The Market Valuation of
Mobile Internet Channel Additions
Marie Isabelle Adams
Thuy Mo Nguyen
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The Market Valuation of Mobile Internet Channel Additions
Marie Isabelle Adams Thuy Mo Nguyen
Faculty of Economics and Business MSc BA Specialization Marketing Research
Master Thesis June 2012
Admiraal de Ruyterlaan 46b, 9726, GW Groningen, The Netherlands 0630864247
m.i.adams@student.rug.nl s1970402
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Management Summary
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Preface
The following study jointly combines our main interests in marketing research; adopting a fact-based approach and the recent phenomenon of Mobile marketing. By applying event study methodology, we have extended our prior knowledge with respect to market research methods and made an attempt to contribute to the conceptualization of Mobile marketing.
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Table of Contents
Management Summary ... 3
Preface ... 4
Chapter 1 Introduction ... 6
Chapter 2 Literature Review... 11
2.1 Conceptual Model ... 14 Chapter 3 Hypotheses ... 15 3.1 Firm Characteristics ... 16 3.2 Introduction Strategy ... 18 3.3 Marketplace Characteristics ... 21 Chapter 4 Methodology ... 24
4.1 Introduction to Event Study Methodology ... 24
4.2 Our Study ... 25
4.3 Calculations ... 26
Chapter 5 Data ... 30
Chapter 6 Results ... 31
6.1 The Main Effect of a Mobile Internet Channel Addition ... 31
6.2 Regression Model ... 34
6.3 OLS Assumptions ... 35
6.4 Robustness Checks ... 36
Chapter 7 Discussion ... 37
Chapter 8 Implications... 40
Chapter 9 Limitations and Further Research ... 41
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Chapter 1 Introduction
In today’s society, Internet has become a major communication tool used by millions of people all over the world. Information gathering and sharing through this medium is a common phenomenon among organizations, institutions and individual users.
The introduction of Personal Computers (PC) in the 1980s, followed by the beginning of the Internet adoption in the late 1990s were the first technological developments changing lives of individual consumers and the way organizations conduct business (Sabat, 2002). Rapid expansion of Internet across the globe in combination with the revolutionary adoption and usage by consumers, made it inevitable for organizations and institutions to integrate this means of communication into their marketing strategy. Simply said, consumers’ demanding behavior set the requirement for organizations to integrate the Internet into their marketing strategy. Consequently, the addition of the Internet channel changed traditional marketing strategies from containing one-way communication tools towards a revolutionary two-way interactive manner of communication. Given this change in structure of communication, organizations experienced a loss of control; shifting bargaining power from organizations towards consumers (Heinrichs, Lim, & Lim, 2011).
Due to the Internet’s increasing importance, in 2002, organizations began to include the online channel to their traditional distribution and communication channels, shifting from being bricks-and-mortars to bricks-and-clicks. Furthermore, the promising online channel caused the up-rise of pure dot com businesses, seizing the advantage of low operating costs and low entry barriers. As a matter of fact, to keep up with increasing consumer demand and competition, following a multi-channel strategy became a common approach for traditional organizations. Already in 1999, Frazier anticipated that a multi-channel approach had become a standard to conduct business and compete, rather than an exception (Geyskens, Gielens, & Dekimpe, 2002). Besides fulfilling consumers’ demand, the online channel addition facilitated the reduction of distribution costs and was proven to positively influence financial performance of firms on average (Geyskens, Gielens, & Dekimpe, 2002).
| 7 technology and consumer behavior. A technological stream that rapidly developed throughout the years is mobile telecommunications technology, creating numerous novel opportunities for marketers (Ranchold, 2007). The diffusion and development of the mobile telecommunications technology has been moving rapidly since the mid-1990s (Bauer, Reichart, Barnes, & Neumann, 2005). From a traditional communication starting point, in which the ability to make a phone call was the main utility of a mobile phone, mobile technology transformed into a sophisticated “personal digital assistant”. First, mobile technology changed from simply making phone calls to the expanding usage of Short Message Services (SMS) and text messages, followed by Multimedia Message Services (MMS) and Wireless Application Protocol (WAP). The introduction of WAP reflected the starting point of the mobile marketing era. However, mobile marketing did not gain significant importance yet due to the slow speed of services, lack of reliability and major costs involved (Frolick & Chen, 2004). Besides, organizations were challenged by the innovation’s complexity and were inexperienced with respect to designing concise, small, personalized services (Shankar & Balasubramanian, 2009), while consumers were hesitant to adopt mobile services resulting from their evaluation regarding perceived value of mobile services. Commonly recognized determinants of consumers’ adoption of mobile services are; perceived usefulness, convenience, risk, price and speed of use (Frolick & Chen, 2004; Shankar & Balasubramanian, 2009; Kim, Chan, & Gupta, 2007).
| 8 few organizations began to add the Mobile Internet channel to their existing distribution channels due to the lack of evidence regarding its performance-enhancing potential.
To date, many organizations have added the Mobile Internet channel, while, numerous organizations stay behind. One reason for that might be the fact that channel addition and pursuing a multi-channel strategy always comes with a number of opportunities and threats (Alba, Lynch, Weitz, Janiszewski, Sawyer, & Wood, 1997; Gosh, 1998; Booth, 2000; Geyskens, et al., 2002). Previous research revealed that channel additions, amongst other things, expand demand through relationship-deepening (Geyskens, et al., 2002; Shankar & Balasubramanian, 2009) and serving the so-called multi-channel shopper by enabling cross-channel synergy (Verhoef, Neslin, & Vroomen, 2007). Contrarily, cannibalization has been heavily discussed as a major concern (Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002). Some organizations move quickly into new channels seizing the benefits of being first-movers, while others wait until it is proven that the advantages significantly outweigh risks and costs associated with channel additions.
