Keywords

Information and Communication Technology, electronic commerce, consumer happiness, artificial intelligence-enabled features, Search Engine Marketing, WEBQUAL, Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, usability, information quality, service interaction quality, virtual try-ons, chatbots, electronic satisfaction, online fashion enterprises, India.

Introduction

The Information and Communication Technology (ICT) revolution and the advent of the internet are significantly altering consumer shopping and purchasing behaviours. E-business and e-retailing are instigating a paradigm shift in online buying, compelling organisations to perpetually enhance their business operations and innovate new business models to attain distinction and competitive advantage in the marketplace. One of the principal domains of transformation and advancement is website design. The swift progress in technology and AI interventions in e-retailing has compelled businesses to recognise the necessity of customer-centric features and services. Therefore, comprehending client wants and cultivating a personalised relationship is essential for the e-commerce success of an organisation. Contented clients propel the firm and foster enduring partnerships (Anderson, 1996; Hallowel, 1996). Rehman and Khattak (2010) assert that satisfied customers constitute the most significant asset for any firm and play a strategic role inside any organisation. This constitutes e-satisfaction in the digital domain, retaining equivalent meaning therein.

Literature Review

Technology-Enabled Retail in India

The expansion of ecommerce over the past decade, particularly due to the pandemic, has resulted in a significant transformation for both customers and retailers. E-retailing in apparel and fashion is experiencing significant growth, with projected sales increasing from $481.2 billion in 2018 to $712.9 billion by 2022, reflecting a consistent annual growth rate (Orendorff, 2018). The retail industry has undergone a transition due to the significant impact of technology on its digitalisation. Smartphones and intelligent technologies have enabled consumers to own elevated expectations about service and satisfaction. Bhagat et al. (2022) investigates the determinants affecting the practical application of AI and its influence on customers' online purchase intention (OPI). In her blog regarding the future of fashion, Johnson (2019) elucidates the significance of AI interventions as a crucial element in delivering the service. Enhanced functionalities including (i) virtual fitting with mix-and-match options, and (ii) round-the-clock customer service and personalised features utilising chatbots. This enhances productivity and aids in streamlining purchasing and product returns. Websites that utilise technology and AI enable consumers to save time in product searches and enhance their buying experience, ultimately bolstering consumer trust in e-tailing organisations. AI-enabled technology on websites simplifies numerous activities for users, including locating specific products, rapidly comparing characteristics, and obtaining comprehensive information about the products. Agarwal et al. (2022) examine the effects of integrating emerging technologies such as AI in the post-Covid19 era and the transformative influence of these intelligent technologies on enterprises. The authors examine 127 empirical papers spanning several sectors, including healthcare, manufacturing, retail, food services, education, media, entertainment, banking and insurance, and travel and tourism. The authors delineate 39 categories of intelligent technologies, encompassing AI and computer vision technology. Overall, AI currently impacts online shopping across the entire value chain of the organisation. Artificial Intelligence is a nascent phenomenon in this context, resulting in the majority of studies concentrating on technological dimensions rather than its applications and effects on business operations.

TAM and UTAUT

The Technology adoption Model (TAM) elucidates user adoption of technology concerning information systems (Gefen, Karahanna & Straub, 2003) and online purchasing intentions. The Unified idea of Acceptance and Use of Technology (UTAUT, Venkatesh et al. 2003), sometimes referred to as TAM2, expands the idea to encompass the technology underlying the features utilised on a website. UTAUT pertains to individual qualities, including personality traits such as technology readiness and technology acceptance, which influence purchase intentions and behaviours.

In examining the determinants of website usage levels and rates in the Netherlands, van der Heijden (2003) notes that the purchasing behaviour of Dutch online consumers was shaped by their assessment of the website utilised for shopping. Therefore, the usability features and design of a website are critical, and it is essential that vendors pay particular attention to this aspect. Wixom and Todd (2005) assert that user happiness and the Technology Acceptance Model (TAM) can and ought to be amalgamated. Moon and Kim (2001) experimentally establish a novel measure termed 'perceived playfulness' while examining existing variables based on earlier research on TAM, which enhances our comprehension of individual acceptance behaviour on the World Wide Web. This pertains to the hedonic dimension or the "Pleasure of Browsing," which can also affect online purchasing intention (OPI) and electronic contentment.

