key: cord-0930345-bk5amkih
authors: Kathuria, Sakshi; Tandon, Urvashi; Ertz, Myriam; Bansal, Harbhajan
title: Social vacation: Proposition of a model to understand tourists’ usage of social media for travel planning
date: 2020-10-18
journal: Technol Soc
DOI: 10.1016/j.techsoc.2020.101438
sha: b8cfc698f9fa87b20622b0b36814124be9956f29
doc_id: 930345
cord_uid: bk5amkih

This study develops a theoretical model that highlights the determinants of actual social media (SM) usage for travel planning by combining theoretical frameworks from the marketing, psychology and information systems literature. The data was collected through field as well as online survey in India. An online survey questionnaire link was shared on different social media platforms and social networking sites. Besides, field visits were carried out to collect data in-person through face-to-face interviews. The final sample consists of 539 observations. Structural Equation Modelling (SEM) was applied to validate the hypothesized relationships among constructs. The results suggest that technological convenience and perceived enjoyment influence the perceived ease of using SM for travel planning. In turn, perceived ease of use impacts perceived usefulness, along with media richness. Perceived ease of use and perceived usefulness, along with trust positively influence intentions to use SM for travel planning, while perceived risk inhibits those intentions. However, trust increases perceived usefulness and mitigates perceived risk. Importantly, intentions exert a strong impact on actual use. This study contributes to the literature by presenting and validating a theory-driven framework that unveil the factors influencing actual usage of SM for travel planning. The proposed theoretical framework emphasizes the key relationships among factors and provides a research basis for development in other contexts.

strengthens the importance of SM for travel planning (Mariani et al., 2019) . Fifth, many travel platforms such as Expedia, Opodo or Skyscanner, are increasingly accessed via SM platforms such as Facebook, while travel organizations (e.g., airlines, hotels) increasingly invest in SM to allow potential visitors to make their travel arrangements over SM platforms (Mariani et al., 2019) .

Consequently, SM usage has progressively gained higher authority in the tourism sector Lim et al., 2017) . Scholars further recognized that the vast amount of travelrelated user-generated content available on SM, has changed the way potential tourists plan and decide their travels (Xiang and Gretzel, 2010; Mendes-Filho et al., 2017) . Although SM is playing an increasingly predominant role in the tourism industry growth, related theory about how different factors shape and predict actual SM usage in travel planning is still emerging (Yoo and Gretzel, 2010; Ayeh et al., 2013; Christou, 2015; Ponte et al., 2015; Okazaki et al. 2017; Osei et al. 2018) and suffers a series of limitations. Past research predominantly focused on travel-specific SM (e.g., TripAdvisor) (e.g., Filieri, 2016) , commercial third-parties (e.g., Booking,com) (e.g., Mariani and Borghi, 2018) , or very particular non-travel-specific platforms (e.g., Facebook) (e.g., Mariani et al., 2019) . While J o u r n a l P r e -p r o o f laudable, these research endeavours resulted in a fragmented set of models of determinants predicting usage of specific SM for travel planning.

SM provides a platform for tourism managers to evaluate the consumers' complaints and opportunities to sincerely and genuinely respond to such complaints and thus build a strong reputation among the existing and potential tourists (Carnoy, 2017) . Accordingly, the literature shows that social media is playing an increasingly central role in the tourism growth. Despite these promising developments, there is a lack of consideration about how different factors associated with social media usage shape and predict actual tourist behavior of using social media. Some researchers highlighted that social media is being increasingly used as a source for information search and for spreading awareness, but it is unclear which factors increase the use of social media for travel planning (Ponte et al., 2015; Agag and El-Masry, 2016; Okazaki et al. 2017; Osei et al. 2018) . Further, there are limited studies on tour and travels that have hypothesized the relationship considering various key variables such as perceived enjoyment, technology convenience and media richness in the framework developed and also tested outside of India (Rauniar et al., 2014; Abou-Shouk and Hewedi, 2016; Kansakar et al., 2018 ). Yet, a theoretical framework underpinning and predicting the use of SM in general for travel planning remains still missing. In other words, it is unclear which cognitive factors determine the use of SM platforms, in general, and for travel planning, in particular. Besides, most studies posit intentions as the focal construct under study, not actual usage behaviour. Yet, the "intention-behavior gap" exists, as emphasized by several scholars in the tourism literature (Lee et al., 2014; Kah et al., 2016) and this gap puts research findings of studies focused solely on intentions at a disadvantage-. Finally, the majority of studies on SM and tourism industry pertain to developed countries (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010; Burgess et al., 2011; Munar and Jacobsen, 2014; Uşaklı et al., 2017; Mariani et al., 2019 ) and very few apply to developing countries. More importantly, these studies have formulated the different model either considering perceived enjoyment or technology convenience or media richness, and therefore the literature lacks a comprehensive study which investigates the extent of influence of technology convenience and perceived enjoyment on perceived ease of use of using social media, media richness on perceived usefulness as well as perceived ease of use on perceived usefulness of using social media for the travel planning. Another void is that most discussed models are developed in the framework of developed economies while developing markets are emerging as fertile grounds for tourism development both as destinations and as tourist markets. Hence, developing and testing a model to evaluate the association between trust, perceived risk, J o u r n a l P r e -p r o o f perceived ease of use, and perceived usefulness for using social media in the Indian context would provide deeper insights into the developing context to formulate marketing strategies accordingly.

