# Innovative Research by Using IoT Applications on Cross-National English Cultural Communication Based on Crowdsourcing Translation Model

Authors: Puyu Zheng; Guojun Chai
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Social innovation provides a combination of social practices and needs in a specific area of action. Therefore, it leads towards satisfying needs and problem-solving techniques. Social innovation contributes to the society regarding improvement process, learning betterments, meeting new and efficient goods and services, and meeting social needs. Nevertheless, social innovation in cross-national English cultural communication motivates communities about widespread social transformation. Therefore, this paper studies the impact of social innovation, social needs, and society’s capacity towards adopting such social practices on crowdsourcing translation and cross-national English cultural communication. The data was collected from actors and players of cross-national English cultural communicators. All actors and players contributed to social innovation and crowdsourcing translation in cross-national English cultural communication. However, cultural explanations have been sparse. The collected data was analyzed in SMART PLS 3. For analysis, social needs, social innovation, and societal capacity were used as independent variables. In contrast, the crowdsourcing translation (IV for cross-national English cultural communication) and cross-national English cultural communication were used as the dependent variable. The data was collected from 50 actors and players of cross-national English cultural communication.


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## Body

## 1. Introduction

With the Internet and technology advancing, big data has come. The conventional schooling approach is being affected by big data. Digital foreign language teaching is witnessing a tremendous transformation in IT. Also, online interactive foreign language instruction is transforming English language education by including social, cultural, and informational dimensions. The expansion of information networks has led to the Internet of Things (IoT) technology. Information technology is employed in practically all areas of people’s everyday life [1, 2].Social innovation, like citizen science, is a term that has yet to be defined by the scientific community. In this work, however, in 2010, Howard definition of social innovation states that “A social innovation is a brand new mixture and/or new configuration of social practices in unique regions of movement or social contexts, brought on via way of means of unique actors or constellations of actors in an intentional focused way with the purpose of higher pleasant or answering desires and issues than is viable on the idea of mounted practices,” in step with the definition [3]. Social innovation, process, activities, services, and new goods meet social needs and improve a society’s capacity to act emerge. All players must contribute to social innovation. Unlike other types of invention, social innovation does not maximize profits and has a competitive advantage [4]. However, it is affected by concerns for communities (a problem or social need) and results in massive social change [5]. Individuals, groups, and social movements have all been considered agents in social innovation. Although other agents such as governments and businesses exist, they can only cooperate [6]. The creator claims that “the Internet offers a platform for finishing obligations the use of the information of a self-decided on network of net volunteers.” Large user groups can now interact and collaborate online because to improvements in the Internet and technology [7]. Translation of popular culture, such as fandubbing, in which fans write subtitles for movies or TV shows in another language, subtitles of TED speeches by users of social media platforms, such as Facebook [8]. Crowdsource translation is also used through civic science endeavors [9]. No experience required. Only the ability to communicate in more than one language is required. Emerging translation demands are addressed through both translation technologies and mobs. Both should boost productivity and shorten the time it takes to translate big amounts of text (and reduce translation costs) [10].The relationship between social innovation and translation in citizen research, on the other hand, is still poorly understood. Social innovators are people who help people to change their lives for the better. Individuals or entire groups can be actors, regardless of the sector of society they belong to [11]. However, since all sectors must participate in the process of social innovation to solve a complex problem, social change can only be possible if all sectors are involved [12]. Furthermore, the distinction between the result and process is another crucial distinction in social innovation. In terms of the final outcome, social innovation stresses the formation of new social institutions and improved interpersonal connections, as well as the fulfilment of a specific demand through innovation [13]. Social innovation, in the long run, should strengthen a society’s ability to act through systemic societal reform [14]. Social innovation, on the other hand, is a collaborative process that improves interactor relationships while promoting social resilience and providing access to resources to meet future needs [15].The popularity of social innovation and civic science has increased [16]. Translation has also been described as facilitating (or hindering) social and cultural change. No study has ever looked at the relationship between social innovation, civic science, and translation [17]. As a result, this study examines the role of translation in civic science initiatives and how it can use civic science to aid in social innovation [18]. In civic science initiative, although some studies have been done on translation and a citizen science translation hub has been established, there is a lack of scientific awareness about civic sciences and social innovations, especially in the field of translation and cultural difficulties [19]. Understanding the relationship between translation and social innovation in civic science initiatives may help to consider these factors in civic science in the future. To approach a nation’s culture philosophically, Hofstede’s dimensions were derived in one multinational corporate firm in 40 countries in a prior study and now extend to 74 countries [20]. According to Hofstede, values are broad preferences for one condition of affairs over another [21] [22]. On the basis of these observations, uncertainty avoidance, power distance, individualism-collectivism, and masculinity-femininity are four elements of national culture that Hofstede identified [10]. The fifth component was then added: long-term orientation [23] [9]. Rather than describing people, the indices are meant to depict society.The dimensions have been rigorously validated by cross-time stability studies and comparisons to other national society features [24]. Crowdsourcing is a web-based activity that taps into the creative talents of a wide, diverse network of people (the crowd) by putting out an open call for participation and contributions. Following the open appeal, several perspectives on crowd response or composition in terms of participation and contribution emerge [25]. Nielsen has studied the topic of ‘participation inequality’ in online groups and supports a general 90-9-1 rule based on existing research:
(1)
90% of people are ‘lurkers,’ meaning they read or watch but do not contribute(2)
9% of users donate from time to time, although other commitments take precedence(3)
1% of people participate frequently and make up the majority of contributions (it may appear as though they do not have lives because they frequently post minutes after the event, they are commenting on happened)As a result, participation inequality has been labeled a human behavior trait that is nearly impossible to overcome [14]. Due to severe time limits and work pressures that severely limit employee participation, as well as the fact that motivation and incentives differ from those in the public arena, crowdsourcing inside the enterprise (inside a company’s firewall) poses a significant problem [26]. As a result, the only viable option is to shape inequality curves slanted differently from the 90-9-1 distribution using online crowdsourcing groups within the organization [4]. This information-driven world with a generation that is continually leaping into globalization. While information technology has made significant progress in overcoming the economic, social, and political divides among culturally diverse populations, the language barrier continues to obstruct meaningful communication and information exchange [27]. At best, the modern planet can be viewed as a collection of interconnected islands. People to keep their cultures and languages alive, so that they will be producing data in local languages for the foreseeable future [23]. The world wants intelligent systems to analyze this data and deliver it where it is needed [27]. Machine translation has remained a fascinating subject and a difficult subfield in natural language processing, with the goal of bridging this precise language barrier by creating machines that can automatically translate between human languages [28].After World War II, the first known application of machine translation (MT) was for military purposes, decoding Russian phrases into English [23]. Although it is still mostly utilized in government sectors to comprehend intelligence across borders, it is increasingly being used in legal papers, patents, and the study of foreign countries’ histories and cultural heritages, among other things [10]. With falling hardware and computational costs, translation technology that was once only available to a privileged few with access to government supercomputing resources is now available to the general public, running on a commodity personal laptop and, in some cases, mobile and hand-held devices as well, for example, in the hands of a tourist translating foreign language on sign-boards in real-time [9].


