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  1. 1.   1   MSc  Marketing  &  Strategy   (MSMS)   2014-­‐2015           Student  Name:  Udit  Karan  Chandhok   ID  Number:   1464519   Supervisor:    Dr.  David  Arnott   Date  of  submission:  September  2015   Word  Count:  15021           Cross-Pollination via the online grocery channel in the UK and exploring the need for virtual in-store experience
  2. 2.     2   Cross-Pollination via the online grocery channel in the UK and exploring the need for virtual in-store experience Submitted  by  Udit  Karan  Chandhok   Year  of  submission:  2015       Declaration     This  is  to  certify  that  the  work  I  am  submitting  is  my  own.  All  external  references  and   sources  are  clearly  acknowledged  and  identified  within  the  contents.  I  am  aware  of  the   University  of  Warwick  regulation  concerning  plagiarism  and  collusion.     No  substantial  part(s)  of  the  work  submitted  here  has  also  been  submitted  by  me  in   other  assessments  for  accredited  courses  of  study,  and  I  acknowledge  that  if  this  has   been  done  an  appropriate  reduction  in  the  mark  I  might  otherwise  have  received  will   be  made.     Copyright   This  dissertation  protected  by  original  copyright.      
  3. 3.   3   Acknowledgement First  and  foremost,  I  would  like  to  thank  Dr  David  Arnott,  my  dissertation  supervisor,   whose  patience,  advice  and  guidance  have  been  highly  valued  and  appreciated.  His   expertise  really  helped  me  to  shape  my  dissertation  overall.  I  owe  my  success  to  Dr   Arnott’s  thorough  and  always  helpful  feedback.   I  would  not  be  here  today  if  it  were  not  for  the  unconditional  help  and  support  I  always   received  from  my  family.  They  have  always  shown  immense  faith  in  me  and  have   driven  me  to  out  perform  myself.  I  also  would  like  to  thank  God  for  showing  me  the   right  path.   Finally,  I  would  also  like  to  thank  my  friends  for  always  being  there  to  motivate  me  and   be  there  for  me  whenever  I  needed  them.
  4. 4.     4   Table  of  Contents   Declaration   2   Acknowledgment   3   Chapter 1: Introduction   7   Chapter 2: Literature Review   9   2.1: Modern Consumption and Identity   9   2.2: Models of Consumer Behaviour   10   2.3: Theory of Reasoned Action and Theory of Planned Behaviour   13   2.3.1: Theory of Reasoned Action   14   2.3.2: Theory of Planned Behaviour   14   2.4: Online Shopping   16   2.4.1: Online Grocery Shopping   18   2.4.1.a: UK Online Grocery Market   21   2.4.1.b: Operational Strategy adopted for online grocery   22   2.4.1.c: Customer Segmentation   24   2.5: Future of the Grocery Industry   26   Chapter 3: Research Methodology   28   3.1: Research Strategy   28   3.2: Research Design   29   3.2.1: Primary Research   29   3.2.1.a: Semi-Structured Interviews   30   3.2.1.b: Self-Administered Questionnaires   30   3.2.1.c: Sampling   31   3.2.2: Secondary Research   32   3.3: Research Ethics   32   3.4: Reliability   33   3.5: Validity   33   3.6: Generalizability   34   3.7: Limitations   34   Chapter 4: Findings and Interpretations   36   4.1: Online Shopping   36   4.1.1: Frequency of online shopping and Average basket size   36   4.1.2: Delivery Cost influencing online shopping behaviour   38   4.1.3: Products purchased online   40   4.2: Behaviour of online shoppers   42   4.2.1: Risk factors in online shopping   46   4.3: Online Grocery Shopping   47   4.3.1: Cross-Pollination via online grocery shopping   51   4.4: Virtual in-store experience   55        
  5. 5.   5   Chapter 5: Conclusions   57   5.1: Consumer behaviour being planned   57   5.2: Typology of Consumers   58   5.3: Cross-pollination via the online grocery channel   58   5.4: Virtual in-store experience   59   Chapter 6: Managerial Implications   60   Chapter 7: Future Research   61   References   62   Appendices   76  
  6. 6.     6   Abstract The research focuses on the food retailing industry in the UK and evaluates the online grocery shopping industry and its value propositions for customers. Based on the typology of consumers, the research aims to suggest possible cross-pollination avenues for the online grocery shopping industry, which includes strategic alliances, brand associations, service associations etc. The research first takes in the feedback of the respondents via a semi-structured interviewing approach and then carries out a detailed research using the questionnaires. Conclusions of the research have been discussed in depth in the fifth section suggesting the impact of delivery cost on the frequency of shopping and hence the average basket size of the consumers. The research also identifies the factors that consumers would prefer to see in the cross-pollination strategy along with their adoption percentage.
  7. 7.   7   Chapter 1: Introduction The research has been conducted with the objectives of studying the online shopping industry along with the online grocery shopping industry; to explore the possible cross- pollination avenues, the online grocery industry could adopt to offer value propositions to the consumers. The research lays its foundations on the theory of reasoned action and the theory of planned behaviour as the defining factors of consumer behaviour in the online retailing industry. Conducting the research primarily in the United Kingdom, significant changes have been identified in the food-retailing industry and the varying consumer needs, which is an enabling factor to study these developments and suggest synergies that the industry could adopt to service the consumer better. The relationship between modern consumerism and identity has been traced back to identify the various consumer behaviour models. Social-psychological theories of behaviour and change have been used in the study to analyse the varying needs of the consumers. Strategic outcomes based on the social-psychological theories have been mapped to the ‘Big Middle theory’ that is used to create shopping motivations in consumers. The theory has been used to identify the similarities in the online shoppers and the online grocery shoppers. Focus has been laid on testing the typology of consumers in the industry using the conceptual framework developed by Huang & Oppewal (2006). The findings have been used to analyse the possibilities of cross- pollination via the online grocery-shopping channel. The cross-pollination objective of the research focuses on identifying the common needs of the online shopping consumers and suggest combined services, which can be offered to consumers in terms of product line expansion using the ‘Big Middle Theory’. Supportive facts such as the growing market size of the online shopping industry and the online grocery shopping industry have been discussed in chapter 2.
  8. 8.     8   Customer segmentation techniques have been identified in the online shopping industry and a need for delivery cost and time frame based segmentation is found. The research has mapped delivery cost to the frequency of online shopping and also its possible effects on the average basket size of the consumers. Understanding the influence of delivery and the importance of convenience factors for the customers, the research also focuses on cross-pollination via the online grocery channel. The product line extension and strategic alliances between different online retail verticals are suggested and tested to provide the customers with a more convenient and well priced service. The study also highlights the parameters with which the online channel should look at possible integration of services. This research is important to give more value propositions to the customers and to co-create value in the longer run. The importance of the study lies with the dependence of the industry on the seeking newer value propositions that can help maintain differentiation of services provided as against their competitors. The study adopts a semi-structured interviewing approach to frame the basic ideology of the questionnaire and the objectives of the research. The study has focused on the long-run strategy that could help the food retailing industry at large and suggests the first movers advantage on such a platform.
