A consumer perspective on Corporate Digital Responsibility: an empirical evaluation of consumer preferences

Abstract

While digitalization offers numerous new possibilities for value creation, managers have to overcome a number of threats and obstacles that it harbors. In this context, the concept of Corporate Digital Responsibility (CDR) is of increasing interest to practitioners. Drawing on the well-established paradigm of Corporate Social Responsibility, CDR comprises a set of principles designed to encourage the ethical and conscientious development, adoption, and utilization of digital technologies. This work aims at contributing to the evolving research base by empirically assessing consumer preferences and a consumer segmentation approach with regard to companies’ concrete CDR activities, thus supporting the operationalization of CDR. Hence, this work provides concrete guidance for firms’ CDR activities in practice. To this end, a series of Best–Worst Scaling and dual response studies with a representative sample of 663 German-speaking participants assesses consumers’ perspectives on firms’ concrete (possible) activities within several CDR dimensions. Both DURE studies reveal the potential halo effect of data privacy and security activities on the perception of the CDR engagement at large, suggesting a more holistic approach to digital responsibilities. Besides, the findings reveal that in case of CDR one size does not fit all. Especially in terms of informational approaches, consumer preferences are rather heterogeneous suggesting that consumer segmentation is beneficial for companies. Additionally, the high importance of price for the consumers’ evaluation shows that it can be useful to offer a slimmed-down version in terms of CDR activities for more price-conscious consumers.

Full text

Vol.:(0123456789)
Journal of Business Economics (2024) 94:979–1024
https://doi.org/10.1007/s11573-023-01142-y
1 3
ORIGINAL PAPER
A consumer perspective onCorporate Digital
Responsibility: anempirical evaluation ofconsumer
preferences
K.ValerieCarl1 · CristinaMihale‑Wilson1 · JanZibuschka2 · OliverHinz1
Accepted: 13 February 2023 / Published online: 2 March 2023
© The Author(s) 2023
Abstract
While digitalization offers numerous new possibilities for value creation, manag-
ers have to overcome a number of threats and obstacles that it harbors. In this con-
text, the concept of Corporate Digital Responsibility (CDR) is of increasing inter-
est to practitioners. Drawing on the well-established paradigm of Corporate Social
Responsibility, CDR comprises a set of principles designed to encourage the ethi-
cal and conscientious development, adoption, and utilization of digital technologies.
This work aims at contributing to the evolving research base by empirically assess-
ing consumer preferences and a consumer segmentation approach with regard to
companies’ concrete CDR activities, thus supporting the operationalization of CDR.
Hence, this work provides concrete guidance for firms’ CDR activities in practice.
To this end, a series of Best–Worst Scaling and dual response studies with a repre-
sentative sample of 663 German-speaking participants assesses consumers’ perspec-
tives on firms’ concrete (possible) activities within several CDR dimensions. Both
DURE studies reveal the potential halo effect of data privacy and security activi-
ties on the perception of the CDR engagement at large, suggesting a more holistic
approach to digital responsibilities. Besides, the findings reveal that in case of CDR
one size does not fit all. Especially in terms of informational approaches, consumer
preferences are rather heterogeneous suggesting that consumer segmentation is ben-
eficial for companies. Additionally, the high importance of price for the consumers’
evaluation shows that it can be useful to offer a slimmed-down version in terms of
CDR activities for more price-conscious consumers.
Keywords Corporate Digital Responsibility· Ethical guidelines· Consumer
preferences· Discrete choice experiments· Dual response· Consumer segmentation
* K. Valerie Carl
kcarl@wiwi.uni-frankfurt.de
1 Chair ofInformation Systems andInformation Management, Goethe University Frankfurt,
FrankfurtamMain, Germany
2 Robert Bosch GmbH, Renningen, Germany
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JEL codes O30
1 Introduction
More advanced and efficient digital products and services continuously enter the
market due to the ongoing progress in digital technologies. This progress allows
connecting and equipping a wide range of objects, devices, machines, or build-
ings with sensors, tags, or software. However, while these more sophisticated
goods and services offer new possibilities for value creation, managers have to
overcome a number of threats and obstacles that digitalization also harbors (Hess
etal. 2016). In an effort to reap the benefits of digitalization and appropriately
manage the associated risks, a debate on Corporate Digital Responsibility (CDR)
has emerged (Lobschat et al. 2021; Mihale-Wilson et al. 2021). CDR closely
relates to the concept of Corporate Social Responsibility (CSR), both summa-
rized under the concept of Corporate Responsibility. The concepts pursue similar
goals, namely minimizing negative impacts and maximizing the positive impacts
of corporate practices, despite different foci. CSR addresses socially and environ-
mentally relevant issues (Maignan and Ralston 2002), while CDR efforts focus
mainly on effects of corporate digital activities and digitalization in general to
establish ethical and responsible practices for the development, deployment, and
use of digital technologies and data. The concept of CDR pursues the goal to
provide a more holistic approach to responsibilities emerging in the digital con-
text rather than addressing them in an isolated manner like issues related to data
privacy or access. Accordingly, such a concept and the broad approach associ-
ated with it tend to reflect the reality in which (digital) responsibilities also do
not occur in isolation. However, the concrete implementation of these concepts
hinges on the individual understanding of the concept within the company or the
implementing individuals (van Marrewijk 2003).
Recently, the concept CDR gains increasing attention from research and prac-
tice. Previous research shed light on defining CDR and its underlying responsibil-
ities (e.g., Lobschat etal. 2021; Herden etal. 2021), discussed CDR as a special
application to Artificial Intelligence (AI) governance (e.g., Elliott etal. 2021),
or in different industry and economic settings (e.g., Etter etal. 2019; Jones and
Comfort 2021). In short, most research on CDR is rather conceptual yet (Mueller
2022). As part of this debate, several approaches share a common understanding
of various areas covered by the concept (Mihale-Wilson etal. 2022). However,
current research on CDR calls for a more empirical approach to the issue because
the conceptualization converges increasingly (Mihale-Wilson etal. 2022; Mueller
2022). Hence, this work contributes to existing research on the subject of CDR by
adding a more empirical angle to the discussion (see Fig.1 for an overview on the
status-quo of CDR research). The publication aims at operationalizing CDR in
practice by empirically assessing concrete CDR activities in a quantitative fash-
ion. Based on an initial empirical approach to the issue by ranking the dimensions
of CDR (Mihale-Wilson etal. 2021), we assess the operationalization of concrete
CDR-related activities on measure-level and evaluate a possible segmentation,
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thus individualization of the offered products and services. The adoption of
standards and practices can take place in a variety of manners and levels (e.g.,
Matten and Moon 2008) and address different stakeholder groups (e.g., consum-
ers, employees, suppliers, the society). The different demands and expectations
of the CDR concept are stakeholder-dependent (e.g., Trittin-Ulbrich and Böckel
2022). Accordingly, research and practice should consider preferences of all rel-
evant stakeholder groups for a broad understanding of CDR. Besides, implement-
ing CDR activities in practice grounds on different motivations (Schaltegger and
Burritt 2018). Motivation can be either intrinsic or extrinsic. Intrinsic motiva-
tion depends on the personality of the implementing persons or their manage-
ment. Intrinsic motivation proved to be very central in the implementation of
CSR (Schaltegger and Burritt 2018). We can already observe the first efforts to
drive CDR from intrinsic motivation in practice. Another source of motivation
can be extrinsic motivation triggered by stakeholder demands (Schaltegger and
Burritt 2018). This study focuses on the operationalization of CDR in practice
originating in extrinsic motivation. Usually, companies have a limited budget for
conducting activities related to CSR and CDR. Hence, the successful deployment
of CDR depends—in case of extrinsically motivated activities—on the ability of
firms to implement dimensions and activities in a manner that matches stakehold-
ers’ demands (Kesavan etal. 2013). Research can help to align corporate engage-
ment regarding CDR and stakeholders involved to maximize the potential of the
CDR activities. Consumers’ perception of implemented CDR activities has the
capability to influence the opinion about a company and hence consumption and
adoption decisions (e.g., Schreck and Raithel 2018; Edinger-Schons etal. 2020).
