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The Role of Users and Customers in Digital Innovation: Insights from B2B Manufacturing Firms



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Abrell, T., Pihlajamaa, M., Kanto, L., Vom Brocke, J., & Uebernickel, F. (2016). The
role of users and customers in digital innovation: Insights from B2B manufacturing
firms. Information & Management, 53(3), 324-335.
The Role of Users and Customers in Digital Innovation: Insights from B2B
Manufacturing Firms
Diffusion of digital technologies into the manufacturing industry has created new
opportunities for innovation that firms must address to remain competitive. We investigate the
role of customer knowledge and user knowledge in three global B2B manufacturing
companies’ digital innovation processes. We find that the B2B manufacturing industry’s
characteristics influence how users and customers may be leveraged. Customers making the
purchasing decisions are used for knowledge about short-term changes in market needs, while
users working directly with the products provide long-term guidance for digital innovation.
We identify practices for acquiring, distributing, and using customer knowledge and user
knowledge for digital innovation.
digital innovation; customer knowledge; user knowledge; innovation management;
knowledge-based view
The diffusion of new digital technologies into the manufacturing industry creates new
opportunities for digital innovation [13]. Combining digital technologies like sensors, RFID
tags, and cloud computing with non-digital products and services may give products and
services new properties [4] and provide significant opportunities for new innovation [58].
Digitization of products is also likely to have significant disruptive effects [812] in that
firms’ competitiveness will depend heavily on the firms’ ability to innovate using digital
Extant research on digital innovation has focused on the use of digital tools to facilitate
innovation processes [10,1315] and the innovation opportunities digital technologies create
[4,1618]. There has also been interest in the managerial challenges associated with digital
innovation processes, as the introduction of digital technologies to traditional innovation
processes induces changes in architectures, development practices, cognitive framing of
problems, organizing logic, and interactions between actors [2,11,1720]. However, little is
known about the factors that influence the success of digital innovation processes [2,4].
To develop digital innovations, firms must have proficiency with digital technologies, but
they must also understand about their markets’ needs [21], for which they must focus on their
customers and users [2225]. According to the knowledge-based view of the firm,
interactions with customers and users can be examined from the viewpoint of customer
knowledge and user knowledgethat is, knowledge about customers and users and
knowledge obtained from customers and users [26]. How firms manage the various types of
customer knowledge and user knowledge is an important antecedent of their innovation
outcomes [27,28]. User knowledge, in particular, is important for technological innovation
and for the development of radical innovations [29].
This study addresses how B2B manufacturing firms leverage customer knowledge and user
knowledge for the purposes of digital innovation. We analyze the data from a multi-case
study of three firms that are presently adopting digital technologies for use in their product
and service designs. The context of the study is that of a heavy manufacturing industry, and
the case companies are component, system, and service providers related to, for example,
marine vehicles, power plants, and production facilities. In this context, there is a need to
distinguish between the roles of customers and users. Userswho are, for example, vehicle or
plant operatorsare not typically involved in purchasing decisions, and the customerswho
make the purchasing decisionsdo not directly use the products. The users are located in the
customers’ organizations or farther down the supply chain. We focus on the differences
between customer knowledge and user knowledge with respect to knowledge type and its
influence on digital innovation. We also report on the practices firms use in acquiring,
distributing, and using customer knowledge and user knowledge to support digital innovation
and the challenges they face in doing so.
The study is organized as follows. First, we establish the theoretical background of the study
by reviewing the literature on digital innovation and the role of customer knowledge and user
knowledge in innovation. Next, we describe our research design, case selection, data
collection, data analysis, and research context. Then we report the findings from the three
cases. Finally, we explain our contributions to theory and practice.
Theoretical Background
Digital technologies may have two kinds of impact on the innovation processes [1] of the
manufacturing industry: They may facilitate the innovation process itself by providing tools
with which to improve how innovation process tasks are performed, and they may make new
kinds of digital innovations possible by adding new properties to existing non-digital products
and services. Following Yoo et al. [17], who build on a Schumpeterian definition of
innovation, we define digital innovation as “carrying out new combinations of digital and
physical components to produce novel products and services.
Nambisan [15] reviews the extant literature on the use of digital technologies to facilitate
innovation processes. Nambisan identifies four key dimensions of these technologies: First,
IT tools for process management may help in the adoption of customized process models and
promote integration between new product development and other organizational functions.
Second, IT-based project management systems allow access to project information from
diverse sources, which helps in project portfolio management and task coordination. Third,
information management systems support information capture, sharing, and combination
across the organization. Last, digital technologies provide new means of collaboration and
communication, such as virtual innovation teams. More recent studies have investigated the
adoption of digital 3D-visualization tools [10]; the use of digital tools, such as computational
chemistry, in drug development [14]; and the effect of simulation models on the automotive
development process [13].
The second branch of literature considers the impact of digital technologies on innovation
outcomes [1]. As the price-to-performance relationship of digital technologies improves,
opportunities for digital innovations open up in diverse contexts [2,3,8]. In the manufacturing
industry, digital technologies like sensors, RFID tags, and cloud computing can be embedded
into non-digital products and services to produce networked “smart factories” with flexible
and adaptive production processes [5,6,7] and give existing non-digital products and services
new properties. Yoo [4] proposes seven properties of digital artifactsprogrammability,
addressability, sensibility, communicability, memorability, traceability, and associability
which provide digital artifacts the ability to modify their operation logics, respond to
messages and environmental changes, send and receive messages, record and store
information, interrelate events over time, and relate to and identify with other entities. These
properties provide significant possibilities for digital innovations [8].
While the innovation potential of digital technologies is widely recognized, few studies
address the management of digital innovation processes [17,18,20]. Digital technologies have
induced “non-reversible and deep changes in computing infrastructures, development
practices, and cognitive framing of computing problems” in both services and development
processes [11]. Compared to traditional product innovation, digital innovation poses new
challenges with respect to organizing logic, market dynamics, and architectural design [20].
Yoo et al. [17] propose that new digital innovations give birth to novel product architectures
that require substantial organizational changes in the innovating companies. Moreover, digital
innovation processes may instigate interactions between heterogeneous actors which creates
coordination challenges [18]. Additional challenges arise in matching digital and traditional
innovation processes with varying paces of development when digital technologies are
embedded into non-digital products [2,19].
The diffusion of digital technologies into new areas of application is likely to have far-
reaching effects. Digital technologies open new opportunities but in doing so also create
competitive pressure. Since digital innovation has the potential to revolutionize the
manufacturing industry by disrupting existing product designs and business models [7,30,31],
manufacturing firms must learn to adapt to such disruptions in order to ensure their long-term
survival [32,33]. In the process of Schumpeterian creative destruction, some firms fail and
new entrants take their place, as incumbent firms often have difficulty adapting to disruptions
from technological developments [34,35]. When disruptive technologies enter an industry,
firms compete to develop novel designs based on new technologies and try out variations
based on emerging opportunities [36]. As disruptions create opportunities outside traditional
areas, they also enable radical innovation, that is, innovation that creates a new development
trajectory [37]. In the end, dominant designs emerge as winners and become industry
standards. Under such circumstances, managers should focus on the right innovation
processes, but little is known about the factors that influence the success of digital innovation
in organizations [4]. Our study addresses this issue.
To develop successful innovations, firms need knowledge about market needs and the
solutions to fulfill those needs [21]. The properties of digital technologies make solutions for
a wide range of needs possible [4,8], so understanding what kinds of needs there are and
which needs should get the most attention is important. Digital innovation is usually
associated with agile innovation processes, characterized by short development cycles and
quick accommodation to changing needs [38,39]. However, in the B2B manufacturing
industry on which the present study focuses, products are complex and their life cycles are
long. Therefore, innovation comes with large investment, and major changes in development
trajectories are rarely feasible. Consequently, identifying the right long-term direction early
on is more important in this industry than it is in industries in which frequent adaptive
readjustments are more practicable.