## 2. Literature Review

m-Learning has a number of cognitive and psychological advantages [29], including collaborative learning among students, which can boost their self-esteem and confidence [30]. In 2005, Attewell’s claim was supported by more recent research from a variety of countries [23]. In 2010, Akdemir enrolled the assistance of 60 college understudies from Turkey’s Black Sea district to test the viability of learning English jargon through cellphones versus customary cheat sheets [31]. The trial’s findings revealed that participants’ mobile learning benefited them and that they had a favorable attitude about learning the English vocabulary [32]. In 2011, Zhang did a study in China that found similar results but recommended utilizing mobile devices as a supplement to classroom instruction rather than as the primary mode of instruction, raising concerns about the constraints of employing m-Learning the sole mode of delivery. In 2011, Yamada investigated m-Learning for business English, whereas the two studies above focused on using it to teach and acquire English vocabulary [33]. They discovered that it helped Japanese EFL students with their English. In 2011, Yamada’s findings, on the other hand, differed from those obtained by Idris’s study conducted in Stockwell, Japan, and Malaysia in 2010 [34–36]. Even though Malaysia has a mobile phone penetration rate of over 100%, learners still struggle with learning transfer in m-Learning contexts [23].Cultural concerns may be a consideration in m-Learning and the popularity of mobile technology and a learner’s digital competence [30]. From 2007 to 2009, a (three-year) study conducted and determined that neither teachers nor students sought to employ mobile devices as an educational platform in Japan. In the studies stated above [14, 37], researchers looked at discrepancies in learners’ perceptions regarding m-Learning primarily from a technology standpoint [14]. m-Learning allows learners to participate in authentic activities, and earlier research has showed that meaningful engagement in either an e-Learning or m-Learning context does not occur on its own [26]. In 2010, according to the pragmatic role of learners in the interaction in a virtual learning environment, Malmmose and Lueg identified five categories of contributing involvement [4]. In Web 2.0 learning environment, among the studies available, only Merchant (2009) has looked at the role of participatory culture [29]. Participatory culture, according to Jenkins (2006), is described as a group of individuals who believe their contributions matter and feel a feeling of social connection with one another [32]. Power imbalances and social anxiety are among the issues addressed by cultural concerns. As a result, people’s thoughts about the implications of participating in discussions and engagements are influenced by their cultural background [28]. In 2010, Joy and Yau argued that learners from different ethnic groups could have different attitudes towards m-Learning [38], indirectly backed up this claim. Their research, on the other hand, failed to provide practical answers to their research issues or to elaborate on the causes for their concerns [39].Nonetheless, while the Hofstede aspects were initially settled for modern brain science, they can likewise be appropriate to medical services and clinical brain research. In correspondence of specialist patient, for instance, progressive versus equalitarian relations [31], level of traditionalism, data trade, and shared navigation [30]; in vulnerability evasion, these could incorporate patient anxiety, uneasiness and feeling ([31, 33], confidence in subject matter experts, specialist’s vulnerability aversion, and level of medicalization; in vulnerability aversion, these could incorporate patient emotionality or nervousness and stress, specialist task-direction, inclination for mechanical arrangements [29], confidence in trained professionals, and specialist’s vulnerability. Individualism introduces itself in a huge number of ways [7], including patient emotionality, nervousness, and pressure, specialist task-direction, a longing for specialized arrangements, and confidence in trained professionals [9]. From the angle of social hypothesis, the current review takes into consideration the following, of which social factors are connected with clinical correspondence and situational perspectives in the setting of medical services conveyance [38].Patients’ assumptions, specialists’ perspectives, and clinical correspondence, mentalities as far as counsel length, instrumental versus expressive conduct, and determination all contrast from each other in various nations [38]. Cultural explanations have been limited, and those focusing on the east-west or north-south axis have been woefully underdeveloped. For example, Polish doctors are more aggressive and directive in their communication than their North American counterparts [40], but they are also significantly kinder, expressing favorable feelings for their patients in their interactions with them. Comparatively speaking, they can be classified as “warm autocrats,” as opposed to North American “cold specialists.” More so than the United States, Poland has a greater power distance from the United States of America [13].It indicates that medical communication is more instrumental (i.e., masculine) in the United States and the United Kingdom, compared to medical communication in the Netherlands. Belgian doctors, on the other hand, are more disease-focused, while Dutch doctors are more patient-focused [41], with British doctors falling somewhere. Japanese consultations are more effective than consultations in the United States or Canada [42]. Ailments that are unique to a particular country include crise de foie (liver difficulties) [43], Herz insufficiency and Kreislauf kollaps (blood circulation diseases) in Germany, and a wide range of chills and bowel movement obsessives in the United Kingdom, among other conditions [44].


## 3. Methodology

In order to conduct innovation research on the cross-national English cultural communication based on crowdsourcing translation model, this paper studies the impact of social innovation, social needs, and society’s capacity towards adopting such social practices on crowdsourcing translation and cross-national English cultural communication. The data was collected from actors and players of cross-national English cultural communicators. All actors and players contributed to social innovation and crowdsourcing translation in cross-national English cultural communication. However, cultural explanations have been sparse. The collected data was analyzed in SMART PLS 3. For analysis, social needs, social innovation, and societal capacity were used as independent variables. In contrast, the crowdsourcing translation (IV for cross-national English cultural communication) and cross-national English cultural communication were used as the dependent variable. The data was collected from 50 actors and players of cross-national English cultural communication as show in Figure1.Figure 1
Research framework.