Electronic Satisfaction

Anderson & Srinivasan (2003) define e-satisfaction as "the satisfaction or contentment of customers regarding their prior purchase experience with an online shopping site." OCE encompasses the entire customer journey on a website (Zomerdijk & Voss, 2010). E-satisfaction encompasses the satisfaction derived from information quality and the satisfaction associated with website features and usability. This indicates that e-satisfaction is the aggregate of system satisfaction and information satisfaction. The system satisfaction component is based on attributes including page loading speed, website telepresence technology, navigability, security features, and personalisation. A system that fails to deliver accurate, timely, and well-organised information does not achieve user satisfaction (Kim, Kishore, and Sanders, 2005). Thus, the physical architecture of a website is the principal factor influencing its usability. Information satisfaction is contingent upon information quality. This pertains to delivering pertinent information to the potential customer. A correlation exists between information satisfaction and performance expectancy, as well as between system satisfaction and effort expectancy (Wixom and Todd, 2005). The information provided by an e-commerce website aids users in product comparisons prior to purchases and is a critical determinant of user happiness with the site. Information satisfaction include relevance, comprehensibility, and consistency. Online purchasing offers personalisation, enabling clients to experience a tailored interface with direct connection (Murthi & Sarkar, 2003). This customised service is typically provided today by technologies known as chatbots. Telepresence is the establishment of a "touch and feel" realism within a virtual environment. Currently, numerous AI interventions, including recommendation systems, augmented reality, virtual size-fit features, virtual try-ons, 3D graphics, and chatbots, are employed on websites to enhance usability and engagement.

WebQual

WebQual is a method for quantifying the dimensions and characteristics of a website's quality attributes based on user perceptions. The components of WebQual 4.0 can be utilised to assess e-satisfaction with an online website. The technology implemented on the website will also affect service quality and customer happiness. The three fundamental constructs of WebQual 4.0, as formulated by Barnes and Vidgen (2003), are: Usability Quality, Information Quality, and Service Interaction Quality.

Usability Excellence

Functionality Quality pertains to the design and functionalities of a website, implying that it should be user-friendly. Services such as expedited product search durations and diverse payment channels might diminish customer effort and enhance online purchasing intent. This pertains to system characteristics and is grounded in research from telemetrics and Human-Computer Interaction (HCI), which focusses on assessing website usability (Barnes & Vidgen, 2003). Favourable perceptions of the website and its content enhance online pleasure (e.g., Polites et al., 2012; Rodgers, Negash, & Suk, 2005).

Quality of Information

Muslimin et al. (2017) assert that the information quality construct delivers pertinent and timely information to consumers during online purchase. The proliferation of online content enables consumers to become informed, facilitating improved decision-making and enhancing happiness with their purchases.

Quality of Service Interaction

Service Interaction Quality derives from service quality in e-retailing and its influence on customer satisfaction. The calibre of engagement on a website directly correlates with the trust and empathy extended to a consumer in the digital realm. A website functions as a system that disseminates information, and the Technology Acceptance Model (TAM) serves as an appropriate theoretical framework for delivering this information to users (Gefen et al. 2003).

Personalisation of User Experience

Service personalisation is particularly advantageous in online buying contexts, as the user lacks direct interaction with store personnel. User personalisation pertains to the customised shopping lists generated from previous purchases, interactions with customer service via chatbots, and other individualised services, which can effectively diminish perceived risk and foster emotional trust in online retailers (Ball et al., 2004; Rajamma et al., 2007). This "service personalisation" is what online retailers strive for to boost e-satisfaction.

Necessity and Significance of the Research

The literature evaluation indicates the necessity and significance of this investigation. Recently, technology and AI underpin every website that facilitates online retailing. Technologically advanced websites enhance value for e-retailers; nevertheless, minimal study has been conducted to connect website design with customer pleasure. While traditional retail literature has extensively examined customer happiness measurement, Evanschitzky et al. (2004) contend that the investigation of dimensions and drivers of customer satisfaction in the context of e-commerce remains in its early stages.