Firms must focus on their customers and users to understand the need side of innovation.
The role of interacting with customers and users in the development of new processes and
services has been recognized since the 1960s [22,23,25]. More recently, the literature on open
innovation in particular has shown how firms can leverage customers and users to benefit
innovation processes. Acquiring users’ knowledge by involving advanced users early in the
innovation process helps firms to understand their needs and guides development in the right
direction [28,40,41]. There is also evidence that orienting innovations on the customer
improves the innovations’ performance in times of technological turbulence [42,43]. Because
of the wide use of IT technologies, new ways to acquire customer knowledge and user
knowledge, such as online forums on product web sites and open source communities, have
emerged [44,45]. Companies are also turning to end users in the search for new ideas [46].
Product inventions in which users are involved are more likely to succeed leading to
innovations that the firm would not otherwise have developed [29]. However, systematic
evidence is lacking in terms of the impact of user contributions to corporate innovation, when
the collaboration with users is most beneficial [29], and how user involvement in the early
phase of development should be managed [46].
The literature on the use of customers and users in innovation has either had a strong B2C
focus (e.g., [47]) or provided no clear distinction between customers and users. The terms
customer and user are often used interchangeably (e.g., [4852]). Von Hippel [21]
describes users as both firms and individual consumers. Firms are also sometimes referred to
as intermediate users [53], although case study reports of intermediate users often refer to
individual users, such as physicians [29], surgeons [41], and librarians [54]. In any case, we
consider users as those who expect to benefit from usingnot sellinga service or product
Research on customers and users in innovation in the B2B sector focuses on lead user
innovation [40,5658], user communities [5962], local or “sticky” information [6366], and
user involvement in innovation processes [29,67,68]. Other literature streams include
innovative users [54,69,70] and user entrepreneurship [7174] . Despite the well-reported
benefits, a firm’s strong orientation toward its customers and users may have its downside.
For example, new technologies may provide opportunities for innovation that do not fit
existing customers’ and users’ needs [75] but may fit the needs of other cohorts, so firms that
focus only on their own users and customers may miss the opportunity to acquire new
customer bases. Close ties to existing customers may also limit the firm’s response to changes
in the industry’s value networks. Customers and users may also reject radically new
innovations if adopting them undermines their existing competencies and requires new
capabilities [76,77].
The present study uses the heavy B2B manufacturing industry, which has specific
characteristics with respect to its customers and users. First, the industry’s customers are not
individual consumers but large companies for which our case companies act as suppliers.
Second, the supplier firms have close relationships with their key customers, who are
responsible for a major share of their sales. According to Bonner and Walker [78], in a B2B
context, a relatively small set of customers can influence new product development. Third, the
end users do not make purchasing decisions but are typically located within the customer
organization or at the customer’s customer’s organization. To address these characteristics,
we concentrate on the distinct roles of customers and users in digital innovation: that is, users
are employees of the customer organization or customers of the customers who work daily
with a product.
Leveraging customers and users in the process of digital innovation can be examined from the
perspective of how the firms acquire and use their customers’ and users’ knowledge [79,80].
We use the knowledge-based view of the firm, which is based on the notion that the “critical
input in production and primary source of value is knowledge” [79]. Creating and transferring
knowledge across a firm’s boundaries may help firms exploit new opportunities [81]. Framing
our argumentation within the knowledge-based view, we state that acquiring knowledge about
customers and users and knowledge obtained from customers and users [26] is critical for
organizations to be able to develop digital innovations [82,83]. Therefore, it is important for
organizations to build resources and capabilities that will allow them to use these kinds of
knowledge [84,85].
A key distinction in the literature related to the knowledge-based view is that between explicit
knowledge and tacit knowledge. While explicit knowledge is transmittable in formal,
systematic language, such as text, tacit knowledge entails action, commitment, and
involvement [86,87]. Tacit knowledge may reside in individuals’ plans, skills, and habits, or
collectively in the firm’s culture, past collaborative experiences, and routines [83,88]. It is
acquired by means of personal experience and is difficult to express and observe, whereas
explicit knowledge may be observed, articulated, and documented in formal language, print,
and electronic media [88,89]. However, tacit knowledge and explicit knowledge should not be
seen as dichotomous but as a continuum [88].
We adopt knowledge as a key concept in our study and explore how firms leverage customer
knowledge and user knowledge for digital innovation. We look at an industry in which firms
are currently adopting digital technologies for product and service innovation to identify ways
firms can benefit from customer knowledge and user knowledge in digital innovation
processes. More specifically, we seek the links between digital innovation and these kinds of
knowledge and identify the methods and challenges of acquiring, distributing, and using it.
Research Context and Methodology
We chose a multiple holistic case study design [90], which is particularly suitable for an
exploration of why and how the phenomenon of digital innovation occurs in the context of the
cases we chose for our study. Case study research refers to an “empirical inquiry that
investigates a contemporary phenomenon within its real-life context, especially when the
boundaries between phenomenon and context are not clearly evident” [90], such as the topic
of the present study. Multiple case studies can lead to generalizable and robust results, as
findings have multiple empirical sources [91].
We chose our case companies from the heavy manufacturing industry, a B2B environment.
We were interested in companies that produce complex technical products and that are
concerned with digital innovation because of its recent diffusion into the industry. This type
of industry provides a fruitful setting in which to investigate the emerging practices of digital
innovation [57]. We also concentrate on B2B companies in order to provide robust findings
that are applicable to a specific domain [90].
Our study is comprised of three case companies to which we refer as Company 1, Company 2,
and Company 3 for reasons of confidentiality. The companies are all multinational
manufacturing companies with headquarters in Europe. The cases were chosen in keeping
with theoretical sampling [91,92] using the criterion of manufacturing companies that are
interested in digital innovation. Table 1 presents an overview of the case companies.
We evaluated the firms’ attitudes toward and current positions related to digital innovation by
reviewing public strategy documents, annual reviews, and public presentations by key
innovation and strategy managers, and by means of discussions with firm representatives. The
companies differ in terms of the maturity of their digital innovation processes: Company 1 has
not yet launched any large-scale digital innovation projects but is experimenting with digital
technologies and building a future vision for taking advantage of the opportunities digital
innovation offers. Company 2, on the other hand, has chosen digital innovation as a key
strategic element, as manifested in its high level of investment in digital innovation projects
of significant size and restructuring of innovation practices to support digital innovation
processes. Company 3 is involved in digital innovation but is hindered by unresolved
challenges related to customers privacy concerns and access to users. Managers in Company
3 are also concerned that, because of the company’s large size, it will not be sufficiently
flexible to adapt to the changing needs of the market. They perceive digital innovation in the
form of virtual simulation processes as crucial for their product development process, for
enhancing their products’ digital capabilitiessuch as those that support maintenance
processesand for creating interfaces between manufacturers and customers with the vision
of creating a digital factory.
Company 1 develops a wide range of marine systems and components related to power and
propulsion, automation, and control. The company is currently envisioning how to use digital
technologies to provide new features for ships, such as remote control, intelligent bridge
control, and lower power demand. Their customers include ship companies that own and
operate freight, passenger, fishing, and military ships. Users include ship captains and the
technical personnel who operate the technologies. Company 2 provides automation and
control systems and processing technologies for industrial customers that operate in industries
like chemical, oil, pulp and paper, power generation, and mining. Typical users of Company 2
are production plant operators and power plant operators. Company 3’s offering is a diverse
product portfolio in the B2B sector, ranging from large-scale plants and component
technologies to marine systems. The firm’s customers are large organizations, and users are
employees working in the customers’ organizations. Typical users are operators of production
The main data collection method was semi-structured interviews with thirty experts. The
interviews with Company 3 were conducted by phone, while those for Company 1 and
Company 2 were conducted face-to-face. The interview themes were based on our
investigation of the potential of using customer knowledge and user knowledge in developing
digital innovations. The first interview guide was open-ended so the research themes could be
explored without being structured too tightly. After the first round of interviews, the interview
guide was refined to deepen the discussion of emerging topics and to increase the findings’
reliability [90]. All interviews were recorded and transcribed verbatim in order to establish a
chain of evidence and strengthen the validity of the emerging constructs [90]. Using multiple
sources of evidence, such as public strategy documents, annual reviews, and public
presentations, to complement the interview data also supported construct validity.
