Emergence of social media has transformed and revolutionized the functioning of travel and tourism industry, which is experience-oriented in nature (Wang, Kirillova and Lehto 2017). Social networking sites provide easy and hassle-free platform for travelers to post and share their travel experience (Wang, Huang, Li and Peng 2016), information (Rode 2016), pictures (Sung, Ah Lee, Kim and Choi 2016), stories (Munar and Jacobsen 2014) and simultaneously receive suggestions and recommendations from other travelers (Lee, Reid and Kim 2014; Wang, Kirillova and Lehto 2017). The most significant impact of social media platforms on the travel business is the democratization of online reviews, which have emerged as powerful marketing and service information channel that influences customers purchase decisions (Crespo, Gutiérrez and Mogollón 2015; Neirotti, Raguseo and Paolucci 2016). Amaro and Duarte (2015) observed that reviews posted by travelers are perceived to be more reliable and trustworthy as compared to direct communication by the marketers. Recently, Yan and Wang (2018) observed that online reviews have potential to create referral value, knowledge value and influential value.
According to industry estimates, 89% of millennials finalize their travel plan based on online travel reviews posted by fellow travelers (Carnoy 2017) and 76% travelers believe that online travel reviews give them information and knowledge which is not available anywhere else (Deloitte Consulting LLP 2015). Recent report by Trip Advisor, indicates that 83% of travelers actively read reviews before booking a hotel and 53% of travelers will not book a hotel that is without reviews; 60% of people read review about restaurant or café before visiting them; 68% of travelers refer to online reviews before selecting an attraction ("How Reviews Help Your Business | TripAdvisor Insights" 2018), ("How Reviews Help Your Business | TripAdvisor Insights" 2018),
Lee et al. (2014) in their study observed that many online communities fail to grow beyond a certain limit because only 10-20% of its members share their knowledge on these platforms. Barreda, Okumus, Nusair and Bilgihan (2016) also examined the knowledge sharing behaviour on online social networks and observed that the biggest challenge for tourism firms is to convince people to share their experiences on various online social networks. Posting content on social media is an integrative process which constitutes of three fundamental elements: experience, motivation and technological acceptance factors. Experience, either good or bad about a destination or service, acts as stimulus and encourages consumers to contribute content on social media; consumer’s motivation (internal or external) further activates this process and technology facilitates posting intentions and technological acceptance factors moderate the relationship between satisfaction and eWOM intentions (Yang 2013). Riege (2005) noted that technological barriers influence the knowledge sharing activities and hence should be examined while studying intentions to share knowledge (Hsu and Lin 2008).
In context of online travel reviews, there is no study that combines motivational factors and technology acceptance factors in a single framework. To address this gap, the present study combines technological acceptance factors and travelers’ motivations and examine their role in influencing the behavioural intentions and actual behaviour to post travel reviews on social media platforms. In this study, we use the Uses and Gratification theory (U&G) to derive travelers’ motivation and the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to derive the technology acceptance factors. As UTAUT2 has a better predictive validity and examines technology acceptance from consumers’ perspective (Venkatesh et al. 2012), we deviate from the existing studies that have used Technology Acceptance Model (Hsu and Lin 2008; Yang 2013). The paper intends to contribute to academic literature by examining the role of technological factors in intention to post online travel reviews and also, we hope that our findings will of use to travel firms and destination marketers who intend to encourage travelers to share their travel experiences on various social media platforms. This paper is structured as follows: Section two explains the theoretical background. Section three describes the research model and hypotheses, section four put forward the research methodology, section five explains the data analysis & results and section six concludes the paper by discussion, major research implication and limitations.
