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Master's Thesis
Competition, product line length, and firm survival
Min Jeong
Department of Management Engineering
Graduate School of UNIST
2017
Min Jeong
Department of Management Engineering
Graduate School of UNIST
Competition, product line length, and firm survival
ABSTRACT
Adjusting product line length is one of the major strategies that firms employ to sustain their market position in competitive environments. This study extends existing studies on product line strategies by adding empirical results on both the relationship between competitive intensity and product line length and the performance of firms following the suggested product line strategy. The analysis of data on 1,849 printer products introduced by 342 manufacturers from 1983 to 2002 shows an inverted U-shaped relationship between
competitive intensity and product line length, and that firms following such a strategy have a significantly higher survival rate in the market. Our findings might shed light on conflicting theoretical viewpoints on and empirical examinations of the relationship between product line strategies and firm performance.
Keywords: product line length; competitive intensity; product line strategies; firm survival;
U.S. printer industry
Contents
1. Introduction --- 1
2. Theoretical background --- 3
2.1 Determinants of product line length --- 3
2.2 Consequences of product line extension --- 4
3. Hypotheses --- 5
3.1 Competition and product line strategies --- 5
3.2 The benefits of product line strategies considering competitive intensity --- 8
4. Methods --- 10
4.1 Sample --- 10
4.2 Statistical analysis --- 12
4.3 Measures --- 13
5. Results --- 17
6. Discussion and Conclusion --- 19
List of Figures & Tables
Figure 1. Number of firms, exits, and entries --- 11
Table 1. Summary statistics and correlations between variables --- 16
Table 2. Results of negative binomial regressions on the product line length --- 20
Table 3. Results of Cox proportional hazard model --- 21
1 Introduction
Considering new product introduction rate and a wide diversity of products today, the fact that many firms establish product line strategies is evident in a lot of industries. Given its prevalence in practice, studying the antecedents and consequences of a firm’s product line strategies will provide meaningful contributions to the industries. In the strategy field, it is believed that product line strategies in a product market affects a firm’s performance and its very survival, making product line length one of the most important strategic weapons for confronting market competition. Prior literature has stated that firms gain several benefits from extending their product line, such as acquiring diverse customer base (Bayus and Putsis Jr 1999), keeping customer loyalty (Klemperer 1995), experimenting product configurations (Dowell 2006), deterring new competitors’ entry (Bordley 2003) and economies of scope (Zahavi and Lavie 2013). Although firms can be more profitable by extending their product lines, an optimal length of a firm’s product line exists because of product self-cannibalization (Axarloglou 2008), greater overlap with competitors’ product line (Gourville and Moon 2004), high inventory costs (Bayus and Putsis Jr 1999), greater distribution and marketing costs (Aaker 2000), and diseconomies of scale (Putsis Jr 1997).
Based on these opposite theoretical views, prior literature has studied the antecedents and outcomes of product line strategies. The first stream of literature includes studies that focus on the determinants of the length of firms’ product lines, such as market share, price, market growth rate, firm age, and industry concentration (Bayus and Putsis Jr 1999;
Draganska and Jain 2005; Giachetti and Dagnino 2014; Putsis Jr and Bayus 2001; Wilson and Norton 1989). The second stream of literature includes studies that analyze the impact of product line extension on firm performance, considering profitability, market share, price, and firm survival (Barroso and Giarrataana 2013; Dowell 2006; Giarratana and Fosfuri 2007;
Kekre and Srinivasan 1990). However, the previous literature has shown inconsistent
findings regarding the outcomes of the product line extension. We thus posit that the benefits of product line extension are contingent on the level of external change. We believe that competitive intensity in a market is one of the major factors that firms should consider when they adjust their product line length. Consequently, in this paper we attempt to identify the relationship between product line extension and firm performance considering a contingency,
2 the level of competitive intensity in the market.
In this paper, we firstly investigate how firms respond to competitive pressure by adjusting their product line length. The degree to which a firm senses market competition is one of the crucial determinants of its product line strategies (Bayus and Putsis Jr 1999).
