電子商務性能的影響因素研究留學生dissertation代寫
08-10, 2014
根據不同的數據，我們預測商務活動的價值在未來的幾年里會增長。據估計，業務消費者(B2C)[1]收入會增加至4544億美元，這是一個不能錯過的機會。因此電子商務的話題變得越來越引起相關管理領域的研究,特別是營銷行業。然而目前在線零售仍處于對不成熟階段(瓦里安1998貝利），營銷科學研究把電子商務作為他們領域的“黃金主題”進行研究，在2000至2002年，他們一直致力于研究電子商務主題/電子商務發展/互聯網的影響……這足以表明MSI成員公司和學術界的成員對電子商務的深入研究，這段工作試圖做出這樣一個守時有意義的貢獻，它的目標是看看公司的類型對電子商務性能的影響，雖然這些因素最初是沒有相關性的，但是事實上,我們將發現的三個因素密切影響著早期電子商務的性能。
This dissertation considers three factors, type of firm (Clicks and Mortar, Pure Player or Cataloguer), timing of entry into the market and the extent of digitisation in the product sector, in order to examine their effect on e-commerce performance. I also consider any relationship that the later of these two issues has with the type of firm. Specifically whether a particular type of firm entered the electronic marketplace earliest and also whether particular types of firms are better suited to selling products with a high or low digital content. The work uses some novel methodology in specifying the level of digitisation in a sector. The main results demonstrate that Pure Players perform best in the operational side of the business but are not effective at attracting visitors to their websites. Earlier entry into the market significantly increases performance and Cataloguers and Pure Players engaged in Internet retailing significantly earlier than the traditional retailers did. Finally, as the extent of digitisation in a product sector increases firm performance does not increase as many authors have predicted. Rather, Pure Players perform better as the extent of digitisation increases with the traditional firms performing better in less digitised sectors.
Table of Contents
1 INTRODUCTION.....................................................................................................4
1.1 Motivation............................................................................................................4
1.2 Context and Rationale.....................................................................................4
1.3 Theoretical Overview.......................................................................................6
1.4 What do we mean by Internet Performance................................................8
2 THEORY...................................................................................................................9#p#分頁標題#e#
2.1 Innovation and the Incumbent.........................................................................9
2.2 Clicks and Mortar versus Pure Players.....................................................10
2.3 The Catalogue model of retailing................................................................17
2.4 First movers......................................................................................................19
2.5 Product Attributes and Performance.........................................................22
2.6 Theoretical Summary......................................................................................24
3 METHODOLOGY AND SAMPLING...................................................................26
3.1 Measures.............................................................................................................26
3.2 Model Specifications........................................................................................28
4 RESULTS................................................................................................................30
4.1 Regression Results...........................................................................................30
4.2 Type of Firm and Performance.......................................................................32
4.3 Type of Firm and entry into the Market......................................................33
4.4 Advantages from Entering the Market Early............................................34
4.5 Digitisation and Performance........................................................................35
4.6 Type of Firm and Extent of Digitisation......................................................35
5 DISCUSSION..........................................................................................................37
5.1 Differences and Similarities between Gomez Scores and Alexa Hits........37
5.2 Analysis of Main Findings..............................................................................38
5.3 The Special Case of Amazon.com..................................................................41
5.4 The Fear for Pure Players.............................................................................41
6 CONCLUSION......................................................................................................43
6.1 LImrtations and further avenues of research..........................................43
6.2 Concluding Remarks......................................................................................43#p#分頁標題#e#
APPENDICES
Appendix A: List of Firms that were Examined...............................................46
Appendix B: List of Sectors and digitisation Scores.....................................47
BIBLIOGRAPHY.....................................................................................................48
List of Figures and Tables
Chapter 1
Figure 1: Theoretical Context of the Paper
Chapter 4
Table 1: Regression Results, using Gomez Ratings as the Dependent Variable
Table 2: Regression Results, using Alexa Hits as the Dependent Variable
Table 3: Mean Gomez Scores and Alexa Hits for the Different Types of Firms
Table 4: Type of Firm and Mean Time since Entry
Figure 2: A Scatter Plot of Gomez Rating on Entry Time to Rating
Figure 3: A Scatter Plot of Alexa Hits on Time since Entry
Figure 4: A Scatter Plot of Mean Gomez Scores for Each Type of Firm at Particul Digitisation Levels
Chapter 1 Introduction
"All the evidence points to unstoppable momentum, an inexorable force that will drive electronic commerce forward to reach out and make it so persuasive and accessible that it can't fail to impact shopping...Retailing will never be the same - home shopping will revolutionise retailing"
(De Kare-Silver 2001)
1.1 Motivation
The figures vary as to what the value of electronic-commerce activities over the coming years will be, although the majority of predictions tend to indicate growth. One estimate expects Business to Consumer (B2C)[1] revenues to be a much as $454.4Bn by 2004 (Forrester Research), ensuring that it is an opportunity that cannot be missed (Green 1999). The topic of e-commerce is therefore becoming increasingly pertinent in management studies and particularly in the field of marketing. However at present online retail is still in a relatively immature phase (Bailey 1998: Baylis and Perloff 2000; Varian 2000) and little is understood about this channel of commerce (Ward 1999). The Marketing Science Institute list this domain as their 'Gold Topic' for research, 2000-2002 and state, "The Topic of E-Business / E-commerce / Impact of the Internet...have been identified by the MSI member companies and academic trustees as being sufficiently significant and timely that they deserve intensive research attention."[2] This piece of work attempts to make such a punctual meaningful contribution. In particular it aims to look at how the type of firm, the entry time of the firm and the product that it sells, effects its e-commerce performance. Initially these factors may appear unrelated, however in actual fact, as we shall discover later fact the three factors are very closely inter-linked in their effects on e-commerce performance.#p#分頁標題#e#
1.2 Context and Rationale
Strictly speaking 'e-retail' includes a range of electronic medium that can be used to sell products (Alba, Lycnh, Weitz, Janiszewski et al 1997), although of these channels the Internet is developing into the most prominent (De Kare-Silver 2001) and this is the channel that this paper focuses on. The e-retail market comprises broadly of two types of firms, (Enders and Jelassi 2000) Clicks and Mortar firms, and Pure Players (Clicks only). The first of these has a conventional business in the bricks and mortar sense but also engages in e-commerce (Subramani and Walden 1999). These firms are the incumbent (Chandy and Tellis 2000) or the dominant firm (Chandy and Prabhu 2000) in the industry. Examples of such firms include Borders and Wal-Mart. Pure Players on the other hand, consist mainly of young, new, start-ups that rely on the Internet as their primary retail channel. The most notable example is Amazon.com. This paper attempts principally to examine how the Internet performance of these two models differs. The dissertation also aims to show that how the type of firm performance is driven by two other issues, which also affect performance. The following diagram should help clarify this:
Figure1: Theoretical context of this piece of work
The diagram illustrates the theoretical framework of the dissertation. The work aims to test the impact on e-commerce performance of the type of firm, the timing of entry into the market and the content of digitisation in a product sector. The later of these two variables are also expected to have a relationship with the type of firm type of firm. Specifically did a particular type of firm enter the market first and do particular types of firms' cope differently with the extent of digitisation in a sector.
1.3 Theoretical Overview
I shall do this by examining in turn these three related subdivisions, the first of these asks, which of these business models will perform best on the Internet?
Ever since Schumpeter (1942), academics have questioned whether large dominant firms would be more innovative than smaller less endowed firms (see Baldwin and Scott 1987; Cohen and Levin 1989). Schumpeter argued that larger established firms would be able to spend more on Research and Development and hence be more innovative. This notion has been backed up by a number of studies that say the slack (Nohria and Gulati 1996) that these firms have, allows them to experiment to a greater degree (Bourgeois 1981; Cyert and March 1963), giving them greater scope to enter new markets (Hambrick and Snow 1977; Moses 1992). If one allies this to other advantages the traditional firms have such as brand name (Lane and Jacobson 1995), greater consumer knowledge (Chandy and Tellis 2000) and the ability to use their store in order to promote their Internet operations (Enders and Jelassi 2000) we see that established firms seem to have a great platform from which to exploit e-commerce.#p#分頁標題#e#
In spite of this, not all the arguments are so pervasively in favour of the dominant firms and many studies have illustrated that incumbents in a particular industry may actually be slower to embrace new technology relative to new firms (Ghemawat 1991; Utterback 1994). Chandy and Tellis (2000) highlight the example of Chester Carlson (dry copiers) to demonstrate this and term it, 'The Incumbents Curse'. Internet start-ups are considered to have a more flexible culture (Naman and Slevin 1993) and to be more technologically astute (De Kare-Silver 2001; Subramani and Walden 1999) than incumbents.
In addition to the 'Clicks and Mortars' and 'Pure Players,' there is also a third category, which this study will examine; Cataloguers. Catalogue Shopping is potentially able to enjoy the asset benefits of the traditional retailer and yet as a direct to customer marketing channel is more akin to online shopping (Ward 1999). Thus the Internet performance of 'Cataloguers' such as, Lands End and J Crew is also of interest to me.
The next issue, which this paper deals with, is the importance associated with instance of entry and specifically, does the time when a firm enters the online market effect its performance in e-tail? At first glance this may appear to be very separate from the question regarding which type of firm is best suited to -tail. However the paper also attempts to test the notion that most of the Pure Players are likely to be the first movers. Many authors have discussed the advantages accrue from entering the market first (Lieberman and Montgomery 1998; Scherer 1986 Schmalensee 1982). This paper aims to examine whether such advantages will enable the early entrants to prosper.
