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

April 2016
Information & Management 53(3)

DOI:10.1016/j.im.2015.12.005

Authors:

Matti Pihlajamaa

VTT Technical Research Centre of Finland

Jan vom Brocke

ERCIS - European Research Center for Information Systems

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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.

https://doi.org/10.1016/j.im.2015.12.005

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

Manufacturing Firms

Abstract

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.

Keywords

digital innovation; customer knowledge; user knowledge; innovation management;

knowledge-based view

Introduction

The diffusion of new digital technologies into the manufacturing industry creates new

opportunities for digital innovation [1–3]. 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 [5–8].

Digitization of products is also likely to have significant disruptive effects [8–12] in that

firms’ competitiveness will depend heavily on the firms’ ability to innovate using digital

technologies.

Extant research on digital innovation has focused on the use of digital tools to facilitate

innovation processes [10,13–15] and the innovation opportunities digital technologies create

[4,16–18]. 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 [2,11,17–20]. 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 [22–25]. 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 knowledge—that 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. Users—who are, for example, vehicle or

plant operators—are not typically involved in purchasing decisions, and the customers—who

make the purchasing decisions—do 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 artifacts—programmability,

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., [48–52]). 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 using—not selling—a service or product

[21,55].

Research on customers and users in innovation in the B2B sector focuses on lead user

innovation [40,56–58], user communities [59–62], local or “sticky” information [63–66], and

user involvement in innovation processes [29,67,68]. Other literature streams include

innovative users [54,69,70] and user entrepreneurship [71–74] . 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 [5–7]. 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 capabilities—such as those that support maintenance

processes—and 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

plants.

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

Company 1

Company 2

Company 3

Field

Marine products

Automation and

systems technologies

Plant and component

technologies

Size (# of

employees)

52,000

16,000

157,000

Location of the unit

interviewed

Finland

Germany

Interviewees’

profiles

Design managers,

product managers,

sales support

managers, sales

managers,

maintenance

managers

R&D managers,

R&D directors, top

managers

R&D managers,

innovation managers,

IT application

support managers

Number of

interviews

Length of

interviews

49-80 minutes

(mean: 63 minutes)

55-143 minutes

(mean: 84 minutes)

54-78 minutes

(mean: 64 minutes)

Type of interview

Face-to-face

Telephone

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

[96,99].

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.

Findings

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

collectively:

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

trajectories.

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 suppliers—that is, our case companies—have 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

Customers

Users

Type of knowledge

Mostly explicit

Mostly tacit

Knowledge sources

Distributed

Individuals

Guidance for innovation

Short-term changes in

market needs

Long-term vision for digital

innovation

Innovation goals

Mostly incremental

improvement of existing

products and services

Introducing radically new

products and services and

improving existing ones

Examples of knowledge

Category

From

Practice

Description

Purpose

Type of

knowledge

Companies

Acquiring

knowledge

Customers

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

Ongoing

discussions with

customers to keep

up with their

changing needs.

Learning about new

product

requirements and

problems with

existing products.

More explicit

1, 2, 3

Management

meetings

Regular meetings

with important

customers’

management.

Understanding the

customer’s big

picture and long-

term goals.

More explicit

1, 3

Users

Use of front-line

employees

Using maintenance

staff and field

service engineers as

knowledge sources;

hiring R&D

employees with

user backgrounds.

Understanding

workflows and the

usability of current

products without

involving the users

themselves.

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

practices.

Prototyping

Demonstrating new

concepts with

tangible prototypes.

Demonstrating and

discussing novel

products and

features.

More tacit

Distributing

knowledge

Customers

IT systems

Updating new

customer

knowledge into, for

example, a CRM

system.

Bringing relevant

parties up to date

on changes in

customer

requirements.

More explicit

1, 2, 3

Users

User-insight

workshops

Regular internal

workshops to

disseminate user

knowledge

throughout the

company.

Making the most of

the limited access

to user knowledge;

sharing information

about users.

More tacit

Informal cross-

functional

interactions

Encouragement for

ad hoc discussions;

open cross-

functional

workspaces.

Promoting the

sharing of user

knowledge across

organizational

functions.

More tacit

1, 2

Using

knowledge

Customers

Product

specification lists

and roadmaps

Compiling

customer

requirements and

user requirements

into specification

lists.