It should also be stressed that past research considered either different or few constructs.

For instance, Yoo and Gretzel (2010) focused on trust and its antecedents. Agag and El-Masry (2016) tested the associations between relative advantage, compatibility, perceived ease of use (PEOU), perceived usefulness (PU), attitude and positive word of mouth. Further, Christou (2015) tested relationships between SM brand characteristics, and trust in SM and established that trust in travel SM brand leads to brand loyalty. Okazaki et al. (2017) illustrated the associations between SM for information search, social interaction ties, trust, shared vision, and SM for sharing knowledge. In recent times, social media has been playing an increasingly important role in the Indian tourism industry. Therefore, it is more than a necessity to analyze and understand the tourists' viewpoints about the use of social media for travel planning, the Indian tourism destinations, products, and services providers are fighting for potential consumers. Therefore, improving marketing capabilities especially by crafting relevant social media strategies to reach consumers effectively and influence their decisionmaking processes, are top priorities.

Past research thus lacks an integrative model that conceptualizes the interconnections of constructs pertaining to diverse theoretical frameworks such as trust, perceived risk, from the utility framework; perceived ease of use (PEOU), perceived usefulness (PU), behavioural intention towards SM usage, from the technology acceptance model (TAM); media richness from media richness theory; and the actual SM usage for travel planning, to further evaluate the potential interactions of such constructs. To bridge the aforementioned gaps, this study aims at developing a single model considering all the mentioned constructs including actual behaviour in a developing country setting. The development of such a model would help destination firms and authorities in understanding tourists' perception and actual use of SM for travel purposes in the context of developing countries. This research combines two trends as SM is increasingly used by potential tourists and guests in travel planning, while tourists from developing countries are also a growing segment in international tourism.

For this study, the tourism sector in India has been considered due to the rich cultural and historic heritage, diversity in ecology, places of natural beauty throughout the country, business opportunities and medical facilities (IBEF, 2019a; IBEF, 2019b) . In 2017-18, the sector accounted for 12.38% of the total employment opportunities and provided jobs for J o u r n a l P r e -p r o o f 81.1 million individuals (IBEF, 2019a) . This contribution is anticipated to increase by 2% per annum to 52.3 million jobs at the end of 2028 (IBEF, 2019b) . India further ranks seventh among 184 countries in terms of the contribution of the travel and tourism sector to the national GDP. The total contribution of the sector to the country's GDP is anticipated to rise from Rs 15.24 trillion in 2017 to Rs 32.5 trillion by the end of 2028. Furthermore, the hotel and tourism industry attracted approximately USD 12 billion of foreign direct investment, and the industry maintains huge growth potential (IBEF, 2019b) . On the other hand, in terms of quantum of internet users, India is ranked second with 560 million users just next to China which has 829 million users (Internet World Stats, 2019). Besides, SM users reached 326.1 million in 2018 and an anticipated 448 million is expected by 2023 (Statista, 2019) . A recent study claimed that 89.30% of tourists used SM to search for information about tourism destinations located within India (Ministry of Tourism India, 2017).

The Indian Government has undertaken several initiatives to increase the arrival of foreign tourists while setting a target of 20 million foreign tourist arrivals by 2020 (IBEF, 2019). Moreover, and especially in the wake of the Covid-19 pandemic, the government seeks also to promote local tourism. A total of USD 15.534 billion have been provisioned in the Union Budget 2019-20 for the development of tourist circuits. Furthermore, the promotion of the Statue of Sardar Vallabhbhai Patel also known as Statue of Unity (highest standing statues in the globe), the "Incredible India 1 " programme, and the "Atithi Devo Bhava" (is a phrase equating guests to God) programme, are such initiatives (IBEF, 2019).

"Atithi Devo Bhava" initiative focuses upon media, messaging, and an organized set of communication activities to engender explicit consequences in a large number of individuals and for a specific period of time (Geary, 2013) . Such campaigns require coordinated social media efforts with a blend of community-based channels. Hence, a cross-sectional study exploring social media usage at the different stages of the travel planning process in the Indian context, would provide the deep understanding for practitioners, destination management authorities, and researchers. This paper proceeds as follows: Section 2 discusses the theoretical background and hypotheses development. The measurement development and data collection is explained in Section 3. Section 4 includes the statistical analysis and testing of research hypotheses.

Lastly, Section 6 discusses the results, the implications, and the limitations of the study.

This research combines a variety of theoretical frameworks in order to account for the richness and diversity of cognitive constructs underlying SM usage for travel planning. As a technological phenomenon, SM is related to notion of technology acceptance. Technology acceptance encompasses how people adopt technology for use (Louho et al., 2006) . In this context, different models for the introduction and adoption of information technology innovations have been elucidated by previous researchers such as Social Cognitive Theory (SCT) (Bandura, 1986) , the Technology Acceptance Model (TAM) (Davis, 1989) , the Theory of Planned Behavior (Ajzen, 1991) , extended TAM (Venkatesh & Davis, 2000) , the model combining TAM and the Theory of Planned Behavior (Taylor & Todd, 1995) , and the Model of PC Utilization (Thompson et al., 1991) . Among these, the Technology Acceptance Model (TAM) proposed by Davis (1989) has been applied widely in a variety of domains to understand users' behaviour concerning different technologies. Davis (1989) proposed two discrete but vital determinants in technology adoption, namely perceived ease of use (PEOU), and perceived usefulness (PU). PEOU is defined as the extent to which "people believe that the systems are too hard to use and that the performance benefits of usage are outweighed by the effort of using the application" (Davis, 1989, p. 320) , and PU is defined as the extent to which "people tend to use or not use an application to the extent they believe it will help them perform their job better" (Davis, 1989, p. 320) . The scientific literature indicates that TAM has been postulated as the most persuasive and widely used theories in describing the users' acceptance of information systems (Haryani et al., 2014; Ayeh 2016; Balouchi et al., 2017) .

