Arslan Ahmed

MANAGEMENT STUDIES DEPARTMENT Research Proposal “Comparison of physical and online mode of retail in brands in Lahore selling casual readymade garments” By Hamza Mushtaq, Arslan Ahmed, Maryam Batool, Saba Javed Submitted to M.Amaad Uppal Date of submission: People of Pakistan use their mobile phones Introduction Since the late 1990s, online shopping is becoming popular day by day and the number of consumer purchases online is increasing too, while also the number of diversified products on the internet is growing as well. It boosts the retail sales from US $ 172 billion in 2005 to US $ 329 billion in 2010. (Zhou, Dai et al. 2007). Online shopping has 28 percent share of the In Asia, e-commerce industry is booming throughout the region, like in India Flipkart raised a record of US $ 1 billion in investment and Alibaba – China‘s ecommerce giant‘s market capitalization is estimated to be over US $ 250 billion. Pakistan, though a late entrant in ecommerce world, has seen in past few years boom functions like the use of internet, social networking and many more. Statistics show that 149.2 million people which are 79% of the total population of Pakistan use smart phones. So some retailers are now using mobile phones to connect with shoppers even before they walk into the store. They develop their mobile apps which help consumers to find where they can buy a total sales. a not only for making calls but also for other in online shopping. Such exponential growth trends - with US $ 30 million being spent on internet shopping (Express Tribune) - depict a highly positive picture for the future. These trends are increasing without the barriers of industry or products: sales are increasing in every field or every industry through e-commerce. Mobile shopping also helps to improve the growing share of e-commerce. The statistics shows that 20 percent sales of e-commerce are done by mobile shopping. product, check whether or not it is in stock and locate the best possible store to buy. These apps have many features like barcode scanners; you can search your desired product by just scanning the barcode through your mobile phone camera. The topic of our study is to find out the growth or importance of Omni channel retailing in Pakistan. Our study will illuminate the current level of shopping by the female customers of casual ready to wear garments and more importantly see how the level of shopping is divided in buying from online stores versus buying from physical stores. Hopefully, our study will be useful for brands in Pakistan that wish to explore the current level of online versus physical retail in women‘s casual clothing, particularly useful to those brands that provide both the physical and the online Research Proposal platforms for buying and selling so that such brands can decide on the best budgets to ―…a prediction that retail sales will migrate allocate to online and physical retail online and physical retail will be virtually development activities. extinguished, and a prediction that future shoppers will almost all be heading to giant Literature Review The authors we reviewed display four main supercenters.‖(Hortaçsu and Syverson 2015) themes:  The evolution of retail from small scale, dispersed nature to mega scale retailers as well as to online retail.  A partial displacement of physical retail in many categories of products and services by the development of e-commerce leading to digital disruption.  The amalgamation of the physical and the digital into a new way of retailing termed as Omni channel retailing capable of combining the advantages of both the physical retail and the digital platforms available for retail is the new way forward in running successful businesses in today‘s age.  physical stores like warehouse clubs and A reorganization of traditional firms is needed to suit the transition to an Omni channel way of retailing. There is also another theme in the literature the authors address in their articles or papers, namely: Mostly feel however, it may be too soon to call for a complete disappearance of the offline channel for retail as worldwide online retail still is significantly small a portion of overall global retail. However still when looking at the first theme that of the evolution of retail from small scale, dispersed nature to mega scale retailers we find that in a period of 1520 years or over a couple of decades ―the adoption and diffusion of modern retailing technology represents a substantial advance in productivity, providing greater product variety, enhanced convenience and lower prices.‖(Bronnenberg and Ellickson 2015) Over here the author means by ‗modern retailing technology‘ the mega scale retailers as well as e-commerce stores or websites. This shows that although online retailing may take little portion of global sales still it does offer huge advantages as echoed by another author: ―Indeed, shoppers [who] are looking to the convenience (shopping at 2 3 Research Proposal a.m. from your sofa in your pajamas), niche products.‖(Brynjolfsson, Hu et al. selection (42,465 results found!), and price 2013). The same author also found that advantage (comparing prices with one click) customers who had seven or more physical are moving online.‖(Yuan 2014). Another stores nearby tended to shop less (by an author mentions the slim proportion of the average 4.2 percent) from online modes for global sales that online retail takes even then popular products than from physical modes. it gives significant profit advantages: ―Globally, digital About the idea of disruption, as retailing is explained by an author as ―the business probably headed toward 15% to 20% of total world is rapidly digitizing, breaking down sales, though the proportion will vary industry significantly by sector. Moreover, much opportunities digital retailing is now highly profitable. successful Amazon‘s five-year average return on Woerner 2015), one author so aptly puts: barriers while and creating new destroying business ―More investment, for example, is 17%, whereas and long- models‖(Weill more shoppers and are traditional discount and department stores finding that online shopping offers greater average 6.5%.