  9. 9.   9   Chapter 2: Literature Review In the last decade or so, food-retailing industry has seen significant changes with the emergence of new store formats and the increased prevalence of large retail chains (Dobson et al., 2003). Such developments have added to the changing consumer behaviour towards shopping habits and increased convenience attribute of shopping (Dobson et al., 2003). Enriching the shopping experience and making it a one-stop shop for consumer needs, retailing giants have diversified retail channels with multiple category stores and have been worked closely on improving the convenience factor with the online retail channel. This pattern of development in the industry has been common across Europe (Dobson et al., 2003). The chapter focuses on the modern consumption and identity, which has directed researchers to study the concept of consumer behaviour. The literature review aims at analysing the varying behaviour of consumers with respect to online shopping and more specifically the online grocery shopping industry and the strategies organizations adopt to broad base their product offerings to the consumers. Studying the online grocery industry in the UK, typology of consumers in the online shopping industry has been identified to structurally approach the task of future value propositions that the online grocery shopping industry can offer and at large the food retailing industry. The chapter also identifies the need for improvements in the enjoyment factors of shopping online with improvements in ease of use. 2.1: Modern Consumption and Identity Consumption in the words of Miller, a social scientist has become the ‘vanguard of history’ (Miller, 1995). Irrespective of tension between conspicuous and inconspicuous consumption, modern society has struck a broader agreement on the fact that, consumption is in some sense inextricably linked to the personal and collective identity (Jackson, 2005). Over the past few years authors have debated and taken stands on the
  10. 10.     10   relationship between identity and consumerism, arguing for it to be a good or a bad thing. Jackson (2005) complied these thoughts in his study and identified; an accessible choice of consumer goods is an important aspect in defining the individualistic modern consumer (Campbell, 1997). Some author’s views observed by Jackson (2005) also referred to the ‘empty self’ of the modern consumer with a continuous need of ‘filling up’ their desires (Cushman, 1990). Even with differences in the various schools of thoughts on the modern consumption, the link between the consumption of material goods and the construction and maintenance of personal identity is one of the most profound elements in modern understanding of consumer behaviour (Jackson, 2005). 2.2: Models of Consumer Behaviour The field of consumer behaviour embraces a lot of ground by studying the processes involved in the selection, purchasing, using or disposing of any goods; that individuals or groups engage in to satisfy their needs and desires (Solomon et al., 2013). Consumers can be of diverse age groups and may consume anything from tinned peas to a massage, democracy, pop music or a celebrity and fulfil desires ranging from hunger and thirst to love, status or even spiritual fulfilment (Solomon et al., 2013). Consumers get passionate about a broad range of products whether it is Nike’s MAG shoe, the perfect coffee bean or even the latest tablet computers; leading us to explore platforms that can give consumers a marketplace stage to co-create values by enriching the online shopping experience with cross-pollination. Innovation in the field of online shopping needs to be evaluated using the models of consumer behaviour to give an understanding of how consumers behave when their convenience can be amplified. Table 1 describes some of the various developed models on the social-psychological theories of behaviour and change highlighting the model the study would be adopting the theory of
  11. 11.   11   reasoned action and the theory of planned behaviour which have been discussed in depth below (refer 2.3). Table 1: Social-Psychological theories of Behaviour and Change Social Psychological Theory Key References Description Attitude-Behaviour- Context (ABC) Theory Stern and Oskamp, 1987 A kind of field theory (Lewin, 1964), which explores the environmental significant of behaviour. The theory suggests, behaviour (B) to be a collaborative product of the ‘internal’ attitudinal variables (A) and the ‘external’ contextual factors (C) that influence individuals (Stern & Oskamp, 1987). Cultural Theory Thompson et al, 1990 The theory incorporates the hypothesis of a four-fold typology of cultural ‘types’ with wide-ranging philosophies about governance and the good life: hierarchists, egalitarians, individualists and fatalists (Thompson et al., 1990). Elaboration-Likelihood Model Petty and Cacioppo, 1981 A persuasion model, which predicts the long-term success of a persuasive message depending on the level of mental processing or ‘elaboration’ of the message undertaken by the subject (target) (Petty & Cacioppo, 1986). Expectancy-Value Theory Ajzen and Fishbein, 1980 A broad class of theories based on the idea that behaviour of an individual is inspired by the expectations they have about the results of their behaviours and the values they attach to the outcomes of their behaviour (Jackson, 2005). Motivation-Ability- Opportunity Model Ölander and Thøgersen, 1995 An integrated behavioural model that combines both of the internal motivational variables – usually based on the theory of reasoned action developed by Ajzen & Fishbein (1980) – with external contextual variables of opportunity and ability (Jackson, 2005). Norm Activation Theory Schwartz, 1977 One of the better known attempts to model pro-social or altruistic behaviours: a personal norm (PN) to
  12. 12.     12   behaviour in a manner that is very pro- social and is activated by awareness of the consequences (AC) of one’s actions and the ascription of personal responsibility (AR) for them (Schwartz, 1977). Persuasion Theory Hovland et al., 1953; Petty et al., 2002 A set of theoretical approaches to the ‘art of persuasion’ that identifies (1) the credibility of the source, (2) the message, and (3) the thoughts/feelings of the receiver as the three critical structural elements in the success of persuasion strategies (Petty et al., 2002). Rational-Choice Theory Elster 1986 The fundamental basis of most economic theories of consumer preference and several other social- psychological theories of behaviour, suggesting that behaviour is the outcome of rational deliberations in which individuals seek to maximise their own expected ‘utility’ (Jackson, 2005). Self-Discrepancy Theory Higgins 1987 Feelings aroused by the perceived gap between their actual and ‘ideal’ selves motivate people’s actions (Higgins, 1987). Subjective Expected Utility (SEU) Ajzen and Fishbein, 1980; Eagly and Chaiken, 1993 A form of expectancy value theory (Ajzen & Fishbein, 1980) closely related to the ration (Ajzen & Fishbein, 1980)al choice (Elster, 1986) model, SEU theory suggests that behaviour is a function of the expected outcomes of the behaviour and the value assigned to those outcomes (Eagly & Chaiken, 1993). Structuration Theory Giddens 1984 Attempts to provide a model of the relationship between agency (how people act) and structure (the social and institutional context). Giddens structuration theory relies on a distinction between ‘practical’ and ‘discursive’ consciousness (Giddens, 1984). Theory of Planned Behaviour (TPA) Ajzen, 1991 Adjusts the Theory of Reasoned Action to encompass the actor’s apparent control over the consequences of his or
  13. 13.   13   her behaviour (Ajzen, 1991). Theory of Reasoned Action (TRA) Ajzen and Fishbein, 1980 The best-known social-psychological attitude-behaviour model, the Theory of Reasoned Action adjusts expectancy value theory to incorporate normative social influences on behavioural intention as an element of understanding the influence of social intentions on the consumers’ behaviour and also on innovation (Ajzen & Fishbein, 1980) (Jackson, 2005). Value-Belief-Norm Theory Stern et al., 1999; Stern, 2000 An attempt to modify Schwartz’s Norm Activation theory by including a more sophisticated association between values, beliefs, attitudes and norms (Stern, 2000). Source: Adapted from (Jackson, 2005) 2.3: Theory of Reasoned Action and Theory of Planned Behaviour In recent years, many theories and models have been developed and proposed aiming to explain and predict consumer’s behaviour online. Klein (1998) in his study suggested, “The Internet is particularly useful for seeking information in relation to search products due to low perceived search costs”. Theory of diffusion of innovations approach was adopted to devise the possible determinants of consumers’ adoption of electronic grocery shopping by Verhoef and Langerak (2001). According to the online pre- purchase intention model by Shim et al. (2001) which basis its findings on the theory on planned behaviour (Ajzen, 1991) conclude an important aspect of predicting consumer online buying intention. Hence it is important to have a detailed understanding of the theory of reasoned action and the theory of planned behaviour, which has been used to carry out the study.
  14. 14.     14   2.3.1: Theory of Reasoned Action The theory of reasoned action by Fishbein and Ajzen (1975) regards a consumer’s behaviour as determined by the consumer’s behavioural intention, where behavioural intention is a function of ‘attitude towards the behaviour’ and ‘subjective norm (SN)’. Chang (1998) described in his study on the theory, describes ‘attitude toward the behaviour’ as the “general feeling of favourableness or unfavourableness for that behaviour” and ‘subjective norm’ as the perceived opinion of other people in relation to the behaviour that is subject to questioning. The theory makes a conjecture on the intention of the consumer to perform a behaviour based on the attitude towards the behaviour rather than the consumer’s attitude towards a product or service (Hansen et al., 2004). The theory of reasoned action is concerned with the rational, volitional and systematic behaviour (Fishbein & Ajzen, 1975), that is, behaviours which are under the control of the individual (Thompson et al., 1994). This assumption has been widely criticised by researchers such as Sheppard et al., which suggest that, “actions that are at least in part determined by factors beyond individuals volitional control fall outside the boundary conditions established for the model” (p. 326). Such considerations have been incorporated into the theory of planned behaviour (Hansen et al., 2004). 2.3.2: Theory of Planned Behaviour The theory of reasoned action extends to the theory of planned behaviour where an addition of the ‘perceived behaviour control’ is made, which is a determinant of behavioural intention (Hansen et al., 2004). Figure 1 is an illustration of the theory of planned behaviour.
