1. Introduction
The outbreak of coronavirus disease 2019 (COVID-19) has had a profound impact on various industries, including tourism and event management [1]. The cancellation or postponement of events, including academic conferences, has led to significant disruptions in the exchange and circulation of scientific knowledge [2]. The use of video-conferencing technologies and learning management system platforms has enabled the scientific community to meet virtually, and this has become a new experience for attendees who seek to justify the benefits of attending virtual conferences [3].
Despite the widespread use of online conferences, the perceived benefits and attitudes toward these conferences remain unstudied in the literature [4]. However, there have been a limited number of recent empirical studies on the factors affecting the intentions of attending future conferences [5]. There has been a particular scarcity of empirical research on attending online academic conferences following the outbreak of COVID-19 [6]. Previous studies focused on factors affecting attendance and perceived quality, such as facility services, site attraction, and accessibility [7]; professional education, knowledge sharing, and site environment [8]; commitment, trust, and satisfaction [9]; attendance motivation and barriers [10,11]; conference attitudes and word-of-mouth publicity [12]; service quality and destination image [13]; and travel ability, health situation, safety, and well-being [14]. Moreover, quality and social and psychological factors were investigated during the COVID-19 pandemic [6,15,16]. However, despite the factors that have been identified as affecting behavioral intention, few empirical studies have focused on the behavioral intention of attending future online conferences or examined how factors such as conference interactivity and perceived service quality affect behavioral intentions.
Virtual conferences have become increasingly common in recent years, and the COVID-19 pandemic only accelerated this trend. As traditional face-to-face conferences have been replaced by online events, it is important to understand the factors that influence attendees’ adoption and usage behavior regarding online conference platforms [17,18]. While online conferences have provided greater accessibility and reduced costs, particularly in developing countries [19], it remains unclear whether they will be as appealing to attendees as traditional in-person events [15]. The technology acceptance model (TAM) is a well-established theoretical framework that has been widely used to explain individuals’ adoption and usage behavior regarding technology, including e-learning, e-commerce, and online platforms [20,21,22,23,24]. The TAM derives from social psychological theories, including the theory of reasoned action (TRA) and the theory of planned behavior (TPB), to predict and offer an understanding of human behavior regarding information technology adoption and usage [25,26]. According to the TAM, behavior is determined by users’ intention to perform the behavior, which is in turn determined by their attitude toward the behavior. By extending the TAM to the context of online conferences, this study aims to provide a comprehensive explanation of the factors that influence attendees’ adoption and usage behavior of online conference platforms and how these factors can help conference organizers to design more effective and engaging online conferences.
Recent studies have investigated the influence of virtual conferences, but there are still gaps in the literature that need further attention [2,18]. Specifically, the COVID-19 pandemic has significantly modified attendees’ behaviors toward video-conferencing technologies [27,28], and their adoption and diffusion processes remain challenging [29,30,31]. To address this challenge, the current study aims to understand the antecedents of behavioral intention to participate in virtual conferences and propose strategies for engagement post-COVID-19 era.
Furthermore, while previous research has identified various factors that improve attention and interaction between presenters and attendees [15,32,33,34,35], the pandemic has increased pressure on conference organizers to accelerate the release of further enhancements in order to remain competitive [36,37]. Investigating the characteristics of these developments is crucial for video-conferencing technology adoption [15,38,39,40]. None of the existing models can distinctly explain the wide range of issues that have emerged during the rapid growth in popularity of video-conferencing technologies, such as interactivity and quality.
To address these gaps, our study aims to examine the impact of perceived conference interactivity and quality on behavioral intention in the context of virtual conferences in the post-COVID-19 era. We also extend the TAM by treating perceived conference interactivity as the antecedent construct of the TAM via the determination of the mediating role of perceived conference quality. Moreover, the moderating role of self-congruity was incorporated to further understand the behavioral intention to attend future virtual conferences. By fulfilling these objectives, the findings of this study will contribute to the understanding of consumer behavior in virtual conferences, particularly in the post-COVID-19 era, and offer insights that event managers can use to enhance virtual conference quality and attract more attendees.
The remainder of this paper is structured as follows. In Section 2, the theoretical foundation of this study is provided based on the literature review. Section 3 proposes the research model and hypotheses. Section 4 describes the methodology for analyzing the research model. Section 5 explains the key findings of this analysis. Finally, Section 6 suggests implications for future research and practice.
2. Literature Review and Theoretical Framework
2.1. Technology Acceptance Model (TAM)
In the field of technology acceptance, numerous theories have been employed by scholars to investigate and predict users’ behavioral intentions and the adoption of technology, including the theory of reasoned action (TRA), technology acceptance model (TAM), innovation diffusion theory (IDT), and unified theory of acceptance and use of technology (UTAUT). The TAM has been widely accepted as a suitable theoretical framework due to its robust and predictive capacity [41]. The TAM is particularly effective for explaining intentions to use technology, and it includes variables such as perceived ease of use, perceived usefulness, and attitudes toward technology that determine user motivation [42]. In addition, the TAM has successfully been applied in an educational context to study technology acceptance and intentions [43]. UTAUT and UTAUT2 have also been developed based on psychological theories, such as the theory of reasoned action and the theory of planned behavior [44,45]. However, the TAM is the most recognized and widely accepted model for studying actual technology use and use intentions. In this study, we use the TAM as the theoretical framework to investigate the impact of perceived conference interactivity on perceived conference quality and the behavioral intention of attending future online conferences.
The TAM developed by Davis [42] has been extensively used to explain individuals’ adoption of and behavior toward various information technology products or services [24]. For instance, the TAM has been applied to understand the adoption of virtual reality [21], online learning [22], and online shopping [23]. According to the TAM, individuals’ behavioral intentions to adopt new technologies are determined by two main factors, i.e., perceived ease of use and perceived usefulness [42,46,47]. Perceived ease of use refers to the degree to which an individual perceives that using a particular system is effortless, while perceived usefulness is the degree to which an individual believes that using a particular system enhances job performance [42]. The two variables differ in individuals’ perceptions of using technologies. Perceived ease of use focuses on reduced effort, whereas perceived usefulness emphasizes improved performance or efficiency [42,47]. Furthermore, the TAM suggests that perceived ease of use leads to perceived usefulness since the easier a technology is to use, the more useful it can be [48]. The interplay of perceived ease of use and perceived usefulness determines an individual’s behavioral intention of using a technology product or service.
