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Article

Determining Factors Affecting Behavioral Intention to Organize an Online Event during the COVID-19 Pandemic

by
Poonyawat Kusonwattana
1,2,
Yogi Tri Prasetyo
1,3,4,*,
Stefanus Vincent
5,
Jefferson Christofelix
5,
Aryadaksa Amudra
5,
Hazel Juan Montgomery
5,
Michael Nayat Young
1,
Reny Nadlifatin
6 and
Satria Fadil Persada
7
1
School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
2
School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
3
International Program in Engineering for Bachelor, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taiwan
4
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taiwan
5
Department of International Business Engineering, Petra Christian University, Siwalankerto No. 121-131, Surabaya 60236, Indonesia
6
Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
7
Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(20), 12964; https://doi.org/10.3390/su142012964
Submission received: 20 August 2022 / Revised: 26 September 2022 / Accepted: 29 September 2022 / Published: 11 October 2022
(This article belongs to the Section Sustainable Management)
Browse Figures
Versions Notes

Abstract

:
An online event, such as an online concert or online graduation, has been widely utilized as one of the solutions to connect people during the COVID-19 pandemic. The purpose of this study was to determine factors affecting behavioral intention to organize an online event during the COVID-19 pandemic by integrating Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Overall, 366 sets of data were collected from respondents through a convenience sampling approach from several social media platforms. They were asked to fill the online questionnaire which had 40 questions under 8 segments. Structural equation modeling (SEM) showed that hedonic motivation was found to have the strongest effect on attitude, which subsequently led to behavior intention. In addition, online event promotion and perceived usefulness were also found to have significant effects on attitude, which also subsequently led to behavior intention. This study is one of the first studies that explored the behavioral intention to utilize an online event during the COVID-19 pandemic. The findings would be beneficial mainly for online event organizers, particularly for enhancing the online event performance based on the contributing factors. Additionally, this study could act as a stepping stone to help future researchers understand online event better. Finally, the technology acceptance model and theory of planned behavior in this study can be extended for exploring the acceptance of an online event worldwide.

