Design Orientation and New Product Performance

Innovation and Strategy

ISBN: 978-1-78754-829-9, eISBN: 978-1-78754-828-2

ISSN: 1548-6435

Publication date: 3 July 2018

Abstract

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Citation

Srinivasan, R. and Lilien, G. (2018), "Design Orientation and New Product Performance", Innovation and Strategy (Review of Marketing Research, Vol. 15), Emerald Publishing Limited, pp. 229-253. https://doi.org/10.1108/S1548-643520180000015011

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Emerald Publishing Limited

Copyright © 2018 Emerald Publishing Limited


Firms take different approaches in the new product development domain to achieve a competitive advantage, usually relying either on a market-pull or a technology-push approach (Dosi, 1992). The market-pull approach (Lilien, Morrison, Searls, Sonnack, & von Hippel, 2002; Urban & von Hippel, 1988) views the innovation process as driven by the firm’s customer orientation, developing products that respond to the explicit needs of the firm’s customer base (Atuahene-Gima, Slater, & Olson, 2005; Sinkula & Baker, 2005). The technology-push approach (Abernathy & Clark, 1985), driven by the firm’s technological orientation (Gatignon & Xuereb, 1997), focuses on the development of new technologies and products with superior functionality. Both the market-pull and the technology-push approaches have been shown to be effective in developing successful new products (Atuahene-Gima et al., 2005; Gatignon & Xuereb, 1997).

An alternative way to develop new products is the design-driven approach. Drawing on evidence from Italian firms such as Alessi and Artemide, Verganti (2008) proposes that the products of firms following the design-driven approach are driven neither by the needs articulated neither by their customers nor by new technology developments. Rather, those products emerged because of the firms’ ability to integrate functionality, aesthetics (styling including appearance, sound, touch, smell, and feel), and meaning into products that make the product more relevant to customers and produce a superior customer experiences, implementing what Srinivasan, Lilien, Rangaswamy, Pingitore, and Seldin (2012) termed the total product design concept.

Swatch watches, launched in 1983, are an illustration of the total product design concept in action, achieved through a design-driven approach. Swatch’s adoption of a new product line characterized by plastic materials, colorful styles, and lower prices positioned watches as fashion accessories (Glasmeier, 1991). Swatch watches entailed neither market-pull (users did not articulate a need for watches as fashion accessories) nor technology-push (no new technology).

In this chapter, we conceptualize design orientation, as a firm’s ability to implement the total product design concept, that is, to integrate functionality, aesthetics, and meaning in its products (Srinivasan et al., 2012). We note that this conceptualization of the total product design concept is consistent with the framework proposed by other scholars in the literature (e.g., Park, Jaworski, & Maclnnis, 1986 who propose functionality, experiential, and symbolic attributes of brands). We develop and test a model that relates the firm’s design orientation, along with other firm-level capabilities to new product performance.

Using data from a survey of senior marketing executives in 252 strategic business units of US firms in the goods sector, we validate the construct of design orientation and establish its distinctiveness from related constructs of creativity, technological orientation, and customer orientation. Using a structural equation modeling approach, we test the proposed model and both find support for it and find that the findings are robust to alternative samples, model specifications, and measures.

We find that a firm’s design orientation, customer orientation, and technological orientation each individually improve new product performance. Customer orientation and design orientation interact negatively while willingness to cannibalize (Chandy & Tellis, 1998) and design orientation interact positively in their effect on new product performance. Technological orientation does not interact with design orientation to affect new product performance. Marginal effects analyses uncover an unexplored opportunity – most managers (69%) perceive that their firm can improve new product performance by increasing their design orientation.

In the next section, we present the theory and hypotheses. Following that, we describe the method, data, hypotheses tests, and analyses that examine the robustness of the findings. We conclude by discussing the chapter’s theoretical and managerial contributions, limitations, and opportunities for further research.

Theory

Design Orientation

Strategy theorists describe how firms build competitive advantage by developing superior capabilities (Wernerfelt, 1984), which include skills reflected in organizational culture and processes that enable firms to develop competencies (Day, 1994; Leonard-Barton, 1992). Employing these ideas, we view design orientation as a capability reflecting the firm’s view that it can draw on its internal capabilities to develop design-driven products that address customer needs that may be, as yet, latent or unarticulated. An example of such a firm is Apple under Steve Jobs: “We built [the Mac] for ourselves. We were the group of people who were going to judge whether it was great or not” (Sheff, 1985). As Isaacson (2011) notes in his biography, what Jobs had in mind was functionality combined with superior aesthetics that had emotional resonance with the target consumers.

New Product Performance

Developments in the strategy literature indicate that different capabilities entail distinctive cultures, behaviors, resources, processes, and outcomes. Thus, when capabilities co-occur, there may be interactions that either improve or harm outcomes (Burton, Lauridsen, & Obel, 2002; Venkatraman & Camillus, 1984). Hence, we propose interactions between design orientation (design-driven) and (1) customer orientation (i.e., market-pull), (2) technological orientation (i.e., technology-push), and (3) willingness to cannibalize, on new product performance. We denote the proposed relationships in Fig. 1.

Fig. 1. 
Conceptual Model.

Fig. 1.

Conceptual Model.

Design orientation. As a firm’s design orientation increases, it may begin to consider itself the arbiter of taste, perhaps downplaying or even disregarding inputs from customers (Wessel, 2012). For example, Jonathan Ive, Apple’s Senior Vice President, noted: “Our goal isn’t to make money. It sounds a little flippant, but it’s the truth. Our goal and what makes us excited is to make great products. If we are successful, people will like them and if we are operationally competent, we will make money” (Solon, 2012). This quote suggests that as a firm’s design orientation increases, it may introduce products that do not appeal to consumers, which may hurt new product performance.

Other evidence indicates that, all else equal, superior product design increases customers’ preferences and satisfaction (Creusen & Schoormans, 2005; Srinivasan et al., 2012) as well as sales, profits, and stock returns (Hertenstein, Platt, & Veryzer, 2005). Moreover, a firm’s design orientation may be a rare and inimitable capability, which enables it to develop new products that are well differentiated and superior to competitive products in the marketplace. All of these arguments suggests a positive effect of design orientation on new product performance. Thus, we propose H1:

H1:

The higher a firm’s design orientation, the higher its new product performance.

