Hyper-personalization – fashionsustainability through digital
clientelingGeetika Jain
Faculty of Management, Uttar Pradesh Technical University, Lucknow, India
Sapna RakeshIMS, Ghaziabad, Noida, India
Mohd Kamalun NabiDepartment of Commerce and Business Studies, Jamia Millia Islamia,
New Delhi, India, and
K.R. ChaturvediSchool of Management, Krishna Institute of Engineering and Technology,
Ghaziabad, India
AbstractPurpose – This study aims to find the model fit to understand the consumer behavior in context to thehyper-personalization through digital clienteling by using structural equation modeling. The traditionalmethod of customer passive observance has been transformed to dominance, where, the fundamentalchallenge for companies is to understand consumer behavior, work on cost-efficiency and implementsustainable innovation.
Design/methodology/approach – To investigate this emerging issue, this study aims to find themodel fit via applying “Technology Acceptance Model” (TAM) and “Theory of Reasoned Action” (TRA)in context to the hyper-personalization through digital clienteling with special reference to womenethnic fashion wear.
Findings – The study findings depict the perceived ease of use (PEOU) and perceived usefulness (PU) oftechnology, attitude toward clienteling and subjective norm toward customization impact on customerintensions. The findings posited that perceived usefulness is having the strong relationship with purchaseintention as compare to other variables. So, the analysis postulated that customer considered hyper-personalization is having perceived usefulness for customer and it also helps customer in getting theinformation about the product on theWeb page.Research limitations/implications – Because of lack of availability of resources, a specified samplingmethod has been used for this study. A new research, which will cover the fashion apparel from all thecategories with a detailed study from the branded and non-branded point of view, will provide betterdescription on this topic.Practical implications – By having personalized Web page through big data analytics, customer willhave positive experience and positive association with the company. The other parameters also play animportant role toward the customer behavioral intention. The current study approaches new way ofunderstanding the participative management of the personalization and tool to guide the work of strategyprofessionals and management of fashion e-commerce sector internationally and even in the other sectorsalso.Social implications – Because of advancement of technology, the usage of online media is increasing dayby day and this change is having high impact on the society, though we can innovate in any field or industry.Hyper-personalization has an impact on the online consumer buying behavior, which will affect the methodsof searching information for consumers.
Originality/value – This new area of research is having large scope of future research from the fashionindustry point of view. This paper is working as one of the element in the area of hyper-personalizationthrough digital clienteling to gain sustainable results in the fashion industry.
IntroductionIn anticipation of creating customer positive association, online retail companies are tryingtheir hands on digital transformation, where touching every line of customer shopping storyis the need of the hour. Achieving fashion sustainability through digital clienteling is thenew buzz world in the fashion industry. Understanding the requirement of customers in atraditional clienteling approach is feasible and entirely confined. But, in the digital age,where the customer is exposed to new platform, online websites and social media areworking as the mode of communication. As per Deloitte perspectives (Lay, 2018), companiesare trying their level to bridge the digital divide where brand heritage and identity is at riskand new digital path is very delicate in nature. Providing products and services to thecustomers are no longer work as an online shopping experience where traditional method ismisaligned in the current environment and can be a threat to brand reputation. So, it’s veryimportant for companies to have sustainable business strategy in new platform which willcreate a balance between growth aspirations and future operational plans with existingstrategic risk. Retailers need to understand all aspects of this journey where customer islooking for “personal factor” or “personal interaction”. They need to understand and studythe past record of product shopping, searched, added to cart and shopping wish list, whichcan enhance, resemble and complement their past purchase by using the big data. It’s theneed of the hour to understand the customer from all the spheres by foraying into digitaltransformation. To make online shopping as “feel good” experience, companies need toconquer clinteling and provide unique experience, retailer need to communicate to customerby using real-time through all the channels and provide personalized communication usinglifestyle-relevant data.
