Behavioral intention of traveling in the period of COVID-19: an application of the theory of planned behavior (TPB) and perceived risk

Sujood (Department of Commerce, Aligarh Muslim University, Aligarh, India)
Sheeba Hamid (Department of Commerce, Aligarh Muslim University, Aligarh, India)
Naseem Bano (Department of Commerce, Aligarh Muslim University, Aligarh, India)

International Journal of Tourism Cities

ISSN: 2056-5607

Article publication date: 15 September 2021

Issue publication date: 4 May 2022

2845

Abstract

Purpose

This paper aims to examine travelers’ behavioral intention of traveling in the period of coronavirus by using the theory of planned behavior. The framework incorporates attitude, subjective norms, perceived behavioral control and a very crucial construct, i.e. perceived risk, as per the current critical scenario of COVID-19.

Design/methodology/approach

Data was collected using a survey instrument on the internet by posting the questionnaire link over social network web pages of online traveling websites. The data was analyzed using structural equations modeling with AMOS 22.0 and SPSS software and the proposed hypotheses were statistically tested. The sample under consideration constitutes 417 responses.

Findings

Empirical findings suggest that attitude, perceived behavioral control and perceived risk are significant for predicting behavioral intention while subjective norms do not. Then, these variables explained about 35% of the variance in the behavioral intention of traveling in the period of coronavirus.

Research limitations/implications

This study can benefit travelers, the tourism and hospitality industry, governments, the aviation industry and other relevant organizations as this paper offers the latest updates and essential information regarding traveler’s intention of traveling in the period of coronavirus. The study mainly focuses on India, so the generalizations of results to other countries are unwanted.

Originality/value

The primary value of this paper is that it tested the theory of planned behavior by incorporating perceived risk in the context of COVID-19. To the best of the authors’ knowledge, in the Indian context, there is no study, which has tested the TPB by adding perceived risk in explaining the Indian citizens’ behavioral intention of traveling in the period of Coronavirus.

Keywords

Citation

Sujood, Hamid, S. and Bano, N. (2022), "Behavioral intention of traveling in the period of COVID-19: an application of the theory of planned behavior (TPB) and perceived risk", International Journal of Tourism Cities, Vol. 8 No. 2, pp. 357-378. https://doi.org/10.1108/IJTC-09-2020-0183

Publisher

:

Emerald Publishing Limited

Copyright © 2021, International Tourism Studies Association.


Introduction

The inception of coronavirus erupted in Wuhan, China, in December 2019 and spread all over the world (De Vos, 2020). The disease was novel to humans and caused the development of severe acute respiratory illness (Kim et al., 2017). The number of confirmed cases of Coronavirus worldwide reached 17,36,09,772 and death count to 37,42,653 till June 09, 2021 (WHO Coronavirus Disease (COVID-19) Dashboard, 2020) while in India, as of June 07, 2021, the number of confirmed cases reached 2,89,95,457 while death count reached to 3,51,335 (Statista, 2021). As Coronavirus attacked the world, the scenario of the traveler’s intention of traveling changed. Travelers are more concerned about their safety and security. Fear of being infected is widespread globally; people are abstaining from even regular everyday practices such as going out and the public’s reaction to fear led to changes in travel behavior. On March 25, 2020, India, the world’s largest democracy, was placed under the world’s biggest lockdown to control the spread of COVID-19 (Pittsburgh Post-Gazette, 2021). As per Statista (2020), this lockdown will have a substantial detrimental impact on India’s domestic and foreign tourism, particularly in the hotel and service industries (Gupta et al., 2021). As per industry chamber CII, “this is one of the worst crises ever to hit the Indian tourism industry impacting all of its geographical segments – inbound, outbound and domestic, almost all tourism verticals – leisure, adventure, heritage, MICE, cruise, corporate and niche segments (The Economic Times, 2021).” Many other nations enforced different travel regulations and restrictions such, as lockdowns and stringent social distancing measures (De Vos, 2020), which also influenced people’s behavioral intention of traveling. Major changes in travel behavior have been seen like; a shrinkage in the intention to use public transport and a rise in the intention to travel by private vehicle (Nguyen and Coca-Stefaniak, 2020). To make decisions and to make policies based on people’s travel needs during any pandemic, it is important to consider and predict travel behavior.

The theory of planned behavior (TPB) given by Ajzen (1991) is an extended version of the theory of reasoned action (Fishbein and Ajzen, 1975). The TPB was proposed to forecast people’s intention to perform a behavior. The theory states that behavior is observed by intention and intention can be predicted by three constructs, i.e. attitude, subjective norms and perceived behavior control (PBC). There are three core proposals of TPB:

  1. individuals possibly practice a specific kind of behavior if they assume that such behavior will give a specific and beneficial result.

  2. their peers will respect and support the behavior.

  3. they have the requisite skills, tools and chances to conduct such behavior (Ajzen, 1985; Conner et al., 1999; Hsu and Huang, 2012).

There are many studies (Han et al., 2020; Jordan et al., 2018; Kaplan, 2015; Al Ziadat, 2015; Horng et al., 2013; Teo and Lee, 2010; Cheng et al., 2006) in which the application of TPB has been tested. The current study checks the application of TPB in the context of the traveling intention of travelers in the period of Coronavirus, it hypothesizes that travelers’ traveling intention can be predicted by travelers attitude toward traveling, social influence to the travelers for traveling and perceived behavioral control over traveling (Liao et al., 1999; Shim et al., 2001; Athiyaman, 2002; Lin, 2006).