Design orientation and customer orientation. Customer orientation is a capability driven by beliefs that put the customers’ interests first. A customer-oriented firm creates superior value for customers by developing offerings that meet the articulated needs of their customers (Deshpandé, Farley, & Webster, 1993, Jaworski & Kohli, 1993; Narver & Slater, 1990). Customer orientation enables the firm to learn about the needs of its customers, which are used to develop new products. Thus, greater customer orientation provides superior market insights leading to new products addressing customers’ articulated needs and improved product performance (Atuahene-Gima et al., 2005; Sinkula & Baker, 2005). Our interest here is in the interaction effect between the firm’s design orientation and customer orientation on new product performance, which we discuss next.

When a customer-oriented firm also has high design orientation, there will be an emphasis on incorporating the expressed needs of its customers in its products. Trying to satisfy those expressed needs may impede such a firm’s ability to effectively integrate functionality, aesthetics, and meaning in its products. Market-pull and design-driven approaches are characterized by different processes and metrics. For example, in the market-pull approach, there is likely to be extensive pretesting of new products with the firm’s customers. In such pretesting, customers may reject design-driven products which do not appear to fit their current needs, calling for redevelopment efforts. Hence, design orientation and customer orientation may result in a conflict between the design-driven and market-pull approach to new product development. As a result, product development in such firms may be driven in different directions and the resulting new products may not benefit from the advantages of either approach. Thus, we propose H2:

H2:

The higher a firm’s customer orientation, the weaker the positive effect of design orientation on its new product performance.

Design orientation and technological orientation. Technology-oriented firms invest in research and development, are proactive in acquiring new technologies, and use them in building new products with superior functionality (Gatignon & Xuereb, 1997). Such firms can use their technical knowledge to build solutions to meet the needs of their customers. The performance of new products in such firms is linked to the development of new technologies.

Technologically oriented firms may be willing to explore radical aesthetics and meanings (enabled by high design orientation) to showcase new products’ superior functionalities powered (Geels, 2004). As A. G. Lafley, Chief Executive Office, Procter and Gamble, noted (2004), “Design can unlock the technological performance we build into a product and help the consumer see it, touch it. Good design is serious business.” Thus, when technologically oriented firms also have high design orientation, they will integrate the high functionality of their products from their new technologies with related superior aesthetics and meaning, which should result in superior new product performance. Thus, we propose H3:

H3:

The higher a firm’s technological orientation, the stronger the positive effect of design orientation on new product performance.

Design orientation and willingness to cannibalize. Willingness to cannibalize is the extent to which a firm is prepared to reduce the value of existing investments to explore new directions (Chandy & Tellis, 1998). It is a trait of the firm and resides in its culture. Firms that are willing to cannibalize their existing products are more likely to introduce radical innovations. The question of interest to us is the interaction effect between a firm’s design orientation and its willingness to cannibalize on its new product performance.

Firms with high design orientation are sensitive to the importance of effective integration of functionality, aesthetics, and meaning in products, which can result in new products with radical new meanings (Verganti, 2008). Such products with radical meaning may undermine the firm’s current investments in existing product platforms, brands, and channels (Christensen, 1997). Hence, as with radical innovations (radical on technology and customer benefits), we anticipate that the firm’s willingness to cannibalize may be synergistic with the firm’s design orientation so that they may be viewed as strategic complements, resulting in a joint positive effect on new product performance. Thus, we propose H4:

H4:

The higher a firm’s willingness to cannibalize, the stronger the positive effect of design orientation on new product performance.

Method

We view a firm’s design orientation as a capability, and capabilities are embedded in the organization’s culture, systems, and processes (Leonard-Barton, 1992; Teece, Pisano, & Shuen, 1997). A firm’s culture is best articulated through its senior management, suggesting a primary data collection strategy as most appropriate to test our hypotheses. In order to attempt to reduce functional bias in response (Kumar, Stern, & Anderson, 1993), we sought a single, identical senior management function across firms. As new product performance connects the firm to the marketplace, we chose senior marketing executives as the most appropriate class of respondents.

We engaged with six senior marketing and management academics with expertise in the product development area to develop our measures. We pre-tested the measures with six senior managers and after refinement, administered a pre-test of the survey to 30 managers. We then made final adjustments, resulting in the scale items listed in Appendix A.

We sought senior marketing executives as key informant-respondents from a cross-industry sample of firms. We focused on firms in the goods sector and excluded services (e.g., banking, insurance) whose intangible product characteristics differ from those of tangible goods. We drew our sample from amongst the business school alumni of large public university in the Southwest part of the United States (n = 1,752) and from the professional membership lists of two leading design and product development organizations (n = 307) in the US goods sector for a total sample size of 2,059. The unit of analysis within the firm is the strategic business unit (SBU). As an incentive for completing the survey, we offered respondents a Kindle book on product design (The Future Design of Things by Donald Norman) and a copy of the study’s findings.

The surveys were fielded online using Qualtrics online survey software. Of those fielded, 105 emails were returned because of incorrect addresses, 60 emails were returned with out-of-office responses, and 40 managers returned the surveys because they believed that they were not qualified to respond, yielding 1,795 surveys that we believe reached a potentially qualified respondent. We received 252 completed surveys, yielding an effective response rate of 14%. 1

In Panel A of Table 1, we provide the profiles of respondents; most (64%) were at the level of Director and above. In Panel B of Table 1, we provide the profiles of sample firms by industry, sales, and number of employees.

Table 1.
Panel A: Profile of Respondents in Sample
Title of Respondent Number of Respondents Percentage
Manager 90 36
Director 85 34
General manager 12 5
Vice president 28 11
Senior vice president 22 9
Chief executive officer/founder 15 6
Total 252 100
Panel B: Profile of Sample Firms
Number Percentage
Industry groups
Computers 17 7
Consumer electronics 13 5
Construction 11 4
Consumer products 54 21
Food and beverage 14 6
High technology 46 18
Industrial products 42 17
Pharmaceuticals 9 4
Software 24 10
Other 22 9
Total 252 100
Sales
≤$100 million 86 34
$101–499 million 36 14
$500–999 million 22 9
$1–4.99 billion 38 15
≥$5 billion 70 28
Total 252 100
Employees
<100 63 25
101–1,000 61 24
1,001–10,000 49 19
10,000–100,000 46 18
101,000–200,000 20 8
>200,000 13 5
Total 252 100

Measures

We developed new measures for design orientation and adapted existing scales for the other measures. To reduce response burden, we used a subset of scales from past research. All measures (reported in Appendix A) were Likert-type scales anchored at 1 (strongly disagree) and 7 (strongly agree).