According to Retail Info Systems (Inniss and Ryan, 2015), customers do not think aboutthe channel and platform while searching for information about the product. So, companiesneed to understand the next step and way beyond to the omnichannel presence. Having apresence online would not fulfill the purpose of customer engagement; hence, companiesneed to understand the complex shopping journey of customers. To understand thecustomer experience and to provide unified customer experience, companies should wearthe customer shoe and find the pinching area. Companies need to curate products which canwork as a catalyst in the story of a customer’s journey based on their lifestyle, choices andpast shopping history. It makes more sense to customer where the products will be offeredon the basis of data given by customer inputs, search engine records, virtual communicationdata and behavioral data. Those were days, where companies used to have push strategyeither by providing offers and discounts. But, these days, companies need to address thecomplex behavior of customers by understanding the pain area of customers and alleviatingtheir pain by using technology provided solutions. An online shopper or a website visitorwould never ever provide the personal information by filing long survey questionnaires,feedback forms, feedback mails and return forms. According to Fujitsu America (2011)report, customers are having access to information through various social networkingwebsites like Facebook, Twitter and blogs. Customer’s data which have been collected bycompanies is having no relevance in the current scenario because of fast-moving
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conversations. Digital clienteling provides better customer information, proactive customerresponses and cross-sell opportunities. The companies need to take an initiative to foray intoa digital clienteling, where building relationship with customers in an automated nature willexist and flourish it by providing consistent solutions across various touch points.
Digital clienteling – one size does not fit allGoing one step ahead is the requirement where customer expects the high level oftransparency from the retailer. According to Retail Info Systems (Inniss and Ryan, 2015),companies need to deliver dynamic digital clienteling solutions which will provide theconsistent solutions to customer by all means through omnichannel like providing access tothe product assortment data, return policy, tracking record of product delivery, premiumshipping services and detail on the availability of product based on price. Engagement withthe customers should be through all the channels even before, during and after the shoppingexperience. To perform better on the digital platform, companies need to close all the gapsbetween the customer service expectation and delivered customer service. So, when afashion brand is going digital then it does not mean that merely they need to provideservices online. They need to re-structure, re-imagine and re-design the whole world ofdigital shopping through hyper-personalized way. Digital transformation is the new buzzworld in the fashion industry. Though fashion industry specifically to women ethnic wearhas major customer base from the offline customer through physical channels, butonline customer base is also growing at a fast pace through omnichannel. By usingomnichannel customer experience, companies try to touch every step of emotional andvirtual aspects of customer experience and shopping journey. Clienteling is defined as anapproach which primarily focuses on one-to-one marketing to have positive association andhealthy customer loyalty by having personalized customer communication. The mainfeature of clienteling is that it will be operated through sales-person only and the interactionwill be one-on-one basis. As per Forbes article (Published in 2017), Apple store andNordstrom are applying best practices of clienteling with digital presence. Both thecompanies have worked on the three major aspects of clienteling, i.e. information,personalized information and seamless checkout.
Hyper-personalizationAccording to Subramanyan (2014), Hyper-personalization is defined as the use of big data toprovide more specialized and personalized products, services and information to thetargeted segment. With the help of hyper-personalization, companies can create an authenticcustomer experience online based on customer requirement. With the advent of technology,customers are trying to curate their surrounding according to their liking, interests andbeliefs. Customers want to control the way of accessing the information. Companies can usethis concept and provide the information as per the customer requirements. Hyper-personalization works as a tool to marketer to provide the personalized information aboutthe customers. Hyper-personalization has three major focus areas like social listening, dataanalysis and content. According to Simon (2014), Netflix has 60 per cent got of rentalcustomer business with the help of hyper-personalization data application only. With thehelp of online data collection, companies can track their customer previous purchaserecords, demographic information, advertisement clicks and subscriptions of emails.
InformationInformation plays a crucial role in the implementation of clienteling, where informationplays a role of a catalyst in providing detailed information of customer to sales associates.