2. LITERATURE REVIEW
2.1. Uses and Gratification
Uses and Gratification theory has been widely used to understand the psychological needs of a consumers and their motives to use a particular media (Katz, Blumler and Gurevitch 1974). Uses refer to the selection of particular media by the users to satisfy their needs or desires; Gratifications refer to the level of satisfaction obtained from using the media and motivation refers to the stimulation which facilitates the use of particular media (Alremeithi and Faisal 2017). Although, Uses and Gratification framework is deeply rooted in mass communication literature (Blumler and Katz 1974) and traditional form of media such as radio (Herzog 1944), newspaper (Elliott and Rosenberg 1987), television (Rubin 1983) but with the evolution of ICT’s this theoretical perspective has proved to be an effective approach to investigate consumers’ media usage (Ruggiero 2000), and one of the key motivations to study social media usage (Chiang 2013; Krause, North and Heritage 2014 and Quan-Haase and Young 2010). U&G theory has been empirically validated by various researchers in context of virtual community participation (Dholakia et al.2004); blogs (Liu, Cheung and Lee 2015); Social networking sites (Chiang 2013). Prior literature has identified different user-oriented gratifications attained from using different type of media such as information, entertainment, remunerative and relational (Dolan, Conduit, Fahy and Goodman 2015); content, process and social (Stafford and Gillenson 2004); social/affection, venting negative feelings, reorganization needs, entertainment and cognitive needs (Leung 2013); belongings, hedonism, Self - esteem, reciprocity (Pai and Arnott 2013); altruistic, personal, hedonic and social (Ab Rahman 2017); information, entertainment, social interaction, self-expression and impression management (Gao and Feng 2016).
In order to understand the role of technological acceptance factors extended unified theory of acceptance and use of technology is applied.
2.2. Extended Unified Theory of Acceptance and Use of Technology
Unified theory of acceptance and use of technology is an amalgamation of different theories/ models i.e: theory of reasoned action (Fishbein and Ajzen 1975), the theory of planned behavior (Ajzen 1991), the technology acceptance model (Davis 1989), the motivational model (Vallerand 1997), the model of PC utilization (Thompson, Higgins and Howell 1991), the innovation diffusion theory (Rogers 1995) and the social cognitive theory (Bandura 1986) which has identified four key factors: performance expectancy, effort expectancy, social influence and facilitating conditions as predictors of behavioural intentions to use a technology (Venkatesh, Morris, Davis and Davis 2003). To overcome the limitations of UTAUT, an extended version of UTAUT was developed by Venkatesh et al. (2012) which mainly focused on the consumers rather than organization. In order to be consumer centric UTAUT 2 comprises of three additional constructs i.e. hedonic motivations, habit, price value in comparison to UTAUT model. UTAUT2 has been empirically validated in various contexts such as mobile banking (Alalwan, Dwivedi and Rana 2017), mobile apps (Antunes and Amaro 2016), education (Escobar-Rodríguez and Carvajal-Trujillo 2014), social media (Herrero, San Martín and García De los Salmones 2017) and tourism industry (Morosan and DeFranco 2016).
The proposed model includes three gratifications which are relevant to the context of the study: a) altruism-which is one of the most important motivating factor which reduces communication barriers and promote participation and contribution on online communities (Yoo and Gretzel 2008; Cheung and Lee 2012; Magno et al. 2018) b) economic rewards-according to social exchange theory, individual always weigh cost (efforts) and benefits (rewards) before indulging in an activity. Rewards have been studied as an important extrinsic motivator for contributing content on virtual communities (Muntinga et al. 2011;Goes et al. 2016) and c) reciprocity which is regarded as important facilitator for knowledge sharing in online communities (Wasko and Faraj 2005; Happ et al. 2016; Belanche et al. 2018).
Further, four technological factors derived from UTAUT2 have been included in the proposed model which includes a) effort expectancy- a prospective contributor will participate more on online platforms if the given technology is user friendly and involves minimum physical and cognitive efforts (Ayeh et al. 2013); b) social influence- It is believed that in a social setting people tend to behave in a manner which is acceptable in their social circle, thus influence of important others play a vital role in participating in online communities (Hsu and Lin 2008); c) hedonic motivation – previous studies suggest that individual attributes like social status, entertainment needs etc. have a significant impact on the willingness to contribute on social networking platforms (Lee and Ma 2012) ; and d) habit- Shah (2006), observed that the motivation of regular contributors to post frequently on e-platforms is not limited to extrinsic factors like economic rewards or social influence but is often a result of routinised behaviour i.e. habit. The constructs excluded in the model include – performance expectancy, price value and facilitating condition. As the benefits or expected utility of posting online reviews is captured through the three gratifications, we dropped performance expectancy to avoid confounding effects. As there is no monetary cost involved in posting online reviews, price value was irrelevant to the context and hence was not considered in the proposed model. Finally, this study included only those people, who had an experience of posting online reviews, and have easy access to internet thereby making facilitating condition irrelevant to the context of the study. The next section will discuss the proposed hypotheses based on U&G and UTAUT-2 theories in context of online review posting intentions.
