Cultural Dimensions and Social Media Empowerment in Digital Era: Travel-Related Continuance Usage Intention

Abstract

In this digital age, technological advancements have dramatically transformed consumers′ travel behavior. Among these, social media has been identified as the most notable and popular digital platform which appeals to consumers from different cultures, mindsets, and demographics. Culture may stimulate individuals’ attitudes and subsequent behavior. Thus, prior research concentrated on technology adoption in the cross-cultural perspectives, rather than the influence of technological empowerment in an individual context. The significance of the cultural dimensions in shaping behavioral patterns of travel consumers has long been recognized, but the topic remains understudied. Moreover, the usage of social media has seen exponential growth in popularity, but little research has incorporated cultural values at an individual level, mostly for travel purposes. Hence, this current study aims to examine cultural values and their impact on social media continuance usage intention from a travel viewpoint. Therefore, an integrated framework is proposed, grounded in the technology acceptance model (TAM), and extended by cultural values (i.e., collectivism, long-term orientation, and uncertainty avoidance), and social media self-efficacy. A cross-sectional data survey (n = 346) was conducted on travel consumers and a structural equation modeling (SEM) was carried out. Results show that collectivism and long-term orientation are significantly linked with the perceived ease of use and perceived usefulness, which influences the social media continuance usage intention, whereas uncertainty avoidance does not have any relevance. Moreover, social media self-efficacy significantly (though indirectly) affects the continuance usage intention via perceived usefulness. From our findings, travel providers are encouraged to follow the cultural effects and individuals’ continuance usage intention while providing tourism services on social media. The theoretical and practical implications of these results have also been an area of focus of this paper.

1. Introduction

Empowerment is a progression that encourages people, organizations, and communities to take control of life within society [1]. In terms of digital advancements, it may give unique opportunities to connect, interact, share, and, thus, can support technical enablement [2]. The advent of digital technology, such as social media, has significantly transformed individuals’ lives and added new dimensions to the concept of empowerment [2]. A users’ ability to take combined action and mandate social change can be heightened by social media connectivity [3]. This empowered action can influence a large population, having a significant effect on culture and society [4]. This platform has revolutionized and brought technological empowerment by providing new opportunities [5], which may differ from one culture to the next [6].

An understanding of national culture originates from the acceptance of values [7], which reflect composite collective attitudes and behaviors across a society [8,9]. National culture also influences a wide range of basic psychological processes [10], which can be an essential factor in a technology acceptance setting [11]. Moreover, culture and values are important impacts on the adoption of internet services [12] and information technology usage at the national level [13]. Hence, an investigation of the individual nation could be extremely beneficial from the national cultural dimensional model by Geert Hofstede [14,15,16], which has been tasked with measuring and investigating different countries from a cross-cultural perspective [17,18].

As social media becomes a vital apparatus for entertainment and communication, travelers have been increasingly consuming it to gather travel information [19], social media-based content for tourist satisfaction [20], to share their travel experiences [21], or to select a sustainable destination [22], all leading to an increase in the sociability and efficiency of such tools, platforms, and services. The utilization of social media for travel and tourism varies by country, generation, and culture [23]. Furthermore, the efficacy of social media is heavily dependent on consumers’ perceptions of social media credibility [24]. Nevertheless, cultural differences influence human behavior [25,26] and, in this setting, it is critical to understand whether national cultural values affect the acceptance of technology [27,28]. Despite all these pieces of evidence, there is a relative absence of research on cultural values and technological empowerment that consciously influences the creation, inclusion, and usage of travel-related information on social media.

To examine social media usage intention, some technological models have been incorporated, such as the main technology acceptance model [29], extended TAM-2 [30], the unified theories of technology acceptance and usage (i.e., UTAUT) [31], and the theory of planned behavior [32]. The following can be considered as examples of these theories or models: social media practice by young Latinas [23], the cultural impact on the use of mobile social media apps [33], acceptability of online hotel reviews across cultures [34], national culture and its variances to use social networking sites [26], and purchases conducted on social media across cultures [35]. However, evidence regarding the tourism context, social media usage by individuals and its determining factors based on the TAM and cultural values have not been integrated before. Thus, the current study aims to explore a different dimensional outline of cultural values towards social media efficiency and empowering to continue using it in a travel context. Based on this, the following questions are offered:

• RQ1: Do national cultural values (e.g., collectivism, uncertainty avoidance, and long-term orientation) empower travel consumers concerning social media usage for travel purposes?
• RQ2: Does social media self-efficiency have a considerable role in continuance usage intention, in a travel setting?

As a result, our present work makes three contributions. First, we consider the hypothetical underpinnings set up on the technology acceptance model [29,30]. Second, Hofstede’s national cultural dimensions model [14,15,16] is integrated to identify its impact on technology acceptance and continuance usage. Hence, three cultural values (i.e., collectivism, uncertainty avoidance, and long-term orientation), and technological factors (perceived usefulness and perceived ease of use) are employed. Finally, individual-specific self-efficacy towards social media [36,37] and continuance usage intention in the travel background is also examined.

The rest of this study is structured as follows: firstly, a review of the previous literature focused on national culture and collectivist values, followed by the theoretical framework based on the TAM and social media self-efficacy. Secondly, hypothetical interactions and a proposed research model with significant constructs have been presented. Thirdly, quantitative research methods and design are outlined in the methodology part. Fourthly, data analysis and its outcomes are offered in the next unit. Fifthly, discussion and implications are presented, followed by conclusions, limitations, and additional recommendations.

