Omni and Multi-Channel: Relationship with Utilitarian/Hedonic Benefits, Shopping Value and Channel Patronage

This study makes an attempt to examine channel patronage, a narrow research gap from literature in the omni and multichannel concept, additionally it has taken up shopping value, utilitarian and hedonic benefits . It examined the characteristics and relationship of omni and multi-channel. It observed whether channels deliver shopping value to customers. It found shopping value is created have significant relationship with utilitarian and hedonic benefits. Ultimately this study fills the research gap by throwing light on why customers show patronage for a retail channel and does shopping value effect it. Reflective first and second order model was used. Both EFA and CFA were performed. Average variance extracted, composite reliability, Cronbach’s alpha, cross loadings and Fornell and Lacker’s values were used to assess convergent and discriminant validity for measurement model. Path coefficient was used to assess structural model. Validation of structural model was done using R 2 , f 2 and Q 2 . It was found that respondents opine omni is different and better than multi-channel. Utilitarian and hedonic have relationship with both, but are better in omni than multi-channel. Independently, shopping value and customer patronage have relationship with utilitarian and hedonic; omni and multi-channels; but they are better in hedonic than utilitarian; and omni than multi-channel. Customer patronage has relationship with shopping value and is better in omni than multichannel when moderated by later.


INTRODUCTION
Rapid development of electronic, digital and internet technologies gave opportunity for retailers to adopted channels to communicate, connect, transact and build relationship seamlessly with customers (Hure et al., 2017;Park and Lee, 2017). For long retailers worked with single channel; retail store. Since few decades rise of information technologies lead retailers to prefer multi-channel extending to omni-channel practice (Melacini et al., 2018;Zhang et al., 2018). Coupey et al. (2015) in their study viewed that rise of digital devices and plethora of touch points lead to omni channel concept. Payne et al. (2017) in their research had thrown light on touch point definition given by (Baxendale et al., 2015) as "episode of direct or indirect contact with the brand." Retailers prefer multi-channel and omni channel, since they fit todays technological environment. Coupey et al. (2015) in their study investigated how e-retailers address challenges and adopt omni channel, they also view that omni channel has higher fit with e-retailers, alternately  say that this argument does not have enough evidence.
Either multi or omni channel, they offer customers different levels of benefits ranging from utilitarian to hedonic. Both utilitarian and hedonic benefits provide value to customer in terms of purchase and post-purchase experience (Tapas and Debasis, 2019) through respective touch points offered by multi and omni channels. Yrjola et al. (2018) in their study had elaborately discussed how customer value creation is done by utilitarian and hedonic benefits by retailers using multi and omni channels. Customers become judgmental about retailers and channels; means they patronize them.
The omni channel approach is a recent practice of some retailers, conceptualization started from last 5 years. Galipoglu et al. (2018) revealed that upon a search of 2000 peer reviewed journals, found no articles on the topic multi and omni channels between years 2000and 2002. They found 19, 16, 23 articles between 2009-2011, 2012-2014and 2015 respectively, concluded that multi and omni channel concept is emerging. Kazancoglu and Aydin (2018) also confirm that multi and omni channels concept from retailer point of view is largely understudied and from the consumer point of view the concept is just picking up. This indicates multi and omni channel is under-studied topic.
Researchers like (Payne et al., 2017;Hure et al., 2017;Verhoef et al., 2015) discussed gaps to be filled in the multi and omni channel theory. Payne et al. (2017) identified need for omni channel study across plethora of consumer theories including behavioral intention, comparison of utilitarian and hedonic experiences. Kazancoglu and Aydin (2018) reported consumer behavioral topics studied in this area with more frequency are willingness towards online payment, impulsive behaviour, product type preference, channel choice, purchase intention. But there was no mentioning of channel patronage. Xua and Jackson (2019) have attempted to study a selective variable influencing channel selection by customers, and there is lot to explore in this narrow area. Taking into consideration gaps this study aims to understand the relationship between omni-multi-channel (MOC) with shopping value (SV) derived from utilitarian/hedonic benefits(UHB) and further effect on channel patronage (CP).

