwhile reduces their costs.

Table 2.

Impacts of ICT adoption on tourism participation and expenditure of tourism participants.

	RBP model		TE model
Variables	ICT adoption (marginal effects)	Tourism participation (marginal effects)	ICT adoption (coefficients)	Tourism expenditure (coefficients)
ICT adoption		0.186 (0.045)***		−0.442 (0.147)***
Age	−0.009 (0.000)***	−0.002 (0.001)**	−0.025 (0.003)***	−0.028 (0.011)***
Gender	−0.018 (0.009)***	−0.013 (0.009)	−0.155 (0.060)***	0.294 (0.153)*
Education	0.015 (0.001)***	0.003 (0.001)**	0.080 (0.009)***	−0.059 (0.031)*
Marital status	0.091 (0.023)***	0.034 (0.018)*	0.230 (0.134)*	0.443 (0.204)**
Employment	−0.027 (0.010)***	0.044 (0.010)***	0.073 (0.072)	−0.598 (0.235)**
Household size	0.034 (0.002)***	0.004 (0.003)	0.054 (0.016)***	−0.178 (0.042)***
Farm size	−0.002 (0.000)*	0.001 (0.000)***	−0.003 (0.001)***	−0.001 (0.000)
Income	0.015 (0.001)***	0.002 (0.001)*	0.044 (0.004)***	0.029 (0.004)***
Distance	−0.033 (0.005)***	0.006 (0.005)	−0.192 (0.035)***	−0.061 (0.039)
Central	−0.096 (0.011)***	−0.043 (0.012)***	−0.199 (0.076)***	−0.209 (0.081)***
Western	−0.071 (0.011)***	−0.068 (0.011)***	−0.253 (0.071)***	0.189 (0.102)*
Telephone access	0.032 (0.005)***		0.057 (0.032)*
Constant			0.485 (0.241)**	3.346 (1.026)***
$ρ_{ε μ}$	−0.311 (0.097)***
$ρ_{εσ}$			0.073 (0.010)***
Log likelihood	−11,632.92		−6708.92
Wald test	3004.59, Prob > χ² = 0.00		624.83, Prob > χ² = 0.00
Observations	11,000		2419

Note: ICT: information and communication technology; RBP: recursive bivariate probit; TE: treatment effects. Robust standard errors are presented in parentheses; the reference region is Eastern region; the tourism expenditure is measured at 1000 Yuan/capita.

***p < 0.01, **p < 0.05, *p < 0.1.

Among other factors, we find that rural residents’ decisions to participate in tourism are positively associated with education, marital status, employment, farm size, and household income. Tourism expenditure is negatively associated with age, education, employment, and household size. The results in columns 2 and 4 of Table 2 show that education, marital status, household size, and household income mainly positively determine rural residents’ ICT adoption decisions.

The disaggregated analyses (Table 3) show that ICT adoption empowers rural women for tourism consumption because it has larger and significant effects on women’s tourism participation and expenditure relative to men.

Table 3.

The disaggregated effects of ICT adoption by gender.

Outcomes		Observations	Marginal effects	Coefficients
Tourism participation	Male	5163	0.185 (0.062)***
Tourism participation	Female	5837	0.190 (0.063)***
Expenditure of tourism participants	Male	1124		−0.418 (0.189)**
Expenditure of tourism participants	Female	1295		−0.462 (0.226)**

Note: ICT: information and communication technology. Robust standard errors are presented in parentheses.

**p < 0.05, *p < 0.1.

Conclusion

In this research note, we provided evidence that ICT adoption increases the probability of tourism participation but decreases the expenditures of tourism participants. ICT adoption also empowers rural women for tourism consumption. Our findings highlight the importance of developing and improving the rural ICT infrastructure to increase the tourism consumption of rural residents in developing and emerging countries. Improving the well-being of rural residents via stimulating tourism consumption requires more than ICT adoption. Thus, future studies may investigate how other factors such as transport service, tourism information propaganda, and social security policy affect rural residents’ tourism participation decisions and expenditure.

Supplemental material

Supplemental Material, sj-pdf-1-teu-10.1177_13548166211000478 - ICT adoption and tourism consumption among rural residents in China

Supplemental Material, sj-pdf-1-teu-10.1177_13548166211000478 for ICT adoption and tourism consumption among rural residents in China by Zhongkun Zhu, Wanglin Ma and Chenxin Leng in Tourism Economics

Footnotes

Data availability statement

The data that support the findings of this study are available from the leading author, Zhongkun Zhu, upon reasonable request.

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: The authors would like to acknowledge the funding support from the National Natural Science Foundation of China Project (Grant No. 71903062).

ORCID iD

Wanglin Ma

Supplemental material

Supplemental material for this article is available online.

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