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With the help of big data, sales associates will be having access to the information related tocustomer clothing size, color preferences, their likes and dislikes, purchase history recordsand purchase amount. As per Forbes article (Published in 2017), detailed information helpssales associate in driving customer engagement and also helps in providing personalizedrecommendation and assistance through 360-degree view. Because of information age, thephysical clienteling has transformed into digital clienteling where the personalized servicesprovided to customers will be purely based on their big data analytics. The big dataanalytics will be based on customer past search record through various search engines,search on mobile applications and website visitor information. The big data will play acrucial role in this big picture by having detailed information on customer wants, needsbuying history, purchase pattern, purchase amount, online search pattern, behavior. Onlineretailer can use this information for providing the hyper-personalized products and servicesto customer in fashion industry with special reference to women ethnic wear.
CommunicationCommunication has its own relevance in the digital clienteling scenario. However,sending communication mails to customer on frequent basis and informing customers aboutthe ongoing offers and discounts will not solve the purpose. Companies need to understandthe requirement of the customer and personalized such communication mails. Thepersonalized mail opens the message which will contain picture or an image ofrecommended products to customer based on customer’s previous purchase and searchhistory. The message will have the feature to shop directly with personalized productassortment to increase customer experience and positive customer engagement.
Seamless checkoutRetailers can think of right technology to provide safe and seamless checkout process tocustomers. By using the data of previous mode of payment, shipping and billing addressand delivery preference, companies can dramatically improve the customer shoppingexperience. Providing transparency in various features like tracking status of the productand return processing of the product can create loyalty and positive association betweenretailer and shopper. Amazon Go is an existing example of seamless checkout of physicalretail store where customer can have the grab-and-go shopping experience in place of anysales associate assistance. Online retailers need to provide the same shopping experienceonline and have seamless checkout by using big data.
Women ethnic wearUrbanization in India is growing in a fast manner where it is estimated that within another20 years, the Indian population living in cities and towns and municipalities would increaseby 300 million. With the rapid increase in urbanization, the rise of selecting and lookingforward to different styles would be noticeable. To maintain the new social status earned,the population shifting the base to urban location would search for fresh fashion trends andstyles so as to live up to the new lifestyles according to Pani and Sharma (2012) study. In theIndian subcontinent, wearing any particular styled dresses of various brands, acts as theethnic identity which depends on the variant across several religion, caste, region or class.Though there are variant options, saree is common ethnic attire including little variation inrespect to various cultures or traditions or community. In the Indian market, though there isavailability for foreign made or foreign branded apparels, it is a common observation thatlocal manufacturer are more in trend and in similar way it can be observed that inspecialized apparel market such as ethnic wears, unbranded local products are more in use
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according to Basil and Ramalakshmi (2013) study. Chattaraman and Lenon (2008) hadstated that as sari is being treated as a formal dress in the Indian society, along withstronger cultural set up, the doors of ethnic apparels would make an advantage in themarket; as in present time, ethic wear can be calculated to pose one fourth of section in theretail apparel market according to RNCOS (2016) report. As per a report by Trivedi et al.(2017), a large number of women and older buyers will start buying online. There would betwice as many online women shoppers in 2020 and thrice as many older online shoppers. Adramatic shift is in the demand drivers for women from discount-driven to variety-driven.Older online buyers care more about convenience and shorter delivery times. Shopping onsmart phones has gained prominence in fashion e-commerce as 85 per cent of onlineshoppers prefer to shop on their smart phones.
Theoretical backgroundTheory of reasoned actionAccording to Ajzen and Fishbein (1980), theory of reasoned action (TRA) depicts anindividual’s intentions toward a specific behavior. There are two basic determinants thatpredict an individual’s behavior, i.e. attitude and subjective norm. The first determinant,attitude, reflects an individual’s beliefs based on his/her likes and dislikes which lead to anoutcome in terms of behavioral intentions. It has inferred in the model that there are twomajor types of beliefs, i.e. attitude beliefs and normative beliefs. Various theorists haveanalyzed the attitude of an individual on an object and then tried to predict the behavioralintention of that individual. But, Fishbein posited an individual’s intention which has beendemonstrated using attitude of certain behavioral factors as compared to analyzing theattitude of an individual’s certain object. Theory of reason action asserted the two mostimportant determinants, i.e. behavioral intention and subjective norms which have the directeffect on performing a certain behavior.
