5 Customer participation and new product performance

Research Policy 47 (2018) 498–510
Contents lists available at ScienceDirect
Research Policy
journal homepage: www.elsevier.com/locate/respol
Customer participation and new product performance: Towards the
understanding of the mechanisms and key contingencies
T
⁎
Todd Morgana, , Michael Obalb, Sergey Anokhinc
a
Department of Management, Haworth College of Business, Western Michigan University, 600 Marion Ave., Kalamazoo, MI 49006, United States
Department of Marketing, Entrepreneurship and Innovation, Manning College of Business, University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854,
United States
c
Department of Marketing and Entrepreneurship, Kent State University, PO Box 5190, Kent, OH 44242, United States
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Customer participation
New product development
Absorptive capacity
Innovativeness
Co-creation
In a study of 243 firms of varying sizes across 14 different industries, we investigate the effect of customer
participation on new product development performance. We confirm that overall customer participation is
positively related to new product development performance and that the effect is mediated by innovativeness.
We also demonstrate that these effects are contingent upon absorptive capacity of the firm in question such that
firms with high absorptive capacity stand to gain more from engaging their customers in new product development than firms with low absorptive capacity, especially at the later stages of the NPD process. The results are
robust to alternative estimation techniques, measures employed to operationalize key concepts, and the industrial makeup of the sample. Post hoc analyses provide non-trivial managerial implications for the decision
makers at the firm level.
1. Introduction
Extant research has acknowledged that to ensure new product
success, customer participation in the new product development (NPD)
process is essential (Chang and Taylor, 2016). In fact, the overall idea
has run the gamut from a low-key, early insistence on “listening to the
voice of the customer” in search of unmet customer needs and solutions
(see, e.g., Griffin and Hauser, 1993), all the way to embracing customer
input in all stages of the NPD process. This evolution is reflected in a
plethora of concepts, such as crowdsourcing and open innovation, that
have truly blossomed over the last decade (e.g., Chesbrough and
Crowther, 2006; Enkel et al., 2009; Poetz and Schreier, 2012; Afuah
and Tucci, 2012; Cui and Wu, 2016). Recently, customer participation
itself has been considered “the extent to which the customer is involved
in the manufacturer’s NPD process” (Fang et al., 2008, p. 91) thus
postulating it as an integral part of the NPD sequence. Essentially,
customer participation is the integration of customers into firm activities where they share needs- and solution-related inputs into the firm’s
NPD processes that the firm may lack internally (Nambisan, 2002;
Poetz and Schreier, 2012; Chang and Taylor, 2016). This entails customer involvement in various NPD activities such as ideation, resource
inputs, knowledge exchange, and co-development (Fang, 2008; Chang
and Taylor, 2016).
⁎
While many studies enthusiastically proclaim the benefits of involving customers into the NPD process to achieve greater success via
reduction of costs (Auh et al., 2007), decision making improvement
(Griffin and Hauser, 1993), increased complementary knowledge and
resources (Coviello and Joseph, 2012), and enhanced new product innovativeness (Fang, 2008), a small number of studies suggest that there
may be negative aspects to integrating customers into the NPD process.
Previous research suggests that customer participation may lead to inefficient NPD processes and lower NPD performance (Chang and
Taylor, 2016). Potential reasons for this may be that customers can
sometimes be a limited source of innovation because they lack creative
ideas (Christensen, 1997), are unable to clearly articulate latent needs
(Franke et al., 2013), and increase the complexity for the focal firm
trying to manage internal and external knowledge for NPD (Hoyer
et al., 2010; Chang and Taylor, 2016).
This research contends that customer participation’s effectiveness in
regard to NPD performance and commercializing innovative new products is contingent on the absorptive capacity (ACAP) of the firm. ACAP
is a dynamic capability that can help utilize the firm’s knowledge
structure to acquire, transform, assimilate and exploit external knowledge and apply it to commercial ends (Zahra and George, 2002; Cohen
and Levinthal, 1990; Flatten et al., 2011). Key components to enhancing the success of customer participation include the ability of the firm
Corresponding author.
E-mail addresses: todd.morgan@wmich.edu (T. Morgan), Michael_obal@uml.edu (M. Obal), sanokhin@kent.edu (S. Anokhin).
https://doi.org/10.1016/j.respol.2018.01.005
Received 26 January 2017; Received in revised form 4 January 2018; Accepted 4 January 2018
0048-7333/ © 2018 Elsevier B.V. All rights reserved.
Research Policy 47 (2018) 498–510
T. Morgan et al.
Salter (2006) show that 66% of UK manufacturing firms in their sample
indicated that customers and clients were a source of knowledge or
information in their innovation processes (Foss et al., 2011). This tactic
of integrating end users in various stages of the development process –
as practiced by P&G, Unilever, and other firms both small and large –
can be described as customer participation (Fang et al., 2012). Fang
et al. (2008, p. 91) define customer participation as “the extent [italics
added] to which the customer is involved in the manufacturer’s NPD
process.” This definition aligns with the progressive user-involvement
in NPD ideology (Von Hippel, 2005; Schulze and Hoegl, 2008) as opposed to the arm’s length market orientation ideology of simply “listening in” to the customer (Urban and Hauser, 2004). Customer participation in NPD is the degree to which customers and firms create new
product value through ongoing interactions (Blazevic and Lievens,
2008). In contrast to market orientation, customer participation goes
beyond simply collecting and disseminating information gathered from
and about customers, then developing offerings around those customer
needs (Atuahene-Gima and Ko, 2001). Instead, customer participation
is closer to a partnership in which customers are integrated into some or
all NPD activities, including product design, business evaluation, team
formation, and concept screening (Fang et al., 2008). Research suggests
that products generated through customer participation will more closely meet customer needs than products generated solely internally
(Hoyer et al., 2010). Importantly, customer participation has been
found to positively impact NPD performance across multiple NPD
stages (Chang and Taylor, 2016; Troy et al., 2008). During ideation,
customers are an abundant source of new product ideas (Von Hippel,
1978) since they provide first hand solutions to the actual problems
they face (Yli-Renko and Janakiraman, 2008). In the development
stage, customers can provide greater access to important resources and
contacts (Coviello and Joseph, 2012). Customers also serve as effective
testing outlets during product testing and launch stages (Griffin and
Hauser, 1993). In essence, customer participation in NPD may help
create products that are less easily imitable, solve customer needs, and
decrease costs.