2. Literature Review and Theoretical Background

2.1. Cultural Values in the Collectivist Context

In the terminology of culture or cultural dimensions, “the united programming of the observance” encompasses such factors as common values, beliefs, attitudes, needs, perceptions, motivations, behavioral norms, and verbal and non-verbal behaviors [14,15,16]. Cultural practices were categorized by Hofstede into the following national cultural dimensions: power distance (PDI), masculinity versus femininity (MAS), uncertainty avoidance (UAI), and individualism versus collectivism (IDV) [14]. Furthermore, pragmatic versus normative (PRA) and indulgence versus restraint (IND) were more recently introduced [16]. The different cultures of individual users play an important role in the discussion and studies on technology integration and adoption [38]. Several studies exploring the relationship between culture, technology, and intention at the national level and in a cross-cultural context already exist. For example, the difficulty of predicting individual behavior based on national cultural values is not sufficient to accomplish equality among all individuals in a specific culture [17].

The present study implemented three dimensions of Hofstede (collectivism, long-term orientation, and uncertainty avoidance) [16]. There are several reasons behind this: Firstly, the national cultural dimensions proposed by Hofstede represent a well-known theory in the cultural studies context [39,40,41,42] and, also, apply to the IS/IT field. Secondly, this theory has also been used in the context of mobile technology [8,43], the adoption of social networking sites [44], technology acceptance and readiness by hotel employees [45], acceptance of travel apps [8], and trustworthiness towards social media brands [25]. Moreover, travel and tourism are progressively being transferred to technology-based [46]. There is an accelerating trend among marketers to utilize IT-based marketing, advertising, and promoting destination products and services [47]. Thus, this study focused on social media acceptance by Chinese travel consumers′ in terms of the collectivist culture, which is the trendy revolution in the IT sector. Thirdly, the validity of collectivism and long-term orientation have been used to explain hotel employees’ technology acceptance [45], and UAI implemented towards the mobile banking acceptance [48], but no previous studies have integrated these dimensions in social media and travel contexts at an individual level. A fourth argument is that the background of the study and the sample is China, one of Asia′s most collectivist countries: in this case, we hypothesized a connection between collectivism and uncertainty avoidance on the one hand, and the acceptance of (or rather anxiety towards) social media towards travel on the other hand. Finally, the Chinese Confucian culture is also characterized by long-term orientation perspectives, that Confucian instruction is thorough, emphasizes the future [49], conserves social customs, follows family values [50], and, thus, considers the longitudinal benefits [8]. As a result, the main input of this research was to gain a stronger comprehension of these cultural values concerning social media acceptance for the long-term welfares towards travel commitments.

Eastern cultures, (i.e., China, Korea) are collectivistic [42]; in contrast, Western societies (i.e., the USA, the UK, Australia) are distinctively individualistic [51], their values are based on self-reliance and independence [52]. China is a highly collectivistic country (Hofstede country culture score 20, see Figure 1), where people in society are linked in groups (i.e., family, friends, a community of social media, travel consumers, etc.) [53]. The Chinese collectivist culture shows a positive attitude towards behaviors to assist this society to prosper as a whole [8]. Based on this, we presumed that social media use for travel purposes is fit for travel communities, social media groups, family members, and friends.

According to Hofstede, uncertainty avoidance (UA) represents the acts and guidelines that tend to reduce undefined situations [14]. Individuals with low uncertainty avoidance are more tolerant of risks and feel less concerned about new decisions (i.e., USA, China, etc.) [12,40]. China is low in UA (Hofstede score of 30), suggesting that they are positive with vagueness [53]. Thus, social media itself is not uncertain, but the travel information provided on the platforms may be different and unclear.

Long-term orientation is the parameter that analyzes cultural time orientation. A society with a high LTO (e.g., Asian countries) are especially pessimistic about the future, while countries with a low long-term orientation (e.g., Western countries) are mostly involved with the past and present [14]. For example, China scores high in long-term orientation (e.g., score 87, see Figure 1), which means that it is a culture where individuals assume that the truth is highly contextual, depending on circumstances and the moment [53]. The cultural scores of China are presented in Figure 1.

2.2. Technology Acceptance Model

The technology acceptance model has been widely used to analyze the acceptance of new technology, by providing a relationship between the technology’s characteristics and its acceptance [54,55]. The use of an information system is determined by its usability and the users’ attitude towards it. It comprises two factors: perceived usefulness (PU) and perceived ease of use (PEOU). Here, PU considers ones’ reliance on using a specific system and PEOU describes a hedonistic component to using a particular technology without any efforts [29].

Given its originality, the TAM has become the foremost model in research on individual attitudes towards technology. This model has been extended with relevant external factors, by fitting in the technology inclination of consumers to observe their acceptance of the electronic service system [56]. System affirmation is the intermediary of a behavioral intention in the technology acceptance model [57]. There is a substantial relationship between PU, PEOU, and intention; additionally, behavior is quantified by usage intentions. The research also found a positive link between intention and engaging in the actual behavior [31,57]. Another research extended the model with perceived enjoyment, social interaction, social facilitation to investigate young consumers’ actual behavior towards social media [23].

