Article Sidebar

Download PDF
Published: Nov 1, 2023
DOI: https://doi.org/10.2224/sbp.12705
Issue: Vol. 51 No. 11 (2023)
Keywords:
mobile online-to-offline service users, continuance usage intention, commitment, habit, attractiveness of alternative vendors

Understanding mobile online-to-offline service users’ continuance usage intention: An integrated model and empirical study

Main Article Content

Fang Wang

School of Business Administration, Tongling University, and Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology

Wengjuan Mei

School of Management, Jinan University
Cite this article:  Wang, F., & Mei, W. (2023). Understanding mobile online-to-offline service users’ continuance usage intention: An integrated model and empirical study. Social Behavior and Personality: An international journal, 51(11), e12705.


Abstract
Full Text
References
Tables and Figures
Acknowledgments
Author Contact

Drawing upon commitment–trust theory and the investment model, this study constructed a research model for understanding the mechanism of mobile online-to-offline (O2O) service users’ continuance usage intention. Results from a survey of 299 O2O users in China revealed that users’ trust was positively related to their commitment, habit, and continuance usage intention, and negatively related to the perceived attractiveness of alternative vendors. Further, users’ commitment was positively related to their habit and continuance usage intention, and negatively related to the attractiveness of alternative vendors. Finally, users’ habit was positively correlated with their continuance usage intention, and the attractiveness of alternative vendors was negatively correlated with continuance usage intention. This study provides a new perspective inspired by the investment model to understand the antecedents of continuance usage intention of mobile O2O services.

Article Highlights

  • We found dual paths, comprising existing relationships (habits) and alternative relationships (attractiveness of alternative vendors), which expand understanding of the logical chain between commitment and continuance usage intention.
  • Continuance usage intention was influenced by users’ trust and commitment via users’ habit and the attractiveness of alternative vendors.
  • Our findings have expanded the application field of commitment–trust theory and the investment model.

Online-to-offline (O2O) services refer to a business combination model that comprises both online and offline platforms (Shen et al., 2019). This business model effectively gathers buying groups, making the internet a front desk for offline transactions and realizing the conversion of online users to offline consumers (Cho et al., 2019). O2O commerce is location-oriented and focuses on local service industries (Yao et al., 2022). Furthermore, mobile technologies enhance the prevalence of O2O (Lee et al., 2022). The development of O2O commerce based on location is inextricably linked to the characteristics of mobile devices, which are more conducive to O2O commerce because mobile technologies can locate consumers in real time, thus achieving accurate recommendation capabilities (Lee et al., 2022).

However, the exposure of phenomena such as eavesdropping, when a mobile application maliciously accesses and uses the microphone on a device to listen in on conversations without the user’s consent (Kröger & Raschke, 2019), has led to frequent leakage of users’ privacy data, triggering a crisis of trust among long-term customers (Lee et al., 2022; Wang, 2019). Although the attributes of mobile devices are conducive to the development of O2O commerce, this privacy vulnerability inhibits users’ continuance usage behavior of application programs. On the basis of the behavioral paradox, according to which mobile O2O commerce consumers want to obtain better personalized recommendations by disclosing private information, but also worry about privacy leakage, this study explored the impact of users’ trust and the mechanism of this trust on the continuance usage intention of mobile O2O commerce in the context of local life services.

Scholars have investigated users’ continuance usage behavior of information systems and mobile application services involving various types of mobile commerce, such as mobile healthcare (Chen et al., 2018) and mobile social networks (Hew et al., 2016). However, research on continuance usage of mobile O2O commerce is scant and has generally been focused on the adoption or initial usage behavior stages (Roh & Park, 2019). Furthermore, most existing literature on mobile commerce continuance usage has been conducted with theories such as the theory of planned behavior, which proposes that behavioral intentions are influenced by three related factors: attitude, subjective norm, and perceived behavioral control (Ajzen, 1991); immersion theory, which explains why people become fully engaged in a situation, concentrate, and filter out all irrelevant perceptions when performing certain daily activities, thus entering a state of immersion (Csikszentmihalyi, 2000); the expectation confirmation model, which proposes that consumers will be satisfied with a product or service based on the comparison of prepurchase expectation and postpurchase performance, and satisfaction becomes a reference for repurchase intention (Oliver, 1980); and the unified theory of acceptance and use of technology, which proposes that user acceptance and usage behavior are affected by performance expectations, convenience, social impact, and effort expectations (Venkatesh et al., 2003). In short, although scholars have begun to explore mobile O2O adoption behavior, the study of continuance usage behavior in this setting is still in its infancy. Furthermore, relevant research has mostly used traditional information system continuance usage research models as the theoretical lens. To this end, this study constructed a model of mobile O2O users’ continuance usage based on commitment–trust theory and the investment model, and sought to reveal the mechanism of trust’s effect on users’ continuance usage intention.

