Do early entrants attract better customer evaluations?
Evidence from the digital camera industry
Purpose— In addition to pioneering, empirical work on entry order increasingly addresses fast followers and laggards and the potential advantages they are able to capture. There is also a growing consensus in the academia, that current measures of firm performance used in the entry order literature to study these advantages are inadequate. This study analyzes the relationship between entry order and customer evaluations, which, depicting the performance of the firm’s products in the market, are used as a proxy for firm performance.
Methodology—The study is set in the digital camera industry, analysing entries into each new technology
level, in terms of the sensor resolution of compact and bridge cameras. The complete dataset consisted of 1,816 digital camera models introduced between January 1996 and December 2017. The data are analysed using hierarchical multiple linear regression.
Findings—The study finds evidence of early-mover advantage for the compact product category. In the
compact camera consumer market, both first-movers and fast followers outperform late movers. Furthermore, the difference in performance in comparison to laggards is greater for first-movers than for fast followers.
However, in the bridge category which consists of a more heterogeneous set of products, no significant entry- order effects are detected.
Value— The results clearly indicate that there exists an early mover advantage. Furthermore, the results are
not consistent across different product categories within an industry, hence caution needs to be exercised when analysing industry dynamics and entry order effects. Finally, our novel conceptualisation of firm performance measured as online customer evaluation add new opportunities to investigate firm success.
Johanna Kirjavainen and Saku J. Mäkinen
Industrial Management, Tampere University, Tampere, Finland Ozgur Dedehayir
School of Management, Queensland University of Technology, Brisbane, Australia
I. Introduction
The timing of market entry is a major concern in competitive strategy, and as such, it has received a lot of attention from both managers and researchers alike (Markman et al. 2019; Jiang et al. 2018; Barlow et al. 2019).
Entry choices present a complex array of decisions most often made under great uncertainty, requiring the consideration of a diverse set of contingencies (Markman and Waldron 2014; Ozcan 2018). In a highly competitive environment, both entering the market prematurely or entering too late pose risks for the firm (Lilien and Yoon 1990). In addition to the timing of entry, entry decision making needs to consider price- quality levels attainable in a given launch window, and choices like the mode of entry, as well as target market (Zachary et al. 2015; Feng et al. 2015). Changes in the general economy, customer preferences, and the evolution of the industry’s life cycle all affect the risks and opportunities related to a new product entry (Lilien and Yoon 1990).
From these themes, the existence of first-mover advantages and disadvantages has been a popular and much- debated topic since the publication of Lieberman and Montgomery’s (1988) seminal article. Empirical studies on the subject have found differing results, and the literature on entry timing has advanced from focusing only on the first-mover, into a research stream considering the timing and order of market entry more broadly as predictors of firm performance (Fosfuri et al. 2013; Wang 2017). Nonetheless, there is still a lack of consistent empirical evidence on many aspects of entry timing advantages (Fosfuri et al. 2013; Klingebiel and Joseph 2016).
One notable problem relates to the measurement of these entry order advantages. Over the years, a number of measures have been utilized by researchers, ranging from the most common ones of profit, market share, and survival (Lieberman and Montgomery 2013), to return on investment (De Castro and Chrisman 1995), brand trial penetration (Kerin et al. 1996), and net income, as well as subsequent combinations of these (Boulding and Christen 2003). Most of these measures have been found to be inadequate or inconsistent in reliably measuring the advantages associated with market entry order. What these measures have in common
nevertheless, is the implicit idea that superior firm performance is achieved through superior performance in the market, which is in turn dependent on what customers think of the firm’s products.
In this explorative study, we set out to analyze the relationship between entry order and customers’ online product evaluations as a proxy for firm performance. Online customer evaluations are a critical aspect for firms, since they provide information on the value the firm’s products offer customers, which is directly linked to firm performance. This is demonstrated saliently, for instance, in studies of electronic word-of-mouth (eWOM) effects, which show that online reviews, both in terms of the quality and quantity of reviews, correlate with the firm’s sales performance (Blal and Sturman 2014). However, to the best of our knowledge, existing literature, with the exception of Bohlmann et al. (2002), remains mostly absent of this important link between entry order and firm performance. To address this knowledge gap, we investigate the digital camera industry and measure firm performance in relation to order of entry into new competitive domains defined by sequentially increasing technology levels reflected in mega pixels of the camera sensor.
Our paper is structured as follows. We first take a short look at the present status of entry order literature and in its relation to competitive dynamics. We then analyze the data and present our findings. Finally, we consider the limitations of our study and offer managerial implications along with avenues for future research.
