Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1528 https://internationalpubls.com To Study the Influence of Web Atmospherics on Customer Purchasing Behaviour with Respect to Fashion Websites Gagandeep Kaur1, Kamalpreet Kaur1 1 CT University, Punjab, India Article History: Received: 12-12-2024 Revised: 25-01-2025 Accepted: 05-02-2025 Abstract: Today is a digitalisation era and many of us have access to smartphones and the Internet. E-businesses are developing day by day due to many factors like acceptance of new technologies, increasing the young population, increasing standard of living, and increasing usage of smartphones. Day by day people are using smartphone apps to order clothes online to save time and crowd time. This research article's primary objective is to examine influence of web atmospherics on customer purchasing behaviour with respect to fashion websites Using a structured questionnaire, non-probability convenience sampling was used to perform the study. The various constructs measuring the web atmospherics were identified through EFA and then descriptive analysis was conducted for the statements of the web atmospherics. As it represents the psychological behaviour of the respondents toward the web atmosphere and their presence in the purchase behaviour. Keywords: Website Atmospherics, Online Shopping, Website Cues, Purchasing Behaviour. 1. INTRODUCTION: The Internet offers great opportunities for everyone as it allows businesses to survive in cyberspace and even benefit from connecting people around the world regardless of geography. Today, users can order services or goods online anytime, anywhere. In marketing, the term atmospherics is used to describe the discipline of designing commercial spaces. Atmospherics was coined by Philip Kotler in a 1973 article in the Journal of Retailing.(4)Atmospherics covers three major forms that are related to retail architecture: interior architecture, exterior structure, and design of window displays. The atmospherics of that place helps in creating attention, attracts the customer, and affect its decision-making process. Atmospherics are defined as ‘‘the deeply planning of an area to form purchaser effects, specifically, the planning of purchasing environments to induce specific connectional effects within the purchaser that enhance purchase chance.” (Kotler, 1973–74, p. 50) Online shopping is becoming a new distribution channel for retailers. Online retailing allows the constraints of time and space to disappear (Kalakota and Whinston (1997). Online shopping has been affected by same psychological and behavioural qualities as the traditional store environment. In the traditional retail store, certain aspects of the physical environment have been shown to impact buying behaviour. It has been seen those similar aspects such as satisfaction, time spent in the online store, and the amount purchased are all influenced by online https://en.wikipedia.org/wiki/Marketing https://en.wikipedia.org/wiki/Philip_Kotler https://en.wikipedia.org/w/index.php?title=Journal_of_Retailing&action=edit&redlink=1 https://en.wikipedia.org/w/index.php?title=Journal_of_Retailing&action=edit&redlink=1 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1529 https://internationalpubls.com web atmospherics (Eroglu, Machleit, & Davis, 2001).Web atmospherics play an important role in impacting decisions of customer while shopping online. It had a significant influence on consumer choice (Mandel & Johnson, 2002). It is found that by manipulating online atmospheric variables, such as colour, images, and background consumer choice is affected. The development of the Internet and associated technologies has had far-reaching effects on people all around the world. The proliferation of online marketplaces has had far-reaching effects on the marketing industry. Products and services are available around the clock, seven days a week, regardless of location or time of day. Despite the fact that many marketers recognise the need to include the Internet in their marketing mixes, there has been little empirical testing of the essential factors that affect an individual's decision when buying things or services online. 2. REVIEW OF LITERATURE: Kim et al (2009) investigated the effects of web atmospherics on consumer responses. Music and product presentation are two main elements of web atmospherics that have been taken into consideration. The findings of this study are that product presentation has a great positive impact on consumer emotional cognitive and conative responses but music has no effect on customer responses. Results provide an indication that website cues can be used to improve emotional responses that ultimately affect shopping response. Broekhuizen and Huizingh, (2009) stated that making web designs and portals novel and sophisticated and web atmospherics friendly is a key to attracting visitors. Moreover, if online stores want to convert visitors into buyer, they should improve their website by offering customers a comfortable, logical, interesting and hassle-free process and easy language by creating a fast website with a functional design as smooth as possible. Bjork (2010) identified link between atmospherics and emotional response on tour operators’ websites. This