‖(Rigby 2011) convenience, About the second theme the authors lower prices, more information, and a more personalized user say that physical retail is certainly affected experience by the presence and development of online preferable to going to a store. And as free retail, however, it is not just a one way shipping becomes more common, one of the effect as online retail is in competition from last remaining barriers to e-commerce is the physical environment too as one author falling. The shift was reflected in this year‘s found out that: (2013) Black Friday weekend numbers. level that makes buying online ―We empirically studied how the Although sales were essentially flat overall, of competition between online sales grew as a share of total sales by Internet retailers and traditional stores varied across 28 products. We found that Internet retailers year.‖(Matthew Egol 2013) faced significant competition from brickand-mortar retailers when percent from the previous All authors have a consensus to a selling greater or lesser degree of emphasis that in mainstream products but were virtually today‘s age reliance on brick or mortar immune from competition when selling format or online format alone is not 4 Research Proposal sufficient as a combination of physical and  The use of the physical to allow digital is needed to effectively participate in show rooming (the use of physical the consumer market. That is what is called space to observe or participate while Omni channel retailing as explained by one the order or the transaction happens author: online) ―As it evolves, digital retailing is quickly morphing into happen(Rigby 2014) Conversely, the use of the online so space to allow web rooming (the use different that it requires a new name: Omni of the web to know about the best channel retailing. The name reflects the fact product to buy through a comparison that retailers will be able to interact with and later heading to the physical customers through countless channels— store to buy) to happen. For online websites, physical stores, kiosks, direct mail retailers, the physical in the form of and catalogs, call centers, social media, permanent stores, or even pop-up mobile consoles, stores or concept stores allow those televisions, networked appliances, home customers to touch and feel the services, and more. Unless conventional products or to interact with them merchants who are missing out on this privilege devices, adopt perspective—one something to gaming an that entirely allows new them to integrate disparate channels into a single in online settings.  The use of mobile technology seamless Omni channel experience—they through smart phone apps to attract are likely to be swept away.‖(Rigby 2011) customers to physical stores, by Another author while not talking directly allowing online sales to occur at hints at such retailing ―…it suggests a physical potential for an extensive future role for messages at critical timings about the ‗bricks and clicks‘, hybrids that combine e- products commerce physical while entering their frequented stores platforms.‖(Hortaçsu and Syverson 2015). and by allowing better search for The authors‘ viewpoints combined present products at the right prices.(Matthew certain points about how Omni channel Egol 2013) and retailing can be used or is advantageous: settings, customers by sending usually buy  Omni channel retailers enjoy a competitive advantage over other 5 Research Proposal retailers who carry just one mode.  Have formal organizational They are successful as they merge structures for both online retailing both physical and digital worlds of and offline retailing that are distinct consumer together making it easier but allow coordination to happen as for them to shop from wherever they and when needed. The employees want either from sitting home on also need to be imaginative and their sofas or by stepping in a shop. innovative with an ability to think For Omni channel retailers, websites out of the box.(Rigby 2011) and mobile apps are not just ecommerce ordering vehicles, they Gaps in the literature are front doors to the stores and because of this many online stores have added physical stores like mentioned in the articles Warby Parker, Athleta, BaubleBar, and Bonobos.(Rigby 2014) analytics has become necessary as part of the Omni channel retail strategy.(Brynjolfsson, Hu et al. 2013) significantly of clear the authors examples showcasing the use of Omni channel retailing, there does not exist any evidence made across different industries and categories of products and services. In particular, there is nothing on the women‘s clothing industry and for the purposes of our research we would like to find more insight increased emphasis is on bringing the customers inside the firm to develop an ever stronger relationship with them so that such products and strategies are made as suits those customers. This means that relationships with customers are very important.(Weill and Woerner 2015) identify most on how the use of such retailing can be  Collection of data through digital  The Although, into how the Omni channel strategies have been incorporated into this industry. Also, although there is evidence from developing countries that there is a significant rise in ecommerce there is currently no research existing on the developing countries and certainly not on Pakistan on how the use of Omni channel retail has been happening or has already happened. There is evidence though that the developed world has 6 Research Proposal achieved a complete transition to modern of the customers who bought from the retail technology that of online retail and company and how many women clothing mega or superstores at the physical level, stores each of those customers had in however, nations proximity. It then found similar patterns of especially Pakistan the change into the store proximity in the overall US population physical stores of mega scale is still limited hence it made it easy to generalize to the while e-commerce adoption is rising at a overall population of the US. The other better rate. Also, there is no study currently researchers engaged in a series of virtual existing in Pakistan about how under Omni roundtables with senior executives from 13 channel retailing the sales from online stores large corporations across a variety of compare with sales from physical stores industries and then followed up with an in- especially in the casual readymade women‘s person roundtable with executives from 17 wear market. companies to whom they posed: ―Describe a in the developing breakthrough organizational change enabled Methodologies used by the by digitization where your company has significantly changed the way you operate reviewed authors with early indications of good results.