The concept of CDR covers a wide range of fields, but in particular puts consumer
Fig. 1 Status-quo of CDR research
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needs and their rights in a digital world in the center of attention. Thus, this work
concentrates on consumers as one key stakeholder group to be able to guaran-
tee a profound evaluation. Additionally, prior research indicated the appropriate-
ness for companies to address different consumer segments of digital products
and services individually due to heterogeneous preferences (e.g., Naous and Leg-
ner 2017; Mihale-Wilson etal. 2019). Hence, this publication assesses consumer
preferences and quantifies them. As is usually the case with new concepts or
technologies, consumers are not equally enthusiastic about these developments.
Accordingly, there is always a group of consumers who are not enthusiastic about
this development, an undecided group, and, usually, a group of consumers who
would value this development. Thus, it is important to understand each of these
different consumer groups in their heterogeneity. With this study, we pursue con-
sequently two main goals: first, to assess why some consumers do not value the
operationalizing of CDR. Second, this study pursues a consumer segmentation
approach to evaluate the additional earnings potential for firms accompanied by
an individualization of CDR activities conducted. In this vein, this work provides
concrete guidance for the operationalization of CDR activities and consumer seg-
mentation in practice, supporting the broad adoption of the concept in corporate
practice (i.e., also by targeting consumers not yet enthusiastic).
BWS 2
Aim: Sequencing the
sub-dimensions of
the top-ranked CDR
dimension
informs
BWS 1
Aim: Sequencing
the proposed CDR
dimensions
informsDURE 1
Aim: Consumers’
perception of concrete CDR
activities within the top
three sub-dimensions of the
top-ranked CDR dimension
DURE 2
Aim: Consumers’
perception of concrete CDR
activities within the further
CDR dimensions
Set of quantitative pre-studies (Mihale-Wilson et al. 2021) Set of main studies
(a) Conducted sets of studies
Expert discussion
Aim: Discussing
the scope of CDR
and different
approaches
Qualitative pre-study
informs
Top ranked
CDR
dimension
Lower ranked CDR dimensions
Three top
ranked sub-
dimensions
Sub-dimensions of the lower ranked CDR
dimensions
Lower ranked
sub-dimensions
CDR dimensions
Sub-dimensions
Activities
One activity
per sub-
dimension
One activity
per sub-
dimension
One activity
per sub-
dimension
One activity
per sub-
dimension
One activity
per sub-
dimension
One activity
per sub-
dimension
One activity
per sub-
dimension
BWS 1 (Mihale-Wilson et al. 2021)
BWS 2 (Mihale-Wilson et al. 2021)
DURE 1 DURE 2
(b) Research subjects per conducted study
Fig. 2 Conducted set of studies and their research subjects
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We conduct a series of studies with 663 German-speaking participants to
derive insights into consumers’ valuation of CDR dimensions, their correspond-
ing sub-dimensions, and concrete CDR activities (see Fig.2, Fig.7 for a detailed
overview). We have selected Germany as the first application testing country, as
a high level of regulatory requirements (e.g., the GDPR) already applies, which
requires a greater commitment for activities classified as CDR activities. In
addition, there is already a high level of awareness and sensitivity for corporate
responsibilities in the digital context in Germany, and organizations and research
located here play a leading role in the further development of the concept of
CDR. For example, governmental efforts in Germany target bringing together
companies in the so-called “CDR initiative” to further develop and anchor the
concept in practice. The empirical evaluation grounds on two types of Discrete
Choice experiments (DCEs) with a strong foundation in behavioral and mar-
ket research, especially for products not yet on the market (Swait and Andrews
2003; Naous and Legner 2017). Before designing and conducting the set of main
studies, we performed a set of pre-studies (Mihale-Wilson etal. 2021) featur-
ing two different types of studies to limit the number of attributes evaluated by
the participants. Firstly, we conducted a (qualitative) expert discussion to dis-
cuss topics summarized under the umbrella concept CDR, applied eight dimen-
sions (Thorun etal. 2017) as the best fitting discussed concept, and developed
according sub-dimensions and concrete activities. Secondly, we conducted a set
of pre-studies employing two Best–Worst Scaling (BWS) experiments (Mihale-
Wilson etal. 2021). The first BWS experiment evaluated the importance of the
CDR dimensions for consumers. We then explored these insights more in depth
by sequencing several sub-dimensions of the most important CDR dimension
in BWS 2. Summing up, aim of the set of pre-studies was to sequence the pro-
posed CDR dimensions and the sub-dimensions of the top-ranked CDR dimen-
sion by importance (Mihale-Wilson etal. 2021). With the help of this set of pre-
studies, we cannot give recommendations for concrete CDR activities, but we
could identify possible fields of action that are most important for consumers.
Thus, the results from both BWS pre-studies (Mihale-Wilson etal. 2021) inform
the design of the set of main studies consisting of two Dual Response (DURE)
experiments. DURE 1 focuses on the most valued activities within the top three
sub-dimensions of the top-ranked dimension, and DURE 2 addresses the remain-
ing five dimensions of CDR. By this means, we provide concrete guidance for a
comprehensive set of CDR activities and the concept’s operationalization, thus
supporting a broader adoption of the concept in practice. Our results enable a
consumer segmentation approach to CDR activities, thus to individualize digital
offerings.
The next section introduces CSR and a conceptualization of CDR based on
existing literature completed by the state of research in the field of DCEs. Sec-
tion three (set of pre-studies, Mihale-Wilson etal. 2021) and section four (set
of main studies) introduce the methodology, study design, and the results of our
conducted set of studies. We conclude with a discussion of the results and impli-
cations for theory, practice, and future research.
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2 Related work
As discussed earlier, research regards the concept of CDR and CSR as inter-
twined. Both concepts belong to the subordinate concept of Corporate Respon-
sibility. However, CDR deserves the independent attention of the research
community as it focuses on the unique responsibilities made necessary by the
ongoing digitalization (Lobschat etal. 2021). The strong technological focus of
the concept conditions the distinction between CDR and CSR (Mihale-Wilson
etal. 2022). To elaborate the gap between CSR and CDR, we first review the
main underlying ideas of CSR. We then discuss the core components of CDR
referencing to existing literature on unique ethical and social issues posed by
the digital era. Subsequently we present the methodological foundation of our
research.
2.1 Corporate Social Responsibility
A widely used and established definition of the concept (Pirsch et al. 2007)
describes CSR as the society’s expectations towards companies in economic,
legal, ethical, and discretionary (philanthropic) matters (Carroll 1979). Corpo-
rate responsibilities in the context of CSR capture these expectations and per-
ceived responsibilities towards society. Hence, these responsibilities set the
frame for interactions between companies and society (Matten and Moon 2007).
For instance, economic responsibilities regarding CSR refer to the company’s
purpose to achieve profits, satisfy affected stakeholders, and create sustainability
in the long term. While organizations must follow legal obligations (i.e., regula-
tions, laws) when offering products or services, ethical responsibilities in the
context of CSR relate to behavior according to “what is right, just and fair, even
when they are not obliged to by the legal framework” (Matten and Moon 2007,
p. 181). Discretionary responsibilities related to CSR describe behavior foster-
ing the well-being of the associated communities. In particular, the economic
and legal responsibilities are a necessary prerequisite for companies’ survival,
while ethical and philanthropic commitments are desirable additions (Matten
and Moon 2007). However, based on the notion that organizations can deter-
mine in the short term the extent to which they will undertake certain respon-
sibilities, the CSR concept anticipates that organizations will adopt activities
and initiatives that exceed the requirements necessary for them to run their busi-
ness. The motivation for implementing such CSR activities differ fundamen-
tally and can be extrinsic (i.e., requirements of relevant stakeholder groups) or
intrinsic (e.g., motivation of involved employees and managers, so-called change
agents) (Schaltegger and Burritt 2018). CSR initiatives can encompass a vari-
ety of actions—depending on the individual understanding of the concept—that
address the environment, (physical) product safety, human rights, human dig-
nity, economic development, sustainability, community involvement, and many
more (Kesavan etal. 2013).