research article identifies emotion-stimulating website features that stimulate an emotional response. These features affect cognitive and affective responses which are both independent and integrated systems. The important atmospherics are pictures and information content and its structuring. Results indicate that all these website features are involved in the decision-making process. Vaiciukynaite (2012) studied the impact of website atmospherics on consumer emotions and behaviour. The results indicated a substantial effect on consumer emotions because of website atmospherics which leads to approach behaviour, which rests on information content, text and graphical elements of a website. The PAD scale is used when the research is focused on the measurement of the dimensions of the emotional states of consumers. Study has used the stimulus- organism-response (S-O-R) model, which is divided into three parts: stimulus, organism and responses. web site atmospherics consist of three dimensions context, content, communication dimension, which will act as stimuli and these stimuli affect the consumer emotions (organism) and that ultimately result in consumer behaviours (Approach/Avoidance). Kawaf (2012) explained the shopping experience of online shoppers. Consumers are the best source to know about consumers. This research has used both quantitative and qualitative analysis and the results show that high-quality 3D images and catwalk videos are two main dominant factors that affect Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1530 https://internationalpubls.com purchasing decisions. Putting images and videos in web atmospherics generates positive responses. It has used the SOR framework to study the online shopping environment. Mazaheri et al. (2013) examined how emotions and website atmospheric cues affect service tangibility and consumer attitudes. The proposed model compared three cultures: North America (Canada and U.S.), China, and the Middle East. the outcomes propose how the influences of two emotional dimensions (pleasure and dominance) have effect on consumer perceptions of site atmospherics. Furthermore, the effects of service tangibility dimensions on consumers' attitudes vary significantly across the three cultures. Vignali et al. (2014) studied that by Analysing online shopping attributes can influence consumer purchasing for fashion products Now fashion industry is stepping into digitisation. With the usage of internet, online websites play an important role for attracting customers. There are various attributes to choose from, such as e-service quality, website design, interactivity, order status tracking search engine and so on. Each of the attributes performs a particular function and is different from the rest of the attributes within online shopping. Online atmospherics and social media are the major factors that affects consumer behaviour. Hsieh et al. (2014) focused majorly on the ignored role of perceived dominance. In online shopping perceived dominance influences purchase intentions both directly and indirectly through pleasure. The impact of perceived dominance on pleasure is moderated by situational involvement. This research after taking into consideration moderating effect of involvement offers a more detailed explanation and explains the factors which influence online customers’ perception of dominance. Gao and Bai (2014) investigated connection relationship of website atmospherics cues and flow experience. Using the SOR model this study examines the impact of consumer perceptions of website atmospherics cues on the development of flow and its further impact on purchase intention. This study focused on that proper web atmospheric design changes consumer shopping experience which increases favourable purchase intention. Kim et al. (2015) aimed to explore the relationship between atmospheric qualities of luxury fashion brand websites and their impact on consumer attitude, which is vital to building valid plans for e- retailing. An empirical study was conducted to explore the relationship and quantitative analysis done that confirm the impact of atmospherics on consumers’ attitudes. Ramoniene et al. (2016) studied these atmospheric variables: colour, layout, and graphics which demonstrated that all these atmospheric variables have an influence on the behaviour of buyer. The major objective was to govern the effect of website atmospheric variables on the emotional behaviour of the buyer, which ultimately impacts consumer behavioural response to online stores. 3. RESEARCH METHODOLOGY: A review of associated literature stimulates the research methodology chapter. This section intentions to present research methodologies and procedures, as well as the feasibilities of sampling design, recognizing variables, tool development, standardization of self-developed tools, and pilot study. This research looked at satisfaction level of people in Ludhiana, Jalandhar, Amritsar, And Patiala. Data analysis approaches, software tools, and methods are also inspected in this chapter. A practical and Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1531 https://internationalpubls.com unbiased stratified sampling procedure is used for the set of target populations. The self-developed instrument is delivered to individuals for data collection. The current study uses a descriptive exploratory research approach. Data were congregated from the self-developed tool associated with the factors affecting consumer behaviour towards fashion delivery apps in be Ludhiana, Jalandhar, Amritsar, And Patiala. The results of the findings were developed through data analysis, followed by the conclusion of the study. 