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The Wheel of Retailing revisited: toward a “Wheel of e
Kutztown University of Pennsylvania
Kutztown University of Pennsylvania
ABSTRACT
A well-worn paradigm in retailers evolve from low differentiation, low margin operations to high differentiation, high margin operations. This study utilizes archival data from the Internet auction site eBay to determine whether the pattern of development proposed by the Wheel of Retailing holds for business-to-consumer (B2C) e-commerce practitioners. Specifically, the study examines 1640 completed sales on eBay and compares the number of years each seller has been in practice to sales volume, whether the seller has an online store, the seller’s feedback rating, whether the seller is designated as a “Power Seller,” whether the seller has a “Me” page, and the sales price of the item sold. Seller experience was found to be si of the six predictor variables, suggesting that, with limitations, eBay sellers do evolve in a pattern consistent with the Wheel of Retailing.
Keywords: e-commerce, internet retailing, e pricing, retail strategy, online retailing, online auctions.
The Wheel of Retailing revisited: toward a “Wheel of e-Tailing?”
Victor J. Massad Kutztown University of Pennsylvania
Mary Beth Nein Kutztown University of Pennsylvania
Joanne M. Tucker Shippensburg University
worn paradigm in marketing known as the Wheel of Retailing suggests that retailers evolve from low differentiation, low margin operations to high differentiation, high margin operations. This study utilizes archival data from the Internet auction site eBay to ether the pattern of development proposed by the Wheel of Retailing holds for commerce practitioners. Specifically, the study examines 1640 completed sales on eBay and compares the number of years each seller has been in ice to sales volume, whether the seller has an online store, the seller’s feedback rating, whether the seller is designated as a “Power Seller,” whether the seller has a “Me” page, and the sales price of the item sold. Seller experience was found to be significantly related to four of the six predictor variables, suggesting that, with limitations, eBay sellers do evolve in a pattern consistent with the Wheel of Retailing.
commerce, internet retailing, e-tailing, wheel of retailing, differentia pricing, retail strategy, online retailing, online auctions.
Tailing?”
marketing known as the Wheel of Retailing suggests that retailers evolve from low differentiation, low margin operations to high differentiation, high margin operations. This study utilizes archival data from the Internet auction site eBay to ether the pattern of development proposed by the Wheel of Retailing holds for commerce practitioners. Specifically, the study examines 1640 completed sales on eBay and compares the number of years each seller has been in ice to sales volume, whether the seller has an online store, the seller’s feedback rating, whether the seller is designated as a “Power Seller,” whether the seller has a “Me” page, and gnificantly related to four of the six predictor variables, suggesting that, with limitations, eBay sellers do evolve in a
tailing, wheel of retailing, differentiation, eBay,
INTRODUCTION AND BACKGROUND
One of the oldest paradigms in modern retailing is known as “The Wheel of Retailing” (WOR). The framework was developed in the 1950’s in order to establish a theoretical basis for understanding the rapid growth and development of post in western industrialized nations. As 21 st^ century, there has occurred a similar, though less visible, growth and development on the electronic platform known as online retail experience to observe, the question arises as to whether between the post-war boom in brick and mortar retailing and the more recent evolution of e tailing. The Wheel of Retailing was originally developed by Professor Malcolm P. McNair in
- McNair reasoned that when start under the premise of being low-price, low margin performers. They d most cost-effective way to acquire as many customers as possible to make a mark in their competitive fields of choice. As realization that not all customers are created equal. Thus, the retailer will seek out those customers who are loyal Over time the retailer will tend to focus his business on these higher margin customers, leaving the lower margin customers to new market entrants The Wheel of Retailing is one of the oldest theoretical constructs in the marketing discipline, and continues to be cited in example, Kotler & Keller 2009). The framework paradigm in the academic literature through the 1960’s and early 1970’s. As late a was used to provide a theoretical foundation for identifying variables that were likely to contribute to a retailer “trading up” by offering a greater number of services (Goldman 1975). As new patterns in retailing began to emerge in the latter to be regarded less favorably as an adequate paradigm to explain many of the trends of that time. Kalkati (1985) pointed out that many high margin retailers, contrary to the pattern proposed by WOR, were lowering prices entering the market. May (1989) pointed out that deficiencies were appearing in the framework based on the speed at which retailing was changing, the fact that many retailers were past maturity, and that there were retail forms that had come into being in a was contrary to that predicted by WOR. The emergence of huge discount bricks Home Depot in the late 20th^ century played a significant role in causin Retailing to become out of favor among academic researchers. These large retailers started out as large discounters, and maintained their identity as large retailers as time went on. Since they had become dominant in the marketplace with t lost its relevance as anything more than an historical oddity insofar as being a robust model for explaining and predicting retail behavior. advocates concluded that “the number hypothesis is not valid for all retailing (Hollander 1996).” One purpose of this research is to examine the more recent phenomenon of Internet based retailing through the lens of the Wheel of Retailing and provides a theoretical basis for understanding the evolution of e Can this relic from the 1950’s and 60’s in post-war retailing -- be dusted of Internet Marketing some 50 years later? Whether the domain is real or virtual, there may be certain consistencies that occur, especially among smaller retailers, as businesses with limited