3. HYPOTHESIS DEVELOPMENT
Altruism is defined as a virtue of selfless and unconditional kindness towards others without any expectation of return (Cheung and Lee 2012). Altruism is an intrinsic motivation through which an individual feel contented by helping others. In travel and tourism industry travelers often share their experiences and knowledge with others in order to help or warn them, to appreciate or give feedback to the service providers. The online content contributors who feel good while helping other consumers are more likely to engage in reviews posting behavior (Bronner and de Hoog 2011; Tong et al. 2013). Altruism has been considered as an important factor to motivate consumers to engage in WOM (Sundaram et al. 1998), eWOM (Bronner and de Hoog 2011; Tong et al. 2013; Wang and Fesenmaier 2013) and online travel reviews (Parikh et al. 2015). Previous studies have established a positive effect of altruism on individual’s intentions to post content (Alexandrov et al. 2013; Wang and Fesenmaier 2013; Yoo et al. 2013). Individuals with higher altruistic motivations are eager to spend more time and energy on online communities, further leading to adopt technology more readily and easily (Hsu and Lin 2008; Hung, Lai and Chang 2011).
Based on the above findings, the first hypothesis is:
H1a: Altruism positively influences the travelers’ intentions to post online reviews.
H1b: Altruism positively influences effort expectancy of travelers to post online reviews.
Online knowledge communities are embedded in social exchange process and therefore, for the sustainability and survival of these communities the exchange must be equitable (Lai and Chen 2014). Reciprocity can be defined as an individual’s obligation to pay back the favour received from online communities (Bjørndalen 2014). People generally have limited time, energy and knowledge, therefore, they often expect benefits from sharing these restricted resources (Lai and Chen 2014). Reciprocity is a form of negotiated exchange in which contributors assume future benefits for their present behaviour (Yiu and Law 2012). Previous literature has suggested reciprocity as an important extrinsic factor which motivates the individual to contribute on social media platforms (Feng and Ye 2016; Moghavvemi, Sharabati, Paramanathan and Rahin 2017). Reciprocity has two main principles a) people should return favor to those who have helped them b) people should not harm those who have helped them. Various researchers have established that reciprocity positively affect the intentions of individuals to post content online (Hew and Hara 2007; Oh 2011). People who have higher reciprocity motivations exhibit more efforts on social media platforms, thus, overcoming the resistance to use technology (Hsu and Lin 2008; Hung, Lai and Chang 2011).
Thus, we propose the following hypothesis:
H2a: Reciprocity positively influences travelers’ intentions to post online reviews.
H2b: Reciprocity positively influences effort expectancy of travelers to post online reviews.
3.3. Economic Rewards
In accordance with Economic Exchange Theory, people expect economic rewards in exchange of their knowledge (Bock and Kim 2002). Economic rewards in the form of incentives, bonus points, monetary benefits, discounts, giveaways and prize distribution are extrinsic factors that motivate the individuals to post online reviews (Krasonikolakis et al. 2014; Tong et al. 2013). Yang and Lai (2010) in their study also found similar results that individuals who receive economic rewards in exchange of their participation are keener to post content online. Economic rewards are considered as an expression of acknowledgement to the contributors in order to appreciate their decision of posting reviews on these communities (Hennig-Thurau et al. 2004). Previous literature has established positive relationship between economic rewards and intentions to contribute on social media platforms (Barreda, Okumus, Nusair and Bilgihan 2016; Yang and Lai 2010).
Thus, we propose the following hypothesis:
H3: Economic rewards positively influence travelers’ intentions to post online reviews.