II. Theoretical background and hypotheses
A. Competitive dynamics and firm performance
The roots of competitive dynamics research can be traced back to Schumpeter’s theory of creative destruction, which describes the process of competition through the actions and reactions of firms in their pursuit of market opportunities (Schumpeter 1934; Schumpeter 1942). He characterized this process as a
‘perennial gale’, in which the first-moving firm earns extraordinary profits through its actions as a pioneer in the industry, gaining temporary advantage over its competitors. These profits, in turn, motivate the firm’s competitors to act and react in hopes of enjoying similar profits, thus eventually eroding the pioneer’s advantage
(Grimm et al. 2005). The literature on entry order advantages considers the order and timing of these actions and reactions (Ketchen et al. 2004).
The research on competitive dynamics was influenced by Austrian economics and Schumpeter more broadly, too. Austrian economists believed that a perfect market equilibrium only occurs in the absence of competition, and as this virtually never happens, the markets are constantly moving toward and away from equilibrium (Smith et al. 2001; Mises 1949; Schumpeter 1934). This movement is caused by innovative actions by firms attempting to fulfill consumer needs and respond to market opportunities, defined as ‘entrepreneurial discovery’. Competitive dynamics scholars have focused a lot of attention on innovative actions, as well as the advantage gained through them and their effects on markets and profits, stemming from the idea of entrepreneurial discovery (Smith et al. 2001). Many of the key concepts of competitive dynamics are subsequently rooted in Austrian economics, including the focus on action, response and their timing, industry structure, and competition (Smith et al. 2001).
One of the conundrums in the entry order literature is the performance measure. There are problems associated with the most common performance measures used in the literature - market share and survival. On the one hand, market share falls short of being a good measure of performance especially when a firm is following a niche strategy, and is therefore not attempting to acquire a majority share of the market at large (Lieberman and Montgomery 2013). Firm survival, on the other hand, is not the aim of all companies and doesn’t necessarily depict the performance of a firm at all. In fact, many startups might aim to be acquired at a good price, measuring their performance by the size of the offer they get from a bigger firm (Lieberman and Montgomery 2013). Firms may also survive for long periods of time without making any profit (Mitchell 1991).
As these different and somewhat problematic variables are used to measure firm performance, researchers attain contradicting empirical results that cannot be compared and valuated against each other (Klingebiel and Joseph 2016).
Both the performance and success of a firm are fundamentally linked with its ability to satisfy the customer by meeting their requirements and expectations (Churchill et al. 1982; Anderson et al. 1994; Herrmann et al.
2000). Customer satisfaction is significantly impacted by disconfirmation, the extent to which the perceived quality of the product fails to match the customer’s expectations (Anderson and Sullivan 1993; Szymanski and Henard 2001). These prepurchase expectations, in turn, are often shaped by information acquired online, since customers commonly seek information on the specifications and quality of new products online prior to making the decision of purchasing them (Clemons 2008; Clemons and Gao 2008; Zhu and Zhang 2010; Archak et al.
2011). A study conducted by Hu et al. (2014) showed that over three quarters of the participants reported trusting reviews by other customers, and over two thirds read at least four reviews before the purchase.
Consequently, as the digital era has multiplied the power of word of mouth (Dellarocas et al. 2004), online customer reviews increasingly influence customers’ purchasing decisions and thus, product sales as a whole (Godes and Mayzlin 2004; Liu 2006; Dellarocas et al. 2007).
Despite the subjective nature of consumer evaluations, they are often perceived to be more credible and trustworthy, and attract more interest from customers than traditional sources of information or information by the vendor (Bickart and Schindler 2001). Prior research suggests that consumer evaluations affect customers’
product purchase decisions and consequently the sales of a product (Dellarocas et al. 2004; Park et al. 2007;
Lin et al. 2011). Recent research on online customer evaluations posits that they are in fact not an accurate measure of a product’s quality (Hu et al. 2006; Koh and Hu 2010), but rather act as a representative measure of customer satisfaction (e.g. Engler et al. 2015).
As the success and performance of a product is directly linked to its ability to satisfy the needs and expectations of customers (Churchill et al. 1982; Anderson et al. 1994; Herrmann et al. 2000), online customer evaluations are strongly linked to product sales and firm performance (Anderson et al. 1994; Fornell et al. 1996;
Anderson et al. 1997; Williams and Naumann 2011). This evident link between customer evaluations and
customer satisfaction presents the opportunity to apply online customer evaluations as a measure of a firm’s success in the market.
B. Entry order
Entry order and its effects on firm performance has emerged as a popular topic of interest for academics and practitioners alike. Since the publication of Lieberman and Montgomery’s (1988) seminal article on first mover advantage in 1988, the research on the subject has thrived (Lieberman and Montgomery 2013; Suarez and Lanzolla 2007). More recently, the focus of the stream has broadened to consider entry order advantages in a more comprehensive manner.