Additionally, customer participation has been suggested to impact
the innovativeness of new products, which we deem an important
mediating factor between customer participation and NPD performance. Customers bring in external knowledge and are not susceptible
to organizational inertia and oftentimes provide ‘outside-the-box’
thinking (Yli-Renko and Janakiraman, 2008), thus their ideas should be
more innovative (Chang and Taylor, 2016). Conversely, employee ideas
may be less innovative as they are more likely to rely on the firm’s
resource base and improvement of current product lines to avoid product cannibalization (Chandy and Tellis, 1998). This is essential as
highly innovative products can provide a firm with a differentiated
market position that less innovative products cannot, thus enabling
higher product performance (Kleinschmidt and Cooper, 1991; Rubera
and Kirca, 2012). Past literature has shown that innovative products
can be sold successfully based primarily on technological advantages
and uniqueness from competitive offerings (Avlonitis and Salavou,
2007). Thus, we posit that product innovativeness may be a mechanism
through which customer participation positively impacts product performance. When firms become overly embedded in their processes, they
fail to meet changing market demands (Atuahene-Gima and Ko, 2001)
that could be met by integrating customers into NPD (Coviello and
Joseph, 2012). Substantively, customer co-development will lead to
differentiated product attributes and increased product innovativeness,
thereby enhancing NPD performance. An overview of the previous literature on the relationships between customer participation, NPD
performance, and innovativeness can be seen in Table 1. As a foundation to our study, we postulate the following as baseline hypotheses:
to successfully acquire external knowledge and the ability of a firm’s
existing systems and capabilities to identify, assimilate and exploit external know-how (Huang and Rice, 2009; Foss et al., 2011). ACAP assists the firm in identifying more marketable external ideas, filtering
through information, redefining and reclassifying problems, and using
domain specific knowledge to implement new product solutions
(Chandy et al., 2006; West et al., 2014; Cohen and Levinthal, 1990;
Zahra and George, 2002; Robertson et al., 2012). Furthermore, ACAP
assists firms in transforming ideas into more novel and usable forms
that build upon current firm processes and capabilities (Cohen and
Levinthal, 1994; Lane et al., 2006), thus projecting a greater fit to
current and future customers. Substantively, we suggest that ACAP is a
key contingency to enhance customer participation’s impact on NPD
performance and innovative new products.
Our principal contribution is the acknowledgment of a key contingency affecting the effectiveness of customer participation in the
NPD process. Specifically, we suggest that the ability of new customers
to positively affect NPD performance depends on the ACAP of the focal
firm (Cohen and Levinthal, 1990). We suggest that when the focal firm
has greater levels of ACAP, its ability to source valuable input from
customers engaged in the NPD process is far more developed, which
manifests in better performance. That is, the direct effect of customer
involvement on performance should be higher when firms have a welldeveloped ACAP. In the same vein, the relationship between customer
involvement and new product innovativeness should be affected by the
focal firm’s ACAP. This makes ACAP a key concept in understanding the
relationship (both direct and indirect) between customer participation
in NPD and performance.
In line with previous research, our results show that customer participation does indeed impact NPD performance directly and indirectly
through new product innovativeness. In support of our principal contribution, the results also show that ACAP is a key contingency for firms
seeking to enhance NPD efforts through customer participation. ACAP
is shown to be a contingency for customer participation’s impact on
both NPD performance, defined here as “the degree to which a new
product is perceived to have achieved its market share, sales growth,
customer use, and profit objectives” (Atuahene-Gima and Ko, 2001, p.
58), and new product innovativeness.1
The paper proceeds as follows. In the next section, we provide a
brief overview of the customer participation literature and formulate
testable hypotheses that formalize its proposed direct, mediated, and
moderated effects on new product performance. This is followed by the
description of our empirical strategy including data sources, measures,
and methods. A section on results provides evidence in support of our
hypotheses, followed by a battery of robustness checks and a separate
post-hoc analysis section to provide additional insights for decision
makers. The paper concludes with a discussion of our results, their
implications for scholars and practitioners, identifies important limitations, and makes suggestions for future research.
2. Literature review and hypotheses development
2.1. Customer participation in new product development
Researchers argue that developing new products solely with internal
knowledge is no longer enough to retain or strengthen competitive
positions (Joshi and Sharma, 2004). As such, the emergent open innovation approach integrates external resources and stakeholders into a
firm’s innovation processes (Gassmann and Enkel, 2004). Laursen and
1
While previous customer participation literature has utilized primary data and subjective performance measures to examine relationships (see Table 1 for overview of
customer participation research), we understand that primary data presents a limitation
to this study in the form of potential common method bias and subjectivity in regard to
performance. We discuss these issues further in the method and limitations sections of the
paper.
H1. Customer participation in NPD is positively related to NPD
performance.
H2. New product innovativeness mediates the relationship between
499
500
Survey, subjective; quantitative
Survey, subjective; quantitative
Survey, subjective; quantitative
Experimental; subjective; quantitative
Mahr et al., 2014
Bonner, 2010
Joshi and Sharma,
2004
Fuchs and Schreier,
2011
Survey, Likert scale, subjective;
quantitative
Carbonell et al., 2009
Interviews, survey, Likert scale,
subjective; quantitative and qualitative
Meta-analysis; objective; quantitative
Chang and Taylor,
2016
Fang et al., 2008
Customer portfolio size (number of
customer relationships), Customer
relational embeddedness
Longitudinal data: mail survey,
telephone interviews, web searches,
and archival data. Subjective and
objective; quantitative and qualitative
Yli-Renko and
Janakiraman,
2008
Customer empowerment in NPD;
perceived risk
Creation of cross-functional NPD teams,
failure/reward systems, integration mode
of conflict resolution, and championing
product leadership.
Customer interactivity; customer
information quality
Value of customer cocreated knowledge,
lead user status, closeness in customerfirm relationship
Customer participation, information
sharing, coordination effectiveness, and
relationship specific investment
Customer involvement, technical novelty,
technical turbulence, innovation speed,
technical quality
Customer participation
Customer participation as an information
source; customer participation as
codeveloper
Survey, Likert scale, subjective;
quantitative
Fang, 2008
Independent Variables
Customer Participation Measurement
Authors
Table 1
Previous research on customer participation outcomes.
Failure/reward systems, integration
mode of conflict resolution,
Product newness; product
embeddedness
Communication channels
Technical turbulence, emerging/
developed country, high/low tech
industry, B2B/B2C, firm size
There is a trade-off between NPD innovativeness
and speed to market. Customer participation as an
information source can improve speed to market,
especially in the presence of downstream customer
network connectivity. Customer participation as a
codeveloper is negatively moderated by process
interdependence.
The impact of customer participation onto NPD
performance depends on the customer portfolio size
and relational embeddedness of customers into the
firm. Firms develop more new products when
customers are closely embedded into the firm.
Customer portfolio size leads to more products,
although this relationship is inverse U shaped.
Customer participation in the ideation and launch
stages improve NPD performance whereas customer
participation in the development phase slows time
to market and NPD performance. The benefits of
customer participation are greater in emerging
countries, low-tech industries, during technological
turbulence, for business customers, and for small
firms.
Customer involvement in NPD leads to improved
technical quality and innovation speed, which
consequently impacts new product sales
performance and competitive superiority. Customer
involvement is more likely during technological
turbulence.
Customer participation in NPD influences
information sharing, coordination effectiveness and
relationship specific investments, which positively
new product value.
Customer cocreation leads to highly relevant
knowledge development, thereby leading to
positive NPD performance. Lead user participation
leads to both relevant and novel knowledge, which
positively impact market/financial performance.