The literature study indicates that e-satisfaction is a crucial factor that warrants examination within the technological setting. This study examines the interplay between technological aspects on a website and their effect on consumer satisfaction. Nearly all e-retailers, regardless of size, are employing various AI-enabled technologies to enhance their value chain. Today, a company's success hinges on its capacity to incorporate website design components that replicate the physical retail experience in product presentations on their webpages. This will elicit favourable experiences for the customer and enhance OPI and OCE. This enables them to disseminate information and maintain market relevance. The study gap examines the significance of technology in marketing within the online retail sector in India. Particularly within the Indian context, there exists a scarcity of studies examining the influence of technological attributes such as rotation and zoom functionalities, product reviews, size and fit specifications, ratings, virtual try-ons, and their effect on e-satisfaction. The influence of personalisation and tailored services through chatbots on e-satisfaction has been insufficiently examined, highlighting a notable gap in the research.

Research Methodology

Based on the need for the study, the following research questions have been proposed for the study:

RQ1: How does the different technology enable factors influence e-satisfaction

RQ2: What are the effects of different website features and AI interventions on e-satisfaction?

Research Objectives and Hypotheses

Based on these research questions, the following objectives have been framed.

Objectives of the Study

RO1: To evaluate the influence of usability quality attributes of a website on electronic satisfaction. ease of navigation, user-friendliness, and aesthetic appeal, constitutes necessary conditions; however, customer satisfaction is derived from features that facilitate rapid product comparisons and sizing tools for fashion apparel, enhancing the online shopping experience.

RO2: To evaluate the influence of information quality attributes of a website on electronic satisfaction.

Product reviews, integral to the Information Quality architecture, have emerged as a significant source of information for both customers and retailers, as well as producers. The substance and detail of a website influence the trust clients have in the information presented.

RO3: To evaluate the influence of online Service Interaction quality on E-satisfaction

This construct pertains to the service rendered by the website and the customer's assessment of the quality of that service, namely, e-service quality. E-S-QUAL was developed by Parasuraman et al. (2005). The findings of the present study indicate that the service quality variable significantly affects e-satisfaction, defined as customer contentment with e-commerce websites. The participants in this study believe that chatbots offer effective service engagement and facilitate communication.

RO4: To evaluate the influence of Personalisation Features on Electronic Satisfaction

This concept encompasses items pertaining to the personalisation component of purchased products and its impact on satisfaction levels. This architecture encompasses elements related to product reviews, online conversations, and product suggestions, all of which are currently enhanced by AI-driven machine learning algorithms that diminish operational response times on websites. Customised product recommendations are essential for consumer pleasure.

RO5: To evaluate the influence of Browsing Enjoyment on Electronic Satisfaction

This notion pertains to the emotional experience of internet buying rather than the practical aspects of convenience and efficiency. The hedonic value of internet purchasing pertains to the enjoyment derived from the experience itself (Kim & Forsythe, 2008b). The technology that supports the website facilitates these customised experiences. Research findings indicate that customers subsequently disseminate these experiences through social networks. This generates favourable word of mouth (WOM) for the products.

RO6: To evaluate the influence of Site Security Features on E-satisfaction

The term pertains to the degree to which site security influences consumers' online purchasing habits. The website's security features include payment alternatives and the safeguarding of financial information privacy.

Hypothesis Construction

As reiterated in the review of literature WEBQUAL 4.0 scale and constructs have been used along with some of the other constructs which have emerged after the pilot study and exploratory study done before the main study. To test the relationship between these variables and the dependent variable e-satisfaction, the following hypotheses have been framed and tested in this study.

H1: There exists is a positive relationship between website technology features and e-satisfaction.

Satisfaction towards an e-retailer’s website will have a positive effect on OPI. This will lead to several sub hypotheses such as:

Hypothesis H1a: “Usability Quality of the websites is significantly influencing the User E Satisfaction.”

Hypothesis H1b: “Information Quality” of the websites significantly influencing the User E Satisfaction.”

Hypothesis H1c: “Service Interaction Quality with the users in the website significantly influencing the User E satisfaction.”

Hypothesis H1d: “Personalization Features in the websites significantly influencing the User E satisfaction.”

Hypothesis H1e: “Fun of Browsing the websites is significantly influencing the User E satisfaction.”

Hypothesis H1f: “Website Security Features significantly influencing the User E satisfaction.”