TAM has been empirically validated by previous researchers to understand employees' adoption of information technology in upscale hotels (Lam et al., 2007) ; information systems regarding hotels (Huh et al., 2009) , computerized reservation systems by a travel agency and SM usage intentions in travel planning (Mariani et al., 2019) .

Nonetheless, there are other variables, besides PEOU and PU, which influence the adoption, acceptance, and diffusion of technologies among users (Balouchi et al., 2017) . Legris et al., (2003) entrenched TAM as an indispensable model; but, to enhance its predictive power, it needs to be unified into a broader model comprising variables associated with human factors. Similarly, other researchers also supported that TAM may be made J o u r n a l P r e -p r o o f robust by validating various other predictors or by integrating it with other information system theories (Marangunić and Granić, 2015; Balouchi et al., 2017) .

SM includes a wide range of online, word-of-mouth forums including blogs, companysponsored discussion boards and chat rooms, consumer-to-consumer email, consumer product or service ratings websites and forums, Internet discussion boards and forums, and social networking websites" (Mangold & Faulds, 2009, p. 358) . SM are web-based services (also known as "Social Networking Sites") which refer to "network of relationships and interactions among different users (groups or individuals)" (Kempe & Kleinberg, 2003) .

Travelers search for a variety of information using social media such as sightseeing, weather conditions, hotel reviews, tourist guides, car rental, transportation booking, and restaurants/bars or prices (Buhalis and Law, 2008; Miguéns et al., 2008) . Consequently, social media is emerging as one of the most trustworthy informative sources in the tour and travel industry. Since tour and travel associated products and services are highly-priced, and since the Covid-19 pandemic has provided additional constrains for tourists in some destinations (e.g., quarantine, social distance, facial mask, closed borders), the opinions of different people on social media facilitate travel consumers to make comparisons which minimize the risk of wrong decisions (Leung et al., 2013; Narangajavana et al., 2017; Tandon, 2020) . Researchers have strongly advocated that the user-generated content about travel available on social media have changed the way potential tourists plan and decide their tours (Xiang and Gretzel, 2010; Mendes-Filho et al., 2017) . Many social media platforms facilitate tourism-consumers in writing and sharing their travel-associated queries, viewpoints, feelings and personal experience in more innovative manners (Ribeiro et al., 2014; Narangajavana et al., 2017) . Hence, tourism authorities are paying significant attention to effectively manage their social media presence to better equip tourists with useful and appealing information (Hays et al., 2013; Marine-Roig and Clavé, 2015) . From the preceding discussion, it is amply clear that the influence of the social media, tourists' engagement in online travel communities, and the availability of user-generated content are vital aspects to impact the travel destination choices of potential tourists (Marine-Roig and Clavé, 2015) .

Hence, this study aligns well with the need to promote tourism destinations using social media as a success tool. There are limited studies that have evaluated TAM for SM usage, especially for travel planning. Additionally, there is no uniformity in the findings of the studies related to the usage of SM in travel planning. Therefore, this study aims to validate the extended version of the TAM model in the travel planning context. Accordingly, apart from the two main factors of the TAM model, this research also considers perceived enjoyment, perceived risk, media richness and trust as extension factors expected to influence tourist behavior in the context of SM technology usage.

Convenience is defined as something that adds to individuals' comfort by being easy to operate or proceed without any hurdles (Jiang et al., 2013) . The greater the technology usage complexity, the lower the perceived convenience (Berry et al., 2002) . Lee et al. (2008) advocated that as technology convenience increases, more users perceive ease of use to adopt a particular technology. However, the users' perception of convenience is adversely affected by users' cognitive, physical, and emotional situations (Jang, 2015) . Likewise, researchers addressed convenience as a predictor of technological PEOU and satisfaction (Durkin 2004 ).

Thus, a convenient technology can offer more process flexibility as well as a reduction in the total time required to perform that task (Collier et al.,2013) . The convenience of using digital platforms by tourists to browse, plan and pick activities fosters the frequent use of technology throughout the different stages of travel planning (Kansakar et al., 2018) . For instance, the convenience of using SM to share personal experiences enhances SM usage for travel.

Further, Lee et al. (2018) found a positive link between technology convenience and PEOU. Therefore, the research posits the following hypothesis:

Perceived enjoyment is an important construct which considerably affects technology acceptance (Davis, 1989) . In the literature, it is defined as an "extent to which activity of using the computer is perceived to be enjoyable in its own right'' (Davis et al., 1992) . In the SM context, Rauniar et al., (2014) defined it as the extent to which the SM-related activities are supposed to be fun and enjoyable apart from any performance consequences that may be anticipated. Hence, perceived enjoyment is a motivating force that increases the travelers' engagement on SM (Abou-Shouk and Hewedi, 2016). Notably, intrinsic motivations enhance SM usage among travellers as it offers enjoyment (Rauniar et al., 2014) . According to Castañeda et al., (2007) , for travel-related information search, potential travellers may be more engaged when browsing user-generated content on SM such as reviews, photographs, and videos. Besides, SM promotes social interaction among the users which adds fun and joy.