Participation by customers who are close to the firm
is low cost and relevant, but not as novel.
Customer interactivity positively influences
customer information quality when developing
innovative products embedded in the customer
environment. This subsequently positively impacts
NPD performance.
Customer knowledge development has a positive
impact on NPD performance. Customer knowledge
development is driven by creation of crossfunctional NPD teams, failure/reward systems,
integration mode of conflict resolution, and
championing product leadership.
Customer empowerment and interaction in the NPD
process leads to improved levels of perceived
customer orientation, more favorable attitudes
(continued on next page)
NPD Speed to Market
Downstream customer network
connectivity; process
interdependence; process
complexity
Perceived customer orientation,
attitudes towards the firm,
purchase intentions
NPD performance; customer
knowledge development
New product performance
Market/financial success;
innovation outcomes
New product value
New product sales performance,
competitive superiority
NPD Performance
Number of new products
developed
Findings
Dependent Variable
Moderating Variables
T. Morgan et al.
Research Policy 47 (2018) 498–510
Customer Participation Measurement
Survey, subjective; quantitative
Survey, Likert scale, subjective;
quantitative
Qualitative interviews, biographic
histories, and archival documents;
subjective and objective.
Qualitative interviews; subjective
Literature review, case studies;
subjective; qualitative
Case study analysis; subjective;
qualitative
Secondary data − multiple sources;
objective and subjective; quantitative
Netnography (publicly available
Internet posts); Ratings of coders;
qualitative and quantitative
Authors
Ngo and O'Cass, 2013
Fang, 2008
Coviello and Joseph,
2012
Griffin and Hauser,
1993
Von Hippel, 1986
Lettl et al., 2006
Chatterji and Fabrizio,
2014
Mahr and Lievens,
2012
Table 1 (continued)
Customer status as a lead user,
technological eagerness of
customer, and customer financial
commitment
Customer participation
501
Customer NPD collaborations (current and
prior year), age of technological area, R&D
expenditures, number of employees,
accumulated knowledge stock
Aspects of a lead users co-developer
contribution: Focus, content, initiation,
and codification
Lead user characteristics: familiarity with
future market needs, product use
experience, driven to find solutions
Lead users in customer participation:
technology openness, supportive
environment, intrinsic motivation
Voice of the customer
Downstream customer network
connectivity; process
interdependence; process
complexity
Moderating Variables
Customer participation as an information
source; customer participation as
codeveloper
Customer participation; technical
innovation capability; non-technical
innovation capability
Independent Variables
New product novelty; New
product relevance; new product
value
NPD innovativeness (radical vs.
incremental) and number of
innovations
Radical innovation development
NPD Innovativeness: product
concepts, product designs
NPD Innovativeness
NPD Innovativeness and
performance
NPD Innovativeness
service quality and, subsequently,
improved firm performance
(sales, market share, and
profitability)
Dependent Variable
Lead users involved in customer participation can
positively impact the development of radical
innovations. This is especially true for customers
who are open to new technologies, are embedded in
a supportive environment, and have strong intrinsic
motivation.
Product innovation is enhanced at the corporate
level through customer participation, especially in
new technological areas and when developing
radical innovations.
Lead users are more capable than other users of
providing valuable contributions in a customer
participation in NPD setting as they can suggest
solutions instead of simply describing problems.
Lead users can help the development of new
product functionalities, but are less successful with
design and usability contributions.
towards the firm, and stronger intentions to
purchase from the firm.
Customer participation in NPD leads to improved
service quality and, subsequently, improved firm
performance (sales, market share, and
profitability). Customer participation is driven by
both technical and non-technical innovation
capability of the business unit.
Downstream customer network connectivity
negatively moderates the impact of customer
participation as an information source onto NPD
innovativeness. Customer participation as a
codeveloper onto NPD innovativeness is positively
moderated by process interdependence.
Firms with successful innovations had customer
participation in the following stages: opportunity
recognition, customer-based funding, development
and testing, commercialization, and feedback.
Success was more likely when customers were lead
users, technologically eager, and financially
committed.
Customers can serve as the testing ground for a new
product’s innovativeness and relevance during
product testing stage, but the link to profits and
sales is less clear.
Lead customers are an abundant source of new
product ideas, but identifying them is crucial.
Findings
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conversion of ideas into new products may be viewed as a problem
solving process (Coviello and Joseph, 2012), and organizations that
possess greater ACAP have a better understanding of ideas and technologies to solve problems.
Conversely, without high ACAP, firms may succumb to increased
costs, information overload, and the development of new products that
are too easily imitated by competitors. This problem has been long
acknowledged as a major challenge in the open innovation literature,
albeit in somewhat different contexts (see, e.g., Anokhin et al., 2011),
and we suggest that the shortening exploitation horizons due to insufficient ACAP should be acknowledged as the new reality. Even when
the ideas offered by customers are plentiful, without high ACAP the
firm may be at a loss at channeling them properly. For example, while
customers participating in NPD may offer unique ideas, they may also
offer an overabundance of ideas that are unlikely to focus on satisfying
the heterogeneous needs of all customer groups or consider the product’s complexity to a general population (Fang et al., 2011; Von
Hippel, 2005). ACAP may allow the firm to turn these ideas into products that are appealing to a broader base of end users as high levels of
ACAP allow easier matching between ideas from customers and the
knowledge base of the firm (Chandy et al., 2006). Moreover, ACAP
allows the implementation of solutions with greater ease suggesting
that firms can take a wide array of problems and minimize the complexity of the task to reduce costs and thus enhance the overall performance outcomes. In other words, we expect ACAP to moderate the
relationship between customer participation and NPD performance. To
state formally:
customer participation and NPD performance.
2.2. Unpacking contingencies: the moderation effect of absorptive capacity
While an abundance of benefits has been explicated, there have
been a small number of studies that have examined some potential
negative consequences of customer participation, such as inefficient
processes and subsequently poor product performance (Chang and
Taylor, 2016). Moreover, research has suggested that knowledge
transfer may become cumbersome between the firm and customer due
to language differences or the increased complexity of meeting firm
objectives and customer interests simultaneously (Hoyer et al., 2010;
Franke et al., 2013). Similarly, Foss et al. (2011) found that the link
between customer knowledge and firm innovativeness is dependent on
firm practices. As detailed below, we suggest that ACAP is a necessary
prerequisite for building innovative and commercially successful new
products when utilizing customer participation. Managing resources
and capabilities in a dynamic environment is a challenging task even
when they are internal to the firm (e.g., Leonard-Barton, 1992). Incorporating insights from the outside – that is, introducing diverse,
potentially discontinuous ideas into the inner workings of the firm –
makes this task particularly daunting (Fang et al., 2011) and requires
the capability to be integrated effectively and efficiently (Kavusan
et al., 2016). We suggest that ACAP is the capability that allows firms to
do so as it entails both the acquisition/internalization and the creative
deployment of the extraneous knowledge.