Conceptual Background
Channels are the means to reach customers in large scale. Channels are the interface between marketers and customers, they act as a medium through which marketers can successfully communicate and transact with customer successfully (Payne et al., 2017). Coupey et al. (2015) revealed that in the literature there are varied definitions for omni and multi channels and lack of acceptance for standard definitions prevail among research fraternity. Supportive to this view Coupey et al. (2015) in their study had discussed several definitions of multi and omni channel from past literature, Omni channel "consists of the synergistic management of the numerous available channels and customer touch points, in such a way that the customer experience across channels and the performance over channels is optimized" given by (Verhoef et al., 2015). Multichannel "is the design, deployment, coordination, and evaluation of channels through which firms and customers interact, with the goal of enhancing customer value through effective customer acquisition, retention, and development" given by Neslin et al. (2006). Ye et al. (2018) had presented definition of omni channel given by (Frost and Sullivan, 2015) as 'seamless and effortless, high quality customer experiences which occur within and between contact channels.
There is scope for misunderstanding the difference between multi and omni channel, as both use to some extent same channels, but many studies had given a clear differentiation between them. Ye et al. (2018) had given a clear differentiation between multi and omni channels using six components via, definition, channel scope, channel focus, market focus, channel objective and attitude toward market competition. By definition multichannel operates channels independently, while omni channel operates integrated channels (Melacini et al., 2018). Channel scope; multi has limited on plate, omni has exhaustive list. Multi focuses on one clear leader dominant channel, omni plays balance within channels. Sales is the primal focus of multi, whereas product and brand are the focus of omni (Melacini et al., 2018). Multi chases individual channel for meeting sales targets, while omni counts overall sales of channels. Multi encourages channels for aggressive competition, on the other hand omni motivates channels to complement each other to satisfy the customer expectations (Zhang et al., 2018). Galipoglu et al. (2018) explained that they found cross comparative studies between traditional and electronic channels, but did not found any difference between multi and omni channels. Adversely Xua and Jackson (2019) found that customers differentiate and have intentions towards channels which offers better prices, less perceived risk, seamlessness, convenience, faster order fulfillment, from the perceptive of omni channel. Similarly Park and Lee (2017) found multi considered traditional channels and omin practicing mobile channels.  discussed that it is obvious for marketers to start work with single channel to multiple then extend to all or omni channel. Single channel is on the brink, with multiple and omni channels on the rise. Hence there is a growing ambition among marketers to create seamless channels or touchpoints to reach customers leading to adoption of multi-channel and omni channel marketing (Payne et al., 2017;Bernon et al., 2016).
Both multi-channel and omni channel give customers shopping value (Yrjola et al., 2018). (Hure et al., 2017) investigated past studies and viewed there is varied explanation and meaning about shopping value (Mahajan, 2020), but they confined to the idea given by (Babin et al., 1994) as "shopping value has both a utilitarian and hedonic dimension (Han et al., 2018). Utilitarian value dimension reflects the task completion aspect of the shopping experience, whereas hedonic indicates the shopping experience's ability to offer pleasure and fun" (Tapas and Debasis, 2019;Babin and Krey, 2020;Singh, 2015). Kazancoglu and Aydin (2018) studied the utilitarian and hedonic motivational dimensions towards omni channel shopping and had similar findings. Customer perception of value is bisectional between utilitarian and hedonic derivation of benefits which ultimately paves way to channel patronage (Molinillo et al., 2017).
Customer shopping experience creates conviction for repeat purchase. Channel patronage(combined offline and online) is the overall customer's experience of reachability, location (Goswami and Mishra, 2009), easy of access, ease of use, speed, product availability, product range, affordable price range, promotions, manufacturing and expiry dates, billing time and so on categorized as utilitarian. Playfulness, shoptainment, image, social class of footfall, ambience like air-conditioning lighting, interiors, employee reception, convenient parking, store timing and so on are part of hedonic. Both utilitarian and hedonic benefits have direct impact on customer choice of retail channel.

