How Does Online Shopping Affect Retail Business - Research Paper Example

How is the Increasing Amount of Online Retailers affecting the Footfall in Retail Shops and Sales? Introduction Today, multiple ways exist in which individuals can easily shop and get a variety of products via different retail channels (Chang, Cheung and Lai, 2005). It is very moving to be part of this change; a transformation that sees retail shopping driven by technology and radically change how every person conducts their future purchasing of products and services. The consumer has spoken and the swelling figures of online buyers can reveal these actions; that multichannel and or online shopping is here forever. Consumers are having a great experience of being able to shop anywhere, anytime and with any device and this have resulted in retail shop owners to be challenged with respect to their sales and footfall as many are in favour of online shopping. The key question that the research paper is set to answer is how is the increasing amount of online retailers affecting the footfall in retail shops and sales? Research Problem How does online shopping affect retail business?  Hypotheses i. Increased online retailers and shopping has both substitution and complementarity effects on traditional in-store retail shops. ii. Increased online shopping reduces footfall in retail shops and sales. Online retailers have the capability to replace traditional retail stores/shops. Today, technological advancements have resulted in changes in the way operations are carried out in the business world, how and where people work, shop as well as the lifestyles of different people across the globe. Various successful research attempts have been made to explain the impacts of technology on how and where people work and how this affects their travel trends. Most recent developments have focused on e-shopping/online shopping/online retailers due to its unparalleled proliferation. A 2007 US research by InternetRetailer.com (2008) revealed that the number of online shoppers in the country had increased with their estimated spending rising by a 19 percent margin as well, recording a figure in the regions of 136 billion US dollars in 2007. Despite this increment, online shopping accounted for only 4 percent of the country’s total retail sales. Another Netherlands research showed similar trends with respect to online sales; increasing from just below 200 million Euros in 1999 to just above 1.5 billion Euros in 2004 (Farag, 2006). Online buying could be dominant in certain specific future markets like digital assets. In essence, online shopping could be a substitute for traditional shops. For the purposes of this research, the use of the term online shopping refers to online retailers as well as online searching or rather product information search, unless stated otherwise. Literature Review Many studies have been conducted in the technology field with respect to e-shopping yet little empirical studies exist that relate to e-shopping and the number of shoppers entering traditional shops on a given business day and or the sales made by traditional retail shops. In the context of e-shopping, Mokhtarian (2004) reveals that substitution refers to the replacement of the physical trip to traditional shopping stores with online transactions. Furthermore, the concept of complementarity emerges with respect to information search where e-shopping results in the emergence of new demands for trips to traditional stores. Another research conducted by Anderson, Chatterjee and Lakshmanan (2003) revealed that online shopping does not change the number of people visiting a store, instead it alters the trips’ characteristics, for instance chaining and timing. According to a study by Sim and Koi (2002) involving a sample of 175 online shoppers from Singapore, 12 percent reduced their trips to traditional stores. Another duo of researchers found that some users of the internet in the Knoxville metropolitan region of the United States had reduced their travel trips to traditional stores. A study by Weltevreden and Van Rietbergen (2007) in the Netherlands found that 20 percent of the respondents had reduced trips to city centre stores. Cao and Mokhtarian (2005) carried out a study involving a sample size of 538 UK users of the internet. The report revealed that 80 percent of the respondents had at least once opted for online shopping rather than travel to a traditional shop. Another study by Farag, Krizek and Dijst (2006) applying a multivariate model using a sample size of 634 in the Netherlands set out to investigate the impacts of the frequency of online shopping on the frequency of trips made to traditional retail shops for purchases while controlling for spatial attributes, internet experience, socio-demographics and attitudes. The study found that frequent online shoppers had a higher likelihood of making shopping trips to traditional shops. As a result, the study concluded that “the relationship between online buying and in-store shopping is not one of substitution but of complementarity” (Farag, Krizek and Dijst 2006:43). Farag, Schwanen and Dijst further investigated another group of Dutch respondents comprising 826 internet users in an urban centre and three other suburban surrounding the urban centre. The model had six variables namely online searching frequency, internet use frequency, online buying frequency, in-store shopping frequency, as well as two attitudes towards traditional retail and or in-store shopping and online shopping. The study revealed that online searching frequency had a positive impact on the in-store sales and footfall. After controlling for shopping attitudes, the study found that people shopping frequently in traditional stores often tend to shop online but online shopping had no significant impacts on the number of sales in traditional stores. Ferrell (2005:212) used a BATS 2000 (San Francisco Bay Area Travel Survey) to explore the relationships between teleshopping