ACAP’s effects manifest through improving both intraorganizational
knowledge transfer (Tsai, 2001) and interorganizational learning (Lane
and Lubatkin, 1998) in various contexts including interorganizational
partnerships (Lane et al., 2001). We thus expect that a similar pattern
should be observed when studying the effect of involving customers
into the new product development process. Given the complex, multidimensional nature of ACAP – namely, the fact that it comprises acquisition, assimilation, transformation, and exploitation of knowledge
(Zahra and George, 2002) – we anticipate that it will moderate both the
principle customer participation–NPD performance relationship and the
key mediation path that links customer participation to superior innovativeness of the focal firm. ACAP focuses on the recombination of
internal and external knowledge beyond customers and competitors,
developing and refining routines that facilitate the existing knowledge
with acquired and assimilated knowledge for future use (Zahra and
George, 2002) and deploying key resources that the focal firm possesses
to obtain and sustain competitive advantage. Substantively, firms with
high ACAP may utilize a push market strategy in regard to NPD and
innovation.
H3. Absorptive capacity positively moderates the relationship between
customer participation and NPD performance.
2.2.2. Absorptive capacity and the customer participation – innovativeness
relationship
Successful innovation often entails recombining existing knowledge,
both internal and external (Galunic and Rodan, 1998; Petruzzelli and
Savino, 2014), especially when dealing with distant and/or diverse
knowledge (Kaplan and Vakili, 2015). Yet, as the breadth of areas
tapped for innovative ideas increases, returns to exploiting recombinations may diminish (Cecere and Ozman, 2014). Managing the
diversity of ideas – especially as they become more distant from the
firm’s knowledge base, which is the case when involving customers into
the NPD process – requires specialized capabilities that help the firm
assess new knowledge (Cohen and Levinthal, 1990), assimilate and
transform it to pursue innovative ends (Kim, 1998), and to harvest the
new markets by exploiting the innovative outcomes (Zahra and George,
2002).
Firms that have low ACAP may have difficulties in integrating the
customer knowledge into firm routines and procedures. They will most
likely not have the knowledge structure and developed schema to take
overly novel product ideas and convert them to marketable form.
Furthermore, firms with lower levels of ACAP may have greater difficulty projecting future success of new products derived from customer
input (Lane et al., 2006; Todorova and Durisin, 2007) that are initially
deemed new to a wider range of segments. As such, their innovative
efforts will not be guided by a strong, unified vision of the future and
may render mediocre results (Talke et al., 2010).
By definition, a firm with higher ACAP would be better equipped to
utilize new ideas from customers that may have not been suggested by
internal employees (Tsai, 2001; Szulanski, 1996). Customer participation on its own only ensures that external knowledge and resources
become available to a firm; it is the ACAP of the firm that increases the
likelihood of the external knowledge being applied to new products in
meaningful way (Chandy et al., 2006). Similarly, a firm with high ACAP
but no source of external knowledge would not have the information
necessary to develop products that are drastically different than their
previous products; product developers would simply rely on their
2.2.1. Absorptive capacity and the customer participation – NPD
performance relationship
NPD success hinges on the confluence of multiple factors
(Schemmann et al., 2016), and it is in this context that ACAP emerges as
a major contingency that affects the impact of customer participation
on NPD performance. As Zahra and George (2002, p. 188) suggest,
ACAP influences the firm’s ability to create and deploy the knowledge
necessary to build a wide range of capabilities. Because ideas sourced
from customers are not limited to any specific phase of the NPD process
but are relevant across its steps (Fang et al., 2008), ACAP can facilitate
their acquisition and assimilation, ensure effective transformation, and
pave the way to effective exploitation. Where customer participation
brings about new knowledge, resources, and potentially NPD team
members from the customer firm, the focal firm’s ability to assimilate
them, weave them into its production processes, and effectively distribute new products will reward the firm with superior performance.
All this suggests that having high ACAP facilitates the conversion of
customer input into NPD performance and thus should increase the
effectiveness of customer participation. It has been argued that the
502
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T. Morgan et al.
existing resource base (Jiminez-Jiminez and Sanz-Valle, 2011). It is the
interaction of customer participation and ACAP that is critical to developing new, differentiated products relative to what the firm currently offers. While customers are capable of providing ideas based on
their unique experiences (Yli-Renko and Janakiraman, 2008), the ACAP
of the developing firm will determine how much those ideas are applied
to the NPD process (Zahra and George, 2002). Therefore, we hypothesize:
approximately $141 million while the firms averaged 34 years in existence. 33% of firms within the sample sold goods, 44% sold services,
and 23% sold both. 42% of firms sold to businesses, 21% sold to consumers, and 36% sold to both. 60% of respondents were senior managers while 40% were employed as functional management (e.g. new
product development manager). On average, the respondents had been
employed at their firm for 11.5 years and identified themselves as
having detailed knowledge of NPD activities.
H4. ACAP positively moderates the effect of customer participation on
innovativeness.
3.2. Measures
3.2.1. New product performance
The new product performance scale for this measure was adopted
from Atuahene-Gima and Ko (2001). New product performance is defined as, “the degree to which a new product is perceived to have
achieved its market share, sales growth, customer use, and profit objectives” (Atuahene-Gima and Ko, 2001, p. 58). It was measured using
six items on a seven-point scale ranging from 1 = strongly disagree to
7 = strongly agree. The scale measures management’s perspectives regarding how new products met overall performance, sales, market
share, and customer usage goals. The use of a perceptual measure of
performance is acceptable due to previous studies showing that subjective measures have high correlations with objective performance
measures (Baker and Sinkula, 2007). An additional benefit is the ability
to compare new product performance among firms across multiple industries (i.e. performance across industries inflates variance and reduces comparability) (Atuahene-Gima and Ko, 2001; Zahra, 1993). Similar studies have utilized subjective, interval scales to measure various
aspects of NPD performance. For example, Carbonell et al. (2009) and
Joshi and Sharma (2004) captured NPD performance by using subjective, interval scale items measuring market share, sales growth, and
sales objective accomplishments/sales growth rate. Bonner (2010) and
Gatignon and Xuereb (1997) also used subjective, interval scale items to
capture new product performance. Relatedly, Fang (2008) used a sevenpoint semantic differential scale to capture NPD speed to market while
Ngo and O'Cass (2013) utilized a seven-point Likert scale to measure
business unit success for firms launching a new product. While experimental (Fuchs and Schreier, 2011) and archival methods (Yli-Renko
and Janakiraman, 2008) have been used in other studies, subjective,
interval-scale based appear to be the standard when capturing NPD
performance. Furthermore, numerous studies have addressed the subjective-objective data debate and demonstrated that subjective measures consistently have high correlations with objective performance
measures (Baker and Sinkula, 2007; Boso et al., 2013; Dess and
Robinson, 1984). All NPD performance items loaded onto their respective factor (α = 0.93).