Studies in this stream have investigated different types of ”new markets” in their analyses of entry advantages: new-to-the-world products, new generations of a product, or introducing existing products into new geographic locations (Lieberman and Montgomery 2013). We derive our definition of entry order from Zachary et al. (2015), stating that it refers to “the order of entry into a new or existing space (e.g. market, industry, or geographic region), relative to competitors, technology development, product life cycle, or other contextual referents”. Despite extensive research, many of the central assertions on the entry order and performance relationship are still under debate, which is why further study is needed (Klingebiel and Joseph 2016).
The literature on entry order advantages has developed around three streams (Suarez and Lanzolla 2007).
The first one is focused on identifying “isolating mechanisms”, which favor early movers in allowing them to protect themselves from imitative competition (Rumelt 1987; Lieberman and Montgomery 1988). The most widely used classification of these favorable mechanisms includes three categories: technology leadership, preemption of scarce assets, and switching costs (Lieberman and Montgomery 1988).
The second stream is based on the premise that a firm’s possession of resources and capabilities affects its ability to benefit from early entry (Fosfuri et al. 2013). Academics have argued that a number of complementary
assets are required to enable a firm to fully take advantage of early entry, and that industry incumbents are often better positioned in terms of the resources they possess and readily have at their disposal (Teece 1986; Mitchell 1991; Agarwal et al. 2002). Even though these resources may have been applied previously by the incumbent in a different industry, they may be general-enough by their nature to be transferrable to new industries, thus lowering the firm’s exit hazard from this new industry (Klepper and Simons 2000; Klepper 2002).
The third stream has focused on the role of environmental-level conditions in enabling or disabling early entry advantages. Porter (1985) was one of the first to argue that entry order advantages depend on industry characteristics, particularly on the levels of technological change embedded within products and processes.
Later on, a number of different environmental conditions have been considered by academics in this and other related streams, including (i) the pace of technology evolution and the pace of market evolution (Suarez and Lanzolla 2007);(ii) variability and uncertainty (Lambkin 1988);(iii) the degree of competition (Gal-Or 1985);
and (iv) the existence of network effects (Farrell and Klemperer 2007).
Despite decades of research and dozens of published articles, why is it that there still exists little generalizable knowledge on the entry order – performance relationship? Lieberman and Montgomery’s (2013) review of this conundrum finds numerous underlying problems, such as the definition of ‘advantage’ in the literature, the measure of performance utilized, measuring the duration of advantage, defining core constructs (e.g. new market, first movers, and later movers), and biases in sample selection. Additionally, most studies in entry order focus on the emergence of a new product market, which does not occur frequently. This makes it difficult to draw reliable comparisons, since contextual factors set many new markets apart from each other.
(Klingebiel and Joseph 2016.)
Furthermore, Lieberman and Montgomery (2013) address definitional problems such as how to define a market, a first mover (or first movers), and followers. For example, most studies do not distinguish between different types of new markets and innovations, although defining market boundaries is essential for comparing
market share levels in different studies (Kalyanaram et al. 1995). As mentioned earlier, a new market can be made up of new-to-the-world products, or new generations of a product, and can be created when existing products are introduced to new geographic locations.. All of these considerations have an impact on the market timing advantage. Also, identifying the starting point of a new market is often very difficult and may lead to differing results in defining businesses as first movers or followers. (Lieberman and Montgomery 2013)
As we have noted earlier, most of the commonly used measures for firm performance are inadequate.
Furthermore, many studies on entry order advantages consider only entry into completely new markets, which are naturally limited number, with market-specific contingencies making their comparison additionally difficult. The definition of entry order adopted in this study allows for an examination from a broader perspective, namely that of entry into new competitive domains defined by technology levels inside the same market. This view brings forth the idea of order of entry into an already existing space, relative to competitors, technology development, and product life cycle. In this paper, we utilize customer evaluations of the product as a proxy for product performance, which is used as a measure of firm performance.
Based on previous research, there are a number of benefits associated with being the first to enter a market or a niche (VanderWerf and Mahon 1997). The first-mover is often able to build isolating mechanisms through technology leadership, gaining control of scarce assets, acquiring expertise, and creating switching costs for the customers (Lieberman and Montgomery 1988; Finney et al. 2008). The exploitation of these mechanisms by the first-mover is likely to hinder the possibilities of its rivals to compete in the market, resulting in better performance for the first-mover.
However, there are also potential disadvantages to being the first to enter. In the early stages of a new market, there are uncertainties related to the utilized technologies and the market itself, as well as customer requirements, which may still be fluid and shift after the pioneer has entered (Lieberman and Montgomery
1988). New products can be very expensive and risky to develop, and later entrants might be able to enter with superior technology, positioning, or brand name (Golder and Tellis 1993; Lieberman and Montgomery 1988).