Specifically, commitment–trust theory (CTT; Morgan & Hunt, 1994) proposes that commitment and trust are the crucial elements of relationship construction and consolidation. The investment model suggests that under conditions of high satisfaction, poor substitute options, and high investment, commitment will be strengthened and the relationship will continue (Rusbult, 1980). The investment model explains the mechanism of choice trade-offs between existing and alternative relationships, and is therefore appropriate for analysis of the problem of maintaining the relationship between users and mobile O2O platforms in this study. Moreover, with the gradual maturity of the mobile O2O industry, users’ evaluation criteria for applications tend to be rational and consistent, such as in the dimensions of information quality, interface design, and service quality, and as similar service providers become more homogeneous, users’ satisfaction as a consideration factor is of little significance (Sharma et al., 2023; Tam et al., 2020). Users continue to use a mobile service provider as a habit of maintenance (Sharma et al., 2023; Tam et al., 2020), so we took users’ habit as the variable representing the perspective of existing relationships, and took the attractiveness of alternative vendors as the variable representing the perspective of alternative relationships. According to the above theoretical analysis, this study constructed a research model to empirically test the antecedents of mobile O2O continuance usage intention.

The Effect of Users’ Habit

Habit is defined as the degree of learned continuous behavior that occurs spontaneously under the unconscious control of individuals (Limayem et al., 2007). Once a habit is formed, the behavior is performed automatically (Orbell et al., 2001). In the case of mobile O2O services, once users form habits from past repeated use, continued use is more likely to avoid the uncertainty caused by switching costs. As the number of uses increases, it gradually develops into unconscious and spontaneous habitual behavior. Furthermore, empirical evidence has found a positive effect of users’ habit on continuance usage intention (Sharma et al., 2023; Tam et al., 2020). Thus, we proposed the following hypothesis:
Hypothesis 1: Users’ habit of using mobile online-to-offline services will be positively related to their continuance usage intention.

The Effect of the Attractiveness of Alternative Vendors

The attractiveness of alternative vendors refers to the appeal of a firm’s competitors to that firm’s customers, including the reputation, image, and service quality of competing firms, and it is negatively related to consumer repurchase intention (Jones et al., 2000). When other mobile O2O platforms with homogeneous price discounts and special offers appear, this may result in temporary, short-term user shifts or even a long-term or permanent loss of old customers. Furthermore, research has found that the attractiveness of an alternative social networking site service is positively related to users’ intention to switch to that alternative service (Xu et al., 2014), and that users’ intention to switch services is positively correlated with their perception of alternative vendors (Kuo, 2020; Zhang et al., 2013). Thus, we proposed the following hypothesis:
Hypothesis 2: The attractiveness of alternative vendors will be negatively related to online-to-offline service users’ continuance usage intention.

The Effect of Trust

Users’ trust in a mobile commerce platform is based on attitudes and beliefs formed from their past experiences, which, in turn, influence their willingness to continue using the platform. Trust may be a driver of commitment (Bansal et al., 2004; Morgan & Hunt, 1994), such that the higher the level of consumer trust, the stronger is the relationship of dependence on and commitment to the platform (Brown et al., 2019), and the less attractive alternative vendors are to users. During the initial adoption phase of an information system, consumer behavior is influenced primarily by rational perceptions, and individuals’ assessment of their cognitive perceptions plays a key role in their subsequent behavioral intentions (Jasperson et al., 2005). Once consumers build trust in online retailers, they largely reduce their rational calculations of uncertainty and switching costs, which promotes persistence in consumer behavior (Gefen et al., 2003). Thus, we proposed the following hypotheses:
Hypothesis 3: Online-to-offline service users’ trust will be positively related to their commitment.
Hypothesis 4: Online-to-offline service users’ trust will be positively related to their habit.
Hypothesis 5: Online-to-offline service users’ trust will be negatively related to their perception of the attractiveness of alternative vendors.
Hypothesis 6: Online-to-offline service users’ trust will be positively related to their continuance usage intention.