Table 1: The case companies
Company 1
Company 2
Company 3
Marine products
Automation and
systems technologies
Plant and component
Size (# of
Location of the unit
Design managers,
product managers,
sales support
managers, sales
R&D managers,
R&D directors, top
R&D managers,
innovation managers,
IT application
support managers
Number of
Length of
49-80 minutes
(mean: 63 minutes)
55-143 minutes
(mean: 84 minutes)
54-78 minutes
(mean: 64 minutes)
Type of interview
We began the interviews with managers who had a good overview of the innovation activities
in the companies. Then we identified employees who are involved with digital innovation in
the company. Selecting interviewees in advance based on their departments or job titles was
an unsuitable approach for several reasons: Innovation often relies on cross-functional
collaboration to reach shared goals [93], and the ownership of digital innovation activities
varies from company to company (e.g., between IT and R&D departments) [94]. Furthermore,
not all employees in a particular department are knowledgeable about the roles of customers
and users in digital innovation. Therefore, we used snowball sampling to identify the
appropriate informants [95]. During the interviews with our primary contacts, we asked who
would be the best people to talk with about relevant topics, thereby adding new informants to
our sample. Snowball sampling is suitable when the study is exploratory in nature [96] and
the informants are difficult to identify [97], and the approach has been used successfully in
studies on digital innovation [10,19,98]. Data collection was continued until additional data
resulted only in minimal new information, that is, until theoretical saturation was reached
In Company 1, most of the informants were design managers and design specialists who
performed development work but were not in contact with customers or users themselves.
Their responsibilities, that is, to renew their product offerings to meet market demand, were
similar to those of R&D managers in the other companies. One design manager reported
directly to top management and was responsible for gathering customer knowledge via direct
customer contact and diffusing the knowledge to the design teams in the business units. Sales
managers and sales support managers, who had access to and distributed customer knowledge
in the organization, were suggested as potential interviewees, as were maintenance managers,
as they were the design teams’ most importance source of user knowledge.
In Company 2, most of our data was collected from two R&D teams. The first team focused
on hardware, while the second team focused on software. The interviewees, who had titles
like R&D manager and R&D director, were suggested based on their wide view of the firm’s
digital innovation activities. We were also pointed toward employees in R&D who were
knowledgeable about customer knowledge or user knowledge. The informants were able to
describe the operations of the sales team, the main source of customer knowledge in detail.
Members of the sales team were not suggested as interviewees, as they would likely have
added only minimal information. Managing user knowledge was integrated into the R&D
teams, which had assigned certain personnel to interact with users. The first couple of
interviews revealed that the firm was in the process of implementing a new company-level
strategy in which digital innovation is a significant element. This information led us to
interview two top managers who were closely involved in formulating the new strategy.
Whereas digital innovation was primarily the R&D/design teams’ responsibility in Company
1 and Company 2, the IT department had a bigger role in Company 3. Our primary contact
person, a senior IT manager who served as the entry point into the organization, suggested
suitable employees who were concerned with digital innovation in the organization, which
covered the developed products from the IT and R&D perspectives. R&D teams had some
direct contact with customers and users. Similar to Company 2, the informants were able to
describe the operations of the sales team in detail and members of the sales team were not
suggested as interviewees.
Following a replication logic [90,91], we first used qualitative data analysis programs and a
coding scheme deduced from the interview guide to analyze each case individually. The
research team members discussed the findings of each case, and emergent topics led to an
iteration of the coding scheme. With this updated coding scheme, we analyzed each case
again. The coded data formed the base for cross-case analyses, during which we revisited the
data in an iterative process and collected emerging findings into tables. Thus, the analysis
process focused on matching patterns across the cases and building explanations for what
occurred in the data, thereby increasing the research’s internal validity [90].
Our interviewees explained how they perceive digital innovation in their companies and
industries, and their views were in line with extant studies on the topic (e.g. [5]). According to
the interviewees, digital innovation is driven in their industry by the emergence of new digital
technologies and the adoption of existing digital technologies from other sectors (e.g.,
consumer products). Their view is that new digital technologies promote innovation by
creating new opportunities for innovation and by facilitating R&D work. This division
supports Nambisan’s distinction in the role of IT as making new innovation outcomes possible
and facilitating the innovation process [1].
For our case companies, the relevant digital technologies for new digital innovation outcomes
include the Internet of Things, big data, sensor technologies, cloud computing, machine-
machine interaction, and GPS. Those mentioned in the category of new digital technologies
that facilitate R&D work include new digital tools and methods to help R&D and production,
such as 3D tools, engineering applications, communication technologies, and 3D printing and
scanning. We decided to focus on the first category, digital technologies for new digital
innovation outcomes, as it is more relevant to our framework, which emphasizes innovation’s
variance-creation as a key activity in responding to disruptive technologies [36].
The interviewees suggested that digital technologies create two kinds of opportunities for
innovation: incremental innovation, in which existing products and services are improved,
and radical innovation, in which significantly novel products and services are developed.
Integrating new technologies like sensors into existing products and services provides
possibilities for optimizing their use and improving productivity through incremental
innovation. On the other hand, most of the interviewees predicted that novel concepts based
on new digital technologies would transform their industry significantly. Radical digital
innovations could change the industry’s business models, create major leaps in performance,
and/or transform how the products are used. We discovered that, for our case companies, the
limiting factors for exploiting digitization by means of digital innovation are not in
technological capabilities, as those could be developed or sourced easily, but uncertainties
about future market needs and choosing the right trajectories for developing digital
innovations. Informants from all companies described their industry as conservative and slow
to change, but they predicted that digital technologies would bring about dramatic changes in
how their products and services are used in the future. This combination of long product life
cycles, pressure to engage in digital innovation, and lack of specific innovation goals poses a
major managerial challenge. The development of more radical products and services in
particular requires substantial investments in the industry and is not likely to take place
without a clear vision of the future.
The case companies had similar views about how digital technologies could be used for
digital innovation. All of them manufacture physical components and systems for complex
industrial products and facilities, such as marine vehicles and power plants. New
opportunities arise when physical components are combined with digital technologies like
sensors and RFID tags so these components can sense changes in their environment.
Innovation potential is related to connecting these components to a network and gathering and
analyzing the massive amounts of data they sense in order to, for example, optimize a power
plant’s or a production facility’s operations at a significantly higher level than was previously
possible. Other opportunities are related to control systems, such as how to design control
interfaces that take advantage of the increased amount of data collected from the sensors. The
technologies also enable new kinds of services, such as intelligent maintenance services that
anticipate future maintenance needs in detail and big data analysis services.
Our findings are divided into, first, a discussion of how our interviewees perceived their
customers and users and the value for digital innovation they saw in interacting with them.
Second, we discuss and classify the practices the companies use in interacting with users and
customers. Our key results in terms of the differences between customer knowledge and user
knowledge in digital innovation are collected in Table 2.
From Customers to Users
Early in our analysis, we noticed the need for a clear distinction between customers and users.
All three of our case firms operate in B2B environments. End users of the products use the
products directly but do not make the buying decisions, as distinguished from customers, who
make the buying decisions but do not use the products directly. In addition, because the
developed products are heavy industrial components and systems and the end users are in the
customers’ organizations or farther down the supply chain in the customer’s customer’s
organization, neither the customers nor the users much resemble their counterparts in the
consumer industry.
For our case companies, the customers and users roles in guiding the innovation processes
differ. As one interviewee from Company 3 explained,
We innovate new products for our customers for use in our customers’ products,
which are then used by the end user. That means we have to innovate, on one hand,
based on the specifications from our customers .... On the other hand, we have to
anticipate which trends are there for the end user, how the end user uses our product,
and how he [or she] perceives it. [Those two sides go] hand in hand.