INTRODUCTION AND BACKGROUND
One of the oldest paradigms in modern retailing is known as “The Wheel of ). The framework was developed in the 1950’s in order to establish a theoretical basis for understanding the rapid growth and development of post-WWII retailers in western industrialized nations. As retail practitioners head into the second decade of the a similar, though less visible, growth and development on the electronic platform known as the worldwide web. With more than a decade of robust online retail experience to observe, the question arises as to whether there might be parallels war boom in brick and mortar retailing and the more recent evolution of e
The Wheel of Retailing was originally developed by Professor Malcolm P. McNair in
- McNair reasoned that when start-up retailers enter a consumer market, they operate price, low margin performers. They do this because it is the effective way to acquire as many customers as possible to make a mark in their a particular retailer acquires customers, he will that not all customers are created equal. Thus, the retailer, as a matter of course customers who are loyal, buy often and are willing to pay for added value. Over time the retailer will tend to focus his business on these higher margin customers, leaving the lower margin customers to new market entrants (Hollander, 1960). The Wheel of Retailing is one of the oldest theoretical constructs in the marketing cipline, and continues to be cited in marketing and retailing textbooks today (see, for example, Kotler & Keller 2009). The framework survived as a relatively unchallenged in the academic literature through the 1960’s and early 1970’s. As late a was used to provide a theoretical foundation for identifying variables that were likely to contribute to a retailer “trading up” by offering a greater number of services (Goldman 1975). As new patterns in retailing began to emerge in the latter part of the 20th^ century, WOR came to be regarded less favorably as an adequate paradigm to explain many of the trends of that time. Kalkati (1985) pointed out that many high margin retailers, contrary to the pattern proposed by WOR, were lowering prices in response to the proliferation of big discounters entering the market. May (1989) pointed out that deficiencies were appearing in the framework based on the speed at which retailing was changing, the fact that many retailers there were retail forms that had come into being in a contrary to that predicted by WOR. The emergence of huge discount bricks-and-mortar retailers such as Walmart and century played a significant role in causing the Wheel of Retailing to become out of favor among academic researchers. These large retailers started out as large discounters, and maintained their identity as large retailers as time went on. Since they had become dominant in the marketplace with the strategy, it appeared WOR had lost its relevance as anything more than an historical oddity insofar as being a robust model for explaining and predicting retail behavior. By 1996 one of the WOR’s strongest academic he number of nonconforming examples suggests that the wheel hypothesis is not valid for all retailing (Hollander 1996).” One purpose of this research is to examine the more recent phenomenon of Internet based retailing through the lens of the Wheel of Retailing and to determine whether WOR provides a theoretical basis for understanding the evolution of e-tailing since the late 1990’s. Can this relic from the 1950’s and 60’s – a device so useful in bringing into focus the growth be dusted off and be made useful again as we examine the growth of Internet Marketing some 50 years later? Whether the domain is real or virtual, there may be certain consistencies that occur, especially among smaller retailers, as businesses with limited
One of the oldest paradigms in modern retailing is known as “The Wheel of ). The framework was developed in the 1950’s in order to establish a WWII retailers head into the second decade of the a similar, though less visible, growth and development based more than a decade of robust there might be parallels war boom in brick and mortar retailing and the more recent evolution of e-
The Wheel of Retailing was originally developed by Professor Malcolm P. McNair in rs enter a consumer market, they operate this because it is the effective way to acquire as many customers as possible to make a mark in their will come to the as a matter of course, willing to pay for added value. Over time the retailer will tend to focus his business on these higher margin customers, . The Wheel of Retailing is one of the oldest theoretical constructs in the marketing textbooks today (see, for relatively unchallenged in the academic literature through the 1960’s and early 1970’s. As late as 1975 it was used to provide a theoretical foundation for identifying variables that were likely to contribute to a retailer “trading up” by offering a greater number of services (Goldman 1975). century, WOR came to be regarded less favorably as an adequate paradigm to explain many of the trends of that time. Kalkati (1985) pointed out that many high margin retailers, contrary to the pattern in response to the proliferation of big discounters entering the market. May (1989) pointed out that deficiencies were appearing in the framework based on the speed at which retailing was changing, the fact that many retailers there were retail forms that had come into being in a manner that
mortar retailers such as Walmart and g the Wheel of Retailing to become out of favor among academic researchers. These large retailers started out as large discounters, and maintained their identity as large retailers as time went on. he strategy, it appeared WOR had lost its relevance as anything more than an historical oddity insofar as being a robust model By 1996 one of the WOR’s strongest academic of nonconforming examples suggests that the wheel