According to recent studies, the TAM has been widely used as a theoretical framework to explain technology adoption and use behavior. This framework has been applied in various domains, including education, healthcare, and business sectors, to better understand the factors that influence technology adoption and usage [43,49,50,51,52]. These studies found that the TAM provides a robust and valid model for understanding consumers’ intentions and behaviors toward information technologies. For example, Camilleri and Camilleri [38] found that perceived interactivity positively affected the perceived usefulness of video-conferencing technologies, which in turn influenced user attitude and intention to continue using the system. Similarly, Girish et al. [39] found that perceived interactivity positively influenced perceived ease of use, which in turn affected users’ attitudes and intentions regarding the use of online learning systems.
However, when it comes to online conferences, relatively little research has been conducted on the application of the TAM. Camilleri and Camilleri [28] noted that the TAM has not been adequately explored in the context of video-conferencing technologies. This gap in the literature highlights the need for further research that applies the TAM to online conference settings. Specifically, researchers should investigate how the TAM could be applied to understand the adoption and usage of virtual conference platforms. By exploring the relationship between perceived ease of use, perceived usefulness, and behavioral intentions toward virtual conference platforms, researchers can gain insights into how to improve these platforms and increase their adoption among users [28]. In the context of online conferences, perceived interactivity can be considered a measure of usefulness, as it can enhance engagement and interaction among participants [38,39]. On the other hand, perceived quality can be seen as a measure of ease of use, as it can affect the perceived usability and functionality of the platform [6,53]. Therefore, the TAM can provide a suitable theoretical framework to examine individuals’ adoption and use behavior in the context of online conferences.
2.2. Perceived Conference Interactivity
Interactivity is related not only to the real-time interaction of the intended users with video-conferencing technologies but also to interactions with other users. Hence, perceived interactivity in the usage of video-conferencing technologies can help users maintain and build a mutual interactive relationship with other users, which in turn enhances their behavioral intention to attend future online conferences. Perceived interactivity can be operationalized as user-to-system, user-to-content, and user-to-user interactions [54]. Most importantly, user-to-system interactions are referred to as user-to-program and user-to-device interactions [55]. User-to-content interactions are related to the interaction between users and documents [54]. Finally, user-to-user interaction is related to relational and interpersonal interactivity, including the degree to which users can engage in device-mediated conversations in an online environment [56]. Thus, in the online environment, the interaction of the intended user with other user/s greatly relies on computer-mediated conversation, such as chat rooms and email, as well as any other device supported by advanced information technologies [57].
In this regard, perceived interactivity has several definitions in the literature [58,59,60,61,62,63]. In real-time participation, Steuer [64] defined interactivity as ‘the extent to which users can participate in modifying the form and content of a mediated environment in real time’ (p. 84). Liu and Shrum [55] also defined interactivity as ‘the degree to which two or more communication parties can act on each other, on the communication medium, and on the messages and the degree to which such influences are synchronized’ (p. 54). McMillan and Hwang [65] defined perceived interactivity as “a psychological state experienced by a site-visitor during the interaction process” [66]. Similarly, Wu [62] defined perceived interactivity as “a psychological state experienced by a site user during his or her interaction with the website” (p. 91). More recently, from a distance education perspective, based on the McMillan and Hwang [65] conceptualization, Camilleri and Camilleri [38] defined perceived interactivity as “web-based, two-way communications among persons, in real time” (p. 2). In this study, we adopted McMillan and Hwang’s [65] conceptualization, defining perceived conference interactivity as the degree to which users can participate in online events via different interactive video-conferencing platforms via two-way communication through a mediated medium in real time.
In this study, perceived interactivity is investigated from two different perspectives: system interactivity (user-to-device interaction), which focuses on video-conferencing technology features, and social interactivity (user-to-user interaction), which emphasizes interpersonal interactivity through computer-mediated conversation in real time [67]. McMillan et al. [65] conceptualized perceived interactivity as a multidimensional construct, comprising time to find/time to load, choices/control of navigation, and two-way communication [55,62,65]. In this study, a unidimensional measure of perceived interactivity was adopted as a two-way communication medium in real-time conversation [28,38,39].
2.3. Perceived Conference Quality
From the service industry perspective, perceived service quality (PSQ) is categorized into two types: service users (customers) and service providers (organizations). From a customer perspective, service quality can be defined as “a global judgment, or attitude, relating to the superiority of the service” [68], as well as an interactive process between the service delivery and the intended user [69]. Therefore, perceived service quality is viewed as the “degree and direction of the discrepancy between consumers’ perceptions and expectations” [68]. Zeithaml [70] also defined perceived quality as a “consumer’s judgement about an entity’s overall excellence or superiority”. From a service provider’s perspective, service quality is viewed as the degree to which the provided service is the same as it was when initially designed [71]. In this regard, PSQ takes place when users receive the service as intended by the service providers to ensure customer satisfaction [72]. However, these studies were conducted from the customer perspective only, and PSQ during the COVID-19 pandemic is still under-researched, especially in the conference and convention industry [73].