3.1 Research Objectives: The objective of the research is to examine the influence of web atmospherics on customer purchasing behaviour with respect to fashion websites 3.2 RESEARCH DESIGN: The design of the survey is based on descriptive, exploratory scrutiny of the population. Data was assembled utilizing the primary data set via the distribution of the tool. The present study analyzed the level of satisfaction and loyalty among customers towards food delivery apps in Ludhiana, Jalandhar, Chandigarh, and Patiala. In this research, stratified random sampling was used to obtain primary data. Primary data contain a well-constructed tool for the level of satisfaction and loyalty among customers towards fashion delivery apps 3.3 Research Hypothesis: H1: Web atmospherics (WA) has positive and significant influence on the creating value (VU) of fashion websites for better customer purchase behaviour H2: Web atmospherics (WA) has positive and significant influence on the creating variety (VR) of fashion websites for better customer purchase behaviour H3: Web atmospherics (WA) has positive and significant influence on the creating Experience (EX) of fashion websites for better customer purchase behaviour 3.4 Data Collection: It is improbable that the investigator would be able to gather data from all sources to respond to the research questions. A set of people known as sample is chosen from amongst the population. The sampling method used for directing this study is described as follows: • Target Population: The 1st step of the sampling technique is to recognize the target population. The group that an investigator is concerned in studying and evaluating that is known as the target population. A sample frame is then derived from the target population. • Sampling Frame: The population should be presented in the sampling frame. A sampling frame is a folder of potential respondents from which researchers might choose respondents to contribute in a study. • Sampling Technique: Sample selection is the procedure of choosing a subset of a population from a larger population. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1532 https://internationalpubls.com • Sample size: The sample size for this study will be 425 respondents. The sample area will be LUDHIANA, JALANDHAR, AMRITSAR, and PATIALA. (As per the census of 2011 these cities have the highest urban population in Punjab) • Collect Data: Data collection process is executed. The self-developed tool is administered to respondents In this study, the researchers evaluate the hypothesis based on the data obtained. The most important purpose of data collection is to collect informative and reliable data for statistical research, with research observations as the basis for the data. After a pilot study, the main data for this study was obtained through the questionnaire. Data processing, analysis, interpretation, and presentation play an important role in transforming raw data into meaningful presentations. Editing, computerization, coding, classifying open-ended questions, and the creation of diagrams and tables are required. Graphing, tabulating, categorizing, coding, editing, and diagramming study data are all elements of data processing. Based on data processing data reduction is done. After this screening and correcting of data, analysis is done. 4. Data analysis and Interpretation: Part A- Descriptive analysis of demographic variables In this segment of the chapter, all the descriptive questions were analysed and interpreted, including the demographic profile of the respondents. This will indicate about the general information and the preferences of the respondents toward the fashion products from the online websites. The detailed analysis has been presented below: Table 4.1 (demographic profile of respondents) Category Frequency (n=425) Per cent Gender Male 211 49.4 Female 214 50.6 Age (in years) Below 25 127 29.9 25-35 211 49.6 35-45 54 12.7 45-60 30 7.1 above 60 3 .7 Marital status Married 238 56.0 un-married 187 44.0 Qualification Illiterate 3 .7 Higher Secondary 16 3.8 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1533 https://internationalpubls.com Graduation 190 44.7 Post Graduation 214 50.4 Others 2 .5 Occupation Student 90 21.2 Housewife 48 11.3 Business/ Self Employed 174 40.9 Salaried Employee 110 25.9 Retired 3 .7 Region Ludhiana 123 28.9 Amritsar 98 23.1 Patiala 96 22.6 Chandigarh 108 25.4 Location Urban 415 97.6 Rural 10 2.4 Income Below 50,000 46 10.8 50,000-100,000 70 16.5 100,000 -200,000 133 31.3 above 200,000 176 41.4 Interpretation: The table highlighted the nature of the respondents in concern to their gender, location, education, marital status, occupation and income. It can be observed that both male and female have equally participated in the study. As the number of male respondents were 211, and females were 