(Interviewee in Company 3)
The informants perceived users as individuals who work in a specific environment, such as
the operators of marine vehicles and power plants. Because the number of accessible users is
low, decision-making based on collectives of users is difficult. Moreover, the firms
concentrated on acquiring tacit knowledge from the users, which is typically difficult to
separate from personal feelings, habits, and experiences [83]. Therefore, the interviewees saw
understanding users as individuals as beneficial in decision-making.
However, the concept of a customer was more ambiguous. The interviewees described
interacting with customers as dealing with a “stream of opinions” from multiple sources. They
were not able to point out customers at an individual level since buying decisions are made
You have, of course, the problem that one customer is [actually] a community of many
people …. There is not only one opinion, but streams [of opinions]. (Interviewee in
Company 3)
The interviewees perceived customers and users as knowledge sources that could help guide
the digital innovation process and its goals. They emphasized the importance of customer
knowledge and user knowledge for digital innovation because they saw the diffusion of
digital technologies leading to changes in market needs, and they are looking for ways to
create goals for digital innovation in situations where there are no clear development
The interviewees saw a significant challenge in developing digital innovations because of the
rapid pace of change in market needs that results from customer organizations’ adoption of
new digital technologies. The customers and users reported having been exposed to new
digital technologies in other parts of their lives (e.g., consumer electronics) and being
accustomed to certain functionalities and levels of usability. The interviewees emphasized the
increasing need for their organizations to be flexible. Company 2 recently adopted agile
development processes in response to this challenge:
The rapidly developing technologies for consumer products are clearly starting to
influence the manufacturing industry. To respond, we need to increase the pace of our
product development cycles so we can offer new features when there’s strong hype on
certain technologies, instead of five years too late. (Interviewee in Company 2)
Most of the interviewees acquired their knowledge about adapting to rapid, short-term
changes from their customers. Customer knowledge is related to the customer’s purchasing
decision, that is, whether the firm would buy a product or a service and what factors influence
their decision. This knowledge is related to improvement suggestions and performance needs,
as the knowledge is typically articulated in terms of requirements lists, which include both
technical and commercial aspects of their requirements. Interviewees in all three case firms
described their customers as conservative entities that focus almost solely on price, product
performance, and product reliability. Much of the customer knowledge concerns incremental
improvement suggestions, knowledge of malfunctions and problems with the products, and
new industry standards and requirements that must be included in the product specifications.
However, the interviewees reported that customers are often unable to express their needs
beyond short-term improvements and adjustments, and since they cannot specify their
expectations for digital innovation, setting long-term goals is difficult. They are somewhat
knowledgeable about the new technologies but cannot comprehend all the opportunities that
they create. Nevertheless, it was the interviewees’ view that both the case firms and their
customers see the effects of new digital technologies as unavoidable and significant, despite
having a difficult time describing their content and impact in detail, apart from identifying
related technologies. In summary, customer knowledge is available in explicit form, it guides
digital innovation processes to adapt to short-term changes in market needs, but it does not
help in setting long-term development goals.
The big trend is obvious, but everyone has his [or her] own way of approaching
digitization. Like our competitors, we have identified the general direction of where
the industry is going, and we need to show our customers that we are involved in this.
(Interviewee in Company 2)
Not finding long-term goals for innovation in customer knowledge, our case companies
turned to their products’ end users. At one time, the users had been of interest primarily to
user-interface designers, but the interviewees reported that now R&D managers seek guidance
in navigating in the new opportunity space by understanding the users and their work. User
knowledge is employed not only to help improve existing products but also to enable radical
innovations by providing guidelines in setting long-term development goals. Categories of
user knowledge that are relevant to our case firms include understanding the user experience,
feelings, and emotions; usability; and the context of use. This knowledge is often tacit, that is,
it is not codified, so it is difficult to share in written form. According to the interviewees,
R&D employees seek tacit user knowledge to understand how their products are used in
practice, what constitutes the core of the users’ work, and how their work processes can be
improved. The innovation teams are also interested in explicit user knowledge in the form of
usage data, but they have found that the customers are not willing to provide data for fear of
losing competitive advantage. The customers inevitably have some level of user knowledge at
their disposal, but the suppliersthat is, our case companieshave stronger incentives than
their customers do to acquire in-depth user knowledge. There may also be time delay and
information loss in transferring user knowledge to customers and then to suppliers. Our
informants suggested that there is value in involving users directly into digital innovation
processes instead of going through customers:
I believe that these new digital technologies will transform our industry in the next ten
years. We are currently looking at the users and trying to understand their work: goals,
tasks, and experiences that are independent of tools or technologies. In this way we
can find seeds for radical innovations. Otherwise, we’re likely to be constrained to
existing tools. (Interviewee in Company 2)
Our interviewees saw knowledge of the user experience as important because digital
innovation can bring major changes to work practices and because new digital technologies
make possible significant increases in the amount of data and information available to support
the work. Knowledge about the user experience can be used to filter and compress the data so
it is useful and manageable. Many new digital products are based on collecting and analyzing
massive amounts of data, so product designers must understand how the data is relevant to the
end user and which parts of the data are relevant.
The firms face several challenges in acquiring user knowledge. The R&D employees are not
free to contact the users directly but are represented in this effort by the sales organizations
that are responsible for managing the customer interface. However, the sales organizations do
not consider arranging meetings with the end user a high-priority task. For their part,
customers will contribute the time and effort required to arrange meeting between suppliers
and users only if they perceive immediate value from doing so. As a result, they seldom make
it happen:
It’s not easy to convince a customer to allow you to see how the products are used in
practice. We need a lot of knowledge, and all knowledge we get is filtered by the
customer. (Interviewee in Company 3)
Another issue that arises from the distinction between customers and users concerns customer
organizations’ ability to adopt novel innovations. Although one of the case firms can develop
new solutions based on new digital technologies and in-depth understanding of users’ needs,
the interviewees in that firm reported experiencing significant difficulty in convincing
customers of the value of the new innovations:
The basic needs of the customers are very clear. The challenge is to show that new
services are, without doubt, worth the cost and not merely nice additional features.
(Interviewee in Company 2)
Table 2: Differences between Customer Knowledge and User Knowledge
Type of knowledge
Mostly explicit
Mostly tacit
Knowledge sources
Guidance for innovation
Short-term changes in
market needs
Long-term vision for digital
Innovation goals
Mostly incremental
improvement of existing
products and services
Introducing radically new
products and services and
improving existing ones
Examples of knowledge
Feedback on ongoing
projects, improvement
suggestions, problems with
existing products, new
industry requirements
Usability, user experience,
understanding work contexts
and practices, future needs
Methods to acquire
Feedback meetings,
management meetings, sales
On-site user studies, use of
front-line employees,
Methods to distribute
IT systems
User-insight workshops,
informal cross-functional
Methods to use knowledge
Product specification lists,
Internal scenario work,
collaborative research
Convincing customers of the
value of new innovations;
rapid pace of changes in
Difficulty in contacting end
users; restricted access to
usage data because of
privacy concerns
Practices Related to Customer Knowledge and User Knowledge
This section introduces twelve practices that the three case companies use to integrate
customer knowledge and user knowledge into their processes of developing digital
innovations. After discussing practices that relate to acquiring customer knowledge and user
knowledge, we discuss practices for distributing and using tacit user knowledge. We collect
our results on the practices in Table 3.