As mentioned before, the need for a channel addition emerges from developments in technology and changing consumer behavior. However, decisions regarding channel additions have to be carefully evaluated by marketing managers. A reason for this is the current demand for marketing managers to present Return on Investments of their marketing actions in order to be perceived as marketing assets rather than expenditures. This is an effect that emerged from the fact that financial benefits of marketing actions are often noticed on the long-term rather than short-term. Research revealed that influence and power of marketing departments within organizations has diminished; resulting in the loss of its strategic role. In order to reverse and abandon this tendency, marketers must become accountable and more innovative to pinpoint the importance of their actions (Verhoef & Leeflang, 2009). Nowadays, marketing managers are pressured to present the added (financial) value of their actions since top management often perceives them as a cost but not as an investment to create shareholder value (Wiesel, Skiera, & Villanueva, 2008).
| 9 the firm value. Even though considerable research has been conducted regarding the use of mobile channels, key success factors, diffusion and consumer’s acceptance, a lack of research remains with respect to potential financial performance of a mobile channel addition. A question that remains unanswered is whether costs that come with the Mobile Internet channel addition outweigh the benefits and added value from the investors’ perspective. Hence, there is a need for applying fact based marketing to show how the Mobile Internet channel addition contributes to an organization’s performance potential.
The objectives of this paper are twofold. First, we aim to analyze to which extent the Mobile Internet channel addition influences an organization’s performance potential. Second, assuming that the influence of the channel addition on performance potential is moderated by additional variables, we aim to investigate if and to which extent industry characteristics (service vs. product industry), the strategic timing of the addition (early entrants vs. followers), the kind of Mobile Internet service applied (app vs. site) and the point in time of the announcement (before vs. during the financial crisis in 2008) influence company performance potential.
We test our hypotheses by using an empirical investigation on a data set on Mobile Internet channel additions across industries, applying event study methodology. This study builds on prior investigations of the market valuation of channel additions and serves as an extension of the research of Geyskens, Gielens and Dekimpe (2002). Our main contribution to the study of Geyskens, et al. (2002) is the focus on the addition of a Mobile Internet channel and the fact that data across various industries has been collected and analyzed. Besides, we include the above mentioned exogenous variables to further investigate and prove the accountability of Mobile Internet channel additions.
| 10 Due to the fact that collecting and analyzing data in this manner is extremely time consuming, this study is conducted jointly. However, a division was made regarding main responsibilities within the collaboration; Marie Isabelle Adams guides the theoretical background, whereas Thuy Mo Nguyen directs the methodology.
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Chapter 2 Literature Review
The following chapter presents an extensive review of existing research on channel additions and mobile marketing. Furthermore, we present our conceptual model and develop hypotheses deriving from theoretical support.
| 12 In 2001, convergence of mobile technological developments and high penetration rates of Internet lead to the adoption of mobile marketing. A review of mobile marketing research revealed a lack of conceptualization with regards to mobile marketing (Leppäniemi, Sinisalo, and Karjaluoto, 2006). The concept of marketing via mobile channels was interchangeably referred to as mobile marketing, mobile advertising, wireless marketing and wireless advertising (Leppäniemi et al. , 2006; Varnali & Toker, 2010; Smutkupt, Krairit, & Esichaikul, 2010). To date, no generally accepted definition of mobile marketing has been developed (Varnali & Toker, 2010). However, reasonably similar definitions have been conceptualized by different organizations and researchers. The Mobile Marketing Association defines mobile marketing as “A set of practices that enables organizations to communicate and engage with their audience in an interactive and relevant matter through any mobile device or network” (MMA Updates Definition of Mobile Marketing). A fairly similar definition has been conceptualized by Leppäniemi et al. (2006), resulting from an extensive review of existing literature on mobile marketing. The definition proposed is as follows; “the use of the mobile medium as a means of marketing communications” and will be adopted in this study; because it originates from marketing communications and emphasizes the importance of communication within the process of initiating and maintaining customer relationships (Leppäniemi et al. 2006).
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2.1 Conceptual Model
Figure 1: The effect of Mobile Internet Channel Additions on the Firm’s Performance Potential and its Moderators
Main Effect
Moderating Effect
In figure 1 we depict our conceptual model. In our model, we are first interested in the effect of Mobile Internet channel addition on firm performance. This is reflected in the main effect in Figure 1. Channel additions and its performance potential depend on a number of factors. Generally acknowledged factors in marketing strategy are classified into three main collections; firm specific characteristics, introduction strategy characteristics and marketplace characteristics. Each group contains a long-list of factors (Geyskens, et al., 2002). According to Lee and Grewal (2004), it is important to account for the above mentioned characteristics because of their influence on the relationship between the new channel addition and its performance potential. Due to restricted accessibility and availability of information as well as the scope of this study, we only examine four factors of different nature that might have moderating effects on the expected positive relationship of channel addition and performance potential. A firm characteristic that will be tested for interaction effects with the main relationship is industry type (service vs. products). Followed by two introduction strategy characteristics; order of entry (early entrants vs. followers within industry), and type of Mobile Internet channel (website vs. application). Finally, market health will represent the marketplace characteristic where the effects of entering the marketing before economic recession or during economic recession will be examined. Mobile Internet Channel Addition Firm’s Performance Potential Firm Characteristics H2: Industry Type (Product vs. Service) Marketplace Characteristics H5: Economic Recession (Before vs. During)
Introduction Strategy Characteristics
H3: Order of Entry (Early Entrants vs. Followers) H4: Type of Mobile Internet Channel
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Chapter 3 Hypotheses
Due to the importance of Mobile Internet in the mobile marketing communication strategy, researchers and marketing managers expected that adding a Mobile Internet channel was, naturally, the next strategic action to pursue with respect to multichannel management. Adoption of the Mobile Internet channel was anticipated to enhance organizations’ two-way interactive communication with its consumers as it widens reach, increases the interactivity of communication, strengthens consumer relationship with the organization and is accessible at any time (Shankar & Balasubramanian, 2009). Therefore, organizations noticed the wide range of possibilities of the Mobile Internet channel and integrated this new channel into their marketing communications strategy. Research revealed that organizations adding the Internet channel had to face opportunities as well as threats (Geyskens, et al., 2002). First of all, factors scientifically found to be performance-destroying regarding channel addition will be discussed.