3. Method
3.1. Data collection
The data for this study was obtained from 243 U.S. firms and collected via email survey over a total period of six months. The sampling
frame for the study was constructed using multiple sources. First,
multiple Chamber of Commerce directories from four Midwestern states
were used to gain access to contact information of senior level management of firms whose headquarters are in those states. Second, to
provide a broader sampling range of firms within the U.S., a commercial list broker was contacted for additional email addresses of senior
management in the U.S. In total, 1705 emails were sent to potential
respondents. Initially, we received a total of 269 completed questionnaires after two waves of data collection. Although a single respondent for each firm is not ideal, a replication study by Slater and
Narver (2000) using multiple, diverse respondents from companies
reported similar results compared to Narver and Slater (1990), which
used single informants with homogenous backgrounds similar to this
study. Although previous research has alleviated this as a cause for
concern, we tried to minimize any effects of a single informant by
contacting multiple respondents for each firm if contact information
was available.
Provided that the core focus of this article is on innovation activities
of the firm, we removed any respondents that did not have detailed
NPD and innovation knowledge. Of the 269 completed questionnaires
we received, 26 were removed due to lack of respondent knowledge.
The 26 cases were from industries or positions or employment that have
been customarily excluded from previous customer participation and
innovation studies due to lack of innovation activities within the industry or general lack of NPD knowledge by respondents within the
focal industry (e.g. retail) (Fang et al., 2011; Fang, 2008; Chang and
Taylor, 2016). As such, the usable responses for this study were from
243 respondents, representing a 20.8% response rate, after accounting
for the questionnaires sent to potential respondents from the deleted
industries. Comparable response rates have been found in similar primary data collection studies (e.g. Baker and Sinkula, 2002; Gatignon
and Xuereb, 1997; Yli-Renko and Janakiraman, 2008) where response
rates ranged from 14%–24%.
To check for the possibility of non-response bias in our sample, we
conducted a series of tests on the respondent population. First, we
compared early and late respondents: we found no significant differences in terms of features such as sales, employees, industry, management experience, and customer participation intensity (Armstrong and
Overton, 1977; Bruneel et al., 2010). Second, we compared responses
from the two sampling frames and found no significant differences.
These tests increase our confidence that the survey data are reliable. An
additional possible issue with primary data collection via survey is the
order effects of questions within the questionnaire. We tried to reduce
any order effects by randomizing the sets of questions that respondents
saw and randomizing the specific questions within each question set.
Within our sample, 14 different industries were represented, as
categorized by NAICS, with manufacturing being the most highly represented at 26% of all respondents. Average annual sales were
3.2.2. Customer participation
Customer participation was measured on a ten-item scale assessing
the level of participation in various NPD activities (e.g., information
generation, idea evaluation, co-development of the idea, etc.) from
1 = very superficially to 7 = very deeply. The ten-item scale was originally developed by Fang et al. (2008) following extensive qualitative
interviews and quantitative analysis; the scale is useful to our study as it
captures both the breadth and depth of customer participation in the
NPD process. The items in the scale initially ask firm respondents if
customers participate in the ten NPD activities (participation breadth)
and if so, to what extent (participation depth). Specific NPD activities
where a firm did not have customers participate in were coded as “0”.
All items from the scale loaded onto their respective factor and were
used in the analysis (α = 0.91).
3.2.3. New product innovativeness
To assess the degree of product innovativeness, Atuahene-Gima
(1995a,b) degree of product newness to the firm scale has been
adopted. The degree of product innovativeness to the firm is defined as
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“the degree of similarity between the new product and those already
marketed [and produced] by the firm” (Atuahene-Gima,1995). The
scale assesses the degree of product extensions, new product lines to the
firm, and new to the world innovations. Items include, “The products/
services we launch at our firm are generally new product or service
lines to our firm.” Thus, respondents who agree with these statements
view their firm as one that launches products dissimilar to their previous products. The scale consisted of four-item Likert scale where
1 = strongly disagree and 7 = strongly agree. All items loaded onto
their respective factor (α = 0.78).
appropriate levels and no items needed to be dropped. In support of
convergence validity and reliability, Cronbach’s alpha exceeded appropriate thresholds for all items, average variance extracted (AVE)
exceeded 0.50, and factor loadings all exceeded 0.60. Inter-item correlations are higher within factors, thus satisfying criteria for discriminant validity. Furthermore, the AVE values are all higher than the
shared variance values (squared correlations) between constructs, thus
supporting discriminant validity between constructs (Fornell and
Larcker, 1981). To assess common method variance, two methods were
utilized. First, we employed the Harman One-Factor method and found
the first factor to account for approximately 33.14% of the variance,
well below the suggested 0.50. Second, we assessed a common latent
factor in the SEM process and did not find any items that were impacted
beyond appropriate levels for common method variance in regard to
standardized loading comparisons, although it cannot be completely
ruled out and is a potential limitation of this study.
3.2.4. Absorptive capacity (ACAP)
ACAP was measured using the multi-item absorptive capacity scale
developed by Flatten et al. (2011). Past research has used Cohen and
Levinthal’s (1990) measure of R&D normalized by sales (i.e. R&D intensity) as a measure of absorptive capacity, but research has suggested
that there are a multitude of knowledge variables that can affect a firm’s
ability to integrate external knowledge into the firm (e.g. Zahra and
George, 2002; Jimenez-Jimenez and Sanz-Valle, 2011). As such, the
purpose of adopting the scale rather than the traditional method is to
capture a more aggregate level of organizational learning, rather than a
measure that strictly focuses on R&D. The scale had a total of 14 items
assessing a firm’s ability to acquire, transform, assimilate, and exploit
knowledge originating from the external environment. The items
ranged from 1 = strongly disagree to 7 = strongly agree. Previous research has identified multiple dimensions of ACAP; Zahra and George
(2002) suggest two overarching dimensions, potential (i.e. acquire and
assimilate) and realized (i.e. transform and exploit) ACAP, while Flatten
et al. (2011) uncovered four dimensions of the construct—acquire, assimilate, transform and exploit. Our factor analysis identified the four
dimensions following Flatten et al. (2011) and our analysis utilized the
second order construct of ACAP with the four individual dimensions
loading onto the higher order ACAP factor. All items loaded onto their
respective factors (α = 0.90).
4. Empirical analysis and results
A seemingly unrelated regression (SURE) model was employed to
test the relationships in this study. The SURE model is appropriate in
this setting due to the set of regression equations of which measurement
error terms may be correlated across equations (Dudley and Silver,
1988) and overestimates of standard errors can be corrected (Hsieh
et al., 2006). We tested the appropriateness of the use of SURE by two
methods. First, we conducted a Breusch-Pagan test to detect contemporaneous correlations between the error terms (Drechsler et al.,
2013). The test statistic from the Breusch-Pagan test in the system of
equations was significant (χ2(18) = 48.90, p < 0.001). Second, we
compared the standard errors from OLS regression to the SURE equations. The results show that the standard errors are greater in the OLS
models suggesting that SURE is a more appropriate method for the
system of equations.
4.1. Results
3.2.5. Control variables
Needless to say, to make the inferences claimed in this study, we
sought to control for additional variables that may explain variance in
our dependent variables. As such, we controlled for management experience of the respondent, firm size using employees and sales, firm
age, degree of customer learning associated with new products
(Atuahene-Gima, 1995a,b), product type (e.g. goods), customer type
(e.g. B2B) and industry. The correlations and descriptive statistics of the
study can be found in Table 2.