The third question, which I aim to answer, is genuinely unique in its proposal: Will the type of product that the firm sells effect its performance in Internet retailing? Firstly, the Literature has theoretically indicated that for products where attributes can be translated digitally the 'Electronic Selling potential' (De Kare-Silver 2001) will be greater (Alba, Lynch, Weitz, Janiszewski et al 1997; Enders and Jelassi 2000, Lai and Sarvary 1999; De Kare- Silver 2001), this study aims to empirically test this claim. More interestingly however, the study aims to examine whether certain business models are better suited to selling certain products on the Internet. Specifically are Pure Players more capable of selling more digitally biased goods? Whereas the Clicks and Mortar firms seem ideally placed to excel in selling products that contain less digital attributes in assessment. This premise is perhaps the most stimulating and insightful one that the paper has to offer the field of marketing and e-commerce.
This study therefore attempts to identify the effects on performance by examining three closely related issues which are all driven by: Which business model will perform best on the Internet? (Pure Players, Clicks and Mortar or Cataloguers). When is the most prosperous time to engage in e-commerce? (and did a particular business model establish its presence on the net first) and What the product attributes will be of firms that perform well? (and will different business models cope better with certain product attributes?) The close connection between the questions of which when and what are already becoming very clear and will continue to do so even more through the duration of this study.[3]#p#分頁標題#e#
The study is unique in the sense that it compares solely the Internet performance of both traditional firms and Pure Players. Most studies in this area have compared the conventional retailing channel with the Internet retailing channel (e.g. Bailey 1998; Brynjolfsson and Smithl999; Degeratu, Rangaswamy and Wu 1998; Lee 1997). Where they have considered questions such as, are prices lower via the electronic channel and to what extent Internet retailing will compete with the high street. This st however, examines the ability of firms to shape their competencies solely for this new channel from an empirical perspective. Literature in this field has theoretically given reasons as to which type of firm should perform best but none appear to have empirically tested this. The study also looks at products across sectors (like Ward 1999) in order to examine the effect, whereas the majority of papers seem to focus on homogeneous sectors (e.g. Bailey 1998; Brynjolfsson and Smith 1999; Clemons, Hann and Hitt 1998; Degeratu, Rangaswamy and Wu 1998; Lee 1997: Lynch and Ariely 1998).
1.4 What do we mean by Internet Performance
So far I have referred to Internet or Web Performance as the dependent variable that I wish to measure. Here I attempt to define exactly what this study is considering when it implies 'performing well on the Internet'. Primarily I am examining two dimensions of performance, operational and attractiveness.
Operational performance takes into account how efficient and competent the e-business is by considering features such as ease of use, customer confidence, on-site resources and relationship services. Attractiveness aims to measure how effective and popular the e-businesses are amongst consumers. During the next sections factors such as, financial capabilities, brand awareness and networks are discussed. Some will affect attractiveness while others will have more of a bearing on operational procedures. It is also highly likely that some factors will have an effect on both these dimensions of performance.
This study now takes the following format; firstly I shall look at theoretical concepts in order to develop our hypothesis, followed by a methodology and results section, which will then provoke some discussion. Finally, I will conclude with some limitations of the study and avenues for further research in this thoroughly engaging area.
Chapter 2 Theory
2.1 Innovation and the Incumbent
The Internet potentially represents to retailing what is known as a 'radical innovation' (Chandy and Tellis 1998); an innovation that will drastically alter the way that business is practised. Historically, large, dominant, incumbent firms invest less heavily in these innovations due to inertial factors (Chandy and Prabhu 2000) or fear of cannibalisation (Golder and Tellis 1996) that could bring about their own downfall (Miller 1990). A stream of literature supports this hypothesis (e.g. Dessauer 1971; Hiltzik 1991; Henderson 1993; Utterback 1994) and many examples exist to illustrate this phenomenon. One of the most poignant examples is illustrated by Christensen (1997) in his book 'The Innovator's Dilemma.' Where he identifies that a non-incumbent instigated every change in the format of computer disks, from 8.5" to 5.25" to the current 3.5" size even though the dominant firm had the option to initiate the change. Also, in each instance, the new entrant brought about the downfall of the previously dominant firm. The Internet may not spell the end of the high street as the initial hype may have predicted (Kreitzman 1999), but it certainly does present an opportunity (or threat) to firms.#p#分頁標題#e#
In spite of this, a counter argument claims that these larger firms possess superior capabilities and assets that allow them to invest more heavily and effectively in new technologies and hence out perform less dominant firms. Chandy and Prabhu (2000) highlight, "Bank one (with Internet banking), Hewlett Packard (with Ink jet printing technology) and Intel (with RISC technology)" as examples of dominant firms actively and successfully pursuing and embracing new technology.
The following section looks specifically at advantageous capabilities that the incumbent traditional retailers' possess and then later the obstacles that they face as far as their performance in Internet retailing is concerned. Although the literature demonstrates that the threats that are posed to incumbents, lead them to hold back investment in new technologies and hence losing an advantage to the first mover (Yip 1982; Scherer 1986) we shall look at first mover advantages in a separate section. In the following section, all firms, traditional or Pure Play, are assumed to have an online presence. However we consider which business type will have the ability to impose this presence with the most significant outcome.
2.2 Clicks and Mortar versus Pure Players The Advantages of the Incumbents: Financial Capabilities
The dominant incumbents are more likely to be well endowed with the necessary financial resources that are required to be successful in the 'new economy.' This is what Chandy and Prabhu (2000) term the 'wealth effect' or Nohria and Gualti (1996) label as slack resources. Larger dominant firms have greater wealth or at least better access to capital than newer less established firms do (Cohen and Levin 1989), enabling them with a number of advantages over start-ups. They can invest in leading edge technology and also hire the most skilful workers in order to have advanced research and development departments (Chandy and Tellis 2000). This wealth shields the dominant firms better than start-ups from the potential losses (Galbraith 1968) that may occur for a period until the market returns profitability (Chandy and Tellis 2000), thereby inducing less risk averse firms (Arrow 1962). As a consequence, wealthier firms are more able to experiment (Bourgeois 1981; Levinthal and March 1981) with projects that may have uncertain outcomes (March 1976), which may not have been approved if the financial situation was not so secure. In addition, the larger fixed cost that these firms have also enables them to enjoy greater economies of scale from such investments.
A string of literature supports this Schumpterian idea that larger firms have greater innovative ability (e.g. Ali 1994: Damanpour 1987: Delbecq and Mills 1985: Galbraith 1952; Lant 1985; Majumdar and Venkataraman 1993; Zaltman, Duncan and Holbeck 1973). Thus, the greater financial capabilities of dominant firms would tend to allow them to perform better in Internet retailing.#p#分頁標題#e#
Intangible Assets
Intangible assets are highly significant to firms and represent the ability to generate earnings over and above those created by tangible assets (Lane and Jacobson 1995). The traditional retailers enjoy these assets in several forms. Here I shall explore brand reputation, existing alliances and competencies in the market place.
Brand Reputation:
"... we think that there is no substitute for...brand strength which a few e-tailers have art present" (Morgan Stanley Dean Witter)
Traditional retailers are able to leverage their well-known physical brand name in their online stores, not only saving on marketing costs, but also increasing the value for the consumers (Lal & Sarvary 1999), who are more likely to accept a familiar brand than that of a unfamiliar start-up (Farquhar 1989). Purchases via untried channels are viewed by consumers as risky purchases (Bauer 1960; Folkes 1998; Gregan-Paxton and John 1997) and Internet purchases can certainly be deemed as risky for a number of reasons. In particular, famous e-tailing failures (e.g. Boo.com) have harmed the confidence that consumers have in Pure Plays. Consumers perhaps now consider all Pure Plays just as financially insecure and fear that they may not receive the products that they have ordered and paid for. The physical security that traditional firms offer is hard for Pure Players to replicate (Enders and Jelassi 2000). Secondly, on-line shopping requires consumers to provide a vast amount of personal and sensitive information such as, names, addresses and most importantly credit card details. It is likely that consumers will view the sites of established firms as being more secure and also less likely to abuse this information.[4] Thus one of the biggest challenges that the start-ups face, is having to convince the buyers about their service and product quality when they are already familiar with the quality of the incumbent (Schmalensee 1982).
In addition trying the products of unfamiliar firms leads to the consumer incurring search costs (e.g. time) of information (Stigler 1961). Consequently, in order to economise on such costs the consumers may choose the familiar brand. (Rumelt 1987; Zeithaml 1998)
Competencies in the Marketplace:
Traditional firms have generally been established for many years and have huge experience in the marketplace. They have acquired significant knowledge of the product, regarding factors such as the demand and supply conditions. Moreover they know their consumers well and have collected detailed consumer profiles over the years. The socio-demo and psychographic information that incumbents have at hand allows them to understand the needs of the consumer better and how to communicate with them.
