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Miller (1956) first proposed the concept of information overload, which means that at any given time, through psychological research, the processing power of humans is very limited. The maximum amount of information that can consciously be processed is about seven units. Consumer decision-making was later studied by Jacoby et al. (1974) to define the theory of information overload. Furthermore, Matthes et al. (2020) noted that overburdening with information, permanent online interaction with other users, the perception of permanent observation, and demand for reactions result in information overload for mobile social networking sites (SNS) use.

Information overload refers to a state whereby a person believes that there is an imbalance between environmental needs and the available resources that can be used to respond to these needs (Eppler and Mengis, 2004). Other academic disciplines, such as cognitive psychology, focus on the acute phenomenon of information overload, which affects recall, judgment, and decision-making (Bargh and Thein, 1985). Fu et al. (2020) noted that overloading describes a person’s subjective perception and evaluation of the amount of information, persons, or objects that exceed the processing capacity. In an era of information and communication technologies, overload is a key factor in providing negative results (Lee et al., 2016). Various devices and applications can increase personal perceptual overload (Yin et al., 2018).

At the beginning of the COVID-19 epidemic, inaccurate information and information overload caused consumers to engage in panic buying (Herjanto et al., 2021). The complex development of the pandemic and emotional news reports resulted in information overload and there is a lack of experience with decision-making in a situation of information overload (Farooq et al., 2021).

Perceived risks that are related to health, well-being, and safety create a preference for specific travel choices (Ozbilen et al., 2021). Traveling cannot be experienced in advance and it is impossible to know the risks that pertain to travel. Perceived risk is an important factor for predicting travel behavior because it explains the important factors in the choice of a travel destination (Zilker et al., 2020).

Previous research has also demonstrated that the perceptions of service quality, value, destination image, risk, subjective norm, and behavioral control are critical factors for attitude and intention to travel (Abbasi et al., 2021). Perceived risk is also proven to negatively impact participation in out-of-home activities and the frequency of trips (Parady et al., 2020).

Under the basic idea of the theory of reasoned action, Ajzen (1991) proposed TPB to determine the mechanism for behavioral intention, which explains human behaviors. The TPB assumes that individuals are rational and evaluate alternatives and form intentions before taking actions. The common factors of behavioral intention include attitude, subjective norm, and perceived behavior control. Attitude is a person’s general perception of specific behavior, which can be positive or negative. Subjective norms determine the way the opinions of other individuals affect the person in terms of a particular behavior. Perceptual behavior control is a factor that facilitates or hinders behavior. The TPB model states that the three pre-variables of behavior attitude, subjective norm, and perceived behavioral control positively affect behavior intentions (Ajzen, 1991).

Bandura’s (1998) study defined self-efficacy as the ability of an individual to believe in an ability to organize and perform a specific achievement (Bandura, 1998). This is the perceived behavior control construct for the TPB. Ajzen’s (2002) study noted that self-efficacy and perceived behavior control are two mutually interchangeable aspects. Ajzen divided the perceived behavioral control construct into two components: self-efficacy and controllability. Self-efficacy pertains to the degree of difficulty in executing a behavior. Some studies show that self-efficacy has a direct effect on intention (Cooke et al., 2016).

Self-efficacy is defined as strong self-confidence in specific abilities, which reduces stress and anxiety. Response efficacy defines how individuals perceive their confidence in a response to a specific situation. Response efficacy affects the intention to secure information (Menard et al., 2017).

When insufficient useful information is provided by a product or service, perceived risk increases. In a situation of information overload, doubts and uncertainty prevail and the efficiency and quality of decision-making are reduced (Castañeda et al., 2020). In addition, the information about COVID-19 can be overwhelming so the causal relationship between information overload and perceived risk during the COVID-19 pandemic is defined in terms of the following hypothesis:

The TPB explains the behavioral willingness mechanism. After COVID-19, many empirical studies of travel behavior reference the TPB theory and its mechanism. The study by Rahmafitria et al. (2021) uses knowledge, social attention, and implementation risk in the COVID-19 pandemic era to expand the TPB theory in the field of tourism. The study collected questionnaires in Indonesia and found that subjective norms have a greater effect than attitudes. Furthermore, the effect of behavioral control was found to be significant. Knowledge, perceived risks, and social concerns also significantly affect willingness to travel.

