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Online pricing dynamics in Internet retailing: The case of the DVD market
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چکیده
مقدمه
جمع آوری داده ها و خلاصه آماری
جمع آوری داده ها
خلاصه آماری
رقابت بین شرکتهای اینترنتی و خرده فروشان چند کانالی
رقابت در میان شرکتهای اینترنتی و در میان خرده فروشان چند کانالی
مدل های اقتصادسنجی
ویژگی های داده های جمع آوری شده
مدل ضریب رگرسیون تصادفی
ویژگی های مدل رگرسیون ضریب تصادفی
نتایج تجربی
تجزیه و تحلیل سطوح قیمت
بحث و نتیجه گیری
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[toggle title=”ترجمه چکیده”]
رشد انفجاری خرده فروشی اینترنتی یک فرصت عالی برای جمع آوری قیمت های آنلاین در سطح انفرادی (به عنوان مثال، قروشگاه شخصی و / یا محصولات شخصی منحصر به فرد) در طول زمان و به منظور بررسی تکامل بازار اینترنت فراهم می کند. در این مقاله، ما نتایج به دست آمده از تجزیه و تحلیل استاتیک موجود را تعمیم داده و دو مدل رگرسیون ضریب تصادفی را برای بررسی پویایی قیمت ها در بازار آنلاین DVD در ایالات متحده آمریکا توسعه می دهیم. بر اساس این مدل ها، فرضیه هایی را برای مقایسه میزان تغییر در سطح قیمت و در پراکندگی قیمت در هر دو شعب آنلاین و تجاری خرده فروشان چند کانالی در بازار DVD آزمایش می کنیم. نتایج به دست آمده، بر اساس تجزیه و تحلیل برگرفته از 6759 قیمت در بیش از یک دوره 12 ماهه، نشان می دهد که خرده فروشان چند کانالی به طور موثر خود را از شرکتهای اینترنتی خالص در ابعاد غیرقیمتی متمایز ساخته اند به طوری که آنها قیمت های بالاتری داشته و در طول مدت زمان مطالعه تفاوت سطح قیمت ها را حفظ کرده اند. تمایل به رقابت قیمت سر به سر با شرکتهای خالص، شدید تر است. همچنین این نتایج نشان می دهد که در حال حاضر نشانه ای از بلوغ بازار آنلاین DVDدر ایالات متحده وجود دارد.
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اگر چه در سال های اولیه خرده فروشی اینترنتی، به طور گسترده ای پیش بینی شده بود که بازاریابی آنلاین، منجر به اصطکاک در تجارت الکترونیک می گردد (Alba و همکاران 1997، Bakos 1997)، تعداد قابل توجهی از مطالعات اخیر، نشان داده اند که این مساله غالبا درست نیست (به عنوان مثال، نگاه کنید به Lal و Sarvary 1999، Brynjolfsson و Smith 2000، Pan همکاران 2004). این مطالعه نقطه تمرکز تحقیقات را تغییر می دهد و با بررسی رفتار قیمت گذاری در نظر دارد، درک بهتری از چگونگی رقابت انواع مختلف خرده فروشان با یکدیگر در بازارهای آنلاین به دست آورد.
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[toggle title=”مقدمه انگلیسی”]
lead to frictionless e-commerce (Alba et al., 1997 and Bakos, 1997), a considerable number of recent studies have overwhelmingly shown that this is not true (see e.g., Lal and Sarvary, 1999, Brynjolfsson and Smith, 2000 and Pan et al., 2004). This study shifts the research focus and by investigating pricing behavior aims to gain a better understanding of how different types of retailer compete with each other in online markets. The issue of online pricing is of particular importance in the online marketing research. This is because pure dotcoms tend to differentiate themselves from other types of retailer via flexibly pricing their products. In addition, the competition among dotcoms also tends to be on the price dimension. Such competition leads to substantial price dispersion in the Internet markets. It is thus crucial for researchers to understand the characteristics of online pricing behavior and how it evolves over time. The results on this research issue also have important managerial implications. Most of the earlier empirical studies performed a static analysis where price competition was measured in terms of price levels and price dispersion. From the perspective of marketing research, both price levels and price dispersion are summaries of the price distribution in a market that reflects how retailers interact with each other. In these empirical studies price levels and price dispersion were compared between bricks-and-mortar (traditional) and online retailers (Bailey, 1998, Brynjolfsson and Smith, 2000, Erevelles et al., 2001 and Clay et al., 2002). Further studies focused on comparisons of various retailing channels. They compared pure Internet retailers (hereafter dotcoms) and online branches of multichannel (hereafter multichannel) retailers (Tang and Xing 2001); or traditional retailers, dotcoms, and multichannel retailers (Ancarani and Shankar 2004). These studies resulted in some interesting findings that suggest substantial differences in pricing behavior among different retailing channels. It has also been recognized that the stage of development of Internet retailing has a substantial influence on the pricing behavior of retailers. In the early stage, for instance, online retailers priced products at a higher-level than traditional retailers (Bailey, 1998 and Erevelles et al., 2001). As Internet markets developed in the early years of this century, online retailers substantially lowered their prices. During the transition period there was a mixture of findings, some of which contradicted each other. For example, Clay et al. (2002) did not find any significant difference in prices between online retailers and traditional retailers, whereas Brynjolfsson and Smith (2000) compared prices of CDs and books and found that online retailers had a lower price level than traditional retailers. There were also conflicting results on price dispersion. See Pan et al. (2004) for a comprehensive review. Internet markets are now more mature. It is thus of interest to investigate which of the earlier findings on Internet retailing can be generalized to the current online markets. In addition, since the majority of the existing researches were carried out at a fixed time-point, it is of interest to investigate which of the findings in these static analyses can be generalized to a longer time period so that the evolution of online