Technology acceptance modelTechnology acceptance model (TAM) (Davis, 1993; Davis et al., 1989) is a well-known theoryof information technology and it asserts the intention of an individual to use a system. Theintention of an individual is having two major determinants, i.e. perceived usefulness (PU)and perceived ease of use (PEOU). These two beliefs have been used as external variables inthe model to assert the effect of PU and PEOU on the attitude of an individual, which leadsto change in the behavioral intentions. These two beliefs have been used by variousstudies to determine and predict the purchasing intentions of an individual while usingsystem and technology (Koufaris, 2002; Gefen et al., 2003; Pavlou, 2003). This theory havebeen accepted and applied to diverse situations to understand the attitude of potentialconsumers from an e-vendor website (Venkatesh and Davis, 1996; Agarwal and Karahanna,2000). Pavlou (2003) has proposed the integration of existing TAMmodel with the trust andperceived risk.
AttitudeAccording to TRA model (Ajzen and Fishbein, 1980), an individual’s intentions have a largeimpact on its buying behavior. Attitude plays a role of catalyst in the construct of TRA,where attitude posits the psychological condition of an individual toward online shopping(Jahng et al., 2002). Attitude has shown as the behavioral beliefs in which an individualshows focal behaviroal to get an information about the product and service. After gettingand searching for information, an individual may show some desire toward purchase of thatproduct:
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H1. Attitude toward the behavior of hyper-personalization by using digital clientelingis postively related to intention of purchasing a women ethnic wear online.
H5. Behavioral intentions of hyper-personalization by using digital clienteling ispostively related to purchase behavior of women ethnic wear online.
Subjective normsSubjective norms refer as an individual’s independent variable which has a lot of influenceof others on the person’s buying behavior pattern. According to Ajzen and Fishbein (1980),subjective norms considered as important determinant to understand the social influenceand perceived pressure on person’s behavior intention toward purchasing a product. So,subjective norms show the behavior of an individual which may get affect based on theperception of immediate society (for e.g. friends, family, relatives, colleagues and referencegroups). The previous studies based on the literature prove the positive relationshipbetween the subjective norms and planned behavior, while empirical work shows a differentpicture of the relationship where behavioral intentions have the influence of subjectivenorms. The influence of subjective norms plays a big role because human behavior is led byits intentions (Karahanna et al., 1999):
H2. Subjective norm related to hyper-personalization by using digital clienteling ispostively related to intention of purchasing a women ethnic wear online.
Perceived ease of usePerceived usefulness and perceived ease of use has been viewed as the cognitive componentas the part of the TAM. It has been studied that perceived usefulness and perceived ease ofuse have been seen as the antecedents of TAM, though they are not directly predicting theattitude and behavioral intentions (Gefen et al., 2003). The contribution of TAM and othermodel is phenomenon in explaining the online transaction and its contribution from thetechnology point of view. These models posit the hedonic features related to the technologywhich boosts the consumer’s intentions to shop online and it also demonstrates theimportance of these features for an e-retailer (van der Heijden et al., 2001). Perceived ease ofuse is one of determinant of TAM where an individual believes that the technology will beeffortless (Davis et al., 1989). In case of online buying behavior, perceived usefulness is anextent to which an individual perceives that his effectiveness of getting information willincrease from the website; similarly, perceived ease of use is defined as the degree anindividual’s belief toward technology to make his job effortless. Davis (1989) theorized thatall the external factors such as technology-specific factors and system-specific factorsinfluenced by perceived usefulness and perceived ease of use:
H3. Perceived ease of use related to hyper-personalization by using digital clienteling ispostively related to intention of purchasing a women ethnic wear online.
Perceived usefulnessResearchers considered perceived usefulness (PU) as an important ascendant of anindividual’s believe of using a system and technology to enhance his performance(Davis, 1989). It is an extent to which an individual perceives that his effectiveness of gettinginformation will increase from the website. TAM posits the PU and PEOU (perceived ease ofuse) as the important determinants of predicting the behavioral intentions toward online
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shopping. Because of these perceptions, consumers’ attitude will get influenced and willaffect their intentions toward online shopping. There is a very strong relationship betweenperceived usefulness and intention of an individual toward online shopping, whereas, therelationship between perceived usefulness and attitude is comparatively weak (Davis et al.,1989; Jackson et al., 1997; Lucas and Spitler, 1999):
H4. Perceived usefulness related to hyper-personalization by using digital clienteling ispostively related to intention of purchasing a women ethnic wear online.