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2.2 Corporate Digital Responsibility
CDR focuses on challenges to the ethical practices of companies that are peculiar
to digitalization and the digital era. Associated concrete goals, norms, and values
depend on the individual understanding of the implementing organization or the
personal understanding (van Marrewijk 2003). Previous research on the ethical and
social implications of digitalization indicates that digital technologies (e.g., Internet
of Things (IoT), robotics, digital platforms, AI, social media) result in key societal
and ethical issues for privacy, security, autonomy, justice, human dignity, and bal-
ance of power (Royakkers etal. 2018). The relevance of these topics is rising, driven
by the “exponential growth in technological development, malleability of technol-
ogies and data in use, and pervasiveness of technology and data” (Lobschat etal.
2021, p. 876). Hence, CDR focuses on unprecedented risks and obstacles of (digital)
technologies rather than the relatively broad goal of CSR concerning society where
technology plays only a subordinate role (Mihale-Wilson etal. 2022). Overall, the
concept of Corporate Responsibility comprises both CSR and CDR, two partially
overlapping concepts. Nevertheless, the distinct issues arising in a digitalized world
suggest that an expanded conceptualization of Corporate Responsibility in the digi-
tal setting is worthwhile, thus motivating separate conceptualizations of CSR and
CDR applying simultaneously.
CDR gains increasing traction in research and practice alike. The current schol-
arly debate shares a common understanding of different areas of CDR activities
aimed at consumers despite differing nomenclature. Hence, the conceptualization
converges increasingly (Mihale-Wilson et al. 2022). Therefore, this publication
rather moves in the encouraged direction of operationalization and empirical assess-
ment of the CDR concept (Mueller 2022). To empirically assess the operationaliza-
tion of CDR in practice, this work needs to choose one of the systematic approaches
to the commonly agreed areas addressing consumers. To provide concrete guid-
ance for practice it is crucial that the approach is easy to access for practitioners.
Hence, we opted for a practice-driven approach comprising eight dimensions (Tho-
run etal. 2017) that reflect the common understanding of CDR activities (Mihale-
Wilson etal. 2022). For example, compared to some other conceptual approaches,
this nomenclature makes the ecological component less central as a separate dimen-
sion. Still, the difference between the concepts does not reside in the scope of CDR
but rather in the division and nomenclature of the dimensions and thus different
foci. The selected approach and its dimensions (Thorun etal. 2017) are suitable for
encouraging the ethical and responsible deployment of technology and data. Even
though these dimensions emerged in the context of the practice-oriented CDR dis-
course, previous research on Information Systems (IS) and Business Ethics theo-
retically validate the eight dimensions. Especially for the concept of CDR, prior
research on IS is of immense importance, as the field of IS deals with operationali-
zation on technology- and product-level in practice and thus matches the understand-
ing and approach to CDR. Table1 establishes this relationship between the practi-
cally formulated dimensions and existing research concerning some dimensions of
the concept. Although the CDR concept is new in research, the individual elements
of it are not new to IS research (e.g., Mason 1986; Hsieh etal. 2008). Approaching
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Table 1 Overview of the eight CDR dimensions (adapted from Mihale-Wilson etal. 2021)
CDR dimension Description (based on Thorun etal. 2017) Exemplary related work
I. Access Consumers should have access to basic digital goods and
services
Hsieh etal. (2008) and Lameijer etal. (2017)
II. Education and awareness Consumers should be educated. This includes their awareness
of ecological, social, and societal aspects and the economic
consequences of their consumption decisions
Hsieh etal. (2008) and Venkatesh and Sykes (2013)
III. Information and transparency Consumers should have access to appropriate information so
that they can be informed according to their individual wishes
and needs
Awad and Krishnan (2006) and Granados etal. (2010)
IV. Economic interests The economic interests of consumers should be protected and
promoted
Lewis (2013) and Bourreau etal. (2015)
V. Product safety and liability Consumers should be protected from risks to their health and
safety
Daughety and Reinganum (1995) and Smith (2017)
VI. Data privacy and security The protection of consumers’ privacy and the free flow of
information should be ensured, and both protected and secure
payment mechanisms should be offered
Mason (1986), Bélanger and Crossler (2011), and
Heimbach and Hinz (2018)
VII. Dispute resolution and awareness Consumers should have access to effective dispute settlement
and appeal procedures
Turel etal. (2008) and Ang and Buttle (2012)
VIII. Governance and participation mechanisms Legal organizations and regulators should ensure that there are
appropriate governance and participation mechanisms in place
Thorun etal. (2017)
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these yet often isolated dimensions under the overarching concept of CDR helps, for
instance, companies to have a comprehensive approach to the topic, consumers to
have a broader awareness, and supports legislative organizations in regulation.
(I) Access refers to the ability to physically and mentally access digital tech-
nologies, products, or services (Hsieh etal. 2008; Lameijer etal. 2017). This
dimension ensures digital inclusion alongside affordability, perceived ease of
use, or required prior knowledge (Venkatesh and Brown 2001; Díaz Andrade
and Techatassanasoontorn 2021). Besides, companies can offer access to
services and products without entering personal data to reduce consumers’
privacy concerns.
(II) Education and awareness comprises all actions that empower consumers
with information and advice related to the process of purchasing online,
data required for online transactions, consumer rights related to data privacy
and security, how to exploit these rights, and understanding technologies
(Thorun etal. 2017; United Nations 2018). In addition, this CDR dimen-
sion encourages companies to equip consumers with tools allowing them to
comprehend the consequences of their digital use and behavior, and to enable
better-informed decisions about digitalization in the future, e.g., concerning
digital well-being, environmental, and societal issues. Both, the access and
the education dimensions, seek to reduce the digital divide that results from
differences in technology access and capabilities (e.g., Hsieh etal. 2008;
Venkatesh and Sykes 2013) therefore pursuing digital empowerment. Prior
research broadly agrees that inequalities in technology access or technology-
related abilities have negative impacts on both individuals and society. Thus,
an ethical, conscientious, and enduring approach to technology should incor-
porate measures to mitigate inequities in technology access and capabilities.
(III) Information and transparency In addition to education, another key require-
ment for informed decision-making is information and transparency. With
the advent of the Internet and immediate availability of information, consum-
ers’ desire for information and transparency also constantly increased (Awad
and Krishnan 2006; Granados etal. 2010) when consumers “expect to be
very well informed, spoiled, and empowered” (Granados and Gupta 2013,
p. 637). Thus, there is a pressure on companies to provide more information
and transparency. Transparency is not just about explicitly outlining the
capabilities of a product or service but instead related to pricing, products’
provenance, the resources such products were made of, quality, certifications,
internal governances, and especially overlapping also with the dimension of
data privacy and security (e.g., Granados and Gupta 2013; Carl and Mihale-
Wilson 2020).
(IV) Economic interests Similar to information transparency, the CDR concept
also encourages businesses to consider the economic interests of their con-
sumers, e.g., by the adoption of an appropriate competition policy (United
Nations 2018). There is a broad literature base on competing economic inter-
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ests between consumers and business in the digitalized setting, for instance,
related to net neutrality (e.g., Bourreau etal. 2015), price strategies (e.g.,
Weisstein etal. 2013), interoperability (e.g., Lewis 2013), unbiased recom-
mendation systems (e.g., Xiao and Benbasat 2011) and AI (e.g., Berente
etal. 2021), and resource consumption and sustainability (e.g., Truby 2018).