In addition to first-movers, researchers have also investigated a more detailed picture on the order of entry and its effects on firm performance. Studies have focused not only on the first-mover, but early entrants in general (e.g. Makadok 1998), fast followers (Lilien and Yoon 1990), and laggards (Shamsie et al. 2004;
Lévesque et al. 2013). Previous studies have found earlier entry to be advantageous in terms of a brand’s market share (Urban et al. 1986; Makadok 1998; Magnusson et al. 2009; Lilien and Yoon 1990). Fast followers might benefit from the additional time to further develop an otherwise underdeveloped product to exceed the quality of the pioneering product (Lilien and Yoon 1990). They can attempt to catch up to the pioneers by securing access to technology through e.g. licensing, purchase of capital equipment, securing supply contracts, or entering into strategic alliances (Mathews et al. 2011). Additionally, the fast follower might be able to benefit from “free-rider effects”, i.e. the ability to imitate the innovation made by the pioneer and thus save in R&D costs (Gilbert and Birnbaum-More 1996). Fast followers also have the potential to take advantage of the hurdles facing pioneers: possible market, technology, and/or regulatory uncertainties might be resolved, and changes in the requirements for the new technology and the needs of the customers might arise after the pioneer’s launch of a new product, and the followers have the possibility to time their response accordingly (Gilbert and Birnbaum-More 1996). Due to these factors, early followers have been found to enjoy advantages over first- movers (Golder and Tellis 1993).
Being the fast follower is not so easy, however. Decision-making timeframes, product development cycles and lengthy sales processes may delay a firm’s entry into a quickly-moving market so much that they may find themselves entering the market too late (Wunker 2012). However, even late movers can sometimes thrive. For example in the mobile telephone market, Motorola was the first to develop a cell phone, Nokia and Ericsson were fast followers and eventually early market leaders, but today, Samsung, LG, and Apple are global leaders in the industry, despite entering the category much later (Wunker 2012). This might be due to differing
complementary assets possessed by later entrants, and changing industry conditions (Lévesque et al. 2013).
Especially in the case of the mobile telephone market, the industry has experienced a significant disruption through the development of smart phones, which opened the doors to new entrants. In all, current research on entry timing seems to point towards the importance of considering the timing of entry and related factors as a whole, and not merely attempting to be the first-mover (Lévesque et al. 2013; Bohlmann et al. 2002; Klepper and Simons 2000; Mitchell 1991).
Previous research has found mixed results in terms of the effect of age on firm performance, and its effects on entry order and firm performance (Durand and Coeurderoy 2001). Based on the idea that an older firm is able to acquire greater experience than younger firms, researchers have argued that post-entry performance is positively related to age after having survived a sufficient period of time (Audretsch 1995). Many studies utilize both the firm age and firm tenure variables, as firm tenure captures more accurately the experience the firm has in the market in question, as learning effects offer firms with longer tenure a potential over later entrants (Scherer and Ross 1990).
However, researchers of the opposing view state that, on average, younger firms outperform older ones due to problems arising from oldness that offset the benefits gained from experience (Dunne and Hughes 1994).
Thus, even though older firms are more likely to survive, they suffer from, for example, conservatism and blindness, which weaken their performance compared to younger firms (Dunne and Hughes 1994; Evans 1963;
Durand and Coeurderoy 2001).
In previous studies on the topic, researchers have quite consistently found a positive relationship between firm size and survival rates (Agarwal and Audretsch 2001). This idea stems in part from the argument that larger organizations have better access to capital and trained workers (Aldrich and Auster 1986), and legitimacy with external stakeholders (Baum and Oliver 1991). Larger firms possess a greater resource base in general, enabling larger investments in R&D (enabling the development of better, higher quality products), and the
ability to obtain and maintain various resources required (Agarwal et al. 2002). In the specific context of order of entry, size is of particular importance especially due to the larger resource base: larger firms might have the possibility to wait longer for the resolution of market and technology uncertainties, and still be able to catch up with pioneers and earlier innovators (Wernerfelt and Karnani 1987).
The intensity of competition in an industry plays an important role in shaping the competitive environment of firms. It affects resource availability (Barnett 1997), profitability (Bettis and Weeks 1987), pricing (Gimeno and Woo 1999), market positioning (D’Aveni 1994), andthe firm’s strategies as well as its survival (D’Aveni 1994; Gimeno and Woo 1996; Barnett 1997). As competition intensifies, product variety increases, and firms require more resources to compete effectively. Furthermore, industry size is linked with entry order, as previous researchers have concluded that the phase of the industry life cycle might be affected by the actions of early entrants and that successful early entrants might be able to better spot the start of the growth phase in the industry (Golder and Tellis 1993).