Intention predicting purchase behavior toward hyper-personalization through digitalclientelingThis study is an effort to understand the extended model or conceptual model by combiningtwo main models, i.e. TRA and TAM. An integrated approach has been taken to understandthe intentions to predict purchase behavior while doing hyper-personalization by usingdigital clienteling. TRA and TAM propose that attitude, subjective norm, PEOU and PUeffect on behavioral intentions and resulting in purchase behavior. The current study hasreviewed various articles where the integrated approach has been considered to studyincluded from MIS Quarterly, Journal of Management Information Systems, InformationandManagement (Figure 1).
Model testingThe study aims to understand the effect of hyper-personalization performed through digitalclienteling impact on behavioral intention and purchase behavior also. After looking atcurrent scenario, the requirement of such type of studies arises where customers have anaccess to different sources which impact the customer’s attitude toward online shopping.Various factors, which have an impact on the customer’s attitude, are also impacting theirpurchase intention and purchase behavior. To understand the customer behavior towardhyper-personalization done through digital clienteling, structural equation modeling (SEM)has been used in the study. According to a study performed by Bollen and Long (1992),various relationship networks has been derived based on theory by using statisticalmethodologies like SEM. In the current study, SEM has been performed in two steps.Primary, confirmatory factor analysis (CFA) has been performed to find the measurementmodel acceptability, and SEM has been performed to check the model fit of the structuralmodel based on theoretical approach.
Figure 1.Conceptual model
Attitude
Subjective Norm
Perceived Ease of Use PEOU)
Perceived Usefulness (PU)
Purchase Behavior
Behavioral Intension
H1
H2
H3
H4
H5
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MethodSample designThis paper is describing the consumer purchase intention toward hyper-personalizationthrough digital clienteling with special reference to women ethnic wear. So, in the currentstudy, the target segment taken is from 18 – 45 years women respondents, who have doneonline shopping within 4-5 months previously for ethnic wear. Total 300 respondents havetaken for this study, out of which 270 have been used for the analysis.
The demographic analysis of the study is as follows (Table I, II and III):
Table I.Sample
demographics
Variable Category Count (%)
Gender Female 270 100Internet access hardware Mobile phone 82 30.3
Desktop/laptop 188 69.6Marital status Married 158 58.5
Single 112 41.4Education Undergraduate 93 34.4
Postgraduate 117 43.3Others 60 22.2
Monthly personal income Below 10K 64 23.710K-25K 22 8.1525K-50K 42 15.550K-100K 123 45.5Above 100K 19 7.03
Table II.Cross-tabulation
Cross-tabulationMonthly personal income
Below 10K 10K-25K 25K-50K 50K-100K Above 100K Total
How many times purchased women ethnic apparel online1-2 times 38 10 14 49 3 1142-3 times 17 8 9 0 5 393-5 times 9 4 17 11 0 415-8 times 0 0 0 63 0 63>8 times 0 0 2 0 11 13Total 64 22 42 123 19 270
Table III.Cross-tabulation
Cross-tabulationMarital status
Married Single Total
How many times purchased women ethnic apparel online1-2 times 67 30 972-3 times 8 47 553-5 times 8 35 435-8 times 52 0 52>8 times 23 0 23Total 158 112 270
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Research instrumentA questionnaire has been designed to measure the variables based on the two theorieswhich have been used in the studies. There are 20 items have been considered for thethis and all the items have been analyzed on Likert scale for four major categories offactors, i.e. attitude, subjective norms, perceived ease of use and perceived usefulness.To find the model between the theoretical model and current study analysis,structural equation modeling (SEM) has been used (Hooper et al., 2008). The researchinstrument has considered using in this study based on previous studies relevance(Table IV).
The Cronbach’s alpha value for the 20 items proves the reliability test (Hair,2007).