1. Introduction

An online event, also known as a virtual event or virtual experience, is an event where people interact in an online environment on the internet. It uses technology specifically designed to replicate the event experience over the internet rather than meeting in a physical location. An online event is usually organized by the committee of the event. For instance, if there is an online event of a school or university such as an online school anniversary or online graduation, the event is led by the student council or committee chosen from among the university students. If the online event is held by an organization or a business company, they are led by a range of key stakeholders like associations, professional meeting planners, company executives, division managers, and various departments.
Online events have several advantages. First, during the COVID-19 pandemic, an online event has become a solution to connect people while staying at home. Due to the possible transmission of the COVID-19 virus, most people cannot go outside and are forced to stay at home. This new way to interact with people has become one of the solutions. Second, online events are also affordable. Costs such as rent of premises, foods, and permits can be avoided by using an online platform as the medium of the events. Last, another advantage of an online event is flexibility. Rather than going to a certain place to join an event, people would be able to access and join an online event from across the world, regardless of their time and location.
There are a large number of platforms that can be utilized for arranging online events, and each of those has unique features and benefits. These platforms can be free platforms such as YouTube, Facebook, and live Instagram, or paid platforms such as ZOOM and Google Meet. Table 1 shows the number of ZOOM daily meeting participants that have grown by 2900% only in 4 months, from 10 million daily meeting participants on 31 December 2019 to 300 million daily meeting participants on 21 April 2020.
Previously, there were several recent studies related to online events or virtual events. In Singapore, Perdana and Mokhtar [2] conducted a study regarding an adaptation of elderlies to virtual events during the COVID-19 Pandemic. By utilizing social exchange theory, they found that social influences and perceived ease of use had significant effects on perceived benefits, which subsequently led to intentions to utilize virtual event platforms. In London, Yates et al. [3] investigated a study regarding the participation and satisfaction of virtual conference. They highlighted that virtual conferences had several challenges, such interaction problems, time-zone differences, and even device issues. Furthermore, in Hongkong, Chen et al. [4] also investigated the effect of online meeting on business travel among potential air travelers. Interestingly, they found that those potential air travelers preferred face-to-face meeting than online meeting [4].
While attending online or virtual event, there are several issues that need to be raised to enhance the engagement of the attendees. Ton and Le [5] mentioned that experience, storytelling, and different intentions from stakeholders involve the engagement of attendees during virtual events. Klaus and Maklan [6] show that the experience that consumers get is one of the leading factors in the success of obtaining consumer engagements in virtual or online environments. Moreover, Kharouf [7] showed that marketing content should be one of the online communication strategies in managing an online event experience.
Despite the availability of several studies related to online events, it is a necessity to understand how online event organizers can organize their event in such a way that the consumers can receive their intended benefits, particularly during the COVID-19 pandemic. Kharouf et al. [7] only discussed the consumer interaction of an offline event in online platforms such as websites and Instagram. Similarly, Pedaste and Kasemets [8] only put a focus on online conferences, particularly International Conference on Advance Learning (ICALT) 2020, while not mentioning other online events such as webinars and public lectures. Moreover, Ton and Le [5] also only based their research on interviews with a small number of people, which can lead to biased research results. Thus, a further investigation regarding the acceptance of online event or virtual event during the COVID-19 should be explored further.
The intention of people to adopt new technology, such as an online event, can be measured using the Technology Acceptance Model. This model is generally used in research to measure the likelihood of people adopting new technology, like online meeting platforms. Fred Davis, who developed the theory, divided the primary factors which influence a person’s intention to adopt new technology into two: Perceived Ease of Use and Perceived Usefulness [9]. Perceived usefulness is described as the degree to which someone thinks that the use of a particular system will improve their job performance. Perceived ease of use is described as the degree to which an individual believes that using a particular system will be free from physical and mental effort [9]. In analyzing the behavior of users while utilizing online events, TAM can be combined with Theory of Planned Behavior (TPB).
The Theory of Planned Behavior (TPB) is systematic and consists of several interrelated concepts. It is focused primarily on one’s perception and how one reacts toward a perceived behavior. This theory enables people to observe the attitude, subjective norm, perceived behavior control, intention, and how they affect the observed subject’s final behavior. Two students from Indonesia used TPB in their research about the intention to use electronic money. The analyses from the study showed that the subjective norm and perceived behavior control of low economic students influence their intention and final behavior to not use electronic money [10]. Moreover, TPB has proven to be useful in many other case studies, and has helped several sectors such as healthcare, politics, businesses, and organizations. Thus, combining TAM and TPB could be very useful in determining factors affecting the perceived usefulness of an online event.
The purpose of this study was to determine factors affecting behavioral intention to utilize an online event during the COVID-19 pandemic. Several factors under TAM and TPB were analyzed simultaneously by utilizing structural equation modeling. This study is one of the first studies that explored the behavioral intention of utilizing an online event during the COVID-19 pandemic. Expectantly, this study of online events will benefit people, mainly online event organizers who seek out the contributing factors. Subsequently, those people would be able to understand their participants’ behavioral intention and how to enhance the overall performance of the previously planned online event. Finally, the TAM and TPB constructs in this study can be extended for exploring the acceptance of an online event worldwide.