III. Methodology
A. Industry Context
We explore the question of whether first-movers or fast followers outperform later movers in product performance, namely through customer evaluations. Our study is set in the digital camera industry, which is commonly considered to have emerged after the launch of the first consumer digital camera, Dycam Model 1, also known as Logitech Fotoman, in 1990, and the first digital single lens reflex (DSLR) camera, Kodak DCS- 1, in 1991. The camera industry is heterogeneous and large, witnessing long periods of intense competition between numerous camera manufacturers. Overall, 129 different brands entered the market after 1990, introducing over 4,000 camera models. Of these companies, only 19 introduced new digital cameras after 2015, leading to the conclusion that 110 companies or brands (over 85 percent) once taking part in the industry have
exited. During the evolution of the industry, three categories of digital cameras subsequently formed: (i) DSLR (high-end), (ii) bridge (mid-range); and (iii) compact (low-end) cameras.
A digital still camera is a device designed to capture information about our environment and translate it into an image. The camera converts the information it captures into electronic signals and stores them in digital format. An image sensor behind the optical lens of the camera is used to record and convert the optical image into electronic signal. The image sensor consists of an array of pixels that convert the incoming light into a signal charge. (Toyoda 2006.) There are currently two main types of technology for imaging sensors: the Charge-Coupled Device (CCD) sensor and the Complementary Metal-Oxide Semiconductor (CMOS) sensor.
As the image quality of the CMOS sensors has improved in recent years, digital camera manufacturers have increasingly started to utilize them instead of CCD sensors in all camera segments.
Within an industry, there are particular product attributes that shape and define the criteria, which customers use to evaluate and rate the products. Those attributes create the basis of competition in the industry (Christensen 1997). In the digital camera industry, based on the previous account of sensor technologies and in industry professionals’ accounts, resolution and sensor size are especially important, particularly in consumer segments such as bridge and compact cameras. For many years, digital camera manufacturers have constantly attempted to reach higher resolutions, and digital camera marketing has focused intensely on communicating these values to the customers.
In this study, we focus our analyses on the two consumer product categories of bridge and compact cameras.
The digital camera industry, and particularly these product categories, provide an ideal setting for our purposes, since competition in the industry has been intensive, and based primarily on the evolution of few product characteristics in particular, such as resolution that is measured by the quantity of mega pixels in a camera.
This is evident from past marketing materials which invariably highlighted the cameras’ quantity of mega pixels, and from past industry experts’ opinions. As a product attribute, the quantity of mega pixels has
improved quickly during the evolution of the industry, giving us numerous opportunities to analyze the entry order of firms into new competitive domains defined by new technology levels (i.e. sequentially higher camera resolutions). Additionally, data on digital camera evolution are quite readily and extensively available from a wide range of sources due to the popularity and wide distribution of consumer digital cameras.
Of the three product categories, DSLR type cameras were excluded from the analysis since they experience very differing dynamics in terms of both demand and supply. DSLR’s are aimed at professionals, whereas compact and bridge cameras are mainly consumer products. On the supply side, DSLR’s often experience a shelf-life of several years, in contrast to compact and bridge cameras with significantly shorter lifecycles. In the DSLR category, lenses and lens packs also play a major role, differentiating them from compact and bridge cameras even further.
B. Data
Our original dataset consisted of 2,030 digital camera models for which we were able to find online customer evaluation data. The introductions of camera models span the period of January 1996 through to December 2017. Of these, 1,816 digital cameras were bridge and compact cameras. After taking into account the industry, firm, and product control variables, and removing significant outliers utilizing Cook’s outlier handling, our final dataset included 1,477 digital cameras.
We gathered technical parameters, release dates, and customer evaluations for digital camera models from a wide range of online sources to ensure highest possible coverage of digital camera introductions globally.
The data were gathered from reputable online sources, including Amazon, Digital Photography Review, and What Digital Camera. We triangulated the data between three researchers to ensure the consistency of information on our dataset.
Originally, the digital cameras were categorized into three product segments mainly based on information from the websites, but the categorization was reviewed by the researchers and missing data were added based
on researchers’ evaluations of the technical parameters of the product category. DSLR cameras are the most advanced in terms of technology, and the most expensive. They were distinguished by two simultaneous attributes: the availability of instant return mirror mechanism and prism system, and lens interchangeability.
Bridge cameras are also highly advanced and usually do not include a mirror system. They are usually less expensive than DSLR’s. The category contains products such as electronic viewfinder (EVF) cameras, mirrorless interchangeable lens cameras (MILC), compact system cameras (CSC) without mirror mechanism, cameras with mirror mechanism but with fixed lenses, single lens display (SLD) cameras, rangefinder-style cameras, mid-sized cameras with manual settings of shutter speed, aperture, ISO sensitivity, color balance and metering, advanced mid-sized cameras with large imaging sensor and advanced mid-sized cameras with extended zoom. Compact cameras include pocketable cameras that most often do not feature an optical viewfinder. Additionally this category contains pocketable zoom cameras, and consumer point-and-shoot cameras that comprise basic functionality and optics. These cameras generally belong to the lowest price segment. Our analysis includes bridge and compact type cameras.
C. Measures
Dependent variable: Online customer evaluations
As noted earlier, online customer evaluations have been deemed to capture customers’ satisfaction with the product and also directly affect its sales (Anderson et al. 1997; Williams and Naumann 2011; Engler et al.