The Effect of Commitment

Scholars are increasingly aware of the importance of commitment in predicting the continuous use of information technology in organizations (Barnes, 2011). Commitment can build a psychological bond between users and providers, and resist individuals chasing other, better entities, which helps to avoid making the current structural connection unstable. Therefore, commitment is a necessary condition for successful long-term relationships (Kim & Son, 2009; Meyer & Herscovitch, 2001). Such a psychological bond and fixed connection between users and mobile O2O service providers will further enhance users’ unconscious inner driving behavior, promoting habit formation. Some studies have also shown that commitment plays a mediating role in the relationship between favorable customer behaviors (e.g., repurchase intention) and prior commitment (Bettencourt, 1997; Garbarino & Johnson, 1999). We proposed that the more emotional investment or emotional dependence users have on a mobile O2O platform, the higher their commitment will be, and the more beneficial it will be to cultivate users’ habit of using the platform. Further, the less attractive alternative vendors are to users, the higher the continuance usage intention will be. Thus, we proposed the following hypotheses:
Hypothesis 7: Online-to-offline service users’ commitment will be positively related to their habit.
Hypothesis 8: Online-to-offline service users’ commitment will be negatively related to their perception of the attractiveness of alternative vendors.
Hypothesis 9: Online-to-offline service users’ commitment will be positively related to their continuance usage intention.

Method

Participants and Procedure

We recruited participants via Wenjuanxing (https://www.wjx.cn/), which is one of the largest online survey platforms in China. We received 299 valid questionnaires, with a 92.3% response rate. The sample included 123 (41.1%) men and 176 (58.9%) women, and 252 (84.3%) were aged between 18 and 35 years, with the rest being over the age of 35 years. Of the participants, 96.7% had a college education or above and 3.3% had a lower education level. The distribution of monthly average income was as follows: 13.4% earned less than RMB 1,000 (USD 139), 13.4% earned RMB 1,000–3,999 (USD 139–556), 21.1% earned RMB 4,000–5,999 (USD 556–834), 26.7% earned RMB 6,000–8,000 (USD 834–1,112), and 25.4% earned over RMB 8,000 (USD 1,112).

All procedures were performed in accordance with relevant institutional standards and the 1964 Helsinki declaration and its subsequent amendments or comparable ethical standards. Informed consent was obtained from all participants.

Measures

We translated established scales into Mandarin Chinese using a translation/back-translation procedure. Meituan (the largest O2O group purchase website in China; Li et al., 2019) was the context object in the questionnaire. Thus, we adjusted the translated scales to fit our research context. All items were scored on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.

Trust

We used a four-item scale adopted from Gefen et al. (2003) to measure trust. A sample item is “On the basis of my experience with Meituan in the past, I know it is honest.” Cronbach’s alpha was .84 in this study.

Commitment

We used a three-item scale adapted from Bansal et al. (2004) to measure commitment. A sample item is “I feel a strong sense of belonging to Meituan.” Cronbach’s alpha was .83 in this study.

Attractiveness of Alternative Vendors

We used a three-item scale adapted from Jones et al. (2000) to measure the attractiveness of alternative vendors. A sample item is “If I need to change apps, there are other good apps to choose from.” Cronbach’s alpha was .85 in this study.

Habit

We used a three-item scale adapted from Limayem et al. (2007) to measure habit. A sample item is “Using Meituan has become automatic to me.” Cronbach’s alpha was .84 in this study.

Continuance Usage Intention

We used a three-item scale adapted from Bhattacherjee (2001) to measure continuance usage intention. A sample item is “I intend to continue using Meituan rather than discontinue its use.” Cronbach’s alpha was .82 in this study.

 

Results

Preliminary Tests

We used Amos 18.0 software for confirmatory factor analysis. Results showed that the standardized loadings of each variable were higher than .70. The composite reliabilities of the trust, commitment, attractiveness of alternative vendors, habit, and continuance usage intention scales were .84, .84, .85, .83, and .82, respectively, indicating good convergent validity.

Furthermore, we tested the discriminant validity by comparing the correlation coefficients between the square root of average variance extracted (AVE) and the latent variable correlation. As shown in Table 1, the square roots of AVE were higher than the interconstruct values, indicating that the scale had good discriminant validity.

Table 1. Results of Correlation Analysis and Discriminant Validity Assessment

Table/Figure
Note. The square roots of the latent variable average variance extracted values are reported on the diagonal.
** p < .01.