Tasci and Sönmez (2019) noted that an individual’s attitude is significantly affected by perceptions. Whenever there is a decision to travel, the perceived risk is the factor that tourists take into consideration before traveling (Beirman, 2006; Rittichainuwat et al., 2018). Furthermore, uncertainty, chaotic information, and the development of COVID-19 might also confuse travel behavior.

Previous studies determined the effect of information overload on perceived risk. More contradictory information results in unproductive attitudes (Cavlek, 2002; Rahmafitria et al., 2021). This study proposes that the construct of perceived risk significantly affects traveling attitude and intention in the COVID-19 era. The TPB also shows that traveling attitude has a direct effect on intention. Hence, the following hypotheses are postulated.

In the context of global pandemic research, response efficacy accurately predicts individuals’ adaptive behaviors, such as vaccination behaviors (Davis et al., 2021). This study uses response efficiency to determine individuals’ confidence in their response to difficulties during the COVID-19 pandemic while traveling. The TPB states that traveling response efficacy directly impacts the intention to travel. Hence, the following hypotheses can be postulated.

This study determines the way information overload and perceived risk affect tourists’ intention to travel since the COVID-19 pandemic. The greater the information overload, the greater the perceived risk of traveling. Furthermore, the perceived risk decreases the intention to travel. Therefore, the mediating effect of the perceived risk on the intention to travel is tested using the following hypothesis:

During the COVID-19 pandemic, government travel rules required nucleic acid testing, wearing of masks, isolation in hotels, and home isolation for travelers. In this way, the opinions of others might affect the travel decisions of potential tourists. Furthermore, research conducted by Schepers and Wetzels (2007) analyzed subjective norms, the technology acceptance model (TAM), and, behavioral intention. The research also included moderating effects of an individual-associated variable, a technology-associated variable, and a dependent variable. Findings suggested a substantial impact of the subjective norms with perceived usefulness and intention. Moreover, moderating impacts were discovered for each designated variable (Schepers and Wetzels, 2007). This study proposes the use of a subjective norm as a moderating variable to determine the significance related to the opinions of others. Hence, the following hypothesis can be postulated.

According to the above hypothesis development, the research framework is proposed in Figure 1.

To study the intention to travel after the COVID-19 epidemic, this study surveyed individuals over the age of 20. Self-administered questionnaires and online electronic surveys were used for data collection. Questionnaires were distributed in major resort hotels, stations, and airports to collect data in Hainan from 1 May 2021 to 30 May 2021. A total of 518 effective questionnaires were collected.

The research variables for this study include the background information for tourists and research variables. To verify the concepts of the research variables, the operational definitions of the following variables are explained as follows. The basic personal data is measured in the first part of the questionnaire and includes seven items: gender, age, marital status, occupation, home environment, and location. All research variables are measured using a 7-point Likert scale, for which one means strongly disagree and seven means strongly agree. The survey instructions for each construct are shown as follows.

This study refers to the definition of information overload in the study by Chen et al. (2009) to determine the effect of information overload on decision-making for e-retail consumers. The results of that study show that an excess of information leads to a high degree of information overload. Four items are excessive information, processing pressure, difficulty in understanding information, and difficulty in obtaining information sources.

The variable of perceived risk variable includes a total of six items. The items were adopted and modified from a study by Abrahão et al. (2016) that compared the mobile phone customers for a telecommunications company in southeastern Brazil and determined the relationship between performance expectations, effort expectations, social influence, and perceived risk.

The items for the traveler’s attitude were adopted and modified from a study conducted by Paul et al. (2016), which defined the items for consumer attitudes to green products. This study showed that attitude and behavior control perception for green products accurately predict purchase intentions and subjective norms do not. The Cronbach’s α value for the attitude construct is 0.897. This study also employs some items from Mehrad and Mohammadi’s (2017) study, which determined the effect of Iran’s mobile banking reputation on the adoption rate. The results showed that word-of-mouth is the main factor affecting users’ attitudes toward mobile banking. In terms of the subjective norm, this study uses the items suggested by Taufique and Vaithianathan’s (2018) study. There are five items for subjective norm constructs.