‖ Most of the authors reviewed have used predictive analysis based on empirical Objectives evidence shared through practices happening in the world of retail by notable or newly developing brands or businesses. Others have also focused on primary statistics of sales data to prove the major trends developing in the physical and online retail. Two authors explicitly mention their methods of research. One of the researchers analyzed the data from a medium size women‘s clothing company selling both through website and a printed brochure and found the location and the transaction details Main Objective To ascertain the extent and prevalence of Omni channel retailing in women’s casual readymade clothing business in Lahore with an emphasis on a comparison between the sales from physical stores and the sales from online modes of retailing. Sub objectives  To ascertain the proportion of women who use internet versus those who do not. 7 Research Proposal  To find out the prevalence of Omni selling casual readymade wear to channel retailing within the female women. clothing markets. Variables  To indentify the comparative sales of online stores and physical stores in the women‘s apparel business. We have got two variables:  To determine the factors that make  Physical retail and women choose physical retail over  Online retail. Study Design online retail or online retail over physical retail. The study was primarily designed to  To study the current attitudes of find out the comparison between the sales women toward physical and online from physical stores and the sales from modes of retailing. online stores Hypothesis in the women‘s casual readymade wear market in Lahore. It was to find out that in times where people are A hypothesis is the statement created always looking for convenience and the best by researchers when they speculate upon the alternative at a best price does the online outcome of a research or experiment. part of Omni channel retailing in women‘s Hypothesis can be more than one, however clothing make any difference. Is it currently in our case we derived a single hypothesis a significant revenue stream capable of by thoroughly going through eight different taking over the sales made in physical HBR and JCR articles. outlets? Our hypothesis is as below:   We did cross sectional study for our H1 = In Omni channel retailing, research. It is also called one shot study and online stores are giving more sales the most simple study design. We did the than are physical stores to businesses cross sectional study of our population by selling casual readymade wear to finding out the prevalence of our query. women. Advantages of using this design are the Ho = In Omni channel retailing, following: online stores are giving less sales than are physical stores to businesses  Get the answers easily and faster in a short time frame. We do not have to 8 Research Proposal   collect data over and over again as location where internet was mostly used and what we need will be sufficiently whether the respondents shopped online or collected in one go. shopped online for casual ready to wear. If Not costly to perform and does not the respondents answered yes to shopping require a lot of time online for casual ready to wear then they had No follow ups to answer questions 11 to 13 otherwise they were asked to move directly to question Disadvantages of using this design are the fourteen. Questions eleven to thirteen asked following: the respondents about whether they mostly    Cannot be used to analyze behavior bought from physical stores, online stores or over a period to time both, also about the frequency of their Does not help determine cause and purchases from online stores and their effect preferred brand while purchasing online. Only a snapshot: the situation may provide differing results if another The age and the other demographic time-frame had been chosen. questions were given brackets or options which the respondents had to choose from. Research Instrument Design This made all of the demographic variables categorical nominal variables except age and We decided to develop a questionnaire with monthly household income which are twenty two questions the first five of which considered were about the demographics and included questions from 6 to 19 were nominal in age, highest educational level, area of nature as they were all qualitative in nature residence in Lahore, monthly household and mostly yes or no questions or otherwise income and current work status. Question questions concerning the frequency of six asked about whether the respondents purchase which was also coded into used internet or not. If someone used categories like on a weekly basis, on a daily internet then they had to answer the basis, on a yearly basis, etc. Question 20 to questions from seven to thirteen. If not they 22 were categorical and ordinal variables as were asked to move directly to question they were the Likert scale questions and fourteen. The questions seven to ten asked Likert scale questions have a certain order as ratio variables. The other about the frequency of use of internet, the 9 Research Proposal