  15. 15.   15   Figure 1: The Theory of Planned Behaviour Image credits: (Ramus & Nielsen, 2005) Theory of planned behaviour frames the intention to perform an action on three constructs: attitude towards the action, subjective norm and perceived behavioural control (Ramus & Nielsen, 2005). Attitude towards the action and subjective norm have been discussed in depth under theory of reasoned action (refer 2.3.1). Perceived behavioural control refers to a person’s ability to perform a given behaviour (Ramus & Nielsen, 2005). According to Ajzen (1991), perceived behavioural control is expected to have an effect on the formation of intentions and on the behaviour itself (Ramus & Nielsen, 2005). The determinants of perceived behavioural control are the beliefs about factors that impede the performance of the behaviour (Ramus & Nielsen, 2005). In relation to the Internet purchase behaviour various researchers have adopted this model. Hansen (2008) suggests that the theory is well suited for the purpose of investigating consumer online grocery shopping behaviour. His research indicates that consumers may perceive obstacles and difficulties in performing online shopping which is based on the study carried out by Shim & Eastlick (2001) which stated that, “ when studying consumers, Internet purchasing behaviour, researchers should take perceived behavioural control into consideration in that Internet shopping does require skills, opportunities, and resources, and thus not occur merely because consumers decide to
  16. 16.     16   act” (Shim et al., 2001; Hansen et al., 2004). Hansen also identified that consumers may perceive risk and difficulties when considering online shopping, as they can be expected to use their cognitive resources to form their beliefs towards a related attitude, which in turn would result in the development of an overall feeling towards the behaviour in question (Zaichkowsky, 1985) (Rossiter & Percy, 1987). When trying to reduce perceived risk, consumers may also seek normative guidance from relevant others (Hansen, 2008). Consumer values are a central aspect to the consumer decision-making process. Claeys et al., (1995) claim, “values are the ultimate source of choice criteria that drive buying behaviour” (p.193). Social values define the desired behaviour or the end result for a society or the group, whereas personal values define desired behaviour or the end state of an individual (Blackwell et al., 2001). Social values are indirectly inherent in the theory of planned behaviour through the conceptualization of ‘social norm’, however personal values are not explicitly dealt with in the theory. Thus, both social norms and personal values are an important factor influencing Internet purchasing practices (Hansen, 2008). 2.4. Online Shopping Forecasts in the past few years have predicted that the value of goods and services purchased over the Internet could increase rapidly (Burger, 1996). E-commerce is the fastest growing retail market in Europe with sales in the UK, Germany, France, Sweden, The Netherlands, Italy, Poland and Spain are expected to grow from £132.05bn [€156.28bn] in 2014 to £156.67bn [(€185.39bn] in 2015 (+18.4%), reaching £185.44bn (€219.44bn) in 2016 (Centre for Retail Research , 2015). Researchers have examined the impact of online shopping environments on consumer choice (Swaminathan et al., 1999), the role of Internet shopping as a channel of distribution (Alba et al., 1997), factors influencing shopping online (Swaminathan et al., 1999), the impact of online
  17. 17.   17   shopping on price sensitivity (Shankar et al., 1999). Rohm & Swaminathan (2004) carried out a study describing the typology of online shoppers basing it on shopping motivations. They identified a need that was highly relevant in competitive online retail markets. Based on the Big Middle Theory, Ganesh et al. (2010) examined the online patronage behaviour and a comparison of shopper typologies, which would help; reveal shopper segments similar to those found in the traditional store formats. Levy et al. (2005) define the Big Middle Theory as “the market space in which the largest retailers compete in the long run, because this is where the largest number of potential customers reside” (p.85). Short term success can be found outside the Big Middle Theory, but many authors argue that over the long-term most successful niche or segment retail players would migrate towards the largest market segment by expanding their merchandising mix, increasing inventory turnover rates, lowering product margins and eliminating certain customer service elements (Ganesh et al., 2010). In the retail landscape, retailers adopt the Big Middle position – as either “Low-price” or “ Innovative” players (Ganesh et al., 2010). Innovative players target quality-conscious customers seeking high-end products while the Low-price retailers target price- conscious customers (Ganesh et al., 2010). Consumers from all segments gravitate retailers, which excel at innovating, offering low prices or both. With increased consumer acceptance for online purchasing and the continuous advancements in technology, some online retailers have adopted to the Big Middle market space like, and EBay Stores (Ganesh et al., 2010). Today these online retailer have diverse product lines and have been successful in lowering margins and expanding their concepts such as; affiliated marketing programs, blogs, cross- selling push technology, diversification into high priced segment of products (Ganesh et al., 2010). Retailers are always adapting to the ever-changing customer needs hence it is
  18. 18.     18   important to adapt the Big Middle theory which suggests “the existence of a core group of shoppers seeking a relatively consistent and more demanding bundle of retail attributes: broad and deep product mixes with consistently low prices” (Ganesh et al., 2007). Chang et al. (2005) describe the relative advantages of online shopping for customers as time saving, product value (price and quality), ease to order and decreased transaction costs. Their study also complied studies on online shopping experience, which highlighted concerns on user-friendliness and aesthesis of online shopping websites (Chang et al., 2005). Service quality is a factor of superiority or excellence in the online shopping industry (Parasuraman et al., 1985) as it has a positive impact on the purchase intention of the customer to shop online. Jeff Bezos of said “one secret of the company’s success is thinking of ways to make online shopping experience more fun” (Star Tribune , 1999). 2.4.1 Online Grocery Shopping A study conducted by the University of Michigan concluded that among the 22 favourite household tasks, grocery shopping came next to last (Richards, 1996). The founders of the online grocer suggested in their research that consumers regarded grocery shopping as the chore they dislike the most next to dentist (Corral, 1999). These findings lead to hypermarkets and large food retailers to develop the online channel of grocery shopping. Internet grocery shopping has faced serious difficulties such as transaction obstacles, slow load times, inability to locate items, incomplete information, lack of human interaction, missed or late deliveries; which has affected the adoption percentage (Kaufman-Scarborugh & Lindquist, 2002). Research has also highlighted issues such as ease of use and security (Elliot & Fowell, 2000). Consumer adoption of online grocery buying has driven researchers to study the
  19. 19.   19   perceived characteristics of innovation that enable this adoption such as perceived compatibility, perceived relative advantage, and perceived complexity (Rogers, 1983) are some of the factors studied (Huang & Oppewal, 2006). A conceptual model (refer figure 2) developed by Huand and Oppewal (2006) tests the consumers’ choice of channel for grocery shopping. The study measures the effects that are mediated by the perceived differences between online and in-store shopping conditions considering factors such as costs, convenience, enjoyment and risk. Cost factors in the study concerns with the difference in monetary cost perceived by consumers when comparing online and in-store grocery shopping (Huang & Oppewal, 2006). Bell et al. (1998) identified ‘fixed costs as travel costs associated with going to a store plus a shoppers’ inherent preference and historic loyalty for the store, while the variable costs depend on the consumers’ shopping list.’ Convenience factors concerns with the psychological costs and other forms of non- monetary costs such as time, effort and stress (Huang & Oppewal, 2006). Shopping convenience can be defined as ‘a reduction of the opportunity costs of effort and time involved in shopping activities’ (Berry et al., 2002). Compared to in-store shopping, online shopping offers a great deal of comfort to the consumer possibly from anywhere and anytime. Online shopping also has inconveniences such as Internet connectivity and ease of use. However, time and effort are the some of the selling points in the online grocery industry. Enjoyment factors or shopping enjoyment as defined by Beatty and Ferrell (1998) is ‘the pleasure one obtains from the shopping process.’ The concept of shopping enjoyment relates the differences between hedonic and utilitarian shoppers (Huang & Oppewal, 2006). In the online setting and with the technological advancements, “virtual
  20. 20.     20   reality” and “interaction” with customers and providers has aided the process of online shopping which was identified as one of the future implications in their study. Figure 2: A conceptual model to test consumers’ choice of channel for grocery shopping Source: (Huang & Oppewal, 2006) Risk factors are the most crucial aspect the industry as a whole has been trying to counter for years. A wide array of research on perceived risk and its impact on consumer behaviour has been carried out (Mitchell, 1999). It is of particular interest that perceived financial risk and leakage of personal information is only one segment of the perceived risk customers have when adopting online shopping (Forsythe & Shi, 2003). Product performance risk was a key factor identified through this study (Forsythe & Shi, 2003). The conceptual model to test the consumers’ choice of channel for grocery shopping and the Big Middle Theory (refer 2.4) has been integrated to examine the responses in this study along with consumer behaviour model (refer 2.3).
  21. 21.   21   2.4.1.a: UK Online Grocery Market In the UK market, key players in the online channel have been the traditional retailers, including Tesco, Sainsbury, Asda and Waitrose (Mills, 2001; Stewart, 2000; Thomas, 2002; Wearden, 2002); with only Ocado being the exception to the market with only online channel of servicing (Thomas, 2002). Various other players have entered in the market ever since such as Aldi, Lidl, Marks & Spencer’s which have been doing well in the store concept but not scoring so well in the online channel of retail. The online grocery market in the UK is about £8.9bn in the year to March 31sr 2015 as shown in figure 3 (The Institute of Grocery Distribution , 2015). As a whole, the UK grocery market was worth £177.5 billion in the year to March 31st 2015, an inflation adjusted increase of 1.7% on 2014 as shown in figure 4 and as forecasted by IGD in their survey the UK grocery market value in absolute terms would be worth £200.6bn in 2020, a 13.0% increase in 2015 (The Institute of Grocery Distribution , 2015).