In tourism research, the TAM has been adopted and extended by various external factors towards technology acceptance, such as perceived risk and e-WoM for the destination choice [58], user-generated content and technology empowerment for travel planning [59], consumer-generated content, perceived trustworthiness; perceived enjoyment, again, for travel planning [60]; hotel employees′ technology acceptance and readiness [45]. Nevertheless, in a tourism context, the effects of cultural values towards social media acceptance based on the TAM have not previously been discovered. Hence, in terms of the tourism sector, national culture shapes human behavior; therefore, it is significant to know the social media acceptance behavior of individuals from a theoretical perspective.

2.3. Social Media Self-Efficacy

The research on self-efficacy confirmed that people’s mental state, opinions, motivation, and actions are all subjective by their sense of self-efficacy [36], which affects people’s feelings, reflections, persuasion, and actions. This is related to an individual self-assessing their ability to use technology to perform a particular behavior [36,61]. In the age of digital expertise, individuals’ self-efficacy can be set by their confidence and ability to accept it [62]. The study considered self-efficacy as individuals’ efficiency of the computing technology or information systems [63]. Prior studies showed that self-efficacy affects the intention to adopt a computer [64]. The study also discovered a direct and indirect effect on the acceptance of mobile health apps [65]. In the self-efficacy theory, an individuals’ views are affected by the ability to achieve purposes in the domain of social media utilization [36,66]. Accordingly, the current study expected that individual social media continuance usage may make a distinctive impact on the development of self-efficacy in a cultural setting. Furthermore, self-efficacy can gain subjectivity to believe in an individual’s ability to continue the habit of social media. Using the concept of social media self-efficacy, this is an individual’s level of content creation, adoption, proficiency, and self-belief in their capability to effectively find social media-based data [37,67].

3. Hypothesis Formulation and Research Model

Countries with high collectivism (i.e., low individualism) typically place a greater emphasis on the cluster; on the other hand, low-collectivist societies (i.e., high individualism) place a greater emphasis on individual performance [16]. Moreover, collectivism significantly impacts the two main attributes (i.e., PU and PEOU) of the TAM at the national level [68]. The research proved that national culture affects PU, PEOU, and technology acceptance intention [69]. Thus, the hypotheses were expressed as underneath:

Hypothesis 1a (H1a).

Collectivism is positively associated with the perceived ease of use of social media.

Hypothesis 1b (H1b).

Collectivism is positively associated with the perceived usefulness of social media.

Moreover, individuals’ usage preference is heavily influenced by their ability to avoid uncertainty, which stems from the fact that the implementation of technology comprises risk and pressure [16]. People from low uncertainty avoidance countries (e.g., the USA, China, etc.) accept new technology with more favorable attitudes [12,40] than a culture with a high uncertainty avoidance (i.e., Jordan, Portugal, etc.) [12,48]. Moreover, positive electronic word-of-mouth stimulated Chinese users to accept the travel apps [8]. Thus, UA is relevant to the current study because selecting a social media platform for travel and tourism does not cause anxiety, but the information over there can be an assessment of risks [39]. This can be easily adaptable by low uncertainty cultures [70]. Hence, the hypotheses theorized as below:

Hypothesis 2a (H2a).

Low uncertainty avoidance is positively associated with the perceived ease of use of social media.

Hypothesis 2b (H2b).

Low uncertainty avoidance is positively associated with the perceived usefulness of social media.

Long-term orientation has a noteworthy consequence with the PU and PEOU of technology at the national level [68]. Eastern societies have a high long-term orientation, and members belonging to groups protect each of its members for unconditional loyalty [68]. A higher degree of LTO contains a logical culture, and the belief in an element depends on the circumstances, perspective, and time [16]. Individuals with a high long-term orientation are presumed to have an optimistic approach towards a social media cause for future profits. Hence, hypothesized as below:

Hypothesis 3a (H3a).

Long-term orientation is positively associated with the perceived ease of use of social media.

Hypothesis 3b (H3b).

Long-term orientation is positively associated with the perceived usefulness of social media.

According to the social cognitive theory, self-efficacy is documented as the focal cognitive mechanism of users’ behavior in a cultural setting [36,66,71]. Self-efficacy showed significant association with cultural and psychological adjustments in cross-cultural environments [24,71]. In particular, the study adopted social media self-efficacy as an influential factor [62]. According to the TAM, PEOU and PU are categorized as users’ trust in a feature’s ability to capture required data. These also generate desired travel information for exchanging and advancement and improve their decision-making processes from a technological perspective [72,73,74]. Based on this, the present study hypothesized that travel consumers’ continuous intentions to use social media have a substantial outcome in terms of travel and empowering their technological practices as follows:

Hypothesis 4 (H4).

Perceived ease of use is positively associated with the perceived usefulness of social media.

Hypothesis 5a (H5a).

Social media self-efficacy is positively associated with perceived usefulness.

Hypothesis 5b (H5b).

Social media self-efficacy is positively associated with the continuance usage intention of social media.

Hypothesis 6 (H6).

Perceived usefulness is positively associated with the continuance usage intention of social media for travel purposes.

Figure 2 depicts the research model grounded on the previous literature and hypothetical relationships.