Existing Network
It was the work of Richardson in the 1970's that first stressed the importance of networks to firms. Since then a huge body of literature has illustrated why and how alliances are vital to success of firms (e.g. Achrol and Kotler, 1999; Anderson, Hakansson, and Johanson, 1994; Gulati 1998; Homburg, Workman, and Krohmer, 1999; Maltz, and Kohli, 1996; Walker and Ruekert, 1987). In particular with B2C e-commerce the economies of scale are smaller, implying firms have a lower minimum efficiency of scale (Earley 2001). As a result, there will be a greater need for firms to outsource activities, for which they will require a strong network. Additionally, these networks are said to foster innovation for several reasons; the ability to learn from one another, develop products together, and also due to 'peer pressure' (Porter 1998). These existing supplier relationships, reputations and relatively large purchase quantities of incumbents facilitate in bargaining better terms and conditions, with regards to price, delivery time, and quality.#p#分頁標題#e#
Channel Synergy
Clicks and Mortar retailers can benefit greatly by being multi-channel operators as they can leverage their stores in order to promote their e-tailing arm. The physical stores, in which consumers can enjoy a shopping experience and meet face to face with staff, presents a significant opportunity for traditional retailers. Enders and Jelassi (2000) use the example of Gap, who have over 2,600 physical stores with which to publicise and support 'GAP.com.' One of the largest problems facing e-retailing is its inability to deliver goods on purchase. Gap however, are able to use their stores as a location for consumers to immediately pick up orders, exchange goods and collect refunds for returned items. Perhaps the largest benefit however, is the saving that arises in marketing costs. Gap uses shop window displays and adverts at cash registers and on shopping bags to promote their website, sales staff are also trained to refer customers to the website. This huge saving allows traditional retailers to acquire online customers at a fraction of the cost that the Pure Players expend. In fact one report suggests that traditional retailers need to spend less than $5 a head in order to attract customers on line, whereas their Pure Play counterparts need to spend an average of $45 per head (Calkins, Farello and Shi 2000). These huge savings in sunk costs and marketing activity allow the traditional firms to focus on other enhancements to their e-tail performance.
Now that the paper has discussed the case for the Clicks and Mortar model to give a superior e-tail performance, I shall look at the reasons why the advantage may lie with the Pure Players.
Fear of Cannibalisation
" Retailers are allegedly frightened by the implications of responding to the threat of electronic shopping. If they set up their own websites will they simply cannibalise their store sales? ... they have made massive investments in their physical infrastructure. Should they now jeopardise all that and undermine long-established customer relationships ? " (Verdict Research)
Incumbents that are enjoying success from present technologies are not likely to be strong proponents of innovations that may harm the returns of current procedures (Ali 1994). Traditional retailers therefore have a lower 'marginal incentive' (Chandy and Tellis 2000), relative to start-ups with regard to engaging in e-commerce
Established firms have invested huge sums in their current infrastructure and technology and are therefore likely to be more committed to these investments and processes, as opposed to switching resources to new technology. Chandy and Prabhu (2000) term this an 'escalation of commitment', whereby the greater commitment firms have to the existing technology, the greater the perceived losses that they face from shifting to newer technology will be (Brockner and Rubin 1985). Traditional retailers have made many investments in physical stores (many of which have long leases (De Kare -Silver 2001)), sales techniques and human capital for example. The Internet, which, threatens to undermine these investments, naturally poses a threat to these firms.#p#分頁標題#e#
Further, the sales that they make from their primary channel now face direct competition from the sales they make from their e-tail business. This leads to the fear that they will cannibalise their established channel and can leave firms hesitant to effectively embrace the new technology (Enders and Jellasi 2000; Golder and Telis 1996). By threatening the sales via the current channel, the jobs of managers and their sales staff are under risk. This suggests why employees may not be eager to see their employers e-commerce arm prosper.
Organisational Procedures
Established firms are more likely to have rigid processes in place that may not be conducive to innovation. These procedures come specifically in two forms 'organisational filters' and 'organisational routines' (Chandy and Tellis 2000; Henderson and Clark 1990)
Organisational Filters:
These are procedures that firms have in place in order to filter information that is not important to the firm's core activities. It has long been argued that these activities help to keep the organisation focused and allow it to excel in the current market. However they are in fact faced with a double-edged sword. The very procedures that make firms successful in the current market hinder them from innovating and successfully entering future markets. This is because the information that they need to engage successfully in these markets is filtered out as irrelevant (Hannan and Freeman 1977).
Organisational Routines:
The operating routines of established firms are obviously well suited to the current marketplace. They can be cited as a reason for a firm's success (Nelson and Winter 1982) and therefore naturally become the norm (Boecker 1989; Bonoma 1981; Davenport 1992; Gersick and Hackman 1990; Hannan and Freeman 1984). Incumbents develop these routines over a period of time enabling them to accomplish current organisational tasks efficiently (Hannan and Freeman 1977; Henderson 1993). Chandy and Tellis (2000) argue that these routines stretch to the Research and Development department where routines are geared toward 'incremental' innovations based on present infrastructure and are therefore liable to prevent 'radical innovation'. Moreover, there exists a switching cost and a risk of an uncertain pay off that is associated with departing from present routines.
Thus the above organisational procedures are also likely to stop managers from appropriately investing in and readily accepting Internet retail developments that could undermine their current success.
Size and Inertia
Established firms can be victims of their own success due to their size. They are firms that have been in the market for a considerable period of time and have therefore grown significantly. This size can lead to a formal hierarchical structure that may stifle their progress in e-commerce. The bureaucracy associated with larger firms' makes them less nimble in responding to a change in market conditions (Tomatzky and Fleischer 1990, Link and Rees 1991). As firms grow in size the number of employees increases, causing firms to have more administrative layers and create increasingly formal methods of communication (Blau and Schoenherr 1971; Kasarda 1974; Terrien and Mills 1955). Therefore, when an idea is developed in a large firm the formal channels through which it has to pass, in order to be accepted, can be vast and tedious. There is also more likelihood of the idea being rejected at each stage of the process (Chandy and Tellis 2000). This bureaucracy also increases the burden on creators and does not allow them to focus on their fundamental tasks. (Acs and Audretsch 1991; Scherer 1980). To add to this, employees in large firms have less of an incentive to produce new ideas, as they are unlikely to capture significant benefits from them (Cohen 1995; Schumpeter 1942). The benefits will be divided inequitably throughout the firm with top managers as opposed to the inventors themselves likely to be the best off.#p#分頁標題#e#
This inertia may impair traditional retailers, as initiatives to improve e-retail performance are never realised. Start-ups on the other hand are smaller in size and have teams that openly communicate. In fact their entrepreneurial culture is likely to foster innovative ideas (Burgelman 1985) in a risk-loving environment (Sykes and Block 1989). Thus their technological know how and the ability to react quickly (Yoffie and Cusumano 1999; Warner 1999) to changes, in a still unfamiliar market place (Bailey 1998; Baylis and Perloff 2000; Byronjolffson and Smith 1999), will be an important factor in their performance.
Aside from the fear of cannibalisation, organisational procedures and size inertia, some of the factors that we earlier classed as advantages for the traditional retailers, slack, brand reputation and competencies in the market place could in some respects be labelled as disadvantages.
Too Much Slack
Economists including Williamson (1963, 1964), have viewed slack as wasted resources within a firm that reflect, "managerial self interest and incompetence," (Nohria and Gulati 1996). The view is that too much slack causes 'principal-agent' dilemmas (Jensen and Meckling 1976). The agents who are the managers invest these auxiliary funds in 'pet projects', which are developments that may not be beneficial to the principal (Jensen 1993). This slack is said to cause X-inefficiency in firms (Leibenstein 1969). Thus as far as e-retail is concerned it is possible that mangers may not invest funds in necessary e-retail activities, causing their performance in this department to suffer.
Destruction of Brand Reputation
The brand reputation, which is such a vital asset to the traditional retailers, can be put at risk by leveraging (Aaker 1990; Sullivan 1990, 1991: Tauber 1981). By associating the brand with online retail they may actually damage both their e-tail and conventional business. This may occur due to inconsistent brand leveraging (Lane and Jacobson 1995) that can lead to confusion or dilution of the brand image (Loken and Roedder 1993) and thereby harming earnings via the brands original channel. It is also difficult to try and rescue the brand if the new channel is unpopular or suffers from an adverse incident (Aaker 1990; Jarrell and Peltzman 1985; Simon and Sullivan 1993). In fact research shows, "... 6% of customers who have a negative online shopping experience stop visiting the retailer's physical outlet" (Brown 2000)
Inappropriate Competencies
In the previous section it is argued that incumbents have competencies in the current market place and a knowledge of consumers that is difficult for the start-ups to acquire. However here I argue that these competencies and consumer profiles may not be what is required for the electronic marketplace. The Internet can be viewed as a 'competence destroying' (Tushman and Anderson 1986) technology and the competencies that are required to succeed with a technology such as the Internet may cause the traditional firms to perform worse in e-tail compared to start-ups, whose skills are specific for this market.#p#分頁標題#e#
Furthermore, the market orientation of traditional firms is likely to be focused on their traditional markets, whereas the start-ups are likely to put a greater focus on the emerging market (Von Hippel 1986). This is likely to leave the Pure Players more aware of new technologies and customer requirements in this market. (Moorman 1995).
Summary
The literature provides mixed evidence as to whether incumbents or start-ups are likely to be more able in e-commerce. These larger firms face many obstacles that cause them to embrace the new technology less effectively and therefore we would expect these factors to impair their performance. Overall however, the greater assets, both tangible and intangible, certainly favour the established firms in performing well in e-tail and it is these factors that I expect to outweigh any negative consequences that may hinder the performance of incumbents.