  22. 22.     22   Figure 3: What channels make up the UK grocery market? Source: IGD UK Grocery: Market and channel forecasts 2015-2020 (The Institute of Grocery Distribution , 2015) Figure 4: Market Forecasts on previous trends Source: IGD UK Grocery: Market and channel forecasts 2015-2020. (The Institute of Grocery Distribution , 2015) 2.4.1.b: Operational Strategy adopted for Online Grocery The operations strategy process is most often modeled as a hierarchical one under which functional strategies such as operations, logistics, marketing and finance that are driven
  23. 23.   23   by the business level strategy of the organization (Boyer & Hult, 2005). A key element of the strategic framework involves co-ordination of the functional level strategies to work in concert to achieve the overall business strategy of the firm. Majority of studies carried out on the operational strategy of e-commerce industry, implicitly assumes the business methods to provide a new means of seamless integration in the functional level strategies (Boyer & Hult, 2005). Figueiredo (2000) provides a conceptual mapping of the marketing characteristics onto the operating characteristics to identify the promising e-commerce strategies. Benefits of syndication were examined by Werbach (2000) to identify the strategies for using the Internet to revise supply chain management. Analyzing the operational strategy of Webvan in the US and Tesco in UK, Boyer & Hult (2005) highlight completely different operational approaches adopted for online grocery retailing. The study highlights first and foremost challenge for companies which channels there sales through online grocery as, the delivery of groceries to the customer being stricken with severe logistical difficulties (Boyer & Hult, 2005). Webvan tried to build their market share by offering its customers door delivery in a 30- minute time window. Webvan’s marketing and operations strategy were or well matched (Boyer & Hult, 2005). Providing low cost groceries and providing timely delivery could not be matched with the operational resources required such as warehouses, logistics and supply chain management with a very difficult marketing strategy to accomplish (Boyer & Hult, 2005). On the other hand, Tesco adopted a completely rational and successful strategy. Tesco’s marketing strategy for its online channel is convenience and not a low-price option. Secondly, Tesco has kept the operations of grocery delivery simple by using their existing assets to help integrate the channel operations (Boyer & Hult, 2005). Online orders are filled by employees at the nearest Tesco store and are followed through by the delivery team at each store (Boyer & Hult, 2005). Taking an alternative approach of
  24. 24.     24   marketing their services to customers, Tesco provides convenience as an added option that cost’s customers more, but which can in turn be supported by operations at a little extra financial cost (Boyer & Hult, 2005). In the UK, the major players in the online grocery-retailing segment follow a similar model other than Ocado, which follows a single warehouse practice to deliver groceries (Ocado Group plc, 2013). 2.4.1.c: Customer Segmentation A variety of shopping behaviors have been identified by researchers such as; economic, store-loyal, recreational, convenience, price-oriented, brand-loyal, name-conscious, quality, brand conscious, and impulsive shoppers (Siu et al., 2001). Sin & Tse (2002) in their research found that convenience oriented and impulse consumers were more inclined to shop online, however time conscious consumers were not. Customers with more experience purchasing approach tend to avoid online shopping and recreational shoppers had mixed approaches towards online shopping (Sin & Tse, 2002). With today’s social structure and corporate and business pressures, recreational shopping should be one of on-go activities people engage in as expressed by the growing online shopping market (refer section 4). However, the study also highlighted the fact that online shopping was not a preferred choice for price-oriented and brand conscious customers (Sin & Tse, 2002). Consumer demographics researches have incorporated mixed variables such as; gender, age, education, and income, which have been found to have positive relationships with online shopping in general (Chang et al., 2005). Researchers have often concluded that probably, demographic variable do not generate the relationships, but they are caused by the deeper structure variables as discussed above (Chang et al., 2005). Innovativeness, which is regarded as a personality characteristic (Chang et al., 2005), is another segmentation factor that separates online shoppers from the traditional in-store shoppers in today’s times. Goldsmith (2001) suggested that the innovative behaviour is not apparently consistent across domains and hence devised the domain specific
  25. 25.   25   innovativeness (DSI) index, to study online shopping behaviour and segmentation of customers. Researchers have found positive relations of the DSI index and the intention to purchase online (Limayem et al., 2000). Personality variables have also been the determinant factors of consumers shopping online. Theory of planned behaviour (Ajzen, 1991) and the Traindis model (Triandis, 1980) have been used to guide psychosocial researches in the field (Chang et al., 2005); supporting the study using the Theory of planned behaviour to evaluate the typology of consumers shopping online. Figure 5: View of the shopping typology literature Source: (Rohm & Swaminathan, 2004) As shown in Figure 5, Rohm & Swaminathan (2004) identified numerous motives to research on the typology of online shoppers and their research focused on the online grocery shopper segment. Their literary review identified convenience as a distinct motive for store choice in the offline setting for the convenience shopper. Information seeking in the retail setting is a shopping motive explicit in the offline context in the early times (Bellenger & Korgaonkar, 1980). Rohm & Swaminathan (2004) also highlighted the findings of Balasubramanian (1998), which suggested that direct marketers could reduce consumer resistance to catalog or Internet purchases by fine
  26. 26.     26   scaling the delivery time frame. Social interaction as a source of shopping motivation was another factor that Rohm & Swaminathan concluded in their literary research. Conclusions of their study mapped the store-oriented shoppers to be motivated by factors such as immediate possession and social contact (Rohm & Swaminathan, 2004). Variety seekers were characterized as seeking in retail alternatives or products and brands and the balanced buyers exhibited lower propensity to the planned purchases suggesting more impulsive purchases online (Rohm & Swaminathan, 2004). Enriching the online retail shopping experience was one of the crucial industry implications that the study concluded along with indicative managerial suggestions to adopt strategic alliances with a long run objective to address the rising needs of online shoppers of all segments (Rohm & Swaminathan, 2004). Hence, delivery cost and time frame have been identified to be critical factors influencing the consumer purchase behaviour of online shopping resulting in better customer segmentation practices in terms of minimum cost of delivery depending on the basket size of the shoppers. 2.5: Future of the Grocery Industry Terbeek (1996) suggests, “The future of the retail food industry is less about the incremental supply chain improvements and more about redistributing rewards and profits along the consumer’s value chain according to the value created” (p. 93). Retailers should aim at enabling consumers to increase the number of tasks they can accomplish in one trip or reduce the time required to complete the shopping task by adding product lines and thus improving convenience for shoppers (Kinsey & Senauer, 1996). Product assortment and addition of service variety are some of the key dimensions highlighted by Kinsey & Senauer (1996) to counter the convenience factor of food retailing shopping. Another dimension suggested by them was home shopping (Kinsey & Senauer, 1996), which is central task in this study. Online grocery shopping
  27. 27.   27   services have the potential to fulfill the goals of both consumers and grocery store operators. The online shopping industry is almost a reflection of our culture today and the practice is becoming widely acceptable. Barriers to entry and exit are relatively lower in the industry as compared to the traditional supermarkets (Keh & Shieh, 2001). The online grocery industry has vied its solid presence in the e-commerce derby. Bargaining power of retailers and the buying power of consumers is really high in the industry that are big supermarket players with more leverage on relationships on both ends (Keh & Shieh, 2001). The industry has also been growing rapidly as it today constitutes about 5% of the overall industry revenue in the UK (refer 2.4.1.a) and is part of a £132.05bn online retail shopping industry (refer 2.4). The industry offers lucrative returns on expansion of the product lines or strategies alliances using cross-pollination in the retailing segment. The chapter has helped identify the research gap and empirically supports the exploration of the relationship between online grocery shopping and online shopping habits of consumers. The chapter has also identified the cost of delivery of goods being a major influencing factor in the purchasing decision of consumers. With convenience being an important factor in the online channel of retail, ease of use and enjoyment enrichment for consumers is a key finding in the chapter. This has helped support the need for ‘Virtual in-store shopping’ which is one of the research objectives.
  28. 28.     28   Chapter 3: Research Methodology This chapter discussed the research strategy and the research methodology that has been used test and answer the research questions. The chapter begins with the explanation of research strategy and the research design that are adopted to carry out the study. Data collections and sampling techniques are discussed elaborately in the chapter as well. The chapter is concluded with limitations of the research strategy chosen. 3.1: Research Strategy The central objective of the research is to identify possible value propositions for the food retailing industry through the online channel of retail. To test the ‘Big-Middle theory’ and the conceptual model developed by Huand and Oppewal (2006), it is crucial to carry out an in-depth analysis to conceptualise the future model of retailing that the industry could adopt. As suggested by Bryman & Bell (2011), “qualitative research has an inductive view of the relationship between theory and research” (p.286). The study not only works on the generation of theory but also tests the theory in the deductive process. The study also embraces the epistemological basis of the natural sciences to be constructed referred to as realism (Bryman & Bell, 2011). Fleetwood (2005) suggests that critical realism offers a more effective alternative to postmodernism for organization and management studies as it shadows the ambiguity associated with the postmodernism that curtails from ontological exaggeration. Cognitive mapping is used to capture individual perspectives as the process is based on the assumption that people interpret data differently and hence will have different ways of comprehending a problem (Eden, 1992). The method of research draws inferences from the personal construct theory (Kelly, 1955), which supports the use of repertory grid technique and is based on the assumption that there is active engagement of the respondents in construction of models, hypothesis, or representations on the real-life scenarios (Bryman & Bell, 2011). The mapping process involves participants in