4. Research Methods

4.1. Instrument and Measures

This study implemented a cross-sectional questionnaire. The questionnaire was developed according to the previous literature and an examination of the specific characteristics of cultural values and technology acceptance [11,45,67]. It consisted of seven constructs, including collectivism, uncertainty avoidance (UA), long-term orientation (LTO), perceived ease of use (PEOU), perceived usefulness (PU), social media self-efficacy (SMSfEf), and continuance usage intention (CUI). The questionnaire had five sections and thirty-eight questions in total. Section 1 consisted of two screening questions, one being about respondents′ active accounts on social media platforms. The other question referred to respondents′ social media consumption behavior in the context of travel. Those who did not meet these two criteria did not further answer the questionnaire. Section 2 measured information about social media-based travel activities with four items; Section 3 used 15 items to measure cultural values (i.e., collectivism, UA, and LTO). Section 4 used 14 items to measure technology acceptance (PEOU, PU, SMSfEf, and continuance usage intention). The scale items (for example, in Section 2 and Section 3) were measured by a Likert-type scale (i.e., from 5 = “strongly agree” to 1 = “strongly disagree”). Finally, five items were included in Section 5 to represent respondents’ demographic profiles.

To assure the questionnaire’s content rationality and consistency, the questions for all variables were modified from relevant studies to adequate the study framework (Appendix A). Collectivism (five-item scale), UA (five-item scale), and LTO (five-item scale) were derived from CVSCALE, particularly from the individual-level study [75]. This scale was developed to quantity Hofstede’s national cultural characteristics for an individual while also being effective on a national-level generalization [16]. Social media self-efficacy (three-item scale) was adapted and modified from research on the theory of self-efficacy [36], and self-efficacy for cultural intelligence [24]. This scale has also been used throughout various research on a social media-based online information evaluation [67] and the adoption of mobile health apps [65]. Finally, technology acceptance was used and revised to measure PEOU (four-item scale), PU (three-item scale), and continuance usage intention (four-item scale) in terms of technology adoption by Davis [29], Venkatesh et al. [31], and Bhattacherjee [76] in the setting of IS/IT usage intention.

The research instrument (e.g., on-site survey form) was prepared in English and transformed into Chinese. The back-to-back translation technique was utilized with the help of scholars to increase translation correctness and consistency [77]. Before the questionnaire was distributed to the respondents, it was pre-tested with three scholars to ensure its face and content were valid. For the pilot study, 46 travel consumers, who were familiar with social media-based travel activities, were selected using a purposive sampling technique and, then, modified in the scope of this research project [78,79]. Based on the pilot test, the questions for the final selection were revised and improved to ensure that they were unambiguous.

4.2. Sample and Data Collection Procedures

Chinese travel consumers aged 18 years and above were chosen as the population sample for this study. The tourism industry in China is extensive, with was approximately worth RMB 5.7 trillion in revenue in 2019 [80,81], and travel consumers are gradually becoming one of the excessive influencers′ of domestic tourism. Moreover, the Chinese leisure industry has evolved into inbound and outbound markets in the world [82].

Regarding the two cultural destinations that were used as sample sites, one was located in the Jiangsu province (e.g., the classical gardens of Suzhou) and another one was in the Shanghai province or municipality (e.g., Xitang, the ancient town), both in Eastern China. These places were chosen in part due to the regions’ high population density, of approximately 26 million in 2019 [83]. Moreover, these cities have diversified domestic and international tourists. In other words, they can be summarized as a good representation of the population’s various ages, backgrounds, and races. The survey was led using a convenience sampling procedure, with the lack of an original sampling frame [84]. This style has been widely accepted in tourism studies; for example, study on destination brand value by Luo et al. [85], research on creative tourism destinations by Chen and Chou [86], and tourists′ revisit intention towards the home-based accommodation by Meng and Cui [87]. Likewise, research on social media-based user-generated content such as Kaosiri et al. [20], social media acceptance for a tourist destination Narangajavana et al. [88], and social networking by retail consumers by Fotiadis and Stylos [73] have also employed this sampling technique. Conceptually, this approach is most valid in the case of a large population, reducing the biases even when it is difficult to implement independent random sampling [84].

The on-site face-to-face survey lasted from the 10th of September to the 12th of October 2019 (weekdays and weekends) to acquire sufficient responses. The survey was carried out with the assistance of three researchers with equivalent subject knowledge. To confirm their eligibility, the responders were asked two screening questions (i.e., the respondents were active social media users and were accustomed to social media-based travel activities). Every fifth person visiting the cultural destinations was picked randomly, and after the completion of the survey, a small token gift was provided.

There were 600 questionnaires distributed, 525 of which were returned, and 346 of these usable questionnaires were employed. The survey’s return and validity rates were 88% and 66%, respectively. Generalized guidelines for SEM sample size requirements are difficult to develop [89]. Regardless, Kline [90] contended that the sample size should be at least 200, while other authors suggest that a minimum of 10 cases are required for each parameter or item in the statistical analysis [90,91]. The representative sample also surpassed the general guideline that the minimum number must be 10 times greater than a single latent component within the structural model [92]. Thus, the final sample size of 346 was satisfactory in this study for 38 scale items (seven constructs).

Meanwhile, an independent sample t-test was castoff to observe initial and late respondents for potential non-response bias [93]. According to the results, there were no statistically significant differences between these respondents [94]. As a consequence, it was rational to settle that non-response bias was not a significant problem.

5. Results

5.1. Data Analysis

The data were examined by statistical software (e.g., IBM SPSS V. 23.0 and AMOS 21.0). According to the recommendations, the measurement model and the structural equation model (SEM) were used to examine the connecting relationships of the hypotheses [95]. Frequency, confirmatory factor analysis (CFA), and correlation were also performed [91].

5.2. Descriptive Statistical Analysis

There were more females (58.09%) than males (41.91%) among the 346 respondents (see

Table 1. Demographic information (n = 346).