Although the focus and range of research questions explored in existing
research varies, the recent literature indicates that protecting consumers’
economic interests can be rewarding for companies (e.g., Weisstein etal.
2013).
(V) Product safety and liability In the real world, product safety describes the
degree of potential risks and injuries due to the handling and use of products
while liability relates to the actions of product or service providers in the
event of injury (Daughety and Reinganum 1995). In a purely physical world,
organizations are unable to limit their liability to the consumer at all, since
the source of most injuries is undisputed (Daughety and Reinganum 1995).
Conversely, in a digitalized world, it can be much more difficult to find the
indisputable cause of injuries and losses due to interconnected products
and services from different suppliers continuously sharing and using data
to deliver personalized products and services (Smith 2017). Additionally,
consumers may suffer not only physical but also mental harm from digital
goods and services (Gross etal. 2016), which further complicates the prod-
uct safety and liability implications. In this vein, the CDR concept enforces
businesses to engage in a variety of issues related to product safety, liability,
accountability, and reliability of digital products.
(VI) Data privacy and security are among the most important issues in the devel-
opment, deployment, and use of information technologies (e.g., Mason 1986;
Mihale-Wilson etal. 2017). It is therefore logically consistent that these top-
ics attract considerable attention from policymakers and researchers alike
(e.g., Bélanger and Crossler 2011; Heimbach and Hinz 2018). Regulations in
the field (e.g., the GDPR) define minimum requirements that organizations
must meet. To count as a CDR activity, companies must voluntarily exceed
the minimum legal requirements. Compliance with the minimum require-
ments does not attract positive attention, but non-compliance can have seri-
ous financial and legal implications for companies (Goel and Shawky 2009).
As businesses can use strategic initiatives to positively affect consumer per-
ceptions (Hann etal. 2007), this dimension promotes organizations to exceed
the privacy and security regulations currently in place, for instance, related
to secure handling and storage of data, and digital freedom.
(VII) Dispute resolution and awareness The CDR concept also covers dispute
resolution and awareness, e.g., with regard to possible difficulties caused
by the interoperability and interconnectivity of products and services from
different vendors. Dispute resolution more generally refers to dispute resolu-
tion mechanisms aimed at enabling consumers who have experienced (e.g.,
economic) loss or damage in transactions to resolve their grievances and
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obtain redress (Ang and Buttle 2012). With digitalization allowing organiza-
tions to engage across borders, the CDR concept proposes straightforward,
uniform, and efficient dispute resolution and awareness tools for all consum-
ers. In practical terms, CDR proposes that consumers should be able to file
complaints easily and at no cost, while complaint handling should be fair,
fast, and transparent (Turel etal. 2008).
(VIII) Governance and participation mechanisms Finally, it is noteworthy that
CDR also recognizes the necessity for governments to continually align
the regulatory requirements to “steer the digitalization process in the right
direction” (Thorun etal. 2017, p. 91). To this end, the CDR concept pro-
motes appropriate governing and participatory mechanisms at state level
(i.e., efficient lawmaking, regulatory frameworks, and well-functioning
enforcement) in a digitalized environment. In this context, governance and
participation engagement lies in the responsibility of policymakers and
other non-governmental regulatory organizations (Thorun etal. 2017) and
is therefore an exogenous force within a company’s CDR concept. Internal
company governances have no application in this dimension, but are subject
to other dimensions such as information and transparency, and economic
interests. Thus, we exclude the dimension of governance and participation
mechanisms from further investigation as this study evaluates the operation-
alization of CDR in companies rather than exogenous forces for companies.
Summing up, the concepts of CSR and CDR share the idea of voluntariness,
although current legislation already regulates several aspects of the aforemen-
tioned CDR dimensions. Nevertheless, it is crucial to recall that CDR constitutes
actions that companies may undertake voluntarily and in supplement to any mini-
mum requirements that may be in place. Only fulfilling legally required minimum
actions is not sufficient to count as CDR activities. Accordingly, the understanding
of CDR differs worldwide, as there apply different legal minimum requirements.
CDR efforts involve additional expenses and investments that companies must con-
sider if they decide to pursue these kinds of activities. One motivation for addressing
additional corporate responsibilities can be stakeholders’ growing interest as seen
for the concept of CSR (Schaltegger and Burritt 2018). Thus, it is inevitable to bet-
ter understand and to take consumer preferences into consideration when developing
and establishing CDR activities also employing them for market segmentation (e.g.,
Naous and Legner 2017; Mihale-Wilson et al. 2019). For companies, consumer
acceptance of their CDR activities is one decisive success factor to prevail also in
the future. Yet, research on CDR mostly focuses on the conceptualization (Mueller
2022) rather than approaching the topic empirically. One initial research approach
evaluated consumer preferences for CDR on dimension-level (Mihale-Wilson etal.
2021). However, this is not sufficient for companies to have a concrete understand-
ing for operationalizing the concept on a measure-level (Mueller 2022). This could
even slow down the adoption of CDR activities in practice. Accordingly, this study
aims to remedy this. To the best of our knowledge, there is no previous research that
assesses CDR operationalization by evaluating consumers’ preferences for concrete
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CDR measures and an according consumer segmentation quantitatively in-depth, a
gap this study aims to close.
2.3 Discrete Choice experiments andrandom utility theory
In a developing field like CDR, it is essential to evaluate consumer preferences.
These preferences set the direction for the development of CDR as a concept but
also for the implementation of CDR strategies in companies. DCEs are a state-of-
the-art method for assessing consumer preferences (e.g., Swait and Andrews 2003;
Gensler etal. 2012; Schlereth and Skiera 2017).
Respondents repeatedly make trade-off decisions between a set of product
alternatives characterized by their attributes and attribute levels, selecting the one
that maximizes their utility. Thus, the attractiveness of each attribute level is evi-
dent with this approach. Random utility theory lays the foundation for evaluating
DCEs (Train 2009; Louviere etal. 2013). DCEs are similar to real-world choices
and therefore suitable to explain the value of specific product features and actual
purchasing behavior (Swait and Andrews 2003; Gensler etal. 2012). Compared to
self-explicated methods, rating- or ranking-based conjoint analysis, DCEs provide a
direct link to the participants’ actual choices (Hinz etal. 2015).
3 Set ofpre‑studies: consumers’ valuation oftheCDR dimensions
andits sub‑dimensions
Aim of this entire set of studies is to provide guidance on concrete CDR activities
and consumer segmentation for firms based on consumers’ perceived importance
of these activities. However, the seven relevant out of the eight CDR dimensions
(see Table 1, except for the excluded dimension of governance and participation
mechanisms) cover a wide range of possible sub-dimensions, each featuring several
concrete activities firms can perform (see Fig.2). Therefore, we conducted a set of
pre-studies employing two BWS experiments (Mihale-Wilson et al. 2021) before
proceeding to a preference evaluation deploying two DURE studies (i.e., set of main
studies) based on the insights from the set of pre-studies. The first BWS experiment
evaluated consumers’ perception of the CDR dimensions (see Table1, except for
the excluded dimension of governance and participation mechanisms). We then
explored these insights more in depth by sequencing several sub-dimensions of the
most important CDR dimension in BWS 2. Summing up, aim of the BWS pre-stud-
ies was to sequence the proposed CDR dimensions and the sub-dimensions of the
top-ranked CDR dimension by importance (Mihale-Wilson etal. 2021). The results
from both BWS experiments then inform the design of the two DURE experiments.
Since established methods like DURE studies can only deal with a limited number
of attributes, this consecutive approach is necessary to evaluate concrete CDR activ-
ities. The following sections introduce the methodology, study setup, and results of
the set of pre-studies (Mihale-Wilson etal. 2021).