We utilized a two-step procedure to examine the relationships in the
study. Model 1 examines main effects and Model 2 added the interaction term. Overall, the system of equations for Model 1 demonstrated a
good fit to the data (χ2(29) = 216.49, p < 0.001). Hypothesis 1 predicted a positive relationship between the level of customer participation and NPD performance. The results show that customer participation is indeed positively related to NPD performance, indicating that
greater integration of customers into NPD activities increases the performance of new products (β = 0.12, p < 0.05). Thus, a standard deviation increase in customer participation increased NPD performance
measure by 12%. This provides support for Hypothesis 1.
Hypothesis 2 predicted innovativeness as a mediating factor between customer participation and NPD performance. A bootstrapping
procedure was conducted to assess the indirect effect of customer
3.3. Validity, reliability, and common method variance
A confirmatory factor analysis was run via AMOS 22.0 to confirm
the validity and reliability of the data and the measures used. The results of this analysis can be seen in Table 3. Model fit metrics all met
Table 2
Correlations and Descriptive Statistics.a
1
2
3
4
5
6
7
8
9
NPD performance
Innovativeness
Customer participation
Absorptive capacity
Management experience
Employees
Salesb
Degree of customer learning
Firm age
Mean
St. Dev.
1
2
3
4
5
6
7
8
4.97
4.44
2.44
4.92
11.54
170.53
141000
3.43
33.62
1.12
1.26
1.92
1.20
8.41
830.35
8370
1.41
34.76
0.44
0.25
0.57
−0.01
0.01
0.02
0.02
−0.05
0.33
0.48
−0.05
−0.03
−0.00
0.42
−0.04
0.26
−0.19
−0.07
−0.01
0.40
−0.10
−0.03
−0.03
−0.03
0.18
−0.10
0.15
0.09
−0.09
0.23
0.79
−0.06
0.06
−0.04
0.04
−0.01
All correlations above 0.10 significant at p < 0.0
a
Dummies not reported.
b
In thousands.
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Table 3
Variables and Measures.
Construct and Items
Factor Loadings
NPD Performance adopted from Atuahene-Gima and Ko (2001)
1. New products/services at my firm generally achieve its market share objectives.
2. New products/services at my firm generally achieve its sales and customer use objectives.
3. New products/services at my firm generally achieve its sales growth objectives.
4. New products/services at my firm generally achieve its profit objectives.
5. Our new products/services meets the performance objectives set for them.
6. Overall, our new products/services are successful.
0.69
0.81
0.86
0.90
0.89
0.77
Customer Participation adopted from Fang et al. (2008)
How deeply do customers participate in the following activities
1. Idea generation
2. Concept screening
3. Product specification
4. Business evaluation
5. Product design
6. Product engineering
7. Prototyping
8. Product testing
9. Formation of cross-functional new product development team
10. Controlling and monitoring of the development process
Response format: 1 = 'very superficially' to 7 = 'very deeply'
AVE
MSV
Cronbach's Alpha
0.74
0.27
0.93
0.60
0.22
0.91
0.82
0.27
0.90
0.53
0.31
0.78
0.54
0.30
0.88
0.73
0.75
0.71
0.66
0.76
0.81
0.81
0.64
0.91
0.88
Absorptive Capacity adopted from Flatten et al. (2011)
Acquire
1. The search for relevant information concerning our industry is ever-day business in my company.
2. Our management motivates the employees to use information sources within our industry.
3. Our management expects that the employees deal with information beyond our industry.
Assimilate
4. In our company, ideas and concepts are communicated cross-departmental.
5. Our management emphasizes cross-departmental support to solve problems.
6. In our company there is a quick information flow.
7. Our management demands periodical cross-departmental meetings to interchange new developments, problems, and
achievements.
0.71
0.93
0.70
0.91
0.93
0.70
0.73
Transform
8. Our employees have the ability to structure and use collected knowledge.
9. Our employees are used to absorbing new knowledge as well as to prepare it for further purposes and to make it available.
10. Our employees successfully link existing knowledge with new insights.
11. Our employees are able to apply new knowledge in their practical work.
0.87
0.88
0.89
0.86
Exploit
12. Our management supports the development of prototypes.
13. Our company regularly considers technologies and adapts them accordant to new knowledge.
14. Our company has the ability to work more effectively by adopting new technologies.
0.70
0.93
0.84
Innovativeness adopted from Atuahene-Gima (1995a, 1995b)
The products/services we launch at our firm are generally…
1. improvements of existing products or services, for example improved quality.
2. line extensions, for example adding a new model to an existing product/service line.
3. new product or service lines to our firm
4. real, new-to-the-world innovations.
0.70
0.69
0.85
0.74
Degree of customer learning adopted from Atuahene-Gima (1995a, 1995b)
1. New products/services usually require major learning efforts or experience by our customers.
2. It usually takes a long time before our customers can understand the full advantage or our new products/services.
3. Our new products/service concepts are usually difficult for our customers to evaluate and understand.
4. Our new products/services usually require considerable advance planning by the customers before use.
5. Our new products/services usually involve high changeover costs for the customers.
6. Products/services we launch nowadays are usually more complex than products/services previously launched by our firm.
0.81
0.74
0.73
0.82
0.71
0.70
Model Fit: Chi-square = 1386.56; df = 713; χ2/df = 1.95; RMSEA = 0.06; SRMR = 0.07; CFI = 0.95; NNFI = 0.94; IFI = 0.95.
Average variance extracted (AVE) score is calculated according to Fornell and Larcker (1981) and should be greater than 0.5. AVE = Σ(λyi)2/[Σ(λyi)2 + ΣVar(εi)], where λ is the
loading of each item.
N = 243 respondents.
df, degrees of freedom; RMSEA, root mean square error of approximation; SRMR, standardized root mean residual; CFI, comparative fit index; IFI, incremental fit index; NNFI, Tucker
Lewis index.
standardized indirect effect of 0.08 (p < 0.001).
The system of equations for Model 2 demonstrated a good fit to the
data (χ2(31) = 224.38, p < 0.001). Hypothesis 3 predicts that a firm’s
ACAP positively moderates the relationship between customer participation and new product performance. The results from the analysis
show that the coefficient is positive and significant (β= 0.12,
p < 0.05); thus, a standard deviation increase in ACAP improved the
relationship between customer participation and new product
participation’s impact on NPD performance through the innovativeness
of new products. Although the Sobel test and Baron and Kenney’s
procedure are alternatives for testing mediation effects, extant research
suggests that the bootstrapping procedure is more appropriate as it
reduces type I error rate and has a higher level of statistical power
(Preacher and Hayes, 2008; Hayes, 2009). The results of the bootstrapping procedure show that innovativeness mediates the relationship
between customer participation and NPD performance with a
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Table 4
Results from analysis.a
Independent Variables
Customer Participation in NPD (CP)
Model 1
Model 2
Dependent Variables
Dependent Variables
NPD Performance
0.14**
(0.06)
0.45***
(0.06)
Absorptive Capacity (ACAP)
Innovativeness
0.16**
(0.06)
.41***
(0.06)
CP * ACAP
Innovativeness
.27***
(0.06)
0.01
(0.05)
0.01
(0.01)
−0.07
(0.06)
−0.22***
(0.06)
0.01
(0.14)
χ2 = 177.13***
0.37
0.43
Firm Age
Management Experience
Employees
Customer Learning
Constant
Test Statistic
R2
Model 1 System Adjusted R2 [F(29,514) = 22.02***] Model 2 System Adjusted
R2[F(31,514) = 21.63***]
Standard errors in parentheses.