Acquiring customer knowledge and user knowledge. We find that the practices our case
companies use to acquire customer knowledge differ from those they use to acquire user
Customer knowledge consists of requirements for new products and services and information
about problems with existing ones, so it is mostly in explicit form. It is acquired through
meetings with representatives at various levels of the customer’s organization. An important
source of customer knowledge is sales meetings organized by the key account organization
(or similar) in the innovator firm, the purpose of which is to learn about changes in the
customer firm’s needs. Interaction is frequent, especially with important customers:
A B2B customer will always come to you with requirements. That customer will be
able to say precisely what the functionality needs to be. (Interviewee in Company 3)
Our sales persons have discussions with important customers daily. The customers
follow how regulations develop in their industry and what kinds of new standards and
legislation are coming. (Interviewee in Company 2)
In addition to sales meetings, management-level development meetings are conducted
occasionally with the goal of identifying the customer’s overall goals and trends and future
challenges that may need to be addressed:
We do have regular exchanges with our customers on the operator level, but also in
management development meetings, where we discuss on the management level new
development topics. (Interviewee in Company 3)
Customers also provide feedback in the course of innovation projects. They are shown new
functionalities before the products are built in order to use their knowledge in steering the
direction of development. Feedback includes also discussions of current products and
implications for future development:
[We discuss] the feedback on the product we delivered: what is good, what is bad,
what could be improved, what have been weaknesses of our delivered products.
(Interviewee in Company 3)
Unlike customer knowledge, user knowledge consists of understanding the user experience
and users’ work context, views on usability, and future needs, so it is mostly in tacit form,
which is difficult to identify and transfer. We identified three practices the case companies
employ to gather user knowledge: prototyping, use of frontline employees, and on-site user
studies. Although our informants perceived users as individuals, knowledge about these
individual users is accumulated in the knowledge acquisition and subsequent steps.
The interviewees reported that they use prototypes as a tool to experiment with new ideas. By
presenting users with concept drafts and tangible prototypes, they encourage users to think
about how the new features will affect their work. Prototypes are also used in triggering
discussions with customers, but the interviewees indicated that showing prototypes to users is
more valuable in creating a long-term vision for digital innovation than is showing them to
customers, where the feedback tends to focus on whether the new ideas will help the
customers reach short-term goals and meet existing performance goals:
We take prototypes with us to the workplace and discuss what kind of operating
situations they would make easier. These discussions have a strong future orientation.
(Interviewee in Company 2)
In the B2B context, users are often within the customer organization and are not easily
accessible to the innovator/supplier firms. Our case companies often use their own employees
to get a user perspective without involving the actual end users:
We have no direct access to external users. If we need access, we have to go via our
colleagues responsible for contracts in engineering. (Interviewee in Company 3)
R&D often employs front-line employees in its own company who have spent time with end
users and thus are able to relate to them to provide valuable insights about users’ needs
without involving users from the customer organization. The R&D teams also get insights
about users’ needs from new recruits in their own companies who have work experience in
positions similar to those of the end users:
[It is] the maintenance staff that meets the users … and has the best contact with the
users. They have the best knowledge of the actual situations. (Interviewee in
Company 1)
[We] have many specialists who know their systems but are not fully aware of how
their systems are operated later on. They just have their isolated view on their own
work. To have a more general overview, we try to employ former [users] if we can.
(Interviewee in Company 3)
A third practice for acquiring tacit user knowledge is that of conducting on-site user studies to
observe their work and interview them at the “feelings” level in order to gain empathy with
I have been travelling around [the user sites] and taken the younger colleagues with
me…. There is a lot of knowledge that is not written anywhere, and you can
understand the meaning better from gestures than from written words. (Interviewee in
Company 1)
We sit down with the users at their workplaces. We ask how they work and try to
understand it and get the feel of their work. We have some good examples of when the
users have spontaneously started to tell us about the stress and tension they feel in
their jobs. (Interviewee in Company 2)
Table 3: Practices Related to Customer Knowledge and User Knowledge
Type of
Feedback meetings
Meetings with
customers mid-
project to evaluate
progress and
confirm direction.
Gathering feedback
from customers on
works in progress.
More explicit
Sales meetings
discussions with
customers to keep
up with their
changing needs.
Learning about new
requirements and
problems with
existing products.
More explicit
1, 2, 3
Regular meetings
with important
Understanding the
customer’s big
picture and long-
term goals.
More explicit
1, 3
Use of front-line
Using maintenance
staff and field
service engineers as
knowledge sources;
hiring R&D
employees with
user backgrounds.
workflows and the
usability of current
products without
involving the users
More tacit
1, 2, 3
On-site user studies
Visiting users at
their workplaces;
interviewing and
observing the users.
Empathizing with
users; identifying
latent user needs;
understanding work
More tacit
1, 2, 3
context and work
Demonstrating new
concepts with
tangible prototypes.
Demonstrating and
discussing novel
products and
More tacit
IT systems
Updating new
knowledge into, for
example, a CRM
Bringing relevant
parties up to date
on changes in
More explicit
1, 2, 3
Regular internal
workshops to
disseminate user
throughout the
Making the most of
the limited access
to user knowledge;
sharing information
about users.
More tacit
Informal cross-
Encouragement for
ad hoc discussions;
open cross-
Promoting the
sharing of user
knowledge across
More tacit
1, 2
specification lists
and roadmaps
requirements and
user requirements
into specification
Setting goals for
new development
More explicit
1, 2, 3
Internal scenario
Building future
scenarios that
describe trends and
future use cases.
Creating explicit
goals and vision
based on tacit user
More tacit
2, 3
research projects
research projects
with research
institutes and other
firms on
technologies and
ways of working.
Leveraging external
organizations to
help envision future
More tacit
1, 2
Distributing customer knowledge and user knowledge. As customer knowledge is usually
explicit and easily transferable, the companies can use IT systems like Customer Relationship
Management (CRM) systems to distribute the latest knowledge about customers’ needs.
However, the R&D teams rarely access the CRM systems themselves; they tend to be used by
the firms’ sales and sales support teams. We found that personal face-to-face meetings and
discussions, not IT systems, are used to distribute user knowledge. The interviewees considered
distributing knowledge about users and their work throughout the R&D units to be important in
guiding R&D employees in their everyday decisions:
Operative R&D employees make lots of small decisions based on how they comprehend
the users. We’ve made drastic mistakes in our product designs that would have been
avoided by spending a little time with the users. (Interviewee in Company 2)
Because of the limited access to users and the resources needed for acquiring user knowledge,
the companies adopted practices to distribute acquired user knowledge. In Company 2 telling
stories about users that are accompanied by drawings and photographs was highlighted as a good
way to transfer user knowledge. Company 3 uses regular user-insight workshops to disseminate
user knowledge throughout the organization. Company 3’s interviewees said that user knowledge
must be actively distributed because direct access to it is limited. The workshops are usually
organized after users have been visited for purposes like testing new products with them.
About the workshops: words, discussions are the most important methods. (Interviewee
in Company 3)
Company 1 and Company 2 also encourage their employees to engage in informal interactions
and discussions about users across functional lines. Company 2 moved the product development
staff to a single open workspace where R&D managers, R&D employees, product managers, and
others are located in order to advance the diffusion of user knowledge and generate fertile ad hoc
The layout of our R&D space is one thing that has promoted innovation and helped reach
user experience goals. We have one space with a few big areas without any privacy
screens. We just have tables in different formations. R&D employees, specificators,
business representatives (such as product managers), and R&D managers are all in the
same space. Spontaneous ad hoc conversations are especially encouraged. (Interviewee in
Company 2)
Using customer knowledge and user knowledge. Finally, we identified practices for using
customer knowledge and user knowledge for digital innovation. The widespread impression
among our interviewees was that their customers have difficulty envisioning their long-term
needs and wishes as they relate to digital innovation, so customer knowledge is not readily
available for purposes with long-term future orientations. Those purposes rely heavily on tacit
user knowledge instead. However, customer knowledge does provide guidelines for the near
future and for guiding short-term development, such as product specification lists. Customer
knowledge may also be particularly applicable for short-term purposes because it is available in
explicit form and can be used straightforwardly. The case firms collect customers’ needs and
wishes into requirements lists and sometimes launch innovation projects based on the needs of a
single large customer. In other cases, the firms develop innovation roadmaps based on the
requirements lists and launch new projects according to the roadmaps:
Ideas go to R&D groups for evaluation. The groups have product management and
businesspersons. Requirement specifications govern the development to a large extent.