## 4. Discussion and Analysis

### 4.1. PLS Algorithm

For the purposes of this study, the SMART PLS 3 software was utilized to design the PLS algorithm and to evaluate the model’s fitness. Cross-national cultural communication is investigated using structural equational modeling, which is done from the standpoint of organizational management. The CT (application of crowdfunding translation method) was used to investigate the effects of SN (social needs), SC (societal capability), and social innovation (SI) on CT as well as the impact of CT on CNEC. This model, using these latent variables, illustrates the factor loadings of the variables against each item in the data set. Social innovation (SI) has five items, societal capacity (SC) has five items, social needs (SN) have four measuring items, CT (crowdsourcing translation) has 4 measuring items, and CNEC (cross-national English cultural c09ommunication) has three measuring items. The synthesis problem has had a favorable impact on SN, SI, and SC on CT and CNEC, as well as a low negative loading of CT against CNEC, at rates of 0.132, 0.005, 0.685, 0.508, and -0.064 as shown in Figure2.Figure 2
PLS algorithm.The overall results of the model reveal that IVs have a beneficial impact on DV, as well as that they have a strong overall correlation.The path coefficients of the link between two variables are shown in Table1. Thet-statistics result is between -2 and 2 and close to zero, indicating that the data collected is legitimate and acceptably representative. P values indicating a relationship between variables are acceptable. Consequently, the association between variables is statistically significant when the P value for CT->CNEC is 0.001, SC->CT is 0.001, SI->CT is 0.005, and SN->CT is 0.000. Therefore, the relationship between variables is significant.Table 1
Path coefficients.
Original sample (O)Sample mean (M)Standard deviation (STDEV)T statistics (∣O/STDEV∣)SignCT->CNEC-0.064-0.0230.2410.2650.001SC->CT0.0050.0030.1130.0420.001SI->CT0.1320.1020.2040.6470.005SN->CT0.6850.6720.0808.5960.000Table2 shows the indirect impact of factors that influence the association of non-SC->CT->CNEC is 0.000, SI->CT->CNEC is -0.008, and SN->CT->CNEC is -0.044. As a result, the table demonstrates that only societal capacity has no effect on the independent variable, whereas SI and SN has a negative impact on CT and CNEC. Everything else, with the exception of SC (social capacity), results in a positive relationship between the latent variables.Table 2
Specific indirect effects.
Specific indirect effectsSC->CT->CNEC0.000SI->CT->CNEC-0.008SN->CT->CNEC-0.044Table3 and Figure 3 underneath show the total effect of latent variables on each other. CNEC->CT has negative effect on each other with the ratio of -0.064, CNEC->SI is -0.008, and CNEC->SN has negative impact as well with the ratio of -0.044. However, rest of the variables has positive impact on each other. Figure 3 shows the CT->CNEC has a negative path coefficient.Table 3
Latent variable covariances.
CNECCTCNECCT-0.064SC0.0000.005SI-0.0080.132SN-0.0440.685Figure 3
SEQ figure \∗ ARABIC 2: path coefficients.The correlation between the variables is depicted in Table3 to the right. As a result, the findings revealed that factors are positively associated with one another. A negative moderate correlation between CNEC and CT is demonstrated by the results, which reveal that they have an impact on each other of -6.4%. CNEC and SC were shown to have a substantial negative association (17.2%), CNEC and SN have negative association (-2.1%), and SC and SI have a negative association (-15.5%), according to the findings as show in Table 4.Table 4
LV descriptive.
CNECCTSCSISNCNEC1.000-0.064-0.1720.011-0.021CT-0.0641.0000.1830.2080.701SC-0.1720.1831.000-0.1550.291SI0.0110.208-0.1551.0000.113SN-0.0210.7010.2910.1131.000Table5 shows the importance of descriptive and latent variables related to values. The results show that the Min and Max values in the descriptive statistics table are 2-5 and that the values are within the acceptable range of 2-5. In addition, the skewness value varies from 1 to +1 and negatively distorted variables are reasonably symmetric and acceptable, while positively distorted variables are moderately symmetric and unacceptable. Since the skewness of the variable value is negative, the variable may be left fringed, resulting in a median and mean value that is less than the mode of the variable as show in Table 6.Table 5
Descriptive and latent variables related to values.
MedianMinMaxExcess kurtosisSkewnessCNEC-0.013-3.1431.5352.388-1.467CT0.098-2.0691.849-0.653-0.275SC0.001-1.8302.4680.4600.400SI0.122-1.9161.914-0.8180.183SN-0.019-1.5511.681-1.0780.109Table 6
Confidence interval bias corrected.
Original sample (O)Sample mean (M)Bias2.5%97.5%CT->CNEC-0.064-0.0230.041-0.3810.394SC->CT0.0050.003-0.002-0.2230.218SI->CT0.1320.102-0.030-0.3690.394SN->CT0.6850.672-0.0120.5130.823The collected data was bootstrapped to 500 responses. The bootstrapping reduced the error responses in the questionnaire and at 97.5% of confidence interval bias correlation the relationship between the variables has strengthened the association.According to the residual correlation of the inner model, CT (crowdsourcing translation) and CNEC (cross-national English communication) showed weak negative correlation, with the link between the variables changing with a degree of -2.6%, as shown in Table7.Table 7
Inner model residual correlation.
CNECCTCNEC1.000-0.026CT-0.0261.000The residual descriptive of the inner model is shown in Table8. It can be seen from the table that the minimum and maximum values of CNEC and CT are between 2 and 5, respectively. The information was gathered from a total of 50 participants. The skewness and kurtosis values were in the range of -1 to 1 and were close to zero, indicating that the data was legitimate and that it was not skewed. When it comes to CNEC, the curve is negatively skewed, which indicates that the longer side is located on the left side of the curve. The CT, on the other hand, is positively skewed, which implies that the longer side of the curve is on the right side of the symmetry equation.Table 8
Inner model residual descriptive.
MedianMinMaxStandard deviationExcess kurtosisSkewnessCNEC0.066-3.0901.6280.9981.000-0.420CT0.048-1.2621.7770.7010.0300.274