Hypothesis Testing

We used maximum likelihood estimation to test the structural equation model. The model fit was good, chi square/degrees of freedom = 1.36, goodness-of-fit index = .95, adjusted goodness-of-fit index = .93, comparative fit index = .99, incremental fit index = .99, root mean square error of approximation = .035. As shown in Figure 1, 71% of the variance in users’ continuance usage intention was explained by the research model, indicating that the model had a good predictive effect and the variables significantly explained users’ continuance usage intention. Results showed that users’ habit, β = .159, p < .01, 95% confidence interval (CI) [0.049, 0.271], and the attractiveness of alternative vendors, β = −.271, p < .001, 95% CI [−0.406, −0.151], were positively related to continuance usage intention; thus, Hypotheses 1 and 2 were supported. Trust was positively related to users’ commitment, β = .732, p < .001, 95% CI [0.512, 1.016], habit, β = .474, p < .001, 95% CI [0.224, 0.748], and continuance usage intention, β = .302, p < .001, 95% CI [0.140, 0.473], and was negatively related to the attractiveness of alternative vendors, β = −.368, p < .001, 95% CI [−0.593, −0.218], supporting Hypotheses 3–6. Users’ commitment was positively related to their habit, β = .273, p < .001, 95% CI [0.108, 0.433], and continuance usage intention, β = .191, p < .001, 95% CI [0.080, 0.306], and was negatively related to the attractiveness of alternative vendors, β = −.274, p < .001, 95% CI [−0.441, −0.133]; thus, Hypotheses 7–9 were supported.

Table/Figure
Figure 1. Path Coefficient Estimation of Structural Equation Model
Note. ** p < .01. *** p < .001.

Discussion


Previous research has found that trust, habit, and the perceived attractiveness of alternative vendors are crucial antecedents of the continuance usage intention of mobile O2O commerce. Further, users’ trust and commitment are important to cultivate habit formation, and users’ trust and commitment affect the perceived attractiveness of alternative vendors. Different from existing studies that included habit as a moderating variable in the research model (Gwebu et al., 2014; Limayem et al., 2007), this study used habit to expand understanding of the logical chain between commitment and continuance usage intention.

Existing studies have found a positive relationship between users’ trust and continuance usage behavior in some mobile commerce contexts (Chen et al., 2018; Wang & Lin, 2017). This study supplemented the extant research by examining the context of mobile O2O business. In addition, previous studies paid more attention to verifying the influence of satisfaction and trust on users’ continuance usage intention (Chong, 2013; Hwang & Kim, 2018), while the mechanism of how trust and commitment influence users’ continuance usage intention remained underexplored. We found dual paths from habit and the attractiveness of alternative vendors to expand understanding of the logical chain between commitment and continuance usage intention. According to the CTT model, the attribute characteristics of mobile O2O can generate cognition and emotion for users, and we explored how trust and commitment affect consumer behavior. Thus, our findings also expand the application field of CTT.

Finally, little research has explored the long-term relationship between O2O users and platforms based on the investment model. This study has filled this gap in the literature. In essence, users’ continuance usage intention is their behavioral preference after choosing between the existing relationship and an alternative relationship. Therefore, the investment model may be applicable to the study of users’ intention to continue using a service, and our research conclusion confirms the rationality of this idea, providing a new perspective for future research on the continuous use of mobile O2O business and information systems.

As regards practical implications, cultivating user habits by enhancing user trust and commitment is more conducive to promoting customer loyalty than is passively retaining users (increasing conversion costs), as greater user trust and commitment can weaken the perceived attractiveness of alternative vendors and prevent users from shifting platforms. Mobile O2O systems should, thus, focus on these areas to increase customer retention and usage intention.

This study also has some limitations. First, our respondents were all from China. Future research could recruit participants from different cultures to enhance the robustness and universality of the results (Hofstede et al., 1990). Second, a longitudinal study could be considered in the future to better reflect the dynamic nature of users’ intentions and behavior in different periods. Third, we focused on exploring the pairwise relationships between our core variables, and future research could further explore potential mediating effects in this model.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
 
Bansal, H. S., Irving, P. G., & Taylor, S. F. (2004). A three-component model of customer to service providers. Journal of the Academy of Marketing Science, 32(3), 234–250.
 
Barnes, S. J. (2011). Understanding use continuance in virtual worlds: Empirical test of a research model. Information & Management, 48(8), 313–319.
 
Bettencourt, L. A. (1997). Customer voluntary performance: Customers as partners in service delivery. Journal of Retailing, 73(3), 383–406.
 
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.
 
Brown, J. R., Crosno, J. L., & Tong, P. Y. (2019). Is the theory of trust and commitment in marketing relationships incomplete? Industrial Marketing Management, 77, 155–169.
 
Chen, Y., Yang, L., Zhang, M., & Yang, J. (2018). Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. International Journal of Medical Informatics, 116, 33–45.
 
Cho, M., Bonn, M. A., & Li, J. J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108–116.
 
Chong, A. Y.-L. (2013). Understanding mobile commerce continuance intentions: An empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22–30.
 
Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. Jossey-Bass.
 