they can be given a rank of 1 to 5 starting We used sample size calculator to find out from 1 for strongly disagree and ending at 5 our sample size which came out to be 384. for strongly agree meanwhile the distances (http://www.surveysystem.com/sscalc.htm). in the middle of the ranks are not the same As our study was qualitative in nature our or cannot be taken to be the same. Hence, all sampling the questions are qualitative in nature combination of quota sampling, convenience however the questionnaire was set out to sampling and judgmental sampling. We used determine the respective percentages for quota sampling as we were primarily online sales and physical sales therefore the concerned overall hypothesis is still concerned with respondents and finding them in locations numbers hence we felt it was important to convenient to us, however as we wanted our have a large sample size that could satisfy sample to be generalizable and also be the requirement for being able to generalize representative we made efforts to get for the whole female population of Lahore. responses selection with the design gender was of a our from multiple areas in Lahore and getting to the convenient respondents in these areas. Hence, we did not just stick to Population, Sampling and our university for the data collection and Mode of Data Collection chose other areas too. We were also The population of our study is all the women in Lahore which we estimated to be around 5 million. Since we had the 1998 figure of census for total population of Lahore which was 5.144 million we assumed that by this time (almost two decades later) this population figure would have doubled so we took the overall population to be 10 million. Out of this ten million almost half was estimated to be the female gender as almost 48.63 percent of the total Pakistani population in 2014 were considered female, hence half of ten million meant 5 million. judgmental to the extent that we chose those respondents we knew were in the best condition, literacy, finance and awareness wise to give us responses. We could predict that with some areas it could be expected that the residents in there would not have access to internet or have the literacy level to understand our questionnaire or the finance level to engage in consumption of casual ready to wear and hence they were not at all likely to engage in online shopping and with whatever resources they possessed they were the least likely to shop for the 10 Research Proposal brands we had mentioned in our physical and some other colleges was that colleges shopping questions. Hence we did not use show a diversity of living areas, income such the levels and behaviors and hence it could questionnaire was long we had to find serve as a suitable sampling place. However, mostly such respondents as that were not too the disadvantage of data collection from the taxed for time. We focused primarily on universities was that most of the respondents three samples: were between the age groups of twenty respondents. 1. Female Also, students of since government College University and some other years or below or twenty one years to forty years universities. ( Paper questionnaires ) Other than this we also conducted 2. Our female friends, relatives and online survey (Google Forms) and asked neighbors (Google forms survey relatives and friends and neighbors to fill the filled online questionnaire within the dead line. It online and also paper questionnaires) took us a good ten to twelve days to get the 3. Wherever possible we also included results from the online questionnaires. some other areas of Lahore which Survey had sufficient information in the were accessible and where we could introductory paragraph to acquaint them find women willing to respond to our with the objective of this questionnaire and questionnaires. what we wanted from them. We also (Using paper questionnaire) organized the questionnaire in such a way to avoid Female students of GCU University were made to fill out the questionnaires consisting twenty two questions regarding women clothing through Omni channel retailing. They were shortly briefed before handing them over the questionnaires to avoid any ambiguity they have related to study. These questionnaires were collected on the spot after they were done. The reason we chose Government College University any ambiguity regarding which questions were to be answered and which not. However if they had any issue or query related to any question we were present online to assist them. All these modes gave us enough insights to conclude our result. In order to study the group of female students we have to look for demographics like age, professional status and shopping behavior either they are shopaholic or not. 11 Research Proposal According to study done by Aurora mostly shopped online or from physical (aurora.dawn.com/news/-/the- stores, it was easy for us to ask them by aurora-fast-fashion-survey) women are more using close ended questions. Using close interested in constitute 92% buying prêt wears and ended of them are weaknesses but using them allowed us to ,58% professionals and 23% are students. Moreover our research concerns an industry that is women fashion industry and questions may have certain perform a better analysis that could readily help us analyzes the strength and applicability of our hypothesis. specific to casual wear and according to same survey done by Aurora, Khaadi and J.Dot are the top most successful brands which have gained importance in the eyes of customers. Both of these brands sell casual wears along with other items like jewelry, men‘s wear and kids wear. We therefore Close ended questions: Advantages  It is easier and quicker for respondents to answer. It is easier and quicker for respondents to answer. included in our research the questions about  The answers are easily comparable. the brands operating in the physical and the  The answers are easy to code and online mode that the women most preferred and bought most often from. Measurement procedures: analyze.  