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3.1 Best–Worst Scaling
Best–worst scaling has been widely used to assess consumer preferences for dec-
ades (Finn and Louviere 1992; Hinz etal. 2015) also for consumer perspectives in
CSR research (Auger etal. 2007). BWS is an advanced version of paired compari-
son (Cohen and Orme 2004; Auger etal. 2007), where participants each choose their
most and least preferred attribute from a varying set of attributes (Hinz etal. 2015;
Kaufmann etal. 2018). In this manner, researchers can compare subjects and peo-
ple minimizing bias due to the utilization of scales or the consumers’ cultural back-
ground (Auger etal. 2007). Besides, BWS is superior to ranking methods when the
number of employed attributes is large, and the equal differences between two con-
secutive attributes cannot be assumed (Hinz etal. 2015). Furthermore, BWS studies
are particularly suitable in the case of heterogeneous groups, e.g., with regard to
education or knowledge (Hinz etal. 2015). Thus, this methodology fitted well with
our investigation of the evolving CDR concept (Mihale-Wilson etal. 2021).
We opted for the counting method to analyze individual and aggregate sample
preference estimations regarding the most and least important attribute choices
(Finn and Louviere 1992). Within the BWS studies, we evaluated seven attributes
within seven choice sets each featuring three alternatives. Thus, with a balanced
design, each attribute appeared three times to the sample (= 7 × 3/7). In this case,
Best–Worst (BW) scores ranged between − 3 (worst) and 3 (best) depending on the
frequency consumers chose this attribute (Mihale-Wilson etal. 2021).1 This analysis
was sufficient to rank the dimensions according to their perceived importance (Hinz
etal. 2015).
3.2 Study setup
The design of our BWS studies followed the one of Auger etal. (2007) and utilized
DISE implemented by Schlereth and Skiera (2012). Both BWS studies employed
the same questionnaire schema (see Supplementary Information). The first part
comprised a brief explanation of the topic of CDR in general, to prepare partici-
pants for the BWS part, followed by further questions exploring socio-demographic
Least importantMost important
Seven choicesetsshowing three CDR dimensions each
CDR dimension1
CDR dimension2
CDR dimension3
Fig. 3 Study design—exemplary choice set in the set of pre-studies (Mihale-Wilson etal. 2021)
1 Accordingly, the BW score can be calculated by performing + 1 each time the attribute is chosen as the
best one, − 1 each time the attribute is chosen as the worst one, and ± 0 if the attribute is neither best nor
worst.
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data (i.e., gender, age, education, employment). Consumers indicated the most and
least important CDR attribute within each of the seven choice sets (see Fig.3). The
design of our choice sets employed the Balanced Incomplete Block Design featuring
(1) the same number of attributes within each choice set; and (2) the same number
of occurrences of every attributes across choice sets (Kaufmann etal. 2018).
An examination of relevant personality traits and participants’ attitude towards
technology in general followed the BWS part. Consumers indicated psychographic
attributes on 7-point Likert scales (Bruner 2009) measuring established constructs
from psychology and marketing.2 Before conducting the BWS experiments, we did a
pilot test to check for clarity of the questionnaire and easiness to fill in.
3.3 Data
Both BWS studies employed the same participant sample as results directly build
upon each-others. A market research institute recruited a representative sample of
the German population. Out of 791 participants, 663 participants finished both BWS
studies also passing the attention checks. The sample had an almost equal gender
split and is between 17 and 87years old (see Table13 in the Appendix).
3.4 Results
Firstly, one BWS experiment determined an overall ranking of the various CDR
dimensions. An additional BWS experiment addressing the favored sub-dimensions
of the top-ranked dimension in detail then complemented the first BWS experiment.
This second BWS experiment aimed at evaluating various possible sub-dimensions
of the wide-ranging, most important CDR dimension (Mihale-Wilson etal. 2021).
3.4.1 Overall ranking oftheCDR dimensions
Figure4 ranked the averaged BW scores (standard deviations (SD) in parentheses)
of the assessed CDR dimensions in decreasing order complemented by the distances
between the BW scores of the top three consumer choices (Δ). BW scores reflect the
relative importance of choice sets ranging from − 3 to 3. We opted for the counting
method to analyze individual and aggregate sample preference estimations regard-
ing the best and worst attribute choices (Finn and Louviere 1992).1 Thus, averaged
BW scores reflect the relative importance of choice sets across the entire partici-
pant sample. In short, consumers saw data privacy and security, product safety and
liability, and information and transparency as the most important CDR dimensions.
While consumers appreciated corporate activities related to access, economic inter-
ests, and dispute resolution less. Expanding on these results and consumer prefer-
ences regarding the CDR dimensions, we conducted another BWS experiment to
2 For detailed information see: Jackson (1976), Costa and McCrae (1992), Steenkamp and Baumgartner
(1995), Steenkamp and Gielens (2003), Kumaraguru and Cranor (2005) and Meuter etal. (2005).
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assess consumers’ valuation of possible sub-dimensions within the highest-ranked
CDR dimension—data privacy and security.
3.4.2 Detailed ranking ofdata privacy andsecurity sub‑dimensions
The following BWS experiment served as a starting point for the DURE analysis
of the most important CDR dimension since DURE studies can only capture a lim-
ited number of deployed attributes. Thus, we first assessed the most preferred sub-
dimensions before the evaluation of concrete activities within these dimensions in
the form of a DURE experiment.
There is a broad, multifaceted research base on the importance of data privacy and
security in IS covering various different aspects (e.g., Bélanger and Crossler 2011).
Several classification schemes exist to describe the scope of privacy. For example,
Smith etal. (1996) name (1) data collection; (2) unauthorized secondary usage; (3)
improper access; and (4) information accuracy as four main aspects. In practice, data
privacy and security regulations incorporate these aspects, for instance, resulting in
eight main principles as in the OECD Privacy Framework (2013): (1) collection
limitation; (2) data quality; (3) purpose specification; (4) use limitation; (5) security
safeguards; (6) openness; (7) individual participation; and (8) accountability princi-
ple. The BWS experiment examined seven of the eight sub-dimensions within the
OECD Privacy Framework excluding the principle of accountability. Accountability
is an important framework condition for the compliance with other principles and
therefore excluded. The seven sub-dimensions used for our BWS experiment cap-
tured previous research on data privacy and security as well as the current state of
legislation (see Table2).
Figure5 indicates the average BW scores of the data privacy and security sub-
dimensions. The results underlined the importance of secure storage and processing,
Data
privacy &
security
Product
safety &
liability
Information &
transparency
1.30
(1.60) 1.09
(1.55)
0.26
(1.44)
Δ= 0.21 Δ= 0.83
Δ= 1.05
Education &
awareness
Dispute
resolution &
awareness
-0.49
(1.76) -0.78
(1.58)
-1.22
(1.56)
Access
Economic
interests
-0.17
(1.93)
Fig. 4 Averaged BW ratings (SD) of CDR dimensions across participants
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Table 2 Data privacy and security sub-dimensions in BWS 2 (Mihale-Wilson etal. 2021)
Sub-dimension Description Exemplary related work
Openness about data processing practices Businesses need to be transparent about their data processing
practices
Turilli and Floridi (2009), GDPR, and Thorun etal. (2017)
Restricted data collection The collection of (personal) data must be limited, lawful, and
fair, usually with the knowledge and/or consent of the user
Smith etal. (1996), GDPR, Felzmann etal. (2019), and Wier-
inga etal. (2021)
Clear purpose of data collection The purpose of the data collection must be clearly stated at
the time of collection
Smith etal. (1996), GDPR, Thorun etal. (2017), and Wieringa
etal. (2021)
Restricted data use The use or disclosure of data must be limited to the previ-
ously agreed purpose(s) or only for closely related purposes
Smith etal. (1996), GDPR, and Thorun etal. (2017)
Secure storage and processing of user data The storage and processing of user data must be subject to
appropriate security
GDPR, Thorun etal. (2017), and Wieringa etal. (2021)
Data quality User data collected and stored by companies must be rel-
evant, accurate, and up-to-date
Smith etal. (1996), Martin (2015), and Thorun etal. (2017)
Access and correction Users must be able to view and correct the user data stored
by companies
Smith etal. (1996), Martin (2015), GDPR, and Thorun etal.