Thus, some scholars have applied this concept to traditional academic conferences. For example, Kim et al. [74] and Lee and Min [75] suggested five factors for measuring academic convention quality: professional education, social networking, site environment, extra convention opportunity, and accessibility. However, these PSQ dimensions are not appropriate for applications in online academic conferences during the COVID-19 pandemic. During this time, the perceived conference quality greatly depended on the service design of video-conferencing technologies. Thus, after analyzing the different conceptualizations of the PSQ within and across disciplines of social science, “it is not surprising that conceptual ambiguity about quality persists” [71]. For example, Reeves and Bednar [76] argued that “no universal, parsimonious, or all-encompassing definition or model of quality exists” (p. 436) and that “different definitions of quality are appropriate under different circumstances” (p. 419). Due to the conceptual ambiguity of PSQ definitions, perceived conference quality can be defined as the overall quality of video-conferencing technologies in terms of online attendance [7]. More recently, Jang and Choi [73] identified five factors for measuring the perceived quality of online conferences: interaction, information, security, usefulness, and fun. This study examined user interactions with video-conferencing technologies from different perspectives. One perspective investigates users’ experiences and attitudes toward video conference design. The other perspective is to study user–device interactions. Thus, this study conceptualizes perceived conference quality in the event management industry during the COVID-19 pandemic based on WebQual 4.0 dimensions, including service interaction quality, information quality, and security [72,73,77].
Accordingly, recent studies have demonstrated that perceived quality is positively related to users’ future intention to use technology [6,15,78,79,80]. For example, Nguyen et al. [53] found that high perceived service quality dimensions (interface quality, responsiveness, and security) are positively associated with attitudes toward usage, which eventually result in behavioral intentions to use video teller machine services. In addition, a conceptual study explored the relationship between website quality and behavioral intention, revealing that, with the exception of information quality, four website quality dimensions—learning, playfulness, interactivity, and connectivity—were positively associated with users’ behavioral intentions toward using websites [81]. Loureiro [82] studied the relationship between website quality and recommended user intentions. The findings indicate that a high perception of quality is positively linked to users’ emotions and contributes to the intention of recommending the website to others. In this study, we argue that users with a high perception of conference quality, such as service interaction quality, information quality, and security, will have a higher intention to attend future online conferences.
3. Research Model and Hypothesis Development
Based on the TAM, this section aims to address a gap in prior literature and provide insights into consumer behavior toward virtual conferences. This involved developing a conceptual research model (depicted in Figure 1) to examine the relationship between perceived interactivity and behavioral intentions to attend future virtual conferences. At the same time, the moderating role of self-congruity and the mediating effect of perceived quality were also investigated.
3.1. Perceived Conference Interactivity, Quality, and Behavioral Intention
Perceived interactivity and perceived quality are two key determinants of technology adoption and use behavior in the TAM [6,38,39,83]. According to previous research, perceived interactivity plays a vital role in shaping individuals’ attitudes and intentions toward technology use [84]. Specifically, studies have found that higher levels of perceived interactivity are associated with a more positive attitude toward technology and a greater intention to use it [28,38,39,85,86,87]. Perceived interactivity has also been identified as a significant performance indicator of information and communications technology, and it determines users’ usage experience of a technology [88]. High interactivity is shown to be positively related to user attitudes and behavioral intentions to use technology [44]. In the context of online academic activities, previous studies have found that higher levels of interactivity with video-conferencing technologies lead to a higher intention of attending future events [28,38,39]. Furthermore, research has shown that perceived interactivity can also positively impact perceived quality in the context of technology use [55,89,90]. As a performance indicator of information and communication technology, perceived interactivity can enhance a user’s experience with a technology [85]. In the context of online conferences, understanding the impact of perceived interactivity on perceived quality can provide insights into the adoption and usage behavior of attendees of online conference platforms, which can help conference organizers to design more effective and engaging online conferences. This suggests that users who perceive high interactivity with video-conferencing technologies are more likely to perceive a higher quality in their virtual conference experience. Based on these studies, we hypothesize that:
Perceived conference interactivity positively influences behavioral intention of attending future online conferences.
Perceived conference interactivity positively influences the perceived service quality of online conferences.
3.2. The Mediating Role of Perceived Quality
Perceived conference quality, including service interaction quality, information quality, and security, can positively influence users’ behavioral intention to attend future online conferences [53,73,91]. Previous studies have shown that quality factors constitute a significant predictor of technology adoption and usage behavior, including online learning systems such as Massive Open Online Courses (MOOCs) and mobile learning [92,93,94,95]. In line with the successful TAM and IS models developed by DeLone and McLean [42,92,96,97], we believe that quality factors play a crucial role in predicting the success of information systems. The COVID-19 pandemic has led to a rapid increase in the use of video-conferencing technologies [98], highlighting technical issues and unfamiliarity with software, which can negatively impact user experience and satisfaction. Research suggests that the highest level of technical features and functions are required to support the requirements of video conferencing, which can contribute to the overall perceived quality of the conference [71]. Perceived quality is determined via the continuous assessment of the experience with a particular technology and can be a summarized judgment of the customer’s experience of the technology [99,100,101]. Based on these studies, we hypothesize that perceived conference quality has a significant impact on attendees’ behavioral intention of attending future online conferences. Specifically, we expect that higher levels of perceived conference quality will lead to greater behavioral intention of attending future online conferences. Moreover, research has demonstrated that perceived quality mediates the relationship between other constructs, such as perceived usefulness, user satisfaction, and behavioral intention to use technology [102,103,104,105,106]. In the context of online conferences, perceived quality can help conference organizers to design more effective and engaging online conferences [15]. We argue that users with a high level of interactivity with video-conferencing technologies will perceive a higher conference quality, which will eventually result in greater intention of attending future online conference. Therefore, we propose the following hypothesis:
Perceived conference quality positively influences the behavioral intention of attending future online conferences.
Perceived conference quality mediates the effect of perceived interactivity on the behavioral intention of attending future online conferences.