It is important to note that in this section we are not dealing with the advantages of the first mover (we deal with this separately in a later section). Although many of the above factors may cause the incumbents to lag behind in (or pioneer) these technologies, this is not considered specifically here. Rather, here I am considering whether these factors will cause the necessary investments to take place and also whether the necessary mindset and structures will exist in order to support this investment. Thus at this point I hypothesize,
H1: The greater tangible and intangible assets that the traditional retailers possess, are likely to enable their 'Clicks and Mortar' business model to perform better in e-tail than Pure Players
2.3 The Catalogue Model of Retailing
"Some of the most successful retailers on-line for instance, have been catalogue retailers...established companies (that) have such systems in place and can use them as the cornerstone of a new Web-based business" (Boston Consulting Group)
The traditional catalogue model of retailing is based on a direct to consumer model of retailing whereby products are demonstrated via pictures and descriptions in catalogues. Thus the catalogue model of retailing naturally possesses many similarities to the model of e-tailing and Ward (1999) finds that consumers consider online and catalogue shopping to be closer substitutes of each other, rather than traditional retailing and the other channels. A number of reasons are stated for this finding and these reasons are likely to allow Cataloguers to perform well on the Internet.
I mentioned earlier how Internet retailing may be regarded as 'competence destroying' (Tushman and Anderson 1986) for traditional retailers. However for Cataloguers the converse is likely to be true and Internet retailing can undoubtedly be thought of as a 'competence enhancing' (Tushman and Anderson 1986) technology. This new channel of commerce should allow Cataloguers to be in their element. Firstly, unlike traditional retailing in both the models of online and catalogue shopping, the customer does not receive the goods at the actual time of payment. We would expect the Cataloguers, with their years of experience, to have the necessary distribution infrastructure in place such that consumers can be assured of timely delivery as well feeling secure of the 'going concern' of the business. Secondly payment for goods is typically in credit form. I would expect the Cataloguers to have the necessary experience in financial matters and also expect consumers to be able to trust that the Cataloguers will deal with the information securely and in an error-free manner. Thirdly, both rely on conveying information through photographic images or descriptions and we would assume Cataloguers to have the 'know how' to do this in an effective manner (Alba, Lynch, Weitz, Janiszewski et al 1997).#p#分頁標題#e#
In addition, Cataloguers have the most to lose as well as the most to gain from Internet technology. They have many similarities to e-tailers as mentioned above and will find it more difficult than traditional retailers to differentiate themselves significantly from e-tailers. The Internet therefore poses a larger threat to their business than to that of the orthodox retailers, which will cause them to shift more urgently onto this channel.
However, they have much of the necessary infrastructure, as well as current audience (Alba, Lynch, Weitz, Janiszewski et al 1997) in place and e-tailing is likely to lower their costs (they no longer have to print expensive magazines). Thus Cataloguers have many advantages over established firms, such as, financial resources and intangible assets as well as the experience that places them at an advantage to start-ups. They possess many of the capabilities that are required for Internet retailing and do not have to overcome the difficulties that prevent established firms from successfully implementing this technology. In addition, Cataloguers can definitely see the enhancement effects to their businesses from lower costs (Alba, Lynch, Weitz, Janiszewski et al 1997), whereas traditional retailers may not be so convinced by its enhancement effects. So,
H2: Catalogue retailers are likely to perform better online than both 'Clicks and mortar' firms and 'Clicks only' firms
2.4 First Movers
While the previous section asked if the focused single channel strategy of the Pure Players would make them more likely to be successful than the lager multi-resource established firms. This section looks specifically at the advantages of the first mover against the advantages that may be gained from entering the market at a later stage. As I discussed earlier, the Pure Players are generally thought of as being the pioneers in e-tailing (Calkins, Farello and Shi 2000; Leadbeater 2001; De Kare-Siler 2001). The factors that I discussed earlier, (e.g. fear of cannibalisation, organisational procedures and size inertia) are thought to leave incumbents behind in embracing this technology. Hence, even though the traditional firms may have the capabilities to perform better than Pure Players when they eventually do engage in e-commerce (as H1 indicates), there are many factors that are likely to mean that they are slow at actually getting into the market in the first instance. Most of the literature does tend to suggest that Pure Players have sensed that opportunity that exists and do not suffer from any of the inertial the factors that hinder the traditional firms (Subramani and Walden 1999). Rather they are excited by this new channel (Kreitzman 1999), thus enabling them to move quickly into the market. So,
H3: The Pure Players are likely to be the first to enter the e-commerce market
Advantage of the First Mover
Developments are taking place at lightening speed.....First mover advantage is becoming more important; joint ventures and alliances ever more critical. There's no place to hide! It's like the gold rush. You have to put your stakes in the ground now. And there's only a certain amount of territory left!#p#分頁標題#e#
(De Kare-Silver 2001)
I often use the phrase 'Internet speed' or 'Internet time' (Subramani and Walden 1999) in this piece of work. This is the notion that the Internet has shortened the time that transactions take and in particular shortened the time that it is required to set up a business. This also refers to the excitement surrounding the pace of innovation associated with the Internet. This speed seems to place even more of an emphasis on firms to enter the market early or risk loosing out to the first movers.
By entering the market first, firms aim to capture the distinct advantages that accompany such a move (Lieberman and Montgomery 1998; Scherer 1986 Schmalensee 1982). First movers are traditionally able to grow faster than later entrants are and retain significantly more consumer loyalty. Also by entering early they can erect barriers to entry, forge supplier relationships and learn first hand about the emerging market. There is a lot of literature that supports pioneers outselling late entrants in many markets (e.g. Kalyanaram and Urban 1992; Robinson 1988; Robinson and Fornell 1985; Urban et al 1986).
Advantage of the Late Entrant
To be the first to the market was the rallying cry of the dotcoms. But the advantage was never all it was cracked up to be...the dotcom experiment has been a massive exercise in first mover disadvantage... Pioneering entrepreneurs often prove a concept can work, only to see large companies come into the market after them and pick up the profits.
(Leadbeater 2001)
Authors have often argued that the advantages of the first mover have in many instances been overstated and depend on individual circumstances. Therefore in some cases firms may profit from waiting and observing the market, before entering. A growing body of literature supports this view (e.g.Golder and Tellis 1993 Lieberman and Montgomery 1988; Lilien and Yoon 1990). Shankar, Carpenter and Krishnamurthi (1998) term this as 'Late mover advantage' and describe two ways in which the late mover can out sell the pioneer. Firstly the pioneer defines the category concept and the consumers preferences in that category (Carpenter and Nakamoto 1989). The late entrant can then monitor and learn about these preferences as well as any weaknesses in the pioneers business model and use this knowledge to out-perform the first mover. Secondly a late mover can 'leapfrog' the pioneers by bringing a superior innovation into the market.
History shows many examples where a late mover has been more successful in a market place. EMI (a British music company) invented Computed Tomography scanner (a great medical innovation). However due to EMI's inexperience in the medical field it soon faced many competitors, was forced to exit the market that it had pioneered and saw General Electronic become the market leader. The personal computer, wine cooler and video game markets (Shankar, Carpenter and Krishnamurthi 1998) tell similar stories.#p#分頁標題#e#
Internet retailing may be a market where the first mover advantage is not so easy to press home. E-retailing has not taken off as greatly as some pundits had initially predicted (Steidmann 2000), which has meant that first movers have not been able to substantially increase their online operations, leaving them small and not so threatening to late entrants. It is also difficult to form barriers to entry in online. The Internet is inherently famed for demonstrating low barriers to entry for firms (Choi, Stahl and Whinston 1997) making it increasingly difficult for first mover to sustain an advantage.
I see that there are advantages to entering the Internet market first as well as advantages in waiting and entering late. However with Internet retailing it is likely that firms that have been around longer will on the whole do better than the late entrants, at least at this relatively short run stage. The firms have been able to learn first hand about this new business model and are likely to have the most online brand awareness as well as the more competent business and are probably going to be able to outweigh the gains of the late entrant. So,
H4: Firms that engage early in e-commerce are likely to benefit from the advantages that accompany this timing and hence perform better in e-tailing than late entrants.
2.5 Product Attributes and Performance
Chandy and Prabhu (2000) state that new technology can affect existing markets in several ways; it can obsolete, enhance, or have no affect on the current market. As far as e-tail is concerned it is likely that the Internet will affect varying product sectors in different ways. Some sectors are more likely to be conducive to e-tail than others are. I see in reports by Jupiter Communications and a study by Ward (1999) that the products that are more heavily purchased on the Web are music, books, and computer hardware and software, whereas the spend on groceries, for example is considerably lower.
La1 and Sarvary (1999) distinguish between two types of product attributes, digital and 'non-digital.' Digital attributes are those that can be communicated through the web. These attributes are normally those, which can be, inferred either via a visual inspection, such as colour (Alba, Lynch, Weitz, Janiszewski et al 1997), or those that can be communicated aurally, "when purchasing a C.D. consumers can listen to samples from the music of their choice" (La1 and Sarvary 1999). The second category, non-digital attributes, are those that can only be evaluated by physical inspection. Examples of such attributes include, the feeling of texture, the fit of a garment, or the smell and taste of supermarket produce. La1 and Sarvary's thoughts are interesting, not only in the insight that they offer us but also in the similarity they have with De Kare- Silver's view offered in his book 'E-shock'and that of Alba, Lynch, Weitz, Janiszewski et al (1997).#p#分頁標題#e#
De Kare -Silver defines product characteristics as "the primal appeal to the senses." Products are said to appeal to the five senses of sight, sound, touch, taste and smell. The first of these two senses are what La1 and Sarvary would class digital senses and is what De Kare-Silver (2001) defines as those that offer a "higher electronic selling potential." On the other hand, the non-digital attributes or senses of touch, taste and smell are classed by De Kare -Silver (2001) as, "prima facie, not suitable of electronic selling," as they require physical experience before purchases (Anderson 1995). Alba, Lynch, Weitz, Janiszewski et al (1997) offer similar views in their paper.