The conditional likelihood of people’s engagement in behavior is behavioral intention; it refers to the motivational factors affecting a certain behavior in which, the greater the intention to conduct the behavior, the more likely it is to perform the behavior. A traveler’s expected or intended future behavior may be characterized as behavioral intention (Swan, 1981; Lam and Hsu, 2006). In the context of this study, the meaning of the behavioral intention can be understood as an Indian traveler’s intention of traveling in the period of Coronavirus. Prior studies have shown that behavioral intention is a product of attitude, subjective norm and PBC (Lam and Hsu, 2004, 2006; Lin, 2006; Hsu and Huang, 2010; Chen and Tung, 2014). In addition to TPB constructs, there is another important construct, which is applied in this study, i.e. perceived risk. Chew and Jahari (2014) described Perceived risk in traveler’s perception as “the probability that an action may expose them to the danger that can influence travel decisions if the perceived danger is deemed to be beyond an acceptable level” (Bae and Chang, 2020). Travel is restricted during pandemics and travelers perceive a higher risk for all sorts of traveling, avoiding regions where they perceive a medium to high risk (Hotle et al., 2020; Abdullah, 2020). Most travelers make travel-related decisions based on their risk perception, so the prediction of perceived risk is valuable for investigating tourist behavior (Zhan et al., 2020). Global pandemics have made a significant contribution to increased levels of fear among travelers regarding travel-related risks (Gupta et al., 2021). There are a number of studies related to severe diseases (MERS, SARS, Ebola and Avian Flu), in which perceived risk and its influence on travel intention, travel decision-making and travel behavior have been examined (Huang et al., 2020; Floyd et al., 2004; Lee et al., 2012; Bae and Chang, 2020). In this direction, this study is an effort to predict Indian citizen’s behavioral intention of traveling in the period of Coronavirus by applying TPB and perceived risk.

The remainder of this study is structured as follows: a range of published literature on travelers’ behavioral intention of traveling, TPB and perceived risk is reviewed. Afterward, in the next section, the methodology which comprises the details of the research instrument, data collection method and data analysis techniques are presented. Then the results obtained through statistical analyzes are discussed along with findings. At last, a discussion and conclusion are given with the implication and limitations of the study.

Review of literature and hypotheses formulation

Theory of planned behavior

When the entire world is struggling with Coronavirus, there is a problem in understanding and predicting traveler’s behavioral intention of traveling, but the TPB is one of the most studied theories for the prediction of behavioral intention (Fielding et al., 2008; Soliman, 2019). Hence, the behavioral intention of traveling during coronavirus can be predicted by applying TPB. Lam and Hsu (2004, 2006) showed that a rise in the components of TPB i.e. attitude, subjective norm and PBC would lead to an increase in the behavioral intention of traveling. The main area of TPB is that travelers possibly conduct a specific kind of behavior as traveling if they trust that such traveling will result in an outcome they value, their key referents will assess and favor the traveling and they possess the essential means, skills and chances to travel (Ajzen, 1985; Lam and Hsu, 2006). An individual’s act is decided by intentions, that are successively impacted by attitude, subjective norms and PBC (Ajzen, 1991). The TPB suggested by Ajzen in 1985, forecast the individual’s behavior (Hsu and Huang, 2012) which is not in the control of free will (Tsai, 2010) propounded that the human beings are coherent (Japutra et al., 2019) and their performance is determined by the behavioral intentions which are based (Teo and Lee, 2010) on their attitude, subjective norms and PBC toward the travelers’ decision-making process (Hsu and Huang, 2012). The main element of the theory is personal intention (Chen and Yang, 2018) to complete a particular action (Buess, 2012). The TPB has been practiced by analysts for the past 20 years (Teo and Lee, 2010) and recently to predict the post-pandemic traveling intention of US travelers, Han et al. (2020) developed a rigorous theoretical framework, which relates the perceived knowledge of coronavirus to the variables of TPB encompassing the moderating effect of psychological risk. The framework enhanced the predictability of the TPB and is widely applicable in different tourism areas, particularly when describing traveler’s behavioral intention of traveling (Han et al., 2020). In the tourism and travel context, there are several studies, which have acknowledged the relationships among the variables of TPB and travel intention (Lam and Hsu, 2006; Han, 2015; Han et al., 2010; Hsu and Huang, 2012; Quintal et al., 2010, 2015; Park et al., 2017). Attitude, Subjective Norms and PBC have been shown a direct and positive impact on behavioral intention in previous studies (Hsu and Huang, 2012; Bae and Chang, 2020).

Behavioral intention

Behavioral Intention represents the people’s expected and arranged upcoming actions (Lam and Hsu, 2004). It shows one’s expectations related to a specific behavior in a certain mechanism and may be taken as the possibility of performing an action (Fishbein and Ajzen, 1975; Lam and Hsu, 2006). Behavioral Intention in the TPB works as the most powerful medium of an individual’s action, also expresses how eagerly an individual wants to perform the actions (Chen and Yang, 2018). Attitude and PBC were associated with the intention of traveling (Lam and Hsu, 2004). When there is a chance to perform an action, the intention leads to behavior and if the intention is estimated perfectly, it will give the strongest prediction of behavior (Fishbein and Ajzen, 1975). In this study, One’s expectation of future traveling in the period of coronavirus is termed as the behavioral intention of traveling. As per the human behavior theories, the strongest forecaster of behavioral intention and upcoming behavior of a traveler is the occurrence rate of earlier behavior (Lam and Hsu, 2006; Quellette and Wood, 1998; Sönmez and Graefe, 1998). The intention is observed as a direct antecedent of traveling behavior. The behavioral intention is determined as the direct and the strongest determinant of behavior out of all other determinants of behavior. Behavioral intention is a sign of a traveler’s readiness to travel, one’s actions of a definite behavior are measured by his intent to execute that particular behavior (Al Ziadat, 2015). Behavioral intention plays a key role in developing actual behavior (Casaló et al., 2010).