One purpose of this research is to examine the more recent phenomenon of Internet- to determine whether WOR tailing since the late 1990’s. a device so useful in bringing into focus the growth f and be made useful again as we examine the growth of Internet Marketing some 50 years later? Whether the domain is real or virtual, there may be certain consistencies that occur, especially among smaller retailers, as businesses with limited
greater information available to them prior to making purchase decisions; and because of the sheer volume of competitors, charging the lowest price does not guarantee that any particular e-tailer will gain an advantage. Thus, e themselves with non-price attributes such as service quality and merchant brand recognition. This is no doubt a reality that Internet recognition, reputation and many other sources of different follows that more mature e-tailers would themselves than newer e-tailers. Internet retailers must incorporate selling strategies that will allow buyers to sense the product in all aspects: visual, audio and kinesthetic. Sellers must be mindful that most of their customers are “goal-directed.” Buyers usually have a specific buying objective in mind when they search for retail websites to purchase an item Another way for e-tailers to signal to consumers that they can expect higher levels of differentiation is to charge higher prices. Mitra and Fay (2010) suggested that the potential sources of differentiation available to online retailers were co those available to traditional bricks likely to use higher price to signal differentiation than their bricks These researchers found that e-ta manipulate price in order to manage service expectations. The importance of finding a source of competitive advantage early and using it to establish a strong customer base is emphasized in research by Pinguin and Talaga (2006), who found that early entrants to the Internet retail domain were able to more efficie command higher customer share than late movers. This would be congruent with postulation that older e-tailers would be more likely to have established sources of differentiation than newer retailers. As for cost and price-oriented e having a lower cost structure than more established competitors. There is some evidence from extant research that cost-based competitors on the Internet are more likely to spurn expensive graphic interfaces and less likely to offer money & Sun, 2009). Shipping and handling costs are another area where e strategies. Under the assumption that buyers may be more focused on selling price, some discount Internet sellers charge inflated shipping and handling costs and recover some of the deficiency that might occur from charging a lower top There is also evidence that larger discounters are utilizing drop products to reduce shipping costs Taking into the consideration the original paradigm and subsequent literature, the WOR predicts that there will be differences between experienced retailers in terms of differentiation, volume and pricing. Experienced retailers will be more oriented toward higher differentiation as a source of competitive advantage; whereas, less experienced retailers will be more oriented toward offering lower prices and less differentiation. By virtue of their experience advantage, higher experienced retailers can be expected to have higher overall sales volume growth rate in margins would be expected to exceed the growth rate in sales. Les experienced retailers would be expected to have in sales exceeding the growth rate in margins. By virtue of their greater dependency on differentiation, experienced retailers would be expected to have higher av higher prices than their less experienced counterparts. These relationships are illustrated in Figure 2 (Appendix).
ter information available to them prior to making purchase decisions; and because of the sheer volume of competitors, charging the lowest price does not guarantee that any particular tailer will gain an advantage. Thus, e-tailers must develop a way to differentiate price attributes such as service quality and merchant brand recognition. This is no doubt a reality that Internet-based retailers discover over time. Since brand recognition, reputation and many other sources of differentiation take time to develop, it tailers would tend to be more focused on differentiating
Internet retailers must incorporate selling strategies that will allow buyers to sense the spects: visual, audio and kinesthetic. Sellers must be mindful that most of their directed.” Buyers usually have a specific buying objective in mind when they search for retail websites to purchase an item (Mathwick, Malhotra, & Rigdon tailers to signal to consumers that they can expect higher levels of differentiation is to charge higher prices. Mitra and Fay (2010) suggested that the potential sources of differentiation available to online retailers were considerably fewer in number than those available to traditional bricks-and-mortar retailers. Thus, online retailers were more likely to use higher price to signal differentiation than their bricks-and-mortar counterparts. tailers at both ends of the spectrum were more likely to manipulate price in order to manage service expectations. The importance of finding a source of competitive advantage early and using it to establish a strong customer base is emphasized in research by Pinguin and Talaga (2006), early entrants to the Internet retail domain were able to more efficie command higher customer share than late movers. This would be congruent with tailers would be more likely to have established sources of differentiation than newer retailers. oriented e-tailers, there may be less opportunity to capitalize on having a lower cost structure than more established competitors. There is some evidence based competitors on the Internet are more likely to spurn nd less likely to offer money-back guarantees (Li, Srinivasan,