### 4.2. Quality Criteria

Table9 shows the value of R-square and the adjusted R-square for various scenarios. The values demonstrate that SC, SI, and SN have a moderately beneficial impact on the variable CT and CNEC. The results reveal that the value of R-square is 0.004, that the present 0.4 percent square values have an adjusted R-square of -0.017, and that the 01.70 percent model fit for the study of CNEC is demonstrated by the results. However, the value of R-square for CT is 0.508, and the current 50.8 percent square values have an adjusted R-square of 0.476, which indicates that 47.6 percent of the model is suitable for analysis.Table 9
R-square.
R-squareR-square adjustedCNEC0.004-0.017CT0.5080.476Table10 underneath shows the value of f-Square. When it comes an endogenous variable in research model, f-square shows the changes in R-square. The table underneath shows that if an endogenous variable change it will provide a negative change in the relationship of CNEC and CT with a very minimal ratio of 0.4%. However, the results in the table below show that if an endogenous variable changes in the relationship of CT and SN, it will bring a positive change of 0.848 (84.4%) (which is a huge change).Table 10
F-square.
CNECCTCNECCT0.004SC0.000SI0.034SN0.848The construct reliability and validity of the study are depicted in Table11. In this case, the Cronbach alpha value is more than 0.70 (which shows that the collected data was reliable for the study). In contrast, the CNEC (cross-national English communication) value was 0.718, which is acceptable for a restricted number of measuring items because of the low number of measuring items. The composite reliability rates are represented by the rho-A values, and the results demonstrate the average variance of all variables gathered in the study. The graphical representation of Cronbach’s alpha and composite reliability is depicted in the following figure. The composite reliability rating for hidden variables, as a result, is likewise considered satisfactory. The average variation of the retrieved value for CNEC is not satisfactory with 0.336, which indicates that the data has a variance of 33.6 percent on average as show in Figures 4(a) and 4(b).Table 11
Construct reliability and validity.
Cronbach’s alpharho_AComposite reliabilityAverage variance extracted (AVE)CNEC0.718-0.4920.4400.336CT0.7390.7990.8350.566SC0.910-7.0800.2260.102SI0.7670.5680.7920.448SN0.8610.8640.9050.705Figure 4
(a, b). Cronbach’s alpha and composite reliability.
(a)(b)The Fornell-Larcker criterion (FLC) used in the study is shown in Table12. It is used to determine the extent to which SN, SI, SC, CT, and CNEC are influenced by one another. Results demonstrate that the variables have a positive degree of share variance in terms of their respective shares. In this case, the degree of shared variance between the variables is 0.208 (CT->SI), which means that if one unit of CT changes, there is a 20.8 percent change in the variance between the variables (which is a moderate variation).Table 12
Fornell-Larcker criterion.
CNECCTSCSISNCNEC0.580CT-0.0640.752SC-0.1720.1830.319SI0.0110.208-0.1550.669SN-0.0210.7010.2910.1130.840To determine whether or not a variable is discriminately valid, the values for the heterotrait-monotrait ratio (HTMT) are employed (as shown in Table13 and Figure 4(b)). It indicates the degree to which the latent variables are comparable to one another. Consequently, the findings imply that if CNEC (cross-national English communication) and CT (crowdsourcing translation method application) are comparable, the connection will have 0.143 (14.3 percent) similar validity. In this chart, the maximum validity is depicted between CNEC and SC (22.4%), CNEC and SI (29.9 percent), and CNEC and SN is (9.7 percent).Table 13
Collinearity statistics (VIF).
CNECCTSCSISNCNECCT0.143SC0.2240.111SI0.2990.2690.157SN0.0970.8320.1900.208Figure5 shows the values of other latent variables, which correspond to the values in table. The validity score between the variables was positive, indicating that the relationship between all variables was accepted. The association between all variables showed significant validity.Figure 5
Heterotrait-monotrait ratio (HTMT).


### 4.3. Outer VIF Values

Table14 shows the outer VIF values for the survey items used to measure variables. The outer VIF value represents a statistical analysis of colinearity between all the items used to evaluate the variable. As a result, the findings reveal that the values of VIF show rate fall between 1 and 10 on a scale from 1 to 10. Consider the following examples: The CNEC3 (3rd question of cross-national English communication) has the low outer VIF value of 1.168, and the SC3 has the greatest outer VIF value of 6.413 (3rd question of social capacity). The correlation between the items and the variables is represented by the outer VIF values. As a result, the results in Table 14 demonstrate that all items were positively linked with the hidden variables.Table 14
Inner VIF values.
VIFVIFVIFCNEC12.881SC25.814SI51.420CNEC22.630SC36.413SN11.693CNEC31.168SC42.496SN22.047CT11.156SC51.941SN32.136CT22.466SI11.543SN42.443CT32.120SI21.630CT41.424SI31.687SC12.251SI41.836Table14 shows the inner VIF values of the variables against measuring items. Therefore, the VIF values of the variables are acceptable.