Research Design

A pilot study involving 30 respondents was done to clarify the constructs and independent factors in this emerging field of study. Participants were administered a questionnaire alongside images of websites they often accessed and were familiar with. An extensive interview was performed to elicit their responses to the websites, accompanied by other enquiries. This cohort of responders encompassed individuals from diverse demographic characteristics, facilitating the selection of participants for the primary study. The pilot survey was conducted through personal interviews to assess the face validity of the questionnaire. Subsequently, the researcher consulted with data science professionals who supply algorithms for online fashion platforms to enhance their efficacy. Based on their feedback and insights from academic advisers, WEBQUAL constructs were employed for the primary study. The research sample was selected to accurately represent the demographic in question. Given that respondents will consist of individuals with internet access who purchase fashion products online and should reflect various age groups, economic levels, and geographical regions, judgement sampling was utilised. The criterion established was that the respondents ought to have engaged in internet shopping for fashion products recently. A survey including 10 straight closed-ended questions, including demographic enquiries, and 41 rating statements utilising a 5-point Likert scale was presented to the participants. The Likert Scale employed ranged from 5 for Strongly Agree to 1 for Strongly Disagree to assess respondents' levels of agreement with various issues.

The sample size formula for large populations utilised in this investigation is derived from Cochran (1963). The estimated necessary sample size with an accuracy level of 5%, with a confidence level of 95% and p=0.5, is 384. The formula employed for sample calculation was:

Where N= Size of the population; e = Margin of Error (Confidence Interval); z=Z - Score of Confidence Interval; p = standard error (0.5)

A Google Form was utilised to gather survey data by email and WhatsApp from approximately 450 respondents. Physical paper surveys were distributed to certain respondents. This yielded 450 completed questionnaires from Bangalore, Chennai, Hyderabad, and a few from Calicut and Cochin in Kerala. The gathered data was subsequently edited, processed, tabulated, and categorised for analysis. A total of 419 valid responses were obtained, upon which the data analysis was conducted.

Final Model of the Study

Based on the critical literature review to identify the gaps leading up to e-Satisfaction in the online retail fashion industry, we identify the variables such as Usability Quality, Information Quality, Service Interaction Quality, Personalization, Fun of Browsing and Site Security Features. This is shown in Figure 1 below:

Model of the Study

Data Examination and Findings

This section offers a concise overview of the sample and details the statistical tests conducted along with the results achieved. Data analysis utilised confirmatory factor analysis (CFA) (Hair & Black, 2010). After confirming the alignment between the model and the data, structural equation modelling (SEM) was conducted.

Exemplar Description

The demographic profile of the sample is presented in Annexure 1. Among the 419 responders.55% are male, while 45% are female. Forty-seven percent of the respondents own a professional technology degree, and twenty-two percent hold a postgraduate degree, indicating that the majority of the sample is highly educated. All respondents have engaged in online shopping, particularly for fashion items.

Data Analysis SPSS 22 and AMOS software are employed for data analysis and structural equation modelling (SEM). Confirmatory factor analysis (CFA) was performed to evaluate the measurement model, subsequently verifying the structural model and research hypotheses.

The model depicted above has six exogenous variables: Usability Quality, Information Quality, Service Interaction Quality, Personalisation Features, Fun of Browsing, and Site Security Features, with e-satisfaction as the endogenous variable. The six hypotheses delineated in the model (shown in Figure 1) were evaluated. The model's validity and reliability were assessed using Confirmatory Factor Analysis (CFA), and the results are presented in Table 1 below. The measurement model's efficacy lies in its specification of the relationship between observed variables and the latent construct (unobserved variables) in the study. The outcomes derived from the CFA are delineated below:

Table 1 Model (CFA)
	Construct Name	Construct	Composite Reliability	Average Variance Extracted	Maximum Shared Variance
		Loadings
UQ7	Usability Quality	0.732	0.904	0.576	0.391
UQ6		0.653
UQ5		0.779
UQ4		0.752
UQ3		0.755
UQ2		0.802
UQ1		0.826
IQ8	Information Quality	0.782	0.914	0.571	0.539
IQ7		0.753
IQ6		0.741
IQ5		0.74
IQ4		0.774
IQ3		0.758
IQ2		0.692
IQ1		0.8
SIQ6	Service Interaction Quality	0.794	0.903	0.61	0.594
SIQ5		0.773
SIQ4		0.768
SIQ3		0.844
SIQ2		0.675
SIQ1		0.822
PF4	Personalization Features	0.861	0.905	0.704	0.594
PF3		0.84
PF2		0.828
PF1		0.826
FBF5	Fun of Browsing	0.773	0.916	0.686	0.576
FBF4		0.867
FBF3		0.836
FBF2		0.862
FBF1		0.799
SSF4	Site Security Features	0.803	0.878	0.646	0.506
SSF3		0.852
SSF2		0.669
SSF1		0.875