Thus authors hypothesise that products, which can be assessed by sight and sound, the digital attributes can be communicated via the Internet, and are therefore more likely to be purchased online. However products that need to be evaluated by non-digital attributes (touch and taste), that cannot be communicated via the web are not likely to be purchases so heavily through this channel. This paper aims to empirically test this proposition. So,
H5: The higher the digitisation content in a product sector the better it is likely to perform in e-tailing
However, perhaps a more interesting insight that this paper aims to offer is in considering whether a particular business model, Clicks and Mortar or Clicks only, will perform better than another when it comes to the content of product digitisation. We have already seen how it may still be necessary to visit a physical store before purchasing some products. In such circumstances the consumer's unwillingness to bear the risk associated with purchasing before inspection will lead to a situation that highlights the importance of the physical store (Alba, Lynch, Weitz, Janiszewski et al 1997; Lai and Sarvary 1999).
Once the product attributes have been evaluated however, consumers can purchase either in store or via the web. With regard to repeat purchases in particular, consumers that have prior beliefs and preferences about product categories use them in order to economise costs (Hauser and Wernerfelt 1990; Ratchford 1982; Roberts and Lattin 1991; Simonson, Huber and Payne 1998). With these beliefs in mind online purchasing will save them search, time and transportation costs (Lai and Sarvary 1999). " Consumers who shop at Next or Marks and Spencer generally know what to expect in terms of quality and fit, they trust the brand. Once a degree of loyalty has been established it becomes easier to sell to the public through other channels... "[5]
Consequently, as far as established firms are concerned we would expect them to use their physical stores in order to emphasise the non-digital attributes that can be conveyed and focus on the individual needs of the consumer (Hauser and Shugan1983). These firms will be better at offering factors that emphasise the utility of the 'total shopping experience' (Tauberl972), regardless of whether the final purchase is in store or via the web.#p#分頁標題#e#
Pure Players on the other hand, will find it more beneficial to emphasise the digital attributes of their products as this is what they can portray equally as well the traditional retailers. By focusing on the sight and sound element of products Pure Players are likely to be better and more experienced at communicating these digital attributes and are therefore likely to fair better on the Internet in the product sectors where these attributes are important, relative to the established firms[6]. Thus,
H6: In sectors where products have a low digitisation content established firms with their 'Clicks and Mortar' model are likely to perform better in e-tail. However in sectors where products have a high digitisation content, Pure Players are likely to perform better in e-tail.
2.6 Theoretical Summary
The paper has now outlined the major theoretical arguments that may affect e-tail performance. The principal question of which business model will perform best is determined by several counter factors although we expect the Clicks and Mortar model to perform better than the Pure Players. The Internet Cataloguers however, seem able to enjoy the advantages of both established firms and focused Pure Players such that we would expect them to perform best on the Internet.
The inertial factors that hinder the traditional retailers and the entrepreneurial nature of the pure Internet firms to lead me to expect that pure e-tailers in the main to be the first to engage in e-commerce. This may have allowed them to profit from any first mover advantages that may have been associated with such a move. Thus even though I expect the Clicks and Mortar model to perform better overall (relative to pure plays) the timing of these new start ups has given them an advantage which will help them offset some of the disadvantages that occur from being sole channel operator.
Finally, it is the question of product digitisation and performance. Firstly, the study highlighted why products with a high digital content should be better suited to online retail. Secondly, I theorized as to why Pure Players would be better suited to selling these products whereas traditional retailers would be better at selling goods with higher non-digital attributes online.
This final hypothesis as mentioned in the introduction, I believe is the most pertinent and unique hypothesis in terms of contribution to the field of marketing and e-commerce is concerned. It appears that this concept has been wholly overlooked by the vast literature that is being produced in this area and the study aims to provide an insight that will be informative as well as providing a framework for future research.
Chapter 3 Methodology and Sampling
The results of this study were conducted primarily by an econometric regression that was carried out on the 'SPSS' computer package.
3.1 Measures#p#分頁標題#e#
Dependent Variable: E-tail Performance
Regressions used two different dependent variables, a Gomez score and Alexa Hits. The Gomez score aimed to capture the quality, competence and efficiency of the business model where as the Alexa Hits aimed to capture the popularity and effectiveness of the business model.
Firstly, like (Brown and Goolsbee, 2000) Gomez.com was used. This is a website that measures the e-tail performance of e-commerce companies on several operational factors, in order to give an overall indicator of performance ranging from 0-10. This data was collected on a longitudinal basis using all the periods for which Gomez.com produced ratings. It is important to note that not all sectors were measured during the same time period and they have also been measured for different number of time periods. For example shoes have only been rated in the 4th Quarter of 2000, where as clothes say, have been rated over several periods. However we control for this distortion as will become clear later. As far as Gomez.com was concerned, all the companies that this website rated, that were in the product categories that sold tangible goods,[7]were chosen in order to avoid any selection bias. Firms rated by Gomez must satisfy minimum criteria, which allow us to compare like for like firms.
Secondly, like Best and Pujari (2000) 'Alexa Hits' were used. Alexa is web crawler that measures the 'hits' of a particular website. The Alexa Hits were measured for all companies that were rated by Gomez in order to avoid any selection bias.
Independent Variables
Time since Start of Online Operation of the Firm until the Period of the Rating
This was identified either by examination of the website, via email correspondence or telephone conversations with the firms. The measure was given as months since entry up until the period of the rating.
Pujari and West (2000) record the date that the company registered its domain name and do find a positive and significant correlation with this measure and Alexa Hits. However this measure of start date is not an accurate measure of when the firm actually began to engage its e-commerce operation. For example Barnes and Noble (a traditional book retailer) registered its domain name, 'www.bn.com' on October 1994, however they did not launch their online retail venture until March 1997. This paper uses the date when the firm actually began to use the website for e-tail purposes in order to calculate this variable.
Digitisation Content
From Gomez.com all the products categories that contained tangible goods were chosen. As we are looking specifically at retail, only these goods were considered for measurement as opposed to service goods such as banking, insurance and travel. A list of the categories chosen can be found in the appendix.
Quantifying the digitisation content of goods is not an issue that has been dealt with before by the literature. This study bases its quantification on De Kare-Silver's notion of senses that can and cannot be translated digitally. Product categories were examined and the senses that were required to evaluate these products were identified. If a product required either the sight and or sound element[8] a score of 10 (ten) was attached to it. For every non-digital sense, touch, taste and feel that was required for evaluation a score of 0 (zero) was attached. The digital and non-digital scores were then aggregated and divided by the total number of digital and non digital-attributes. Hence giving a digitisation scale that ranges from 0 (zero) to 10 (ten).#p#分頁標題#e#
For example, if sector X required the sight (and or sound), touch and smell senses in purchase, it would receive one score of 10 (ten) for the digital attribute(s) and two scores of 0 (zero) for the two non-digital senses. This score of 10 would then be divided by 3 (total number of senses required in evaluation) to give digitisation score of 3.3333.
As mentioned, this method was novel, as the issue of digitisation has not been addressed empirically by the literature. However after conferring with both academics and financial experts that have specialized in the e-tail sector this was judged to be an objective and competent approach. The full list of the sectors, the digitisation scores that they received and how these scores were calculated can be found in the appendix. The only concern that arose was that within sectors, products might require different senses for evaluation. For example, in the grocery sector a cereal purchase may only require sight whereas fruit may require sight, smell, touch and taste. However this was justified by the fact that we assume the marginal cost of purchasing additional products on a same shopping trip to be less than using another channel to purchase certain products within a sector. So in my grocery example I would expect both the cereal and fruit purchases to be carried out using the same shopping experience. As the cost of using one channel (store visit for example) for the fruit and another channel (Internet) for the cereal would be greater than using the store visit for both.
3.2 Model Specifications
Let us begin with the regressions, in which Gomez scores are used as the dependent variable,
Model 1
Yg = α + βi XI + β2 X2 + β3 Dl +β4 D2 + β5 (X2 Dl) + β6 (DX2 D2) + β7 D3 +β8 D4i
Model 2
Yg = α + βi XI + β2 X2 + β3 Dl +β4 D2 + β5 (X2 Dl) + β7 D3 +β8 D4i
Model 3
Yg = α + βi XI + β2 X2 + β3 Dl +β4 D2 + β6 (DX2 D2) + β7 D3 + β8 D4i*
Where i= 1,2,...7
As far as the estimation of the model using Alexa Hits as the dependent variable was concerned the following regression models were estimated:
Model 4
Ya = α + βi XI + β2 X2 + β3 Dl + β4 D2 + β5 (X2 Dl) + β6 (DX2 D2) + β7 D3
Model 5
Ya = α + β1 XI + β2 X2 + β3 Dl + β4 D2 + β5 (X2 Dl) + β7 D3
Model 6
Ya = α + β1 XI + β2 X2 + β3 Dl + β4 D2 + β6 (DX2 D2) + β7 D3
Yg = Gomez rating
Ya = Alexa Hits
XI = Time from start of Internet operation to rating
X2 = Digitisation Content#p#分頁標題#e#
Dl = 1 if firm is clicks only Pure Player and 0 otherwise
D2 = 1 if firm is Clicks and Mortar and 0 otherwise
There are three categories in total, Clicks only, Clicks and Mortar and Cataloguers. Therefore by implication when Dl and D2 both take 0 (zero) a Cataloguer is being considered.