2015). Consequently, we measure firm performance through online customer evaluations, which serve as a proxy for firm performance. To achieve uniformity in the representation of customer evaluations acquired from different sources, numerical evaluations were converted into a scale of 0 to 5. These numerical evaluations included in our dataset were calculated as the weighted average of scores provided by the different websites, based on the number of reviews the product received on each website.
Independent variables: First-movers, early entrants, fast followers and laggards
Entry order was calculated for both of the product categories separately. The subsets were sorted on two criteria. We first created an ordered list of camera models based on their number of megapixels, and then created groups of camera models with the same megapixel count. We treated each group of camera models with the same quantity of megapixels as a unique domain of competition, into which firms entered with particular timing. For each of these groups, or competitive domains, we next sequenced the camera models in terms of their introduction date. We performed two different groupings that we used in our analyses, as we first wanted to analyse the differences between early and late movers, and then analyse the relationships between first-movers, fast followers, and laggards in more detail.
In the first grouping, we defined: (1) early movers in each group as the camera models that were the firsts to be launched with the corresponding technology level within three months of the very first camera model to reach this level; (2) late movers in each group as all the remaining camera models to be launched with the corresponding technology level. In the second grouping, we defined: (1) the first-mover in each group as the camera model that was the first to be launched with the corresponding technology level (i.e. megapixel count);
(2) fast followers in each group as the camera models that were listed between second and sixth positions in the launch sequence with respect to the corresponding technology level; and (3) laggards in each group as all the remaining camera models to be launched with the corresponding technology level (i.e. listed in seventh position onwards in the launch sequence). A similar take on the entry into new technological generations of digital cameras based on megapixels has been adopted by e.g. Benner and Tripsas (2012).
Control variables
At the product level, the effect of the technological performance of a product was controlled with variables measuring sensor size, the quantity of megapixels and sensor type (i.e. CCD or CMOS). The lag between the entry of the first-mover and any other product in a given group was also controlled for by calculating the days between their introductions.
At the firm level, we controlled for firm size, firm age, and firm tenure. The annual turnover of a firm was utilized as a size measure, firm age was calculated from the year of founding, and firm tenure was measured as years from entry to the digital camera industry.
We also controlled for some industry-specific effects. Competitive intensity was measured as the number of products in the industry (e.g. Sorenson 2000), while industry size was measured as millions of yens (mil. ¥).
These industry-related data were collected from online sources covering the digital camera industry and estimating its sales annually.
IV. Analysis and results
This chapter presents the hierarchical multiple linear regression results of the models. Tables I and II report the basic descriptive statistics and the correlations of the variables in this study in both of the digital camera categories. We first examined that there are no problems in terms of multicollinearity. The variance inflation factors in our model are all <6, less than the recommended 10 and hence indicate no multicollinearity problem (Hair et al. 1998).
[INSERT TABLE 1 ABOUT HERE]
[INSERT TABLE 2 ABOUT HERE]
Next, we conducted hierarchical multiple linear regression (HR) analyses on both of the product categories utilizing three models, i.e. six models in total. The first and fourth models are the baseline models, which analyze the relationships between the dependent variable and the control variables. The second and fifth models include early movers as an independent variable and analyse whether there is a statistically significant relationship between entering early and firm performance, measured by online customer evaluations. The third
and sixth models utilize a division of first-movers, fast followers and laggards to identify their interrelationships in terms of firm performance. As a robustness check, the analyses were ran also with differing group divisions in terms of fast followers and laggards, but the results remained relatively consistent. The final results are reported in Table III.
[INSERT TABLE 3 ABOUT HERE]
All of the models in the compact product category are statistically significant at the p < .001 level. According to the R2 values, 11.8 percent of the variance in the dependent variable is explained by the control variables depicted in model 1. The quantity of megapixels and sensor size both have a positive and significant coefficient (β = .022, p < .001 and β = .003, p < .01, respectively). In addition, entry lag and market size have very slight positive, yet statistically significant coefficients (β = .000, p < .05 and β = .000, p < .001, respectively). The sensor type dummy variable has a positive coefficient of β = .178 at p < .001 level, indicating a difference between the cameras utilizing CCD or CMOS sensor technology. The remaining statistically significant control variables of industry product density, firm tenure and firm age have negative coefficients (β = -.001, p < .001;
β = -.016, p < .01 and β = -.003, p < .001, respectively).
Adding the independent variables in models 2 and 3 increases their predictive power by 0.9 percentage points (R2 =12.7). In model 2, the first-mover quarterly variable assumes a negative, statistically highly significant value (β = -.162, p < .001). In model 3, the comparison variables of first-movers vs. laggards and fast followers vs. laggards also have negative and statistically significant coefficients (β = -.157, p < .01 and β
= -.071, p < .05, respectively). In contrast, the comparison variable of first-movers vs. laggards does not assume a statistically significant coefficient.