The requirement specifications come from business, that is, people who have discussions
with our key customers daily. We get information about norms and standards, etc. This
kind of information is put together into business requirements lists. Then the business
prioritizes them, and roadmaps are created based on them, or existing roadmaps are
updated. New development projects are launched based on the roadmaps. (Interviewee in
Company 2)
One use the companies have identified for tacit user knowledge is to create explicit goals for
digital innovation based on that knowledge. The case companies use scenario work and research
projects to determine directions for digital innovation, as building future scenarios is a key
practice for explicating tacit knowledge about users and tacit user knowledge is based on a deep
understanding individual users. The scenarios picture future use cases and describe ongoing
trends in order to explain users to a larger audience and set long-term user experience goals for
the development of digital innovations. The scenarios allow the companies to forecast in which
direction customer needs will develop in the long-term even though the customers themselves
are unable to convey their needs that far in the future. Making R&D employees aware of the
scenarios also helps them make better daily design decisions:
We need to know the technical and operational trends, how the [users] of tomorrow will
operate, and what the technical needs they have for these [products] will be. It is, of
course, a bit tricky to forecast how the situation will be in ten to fifteen years because our
product life cycle is very long, but it is important to forecast what will happen in ten years
and to be prepared for that. (Interviewee in Company 3)
We try to visualize what the potential value for the customer would be, what the
customers could be interested in. It’s mostly based on new functionalities. We aren’t
interested in numbers until later stages, when we are thinking of launching R&D projects.
(Interviewee in Company 2)
Company 1 and Company 2 also use collaborative research projects to complement their internal
scenario work. The research projects are carried out with research institutes and other firms in
order to help firms comprehend the perspectives of future users and their work and how
upcoming new technologies will influence them.
In all of the research projects, we try to sketch radically new ways of working using new
technologies. New technologies overcome lots of the limitations that currently determine
how the user does his [or her] work. We try to picture how our users would work in the
[distant] future and what kinds of user experience goals we could set. (Interviewee in
Company 2)
Research projects with universities and other research units are very important for us.
These are usually the fastest ways to work with radically new ideas and to search user-
driven opportunities for future offerings. (Interviewee in Company 1)
Prior literature has acknowledged the innovation potential of new digital technologies for the
manufacturing industry [3,58], but little is known about how manufacturing firms should
manage their digital innovation processes [2,4,17]. We grounded our observations in data from a
multi-case study with three multi-national organizations that face the diffusion of digital
technologies as an industry-level disruption. We adopted the knowledge-based view framework,
which emphasizes the management of various types of knowledge in developing innovations
[79,80,100], and focused on how the firms leverage customer knowledge and user knowledge for
use in digital innovation.
Our findings contribute to the literature on digital innovation in three ways. First, we propose
that identifying changes in market needs is a twofold challenge for digital innovation processes
in the B2B manufacturing industry. Second, we identify the roles of customers and users in
guiding these processes and we observe differences in the nature and content of relevant
customer knowledge and user knowledge. Third, we identify practices for leveraging customer
knowledge and user knowledge in digital innovation and provide managerial implications.
Reactive and proactive management of digital innovation in the B2B manufacturing industry
Our first contribution addresses the challenges inherent in understanding market needs and their
evolution when developing digital innovations in the B2B manufacturing industry [21]. We
identified two distinct challenges: the need for companies to be able to react to changing market
requirements, and the need for companies to propose new solutions proactively based on new
digital technologies. We also argue that the second challenge is likely to be significantly more
demanding because of the characteristics of the B2B manufacturing industry.
Our findings suggest that the pace of change in market needs has increased because of advances
in digital technologies and their ongoing diffusion from other industries to the manufacturing
industry. Manufacturers must improve their offerings to meet new industry standards and
suggestions from their customers. Pressure to react to emerging needs has led companies to
become more flexible by, for example, shortening their product development cycles by adopting
agile development processes. To keep up with the changing needs of the market, companies must
have systematic ways to integrate knowledge about their environment into their development
processes [32,33].
Merely reacting to customer needs is not likely to lead to making good use of the opportunities
that new digital technologies provide [2,4]; instead, a proactive approach, where companies drive
change by developing new digital innovations and offering them to their customers, is required.
Managing uncertainties about future market needs is the focal challenge of this proactive
approach. It has often been proposed that companies should conduct experimentations to test
market responses to new technologies in the early stages of the innovation process, thereby
reducing uncertainty [37]. Experimentation and associated iterative innovation processes are
especially suitable for digital innovations [1,16,18,38,39,94,101].
The B2B manufacturing industry has characteristics that influence how companies benefit from
the reactive and proactive approaches. Despite some increase in the pace of change in market
needs related to digital technologies, the overall industry dynamics tend to be stable. Customers
are considered to be conservative, valuing reliability and cost-effectiveness, and unwilling to
take risks with novel innovations. With manufactured products like power plants and ships, the
product life cycles may span multiple decades. New products are significant investments for the
customers, as is their development for the manufacturer. Because of these characteristics and the
tendency for changes in market demand to require small-scale improvements, the reactive
approach to digital innovation is often appropriate. For the same reasons, the proactive approach
is challenging, as experimenting with innovations (as the literature has proposed) is not often
practicable. While such experiments may work well for small-scale features, radical breakaways
from current offerings are likely to be costly to develop, and testing their market potential is
likely to run into skeptical, conservative customers. Hence, we find a mismatch between the
rapid pace of digital technologies’ development and the slow movement of the industry [19]. To
overcome this challenge, the companies we investigated rely on planning instead of
experimenting. They seek to understand their markets in a way that allows them to reduce
uncertainties and pursue a long-term direction to guide their digital innovation processes. Thus,
our findings suggest that, to benefit from the changes new digital technologies bring to the
manufacturing industry, companies must be flexible in the short-term and find new ways to set a
long-term direction for digital innovation.
Distinct roles of customers and users
We find that customers and users have distinct contributions to firms’ ability to respond to the
challenge of identifying market needs. Customers, who are responsible for making the
purchasing decisions related to new innovations but who do not use the products and services
themselves, are an important source of knowledge related to changes in short-term requirements
regarding the functions, properties, constraints, and rationale according to which new products
and services should be designed [102]. However, since customers cannot provide guidance for
digital innovation beyond expressing their immediate needs, their input is restricted in large part
to guiding incremental improvements in existing products and services. Their difficulty in
expressing their long-term future needs derives from the fact that the diffusion of digital
technologies has created a wide new opportunity space where dominant trajectories for future
development have not yet been established. In short, customer knowledge is important in
responding to short-term changes in market needs but does not help in setting long-term
guidelines for digital innovation.
Instead of focusing on customers for their long-term innovation goals, the supplier firms we
investigated focus on users. Because of the low numbers of users and limited access to collective
data about them, the companies had to find ways to understand their individual users and derive
their needs from that understanding. By understanding their users’ latent needs and the users’
work context, they can envision future users' needs and usability criteria for new digital products
and services. Our findings suggest that, to respond to the opportunities and challenges created by
the diffusion of digital technologies [36], such supplier firms should find ways to integrate user
knowledge into their digital innovation processes, as focusing solely on customers’ needs may
limit the firm’s ability to innovate proactivelythat is, beyond incremental improvements in
current products and services [103,104]. It is user knowledge that facilitates the radical
innovations that create new trajectories for future developments [37]. This finding supports
earlier research that sees users as especially valuable in radical innovation [29]. Based on our
findings, we emphasize the need to distinguish customers and users, a distinction that has been
lacking in the extant literature [48,49,51,52]. In the B2B manufacturing industry context of our
study, customers and users are associated with different types of knowledge, management
practices, challenges, and contributions to digital innovation processes (Table 2).