(2017)
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a restricted data collection, and data access and correction for consumers, while
consumers seemed to appreciate openness about data processing practices, high data
quality, or a clear purpose of data collection less. This was rather surprising given
that, e.g., the GDPR emphasizes the importance of purpose limitation as one of the
central pillars.
4 Set ofmain studies: consumers’ valuation ofCDR
operationalization
So far, the set of pre-studies (Mihale-Wilson et al. 2021) provided insights into
which CDR dimensions and which related sub-dimensions consumers value most.
Despite these insights in the set of pre-studies, it is still unclear how consumers per-
ceive concrete CDR activities also relative to economic factors. The goal of the set
of main studies is to address this gap and support the operationalization of CDR and
firms’ consumer segmentation strategy. Accordingly, our further assessment deploys
DURE to evaluate possible activities within these CDR dimensions but excluding
the product safety and liability dimension. We could not verify stable product safety
and liability activities for empirical assessment for the sake of them being very
heterogeneous issues across product types and nationally fragmented (Desai 2014;
Howells etal. 2017; Kozup 2017). This notwithstanding, product safety and liabil-
ity are strongly regulated fields. Companies have only very few degrees of freedom
in this context (Jorstad 2000). Thus, companies need to fulfil these regulations but
often cannot use this factor as a unique selling proposition. Aim of this study is to
provide guidance which dimensions to implement first and how to implement them
specifically (i.e., providing guidance on the operationalization of CDR), therefore
focusing on the further CDR dimensions.
1.18
(1.52)
0.24
(1.64) 0.14
(1.57)
-0.47
(1.59) -0.62
(1.56)
-1.05
(1.71)
0.57
(1.65)
Secure
storage &
processing
Restricted
data col-
lection
Access &
correction
Restricted
data use
Openness
about data
processing
practices
Clear
purpose of
data
collection
Data
quality
Δ= 0.60 Δ= 0.34
Δ= 0.94
Δ= 0.09
Fig. 5 Averaged BW ratings (SD) of privacy and security sub-dimensions across participants
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First, we offer some insights into DURE and Choice-based Conjoint (CBC)
analysis complemented by the study setup. Second, we present the results of the set
of main studies. This set consists of two independent experiments and its design
grounds on the initial rankings (i.e., CDR dimensions in general and sub-dimensions
of the data privacy and security dimension) from the set of pre-studies (Mihale-Wil-
son etal. 2021): one DURE experiment focuses on the most valued activities within
the top-ranked data privacy and security sub-dimensions, and the other DURE
experiment addresses the remaining five dimensions of CDR to provide guidance for
companies on possibly useful, concrete CDR activities and consumer segmentation.
Besides, the studies provide insights on specific characteristics of participants not
valuing the implementation of CDR activities in practice, hence not yet in the mar-
ket. Still, it might be sensible for companies to acquire a large consumer base also
by convincing non-purchasers.
4.1 Dual Response
DURE is a modification of the widespread CBC analysis as used in market research
especially for business research and marketing (e.g., Gensler etal. 2012; Hinz etal.
2015; Naous and Legner 2017) and belongs to DCEs (Schlereth and Skiera 2017).
Characteristically, CBC enforces participants to repeatedly trade-off between multi-
attributed product versions in context with a price (Green and Srinivasan 1990).
Conducting a CBC analysis reveals consumer preferences about a product or service.
To better map the market, studies can implement a “no choice” option (Louviere and
Woodworth 1983; Gensler etal. 2012). In case of a traditional CBC analysis, this
option is available parallel to the prompted alternatives, thus losing knowledge about
the preference order and leading to a knowledge bias in case of a selected no choice
option (Brazell etal. 2006). To compensate for this weakness, DURE emerged (e.g.,
Brazell etal. 2006; Hinz etal. 2015; Schlereth and Skiera 2017). Each choice set in
a DURE experiment consists of two trade-offs. Firstly, consumers have to choose
one out of several alternative products that they like most (forced-choice). Secondly,
they have to decide whether they would choose this product or not (free-choice).
The no choice option is no longer parallel to the product alternatives but consecu-
tive thus offering more information on consumer preferences. Consumers who are
currently not yet active on the market also provide insights in this way (Brazell etal.
2006). Especially with an expected high share of no choices, this method can lead to
more stable preferences and reliable results (Brazell etal. 2006). Due to the novelty
of CDR, a high proportion of no choice decisions is likely. Thus, DURE seems to
be suiting to evaluate preferences on CDR. The analysis of the DURE study relies
on random utility theory (Train 2009). Consumers’ valuations of the latent value of
respective CDR activities complement findings from the DURE study. To evaluate
the latent value of CDR activities we employ the concept of the willingness to pay
(WTP) (e.g., Green and Srinivasan 1990). In general, the WTP is defined as the
indifference reservation price meaning “the price at which a consumer is indifferent
between purchasing and not purchasing a bundle” (Meyer etal. 2018, p. 503). To
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provide insights into consumers’ latent value ratings for CDR activities we calculate
the latent values analogous to the concept of WTP (Gensler etal. 2012).
4.2 Study setup
Aim of the DURE studies is to evaluate consumers’ perception of different, con-
crete activities within the previously ranked CDR dimensions and sub-dimensions
(set of pre-studies, Mihale-Wilson etal. 2021) complemented by a consumer seg-
mentation approach. The results from the set of pre-studies condition the attributes
(i.e., activities) chosen for the DURE studies. A literature review reveals respective
attribute levels covering the status-quo in the market but also incorporating fur-
ther improvements. The chosen attribute levels address the ranked dimensions and
their respective sub-dimensions (see Tables3, 8). To reduce the effort for partici-
pants, we conducted two independent DURE experiments covering activities within
the top-ranked data privacy and security sub-dimensions in DURE 1 and activities
within further CDR dimensions in DURE 2 for a comprehensive understanding of
possible CDR activities. The design of the DURE experiments follows established
scientific approaches and data collection methods as an online survey (e.g., Brazell
etal. 2006). The survey consists of three major parts analogous to the set of pre-
studies. To make the more abstract topic of CDR tangible for the participants, a use
case serves as an illustration for the DURE parts. In addition, the effectiveness and
design of CDR activities is partly based on the specific industry in which CDR is
to be implemented (Mihale-Wilson etal. 2022). Accordingly, we employed IoT as
a tangible example of an ongoing digitalization and at the same time as a rapidly
growing market with an ever-increasing importance for our professional and private
everyday life. The first part of each DURE experiment presents every participant an
introductory video showing the amenities, IoT can have in everyday life. Further-
more, descriptions of the assessed attributes (i.e., CDR activities) and their assorted
characteristics appeared.
The DURE experiments use DISE (Schlereth and Skiera 2012) for implementa-
tion. To reduce the complexity and the length of the survey, we limited the number
of choice sets while still producing valid insights. Therefore, we followed the tech-
niques by Street and Burgess (2007) deploying only a limited number of attributes
creating a D-optimal fractional factorial design with 12 choice sets. Participants had
to choose one out of three product versions followed by the question if they would
actually subscribe to this solution or not (see Fig.6).
Further questions exploring socio-demographic data (i.e., gender, age, educa-
tion, employment) as well as relevant personality traits and the participants’ atti-
tude towards technology and innovation in general follow the DURE part. For the
psychographic information, we employ 7-point Likert scales (Bruner 2009) and
established constructs from psychology and marketing.2 We conducted a pilot test to
check for clarity of the questionnaire and easiness to fill in.