Demography	Type	n	%
Gender	Male	145	41.91
	Female	201	58.09
Age (In years)	18–27	66	19.07
	28–37	95	27.45
	38–47	102	29.47
	48 years or above	83	23.98
Education	Undergraduate	90	26.01
	Master’s	192	55.49
	PhD or Doctoral	64	18.49
Monthly Income (Chinese Currency or RMB)	3000–5000	96	27.74
	5001–7000	130	37.57
	7001 or above	120	34.68
Duration of Social Media Using (Year)	0–2	60	17.34
	3–5	95	27.45
	6 years or more	191	55.20
Purposes	Looking for travel information	106	30.63
	Towards travel planning	63	18.20
	For travel purchase	82	23.69
	For travel experience sharing (i.e., photo, video, content)	95	27.45
Popular Social Media Platforms	Renren/QZone	30	8.67
	Youku/TikTok	45	13.0
	WeChat	118	34.10
	Sina Weibo/Weibo/Blogs	90	26.01
	Others	34	9.82

), whose ages ranged from 18 years to above. The majority were aged 38–47 years old (29.47%), followed by 28–37 years old (27.45%), 48 years or older (23.98%), and 18–27 years old (19.07%). The majority held a master’s degree (55.49%), followed by an undergraduate degree (26.01%) and a PhD or doctoral degree (18.49%). Their monthly income identified around 6001–9000 RMB (37.57%), followed by 9001 or above (34.68%), and 3001–6000 RMB (27.74%). According to social media and tourism, 55.20% have used social media platforms for the past 6 years or more, followed by 3–5 years (27.45%), and 0–2 years (17.34%). The purpose of using social media was travel information search (30.63%), followed by travel experience sharing (27.45%), purchase (23.69%), and planning (18.20%). Finally, social media platforms for travel purposes were WeChat (34.10%), followed by Sina Weibo/Weibo/Blogs (26.01%), Youku or TikTok (13.0%), others (9.82%), and Renren/QZone (8.67%).

5.3. Reliability and Validity Testing

A common method bias (CMB) was implemented in the beginning phases to recognize the model fit indices, via Harman’s one-factor test [96]. In total, a 31.971% common variance was originated, less than the recommended value of 70% [97]. As a result, the CMB problem did not apply to this study.

Additionally, the measurement model (MM) was estimated by the confirmatory factor analysis, convergent validity, reliability, and discriminant validity (see

Table 2. Reliability and convergent validity testing.

Constructs	Items	Mean	SD	S.E.	S.F.L.	CR	AVE	Cronbach’s Alpha (α)
Collectivism	COLL1	4.03	0.695	0.037	0.815	0.883	0.603	0.881
	COLL2	4.06	0.697	0.037	0.740
	COLL3	4.01	0.692	0.037	0.855
	COLL4	4.07	0.709	0.038	0.696
	COLL5	4.04	0.684	0.037	0.769
Uncertainty Avoidance	UA1	4.20	0.640	0.034	0.869	0.865	0.564	0.862
	UA2	4.19	0.625	0.034	0.739
	UA3	4.20	0.658	0.035	0.716
	UA4	4.18	0.681	0.037	0.723
	UA5	4.24	0.627	0.034	0.698
Long-Term Orientation	LTO1	4.28	0.603	0.032	0.906	0.903	0.651	0.898
	LTO2	4.27	0.631	0.034	0.758
	LTO3	4.24	0.613	0.033	0.776
	LTO4	4.26	0.611	0.033	0.790
	LTO5	4.31	0.615	0.033	0.798
Perceived Ease of Use	PEOU1	4.25	0.612	0.033	0.795	0.876	0.640	0.883
	PEOU2	4.25	0.611	0.033	0.838
	PEOU3	4.25	0.630	0.034	0.726
	PEOU4	4.27	0.634	0.034	0.836
Perceived Usefulness	PU1	4.30	0.621	0.033	0.772	0.831	0.622	0.832
	PU2	4.29	0.622	0.033	0.781
	PU4	4.30	0.630	0.034	0.814
Social Media Self-efficacy	SMSfEf1	4.39	0.600	0.032	0.825	0.821	0.606	0.820
	SMSfEf2	4.36	0.603	0.032	0.736
	SMSfEf4	4.38	0.631	0.034	0.773
Behavioral Intention	BI1	4.30	0.630	0.034	0.858	0.829	0.625	0.896
	BI2	4.30	0.638	0.034	0.726
	BI3	4.25	0.649	0.035	0.766
	BI4	4.29	0.612	0.033	0.808

Note: SD—standard deviation; S.F.L.—standardized factor loading; S.E.—standard error; CR—composite reliability; and AVE—average variance extracted.

). Cronbach’s alpha (α) [98] ranged from 0.820 to 0.898 to measure the internal reliability. As a result, the reliability of every construct was confirmed at the cut-off point of 0.7 [99]. The standardized path coefficients were 0.696 to 0.906, superior to the recommended value of 0.60 for recognized items [91]. The composite reliability (CR) was also calculated [100], ranging from 0.821 to 0.903, which surpassed the suggested value of 0.60 [101]. Moreover, all the extracted average variance (AVE) ranged from 0.564 to 0.651, slightly above the advised value of 0.50 [91].

The Pearson′s product-moment correlation coefficient (r) was analyzed to detect the significance and interdependence of the constructs (see

Table 3. Pearson′s correlation coefficient.