Accordingly, researchers found a positive link between perceived enjoyment and PEOU Haryani et al., 2014) which improves travel-decisions. In addition, Yoo et al. (2017) found a positive relationship between the enjoyment of using smart tourism applications and the intention to use such applications. Based on the aforementioned arguments, we propose the following:

Media richness theory is amongst one of the most prominent theories to demonstrate the effect of media types on PU. Papathanassis and Knolle (2011) defined media richness as the variety and quantity of information required to fulfill an individual's requirement of being informed. Ayeh (2013) reported the perceived media richness as the perception about the capability of information sources to provide rich information. Notably, the information media source significantly varies in terms of their capability (Lengel and Daft, 1984) . In the literature, four dimensions namely feedback competence, cues, personalization, and variety in language are considered to measure the richness of the media. Thus, the medium is regarded as rich, if it provides on-time feedback (Ayeh, 2013) . For travel planning, the information search can be regarded as uncertainty and equivocality. Hence, to lessen the uncertainty, the medium should bridge the void between the supplied information quantity and quality and the actual required information for the travel planning. Travel-related users' generated content on SM, facilitates prospective travellers to read and evaluate the different experiences and viewpoints (Ayeh, 2013 . Gretzel et al., (2007 found that approximately 80% of online travel-J o u r n a l P r e -p r o o f related information readers assume that user-generated content helps them in minimizing uncertainty and makes it easier to visualize what the destination would be like. Hence, the study posits:

H3: Media richness positively influences PU

According to Singh and Srivastava (2019) , the sites with complicated features and that are difficult to use are ignored by users. Researchers advocate that people prefer to use those SM sites which are perceived user-friendly and require minimal effort to perform a task (Agag and El-Masry, 2016) . In this study, PEOU is referred to as the extent to which a tourist believes that SM sites would be easy to operate. For instance: for obtaining information to choose a travel destination, the individual will be more likely to use the SM site which would be easier to use . Lim et al. (2007) claimed that the greater extent of PEOU of SM could assist travellers to achieve their travel-related goals easily. Other researchers supported this viewpoint (Safko and Brake, 2009; Mendes-Filho et al., 2017) . Nevertheless, few studies reported the direct impact of PEOU on behavioral intention to use consumer- 

The travelers' search for a variety of information using SM related to sightseeing, weather conditions, hotel reviews, tourist guides, car rental, transportation, and restaurants/bars or prices. It is a well-known fact that consumers trust online consumer-generated content on SM platforms more than information provided by travel service providers (Ip et al., 2012) . In this line, trust is defined as an individual's willingness to accept vulnerability on the grounds of positive expectations about the intentions or behavior of another in a situation characterized by interdependence and risk (Ennew and Sekhon, 2007) . Trust enhances the tendency to frequently use SM for information search (Hau et al., 2017) , and a key factor which can also J o u r n a l P r e -p r o o f positively influence PEOU, PU, and behavioral intention (Jalilvand and Samiei, 2012) , and negatively influence the perceived risk (Leung et al., 2013) . As tour and travel associated products and services are high-priced, the opinion of different people on SM facilitate travel consumers to make comparisons which minimize the risk of wrong decisions (Cox et al., 2009; Casaló et al., 2011; Leung et al., 2013; Narangajavana et al.,2017) . Gefen et al., (2003) advocated the positive relationship between the trust and PU and claimed that trust enhances the certain characteristic of PU. Öz (2015) argued that when travellers trust SM for information search, they believe that products and services will be as shown, which enhances PU. Similarly, Gretzel et al. (2008) highlighted that the higher the trust perceived in the SM's reviews and blogs, the higher would be the PU and intention to use SM. Fotis et al., (2012) advocated that trust in SM sites is an important determinant of PEOU, risk, PU, and behavioral intentions for SM usage. Based on the above discussion, the following hypotheses Bauer (1967) defined perceived risk as "a combination of the uncertainty of the outcome involved". Similarly, Bauer (1960) reported it as uncertainty rooted due to unfavorable outcomes against buyers' expectations. In literature, perceived risk is classified into six categories: safety/privacy, performance, social, time, financial, and psychological loss (Cunningham, 1967) . According to Hua et al., (2017) , the perceived risk associated with SM for obtaining travel-related information indicates the inaccuracy of generated content and the vulnerability which information seeker may face about their data. Researchers stressed that, the lower the perceived risk; the higher would be the PU and behavioral intention to SM usage (Gretzel et al., 2008) . Meanwhile, information from reliable review sites is perceived as more credible as it reduces perceived risk, and enhances the PU and use intention (Wu, 2014) . For instance: travellers' participation in online discussion and willingness to purchase products/services increases, if they perceive lower risk associated with SM (Wu, 2014) .