The CFA table above reports the estimated values of construct loadings for all the statements of different dimensions of the website quality are greater than 0.7. The CR for the factors measuring the website quality is found to be greater than 0.7. From the table 2, we can see the values for the different constructs: (Site Security Features: CR=0.878, AVE = 0.646, Usability Quality: CR = 0.904, AVE = 576, Information Quality: CR= 0.914, AVE=571, Service Interaction Quality: CR= 0.903, AVE=0.610, Personalization Features: CR = 0.905, AVE=0.704 and Fun of Browsing: CR= 0.916, AVE =0.686).

Thus, it can be concluded that the measurement scale ensures the presence of convergent validity in the scale. Also, the construct loading shows which items have the maximum influence.

The discriminant validity of the measurement scale is evaluated using the Fornell-Larcker Criteria (1981), which assesses the extent of shared variation among the model's latent variables. The convergent validity of the measurement model can be evaluated by comparing the Average Variance Extracted (AVE) and Composite Reliability (CR) based on this criterion. It additionally contrasts the Average Variance Extracted (AVE) of each dimension of website quality with the Maximum Shared Variance (MSV) values.

The outcome of discriminant validity is presented in Table 2 above. The findings indicated that the diagonal value of the table, which signifies the square root of the AVE for each website quality factor, exceeds the correlation values given by the other entries in the column. The AVE estimate for each construct exceeds the MSV estimate. Consequently, it can be asserted that the measuring scale (website quality) is valid concerning convergent and discriminant validity.

Table 2 Fornell Lacker Criteria
	Site Security Features	Usability Quality	Information Quality	Service Interaction Quality	Personalization Features	Fun Of Browsing
Site Security Features	0.804
Usability Quality	0.39	0.759
Information Quality	0.562	0.625	0.756
Service Interaction Quality	0.693	0.597	0.718	0.781
Personalization Features	0.711	0.587	0.734	0.771	0.839
Fun of Browsing	0.655	0.465	0.675	0.725	0.759	0.828

Statistical Fitness Indices: Following the verification of convergent and discriminant validity of the measurement scale, the model's statistical fitness is subsequently assessed. The statistical fitness results of the model are presented in Table 3 below.

Table 3 above shows that the CMIN/Df index is 2.644 which is less than the required threshold value of 3. This value also known chi-square goodness.

Table 3 Statistical Fitness Indices
Statistical Fitness Indices	CMIN/Df	Goodness of Fit Index	Augmented Goodness of Fit Index	Comparative Fit Index	Tucker Levis Fitness Index	Normative Fit Index	Root Mean Squared Error Approximation
		(GFI)	(AGFI)	(CFI)	(TLI)	(NFI)	(RMSEA)
Estimated Value	2.644	0.83	0.803	0.918	0.911	0.876	0.063
Required Value	Less than 3	Greater than 0.8	Greater than 0.8	Greater than 0.9	Greater than 0.9	Greater than 0.8	Less than 0.08
Remark	Satisfactory	Satisfactory	Satisfactory	Satisfactory	Satisfactory	Satisfactory	Satisfactory

SEM Model

The SEM approach was used to validate the research model. This approach was chosen because of its ability to test the causal relationship between constructs with multiple measurement items particularly related to technology and AI. The result of SEM analysis is shown in figure 2 below and discussed further.

The data shown in the table corroborated the hypothesis that "Usability Quality of the websites significantly influences User E Satisfaction" (path coefficient = 0.307, t-statistic = 6.159).The route coefficient reflecting the influence of website usability quality is determined to be positive and significant, exhibiting the greatest impact at a 5% significance level. The covariance values in the SEM indicate that the usability quality of the website affects its information quality. It may be deduced that technology-related elements, such as product comparisons and sizing and fit tools utilised on the website, boost usability and hence influence the e-satisfaction of online customers. The data presented in Table 4 indicates that all hypotheses from H1a to H1e are supported at a 5% significance level, whereas only H1f concerning Site Security Features lacks statistical support.