prices over time can be investigated. The emergence of Internet data sources offers an impetus to the development of dynamic models that capture price dynamics (Dekimpe and Hanssens, 2000 and Pauwels et al., 2004). Consequently, recent studies on online pricing have used more sophisticated dynamic approaches. As Pauwels et al. (2004) have pointed out, however, few existing studies in marketing research recognize that the neglect of heterogeneity across the entities over which the data are averaged is a serious issue in dynamic modeling. For instance, in the recent analysis in Xing et al. (2006), cross-sectional heterogeneity was absent and the correlation of the prices posted at different retailers for the same product (e.g., a particular DVD title) during the same time period was ignored. Statistically, when aggregation bias is not addressed properly, it may result in parameter estimates being inconsistent, inefficient, and/or biased (Pauwels et al. 2004). This paper incorporates a sophisticated statistical technique to address these econometric issues. On the basis of our models, we focus on the pricing dynamics in online market evolution and investigate how different types of retailer compete with each other in an online market, the US DVD market. The US DVD market is chosen for several reasons. First, it is generally considered that DVDs are relatively homogeneous goods and thus likely to experience strong price competition given the characteristics of Internet channels (see e.g., Bakos, 1997, Lal and Sarvary, 1999, Brynjolfsson and Smith, 2000, Harrington, 2001, Tang and Xing, 2001, Iyer and Pazgal, 2003 and Xing et al., 2006). Secondly, there is a rich literature on the US online DVD market so it is easy to compare and contrast the findings of this study with other results, and in particular to compare the current price dynamics with those presented in Xing et al. (2006). In addition, it is more straightforward to compare DVDs because they are relatively homogeneous. For instance, prices of identical DVDs at different retailers can be compared directly. This is not the case for goods such as clothes, shoes, and electronics where there are many styles and/or models, and similar products may differ from each other to a considerable extent. Finally, the US online DVD market has a long history of Internet retailing and is likely to be more mature than other markets. The existing static analyses have revealed some interesting results on online marketing. Tang and Xing (2001) found that prices at dotcoms were significantly lower than prices at multichannel retailers. In addition, the corresponding price dispersion was much lower among dotcoms than among multichannel retailers. Contrary to Tang and Xing, 2001 and Pan et al., 2003 found that multichannel retailers generally had smaller price dispersion than did dotcoms. Ancarani and Shankar (2004) argued that multichannel retailers can combine the benefits of online shopping with physical inspection, pickup, and return of merchandise via support from their offline stores. In their static analysis they suggested that multichannel retailers may effectively differentiate themselves from dotcoms on nonprice dimensions and charge higher prices. Recently Xing et al. (2006) have investigated the dynamics of online prices in the US DVD market. On the basis of the online price data in the US DVD market collected during years 2000–2001, they have found that multichannel retailers charge higher prices than dotcoms and prices go up with time for both multichannel and dotcoms retailers. In addition, prices of dotcoms go up faster than those of multichannel retailers. In this paper we shall investigate which of these earlier findings can be generalized to the current online DVD market and can be generalized from a given time-point to a longer time period. More importantly, if there exists a difference in price levels between different types of retailer at a given time-point, we shall investigate whether the difference is maintained across the time period. To reveal the competitive pricing behavior of retailers, two dynamic models will be built at an individual product level, one model for price levels and the other for price dispersion. The nature of the data collected in this study raises several challenging issues for dynamic modeling, including extremely high dimensionality, and cross-sectional heterogeneity and the associated random effects. As indicated in Dekimpe and Hanssens, 2000 and Pauwels et al., 2004, it is difficult to address these issues in the framework of the widely used VARX approach. Hence, in this paper we shall consider an alternative approach, random coefficient regression models, to analyze pricing dynamics at an individual product level where the issues of time correlation and cross-sectional heterogeneity can be easily dealt with. We can also link marketing characteristics directly to the rate of change in price levels and in price dispersion so that the research issues of interest can be investigated. The next section is devoted to data collection and summary statistics. We then develop our main research questions. Then we build econometric models and test the formulated hypotheses. Finally we summarize the main results and discuss the managerial implications.
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[toggle title=”منبع”]
Journal : Electronic Commerce Research and Applications, Volume 10, Issue 2, March–April 2011, Pages 227–236
Publisher : Science Direct (Elsevier)
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فایل مقاله : 10 صفحه PDF
فایل ترجمه : 33 صفحه WORD
سال انتشار : 2011
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