Attitude

Attitude means “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Ajzen, 1991). A traveler’s intention to execute a particular action or behavioral intention toward a product or service, in a certain condition is a mechanism of one’s attitude toward the behavior (Fishbein, 1967a). Attitude toward intention leads to behavior and is explained as the traveler’s response; this may be negative or positive. A traveler’s attitude toward a travel product or travel service is decided by measuring one’s beliefs of salient attributes that the product or service has and by the measurement of each attribute (Fishbein and Ajzen, 1975; Lam and Hsu, 2004). Attitude build by understanding and circumstances (Hsu and Huang, 2012) retaliates toward a definite purpose. Attitude is an important factor that predicts, explains and affects the behavioral intention of travelers (Bianchi et al., 2017; Han et al., 2010; Soliman, 2019) to perform a particular behavior such as traveling in the period of crisis or pandemic (Huang and Hsu, 2009; Soliman, 2019; Mainardes et al., 2020). In the midst of a coronavirus outbreak, fostering a positive attitude toward traveling is quite challenging. If a traveler has a favorable attitude toward traveling in the period of coronavirus then the intention of traveling will be intensified and this intensified intention results in actual traveling. The TPB claims that attitude toward a behavior empowers humans’ intention to perform the behavior (Lee et al., 2012). Previous studies approve that attitude positively influences one’s behavioral intention to perform the behavior (Baker et al., 2007; Cheng et al., 2006). Thus, based on the findings of previous studies, we postulate the hypothesis:

H1.

Attitude has a positive effect on the behavioral intention of traveling in the period of COVID-19.

Subjective norms

Subjective norms indicate social influence which encourages or discourages people to perform a particular behavior (Rivis et al., 2009; Bae and Chang, 2020). It is explained as traveler’s perception of social normative pressures created by friends, family, peers and others’ beliefs (Taylor and Todd, 1995; Koundinya, 2019) and social influence is the perception held by travelers of what others who are important to them, think their traveling behavior should be like (Fishbein and Ajzen, 1975; Koundinya, 2019). Subjective norm is a predictor of behavioral intention in the TPB, which influences behavior. It evaluates the significance of people associated with reference groups and others’ willingness to follow these groups’ collective choices, beliefs and attitudes, as travel choices (Quintal et al., 2010; Moutinho, 1987). It refers to the others’ expectations regarding the travelers’ traveling behavior. Subjective norm is measured by one’s normative beliefs regarding what people, who are precious to him consider that he should or should not travel and level of inspiration to which one wishes to follow what one’s referents consider (Ajzen and Fishbein, 1980). There is social pressure during the coronavirus to conform to preventive behavior such as social isolation, wearing a mask while traveling and regular hand washing, so that social accountability cannot be prevented (Bae and Chang, 2020). Ajzen (1991) clarified that if family or peers have a favorable attitude about a certain behavior, the probability of a person performing that behavior would improve to meet their expectations and vice versa (Bae and Chang, 2020). In that case, if family or peers have a favorable attitude about traveling in the period of coronavirus, the probability of traveling by the traveler will increase to meet their expectations and vice versa. There is a number of studies, which have shown that subjective norms positively influence behavioral intention (Grandón et al., 2011; Lam and Hsu, 2006; Hansen et al., 2004). Hence, the hypothesis is indicated as follows:

H2.

Subjective norms have a positive effect on the behavioral intention of traveling in the period of COVID-19.

Perceived behavioral control

PBC is a degree of one’s perception of the difficulty or easiness to perform a behavior (Lam and Hsu, 2006; Armitage and Conner, 2001), like a traveler thinks how stress-free or hard it is to travel in the period of coronavirus. The link between PBC and behavioral intention of traveling depends upon the assumption that an increase in PBC will lead to an upsurge in intention and the possibility of traveling or vice-versa (Armitage and Conner, 2001). The more favorable the subjective norm, attitude, PBC in respect to travel behavior, the more would be the traveler’s intention of traveling (Ajzen and Driver, 1992). PBC helps in forecasting the individualistic objectives that direct the influence of behavior (Ajzen and Madden, 1986). There are two segments of PBC, the first is the ease of accessibility of resources (Buess, 2012; Ajzen, 2002) and the other is the capability to execute the actions (Soliman, 2019). The Perceived Behavioral Control, which refers to an individual’s trust to perform a behavior, is an essential element in the formulation of intention (Ajzen, 1991; Ajzen and Madden, 1986; Lee et al., 2012) and influences decision-making in the TPB (Lee et al., 2012; Ajzen, 1991; Conner and Abraham, 2001; Taylor and Todd, 1995). If we try to understand PBC in the current context of coronavirus, then it refers to the traveler’s expectation of traveling that the traveler will be able to control the condition and handle the resources needed for successful travel. During the lockdown, travel is restricted and people are unable to travel. Hence, perceived behavioral control plays a crucial role in the development of behavioral intention of traveling during coronavirus. Behavioral Intention elucidates that the stronger the attitude and subjective norms in the context of behavior, the more appreciable PBC (Ajzen and Driver, 1992). Previous studies confirm that perceived behavioral control positively influences behavioral intention (Hsiao and Yang, 2010; Lam and Hsu, 2004; Mainardes et al., 2020). Hence, the hypothesis is indicated as follows:

H3.

Perceived behavioral control has a positive effect on the behavioral intention of traveling in the period of COVID-19.