2. Conceptual Framework

The Theoretical Research Framework of this study is represented in Figure 1. There are two building blocks of this proposed framework, which are Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). TAM is a model to measure a person’s intention to use any technology, using two primary factors [9]. Theory of Planned Behavior consists of three independent predictors of an individual’s intention [11], and it is an extension of the theory of reasoned action. These independent predictors include subjective norms and attitudes toward behavior [12].
Based on Figure 1, there were seven exogenous latent variables. These include Utilities Issue/Noise (UIN), Hedonic Motivation (HM), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Online Event Promotion (OEP), and Subjective Norm (SN). In addition, Figure 1 also shows that there were two endogenous latent variables, consisting of behavior intention and attitude. These latent variables were constructed by several indicators which were supported by several studies [12,13,14,15,16,17,18].
The promotion of online events is very important for the publication of information related to online events. People tend to know or get information regarding online events from social media and the internet. Knowing information about the online event, people will be aware of the possible content and outcome of the online event, minimizing preference mismatch. Moreover, exposure to a variety of online event promotion will most likely affect the viewers or listeners to react to the events mentioned, such as by looking for additional information about the event. Consequently, the following hypothesis was proposed:
Hypothesis 1 (H1).
Online Event Promotion had a significant direct effect on attitude.
To participate in online events, supporting technology is needed, one of which is an online platform used to hold online events. There are a variety of platforms that can be used to attend or hold online events, such as ZOOM and Google Meet. Davis defines perceived ease of use as “the degree to which a person believes that using a particular system would be free of effort” [9]. As a result, people tend to like a platform that has straightforward and easy usability. People prefer to use an easier platform to work with because no one wants or intentionally burdens themselves mentally with such hassle. Thus, we proposed the following hypothesis:
Hypothesis 2 (H2).
Perceived ease of use had a significant direct effect on attitude.
In the COVID-19 pandemic era, people’s movements are extremely limited. Consequently, they need to be reliant on online meeting platforms such as ZOOM and Google meet. Perceived usefulness is considered to be a critical attribute to understand people’s intention to use those apps. Davis defines perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance” [9]. People also find that online meeting platforms like ZOOM offer the ability to communicate in real-time despite geographical distance [13]. Thus, we hypothesize the following:
Hypothesis 3 (H3).
Perceived usefulness had a significant direct effect on attitude.
Intrinsic motivation such as fun, happiness, and pleasure derived from using technology can be used to define hedonic motivation [14]. The existence of online meeting platforms such as ZOOM and Google Meet allows people to seek fun and happiness by joining an event. For example, since people are unable to meet one another directly due to social distancing measures, joining an online event through ZOOM or Google Meet acts as the next best thing to do in order to provide some joy and happiness by meeting others. Some people are even willing to spend a lot of money in order to get the joy to make them satisfied in an online event. Thus, we hypothesize the following:
Hypothesis 4 (H4).
Hedonic motivation had a significant direct effect on attitude.
Utilities and tools were made to make progress easier to complete. Humans are versatile tool users which are able to adapt to use new tools for their needs [15]. The issue lies in studying to use the tools effectively because it requires to progress. This requires motivation which derives from the cost–benefit ratio on progressing such progress. Setting up an online event is an activity which requires tools that are unfamiliar for people to use before the COVID-19 pandemic era. This leads toward individuals’ behavior to change for adaptions or not.
Hypothesis 5 (H5).
Utilities Issue/Noise had a significant direct effect on attitude.
Attitude can be defined as “the relatively stable overt behavior of a person which affects their status” [16]. The intent of this behavior stems from status fixing on its individual social subject. The intention can also be defined as motivation that influences the behavior correlated to his attitudes. As in the COVID-19 pandemic era, the social doctrine is to follow health precautions to minimize health risks. The precaution has become an intended behavior that is followed by people motivated to stay healthy and to change their attitude towards social cues. Thus, we proposed:
Hypothesis 6 (H6).
Attitude had a significant direct effect on behavior intention.
Subjective norm can be defined as “perceived social pressure to perform or not to perform the behavior” [17]. In the theory of planned behavior, the subjective norm is the secondary factor that influences an individual’s intention. Parash et al. [18] applied the theory of planned behavior in the context of blood donation, and the results show that subjective norms are both significant to the intention of making a voluntary blood donation. Based on its similarity with cases of online events, we hypothesized this hypothesis:
Hypothesis 7 (H7).
Subjective Norm had a significant direct effect on Joining Online events.

3. Methodology

3.1. Participants

This study uses a cross-sectional design. A cross-sectional design study is utilized to allow the authors to simultaneously compare multiple range of variables, which is appropriate in this study. Due to the current condition of the COVID-19 pandemic in Indonesia, where most activities are still held on an online basis, an online questionnaire was distributed between 27 September 2021 and 16 October 2021 through Google forms using a convenience sampling approach. The questionnaire was distributed to random people from Indonesia via social media, such as LINE, Facebook, Whatsapp, and Instagram. The sampling frame or the targeted audience ranged from high school students to elders. As a result, a total of 366 people responded to the online questionnaire, consisting of a total of 40 questions spread across 8 segments.