Particularly, in a recent study using data on the mobile phone industry, Giachetti and Dagnino (2014) empirically show an inverted U-shaped relationship between competitive intensity and product line length, while earlier research suggests a linear relationship between them (Bayus and Putsis Jr 1999; Putsis Jr and Bayus 2001). Because Giachetti and Dagnino (2014) used data on the mobile phone industry, the generalizability issue is raised. As they mention in their limitation section, mobile phone vendors extensively outsource many activities to third parties, and thus, the determination of product line strategies is likely to be dependent on the level of vertical integration which was not controlled in their analysis (Rothaermel, Hitt, and Jobe 2006). Hence, we aim to confirm the relationship between competitive intensity and product line length. More importantly, although those studies empirically test how firms respond to competitive intensity with their product line strategy but does not provide whether the way firms respond to competitive intensity with such a strategy affects firm performance. Thus, we secondly investigate whether a firm’s response to competitive intensity through its product line strategies really leads to its survival in the industry. We depart from prior work on outcomes of product line extension by considering the contingency that affect the degree to which a firm can get a positive outcome from product line strategies.
To investigate this research issue, we apply our model to 342 U.S. printer
manufacturers with 1,849 products from 1983 to 2002. Using a negative binomial regression to address the first issue and a survival analysis to examine the second issue, we find that firms tend to lengthen their product line length initially and then shorten it as competitive intensity increases and that the firms that follow this product line strategy have a higher chance of survival than those that do not. Our results indicate that product managers need to consider competitive intensity in the market when they make decisions regarding product line adjustments because this leads to their ultimate survival. Our findings might link the
literatures on determinants of a firm’s product line strategy and literatures on consequences of the strategy. Furthermore, they might shed light on conflicting theoretical viewpoints on
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and empirical examinations of the relationship between product line extension and firm performance.
Theoretical Background
Determinants of product line length
Previous research has identified a number of determinants of product line length.
Some researchers suggest market environment factors, such as the market structure (Shugan 1989), market growth rate (Bayus and Putsis Jr 1999; Putsis Jr and Bayus 2001), market demand (Axarloglou 2008), industry concentration (Putsis Jr and Bayus 2001), and competitive intensity (Giachetti and Dagnino 2014). Other researchers provide internal factors such as the firm size (Gatignon et al. 1989), firm age (Putsis Jr and Bayus 2001), ability of a firm to price above marginal costs (Bayus and Putsis Jr 1999), possible product overlap among models (Wilson and Norton 1989), and product line adjustment costs (Putsis Jr 1997). Finally, some researchers show factors related to a firm’s competitive position, such as the market share (Bayus and Putsis Jr 1999; Putsis Jr and Bayus 2001), competitors’ new product introductions (Stern and Henderson 2004), competitors’ product line length (Bayus and Putsis Jr 1999), relative technological age of the firm’s product line (Putsis Jr and Bayus 2001), and market leadership (Shankar 2006). There are two main streams of studies on this topic. The first stream of studies focuses on the factors that affect the optimal size of a product line and propose the significance of the factors. The second stream of studies investigates the trade-offs among factors that influence how firms adjust product line length and suggest the optimal length of firms’ product line.
Consequences of product line extension
Product line extension is a strategic choice with both benefits and costs. As for benefits achieved by product line extension, firms can meet the needs of various consumers and thus acquire a diverse customer base (Bayus and Putsis Jr 1999; Kekre and Srinivasan 1990), attract consumers who are willing to pay more for a high-quality product (Shankar 2006), keep consumers loyal to them (Klemperer 1995), deter new product introductions of a
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competitor (Bordley 2003), identify the product configurations desired by consumers (Dowell 2006), generate demand synergies by offering ‘one-stop shopping’ (Giarratana and Fosfuri 2007; Ye et al. 2012), and achieve economies of scale and scope (Tanriverdi and Lee 2008;
Zahavi and Lavie 2013). However, product line extension also entails costs that could have a negative effect on firm performance. The costs include control and coordination costs (Jones and Hill 1988), self-cannibalization (Axarloglou 2008), greater inventory, distribution and promotion costs (Aaker 2000), learning traps (Stern and Henderson 2004), costs involved with cognitive management capabilities (Simon 1991), and loss of scale economies and production efficiencies (Putsis Jr 1997; Quelch and Kenny 1994). These are the main benefits and costs that firms could encounter when they lengthen their product lines.