. Shipping and handling costs are another area where e-tailers may vary strategies. Under the assumption that buyers may be more focused on selling price, some ellers charge inflated shipping and handling costs and recover some of the deficiency that might occur from charging a lower top-line price (Gurtler & Grund, 2006) There is also evidence that larger discounters are utilizing drop-shipping of standardized products to reduce shipping costs (Tsai & Hung, 2009). Taking into the consideration the original paradigm and subsequent literature, the WOR predicts that there will be differences between experienced retailers and inexperienced ferentiation, volume and pricing. Experienced retailers will be more oriented toward higher differentiation as a source of competitive advantage; whereas, less experienced retailers will be more oriented toward offering lower prices and less on. By virtue of their experience advantage, higher experienced retailers can be higher overall sales volume than less experienced retailers. However, the growth rate in margins would be expected to exceed the growth rate in sales. Les experienced retailers would be expected to have lower overall volume, with the growth rate in sales exceeding the growth rate in margins. By virtue of their greater dependency on differentiation, experienced retailers would be expected to have higher average sales and higher prices than their less experienced counterparts. These relationships are illustrated in
ter information available to them prior to making purchase decisions; and because of the sheer volume of competitors, charging the lowest price does not guarantee that any particular ifferentiate price attributes such as service quality and merchant brand recognition. based retailers discover over time. Since brand iation take time to develop, it be more focused on differentiating
Internet retailers must incorporate selling strategies that will allow buyers to sense the spects: visual, audio and kinesthetic. Sellers must be mindful that most of their directed.” Buyers usually have a specific buying objective in mind when (Mathwick, Malhotra, & Rigdon, 2002). tailers to signal to consumers that they can expect higher levels of differentiation is to charge higher prices. Mitra and Fay (2010) suggested that the potential nsiderably fewer in number than mortar retailers. Thus, online retailers were more mortar counterparts. ilers at both ends of the spectrum were more likely to
The importance of finding a source of competitive advantage early and using it to establish a strong customer base is emphasized in research by Pinguin and Talaga (2006), early entrants to the Internet retail domain were able to more efficiently command higher customer share than late movers. This would be congruent with the WOR tailers would be more likely to have established sources of
there may be less opportunity to capitalize on having a lower cost structure than more established competitors. There is some evidence based competitors on the Internet are more likely to spurn (Li, Srinivasan, tailers may vary strategies. Under the assumption that buyers may be more focused on selling price, some ellers charge inflated shipping and handling costs and recover some of the (Gurtler & Grund, 2006). shipping of standardized
Taking into the consideration the original paradigm and subsequent literature, the retailers and inexperienced ferentiation, volume and pricing. Experienced retailers will be more oriented toward higher differentiation as a source of competitive advantage; whereas, less experienced retailers will be more oriented toward offering lower prices and less on. By virtue of their experience advantage, higher experienced retailers can be than less experienced retailers. However, the growth rate in margins would be expected to exceed the growth rate in sales. Less , with the growth rate in sales exceeding the growth rate in margins. By virtue of their greater dependency on erage sales and higher prices than their less experienced counterparts. These relationships are illustrated in
RESEARCH OBJECTIVE, METHODOLOGY, AND HYPOTHESES
The primary objective of this study is to Retailing model might apply to e retailers in the traditional brick-and upon is: to what extent, if any, do and their sales evolution in terms of pricing, differentiation and volume? In order to examine whether the relationships suggested by might hold in an authentic e-tailing environment, a num sources were considered, including information from Internet retail sites such as Amazon, Tiger Direct and other online direct marketing firms. Unfortunately, much information regarding these sellers is kept proprietary source of comprehensive retail transactional information for the purposes of examining Internet retail activity was found at the auction site eBay. This well intermediary keeps extensive records about sellers, including their track records and levels of experience, and this data is readily available to buyers An ordinary least squares regression model was used to study the relationships between the variables. The eBay population of listings considered in our study included broad array of products available on the site made up of 1640 eBay completed sales were for sampling from eBay’s diverse p from an audio CD at 49 cents to a rare antique vase with a final price of $1750. Therefore, sales were represented from numerous and highly disparate eBay sales catego from consumer electronics to fashion clothing related variables were noted. These variables are: registered on eBay (YRS) which is our dependent variable (FDBCKS) accumulated by a seller divided by YRS defined the variable VOLUME; ( 3 the seller operated an eBay store (RATING); (5) Was the seller was an eBay Power Selle have a “Me” Page (MEPAGE); and (7.) The sales price of the item sold (PRICE). quantitative and categorical measures were helpful in examining the research objective and drawing conclusions. The WOR predicts that there will be differences between experienced inexperienced retailers in terms of differentiation measured differentiation by whether experienced eBay sellers used differentiation tactics such as having an eBay store, qualified to be considered as a Power Seller and having an About Me page. Thus, we measured differentiation through STORE, POWSEL variables. We measured a seller’s volume through the variables VOLUME and RATING. Lastly, we measured pricing through the variable PRICE to these differences are:
H1: Experienced eBay sellers will have of transactions than inexperienced eBay sellers
H2: Experienced eBay sellers will be more likely to have an eBa eBay sellers as measured by the variable STORE.