3.2. Questionnaire

Based on the theoretical framework that has been presented in Section 2, we assembled a self-administered questionnaire for this study to analyze the factors affecting customer satisfaction during an online event (Table A1). The questionnaire consisted of one demographic information section that acts as the introductory section (Age, Gender, Province, Current/Last education, Income per month, Have the respondent ever joined an online event?); and 8 different sections: (1) Perceived Usefulness, (2) Perceived Ease of Use, (3) Behavioral Intention, (4) Online Event Promotion, (5) Hedonic Motivation, (6) utilities Issue/Noise, (7) Attitude, (8) Subjective Norm. A 5-point Likert scale is used for every measurement of every latent construct included in the SEM. The 5-point scale labels from 1 to 5 are disagree, somewhat disagree, neutral, somewhat agree, and agree, respectively.

3.3. Statistical Analysis

Structural Equation Modelling (SEM) is a statistical technique that is used to estimate, identify, and test relationships between model variables [19]. IBM SSPS AMOS 25 was applied to attain the causal relationship of the proposed hypothesis in the conceptual framework by using the maximum likelihood approach.
Similar to the concept of R-squared in the multiple linear regression, the SEM is further supported by indices such as goodness of fit index (GFI), adjusted goodness of fit index (AGFI), incremental fit index (IFI), Tucker Lewis Index (TLI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A good model should have a value above 0.9 in terms of CFI, TLI, and IFI. Additionally, the values of GFI and AGFI should be above 0.8. Lastly, the value of RMSEA needs to be below 0.07.

4. Results

Table 2 presents the descriptive statistics of the respondents. The majority of the respondents, 94.8% of them, came from the region of Java. In addition, 63% of our respondents were female. Interestingly, there are two age categories that became the majority. Those two are the age ranges of 20–24 and 45–54 years old.
Figure 2 represents the preliminary model of this study. However, based on this figure, several hypotheses were found not to be significant: perceived ease of use on attitude (Hypothesis 2), utilization issue or noise on attitude (Hypothesis 5), and subjective norm on behavioral intention (Hypothesis 7). Thus, a revised model was acquired by the elimination of those hypotheses. Figure 3 represents the final revised model of the variables, which consists of exogenous variables and 2 endogenous variables in total. Moreover, some indices were modified in order to enhance the model’s fit.
Table 3 shows the model fit of the final model. Based on Table 3, it can be seen that there is a major improvement from the initial model to the final model. The improvement was indicated by indices, such as IFI, TLI, and CFI, which were greater than 0.90. Furthermore, GFI and AGFI surpassed the 0.80 minimum cut-off. The RMSEA was also seen to be lower than 0.07. Thus, all the parameter estimates in the final model passed the minimum requirement of cut-off.
Table 4 shows the results of factor loadings, construct validity, and reliability. According to Hair et al. [22], an indicator can be considered representative of its variable when its value is higher than 0.5. The results show that the loadings of each indicator were higher than 0.5. In addition, the results of the Average Variance Extracted (AVE) of each variable were higher than 0.5, which was the suggested value for AVE. It showed the close relationship between the indicator and latent construct. Finally, Cronbach’s α and Composite Reliability (CR) were used for internal consistency assessment. The Cronbach’s α is intended to measure the lower limit of internal consistency of a questionnaire, whereas composite reliability measures the real reliability. Each construct has Cronbach’s α and CR that are higher than 0.7, which is the suggested value for both measures. Thus, every construct in our proposed model had internal consistency.