Considering a complex interaction between the benefits and costs, researchers have studied the relationship between product line extension and firm performance. Exploring diverse aspects of firm performance such as profitability (Barroso and Giarratana 2013;
Kekre and Srinivasan 1990; Stern and Henderson 2004; Zahavi and Lavie 2013), market share (Draganska and Jain 2005; Kekre and Srinivasan 1990; Tanriverdi and Lee 2008), and firm survival (Dowell 2006; Giarratana and Fosfuri 2007; Kato 2008), researchers have shown inconsistent results on the relationship between them. Some authors found that product line length has a positive effect on performance (Barroso and Giarratana 2013;
Dowell 2006; Giarratana and Fosfuri 2007; Kato 2008; Kekre and Srinivasan 1990;
Tanriverdi and Lee 2008); others found that product line length has a negative effect on performance (Axarloglou 2008; Romanelli 1989). Zahavi and Lavie (2013) showed that product line length has a U-shaped relationship with performance, while Draganska and Jain (2005) showed that product line length has an inverted U-shaped relationship with
performance. Stern and Henderson (2004) found that that there is no relationship between product line length and performance.
Hypotheses
Competition and product line strategies
Competitive dynamics research has defined competition as the exchange of
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competitive moves between firms in a market; i.e., when a firm initiates an action, its competitor initiates a response (Porter 1980). The action can be defined as a specific competitive move initiated by a firm to improve or defend its relative competitive position, and the response can be defined as an observed and discerned counteraction taken by a competing firm (Chen, Smith, and Grimm 1992). Likewise, firms’ product line strategies can be considered as competitive moves and can be very effective in coping with changing competition (Bayus and Putsis Jr 1999). When firms face competitive pressure from competitors, they might respond with a number of product line strategies: lengthen their product lines by introducing new products, maintain their existing product lines and spend resources on promoting and distributing products in those product lines, shorten their product lines to focus on a limited number of products and spend resources on the qualitative
improvement of their technologies to break new ground, or exit the market (Boone 2000;
Draganska and Jain 2005; Giachetti and Dagnino 2014; Lancaster 1990; Putsis Jr and Bayus 2001; Shankar 2006).
As competition increases, incumbents are likely to defend their profitability and market shares from the attacks of other incumbents and potential new entrants because they have already invested resources into their current operations (Ferrier et al. 2002). Incumbents might respond to increasing competitive intensity by lengthening their current product lines.
Incumbents already have customers; thus, they can effectively lengthen their current product lines to deal with competitors’ attacks. By offering a wide variety of products, incumbents can keep customers loyal to their brand and prevent them from switching to competitors (Klemperer 1995). Moreover, long product lines may deter competitors from introducing new products (Bordley 2003). Newcomers must attack incumbents and deprive them of their current market share and profitability. Lengthening product lines allows newcomers to probe the niche markets and determine product configurations that customers desire but have not yet received without using substantial resources (Dowell 2006; Schot and Geels 2008).
Under conditions of low to moderate levels of competition, lengthening product lines typically represents a useful and effective strategy in responding to competitive pressures, while at high levels of competition, it might not be useful or effective. If firms continuously lengthen their product lines as competitive intensity increases and becomes relatively high, they are likely to face difficulties in product line coordination, which reduces their internal
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consistency and leads to slow decision-making speed (Jones 2003). In a highly competitive environment, a firm’s capability to rapidly and flexibly cope with competitive challenges is very important (Draganska and Jain 2005). Thus, firms are likely to shorten their product lines and renounce the benefits of product variants when they face high levels of competition to respond rapidly and flexibly to dynamic competitive challenges from their rivals.
From an industry evolution perspective, market competition is one of the main factors affecting firms’ product line strategies. According to industry life cycle theory, after a
dominant design emerges in the industry, competitive intensity increases over time until a new technological opportunity appears and thus generates discontinuity (Klepper 1996;
Peltoniemi 2011). Under conditions of low to moderate levels of competition, firms that have confidence in potential market share gains and profits enter and exit the market frequently.
All firms have an opportunity to achieve market dominance in this period because of high market uncertainty; thus, they may attempt to have long product lines. At the very start, firms must identify the criteria that drive customer acceptance and the importance of the different criteria in the market (Henderson and Clark 1990). In this sense, introducing and providing various products allows firms to benefit from probing the market and to determine product configurations that will drive the majority of demand (Dowell 2006; Draganska and Jain 2005).
A very high level of competition is achieved over a period of time that market uncertainty has been resolved and product standardization has been established. In this period, firms tend to focus on core product models within the line and invest in process innovation rather than product innovation because the benefits of high product variants normally cannot exceed losses resulting from them. Hence, firms’ product line length initially increases and then decreases as competitive intensity increases. Overall, we assume that the competitive intensity of the overall market will have a positive relationship with product line length up to a moderate competitive intensity level but will have a negative relationship when competitive intensity is extremely high. This logic leads to the following hypothesis:
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Hypothesis 1: There is an inverted U-shaped relationship between competitive intensity and the length of a firm’s product line.