We measured new product performance covering the commercial performance of the products in the market using a three-item scale adapted from Griffin and Page (1996).

As capabilities include culture, processes, and behaviors (Leonard-Barton, 1992; Teece et al., 1997), we measured design orientation using five items covering the firm’s culture, processes, and behaviors pertaining to the integration of functionality, aesthetics, and meaning in its products.

We measured customer orientation using a four-item scale adapted from Deshpandé and Farley (1998). For technological orientation, we used a three-item scale adapted from Gatignon and Xuereb (1997); for willingness to cannibalize we used a three-item scale adapted from Chandy and Tellis (1998). We also measured creativity, using a three-item scale adapted from Amabile (1988), to establish the discriminant validity of design orientation.

The reliabilities of the various measures were high and ranged from 0.734 to 0.945. The dependent variable, new product performance measure, displayed good variability (mean = 8.255, standard deviation = 3.229, range = 3–20 (scale range: 3–21), which we attempt to explain in our model.

To assess the validity of the measures, we conducted two additional analyses. First, we conducted exploratory factor analyses of the constructs in our model; the loadings were higher than 0.70 for all the scale items. Second, we assessed the validity of the constructs by including all independent and dependent latent variables in one confirmatory factor analysis model with the free correlations among the factors (Bagozzi, Yi, & Phillips, 1991). The model fit the data well: comparative fit index (CFI) = 0.921; Tucker Lewis Index (TLI) = 0.929; root mean square error of approximation (RMSEA) = 0.082; standardized root mean square residual (SRMR) = 0.070.

Because we use a key informant, we examined possible threats from common method bias. First, we used Harman’s one-factor test (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) and found no evidence of common method bias. Second, we had a second informant for 21 of the 252 firms, a sample too small for a formal multitrait, multimethod assessment. However, t-tests of the differences between the two informants’ reports were not statistically significant (K = key informant and S = second informant): design orientation (K = 14.15, S = 14.24, ns), technological orientation (K = 9.40, S = 9.21, ns), customer orientation (K = 10.29, S = 10.14, ns), willingness to cannibalize (K = 10.66, S = 10.72, ns), and new product performance (K = 8.26, S = 8.30, ns). Thus, the tests do not identify any threats to the validity of the results from the mono-method source of the measures.

Table 2 provides the descriptive statistics and correlations of the constructs. The correlations between design orientation and customer orientation, technological orientation and willingness to cannibalize are positive and significant, suggesting that they co-occur in firms.

Table 2.

Descriptive Statistics and Correlation Matrix.

Construct Mean (Std. Deviation) 1. 2. 3. 4. 5.
Min–Max
1. New product performance 8.255 (3.229) 1.000
3–20
2. Design orientation 14.147 (7.132) 0.517 1.000
5–35
3. Technological orientation 9.401 (4.361) 0.659 0.426 1.000
3–21
4. Customer orientation 10.290 (4.509) 0.474 0.453 0.391 1.000
4–28
5. Willingness to cannibalize 10.655 (3.991) 0.510 0.472 0.441 0.512 1.000
3–21

All correlations above 0.15 significant at p < 0.05 and correlations above 0.20 significant at p < 0.01.

Validity of Design Orientation

Reliability and Discriminant Validity. Cronbach’s alpha for the five-item design orientation scale was 0.945 indicating high reliability. The design orientation scale exhibited good variance, ranging from 5 to 35, covering the full range of the scale with a mean of 14.1 (standard deviation = 7.1). We examined the discriminant validity of design orientation from the related constructs of customer orientation, technological orientation, and creativity. The factor loadings were large and significant (p < 0.01). The factor loadings for design orientation ranged from 0.842 to 0.905. There was a good fit of the model to the data (CFI = 0.926, TLI = 0.91, RMSEA = 0.090, SRMR = 0.070). We then examined the convergent and discriminant validity of these four constructs (Fornell & Larcker, 1981). The composite reliability (CR) and average variance extracted (AVE) (design orientation: CR = 0.943, AVE = 0.769; customer orientation: CR = 0.865, AVE = 0.500; technological orientation: CR = .893, AVE = 0.738; creativity: CR = 0.926, AVE = 0.806) indicate internal consistency and convergent validity. Regarding discriminant validity, the 95% confidence intervals of the correlations between the constructs were well below 1.00 and their AVEs exceeded the squared correlations between them. Thus, design orientation is distinct from customer orientation, technological orientation, and creativity.

Nomological Validity. To establish nomological validity, we assessed the degree to which a construct behaves as it should within a system of related constructs called a nomological net (Peter, 1981). The firm’s product design history should be positively related to its design orientation, which, in turn, should be positively related to product design quality. Thus, we estimated a path model relating the firm’s product design history (two-item scale, see Appendix A) to design orientation, which, in turn, is related to product design quality (a two-item scale also in Appendix A). The data fit the model well (CFI = 0.936, TLI = 0.893, SRMR = 0.071, RMSEA = 0.083) and product design history was related to design orientation (b = 0.765, p < 0.01), which, in turn, was related to product design quality (b = 0.637, p < 0.01). Overall, these results provide evidence of nomological validity for design orientation.

Results

Model-Free Evidence: Bivariate Relationships

We examine the relationships between design orientation and (1) customer orientation, (2) technological orientation, and (3) willingness to cannibalize (median splits) and new product performance. In Fig. 2, we report the bivariate relationship between design orientation, customer orientation, and new product performance. High customer orientation is positively related to new product performance when design orientation is low (6.12–8.36, p < 0.01) but not when it is high (9.42–10.07, ns). High design orientation positively related to new product performance when customer orientation is both low (6.12–9.42, p < 0.01) and high (8.36–10.07, p < 0.05). As the firm’s design orientation increases and customer orientation decreases, new product performance increases (8.36–9.42, p < 0.10).