In the bridge category, all of the models are statistically significant at the p < .001 level. The predictive power of the models varies from between 20.2 percent and 20.7 percent. Of this, 20.2 percent is explained by the control variables presented in model 4. Of the control variables, firm tenure, firm age, and firm size have
negative and statistically significant coefficients (β = -.016, p < .05; β = -.003, p < .05 and β = -.000, p < .01, respectively). In contrast, entry lag and the quantity of megapixels have positive ones (β = .000, p < .01 and β
= .020, p < .001, respectively). In models 5 and 6 the predictive power of the models increases slightly (R2
=20.5 and 20.7), but the independent variables measuring possible first-mover advantages and differences between first-movers, fast followers, and laggards are not statistically significant.
V. Conclusions and discussion
In the compact camera consumer market, we find that both first-movers and fast followers outperform late movers. Furthermore, the difference in performance in comparison to laggards is greater for first-movers than for fast followers. Hence, we can conclude that in a pure consumer high-technology market such as this, earlier entrants in general outperform later entrants. However, in the bridge category which consists of a more heterogeneous set of products, we do not find significant entry-order effects.
The key result of the study is that early entrants outperform later entrants in the compact product category.
Based on the more nuanced analyses of models 2 and 3, the earlier the firm is to enter, the higher the customer evaluations and thus, market performance. Fast followers generally perform better than later entrants, but the difference is even more significant in terms of the actual first-movers. However, the difference between first- movers and fast followers is not statistically significant, indicating that early entrants in general enjoy advantage over late entrants.
In contrast, there were no significant entry-order effects detected in the bridge product category. The sample size for this category is significantly smaller, which might explain the results. Furthermore, the overall makeup of the bridge category is also far more heterogeneous, with some cameras quite near the specifications of DSLRs and some more akin to compact cameras. Nonetheless, our result also showcases the dangers of conducting industry level analyses. If we had studied all the categories together, the differences between the categories would not have been detected, as the compact category dominates our empirical data in terms of the
number of new product introductions and significance of the results. Considerations related to entry order and the timing of entry are of utmost importance to managers. Our results point towards an early-mover advantage as suggested by some previous studies (Szymanski et al. 1995; VanderWerf and Mahon 1997; Lieberman and Montgomery 2013).
As always, our study has a number of limitations. Firstly, the empirical data are from only one industry, limiting the generalizability of the findings. Although reporting on a high-tech consumer good industry the results may be representative of other similar industries. Secondly, our modelling approach could be changed to a number of differing forms such as moderated models or survival analysis or the like. Thirdly, we do not consider external contingenecies that may have changed the industry dynamics. For example, the introduction of smart phones led to a rapid decline in digital camera markets. Fourthly, brand aging and the like have not been considered as such but our used of control variables such as firm age and tenure might partially capture these kind of dynamics. Fifth, the analyses have been performed on the product category level. Thus, we do not know if similar effects would be detected at the industry level, when all digital cameras would be combined in the data.
In the entry order literature, there has been discussion on how to take into account the issue of potential first- mover mortality (e.g. Mitchell 1991; Vidal and Mitchell 2013). To respond to this concern of identifying the actual first-mover, our data take into account all of the product launches we were able to find an online record of, regardless of potential firm or product exit later on. However, the data only include the digital cameras for which online customer evaluations were found, which might distort the data. Nevertheless, we expect these effects to be randomly symmetric and hence not bias our data.
Finally, our online customer evaluations may be distorted or biased by nature. As a firm performance measure, it is a simplified, single point estimate of customers’ satisfaction with a product. It compresses all of the reviews of the product into a mean value of customers’ opinions, resulting in the loss of the underlying
distribution of the reviews, which might, in fact, more closely resemble a bimodal, U-shaped distribution than a normally distributed one (Hu et al. 2006). In addition, both extremely satisfied and extremely dissatisfied customers are often more likely to share their views on the products (Anderson 1998), which might further bias the review data. This same bias can, however, also be seen to improve the validity of the mean value of customer evaluations as a performance measure, as it averages out the extremes, possibly resulting in a value more reflective of the views of the average customer that was neither satisfied nor dissatisfied enough to review the product. Furthermore, product performace may lead to customer satisfaction which further may be reflected in online reviews. And this may reflect back to customers reading reviews and viewing the product performance in more positive way and increase their satisfaction. This is by no means necessarily as straightforward a measure as we used here and warrants ample future research.