3.3. The Moderating Effect of Self-Congruity
Self-congruity reflects “the extent to which a consumer perceives a brand similar to his or her self-concept” [107]. Previous studies on marketing and consumer behavior revealed that self-congruity has a significant impact on individuals’ behavioral traits, such as consumer motivation and purchase intention [108,109]. Most importantly, individuals favor using and selecting services and goods that match their self-concept [110]. In theory, self-congruity can be defined as “congruence resulting from a psychological comparison involving the product-user image and the consumers self-concept” [111]. In this regard, Aaker [112] argued that “the crux of self-congruity is that consumers prefer brands associated with a set of personality traits congruent with their own”. Malär et al. [107] demonstrated the important role of building and establishing a customer–brand relationship via the consideration of brand attachment and self-congruity. In this vein, Malär et al. conceptualized the concept of self-congruity by merging the perspectives of Sirgy [113] and Aaker [114] together and refer to self-congruity as “a fit between the consumer’s self and the brand’s personality or image” [107]. More recently, Sirgy [115] revised his conceptualization again by noting that “self-congruity reflects the match between consumer’s self-concept and brand personality or brand user image”. In this study, self-congruity is conceptualized as a unidimensional construct based on personality and image congruence when attending an online event [116]. It can be defined as the match between the self-concept of the conference attendant and the image of the conference in an online environment.
In the tourism domain, Chon [117] was the first to introduce self-congruity to tourism research and investigate the impact of tourist self-congruity on satisfaction. In a tourism behavioral study before the COVID-19 pandemic, Hung and Petrick [118] asserted that self-congruity is positively related to travel intentions to take cruise vacations. Usakli and Baloglu [119] found that self-congruity has a positive and significant relationship with tourists’ behavioral intentions. Fu et al. [120] argued that self-congruity is one of the main predictors of visitor attitude. Sop and Kozak [121] showed that self-congruity was positively associated with hotel brand loyalty. More recently, Joo et al. [122] found that self-congruity is positively related to emotional solidarity dimensions (i.e., commonality and fairness), which, in turn, influence travel satisfaction, eventually resulting in destination loyalty. Other studies have illustrated that self-congruity is positively associated with revisit intentions [123,124].
Recent research has merged self-congruity with event image and defined it as “the extent to which attendees perceive the image and personality of themselves to be similar to the image of the event” [116]. Previous studies have investigated the impact of self-congruity with event images on behavioral intentions [125]. Sirgy et al. [126] investigated the impact of self-congruity with a sponsorship event on brand loyalty. Others have developed this research topic by introducing the mediation effect of event and brand attitudes, through which self-congruity with the event influences brand loyalty [127]. More recently, Thompson et al. [116] asserted that self-congruity with an event image is positively associated with the visit intentions of live events. During the COVID-19 pandemic, Zhang et al. [128] found that destination personality influences self-congruity and destination image, similarly influencing the behavioral intention of golf events. They also found that self-congruity and destination image mediate the relationship between destination personality and behavioral intention [128].
Previous consumer behavior studies have also investigated the moderating role of self-congruity [129]. For instance, Kourouthanassis et al. [130] asserted that self-congruity moderates the relationship between satisfaction and the continued intention to use social network services. El Hedhli et al. [131] examined the moderating effect of self-congruity on shopping values and well-being. Li et al. [132] demonstrated the moderating effect of self-congruity on the relationship between internal personal factors (i.e., emotion and motivation) and experiential value in the context of festival events. Building on these studies, we expect that the higher the match between the self-concept of the conference attendant and the image of the conference, the stronger the relationship between conference interactivity and the perceived service quality of online conferences, which may further induce behavioral intentions to attend future online conferences. Based on the theoretical foundation outlined above, the following hypotheses are proposed:
Self-congruity moderates the relationship between conference interactivity and the perceived quality of online conferences.
Self-congruity moderates the relationship between perceived conference quality and behavioral intentions to attend future online conferences.
4. Research Methodology
4.1. Study Site
To answer our research questions and prove the related hypotheses, this study was conducted at higher education institutions in Lebanon. Lebanon is a country located in the Middle East with a population of approximately six million people. The country has a long history of education, with the first modern university established in the 19th century [133]. Today, Lebanon has a diverse range of educational institutions, including public and private universities [134]. However, the country has a strong tradition of education, and many universities have a reputation for producing highly qualified graduates. Higher education in Lebanon plays a significant role in the country’s economy and society [135]. Universities in Lebanon are engaged in R&D, which can help to develop new technologies and innovations that can drive economic growth and development [136]. With more than 40 private universities and 1 public university operating in the country [137], these institutions also provide a platform for the exchange of ideas and knowledge, which can foster creativity and innovation [138].
4.2. Sample Design and Data Collection
To obtain the sampling frame of this study, 25 medium- and large-sized universities in Lebanon were contacted via an invitation letter to their general directors, but only 11 agreed to participate in the online survey. Thus, small universities hosting between 200 and 2700 students were excluded from this sample frame [139]. Then, we asked these directors to identify all of their full- and part-time academic staff and to send them a memo encouraging them to participate in the survey [140]. Follow-up phone calls were made to support the study and answer any questions. Questionnaires were distributed to all 11 participating universities, with 75 given to academic staff (both full-time and part-time) at each university. Specifically, we randomly selected, from a list of registered attendees for a virtual conference from a range of universities in Lebanon through a random sampling selection procedure. The use of this method ensures the representativeness of the sample and increases the generalizability of the findings. This method has been shown to be effective in obtaining representative samples and reducing bias in academic research [141]. This method resulted in a total of 825 academic staff participating in the study; 327 questionnaires were returned and used for further analysis, representing a response rate of 39.6%.
In this study, the majority of participants were full-time academic staff (85.3%), and 53.2% of participants were female. The largest age group represented was 30–40 years old (34.3%), followed by 20–30 years old (23.9%), and over 60 years (22.9%). The respondents had a range of educational backgrounds: 65.1% held a doctoral degree, 29.1% a master’s degree, and 5.8% a bachelor’s degree. In terms of years of employment at the university, the largest group (31.8%) had been working at the university for 3–6 years, followed by those with more than 10 years of experience (31.2%) and those with 6–10 years of experience (15.6%, 51). Additional information about the respondents’ profiles is provided in Table 1.