Fig. 2. 
Design Orientation (DO), Customer Orientation (CO), and New Product Performance. Notes: Customer orientation is positively related to new product performance, when design orientation is low but not when it is high. However, design orientation is positively related to new product performance when customer orientation is both low and high.

Fig. 2.

Design Orientation (DO), Customer Orientation (CO), and New Product Performance. Notes: Customer orientation is positively related to new product performance, when design orientation is low but not when it is high. However, design orientation is positively related to new product performance when customer orientation is both low and high.

We get similar results (provided in Appendix B: Figs. B1–B2) for the bivariate analysis of design orientation and (1) technological orientation and (2) willingness to cannibalize and new product performance. The model-free evidence suggests that (1) design orientation, customer orientation, and technological orientation each increases new product performance and (2) there may be interactions between design orientation and (a) customer orientation, (b) technological orientation, and (c) willingness to cannibalize on new product performance.

Estimation Results

Our conceptual model proposes both main and interaction effects of the explanatory variables on new product performance (Fig. 1). We estimate the model using the full-information maximum likelihood approach (Muthén, 2010). The estimation approach does not provide the traditional fit statistics, such as RMSEA and CFI commonly used in path models. Following Muthén (2010), we assessed model fit by estimating a model with only main effects (baseline model) which we compared with the hypothesized model with the interaction effects using the associated log-likelihoods. We provide the results of this baseline model in Column 1 of Table 3. We also include the following control variables in the baseline model: creativity, firm size, a scaled measure of the total number of employees, competitive intensity, a three-item measure adapted from Jaworski and Kohli (1993) and industry design intensity.

Table 3.

Design Orientation and New Product Performance.

Variables Column 1 Column 2 Column 3
Baseline Model Model for Hypotheses Testing (N = 252) B2B Sample (N = 193)
Parameter Estimate (std. error) Parameter Estimate (std. error) Parameter Estimate (std. error)
Design orientation (H1) 0.120 (0.072)* 0.200 (0.101)** 0.136 (0.070)*
Design orientation × Customer orientation (H2) −0.136 (0.055)** −0.122 (0.060)**
Design orientation × Technological orientation (H3) 0.024 (0.037) 0.007 (0.041)
Design orientation × Willingness to cannibalize (H4) 0.172 (0.084)** 0.165 (0.098)*
Technological orientation 0.356 (0.097)*** 0.183 (0.082)** 0.360 (0.099)***
Customer orientation 0.095 (0.102) 0.200 (0.101)** 0.150 (0.117)
Willingness to cannibalize 0.144 (0.164) 0.050 (0.145) 0.146 (0.159)
Creativity 0.124 (0.122)** 0.132 (0.108) 0.119 (0.108)
Size −0.007 (0.052) −0.034 (0.038) 0.001 (0.042)
Competitive intensity 0.006 (0.069) −0.014 (0.072) −0.001 (0.071)
Industry design intensity 0.025 (0.056) 0.019 (0.052) 0.031 (0.056)
Model fit
Log-likelihood −10,020.019 −13,027.199 −10,017.096
Akaike’s Information Criterion (AIC) 26,326.399 20,306.037 20,343.193
Bayesian Information Criterion (BIC) 26,805.861 20,739.284 20,749.212
Sample-size adjusted BIC 26,374.725 20,317.981 20,328.406

***denotes p < 0.01, **p < 0.05 and *p < 0.10.

To test the hypotheses, we estimate a model that includes the main effects of explanatory variables, the control variables, and the related interaction effects on new product performance. We get similar results without the inclusion of the control variables. We report the results of this estimation in Column 2 of Table 3. A likelihood ratio test of the baseline model (Column 1) versus the model with interaction effects (Column 2) yields a χ 2 difference of 445.784 (degrees of freedom = 4, p < 0.01) supporting the hypothesized model.

We find that as hypothesized in H1, design orientation (b = 0.200, p < 0.05) increases new product performance. With regard to its joint effect with customer orientation on new product performance, we find that as hypothesized in H2, the higher the customer orientation, the lower the positive effect of design orientation on new product performance (b = −0.136, p < 0.05). (Note that the empirical support for the negative interaction effect between customer orientation and design orientation offers further evidence that shared variance from the mono-method data is not a threat.)

We find no support for H3, the joint effect of technological orientation and design orientation on new product performance (b = 0.024, ns). However, as hypothesized in H4, the higher the firm’s willingness to cannibalize, the higher the positive effect of design orientation on new product performance (b = 0.172, p < 0.05).

With respect to the main effects of the key explanatory variables, both technological orientation (b = 0.183, p < 0.05) and customer orientation (b = 0.200, p < 0.05) increase new product performance, while, willingness to cannibalize (b = 0.050, ns) has no main effect. The control variable of creativity (b = 0.132, ns), firm size (b = −0.034, ns), competitive intensity (b = −0.014, ns), and industry design intensity (b = 0.019, ns) have no effect on new product performance. We performed additional analysis which examines the validity of the results.

Robustness Analyses

Multigroup Analysis. There are many differences between business-to-business (B2B) and business-to-consumer (B2C) firms (Grewal & Lilien, 2012) that suggest differences in the impact of design orientation on new product performance. To assess this possibility, we re-estimated the path model using only the data on B2B firms (n = 193; the B2C sample was too small to estimate the full model).

We report the results in Column 3 of Table 3. Consistent with the full sample (Column 2 of Table 3), the results support the main effect (H1) of design orientation (b = 0.136, p < 0.10), the interaction effect (H2) between design orientation and customer orientation (b = −0.122, p < 0.05) and the interaction effect (H4) between design orientation and willingness to cannibalize (b = 0.165, p < 0.10) on new product performance. Hence, the results are robust to samples of B2B versus B2C firms.

Validity of Measures. As we used key informants, we do not have objective data for the measures. Hence, we examine the validity of the subjective measures for product design quality and business performance for those firms for whom we are able to get objective data from secondary sources. Of the respondents, 155 provided the name of their firm, an optional response item. For those firms, we used the Factiva database to collect the number of product design awards they had won over the past five years and correlated that number with the two-item measure of history of product design quality obtained in the survey. The correlation was 0.674, (p < 0.01), providing support for the subjective product design quality measure. In addition, we collected a two-item subjective measure of the firm’s business performance in the survey (Performance relative to competitors, performance relative to last year (1 – much worse than, 7 – much better than) (alpha = 0.680). For the 28 publicly listed firms, we correlated the subjective measure of business performance with the equivalent objective performance measure of return on assets (Fiscal year 2011) adjusted by industry (4-digit Standard Industry Classification code) from Standard and Poor’s Compustat database. The correlation was 0.652, (p < 0.01), providing support for the subjective measure of relative performance. These results support the validity of the information provided by the key informants.