Mulit and Omni Channel Marketing
Multichannel marketing is promotion of multiple channels with touch points or contact points by retailers to customers, whereas Omni channel marketing is using all available channels at a point of time to reach customers (Payne et al., 2017). These channels can be physical store, phone call, store app, television advertisement, flyers, brochures, email, chat box, social media; Facebook, twitter, and so on. The design of multi and omni channel is to give customers a rich experience of shopping (Coupey et al., 2015). The Multichannel marketing focuses on individual channel performance as such some channels are outcompeted by others. Payne et al. (2017) had discussed, Omni channel marketing is an integrative effort and performance is expected to give customer a rich shopping experience. Multichannel marketing allows each channel to compete with others, resulting in diverse customer experience, satisfaction and benefits across channels. Contrastingly in omni, customers will achieve unidirectional services, as all channels are synchronized and try to complement each other's to seamless customer experience (Coupey et al., 2015) alternatively,  found 70% of the respondents opine the opposite. Kazancoglu and Aydin (2018) found omni shopping experience gave speed, access, time, price incentives, ease of use, and other benefits to shoppers. Zhang et al. (2018) argue that omni channel retailing gives rich shopping experience to consumers with positive purchase intention establishing satisfaction and trust, contrary to this Xua and Jackson (2019) discussed that omni retail settings need transparency, convenience, and uniformity to drive customer purchase intention. Bernon et al. (2016) found integration of channels is one of the major issue. Ye et al. (2018) had given an interesting story of a Chinese company named "CasualCo" a casual wear retailer moving towards omni channel. Initially the retailer operated multichannel format. The company grown to 3000 retail outlets with majority of them being franchisee outlets, company owned some medium outlets and few large outlets. Company was successful in the initial years but faced problems from competitors. Later the company decided to develop Enterprise Resource Planning (ERP). ERP further helped to initiate mobile applications, social media platform "WeChat," supply chain solutions, franchisee networking, and data mining tools. The omni channel initiative contributed more than half of company sales with an improved value to customer. Park and Lee (2017) had found that mobile and internet channels have more preference than traditional channels, which indicates customer choice is more towards omni channels, but with several challenges and cautions.
H 1a : Multichannel and omni channel types are similar in nature. H 1b : The customer experience of omni channel is better than multichannel.

Utilitarian and Hedonic Benefits
When customers use multichannel or omnichannel they experience a set of utilitarian and hedonic benefits. In both multi and omni channel marketing, customers derive utilitarian and hedonic benefits (Hure et al., 2017;Payne et al., 2017). Han et al. (2018) found that both benefits have significant influence on customer post purchase intention or repeat buying (Molinillo et al., 2017). Singh (2015) found both utilitarian and hedonic benefits lead to patronage. Utilitarian is much of transactional based, hedonic is emotional (Yrjola et al., 2018). Customers buying pattern involves task based and experience based. In task-based buying customers perceive purchasing as a means to fulfil certain specific goal. Whereas in experience orientation, they would not like just buying, want to experience the whole buying activity including product or service they purchase. Kazancoglu and Aydin (2018) respondents of omni channel were more satisfied with transactional aspects like ease of access, ease of use, speed of transaction, saving time, but dis-satisfied with emotional aspects like product variety, timing of buying, risk of buying online, social interaction. Contrastingly Park and Lee (2017) found omni channel types like mobile apps and internet gave customers more transactional and emotional benefits than multi and traditional channels.
H 2 : Utilitarian and hedonic benefits have positive relationship with multichannel and omnichannel. H 3a : Utilitarian benefits are better in omnichannel than multichannel. H 3b : Hedonic benefits are better in omnichannel than multichannel.

Shopping Value
Shopping value is overall experience of shopping activity with a particular retailer/s and set of channel/s leading to repeat purchase, Han et al. (2018) found similar customer behavior with airport retailers; Molinillo et al. (2017) found shopping value has significant relationship with customer loyalty among online clothing retailers. Shopping value is not unidimensional. The fundamentals of measuring shopping value are very subjective in nature. Each customer purchase involves shopping value which is inter-different within and intra-different from other customers. Studies indicate that shopping value comes from product quality (Han et al., 2018), design, color, shape, price, discounts, availability, variety, retailscapes (Yrjola et al., 2018;Mahajan, 2020). The value itself comes from overall shopping experience a particular channel or retailer gives to consumer, further it leads to satisfaction (Tapas and Debasis, 2019;Zhang et al., 2018). Sadachar and Fiore (2017) identified shopping value dimensions as (education, entertainment, escapist and esthetics) and have direct relationship with customer patronage. The question, what motivates a consumer to shop? leads definitely to a horizontal thought; what value proposition customers attain from the shopping. Customer compares price versus non price benefits, emotional benefits versus rational benefits (Payne et al., 2017). In literature, shopping value is both utilitarian and hedonic (Hure et al., 2017;Babin and Krey, 2020;Han et al., 2018), both benefits create value to customer (Babin et al., 1994). Kazancoglu and Aydin (2018) said that consumers derived value in terms of joy as a part of hedonic benefits in the omni channel shopping process. The overall service experience given by channel type will enhance hedonic benefits. Ye et al. (2018) had given enablers of omni channel, stated the online digital environment connected to offline platform creates rich experience and customer value.
H 4 : Utilitarian and hedonic benefits have positive relationship with shopping value. H 5a : Shopping value from hedonic benefits is better than utilitarian benefits. H 5b : Shopping value from omni channel is better than multichannel.