3.4. Effort Expectancy
Effort expectancy is one of the most important predictors of behavioural intention and can be defined as the degree of ease associated with the use of particular technology (Venkatesh et al. 2003). Venkatesh (2003) adapted this variable from the preexisting constructs, i.e. perceived ease of use of TAM/ TAM 2; Complexity of The Model of PC Utilization and ease of use of The Innovation Diffusion Theory. In the context of social media, effort expectancy is the level of exertion, individual associates with the use of social media. While using social media individual might experience a component of complication which is related to their cognitive abilities. Therefore, in our study effort expectancy is the degree of effort exerted by a traveler to post reviews on social media platforms (Davis 1989). Consumers who perceive social media platforms easy to use and operate are motivated to post content online. Extant literature has established a positive relationship between effort expectancy and consumers’ adoption of social media platforms (Al-Busaidi and Olfman 2014; Kwon, Park, and Kim 2014; Hsu and Lin 2008). Previous literature has also argued that user friendly technology results to a stronger habit formation (Herrero et al. 2017). Pillet and Carillo (2016), in their study revealed a significant and positive relationship between effort expectancy and habit of using that particular technology.
Therefore, we posit that:
H4a: Effort Expectancy positively influences the travelers’ intentions to post online reviews.
H4b: Effort Expectancy positively influences habit of travelers to post online reviews.
3.5. Social Influence
Social influence is described as the level to which consumers perceive the opinion of the people (family and friends) important for them to adopt a particular technology (Venkatesh et al. 2003). It is originated from three constructs of existing models, i.e. subjective standards in Theory of Reasoned Action, The Technology Acceptance Model 2, The Theory of Planned Behavior; social factors in The Model of PC Utilization and ease of use and image in The Innovation Diffusion Theory. Previous literature has proved that social influence is an important predictor of behavioural intentions in various context, for example m- commerce (Chong 2013), education (Lewis et al. 2013), online music services (Martins 2013) and social networking sites (Nikou and Bouwman 2013).
In our study social influence is about whether a traveler believes his/her social circle will appreciate them for contributing in online communities or not.
Therefore, we hypothesize:
H5: Social Influence positively influences travelers’ intentions to post online reviews.
3.6. Hedonic Motivation
Hedonic motivation refers to the fun and pleasure associated with the use of particular technology (Venkatesh et al. 2012). It is an intrinsic motivation and is considered similar to the perceived enjoyment of Technology Acceptance Model (Venkatesh et al. 2012). Hedonic motivation is viewed as a key predictor of behavioural intention for the adoption of technology (Brown and Venkatesh 2005; Venkatesh et al. 2012). Various researches have established a positive relationship between hedonic motivation and behavioural intentions (Alalwan, Dwivedi and Williams 2014; Herrero, San Martín and García de los Salmones 2017). In our study, hedonic motivation can be defined as fun, entertainment or pleasure, travelers derive from posting a review on social media platforms. Greater the fun and pleasure travelers derive by posting an online review, the stronger will be their intentions to post online travel reviews.
In consequence, we propose the following research hypothesis:
H6: Hedonic motivation positively influences travelers’ intentions to post online reviews.
Habit can be defined as an extent to which an individual performs certain behaviour subconsciously because of learning (Alazzam, Basari and Sibghatullah 2015; Venkatesh et al. 2012). This construct not only reflect the results of past experiences but also predicts the present and future behaviour of individuals (Ajzen 2002; Venkatesh et al. 2012). Prior research found a significant influence of habit on behavioural intention and adoption of technology (Lewis, Fretwell, Ryan and Parham 2013; Venkatesh et al. 2012; Wong, Tan, Loke and Ooi 2014). For a non- user of social media platforms, it is impossible to form a habit therefore, In the context of present study, frequent and repetitive use of social media (social networking sites, blogs, Instagram) by the consumers for travel planning and information searching will positively influence their intentions to post online travel reviews and actual usage (Escobar-Rodriguez and Carvajal-Trujillo 2014; Hsiao et al. 2016; Järvinen, Ohtonen and Karjaluoto 2016).