5. Discussion

Hedonic motivation (HM) was found to have the strongest effect on attitude (β = 0.631, p = 0.012). Several indicators, such as the perceived personal joyfulness, enjoyment, and entertainment, seemed to be the driving factors. HM2, HM3, and HM4 were found to have significant effects on the positive attitude towards an online event. These hedonic motivations have similar features, such as personal feelings gained from such tech-related activity [23]. These findings were also supported by other recent studies about tech-related activity use, and it is stated that intention to use tech-related activity came from personal perceived subject use [24]. Additionally, the past findings tended to overlook important aspects of utility issues in the leveraged tools during an online event, since the findings were done before the COVID-19 pandemic.
SEM found that perceived usefulness (PU) had a powerful direct effect on attitude (ATT) (β = 0.238, p = 0.012). Most of the respondents perceived that online event presence is very useful and efficient during the COVID-19 pandemic era. In addition, most respondents also perceived that an online event could enhance the productivities which lead them to organize an online event. Furthermore, an online event is perceived as useful because of its ability to connect people in spite of dispersed locations [13]. Moreover, in a recent study about online event participant experience, it is stated that online events offer an inclusive and flexible mode of participation, which could be perceived as useful for the participants [25].
Online event promotion (OEP) was also found to have a strong direct effect on attitude (ATT) (β = 0.372, p = 0.006). OEP is the medium to let people know the presence of an online event. Advance information regarding an online event will make people aware of the possible event content and outcome. Social media played a big role in OEP, and the majority of the respondents look for online events while using their social media. If they see an advertisement for an online event, most of the respondents will look further into it. Additionally, when they see their friend post a Twibbon for an online event, they tend to look further into it. In addition, most of the respondents will be more likely to register for an online event if their friends or colleagues, and their relatives recommend an online event.
In addition to all previous latent variables, the study has also confirmed Hypothesis 6, “Attitude had a significant direct effect on the Behavioral Intention” (β = 0.892, p = 0.007). The value in the SEM also shows that the effect’s magnitude is quite high. This finding was supported by a study by Najmi et al. [26], where attitude was found to be one of the most significant factors that affected behavioral intention in consumer participation. Likewise, another study by Xenaki et al. [27] showed how significantly attitude affects the behavioral intention of Norwegian dental health workers. Therefore, if people have a stronger positive attitude regarding online events, the stronger their intention is to join and enjoy an online event.
Interestingly, Utilities Issue/Noise (UIN) was found to have an insignificant effect on attitude, with β = 0.11. As discussed earlier in the conceptual framework, utilities and tools were made to make progress easier to complete. Utilities, such as Computers or Gadgets, and Internet Connection, are integral tools to support how participants can participate in an online event. Nonetheless, the result proven with Hypothesis 5 “Utilities Issue/Noise had a significant direct effect on attitude”, which was shown to be insignificant in the SEM result due to a low beta value. This finding contradicts what Dontre found in his study about distraction in online learning [28]. This difference is logically correct since people who attend an online event choose to attend an event at a short and specific time, which implies that they would properly set themselves and the conditions around them up to support their participation. On the other hand, during online learning, the duration tends to be longer and sometimes the classes are not based on their choices but are mandatory for them. Thus, it is less likely for noise or utility issues to bother a participant of an online event, rather than a student in an online class.
Additionally, subjective norms (SN) were also found not to have a significant effect on the BI, with β = 0.19 subjective norm determined by the perceived social pressure from others for an individual to behave in a certain manner and their motivation to comply with those people’s views [29]. This result from Hypothesis 7, which stated that subjective norm had a significant direct effect on joining online events and was found not to be significant, proven by the low beta value. However, previous research about attending an online event was not found. The closest substitute to this was from a study about the intention of joining soccer events. In that specific study, a subjective norm was eliminated due to low factor loadings, which was the same case in this study about online events [30]. Logically, people are unlikely to ask for consent from their closest person, for instance, parents, relatives, or close friends. In general, if people want to join a specific event, they will look into the event and will casually join it of their own will.
Surprisingly, with β = 0.01, Perceived Ease of Use (PEU) was also found not to have a significant effect on attitude. It was previously hypothesized in Hypothesis 2 that the PEU would have a significant direct effect on attitude. However, this hypothesis was rejected by the SEM result, which showed it to be insignificant with its beta value. The insignificance possibly came from the efforts expected by the respondents whenever they wanted to join an online event. Such a study about online event cases was not found, hence a study about problems of online learning was taken into consideration instead. The author pointed out that one of the key points of online learning is simplicity. In addition, most of the problems stated in the study came from the simplicity issue [31]. If an online event or online learning does not have the simplicity to offer, the audience will need to put some effort into the event. Oftentimes, participants of an online event are required to fill a registration in form, registration form, evaluation form, be online the whole event, and participate actively. These activities will contradict the perceived ease of use factor and reduce its significance on attitude.