### 4.4. Fit Summary

The summary of model fitness is shown in Table15, and the findings are shown in the next table, which illustrates the model fitness analysis with the help of the saturated model and the estimated model. The SRMR value of the saturated model is 0.179, indicating that the model fits the analysis 17.9% (reasonably good goodness-of-fit).Table 15
Model fitness.
CNECCTCNECCT1.000SC1.136SI1.054SN1.123The rate is 0.180 in the estimated model, which also demonstrates a similar fitness analysis of the variables. When the d-ULS data are computed, they reveal that the rate is 2.256%. This rate demonstrates the beneficial impact of SC, SN, SI, CT, and CNEC, as well as the favorable impact of CT on CNEC.Table16 shows the RMS theta. The root mean squared residual covariance of the variable’s outer model residuals is displayed in this table. It is calculated that 24.6 percent of the outer model is fit by RMS theta, which equals 0.246 as show in Table 17.Table 16
RMS theta.
Saturated modelEstimated modelSRMR0.1790.180d_ULS7.4007.476d_G2.2562.263Chi-square447.952449.658NFI0.3360.334Table 17
RMS theta.
RMS theta0.246


## 4.1. PLS Algorithm

For the purposes of this study, the SMART PLS 3 software was utilized to design the PLS algorithm and to evaluate the model’s fitness. Cross-national cultural communication is investigated using structural equational modeling, which is done from the standpoint of organizational management. The CT (application of crowdfunding translation method) was used to investigate the effects of SN (social needs), SC (societal capability), and social innovation (SI) on CT as well as the impact of CT on CNEC. This model, using these latent variables, illustrates the factor loadings of the variables against each item in the data set. Social innovation (SI) has five items, societal capacity (SC) has five items, social needs (SN) have four measuring items, CT (crowdsourcing translation) has 4 measuring items, and CNEC (cross-national English cultural c09ommunication) has three measuring items. The synthesis problem has had a favorable impact on SN, SI, and SC on CT and CNEC, as well as a low negative loading of CT against CNEC, at rates of 0.132, 0.005, 0.685, 0.508, and -0.064 as shown in Figure2.Figure 2
PLS algorithm.The overall results of the model reveal that IVs have a beneficial impact on DV, as well as that they have a strong overall correlation.The path coefficients of the link between two variables are shown in Table1. Thet-statistics result is between -2 and 2 and close to zero, indicating that the data collected is legitimate and acceptably representative. P values indicating a relationship between variables are acceptable. Consequently, the association between variables is statistically significant when the P value for CT->CNEC is 0.001, SC->CT is 0.001, SI->CT is 0.005, and SN->CT is 0.000. Therefore, the relationship between variables is significant.Table 1
Path coefficients.
Original sample (O)Sample mean (M)Standard deviation (STDEV)T statistics (∣O/STDEV∣)SignCT->CNEC-0.064-0.0230.2410.2650.001SC->CT0.0050.0030.1130.0420.001SI->CT0.1320.1020.2040.6470.005SN->CT0.6850.6720.0808.5960.000Table2 shows the indirect impact of factors that influence the association of non-SC->CT->CNEC is 0.000, SI->CT->CNEC is -0.008, and SN->CT->CNEC is -0.044. As a result, the table demonstrates that only societal capacity has no effect on the independent variable, whereas SI and SN has a negative impact on CT and CNEC. Everything else, with the exception of SC (social capacity), results in a positive relationship between the latent variables.Table 2
Specific indirect effects.