Multiple Respondents. We have multiple respondents for 21 sample firms. We used the more senior of the respondents in the sample of 252 firms which we used for hypotheses testing (Column 2 of Table 3). We examined the robustness of our hypotheses tests using two alternative approaches. First, we averaged the responses of multiple respondents and then re-estimated the model as if we had only a single response from the firms. Second, we selected a random respondent and re-estimated the model. The results in both approaches (not reported here) are consistent with those in Column 2 of Table 3, reinforcing our confidence in the responses of our key informants.

Marginal Effects Analysis

To quantify the effects of moving from a firm’s status quo, we use the model estimated in Column 2 of Table 3 to obtain firm-specific marginal effects of design orientation and customer orientation on new product performance. As each marginal effect combines estimates of the main and interaction effects (Table 3), it is a multivariate function of random variables. We use the delta method (Mantrala, Prasad, Sridhar, & Thorson, 2007) to obtain the standard error and confidence intervals of the firm-specific marginal effects. We obtained the upper and lower limits of a 95% confidence interval for each marginal effect to assess whether it was positive and statistically significant, negative and statistically significant, or not significantly different from zero. The marginal effect of an increase in design orientation and associated statistical significance (negative, positive or not different from zero) indicates whether the firm has over-, under- or at about the right level of design orientation with respect to its effect on new product performance. We performed the same analysis for customer orientation. We summarize the marginal effects of design orientation and customer orientation for the sample firms in Panels A and B in Fig. 3.

Fig. 3. 
Marginal Effects Analysis. Panel A: Design Orientation and New Product Performance. Panel B: Customer Orientation and New Product Performance. Note: In Panel A, % of sample firms that can benefit (i.e., improve their new product performance) by (1) increasing their design orientation (69%), (2) with no change (28%), and (3) decreasing their design orientation (3%).

Fig. 3.

Marginal Effects Analysis. Panel A: Design Orientation and New Product Performance. Panel B: Customer Orientation and New Product Performance. Note: In Panel A, % of sample firms that can benefit (i.e., improve their new product performance) by (1) increasing their design orientation (69%), (2) with no change (28%), and (3) decreasing their design orientation (3%).

From Panel A in Fig. 3, we see that managers of most firms (69%) perceive that their firm can benefit (i.e., improve their new product performance) by increasing their design orientation. Managers in 28% of firms perceive their design orientation to be about right (i.e., will neither benefit nor be hurt by an increase or decrease) and managers in only 3% of firms perceive that their firms will benefit by decreasing their design orientation.

These three groups of firms in Panel A in Fig. 3 do not differ on firm size, industry design intensity, and competitive intensity. However, firms that are about right on design orientation and those that can benefit from a decrease in design orientation (relative to those which can benefit from an increase in design orientation) have higher design orientation (p < 0.01), are more customer-oriented (p < 0.01), and more willing to cannibalize (p < 0.01).

From Panel B of Fig. 3, we see that managers in most firms (95%) perceive that their firms will benefit (i.e., their new product performance will improve) by decreasing customer orientation and managers in 5% of firms perceive that their firms’ new product performance will not change by increasing customer orientation.

Discussion

As technological capabilities become more universally available, firms are exploring other venues when seeking a competitive advantage. One such venue is that of product design. Although practitioners have acknowledged the importance of design as a strategic marketing issue (Nussbaum, 2006), there is little in the academic literature that aids in understanding the role of how firms can benefit from building capabilities in the design domain, the issue we focus on in this chapter.

The findings on the domain and distinctiveness of design orientation indicate that the capability to develop design-driven products is distinct from what is needed for products driven by market insights or technology. The findings support a contingency-based model of new product performance rewards to firms’ design orientation, dependent on their customer orientation and willingness to cannibalize. The marginal effects analyses uncover an unexplored opportunity – most firms’ managers (69%) perceive that their firm can improve new product performance by increasing their design orientation. Managers in only 28% of firms consider that they are at about the “right” level of their design orientation (i.e., they cannot benefit from an increase) with respect to new product performance.

Managerial Implications

Note that most firms in our sample report did not report high levels of new product performance (mean = 8.255, standard deviation = 3.229, minimum = 3, maximum = 20; scale range = 3–21). Given the importance of new products in improving business performance, our findings should be useful for firms looking to improve in this domain.

Our findings on the domain and distinctiveness of design orientation indicate that the capabilities to develop design-driven products and to develop new products in response to new technologies and articulated customers’ needs are distinct. For firms looking to improve new product performance, customer orientation and design orientation represent strategic substitutes, representing distinct mechanisms to new product performance. In contrast, design orientation and willingness to cannibalize reinforce one another to improve new product performance.

Managers can use their firms’ and competitors’ organizational characteristics to generate firm-specific estimates of the effectiveness of design orientation in new product development. Table 4 illustrates this approach for four firms in our sample. For Alpha Corp., a small B2B firm, the marginal effects of design orientation (5), customer orientation (16), and willingness to cannibalize (7) are 0.224 (p < 0.01), −0.529 (p < 0.01), and −0.690 (ns), respectively. For Beta Corp., a medium-sized, B2B firm, the marginal effects of design orientation (9), customer orientation (19), and willingness to cannibalize (15) are 0.390 (ns), −1.114 (p < 0.01), and −0.995 (ns), respectively. For Gamma Corp., a large B2C firm, the marginal effects of design orientation (30), customer orientation (9), and willingness to cannibalize (7) are 1.140 (p < 0.01), −4.970 (ns), and −1.148 (ns), respectively; for Delta Corp., a small B2C firm, the marginal effects of design orientation (33), customer orientation (28), and willingness to cannibalize are 0.333 (ns), −4.661, and 2.320 (p < 0.01), respectively.

Table 4.

Illustrative Application of Marginal Effects for Four Sample Firms.