There are fewer irrelevant or confused answers to questions. In order to determine the relative  Respondents can clarify the meaning importance of physical retail and online of a question from the given choices. retail in women‘s wear businesses in Lahore  Respondents are more likely to the female respondents were given a answer about sensitive topics. questionnaire consisting of closed ended  These types of questions are done questions only. We chose this tool because a faster than other type of questions large amount of information can be collected and so a wide range of problem can from a large number of people in a short be covered in a time available. period of time and in a relatively cost Disadvantages effective way. Moreover as we wanted to know whether our target segment (females) 12 Research Proposal  They suggest ideas to the respondents by giving options. validity we ran the Cronbach’s Alpha of all the variables as well as of the  Even the respondent with no opinion can answer that. questions that constituted our two key variables that of online retail and  Sometimes the respondents don‘t physical retail. For the analysis of find the desired answer in option so frequencies we took out the frequency become irresistible. tables and for the descriptive, we took  It gets confused when many choices are offered. out medians and modes as they are more suitable for qualitative data. Only the  These questions force students to means of income and age were taken make choices out of the given out. The other descriptives were the options. skewness coefficient, the quartiles and  Sometimes marked wrong when choices are question is misinterpreted. the variance simple option to the complex issues. well as standard deviation. Since we wanted to know associations  They force respondents to select as among some of the variables we found out crosstabs as well as took out the chi square tests in some cases too. For correlation studies, we Data Processing decided to go for Spearman correlation The data after collection was fed into the Google forms online software. After that the Excel file that was gained was converted into an SPSS file. The as our data is qualitative and has nominal or ordinal variables. Analysis of the results software that was primarily used for data The first step after the data conversion from processing was SPSS. SPSS is a Google forms into the SPSS file was to run powerful analysis tool that allowed us to the Cronbach‘s Alpha test of reliability. The turn out tables for frequency, descriptive Cronbach‘s Alpha test for reliability is a test statistics, run tests of research instrument to show the internal consistency of the reliability and run further tests to find research instrument. A reading of 0.6 or associations above is a satisfactory reading for the among the different variables contained in the data. For test 13 Case Processing Summary Research Proposal Case Valid s Exclud N % 60 15.6 324 84.4 and to run the tests of chi square for a ed assessing the amount of association between Total 384 100.0 the variables. a. Listwise deletion based on all Frequencies variables in the procedure. Reliability Statistics The frequencies we found out were for age, Cronbach's area of residence, highest educational level, Alpha N of Items 0.766 34 monthly household income, work status, the question do you use internet, the question do Cronbach‘s Alpha. Above we can see that Cronbach‘s Alpha is 0.766 and suitable, showing that out instrument is fairly you shop online for casual ready to wear, the question have you ever before shopped online for casual ready to wear, do you have bad experience with online shopping and reliable. finally the question did you switch to the shopping at physical stores because of a bad reliability analysis for our two variables that experience with online shopping. The results of physical retail and online retail and found are displayed in appendix (See table1, 2 and the readings above 0.6 as well for both. 3 and figures 1 to 5). The most important Furthermore, we also performed frequencies among these are the frequencies The next step was to have a look at the of the demographics as they show the descriptives, namely the frequencies and the characteristics of our study sample. The crosstabs or the contingency tables. After mode for our age is 21 to 40 years inclusive that, the chi square tests analysis was with 43 percent respondents within this age performed whether there was group. The median for area of residence is goodness of fit as well as whether the Iqbal Town whereas the highest chosen crosstabs association category is other as the mode shows 9. The between the variables chosen for the rows mode for highest educational level is the and the columns. The further step was to Bachelors with 153 respondents having the find out the spearman correlations for our highest qualification of Bachelors. The major variables. The final step was to find largest number of respondents is not out the crosstabs or the contingency tables employed as can be seen in 52.9 percent for to see actually showed 14 Research Proposal the not employed category in the work within the factors that made people choose status. offline shopping over online shopping.