H3: Experienced eBay sellers will have higher feedback ratings than inexperienced eBay sellers as measured by the variable RATING.
RESEARCH OBJECTIVE, METHODOLOGY, AND HYPOTHESES
objective of this study is to examine the degree to which the Wheel of apply to e-tailers in the Internet environment; as it had in the past to and-mortar environment. The underlying question does the Wheel of Retailing model have relevance to e and their sales evolution in terms of pricing, differentiation and volume? In order to examine whether the relationships suggested by the WOR in the foregoing tailing environment, a number of Internet-based archival data sources were considered, including information from Internet retail sites such as Amazon, Tiger Direct and other online direct marketing firms. Unfortunately, much information regarding these sellers is kept proprietary and therefore is inaccessible. Ultimately, the best source of comprehensive retail transactional information for the purposes of examining Internet retail activity was found at the auction site eBay. This well-established online sive records about sellers, including their track records and levels of experience, and this data is readily available to buyers and the general public. An ordinary least squares regression model was used to study the relationships The eBay population of listings considered in our study included array of products available on the site, with the exception of automobiles. completed sales were selected at random. The large sample allowed sampling from eBay’s diverse population of listings and included sales of items ranging from an audio CD at 49 cents to a rare antique vase with a final price of $1750. Therefore, sales were represented from numerous and highly disparate eBay sales categories ranging consumer electronics to fashion clothing. For each listing, values for seven related variables were noted. These variables are: (1) The number of years the seller has been which is our dependent variable; (2) The total number of feedbacks (FDBCKS) accumulated by a seller divided by YRS defined the variable VOLUME; ( 3 the seller operated an eBay store or not (STORE); (4) The seller’s feedback rating seller was an eBay Power Seller or not (POWSEL); (6) ); and (7.) The sales price of the item sold (PRICE). measures were helpful in examining the research objective and
there will be differences between experienced retailers and inexperienced retailers in terms of differentiation, volume and pricing. For this study, we measured differentiation by whether experienced eBay sellers used differentiation tactics an eBay store, qualified to be considered as a Power Seller and having an About Me page. Thus, we measured differentiation through STORE, POWSEL, variables. We measured a seller’s volume through the variables VOLUME and RATING. through the variable PRICE. The research hypotheses that relate
H1: Experienced eBay sellers will have higher annual sales volumes as measured by number than inexperienced eBay sellers, indicated by the variable VOLUME.
H2: Experienced eBay sellers will be more likely to have an eBay store than inexperienced as measured by the variable STORE.
H3: Experienced eBay sellers will have higher feedback ratings than inexperienced eBay as measured by the variable RATING.
the Wheel of as it had in the past to underlying question focused ling model have relevance to e-tailers
WOR in the foregoing based archival data sources were considered, including information from Internet retail sites such as Amazon, Tiger Direct and other online direct marketing firms. Unfortunately, much information and therefore is inaccessible. Ultimately, the best source of comprehensive retail transactional information for the purposes of examining established online sive records about sellers, including their track records and levels of
An ordinary least squares regression model was used to study the relationships The eBay population of listings considered in our study included the A sample The large sample allowed opulation of listings and included sales of items ranging from an audio CD at 49 cents to a rare antique vase with a final price of $1750. Therefore, ries ranging seven seller- ) The number of years the seller has been (2) The total number of feedbacks (FDBCKS) accumulated by a seller divided by YRS defined the variable VOLUME; ( 3) Did he seller’s feedback rating ) Did the seller ); and (7.) The sales price of the item sold (PRICE). Both measures were helpful in examining the research objective and
retailers and , volume and pricing. For this study, we measured differentiation by whether experienced eBay sellers used differentiation tactics an eBay store, qualified to be considered as a Power Seller and having an MEPAGE variables. We measured a seller’s volume through the variables VOLUME and RATING. The research hypotheses that relate
as measured by number variable VOLUME.
y store than inexperienced
H3: Experienced eBay sellers will have higher feedback ratings than inexperienced eBay
Figure 6 (Appendix) shows found to be below the .05 threshold for significance relationship based was positive. inconsistent with the corresponding showing the greatest linearity to the dependent variable was PRICE and as indicated in Table 3 (Appendix). The ranking of independent variables based on their p RATING, VOLUME, STORE and POW similar to ranking of variables in Table 3 with the exception of POWSEL and STORE positions of 5 STORE are moderately correlated which may be the source of the position re Overall, H1 (VOLUME), H3 (RATING), supported by our analysis. Thus, supports the WO experienced retailers and inexperienced retailers in terms of differentiation pricing. However, H2 (STORE) differentiation are not supported.