As demonstrated by Dougherty (1992 pp.268), the reference category was chosen as follows:
Parity (0)D1 =D2 = 0
Parity (1)D1 = 1,D2 = 0
Parity (2)D2= 1,D1 =0
(X2 Dl) = Interaction variable between digitisation and Pure Players
(X2 D2) = Interaction variable between digitisation and traditional firms
D3 = 1 if U.S. firm and 0 otherwise
D4i = This controls for the period in which the rating was given in order to control for any period specific affects. (For Alexa this variable of obviously omitted)
In order to test H3, which aims to examine whether the Pure Players entered the market in the majority of cases. A test for the difference in means, for the number of months since entry for Pure Players and traditional retailers was calculated using Microsoft Excel.
The overall mean scores of the different types of firms were calculated for Alexa and Gomez.com and a test fort the difference in means was calculated in order to see the specific affects. A number of graphs were also used in order to highlight some of the main findings.
Chapter 4 Results
A total number of firms that Gomez's shopping section assessed was 151[9] across 12 sectors and 8 different time periods. Examining these companies across a longitudinal basis gave us a total of 409 observations. Out of these the start date for 8 companies could not be determined and 2 had re-launched their websites during the time in question. Of these 151 companies assessed by Gomez it was possible to obtain Alexa Hits for 141, of these firms I was unable to establish the start date of two of them.
The results will now be presented in the following manner; firstly a table that shows the regression results of the models will be displayed. Then this study looks at each hypothesis in turn using the regression results in conjunction with other measures to examine their significance.
4.1 Progression Results
Table 1 Regression Results
Dependent Variable: Gomez Ratings
Independent VariableModel 1Model 2Model 3
Standardised ßStandardised ß Standardised ß
(Significance)(Significance)(Significance)
ConstantN/A (.000)N/A (.000)N/A (.000)
Time since entry ( X1 ).090 (.080).103 (.047)0.89 (.082)
Content of Digitisation (X2)-.071 (.615).309 (.000)-.116 (.213)#p#分頁標題#e#
Pure Play (Dl)-.488 (.037).192 (.009)-.551 (.001)
Clicks and Mortar (D2).024 (.917).529 (.001)-.059 (.406)
Digitisation* Pure Play (X2 Dl).807 (.002)-.884 (.000)
Digitisation* C & M (X2 D2)-.094 (.702)-.668 (.000)-
US (D3).145 (.005).529 (.001).146 (.005)
Ql 2001 (D4)l.038 (.631).54 (.491).039 (.620)
Q4 2000 (D4)2-.018 (.833).003 (.969)-.018 (.832)
Q3 2000 (D4)3-.050 (.692)-.056 (.662)-.048 (.704)
Q2 2000 (D4)4-.035 (.793)-.053 (.696)-.031 (.814)
Ql 2000 (D4)5-.110 (.341)-.117 (.314)-.107 (.350)
Q4 1999 (D4)6-.113 (.282)-.131 (.218)-.110 (.294)
Q3 1999 (D4)7-.086 (.446)-.096 (401)-.83 (.459)
Adjusted R square.176.157.178
Model Significance, F value (Sig.)6.630 (.000)6.268 (.000)7.146 (.000)
Table 2 Regression Results[10]
Dependent Variable: Alexa Hits
Independent VariableModel 4Model 5Model 6
Standardised ßStandardised ßStandardised ß
(Significance)(Significance)(Significance)
ConstantN/A (.469)N/A (.020)N/A (.375)
Time since entry ( XI ).453 (.000).450 (.000).452 (.000)
Content of Digitisation (X2)-.076 (.705).164 (.110)-.099 (.336)
Pure Play (Dl)-.469 (.144)-.053 (.634)-.498 (.035)
Clicks and Mortar (D2).296 (.373).609 (.014).254 (.025)
Digitisation* Pure Play (X2 Dl).508 (.167)-.544 (.026)
Digitisation* C & M (X2 D2)-.049 (.894)-.429 (.080)-
US (D3).068 (.385).066 (.401).69 (.380)
Adjusted R square.216.211.222
Model Significance, F value (Sig.)6.56 (.000)7.281 (.000)7.707 (.000)
The adjusted R Square value ranges from 15.7% to 22.2%. All the models were significant.
The work will now for the sake of clarity take each hypothesis in turn[11] and present findings in conjunction with the above regression results (obviously due the nature of regression and this paper their will be some unavoidable but welcome overlap). At this stage the results will merely be presented and analysis and reasons for some of the findings will be left until the next section.
4.2 Type of Firm and Performance
Type of FirmMean Gomez ScoreMean Alexa Hits
Clicks & Mortar5.87116265.82
Pure Players6.5262025.72
Cataloguers6.0696174.30
Table 3: Mean Gomez scores and Alexa Hits for different types of firms
The Pure Players do significantly (p<. 001) better than the Clicks and Mortar firms and significantly (p<. 002) better than Cataloguers as far as the Gomez scores are concerned. Cataloguers on average perform better (p<. 1) than Clicks and Mortars firms in this dimension. However as far as the Alexa Hits are concerned, a measure of popularity and effectiveness, the picture is somewhat different. Clicks and Mortar firms on average outperform the other business models. This difference however is only significant with Pure Players (p<. 01). The Cataloguers proved on average more popular than the Pure Players (p =< .1). These results may not have been exactly what were expected, especially the different results indicated by Alexa and Gomez however they actually provide us with some very interesting insights, which we shall discuss in the next section. Also the results directly above looked at the mean scores in isolation without controlling for other factors. Let us now do this and inspect the results of the regressions.#p#分頁標題#e#
In the three models (2,5 and 6) that the Clicks and Mortar coefficient is significant and it has a positive affect on the dependent variable. We notice that it is significant in both the cases (models 2 and 5) when the variable Digitisation * Pure Play (X2 Dl) is dropped. In both cases the Digitisation * C&M (X2 D2) is also negative and significant. Suggesting that controlling for certain variables Clicks and Mortar firms do perform better in some sectors. Further in models 1,3 and 6 where we included the variable Digitisation * Pure Play (X2 Dl) the coefficient for Pure Players was negative and significant (p<. 15 in all cases). Suggesting that controlling for certain variables Pure Players do not perform as well ceteris paribus as Clicks and Mortar firms.
Table 3 illustrated that the average performance of Cataloguers is not significantly superior to both the other models and thus does not support H2. What we do find however, is that even though Cataloguers performed significantly better than Clicks and Mortar firms with the basic Gomez means. When we take into account all the other variables, as in regression model 1, we find that the coefficient for Clicks and Mortar firms is not significantly different from Cataloguers, (which is the reference category (Dougherty 1992)). The difference for these two firms was not significant for mean Alexa Hits and this was confirmed by regression model 4. The Cataloguers still significantly perform better as expected than Pure Players in terms of Alexa Hits.
4.3 Type of Firm and Entry into the Market
Type of FirmMean Time Since Entry (Months)
Clicks and Mortar29.04
Pure Play37.67
Cataloguer48.47
Table 4: Type of firm and mean time since entry
Pure Players on average have been operating online for a significantly (p<.05) longer period of time than the Clicks and Mortar firms supporting H3. Cataloguers have been around on average significantly (p< .05) for the greatest time period. Even though we did not hypothesise about Cataloguers and entry time this discussion will be useful for my discussion.
4.4 Advantages from Entering the Market Early
Figure 2 A scatter plot of Gomez rating on entry time to rating
Figure 3 A scatter plot of Alexa Hitson time since entry
The above figures demonstrate scatter plots of Gomez scores (Figure 2) and Alexa Hits (Figure 3) over time[12]. We see that there is a clear upward trend using the 'SPSS' regression fit line in both figures suggesting that the longer the firm has been operating online the better it scores in Gomez ratings and also is more successful in the amount of Alexa Hits that it receives.
All the regression models (whether Alexa Hits or Gomez score is the dependent variable) confirm the above figures where firms that have been around longer perform significantly (p< .1 in all cases) better. This provides overwhelming support for H4.#p#分頁標題#e#
4.5 Digitisation and Performance
The coefficient for the content of digitisation is only significant in models 2 and 4, where the variable Digitisation * Pure Play (X2 Dl) has been excluded. In these cases it is positive. In all other models the coefficient of Digitisation * Pure Play is positive and significant. Suggesting that it is not the content of digitisation that positively influences the Gomez and Alexa dependent variables but rather Pure Players that sell products with a digitisation content. We examine this specifically with our final hypothesis but conclude that we cannot reject the notion that, higher digitisation content in product sector does not significantly increase the e-tail performance and hence we do not find evidence to support H5.