(See Table 5 in Appendix) We wanted to see if Look at Table 4 in appendix and also figure 4 to see that the mode for the answer to the question do you shop online for casual ready to wear is the option of No as only 43.9 percent of the people said Yes to shopping online for causal ready to wear. Other important questions are whether the respondents have ever before shopped for casual ready to wear and 213 said yes and when asked if they had a bad experience people were thinking of these factors at the same time. We found that the correlation of the factors of the visiting experience in store/mall and the ability to feel touch and try on the product was the highest in the bunch at 0.66 with significance value of 0.01 and the greatest positively correlated as well. This meant people thought the most of these two factors in conjunction while choosing offline shopping. with online shopping out of those 213 (see figure 8 in appendix), 115 said yes they had Next we looked at the women‘s responses to a bad experience. And further when asked the factors that made offline shopping better whether they switched to offline shopping than online shopping and their correlations because of a bad experience with online with the statements in question 22. We shopping a large 89 respondents out of 115 found that the strongest coefficient out of all said Yes. It means that 77.4% of women was negative and showed a reading of - who have a bad experience with online 0.434. This correlation was between the shopping have switched to offline shopping factor of visiting experience in store/mall because of that experience. and the statement of online store does not need a physical store to support it. This Correlations Spearman‘s correlation was chosen for our analysis as it is a correlation which is nonparametric and can be applied to ordinal meant that as people agreed with the statement they tended to disagree with this factor and vice versa. Crosstabs data which does not have a normal distribution. For this correlation analysis, we Crosstabs in tables 9.1 to 9.5 in the appendix wanted to find out the internal correlations show the association between the questions 15 Research Proposal of Do you shop online for casual ready to and the physical channels, with those who wear and the factors that make online do sales mostly from physical stores greater shopping better. There is an association in number than those who do them from found in each one of the five tables for the online stores mostly. Overall, online retail is five factors for online retail that show that done by just 44 percent of the respondents there is a relationship among the two which which becomes 153 in number. There is can mean that the decision to buy online is great usage of internet amongst the studied affected by these factors. sample but the sales on online stores are still quite low. Amongst the physical stores Hypothesis testing Finally there is a hypothesis test done through the chi-square at the end of appendix to check whether it is true that online retail is generating more sales for the doing Omni channel retail Khaadi is the most popular. The top two factors people agree with while deciding to buy offline over online are instant product delivery and ability to feel/touch and try on the product. omnichannel retailers in the women‘s ready The limitations of the study are that it can be to wear clothing market. The null hypothesis generalized to some extent only to the whole is that there is no difference in the expeceed population of Lahore as effort was made to and the observed values. Since the sig value have a sample representative of the bigger is 0.00 ans is less than 0.05 the null population of the Lahore city. In future this hypotheis gets rejected and there is a study can be made stronger if it used difference in the expected and the observed quantitative variables and if it were to use values therefore online retail in comparison methods is not greater than physical retail. studies. of sampling for quantitative Conclusion To conclude it can be said that online retail is not giving more sales in comparison to physical retail. In turn there is an equal amount of sales that most of the people who do buy online are doing from both the online 16 Research Proposal Appendix Table 1 N Valid Missing Median Mode Skewness Std. Error of Skewness Percentiles- 4. Monthly 3. Highest household 2. Area of educational income (in 1. Age residence level PKR- .141 -.297 -.280 -.365 .125 .125 .125 - 5. Work status- -.512 - 6. Do you use internet?- - Table 2. Area of residence Valid Frequency Percent Valid Percent Cumulative Percent Shalimar Town 25 6.5 6.5 6.5 Aziz Bhatti town 17 4.4 4.4 10.9 Data Gang Baksh town 28 7.3 7.3 18.2 Gulberg town / Model Town 68 17.7 17.7 35.9 Samanabad town / Gulshan Town 37 9.6 9.6 45.6 Iqbal town 34 8.9 8.9 54.4 Wapda Town / Johar Town 40 10.4 10.4 64.8 Lahore Cant / Defence 59 15.4 15.4 80.2 Other. 76 19.8 19.8 100.0 Total 384 100.0 100.0 17 Research Proposal Figure 1 Table 3. Highest educational level Valid Frequency Percent Valid Percent Cumulative Percent Below Matric/O'level 3 .8 .8 .8 Matric/O'level 45 11.7 11.7 12.5 Intermediate/A'level 81 21.1 21.1 33.6 Bachelors 153 39.8 39.8 73.4 Masters 90 23.4 23.4 96.9 PHD 12 3.1 3.1 100.0 Total 384 100.0 100.0 18 Research Proposal Figure 2 Figure 3 19 Research Proposal Figure 4 Figure 5 20 Research Proposal Table 4 10. Do you 14. Have you ever shop online for before casual ready to online N Valid 16. switch to shopped 15. Do you have a shopping at physical stores for casual bad experience with because of a bad experience ready to wear? online shopping with online shopping 346 384 212 115 0 172 269 Median 2.00 1.00 1.00 1.00 Mode 2 1 1 1 Skewness -.246 .221 .172 1.327 of .131 .125 .167 .226 Percentiles 20 1.00 1.00 1.00 1.00 25 1.00 1.00 1.00 1.00 40 1.00 1.00 1.00 1.00 50 2.00 1.00 1.00 1.00 60 2.00 2.00 2.00 1.00 75 2.00 2.00 2.00 1.00 80 2.00 2.00 2.00 2.00 Error you wear? Missing 38 Std. Did Skewness 21 Research Proposal Figure 6 Figure7 22 Research Proposal Figure 8 Table 5. Pearson Correlations Spearman' 20. Factor i:Correlation s rho [The visitingCoefficient experience inSig. (2-tailed) store/mall] N 20. Factor ii:Correlation [Ability toCoefficient feel/touch andSig. (2-tailed) try on theN product] 20. Factor iii:Correlation [Word ofCoefficient mouth of othersSig. (2-tailed) like N friends/relatives ] 20. Factor iv:Correlation [Instant Coefficient Product Sig. (2-tailed) delivery] N 20. 20. Factor Factor ii: i: [The [Ability to 20. Factor iii: 20. Factor visiting feel/touch [Word of mouth iv: 20. Factor v: experience and try on of others like [Instant [Communicatio in the friends/relatives Product n with store store/mall] product] ] delivery] manager/staff] ** ** 1.000 .660 .400 .482** .260** . 