DISCUSSION
Apart from the results reported in the previous section on our six research hypotheses, there are a number of interesting revelations regarding eBay and its evolution as an online retail facilitator that arose from the study. One eBay’s revenues, which underscores the company’s shift in strategic direction from its traditional posture as an online auction site that secondarily offered fixed price merchandise, to a fixed price priced site that secondarily offers auction mercha roughly 20 percent of the sellers accounting for about 85 percent of the firm’s volume (nearly half the volume being accounted for by the top 5 percent), it is not surprising that embraced a business model that is more security than the turbulent and risk The fact that VOLUME was very significant is not surprising since the variable was a ratio in which one of the components was the dependent variable. However, the fact that it was less significant than PRICE was very revealing since it indicates that as sellers gain in experience selling on eBay (and by extension, online) they engage in more transactions at higher average prices. This is consistent with retail evolution predicted according to The Wheel of Retailing paradigm expressed in The fact that 43 percent of all sellers pay to maintain an eBay store the merchandise is inventoried longer percentage of sellers are adapting to the new environment and operating more like traditional retailers. This necessitates that sellers maintain larger levels of working capital and endure higher turnover rates in exchange for improved margins. explanation for this phenomenon, however STORE was not found to be significantly correlated to longevity, and in fact the model yielded a reverse relationship. One exp is that eBay may more recently be attracting retailers that were previously selling on fixed price sites such as Amazon. If this is the case, these retailers would be newer to eBay but still quite experienced in online retailing. Slightly more than a third of Sellers.” In order to become a Power Seller, a seller must have sales consistently over one thousand dollars per month, 98 percent feedback rating, and average better than 4.5 stars (out of a possible five) on four service dimensions. necessarily be more focused on the service dimensions of their businesses than other eBay sellers.
shows that of the six predictor variables, only POWSEL was found to be below the .05 threshold for significance, however the sample showed the
. The variable, STORE, was significant, but was the corresponding hypothesis. Of the five significant variables, t showing the greatest linearity to the dependent variable was PRICE and as indicated in Table
The ranking of independent variables based on their p-value is PRICE, MEPAGE, RATING, VOLUME, STORE and POW similar to ranking of variables in Table 3 with the exception of POWSEL and STORE positions of 5th^ and 6th^ are reversed. POWSEL and STORE are moderately correlated which may be the source of the position reversal. (VOLUME), H3 (RATING), H5 (MEPAGE) and H6 (PRICE)
. Thus, supports the WOR constructs of differences between experienced retailers and inexperienced retailers in terms of differentiation, volume and (STORE) and H4 (POWSEL) as surrogates for the construct of are not supported.
results reported in the previous section on our six research hypotheses, there are a number of interesting revelations regarding eBay and its evolution as an online arose from the study. One was the degree to which larger sellers a eBay’s revenues, which underscores the company’s shift in strategic direction from its traditional posture as an online auction site that secondarily offered fixed price merchandise, to a fixed price priced site that secondarily offers auction merchandise (Holahan 2008). With roughly 20 percent of the sellers accounting for about 85 percent of the firm’s volume (nearly half the volume being accounted for by the top 5 percent), it is not surprising that embraced a business model that is more oriented toward maintaining price consistency and security than the turbulent and risk-oriented marketplace that typified its earlier existence. The fact that VOLUME was very significant is not surprising since the variable was a components was the dependent variable. However, the fact that it was less significant than PRICE was very revealing since it indicates that as sellers gain in experience selling on eBay (and by extension, online) they engage in more transactions at r average prices. This is consistent with retail evolution predicted according to The Wheel of Retailing paradigm expressed in Figure 1. percent of all sellers pay to maintain an eBay store (Table 2) longer-term and sold at a fixed price, indicates that a large percentage of sellers are adapting to the new environment and operating more like traditional retailers. This necessitates that sellers maintain larger levels of working capital and endure er turnover rates in exchange for improved margins. The Wheel of Retailing provides an explanation for this phenomenon, however STORE was not found to be significantly correlated to longevity, and in fact the model yielded a reverse relationship. One exp is that eBay may more recently be attracting retailers that were previously selling on fixed price sites such as Amazon. If this is the case, these retailers would be newer to eBay but still quite experienced in online retailing. a third of the sellers in the study were identified as “Power Sellers.” In order to become a Power Seller, a seller must have sales consistently over one thousand dollars per month, 98 percent feedback rating, and average better than 4.5 stars (out ) on four service dimensions. This suggests that Power Sellers would necessarily be more focused on the service dimensions of their businesses than other eBay
only POWSEL was , however the sample showed the significant, but was directionally Of the five significant variables, the variable showing the greatest linearity to the dependent variable was PRICE and as indicated in Table
is PRICE, MEPAGE, RATING, VOLUME, STORE and POW similar to ranking of variables in Table 3 with the are reversed. POWSEL and versal. (PRICE) are constructs of differences between , volume and construct of
results reported in the previous section on our six research hypotheses, there are a number of interesting revelations regarding eBay and its evolution as an online was the degree to which larger sellers affect eBay’s revenues, which underscores the company’s shift in strategic direction from its traditional posture as an online auction site that secondarily offered fixed price merchandise, ndise (Holahan 2008). With roughly 20 percent of the sellers accounting for about 85 percent of the firm’s volume (nearly half the volume being accounted for by the top 5 percent), it is not surprising that eBay has oriented toward maintaining price consistency and oriented marketplace that typified its earlier existence. The fact that VOLUME was very significant is not surprising since the variable was a components was the dependent variable. However, the fact that it was less significant than PRICE was very revealing since it indicates that as sellers gain in experience selling on eBay (and by extension, online) they engage in more transactions at r average prices. This is consistent with retail evolution predicted according to The