4.6 Type of Firm and Extent of Digitisation
Figure 4 (see below) plots mean Gomez rating[13] for different levels of digitisation for each type of firm. A 'best-fit' regression line produced by the 'SPSS' package was then used to examine any effects. The figure demonstrates quite neatly that as the content of digitisation in a product sector increases the Gomez performance of Clicks and Mortar firms declined (as demonstrated by the blue line). The converse occurred in the case of the Pure Players where the Gomez performance increased as the content of digitisation increased[14](The red line). This later concept is well supported by the regression models. Every time that the variable Digitisation * Pure Play is included in the regression models it is positive and shows a degree of significance. The regression results do not show quite so conclusive results for the variable Digitisation * Clicks and Mortal (X2 D2). The coefficient was significant in models 2 and 5 when the variable Digitisation * Pure Play was dropped. Indicating as figure 4 shows a negative relationship between Clicks and Mortar firms and the content of digitisation in a product sector. However this hypothesis is not conclusive as when all the variables are included as in model 1 and 4 the coefficient was still negative but no longer significant (p >. 7). Thus we do have evidence that demonstrates that Pure Players perform better as the digitisation content of product sectors increase. The results also seem to point towards the performance of Clicks and Mortar firms deteriorating as the content of digitisation in the product sector increases, however the evidence for this later proposition are not so robust.
Figure 4: A scatter plot of the mean Gomez scores for each type of firm at particular digitisation level
Chapter 5 Discussion
The results section, for the sake of clarity examined each hypothesis in turn. In this section however I aim to bring together a number of the interesting findings in order to show the relationships between the hypotheses and discuss the appealing insights that arise.
5.1 Differences and Similarities between Gomez Scores and Alexa Hits#p#分頁標題#e#
Firstly it is necessary at this point to restate clearly the distinction between the Alexa Hits and the Gomez scores. Each measures different dimensions of e-commerce performance. Alexa Hits is an indication of how effective the site is at attracting visitors. This is dependent on a variety of factors illustrated in our theory section such as, financial capability, size and perhaps principally brand awareness. Gomez.com also depends on similar factors but rather in this instance specifically examines how these factors affect the quality of the operational business model. Therefore some factors will influence Gomez performance more directly while others will be more critical in determining Alexa Hits, however it is likely that most will affect both to some extent. For example the financial capabilities of a firm may lead it to perform better (or worse) in terms of its operations and this could also have an effect on how many visitors the website is able to attract. Thus, the measures that we used above cover both of these dimensions therefore assessing a breadth of e-commerce performance.
Now this distinction has been made the results be even more striking. The results that I achieve from the regression models where Gomez.com is the dependent variable are very similar to the results that are obtained from the regression models when Alexa Hits were the dependent variable. Both models demonstrated in the main that timing of entry into the market was positive and significant and both indicated that Pure Players on the whole perform significantly worse than other business models. However perhaps most interestingly both showed that this weak overall Pure Play performance was significantly offset by the interaction of Pure Players selling goods with a high content of digitisation. As we have just mentioned Alexa Hits and Gomez .com are dependent on similar factors but measure different facets of performance. Thus the fact that from the results that they produced I am able to draw many parallels greatly increases the robustness of our findings.
5.2 Analysis of Main Findings
I am now considering the main findings of this study. As mentioned the most interesting and notable hypothesis and discovery of this paper has to be that of the results that were portrayed for interaction between the type of firm and the extent of digitisation in a product sector. The results showed clearly that as the extent of digitisation increased the performance of Pure Players also increased. Suggesting that Pure Players are better than other firms at effectively communicating digital attributes to consumers (see figure 4). They are likely to be more technologically astute and focused (Agrawal, Arjona, and Lemmens 2001) in promoting the sight and sound attributes and are more likely to perform well in these product sectors. As expected these pure plays are not so proficient as the level of digitisation falls. They are unable to offer the total shopping experience (Kreitzman 1999) and have not as of yet mastered the ability to promote non-digital attributes with equal success. The converse holds as expected for Clicks and Mortar type firms.[15] These firms perform best in non-digital sectors where their traditional stores prove invaluable in product assessment.#p#分頁標題#e#
However, it is plausible that this may not remain the case in the long run. Cataloguers at a glance have a similar channel to Pure Players in the promotion of their products. In spite of this they have now for many years sold goods without a complementing traditional bricks and mortar store. Figure 4 quite clearly demonstrates that Cataloguers are indifferent as far as the their performance in highly digitised or lowly digitised sectors is concerned. This seems to suggest that they have gained the necessary skills to perform in a variety of sectors regardless of digitisation content. This perhaps is best illustrated by the clothing sector. Products in this sector are assessed via sight (digital attribute) as well as touch or fit (Lal and Sarvary 1999) (non-digital attribute). Cataloguers however perform well in this sector due most probably to the experience that they have gained.
Thus, even though at present the Pure Players have performed better up to now in highly digital sectors and Clicks and Mortar firms have performed better in the less digitised sectors there certainly exists in the long run a prospect for this to change. The possibility of different types of firms, acquiring through experience the different skills necessary to compete more effectively in other sectors is possible as demonstrated by Cataloguers. I discuss this long run time element further in my conclusion.
What is further interesting on the subject of digitisation is that the extent of digitisation does not appear to affect performance. I expected in H5 to empirically test what many authors would have had expected, which is as the content of digitisation in the product sector increases the e-commerce performance of all firms should also increase. However I actually find that this is not necessarily the case and in fact the type of firm interaction with digitisation is what is stronger than any potential digitisation affects.
A second very interesting result is that we notice is that the mean Gomez score for Pure Players is significantly higher than the mean score for Clicks and Mortar firms. On the other hand, the converse is true for the Alexa Hits. This was not expected in any of the hypothesis but allows us post-results to suggest a likely rationale. As mentioned earlier Gomez.com is an indication of the quality of the business model and illustrates how operationally competent the firm is in its e-commerce. It appears that the focused, flexible and original strategies of the Pure Players have enabled them to excel in this department. However, the large brand awareness (Aaker 1990) and consequently lower acquisition cost that the incumbents possess has allowed them to leverage their brands and perform better as far as market appeal is concerned.
The results for Cataloguers were not as we may have necessarily expected. The Cataloguers performed significantly better than traditional firms did as far the Gomez measures were concerned, but significantly worse than the Pure Players did. This suggests that operationally they are as expected more competent than the Clicks and Mortar model. However even though the Internet is thought of as a 'competence enhancing' (Tushman and Anderson 1986) technology and they are likely to have a lot of the experience necessary to succeed, they still do not outperform the Pure Players. This result occurs despite the fact that they entered the marketplace significantly earlier than the Pure Players did. This implies that although they saw the opportunity that the Internet has to offer, they have not been as successful as we would have expected them to be in terms of leveraging their operational skills to this channel. However they did perform as well as traditional firms did in terms of Alexa Hits, suggesting that they were able to achieve a relatively high number of visitors due their existing brand awareness and early timing of entry into the market.#p#分頁標題#e#
As I mentioned earlier the timing of entry and the content of digitisation in a product sector are likely to be linked with the type of firm as well as performance. Let me now consider the overall direction and magnitude of all these affects. Firstly in the case of the Pure Players it is apparent that when all variables are included both the Gomez and Alexa performance is negatively affected by approximately the same magnitude (the standardised coefficients are-. 488 and -. 469 respectively). Also we saw that the time since entry was positive and significant in all models. However the magnitude of the coefficient when Gomez.com was the dependent variable was considerably lower than when Alexa was the dependent variable (.090 in model 1 compared to .453 in model 4). This result is likely to arise from the fact that the longer a firm has been operating online the better it is able to cope with and understand the emerging channel and the consumers with in this market. It has had the chance to learn first hand, forge networks and thence be in a superior position to satisfy consumers. In spite of this, what is more evident is that the longer a firm has been operating online the more likely it is to have attracted visitors. The first of these concepts is fairly subtle and also is dampened by any late mover advantages (Shankar, Carpenter and Krishamurthi 1998) whereas the later is quite an obvious and direct link, the longer that a firm has been online the more visitors it is able to attract.
As mentioned earlier the chief finding concerned Pure Players and Digitisation. The positive magnitude of this coefficient offsets the negative coefficient of being a Pure Play firm as the extent of digitisation increases. This increase is greatest with Gomez (.807 in model 1 compared to.508 in model 2). This suggests, that Pure Players overall perform better when the extent of digitisation increases. Consumers are more likely to visit their sites when these digital attributes are most required, however this increase in performance is most evident in the quality aspect of their business. Suggesting that their technological and innovative ability to clearly communicate these attributes is operationally better than the Clicks and Mortar model.