The benefits of product line strategies considering competitive intensity
We hypothesize that firms are likely to choose to lengthen their product line under conditions of low to moderate competitive intensity and shorten their product line when competitive intensity is moderate to high. This hypothesis explores how firms respond to competitive intensity by adjusting their product line length but does not directly consider the implications of firms’ product line strategies based on competitive intensity for their
performance. A number of studies on product line strategies have found inconsistent results about the relationship between product line length and firm performance. In order to have better understanding of the relationship, the contingency that affect the degree to which a firm can get a positive outcome from product line strategies should be taken into
consideration. We posit that firms’ product line strategies are likely to be affected by certain circumstances such as competitive pressure; thus, we aim to explore the relationship between product line length and performance considering an antecedent of the relationship,
competitive intensity.
We posit that in situations of low to moderate levels of competition, firms receive several benefits from extending their product lines. Product line extension enables firms to determine the product configurations most desired by customers (Dowell, 2006), to meet the needs of a diverse customer base and acquire different customer segments (Bayus and Putsis Jr 1999; Kekre and Srinivasan 1990), to attract consumers who are willing to pay more for a high-quality product (Shankar 2006), to keep customers loyal to the firms (Klemperer 1995), to deter competitors’ introduction of new products (Bordley 2003) and to achieve the benefits of economies of scope (Zahavi and Lavie 2013). These are the main benefits that firms can enjoy and lead to the firms’ larger market share and higher profitability. Hence, to cope with increasing competition, firms are likely to select the product line extension strategy.
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However, firms may decide not to lengthen their product lines continuously as competitive intensity increases. Basically, long product lines can lead firms to experience several coordination problems: 1) self-cannibalization, i.e., a firm’s newly introduced
products reduce the sales of existing products (Axarloglou 2008); 2) a high degree of product overlap with rivals caused by the overall increase in product models in the market (Gourville and Moon 2004); 3) greater inventory, distribution and promotion costs caused by complex product lines (Aaker 2000); and 4) loss of scale economies and production efficiencies that can be achieved through standardization (Putsis Jr 1997; Quelch and Kenny 1994). Up to moderate levels of competition, firms are likely to enjoy the benefits from product line extension mentioned above and attempt to manage these coordination problems because the benefits exceed the costs of managing the coordination problems. At very high levels of competition, the coordination problems become more difficult for firms to manage; thus, the costs of managing the coordination problems may exceed the benefits received from having long product lines. Moreover, if firms cannot manage these problems well, they might experience structural inertia. Thus, they are likely to die in the market because in highly competitive environments, firms need to rapidly and flexibly respond to dynamic competitive challenges to survive. Firms’ structural inertia could decrease the speed and flexibility of their decision making. Hence, at high levels of competition, firms rather shorten their product lines to address these problems.
Overall, we posit that firms lengthen their product line initially and then shorten it as competitive intensity increases and that firms that follow this product line strategy are more likely to survive than those that do not. This logic leads to the following hypothesis:
Hypothesis 2: Firms that follow a strategy in line with an inverted U-shaped
relationship between competitive intensity and the length of their product line are more likely to survive than those that do not.
9 Method
Sample
We tested Hypothesis 1 and Hypothesis 2 using a sample of firms from the U.S.
printer industry. Data on 1,849 printer models introduced by 342 printer manufacturers from 1983 to 2002 were collected from Data Sources1. Data Sources contains product-level information such as models’ technical, design and price information and manufacturing company profiles. Our sample comprises product models that each manufacturer sells, and we treat each model with a unique model name as a distinct product. Thus, we can identify when a model is introduced to and withdrawn from the industry. We also integrate financial and accounting information from Compustat to control for firm heterogeneity, such as R&D intensity, CAPEX intensity, debt to assets and sales.
The U.S. printer industry has shown large fluctuations in terms of the number of firms in the industry and a substantial amount of product launches and subsequent exits between 1983 and 2002. As Figure 1 illustrates, the number of printer manufacturers in U.S. market peaked at 290 in 1987 and it decreased over time. In 2002 the number firms in our sample remained at 65. 127 firms have entered in 1986 and 89 firms have exited in 1993. This pattern of US printer manufacturers illustrates a traditional industry evolution. Previous researchers have frequently used data from the printer industry to analyze product-level dynamics in competitive market situations (De Figueiredo and Kyle 2006). Therefore, the printer industry appears to be an appropriate setting in which to test product line strategies at different levels of competitive intensity. Additionally, because the entry and exit rates of firms in this industry are relatively high, a survival analysis is feasible.