Integrating a new channel into an existing marketing communication strategy always comes with additional costs for various purposes, e.g. R&D investments and promotional expenditures (Geyskens, et al., 2002). According to Frazier (1999), channel addition complicates interfirm communication. Research further revealed that adding a new distribution channel might lead to channel cannibalization, more precisely, existing consumers shifting from one channel to another (Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002). Hence, the new channel might have a substitution effect instead of a complementary effect (Neslin & Shankar, 2009). Besides, offering multiple channels to consumers enhances their bargaining power due to the wealth of informational resources (Deleersnyder et al., 2002).
| 16 supplementary positive influence on another channel. Thus, the authors conclude that integrating a new channel does have a complementary effect. To oppose organizations’ major concern of cannibalization, Deleersnyder et al. (2002) proved that this presumption is largely over-stated. Given that the Mobile Internet channel is still in its infancy (Bauer, et al., 2005; Leppäniemi, et al, 2006; Ong, 2010), there is no scientific research to date revealing the potential performance of this channel addition. Geyskens, et al. (2002) conclude that adding new media to traditional channel is a necessity to maintain a competitive advantage. Additionally, the authors provide support for the claim that benefits of the Internet channel addition on average outweigh investment endeavors for established companies. Following the research stream of Junglas & Watson (2003), Mobile Internet is perceived as an extension of stationary Internet in this study. Hence, we expect a similar main positive effect of the channel addition on company performance as found by Geyskens, et al. (2002). Mobile Internet enhances interactivity with consumers, without regards to time or location, creating stronger ties between company and consumer (Shankar & Balasubramanian, 2009). Furthermore, the high marketing potential (Barnes, 2002; Smutkupt, et al., 2010) and increasing consumers’ demand (Ranchold, 2007; Barnes, 2002) support the assumption of performance-enhacing effects arising from Mobile Channel additions. We therefore hypothesize the following:
H1: The addition of the Mobile Internet channel is positively related to the company’s performance potential.
3.1 Firm Characteristics
| 17 Previous research has proven that industry type has a moderating effect on various marketing strategy activities and the resulting firm performances (Anderson, Fornell, & Rust, 1997; Peng & Luo, 2000; Banerjee, Iyer, & Kashyap, 2003; Capar & Kotabe, 2003; Anderson, et al., 2004; Sin, Tse, Yau, Chow, & Lee, 2005). When developing marketing strategies, organizations cannot leave out the consideration of the surrounding environment because its effectiveness depends on the environment’s dynamics (Sin, et al., 2005). Fiorino (1996) claims that industry has a moderating role due to the fact that different sectors of the economy cannot be addressed similarly, it requires different approaches.
| 18 same reasons e.g. reducing labor costs and market access. Even though motivations may be of similar nature, unique characteristics of both industries lead to different performance outcomes (Capar & Kotabe, 2003). Anderson, et al. (1997) provided evidence for significantly different outcomes of marketing strategies across goods and service industries. The finding was supported by various other studies (e.g. Anderson, et al. 20040; Sin, et al., 2005).
Generally, channel additions add more value in service industries attributable to opportunities to serve various consumer segments and their differentiating demands (Verhoef, Neslin, & Vroomen, 2007). New channel potential and added-value are less appreciated in product industries due to threats, e.g. substitution effect, as mentioned previously. Mobile Internet is presumed to enrich consumers’ experience, involvement and deepen relationships between user and company (Shankar & Balasubramanian, 2009). This is enabled by the Mobile Internet’s capabilities of continuous interactive connectivity and personalized location-based marketing and communication (Shankar & Balasubramanian, 2009).
As mentioned previously, service oriented organization appear to obtain more benefits from relationship marketing compared to organizations in product industries. Accompanied by the fact that Mobile Internet channel additions evolve from consumer demands, additionally supported by its consumer relationship enhancing competences, we hypothesize the following:
H2: The Mobile Internet channel has a stronger performance potential across organizations in the service industry, relative to organizations operating in a product industry.
3.2 Introduction Strategy
When introducing a new communication and/or distribution channel to existing traditional channels, introduction strategy plays an important role. The introduction strategy determines the competitive advantages an organization can obtain with this marketing action (Geyskens, et al., 2002). We consider order of entry and nature of the channel addition as two introduction strategy characteristics on which performance potential of the new channel might depend.
| 19 and threats (Rodriguez-Pinto, Carbonell, & Rodriguez-Escudero, 2011). Reviewing research regarding order of entry in the Internet context, two different positions are taken and supported with evidence. The opposing findings evolved from different natures of the studies and the markets in which the research was designed. Some claim that pioneers and first-movers hold an advantage of organizations that follow later, due to their ability of setting the standard, perceived uniqueness, and the opportunity to establish a reputation of being market leaders (Lieberman & Montgomery, 1988; Lee & Grewal, 2004; Frynas, Mellahi, & Pigman, 2006; Varadarajan, et al., 2008). On the contrary, others claim that followers hold an advantage over those who enter the market as pioneers and first-movers, due to the fact that they do not have to deal with the initial adoption of new approaches; they have time and ability to shape customer preferences (Carpenter & Nakamoto; 1989; Porter, 2001; Geyskens, et al., 2002).
| 20 H3: The Mobile Internet channel has a stronger performance potential among early entrants relative to the performance potential of the Mobile Internet channel among followers.