Perceived risk

Perceived risk is measured by people in a particular situation (Haddock, 1993) which refers to people’s belief that there is uncertainty and adverse effects in buying a product or performing a behavior (Dowling and Staelin, 1994; Reisinger and Mavondo, 2005). Perceived risk is an important determinant in travelers’ behavioral intentions (Verhage et al., 1990; Moutinho, 1987; Lepp and Gibson, 2003; Tavitiyaman and Qu, 2013). The inappropriate high-risk realization may conduct negative notions of safety (Brug et al., 2009). Sönmez and Graefe (1998) defined the types of risk for international traveling in 10 categories, i.e. health risk-chances of being infected or feeling sick during traveling, psychological risk – the risk to people, physical risk – the threat of danger or damage, equipment risk – chances of an issue with equipment and company while traveling, financial risk – a risk of not providing value for the money spent, social risk- includes preferences which a traveler makes that impact the other’s opinions, time risk- the risk of traveler’s spent time in traveling, terrorism risk- chances of being involved in a terrorist act, political instability- chances of political chaos in the visiting state, satisfaction risk- personal satisfaction may not be delivered while experiencing travel. In tourism, the idea of perceived risk was explored in several studies (Hales and Shams, 1991; Roehl and Fesenmaier, 1992; Reisinger and Mavondo, 2005; Moutinho, 1987; Yavas, 1987). Perceived risk in traveling has primary value in travel decision-making due to its potential to influence travel intention (Sönmez and Graefe, 1998; Khan et al., 2017). Previous studies indicate that incidents of violence, virus outbreaks, disasters affect tourist behavioral intention of traveling and increase risk perceptions (Carter, 1998; Fuchs and Reichel, 2006; Rittichainuwat and Chakraborty, 2009; Chew and Jahari, 2014; Khan et al., 2017). Perceived risk is characterized as a traveler’s perceived vulnerability and adverse effects of traveling during any pandemic or chaos (Dowling and Staelin, 1994; Khan et al., 2017). The perceived health risk refers to the harm to the traveler’s health while participating in travel activities (Olya and Al-ansi, 2018) and the risk of getting infected with coronavirus is very high in this period. Perceived risk is an important factor, which affects the traveling intention of travelers (Law, 2006; Sönmez and Graefe, 1998; Seow et al., 2017). Given the background of perceived risk in traveling during any pandemic/natural disaster, the hypothesis is indicated as:

H4.

Perceived risk has a negative effect on the behavioral intention of traveling in the period of COVID-19.

Figure 1 shows the conceptual model and proposed hypotheses of the study. Table 1 represents a brief scenario of studies related to the TPB, perceived risk and travel intention published in previous years.

Research methodology

Research instrument

An online questionnaire was developed after a comprehensive review of traveler behavior in the period of pandemics and virus outbreaks which was based on the measurement scale of five-point Likert where strongly agree = 5 and strongly disagree = 1 and, designed using Google forms. There are two parts to the questionnaire; the first part consisted of questions regarding respondents’ demographics, i.e. gender, education, age, marital status, monthly income, occupation and nationality. The second section of the questionnaire contained the questions measuring attitude, subjective norms, PBC, perceived risk and behavioral intention. The items to measure attitude were adapted from Lam and Hsu (2006), items to measure subjective norm were adapted from Ajzen (1991) and PBC was measured through the items adapted from Taylor and Todd (1995) and Ajzen (1991), the items used for measuring perceived risk-adapted from Sönmez and Graefe (1998) and the measurement of behavioral intention was done using items adapted from Venkatesh et al. (2003) and Taylor and Todd (1995). Measurement items are given in Appendix.

Data collection

Data was collected using survey instrument over the internet by posting the questionnaire link over social network web pages of online traveling websites from May 18, 2020, to June 29, 2020, during this period the number of coronavirus cases showed a rising trend as of May 18, 2020, there were 96,169 confirmed cases, 3,029 deaths and on June 29, 2020, there were 5, 49,035 confirmed cases, 16,492 deaths (India: COVID-19 cases timeline, 2020 | Statista, 2020). These websites were selected based on Alexa rank, which is a ranking agency worldwide, amazon.com’ subsidiary organization which ranks a huge number of websites in order of popularity. The top five traveling websites in terms of booking as per the Alexa ranking website are booking.com, tripadvisor.com, xe.com, hotels.com and vrbo.com. The reason for posting the questionnaire on such social networking web pages was to maximize exposure in gathering larger samples (Alexa – Top Sites by Category: Top/Recreation/Travel, 2020).

Data analysis

Data was collected through the questionnaire and computed using SPSS and AMOS 22.0 software. SPSS was used to produce descriptive and inferential statistics. A pilot test of 30 respondents was conducted before posting the questionnaire link over social network web pages of online traveling websites for verifying that the questions were clearly understood. The reliability of the items was checked, alpha values (Cronbach’s) for all the constructs were higher than the recommended value, indicating that reliability was attained. In total, 453 responses were received in total and 36 cases were excluded due to missing values. The final sample of 417 valid and usable responses was considered for the study.

Results

Profile of respondents

The population of this study was composed of Indian citizens and 417 responses were considered for the study (Table 2). The gender breakdown was 255 men (61.2%) versus 162 women (38.8%); most of the respondents (39.6%) were between 28 and 35 years of age. Respondents were well educated as 49.2% (205 respondents) had post-graduation degrees and 30.5% (127 respondents) had PhD degrees. Most of the survey respondents were employees 184 (44.1%) and students 135 (32.4%). In terms of marital status, 233 (55.9%) were single and 183 (43.9%) were married.

Descriptive statistics

Table 3 shows the mean scores of constructs and all the items.

Measurement model

The very primary step in assessing a research model is to check the results of the measurement model to inspect the internal consistency reliability, discriminant and convergent validity (Hair et al., 2010). To measure the internal consistency, evaluating the constructs’ coefficient alpha is the best method. If the values of Cronbach’s alpha exceed 0.70, then measures are stronger in terms of their internal consistency reliability (Nunnally and Bernstein, 1978). Table 4 shows that values of Cronbach’s alpha are exceeding the recommended value.