  29. 29.   29   identifying the factors that affect a particular decision making goal (Bryman & Bell, 2011). This method has been used in carrying out the study to analyse the factors that influence the purchasing decisions of consumers via the online channel of retail. Hence, in-depth interviewing is one of the approaches of data collection. 3.2: Research Design Qualitative research tends to view social life in terms of processes and lays strong footings on ‘the sequence of individual and collective events, actions and activities unfolding over time in context’ (Perrigrew, 1997). As explained the section 3.1, the research uses cognitive mapping techniques to gain feedback on the respondent behaviors to frame the hypothesis and test them in real time. The research employs open-ended questions as favour respondents to answer questions in their own terms and also allows unusual responses to be derived (Bryman & Bell, 2011). The study uses the survey methodology of research, which comprises of the cross-sectional design in relation to which data are collected predominantly by questionnaire or structured interview (Bryman & Bell, 2011). The research follows a cross-sectional design in the second phase that helps entail the collection of data on more than one case at a single point in time and collects both qualitative and quantitative data in connection with one or more variables that are analyzed to detect patterns of association (Bryman & Bell, 2011). 3.2.1: Primary Research Vidich & Shapiro (1955) suggested “Without the survey data, the observer could only make reasonable guesses about his area of ignorance in the effort to reduce bias” (p. 31). Researchers using qualitative methodology should be encouraged to systematize observations using sampling techniques and developing quantifiable schemes for answering complex problems (Jick, 1979). Primary research is carried out in two phases. Phase one focused on the in-depth interviewing of the respondents and phase
  30. 30.     30   two took a more quantifiable feedback using questionnaires, which was formulated on the learning from phase one of the research. 3.2.1.a: Semi-Structured Interviews Business research interviewers aim to elicit all manners of information from the interviewees about their behaviour or that of others, attitudes, norms, beliefs and values (Bryman & Bell, 2011). Many writers embrace qualitative interviewing as being both the semi-structured and unstructured kind (Carol, 2002). The study adopts the semi structured approach interviewing to collect data in a general frame of reference with latitude to ask further questions in responses to find significant replies (Bryman & Bell, 2011). Under phase one of the data collection, 10 interviews were collected from online shoppers and online grocery shoppers. The in-depth interviewing approach was adopted to probe important aspects of the online shopping experience and the personal characteristics and behaviors of shopping via the online channel. Probing is a highly problematic area for researchers employing a structured interviewing technique (Bryman & Bell, 2011). This directed the study to adopt the semi-structured approach in order to help respondents understand the concepts being tested and also get adequate answers and feedback on the same for analysis. The probing approach has directed the study to unexplored knowledge and has given consumer attitude towards shopping (Bryman & Bell, 2011). Also the interviewing technique has helped develop the questionnaire for the second phase of data collection. 3.2.1.b: Self-Administered Questionnaires In the second phase of primary research, questionnaires responses were collected from the respondents. Self-completion questionnaire or self-administered questionnaire is the approach where the respondents answer the questions by completing the questionnaire themselves (Bryman & Bell, 2011). The questionnaire focused on the objectives of shopping online for respondents and also measures the factors such as convenience,
  31. 31.   31   enjoyment, risk and price that influence their decision-making. The questionnaire also tests the possibility of virtual in-store experience, which is one of the research objectives. The questionnaire uses open-ended questions on the feedback users would have on the concept of virtual in-store and would it enrich the factors of enjoyment for the respondents. The questionnaire adapts its scale from the study conducted by Huand and Oppewal (2006) on the typology of online grocery shoppers. 3.2.1.c: Sampling Phase one of data collection used the snowball sampling technique to individuals and groups of people for whom there is no sampling frame. Pettigrew and Mchulty (1995) used the same technique to carry out in-depth interview for their research into part time board members of top UK firms. When there is no accessible sampling frame for the population from which the sample can be derived and the study has difficulty in defining the sampling frame snowball sampling should be applied (Bryman & Bell, 2011). This approach has been helpful in getting respondent referral to widen the consumer perspective on online shopping and its influencing purchasing factors. The drawback of the sampling technique has been with the representation of the population, which lead to the second phase of the research. Stratified sampling was applied for the second phase of data collection. Under stratified sampling, the population is stratified by a criterion and selecting either a simple random sampling or a systematic sampling from each of the resulting strata (Bryman & Bell, 2011). The study creates strata on the basis of gender and the consumer shopping habits via the online channel. A simple random sampling has been applied to connect to 100 respondents out of which 54 respondents replied to the questionnaire. Time and cost considerations were relevant factors influencing the sample size. The response rate was calculated to be 54% with 37 responses being unsuitable or uncontactable member of the sample. The sampling frame is also adapted from the study conducted by Rohm &
  32. 32.     32   Swaminathan (2004), which segments the users as being online shoppers, online grocery shoppers, online shoppers and online grocery shoppers, and people who do not engage in online shopping. 3.2.2: Secondary Research Operational theories for cross-pollination and the outsourcing strategy by various online grocery retailers have been adapted to study the cross-pollination strategy for online grocery stores. Secondary research has also been used to understand the ‘Big Middle Theory’ of online shopping (Levy et al., 2005) and its possible linkages with the conceptual online grocery-purchasing model (Huang & Oppewal, 2006). Advantages of the cataloging system in the online shopping experience (Gehrt & Shim, 1998) as compared to the proposed virtual in-store experience have been compared. Also the diminishing risk factors via the online shopping channel have been evaluated using literature and the data collected in the primary research as more and more organizations have moved to a transparent returns policy and the e-commerce industry providing safer monetary transactions. 3.3: Research Ethics The research has been conducted ethically and the questions asked during both the phases of data collection have been strictly under the boundaries of ethical code of conduct and are concerned with the purpose of the study. A growing concern has been expressed with the ethical ways of data collection and ensuring anonymity in the data collection (Bryman & Bell, 2011). The research has been conducted keeping the privacy of respondents in mind. The interviews were recorded with consent of the interviewees and anonymity has been maintained. In the second phase of data collection, only the required sign in to access the questionnaire and a time stamp of the response being submitted have been recorded. The respondents have been given an option of not answering the question if deemed inappropriate and irrelevant to the study.
  33. 33.   33   By taking the above stated measures, the researcher has aimed in creating a stress free setting for interviewees and respondents to revert while maximizing the effectiveness of the research. 3.4: Reliability Reliability concerns with the fact whether the results of the study are repeatable (Bryman & Bell, 2011). The interviewing phase of data collection may not be entirely repeatable due to differing viewpoints of the interviewees, which is one of the disadvantages of using the interviewing approach to collect data. The questionnaire phase of data collection is repeatable as the scaling technique of the questionnaire has been adapted from the study on online grocery shopping conducted by Huang & Oppewal (2006). Using the cross-sectional study the problems with the reliability of the research is primarily related to the quality of the measures that are employed to evaluate the concepts (Bryman & Bell, 2011). Replicability is highly present in the study as the study employs the process of selection of the respondents, designing of the measures of the concepts, administrating the research instruments and analyzing the data collected (Bryman & Bell, 2011). Hence, the replicability of the study is highly possible as the survey research design has been used. 3.5: Validity Validity is concerned with the integrity of the conclusions that are generated from the research (Bryman & Bell, 2011). Measurement validity addresses the question whether or not a measure that is devised of a concept reflect the concept it should denote (Bryman & Bell, 2011). The causal impact of the independent variable on the dependent variable defines the internal validity of the research. The study has embraced the concepts of internal validity research and has measured the results, which are stable leading to its reliability. The study is also ecologically valid as it credible, transferable, dependable and confirmable as suggested by Lincoln & Guba (1985). Conclusions of
  34. 34.     34   the study develop a causal impact of the delivery costs on the frequency of purchase and also on the average basket size of shopping and also affecting the consumers’ behaviour. Internal validity in the cross-sectional study is typically weak, however, the external validity is quite strong (Bryman & Bell, 2011). The results are stable leading to reliable and generalizable research as the research employs the stratified sampling techniques. 3.6: Generalizability Generalizability concerns with the question whether the results of the study can be generalized beyond the specific research context (Bryman & Bell, 2011). The results of the research can be applied to other industries seeking future value propositions and also adapted by the food retailing industry in the other channels of retail. The study analyses the typology of customers and test the theory of planned behaviour in the decision making process. This aspect of the research can be validated in the external scenario with industries such as the technological retailing and the gaming industry with consumer needs on virtual reality. 3.7: Limitations The study has been constrained with a time bound response rate and the sample size taken into consideration is not adequate to suggest strong organizational or theoretical changes. The spread of the strata in the sampling technique are not sufficient to give a detailed analysis of the cross-pollination need in the food retailing industry. This has bounded the research to present facts generalizable only to a certain extent. The interviewing phase of data collection suffered the gender disparity in the sample and a more influencing conclusion could not be drawn, as aspects of primary shopper in the family could not be identified in many cases. The qualitative aspect of the research could not be supported due to financial aspects involved in creating a virtual in-store experience platform where the interviewees could give detailed feedback. The
  35. 35.   35   questionnaire approach was adopted for data collection with lesser open ended questions to improve the effectiveness of the research and hence could not incorporate a broader perspective of the respondents, as the questions were self-administered and no probing could be done. 36 respondents could not be contacted again for further feedback on the questionnaires that were incomplete. The study only concerns with the consumer aspect of the online shopping experience and rely on previous researches to help give a more operational and commercial reasoning for the suggestions. There is empirical need to carry out research on the operational aspect of cross-pollination strategy in the food retailing industry.