and in-store shopping. The study revealed that “people substitute home teleshopping time for shopping travel time and that teleshoppers take fewer shopping trips and travel shorter total distances for shopping purposes”. At this stage it is crystal clear that the relationship between online shopping and in-sore shopping is quite complex than first thought: different survey methods have generated different results and conclusions. Methodology Data The data was collected from an online shopping survey conducted in a Metropolitan centre in 2010 and 2011. The purpose of the study is to investigate shopping via internet, and as a result an adult population is preferred. Identification of sample frame was initially expected to be done with the aid of data from local DSL and cable internet providers. However, the major issues associated with these sets of data was its inherent incompleteness; for instance internet subscribers using different connections besides the two covered (cable and DSL) are not in the list provided by the internet providing companies. In addition, no company was in a position to provide subscriber data only for purposes of research. As a result, the research decided to sample households from the chosen neighbourhoods. Due to unavailability of data on internet penetration, household income was used to denote the accessibility to internet connections where the sample was drawn from households living in certain areas with relatively high income. Past research demonstrates that accessibility to retail services has a greater hand to play in the consumers’ adoption of online shopping as well as the relationship between online shopping and footfall in traditional retail shops and sales (Ren and Kwan, 2009; Weltevreden and Van Rietbergen, 2007). As a result, the study took into considerations sampling respondents with respect to location i.e. urban neighbourhoods, exurban, and suburban neighbourhoods to represent the different shopping accessibility levels. It is obvious to have lower internet connection in exurban areas as compared to suburban and urban areas and as a result, the study considered oversampling the exurban area in order to obtain a sample of internet users that is relatively balanced. Administration of the survey was done on a web-based survey provider called Survey Monkey between December 2010 and January 2011. After removal of duplications and four non-adult participants, the respondents totalled 590. Table 1: Number of respondents per neighbourhood Response rates per neighbourhood (N=590) Neighbourhood Respondents Urban 182 Suburban 153 Exurban 255 Table 1 is a representation of the number of respondents from each of the three neighbourhoods while table 2 below represents the chosen demographic characteristics of the respondents of the survey. In general, the respondents have good educational standards and are well paid with few limiters to their shopping behaviours like lack of driver’s licence, relatively low income or lack of credit card (s). Table 2: Characteristics of sample Variables Number(mean) percent Age (n=590) 49.49 Gender Female Male 350 210 64.41 35.59 Size of household 2.1 Number of vehicles per household 2.80 Driver’s licence Yes No 588 2 99.66 0.34 Credit card Yes No 575 15 97.46 2.54 Income of household (‘000) Less than $25 25 – 39.999 40 – 54.999 55 – 69.999 70 – 84.999 85 – 99.999 100 – 114.999 115 – 129.999 130 or more 10 22 40 40 87 83 53 80 175 1.69 3.73 6.78 6.78 14.75 14.07 8.98 13.56 29.66 Employment status Fulltime Part-time Unemployed Retired 399 78 47 66 67.63 13.22 7.97 11.18 Occupation Student Manager Crafts/construction Repair/service Sales Technical/professional Homemaker Clerical support 5 140 30 20 35 300 40 20 0.85 23.73 5.1 3.39 5.93 50.85 6.78 3.39 Education High school or less Technical school or college Associate’s degree (2 years) Technical degree (4 years) Some graduate school Graduate (complete) 35 89 32 205 60 169 5.93 15.08 5.42 34.75 10.17 28.64 Variables Variable development was as follows. Does an upbeat or downbeat relationship between the behaviours of shopping indicate that online shopping has a substitution or complimentarity effect on traditional in-store shopping? However, the answer to this question is not straightforward. The following three mechanisms can be attributed to the positive association (Mokhtarian and Circella, 2007). First, online shopping results in the need for other related products like accessories; second, time saved via purchasing online is used for other shopping activities; and third, factors antecedent to both behaviours of shopping. Antecedent factors can be derived from a variety of sources for instance the likelihood of individuals with higher affluent having more demand for online shopping than the poor. Attributes related to accessibility to shopping and locations have a low priority with respect to the efficiency hypothesis that reads: people living in exurban areas and or with low accessibility to shopping make reduced traditional in-store shopping, but increased online shopping (Anderson et al., 2003). Instruments were developed in this survey to account for the influences caused by the antecedent factors on traditional shopping and online shopping. The variables of the study include six groups namely: shopping attitudes, behaviour, accessibility, internet experience, household responsibility and demographics. Variables of interest include namely online searching frequency, internet use frequency, online buying frequency, in-store shopping frequency, preferred alternative to internet shopping. Results and Analysis The survey employed an ordinal scale to represent the respondents’ propensity to traditional in-store shopping ranging from 0 (no visits per month) to 3 (more than once a week). The survey employed a technique similar to Choo, Lee and Mokhtarian (2007) where it asked directly what the respondents would do if their last purchase over the internet was unavailable over the same channel. This question yielded