Factor analysis with PCA and varimax rotation technique was implemented and to regulate the number of factors, a minimum eigenvalue of one was used. Table 4 showed that the cumulative% of factors variance was 71.703%, with a KMO of 0.871, which was above the minimum threshold of 0.60 (Garson, 2001). The Bartlett test was 4,676.036 with a significance of 0.000.

The correlation between two or more items is denoted by convergent validity, intended to assess the same variable. Fornell and Larcker (1981) have given 03 ways to check the items’ convergent validity; first, reliability of every measure, second, composite reliability of every construct and third the average variance extracted. The reliability was calculated by factor loading into the underlying construct. Item reliability has already been tested and approved as Cronbach’s alpha exceeds the cut-off value, as per Steenkamp and Geyskens (2006) acceptable benchmarks, standardized factor loadings should be statistically significant and greater than 0.6, our results confirm the same. As shown in Table 5, the CR values of all the constructs were above the minimum threshold of 0.70 (Bagozzi and Yi, 1988), representing that the measurement items for each variable are internally reliable and consistent. The AVEs of all the constructs are greater than the cut-off value of 0.50, which confirms the convergent validity (Fornell and Larcker, 1981).

Discriminant validity refers to the degree to which factors differentiate between latent variables. It is checked on the basis of the guidelines of Fornell and Larcker (1981), as shown in Table 6 the square roots of the average variance extracted are larger than the off-diagonal values in the consistent columns and rows, greater than the correlations among a given construct and others in the model, it recommends that a construct is correlated with its items strongly and with the other constructs in the model (Teo and Lee, 2010).

Structural model

In the measurement model, there was no issue regarding the reliability and validity, it was considered to be valid and acceptable, so in the next step, the structural model was checked. CFA using the maximum likelihood procedure was carried out in SEM. Several fit indices measured model fit adequacy recommended by Hair et al. (2010). The measurements of fit indices recommend that the model is a good fit (χ2/df = 2.366, GFI = 0.917, NFI = 0.921, CFI = 0.952, TLI = 0.943, RMSEA = 0.057) as the fit indices values were higher the model adaptability standard recommended by Hair et al. (2010)2/df < 3, TLI > 0.90, CFI > 0.90, RMSEA < 0.07).

Figure 2 represents the SEM model path diagram, which showed the degree and direction of the impact with the signs, the path coefficients and the value of the standardized coefficients. The paths from Attitude, PBC and perceived risk are significant at p < 0.001. Attitude (H1) (β = 0.236, t-value= 5.940, p < 0.001) and perceived behavioral control (H3) (β = 0.406, t-value = 10.244, p < 0.001) have a positive effect on behavioral intention. subjective norms (H2) (β = −0.183, t-value = −4.625, p < 0.001) has a negative effect on behavioral intention, in contrast with H2 and perceived risk (H4) (β = −0.302, t-value = −7.606, p < 0.001) has a negative effect on behavioral intention. Hence, hypotheses H1, H3 and H4 are all supported but H2 is not (Table 7).

The above results represent that attitude, perceived behavioral control and perceived risk are significant for predicting behavioral intention while subjective norms do not. Then, these variables explained about 35% (R2 = 0.345) of the variance in the behavioral intention of traveling in the period of coronavirus. This result is in line with Armitage and Conner’s (2001) results in which they showed that in the variance of humans’ behavioral intention, TPB accounted for 27% and 39%, respectively (Chien et al., 2012).

Discussion and conclusion

The several fit indices, i.e. GFI, NFI, TLI, CFI and RMSEA, showed that the model is a moderately good fit. The descriptive statistics determine that Indian citizen’s attitude toward the behavioral intention of traveling is lower than the medium value of 3 (mean = 1.86) which is the lowest among all the variables, subjective norms is also lower than the medium point (mean = 1.91) but perceived behavioral control over traveling (mean = 3.17) and behavioral intention of traveling (mean = 3.44) are higher than the medium value, at last, the perceived risk (mean = 2.18) is also lower than the medium point which indicates negative perception toward traveling in the period of coronavirus (Table 3).