4.3. Measurement of Variables
The measurement items were translated into Arabic, the native language of the participants, to ensure that they fully understood the questions. The translated items were then reviewed by bilingual experts to ensure their accuracy [142]. Moreover, a pretest was conducted on a small sample of academic staff to ensure the validity and reliability of the measurement items. Before posting the online survey, a pilot study was conducted for 25 academic staff, and the scale reliability and internal consistency of the questionnaire items were confirmed. As a result, the survey items demonstrated satisfactory internal consistency, as evidenced by Cronbach’s alpha values, which exceeded the recommended criterion of 0.70 [143], and strong correlations among items within each construct. Additionally, respondent feedback was used to assess the clarity of the items, and a few minor adjustments were made before finalizing the survey instrument.
4.3.1. Perceived Conference Interactivity
Perceived interactivity was assessed using a scale adapted from McMillan and Hwang [65] and Camilleri and Camilleri [28], which consisted of 4 items rated on a scale from 1 (strongly disagree) to 7 (strongly agree). The items included: “Video conferencing technologies enable two-way communication”, “Video conferencing technologies enable concurrent communication”, “Video conferencing technologies are interactive”, and “Video conferencing technologies enable conversations”.
4.3.2. Perceived Conference Quality
To measure the perceived service quality (PSQ) of online conferences, three dimensions were identified: service interaction quality, information quality, and security. These dimensions were measured using a 12-item scale adapted from previous studies [53,73,91], which used a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The three dimensions were averaged to represent the overall PSQ. Service interaction quality was measured using four items: “Communication with the moderator (service provider) was good in the video conference”, “Communication with the presenter was good in the video conference”, “Communication with another participant was good in the video conference”, and “Overall communication was good in the video conference”. Information quality was measured using four items: “The video conference provided accurate, reliable, and timely information”, “The video conference provided information relevant to me”, “The video conference information was easy to understand”, and “The video conference delivered information in an appropriate format”. Security was measured using four items: “Participation in the video conference was safe in terms of personal information management”, “The video conference was secure”, “Personal information will never be leaked through the video conference”, and “A safe payment system was operated for personal security in video conferences”.
4.3.3. Self-Congruity
The moderating effect of self-congruity was measured using a four-item scale adapted from Hashemi et al. [7]. The items included: “Attending online conferences helps me achieve the image and character which I seek”, “Attending online conferences helps reflect my status”, “Attending online conferences fits well with my image”, and “Attendees similar to me professionally, attend online conferences that I have participated in”.
4.3.4. Behavioral Intention
To measure intentions to attend future online conferences, five indicators were adopted from Camilleri and Camilleri [28,38], based on Venkatesh et al.’s [44,45] behavioral intention scale. The items included: “Most probably, I will continue using video-conferencing technologies in the future”, “It is very likely that I will use video-conferencing technologies for other purposes in my daily life”, “Having used video conferencing technologies, I would recommend online conferences to someone who seeks my advice”, “I would encourage my colleagues to attend online conferences”, and “I would say positive things about online conferences to other colleagues”.
4.4. Common Method Variance Check
The common method variance (CMV) test is a method used by researchers to gather data on both exogenous and endogenous constructs using a questionnaire. The purpose of the CMV test is to prevent potential disturbances in the study data that can arise when multiple variables are collected from a single source [144]. Herman’s single-factor method [145] was used to determine the presence of CMV. If the total variance was <50%, the data were considered free from CMV. The CMV value in our study was 37.89%, which was below 50%, indicating the absence of CMV infection.
5. Analysis and Results
This study used partial least squares structural equation modeling (PLS-SEM) to test the study hypotheses, with the assistance of SmartPLS 3.0 software [146]. PLS-SEM is considered a suitable approach for predicting outcomes, as confirmed by Hair et al. [147], who suggested that confirmatory composite analysis (CCA) should be considered when the focus of research is on prediction. PLS-SEM is particularly useful for examining the interplay between prediction and theory testing, and the results should be validated accordingly [148]. Recently, scholars have proposed new evaluation procedures specifically designed for the prediction-oriented nature of PLS-SEM [148]. This approach has also been applied in research within the hospitality and tourism sector [149].
According to PLS-SEM literature guidelines, a two-step approach was used to analyze the results [150]. First, the measurement model was examined to assess interitem reliability, convergent validity, and internal consistency reliability. In the second step, the structural model was examined to test the hypotheses and assess predictive capability.
5.1. Measurement Model
The measurement model, also known as the outer model, is evaluated using four tests. One such test is interitem reliability, which is used to determine the model’s factor loadings. According to Hair et al. [151], the standardized loading value should be at least 0.70. As shown in Table 2, the factor loadings for the model range from 0.81 to 0.91, which exceed the recommended value. Another criterion for the measurement model is internal consistency reliability, which is calculated using the composite reliability (CR) of the variables, which was found to be above the threshold of 0.70 [151]. In this study, the CR values for the model ranged from 0.88 to 0.94, indicating strong internal consistency. The third criterion for the measurement model is convergent validity, which is assessed using the average variance extracted (AVE) of the constructs. Convergent validity refers to the extent to which the items of a variable measure the same underlying concept [152]. According to Fornell and Larcker [152], the AVE should be greater than 0.50. The AVE values for the model ranged from 0.526 to 0.739, indicating strong convergent validity. To check for multicollinearity, we also calculated the variance inflation factor (VIF) for each variable. Hair et al. [153] recommended a threshold value of less than 3 for an ideal VIF. As shown in Table 2, all VIF values were below 3, indicating that there were no multicollinearity issues. The results of the measurement model, mean, and standard deviation for each variable and its corresponding items in the current study are presented in Table 2.
5.2. Discriminant Validity
To evaluate discriminant validity, we used the heterotrait–monotrait ratio of correlation (HTMT) method [154]. We chose to use the HTMT method because of recent criticisms of the criteria proposed by Fornell and Larcker [152]. The HTMT is a crucial component in the evaluation of measurement models, along with interitem reliability, VIF, CR, and AVE. To accurately assess hypothesized structural paths, discriminant validity must be confirmed. It has been suggested that if the HTMT value exceeds 0.85 or 0.90 [153], there may be a problem with discriminant validity [154]. Our results, presented in Table 3, indicate that all HTMT values met the suggested criterion of less than 0.85 [155].