Likewise, Gretzel and Yoo (2007) demonstrated that positive online reviews and the online J o u r n a l P r e -p r o o f rating systems lessen the risk, facilitating travelers' task of choosing their destinations and accommodations, and thus fostering travel decision making. Researchers also argued that internet users' privacy issues significantly affect risk beliefs which in turn affect behavioral intentions (Malhotra et al., 2004, Tandon and Kiran, 2019) . Few previous contributions found that risk negatively impacts intentions to use SM platforms (Roback and Wakefield, 2013) .

Others found a negative relationship between perceived risk and PU of SM for travel-related information search Schroeder et al., 2013 . Based on the above discussion, we posit the following:

H10: Perceived risk negatively influences PU H11: Perceived risk is negatively related to behavioral intentions

According to Singh and Srivastava (2019) , PU reveals the extent to which travelers' believe that SM usage will facilitate them in taking their travel decisions. Mariani et al., (2019) defined it as travelers' expectations that using SM will improve their travel decisionmaking and recognize it as a fundamental force of technology deployment. To confine the risk of trip failure, travelers' consult as many resources as possible. Hence, they will use SM if they perceive it more beneficial than traditional information sources (Singh and Srivastava, 2019) . Several researchers reported that easy availability of information on SM saves time, efforts, and money and thus strengthens positive behavioral intentions (Fotis et al., 2012; Jadhav et al., 2018) . The direct association between PU and intentions has also been justified by prior investigations on SM (e.g., Ayeh et al., 2013; Rauniar et al., 2014; Agag and El-Masry, 2016; Mariani et al., 2019) . Empirical evidence suggested an indirect impact of usefulness on intentions, a direct impact of use on intentions to contribute to virtual travel communities (Casaló et al., 2010) . Focusing on travelers, a few studies also measured a direct association between the aforementioned constructs (Besbes et al., 2016; Dieck and Jung, 2017; Mendes-Filho et al., 2018) . Rauniar et al. (2014) recommended that travelers' who find user-generated content of use are likely to have positive intentions to use it for travel purposes. Based on the above discussion, the following hypothesis has been proposed: Ajzen (2005) reported behavioral intentions of individuals' readiness to engage in a given behavior are an immediate antecedent of actual behavior. According to Davis (1986) , intentions signal a choice that an individual has made on whether to perform a particular action or not. Besides, intentions are the outcome of a mental deliberation procedure and commitment that possibly requires a significant amount of time. Besides, Rauniar et al., (2014) defined the actual SM usage as a frequency of using SM sites for information search and decision-making. Prior studies argued that the actual behavior of SM users is determined by their intentions to perform the behavior (Park et al., 2015; Zhao et al., 2016) . Further, Rauniar et al., (2014) also confirmed a positive relationship between intentions and actual use. Thus, the following hypothesis is proposed:

H13: Behavioural intention positively influences the actual use of SM.

Based upon above discussion, a theoretical model has been proposed. Figure were adopted from Davis (1989) as well as from Ayeh et al. (2013) . Technical convenience was measured with items adapted from Lee et al., (2008) . A variety of studies conducted by Ayeh (2013) , Chung and Koo (2015) , and Chang et al. (2016) were used to develop a scale for measuring perceived enjoyment. Measurement items suggested by Bauer et al. (2005) , Nusair et al., (2013), Fuchs and Reichel (2006) , and Tseng and Wang (2016) were used to measure perceived risk. Likewise, the media richness scale suggested by Ayeh (2013) was taken into the consideration, and perceived trust was measured using the scale of Pavlou (2003) and Chang et al. (2016) . To measure the behavioral intentions to use SM, a series of items were adapted from studies conducted by Ayeh et al. (2013) and Chang et al. (2015) .

Lastly, actual use of SM for travel planning was measured with items taken from Chung and Koo (2015) . The items of all the constructs were measured with five-point Likert-type scales (1 = "strongly disagree" to 5 = "strongly agree").

An item screening test was conducted with an expert panel of twelve industry experts, researchers, and scholars to confirm the face validity of the scale items. This panel suggested minor amendments in language and applicability as well as alternatives where applicable, and the scales were modified accordingly. Since, the population of North Indian tourists is unknown and hidden, therefore, mixed method sampling technique was employed. According to Onwuegbuzie and Collins (2007) , mixed method sampling is highly imperative where the respondents are unknown and difficult to reach. Therefore, non-probability sampling techniques such as convenience, purposive (also known as judgemental), and snowball sampling methods, have been used to contact respondents. Further, considering the mixed methodology approach (both field and online survey), a survey was carried out in Northern Indian States. Notably, only tourists who belonged to Northern Indian states and have used social media in the last twelve months for travel planning were considered. In the field study, different tourism sites located in India were visited. Based on convenience sampling, tourists were approached to take part in the survey. Subsequently, using purposive sampling method respondents were recruited from Facebook, Instagram, and YouTube for the online survey. In doing so, authors visited online pages of tourist sites available on Facebook, Instagram, and YouTube, and those individuals who had recommended, liked, disliked, and commented on these pages as well as shared their tours' photographs were considered as potential respondents for this study. Authors sent detailed messages on their related social media accounts to know their willingness for inclusion in the survey. Next, the researchers sent the J o u r n a l P r e -p r o o f questionnaire to those individuals who were willing to take part in the survey. Lastly, the researcher recruited respondents using snowball sampling methods from social groups/networks, and questionnaires were distributed using both online and offline modes as per convenience. According to De Leeuw et al., (2008) , the use of mixed methods reduces the bias caused by the single method, saves time and improves the survey response rate.