5.1. Theoretical Contributions

With the rise of the online and hybrid events in Indonesia, this study contributes to the initiation of theoretical contributions to the non-existing literature on organizing an online event in Indonesia. Firstly, this study delivers originality about factors that affect an online event, which have been booming from the beginning of the COVID-19 pandemic. As the number of online events keeps increasing, there ought to be a study about what factors the participants consider in an online event. These factors were analyzed and modeled by utilizing structured equation modeling (SEM). Secondly, the contribution of this study would be associated with two theories used for this study. The integration of technology acceptance model (TAM) and extended theory of planned behavior (TPB) were used to show their relevance in this relatively new COVID-19 pandemic situation. Following the existing studies which utilized TAM and TPB, latent variables such as perceived ease of use, perceived usefulness, attitudes, and subjective norms from the participants’ perspective were properly examined.

5.2. Practical Implications

Due to the importance of maintaining communication and social within society in the COVID-19 pandemic, online events have become one of the main tools in satisfying that need. Online events also contributed to how events were orchestrated. Before the COVID-19 pandemic, online events were a subtle addition to an event to attract more participants to the event [32]. Offline events before the COVID-19 pandemic were usually seen as how events should be done. However, a study has also shown that online events before the COVID-19 pandemic proved to be preferred because of more new ways to interact within the event [33]. This can be seen as a driving factor by participants finding something new and perceived personal gain from the event compared to offline/face-to-face events.
Consequently, a recent study has also shown the engagement of the audiences after the events, for instance, the Olympic Games 2012 [34]. The study showed where the event games were shown offline face-to-face and broadcast on TV to a wider audience; the result is the audience is much more likely to put more interest in the sport games festival than the sport itself. This means the participant/audience wants the emotion’s perceived personal gain from the event. This matches with the finding of this study and can also be utilized for elevating future online events.
Based on this study, it can be found that hedonic motivation was a major factor in driving participants in gaining interest in joining [35]. This suggested targeting and marketing online events to demographics by narrowing in on the participants’ perceived personal gain. Thus, the event must also deliver good marketing to gain awareness of how the demographic perceived the event phase.

5.3. Limitations and Future Research Direction

Despite the results and success of this study, there were still some limitations. First, the respondents of this study were not equally distributed in demographic terms. For example, 94.81% of the respondents came from the area of Java. Consequently, the sample in this study may not completely capture the targeted participants of an online event, who should have come from diverse regions in Indonesia. Future studies can expand their demographic reach in order to capture the whole target market of online events.
Second, the researchers misestimated the income backgrounds of the respondents. This can be seen through the data where the majority of the respondents, 56.01% of them, address themselves in the area of extreme values of the questionnaire (monthly income at below 2 million IDR or greater than 10 million IDR per month). This means that our questionnaire failed to capture the information on the income spread of the respondents. Hence, this study may not be useful in terms of mapping the correlation between a great event and the participant’s income.
Third, the scope of this study is limited to online events during the COVID-19 pandemic, particularly between September and October 2021. As mentioned by Gupta, with an increase in the number of vaccination in countries around the world and changes in regulation that imposes less restriction on people’s movement, we can expect the number of offline or hybrid events to rise in the near future [36]. This means that the result of this study may become obsolete in the future if the number of online events keeps decreasing as countries shift back to normal condition after the COVID-19 pandemic. Future research can broaden the scope to cover hybrid and potentially new normal offline events.
Lastly, there is a possibility that the respondents misinterpreted the questions in the questionnaire. The questionnaire’s questions are designed to elicit what people feel about their current situation or condition. However, people sometimes misunderstand the question and answer the questions with the condition that is ideal for them. Therefore, there is a possibility that the data that were collected in this research do not have the same intention as what the researcher intended in the questionnaire.

6. Conclusions

Due to social distancing regulation during the COVID-19 pandemic [37] and the rising popularity of online meeting platforms [38,39], the idea of holding online events became popular. The purpose of this study was to determine factors affecting behavioral intention to organize an online event during the COVID-19 pandemic by integrating Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Overall, 366 valuable respondents participated in answering the questionnaire, which was made up of 40 questions and split evenly among 8 sections. The final structural equation modeling (SEM) showed that attitude had the most significant direct effect on behavioral intention (BI), followed by hedonic motivation (HM), online event promotion (OEP), and perceived usefulness (PU). This study is one of the first studies that explored the behavioral intention in utilizing an online event. The results could be used as a reference for online event organizers to enhance the online event quality and reach more audiences. Finally, the technology acceptance model and theory of planned behavior can be extended for exploring the acceptance of an online event worldwide.