Specific indirect effectsSC->CT->CNEC0.000SI->CT->CNEC-0.008SN->CT->CNEC-0.044Table3 and Figure 3 underneath show the total effect of latent variables on each other. CNEC->CT has negative effect on each other with the ratio of -0.064, CNEC->SI is -0.008, and CNEC->SN has negative impact as well with the ratio of -0.044. However, rest of the variables has positive impact on each other. Figure 3 shows the CT->CNEC has a negative path coefficient.Table 3
Latent variable covariances.
CNECCTCNECCT-0.064SC0.0000.005SI-0.0080.132SN-0.0440.685Figure 3
SEQ figure \∗ ARABIC 2: path coefficients.The correlation between the variables is depicted in Table3 to the right. As a result, the findings revealed that factors are positively associated with one another. A negative moderate correlation between CNEC and CT is demonstrated by the results, which reveal that they have an impact on each other of -6.4%. CNEC and SC were shown to have a substantial negative association (17.2%), CNEC and SN have negative association (-2.1%), and SC and SI have a negative association (-15.5%), according to the findings as show in Table 4.Table 4
LV descriptive.
CNECCTSCSISNCNEC1.000-0.064-0.1720.011-0.021CT-0.0641.0000.1830.2080.701SC-0.1720.1831.000-0.1550.291SI0.0110.208-0.1551.0000.113SN-0.0210.7010.2910.1131.000Table5 shows the importance of descriptive and latent variables related to values. The results show that the Min and Max values in the descriptive statistics table are 2-5 and that the values are within the acceptable range of 2-5. In addition, the skewness value varies from 1 to +1 and negatively distorted variables are reasonably symmetric and acceptable, while positively distorted variables are moderately symmetric and unacceptable. Since the skewness of the variable value is negative, the variable may be left fringed, resulting in a median and mean value that is less than the mode of the variable as show in Table 6.Table 5
Descriptive and latent variables related to values.
MedianMinMaxExcess kurtosisSkewnessCNEC-0.013-3.1431.5352.388-1.467CT0.098-2.0691.849-0.653-0.275SC0.001-1.8302.4680.4600.400SI0.122-1.9161.914-0.8180.183SN-0.019-1.5511.681-1.0780.109Table 6
Confidence interval bias corrected.
Original sample (O)Sample mean (M)Bias2.5%97.5%CT->CNEC-0.064-0.0230.041-0.3810.394SC->CT0.0050.003-0.002-0.2230.218SI->CT0.1320.102-0.030-0.3690.394SN->CT0.6850.672-0.0120.5130.823The collected data was bootstrapped to 500 responses. The bootstrapping reduced the error responses in the questionnaire and at 97.5% of confidence interval bias correlation the relationship between the variables has strengthened the association.According to the residual correlation of the inner model, CT (crowdsourcing translation) and CNEC (cross-national English communication) showed weak negative correlation, with the link between the variables changing with a degree of -2.6%, as shown in Table7.Table 7
Inner model residual correlation.
CNECCTCNEC1.000-0.026CT-0.0261.000The residual descriptive of the inner model is shown in Table8. It can be seen from the table that the minimum and maximum values of CNEC and CT are between 2 and 5, respectively. The information was gathered from a total of 50 participants. The skewness and kurtosis values were in the range of -1 to 1 and were close to zero, indicating that the data was legitimate and that it was not skewed. When it comes to CNEC, the curve is negatively skewed, which indicates that the longer side is located on the left side of the curve. The CT, on the other hand, is positively skewed, which implies that the longer side of the curve is on the right side of the symmetry equation.Table 8
Inner model residual descriptive.
MedianMinMaxStandard deviationExcess kurtosisSkewnessCNEC0.066-3.0901.6280.9981.000-0.420CT0.048-1.2621.7770.7010.0300.274