Garbarino, E., & Johnson, M. S. (1999). The different roles of satisfaction, trust, and commitment in customer relationships. Journal of Marketing, 63(2), 70–87.
 
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
 
Gwebu, K. L., Wang, J., & Guo, L. (2014). Continued usage intention of multifunctional friend networking services: A test of a dual-process model using Facebook. Decision Support Systems, 67, 66–77.
 
Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? Computers in Human Behavior, 59, 142–154.
 
Hofstede, G., Neuijen, B., Ohayv, D. D., & Sanders, G. (1990). Measuring organizational cultures: A qualitative and quantitative study across twenty cases. Administrative Science Quarterly, 35(2), 286–316.
 
Hwang, S., & Kim, S. (2018). Does mIM experience affect satisfaction with and loyalty toward O2O services? Computers in Human Behavior, 82, 70–80.
 
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.
 
Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000). Switching barriers and repurchase intentions in services. Journal of Retailing, 76(2), 259–274.
 
Kim, S. S., & Son, J.-Y. (2009). Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly, 33(1), 49–70.
 
Kröger, J. L., & Raschke, P. (2019). Is my phone listening in? On the feasibility and detectability of mobile eavesdropping. In S. Foley (Ed.), Proceedings of the Data and Applications Security and Privacy XXXIII: 33rd Annual IFIP WG 11.3 Conference (Vol. 11559, pp. 102–120). Springer International Publishing.
 
Kuo, R.-Z. (2020). Why do people switch mobile payment service platforms? An empirical study in Taiwan. Technology in Society, 62, Article 101312.
 
Lee, P. T. Y., E, F., & Chau, M. (2022). Defining online to offline (O2O): A systematic approach to defining an emerging business model. Internet Research, 32(5), 1453–1495.
 
Li, X., Li, Y., & Cao, W. (2019). Cooperative advertising models in O2O supply chains. International Journal of Production Economics, 215, 144–152.
 
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.
 
Meyer, J. P., & Herscovitch, L. (2001). Commitment in the workplace: Toward a general model. Human Resource Management Review, 11(3), 299–326.
 
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38.
 
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469.
 
Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31(1), 31–47.
 
Roh, M., & Park, K. (2019). Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, 47, 262–273.
 
Rusbult, C. E. (1980). Commitment and satisfaction in romantic associations: A test of the investment model. Journal of Experimental Social Psychology, 16(2), 172–186.
 
Sharma, S., Cheng, Y., & Sharma, P. (2023). Determinants of continuous use intention of news apps in India: Towards an integrated model. International Journal of Human–Computer Interaction, 39(12), 2450–2467.
 
Shen, C., Chen, M., & Wang, C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media. Computers in Human Behavior, 101, 474–483.
 
Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile apps: Extending the expectation confirmation model. Information Systems Frontiers, 22, 243–257.
 
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
 
Wang, E. S.-T. (2019). Role of privacy legislations and online business brand image in consumer perceptions of online privacy risk. Journal of Theoretical and Applied Electronic Commerce Research, 14(2), 59–69.
 
Wang, E. S.-T., & Lin, R.-L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2–10.
 
Xu, Y. C., Yang, Y., Cheng, Z., & Lim, J. (2014). Retaining and attracting users in social networking services: An empirical investigation of cyber migration. The Journal of Strategic Information Systems, 23(3), 239–253.
 
Yao, P., Osman, S., Sabri, M. F., & Zainudin, N. (2022). Consumer behavior in online-to-offline (O2O) commerce: A thematic review. Sustainability, 14(13), Article 7842.
 
Zhang, R., Chen, J. Q., & Lee, C. J. (2013). Mobile commerce and consumer privacy concerns. Journal of Computer Information Systems, 53(4), 31–38.

Table 1. Results of Correlation Analysis and Discriminant Validity Assessment

Table/Figure
Note. The square roots of the latent variable average variance extracted values are reported on the diagonal.
** p < .01.

Table/Figure
Figure 1. Path Coefficient Estimation of Structural Equation Model
Note. ** p < .01. *** p < .001.

The datasets used in this research are available upon request from the corresponding author.

This research was supported by the Anhui Provincial Natural Science Foundation General Project (2108085MG246), The Open Fund of Key Laboratory of Anhui Higher Education Institutes (CS2023-02), and the Humanities and Social Sciences Project of Anhui Province's Universities (SK2020ZD41).

Wengjuan Mei, School of Management, Jinan University, No. 601, Huangpu Avenue West, Tianhe, Guangzhou, People’s Republic of China. Email: [email protected]

Article Details

© 2023 Scientific Journal Publishers Limited. All Rights Reserved.