1. Data Sources has been published by the Information Access Company, which has been part of The Gale Group since 1981. It is a comprehensive guide to available data processing/data communications equipment, software, and companies.
Vendors provide the information published through either completed surveys or technical manuals, and it is indexed to lead users to the product or company sought.
10 Figure 1. Number of firms, exits, and entries
Statistical analysis
Because this study aims to confirm the previous finding that market competition influences a firm’s product line strategy and to probe whether firms that follow a product strategy based on market competition survive longer, it consists of two models: 1) a negative binomial regression to test Hypothesis 1 and 2) a survival analysis to test Hypothesis 2. In terms of the first model, the integer and positive count dependent variable causes asymptotic bias and is inconsistent with the ordinary least squares (OLS) model (Greene 2003). A negative binomial regression was used in this study because of its power to handle the problem of overdispersion (Hausman, Hall, and Griliches 1984; Kogut and Chang 1991;
Sanders 2001). The model is rooted in a Poisson regression but is combined with a stochastic component to address the overdispersion issue (Hausman, Hall, and Griliches 1984). In addition, all independent and control variables are lagged by one year to control for
additional firm heterogeneity (Heckman and Borjas 1980). Moreover, in the second model, a survival analysis is conducted using the Cox proportional hazard model. The Cox
proportional hazard model describes the probability of the occurrence of a certain event (in this paper, exit) at a certain time under the condition of other variables. The hazard of exit per year for firm i in year t is
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ℎ𝑖𝑡 = 𝑓(𝑡) exp(𝛽′𝑥𝑖),
where ℎ𝑖𝑡 is the probability of exit in year t for firms that have not yet exited by year t, 𝑓(𝑡) is a function of calendar time that allows the hazard of exit to change as the industry evolves, 𝑥 is a vector of variables expected to shift the hazard of exit proportionally in each year, and 𝛽 is a vector of coefficients. The results of the first and second analyses are shown in Table 2 and Table 3, respectively.
Measures
Dependent variable
For the first analysis, product line length was measured as the number of product models of firm i at time t within s major submarket2 in the printer industry. For the second survival analysis, a dummy for a firm’s exit was used.
Independent variable
For the first test, we used the number of firms in the market at time t within s major submarket, and we took the natural logarithm of the number of firms. This measure has previously been used to calculate competitive pressure in the market. Porter (1980) measures competitive pressure as the number of firms and level of concentration. Boone (2000) uses the number of firms to study the effect of competitive pressure on product and process innovations. The recent study performed by Giachetti and Dagnino (2014) also adopts a composite measure by considering both the number of companies and their market shares in the calculation of competitive intensity. For the second test, we first identified firms
following the product line strategy suggested in the first analysis, a strategy in line with an inverted U shape. To categorize all firms, we regressed market competition on each firm’s
2. Our sample includes 9 major submarkets in the U.S. printer industry: serial printers, dot matrix printers, line printers, daisy wheel printers, laser/page printers, inkjet printers, thermal transfer printers, plotters, and special purpose printers.
12 product strategy using the following general form:
𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑙𝑖𝑛𝑒 𝑙𝑒𝑛𝑔𝑡ℎ𝑖 = 𝑏0+ 𝑏1 × 𝑀𝑎𝑟𝑘𝑒𝑡 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖+ 𝑏2 × 𝑀𝑎𝑟𝑘𝑒𝑡 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖2+ 𝑒 If a firm has a positive and significant 𝑏1 and a negative and significant 𝑏2, we can conclude that the firm adjusts its product line length based on market competition; specifically, its strategy takes an inverted U-shaped form. We created a dummy variable for product strategy followed that is coded 1 if a firm has a positive and significant coefficient on the non-squared market competition variable and a negative and significant coefficient on squared market competition and coded as 0 otherwise.
Control variables
Since rivals’ product line length directly affects a focal firm’s product line length (Bayus and Putsis Jr 1999), we included competitors’ product line length, measured as the average product line length of competitors – all firms but the focal firm – at time t within s major submarket. Extant research suggests that experience and entry timing in the certain market has relationship with product line strategies (Dowell 2006; Zahavi and Lavie 2013).