To control for the social desirability bias, respondents were assured about their response anonymity and motivated to respond sincerely as much as possible (Podsakoff et al., 2003; De Leeuw et al., 2008) . Using the aforementioned methodology, a total of 561 filled up responses were received in return. However, 22 were found incomplete or unengaged responses and therefore only 539 valid responses were analyzed. Kline (2010) suggested that a sample of 200 responses or larger is suitable for a complicated path model.

In the sample, there is a fair inclusion of respondents across gender -57.32% males and 42.67% females, and good representation of each age group, education level, employment status, and income. Table 1 reports the characteristics of respondents in more details. 

The data analysis process was conducted by means of a two-step analytical approach. In the first phase, a confirmatory factor analysis (CFA) assessed the measurement model including reliability, validity and fit. Secondly, a structural equation model (SEM) estimated the structural model to test the hypotheses. Further, factor loadings were used to assess the indictors' reliability and 0.50 was taken as a minimum threshold for the retention of measurement items (Fornell and Larcker, 1981) . As shown in Table 3 , all standardized factor loadings were above 0.50 confirming item reliability and factor unidimensionality (Table 3) . Further, convergent validity was assessed through item loadings, composite reliability (CR), and average variance extracted (AVE) of J o u r n a l P r e -p r o o f each construct. Table 3 shows that AVE and CR for each construct is above the minimum suggested cut-off level i.e., AVE > 0.50 and CR > 0.70, thereby confirming convergent validity (Bagozzi and Li, 1988; ) . Further, as can be seen in Table 4 , the results also indicated satisfactory discriminant validity since all constructs are more strongly correlated with their own items compared to the other constructs' items (Fornell and Larcker, 1981; Shashi et al., 2020) . 

The section examines the structural model. Table 5 also indicates the structural model reporting the theoretical associations between constructs. The results strongly support H1-6, H8-9 and H11-13, but fail to lend support to H7 predicting a positive relationship between trust and PEOU; as well as to H10 which predicted that perceived risk negatively influences PU.

The Table 5 concludes the structural model's results by reporting direct β, indirect β, total β, C.R., and significance level for the thirteen proposed claims. Overall, analysis has provided support for the acceptance of eleven proposed claims, and unsupported two proposed claims.

The results claimed the following significant positive and negative direct effects; i) from technology convenience to PEOU (0.63); ii) from perceived enjoyment to PEOU (0.22); iii) from media richness to PU (0.17); iv) from trust to perceived risk (-0.35), behavioural intention (0.14), and trust to behavioural intention (0.15); v) PEOU to PU (0.31) and

behavioural intention (0.11); vi) perceived risk to behavioural intention (-0.43); vii) PU to behavioural intention (0.27); and viii) behavioural intention to actual use of SM (0.74) ( Table   5 ). As indirect effects are concerned (Table 5) , the indirect effect of trust on PU was 0.02.

The indirect effect of trust on behavioural intention was 0.20, which is highest among all indirect effects. The indirect effect of PEOU on the behavioural intention was 0.08. The indirect effect of the perceived risk on the behavioural intention was -0.02. Finally, the indirect effect of PU on behavioural intention was 0.05. The study findings build an understanding about the complex relationships among the technology convenience, perceived enjoyment, media richness, trust, PEOU, perceived risk, PU, behavioural intention, and the actual use of SM for travel planning. 

The study findings confirmed the positive influence of technology convenience on PEOU (Collier et al., 2013 Lee et al., 2008 Kansakar et al., 2018) . Perceived enjoyment also showed a significant positive influence on PEOU (Abou-Shouk and Hewedi, 2016; Rauniar et al., 2014; Yoo et al., 2017) . Besides, technology convenience seems to have a much stronger influence on PEOU than perceived enjoyment. This confirms the vital role of technology convenience in PEOU of SM. Media richness also emerged as a significant factor influencing PU. The study findings also supported the negative influence of trust on perceived risk which is in line with Leung et al. (2013) . Further, the positive influence of trust and PEOU on PU and behavioural intention were also found, while perceived risk exerts a negative influence on behavioural intentions. Further, the findings of the study also confirmed the positive influence of the PU on behavioural intention and accordingly supported the results of Ayeh (2016) . Lastly, the findings affirmed that behavioural intention positively influences the actual use of SM for travel planning. This indicates that SM sites as an important source of information sharing. The tourists consider statements, reviews and recommendations written by the friends, neighbours and peers from SM suites more reliable as compared to advertisement.

Technology convenience was found to have the highest loadings among all other constructs. These findings lead us to presume that Indians' need to know more about the tourist places before finalizing their trip will be most adequately addressed if the underlying technology for information search on social media is convenient. Hence, an updated and widespread information that is easy to access and follow on social media will facilitate travellers (Sakshi et al., 2020; Tandon and Kiran, 2019; Sanjeev and Birdie, 2019; Gupta et al., 2019; Khan et al., 2019) . On finding the useful information easily, tourists may discuss their positive experiences with their friends, peers and other social groups leading to an additional increase in traffic for information search.