Sl. No.	Constructs	COLL	UA	LTO	PEOU	PU	SMSfEf	BI
1.	Collectivism	1	0.440 **	0.402 **	0.468 **	0.412 **	0.330 **	0.342 **
2.	Uncertainty Avoidance	0.440 **	1	0.439 **	0.407 **	0.416 **	0.340 **	0.369 **
3.	Long-term Orientation	0.402 **	0.439 **	1	0.406 **	0.566 **	0.420 **	0.370 **
4.	Perceived Ease of Use	0.412 **	0.416 **	0.566 **	0.444 **	1	0.401 **	0.447 **
5.	Perceived Usefulness	0.468 **	0.407 **	0.406 **	1	0.444 **	0.424 **	0.421 **
6.	Social Media Self-efficacy	0.330 **	0.340 **	0.420 **	0.424 **	0.401 **	1	0.380 **
7.	Behavioral Intention	0.342 **	0.369 **	0.370 **	0.421 **	0.447 **	0.380 **	1

Note: ** Correlation is significant at the p < 0.01 level/Sig. (2-tailed).

). It ranged from 0.330 to 0.566, n = 346 (** p < 0.01), a moderate correlation.

5.4. Discriminant Validity

To test the discriminant validity, the AVE of all latent variables (diagonal) outperformed the square correlations (0.330 to 0.566) between any two constructs (non-diagonal) [102]. Furthermore, for each construct, the square root of AVE (0.75 to 0.81) surpassed the correlations between the reliability coefficient and others [101]. As a result, the instrument’s discriminant validity was supported (see

Table 4. Discriminant validity.

Sl. No.	Constructs	COLL	UA	LTO	PU	PEOU	SMSfEf	BI
1.	Collectivism	0.78
2.	Uncertainty Avoidance	0.440 **	0.75
3.	Long-term Orientation	0.402 **	0.439 **	0.81
4.	Perceived Usefulness	0.468 **	0.407 **	0.406 **	0.80
5.	Perceived Ease of Use	0.412 **	0.416 **	0.566 **	0.444 **	0.79
6.	Social Media Self-Efficacy	0.330 **	0.340 **	0.420 **	0.424 **	0.401 **	0.78
7.	Behavioral Intention	0.342 **	0.369 **	0.370 **	0.421 **	0.447 **	0.380 **	0.80

Note: Diagonal values are AVE and off-diagonals are inter-construct squared correlations. ** p < 0.01.

).

The measurement model’s results showed that it closely matches the data, indicating that the model had a tolerable goodness of fit: Chi-square (X²) = 472.647; degrees of freedom (df) = 354; X²/df = 1.335; probability level (p) = 0.000; RMR = 0.017; GFI = 0.918; AGFI = 0.899; NFI = 0.922; RFI = 0.910; IFI = 0.979; TLI = 0.976; CFI = 0.979; RMSEA = 0.031 [91].

5.5. Structural Equation Modeling

The structural model analysis was assessed after the reliability and validity tests [100]. As a dataset for the structural model, a correlation matrix was employed, with the maximum likelihood method. Figure 3 depicts the standardized values of beta (β), t-value, and explanatory power (R²). R² was 0.2 to 0.5 for all endogenous constructs, significant explanatory power [90].

The study’s findings showed that the model accurately reflected the data: Chi-square (X²) = 594.214, degrees of freedom (df) = 362, and were major at a probability level (p) = 0.000. The X²/df ratio of less than five (1.641) was acceptable as the recommended value. The findings revealed that the goodness of fit statistics resided in a satisfactory edge. Furthermore, the root mean square residual (RMR) = 0.049, and goodness of fit index (GFI) = 0.900 were substantial. Additionally, the adjusted goodness of fit index (AGFI) = 0.879, normed fit index (NFI) = 0.902, relative fit index (RFI) = 0.890, incremental fit index (IFI) = 0.959, Tucker–Lewis index (TLI) = 0.954, comparative fit index (CFI) = 0.959, and root mean square error of approximation (RMSEA) = 0.043, were also supported [91].

5.6. Hypotheses Testing

Table 5. Outcomes of hypotheses testing (direct).

Hypothesis	Path	Standardized Regression Coefficient (β)	Standardized Error (S.E.)	C. R. ( = t-Value)	Decision
H1a	COLL → PEOU	0.350	0.057	5.337 ***	Supported
H1b	COLL → PU	0.124	0.051	1.938 *	Supported
H2a	UA → PEOU	0.189	0.068	2.872 **	Supported
H2b	UA → PU	0.126	0.060	1.937	Not Supported
H3a	LTO → PEOU	0.207	0.063	3.373 ***	Supported
H3b	LTO → PU	0.526	0.064	8.012 ***	Supported
H4	PEOU → PU	0.142	0.060	2.256 *	Supported
H5a	SMSfEf → PU	0.103	0.045	2.09 *	Supported
H5b	SMSfEf → BI	0.231	0.061	3.945 ***	Supported
H6	PU → BI	0.454	0.070	7.345 ***	Supported

Note: *** p-value < 0.001, ** p-value < 0.01, * p-value < 0.05.

illustrates the fact that, except for one, nine out of ten proposed hypothetical associations were accepted. First, collectivism found a significant connection with the PEOU (H1a: β = 0.350 ***, p < 0.001, t-value = 5.337) and PU (H1b: β = 0.124 *, p < 0.05, t-value = 1.938), supporting H1a and H1b.