214. Indicating that the results of the study would be generalised to both the gender categories. While taking into account the majority of the respondents were from the age group of 25- 35, i.e. 211. While, 127 (29.9%) were below 25 age group. However, 54 (12.7%) were 35-45. And 30 (7.1%) were of age 45-60 and only 3 were from the age group of above 60. Further, taking into account the marital status of the respondents as highlighted above 238 (56.0%) of the respondents were belonging to the married category. While 44 % or 187 respondents were unmarried. Many studies have highlighted that qualification has a positive impact on the nature of purchase and the buying habits, thus in this study the response from every qualification category has been focused as it is highlighted in the above table that majority of the respondents 214 (50.4%) were having post graduate Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1534 https://internationalpubls.com qualification. While 190 (44.7%) were graduate. On the other hand, 3 (0.7%) were illiterate and 16 (3.8%) were having only higher secondary education. Viewing the occupation of the respondents, Majority of the respondents were self-employed as their number was 174 (40.9%), whereas 110 (25.9%) were salaried employees, 90 (21.2%) were students, 48 (11.3%) were housewives, and only 3 (0.7%) were retired personals. The above table presented the different region of residence of the respondents. It was found that majority of the respondents were belonging to the area of Ludhiana and Chandigarh as their number were 123 and 108. However, 98 respondents were from Amritsar and 96 were from Patiala. As many studies have quoted that urban population have the more interest and positive attitude toward the online purchase habits. Thus, in this study it was found that majority of the respondents who has participated were belonging to the urban area (415). Only 10 were from the rural area. As given above majority of the respondents 176 (41.4%) were earning more than 2 lakhs, while 133 (31.3%) were earning between 1-2 lakh. However, 70 (16.5%) were having the income range of 50k to 1 lakh and only 46 (10.8%) were among the income group of below 50k. thus, it indicated that almost all the income group have participated in the study. Which implies that the participation was sufficiently handled and organised. Part -B EXPLORATORY FACTOR ANALYSIS A statistical method known as exploratory factor analysis is used to condense data into a more manageable group of summary variables and to investigate the underlying theoretical framework of the phenomenon. It is employed to determine the nature of the link between the respondent and the variable. 5. RESULTS AND INTERPRETATION Principal component analysis was conducted as a means of data reduction, broadly speaking factor analysis provides the tools for analysing the structure of the interrelationship (Correlations) among a large number of variables by defining sets of variables that are highly inter-correlated, are assumed to represent dimensions within the data. Prior to performing PCA, the suitability of data for factor analysis was assessed by following means. Table 5.1: Cronbach’s Alpha Table 4.1: Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items .866 .867 14 Interpretation: it is the first step to perform the analysis of the variables, to measure the validity of the questions and the scale which have been used in the study, thus, without this no study can achieve the correct results. Cronbach's alpha was used to assess the internal consistency of the questionnaire, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1535 https://internationalpubls.com as it is in the majority of validation studies reported in the literature. Cronbach's alpha was 0.886 in this case, while an alpha value of 0.60 or higher is considered almost acceptable, and values above 0.80 are considered exceptional or good outcomes (Ocak Aktürk et al. 2021). As a result, the findings of the reliability statistics have exceeded the threshold for further factor analysis processing. Table 5.2: KMO & Bartlett test results Table 4.2: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .860 Bartlett's Test of Sphericity Approx. Chi-Square 3540.361 Df 91 Sig. .000 Interpretation: The KMO statistics determine whether the sample size that have been used in the study, is suitably determining the study and is suitable for measuring the constructs, it range from 0 to 1, with values closer to 1 denoting greater adequacy of the factor analysis (KMO ≥ 0.6 low adequacy, KMO ≥ 0.7 medium adequacy, KMO ≥ 0.8 high adequacy, KMO ≥ 0.9 very high adequacy). If the result of Bartlett’s test is < 0.05, factorial analysis can be used (Nievas Soriano BJ et al. 2020). For the present case the KMO value (.860) is above the acceptable limits & is highly adequate, while the results of bartlett’s test is < 0.05. Thus, it states that sample is adequate and suitable for the application of factor analysis. Table 5.3: RELIABILITY STATISTICS (Using communalities, corrected item total correlation, Cronbach’s alpha if item deleted and mean & standard deviation) Variables Communalities Corrected Item total correlatio n Cronbach’ s Alpha if item deleted Mean Std Dev. Initial Extracti on