384 .660** - .000 384 .389** .000 384 .489** .000 384 .113* .000 384 . 384 .000 384 .000 384 .027 384 .400** .389** 1.000 .444** .323** .000 384 .000 384 . 384 .000 384 .000 384 .482** .489** .444** 1.000 .443** .000 384 .000 384 .000 384 . 384 - Research Proposal .260** 20. Factor v:Correlation [Communicatio Coefficient n with storeSig. (2-tailed) .000 manager/staff] N 384 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). .113* .323** .443** 1.000 .027 384 .000 384 .000 384 . 384 22. [Bran ds' mobile 22. phone [Online apps retail is are a nothin [Online great g more 22. store way to than [Internet 22. does shop 22. [Buying just for from 22. Table6. Pearson correlations continued 22. [Internet is good [Internet not allows easy for offers an need a wome physical t for a price easy physica n's brand' buying comparison casual s across comparison l store casual better than s ready to of the competitors. wear stores is suppor to ready buying physica competitors support made from online l ] clothes.] ' products.] it.] wear.] stores.] retail.] .128* -.227** .116* -.434** -.226** .402** -.003 Spearman' 20. Factor i: Correlation s rho [The visiting Coefficient experience in Sig. (2-tailed) .012 .000 .023 .000 .000 .000 .946 store/mall] N 384 384 384 384 384 384 384 20. Factor ii: Correlation .060 -.248** .047 -.290** -.126* .507** .160** [Ability to Coefficient feel/touch and Sig. (2-tailed) .243 .000 .362 .000 .014 .000 .002 try on the N 384 384 384 384 384 384 384 20. Factor iii: Correlation -.227** -.224** .029 -.279** -.246** .180** .193** [Word of Coefficient .000 .000 .567 .000 .000 product] mouth of others Sig. (2-tailed) .000 .000 24 Research Proposal N 384 384 384 384 20. Factor iv: Correlation -.023 -.268** .009 -.202** -.109* .305** .193** [Instant Coefficient Product Sig. (2-tailed) .659 .000 .862 .000 .033 .000 .000 delivery] N 384 384 384 384 384 384 384 20. Factor v: Correlation -.082 .000 -.004 -.068 .008 .013 -.079 .108 1.000 .931 .187 .878 .795 .122 384 384 384 384 384 384 384 like 384 384 384 friends/relative s] [Communicatio Coefficient n with store Sig. (2-tailed) manager/staff] N 22. 22. 22. [Brands' [Buying [Online 22. 22. Table 7. Pearson Correlations continued from retail is 22. phone apps physical nothing 22. [Internet [Internet is offers an [Online are a great stores is more allows easy good for easy store does way to shop better price compariso not need a for than support comparisons casual n of the buying for a across competitor store to casual from brand's competitors. wear s' readymade online ] clothes.] products.] it.] wear.] stores.] retail.] .130* .332** -.205** .070 -.047 -.197** .011 .000 .000 .168 .362 .000 384 384 384 384 384 384 .410** .401** .062 .388** -.175** -.310** .000 .000 .228 .000 .001 .000 Spearman' 21.Factor Correla .427** s rho i:[Wide tion selection of Coeffici products to ent choose from] Sig. (2- .000 buying ready to [Internet mobile physical women's support than just physical tailed) N 384 21.Factor ii: Correla .293** [Ease and tion Convenience to Coeffici shop whenever ent and wherever] Sig. (2- .000 tailed) 25 Research Proposal N 384 21.Factor iii: Correla .528** [Easy tion 384 384 384 384 384 384 .198** .470** -.086 .303** -.175** -.317** .000 .000 .092 .000 .001 .000 384 384 384 384 384 384 .358** .460** .137** .415** -.234** -.257** .000 .000 .007 .000 .000 .000 384 384 384 384 384 384 .226** .316** .259** .282** -.107* -.233** .000 .000 .000 .000 .036 .000 384 384 384 384 384 384 comparison of Coeffici a competitors' ent prices and Sig. (2- .000 offerings] tailed) N 384 Correla .329** 21.Factor iv: [The reduction tion in time and Coeffici effort spent in ent going to a Sig. (2- .000 physical store] tailed) N 384 21.Factor v: Correla .127* [Easy return tion and exchange Coeffici policies] ent Sig. (2- .013 tailed) N 384 Table 8 Spearman correlation continued 21. Factor 21.Factor ii: i:[Wide [Ease and 21.Factor iii: 21.Factor selection Convenience [Easy of [The reduction shop comparison of in time and 21.Factor products whenever competitors' to choose and prices from] wherever] offerings] physical store] policies] .612** .657** .360** Spearman's21.Factor Correlation 1.000 rho Coefficient i:[Wide to iv: effort spent in [Easy and going to v: return a and exchange .287** 26 Research Proposal selection ofSig. (2- . .000 .000 .000 .000 384 384 384 384 1.000 .611** .671** .461** . .000 .000 .000 384 384 384 384 .611** 1.000 .525** .371** (2- .000 .000 . .000 .000 384 384 384 384 384 .671** .525** 1.000 .573** (2- .000 .000 .000 . .000 384 384 384 384 384 .461** .371** .573** 1.000 (2- .000 .000 .000 .000 . 384 384 384 384 384 products totailed) choose 384 N from] 21.Factor ii:Correlation .612** [Ease andCoefficient ConvenienceSig. to (2- .000 shoptailed) whenever 384 N and wherever] 21.Factor iii: Correlation .657** [EasyCoefficient comparison Sig. of tailed) competitors' N prices and offerings] 21.Factor iv:Correlation .360** [The Coefficient reduction inSig. time andtailed) effort spentN in going to a physical store] 21.Factor v:Correlation .287** [Easy returnCoefficient and Sig. exchange tailed) policies] N 27 Research Proposal Table 9.1 Crosstab 21. Do you believe the following factors make online shopping better than offline shopping? Factor i:[Wide selection of products to choose from] Strongly Strongly disagree Disagree Neutral Agree agree Total 10. Do you shopYes Count 17 5 34 77 19 152 online for casual % within 10. 11.2% 3.3% 22.4% 50.7% 12.5% 100.0% ready to wear? % within 21. 77.3% 23.8% 26.4% 52.7% 67.9% 43.9% % of Total 4.9% 1.4% 9.8% 22.3% 5.5% 43.9% Count 5 16 95 69 9 194 % within 10 2.6% 8.2% 49.0% 35.6% 4.6% 100.0% % within 21. 22.7% 76.2% 73.6% 47.3% 32.1% 56.1% % of Total 1.4% 4.6% 27.5% 19.9% 2.6% 56.1% Count 22 21 129 146 28 346 % within 10. 6.4% 6.1% 37.3% 42.2% 8.1% 100.0% % within 21. 