Years in operation were measured as the length of time in years since the company entered the printer industry. Because our data begin with 1983, we consider only those firms that entered after 1983. The number of submarkets entered was measured as the number of major submarket entries of firm i within the printer industry. Technological capability (Speed) was calculated as the normalized deviation of the difference between the product with the fastest speed and the industry mean speed at time t in the same submarket. The equation is as follows:
𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑐𝑎𝑝𝑎𝑏𝑖𝑙𝑖𝑡𝑦 (𝑆𝑝𝑒𝑒𝑑)𝑖,𝑡 = (max𝑝 𝑆𝑝𝑒𝑒𝑑𝑖,𝑗,𝑝,𝑡) −
1 𝑛∑ (max
𝑝 𝑆𝑝𝑒𝑒𝑑𝑗,𝑝,𝑡) 𝑖
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 , where i represents a firm, j is a major submarket, t is a year, and p is a product.
Empirical evidence suggests that a firm’s financial structure affects its product strategy (Phillips 1995). We also included three financial factors: Debt to assets, R&D intensity and CAPEX intensity. Debt to assets was measured as total debt over total assets.
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R&D intensity was measured as R&D expenditures over total assets. CAPEX intensity was measured as total capital spending over total assets. The sales variable was measured as the natural logarithm of the total sales of each firm in each year. For some observations, financial and accounting information were missing. To avoid potential sample selection bias due to excluded observations, we replaced the missing data with industry and year averages and added dummy variables with a value of one when the data were unavailable (Siegel and Simons 2010). The industry phase dummy recorded observations before 1987 as 1 and other observations as 0. The number of firms can be the same when the printer industry is in a growing phase or a declining phase. To control for additional properties of firm, we also included the origin of the company (Klepper and Simons 2000) and prior experience in the U.S. printer industry (Bayus and Agarwal 2007). The international company dummy was coded as 1 if a company is a non-U.S. firm and 0 otherwise. The exit dummy was coded as 1 if a firm exited the U.S. printer industry and 0 otherwise.
14 Table 1. Summary statistics and correlations between variables
VariableMeanS. D.(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) Product line 3.653.92
(2) No of firms*4.050.890.023(3) (No of firms*)217.226.600.0070.983(4) Product strategy followed0.070.260.067-0.049-0.055(5) Competitor’s product line length*4.451.760.2240.2260.198-0.086(6) Years spent*1.160.870.246-0.302-0.3420.0800.283(7) CAPEX intensity0.070.04-0.0220.0960.075-0.043-0.112-0.075 (8) No of submarkets entered*0.300.460.111-0.157-0.1730.1160.0220.256-0.007(9) Tech. capability (Speed) 0.321.44-0.0210.0220.0080.007-0.052-0.0810.086-0.045(10) R&D intensity0.040.040.126-0.417-0.473-0.0050.2560.529-0.0690.128-0.178(11) Debt to assets0.420.200.0230.1910.165-0.0430.1500.0790.2610.1220.0480.009
(12) Sales*1.812.370.154-0.218-0.244-0.0320.3780.371-0.0080.069-0.0730.3790.093(13) Exit dummy0.720.45-0.1280.1760.1960.107-0.297-0.3940.028-0.1500.080-0.308-0.034-0.343
(14) Int. company dummy0.110.310.1140.0550.0530.0680.0610.0830.071-0.091-0.0480.0550.0450.1770.059Note: N = 1,849 (342 firms during the 1983-2002 period)
* Natural logarithm
15 Results
Table 1 provides the means and standard deviations of and correlations between all the variables. From 1983 to 2002, the mean of the product line length in the U.S. printer industry is 3.65, and the maximum is 79 products. Only 7% of printer manufacturers follow the product strategy, which is to reduce the number of products as market competition increases. In a univariate analysis, the correlations between the variables are low to medium, except for the correlation between the number of competitors and its square. All the variance inflation factors (VIFs) are less than 10, which is the commonly accepted threshold indicating potential multicollinearity. The results thus suggest that our models are not likely to have multicollinearity problems.
Model 1 in Table 2 includes only the effect of the control variables on product line length. Model 2 includes the number of firms to assess the linear relationship, and Model 3 adds the number of firms squared to test the curvilinear relationship predicted in Hypothesis 1. In Model 4 and Model 5 in Table 3, we run the survival analysis using the Cox
proportional hazard model. Model 4 includes all control variables, and Model 5 adds a dummy variable for product strategy followed to investigate whether firms survive longer when they follow the suggested product strategy found in Hypothesis 1.