Setting goals for

new development

projects.

More explicit

1, 2, 3

Users

Internal scenario

work

Building future

scenarios that

describe trends and

future use cases.

Creating explicit

goals and vision

based on tacit user

knowledge.

More tacit

2, 3

Collaborative

research projects

Future-oriented

research projects

with research

institutes and other

firms on

technologies and

ways of working.

Leveraging external

organizations to

help envision future

scenarios.

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

discussions:

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)

Discussion

Prior literature has acknowledged the innovation potential of new digital technologies for the

manufacturing industry [3,5–8], 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 proactively—that 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.

Conclusion

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.

Acknowledgements

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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Development of Outcome-Driven Product Roadmaps

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Stefan Trieflinger

Technological innovations of hidden champions: Evidence from patent data

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J PROD INNOVAT MANAG

The hidden champion concept has received much interest in practice. As market leaders in niche markets, hidden champions represent the success of the (German) Mittelstand. Innovation is a key element of their strategy and their focus and niche market strategy are associated with specific technological capabilities. However, thus far, little quantitative empirical evidence exists about the innovation output of hidden champions. Drawing on a capability perspective and using patent data, the present study analyzes differences between hidden champions and comparable non‐hidden champion firms in their technological innovation. Our results show that hidden champions have a significantly larger technological innovation output but do not have a higher efficiency in their innovation creation compared to other firms from the same industry, size, and age. Moreover, the innovations produced by hidden champions show higher levels of technological depth and indicate lower levels of technological breadth. The sources of technological knowledge of hidden champions seem to be more inward oriented. Finally, innovations of hidden champions have similar technological impact, novelty and quality compared to those of other firms. Overall, our study supports many of the anecdotal beliefs about the innovation of hidden champions contributing to a better understanding of what makes hidden champions different from other Mittelstand firms. Practical implications for hidden champions and Mittelstand firms are discussed.

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Nov 2024

The primary objective of the research study is to investigate the influence of innovative digital tools and technologies in attaining the fundamental aspects of Industry 4.0, including automation, integration, traceability, flexibility, safety, and security. This study utilizes a systematic literature review, employing keyword search criteria to examine 187 relevant articles and evaluate the implementation and consequences of technologies, such as artificial intelligence, big data, cyber-physical systems, cloud computing, and digital twin, across a variety of Indian economic sectors, including retail, healthcare, manufacturing, agriculture, entertainment, and e-commerce. The study’s results indicate that digital technologies can improve parameters such as automation, transparency, integration, traceability, flexibility, safety, and security, while promoting sustainable development through reduced waste, operational costs, and time, and optimum resource utilization. The insights from this study will be helpful for future inquiries and economic initiatives, encouraging the adoption of digital innovations in fields such as women’s empowerment, public administration, and skill development.

Enterprise ESG Performance, Digital Transformation, and Firm Performance: Evidence from China

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Oct 2024

Plain language summary Enterprise ESG performance, digital transformation, and firm performance: evidence from China Based on data from A-share listed companies in China from 2016 to 2022, we constructed a regression model to verify the influence of environmental, social, and governance (ESG) performance on company performance under digital transformation. First, the benchmark regression model verified that ESG performance can directly improve firm performance. Second, through a test of the intermediary scheme, we found that promoting enterprise ESG performance to enhance firm performance can also be achieved by promoting enterprise innovation, which plays a partial intermediary role between the two. Additionally, we built a regulated intermediary test model and showed that digital transformation can positively regulate ESG performance and promote firm performance by enhancing enterprises’ innovation ability. Finally, we divided the research sample into groups and discussed the heterogeneity of ESG performance on enterprise performance under different property rights and industry attributes. This research provides significant guidance for ESG development in China and the digital transformation of enterprises.