Confirmatory factor analysisConfirmatory factor analysis has been performed on the 20 items using theconceptual model which is based on an integrated approach of two different model,i.e. TRA and TAM. It consists of four exogenous variables (attitude, subjectivenorm, perceived usefulness and perceived ease of use) and two endogenousvariables (purchase intention and purchase behavior). Purchase intention has beentheorized and hypothesized as one of endogenous variable and works as amediator while proving the relationship between all the exogenous and endogenousrelationships. The regression estimated of the entire observed variable should beabove 0.3 considered as normal and above 0.5 considered as healthy (Hair, 2007).It proves that all the construct items have been confirmed to the validity test(Table V).
So, finally, the remaining 16 items for five constructs have been considered for the finalanalysis. As from the above table, it is clear that all the 16 items have the strong effect on themajor six factors which has been defined by the TRA and TAM. The result of factor loadingof various items proves their relationship with the subsequent factor and the relationshipeffect is proportional to the value of factor loading.
Measurement modelTo measure the theory with various construct, the measurement model has been used. Byusing measurement model, various constructs have been measured to check the model fit.CFA analyzes the relationship between all the constructs, which have been taken based onthe postulated theory on the study. Further, the results of CFA will be compared to the basetheory and then analyze the model fit (Figure 2).
Construct validityIt is used to check the validity of various construct which has been used to measure themodel fit. To confirm, that the items which has been used to analyze, are actually able tomeasure the dimension. CFA has been used to analyze the reliability of items which hasbeen used to perform the analysis on Likert scale. AMOS 19 has been used to perform the
Table IV.Reliability coefficient
Reliability statistics (Cronbach a)
Cronbach’s alpha Cronbach’s alpha based on standardized items No. of Items0.936 0.937 20
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statistical analysis. After performing the analysis, the result values confirm the model fit(Table VI).
After performing the analysis, all the values of various statistic indexes are falling underthe acceptable range and thus, approving the good model fit.
Structural modelTo analyze the model fit, structural model testing is the further step in performing the SEManalysis while analyzing the measurement model validity. The conceptual mode of thecurrent study has been structured on the basis of given model which has been describedunder literature review (Figure 3).
Table V.Measurement model
indices
Variable Code Attribute Factor loadings
Factor 1 (Attitude) 4 items ATT1
ATT2ATT3
ATT4
Online shopping by having personalizedfeature is time savingOnline shopping is 24*7Buying personalized things over theinternet is a good ideaI like to do online shopping when I amhaving personalized product offerings
0.950.500.590.62
Factor 2 (Subjective norm)2 items
SN1
SN2
People who influence my behavior wouldencourage me to use online shopping forpersonalized productsPeople who are important to me wouldencourage me to use online shopping forpersonalized products
0.730.75
Factor 3 (Perceivedusefulness) 3 items
PU1
PU2
PU3
Personalized Web page would be useful forgetting information about the productFor me, valuable information about theproduct is important to mePersonalized information would enhancemy effectiveness in getting useful productinformation
0.490.400.85
Factor 4 (Perceived ease ofuse) 2 items
PEOU1
PEOU2
Getting information specific to the productfrom the personalized Web page would beeasyFor me, getting product information basedon my requirement easily available fromwebsite
0.760.39
Factor 5 (Behaviorintension) 3 items
PI2
PI3
PI1
I intend to use the internet purchasing forpersonalized products as much as possibleI intend to use the internet purchasing in thefuture also if personalized services will beprovidedGiven that I had access to the personalizedinternet purchasing, I predict that I woulduse it
0.810.750.32
Factor 6 (Purchasebehavior) 2 items
PB1
PB2
I would feel comfortable buyingpersonalized things over the internet on myownThe internet is a reliable way for me to takecare of my personal affair
Estimated standardized path coefficientsTo perform SEM analysis and primary requirement of model fit, the estimated coefficientsof all the standardized paths should be significant in nature. As per the analysis, therelationship among all the constructs is significant in nature (Table VII).
In the current analysis, it has been posited that the relationship between variousconstruct is at significance level (0.01*, 0.05**).