5.3. Structural Model
The second step in our analysis was to test the hypotheses and evaluate the significance of path coefficients, as recommended in PLS-SEM studies [150]. For this, we employed a bootstrapping procedure with 5000 subsamples [156] using Smart PLS version 3.3.9 software [146]. The results of hypothesis testing are summarized in Table 4. The results of our first hypothesis (H1) indicate that perceived conference interactivity has a positive influence on the behavioral intention to attend future online conferences (β = 0.280, t = 5.686, p = 0.000). Additionally, our second hypothesis (H2) posits that perceived conference interactivity positively influences perceived conference quality, which is also supported by the results (β = 0.414, t = 7.035, p = 0.000). Our third hypothesis (H3) proposed that perceived conference quality has a positive influence on the behavioral intention to attend future online conferences, which is also supported by the results (β = 0.280, t = 6.689, p = 0.000).
Our fourth hypothesis (H4) speculates that perceived conference quality mediates the relationship between perceived conference interactivity and the behavioral intention to attend future online conferences. To test this mediation, we followed the recommendations of Preacher and Hayes [157] and used bootstrapping to analyze the indirect effects. The results were statistically significant (β = 0.116, t = 4.903, p = 0.000), thus supporting this hypothesis.
According to our fifth hypothesis (H5), the relationship between perceived conference interactivity and quality is expected to be stronger for participants who perceive the conference as congruent with their values. To test the moderating effect of self-congruity on this relationship, we used a product indicator approach with PLS-SEM [158,159]. The results show that the interaction term representing PCI*SC was significant (β = 0.156, t = 2.679, p = 0.007), fully supporting H5.
Similarly, our sixth hypothesis (H6) proposes that the relationship between perceived conference quality and behavioral intention to attend future online conferences is expected to be stronger for participants who perceive the conference as congruent with their own values. To test this hypothesis, we follow the recommendations of Chin et al. [158], and Henseler and Chin. [159]. However, the results show that the interaction term representing PCQ*SC was negative and significant (β = −0.080, t = 2.191, p = 0.028), indicating that H6 was not supported. The structural model test results are shown in Figure 2.
5.4. Strength of Moderating Effects
To evaluate the strength of the moderating effects, we compared the R2 value of the main model to the R2 value of the full model, which includes both exogenous and moderating variables [160]. This was performed using the following formula [160,161].
According to the literature, moderation effect sizes of 0.02, 0.15, and 0.35 are considered weak, moderate, and strong, respectively [160,161]. The results of our analysis, as shown in Table 5, indicate that the moderation effect size was small (0.048). However, it is important to note that a small effect size does not necessarily mean that the moderating effect is unimportant [158]. In fact, “even a small interaction effect can be meaningful under extreme moderating conditions, if the resulting beta changes are meaningful” [158].
5.5. Explanatory Power of the Structural Model
The explanatory power of the model was determined by examining its coefficient of determination (R2). The R2 values were calculated using the PLS algorithm in Smart PLS software, and all values were above the recommended threshold of 0.10 [162]. The R2 value for perceived conference quality was 0.190, and the R2 value for behavioral intention was 0.431.
6. Discussion and Conclusions
This study investigates the impact of perceived interactivity and quality on behavioral intention toward virtual conferences in the post-COVID-19 era, which is an underexplored area in the literature. By extending the TAM by adding the moderating role of self-congruity and the mediating effect of perceived quality, we aimed to provide a more comprehensive understanding of the factors that influence attendees’ attitudes and intentions toward virtual conferences. The results of this study indicate that both perceived interactivity and quality have a positive effect on the behavioral intention of attending future virtual conferences. Additionally, perceived interactivity was found to positively affect the perceived quality of virtual conferences, and this relationship was further strengthened by self-congruity. Moreover, perceived quality was identified as a significant mediator between perceived interactivity and behavioral intention. These findings provide insights into consumer behavior in virtual conferences and expand our understanding of the applicability of the TAM in the context of online events. Furthermore, this study sheds light on the attitudes and perceived benefits of attendees toward virtual conferences, which is particularly relevant given the shift to online conferences that occurred during the COVID-19 pandemic. Our study also has practical implications for conference organizers and policymakers, who can use our results to design more interactive and high-quality virtual conferences that meet the needs and expectations of attendees. The theoretical and practical implications of these results are discussed below.
6.1. Theoretical Implications
This TAM-based study has significant theoretical implications for tourism and hospitality research in the context of event management [163]. TAM is one of the most commonly used models for understanding the acceptance of new technologies [39,42]. TAM is based on the belief that the perceived usefulness and ease of use of a technology are key determinants of the intention to use that technology [24,43,44]. The present study extends the TAM by treating perceived conference interactivity and quality as the antecedent constructs of this model. The findings of this study are consistent with prior research suggesting that perceived conference interactivity and perceived quality are important determinants of behavioral intention [6,28,38,39,79,87,92,164]. Moreover, this study reveals that perceived conference quality mediates the relationship between perceived interactivity and behavioral intention. This finding also corroborates prior research suggesting that perceived quality has a significant impact on user behavior [6,15,79]. This study contributes to the existing research on the TAM by demonstrating the importance of perceived conference interactivity and quality in predicting behavioral intentions in the context of post-COVID-19 virtual conferences.
First, this study contributes to the existing body of knowledge by investigating the impact of perceived interactivity on perceived conference quality and the intention to attend future online conferences. While previous studies addressed the role of interactivity in behavioral intention [28,38,39,86,165], research on the relationship between perceived interactivity and quality is still in its early stages [166,167]. Our study provides a foundation for future studies to build upon across various fields.