In terms of the items for the intention to travel construct, the items suggested by Jang et al.’s (2015) study are used. Their study determined the relationship between precedents in terms of environmental awareness behaviors, perceived consumer effectiveness, environmental concerns (EC), and attitude (AT) associated with the TPB to determine consumer behavioral intentions (BI).

In terms of response efficacy items, this study uses those for the study by Rimal and Real (2003), which defines response efficacy as a subconstruct of the efficacy belief. There are four items for the response efficacy. The items are modified to suit this study and are available in the appendix part of the study as Table 1.

This study collected a total of 518 valid questionnaires in Hainan, at airports, restaurants, hostels, and shopping malls that are frequently visited by tourists, using a convenience sampling method. The sample distribution is shown in Table 2. A total of 304 female respondents accounted for 58.7% of the population and 214 male respondents accounted for 41.3%. In terms of age, 302 people aged between 31 and 40 accounted for 58.3%, and 80 people aged between 20 and 30 accounted for 15.4%. In terms of marriage, 333 were unmarried, which accounted for 64.29%, and 179 were married, which accounted for 34.56%. A total of 186 people took more than three trips per year, which accounted for 35.9%, and 155 people took two trips per year, which accounted for 29.9%. In terms of occupation, 193 students accounted for 37.3% and 102 corporate employees accounted for 19.7%. In terms of educational background, 354 university graduates accounted for 68.3%.

The results for the descriptive statistics showed that the average number of 2.900 for INO4 was the lowest, with 4.760 for ATT3 and ATT4 being the highest. For this study, the value for Kurtosis was between –1.090 and –0.100. Additionally, for Skewness the values were between –0.670 and 0.620, as shown in Table 3. The empirical rule of Kline (2015) states that if the absolute value for skewness is less than or equal to 2 and the absolute value for Kurtosis is less than 7, then the variable conforms to the univariate normality. For this study, the absolute values of skewness and kurtosis are less than 2.

This study uses a two-stage approach to verify the proposed model using structural equation modeling (SEM), as suggested by Anderson and Gerbing (1988). Stage 1 was associated with analyzing the variables’ validity and reliability. Confirmatory factor analysis (CFA) and Fornell and Larcker criterion were used in this stage. The second stage determined the effect of the path on the structural model and its significance (Anderson and Gerbing, 1988; Hulland, 1999). Mediation and moderation analysis were also conducted in Stage 2. Furthermore, to deal with the complex proposed research model, past studies have considered PLS as a suitable choice for the analysis of this study (Chin and Newsted, 1999; Zhao and Khan, 2021).

Table 4 shows the standardized factor loadings for scales from 0.560 to 0.941, which was a reasonable range. The composite reliability for each dimension was 0.818–0.937, exceeding 0.7, as recommended by Nunnally (1994) and Hair et al. (2010), so all dimensions exhibit internal consistency. The lowest value for average variance extracted (AVE) was 0.607, which exceeded the value of 0.5 suggested by Fornell and Larcker (1981) and Hair et al. (1998), so all dimensions exhibit convergent validity.

To confirm the discriminant validity, the square root of the AVE for a dimension is compared with the correlation. As shown in Table 5, the square root of the AVE for all five dimensions of this study is greater than the correlation coefficient, so the data of this study has discriminant validity (Fornell and Larcker, 1981).

Large-sample studies usually have a p-value of less than 0.05, so the SEM model fitting is easily affected by a bad result (Schumacker and Lomax, 2004). Therefore, the model and sample for a quantitative study must be matched and verified using many different methods. This study uses eight methods to test the model fit: Normed Chi-square, CFI, GFI, AGFI, TLI, and RMSEA (as shown in Table 6). All of the models fit criteria signified fit according to the suggested standards.

Table 7 and Figure 2 show the results for path coefficients. According to the results, information overload significantly impacted perceived risk (β = 0.364, p < 0.001). Moreover, the intention to travel was significantly impacted by perceived risk (β =–0.183, p < 0.001) and attitude (β = 0.721, p < 0.001); however, it was not a significantly impacted response efficacy (β = 0.067). Furthermore, perceived risk also significantly impacted attitude (β =–0.142, p = 0.003). These results support the validity of the research model. A total of 13.3% of perceived risk is explained by the information overload construct, and 59.5% of intention to travel is explained by perceived risk, attitude, and response efficacy constructs. In addition, 2% of attitude is explained by the perceived risk construct.