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Table 3 Attributes and attribute levels of data privacy and security—DURE 1
Data privacy and security sub-dimension Attribute Range Attribute levels
Restricted data collection Information regarding data collection (data
protection declaration)
3 Detailed; one pager; tabular form
Access and correction Access and correction of personal data 3 Information; information and correction; infor-
mation, correction, and deletion
Secure storage and processing of user data Notification of incidents 3 On request; affected users only; public broadcast
Price Price per month 3 1€; 2.50€; 5€
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4.3 Consumers’ valuation ofactivities regardingthedata privacy andsecurity
sub‑dimensions
The set of pre-studies Mihale-Wilson etal. (2021) validated the importance of data
privacy and security, also proposing the most-valued sub-dimensions of this CDR
dimension. The more in-depth assessment of consumers’ preferences and latent val-
ues for concrete activities within the three top-ranked sub-dimensions of privacy
and security (DURE 1) advances the initial ranking in the set of pre-studies (BWS
2, Mihale-Wilson etal. 2021). DURE 1 focuses on the top three sub-dimensions
of data privacy and security to avoid participants’ information overload. Each data
privacy and security sub-dimension represents a broad field of application, thus,
we chose one subordinate activity (attribute) per sub-dimension to limit the partici-
pants’ effort.
Limited or restricted data collection must be with the consent of the user, never-
theless many consumers struggle to understand what data companies are really col-
lecting and what it is for, thus making uninformed decisions (Wieringa etal. 2021).
Even though companies have to inform about this in an understandable way accord-
ing to the GDPR (e.g., Felzmann etal. 2019). Whereby this is a rather subjective
legal requirement. Hence, companies can define themselves in the implementation
beyond the legal minimum requirement. Thus, the attribute information regarding
data collection in the form of the data protection declaration covers the aforemen-
tioned sub-dimension. Access and correction of personal data is ranked under the
top three sub-dimensions and (partly) covered by the GDPR therefore captured in
the DURE analysis by its own attribute (e.g., Martin 2015). Here, companies can
implement the access to data required by the GDPR more or less easily for con-
sumers. Hence, the item access and correction of personal data covers this differing
manifestation. Secure storage and processing presents the top-ranked sub-dimension
and goes beyond the mere process in the eyes of the consumers. This sub-dimension
Fig. 6 Study design—exemplary choice set in the set of main studies
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is mainly perceived in the form of incidents and the related notifications (e.g., Tho-
run et al. 2017). Thus, the corresponding attribute is the notification of incidents
concerning stored personal data. Direct, personal information of those affected can
be obligatory for companies (i.e., by the GDPR), depending on the incident and
instructions from the supervisory authority. Beyond this obligation, companies can
also voluntarily assume more responsibility in this respect and exceed the legal min-
imum. Additionally, we include the price per month to assess consumers’ perceived
latent valuation of CDR activities. The price of the service and the data privacy and
security features can influence the WTP for this service. We based our pricing on
the monthly costs of entertainment subscriptions such as Spotify (9.99€), Netflix
(7.99€), or Amazon Prime (7.99€).3 Accordingly, Table 3 comprises the deployed
attributes (activities) and respective levels.
4.3.1 Data
The first DURE study expands the results of the BWS studies and uses the same
sample as the BWS studies. A market research company provided a representative
sample with 404 German participants completing both BWS and the first DURE
experiment also passing the attention checks. The sample has an almost equal gen-
der split and is between 17 and 75years old (see Table13 in the Appendix).
4.3.2 Results
The primary goal of the first conducted DURE analysis is the identification of con-
sumer preferences regarding concrete data privacy and security activities. We first
present characteristics of those who do not value the operationalizing of data pri-
vacy and security activities and thus are not in the market, at least yet. We then
proceed with a consumer segmentation of those who are in the market to guide prac-
tice in individualizing data privacy and security activities to better meet the differ-
ent preferences. Reasonable signs and magnitudes of the parameter values indicate
face validity. Internal validity is high with a hit rate of 90.14%, thus suggesting an
adequate sample quality and high validity of the results.
4.3.2.1 Evaluation ofparticipants’ characteristics not(yet) inthemarket Aggregated
over all 12 choices, we observe 52.23% of the respondents never choosing any of
the presented products (i.e., non-purchasers), while only 15.84% of the consumers
always choose one of the presented alternatives (i.e., always-purchasers). The high
share of no choices supports the choice of DURE. Other established methods like
CBC would lead to a loss of information because the no choice option is available
parallel to the prompted alternatives, thus losing knowledge about the preference
3 In 2018, 76% of the Americans owned a subscription for TV/movie commonly spending $20 (17€) per
month (Waterstone Management Group 2018) proving a high WTP for subscription services. As only
one implemented CDR dimension is under evaluation, we opted for a maximum price slightly below an
entertainment flat rate and correspondingly lower price levels.
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order and leading to a knowledge bias. Analyzed more in detail, for companies, it is
essential to know how to target the type of consumer who is more difficult to acquire
with additional data privacy and security activities, hence the non-purchasers, in
future. When evaluating the characteristics of non-purchasers we found that their
decision not to subscribe to any of the IoT solutions with different data privacy and
security attributes is significantly correlated with a higher age and not very surpris-
ingly with higher privacy concerns (see Table4). High privacy concerns may limit
the overall adoption of such systems (e.g., Carl and Mihale-Wilson 2020) regardless
of the CDR activities conducted. For these consumers, there might be a general lack
of trust regarding companies and their (privacy) activities that hinders general adop-
tion of IoT solutions distrusting the claim of any CDR activities (e.g., Sicari etal.
2015; Khan etal. 2019). Firms first have to establish a sufficient level of trust before
they can distinguish themselves credibly to these non-purchasers through additional
data privacy and security activities.
4.3.2.2 Consumer segmentation regarding data privacy and security activities A
first analysis (see Table5) of the data for the entire sample (i.e., non-, sometimes-, and
always-purchasers) provides us estimated parameter values and importance weights
of the activities (attributes) across the entire sample. The importance weights gained
allow ranking the considered concrete CDR activities for the whole sample. Weight-
ing the four deployed attributes, access and correction of personal data (30.51%), and
price per month (30.18%) seem to play a leading role for consumers regarding CDR
activities in the field of data privacy and security. Access and correction of personal
data outperforms in the in-depth analysis of possible activities. The reason might lie
in the additional understanding and awareness consumers gained when presented with
Table 4 Characteristics of non-purchasers—DURE 1
***p < 0.001, **p < 0.01, *p < 0.05
Logistic regression DV: non-purchaser
(0/1)
Coefficient Standard error Significance
Gender 0.853 0.195 0.484
Age* 1.023 0.009 0.013
Education
Secondary school certificate 0.860 0.292 0.658
Abitur 1.343 0.515 0.442
Bachelor 0.638 0.296 0.333
Master/diploma or higher 0.833 0.323 0.638
Privacy concerns** 1.360 0.158 0.008
Technophobia 1.084 0.088 0.319
Change seeking behavior 0.828 0.129 0.226
Innovativeness 0.778 0.106 0.064
Trust 0.903 0.089 0.301
Risk appetite 0.966 0.102 0.746
Online transaction perception 0.903 0.091 0.313
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K.V.Carl et al.
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the detailed possibilities inherent in this dimension. While consumers might be aware
of the importance of secure storage and processing, access and correction of personal
data might be rather unfamiliar to them. Utility increases substantially when the easy
access to correct or delete personal data is available instead of mere information
about personal data (enhancement of 1.67). Many regulatory regimes set minimum
requirements that companies can voluntarily exceed concerning notifications of inci-
dents concerning stored personal data (19.78%), and information regarding data col-
lection (19.54%), but consumers seem to value these facets of CDR less. The highest
increase in utility regarding the notification of incidents concerning stored personal
data is observable between the information on request and an automatic notification
of affected consumers (increase of 0.34). Consumers even prefer this notification type
compared to a public broadcast. Surprisingly, in the dimension information regard-
ing data collection, utility values decline (decline of 0.49) for a more standardized
declaration (i.e., detailed form and a more standardized overview). Thus, consumers
seem to prefer a more extensive data protection declaration—for the information on
data collection practices—compared to a simpler approach to this matter. Altogether,
these findings go in line with previous technology adoption research. On the one hand
consumers are highly concerned about their personal data stored (e.g., Awad and
Krishnan 2006; Baumann etal. 2019) but on the other hand price plays a striking role
especially for digital goods (Mihale-Wilson etal. 2019).