## 4.2. Quality Criteria

Table9 shows the value of R-square and the adjusted R-square for various scenarios. The values demonstrate that SC, SI, and SN have a moderately beneficial impact on the variable CT and CNEC. The results reveal that the value of R-square is 0.004, that the present 0.4 percent square values have an adjusted R-square of -0.017, and that the 01.70 percent model fit for the study of CNEC is demonstrated by the results. However, the value of R-square for CT is 0.508, and the current 50.8 percent square values have an adjusted R-square of 0.476, which indicates that 47.6 percent of the model is suitable for analysis.Table 9
R-square.
R-squareR-square adjustedCNEC0.004-0.017CT0.5080.476Table10 underneath shows the value of f-Square. When it comes an endogenous variable in research model, f-square shows the changes in R-square. The table underneath shows that if an endogenous variable change it will provide a negative change in the relationship of CNEC and CT with a very minimal ratio of 0.4%. However, the results in the table below show that if an endogenous variable changes in the relationship of CT and SN, it will bring a positive change of 0.848 (84.4%) (which is a huge change).Table 10
F-square.
CNECCTCNECCT0.004SC0.000SI0.034SN0.848The construct reliability and validity of the study are depicted in Table11. In this case, the Cronbach alpha value is more than 0.70 (which shows that the collected data was reliable for the study). In contrast, the CNEC (cross-national English communication) value was 0.718, which is acceptable for a restricted number of measuring items because of the low number of measuring items. The composite reliability rates are represented by the rho-A values, and the results demonstrate the average variance of all variables gathered in the study. The graphical representation of Cronbach’s alpha and composite reliability is depicted in the following figure. The composite reliability rating for hidden variables, as a result, is likewise considered satisfactory. The average variation of the retrieved value for CNEC is not satisfactory with 0.336, which indicates that the data has a variance of 33.6 percent on average as show in Figures 4(a) and 4(b).Table 11
Construct reliability and validity.
Cronbach’s alpharho_AComposite reliabilityAverage variance extracted (AVE)CNEC0.718-0.4920.4400.336CT0.7390.7990.8350.566SC0.910-7.0800.2260.102SI0.7670.5680.7920.448SN0.8610.8640.9050.705Figure 4
(a, b). Cronbach’s alpha and composite reliability.
(a)(b)The Fornell-Larcker criterion (FLC) used in the study is shown in Table12. It is used to determine the extent to which SN, SI, SC, CT, and CNEC are influenced by one another. Results demonstrate that the variables have a positive degree of share variance in terms of their respective shares. In this case, the degree of shared variance between the variables is 0.208 (CT->SI), which means that if one unit of CT changes, there is a 20.8 percent change in the variance between the variables (which is a moderate variation).Table 12
Fornell-Larcker criterion.
CNECCTSCSISNCNEC0.580CT-0.0640.752SC-0.1720.1830.319SI0.0110.208-0.1550.669SN-0.0210.7010.2910.1130.840To determine whether or not a variable is discriminately valid, the values for the heterotrait-monotrait ratio (HTMT) are employed (as shown in Table13 and Figure 4(b)). It indicates the degree to which the latent variables are comparable to one another. Consequently, the findings imply that if CNEC (cross-national English communication) and CT (crowdsourcing translation method application) are comparable, the connection will have 0.143 (14.3 percent) similar validity. In this chart, the maximum validity is depicted between CNEC and SC (22.4%), CNEC and SI (29.9 percent), and CNEC and SN is (9.7 percent).Table 13
Collinearity statistics (VIF).
CNECCTSCSISNCNECCT0.143SC0.2240.111SI0.2990.2690.157SN0.0970.8320.1900.208Figure5 shows the values of other latent variables, which correspond to the values in table. The validity score between the variables was positive, indicating that the relationship between all variables was accepted. The association between all variables showed significant validity.Figure 5
Heterotrait-monotrait ratio (HTMT).