An attitude is a predisposition generated by experience and learning (Lam and Hsu, 2006), which can be favorable or unfavorable such as traveling in the period of disaster or virus outbreak (Moutinho, 1987). The present study revealed that the connection between attitude and behavioral intention is direct. Travelers’ attitude has positive effect (β = +0.236, p < 0.001) on behavioral intention. This finding is in line with past studies (Bae and Chang, 2020; Lam and Hsu, 2004; Hsu and Huang, 2010; Hsiao and Yang, 2010). This virus has changed the attitude of travelers and very few Indian people are interested in traveling, most of the travelers think that their travel would be unfavorable, unpleasant and negative. Hence, there is a need to encourage a favorable attitude toward traveling in the period of coronavirus. In this study, subjective norm means, a traveler’s opinion that people who are close and precious to him, think he should not travel in the period of coronavirus. The results demonstrated that subjective norms have a negative effect (β = −0.183, p < 0.001) on the intention of traveling. However, in contrast with our expectations, the direction of subjective norms is not positive but had a significant negative effect on the behavioral intention of traveling. This result is consistent with the prior study (Casaló et al., 2010). The reason for this surprising finding may be that there is social influence on travelers regarding not to travel in the period of the current pandemic. The intention is associated with the social pressure of not traveling in the period of COVID-19. The relatives/friends of the traveler do not wish that he/she should travel during the pandemic and this may be for two reasons. First, they have the fear that if the person travels, he/she might be infected by a coronavirus. Second, if the traveler gets infected then there is a possibility that the relatives/friends of the traveler might be infected through him/her. PBC is about the difficulty or easiness that one feels during executing a certain behavior (Lam and Hsu, 2006), like how easy or difficult to travel in the period of a pandemic. The study revealed that PBC has a positive effect (β = +0.406, p < 0.001) on intention. This result is consistent with prior studies (Lee et al., 2012; Lam and Hsu, 2006; Hsu and Huang, 2010; Bae and Chang, 2020). The strongest direct influence on traveler’s intention out of all the variables is noted among perceived behavioral control. This relationship is all about easiness or difficulty in traveling during the period of coronavirus. An important finding of this study is that it recognizes the relationship between perceived risk and behavioral intention. The finding illustrated that perceived risk has a negative effect (β = −0.302, p < 0.001) on traveling intention which is consistent with prior studies (Chew and Jahari, 2014; Kwun and Oh, 2004; Qi et al., 2009; Pelaez et al., 2019). This result suggests that Indian travelers will not intend to travel if he/she perceives a high risk of coronavirus so there is an urgent need to concentrate on the ways by which risk perception of travelers can be reduced. The investigation highlights that travelers are concerned about their travel plans as perceived risk is negatively influencing the traveling intention. The greater the perceived risk, the lesser the behavioral intention of traveling, travelers believe that if they travel during coronavirus, they may get infected by the virus, they may not get the value for money, they may not get the personal satisfaction or it may waste their time. The negative effect on behavioral intention shows travelers’ fear toward traveling in the period of coronavirus. Perceptions of risk and travel behavior are likely to influence travel intentions. Perceived risk denotes travelers’ perceptions of the insecurity and undesirable consequences of traveling in the period of a virus outbreak (Dowling and Staelin, 1994). These negative/undesirable outcomes may be disappointing travel experiences or a serious threat to traveler’s health as chances of being infected by the virus (Sönmez and Graefe, 1998). COVID-19 epidemic is being considered a serious and major global health issue (Zhang et al., 2020). The results of this study are in line with prior studies (Hsu and Huang, 2012; Han and Kim, 2010; Han et al., 2010; Lam and Hsu, 2004, 2006).

Theoretical and practical implications

At this point in time, information is limited about how the coronavirus has impacted traveler’s behavioral intention of traveling; this study is an effort for measuring that intention. This study contributes to the academic literature on behavioral intention, travel behavior and risk perception during a pandemic or any other crisis. The results highlighted the usefulness of the TPB model to predict the behavioral intention of traveling in the context of India. These results show that Indian traveler’s attitude, PBC and perceived risk became important factors in forming behavioral intention of traveling in the period of coronavirus. Although subjective norms are negatively related to the traveler’s behavioral intention and it is crucial for concerned authorities. This finding is clearly indicating that in India, there is a social pressure of peers/families/friends who do not want that a person who is close to them should travel in the period of COVID-19. As per the findings, people are fearful and nervous, they do not want to travel during the coronavirus outbreak. In this situation, both the government and industry should devise a comprehensive plan by which few things could be done. First, travelers’ attitudes may be favorable for traveling during the coronavirus. Second, their subjective norm (friends/relatives) may support their travel. Third, they may have all the necessary resources (such as transportation) for traveling as they are unable to travel due to strict restrictions during the lockdown. Fourth, their perceived risk may be the lowest, the lower the perceived risk, the higher the traveling intention. Further, the government should encourage people to travel by directing all travelers and tourism organizations to adopt preventative measures. Physical or social distancing, wearing a mask, following the basics of good hygiene and avoiding locations that are closed, congested or involve close contact is the prevention measures that must be followed while traveling and at the destination.

To the best of the authors’ knowledge, in the Indian context, there is no study, which has tested the TPB by incorporating perceived risk in explaining the Indian citizens’ behavioral intention of traveling in the period of Coronavirus. As per TIME “officially, India has the world’s second-worst Covid-19 outbreak. Unofficially, it’s almost certainly the worst” (Perrigo, 2021) so the results of this study can be useful for those countries which are worst affected by the coronavirus. This study acts as a valuable benchmark for future longitudinal studies examining tourists’ short-term and long-term behavior changes. The findings of this study offer detailed insights into the travel behavior of Indian citizens during infectious disease outbreaks. This research summarizes the crucial function of travelers’ behavior in the specific disease to elucidate their purpose generation mechanism for travel decisions. Thus, the findings of this study could be useful in tourism planning and policymaking during pandemics. This research provides timely and interesting information regarding the impact of coronavirus on the behavioral intention of traveling in this pandemic. This study can benefit travelers, the tourism and hospitality industry, governments, the aviation industry and other relevant organizations as this paper offers the latest updates and essential information regarding traveler’s intention of traveling in the period of coronavirus. The research consists of variables, which influence Indian travelers’ behavioral intention of traveling during Coronavirus. Tour companies in India need to focus on these constructs in their promotional and marketing plans as these constructs have a significant role in generating tour/travel demand and help potential travelers in finalizing their tours. The findings of this research will provide insights regarding the behavioral intention of traveling during the period of virus outbreaks in the future.

Limitations and future directions

The method of questionnaire administration was online. This mode is feasible and convenient in reaching out to the larger gatherings, but the sample composition shows that the respondents are friendly with the use of this technology, young and almost 80% with post-graduate qualification means the major chunk of the population is well qualified. As less qualified populations make up a large part of India’s tourism market, future studies may include them (Gupta et al., 2021). The survey was taken by the users who had internet access and who were able to understand and communicate in English. In this case, the generalizability of the results may be limited as the non-users of this technology have been overlooked; hence other respondents should be included in future studies. There are many countries which severely affected by a coronavirus, so data collection in India may present biasness in the sample because only Indian citizens are included. Sample size with a larger geographic representation of different countries may be included in future studies. In addition, future studies may also include additional variables, i.e. perceived security, perceived safety, perceived trust, etc. After the end of Coronavirus, the behavioral intention of traveling in the period of pandemic/crisis/virus outbreaks may not remain the same.