†
p < 0.10, *p < 0.05, **p < 0.01,
a
dummies not reported.
***
0.00
(0.06)
0.01
(0.01)
−0.02
(0.06)
0.24***
(0.06)
0.33*
(0.14)
χ2 = 165.04***
0.35
NPD Performance
0.12*
(0.06)
0.48***
(0.06)
0.12*
(0.06)
.25***
(0.06)
0.01
(0.05)
0.00
(0.01)
−0.06
(0.06)
−0.22***
(0.06)
0.01
(0.14)
χ2 = 180.03***
0.43
0.55
Innovativeness
0.14**
(0.06)
.44***
(0.06)
0.13*
(0.06)
0.00
(0.06)
0.00
(0.01)
−0.00
(0.06)
0.23***
(0.06)
0.33*
(0.14)
χ2 = 163.10***
0.40
p < 0.001.
Table 5
Results from Mediation Analysis.
Conditional Direct and Indirect Effects at Level of Absorptive Capacity
Path
Absorptive Capacity
Effect
Standard Error
LLCI
ULCI
Significance
Customer Participation − > NPD Performance
L
M
H
−0.06
0.05
0.17
0.09
0.05
0.07
−0.24
−0.06
0.03
0.11
0.16
0.30
NS
NS
*
Customer Participation − > Innovativeness − > NPD Performance
NA*
L
M
H
0.08
0.00
0.03
0.08
0.03
0.02
0.01
0.02
0.03
−0.03
0.01
0.02
0.13
0.04
0.06
0.11
*
NS
*
*
N = 243.
NA* = ACAP as moderator not accounted for in model.
LLCI = lower level confidence interval.
ULCI = upper level confidence interval.
NS = not significant.
* = significant.
Fig. 1. Customer Participation and Absorptive Capacity interaction on
New Product Performance.
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Fig. 2. Customer Participation and Absorptive Capacity interaction on
Innovativeness.
performance by 12%, providing support for Hypothesis 3. The mediation
analysis suggests that only under conditions of high ACAP will customer
participation be effective. Hypothesis 4 predicted that a firm’s ACAP positively moderates the relationship between customer participation and the
degree of innovativeness. The results from the analysis show that the
coefficient is positive and significant (β = 0.13, p < 0.05), thus providing
support for Hypothesis 4. Furthermore, the results from the mediation
analysis suggest that only under conditions of medium and high ACAP will
customer participation be beneficial to developing more innovative products with high ACAP providing the greatest benefits. The results from the
analysis can be seen in Table 4, the mediation analysis in Table 5 and the
interaction plots can be seen in Figs. 1 and 2, respectively.
Table 6
Post hoc analysis of individual customer participation activities.
Customer Participation Stage
Innovativeness
To examine the robustness of the relationships in our model, we
utilized a structural equation modeling procedure. We report on these
analyses below. The full results are not included due to space limitations, but are available upon request. To minimize the effects of the selfreported nature of the data, we utilized a common latent factor
(Podsakoff et al., 2003) in the structural model to reduce any effects of
common method bias. As expected, the results of the SURE model are
supported. Hypothesis 1 suggests that customer participation positively
impacts NPD performance. This hypothesis is again supported
(β= 0.17, p < 0.01). Hypothesis 2 states that innovativeness mediates
the relationship between customer participation and NPD performance.
A boot strapping procedure was conducted using 5000 replications. The
results show that innovativeness mediates the relationship between
customer participation and NPD performance, accounting for 10% of
the total effect (p < 0.01). Hypothesis 3 suggests that ACAP moderates
the relationship between customer participation and NPD performance.
The results from the analysis show that ACAP positively moderates the
relationship (β = 0.12, p < 0.05), thus H3 from the main analysis has
further support. Hypothesis 4 suggests that ACAP moderates the relationship between customer participation and innovativeness. The
results from the analysis show that ACAP positively moderates the relationship (β= 0.14, p < 0.05), thus H4 from the main analysis has
further support. Substantively, our robustness check provides further
support for the hypotheses in this study and the main analysis.
NPD Performance
non-significant
Ideation x ACAP
positive
negative***
negative***
Product specification x ACAP
negative*
non-significant
Business evaluation x ACAP
positive*
positive*
Product design x ACAP
4.2. Robustness check
***
Concept screening x ACAP
negative
*
negative*
***
positive***
Prototyping x ACAP
negative
*
positive***
Product testing x ACAP
positive**
positive***
Product engineering x ACAP
positive
***
Formation of product development team x
ACAP
positive
Controlling and monitoring x ACAP
positive*
positive***
non-significant
* p < 0.05.
** p < 0.01.
*** p < 0.001.
of the analysis can be seen in Table 6. When ACAP is examined as a
moderator in different NPD activities, we uncovered numerous interesting findings. As expected, ACAP has a positive role in stages where
tasks are more complex and firms are acquiring, assimilating, transforming and/or exploiting external information. As explained below,
ACAP may be largely beneficial to integrating customers into complex
activities, but could become counterproductive in stages where firms
should rely on their existing, internally-proven processes, such as concept screening and design.
On the positive side, we find that formulation of a product development team benefits from ACAP. This seems logical – a truly innovative and successful firm will be able to integrate people of varying
viewpoints and backgrounds into a team to work on product development (Zahra and George, 2002; Lane and Lubatkin, 2006). If high levels
of ACAP exist in this stage, individuals with new and different ideas
should be welcome and the resulting team will be able to develop innovative and successful products, as found in our results. Not surprisingly, we also see that ACAP has a positive, moderating influence onto
innovativeness during the ideation stage. These results align nicely with
prior literature that found that team ACAP indeed had a positive,
moderating impact on firm performance (Engelen et al., 2014) and
product innovativeness (Backmann et al., 2015). ACAP also has a positive role in product testing. During the product testing stage, firms
gather new information on their products (often from customers) to
determine what works and what does not work. This is very similar to
the acquisition stage of ACAP (Huang and Rice, 2009; Flatten et al.,
2011). Firms with high levels of ACAP should be able to take this
5. Discussion
5.1. Post hoc analysis and managerial implications
In order to make proper inferences based on our analysis and to
provide more insightful managerial implications, we further set out to
explore effects of customer participation and its interaction with ACAP
on the stages of customer involvement in the NPD process.2 The details
2
Dependent Variable
We would like to thank an anonymous reviewer for this suggestion.