Our findings clearly hint that there are ample avenues for future research. For example, studies might identify and consider different technology generations and a firm’s portfolio of these technologies and how they affect image quality and consequently consumers’ purchasing decisions. Similarly, competitive intensity, firm size, firm age, firm tenure, market size, and/or the technological performance of the product might moderate or mediate the effect of early mover advantage and early mover advantage could be measured in various different ways. Also, non-linear models might find relationships between product performance and early mover advantage. Brand measurement studies could additionally be included in this type of setting to see the effects of early mover advantage and how these are related to marketing tactics. Price-performance ratio could also be a topic of further research, as the consumer’s satisfaction in the product is likely to be affected by the perceived price-quality ratio.
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Do early entrants attract better customer evaluations? Evidence from the digital camera
industry
Tables
TABLEI. DESCRIPTIVE STATISTICS(COMPACT CAMERAS).
1 2 3 4 5 6 7 8 9 10 11
1. Cust omer evaluat ions 1.00 2. First-mover quart erly -.085** 1.00
3. Ent ry lag .029 .507*** 1.00
4. Number of megapixels .158*** -.038 -.099*** 1.00 5. Sensor size .131*** -.163*** -.191*** .152*** 1.00 6. Indust ry product densit y -.077** -.019 -.088** .192*** -.138*** 1.00
7. Market size -.020 -.020 -.071** .105*** -.090** .849*** 1.00
8. Firm tenure .059* .099*** .175*** .800*** .079** .203*** .157*** 1.00
9. Firm age -.177*** -.003 .040 .154*** -.036 .092** .060* .329*** 1.00
10. Firm size .119*** -.029 -.082** .182*** -.033 .154*** .118*** -.159*** -.310*** 1.00 11. Sensor t ype .222*** .032 .261*** .434*** .156*** -.257*** -.262*** .428*** -.007 .031 1.00
Mean 3.795 1.91 913.07 9.313 30.017 499.66 1185.308 13.54 83.440 37981.641 .142
St andard deviat ion .396 .279 534.936 5.012 9.95 160.161 400.395 4.755 19.011 38907.541 .350 Com pact N=1222, Bridge N=255
***p<.001; **p<.01:*p<.05
Variabl es C ompact
TABLEII. DESCRIPTIVE STATISTICS(BRIDGE CAMERAS).
TABLEIII. EFFECTS OF FIRST ENTRY ON PRODUCT PERFORMANCE.
1 2 3 4 5 6 7 8 9 10 11
1. Cust omer evaluat ions 1.00 2. First-mover quart erly .018 1.00
3. Ent ry lag .236*** .530*** 1.00
4. Number of megapixels .313*** -.014 .104* 1.00
5. Sensor size .219*** -.091 -.053 .635*** 1.00
6. Indust ry product densit y .130* .310*** .414*** .458*** .176** 1.00 7. Market size -.270*** -.054 -.532*** -.459*** -.201** -.555*** 1.00
8. Firm tenure .124* .367*** .536*** .384*** -.011 .473*** -.382*** 1.00
9. Firm age -.152** .069 .134* -.284*** -.238*** -.145* .147** .071** 1.00
10. Firm size -.065 -.050 -.170** .223*** .170** .192** -.005 -.225*** -.384*** 1.00
11. Sensor t ype .189** .221*** .367*** .371*** .298*** .585*** -.375*** .286*** -.122* .213*** 1.00
Mean 4.174 1.89 1015.46 16.165 212.209 86.58 977.813 17.45 86.45 48350.270 .757
St andard deviat ion .337 .308 658.934 6.420 235.163 37.815 341.084 3.764 16.228 52275.673 .430 Com pact N=1222, Bridge N=255
***p<.001; **p<.01:*p<.05
Variabl es Bridge
DV: Cu stomer e valuations
Inde pe nde nt variable s Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Early ent rants quarterly -.162*** -.080
First-mover - laggards -.157** -.025
Fast follower -laggards -.071* .056
First-mover - fast follower -.086 -.080
Entry lag 4.964E-5* 9.470E-5** 1.02E-4** 1.44E-4** 1.67E-4 1.46E-4**
Number of megapixels .022*** .022*** .026*** .020*** .019*** .020***
Sensor size .003** .003* .003* -2.872E-5 -2.673E-5 -1.827E-5
Industry product density -.001*** -.001*** -.483E-4*** -.001 -.001 -.001
Market size 2.16E-4*** 2.13E-4*** 2.10E-4 -3.772E-5 -5.952E-6 -1.601E-5
Firm tenure -.016** -.015** -.019*** -.016* -.016* -.017*
Firm age -.003*** -.003*** -.003*** -.003* -.003* -.003*
Firm size 1.445E-8 2.015E-8 -2.812E-7 -1.23E-6** -1.218E-6** -1.179E-6**
Sensor type .178*** .157*** .152*** .068 .067 .062
F-value 18.002 17.661 15.882 6.904 6.307 5.771
R2 11.8*** 12.7*** 12.7*** 20.2*** 20.5*** 20.7***
***p<.001; **p<.01:*p<.05 Compact N=1222, Bridge N=255
Bridge Compact