Channel Patronage
Channels offer services to customers in return the later termed as patron exhibits positive intentions (Blut et al., 2018). Multi and omni-channel are the important medium through which retailers reach customers to market themselves. In multi and omni channel, physical retail channels gain patronage through location, reachability to store (Goswami and Mishra, 2009), parking, interiors, product variety, product quality, customer services, product returns, prices, discounts, offers, brochures, flyers, outbound calls, billboards etc., (Julie et al., 2002), they all play role in customers footfalls. Similarly, online channels reach through mobile apps or other platforms gain patronage by app design, navigation, convenience of transaction, category of products available, customer reviews, customer shopping time, price, promotions (Park and Lee, 2017), online ordering, logistics, distribution and product delivery (Melacini et al., 2018). Customers use channels which provide value for shopping, to which they are loyal or for which they have patronage (Min et al, 2018;Inman, Shankar, and Ferraro, 2004). The value derived satisfaction goes a long way in fire powering customer intentions to repeated buying (Han et al., 2018) and be loyal to channels and retailers (Zhang et al., 2018). Patronage for a particular channel whether multi or omni (online, offline or combined) comes from utilitarian and hedonic benefits and value perceived by customers. Kazancoglu and Aydin (2018) had observed that channel preference depends on situational shopping needs. Multi or omni channels are preferred to satisfy the utilitarian needs such as saving time, lower physical effort, broad product variety, alternative comparisons and so on. Hedonic needs are fulfilled by playfulness of a website, attractive promotional advertisements, in-store music, smell, interior decor, value added information of products and services and so on. Xua and Jackson (2019) had found that particular channel performance influences the consumer decision making, means consumer satisfaction with channel performance can lead to his patronage towards a channel. Singh (2015) found that customer patronage for shopping malls have strong relationship with utilitarian and hedonic benefits offered.

Pilot Questionnaire -Validity and Reliability
Marketing field witnessed omni channel concept evolved in recent times (Galipoglu et al., 2018). Multi-channel is well adopted; omni channel is picking pace with many large retailers and some medium retailers. There is plenty of scope to study omni channel in multi-faceted and multi-dimensional approaches. Based on the extant literature, this study picked up the following constructs; omni and multi-channel, utilitarian and hedonic benefits, shopping value and channel patronage. Arguments, concepts, items and ideas are adapted from (Payne et al., 2017;Neslin et al., 2006;Verhoef et al., 2015;Frost and Sullivan, 2015;Coupey et al., 2015;Ye et al., 2018;Hure et al., 2017;Yrjola et al., 2018;Babin et al., 1994;Kazancoglu and Aydin, 2018;Bernon et al., 2016;Ma, 2017;Willems et al., 2016;Blut et al., 2018;Xua and Jackson, 2019;Zhang et al., 2018 and;Park and Lee, 2017). Initially 23 items were adapted, modified and finalized 20 items, refer "Appendix A". Measurement model initial construct-items, item generation, scale measurement and evaluation were treated using Hinkin (1995). 75 pilot test samples were selected. Seven-point Likert scale with 7 indicating strongly agree and 1 strongly dis-agree was used. Expert team of four business college colleagues with similar research interests were selected to assess the item's content validity-reliability with pilot sample responses on 23 items, this led to elimination of three items (I4, I7 and I21), resulting in 20 items for the final data collection. Expert team's item wise summarized suggestions for content change and elimination are given in "Appendix A".