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 6.4% 6.1% 37.3% 42.2% 8.1% 100.0% No Total Table 9.1 Chi-Square Tests Asymp. Table 9.1 Symmetric Measures Sig. (2Value df sided) a Pearson Chi-Square 40.663 4 .000 Likelihood Ratio 41.984 4 .000 Linear-by-Linear Association 3.237 1 .072 N of Valid Cases 346 Value Approx. Sig. Nominal by NominalPhi .343 .000 Cramer's V .343 .000 N of Valid Cases 346 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 9.23. 28 Research Proposal Figure for Table 9.1 Table 9.2 Crosstab 21.Factor ii: [Ease and Convenience to shop whenever and wherever] Strongly Strongly disagree Disagree Neutral Agree agree Total 10. Do you shopYes Count 5 6 40 69 32 152 online % within 10. 3.3% 3.9% 26.3% 45.4% 21.1% 100.0% 27.3% 28.4% 52.7% 76.2% 43.9% 1.7% 11.6% 19.9% 9.2% 43.9% for casual ready to wear? % within 21.Factor 50.0% ii: % of Total 1.4% 29 Research Proposal No Total Count 5 16 101 62 10 194 % within 10. 2.6% 8.2% 52.1% 32.0% 5.2% 100.0% % within 21. 50.0% 72.7% 71.6% 47.3% 23.8% 56.1% % of Total 1.4% 4.6% 29.2% 17.9% 2.9% 56.1% Count 10 22 141 131 42 346 % within 10. 2.9% 6.4% 40.8% 37.9% 12.1% 100.0% % within 21. 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 2.9% 6.4% 40.8% 37.9% 12.1% 100.0% Asymp. Sig. Table 9.2Chi-Square Tests Value df (2-sided) Pearson Chi-Square 38.299a 4 .000 Likelihood Ratio 39.381 4 .000 Linear-by-Linear Association 24.790 1 .000 N of Valid Cases 346 a. 1 cells (10.0%) have expected count less than 5. The minimum expected count is 4.39. Table 9.2 Symmetric Measures Approx. Value Sig. Nominal by Nominal N of Valid Cases Phi .333 .000 Cramer's V .333 .000 346 30 Research Proposal Figure for table 9.2 Table 9.3 Crosstab 21.Factor iii: [Easy comparison of competitors' prices and offerings] Strongly Strongly disagree Disagree Neutral Agree agree Total 10. Do you shopYes Count 5 13 26 152 online 3.3% 8.6% 17.1% 49.3% 21.7% 100.0% 39.4% 24.5% 47.2% 76.7% 43.9% for casual % within 10. ready to wear? % within 21.Factor 100.0% 75 33 iii: No % of Total 1.4% 3.8% 7.5% 21.7% 9.5% 43.9% Count 0 20 80 84 194 % within 10. .0% 10.3% 41.2% 43.3% 5.2% 100.0% % within 21.Factor .0% 60.6% 75.5% 52.8% 23.3% 56.1% 5.8% 23.1% 24.3% 2.9% 56.1% 10 iii: % of Total .0% 31 Research Proposal Total Count 5 33 106 % within 10. 1.4% 9.5% 30.6% 46.0% 12.4% % within 21.Factor 100.0% 159 43 -% 100.0% 100.0% 100.0% 100.0% 100.0% 9.5% 100.0% iii: % of Total 1.4% 30.6% 46.0% 12.4% Table 9.3 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 42.332a 4 .000 Likelihood Ratio 45.639 4 .000 Linear-by-Linear Association 13.026 1 .000 N of Valid Cases 346 a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 2.20. Symmetric Measures Approx. Nominal by Nominal N of Valid Cases Value Sig. Phi .350 .000 Cramer's V .350 .000 346 32 Research Proposal Figure for Table 9.3 Table 9.4 Crosstab 21.Factor iv: [The reduction in time and effort spent in going to a physical store] Strongly Disagree Neutral Agree agree Total 10. Do you shop onlineYes Count 24 46 56 26 152 for casual ready to % within 10. 15.8% 30.3% 36.8% 17.1% 100.0% wear? % within 21.Factor iv: 36.9% 29.9% 58.3% 83.9% 43.9% % of Total 13.3% 16.2% 7.5% 43.9% 6.9% 33 Research Proposal No Total Count 41 108 40 5 194 % within 10. 21.1% 55.7% 20.6% 2.6% 100.0% % within 21.Factor iv: 63.1% 70.1% 41.7% 16.1% 56.1% % of Total 11.8% 31.2% 11.6% 1.4% 56.1% Count 65 154 96 31 346 % within 10. 18.8% 44.5% 27.7% 9.0% 100.0% % within 21.Factor iv: 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 44.5% 27.7% 9.0% 100.0% 18.8% Table 9.4 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 41.818a 3 .000 Likelihood Ratio 43.333 3 .000 Linear-by-Linear Association 28.982 1 .000 N of Valid Cases 346 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 13.62. Symmetric Measures Value Approx. Sig. Nominal by Nominal N of Valid Cases Phi .348 .000 Cramer's V .348 .000 346 34 Research Proposal Figure for table 9.4 Table 9.5 Crosstab 21.Factor v: [Easy return and exchange policies] Strongly Strongly disagree Disagree Neutral Agree agree Total 152 10. Do you shopYes Count 17 13 68 online % within 10. 11.2% 8.6% 44.7% 25.0% 10.5% 100.0% 19.4% 38.6% 71.7% 64.0% 43.9% for casual ready to wear? % within 21.Factor 68.0% 38 16 v:] No % of Total 4.9% 3.8% 19.7% 11.0% 4.6% 43.9% Count 8 54 108 9 194 % within 10. 4.1% 27.8% 55.7% 7.7% 4.6% 100.0% 15 35 Research Proposal % within 21.Factor 32.0% 80.6% 61.4% 28.3% 36.0% 56.1% v: Total % of Total 2.3% 15.6% 31.2% 4.3% 2.6% 56.1% Count 25 67 176 25 346 % within 10. 7.2% 19.4% 50.9% 15.3% 7.2% % within 21.Factor 100.0% 53 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19.4% 100.0% v: % of Total 7.2% 50.9% 15.3% 7.2% Table 9.5 Chi-Square Tests Asymp. Value df sided) Pearson Chi-Square 44.925a 4 .000 Likelihood Ratio 46.633 4 .000 Linear-by-Linear Association 10.768 1 .001 N of Valid Cases 346 Sig. (2- a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 10.98. Symmetric Measures Value Approx. Sig. .360 .000 Cramer's .360 .000 Nominal by Nominal Phi V N of Valid Cases 346 36 Research Proposal 37 Research Proposal Chi-Square Test for hypothesis Frequencies Do you mostly buy casual ready to wear from online stores or physical stores? Observed N Expected N Residual Online stores 19 53.0 -34.0 Physical stores 25 50.0 -25.0 Both from online and physical stores 109 50.0 59.0 Total 153 Test Statistics 11. Do you mostly buy casual ready to wear from online stores or physical stores? Chi-square 103.931a df 2 Asymp. Sig. .000 a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 50.0. 38 Research Proposal REFERENCES Bronnenberg, B. J. and P. B. Ellickson (2015). "Adolescence and the path to maturity in global retail." The Journal of Economic Perspectives 29(4): 113-134. Brynjolfsson, E., Y. J. Hu, et al. (2013). "Competing in the age of omnichannel retailing." MIT Sloan Management Review 54(4): 23. Hortaçsu, A. and C. Syverson (2015). 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Journal of Electronic Commerce Research 8(1): 41. http://www.tradingeconomics.com/pakistan/population-female-percent-of-total-wb-data.html The encouraging future of e-commerce in Pakistan, Source: Express Tribune. http://tribune.com.pk/story/975430/the-encouraging-future-of-e-commerce-in-pakistan/ 39