Second, this study fills a gap in the existing literature’s exploration of the impact of perceived conference quality on attendees’ future intentions of attending online conferences, a crucial aspect of post-COVID-19 society [6,15,96,168]. Prior research on perceived quality and behavioral intentions mainly focused on in-person academic conferences [7,169], whereas this study explores online conferences. In addition, we found that perceived conference quality acts as a mediator between perceived interactivity and the intention of attending future online conferences. This finding is consistent with previous research, which suggests that perceived quality plays a crucial role in users’ acceptance and adoption of technology [79,88]. Our study extends this research by highlighting the importance of perceived quality in the context of virtual academic conferences.
Third, this study highlights the role of self-congruity as a moderator between perceived interactivity and quality, as well as the relationship between perceived conference quality and the intention of attending future online conferences. This sheds new light on the limited research on the impact of self-congruity on consumer decision-making and provides unique insights using self-concept theory [170,171]. Our findings indicate that, when individuals perceive that a conference aligns with their self-concept, they are more likely to participate and engage in the conference, leading to a higher perception of conference interactivity. Simultaneously, a high level of perceived interactivity can lead to a more positive evaluation of conference quality. The positive moderating effect of self-congruity in this relationship strengthens the association between perceived conference interactivity and the quality of virtual conferences.
Finally, this study contributes to the emerging trend in hospitality and tourism research of examining the determinants of the decision to participate in academic conferences, particularly in the context of the post-COVID-19 pandemic [18,32,172]. The findings of this study may assist in improving and restructuring virtual conferences to align with participants’ values and increase future attendance [5,6,14,173]. Overall, this study provides insights into the factors that influence attendees’ future intentions of attending virtual conferences, which can guide future research in this field.
6.2. Practical Implications
The findings of this study have significant managerial implications for policymakers and event organizations that conduct virtual conferences, including meetings, expositions, conferences, and conventions. The following three critical insights can help organizers improve the quality and interactivity of their virtual events.
First, this study highlights the importance of perceived interactivity in enhancing the quality and intention of attending future online conferences. To improve interactivity and attract attendees to future events, organizers should focus on incorporating interactive sessions, live Q&A, and other engaging activities. Additionally, investing in high-quality audiovisual equipment and virtual event platforms can help improve the quality of virtual conferences. Emphasizing the benefits of virtual conferences, including convenience, flexibility, and cost effectiveness, can encourage attendees to continue attending virtual conferences post COVID-19 pandemic. Regular evaluations of conference experience can help organizers identify areas for improvement and make changes to enhance interactivity and quality in future events.
Second, organizers should aim to entertain attendees via high-quality presentations, engaging activities, and relevant information to increase the likelihood of attendees returning for future online conferences. Encouraging participation and interaction among attendees can improve the conference’s perceived quality. Networking and collaboration opportunities can enhance the interactivity and perceived quality of an event. Regular evaluations and continuous improvement efforts are necessary to ensure that the changing needs of attendees are met in a post-COVID-19 society.
Lastly, aligning attendees’ personal values with those of the conference is crucial for enhancing the quality of virtual conferences. To achieve this, conference managers should communicate conference goals and values to attendees to promote self-congruity. Personalizing the conference experience by creating opportunities for attendees to connect with others who share similar interests and backgrounds can increase self-congruity and improve the conference experience. The structure of conferences should be continuously evaluated and adjusted to maintain high levels of interactivity and positive experiences for attendees. Emphasizing diversity and inclusiveness in all aspects of a conference, including speaker selection, presentation topics, and networking opportunities, can make all attendees feel valued and welcome.
6.3. Limitations and Future Research Directions
This study provides important insights but is also subject to several limitations that present opportunities for future research. First, this study was limited to Lebanese higher education institutions and may not be representative of other cultures or countries. Further research should explore the relationship between perceived interactivity and quality and their impacts on the intention of attending post-COVID-19 online conferences in other cultural contexts. Additionally, the study was conducted during the COVID-19 pandemic and may not be generalizable to other periods. To address these limitations, comparative studies should be conducted across other cultures and countries. Second, the sample was limited to faculty members from higher education institutions in Lebanon, and the results may not be representative of other groups of online conference attendees. Future studies should consider alternative participant groups and sampling methods.
Third, the study focused on self-congruity as a moderating factor; however, other variables such as technical skills, prior experience with online conferences, and individual motivations for attending online conferences may also play a role in the relationship between perceived interactivity and quality and the intention to attend future online conferences. Furthermore, the study only focused on understanding the impact of perceived conference interactivity and quality in predicting the intention to attend future virtual conferences and did not consider other factors that influence this intention. Future studies could examine alternative technology adoption models such as the social presence theory and perceived enjoyment to support interactive video-based conferences.
Finally, the use of a cross-sectional research design only captures the dynamics at one point in time and does not account for the evolution of relationships over time. To address this, future research could employ a longitudinal design and use alternative measurement tools, such as surveys, interviews, or focus groups, to gain a more in-depth understanding of the relationship between perceived interactivity and quality and the intention of attending future online conferences.
Conceptualization, S.A.-G. and H.Y.A.; methodology, S.A.-G. and E.N.; software, H.Y.A.; validation, H.Y.A. and O.L.E.; formal analysis, H.Y.A.; investigation, S.A.-G. and H.Y.A.; resources, S.A.-G. and E.N.; data curation, S.A.-G.; writing—original draft preparation, S.A.-G.; writing—review and editing, S.A.-G., H.Y.A. and O.L.E.; visualization, S.A.-G., H.Y.A. and O.L.E.; supervision, O.L.E.; project administration, H.Y.A. and O.L.E.; funding acquisition, S.A.-G. and E.N. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Informed consent was obtained from all subjects involved in the study.
Not applicable.
The authors extend their gratitude to the four anonymous reviewers for their insightful feedback and the editor for thoroughly reviewing the manuscript. They also extend their appreciation to all the academic staff of Lebanese universities for their participation in this study.
The authors declare no conflict of interest.