Final Questionnaire, Sample and Data Collection
Based on the expert suggestions final questionnaire was designed with five statements at beginning for respondents conceptual understanding and were encouraged to interact with us for clarity.
Statement 1: I have shopping experience with Reliance trends; Shoppers stop; IKEA; and Pantaloons. see a brief on these retailers in "Appendix B". Statement 2: Multi channel is shopping via physical outlet, phone call ordering, flyers like single paper product details, booklets or brochures found at retailer's place, television/ radio/newspaper advertisement. Omni channels is shopping via above all mentioned and email, shopping app, social media, chat box, e-catalogs, blogs, and all other possible ways. Statement 3: Utilitarian benefits are derived from shopping transactions like store location, store accessibility, parking availability, product availability, product variety, price, promotions, shopping app (accessibility, speed, security, ease of use). Hedonic benefits are derived from shopping experience like ambience (interiors, airconditioning, lighting, music), personnel (friendliness, courtesy, responsiveness), value added environment (food courts, children play options, window shopping opportunities). Shopping app (design, colors, navigation comfort, playfulness, creative content). Marketing(information, knowledge, communication, responsiveness, integrity). Statement 4: Shopping value is overall experience of shopping activity over a period of time with a particular retailer/s and set of channel/s. Statement 5: Patronage is satisfaction and repeated shopping with a particular retailer/s and set of channel/s. Data was collected from software employees in Hi-tech city, Hyderabad, Bharat (India). Hi-tech city was targeted due the presence of large number software and ITES (IT enabled services) companies. Majority of the employees in these companies are millennials aged between 21 and 35 years, categorized as "Yuppies". The rationale behind selecting software employees; most of these millennial "Yuppies" earn fat pay, are affluent, higher purchasing power and well educated. They are short of time, tech geeks, internet savvy, early adopters, blend of these characteristics makes them most suitable sample choice for this study.
The study sample group have buying experience and are witness to retailers' transition from mulit-channel to omni in last one decade. Timing of data collection was from evening 5 pm to 7.30 pm between January and February 2020. Data collection was done in three phases; first, responses were taken from 75 samples for pilot study. Second; 200 respondents, of which 30 questionnaires were not usable, third; 150 respondents, of which 12 questionnaires were not usable. A total of 308 usable questionnaires with 88% response rate was achieved. After coding of usable questionnaires, data was first entered into excel sheet for data entry operator's convenience. After final check, data was migrated and analyzed using IBM SPSS 25.0 Microsoft windows version. Instructions from Ramayah et al. (2016) and Hair et al. (2013) were followed for correlations, validity, reliability and fit indices, hypothesis testing for measurement and structural models respectively.