Uncertainty avoidance found a noteworthy association with the PEOU (H2a: β = 0.189 **, p < 0.01, t-value = 2.872); thus, supporting H2a. In contrast, it was non-significant with the PU (H2b: β = 0.126, p < 0.05, t-value = 1.937), which did not support H2b.

On the other hand, long-term orientation had a significant association with the PEOU (H3a: β = 0.207 ***, p < 0.001, t-value = 3.373) and PU (H3b: β = 0.526 ***, p < 0.001, t-value = 8.012); consequently, supporting H3a and H3b.

Moreover, PEOU also found a positive significant outcome on PU (H4: β = 0.142 *, p < 0.05, t-value = 2.256), supporting H4.

Moreover, self-efficacy had a positive significant consequence on PU (H5a: β = 0.103 *, p < 0.05, t-value = 2.09) and CUI (H5b: β = 0.231 ***, p < 0.001, t-value = 3.945). Hence, supporting H5a and H5b.

Finally, PU had a positive significant outcome on CUI (H6: β = 0.454 ***, p < 0.001, t-value = 7.345), which supported H6.

Research findings suggested that the variables related to cultural dimensions and technology acceptance were meaningfully associated with respondents′ continuance usage intention in the context of travel. According to the literature, technology adoption and its continuous usage are higher in individualistic cultures than in collectivist cultures, possibly due to the important roles that personal motivation and choice play in the individual decision to accept it [103]. The more effective the system is ascertained to be in terms of uncertainty avoidance (UA), the more expected it to be reduced. However, the impact of UA at the individual level on PU was found statistically non-significant, not supporting the initial hypothesis (H2b). The analyzed results indicated that the impact of collectivism and LTO were more effective to the perceived usefulness of social media. Furthermore, these findings showed that all TAM variables were significantly related to empowering the continuous use of social media.

6. Discussion and Implications

The empowerment of social media is a wide, multi-level perception that encompasses the specific, operational, and group levels [4,104]. Meanwhile, this concept can be regarded as a progression or a consequence [2,105]. Moreover, the individual empowerment potential of social media is, increasingly, a topic for academic debate [3,5], but to this moment, the topic of travel-related utilization is little discussed in a cultural context. Therefore, the individual utilization behavior concerning cultural values and travel topics is an interesting and relevant phenomenon to examine. In the tourism context, culture is considered an influential indicator of tourist behavior, but due to the direct measurement, it has been less focused upon. Besides this, national cultural differences can influence consumer behavior by shaping consumption patterns for travel products, services, and innovations [14,16,106,107,108]. Based on this, the present study aimed to investigate how cultural values affected social media empowerment and continuance of usage in travel circumstances. Thus, the findings of the current study are presented below.

According to theoretical integration, the impacts of cultural values towards the utilization of social media can also be proven through TAM. Earlier studies concentrated on the moderating influence of cultural attributes on the e-learning model [109] and the e-commerce receiving [11]. However, this present study hypothesized the indirect effects of cultural values towards the intention to accept social media in travel perspectives, which was a novel contribution. Hence, two independent variables, namely, collectivism and a long-term orientation, were shown to have positive and significant associations with perceived usefulness (R² = 36%) and perceived ease of use (R² = 56%). They indicated that there is an association between cultural beliefs, perceived usefulness, and perceived ease of use. As a result, the hypothetical relationships were accepted (e.g., H1a, H1b, H3a, and H3b). These were constant with the previous exploration on approval and readiness of the technology [45].

Even so, another independent variable (uncertainty avoidance), at the individual level, was significantly positive with the PU; thus, H2a was supported. However, it was insignificant with the PEOU, which meant H2b was not supported. Moreover, perceived ease of use also showed a substantial constructive connection with PU, which supported H4. These findings corroborated an earlier study on e-commerce [11]. Moreover, the self-efficacy of social media significantly affected perceived usefulness and CUI, which supported H5a and H5b. This was consistent with prior research on mobile self-efficacy towards perceived usefulness and behavioral intention via competence [110].

Finally, perceived usefulness was significantly related to CUI (R² = 28%), which supported H6. This was consistent with research on mobile-based assessment [110], research on e-learning [109], and online hotel reviews [34]. According to the theoretical model (see Figure 2), the outcomes showed that, except for one (H2b: UA → PU), all nine proposed hypothetical interactions were accepted. The conclusion validated the study on technology acceptance and readiness [45], e-commerce acceptance [11], and e-learning acceptance [109].

6.1. Theoretical Implications

Theoretical suggestions were as follows: Firstly, this study was constructed on the technology acceptance model, to observe social media continuance of use and social media self-efficacy in the case of travel consumers. In addition, technology acceptance (e.g., social media, smartphone-based social media, etc.) was used as an outcome [45,69,110,111]. The study also revealed that PU, PEOU, and perceived enjoyment of mobile marketing were constant with previous discoveries in diverse fields [112].

Secondly, this study also incorporated one essential aspect of technology acceptance (i.e., social media self-efficacy) into the projected model to provide a complete vision of efficiency towards social media usage [111]. Here, self-efficacy represented consumers’ perceived control of skills for successfully performing a behavior. Additionally, the assessment of social media for travel activities and its practices in the area of information seeking or searching, purchase, and planning was still an emerging research possibility among academics and practitioners. In particular, this study used social media self-efficacy, which was important in the travel context, as online travel operators sell tickets, post their social media pages [20,24,40,42]. In China, technology self-efficacy is particularly applicable to the use of social media, because of the dramatic increase in internet users [81]. Thus, more Chinese consumers′ will be capable of searching for online travel information and purchasing tickets on the internet.