Figures

Conceptual model and hypotheses

Figure 1

Conceptual model and hypotheses

The estimated structural model and hypotheses

Figure 2

The estimated structural model and hypotheses

Studies related to TPB, perceived risk and travel intention

Author/s (year) Purpose Country Data collection Sample size Data analysis Variables/constructs Theory/concepts Results/finding
Han et al. (2020) To construct a theoretical framework by applying the TPB which explains travelers’ post-pandemic travel decision formations USA Online survey n = 305 SEM Attitude, subjective norm, PBC, perceived knowledge of COVID-19, psychological risk and behavioral intention TPB The theoretical framework enhanced the TPB's predictive ability and is generally applicable in a number of tourism sectors
Bae and Chang (2020) To investigate the impact of risk perception of COVID-19 on behavioral intention toward untact tourism South Korea Online survey n = 877 SEM Attitude, subjective norm, PBC and cognitive and affective risk perception TPB and HBM The findings indicate that cognitive and affective risk perceptions have a significant effect, but there is a negative effect of affective risk perception on behavioral intention
Soliman (2019) To build an extended model of TPB for the prediction of travelers’ intention to revisit Egypt Egypt Survey n = 302 PLS-SEM Attitude, subjective norm, PBC, destination familiarity, destination image, travel motivation and eWOM TPB The results reveal that the extended model has a robust capability for the prediction of travelers’ revisit intention
Japutra et al. (2019) To explore the relationship between traveler’s mindsets and TPB Portugal Survey n = 312 PLS-SEM Attitude, subjective norm, PBC, growth mindsets, intentions to recommend, travel desires and revisit intentions TPB The findings showed that attitudes, subjective norms and PBC could be predicted by travelers’ mindsets
Park et al. (2017) To explain the intention of Chinese students to travel to Japan by applying extended TPB China Survey n = 736 SEM Attitude, subjective norm, PBC, travel constraint, travel intention and destination image TPB The study’s findings show that the prediction ability of the extended TPB model is robust for travel intention to Japan than the original one
Seow et al. (2017) To investigate tourists’ intention to seek medical tourism in Malaysia by applying the extended model of TPB Asia, Europe, Oceania, Africa, The Americas and Middle East Survey 380 PLS-SEM Attitude, subjective norm, PBC, perceived risk, perceived benefit, resource availability and intention to visit TPB The analysis of TPB has been found to be overwhelmed and little attention has been paid to perceived risks, perceived benefits and resource availability in relation to the TPB
Lee et al. (2012) To implement a novel concept of NPI for influenza and to assess the effect of NPI on the behavioral intentions of international travelers Korea Online survey 397 SEM Attitude, subjective norm, PBC, positive and negative anticipated emotion, perception of influenza, desire, NPI and behavioral intention TPB and MGB The model showed that the intention of tourists was predicted by desire, PBC, frequency of past behavior and NPI, but perceptions of 2009 H1N1 had no impact on desire and intention
Hsiao and Yang (2010) This research adds two constructs, novelty searching (NS) and trust (TR) to the TPB model to assess the intention of travelers to take Taiwan’s High-Speed Rail (HSR) and its antecedents Taiwan Survey 300 SEM Attitude, subjective norm, PBC and novelty seeking and trust TPB Results indicate that attitude, subjective norm, PBC have a positive impact on behavioral intention of taking HSR
NS positively influences attitude and TR positively influences three antecedents of the intention
Lam and Hsu (2006) To assess the application of the TPB model including past behavior variables in predicting the behavioral intention of selecting a destination for travel Taiwan Survey 299 SEM Attitude, subjective norm, PBC, past behavior and behavioral intention TPB The findings revealed that the data fitted fairly well with the TPB model. Attitude, subjective norm, PBC and past behavior were related to the behavioral intention of selecting a travel destination
Lam and Hsu (2004) To assess the fit of the TPB model with travelers from China China Survey 328 SEM Attitude, subjective norm, PBC, past behavior and behavioral intention TPB The results showed that the data fitted reasonably well into the TPB model and described the behavioral intention of travelers
Attitude, PBC and past behavior were linked to travel intention
Notes:

MGB – Model of goal-directed behavior.

HBM – Health belief model.

NPI – Non-pharmaceutical intervention

Respondents’ profile (n = 417)

Variable Classification Freq (%)
Gender
Male 255 61.2
Female 162 38.8
Age
20–27 118 28.3
28–35 165 39.6
36–43 91 21.8
44–51 34 8.2
Above 51 4 1.0
Below 20 4 1.0
Education
Intermediate 5 1.2
Graduation 74 17.7
Post-graduation 205 49.2
PhD 127 30.5
Others 6 1.4
Marital status
Single 233 55.9
Married 183 43.9
Others 1 0.2
Occupation
Business person 62 14.9
Employee 184 44.1
Others 31 7.4
Retired 5 1.2
Student 135 32.4
Monthly income (INR)
Upto 20,000 129 30.9
20,001–40,000 82 19.7
40,001–60,000 37 8.9
60,001–80,000 63 15.1
Above 80,000 106 25.4
Total 417 100