RESULTS DISCUSSION
EFA was used for grouping of items into factors. Same data cannot be considered for running EFA and CFA, which compounds the problem of capitalization on chance variation. Data collected from pilot study was used for EFA. To run factor analysis, minimum sample size of 3 to 20 multiplied by number of items is recommended, hence a sample of 75 responses were taken. Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) result was 0.974 and Bartletts test of Sphericity results was approximately Chi-Square 2733.684 with significance of .000. (where p < 0.05). Hence these results reveal the outcomes to be achieved from study data will be useful. For EFA, common method variance (CMV) was performed suggested by Podsakoff et al. (2003). From the results of un-rotated analysis, through initial eigenvalues four factors emerged as shown in the Table 1. These four factors accounted for 80% of the variance. CMV does not pose a problem as the first factor accumulated 43.207% of variance, which is less than half of the variance explained.
A reflective second order model suggested by Chin (2010) was used for understanding the relationship model of study's constructs via multi and omni channel (MOC), utilitarian and hedonic benefits (UHB), shopping value (SV), channel patronage (CP). Partial Least Squares method (PLS) was applied using Structural Equation Modelling (SEM) for measuring the relationship between latent variables in the inner model i.e., measurement model and for measuring the relationship between latent variables (LV) and their indicators (I) in the outer model i.e., structural model. Analysis and assessment of first order reflective measurement model was done by validity and reliability of constructs involving indicator reliability, discriminant and convergent validity; and internal consistency. Convergent validity was assessed using item loadings highlighted as shown in Tables 2 and 3. Loadings more than 0.7 are acceptable (Hair et al, 2013), hence all the items are acceptable.
To assess the convergent validity of study's measurement model, AVE was calculated along with composite reliability and Cronbach alpha as shown in the Table 2. The AVE acceptable limit of more than 0.50 is achieved for all four constructs, composite reliability values are more than 0.80 and Cronbach alpha values are more than 0.70 reveal that convergent validity is attained fruitfully as recommended by Fornell and Larker (1981). Discriminant validity was used for showing the un-relatedness of measurements/ constructs of this study to be actually not related. Cross loadings are show in Table 3 and Fornell and Lacker's values in Table 4. Cross loadings indicated that in the outer model, loadings between a respective latent variable and its indicators are higher compared to other latent variable and their indicators demonstrating the discriminant validity, based on methodological findings of Hair  (Ye et al., 2018). They both are distinctive by definition, though this is debated among scholars, the scope and strategies give a clear understanding that they are dissimilar (Coupey et al., 2015).
Hypothesis 1b is accepted, where β = 0.614, t = 5.241 tested at (P < 0.05), means customer experience with omni channel is better compared to multichannel. Omni channel has wider horizon than multi in terms of channel variety, integration and communication/interaction among customer, overall gives a better customer experience (Ye et al., 2018). Coupey et al. (2015) had investigated "Direct Opitc" an eyewear retailer from France started in 2008 with multi-channel strategy; the objective was to study the transformation process form multi to omni channels. They found companies shift from multi to omni owing to its success, better strategy and rich customer experience, but this all comes at a price in form of challenges via. financial, marketing, organizational, managerial and cultural; additionally, operational challenges like CRM, IS and retailing mix. Direct optic felt a shift to omni from multi gave them strategic advantages. A similar objective was pursued by , probed whether shifting logistics operations from multi to omni will be successful or not. They found that with an organized organizational set up and effective IT infrastructure for inventory, assortment, picking, delivery and returns can be achieved with higher success rates. Shopping through omni channel reduces stress, gives excitement, makes feel happy, gives more purchasing power, opportunity to use all channels, over all it gives better experience than other ways (Kazancoglu and Aydin, 2018).
Hypothesis two recorded β = 0.441, t = 7.266 tested at (P < 0.05) indicating acceptance. Respondents agree utilitarian and hedonic benefits have significant positive relationship with both omni and multichannel types. Similarly, Hure et al. (2017) also identified utilitarian and hedonic dimensions have positive relationship and impact on omni channels.
Hypothesis 3a and 3b were accepted with β = 0.362, t = 4.639 tested at (P < 0.05) and β = 0.519, t = 6.027 tested at (P < 0.05) respectively indicating both utilitarian and hedonic benefits are delivered better by omni channel type than multichannel. Yrjola et al. (2018) results are contrary, they assessed value propositions across omni/multi-channel and utilitarian and hedonic shopping motivations with a mix of retailers via. Power (consumer electronics), Masku and Jordan's Furniture (furniture and decoration) Nanso Group, Rebecca Minkoff and Oasis (fashion and accessories), The Home Depot (Home decor and renovation), REI (outdoor and camping), Waitrose (groceries). Their results reveal, those retailers who practice omni channel strategy are driving hedonic motivations and utilitarian motivations by multichannel strategy. Kazancoglu and Aydin (2018) had found hedonic benefits like lower stress, happiness, excitement, seamlessness, playfulness and utilitarian benefits like product information, variety, low risk-smooth transaction, delivery, returns are better in omni channels than others.