Hypothesized relationship between various construct is found to be significant andsupport the theorized model fit indices. The values of the model fit indices fall in thepermissible and acceptable range (Table VIII).
Conclusion and implicationsThe current study has performed to test the model fit and hypothesis relationshipwhich has been formed on the basis of literature review and simultaneously developingthe new model based on the present scenario. In the current study the role of variousdimensions like attitude, subjective norms, perceived ease of use and perceivedusefulness have been tested in the hyper-personalization through digital clienteling. In
Figure 3.Structural model
Table VII.Significance (p)
values
Estimate SE P
PI<— Attitude 0.16 0.017 **
PI<— SN 0.15 0.022 **
PB<— PI 0.25 0.032 **
PI<— PU 0.33 0.015 **
PI<— PEOU 0.08 0.010 **
Note: Significant at *p< 0.01 and **p<0.05
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the conceptual mode, the integrated approach of two major theories TRA and TAM(Ajzen and Fishbein, 1980; Davis et al., 1989) has been used for this study to understandthe online consumer behavior toward hyper-personalization through digital clienteling.Two models have been used together to understand the online consumer behavioralintention and its effect on the purchase behavior. To test the conceptual mode, the datahave been collected through random sampling and the target respondents were ofwomen who have done online apparel shopping previously. The data have beencollected from the age group of 18-45 years majorly from metro cities and this can beconsidered as the major limitation of the study.
As per the TRA model (Ajzen and Fishbein, 1980), attitude and subjective normhas the direct impact on online consumer behavioral intentions and the behavioralintentions have direct effect on the purchase behavior. All the relationship amongattitude, subjective norm, perceived ease of use and perceived usefulness aresignificant in nature and having values in the permissible range. The results andfindings proved the relationship of various determinants as per the theoretical model,although the current study aims to prove the relationship of all these determinants forthe hyper-personalization through digital clienteling. The respondents have giventheir responses on the basis of hyper-personalization feature provided by thecompany by using big data and their purchase behavior on the basis of that. Thefindings posited that perceived usefulness is having the strong relationship withpurchase intention as compare to other variables. So, the analysis postulated thatcustomer considered hyper-personalization is having perceived usefulness forcustomer and it also helps customer in getting the information about the product onthe Web page.
Because of advancement of technology, the usage of online media is increasing day byday and this change is having very high impact on the though we can innovate in any fieldor industry and thus facilitating a personalization never known consumer buying behaviorand attitude toward online shopping. This study provides the results of new horizon. Thecurrent study approaches new way of understanding the participative management of thepersonalization and tool to guide the work of strategy professionals and management offashion e-commerce sector internationally and even in the other sectors also. Another,important finding of the study is that personalization has its own importance and customerconsiders personalized products information is having more usability also. By havingpersonalized Web page through big data analytics, customer will have positive experienceand positive association with the company. The other parameters also play an importantrole toward the customer behavioral intention.
Limitations and future researchIn the current study, very less number of variables has been considered for the study. Butfrom the future study perspective, other factors such as social, personal and cultural factorscan be considered for the study to get better insights about the research. As per Deloittedigital market report (Published, 2015), the number of internet users are increasingexponentially and the maximum number of users’ base ranged from 16-35 age group. Themain limitation of this study is to consider the respondents from metro cities only, althoughrural population and tier 2-3 cities are having huge potential in terms of prospect customerbase. Because of lack of availability of resources, a specified sampling method has been usedfor this study. A new research, which will cover the fashion apparel from all the categorieswith a detailed study from the branded and non-branded point of view, will provide betterdescription on this topic. So, this new area of research is having large scope of futureresearch from the fashion industry point of view. In future, a more structured and specifiedresearch can be doable in the area of hyper-personalization through digital clienteling. Thecurrent paper is working as one of element in the area of hyper-personalization throughdigital clienteling to gain sustainable results in the fashion industry.
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Further readingMangtani, N. (2017), Clienteling in 2017 –Defining the Future in-Store Experience, Forbes.
Corresponding authorGeetika Jain can be contacted at: [email protected]
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