Managing customer knowledge and user knowledge
According to the knowledge-based view of the firm, managing different types of knowledge
requires different practices [79]. Our findings support this view by distinguishing between
practices used mainly for managing explicit customer knowledge and those that deal primarily
with tacit user knowledge (Table 3). Our study contributes to the literature on the knowledge-
based view of the firm by identifying twelve knowledge-management practices that we
categorize according to the type of knowledge [83,88]. In the context of our study, customer
knowledge is usually provided in explicit form, and the case firms are experienced in acquiring it
and in integrating it into the specifications of new innovation projects. However, the user
knowledge that is available to the firms we study is usually in tacit form, embedded in the users
and their work practices and, therefore, difficult to express and transfer. In order to create long-
term goals for digital innovation, the firms adopt new methods for acquiring, disseminating, and
using this kind of tacit knowledge. We argue that it may be beneficial for B2B manufacturing
companies to adopt these kinds of practices for managing tacit knowledge in promoting their
digital innovation processes. Tacit knowledge may help them set long-term goals when no
suitable explicit knowledge is available. The identified practices also add to the literature on
customer and user involvement in innovation by providing practical guidelines for managing the
integration of external knowledge into the innovation process and describing open innovation
activities in the context of the B2B manufacturing industry [21,21,23].
Practical implications
Managers in the B2B manufacturing industry face an interesting situation where their customers
expect them to be involved in digital innovation. At the same time, the customers fail to provide
proper guidance on their needs and are conservative when it comes to digital breakthroughs.
These B2B manufacturing companies may want to make arrangements, such as “digital
innovation labs,” where they can explore new digital technologies with users in a controlled
environment. Interacting with users may help them reduce uncertainties about the future, while
separate structures, decoupled from the regular business, provide an environment for continuous
learning, building capabilities in new digital technologies, and developing innovations based on
them. Users provide guidance in choosing which capabilities and which kinds of innovations
should be developed. Arrangements of this type also make it possible to showcase to customers
new ideas and concepts and to demonstrate to them that the company is involved in digital
innovation. Then, when new capabilities reach maturity, the digital innovations may be
integrated into the daily business. Such arrangements have three benefits: a) interacting with
users can unveil customers’ future needs, b) proficiency in digital innovation and digital
technologies can be built without disturbing the daily business, and c) the company can
demonstrate its involvement in digital innovation without yet having digital technologies in its
product portfolio.
The diffusion of digital technologies creates new opportunities but brings with it uncertainties
about the future. This article suggests two ways that B2B manufacturing companies can leverage
customer knowledge and user knowledge to guide digital innovation processes. Customers can
provide explicit knowledge on how the diffusion of digital technologies changes their short-term
needs, and frequent interactions with key customers can help firms develop incremental
improvements in existing products and services, while tacit user knowledge can make possible
the creation of long-term goals for digital innovation. Since customers cannot help
manufacturing companies find the right directions in which to develop digital innovations
beyond the short-term, companies should seek in-depth understanding of their products’ end
users in order to create long-term goals for digital innovation. For this purpose, firms must adopt
new methods of acquiring, distributing, and using tacit user knowledge.
We gratefully acknowledge financial support by Tekes the Finnish Funding Agency for
Innovation, the Otto A. Malm Foundation, and Airbus Operations GmbH. We further would like
to acknowledge the fruitful discussions with Walter Brenner.
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... In recent years, an increasing number of scholars focused on digital innovation, defining it from the perspectives of process and outcome (Yoo et al., 2010;Henfridsson et al., 2018). The process perspective views digital innovation as the process by which companies use digital technologies to discover and maximize digital opportunities (Abrell et al., 2016;Schallmo et al., 2017;Gurbaxaniv and Dunkle, 2019;Hadjielias et al., 2021). In contrast, the outcome perspective views digital innovation as adding new attributes to existing non-digital products and services through digital resources, thereby forming new products, services and business models (Yoo et al., 2010(Yoo et al., , 2012Nambisan et al., 2019;Jahanmir and Cavadas, 2018). ...
Purpose As an effective tool to promote rational resource allocation and facilitate the development of green management practices such as enterprise digital innovation, the green credit policy has recently gained extensive attention. The purpose of this paper is to analyze the relationship between green credit policies and the digital innovation of enterprises, and to further explore the mechanism of action between them and their boundary conditions. Design/methodology/approach Based on micro-level data on Chinese firms from 2007 to 2019, this paper constructs a difference-in-differences (DID) model to investigate the impact and intrinsic mechanisms of green credit policies on firms' digital innovation and its boundary conditions, with the help of a quasi-natural experiment, i.e. the Green Credit Guidelines. Findings Green credit policies inhibit digital innovation and fail to compensate for innovation. The analysis of the mechanism shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms, and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation. Further analysis reveals that the negative impact of green credit policies on digital innovation is more pronounced in state-owned enterprises, enterprises without financially experienced executives, and in the eastern regions of China. Originality/value This study provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, providing also a basis to further improve green credit policies.
... Finally, enterprise digitalization improves the willingness and performance of new product development. Digital technologies, such as virtual customer environments (VCEs), allow customers to participate in the design of new products [9,61], inspiring enterprises to expand in new business areas. Moreover, technology cooperation between enterprises can be more efficient and less costly with the help of digital technology [55,62], and the exploratory innovation of enterprises will proceed more smoothly. ...
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Digital transformation has become an inevitable choice for manufacturing enterprises to sustain innovation in the digital world. This study selected a sample of Chinese provinces and A-share companies listed on the Shanghai and Shenzhen stock markets from 2011 to 2021 to examine the relationship between regional digitalization, enterprise digitalization, and enterprise ambidextrous innovation. We found that (1) regional digitalization can promote exploitative and exploratory innovation of manufacturing enterprises. Digitalization plays an empowering effect in the promotion process. The regional digital talent reserve significantly impacts ambidextrous innovation in manufacturing enterprises more than network infrastructure construction, digital technology development, and digital technology application; (2) Enterprise digitalization can promote exploitative and exploratory innovation of manufacturing enterprises. Digitalization plays an enabling impact in this promotion process; (3) Regional digitalization can promote the level of manufacturing enterprise digitalization. In promoting enterprise ambidextrous innovation, digitalization can realize the transformation from regional digitalization empowering to enterprise digitalization enabling. The regional digital talent reserve significantly impacts manufacturing enterprise digitalization more than network infrastructure construction, digital technology development, and digital technology application; (4) The impact of regional and enterprise digitalization on exploratory innovation in large-scale manufacturing enterprises is more significant, and large-scale enterprises are better able to transform from digitalization empowering to enabling. The conclusions of this study have specific theoretical and practical significance for revealing the relationship between digitalization and enterprise ambidextrous innovation and then promoting regional digitalization development, manufacturing enterprise digital transformation, and innovation development.
... Based on process theory, some scholars proposed that digital innovation is the combination of digital technologies such as information, computing, communication, and connection used in the innovation process [1]. There are also studies based on the theory of synthesis, pointing out that digital innovation should combine the application of digital technology and the results, including both the efficiency in the innovation process and the generation of innovation results [5]. ...
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Improving the level of digital industry innovation is of great significance to enhance the competitiveness of China’s digital industry and improve the sustainable development advantages of the digital economy. Based on the technology–organization–environment framework (TOE framework), this paper establishes a multi-stage configuration analysis model of factors affecting enterprises’ digital innovation, selects six antecedent variables from technology, organization, and environment, including R&D investment, high-level talents, organizational size, top management team heterogeneity, industrial development speed, and regional digitalization level, and explores the driving path for improving enterprise digital innovation intention and digital innovation performance under the influence of multi-factor combinations. The results are presented that single factors or single dimensions struggle to stimulate high digital innovation, and the combination configuration of multiple factors has a significant impact. The configuration paths that drive high digital innovation intention include the technology–environment type (TE type) driven by technological and environmental factors, organization–environment type (OE type) driven by organizational and environmental factors and technology–organization–environment type (TOE type) driven by technological, organizational, and environmental factors; the configuration paths that drive high digital innovation performance include the technology–organization type (TO type) driven by technological and organizational factors, organization–environment type (OE type) driven by organizational and environmental factors. With the evolution of time, there are configuration solutions with relatively high stability, such as the TE type and OE type that drive high digital innovation intention, and the TO type that drives high digital innovation performance. The results provide a reference for revealing the key influencing factors and driving paths of enterprise digital innovation, and improving the vitality and output level of enterprise digital innovation.