Characteristic Alpha Beta Gamma Delta
Firm size Small (<$100 million) Medium ($101–499 million) Large (>$5 billion) Small (<$100 million)
Industry Business-to-business Business-to-business Business-to-consumer Business-to-consumer
New product performance (scale range: 3–20) 4 9 9 17
Design orientation (5–35) 5 9 35 33
Marginal effect of design orientation a 0.224*** 0.390 (ns) 1.140*** 0.333 (ns)
Customer orientation (4–28) 16 19 9 28
Marginal effect of customer orientation −0.529*** −1.114*** −4.970 (ns) −4.661 (ns)
Willingness to cannibalize (3–21) 7 15 7 21
Marginal effect of willingness to cannibalize −0.690 (ns) −0.995 (ns) −1.148 (ns) 2.320***
a

On new product performance.

Overall, these findings suggest that one size does not appear to fit all in assessing the tradeoffs between design orientation, customer orientation, and willingness to cannibalize on new product performance. Managers in Alpha Corp. and Gamma Corp. can increase their new product performance by decreasing their design orientation, while managers in Alpha Corp. and Beta Corp. can improve their new product performance by decreasing customer orientation. Managers in Delta Corp. will not improve their new product performance by increasing either design orientation or customer orientation, but can do so by increasing their willingness to cannibalize.

Design vendors can use our findings to educate their (customer) firms that combining a market-pull approach based on their customer orientation with a design-driven approach fueled by their design orientation hurts new product performance.

Theoretical Contributions

To supplement the two dominant product development approaches of market-pull (driven by customer orientation) and technology-push (driven by technological orientation) in business practice and in the literature, we propose and validate a third firm-level approach (design orientation) to developing new products. Using a cross-industry sample of firms, we measure it reliably, and demonstrate its empirical distinctiveness from customer orientation, technological orientation, and creativity.

We also develop and provide support for hypotheses concerning the interaction between this new construct and three established constructs, customer orientation, technology orientation, and willingness to cannibalize. Our findings show that firms with design orientation can achieve superior new product performance in the absence of technological orientation and/or without customer orientation. Moreover, while customer orientation and design orientation working independently improve new product performance, working together, they emerge as strategic substitutes, being less beneficial for new product performance. Further, the lack of support for the interaction effect between design orientation and technological orientation suggests that represent distinct, independent routes to new product performance.

Limitations and Opportunities for Further Research

Our research has limitations that qualify our findings and present opportunities for further research. Our cross-sectional design limits our ability to study the effects of changes in the key constructs or in the environment on design orientation and its effects over time. (e.g., Are the rewards to design orientation sustainable?) Further research could use a longitudinal methodology to capture the time-dependent dynamics of the new product development process.

In this study, we focused on firms in the goods sector. Future work that examines service firms where there is potential to improve the design of service experiences would be useful. Further, as the unit of analysis in this research is the SBU, we did not relate design orientation to financial performance (e.g., profits, stock returns). Future research using secondary data of publicly listed firms relating their design orientation to financial performance would be a useful extension to this work.

In summary, we believe that both practitioners and scholars will find the design orientation construct useful and that more research remains to be done to refine and extend the construct, explore its drivers, and quantify its impact on organizational outcomes.

Note

1

To assess threats from non-response bias (Armstrong & Overton, 1977), we examined and found no difference in key variables between late (last 25%) and early (first 25%) respondents, (L = late, E = early): Design orientation (L = 14.10, E = 14.14, not significant [ns]), technological orientation (L = 9.30, E = 9.42, ns), customer orientation (L = 10.18, E = 10.34, ns), willingness to cannibalize (L = 10.55, E = 10.75, ns), and new product performance (L = 8.26, E = 8.30, ns).

References

Abernathy & Clark (1985) Abernathy, W. , & Clark, K. B. (1985). Mapping the winds of creative destruction. Research Policy, 14, 322.

Amabile (1988) Amabile, T. M. (1988). A model of creativity and innovation in organizations. In B. M. Staw & L. L. Cummings (Eds.), Research in organizational behavior (Vol. 10). Greenwich, CT: JAI Press.

Armstrong & Overton (1977) Armstrong, J. S. , & Overton, T. S. (1977). Estimating non-response bias in mail surveys. Journal of Marketing Research, 143, 396402.

Atuahene-Gima, Slater, & Olson (2005) Atuahene-Gima, K. , Slater, S. F. , & Olson, E. M. (2005). The contingent value of responsive and proactive market orientations for new product program performance. Journal of Product Innovation Management, 226, 464482.

Bagozzi, Yi, & Phillips (1991) Bagozzi, R. P. , Yi, Y. , & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 363, 421458.

Burton, Lauridsen, & Obel (2002) Burton, R. M. , Lauridsen, J. , & Obel, B. (2002). Return on assets/loss from situational and contingency misfits. Management Science, 4811, 14611485.

Chandy & Tellis (1998) Chandy, R. , & Tellis, G. (1998). Organizing for radical product innovation: The overlooked role of willingness to cannibalize. Journal of Marketing Research, 35(November), 474488.

Christensen (1997) Christensen, C. M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Boston, MA: Harvard Business School Press.

Creusen & Schoormans (2005) Creusen, M. E. H. , & Schoormans, J. P. L. (2005). The different roles of product appearance in consumer choice. Journal of Product Innovation Management, 22(1), 6381.

Day (1994) Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(October), 3752.

Deshpandé & Farley (1998) Deshpandé, R. , & Farley, J. U. (1998). Measuring market orientation: Generalization and synthesis. Journal of Market-Focused Management, 23, 213232.

Deshpandé, Farley, & Webster (1993) Deshpandé, R. , Farley, J. U. , & Webster, F. E., Jr. (1993). Corporate culture, customer orientation, and innovativeness in Japanese firms: A quadrad analysis. Journal of Marketing, 57(January), 2337.

Dosi (1992) Dosi, G. (1992). Research on innovation diffusion: An assessment. In A. Grubler & N. Nakicenovic (Eds.), Innovation diffusion and social behaviors. New York, NY: Springer Verlag.

Fornell & Larcker (1981) Fornell, C. , & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(February), 3950.

Gatignon & Xuereb (1997) Gatignon, H. , & Xuereb, J.-M. (1997). Strategic orientation of the firm and new product performance. Journal of Marketing Research, 34(February), 7790.

Geels (2004) Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33, 897920.