The second introduction strategy characteristic involved the specific Type of Mobile Internet channel. Organizations have the opportunity to integrate Mobile Internet channel in two manners, namely launching either a mobile website or a mobile application (app). Due to the fact that mobile marketing is still in its infancy (Bauer, et al., 2005; Leppäniemi, et al, 2006; Ong, 2010), differences and possible consequences of integrating a mobile website or an app have not been researched yet. However, previous research with respect to the traditional Internet channel made a distinction in the type of Internet channel (e.g. information; text, document, multimedia vs. transactional; product purchasing, multimedia download, online application) and its influence on consumer behavior and firm performance (Jansen, Booth, & Spink, 2008; Nierop, Leeflang, Teerling, & Huizingh, 2011). As the Mobile Internet channel may be perceived as an extension of the traditional Internet channel (Junglas & Watson, 2003), we expect that the type of Mobile Internet channel will have a moderating role between the firm’s potential performance and the Mobile Internet channel addition.
Since no commonly accepted concepts referring to either a mobile website or a mobile web application could be found in scientific papers, we have derived definitions after reviewing several online sources. A mobile website refers to the concept of providing an internet site optimized to browse with a mobile device. Hence, features such as layout and content are adjusted to simplify the access for the user. An app classifies software developed to be used by mobile devices, providing content and can be used for transactional purposes in addition. Since the access to mobile websites is most commonly free of charge, the use of apps might involve costs for the user depending on the provider of the service.
As the concept of mobile website and apps are not clearly separated from each other, a noticeably classification referring to the concept of 3 C’s by Kim et al. (2007) cannot be given. In general, mobile websites rather function as providing contents, while apps incorporate the mobile commerce and mobile communication tools.
| 21 consumer involvement refers to product’s perceived importance or personal relevance based on every consumer’s individual requirements and values (Bloch & Richins, 1983; Celsi & Olson, 1988; Zaichkowsky, 1985). A mobile website can be accessed immediately, providing the demanded service right away. In contrast to this, a mobile app has to be downloaded before consumers can make use of it. Hence, it is expected that the level of involvement is higher when deciding to purchase an app compared to accessing a mobile website. Furthermore, we assume that consumers consider more carefully whether they will download an app or not and in general only purchase apps which they make use of on a regular basis, e.g. to use mobile banking. In conclusion, consumers deciding to download an app have a stronger bond with a brand. Nysveen et al. (2005) state that building strong emotional relationships between consumers and a brand serves as a competitive advantage and is thus highly appreciated by organizations. Since an app creates more psychological and physiological touch points between the brand and its user, it is expected that the bond created is stronger when downloading an app than accessing a mobile website. Hence, the launch of an app can be regarded as being one step further in the direction towards consumer involvement than creating a mobile website. As mentioned before, purchasing apps is not commonly free of charge. As a consequence, consumers might face switching costs, aiming to prevent consumers from changing suppliers (Chebat, Davidow, & Borges, 2011). The use of this defensive marketing tool (Klemperer, 1995) further supports our assumption that consumers who did choose to download an app are more brand loyal than consumers accessing a mobile website. Taking consumers’ strong emotional relationship with their mobile devices (Bauer, et al., 2005) into account, additional reasoning for the assumption that consumers are highly involved when using the Mobile Internet channel is provided. When we total-up these effects, it is expected that the relationship between a consumer and a brand is stronger when the user purchases an app compared to accessing a mobile website. Hence, we hypothesize the following:
H4: The effect of adding a Mobile Application is significantly stronger on company performance potential compared to adding a Mobile Website.
3.3 Marketplace Characteristics
| 22 we focus on analyzing the moderating impact of the global economic recession starting mid 2008 due to the permanent relevance of the event (The Crisis and Beyond, 2012). Especially when it comes to marketing expenditures, stability of the market is of utmost importance. Research revealed (Srinivasan, Lilien, & Sridhar, 2011) that there is still no commonly accepted answer to the ongoing debate between marketers and researchers about whether to invest in or to decrease marketing expenditures in an economic downturn.
According to the IMF (2012), there is no general definition of a global economic recession. Thus, we will apply the concept adjusted by the IMF referring to a global economic recession as a “period of decline in world real per capita real GDP, accompanied by a broad decline in various other measures of global activity”.
Research has proven (Srinivasan, et al., 2011) that recessions are periodic events shaping the world’s major economies. Narrowing it down to individual organizations, economic recessions have a significant impact on marketplace characteristics, e.g. product demand growth. Cutting expenditures is the most common reaction when operating in unstable financial markets, faced with irritated consumers and shareholders (Graham & Frankenberger, 2011). Most likely, organizations begin to cut costs in marketing communications since marketing is in general still enumerated as a cost rather than an investment (Verhoef & Leeflang, 2009). Biel (1998) recommends a contrary approach; more precisely the author suggests increasing marketing expenditures during a recession and is strongly supported by advertising professionals (Graham & Frankenberger, 2011). Additionally, advertising expenditures were found to have a stronger positive effect during a recession than in stable financial markets. (Kamber, 2002)
| 23 that consumers’ demand is growing (Kim, et al., 2007), justifying such an investment in these difficult times is expected to be hard for marketers.
In contrast to this, recent research by Graham et al. (2011) revealed that increasing marketing communications expenditures during a recession creates future value for the firm. Additionally, a positive signal is being sent to shareholders, consumers and future investors. Although this effect has only been proven for B2C product firms, but not for companies operating in the service industry (Srinivasan, et al., 2011), the psychological effect of increasing marketing expenditures might also have an impact on service companies. Also relevant to the discussion are the outcomes of Frankenberger & Graham (2003) and Srinivasan & Lilien (2009) stating that investments in marketing communications have a performance-enhancing effect on profits, not only during, but also after a recession.
In conclusion, recent research suggests that increasing expenditures in marketing communications in an economic recession is recommended. To date, scientific proof of the instrument of channel addition is missing. We therefore hypothesize the following:
H5: The effect of Mobile Internet channel addition on company performance potential is stronger in the economic recession than before.