This site is informative for me 1.00 .589 .374 .866 4.89 1.381 This site has the information I need 1.00 .717 .550 .856 4.96 1.302 This site is useful / useful for me 1.00 .744 .537 .857 4.82 1.345 This site gives me knowledge 1.00 .794 .518 .858 4.85 1.398 Information on this website is convenient 1.00 .726 .491 .859 4.64 1.329 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1536 https://internationalpubls.com Information on this website is accurate 1.00 .613 .512 .858 4.62 1.248 Information on this latest website 1.00 .780 .500 .859 4.11 1.206 Information on this website is complete 1.00 .632 .496 .859 4.25 1.216 ‘Information on this website is relevant’ 1.00 .727 .506 .858 4.04 1.190 This website is fun to explore 1.00 .731 .547 .856 4.24 1.194 This website is interesting 1.00 .733 .579 .854 4.14 1.224 This website is imaginative 1.00 .720 .565 .855 4.19 1.204 This website is entertaining 1.00 .720 .555 .856 4.36 1.266 This website is striking 1.00 .769 .575 .855 4.99 1.368 Source: Primary Data Interpretation: The initial communalities represent the relation between the variables and all other variables (i.e., the squared multiple correlation between the items and all other items) before rotation. The value for communalities using principal component analysis ranged from .589 to .794. here it is admissible to acknowledge that communalities ≥ 0.4 is sufficient for the explanation of construct. All these values show factor analysis has extracted good quantity of variance in the items. Hence all the prerequisites of reliability, validity and one-dimensionality are met. Table 5.4: CORRELATION MATRIX OF ITEMS AND VARIABLES OF WEB ATMOSHPERICS Interpretation: This test indicates how each question is associated (Correlated) with each of the other questions. Note that some of the correlations are high and some are low. Relatively high correlations indicate that two items are associated and will probably be grouped together by the factor analysis. Items with low correlations usually will not have high loadings on the same factor. The mean 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1 2 .585** 1 3 .479** .614** 1 4 .526** .654** .787** 1 5 .132** .224** .244** .196** 1 6 .143** .247** .190** .186** .671** 1 7 .184** .229** .238** .194** .718** .546** 1 8 .132** .280** .220** .212** .514** .528** .637** 1 9 .131** .227** .256** .191** .622** .529** .731** .645** 1 10 0.082 .235** .203** .180** .148** .244** .138** .187** .151** 1 11 .162** .257** .280** .292** .149** .246** .122* .136** .139** .651** 1 12 .121* .219** .221** .193** .175** .222** .157** .205** .206** .633** .673** 1 13 .178** .259** .237** .218** .110* .224** 0.08 .170** .154** .642** .658** .653** 1 14 .134** .261** .230** .240** .127** .183** .131** .183** .166** .718** .679** .675** .678** 1 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1537 https://internationalpubls.com correlation is .309 and it varies from .071 to .807 with a range .736. Thus, there is sufficient correlation to go ahead with the factor analysis. Factor analysis is done using SPSS software with varimax rotated, Principal Component Analysis. Table 5.5: FACTOR ANALYSIS RESULTS (with Factor Loadings, Eigen Values, Percentage of Variance and Cronbach’s alpha for each factor) Varimax Rotated Results (Table: 4.5) WEB ATMOSPHEICS FACTORS Loadings Eigen values % Of Variance Cronbach’s Alpha F1. Website entertainment 5.152 36.799 0.927 The fashion website is fun to explore .845 ‘The more the fashion website imaginative, the more the attractive it is. .834 It is very entertaining to surf through the fashion website .835 The website I used for fashion product is striking .865 F2. Content Effectiveness 2.749 19.634 0.918 Information on the fashion website is accurate .757 Information on the fashion latest website .874 Information on the fashion website is complete .777 ‘Information on the fashion website is relevant .842 F3. Website informativeness 2.095 14.964 0.873 The fashion website is informative for me .763 The fashion website has the information I need .812 The fashion website useful / useful for me .835 the fashion website gives me knowledge .874 Source: Rotated Component Matrix (Primary data) Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1538 https://internationalpubls.com Interpretation: The above table shows all the extracted factors with their statements. Factor analysis is used to extract some common factors which describe the entire data set. Principal component analysis with a varimax rotation is applied. Components having eigen value greater than one (1) are retained. These Three factors explain 71.379% variance which is considered good. The statements with factor loadings greater than 0.5, are retained. the explanation of various factors emerged from factor solution relating to web atmospherics factors is given below. Table 5.5: FACTOR ANALYSIS RESULTS (with Factor Loadings, Eigen Values, Percentage of Variance and Cronbach’s alpha for each factor) Varimax Rotated Results WEB ATMOSPHEICS FACTORS Loadings Eigen values % Of Variance Cronbach’s Alpha F1. Website entertainment 5.152 36.799 0.927 The fashion website is fun to explore .845 ‘The more the fashion website imaginative, the more the attractive it is. .834 It is very entertaining to