Our Hypothesis 1 posits a curvilinear (inverted U-shaped) relationship between the number of firms and the length of the focal firm’s product line, as Giachetti and Dagnino (2014) argue. Model 2 indicates a linear relationship between the number of firms and the length of the focal firm’s product line (β = 0.047, not significant). Model 3 shows a curvilinear relationship between these variables. The squared term of the number of firms shows a negative and significant coefficient (β = -0.114, p < 0.05) and the non-squared term shows a positive and significant coefficient (β = 0.885, p < 0.05), supporting Hypothesis 1.
Thus, firms have a longer product line when the number of firms in the market is moderate;
they have a shorter product line when the number of firms in the market is either small or large. This result confirms the finding of Giachetti and Dagnino (2014).
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In terms of control variables, the number of submarkets entered, representing a firm’s experience and capability, positively influences the length of the firm’s product line. Firms with a larger number of submarket entries have experienced the pursuit of diverse customer demands and have built capabilities to restructure resources to develop new products.
Additionally, non-U.S. firms tend to have a greater product line length compared with U.S.
firms. International firms experience greater information asymmetry in the U.S. market (Wagner 2006) and thus spend more effort in understanding the diverse range of customers in the U.S. market.
Regarding Hypothesis 2, Model 5 in Table 3 illustrates that the dummy variable for product strategy followed has a negative and significant coefficient (β = -0.361, p < 0.05).
When it transforms the hazard ratio, it is 0.692 with p < 0.05. This result implies that when a firm follows the product strategy suggested in Hypothesis 1, it has a 30.8% lower exit rate (1- 0.692 =0.308) compared with a firm that does not follow the strategy. Hence, the result supports our Hypothesis 2, which posits that firms following the product strategy survive longer than firms that do not.
In this analysis, the control variables also provide interesting insights. Firms with more experience in the printer industry show a higher exit hazard rate. Longer experience in the industry implies that firms obtain production experience, form external relationships, develop more technological competencies and establish and implement organizational routines that facilitate new product development (Sørensen and Stuart 2000; Tushman and Anderson 1986). However, an opposing view also exists. For example, Levitt and March (1988) suggest that older firms encounter a ‘competency trap’ as a result of their past success, and R&D areas of expertise succumb to inertial pressures that keep these firms from
researching new technological domains. Moreover, during our test period, firms experienced a technically volatile environment, and the innovation related to their products and
technology became an essential element of firm survival. However, firm age is negatively related to technical quality (Balasubramanian and Lee 2008), and firm age and innovation quality have a negative relationship in some industries (Sørensen and Stuart 2000). These results indicate that older firms’ technical quality and innovation quality are lower.
Therefore, older firms are more likely than younger firms to be forced out of the printer industry market.
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Table 2. Results of negative binomial regressions on the product line length
No of products
Model 1 Model 2 Model 3
No of firms 0.047 0.885*
(0.066) (0.346)
(No of firms)2 -0.114*
(0.047)
Intercept 0.180 0.087 -1.073†
(0.417) (0.458) (0.603)
Competitor’s product line length 0.009 0.009 -0.005
(0.018) (0.018) (0.019)
Years spent 0.085 0.092 0.087
(0.070) (0.073) (0.073)
CAPEX intensity -0.042 -0.091 -0.012
(0.904) (0.912) (0.922)
No of submarkets entered 0.292** 0.294** 0.288**
(0.103) (0.104) (0.103)
Tech. capability (Speed) -0.004 -0.001 -0.012
(0.022) (0.022) (0.024)
R&D intensity -1.066† -0.850 -1.316*
(0.631) (0.569) (0.566)
Debt to asset 0.093 0.089 0.082
(0.207) (0.203) -0.204)
Log (Sales) 0.001 0.003 0.006
(0.019) (0.019) (0.019)
Exit dummy -0.155 -0.160 -0.164
(0.128) (0.127) (0.127)
Intl. company dummy 0.361† 0.356† 0.359†
(0.206) (0.208) (0.207)
Log(alpha) -0.777*** -0.778*** -0.784***
(0.111) (0.111) (0.111)
Log pseudolikelihood -4534.5 -4534.2 -4530.7
Chi-square 246.6*** 249.2*** 265***
No. of firms 1,849 1,849 1,849
Note: N = 1,849 (342 firms during the 1983-2002 period) All independent and control variables are lagged by one year.
No-Data dummies for R&D intensity, CAPEX intensity, and Log (Sales) are included but not reported here.
Cluster-robust standard errors clustered by firms are presented in parentheses.