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Chapter 4 Methodology
To date, financial performance measures of Mobile Internet channel additions do not exist. Due to the recent call of fact-based marketing, Marketing departments are under pressure to present financial benefits deriving from strategic marketing decisions (Wiesel, Skiera, & Villanueva, 2008). Event study methodology has been commonly accepted and will be utilized in this study to present the impact of adding a Mobile Internet channel on firm value (Srinivasan & Hanssens, 2009). This chapter presents a review regarding the methodology, followed by a succinct description of undertaken steps.
4.1 Introduction to Event Study Methodology
In 1933, event study methodology was first utilized in a study by Dolley publishing the principal article applying the approach. Marketing researchers recognized the potential of this method and gradually adopted it from the 1980s onwards to present effects of strategic marketing decision on stock prices and firm value (Johnson, 2007). According to Verbrugge (1997), event studies permit building guidelines for strategic marketing decisions. Hence, it serves as an essential step to go into the direction of fact-based marketing. Nonetheless, studies relating marketing initiatives to financial performance underutilized the method (Srinivasan & Bharadwaj, 2004). This may be caused by three assumptions event studies rely on (McWilliams & Siegel, 1997). First of all, event studies heavily rely on the Market Efficiency Hypothesis (MEH) implying that all relevant information available to market traders is reflected in stock prices. Furthermore, the methodology incorporates no information leakages regarding announcements; more precisely, it is assumed that traders gain information solely from official press releases. Lastly, confounding effects are considered to be absent (McWilliams & Siegel, 1997). Besides, a major downside of event study methodology was presented by Johnston (2007), revealing that it is rather difficult to make general inferences from one study to another.
| 25 prices are suitable to measure expected future cash flows (Rappaport, 1987), reflecting the fact-based potential of the approach. Srivastava, et al. (1998) elaborate on the benefits of using stock prices as a measurement tool; pinpointing their ease of accessibility and accuracy. Additionally, abnormal stock returns are considered to be a unique means to present future impacts (Subramani & Walden, 2001).
After careful deliberation regarding the appropriateness of event study methodology to this study, we came to the conclusion to follow the intercessors due to the power of the approach as a fast and fact-based tool to measure the impact of Mobile Internet channel addition. As previously mentioned, this paper builds on the framework regarding channel additions of Geyskens, et al. (2002). By adopting this technique, it is necessary to take the three general assumptions every event study relies on into consideration.
4.2 Our Study
| 26 proven to be more precise in detecting event effects (MacKinley, 1997; Srinivasan & Bharadwaj, 2004), we will adopt this approach in our study. The market model can be implemented by means of two different techniques, namely the international market model and the adjusted market model incorporating local market indexes (Park, 2004). With respect to accuracy, research revealed no significant differences between the two approaches (Yang, Wansley, & Lane, 1985). Hence, we follow the majority of event studies (Park, 2004) in applying the international market model to obtain expected returns. The international market model assumes that there is a linear relationship between the return of the overall market portfolio and the individual stock’s return (Johnston, 2007). Therefore, Standard & Poors’s 500 index has been collected due to the fact that it is readibly available (Patell, 1976). Besides, this capitalization-weigthed index has been commonly utilized in event studies (Campbell, Lo, & MacKinlay, 1997). This overall market portfolio reflects the stock market as a whole and provides an impression of the overall equity market (Johnston, 2007). An event window, referring to the days stock prices are most likely to be affected by the announcement, will be used to calculate abnormal stock returns. In order to estimate normal stock returns, we make use of an estimation window; a long period well in advance before the event (Johnston, 2007). As stated by Johnston (2007), estimation windows are generally quite large and have to be clearly seperated from the event window. In our study, we apply an estimation window of 220 days, more precisely we select normal stock returns from 250 days to 30 days before the unanticipated event.
4.3 Calculations
| 27 To identify the impact of Mobile Internet channel addition, firstly, stock return has to be obtained. We measure the percentage change in the stock price by using the following formula:
(1) Rit = Pit - Pit – 1
Pit - 1
Where Pit represents the stock price of company i at time t. The percentage change in the stock price captures the impact of unanticipated event announcements between t – 1 and t.
Implementing event study methodology demands a comparison of stock return Rit at the event day with E(Rit)representing the expected return in case the announcement of a Mobile Internet channel would not have occurred. Given that the international market model entails a linear relationship between the overall market portfolio and the individual stock return, E(Rit) can be obtained with the following formula:
(2) E(Rit) = αit + βi Rmt
Coefficients for αit and βit are acquired from ordinary least squares regression (OLS) of Rit on Rmt over an estimation window of 220 days. Rmt represents the overall market portfolio as provided by Standard and Poor’s 500 Index.
Subsequent, the abnormal returns (ARit) relating to the shares of organization i at time t have to be measured. Abnormal returns are estimated by taking the difference between normal returns and expected returns as presented in formula (3).
(3) ARit = Rit - E(Rit) = Rit – ( it + i Rmt )
Following McWilliam and Siegel (1997), we standardize the abnormal returns (SAR) by dividing the abnormal returns (ARit) of every organization by its own standard deviation (SDit). (4) SARit = ARit / SDit
| 28 Internet channel by computing the average of standardized abnormal returns over all announcements.
(5) SAARit = ∑ SAR /N
Hereby, SAARit specifies the average of the standardized abnormal returns, N is the sample size, i refers to the event and t represents the date differing across organizations.