Footnotes
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Demographic profile of the respondents.
| Measures | Item | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Male | 153 | 46.8% |
| Female | 174 | 53.2% | |
| Age | Under 20 years old | 2 | 0.6% |
| 20–30 | 78 | 23.9% | |
| 30–40 | 112 | 34.3% | |
| 40–50 | 60 | 18.3% | |
| Above 60 years old | 75 | 22.9% | |
| Marital status | Single | 188 | 57.5% |
| Married | 116 | 35.5% | |
| Others | 23 | 7.0% | |
| Educational level | PhD | 213 | 65.1% |
| Master | 95 | 29.1% | |
| Bachelors | 19 | 5.8% | |
| Occupation | Academic Staff (full-time) | 279 | 85.3% |
| Academic Staff (part-time) | 48 | 14.7% | |
| University | Private | 302 | 92.4% |
| Public | 25 | 7.6% | |
| Job tenure | Less than 1 year | 24 | 7.3% |
| 1–3 years | 46 | 14.1% | |
| 3–6 years | 104 | 31.8% | |
| 6–10 years | 51 | 15.6% | |
| More than 10 years | 102 | 31.2% | |
| Total | 327 | 100% | |
Measurement model and construct validity.
| Construct/Indicators | Outer Loadings | VIF | Mean | Standard Deviation | Cronbach’s α | CR | AVE |
|---|---|---|---|---|---|---|---|
| Perceived Conference Interactivity (PCI) | 0.83 | 0.90 | 0.69 | ||||
| PCI1 | 0.85 | 2.01 | 5.06 | 1.18 | |||
| PCI2 | 0.83 | 1.96 | 5.18 | 1.29 | |||
| PCI3 | 0.82 | 1.87 | 5.20 | 1.30 | |||
| PCI4 | 0.81 | 1.68 | 5.29 | 1.16 | |||
| Perceived Conference Quality (PCQ) | 0.92 | 0.94 | 0.58 | ||||
| Service interaction quality (SIQ) | 0.86 | 0.91 | 0.76 | ||||
| SIQ1 | 0.87 | 2.00 | 4.92 | 1.33 | |||
| SIQ2 | 0.90 | 2.51 | 4.75 | 1.32 | |||
| SIQ3 | 0.88 | 2.07 | 4.82 | 1.34 | |||
| Information quality (INQ) | 0.87 | 0.92 | 0.75 | ||||
| INQ1 | 0.86 | 2.20 | 5.00 | 1.20 | |||
| INQ2 | 0.88 | 2.43 | 5.03 | 1.42 | |||
| INQ3 | 0.87 | 2.39 | 4.99 | 1.38 | |||
| INQ4 | 0.85 | 2.31 | 5.24 | 1.34 | |||
| Security (SEC) | 0.84 | 0.90 | 0.73 | ||||
| SEC1 | 0.82 | 1.76 | 4.83 | 1.34 | |||
| SEC2 | 0.88 | 2.30 | 4.57 | 1.51 | |||
| SEC3 | 0.87 | 1.92 | 4.78 | 1.54 | |||
| Self-congruity (SC) | 0.83 | 0.88 | 0.75 | ||||
| CS1 | 0.91 | 2.21 | 5.28 | 1.25 | |||
| CS2 | 0.90 | 2.05 | 5.27 | 1.25 | |||
| CS3 | 0.88 | 1.63 | 5.18 | 1.26 | |||
| Behavioral Intention (BI) | 0.88 | 0.91 | 0.79 | ||||
| BI1 | 0.88 | 2.68 | 5.49 | 1.04 | |||
| BI2 | 0.86 | 2.21 | 5.38 | 1.12 | |||
| BI3 | 0.85 | 2.31 | 5.45 | 1.07 | |||
| BI4 | 0.89 | 2.55 | 5.37 | 1.07 | |||
Note: CR = composite reliability, AVE = average variance extracted, VIF = variance inflation factor.
Discriminant validity (HTMT ratio).
| Factors | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1. Behavioral intention | 0 | |||||
| 2. Information quality | 0.526 | 0 | ||||
| 3. Perceived interactivity | 0.608 | 0.441 | 0 | |||
| 4. Self-congruity | 0.571 | 0.229 | 0.552 | 0 | ||
| 5. Security | 0.353 | 0.652 | 0.297 | 0.163 | 0 | |
| 6. Service interaction quality | 0.370 | 0.808 | 0.425 | 0.151 | 0.736 | 0 |
Note: HTMT = Heterotrait–monotrait ratio of correlations.
Results for direct, indirect, and interaction effects.
| Direct Effect | Standardized Coefficients | Standard Errors | t-Values | p-Values | Confidence Intervals | Decision | |
|---|---|---|---|---|---|---|---|
| 2.5% | 97.5% | ||||||
| Direct effect | |||||||
| H1: PCI → BI | 0.280 | 0.049 | 5.686 | 0.000 | 0.188 | 0.380 | Supported |
| H2: PCI → PCQ | 0.414 | 0.059 | 7.035 | 0.000 | 0.297 | 0.525 | Supported |
| H3: PCQ → BI | 0.280 | 0.042 | 6.689 | 0.000 | 0.195 | 0.361 | Supported |
| Indirect effect | |||||||
| H4: PCI → PCQ → BI | 0.116 | 0.024 | 4.903 | 0.000 | 0.071 | 0.164 | Supported |
| Interaction effect | |||||||
| H5: PCI*SC → PCQ | 0.156 | 0.058 | 2.679 | 0.007 | 0.028 | 0.255 | Supported |
| H6: PCQ*SC → BI | −0.080 | 0.036 | 2.191 | 0.028 | −0.154 | −0.011 | Not supported |
Note: PCI = perceived conference interactivity, PCQ = perceived conference quality, BI = behavioral intention, SC = self-congruity.
Strength of moderating effects.
| Interaction Effect | R 2 | F 2 | Effect Size | |
|---|---|---|---|---|
| R2 with the Moderator | R2 without the Moderator | |||
| PCI*SC → PCQ | 0.190 | 0.151 | 0.048 | Weak |
| PCQ*SC → BI | 0.431 | 0.422 | 0.016 | Weak |
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