Detail of the results of descriptive analysis

Construct Items Mean SD
ATT 1.8649 0.7763
ATT1 1.7170 0.8941
ATT2 1.8705 0.8784
ATT3 2.0071 1.0011
SN 1.9137 0.8000
SN1 1.8081 0.8213
SN2 1.8992 0.9195
SN3 1.9688 0.9590
SN4 1.9784 0.9390
PBC 3.1775 0.9413
PBC1 3.2517 1.1649
PBC2 3.1151 1.1710
PBC3 3.2110 1.2003
PBC4 3.1414 1.1732
PBC5 3.3573 1.1432
PBC6 2.9880 1.2604
PR 2.1823 0.8516
PR1 2.1007 1.0187
PR2 2.0671 0.9758
PR3 2.3788 1.1095
BI 3.4436 1.0056
BI1 3.5131 1.1374
BI2 3.5443 1.1217
BI3 3.4412 1.1167
BI4 3.2757 1.1901

Measurement scale factor analysis

Items loadings Eigenvalue % of variance Cumulative (%) Cronbach’s alpha
Perceived 5.444 27.222 27.222 0.882
Behavioral control
PBC3 0.823
PBC4 0.815
PBC5 0.788
PBC2 0.783
PBC1 0.765
PBC6 0.700
Subjective norm 4.554 22.769 49.990 0.901
SN4 0.875
SN3 0.873
SN2 0.826
SN1 0.728
Behavioral intention 2.037 10.184 60.175 0.904
BI3 0.890
BI2 0.868
BI1 0.868
BI4 0.815
Perceived risk 1.301 6.504 66.679 0.760
PR3 0.781
PR2 0.781
PR1 0.748
Attitude 1.005 5.024 71.703 0.788
ATT1 0.829
ATT3 0.740
ATT2 0.725

Reliability and convergent validity test

Standardized CR AVE
Items Factor loading
Attitude (ATT) 0.791 0.558
ATT1 0.72
ATT2 0.80
ATT3 0.72
Subjective norm (SN) 0.903 0.699
SN1 0.77
SN2 0.83
SN3 0.87
SN4 0.87
Perceived behavior control (PBC) 0.884 0.560
PBC1 0.75
PBC2 0.76
PBC3 0.77
PBC4 0.82
PBC5 0.73
PBC6 0.66
Perceived risk (PR) 0.768 0.528
PR1 0.76
PR2 0.80
PR3 0.61
Behavioral intention (BI) 0.905 0.706
BI1 0.85
BI2 0.87
BI3 0.88
BI4 0.76

Discriminant validity test

Construct ATT SN PBC PR BI
ATT 0.747
SN 0.659*** 0.836
PBC 0.244*** 0.158*** 0.749
PR 0.620*** 0.569*** 0.127* 0.727
BI −0.002 −0.131* 0.382*** −0.178** 0.840
Notes:

* p < 0.050, ** p < 0.010, *** p < 0.001; square root of AVE diagonally in italic; ATT = attitude, SN = subjective norm, PBC = perceived behavioral control, PR = perceived risk, BI = behavioral intention

Results of hypothesis testing

Relationship Std β t-value Result
H1: ATT → BI 0.236 5.940 Supported
H2: SN → BI −0.183 −4.625 Not supported
H3: PBC → BI 0.406 10.244 Supported
H4: PR → BI −0.302 −7.606 Supported

Appendix

  1. Attitude (adapted from Lam and Hsu,2006):

    • ATT1: I think traveling in the period of COVID-19 would be favorable.

    • ATT2: I think traveling in the period of COVID-19 would be pleasant.

    • ATT3: I think traveling in the period of COVID-19 would be positive.

  2. Subjective norms (adapted from Ajzen,1991):

    • SN1: My relatives who are important to me think that I should travel in the period of COVID-19.

    • SN2: My friends who are important to me think that I should travel in the period of COVID-19.

    • SN3: My seniors who are important to me think that I should travel in the period of COVID-19.

    • SN4: My colleagues who are important to me think that I should travel in the period of COVID-19.

  3. Perceived behavioral control (adapted from Ajzen,1991; Taylor and Todd,1995):

    • PBC1: I am confident that if I want to, I can travel in the period of COVID-19.

    • PBC2: I am able to travel in the period of COVID-19.

    • PBC3: I have the resources to travel in the period of COVID-19.

    • PBC4: I have the ability to travel in the period of COVID-19.

    • PBC5: I have the knowledge to travel in the period of COVID-19.

    • PBC6: Traveling in the period of COVID-19 is entirely within my control.

  4. Perceived risk (adapted from Sönmez and Graefe, 1998):

    • PR1: I think traveling in the period of COVID-19 will provide value for money spent.

    • PR2: I think traveling in the period of COVID-19 will provide personal satisfaction.

    • PR3: I think traveling in the period of COVID-19 will waste time.

  5. Behavioral intention (adapted from Venkatesh et al., 2003 and Taylor and Todd,1995):

    • BI1: I intend to travel in the near future.

    • BI2: I predict I would travel in the near future.

    • BI3: I plan to travel in the near future.

    • BI4: I intend to travel frequently in the near future.

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Further reading

Ajzen, I. (1988), Attitudes, Personality and Behavior, Open University Press, Milton Keynes.

Fishbein, M.A. (1967b), “Behaviour theory approach to the relation between beliefs about an object and the attitude toward the object”, in Fishbein, M. (Ed.), Readings in Attitude Theory and Measurement, John Wiley, New York, NY, pp. 389-400.

Corresponding author

Sujood can be contacted at: sjdkhancool@gmail.com

About the authors

Sujood is based at the Department of Commerce, Aligarh Muslim University, Aligarh, India.

Sheeba Hamid is based at the Department of Commerce, Aligarh Muslim University, Aligarh, India.

Naseem Bano is based at the Department of Commerce, Aligarh Muslim University, Aligarh, India.

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