As the influences on behavioural intention to use SM for travel planning are concerned, perceived risk and PU have stronger influences than the influence of trust or PEOU. Within J o u r n a l P r e -p r o o f the model, as individual influences are concerned, the positive influence of behavioural intention on actual use of SM was found strongest followed by the influence of technology convenience on PEOU and the negative influence of perceived risk on behavioural intention. This is further followed by the negative influence of trust on perceived risk, the positive influence of PEOU on PU, and the positive influence of PU on behavioural intention. These influences are further followed by the positive influence of perceived enjoyment on PEOU, the positive influence of media richness on PU, the positive influence of trust on PU, and the positive influence of PEOU.

The study aimed at addressing a gap in past literature with regards to the use of SM in general for travel planning. The study analyzed the impact of a wide array of cognitive constructs for which extant literature has found mixed results with regards to their propensity to predict SM usage in general and SM usage for travel planning in particular (e.g., Mariani et al., 2019) . Besides, in contrast to past research and in order to avoid the intention-action gap, the study assesses actual behaviour in addition to behavioral intentions to use SM for travel planning. The study proposed a comprehensive model combining different theoretical frameworks and thus different constructs to predict the behavioural intention and actual usage of SM for travel planning. This study is the first attempt to simultaneously consider nine diverse constructs and assess the related associations among them. The study results provided a deep understanding of the major theories about the role of different constructs in predicting actual SM usage for travel planning.

Overall, in line with past research which showed that SM are primarily used to satisfy functional motives (e.g., Ben-Shaul and Reichel, 2018; Ayeh et al., 2013) the results confirm the prevalence of extrinsic motivational factors such as perceived usefulness over more intrinsic ones such as perceived ease of use. While past research stressed that this is primarily the case for non-travel-specific SM such as Facebook and less so in travel-specific SM such as TripAdvisor we find that when taking different types of SM altogether the prevalence of functional motives exceeds that of more hedonically-related ones. This is not so surprising since the results showed that non-travel-related SM such as Facebook and YouTube have a much higher usage share compared to others.

We complement these past results by showing the pivotal role of trust in those effects. In fact, while trust strengthens PU it does not affect PEOU. The increased effect of functional J o u r n a l P r e -p r o o f motives such as PU is therefore the result of higher levels of trust in SM while this is not the case for intrinsic factors such as PEOU. Besides, the combined effect of TAM constructs (i.e., PEOU and PU) do not compensate for the negative impact of perceived risk on intentions. Rather, the additional impact of trust is needed to trigger intentions. More generally, we precise past results emphasizing the key role of trust in information search using SM (Hau et al., 2017) , by showing that this results from enhancing PU (Jalilvan and Samiei, 2012) while mitigating perceived risk (Leung et al., 2013) . Yet, in contrast to past research (e.g., Jalilvan and Samiei, 2012), we show that in a SM usage context for travel planning, trust is not relevant to enhance extrinsic motivational factors such as PEOU.

Hedonic factors such as technological convenience and perceived enjoyment are much more relevant to this end.

In addition to the trust concept, the media richness theoretical framework (Papathanassis and Knolle, 2011) constitutes a useful addendum to understand the prevalence of PU over PEOU in explaining intentions. In fact, the perception of media richness which is much stronger in SM settings due to the large availability of audio, video, pictorial, graphic, or textual UGC (Ayeh, 2013) fuels PU of SM for information search and travel planning.

The absence of any negative effect of risk on PU -albeit providing lack of support to one of our hypotheses is good news. It shows that the perception of risk does not inhibit the perception of usefulness both appearing to evolve independently. It also illuminates some parts of the "privacy paradox" which stipulates that SM users continue to use SM while claiming to fear for their privacy (Dienlin and Trepte, 2015) . Research suggested that it is because users perceive that the practicality inherent to SM overweighs privacy issues (Hargittai and Marwick, 2016) . We show more specifically that this is because the perception of risk does not inhibit the perception of usefulness both seeming to operate independently at different cognitive levels.

Finally, the very strong relationship between intentions and actual use suggests that an "intention-behavior gap" is very unlikely in the specific context of SM usage for travel planning. This may be explicable by the fact that, unlike other highly involving behaviors such as sustainable tourism (Antimova et al., 2012)-where the gap is very acute-, the actual behaviour under study (i.e., using SM for travel planning) is not so difficult to perform and does not require excessive commitment or motivation. Instead, it fits well in very diverse lifestyles especially since most people use SM now for a variety of purposes.

The study results provide a deep understanding of the major theories about the role of different constructs in predicting the actual use of social media for travel planning. This study is the first attempt that simultaneously considered nine diverse constructs and assessed the association and interactions among them to understand travel planning using social media. In contrast to past related research (e.g., Mariani et al., 2014 Mariani et al., , 2019 , the study demonstrates the use of social media at different stages of travel planning and proposes a comprehensive model to predict the impact of different factors to predict online travel content creation on social media. Additionally, Ayeh et al. (2013) conducted an impactful research in the domain.

The study explored the impact of perceived ease of use, and perceived trustworthiness on perceived usefulness to use social media. Unfortunately, the study did not shed light on the manner in which trust impacts to perceived ease of use and perceived risk, and further to what extent perceived risk impacts to perceived usefulness. In the same vein, Agag and El-Masry (2016) did not explore the relationship between perceived trust and perceived usefulness, and further between perceived risk and perceived usefulness in their measurement model. Ponte et al. (2015) supported perceived risk as an important construct which predicts the perceived usefulness of using social media. Zeng and Gerritsen (2014) The study results provide a deep understanding of the intricate relationships among cognitive factors which determine SM usage for travel decision-making. Therefore, hospitality and tourism managers should consider these factors in actual practice to increase their market share and revenue. The study confirmed that perceived enjoyment contributes to ease of use.