The main effect of the event of interest has to be statistically tested to determine whether the main effect of a Mobile Internet channel addition is significantly different from 0 on the event day t = 0. We test the significance with the Patell statistic (Patell, 1976). Hence the following equation will be operationalized:
(6) SAARt=0 = ∑ SAAR , )
Formula (6) considers the effect of Mobile Internet channel addition in an ideal situation referring to the absence of information leakage. While event study methodology relies on the assumption of investor reaction in ideal situations, research revealed (McWilliams & Siegel, 1997) that the existence of no information leakages does not always hold in practice. Besides, dissemination of information might not be immediate, causing the effect of the announcement to be delayed. To account for all effects, a window of 10 days prior to (-t1) and after (t2) the event will be examined. The standardized abnormal returns can be cumulated over all firms for each day of the chosen event window, to capture a measure of the cumulative abnormal return (SAAR[-t1,t2]) for each organization. This can be estimated by using equation (5) with t varying from -10 to +10. Once more, equation (6) is used to test for the significant effect of each day. Once effects and significances of single days within the chosen event window have been identified, cumulative standardized average abnormal returns (CSAAR) for various times windows surrounding the event date will be examined. By comparing various CSAAR windows, the most significant ones are selected to capture the overall effect of the event.
| 29 After selecting the most significant time window, an ordinary least squares regression will be conducted to test the anticipated hypotheses regarding moderating impacts of firm characteristics, introduction strategy and marketplace characteristics. More specifically, a set of dichotomous variables represent the moderators adopted within this study to analyze whether they significantly interact with Mobile Internet channel additions and its market valuation. The selected time window will serve as the dependent variable in the OLS, whereas above mentioned moderators will act as exogenous explanatory variables, including an error term:
(8) CSAARi[-t1,t2] = αit + β1 * IndustryTypei + β2 * OrderOfEntryi
+ β3 * MobileChannelTypei + β4 * MarketHealthi + εi where,
IndustryTypei = 0 = Product, 1 = Service
OrderOfEntryi = 0 = Followers, 1 = Early Entrants
MobileChannelTypei = 0 = Mobile Website, 1 = Mobile App
MarketHealthi = 0 = During Recession, 1 = Before Recession
i = event announcement
εi = error term for event i
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Chapter 5 Data
We define event day as the date on which the announcement of Mobile Internet channel addition was first mentioned in the media. After collecting data meeting our criteria to be included as described in the methodology chapter, our final sample size consisted of 117 observations. The sample represents publicly listed companies on four different international stock exchanges, more precisely 43 of them being traded on LSE, 34 on NYSE, 22 on AEX and 18 on FSE. Besides, majority of organizations, precisely 58%, operate in a product industry. Furthermore, 52 organizations were found to be early entrants regarding Mobile Internet channel addition within their industry. Defining the exact type of channel addition, it was found that 55 organizations launched an app, 52 developed a mobile website and 10 firms introduced both types of Mobile Internet channels at the same time. When gathering data concerning timing of channel addition with respect to the economic recession; 20 organizations out of the total sample of 117 were revealed to have launched the Mobile Internet channel before the economic crisis. Thus, the remaining 97 organizations integrated the Mobile Internet Channel after mid-2008.
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Chapter 6 Results
The next chapter present the results derived from the analysis as described in the methodology chapter. First, we test for main effects of Mobile Internet channel additions; followed by an analysis of interaction effects of several exogenous variables with the main relationship.
6.1 The Main Effect of a Mobile Internet Channel Addition
Applying the procedure of event study methodology, we estimated the parameters of α and β for every organization by operationalizing equation (2) using an estimation window of 220 days (t = -250 to t = -30). The obtained parameters were used to calculate organizations’ abnormal returns (ARit) with equation (3) for the event day plus a time window of ten days before and after. Operating equation (4), standardization of abnormal returns was conducted after which the average main effect was estimated by means of equation (5).
Table 1: Standardized Average Abnormal Returns for Mobile Internet Channel Additions Event Day SAAR(%) Patell Statistic
-10 -.09 -.969 -9 -.03 -.302 -8 .04 .406 -7 -.04 -.474 -6 *-.19 -2.043 -5 -.01 -.118 -4 .07 .776 -3 -.11 -1.209 -2 .05 .551 -1 -.12 -1.346 0 .13 1.400 +1 .04 .398 +2 .07 .787 +3 -.08 -.888 +4 -.10 -1.054 +5 .06 .615 +6 *.19 2.045 +7 .02 .265 +8 -.13 -1.388 +9 -.12 -1.243 +10 **-.42 -4.546 * p < .05 ** p < .01
| 32 organizations integrating a Mobile Internet channel experienced .13% abnormal returns on t = 0 (Patell Statistic 1.400 < 1.96) and .04 abnormal returns for t = +1 (Patell Statistic 0.398 < 1.96). Although an effect was found, it was revealed not to be statistically significant on both days. For t = +6 (Patell Statistic 2.045 > 1.96) and t = -6 (Patell Statistic -2.043 > 1.96), significant abnormal returns of .19% (p < .05) and -.19% (p < .05) were identified, respectively. However, this effect is not considered to be linked to the event due to the long time distance to the event day. For the same reason, the significant effect of obtaining -.42% abnormal returns ten days (Patell Statistic -4.456 > 2.576; p < .01) after the announcement is not taken into consideration. According to Ryngaert & Netter (1990), significant effects of events were proven to be found in short event windows. Furthermore, a large event window complicates the verification of the absence of confounding events (McWilliams & Siegel, 1997). Thus, despite the fact that no significant main effect could be found on specific days, we analyzed various cumulative event windows surrounding the event day. The analysis of cumulative standardized average abnormal returns (CSAAR) of different windows around the event day shows that a significant effect of Mobile Internet channel additions can be found for CSAAR[0,+1,+2], with abnormal returns of .24% (Patell Statistic 2.586 > 2.576; p < .01). No other significant cumulative event windows were found. ‐0.5 ‐0.4 ‐0.3 ‐0.2 ‐0.1 0 0.1 0.2 0.3 ‐10 ‐9 ‐8 ‐7 ‐6 ‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5 6 7 8 9 10 SAAR
Time (in days before and after the announcement)
| 33 Figure 2 illustrates the SAARs over an event window of t = -10 to t = +10, highlighting the positive SAARs linked to the announcement. According to McWilliams and Siegel (1997), a short event window is a critical condition for market efficiency. With a significant event window of [0,+1,+2], we find an immediate positive reaction in stock prices to the launch of a Mobile Internet channel. Hence, the above mentioned criterion is fulfilled and supported by the lack of significant cumulative effects beyond a two-day period after the event (including the event day). The selection of event window wherein the effect of Mobile Internet channel additions is based on the significance level. Validating the appropriateness of using the event window of [0,+1,+2] in OLS, we examine whether the assumption of normal distribution holds.