Author Contributions

Conceptualization, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; methodology, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; software, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; validation, R.N. and S.F.P.; formal analysis, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; investigation, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; resources, S.V., J.C., A.A. and H.J.M.; writing—original draft preparation, P.K., Y.T.P., S.V., J.C., A.A. and H.J.M.; writing—review and editing, M.N.Y., R.N. and S.F.P.; supervision, Y.T.P., M.N.Y., R.N. and S.F.P.; funding acquisition, P.K., Y.T.P. and M.N.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Mapúa University Directed Research for Innovation and Value Enhancement (DRIVE).

Institutional Review Board Statement

This study was approved by Mapua University Research Ethics Committees and Petra Christian University Research Ethics Committees (FM-RC-21-22).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The researchers would like to extend their deepest gratitude to the respondents of this study despite the current COVID-19 inflation rate.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. The Questionnaire.
Table A1. The Questionnaire.
ConstructItemsMeasurementsReference
Perceived
Usefulness		PU1Online event is very useful during this COVID-19 pandemic[40]
PU2Online event enhance my productivity during this COVID-19 pandemic[40]
PU3Online event makes me easier to organize an event[40]
PU4Online event is very efficient
PU5Online event can minimize the chance for getting infected by COVID-19[41]
Perceived Ease of UsePEU1I find online event is easy to be organized[42]
PEU2Online event makes me feel comfortable
PEU3I find it is easy to interact with the audience during the online event[42]
PEU4Learning to conduct an online event is easy[43]
PEU5Instructions to navigate the online event are clear and understandable[43]
Behavioral
Intention		BI1If I enjoy an event, I would like to give online testimony in my social media[40]
BI2If I find an event interesting, I would like to tell my friends, colleagues, and family to join the event
BI3I am Planning to join more online event in the future[42]
BI4I tend to seek for information about new online event[40]
BI5I am willing to learn new online event platform in order to enjoy more online event[44]
Online Event PromotionOEP1If a relatives recommend me to join an event, I will more likely to register for the event[40]
OEP2I tend to look for an online event when I am using my social media
OEP3If I saw an ads for an online event when I use a social media, I tend to look further about it
OEP4I tend to look further about an online event if I saw my friend posts a twibbon about the event[42]
OEP5If a friend/colleagues tells me to join an event, I will more likely to register for it
Hedonic
Motivation		HM1Free events will more likely attracts me to join it[40]
HM2Online event is fun[45]
HM3Online event is very entertaining[45]
HM4Online event amuses me[42]
HM5I join an online event to gain more friends
Utilities
Issue/Noise		UIN1Internet connection quality will affect how I enjoy and online event[42]
UIN2My Room temperature will affect how I enjoy and online event
UIN3Online Meeting platform will affect how I enjoy and online event[42]
UIN4My Personal Computer/laptop/gadget quality will affect how I enjoy an online event
UIN5The noise around me will affect how I enjoy an event[41]
AttitudeATT1I have fun when joining an online event[46]
ATT2Organizing an Online event is a good idea[47]
ATT3I am positive toward organizing an online event[47]
ATT4Joining an Online event is a good idea[47]
ATT5I enjoy joining an event in online condition rather than in offline condition[47]
Subjective NormSN1The professionality of the committee will affect how I enjoy an event[47]
SN2The professionality of the speaker will affect how I enjoy an event[47]
SN3The body language of the committee will affect how I enjoy an event[42]
SN4The body language of the speaker will affect how I enjoy an event[42]
SN5The duration of the event will affect how I enjoy an event[40]