## 4.3. Outer VIF Values

Table14 shows the outer VIF values for the survey items used to measure variables. The outer VIF value represents a statistical analysis of colinearity between all the items used to evaluate the variable. As a result, the findings reveal that the values of VIF show rate fall between 1 and 10 on a scale from 1 to 10. Consider the following examples: The CNEC3 (3rd question of cross-national English communication) has the low outer VIF value of 1.168, and the SC3 has the greatest outer VIF value of 6.413 (3rd question of social capacity). The correlation between the items and the variables is represented by the outer VIF values. As a result, the results in Table 14 demonstrate that all items were positively linked with the hidden variables.Table 14
Inner VIF values.
VIFVIFVIFCNEC12.881SC25.814SI51.420CNEC22.630SC36.413SN11.693CNEC31.168SC42.496SN22.047CT11.156SC51.941SN32.136CT22.466SI11.543SN42.443CT32.120SI21.630CT41.424SI31.687SC12.251SI41.836Table14 shows the inner VIF values of the variables against measuring items. Therefore, the VIF values of the variables are acceptable.


## 4.4. Fit Summary

The summary of model fitness is shown in Table15, and the findings are shown in the next table, which illustrates the model fitness analysis with the help of the saturated model and the estimated model. The SRMR value of the saturated model is 0.179, indicating that the model fits the analysis 17.9% (reasonably good goodness-of-fit).Table 15
Model fitness.
CNECCTCNECCT1.000SC1.136SI1.054SN1.123The rate is 0.180 in the estimated model, which also demonstrates a similar fitness analysis of the variables. When the d-ULS data are computed, they reveal that the rate is 2.256%. This rate demonstrates the beneficial impact of SC, SN, SI, CT, and CNEC, as well as the favorable impact of CT on CNEC.Table16 shows the RMS theta. The root mean squared residual covariance of the variable’s outer model residuals is displayed in this table. It is calculated that 24.6 percent of the outer model is fit by RMS theta, which equals 0.246 as show in Table 17.Table 16
RMS theta.
Saturated modelEstimated modelSRMR0.1790.180d_ULS7.4007.476d_G2.2562.263Chi-square447.952449.658NFI0.3360.334Table 17
RMS theta.
RMS theta0.246


## 5. Conclusion

The study is about the crowdsourcing translation and cross-national English communication. Social innovation, process, activities, services, and new goods meet social needs and improve a society’s capacity to act emerge. All players must contribute to and contribute to social innovation. Crowdsourced translation (also known as volunteered translation or community translation) refers to translation in which members of an unspecified “crowd” volunteer to translate. With falling hardware and computational costs, translation technology that was once only available to a privileged few with access to government supercomputing resources is now available to the general public, running on a commodity personal laptop and, in some cases, mobile and handheld devices as well. The impact of social innovation, social needs, and society’s capacity towards adopting such social practices on crowdsourcing translation and cross-national English cultural communication was studied in this paper. The results indicated that all actors and players contributed to social innovation and crowdsourcing translation in cross-national English cultural communication. However, cultural explanations have been sparse.


## 6. Recommendations and Limitations

Following are the recommendations of the study;(i)
In the future, the cultural explanations and aspects could be added(ii)
The study lacked the importance and hurdles of learning techniques of cross-national language(iii)
Furthermore, an applied model of crowdsourcing translation method could be added



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