  36. 36.     36   Chapter 4: Findings and Interpretation 4.1: Online shopping Phase one of the data collection shows the interviewees to be online shoppers with engagement in purchasing at least once a week and come under the category of regular online shoppers. In the second phase of data collection, out of the 54 completed responses, 52 respondents engage in online shopping (refer appendix 1). 4.1.1: Frequency of online shopping and Average basket size of the shopping Interviewing phase of the research suggested that, consumers generally shop once or twice a week and some interviewees shopped once a month. Hence the scale was adopted to understand the shopping patterns in the second phase of the research. Data analysis in the second phase revealed that 8% of the respondents shop at least once a week and 9% of the respondents shop twice a week. Both the categories of shoppers come under the frequent category of online shoppers. 25% of the respondents shop at least once in two weeks and 10% of the respondents shop at least once in three weeks. Data analysis has revealed that 50% of the respondents shop at least once a month via the online channel. Figure 6: Average Frequency of shopping online Once  a  Week   8%   Twice  a  Week   9%   Once  in  two   Weeks   23%   Once  in  three   Weeks   10%   Once  a  Month   50%   Average  online  shopping  cycle  
  37. 37.   37   The interviewing phase of the research reflected upon the frequency of online shopping as being associated with the average basket size while shopping online because of the delivery costs involved. Most of the interviewees’ shopped in the price bracket of £20 to £40 when they shopped online. Some of the interviewees’ were explicit about the change in average basket when the delivery cost over a certain amount is free. The stretch in budget often seemed to be a hurdle as stated by one of the interviewee (Refer appendix 2). Generally the feedback in the interviewing phase about the frequency and the average basket size ranged from once a week to once a month with varied time frames, but the average basket size changed drastically in case of family interviewees where their frequency was once in two weeks with an average basket over £100 pounds. In one of the interviews, the respondent said that, “being a man I do like to planned purchases, however my wife is an impulsive shopper.” The respondent also added, “my average basket size varied between £100 to £200 in a week” (refer appendix 4). In the second phase of the research, respondents were asked about their average basket size and frequency of shopping and they have been plotted below in figure 6. Table 2: Average basket size of shopping along with the frequency of shopping (52 respondents) Average  basket   size  (52)   Once  a   week  (6)   Twice  a   week  (5)   Once  in   two  weeks   (12)   Once  in   three   weeks  (5)   Once  a   month   (26)   £10  -­‐  £20  (8)   0   1   1   0   6   £21  -­‐  £40  (28)   1   4   5   5   13   £41  -­‐  £50  (10)   1   0   5   0   4   More  than  £50   (6)   2   0   1   0   3   The data in figure 6 shows a lot of variation in the average basket size of shopping by the respondents. The average basket size shows over 54% of respondents shop for £21 to £40 and 19% in the range of £41 to £50. Only 12% of the respondents shop over £50
  38. 38.     38   and 15% of the respondents are budgeted shoppers in the basket range of £10 to £20. When drilling down the average basket size with the frequency of shopping, as shown in figure 6, the data shows that out of the 8 respondents in the shopping basket range of £10 to £20, 6 of the respondents shop once a month. The basket size of £21 to £40 shows the most variation in the respondent behaviour with only 3.5% of respondents shopping once a week, 14.28% of respondents shopping twice a week, 17.85% of respondents shopping once in two weeks, 17.85% of respondents shopping once in three weeks and 46.42% of respondents shopping once a month in the price range. Data collection shows more preference for this basket size with some variations in the upper and lower brackets as well. An encouraging percentage of online shoppers, shop for over £50 shop in a week with 33.33% shoppers in the segment shop at least once a week and 50% of the shoppers in the segment shop at least once a month. 83.33% of shoppers with a frequency of once in two weeks shop in the price range of £20 to £50. 4.1.2: Delivery Cost influencing online shopping behaviour The in-depth interviewing phase highlighted the factor of delivery costs influencing the frequency of shopping online and the average basket size. One of the interviewee’s respondent to the impact of delivery cost by saying, “I don’t like paying for delivery because I am from India and back home everything was free delivery.” Some of the interviewees’ felt that the free delivery scheme did not benefit them because the delivery time is very elongated (refer appendix 3). One of the respondents said that, “the cost of delivery is okay the only thing I don’t feel good about is the delivery time” explicitly mentioning the discomfort to the factor. One of the interviewee’s gave a reference example of a friend who did not receive his order in over 15 days and had to cancel on the later stage (refer appendix 7). The feedback from the interviewing phase of data collection helped tabulate this factor in the second phase of the research and the response spread is shown in table 3 and table 4. In the strata of 52 respondents who
  39. 39.   39   engage in online shopping, 86.53% find delivery cost to be an influencing factor in the decision-making process of shopping online. Analysis of table 3 and table 4 show that delivery costs influence the purchasing decision of online shoppers, which influences their frequency of shopping online and also the average basket size. This indicates towards the minimum order value to affect the decision making of customers of both the genders and can be seen across age groups. Table 3 discusses the delivery cost influencing the frequency of online shopping. 49% of the respondents shop online only once a month because of the delivery costs being higher and their average basket size is over £20 (refer table 2). The data also highlights another time frame that the consumers generally shop online. 27% of the respondents projected their views about shopping only once in two weeks and the time frame includes a combined average of 47% (refer table 3) Table 3: Delivery cost influencing the frequency of shopping online Delivery   Cost   Once  a   week  (4)   Once  in   two  weeks   (5)   Once  in   two  weeks   (12)   Once  in   three   weeks  (5)   Once  a   month   (26)   No  (7)   1   1   0   1   4   Yes  (45)   3   4   12   4   22   Table 4: Delivery cost influencing the average basket size of shopping online Delivery   Cost   £10  -­‐  £20   (8)   £21  -­‐  £40   (28)   £41  -­‐  £50   (10)   Over  £50   (6)   No  (7)   1   6   0   0   Yes  (45)   7   22   10   6   Table 3 and table 4 highlight the effects of delivery costs on the frequency of online shopping and hence having a resultant effect on the average basket size of the shopping. The data shows that the delivery costs have a strong affects on the frequency of shopping and hence the average basket size. Shoppers aim at reducing effects of the overall cost of shipment by purchasing products within a timeframe when they can
  40. 40.     40   create the basket of the amount to minimize delivery costs for the products they shop online. 4.1.3: Products purchased online In the first phase of the research, interviewees were asked to give feedback on the products they usually buy online. All the respondents who shopped online engaged in buying clothes online. The female interviewees were keener on purchasing clothes online and described that apart from the clothing segment they preferred purchasing shoes and fashion accessories online. One of the respondents also referred to a category of electronic accessories, which they purchased because of a wide variety and the cost factor (refer appendix 5). One of the female respondent showed keen interest in purchasing branded bags online (refer appendix 6). On similar grounds, the second phase of study asked the respondents to choose products that they usually shop for via the online channel and the responses have been charted in figure 7 below. Figure 7: Product respondents purchase online The data in figure 7 shows that clothes are the most preferred products purchased online with 75% of the respondents shopping for clothes online. Fashion accessories are the 39   24   26   21   25   2   Clothes  (75%)  Shoes  (46.2%)   Fashion   Accessories   (50%)   Groceries   (40.4%)   Electronics   (48.1%)   Others  (3.8%)   0   5   10   15   20   25   30   35   40   45   Products  purchased  by  respondents   online  
  41. 41.   41   second most purchased product online with 50% respondents shopping them online. 48% of the respondents liked shopping for electronics online and 46% of them preferred shoe shopping as well. Online grocery shopping is preferred by only 40.4% of the respondents, which has been discussed in depth in the later sections of data analysis. Respondents in the questionnaire suggested books and vehicular accessories to be some other product categories they prefer to shop online. The data reflects on the product categories that the consumers are generally interested in. Clothing being the most preferred category of products purchased online. The analysis was done to get a consumer perspective of what they want to buy when they shop online. Table 6: Cross-tabulation analysis of Gender and Clothes Shopped online Crosstab Clothes shopped online TotalYes No Gender Male Count 12a 9b 21 % Within Gender 57.1% 42.9% 100.0% Female Count 27a 4b 31 % Within Gender 87.1% 12.9% 100.0% Total Count 39 13 52 % Within Gender 75.0% 25.0% 100.0% Each subscript letter denotes a subset of Clothes shopped online categories whose column proportions do not differ significantly from each other at the .05 level.
  42. 42.     42   Table 6.a: Chi- Square Tests Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi- Square 5.991a 1 .014 Continuity Correctionb 4.500 1 .034 Likelihood Ratio 5.959 1 .015 Fisher's Exact Test .022 .017 Linear-by- Linear Association 5.876 1 .015 N of Valid Cases 52 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.25. b. Computed only for a 2x2 table The chi-square test reveals the Pearson Chi-square value to be 5.991 with a significance of 95%. This shows that there is a valid association between the products those consumers buy depending on their gender and hence a dependence on their average basket size and the frequency of shopping can be established. 4.2: Behaviour of online shoppers Big Middle refers to the market-space in which large retailers compete in the longer run with economies of scale, increased revenues and incremental profits (Levy et al., 2005). The model refers to the expansion of offerings into broader and deeper product lines and also expansion of markets to reach a large potential audience (Levy et al., 2005). The theory is well implemented in the online channel of retailing but doesn’t refer to any typology of customers is the potential audience. The interviewing phase of the study focused on identifying the important typology factors that influence respondents’ engagement with online shopping. Most of the
  43. 43.   43   interviewees’ felt that online shopping was very convenient and shared their convenience stories in the study. One of the interviewee stated that, ‘It just saves my time and one gets more product variety with suitable ease of access and use as the shopper can browse through the categories and click and buy it’ (refer appendix 6). “It my gateway to know what’s out there” was another highlighting input by one of the interviewee’s suggesting the cataloging system adopted by online shopping portals provides convenience and ease of use to the shoppers. A reason that was seldom preferred by one of a respondent in the in-store shopping was the queuing and space constraints in the stores, which makes online shopping a much conducive and convenient option (refer appendix 3). One of the interviewee’s categorically mentioned about convenience being the biggest plus point of shopping online and added, “the online shopping is bit cheaper in the overall terms as I don’t like to go far away to buy clothes in London when I can just buy one or two things online” (refer appendix 8). The interviewing phase also highlighted the cost factor as being an advantage when shopping online. An interviewee shared an experience of prices at stores like H&M being almost double of what is available online (refer appendix 3). Another respondent also felt that the offers are a lot better on the online channel and it brings with it a convenience factor, which makes online shopping a very enjoyable experience (refer appendix 9). The respondent also added, “ unless its like a luxury market, like I would love the Louis Vuitton experience, like you enter their showroom and the way they treat you” and referred to it as being a shopping experience which can never be replicated or replaced by online shopping (refer appendix 9). Hence the convenience, ease of use and accessibility, and the price factors were given positive feedback by the interviewees with an interesting feedback on the risk factors, which have been discussed in the section 4.2.1. The questionnaire phase asked the respondents about their perception about these factors affecting their online shopping behaviour.
  44. 44.     44   Figure 8: Graphical plot of the factor important for customers when shopping online Figure 8 is a summary of the responses highlighting the importance of the factors that influence or add as a catalyst factors in helping them shop online. Convenience factors were calculated to be the influencing factors driving the purchase decision with about 63.46% of respondents rating it very important during the survey and another 21.15% respondents rating it an important factor. The cost factors show a divided plot between being a very important and an important factor influencing the respondent’s purchase decision. The study found that 34.61% of the respondents found cost to be a very influencing and important factor in their purchasing decision and 36.53% of the respondents considered it important. However 19.23% of the respondents seemed to find the factor moderately important when shopping online. Ease of use factors deals with the familiarity with the website along with being accessible and the attached factor of browsing the products. According to Huang and Oppewal (2006), ease of use factors add pleasure in the online shopping making it enjoyable. 48.07% of respondents considered it to be a very important factor influencing their purchase decision or making Very   Important   Important   Moderately   Important   Not  so   important   Not  at  all   important   Convenience  Factors   33   11   2   2   4   Cost  Factors   18   19   10   4   1   Ease  Of  Use'  Factors   25   16   6   2   3   0   5   10   15   20   25   30   35  
  45. 45.   45   it easier for users to purchase goods online. The study also found 30.76% of the respondents considering it to be an important factor influencing their decision-making process. Table 7: Correlations between the convenience factor, cost factor and ease of use factor in online shopping Table 5 reveals; the three factors have a positive correlation between the convenience factor, cost factor, ease of use factor and decision of online shopping. The analysis also reveals a 99% significance of the relationship between the variables tested. This supports the fact that the consumer decision-making process involves the convenience, cost and ease of use factors affecting the relationship. The analysis also emphasizes the interrelation between these factors as being a collective force influencing the overall decision-making process of the consumer. The online shopping industry deals with a wide variety of products as it gives a large retail space with boundless accessibility and price range to offer. A combination of these factors influences the consumers purchasing behaviour. Findings in table 5 are indicative towards the concept of virtual in-store experience or virtual reality.