... In recent years, scholars have made many researches on the relationship between "digital technology and enterprise innovation capability". On the one hand, enterprises can use the data integration and analysis advantages of digital technology [1] (Shi Bingzhan, Li Jiantong, 2020) to change the way of enterprise value creation and acquisition [2] (Nambisan S et al., 2019), achieve factor empowerment [3] (Qilei Liu et al., 2021), and thus enable production innovation [4] (Abrel T et al., 2016). On the other hand, digital technology can improve the accuracy of information searching and analyzing [5] (Zhu Qin, 2019), and eliminate the structural barriers of enterprise employees in obtaining information, opportunities and resources [6] (Maomao Chi, 2020), so as to improve the accurate analysis of market demand (Qin Zhu, 2019). ...
Purpose This study aims to investigate the logic between digital transformation and export performance by analysing a large sample of Vietnamese exporters. The theoretical model is further extended by investigating the driving role of digital transformational leadership and mediating effects of firm digital capabilities and employee exploration and exploitation innovation. Design/methodology/approach The study sample comprised 364 middle managers at 107 export firms in Ho Chi Minh City, Vietnam. The data are analysed using a partial least square-structural equation model (PLS-SEM). Findings Digital transformation may be an indispensable tool for enhancing the export performance of businesses. Moreover, digital transformational leadership may drive digital transformation through the mediating mechanisms of firm digital capabilities and employee exploration and exploitation innovations to achieve high export performance. Originality/value This initial study contributes significantly to international business theory through the lens of dynamic capabilities theory, which reveals the role of digital transformation in export performance via mechanisms of digital transformational leadership, firm digital capabilities and employee exploration and exploitation innovation.
Although digital innovation is increasingly important for all types of firms, studies of the drivers of digital innovation rarely cover all sectors. Furthermore, organisational and individual factors are often overlooked. To address these gaps, we use data from a purpose-built cross-industry survey of 1,201 firms in Norway to examine how various types of collaborations and managers attitudes toward them shape firms’ achievement of digital product innovation. We find that collaborations with suppliers, consultants, and universities and research institutes, and the presence of open-minded managers, are positively associated with a firm’s likelihood of digital product innovation. Although much attention has been given to customer relationships, upstream sources of knowledge from suppliers, consultants, and universities and research institutes are also important to strengthen firms’ capacities for digital product innovation. Furthermore, we go beyond examining the existence of partnerships to show that manager’s attitudes towards collaboration also matter.
Christensen and Bower (1996) report the results of a study of how customer power contributes to the failure of leading firms during a period of industry discontinuity. They conclude that developing a customer orientation appears not to be wise advice under these conditions. However, this conclusion is contradicted by long‐standing theory and recent research in marketing. In this commentary we distinguish between two forms of ‘customer orientation’ that are frequently confused. The first, a customer‐led philosophy, is primarily concerned with satisfying customers' expressed needs, and is typically short term in focus and reactive in nature. The second, a market‐oriented philosophy, goes beyond satisfying expressed needs to understanding and satisfying customers' latent needs and, thus, is longer term in focus and proactive in nature. Based on theory and substantial evidence, the advice to become market‐oriented appears sound regardless of the market conditions a business faces. © 1998 John Wiley & Sons, Ltd.
Why might firms be regarded as astutely managed at one point, yet subsequently lose their positions of industry leadership when faced with technological change? We present a model, grounded in a study of the world disk drive industry, that charts the process through which the demands of a firm's customers shape the allocation of resources in technological innovation—a model that links theories of resource dependence and resource allocation. We show that established firms led the industry in developing technologies of every sort—even radical ones—whenever the technologies addressed existing customers' needs. The same firms failed to develop simpler technologies that initially were only useful in emerging markets, because impetus coalesces behind, and resources are allocated to, programs targeting powerful customers. Projects targeted at technologies for which no customers yet exist languish for lack of impetus and resources. Because the rate of technical progress can exceed the performance demanded in a market, technologies which initially can only be used in emerging markets later can invade mainstream ones, carrying entrant firms to victory over established companies.
This paper focuses on dynamic capabilities and, more generally, the resource‐based view of the firm. We argue that dynamic capabilities are a set of specific and identifiable processes such as product development, strategic decision making, and alliancing. They are neither vague nor tautological. Although dynamic capabilities are idiosyncratic in their details and path dependent in their emergence, they have significant commonalities across firms (popularly termed ‘best practice’). This suggests that they are more homogeneous, fungible, equifinal, and substitutable than is usually assumed. In moderately dynamic markets, dynamic capabilities resemble the traditional conception of routines. They are detailed, analytic, stable processes with predictable outcomes. In contrast, in high‐velocity markets, they are simple, highly experiential and fragile processes with unpredictable outcomes. Finally, well‐known learning mechanisms guide the evolution of dynamic capabilities. In moderately dynamic markets, the evolutionary emphasis is on variation. In high‐velocity markets, it is on selection. At the level of RBV, we conclude that traditional RBV misidentifies the locus of long‐term competitive advantage in dynamic markets, overemphasizes the strategic logic of leverage, and reaches a boundary condition in high‐velocity markets. Copyright © 2000 John Wiley & Sons, Ltd.
Why are some firms more successful at introducing radical product innovations than others? Following Schumpeter (1942), many researchers have suggested that firm size is the key organizational predictor of radical product innovation. The authors provide an alternate view and argue that one key variable that differentiates firms with strong radical product innovation records from others is the firms’ willingness to cannibalize their own investments. The authors identify three organizational factors that drive a firm's willingness to cannibalize. Results from a survey of three high-tech industries tend to support the alternate view that willingness to cannibalize is a more powerful driver of radical product innovation than firm size is. These results suggest a need to reconsider conventional wisdom on firm size, cannibalization, and organizational synergy.
This research addresses three questions: (1) Why are some organizations more market-oriented than others? (2) What effect does a market orientation have on employees and business performance? (3) Does the linkage between a market orientation and business performance depend on the environmental context? The findings from two national samples suggest that a market orientation is related to top management emphasis on the orientation, risk aversion of top managers, interdepartmental conflict and connectedness, centralization, and reward system orientation. Furthermore, the findings suggest that a market orientation is related to overall (judgmental) business performance (but not market share), employees’ organizational commitment, and esprit de corps. Finally, the linkage between a market orientation and performance appears to be robust across environmental contexts that are characterized by varying degrees of market turbulence, competitive intensity, and technological turbulence.
This paper focuses on dynamic capabilities and, more generally, the resource-based view of the firm. We argue that dynamic capabilities are a set of specific and identifiable processes such as product development, strategic decision making, and alliancing. They are neither vague nor tautological. Although dynamic capabilities are idiosyncratic in their details and path dependent in their emergence, they have significant commonalities across firms (popularly termed ‘best practice’). This suggests that they are more homogeneous, fungible, equifinal, and substitutable than is usually assumed. In moderately dynamic markets, dynamic capabilities resemble the traditional conception of routines. They are detailed, analytic, stable processes with predictable outcomes. In contrast, in high-velocity markets, they are simple, highly experiential and fragile processes with unpredictable outcomes. Finally, well-known learning mechanisms guide the evolution of dynamic capabilities. In moderately dynamic markets, the evolutionary emphasis is on variation. In high-velocity markets, it is on selection. At the level of RBV, we conclude that traditional RBV misidentifies the locus of long-term competitive advantage in dynamic markets, overemphasizes the strategic logic of leverage, and reaches a boundary condition in high-velocity markets. Copyright © 2000 John Wiley & Sons, Ltd.
- This paper describes the process of inducting theory using case studies from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.