Figure 3: Frequency Distribution of Standardized Abnormal Returns
| 34
6.2 Regression Model
The cumulative event window of t = 0 to t = +2 was found to represent the significant effect of a Mobile Internet channel addition. This window is selected to represent the dependent variable in the linear model. A number of exogenous variables were tested for moderating influences on the relationship between Mobile Internet channel additions and performance potential. We test whether the exogenous variables significantly affect the relationship of Mobile Internet channel addition and organizations’ performance potential. Conducting OLS with CSAAR[0,+1,+2] as dependent variable and the moderating variables; industry type, order of entry, type of Mobile Internet channel addition and marketplace characteristics. Table 2 illustrates the obtained results.
Table 2: Regression Model
Variable Coefficients Std. Error t-values p-values
Early Entrant .265 .363 .729 .468
Service .270 .359 .751 .454
Mobile App .147 .411 .357 .722
Both .075 .644 .116 .907
Before Recession -.607 .538 -1.128 .262
Industry type was found to be insignificant (p > .05), hence hypothesis 2 cannot be supported. Order of entry also appeared to have an insignificant effect on abnormal returns, therefore no scientific support for hypothesis 3 could be found. Besides, type of Mobile Internet channel addition did not reveal a significant impact. Therefore, H4 is not supported. Based on scientific literature, we hypothesized that introducing a Mobile Internet channel during the recession would have a stronger impact on performance potential relative to the launch before the recession. However, results show insignificant effect. As a result, H5 is not supported. Summarizing, none of the exogenous variables revealed a significant impact on abnormal returns. Reflected by a relatively low coefficient of determination R2 = .024.
| 35
6.3 OLS Assumptions
Validating the results of the OLS and the identified insignificant effects of exogenous variables, additional tests were conducted exploring the disturbance term assumptions. The validity of OLS is based on a number of assumptions. All assumptions and instruments used for detection and verification of assumptions are summarized in table 3.
Table 3: Verification of OLS Assumptions
Assumption Instrument for Detection Verification
Independence of Disturbance Term Durbin-Watson Statistic
Homogeneity of Variance Levene Statistic
Independence of Exogenous Variables Collinearity Diagnostics
Normal Distribution of Disturbance Term Normality Plots
First of all, residuals have to be independent to capture dynamics properly. This criterion has been assured by applying the Durbin-Watson (D.W.) Statistic. With a D.W.-value of 2.02, being close to the ideal value of 2, no autocorrelation has been detected. Second, multicollinearity is a significant problem in marketing. Therefore, we ensure independence of exogenous variables by analyzing the collinearity diagnostics. The issue of multicollinearity was not detected, all variables revealed Variance Inflation Factor (VIF < 10) values close to 1. Third, we test for homogeneity of variance across organizations. The total sample was randomly divided into two groups to apply the Levene Statistic. Displaying a Levene Statistic of .372 and a p-value of .543, heteroscedasticity was verified to be absent. Lastly, in OLS, disturbance terms are assumed to be normally distributed. A normal distribution was verified by means of the normality plot of residuals. Thus, statistical validity has been proven.
| 36
6.4 Robustness Checks
We evaluate our results by checking their robustness. First, we analyze whether our findings were driven by outliers. Then, we conduct OLS on different event windows with and without outliers to evaluate the consistency of our results.
Analyzing the distribution of CSAAR[0,+1,+2], five organizations were identified as outliers. In order to determine whether results were driven by these observations, we conducted OLS without outliers. Results are presented in Table 4.
Table 4: OLS excluding outliers
Test Results Robustness
Kolmogorov-Smirnov p-value > .05
Skewness .276
Durbin-Watson Statistic 2.137
Levene Statistic p-value > .05
Collinearity Diagnostics close to 1
Normality Plots bell-shaped distribution
The results shown in table 4 indicate that the appropriateness of OLS remains similar and disturbance term assumptions are met. Findings can be interpreted with statistical validity. All exogenous variables appear to be insignificant, consistent with our previous findings.
Evaluating the robustness of moderator effects, we calculate the CSAARs for t=0 and t=0,+1 following the procedure as described in the methodology. Hence, we use the newly obtained CSAARs as dependent variables computing two new regressions, including the previously used exogenous variables. Similar effects were found for all moderators. No significant effect of exogenous variables can be found in both regressions.
Conducting additional robustness checks, all above mentioned steps were repeated using a control variable (year). In our sample, the frequency of Mobile Internet channel additions linearly increased from 2006 onwards, we included dichotomous variables to ensure that our results were not influenced by this movement. Including the control variable did not change our outcomes.
| 37
Chapter 7 Discussion
In this study, we have made a first attempt to empirically explain the impact of Mobile Internet channel additions on company potential performance. Based on an extensive review of existing literature with respect to mobile marketing and channel additions, we identified a need for scientific evidence to support marketing managers in their decision making regarding channel additions. Applying the commonly accepted event study methodology, we followed a similar approach as Geyskens, et al. (2002). The authors utilized this study to analyze the market evaluation of Internet channel additions. Given that the Mobile Internet channels can be perceived as an extension of traditional Internet channels, this approach is considered to be appropriate. Pursuing this method, we aim to overcome a number of matters as pointed out by Johnston (2007). In this manner, we established a sample including various industries and stock exchanges. In contrast to Geyskens, et al. (2002), we simplify the generalizability of our results. Announcing the addition of a Mobile Internet channel to existing channels, on average, causes significant reactions on abnormal stock returns. Changes in abnormal stock returns appear to be both positive and negative. On average, investors perceive a Mobile Internet channel addition as a favorable performance-enhancing strategy according to our findings. Hence, associated benefits outweigh possible disadvantages. However, along with positive abnormal returns, we identified negative investors’ reactions as well; thus, careful consideration has to be given to the announcement of a Mobile Internet channel addition. Investors are aware of both impacts. They notice potential advantages of adding a Mobile Internet channel, and are also aware of the downsides of such a strategy. Hence, an inventory has to be made when deciding on adding a Mobile Internet channel in which all possible (positive and negative) scenarios should be evaluated.