inconsistent results as compared to previous research on the relationship between online shopping and the footfall of retail shops and sales. The study revealed that 29 percent of the respondents would take a trip to a store to purchase their product or similar product (s). This means that traditional in-store shops can substitute 29.2 percent of online purchases. However, the survey continues to reveal that 18 percent would be unwilling to make the trip. Even though this indicates the possibility of an induced demand, it still shows that the number of people visiting stores will be reduced. According to the results, 24.8 percent of the respondents visit a traditional in-store for shopping less than once in a month; 7 percent visit a traditional store more than once a week; 20.4 percent visit a store once in a week while the remaining 47.8 percent visit a store between once and three times a month. A further 13.9 percent reported that they would find an alternative teleshopping means and the final 39.1 percent were determined to wait until the item is available. The last portion is the most vital segment of the population as having to wait means that they will not visit a store to get the same product that is online and as a result reducing the sales and trips to traditional retail shops. With respect to the information search or rather information gathered via retail online shopping, the survey revealed that 48.3 percent of the sample had made a special trip to a traditional in-store shop thus providing evidence for the effect of complementarity. Continued analysis of the results necessitated the dropping of some variables with a greater positive effect on online shopping and footfall in shops and sales. The results revealed that the income of the respondents has a positive association with frequency of in-store shopping and volume of sales as well. Since the respondents were adults, possibility of having children was considered and as such it was found that the number of children mostly between the age of 12 and 15 years positively affect the number of shop visits. In addition, students were reported to record the least store visits, followed by full-time employees while retirees preferred visiting shops despite having access to internet services. The number of years that respondents had spent using the internet has a negative impact on the frequency of store visits; an indicator that with the continued evolving world, the near future will be free of traditional stores since more and more are using the internet as years pass by. Control for demographics and years spent using the internet resulted in the identification of other variables: how often respondents consult and or search the internet for information about products and how often they buy these products online. These variables are found to have a positive association with frequency of store visits since gathered information drives the consumer to visit a store and as a result are believed to have a complimentary rather than a substitution effect on traditional store shopping. The respondents’ attitudes had a positive association with the frequency of traditional in-store shopping. Some consumers intrinsically enjoy shopping while others shop because of a certain necessity. As expected, those shopping impulsively as well as those who intrinsically enjoy the experience of shopping are more inclined towards shopping in traditional in-store shops. Controlling for this variable means that the consumer (without the enjoyment attitude towards shopping) is left with no alternative but to buy whatever they need with whatever means. In essence, controlling for this shopping attitudes’ variable means that the complimentarity effect between the online shopping and in-store shopping is strengthened. This is in contrast to the expected where when an antecedent variable affecting two outcomes in the same way is controlled; the association between the outcomes tends to diminish (Veall and Zimmermann, 1996). On the other hand, if the antecedent variable affecting two outcomes in opposite ways is controlled; the association between the two outcomes becomes stronger (Cao, 2009). Through further analysis, it was determined that the attitude of shopping enjoyment has a positive association with frequency of traditional in-store visits and a negative association with frequency with which respondents shop online. Therefore, this study has been successful in supporting the hypothesis that increased online retailers and shopping has both substitution and complementarity effects on traditional in-store retail shops. However, the study has failed in its quest to support the hypothesis that increased online shopping reduces footfall in retail shops and sales. Conclusion The objectives of this research paper is to reveal the associations between online retail shopping and the footfall in retail shops and sales using a sample of adult respondents using the internet in a Metropolitan centre. The results or rather data collected was heavily influenced by a number of factors which were effectively controlled. These confounding factors include access to shopping, shopping responsibility, shopping attitudes and socio-demographics. The preliminary results reveal that online shopping tends to affect footfall in retail shops complementarily when other variables are controlled. This paper contributes to the limited research that exists regarding the impacts of online retail shopping on the footfall in retail shops and sales; hence, further research needs to be conducted in this area for future studies. References Anderson, W.P. Chatterjee, L. & Lakshmanan, T.R. (2003) "E-commerce, transportation, and economic geography" Growth and Change, vol. 34, pp. 415-432. Cao, X. & Mokhtarian, P. (2005) "The Intended and Actual Adoption of Online Purchasing: A Brief Review of Recent Literature", Institute of Transportation Studies, University of California, Davis. Cao, X. 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