  46. 46.     46   4.2.1: Risk factors in online shopping Advancements in technology have made online transactions really secure with the shoppers’ credentials and bank details kept secured. Many respondents in the interviewing phase stressed on the fact that the returns policy and the process are really conducive to online shopping. One of the interviewee’s emphasized the role of social media platforms in helping fine tune the online shopping return policy and security of the card details (refer appendix 9). The strengths of social media have been harnessed to create negative word of mouth in case of poor servicing or being wrongly debited (Weber, 2009). Customer services platforms have been strengthened to meet with growing consumer requirements and concerns (Nidumolu et al., 2009). One of the respondents stated, “credit card information is always kept confidential and the return policy is so good that I just have to send the products back and I get the refund on its own without follow ups or reminders” (refer appendix 6). This has not only help build the trust factor in the online shopping industry but has also given the consumers an overall experience enrichment. One interviewee was very upfront with the belief of payments via PayPal because of the risk-free transactions and not being double debited due to poor connectivity at times (refer appendix 3). The interviewee also highlighted the factor of paying more or preferring to shop on website using PayPal as a payment method (refer appendix 3). Consequently with risk-free transactions along with transparent and convenient returns policies have been identified to be the minimalistic influencing factor in the consumer decision-making process and was not tested in the second phase.
  47. 47.   47   4.3: Online Grocery Shopping The interviewing phase revealed consumers averagely shopped for groceries in the timeline of once in two weeks or once a month depending on the size of the family and consumption. Eight interviewees engaged in online grocery shopping and did so largely for the convenience. Feedback from the consumers revealed the familiarity with the products being one of the key factors in shopping for groceries online (refer appendix 5). The more profound reason for doing groceries online has been because of the distance and the opportunity cost involved in shopping for groceries online. As revealed in one of the studies by the founders of, grocery shopping was one of the most disliked household chores (Corral, 1999); this has been an explicitly mentioned by one of the respondents (refer appendix 4). One of the interviewee’s regarded the product line being easily accessible via the online channel of retail and added, “there are so many products that I actually like shopping for groceries online than in store, so unless you know what you are looking for, you are going to have so many heads that you wont have the patience or time for that” (refer appendix 9). The cataloging system for groceries has been referred to by a lot of interviewees as being a convenient and an ease of use factor contributing to their shopping for groceries online. An interviewee gave an example about the shopping experience and said that, “So if I want to buy certain type of meat or other things I cannot find them in the real store but I can get them online” (refer appendix 8). One the interviewees referred to the shopping of online groceries as being a planned activity rather than being an impulsive purchasing behaviour and added, “ In offline you see products in front of you and you end up buying seven to eight things instead of buying five things you had in your list” (refer appendix 9). Interviewees have not regarded cost to be an important factor in the online grocery shopping but rating convenience to be more important. A family respondent stated,
  48. 48.     48   “even when we are paying for delivering groceries, it saves time and we don’t have to carry so much grocery from the store to our residence” (refer appendix 7). The second phase of the study showed that out of the 54 completed responses, 51.85% of the respondents do their grocery shopping online. The respondents were also asked the reasons they like purchasing their groceries online and the responses have been tabulated in figure 9. Figure 9: Why customers engage in online grocery shopping? (28 respondents) The second phase of data collection showed that 92.9% engage in online grocery shopping for the convenience. 28.6% of the respondents’ find cost and ease of shopping to be key benefit them when shopping for groceries online. On some of the interviewees’ feedback on the products being of better quality when purchased online, the study also tested this factor influencing the online grocery shoppers. Only 14.3% of the respondents felt it was the factor pushing them to engage in online grocery shopping. 26   8   4   8   0   5   10   15   20   25   30   Convenience  (92.9%)   Cost  (28.6%)   Products  (14.3%)   Ease  of  shopping   (28.6%)   Reasons  to  engage  in  online  grocery   shopping  
  49. 49.   49   To analyze the relationship between the typology of consumers engaging in online shopping and online grocery shopping a simple linear regression analysis has been carried out to map the factors that influence online shopping. Table 7: Regression analysis for online shopping Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Cost factor Online Shopping, Ease of use Online Shopping, Convenience factor Online Shopping b . Enter a. Dependent Variable: Online shopping b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .637a .405 .370 .151 a. Predictors: (Constant), Cost factor Online Shopping, Ease of use Online Shopping, Convenience factor Online Shopping ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regressio n .781 3 .260 11.365 .000b Residual 1.145 50 .023 Total 1.926 53 a. Dependent Variable: Online shopping b. Predictors: (Constant), Cost factor Online Shopping, Ease of use Online Shopping, Convenience factor Online Shopping
  50. 50.     50   Coefficientsa Model Unstandardized Coefficients Standardiz ed Coefficient s t Sig.B Std. Error Beta 1 (Constant) .573 .070 8.184 .000 Convenience factor Online Shopping .002 .034 .013 .052 .959 Ease of use Online Shopping .041 .032 .291 1.267 .211 Cost factor Online Shopping .058 .027 .382 2.180 .034 a. Dependent Variable: Online shopping The regression analysis accounts for the factors such as cost, ease of use and convenience and establishes how online shopping is influenced by these factors. Model Summary shows the percentage of variability in DV accounted for all the independent variables together. ANOVA table gives the F value from the f-test stating the model to be good and also gives the significance of the model at p value < 0.01. Hence the resulting equation can be understood from the beta-coefficients calculating the online shopping to be Y = 0.573 + 0.002 (Convenience factors) + 0.041 (Ease of use factors) + 0.058 (Cost factors) This above analysis reveals that there is a high degree of correlation between these terms and also that the typology of customers shopping online for products other that groceries also have a similar typology.
  51. 51.   51   The relationship also indicates that cross-pollination via the online grocery channel or vice versa could prove fruitful as value propositions to the consumers. In the interviewing phase of data collection an interviewee suggested, “I feel like if there was a company that said we will come to you doorstep and deliver you the goods and offers a personalized service” and also added, “an organization that could provide a personalized service with of delivering all my deliveries/shopping together it be more convenient” (refer appendix 3). Convenience is a factor that encourages the consumers of both the industries have been seeking and deriving out of the experience. The factor analysis of the online shopping industry and the online grocery industry (refer appendix 10) has facilitated the study of cross-pollination via the online grocery channel. 4.3.1: Cross-Pollination via online grocery shopping Cross-pollination via the online grocery refers to the concept of integrating services from other online retailing firms to be delivered / serviced to the consumer together. The idea is based on the improvement of factors like convenience and cost of delivery, which could be maximized and minimized respectively using this platform. The industry for such cross-pollination currently exists on a small scale with smaller developers like ‘convivo’ aiming to maximize their services across platforms. The research focused on the concepts of strategic alliances in the front end to have the products delivered to the customer together at the desired time and place of convenience. Phase one of the research evaluated the concept of cross-pollination via the online grocery channel and received quite varied perspectives. Enthusiastic shoppers believed that they would try the concept and were depending more to do with the entire experience of shopping online. An interviewee was very pleased with the concept and said that,
  52. 52.     52   “I think combining the services would be a very good option because people these days tend to prefer to shop online and not in the store, and if we can have everything under one roof and coming to your doorstep together rather than shopping two things from here and three thing the other, and also pay separate delivery charges if the basket size is not big enough.” This viewpoint really highlights some very important factors that cross-pollination could offer consumers. One of the interviews suggested if cross-pollination could schedule weekly deliveries of groceries along with other products such as medicines, which are need-based, products could be useful for the consumers and also felt that such a service would add to the convenience and also consumers may not mind paying extra for such a service because of the sheer convenience this would offer (refer appendix 5). Interviewing phase also revealed a factor that there should be a differentiation in the interface for the consumers’ ease of use and the backend could be connected. This would let the consumers have different product categories and needs to meet but have them delivered together or collected to a store closer to them (refer appendix 6). Some of the interviewees mentioned the factor of the service cost or for that instance the delivery cost of such a service as being a deciding factor, whether they would like to adopt it or not. The concept was welcomed well by the interviewees and was tested at the larger audience in the second phase of data collection. The study asked the respondents about whether they would find it convenient if their online shopping was delivered to them along with their grocery shopping or vice versa and their responses were mapped in figure 10.