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Another possible limitation is that NPD performance is perceptual
rather than empirical. While previous research has explicated the correlations between objective and subjective firm performance measures
are high and there are many benefits to subjective measures such as
inter-industry comparisons (Atuahene-Gima and Ko, 2001; Dess and
Robinson, 1984), future research should explore the notion of measuring these variables directly rather than using a perceptual measure.
Further, the addition of objective data would allow us to move away
from correlation-based analysis and perhaps closer to identifying the
existence of causality. Future studies should also explore the notion of
customer participation breadth versus depth and how it affects new
product performance. The cost of integrating customers deeply into the
new product process includes financial commitment, communication
and coordination difficulties, and external knowledge integration.
Customer participation breadth may show different results provided by
this study. While the results show a potential down side to customer
participation when firms have low ACAP, future research should explore how firm resources and capabilities can solve the paradox. Finally, while we added control variables to account for differences in
B2B vs. B2C and goods vs. services firms, future research may explicate
the differences of each within NPD functions. Customer participation
was treated as a single construct in this study, but the post-hoc analysis
suggests that more research should be conducted on what activities
would benefit most by having customers become involved. We hope
this study opens the scholarly dialogue on these issues.
information (acquisition) and apply it to new and improved versions of
their products. The ability to acquire and utilize new information is
truly critical during product testing (Dolan and Matthews, 1993).
On the negative side, we found that concept screening, when interacted with ACAP, has a negative impact on innovativeness and NPD
performance. This stage is intended to reduce innovative ideas into
more realistic and practical solutions. Further, concept screening may
depend on existing internal criteria (e.g. budgetary limitations, time
limitations, etc.). Thus, high ACAP may cloud this stage. Instead, product developers should rely on their existing processes of screening and
drown out external distractions. As noted by Markham and Lee (2013),
many of the most successful product developers have set processes that
do not change drastically from project to project. While the information
sources may change, and the complexity may change, the process stays
about the same internally. Once the external information has been acquired through ideation, firms should rely on those set processes for
effective screening. While ACAP plays a positive role in innovativeness
and NPD Performance, it is not deemed necessary in the concept
screening stage. These results lend themselves to future research possibilities analyzing ACAP as both a positive and negative moderator.
While we can only speculate as to the specific reasons for these
intriguing results, it is possible that absent sufficient ACAP, early-stage
activities cannot be duly incorporated into the corporate routines and
may in fact cost more to the focal firm than the benefits they bring with
them. Furthermore, because these early-stage activities necessarily
imprint the following steps of the NPD process, the cost vs. benefit
challenge exacerbates to an extent where customer participation is, on
balance, a drain on the company’s resources, and not a net benefit
unless the task is more complex in nature. Alternatively, because our
performance measures are perceptual, it may be that incorporating
customer insights at the earliest stages of NPD absent sufficient ACAP is
simply too involved for the individuals at the focal firm involved in the
process, and shifts the reference point such that the (poorly equipped
for absorption) respondents vent their frustration and disappointment
at having to incorporate customer input. Future research would be wise
to compare if this same negative relationship holds when looking at
objective performance metrics. At any rate, however, the managerial
implication of this study is clear: companies would benefit more from
employing customers into the NPD later and not sooner in the process.
In other words, customers are instrumental in bringing the firm strategy
to fruition but not at formulating the product strategy itself.
5.3. Conclusion
In this paper, the direct and indirect effects of customer participation on new product performance were examined. While the results
substantiated previous research’s claim that participation is indeed
positively associated with innovativeness and new product performance
(Chang and Taylor, 2016), the true impact may be more nuanced. This
research suggests that the benefits of customer participation are contingent on high levels of ACAP. The results show that firms without
high levels of ACAP may not gain the true benefits highlighted by
previous customer participation research. A firm must be able to acquire, transform, assimilate, and exploit the knowledge to maximize
any impact that open innovation may have on competitive advantage.
Firms with higher ACAP are more equipped to apply customer knowledge to newer, differentiated products than firms with lower levels of
ACAP. The results of this study indicate that having a greater ability to
internalize external knowledge (i.e. from customers) and exploit it to
commercial ends enhances the relationship between customer participation and new product performance. When firms have a lesser ability
to integrate customer information into the firm’s processes, open innovation may not be as desirable as previously thought. In other words,
if firms wish to have customers participate in NPD activities, they must
be able to build upon the knowledge presented to them in order to
maximize its effectiveness. This is in line with dynamic capabilities and
organizational learning theory (e.g. Moorman, 1995) that suggests that
firms may become overwhelmed with information overload unless they
have the capabilities of sorting and filtering through such information.
Therefore, firms may consider foregoing the involvement of customers
during the NPD process unless they have the capacity to internalize the
information, capitalize on it, and convert it to commercial ends (Cohen
and Levinthal, 1990). Future studies should further explore the notion
of how a firm’s ACAP can impact the integration and exploitation of
customer knowledge throughout various stages of NPD.
Substantively, this research builds upon the continually growing
stream of customer participation literature. While previous research has
shown that customer participation has many positive outcomes (e.g.
Coviello and Joseph, 2012; Chang and Taylor, 2016), this study explicated a key contingency. Firms with lower degrees of ACAP should
encourage activities to increase their ACAP capabilities, such as crossdepartmental sharing of knowledge and dedicating time to external
5.2. Limitations
The results need to be interpreted in light of the study’s limitations.
Common method bias cannot be completely ruled out for this study.
While we tried to examine its effects through the Harman One Factor
method and utilizing a common latent factor, it still is a possibility.
Second, the subjective measures for the variables may be slightly
skewed given the firm’s propensity to enhance its favorability. Parts of
the questionnaire, such as degree of product innovativeness, may be
better off being answered by the customer rather than the firms since
the implications for the firm lie within consumer perceptions and
learning. The customers’ perceptions in regard to this variable would
seem to matter more than the degree of product innovativeness than
managers’ perceptions. While a firm may not perceive a dramatic degree of change in product innovativeness, the customer may feel what is
offered by the firm in fact is a dramatic change. While our study matches the methodology of similar work within this line of literature (e.g.,
Atuahene-Gima and Ko, 2001), a future study could remove any potential issues with the single informant design by utilizing customer
data in combination with firm data. We tried to control for this by
contacting multiple senior level managers at the same firm if contact
information was available, but due to anonymity of the survey and not
having multiple contacts at every firm, this still presents a limitation to
the study.
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information acquisition, to name a few. In fact, firms with low ACAP
levels may need to undertake a substantial culture shift before reaping
the benefits of integrating customers into their NPD processes.
Our second, albeit more minor, contribution is the examination of
customer participation across a broader industry range. Noted in Chang
and Taylor (2016), customer participation research has been embedded
in specific industries such as software development and computer and
electronic product manufacturing, which limits generalization of the
construct. Our approach in this study is that of a broader industry
analysis to assist in widening the scope of customer participation research while controlling for industry.
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