How Government Subsidies Facilitate Digital Transformation of Suppliers

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Full-text available

Sep 2024

Resource and capacity constraints are common challenges for suppliers in digital transformation. This study examines the impact of government subsidies on supplier digital transformation from the perspectives of supply chain diffusion and the local government competition, with supply chain relationship data from A-share listed companies spanning 2010 to 2021. The findings indicate that: government subsidies promote suppliers to expand their diversified downstream customer base, which will boost supplier digital transformation. However, the spillover effect within the same region is higher than that across regions. The heterogeneity analysis results indicate that the effect of government subsidies becomes increasingly apparent as the opportunistic risk escalates; conversely, the impact of such subsidies is considerably constrained by heightened systemic risk. The higher the human capital of suppliers and the smaller the digital divide between suppliers and customers, the better the effect of suppliers receiving innovation diffusion. Further analysis reveals that local government subsidies to upstream suppliers reduce the proportion of downstream local customer purchases outside the jurisdiction, thereby hindering cross-regional spillover effects. This article enriches the research on the promotion mechanism of enterprise digital transformation and the efficiency loss of market segmentation, providing some reference for optimizing local government subsidy behavior.

How Government Subsidies Facilitate the Digital Transformation of Suppliers

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Full-text available

Oct 2024

Digital transformation is increasingly recognized as a key driver of sustainable development, enabling suppliers to improve efficiency, reduce resource consumption, and adapt to changing market demands. However, it remains a challenging process for suppliers, often hindered by resource and capacity constraints. This study investigates how government subsidies can facilitate supplier digital transformation, considering supply chain diffusion and local government competition dynamics. Using data from A-share listed companies in China between 2010 and 2021, our analysis reveals that government subsidies significantly promote supplier digital transformation by encouraging a more diversified downstream customer base. Moreover, customer digital transformation can facilitate supplier digital transformation, but spillover effects are higher within the same jurisdiction than across different jurisdictions. This study further identifies that the impact of government subsidies is more pronounced under higher opportunistic risk but is constrained by systemic risk. Additionally, suppliers with higher human capital and a smaller digital divide with customers exhibit greater effectiveness in adopting innovation diffusion. These findings provide valuable insights into optimizing local government subsidies policies to enhance supplier digital transformation and contribute to the broader goal of sustainable development.

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Exploring Liminal Experience of Entrepreneurs in Information and Communication Technology Innovation in Underdeveloped Regions

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INFORM MANAGE-AMSTER

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Bus Process Manag J

Purpose This study aims to perform a bibliometric and systematic literature review (SLR) to elucidate the current landscape and to propose a framework that defines and explores research strands to guide the formulation of business process management (BPM) strategies anchored in digital innovations. Design/methodology/approach A bibliometric analysis of 238 papers from the last 10 years (2014–2023) that were correlated to BPM strategies and digital innovations was conducted. Additionally, a SLR was performed on 15 selected papers, making it possible to propose a framework that explored research strands through the content analysis. Findings A framework comprising eight research strands (value creation; ambidexterity; governance; agility in processes; strategic process management; external context and culture and integrated systems) was developed. It proposes a research agenda and provides valuable insights as a reference for formulating BPM organizational strategies anchored in digital innovations. Research limitations/implications This study acknowledges the limitation of utilizing a single software for bibliometric analysis. The framework developed in this study has not yet been validated. Practical implications This paper provides insights to assist managers in comprehending BPM strategies anchored in digital innovations and calls for organizations to prepare for future digital needs. Social implications This paper contributes to advancing our understanding of BPM aligned with the demands of a digital age society. Originality/value The developed framework sheds light on the current landscape and research strands that explore the association between digital innovations and BPM strategies. This provides a valuable opportunity for future qualitative and quantitative studies to explore the eight research strands presented in the framework.

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Customer‐led and market‐oriented: let's not confuse the two

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STRATEGIC MANAGE J

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.

CUSTOMER POWER, STRATEGIC INVESTMENT, AND THE FAILURE OF LEADING FIRMS

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Mar 1996
STRATEGIC MANAGE J

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.

Dynamic capabilities: what are they?

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Oct 2000
STRATEGIC MANAGE J

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.

Organizing for Radical Product Innovation: The Overlooked Role of Willingness to Cannibalize

Article

Nov 1998

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.

Market Orientation: Antecedents and Consequences

Article

Jul 1993
J MARKETING

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.

Dynamic capabilities: what are they?

Article

Oct 2000
STRATEGIC MANAGE J

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.

Qualitative research and evaluation methods

Book

Jan 2002

M. Q. Patton

The Accidental Entrepreneur: The Emergent and Collective Process of User Entrepreneurship

Article

Jan 2007

Building theories from case study research

Article

Jan 2002

- 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.

The lead user method: theoretical-empirical foundation and practical implementation

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