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Sustainability 14 12964 g001 550
Figure 1. Theoretical Research Framework.
Figure 1. Theoretical Research Framework.
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Figure 2. The preliminary model.
Figure 2. The preliminary model.
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Figure 3. The final model.
Figure 3. The final model.
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Table
Table 1. The number of ZOOM daily meeting participants in 4 months.
Table 1. The number of ZOOM daily meeting participants in 4 months.
DateNumber of Daily Meeting Participants
31 December 201910 million
31 March 2020200 million
21 April 2020300 million
Note. Reprinted from “ZOOM User Stats: How Many People Use ZOOM in 2021?” [1].
Table
Table 2. Descriptive statistics of respondents (n: 366).
Table 2. Descriptive statistics of respondents (n: 366).
CharacteristicsCategoryN%
GenderMale13536.89%
Female23163.11%
Age15–19174.64%
20–247821.31%
25–29236.28%
30–34143.83%
35–39215.74%
40–44369.84%
45–499325.41%
50–546317.21%
55–59113.01%
≥60102.73%
Income<2 million IDR7520.49%
2–4 million IDR5013.66%
4–6 million IDR6517.76%
6–8 million IDR205.46%
8–10 million IDR267.10%
>10 million IDR13035.52%
Current/Last EducationHigh School5113.93%
Undergraduate (Bachelor)27274.32%
Postgraduate (Master/Doctorate)226.01%
Others215.74%
RegionSumatera20.55%
Java34794.81%
Borneo51.37%
Celebes30.82%
Lesser Sunda Islands82.19%
Moluccas and Papua10.27%
Have ever joined an online eventYes33792.08%
No297.92%
Subscribe to online platformYes16143.99%
No20556.01%
Table
Table 3. Model Fit.
Table 3. Model Fit.
Goodness of Fit Measures of SEMParameter EstimatesMinimum
Cut-Off		Suggested By
Initial ModelFinal Model
Incremental Fit Index (IFI)0.7570.945>0.90Gefen et al. [20]
Tucker Lewis Index (TLI)0.7410.934>0.90Gefen et al. [20]
Comparative Fit Index (CFI)0.7560.944>0.90Gefen et al. [20]
Goodness of Fit Index (GFI)0.6870.879>0.80Gefen et al. [20]
Adjusted Goodness of Fit Index (AGFI)0.6490.844>0.80Gefen et al. [20]
Root Mean Square Error (RMSEA)0.0930.065<0.07Steiger [21]
Table
Table 4. Tests of reliability, validity (Initial Model).
Table 4. Tests of reliability, validity (Initial Model).
Latent
Variables		ItemsFactor
Loadings		Cronbach’s αAverage
Variance
Extracted (AVE)		Composite
Reliability (CR)
Perceived
Usefulness		PU10.620.7810.5050.802
PU20.7
PU30.81
PU40.7
Hedonic
Motivation		HM10.530.880.6110.884
HM20.87
HM30.88
HM40.86
HM50.71
Online Event PromotionOEP10.690.880.5870.876
OEP20.86
OEP30.75
OEP40.79
OEP50.73
Behavioral
Intention		BI10.590.830.5510.828
BI30.82
BI40.84
BI50.69
AttitudeATT10.840.8770.5120.838
ATT20.74
ATT30.6
ATT40.75
ATT50.62
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MDPI and ACS Style

Kusonwattana, P.; Prasetyo, Y.T.; Vincent, S.; Christofelix, J.; Amudra, A.; Montgomery, H.J.; Young, M.N.; Nadlifatin, R.; Persada, S.F. Determining Factors Affecting Behavioral Intention to Organize an Online Event during the COVID-19 Pandemic. Sustainability 2022, 14, 12964. https://doi.org/10.3390/su142012964

AMA Style

Kusonwattana P, Prasetyo YT, Vincent S, Christofelix J, Amudra A, Montgomery HJ, Young MN, Nadlifatin R, Persada SF. Determining Factors Affecting Behavioral Intention to Organize an Online Event during the COVID-19 Pandemic. Sustainability. 2022; 14(20):12964. https://doi.org/10.3390/su142012964

Chicago/Turabian Style

Kusonwattana, Poonyawat, Yogi Tri Prasetyo, Stefanus Vincent, Jefferson Christofelix, Aryadaksa Amudra, Hazel Juan Montgomery, Michael Nayat Young, Reny Nadlifatin, and Satria Fadil Persada. 2022. "Determining Factors Affecting Behavioral Intention to Organize an Online Event during the COVID-19 Pandemic" Sustainability 14, no. 20: 12964. https://doi.org/10.3390/su142012964

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