Glasmeier (1991) Glasmeier, A. (1991). Technological discontinuities and flexible production networks: The case of Switzerland and the world watch industry. Research Policy, 20, 469485.

Green, Carroll, & Goldberg (1981) Green, P. , Carroll, J. , & Goldberg, S. (1981). A general approach to product design optimization via conjoint analysis. Journal of Marketing, 43(Summer), 1735.

Grewal & Lilien (2012) Grewal, R. , & Lilien, G. L. (2012). Business-to-business marketing: Looking back, looking forward. In G. L. Lilien & R. Grewal (Eds.), Handbook of business-to-business marketing (pp. 314). Northhampton: Edward Elgar Press.

Griffin & Page (1996) Griffin, A. , & Page, A. L. (1996). PDMA success measurement project: Recommended measures for product development success and failure. Journal of Product Innovation Management, 13(V), 478496.

Hertenstein, Platt, & Veryzer (2005) Hertenstein, J. H. , Platt, M. B. , & Veryzer, R. W. (2005). The impact of industrial design effectiveness on corporate financial performance. Journal of Product Innovation Management, 22(1), 321.

Isaacson (2011) Isaacson, W. (2011). Steve Jobs. New York, NY: Simon and Shuster.

Jaworski & Kohli (1993) Jaworski, B. J. , & Kohli, A. K. (1993). Market orientation: Antecedents and consequences. Journal of Marketing, 57(3), 5370.

Kumar, Stern, & Anderson (1993) Kumar, N. , Stern, L. W. , & Anderson, J. C. (1993). Conducting inter-organizational research using key informants. The Academy of Management Journal, 36(6), 16331651.

Lafley (2004) Lafley, A. G. (2004). Delivering delight. Fast Talk, 83(June), 51.

Leonard-Barton (1992) Leonard-Barton, D. (1992). Core capabilities and core rigidities: A paradox in managing new product development. Strategic Management Journal, 13(Summer), 111125.

Lilien, Morrison, Searls, Sonnack, & von Hippel (2002) Lilien, G. L. , Morrison, P. D. , Searls, K. , Sonnack, M. , & von Hippel, E. (2002). Performance assessment of the lead user idea-generation process for new product development. Management Science, 48(8), 10421059.

Mantrala, Prasad, Sridhar, & Thorson (2007) Mantrala, M. K. , Prasad, N. A. , Sridhar, S. , & Thorson, E. (2007). Uphill or downhill? Locating your firm on a profit function. Journal of Marketing (April), 2644.

Muthén (2010) Muthén, B. O. (2010). Mplus discussion – Comment on how to evaluate the fit of a SEM with interaction, October 26, 2010. Retrieved from http://www.statmodel.com/discussion/messages/11/385.html?1309276920. Accessed on June 1, 2014.

Narver & Slater (1990) Narver, J. C. , & Slater, S. F. (1990). The effect of a market orientation on business profitability. Journal of Marketing, 54(October), 2035.

Nussbaum (2006) Nussbaum, B. (2006). The best product design of 2006. Business Week, July 10, 3992, 7478.

Park, Jaworski, & Maclnnis (1986) Park, C. W. , Jaworski, B. J. , & Maclnnis, D. J. (1986). Strategic brand concept-image management. Journal of Marketing, 50(October), 135145.

Peter (1981) Peter, J. P. (1981). Construct validity: A review of basic issues and marketing practices. Journal of Marketing Research, 18(May), 133145.

Podsakoff, MacKenzie, Lee, & Podsakoff (2003) Podsakoff, P. M. , MacKenzie, S. B. , Lee, J. Y. , & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 885, 879903.

Sheff (1985) Sheff, D. (1985). Playboy interview: Steven Jobs. Retrieved from http://www.playboy.com/magazine/playboy-interview-steve-jobs. Accessed on June 12, 2014.

Sinkula & Baker (2005) Sinkula, J. M. , & Baker, W. E. (2005). Market orientation and the new product paradox. Journal of Product Innovation Management, 22(6), 483502.

Solon (2012) Solon, O. (2012). Jonathan Ives: Apple’s Goal is not to make money. Retrieved from http://www.wired.co.uk/news/archive/2012-07/30/jonathan-ive-revenue-good-design. Accessed on February 1, 2014.

Srinivasan, Lilien, & Rangaswamy (2002) Srinivasan, R. , Lilien, G. L. , & Rangaswamy, A. (2002). Technological opportunism and radical technology adoption: An application to e-business. Journal of Marketing, 663, 4760.

Srinivasan, Lilien, Rangaswamy, Pingitore, & Seldin (2012) Srinivasan, R. , Lilien, G. L. , Rangaswamy, A. , Pingitore, G. M. , & Seldin, D. M. (2012). Total product design concept: An application to the US automotive industry. Journal of Product Innovation Management, 29(S1), 320.

Teece, Pisano, & Shuen (1997) Teece, D. J. , Pisano, G. , & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509533.

Urban & von Hippel (1988) Urban, G. I. , & von Hippel, E. (1988). Lead user analyses for the development of new industrial products. Management Science, 345, 569582.

Venkatraman & Camillus (1984) Venkatraman, N. , & Camillus, J. C. (1984). Exploring the concept of fit in strategic management. Academy of Management Review, 9(3), 513525.

Verganti (2008) Verganti, R. (2008). Design, meanings, and radical innovation: A metamodel and a research agenda. Journal of Product Innovation Management, 25(5), 436456.

Wernerfelt (1984) Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171180.

Wessel (2012) Wessel, M. (2012). New product narcissism. Retrieved from http://blogs.hbr.org/cs/2012/10/new_product_narcissism.html. Accessed on February 28, 2014.

Yamamoto & Lambert (1994) Yamamoto, M. , & Lambert, D. R. (1994). The impact of product aesthetics on the evaluation of industrial products. Journal of Product Innovation Management, 1129, 309324.