  53. 53.   53   Figure 10: Convenience map for cross-pollination via online grocery shopping As depicted in figure 10, 35% of the respondents feel it would be convenient if they could shop for other products and groceries online and have them delivered together. 22% of the respondents find the concept moderately convenient and 17% of the respondents were neutral to the concept. However, 13% of the respondents felt it would be moderately inconvenient to have their online shopping be delivered with their groceries or vice versa. The study also revealed that 13% of the respondents found the concept to be inconvenient and not conducive to their needs. The study also incorporated the possible product categories that would interest consumers and would meet their needs. Some product categories that came out in the interviewing phase were off the shelf medicines, clothes (brands such as H&M, GAP, Zara), books and electronic accessories. The questionnaire phase of data collection received feedback on various product categories people would like to have along with their grocery shopping. Clothing and electronic accessories were some of the most preferred product categories people would like to be delivered along with their groceries. 35%   22%   17%   13%   13%   A  total  of  54  responses   Convenient     Moderately  Convenient     Neurtal     Moderately  Inconvenient     Inconvenient    
  54. 54.     54   The relationship between the typology of online shoppers and the online grocery shoppers indicates that there is a strong potential for such a value proposition to be rewarding the consumers with services they would prefer, if the experience of such a service were kept up with. An interviewee suggested that if there is such a strategic alliance, then she would be fascinated by the concept of going to her grocery store and buying her favorite clothing brand as well if they can have an in-store kiosk as well (refer appendix 9). Figure 10 also shows a large section of the respondents welcoming the concept of cross-pollination via the online grocery channel. Big Middle theory also supports the concept of cross-pollination by suggesting the broad basing of product lines and addressing a larger range of segments of customers to serve all there possible needs. The concept also adds value to the consumer servicing chain at large. Growing need of the food retailing industry looking at past trends and the financial trends elaborated in section 2.4.1.a supports the development of the cross- pollination platform. Adapting the online grocery conceptual framework (refer figure 2) that states; cost, convenience, enjoyment (ease of use) and perceived risk together define online shopping preference and has been tested to define the typology of online grocery shoppers. The model also holds true for the typology of the online shoppers as well and can be supported by the regression analysis shown in table 7.
  55. 55.   55   4.4: Virtual in-store experience: Ease of use and the overall experience of shopping online have been identified as being some important factors influencing the purchase decision of the customers. Simulation experience or having the real feel of the store would benefit the customers who engage in shopping online as the feeling of the store is missing and there is no human contact. The concept was receipted well but lacked the experience, as the study could not show the interviewees or the respondents with how the virtual in-store concept would be in reality. Reference points were used in the interviewing phase using the ASOS Catwalk concept, which was the only virtual experience in the retail segment. Just as a concept, the interviewees’ felt that it would add in more structuring to the cataloging system in the online shopping websites. One of the interviewee’s referred to the problems of Internet connectivity being a major issue in this case and the cost that it would add to the consumers (refer appendix 3). The enjoyment factor in shopping would only be enriched by such a platform stated on of the respondent in the interview and added, “if I can see everything lined up and spaced out and if I can pick it up in the basket it would be very convenient” (refer appendix 6). One of the interviewee’s related it to a concept developed by ASOS and explained the virtual catwalk concept and said that, “the customer can view a product in 360 degrees and one can see the model walking and turning around helping judge how the garment flows, how thick is the fabric” (refer appendix 9). “The experience and the enjoyment factor is maximized with such a concept and makes purchasing the product easier for the consumer” added the interviewee. When the respondents in the second phase of data collection were probed with the concept (refer figure 11), 24% of the respondents felt that it would be very helpful and an enjoyable experience. 41% of the respondents were positive about the concept but were sure about the experience and another 22% of the respondents were very neutral
  56. 56.     56   about the concept. A small percentage of the population felt it would hardly be helpful and enjoyable on the similar grounds as one of the interviewee’s who believed, “It would be difficult as I prefer the cataloging system which improves ease of use.” 9% of the respondents felt it wouldn’t help them shop online at all. Figure 11: Virtual in-store experience When respondents in the second phase were asked how this would help them, responses remained quite positive. Most of the responses ended it as being a convenient and an alternate shopping experience. Despite execution and experience in reality not being available as a sample to showcase, the respondents were optimistic about the concept adding to there overall shopping experience and being helpful to them. The concept of virtual in-store would improve the overall shopping experience for the shoppers and add to the enjoyment factors of shopping online. The ease of use factors for the online shopping and the online grocery shopping are relatively a higher factor influencing the purchase decision of the customers. The concept when implemented would add to the convenience for the shoppers in both the categories and could be one of the early value propositions that can support cross-pollination via online grocery shopping. Helpful  and   Enjoyable   24%   Helpful   41%   Neutral   22%   Hardly  Helpful   4%   Wouldn't  Help   9%   Virtual  in-­‐store  Experience  
  57. 57.   57   Chapter 5: Conclusions The chapter concentrates in drawing the conclusions from the finding and interpretations done on the data collected. It also summarizes the relationships proved using the data analysis. Models discussed in chapter 2 have been used to support the theoretical framework of the conclusions drawn. 5.1: Consumer Behaviour being planned The study has been able to conclude the factors that classifies the typology of consumers in the online shopping channel and also suggests modifiers in them. The theory of planned behaviour has been identified the behavioral backing for Hunag & Oppewal (2006) conceptual model of typology of customers. This has helped map the perceived behaviour control to the behavioral intention of the consumers. The intention to perform an action is the defined factor developed by the theory of planned behaviour. Hansen (2008) in his study suggested that the theory of planned behaviour could be used to understand the behaviour of online grocery shoppers. The model has also been used to analyze the effects of the delivery cost on the average basket size of the consumers. Price range of £20 to £50 has been identified to be an appropriate basket given the delivery costs involved in attaining the products or services. Delivery cost has also been an influencing in the frequency of online shopping by the consumers and a resultant effect on the average basket size. The average industry pricing for free delivery is £50, but the research identifies that consumers tend to shop for the price where the relative effect of the delivery cost is minimized as opposed to the products attained. Clothes are the most preferred of the products purchased online. 5.2: Typology of Consumers The study had adopted the online grocery shopper typology developed by Huang & Oppewal (2006) to study the typology of online shoppers and to find common factors that influence the purchasing behaviour of both the industries. Pearson’s correlation
  58. 58.     58   coefficient helped establish a positive correlation between adoption of online shopping and the convenience factor, cost factor and ease of use factor in the research with a significance of 99%. The relationship has helped analyze the factor of ease of use to an important factor in the purchasing behaviour. Similarities in the typology of consumers in both the industries has helped map the possibility of cross-pollination via the online grocery channel, hence creating a value proposition the food retailing industry can offer. Studying the typology of online shoppers using the model developed by Huang & Oppewal (2006) has lead to fine tune the model in terms of the risk factors that have been reduced over the years due to technological advancements in the banking and e- commerce sector. Consumer information being kept confidential and the transactions being tracked by the consumer at all points have inculcated a sense of trust with online shopping experience. Platforms such as PayPal, Paypoint, 3-D secure by Visa etc. have been some of the financial securities that have influenced the consumer perception over the last years. An important factor with online shopping that has helped build trust with the consumers is the returns policy being transparent and hassle-free. 5.3: Online Grocery shopping and the factors leading to cross-pollination via the online grocery channel Convenience has been identified to be one of the most influencing factor leading consumers to engage in online grocery shopping. Ease of use and the cataloging system have added to the overall shopping experience. A market need for a personalized servicing was identified in the first phase of the study using interviews and recorded that delivering of the groceries and shopping together would be more convenient. The study then identified the cross-pollination feasibility via the online grocery channel. Using the Big Middle theory, a theoretical support was sourced for cross-pollination being a value proposition for the food retailing industry. The study acknowledged the need in the market for a combined and a personalized serviceability in the online
  59. 59.   59   grocery channel and the online shopping industry. The study was able to identify possible strategic alliance parameters that would enable cross-pollination via the online grocery channel. Some of the parameters were similar pricing strategy and brand appeal like the grocery store. Collection of the products purchased online from a more accessible and convenient store was identified in the study. Developing a larger product line and convenience parameters for the consumers using the model can meet with the growing consumer needs of personalized service. 5.4: Virtual in-store experience The study also explored the need for virtual in-store experience and the enhancement of the shopping experience and the ease of use factor. Ease of use was identified to be the second most influencing factor in the consumer behaviour of shopping online (refer appendix 10). Evaluating the concept results showed that ease of use and convenience factors were very important in the online shopping and the online grocery shopping experience. Hence, developing a platform of such would add to the enjoyment factors that a consumer can experience when engaging in shopping online and also minimize the lacking in-store charm that consumers feel (Forsythe & Shi, 2003).
  60. 60.     60   Chapter 6: Managerial Implications The study brings out the effects of delivery costs that influence consumer behaviour and their purchasing of products online in terms of average basket size of shopping and the frequency of shopping. Understanding the financial factors involved in setting up the delivery costs to make convenience feasible for the consumers in the longer run, platform of cross-pollination could prove beneficial for the consumer in meeting their needs and also for organizations to meet their financial aspects involved in the delivering of goods. Hence a strategic review is needed to reduce the overall cost that the consumers incur in attaining the product at their doorstep along with its financial feasibility for the service providers. Analysis of the data collected also highlighted a large gap between the adoption percentages of online shopping as compared to that of online grocery shopping. This has implicated the need for more value propositions in the online grocery shopping industry. The research also helped implicate some suggestive parameters like pricing strategies, brand image and goals, and consumer-centric goals for the cross-pollination alliances with the online grocery industry. Store layout was identified as one of the concerns that consumers have been moving to online shopping. Hence having a more interactive virtual in-store experience would enrich the overall shopping excitement on the online channel. With the advancements in the technology and the identification of the growing need of the consumers for an online trail room or experience of the product in the clothes retail industry has given a challenge for managers to implement a platform where the consumers can experience the garment. Some of the suggestions made for such a platform were to the ASOS Catwalk, which enables the consumer to look at the flow of the garment and the texture of the cloth and also view it in 360 degrees with a model wearing it to showcase how