For hypothesis four β = 0.381, t = 9.472 tested at (P < 0.05) reveals acceptance and proves both utilitarian and hedonic benefits have significant positive relationship with shopping value (Tapas and Debasis, 2019;Babin and Krey, 2020;Han et al., 2018). Hure et al. (2017) had similar results, establishing utilitarian and hedonic variables to be significantly positive from the point of shopping value, and found them to be effective. Also, they found that hedonic dimension has effective shopping value in all channel formats, whereas utilitarian dimension is also effective, but on mobile   channel front it is not. Fifth hypothesis 5a, β = 0.496, t = 7.002 tested at (P < 0.05) accepted, respondents view shopping value derived from hedonic benefits is better than utilitarian.
Both omni and multi-channel offer shopping value to the customers through propositions (price, quality, variety, brands, service, fun, playfulness, advice, seamlessness etc.) but omni channel offers value and experience whose horizon is better than multi to the customers (Yrjola et al., 2018). Shopping value has significant positive effect on omni channels; in offline and online channel formats shopping value has positive impact (Hure et al., 2017). Hypothesis 5b, has β = 0.703, t = 12.816 tested at (P < 0.05) accepting it proves shopping value derived from omni channels is better than multichannel.
Hypothesis six with β = 0.601, t =18.385 tested at (P < 0.05) proves significant positive relationship between utilitarian and hedonic benefits with customer patronage. Singh (2015) had found utilitarian and hedonic benefits have significant effect on shopping mall patronage. Ma (2017) in his study found that utilitarian factors like delivery time and charges; and free delivery have positive relationship with purchase intention which is a factor of customer patronage. Han et al. (2018) had found that utilitarian and hedonic satisfaction has significant effect on repeat buying. Hypothesis seven scores β = 0.441, t = 1.911 tested at (P < 0.05) indicating hedonic benefits lead customer patronage better than utilitarian benefits. Molinillo et al. (2017) found customer satisfaction is better in hedonic than utilitarian.
Hypothesis eight had β = 0.916, t =12.721 tested at (P < 0.05) proves significant positive relationship between multi and omni channel with customer patronage (Ma, 2017). Choosing a channel depends on its image from the customer experience (Inman et al., 2004), hence they are highly related (Kazancoglu and Aydin, 2018). Xua and Jackson (2019) found that omni channel preference intention has positive relationship in terms of channel risk and price.
Hypothesis nine with β = 0.642, t = 11.109 tested at (P < 0.05) indicates omni channel type derives better customer patronage than multi. Kazancoglu and Aydin (2018) found that respondents believe omni channel is better than other, but expressed dis-satisfaction and suggested improvement on variables; urgent buying, transportation, anxiety, delivery, trust, interaction, perceived risk. Goswami and Mishra (2009) had found that customers perceive kirana retailers (small stores) differently from big-organized institutional retailers, and also customer patronage differs between them, common sense insights a patronage tilt towards later.
Hypothesis ten with β = 0.882, t =8.518 tested at (P < 0.05) proves significant positive relationship between shopping value with customer patronage. Shopping value plays an important role in customer post purchase buying intention (Han et al., 2018). Molinillo et al. (2017) found shopping value has significant positive effect on customer loyalty in online clothing retailers. Sadachar and Fiore (2017) had found the 4E's of shopping value (education, entertainment, escapist and esthetics) has positive influence on patronage towards shopping mall retailers.
Hypothesis eleven recorded β = 0.803, t = 15.182 tested at (P < 0.05) proving omni channel shopping value results in superior customer patronage compared to multichannel. Customer patronage is related to what is being offered by a particular channel or retailer. Zhang et al. (2018) found that omni channels offer seamless shopping experience through effective integrated offline and online channels leading to consumer preference for channels and retailers.
The predictive accuracy of structural model was evaluated using coefficient of determination (R 2 ) categorized suggested by Chin (1998). The values of R 2 for dependent variables are UHB = 0.751, SV = 545, CP = 684 respectively. R 2 for UHB and CP is substantial and for SV moderate, indicating the predictive explanatory power of the study model as valid measurement. Based on the R 2 values it can be stated that 68.4% of variance in CP is explained by UHB and SV. Further 54.5% of variance in SV is explained by MOC and UHB; and 75.1% of variance in UHB is explained by MOC.
The measurement of effect size is done using f 2 to know the effect of independent on dependent variable to be considerable or substantial. f 2 values for MOC effect on UHB was 0.447 which is considered as large effect, SV was 0.394 considered as medium effect and CP was 0.610 considered as large effect. Further f 2 values for UHB effect on SV was 0.459 which is considered as large effect and CP was 0.728 which is also considered as large effect. Similarly, f 2 values for SV effect on CP was 0.591 considered as large effect, f 2 values followed threshold established by (Cohen, 1988).
Predictive relevance for the study was assessed using Stone-Geisser Q 2 with blind folding process as recommended by Hair et al. (2013). For endogenous constructs the threshold values for Q 2 are considered small if less or equal to 0.02 and medium between 0.03 and 0.34 and large if greater or equal to 0.35. The Q 2 values are given in Table 6. Results indicate that among endogenous latent variables, UHB and SV achieved medium predictive relevance level and CP large.