(Table 2), where term and sold at a fixed price, indicates that a large percentage of sellers are adapting to the new environment and operating more like traditional retailers. This necessitates that sellers maintain larger levels of working capital and endure The Wheel of Retailing provides an explanation for this phenomenon, however STORE was not found to be significantly correlated to longevity, and in fact the model yielded a reverse relationship. One explanation is that eBay may more recently be attracting retailers that were previously selling on fixed price sites such as Amazon. If this is the case, these retailers would be newer to eBay but still
the sellers in the study were identified as “Power Sellers.” In order to become a Power Seller, a seller must have sales consistently over one thousand dollars per month, 98 percent feedback rating, and average better than 4.5 stars (out This suggests that Power Sellers would necessarily be more focused on the service dimensions of their businesses than other eBay
Another interesting finding is that only “About Me” page as shown in Table 2 sellers to create an image for themselves and perhaps draw some distinctions between themselves and other eBay sellers. given that less than a third of sellers use it, a majority of eBay sellers may not think non competition makes much of a difference on eBay. tend to be more experienced as MEPAGE was a significan The Wheel of Retailing appears to be a fairly robust and relevant framework for investigating the phenomena of e entrepreneurs who start on a shoestring and build aptly describes the typical eBay seller, and so the model is a good one for investigating this particular subset of online retailers. There is, however, another group of online business consumer (B2C) companies that begin with large sums from venture capitalists and start out as large, well capitalized firms with experienced management. The WOR would not be expected to yield much in studies concerning these operations. Future research should focus on a number additional variables that might be related to e attitudinal variables such as opportunism, customer regarding future strategic direction may better explain the broader range of e (3) examining the question of e-tailer evolution based on any number of possible sociological or economic intervening factors, including such things as industry, the nature of the product, the size of the organization, the cost structure, and the corporate culture of the firm. In conclusion, it can be said that the economic forces that propelled small, upstart bricks-and-mortar retailers in the 1950’s were not entirely different from the forces that moved the small virtual world entrepreneurs of the late 1990’s and early 2000’s. Given limited resources, the desire to serve the most profitable customers and mainta margins would seem to be a sensible goal for the majority of e Nonetheless, the Internet retail marketplace is known to be highly price sensitive, which offers much less in the way of differentiation opportunities to industries where there is manufacturer brand trust this, the value of WOR as a basis for e to the bricks-and-mortar retailers of the past. Entrepreneurs who are interested in developing a retail presence on eBay may find solace in the fact that as sellers gain experience they tend to move away from low margin goods toward higher priced goods w opportunity for new entrants to serve bargain hunters with lower priced goods.
Another interesting finding is that only 23 percent of the sellers utilized eBay’s as shown in Table 2. The About Me page is a free feature that allows sellers to create an image for themselves and perhaps draw some distinctions between themselves and other eBay sellers. Since having an About Me page is free promotion, and given that less than a third of sellers use it, a majority of eBay sellers may not think non competition makes much of a difference on eBay. Consistent with WOR the ones that do tend to be more experienced as MEPAGE was a significant variable in this study. The Wheel of Retailing appears to be a fairly robust and relevant framework for investigating the phenomena of e-tailer evolution, especially as it concerns the nature of small entrepreneurs who start on a shoestring and build their businesses from the ground up. This aptly describes the typical eBay seller, and so the model is a good one for investigating this particular subset of online retailers. There is, however, another group of online business s that begin with large sums from venture capitalists and start out as large, well capitalized firms with experienced management. The WOR would not be expected to yield much in studies concerning these operations. Future research should focus on a number of areas, including: (1) identifying additional variables that might be related to e-tailer experience. Of particular interest are attitudinal variables such as opportunism, customer-service orientation and intentions regarding future strategic directions; (2) developing a more comprehensive framework that may better explain the broader range of e-tailers beyond those who do business on eBay; and tailer evolution based on any number of possible sociological rvening factors, including such things as industry, the nature of the product, the size of the organization, the cost structure, and the corporate culture of the firm. In conclusion, it can be said that the economic forces that propelled small, upstart mortar retailers in the 1950’s were not entirely different from the forces that moved the small virtual world entrepreneurs of the late 1990’s and early 2000’s. Given limited resources, the desire to serve the most profitable customers and maintain higher margins would seem to be a sensible goal for the majority of e-tail practitioners. Nonetheless, the Internet retail marketplace is known to be highly price sensitive, which offers much less in the way of differentiation opportunities to retailers, particularly in industries where there is manufacturer brand trust (Ellison and Ellison 2009). In the face of this, the value of WOR as a basis for e-tail research may be more limited than its applicability mortar retailers of the past. Entrepreneurs who are interested in developing a retail presence on eBay may find solace in the fact that as sellers gain experience they tend to move away from low margin goods toward higher priced goods with more substantial margins. This provides opportunity for new entrants to serve bargain hunters with lower priced goods.