Therefore, we hypothesize:
H7a: Habit positively influences travelers’ intentions to post online reviews.
H7b: Habit positively influences travelers’ usage behaviour to post online reviews.
3.8. Behavioural Intentions
Behavioural Intentions is defined as strength of an individual to perform a particular behaviour (Fishbein and Ajzen 1975). Social media provide a platform for travelers to express their opinion and share their knowledge with other travelers. Prior studies have established that behavioural intentions have a significant impact on actual usage (Ajzen 1991; Escobar-Rodríguez and Carvajal-Trujillo 2014; Venkatesh et al.2003, 2012). In knowledge sharing literature it is established that higher the intentions to post content online, more will be the actual participation in online communities (Wang, Huang, Li and Peng 2016).
Therefore, this study proposes that:
H8: Behavioural intentions positively influence the usage behaviour of travelers to post online reviews.
4. RESEARCH METHODOLOGY
4.1. Questionnaire Design and Construct Measurement
To test the proposed research model, a structured questionnaire was used. Questionnaire was divided into two sections. Section 1 of the questionnaire focused on the demographic profiles of the respondents along with the questions about the online travel site usage behaviour and frequency. Section 2 of the questionnaire consisted of items related to constructs of Uses and Gratification (Altruism, Reciprocity, and Economic Rewards) and Extended Unified Theory of Acceptance and Use of Technology (Effort Expectancy, Social Influence, Hedonic motivation and Habit) along with behavioural intentions of consumers. The study derived three items related to altruism from Bronner and Hoog 2011; Chang and Chuang 2011 and Wetzer, Zeelenberg, and Pieters 2007; three items of reciprocity from Hung, Durcikova, Lai and Lin 2011 and Yoo and Gretzel 2011; three items related to Economic Rewards were adapted from Bronner and De Hoog 2011; Liou, Chih, Yuan and Lin 2016 and Yoo and Gretzel 2011; three items relating to effort expectancy from Herrero, San Martín and García de los Salmones 2017; three items to measure social influence were derived from Oliveira et al. 2016; three items describing Hedonic Motivation from Baptista and Oliveira 2015; three items relating to Habit from Hew, Lee, Ooi and Wei 2015 and Three items to measure Behavioural Intentions were adapted from Venkatesh et al. 2012. Each statement was measured using five-point Likert Scale where 1 denoted strongly Disagree and 5 denoted strongly agree whereas, actual usage was adopted from Venkatesh et al. 2012 and was measured using five-point Likert Scale where 1 denoted Never and 5 denoted always.
In order to evaluate the construct validity of initial instrument two academicians and one industry expert who are the member of different virtual communities were consulted. To simplify the questionnaire, items were revised based on the comments and suggestions of the reviewers. Moreover, to ensure a degree of randomness and to reduce the monotonous/ repetitive responses obtained from measuring the same construct the sequence of items was changed.
4.2. Data Collection
After finalizing the questionnaire, data was collected from June 2016 to November 2016 from major airports of north India. Data collection was carried on different days of month, mixing different days of the week in order to reduce the biasness, sampling error and to increase the heterogeneity of data (Rideng and Christensen 2004). The sample frame was Indian domestic tourists who had experience of posting reviews on any Social Media platforms in past six months. 600 questionnaires were distributed and a response rate of 45.5% was achieved which is considered satisfactory for airport surveys (Rideng and Christensen 2004). The effective sample size was 273. Table 1 displays the demographic profile of the respondents which shows out of 273, 131 were male and 142 were Female. It was also observed that largest proportion of respondents were between the age group 20 to 30 years and lowest were below 20 years.
5. DATA ANALYSIS AND RESULTS
To test our hypotheses, Partial Least Square (PLS) method with SmartPLS software, version 3.0 was adopted (Ringle et al. 2015). It is a variance based structured equation modelling technique and is appropriate and suitable for the present study because it places less confinements on measurement scale, sample size and residual distribution (Chin and Todd 1995; Wasko and Faraj 2005). PLS is considered as one of the most powerful statistical tools for studying research model with several constructs and is widely accepted in Information sharing and marketing research over the past decade (Hulland 1999).