Finally, although the effects of cultural values at the countrywide and cross-culture context have been examined before [11,26], social media continuance usage intention influence by cultural values has never been researched. Therefore, the present study integrated cultural values and extended the TAM from consumers’ travel purposes. A qualitative study observed consumer-generated media implementation in Germany, China, the UK, and the USA, which recommended that varied rates of acceptance can be explained by national culture [113]. Additionally, in uncertainty avoidance, a study found that respondents with higher UA were more able to obtain information from friends and family, revealing different travel behaviors [114].

In conclusion, this study enhanced the growing literature by constructing and integrating the TAM into a model accounting for the effects of cultural values of individuals. This finding supported the prior research [11,45] and confirmed the successful incorporation of these two theories. The study’s analytical findings contained several significant references for practice in the area of travelers’ cultural standpoint. This suggested that the proposed theoretical framework can be used to investigate the associations of technology acceptance and can be used as a framework for new research in cultural perspectives [16,45]. All of the external factors supported cultural values in terms of technology acceptance, demonstrating that users are assuming and utilizing social media in the realm of travel marketing.

6.2. Practical Implications

This research also included some practical suggestions for facilitating communication between consumers and travel service providers. The research findings have the potential to increase trustworthiness and appeal to possible adopters of social media. Firstly, while the mainstream cultural studies focus on a cross-country evaluation, the current study focused on individual acceptance of social media, which was not revealed before. Understanding travel consumers’ acceptance of social media could aid service providers in developing response strategies. Secondly, the outcomes specified that collectivism and the long-term orientation have a noteworthy impact on PEOU and PU of social media. This suggested that there may be benefits associated with social media travel activities carried out through this channel. By considering how social media allows consumers to conduct travel activity more efficiently and effectively, numerous benefits were, thus, provided to individuals. Time savings and the ease of making an online purchase, searching for travel information, booking online, creating content, and sharing experiences, for example, may help to retain more social media users.

Thirdly, understanding cultural traits may be key in shaping and managing social media-based travel solutions. Therefore, this study provided valuable information for marketers and travel providers to identify the use of social media by individuals; thus, supporting the notion that a person’s online behavior reflects their offline cultural identity [26]. Showing an example of social media adoption by consumers is recommended to specify a kind of collectivism, which values the group activity and the opinions of others, which are considered to be more significant [115]. Thus, their acceptance of social media thereby improves the PEOU and PU. As such, travel marketers should deliberate whether or not to utilize social media in collectivist cultures.

Furthermore, China’s great contribution and participation in social media reflect its collectivist culture [11,26]. Besides this, taking the noteworthy influence of long-term orientation (e.g., long-term benefits, the achievements of travel planning, travel purchase) can be used to highlight consumers′ to improve their PEOU and PU of social media.

7. Conclusions

Although the impact of cross-cultural values on national culture and technology acceptance has previously been investigated, prior literature has mostly overlooked the particular case of travel consumers. To address this shortage, cultural values and their effect on social media continuance use based on the TAM and travel context is a new area. It is significant to look at the impacts of cultural values from the perspective of travel consumers and their empowerment towards technology. A broader understanding of such influences can help travel managers identify the technology links of future generations and greatly facilitate the adoption of travel technology. While this study was an example of collectivist countries, travel managers should be given the focus on uncertainty avoidance to provide more information on social media, creating a sustainable platform to adapt it for long-term benefits. This will produce new challenges for researchers seeking to explain the cultural impacts and continuance usage intention in different pathways. For those who are less likely to avoid uncertainty, service providers need to reduce the risk of using social media and, also, to provide solutions that have a constructive effect on user confidence and willingness to use this service. In light of these changes, the self-efficacy of social media services by individuals and their impact on travel activities can make this channel more attractive and improve user performance. These outcomes support the existing discovery of social media acceptance via the cultural dimensions and the TAM, as well as the new technological-relative setting.

Limitations and Future Research

Considering an extensive theoretical framework, research techniques, and processes, our research experienced some limitations that future studies on this topic should keep in mind.

First and foremost, the integrated model confirmed the analytical evaluation of three cultural dimensions (i.e., collectivism, LTO, and UA), in the travel background. As a result, it is unknown whether the findings of the research can be generalized. This research can be extended with the other two dimensions (i.e., power distance and masculinity-femininity). The inclusion of other cultural factors could be intriguing for additional research and provide useful insight into travel activities and the behaviors of consumers.

Second, the research model only incorporated the TAM and three cultural values from Hofstede’s dimensions. Although the integrated model showed significant differences in the endogenous variable, the other types of features play vital roles in consumers′ behavior. Future research could include, for example, technology self-efficacy, perceived threat, and constancy. These factors can be reflected to improve the understanding of consumer social media implementation.

Third, because this study was limited to Chinese travelers, its applicability could be limited. Future research may be functional to other technologically centric countries, while a comparison of current results with other cultures can be performed. Fourthly, this study only revealed the social media acceptance of consumers, and only for travel purposes, meaning that it did not have specific information about distinctive travel behaviors (i.e., travel purchase intention, travel planning, or travel information search). Hence, an additional study can look into the extent to which the social media-based travel experience of consumers affects the results.

Finally, research should be conducted for a better understanding of social media adoption for travel marketing practices from various backgrounds. To address this limitation, future studies can investigate the constructs across a wider geographical range or conduct a comparison.