| 38 additional channels may not be perceived as innovative, resulting in a limited value-adding strategy. This may be caused by the concept that Mobile Internet channels can be perceived as an extension of stationary Internet channels. Organizations integrating a Mobile Internet channel should be aware of the fact that the short-term impact on abnormal stock returns might not be substantial and can be either positive or negative. Therefore, marketing managers should ensure the alignment of offering multiple channels and aim to further justify the addition by reporting consumers’ demand.
Furthermore, analyzing various event windows, we only identified significant cumulative abnormal returns for a time period lasting from the event date until two days after the announcement. Given the significant impact, dissemination of information took three days. The finding of delayed significant effect of Mobile Internet channel additions on firm performance is in line with prior literature. McWilliams & Siegel, (1997) indicated that diffusion of information is rather scattered over a few days. Consequently, changes in abnormal stock returns should be dispersed over a few days as well. Overall, our results indicate performance-enhancing impact of Mobile Internet channel addition on stock returns.
Examining effects of firm specific-, introduction strategy-, and marketplace characteristics, evidence to support our hypothesis regarding their interaction with the main effect remained absent. Our results indicate that the market valuation of Mobile Internet channel additions do not depend on the exogenous variables included in our analysis.
| 39 In our study, we adopted two introduction strategy characteristics; order of entry and type of Mobile Internet channel. Referring to the first characteristic, we did not follow the approach of Geyskens, et al. (2002). More precisely, we simplified the concept of order of entry due to the fact that we research various industries and stock exchanges. Organizations’ announcements only provide data enabling the approach we pursued. Given our finding that a Mobile Internet channel addition does not have a strong impact on abnormal stock returns in general, it was not unexpected that the type of Mobile Internet channel did not have a significant moderating effect. Considering the literature regarding consumer relationship deepening, touch point creation, interactivity, and consumer adoption, merely comparing a mobile website with a mobile app did not depict significant differences. Suggestions for further research will be discussed in chapter 9. Our final exogenous variable represented the timing of the event with respect to the economic recession. Literature suggests that marketing communications expenditures during a recession adds more value to the firm relative to the ones before economic downturn. However, as our research indicates that pursuing a multi-channel strategy has become a business standard, adding a Mobile Internet channel is not perceived as a strategic move by shareholders. Therefore, recent impacts of economic recession are not assumed to be significant anymore, reflected by the lack of support for hypothesis 5. The absence of support may be assigned to the classification of the timing of events, releasing the announcement before or during economic recession. Adopting the definition regarding latest economic recession of the International Monetary Fund, the distinction of before or during recession was made by mid-2008. As stated in chapter 5, the majority of announcements were published after mid-2008. Given this occurrence and the infancy of Mobile Internet marketing, the timing of Mobile Internet channel additions cannot be related to the economic downturn.
In summary, our results indicate that managers do not need to take these factors into account when considering adding a Mobile Internet channel. To date, merely announcing a Mobile Internet channel addition is valued by investors.
| 40
Chapter 8 Implications
Our research indicates implications for researchers and marketing managers. Mobile Internet channel research is still in its infancy. However, this study serves as a first step into the direction of identifying implications of adopting a Mobile Internet channel strategy. For researchers, our study provides valuable insights which may be used to conceptualize the phenomenon of Mobile Internet channel adoption and its influence. For managers, our study proves that to date, adding a Mobile Internet channel favorably influences firms’ potential performance on average. Therefore, organizations that have not pursued this course of action yet, are highly recommended to do so. Our findings provide marketing managers with empirical evidence to support their decision and apply fact-based marketing. While the average effect is positive, organization also experienced negative effects caused by the announcement regarding Mobile Internet channel addition. Thus, the value-perspective of a Mobile Internet channel addition is positive on average, but organizations should not expect an enormous impact on abnormal stock returns on the short-term. Hence, there is a need for the inclusion of long-term metrics to reveal the actual impact of Mobile Internet channel additions on firm performance.
Changing consumer behavior causes demand for availability and accessibility of services at any time. This need can be served by adding a Mobile Internet channel. However, simply adding a Mobile Internet channel is not sufficient. Marketing managers have to ensure that the implementation of this action is aligned to their existing marketing and communication channels. Besides, the functionality and the content of the new channel should correspond with the corporate identity.
| 41
Chapter 9 Limitations and Further Research
Our research reflects a pioneering investigation into the complex field of mobile marketing. As such, various limitations exist leading to further research propositions.
First of all, we applied event study methodology in which the market valuation of Mobile Internet channel is based on stock price data. More precisely, we calculate stock returns to identify the effect. However, this metric is rather short-term oriented. No inferences regarding long-term impacts of Mobile Internet channel additions on firm value can be developed. Additional research on future potential performance is hence recommended to capture the true long-term value. Furthermore, Geyskens, et al (2002) state that not only investors belong to shareholders but multiple groups should be included as well, such as customers. Analyzing customers’ valuation of the channel addition is of great interest as customer equity is acknowledged to be highly important regarding firm value.
Another limitation refers to the usage of secondary data. Compared to primary data, the dependence on secondary data limits the scope of the study. Measurement issues emerged from restricted availability and accessibility of information, driving the simplification of classification of exogenous factors as mentioned in chapter 7.
| 42
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