Table 8 shows that in the total effect of INO→IT, the p-value was less than 0.05, and the bias-corrected confidence interval (CI) did not include 0 (CI of INO→IT = [–0.29,–0.126]). This evidence supported the existence of the total effect. In the indirect effect of INO→PR→IT, the p-value was also less than 0.05 and the bias-corrected confidence interval (CI) did not include 0 (CI of INO→PR→IT = [–0.191,–0.079]), so the hypothesis regarding the indirect effect was supported. The indirect effect of INO→PR→ATT→IT signified that the p-value was less than 0.05, p<0.05, and the bias-corrected CI did not include 0 (CI of INO→PR→ATT→IT = [–0.138,–0.019]), thus hypotheses related to the mediating effect was supported.

The subjective norm was a moderator of the proposed model. As shown in Table 9, the interaction of perceived risk and subjective norm to travel intention was 0.032 (z = | 0.791| <1.96, p = 0.429). Hence, subjective norms did not exist in the moderating effect.

In terms of a causal relationship, information overload was in a significant association with perceived risk, so the higher the perceived information overload, the greater would be the perceived risk. The development of COVID-19 and an excess of information have a significant impact on the average person. Excessive, serious, uncertain, and professional information have been prevalent since the COVID-19 pandemic, and because of the Internet, an overwhelming amount of information has increased the perception of risk.

The results of this study are similar to those of the study by Castañeda et al. (2020) and Abbasi et al. (2021). Modern society features an excessive amount of information, so it is difficult to make quick decisions. The information overload item scores for this study showed that the item with the highest score is “Since the COVID-19 pandemic, I feel that there is too much travel information.” The item with the lowest score was “Since the COVID-19 pandemic, I don’t know where to find the information I need.”

The results of this study showed that there is a significant increase in the perception of travel risk due to information overload. The plethora of COVID-19-related prevention and travel-related information means that a government should use propaganda and its credibility to reduce the deleterious effect of information overload. Various governments used the same accountability window to publish important messages through the media and to clearly convey correct instructions through simple procedures. This is common practice during this COVID-19 pandemic.

The impact of perceived risk on intention to travel was statistically significant. The greater the perceived risk, the lower the willingness to travel. These results were also realized by Rahmafitria et al. (2021). In the post-COVID-19 era, the perceived risk of tourism is an influencing factor for the intention to travel.

The regression coefficient for perceived risk to intention to travel was - 0.273, which was statistically significant. The greater the perceived risk, the lower the willingness to travel. These results were also realized by Rahmafitria et al. (2021) for the post-COVID-19 era. The perceived risk of tourism is an influencing factor for tourism willingness.

In terms of the scores for the perceived risk items, the item with the highest score is: “Since the new crown epidemic, tourism will increase uncertainty.” On the other hand, the item with the lowest score is: “Since the COVID-19 pandemic, I have not felt well protected.” Therefore, although uncertainty about travel has increased significantly, individuals have confidence in self-protection.

According to the findings of the research study, it can be implied that the attitude toward planning behavior theory is a factor that influences intention. The attitude of tourists is the decisive factor and determines tourists’ intention to travel. Hence forth, perceived risk had a significant impact on tourism attitude. The highest score for intention to travel was for “I would like to travel to the resort.” The lowest score was “I will encourage others to travel.” This shows that individuals are willing to travel but are unwilling to encourage others to travel. This result is similar to that of the effect of adjustment for this study.

The effect of response self-efficacy on intention to travel was not significant. For response self-efficacy, the highest score was for the item “I have the necessary knowledge for good COVID-19 measures in keeping me free of COVID-19 during the trips.” This shows that most individuals are confident in their knowledge of self-protection measures for the COVID-19 virus.

In terms of mediation effects, information overload had an indirect effect on the intention to travel through perceived risk. Information overload also had a chain-like mediation effect on the intention to travel through perceived risk and traveling attitude. Subjective norms define the importance of third-party opinions regarding traveling in the post-COVID-19 era. This study has different results from that by Rahmafitria et al. (2021). The subjective norms for this study did not play an important role. It is possible that travelers did not pay too much attention to other people’s perspectives.