Hence, the SM managers can make SM even more useful through adding few attributes on their SM sites which can increase the enjoyment of SM usage. Managers should consider these factors as they enhance the behavioural intention that further increases the actual use of SM. The study findings confirmed that offering a well-designed SM interaction that is useful, trustworthy and easy to use would outweigh the perceived risk associated with SM usage, and enhance intentions of SM usage for tourism-related activities. Hence, social media managers may leverage social media more meaningfully by adding few attributes on their social media sites to increase the enjoyment of using social media in relation to their destination or brand.

More specifically, managers should devote attention to promote ease of use, cultivate media richness on SM, and build their credibility on SM to foster usefulness; they should also build their credibility on SM by holding promises made on SM, publishing valid content or using SM that are trusted by users; they should further attempt to improve the technological convenience first and, to a lesser extent, stress the enjoyment that might result from using SM for travel-related purposes to promote ease of use. Further, as evident from the study, tourism service providers should only share realistic information to minimize the risk, which will positively influence tourists' behavioural intention. This can attract more tourists to go through the travel content available on social media sites and consider it for their travel planning. It will further improve goodwill, market share, and the profitability of both travel destinations and social media operators. All these different measures might act to counter the negative impact of perceived risk which remains an important inhibitor to SM usage.

The present work provides interesting insights in the factors predicting SM usage for travel planning purposes. Yet, it is not without limitations that offer the opportunities to conduct further research in this area. First, in this study, the survey respondents represent both rural as well as urban areas. Therefore, two separate studies, one for rural and another for urban, could facilitate in getting deeper insights into the SM usage in the travel planning process. This is an important aspect to keep in mind in the specific context of developing countries such as India, where a large proportion of the population still lives in rural areas, despite J o u r n a l P r e -p r o o f urbanization trends. Another fact is that the present investigation includes only Indian tourists. To obtain more meaningful results about the differences between respondents from different developing countries or to compare between developing and developed countries, future research might conduct a comparative study by taking an equal sample size of both foreign and domestic (i.e., Indian) tourists to investigate their difference in SM usage for the travel planning process. This may result in formulating more effective marketing and branding strategies. Additionally, the respondents for the study were also chosen based on their user-generated content on three social media sites: Facebook, Instagram, and YouTube, and excluded other social media sites. Undoubtedly, these three are dominant social media sites; nevertheless, the choice of respondents from other social media sites (e.g. Twitter, Snapchat, and Flickr, etc.) would have increased sample size and reliability of the information. Therefore, future research is possible considering other social media sites, along with Facebook, Instagram, and YouTube. Lastly, this study is based on survey data, therefore failing to capture the causality effects between constructs. Future research using experimental or longitudinal research designs might better capture causal relationships and dynamic evolutions over time. Finally, systematic literature reviews, bibliometrics and network analysis could further conclude the developing body of knowledge about SM usage in pretravel decision-making processes.

This research examines the factors influencing the usage of social media (SM) for travel planning.

As tourism firms are considering social media as an essential source of marketing due to their strong reach, tourism authorities are paying significant attention to effectively manage the social media pages of their destinations to allure more tourists. This is especially true in developing markets where social media is being used for a variety of purposes including tourism information search. Therefore, this study takes the context of tourism in Northern India to investigate the impact of several constructs, such as perceived usefulness, perceived ease of use, trust, perceived risk, technological convenience originating from multiple theoretical frameworks, to test a theoretical framework that predicts social media usage for media. This indicates that offering a well-designed social media interaction which is entertaining, fun, enjoyable and easy to use would possibly enhance the use of social media for the tour and tourism-related activities. Hence, managers should frequently attempt to improve the richness of their media and ease of use, build trust and reduce the risk, which will improve the perceived usefulness. Further, tourism service providers should only share realistic information to minimize the risk, which will positively influence tourists' behavioural intention.

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Technical convenience TC1The desired and target information can be located easily on social media platforms for travel decisions TC2 Surfing on social media platforms is difficult for the travelers

The users face difficulties while moving to the social media pages and platforms

In general, technical convenience is required and useful in making travel decisions on social mediaThis study contributes to the literature by presenting and validating a theory-driven framework that unveil the factors influencing actual usage of SM for travel planning.The proposed theoretical framework emphasizes the key relationships among factors and provides a research basis for development in other contexts.

Dear Editor-in-Chief Greetings. Please find attached our manuscript entitled, "Understanding predictors of tourists' behavioral intention and actual usage of social media: An empirical investigation" for possible publication in your journal. We certify that the manuscript has been submitted only to your journal. The biography of the authors is:Ms. Sakshi is a Ph.D. student. Her research and publication interests include tourism, sustainability and social media marketing, etc. She has published papers in journals like Business Strategy and the Environment, and Paradigm.Dr. Urvashi Tandon Dr. Harbhajan Bansal is Professor in Marketing. His recent research areas include tourism management, digital marketing and social marketing. He has presented research papers in conferences both home and abroad.We look forward for working under your guidance to lead the manuscript towards publication.