surf through the fashion website .835 The website I used for fashion product is striking .865 F2. Content Effectiveness 2.749 19.634 0.918 Information on the fashion website is accurate .757 Information on the fashion latest website .874 Information on the fashion website is complete .777 ‘Information on the fashion website is relevant .842 F3. Website informativeness 2.095 14.964 0.873 The fashion website is informative for me .763 The fashion website has the information I need .812 The fashion website useful / useful for me .835 the fashion website gives me knowledge .874 Source: Rotated Component Matrix (Primary data) Interpretation: The above table shows all the extracted factors with their statements. Factor analysis is used to extract some common factors which describe the entire data set. Principal component Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1539 https://internationalpubls.com analysis with a varimax rotation is applied. Components having eigen value greater than one (1) are retained. These Three factors explain 71.379% variance which is considered good. The statements with factor loadings greater than 0.5, are retained. the explanation of various factors emerged from factor solution relating to web atmospherics factors is given below. FACTOR 1. WEBSITE ENTERTAINMENT The first factor (website entertainment) accounted with the variance of 36.799, indicating that this factor can account for 37% change in the web atmosphere. While, the eigen value of 5.152 and Cronbach’s alpha value of 0.927, highlighted the relevance and reliability of the factor. Four statements were constructed in this factor with the factor loading ranging between .834 to .865. however, the highest factor loading was observed for the variable the website I used for fashion product is striking’ indicated that the more attractive the website is the more will be the customer entertainment on the web portal. whereas, exploring fun, imaginative designs and entertaining graphics can create the better source of attraction for the customers. FACTOR 2. CONTENT EFFECTIVENESS The second factor accounted with the variance of 19.634, indicating that almost 20% of change can be acquired by this factor. While the eigen value of 2.749 and Cronbach’s alpha value of 0.918, indicated the reliability and the relevance of the construct. Four statements were constructed in this factor, with the factor loading ranging between .757 to .874, whereas the highest factor loading was observed for the variable ‘website provide latest information’ (.874) indicating that the customers look for the updated and latest information whenever they visit the fashion website. Thus, enhancing the culture of regular updating can enhance more customer credibility. While, convenient design, accuracy of information, complete information and relevant of such information also play important role in shaping the effectiveness of the content. FACTOR 3. WEBSITE INFORMATIVENESS this third factor accounted with the variance of 14.964, indicating that almost 15% of change can be acquired by this variable. Whereas the eigen value of 2.095 and Cronbach’s alpha value of 0.873 highlighted the relevance and reliability of the measures. This factor constructed four statements, with factor loading ranging between .763 to .874, while, the highest factor loading of .874 was accounted for the statement ‘the fashion website gives me better knowledge about the products’ indicating that the customers preferences for the better knowledge and accurate information determine the informativeness of the websites. However, timely information, useful content can also impact the informative aspects of the websites. Thus, all the three factors were found to be relevant for shaping the better web atmosphere for the customers. MEASURIING THE INFLUENCE OF WEB ATMOSPHERICS ON THE CUSTOMER PURCHASE BEHAVIOUR ON FASHION WEBSITES In this segment of the chapter the influence of the web atmospherics on the customer purchase behaviour was assessed. This will enable to highlight the role of the various factors of web Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1540 https://internationalpubls.com atmospherics on the factors of the customer purchase behaviour. For this SEM (Structural Equation Modelling) has been used. the detailed analysis and interpretation have been given as below: Confirmatory Factor Analysis of Measurement Model for Influence of Web Atmospherics on Customer Purchase Behaviour Measurement model examines the strength of the association between the observed indicators (items)to its latent construct (observed variable). In other words, CFA is used to verify the adequacy of the items to its respective construct. In addition, it also helps in assessing the reliability, construct validity (Convergent and discriminant validity) and one-dimensionality of the measurement model. In this process, an overall CFA was conducted to evaluate the quality of the final measurement model available for structural equation model. Following this, the composite reliability, convergent and discriminant validity was evaluated using AVE and CR. As recommended by Fornell & Larcker (1981), the threshold value for AVE and CR of the study constructs is above 0.50 and 0.6 respectively. This is used