The data was analyzed and interpreted in two stages using PLS: a) In first stage the quality of measurements (measurement model) i.e. reliability and validity was assessed and b) In second stage the hypothesized relationship was developed (structural model).
5.1. Measurement Model
In the first stage, the measurement model was assessed through various quality criteria like factor loadings, Cronbach’s alpha, average variance extracted (AVE) and composite reliability. Indicator reliability was tested using factor loadings, the value of which should be above 0.7 (Hair et al. 2010). Table 2 shows factor loadings of each item which is greater than 0.70, with the lowest loading being 0.701 hence, it was found that all the items were statistically significant (Fornell and Larcker 1981) and no item was dropped.
Cronbach alpha and composite reliability were adopted to measure construct reliability, based on criteria that cronbach alpha values should be above 0.7 and composite reliability should be above 0.6 ( Liébana-Cabanillas et al. 2017). Table 3 shows Cronbach alpha values of all the items ranged from 0.700 to 1.000 while values of composited reliability ranged from 0.800 to 1.000 thus, satisfying the criteria for construct reliability.
Construct validity was tested through AVE scores which should be above 0.50, indicating that construct is able to explain atleast 50% of variance of its indicators (Hair et al. 2010; Oliveira et al. 2016). Table 3 also shows AVE scores of all the items ranging from 0.619 to 1.000. These results verify the convergent validity and indicate that measurement model has high internal consistency (Yoo et al. 2013).
The discriminant validity of the constructs is established when the square root of AVE of each construct is greater than level of correlation between them (Fornell and Larcker 1981). The results are shown in table 4. All AVE values in this study are the highest squared correlation in the corresponding rows and columns which suggest satisfactory discriminant validity of the all constructs. Hence, discriminant validity is accepted.
5.2. Structural Model
SmartPLS 3 was used to test the structural model and proposed hypotheses. Figure 1 shows the research model whereas results from the hypotheses tests are shown in Table 5. A bootstrapping procedure with 1000 iterations was performed on a sample of 273 to determine the level of significance of each indicator. Results in figure1 reveal that Altruism, Reciprocity, Reward, Effort Expectancy, Habit and Hedonic Motivations explain 70.6% of travelers’ intentions to post online reviews. Further, Habit and Behavioural intentions contributed in explaining 40% variance in Actual usage. Out of 7 proposed variables for predicting behavioural intentions 6 variables, Altruism (β = 0.291, p <0.000), Reciprocity (β = 0.137, p <0.025), Effort Expectancy (β = 0.139, p <0.029), Habit (β = 0.327, p <0.000) and Hedonic Motivations (β =0.189, p <0.004) were significant hence, hypotheses H1a, H2a, H4a, H6 & H7 were accepted, while social influence (β = 0.032, p <0.571) was not significant therefore, H5 was rejected. However, Economic reward (β = -0.112, p <0.027), was found to have a significant but negative impact on behavioural intentions, Hence, H3 was not accepted. Further, a significant relationship was observed between Altruism and Effort expectancy (β = 0.426, p <0.000); Reciprocity and Effort Expectancy (β = 0.213, p <0.035) and Habit and Effort Expectancy (β = 0.537, p <0.000). Therefore, H1b, H2b, H4b were accepted.
In case of Actual usage both Behavioural intentions (β = 0.403, p <0.000) and Habit (β = 0.287, p <0.000) were significant. Hence, H7b & H8 were accepted.
6. DISCUSSION AND CONCLUSION
In order to enhance our understanding on how traveler motivations and technological acceptance factors affect the review posting behavior on Social media platforms, present study developed and tested a model based on Uses and Gratification and UTAUT-2 theories. This study also argued that review posting motivations such as altruism and reciprocity and technological acceptance factor (habit) can affect effort expectancy of travelers to post online reviews. Numerous interesting findings were emerged from this study.