FUTURE RESEARCH
This study can be extended in three directions. One, as omni and multi channels provide technology interface to customers, technology adoption models like TAM 1, 2, 3 and technology related consumer behavioral models like UTAUT 1&2 can be blended for investigation. TAM gives some of the most timetested constructs like perceived usefulness (PU), perceived ease of use (PEOU) and intention to use (BI). UTAUT offers behavioral constructs like customer expectancy, social influence and demographics. Since online channels or online retailing has faster growth and expected to leap further fueled by Chinese virus (COVID-19); studying omni and multi channels from the perspective of technology models can be worth extension of literature. Two, Omni and multi channels are expected to move to more technological platforms hence new service-related challenges will arise. Service-related scales like SERVQUAL and SERVPERF can be adapted and modified to discover new scale suitable to omni and multichannel environment; just like RETAILQUAL for retailing, BANKQUAL for banking. In technological driven omni and multichannel operations definition of tangibility might change, since it reflects many non-physical facilities, hence there is scope for new constructs to be added. Three, factors of utilitarian and hedonic benefits can be considered for an in-depth investigation.
Similarly shopping value can be segmented into independent factors for study.

CONCLUSION
Few decades before power of retailing was in the hands of tiny and small outlets. The growth of new age technologies paved way for the rise of corporate retailers or institutional retailers. New age retailers had expanded nationally and globally. As the retailers started expanding, they shifted to multi and omni channels. Flotation of virtual (online) channels is the game changer like the retailers of this study. Virtual channels have the strategic advantage in terms of optimal adoption and implementation of omni channel practice. Online and offline channels deliver benefits to customers and retailers. Benefits which fulfill maneuvering kind of purchases categorized as utilitarian and heartwarming benefits as hedonic. There is little evidence in the extant literature that omni channel practice gives retailers competitive advantage, but surely it will help retailers to optimize their channel resources. Similarly patronage of retail channel is affected by type of channel practice. This study had investigated multichannel and omnichannel relationship from the point of benefits, shopping value and patronage; and created an evidence for the extension of body of knowledge in the emerging omni channel concept.
Customers use channels to achieve their buying needs, without acknowledging the concepts and theories behind. The obvious reason is, it does not fit to their conscious reasoning. What customer recognize is the benefits of a particular channel or set of them. This study placed short literature in the form of statements in the questionnaire giving respondents opportunity to know about multi and omni channels. After reading the statements, respondents were comfortable in understanding the clear difference between multi and omni channel and responded omni channel beneficial than multi. Respondents were given opportunity to compare between omni and multi-channel types from utilitarian and hedonic benefits on a whole, but the results may differ if their factors are considered as utilitarian via, product, price, promotions, location, parking, speed, ease of use, security of shopping app and hedonic via personnel, shopping ambience, shopping app comfort, playfulness, navigation. The definition of shopping value is much debated in the literature, definitions given may not represent what exactly customers feel. Shopping value propositions should be considered to give more scope for respondents. Customer patronage tilts towards feeling of higher benefits and better shopping value at macro level, but it needs an in-depth micro level investigation. Psychology of customers is a complex puzzle, more the researchers dig, the mine goes deeper and needs continuous probe.  I8. Marketing channels via limited platforms give benefits by price, promotions, product availability, product variety, accessibility/speed/ security of store app I9. Marketing channels via limited platforms give benefits from store ambience, store personnel, food options, children play, window shopping, design/colors/navigation comfort of store app I10. Marketing channels via unlimited platforms give benefits by price, promotions, product availability, product variety, accessibility/speed/ security of store app I11. Marketing channels via unlimited platforms give benefits from store ambience, store personnel, food options, children play, window shopping, design/colors/navigation comfort of store app Appendix A: Measurement Model initial construct-items and Expert team suggestions and final questionnaire items (Contd...)

Reliance Retail
Reliance retail has two decades of presence in Bharat (India). Its presence is in food, electronics, footwear, clothing, jewelry and telecommunications. Accredited to be the largest retailer in the country. It operates 10,901 stores as of 2019 end. https://relianceretail. com/ (accessed 14 September 2019).

Pantaloons
Pantaloons; a retail brand with three decades of experience in Bharat (India). It is part of Aditya Birla Fashion and Retail Limited (ABFRL). Its product portfolio includes clothing, footwear, jewelry, leather line, cosmetics, perfumes and watches. It has presence in 78 cities in the country. https://www.pantaloons.com/ (accessed 14 September 2019).

Construct
Initial items adopted/ modified from literature Mean value of the response = 5.02 Content change is needed. Mean value of the response = 5.78 Content change is needed. Mean value of the response = 6.13 Content change is needed. Mean value of the response = 5.58 Content change is needed.
I12. I feel more benefited when the shopping experience give me better store ambience, store personnel, food options, children play, window shopping, design/colors/navigation comfort of store app I13. I feel more benefited when the shopping experience give me better price, promotions, product availability, product variety, accessibility/speed/ security of store app I14. I derive more shopping value when marketing channels operated via limited platforms I15. I derive more shopping value when marketing channels operated via unlimited platforms Channel patronage (CP) I16. Retail channels will gain more patronage through Hedonic benefits. I17. Retail channels will gain more patronage through utilitarian benefits. I18. Retail channels will gain more patronage under multi channel environment. I19. Retail channels will gain more patronage under Omni channel environment. I20. Customer patronage retail channels which offer more shopping value. I21. I prefer to shop with Retail channels which offers me better shopping value in some prioritized aspects. I22. I derive higher shopping value with Retail channels which offer marketing channels via limited platforms I23. I derive higher shopping value with Retail channels which offer marketing channels via unlimited platforms.  I16. I prefer to shop with retailers who provide better store ambience, store personnel, food options, children play, window shopping, design/colors/ navigation comfort of store app. I17. I prefer to shop with retailers who provide better price, promotions, product availability, product variety, accessibility/speed/security of store app I18. I prefer to shop with retailers who offer marketing channels via limited platforms I19. I prefer to shop with retailers who offer marketing channels via unlimited platforms I20. I prefer to shop with retailers who offers me better shopping value in all aspects. I21. I prefer to shop with retailers who offers me better shopping value in some prioritized aspects. I22. I derive higher shopping value with retailers who offer marketing channels via limited platforms. I23. I derive higher shopping value with retailers who offer marketing channels via unlimited platforms.

Red color indicated for rejected items
Appendix A: (Continued)