5.3 The Special Case of Amazon.com
It is also worth mentioning the extraordinary results of Amazon.com. Amazon is undoubtedly the most notable Internet retailer amongst all types of firms and the results that we obtained certainly highlighted this phenomenon. Amazon.com received in total 3,456,683 Alexa Hits this was considerably higher than what any of the other firms achieved. The second largest receiver of Alexa Hits was CD Now with 581,368. As we saw the average for the other Pure Players was merely 62,026 and the average for all firms excluding Amazon was 92,223. This illustrates the excitement that Amazon.com has generated in the world of e-tail and also shows how all other firms have not experienced such exponential growth. This corresponds to much of the recent literature on e-commerce, which suggests that Internet retailing has been slower to make an impression than the initial hype may have suggested (Steidtmann 2000). Amzon.com generated much of this initial hype however as we see e-tailing has not been as of yet so pervasive in its appeal.#p#分頁標題#e#
5.4 The Fear for Pure Players
On a final note both dimensions of e-commerce performance that I am considering are important. Thus even though Pure Players receive a significantly higher Gomez rating, if they are unable to attract the visitors to their sites they are not going to be able to convert their superior service into revenue. We have witnessed many famous Pure Plays not being able to maintain their 'going concern' status. Alternatively if the Clicks and Mortar firms fail to offer the consumers the service they require, as we already see for highly digital products, consumers may begin to shop elsewhere. The problems we fear are graver however for the Pure Players. The Clicks and Mortar firms have the current relationship with the consumers as demonstrated by the Alexa Hits. If this trust is broken however by providing a poorer service (as indicated by the Gomez scores) consumers may switch to superior Pure Players. In spite of this, the time that it could take for consumers to make this change may take too long to benefit many specialised Pure Plays. We are frequently hearing of Pure Plays that have been unable to continue their operations due to low revenues and high costs. These problems have been exacerbated by the American economic downturn and the 'tech stock crash' that have recently occurred. It is likely that even many Pure Plays with sound business models may be forced out of the market that they pioneered (Leadbeater 2001).
Chapter 6 Conclusion
6.1 Limitations and Further Avenues of Research
This study aimed to make a noteworthy and unique contribution to the field of e-commerce and marketing. The greater my research into this topic the more I realised the depth of untapped research that exists in this in this domain and the more I realized how I could have augmented this study. The study therefore does have limitations, however I hope that this piece of work will act as a facilitator to researchers to further examine the insights from this preliminary study. This studyI believe has two major limitations. Firstly the data that was collected on Alexa was of a cross sectional rather than longitudinal nature and secondly the study lacked one important aspect of E-commerce performance, a measure of sales. Collecting Alexa Hits over a longitudinal time period will give me more observations as well as allowing me to examine any patterns that may have emerged over time. Sales are the core of any business and even though high hit rates and a good quality business model may lead to high sales to actually measure a sales would prove invaluable in such a study
The limitations are rectifiable and were partly due to the time constraints associated with such a study. The study also raises other questions, for example this study used a country variable to control for any differences. The USA is the country that is in the most mature stage of e-commerce and most of the firms in our sample were US firms. However it would be interesting to see whether there is a difference across countries in the performance of different types of firms. Have the U.S Pure Players benefited from their extensive venture capital market? (Chandy and Tellis 2001), have European and Asian Internet retailers learnt lessons from these firms that have allowed them to perform differently?#p#分頁標題#e#
There is also much opportunity to examine the individual firm affects, in order to test what affect different managerial styles would have on the findings of this paper. In particular work that examines the expectations of the managers with regard to the Internet (e.g. Chandy and Prabhu 2000) will no doubt be valuable in complimenting this study.
6.2 Concluding Remarks
This particular study may be ever increasingly difficult to replicate, as the distinction between Pure Players and traditional firms, is likely to become less clear (Enders and Jelassi 2000). In the long run, many experts expect to see very few Pure Players that will alone be 'Category Killers'[16]. It has therefore been predicted that start-ups will require alliances in the form of established firms in order to compete in the long run (De Kare - Silver 2001). I have already seen the formation of such alliances with the likes of Amazon.com and Toys 'r' Us working together on certain aspects of their strategies.
Moreover, as the market has not experienced the exponential growth that some of the Pure Players had budgeted for, their financial security is under threat (Grande 2001). This poses a major opportunity for traditional firms, they have the resources to acquire such operations and utilise the brand name, experiences and infrastructure of these firms (Connon 2001). Bank of Montreal for example, acquired Security First Network Bank for its leading edge software, technology that is becoming synonymous with the Pure Players. In particular established firms that offer the 'total shopping experience' and hence perform better than Pure Players in the low digitisation sectors could learn how these Pure Players tend to outperform them in the high digitisation categories. There also appears to be an opportunity for Pure Players with leading edge technology to move out of their initial business and instead the supply specialised e-tail software to the traditional retailers wanting to engage in e-commerce.
This study examines traditional firms engaging in e-commerce but there is also the possibility of e-tailers actually investing in physical ventures (Enders and Jelassi 2000). It has been suggested that this may be the only way for these Pure Players to remain financially viable, especially if the products that they are selling are aimed at a mass, as opposed to a niche market. Where firms sell products aimed at a niche market they are likely to perform better on the Internet than firms that sell more general goods and face competition from a greater number of channels[17].
Therefore in summary the results presented appear to be novel to the field and suggest most interestingly that not all firms that sell products with a large extent of digitisation are likely to do well. Rather, it is Pure Players that appear more capable of pleasing consumers when this is the case.
Overall the results indicate that traditional retailers that engage in e-commerce are better positioned to attract consumers and are able to offer these consumers an all round shopping experience although their operations aspect is not as good as Pure Players or Cataloguers. It will certainly be interesting to see the medium to long-term effects of these insights.#p#分頁標題#e#
E-commerce is still in an immature and growth phase of its life cycle and as Jeff Bezos of Amazon said, "...today is just day 1 of the Internet and its still just 1.00am of day 1. "[18] The promise that exists for potential research in this field is awesome. There exist many possibilities to build directly and indirectly upon this study and in such an emerging and fast changing field the research avenues that exist are not only stimulating, cutting-edge opportunities but will also make a very worthwhile contribution to this domain.
Appendix A
Companies that were used in the sample
800.comGrocerlineVarsitybooks.com
Access DVDGrocery WoksWal-Mart
AlbertsonsHifi.comWebvan
Alpha crazeHome GrocerWordsworth.com
AltrecHomeruns.comZanyBrainy.com
AmazonHometownstoresZappos.com
Amazon.co.ukInsightZones
AudioStreetIQVC
Barnes & NobleJ Crew
Best buyJ&R electronics
Big starJ Crew
BirkenstockJC penny
BlockbusterJohnston & Murphy
BloomingdalesKbKids
Blue FlyLands End
BluelightLL Bean
Bookbuyer outletMacys
BooksamillionMichealHoligan
BordersMicrowarehouse
BuildscapeMuzic depot
BuyNecX
CampusplaceNet Grocer
CD NOWNetmarket
CDWNordstrom
Checkout EntertainmentNuttyPutty
Checkout.comOshmans
Circuit cityOurHouse
Comp USAOutpost
Computers4surePatgonia
ComputibilityPc connection
CornerHardwarePcmall
CostcoPeapod
CountdownPolo R L
CrutchfieldPowell's Books
DicksportingREI
DoitbestSaks
EcostSam goody
Eddie BauerSams club
EggheadSears
EtownShoebuy.com
EtoysSimply organic
Express.comSmarterKids.com
FAO SchwarzSpiegel
Fatbrain.comSports Authority
FingerhutSteamline
First soucreTarget
Food FerryThe Territory ahead
FootdogTiger direct
Fresh food coTools America
GapTower Records
GohastingsTrue Value
Appendix B
Product Sectors Examined, Senses Required and Digitisation Scores
Product SectorSenses RequiredDigitization Score
#p#分頁標題#e#
ElectronicsSight10
BooksSight10
MusicSound10
VideosSight10
DIYSight10
ToysSight10
General**see footnote8.333
ClothesSight and touch5
ShoesSight and touch5
SportsSight and touch5
GroceriesSight, touch, taste and smell2.5
** General merchandisers tend to sell electronics, music and clothes therefore their digitisation score was calculated as follows:
(1/3*10) + (1/3*10) + (1/6*0 + 1/6*10), the first two brackets refer to electronics and music, which make up two thirds of the sector (with solely digital attributes) and the final bracket to clothes which consists of the sight and touch senses.
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Other References
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Marketing Science Institute, "Research Priorities 200-2002"
[1] This paper deals with B2C e-commerce also referred to as e-retail or online shopping.
[2] Marketing Science Institute "Research Priorities 2000-2002"
[3] The paper did initially aim to study the locational differences in performance as the title indicates. However due to a lack data available on non-US firms, a country variable was controlled for but we do not propose any major theory in this study.
[4] Report by Boston Consulting group, "Advantage Incumbent"
[5] eBusiness February 2001
[6] Note that we do not expect to find any major difference in Cataloguers with regard to the degree of digitisation. They have been around in non-digital sectors (clothes) for along time and are likely to have experience to cope well with both high and low digital products.
[7] These categories can be found in the appendix.
[8] The senses of sight and sound were treated as one digital attribute this is because a product that requires both of these senses in evaluation does not have a higher electronic selling potential than a product that just needs to be evaluated by one of these senses. Thus only a maximum of 4 senses can be used as the divisor.
[9] There are actually 111 companies however many of these appear in more than one sector.#p#分頁標題#e#
[10] Amazon.com was removed, as it was a highly influential observation in these models. This is further discussed in the next section
[11] The first two hypotheses will be dealt with together.
[12] Influential outliners were removed. However when they were included the results were even stronger.
[13] A similar figure using Alexa Hits was not insightful due to the lower number of observations leading to biases at particular digitisation levels.
[14] The catalogues showed no clear trend (the green line) as to how performance was affected by the content of digitisation. Note that this was not actually related to our hypothesis but is useful for the discussion section.
[15] Although the regression models do not emphasise the same overwhelming significance as for the same interaction with pure play firms
[16] "Category Killers" use e-business to create capture and redefine a market e.g. Amazon.com.
[17] E-Business Report, “clothing on the net”.
[18] As cited in De Kare-Silver (2001)
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