Firstly, altruism and reciprocity were found to have an influential effect on review posting behaviour on Social media platforms which is consistent with the previous research (Alexandrov et al. 2013; Wang and Fesenmaier 2013; Yoo et al. 2013; Feng and Ye 2016; Moghavvemi, Sharabati, Paramanathan and Rahin 2017). Altruism and reciprocity also have significant impact on effort expectancy, which facilitates travelers to contribute on social media. Whenever a traveler develops an altruistic and reciprocity motivation, he or she will voluntary share their knowledge with others thus reducing the efforts and difficulties associated with posting online travel reviews.
Secondly, economic rewards surprisingly have a negative relationship with travelers’ intentions to post reviews on social media. This could be because rewards exhibit outer control and those with high intrinsic motivations assume it as an obligation and eventually lose interest in doing such activities (Bock and Kim 2002).
Thirdly, the technological acceptance factors Effort expectancy, habit and hedonic motivations have a significant effect on travelers’ intentions to post online reviews which is consistent with previous literature (Al-Busaidi and Olfman 2014; Alalwan, Dwivedi and Williams 2014; Escobar-Rodriguez and Carvajal-Trujillo 2014; Kwon, Park, and Kim 2014; Chen et al. 2015; Hsiaoa et al. 2016; Järvinen, Ohtonen and Karjaluoto 2016; Herrero, San Martín and García de los Salmones 2017). Among these factors habit is the strongest determinant of behavioural intentions. Further, the findings also revealed that there is no significant relationship between social influence and review posting behaviour of travelers. Overall, the proposed model achieves acceptable fit and explained 70.6% of variation in behavioural intentions and 40% in use behaviour.
6.1. Theoretical Implications
The findings of the present study expand our understanding of the individual and technological factors which motivate travelers to post reviews on social media. Although the number of studies on online knowledge sharing is increasing, but studies on traveler’s review posting intentions and actual usage are still controversial and scarce. Prior researches have shown a direct effect of individual motivations on behavioural intentions. In this study, we have added technological factors (Habit, Hedonic motivations, effort expectancy and social influence) from the UTAUT-2 Framework and also examined the relationship between effect of effort expectancy and altruism, reciprocity and habit. In particular, key findings of the study have highlighted the role of individual motivations and technological factors in review posting intentions and actual use behaviour of travelers. Thus, the study contributes to the fragmented and intermittent literature on review posting intentions of travelers and actual use behaviour.
6.2. Practical Implications
This study also provides meaningful implications for the managers and practitioners. Firstly, altruism and reciprocity are the two important factors which motivate the travelers to post online reviews. Therefore, in order to enhance the number of reviews and to create consumer generated content, web developers should provide platforms which enhance interaction among the consumers and improve trusts among them. Managers should also provide feedback and regularly appreciate those consumers who have posted reviews in order to encourage altruistic and reciprocity needs of the contributors. Secondly, the results of the study also revealed that rewards do not facilitate review posting intentions, therefore, managers should re-examine the reward system and should focus on rewards which enhance their self-reputation and self-image for example creating a category of contributors and including them in various decision making or panel of advisors which further would boost their voluntary behaviour and will increase their review posting intentions and behaviour. Thirdly, technological acceptance factors like habit, hedonic motivation and effort expectancy also encourage review posting intentions and behaviour, therefore, practitioners should make review sites more entertaining, attractive, simple and easy to use. They should keep on innovating and improvising the websites to enhance user- friendliness, trust and accessibility.
6.3. Limitations and Future Research
Although present study provides significant insights into various individual and technological factors which determine review posting intentions of travelers and actual use behaviour, several limitations should also be noted. Firstly, the model incorporated three individual motivations (Altruism, Reciprocity and Economic Rewards) and four constructs from UTAUT-2 (Hedonic Motivations, Habit, Effort expectancy and Social Influence) more determinants such as perceived usefulness, venting negative feelings, reputation and privacy concerns could also be studied. The moderation effect of age and gender could be studied in future researches. The sample for the present study was comprised of Indian domestic tourists therefore, findings were limited to Indian culture. Motivations may vary across different cultures and lifestyles. Lastly, the study only focused on factors motivating the travelers to post online reviews, thus, future researches could consider factors which inhibit review posting intentions and behaviour.