to validate the study variables. Straub et al. (2004) criterion was used to examine the discriminant validity. The distinct nature of the study variables was established by comparing the square root of AVE with its corresponding construct correlation. FIGURE 4.1: PROPOSED OVERALL MEASUREMENT MODEL Figure 5.1 (MEASUREMENT MODEL) Construct Validity The degree to which an instrument measures what it is intended to measure is referred to as construct validity. Both convergent and discriminant validity are included. The degree to which various Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1541 https://internationalpubls.com instruments suggested to assess the same construct correlate with one another is known as convergent validity. The distinctiveness of factors measured by the particular collection of indicators is defined by discriminant validity (Kline 2015). Table 5.6 Convergent Validity Sr. No Constructs CR AVE 1 WE 0.921 0.746 2 CE 0.933 0.738 3 WI 0.935 0.784 4 VU 0.892 0.680 5 VR 0.849 0.588 6 EX 0.817 0.599 The accepted level of CR is > 0.7 has been achieved for all the study constructs ranging from 0.817 to 0.935. The minimum value of AVE for all the construct validity exceeded the recommended value of 0.5, ranging from 0.588 to 0.784 in the current study (Table 4.12). Therefore, the model established the convergent validity. Notes: AVE Average variance extracted, CR Composite reliability. WE web entertainment, CE content effectiveness, WI web Informativeness, VU value, VR variety, EX experience. Table 5.7: Discriminant Validity In order to achieve discriminant validity, the square root of AVE was compared with its corresponding construct correlation. When the AVE is higher than the inter correlations squares, this shows the independency of the construct. Table 4.26 below suggest that there are no issues with the convergent validity with respect to the study variable. The result indicates that the correlation among all the study constructs was less than the square root of AVE. Thus, in conclusion, the measurement scale had discriminant validity. Table 5.7 WE CE WI VU VR EX WE 0.778 CE 0.597 0.859 WI 0.614 0.674 0.864 VU 0.614 0.752 0.682 0.885 VR 0.581 0.626 0.634 0.633 0.824 EX 0.573 0.682 0.708 0.727 0.643 0.767 More or less assessment of measurement model, multiple fit indexes were performed and accounted from where we can evaluate that the measurement model has cleared all the requirement to be a fit model & is reasonably consistent with the data and al the fit indexes better than the recommended values. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1542 https://internationalpubls.com Table 5.8: COMMON METHOD BIAS In this study, data was collected for study variables using a self-questionnaire. Therefore, there is a high chance of common method bias to inflate or deflate the relationship between the study variables. The first factor in the model accounted for 34 percentage of variance which is less than the threshold of 50 percentage. Thus, absence of common method bias was verified. Total Variance Explained (Table: 5.8) Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 6.927 34.636 34.636 6.927 34.636 34.636 2 3.667 18.333 52.969 3 2.748 13.741 66.710 4 .812 4.059 70.769 5 .689 3.446 74.215 6 .626 3.132 77.347 7 .585 2.923 80.270 8 .507 2.536 82.806 9 .440 2.200 85.007 10 .397 1.983 86.990 11 .364 1.819 88.808 12 .333 1.665 90.474 13 .296 1.482 91.956 14 .290 1.449 93.405 15 .279 1.396 94.800 16 .258 1.291 96.092 17 .243 1.214 97.305 18 .189 .943 98.248 19 .183 .915 99.163 20 .167 .837 100.000 Extraction Method: Principal Component Analysis. 5.9 ASSESSING THE STRUCTURAL EQUATION MODEL Structural equation model combines aspects of multiple regression and factor analysis to estimate a series of interrelated relationships among variables simultaneously (Hair et al. 2010). Statistical information and qualitative cause hypotheses are combined to employ structural equation modelling to assess and completely eradicate causal linkages. Because SEM has no restriction on the quantity of variables, unlike all the other methods, it is regarded as the best approach. SEM makes hypothesis testing easier because it uses the confirmatory method rather than the exploratory method. The data in this model are statistically analysed while accounting for measurement error. Measurement error can Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1543 https://internationalpubls.com be estimated or evaluated using the SEM. It can take into account both latent and observable factors. SEM models use less basic statistical techniques. 