Table 4 Structural Model Results
Hypothesis	Endogenous Construct	Exogenous constructs	Path Coefficient	S.E.	T stats	P	R Square	Remark
H1a	User	Usability Quality	0.307	0.062	6.159	***	71%	Supported
H1b	E-Satisfaction	Information Quality	0.15	0.068	2.576	0.01		Supported
H1c		Service Interaction Quality	0.162	0.066	2.421	0.015		Supported
H1d		Personalization Features	0.175	0.068	2.397	0.017		Supported
H1e		Fun of Browsing	0.142	0.062	2.421	0.015		Supported
H1f		Site Security Features	0.064	0.064	1.195	0.232		Not Supported

Analysis and Managerial Consequences

This study examined the influence of website design and technological factors on the satisfaction of online customers. Despite the proliferation of e-retailing in India, researchers have mostly overlooked the examination of online shopping behaviour. This research enhances the understanding of shopping and consumer purchasing behaviour in the internet environment. The study utilising the SEM approach identifies the aspects influencing customer e-satisfaction on e-commerce websites as usability quality, information quality, and service interaction quality. The elements of personalisation features and the enjoyment of surfing influence the Online Customer Experience (OCE) and electronic satisfaction. All hypotheses, with the exception of the site security elements, are statistically validated by the study's results. The research indicates that website features and the underlying technology facilitate customer selection and purchasing of preferred products while also offering a hedonic value associated with the enjoyment of shopping and browsing the site.

In the realm of online retail, cultivating e-satisfaction is a difficulty, however it is crucial for online companies to distinguish themselves amidst competition. E-satisfaction leads to decreased expenses and enhanced client retention and repeat transactions. E-retailers must offer online functionalities, comprehensive information, and exceptional service to remain pertinent in today's competitive landscape. The performance indicators that affect overall quality are the website's usability elements that ensure a favourable user experience. Secondly, the attributes that furnish clients with comprehensive information (Information Quality) to enhance their comfort level. The study's findings indicate that the hedonic element of "fun of browsing" is significant for customers, as the enjoyment derived from browsing prolongs website engagement and directly influences sales. Website characteristics facilitate customer shopping while also offering a fundamental hedonic value of joy through convenience.

Gaps & Future Scope

The research was confined to Bangalore city and adjacent states, hence the sample may not accurately represent the entire population of internet buyers in India. This study exclusively examined the B2C dimension of online buying, and outcomes may vary in other formats such as B2B and C2C. The future topic of study encompasses two facets: the influence of AI and its sustainability in online fashion shopping. Conversations with fashion industry professionals and data scientists have confirmed that online retailers are increasingly interested in AI and technological interventions to enhance both utilitarian and hedonic value for their customers. Currently, Indian websites lack advanced technologies; however, the introduction of 5G will swiftly alter this landscape. A prospective research domain may investigate the influence of technologies such as augmented reality, avatars, and virtual reality on electronic pleasure, which remains unexamined in this study.

Furthermore, regarding website design and WebQual 4.0 components, longitudinal studies can be conducted on prominent websites such as Amazon, Flipkart/Walmart, or Myntra to examine the impact of technology on delivery, after-sales service, and customer interaction strategies that enhance the website. The WebQual structures can also be evaluated across several domains, including e-government, online tax submission, and education.

This study was conducted in the urban centres of Bangalore, Chennai, and Cochin; however, additional research might be undertaken in rural areas to establish a comparative model of online purchasing behaviour between urban and rural consumers.

Conclusion

This study has formulated and evaluated a theoretical model utilising SEM analysis. The study indicates that consumers with greater familiarity with the technological features of the website experience enhanced delight while purchasing online. The analysis based on the WEBQUAL 4.0 scale indicates that increased consumer satisfaction with the features and usability of the online retail site correlates with higher e-satisfaction scores and greater purchase intention. Consequently, online retailers ought to integrate various components into their websites according to their target demographic. It must be informational, user-friendly, and simultaneously engaging and entertaining. E-retailers should incorporate product visualisation techniques, models, pictures, and videos on their websites to enhance the "Visualise and Feel" component of the site.

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Received: 04-Nov-2024, Manuscript No. AMSJ-24-15421; Editor assigned: 05-Nov-2024, PreQC No. AMSJ-24-15421(PQ); Reviewed: 20-Dec-2024, QC No. AMSJ-24-15421; Revised: 26-Jan-2024, Manuscript No. AMSJ-24-15421(R); Published: 05-Feb-2025