Appendix A: Measures of Multi-Item Scales

New product performance (α = 0.802) (adapted from Griffin & Page, 1996)

  1. We introduce new products that face high consumer acceptance

  2. We are very successful in our new product introductions

  3. Our products are very successful in the marketplace

Design orientation (α = 0.945) (new scale)

  1. We strongly value products that integrate functionality, aesthetics, and brand meaning

  2. We recognize that integrating functionality, aesthetics, and brand meaning is critical in our product development process

  3. Our product developers understand the inter-relationships between a product’s functionality, aesthetics, and brand meaning

  4. Our product development processes enable the seamless integration of functionality, aesthetics, and brand meaning in our products

  5. We integrate functionality, aesthetics, and brand meaning in our products early in the product development cycle.

Customer orientation (α = 0.854) (adapted from Deshpandé & Farley, 1998)

  1. Our business objectives are driven primarily by customer satisfaction

  2. We constantly monitor our commitment to serving our customers’ needs

  3. Our strategy for competitive advantage is based on our understanding of customers’ needs

  4. We are more customer-focused than our competitors

Technological orientation (α = 0.888) (adapted from Gatignon & Xuereb, 1997)

  1. Our company uses sophisticated technologies in new products

  2. We excel at developing new technologies for our products

  3. Our organizational environment is conducive to the development of new technologies

Willingness to cannibalize (α = 0.734) (adapted from Chandy & Tellis, 1998)

  1. We support projects even if they could potentially take away from sales of existing products

  2. We can easily change established procedures to develop a new product

  3. We frequently pursue a new technology even if it destroys the value of our existing investments

Industry design intensity (α = 0.811) (new scale)

  1. Many companies in our industry rely on product design as a source of competitive advantage

  2. Superior product design is expected from companies in our industry

  3. The products in our industry are very distinctive in design

Creativity (α = 0.921) (adapted from Amabile, 1990)

  1. There is a great deal of originality in our new products

  2. Many of our products are very creative

  3. Our products are more novel than those of our competitors

Product design history (α = 0.913) (new scale)

  1. We have an established tradition of well-designed products

  2. We have a history of superior product designs

Product design quality recognition (α = 0.721) (new scale)

  1. Our consumers frequently comment on the superior designs of our products

  2. Many of our products have won product design awards.

Business performance (α = 0.856)

Overall performance of the company

  1. Compared to last year

  2. Compared to our major competitors

Notes: The unit of analysis was the SBU. All items were scored using a seven-point scale, where 1 corresponds to “strongly disagree” and 7 to “strongly agree.”

Appendix B

Figs. B1–B2

Fig. B1. 
Bivariate Analysis of Design Orientation (DO), Technological Orientation (TO), and New Product Performance.

Fig. B1.

Bivariate Analysis of Design Orientation (DO), Technological Orientation (TO), and New Product Performance.

Fig. B2. 
Bivariate Analysis of Design Orientation (DO), Willingness to Cannibalize (WTC), and New Product Performance.

Fig. B2.

Bivariate Analysis of Design Orientation (DO), Willingness to Cannibalize (WTC), and New Product Performance.

Appendix A: Measures of Multi-Item Scales

New product performance (α = 0.802) (adapted from Griffin & Page, 1996)

  1. We introduce new products that face high consumer acceptance

  2. We are very successful in our new product introductions

  3. Our products are very successful in the marketplace

Design orientation (α = 0.945) (new scale)

  1. We strongly value products that integrate functionality, aesthetics, and brand meaning

  2. We recognize that integrating functionality, aesthetics, and brand meaning is critical in our product development process

  3. Our product developers understand the inter-relationships between a product’s functionality, aesthetics, and brand meaning

  4. Our product development processes enable the seamless integration of functionality, aesthetics, and brand meaning in our products

  5. We integrate functionality, aesthetics, and brand meaning in our products early in the product development cycle.

Customer orientation (α = 0.854) (adapted from Deshpandé & Farley, 1998)

  1. Our business objectives are driven primarily by customer satisfaction

  2. We constantly monitor our commitment to serving our customers’ needs

  3. Our strategy for competitive advantage is based on our understanding of customers’ needs

  4. We are more customer-focused than our competitors

Technological orientation (α = 0.888) (adapted from Gatignon & Xuereb, 1997)

  1. Our company uses sophisticated technologies in new products

  2. We excel at developing new technologies for our products

  3. Our organizational environment is conducive to the development of new technologies

Willingness to cannibalize (α = 0.734) (adapted from Chandy & Tellis, 1998)

  1. We support projects even if they could potentially take away from sales of existing products

  2. We can easily change established procedures to develop a new product

  3. We frequently pursue a new technology even if it destroys the value of our existing investments

Industry design intensity (α = 0.811) (new scale)

  1. Many companies in our industry rely on product design as a source of competitive advantage

  2. Superior product design is expected from companies in our industry

  3. The products in our industry are very distinctive in design

Creativity (α = 0.921) (adapted from Amabile, 1990)

  1. There is a great deal of originality in our new products

  2. Many of our products are very creative

  3. Our products are more novel than those of our competitors

Product design history (α = 0.913) (new scale)

  1. We have an established tradition of well-designed products

  2. We have a history of superior product designs

Product design quality recognition (α = 0.721) (new scale)

  1. Our consumers frequently comment on the superior designs of our products

  2. Many of our products have won product design awards.

Business performance (α = 0.856)

Overall performance of the company

  1. Compared to last year

  2. Compared to our major competitors

Notes: The unit of analysis was the SBU. All items were scored using a seven-point scale, where 1 corresponds to “strongly disagree” and 7 to “strongly agree.”

Appendix B

Figs. B1–B2

Fig. B1. 
Bivariate Analysis of Design Orientation (DO), Technological Orientation (TO), and New Product Performance.

Fig. B1.

Bivariate Analysis of Design Orientation (DO), Technological Orientation (TO), and New Product Performance.

Fig. B2. 
Bivariate Analysis of Design Orientation (DO), Willingness to Cannibalize (WTC), and New Product Performance.

Fig. B2.

Bivariate Analysis of Design Orientation (DO), Willingness to Cannibalize (WTC), and New Product Performance.

Acknowledgments

The authors thank the Institute for the Study of Business Markets for funding support and Sandeep Arora, Ethan Burris, Craig Crossland, Frank Germann, Abbie Griffin, Praveen Kopalle, Luis Martins, Violina Rindova, Debika Sihi, Johanna Slot, Hari Sridhar for feedback on earlier versions of the manuscript and George Day, Don Lehmann, Christian Schulze, Christophe Van den Bulte, and seminar participants at the Rotterdam School of Management for their feedback on the research.