†p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001
18 Table 3. Results of Cox proportional hazard model
Cox proportional hazard
Time to exit Model 4 Model 5
Product strategy followed -0.361*
(0.166) Log (Product line length) -0.237*** -0.228**
(0.069) (0.071)
Years spent 0.395*** 0.451***
(0.081) (0.087)
Market competition -0.210 -0.133
(0.272) (0.293) Competitor’s product line length 0.061 0.046
(0.044) (0.046)
CAPEX intensity -0.104† -0.093†
(0.054) (0.055)
No of submarkets entered 2.148 2.429†
(1.417) (1.313) Tech. capability (Speed) -0.433* -0.388*
(0.169) (0.172)
R&D intensity -0.018 -0.021
(0.039) (0.044)
Debt to asset -6.123** -5.836*
(2.335) (2.428)
Log (Sales) -0.048 -0.096
(0.256) (0.275)
Intl. company dummy -0.090* -0.080†
(0.041) (0.042)
Log pseudolikelihood -1620.6 -1504.2
Chi-square 103.0*** 95.2***
No. of firms 342 342
No. of exits 316 296
Note: N = 1,849 (342 firms during the 1983-2002 period)
No-Data dummies for R&D intensity, CAPEX intensity, and Log (Sales) are included, but not reported here.
Cluster-robust standard errors clustered by firms are presented in parentheses.
†p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001
19 Discussion and conclusion
We tested two hypotheses: 1) the impacts of market competition on product line strategies and 2) the effect of such a strategy on firm survival. Our study empirically
confirms that competition has a strong effect on the length of firms’ product lines. Following a previous study conducted by Giachetti and Dagnino (2014), our findings show an inverted U-shaped relationship between competitive intensity and the length of a focal firm’s product line, unlike most previous tests, which showed a strictly linear relationship between these two constructs. In terms of the impacts on firm survival, we found that firms have higher survival rates when they adjust their product strategy based on market competition. Particularly, as the former finding suggested, firms following a strategy in line with a curvilinear relationship between market competition and product line length can survive longer.
In sum, our findings extend the literature on product line length in at least two ways.
First, we provide additional empirical evidence of the relationship between market
competition and product line length in the printer industry, as also shown by Giachetti and Dagnino (2014). Second, we empirically show that firms following the curvilinear product strategy based on market competition really obtain higher performance outcomes in terms of firm survival. In other words, because both benefits and costs of product line extension exist, firms develop their own optimal product line based on market competition, and such
strategies improve their survival. This study complements the product line extension literature with performance implications of product line strategies that is influenced by market competition.
This study also has several managerial implications for product managers and business-level executives. First, an inverted U-shaped relationship between competitive intensity and the length of a firm’s product line indicates that a very long product line is not an effective weapon to cope with moderate to high competitive intensity in terms of the number of firms in the market. Thus, across the moderate to high range of competitive intensity, one of the most important tasks that managers perform is overseeing an entire group of products in a product line and streamlining the product line. For instance, managers may decide to discard product models that lead to high competition between their models, hinder production efficiencies, or cause higher inventory, distribution and promotion costs considering coordination between product models. Second, the survival analysis shows that
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firms that adjust their product line length for competitive intensity are more likely to survive.
In light of this finding, managers should consider not only internal relationships between their own product models but external pressures on firm survival. According to the level of competition manager should decide what should be done to prevent the firm from being forced out of the market. From a low to moderate levels of competition, manager should focus on introducing a variety of products to achieve or maintain market dominance, while at very high levels of competition focus on allocating the resources not only to obtain higher profitability in the present but also to prepare for the next stages (e.g., R&D investment).
Although the results have several theoretical and managerial implications, our study also has certain limitations. First, this study is based on a single industry, the U.S. printer industry. Product line strategies in the printer industry are likely to be affected by some industry-specific variables that are missing from this study. Thus, our finding cannot be easily generalized to other industries. Future research might explore the link between competition and product line strategies and the performance implications of product line strategies affected by competition in different industry settings or multiple-industry settings.
Second, in our analysis, we utilize only the number of firms in the market as the proxy for competitive intensity. Competitive intensity can be measured in various ways such as the number of firms, concentration ratios, the Herfindahl index, or the indicator suggested by Giachetti and Dagnino (2014). Future research could measure competitive intensity more accurately using several proxies or combined indicators of proxies. Finally, the way in which product line length is defined and measured is a very important issue in the extant literature on product line length. For example, Dowell (2006) separates product line length from product line complexity and finds that each element impacts firm survival differently, and Barroso and Giarrataana (2013) distinguish between product breadth and product depth and find that they have different impact on firm margins. Future research could apply these conceptual frameworks to study the relationship between competition and product line strategies.
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