lized eBay’s
. The About Me page is a free feature that allows sellers to create an image for themselves and perhaps draw some distinctions between e promotion, and given that less than a third of sellers use it, a majority of eBay sellers may not think non-price Consistent with WOR the ones that do t variable in this study. The Wheel of Retailing appears to be a fairly robust and relevant framework for tailer evolution, especially as it concerns the nature of small their businesses from the ground up. This aptly describes the typical eBay seller, and so the model is a good one for investigating this particular subset of online retailers. There is, however, another group of online business-to- s that begin with large sums from venture capitalists and start out as large, well capitalized firms with experienced management. The WOR would not be
of areas, including: (1) identifying tailer experience. Of particular interest are service orientation and intentions s; (2) developing a more comprehensive framework that tailers beyond those who do business on eBay; and tailer evolution based on any number of possible sociological rvening factors, including such things as industry, the nature of the product, the size of the organization, the cost structure, and the corporate culture of the firm. In conclusion, it can be said that the economic forces that propelled small, upstart mortar retailers in the 1950’s were not entirely different from the forces that moved the small virtual world entrepreneurs of the late 1990’s and early 2000’s. Given in higher tail practitioners. Nonetheless, the Internet retail marketplace is known to be highly price sensitive, which , particularly in In the face of tail research may be more limited than its applicability
Entrepreneurs who are interested in developing a retail presence on eBay may find solace in the fact that as sellers gain experience they tend to move away from low-priced, low ith more substantial margins. This provides
APPENDIX
Figure 2: E-tailer Characteristics Based on Experience
E-tail
Experience
Volume
High
Lower number of transactions, growth rate of margins exceeds growth rate in volume
Low
Higher number of transactions, growth rate of volume exceeds growth rate in margins
Figure 3: Descriptive Statistics eBay Sellers VARIABLE MEAN YRS 6. VOLUME 1640 STORE RATING 99. POWSEL MEPAGE PRICE 50.
Sales
And
Volume
Figure 1: Wheel Of E
tailer Characteristics Based on Experience Volume Pricing Differentiation
Lower number of transactions, growth rate of margins exceeds growth rate in volume
Average sale and average price above the mean relative to competitors
Primary marketing goal is to find and capture new sources of differentiation
Higher number of transactions, growth rate of volume exceeds growth rate in margins
Average sale and average price below the mean relative to competitors
Primary marketing goal is to expand customer base
: Descriptive Statistics eBay Sellers MEDIAN FREQUENCY
284 .
. .
Time
Total Sales in
Average Sale in
E-Tailing Evolution Of Sales
Differentiation
Primary marketing goal is to find and capture new sources of differentiation
Primary marketing goal is to expand customer base
FREQUENCY
No. of Transactions
Total Sales in $
Average Sale in $
Figure 4: Correlation Table VARIABLE YRS RATING YRS 1 FDBK 0. RATING 0. STORE 0.038851 0. MEPAGE 0.157274 - 0. POWSEL 0.06584 0. PRICE 0.23178 0.
VOLUME 0.075452 - 0.
Figure 5: ANOVA Source SS Regression 2,425. Residual 19,300. Total 21,726.
Figure 6. Individual Variable Mean Values and Regression Output
Hypothesis Variable
H1 VOLUME
H2 STORE YES
STORE NO
H3 RATING
H4 POWSEL
YES
POWSEL NO
H5 MEPAGE
YES
MEPAGE NO
H6 PRICE
RATING STORE MEPAGE POWSEL PRICE
df MS F p- 6 404.2534 34.20 4.49E 1633 11. 1639
. Individual Variable Mean Values and Regression Output
Variable
Mean on YRS
Standard. Coefficient T^ p-value
VOLUME .068 2.72.
STORE YES
STORE NO
.151 6.39 2. 17E
POWSEL
POWSEL NO
MEPAGE
MEPAGE NO
.185 6.93 6.26E-
.234 9.98 7.65E-
VOLUME
- value 4.49E-
value
2. 17E-