5.10 RESEARCH MODEL AND HYPOTHESIS TESTING In the present analysis, the research model is tested through SEM. The structural equation modelling diagram in AMOS is represented in after model fit summary. Table 5.9: MODEL FIT SUMMARY Fit Index Guidelines (Recommended) Model values Chi-Square 1951.337 CMIN/DF ≤ 3 2.772 NFI ≥ 0.80 suggests a good fit .886 TLI ≥ .900 .915 GFI ≥0.80 suggests a good fit .833 AGFI ranges between 0 & 1 .806 RMSEA 0.05 to 0.08 .060 P .000 Hair et al. (2010), (b) Forza & Filippini (1998); (c) Greenspoon & Saklofske (1998) , (d) Awang (2012). Almost all fit indexes reflected in the model are reasonably consistent with the data, and all fiit indexes are greater than the values recommended in the table. Thus, it could be said that the model has a parsimonious fit. Overall, we could say that the model is acceptable in predicting the outcome variables. Figure 5.2: Overall structural equation Research Model Figure 5.2: Structural Equation Modelling of the Research Model with Standardized Estimates Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1544 https://internationalpubls.com 5.11 TESTING OF HYPOTHESIS The hypothesis was framed according to the different variables included in the research. The hypothesis was framed as below: Influence of web atmospherics on customer purchase behaviour of fashion websites. H1: Web atmospherics (WA) has positive and significant influence on the creating value (VU) of fashion websites for better customer purchase behaviour H2: Web atmospherics (WA) has positive and significant influence on the creating variety (VR) of fashion websites for better customer purchase behaviour H3: Web atmospherics (WA) has positive and significant influence on the creating Experience (EX) of fashion websites for better customer purchase behaviour Results: Table 5.10 Impact Standardised Estimate S.E. C.R. P Hypothesis WA-VU .083 .037 2.227 .026 Accepted WA-VR .174 .037 4.774 .000 Accepted WA-EX .077 .037 2.114 .035 Accepted Interpretation: H1: The study assessed the impact of web atmospherics on the value aspect of the customer purchase behaviour. It was found from the above table that the impact of WA on VU was positive and significant as the value C.R. (t-value) is more than 1.96 (2.227) and the P-value is .026 which is less than the significant level of 0.05. Thus, it can be said that web atmospherics has positive and significant impact on the value creation for the fashion websites. These findings are according to the findings of the other researchers as well, it is due to the fact that web atmosphere, positively enhance the quality of surfing experience, which automatically generate the more value of the products listed on that particular website. For example, if the a web shows the auto suggestions of the related apparels this will generate more customer value toward the products that are listing according to the particular customer’s taste. Considering the hypothesis framed we will Accept the alternate hypothesis and reject the Null hypothesis. H2: While taking into account the influence of web atmospherics on the variety aspect of the consumer purchase behaviour, it was found that the impact was positive and significant as the value C.R. (t- value) is more than 1.96 (4.774) and the P-value is .000 which is less than the significant level of 0.05. Thus, it can be said that web atmospherics has positive and significant impact on the variety creation for the fashion websites. Where, the literature review has also supported the fact, that web atmosphere has a great influence on the customer variety seeking criteria. If the web has a positive and customer friendly tone, this will generate more time spending of the customer on that particular website. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 1545 https://internationalpubls.com Considering the hypothesis framed we will Accept the alternate hypothesis and reject the Null hypothesis. H3: Considering the influence of web atmospherics on the Experience aspect of the consumer purchase behaviour, it was found that the impact was positive and significant as the value C.R. (t-value) is more than 1.96 (2.114) and the P-value is .035 which is less than the significant level of 0.05. Thus, it can be said that web atmospherics has positive and significant impact on the better experience creation for the fashion websites. Where, it has been found that in the studies of Cato (2001) that, positive website atmosphere creates positive customer experience. And also create the higher chances of purchase and customer loyalty. Considering the hypothesis framed we will Accept the alternate hypothesis and reject the Null hypothesis. Thus, it has been found that all the factors of web atmospherics have a positive and significant impact on the customer purchase behaviour and can enhance the shopping experience and intentions of the customers in the positive ways. 6. SUMMARY AND CONCLUSION: The era of the "electronic" generation began in the early 2000s. This is solely due to the growth of the Internet. Because of the government's push for digitisation, India's internet and mobile client base has grown significantly in recent years. For a developing country, India's annual GDP growth rate and per capita income have been remarkably impressive. Due to its ability to accommodate people's busy schedules, internet apparel ordering has grown in popularity. Furthermore, in online buying apparel, consumers are solely responsible for selecting items for the cart and confirming by choosing any payment mode available on websites. People prefer to purchase clothing online from stores that let them relax while doing so. A well- designed online business has been shown to increase the likelihood of impulsive purchases. When people shop online for clothing products, their impulsive purchasing behaviour might be influenced by the serviceability of websites. 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