The overall high standard deviations especially of the two top-ranked attrib-
utes price per month (SD = 31.15%), and access and correction of personal data
(SD = 23.12%) indicate heterogeneous preferences within the sample (see Table5).
Thus, a cluster analysis could reveal some additional insights relevant for market
segmentation. For clustering, we employed the two unsupervised learning algo-
rithms Principal Component Analysis (PCA) and K-Means. Before performing
the cluster analysis, we use PCA to generate aggregated principal components
Table 5 Perceived value of the data privacy and security activities—DURE 1
Attribute Attribute levels Aggregated
parameter values
(SD)
Average impor-
tance weights
(SD)
Constant − 2.70 (6.53)
Information regarding data
collection
Detailed 0.28 (0.48) 19.54% (17.51%)
One pager − 0.07 (0.39)
Tabular form − 0.21 (0.51)
Access and correction of per-
sonal data
Information − 0.86 (0.83) 30.51% (23.12%)
Information and correction 0.05 (0.30)
Information, correction, and
deletion
0.81 (0.97)
Notification of incidents On request − 0.19 (0.48) 19.78% (16.21%)
Affected users only 0.15 (0.41)
Public broadcast 0.05 (0.59)
Price per month 3.88 (8.00) 30.18% (31.15%)
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summarizing psychographic attributes (i.e., privacy concerns, technophobia, change
seeking behavior, innovativeness, trust, risk appetite, online transaction percep-
tion). The PCA generated three principal components summarizing the braveness
(i.e., change seeking behavior, risk appetite), the trusting nature (i.e., trust, privacy
concerns (reversed), online transaction perception), and technology affinity (i.e.,
technophobia (reversed), innovativeness). Afterwards, a cluster analysis employing
K-Means effectively divides participants into four segments according to the gener-
ated principal components (i.e., braveness, trusting nature, technology affinity) and
demographic information (i.e., age, education, employment status). To determine
the optimal number of clusters K we applied the “Elbow criterion”. To avoid distor-
tions in our WTP calculation (Gensler etal. 2012), we removed respondents from
this analysis who invariantly subscribed (i.e., always-purchasers) or did not sub-
scribe (i.e., non-purchasers) to the IoT solutions regardless of the privacy attributes.
The thus adjusted sample contains 129 respondents (i.e., the sometimes-purchasers).
We labeled the clusters according to their demographics and the derived principal
component scores to distinguish them (see Table6). Accordingly, segment 1, the
distrustful brave, exhibits the comparatively lowest average score for trust and the
highest for braveness across the various segments. Similarly, we label the other seg-
ments young performer, retired traditionalists, and technology affine conservatives.
Table7 presents the resulting preferred product variations as well as their associated
latent value (WTP). It shows the three most preferred product variations per con-
sumer segment and for the entire sample (i.e., all sometimes-purchasers) alongside
with their associated monthly WTP. For example, for the distrustful brave, the most
preferred product variation features detailed information regarding data collection,
information, correction, and deletion access to personal data, and the notification of
incidents for affected users only, with an associated WTP of 3.17€ per month.
Table 6 Consumer segments—DURE 1
Cluster analysis Distrustful brave
N = 32
Young performer
N = 37
Retired
tradi-tion-
alists
N = 26
Technology
affine conserva-
tives
N = 34
Entire sample
N = 129
Principal compo-
nents (PCA)
Braveness 0.60 0.58 − 0.27 − 0.99 0.00
Trusting nature − 0.87 0.47 − 0.10 0.39 0.00
Technology
affinity
0.00 − 0.21 − 0.22 0.40 0.00
Demographics
Age (average) 47 37 59 46 46
Education (%
university
degree)
12.51% 83.78% 23.07% 20.59% 37.21%
Employment (%
employed)
96.88% 97.30% 0.00% 94.12% 76.74%
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Table 7 WTP and preferred products—DURE 1
Information regarding data col-
lection
Access and correction of personal data Notification of incidents Price
Ranking Detailed One pager Tabular form Information Information
and correc-
tion
Information,
correction, and
deletion
On request Affected
users
only
Public broad-
cast
WTP (per
month)
Distrustful
brave
1st
2nd
3rd
x
x
x x
x
x
x
x
x 3.17€
1.87€
1.69€
Young per-
former
1st
2nd
3rd
x
x
x x
x
x
x
x
x 6.52€
6.29€
5.50€
Retired tradi-
tionalists
1st
2nd
3rd
x x
x
x
x
x
x
x
x − 0.88€
− 1.76€
− 2.55€
Technology
affine con-
servatives
1st
2nd
3rd
x x
x
x
x
x
x
x
x 5.62€
5.46€
5.30€
Entire sample 1st
2nd
3rd
x
x
x x
x
x
x
x
x3.40€
2.65€
2.50€
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The evaluation of consumer preferences unanimously advices companies to com-
mit to easy access to the information on, correction, and deletion of personal data.
We found no interest for other access approaches. Except for this attribute, consumer
preferences are quite heterogeneous (see Table7). In this case, one approach to fur-
ther data privacy and security activities does not fit all equally. Rather, companies
should consider individualizing and targeting their CDR activities related to this
dimension by exceeding legal requirements. Regarding their information practices,
companies can pursue two different approaches. On the one hand, they could offer
several untargeted information approaches to data collection practices simulta-
neously to meet the preferences of a broad mass of people. However, companies
pursuing this strategy of different information sources should take the problem of
a potential information overload into account. On the other hand, firms might con-
sider customizing information approaches for different consumer segments through
targeted communication media to respond to the different preferences. Preferences
for the notification of incidents are rather heterogeneous, too. While retired tradi-
tionalists favor the notification of incidents on request, the other segments value the
pro-active notification of affected users and public broadcasts more. Hence, one size
does not fit all when assessing different approaches to the notification of incidents.
In sum, the results suggest that companies can differentiate themselves more or less
with additionally assumed responsibilities in the context of data privacy and secu-
rity depending on different consumer segments. To appropriately address different
market segments, companies should assess relying on different communication strat-
egies for incident reporting and data collection information, adapting them to dif-
ferent communication channels to address different market segments through their
preferred communication media.
To enable a more informed prioritization of CDR activities, we also determined
the latent value of various CDR activities (see Table7). We found positive latent
values for data privacy and security related activities of companies across the sam-
ple ranging from 3.17€ to 6.52€ for the most preferred bundle except for the seg-
ment of the retired traditionalists. Retired traditionalists reveal negative latent val-
ues for data privacy and security activities. Their lack of technology affinity also
manifests itself in the missing appreciation for such products or services and thus
the evaluated IoT solutions. Recent research on preferred privacy properties of IoT
systems confirms this impression that older users in particular have a lower WTP for
such attributes than younger users (e.g., Zibuschka etal. 2019). Still, the observed
mostly positive latent values for the implementation of more advanced data privacy
and security activities might not fully account for the expenses of companies to
implement these activities. This supports the hypothesis that consumers expect high
standards in this field but only punish the absence instead of being overly excited by
their implementation (Goel and Shawky 2009). Moreover, these results support the
assumption that different consumer segments do not value the implementation of
additional CDR activities (monetarily) equally and, accordingly, companies cannot
define themselves equally strongly towards different consumers. Hence, companies
should pursue consumer segmentation in practice, as one size does not fit all equally.
Besides, companies should evaluate whether they can easily supply different ver-
sions of their digital offerings, also to appeal to more cost-conscious consumers.
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For this purpose, firms could implement different versions with lower levels of data
privacy and security activities, still exceeding legal minimum requirements. In addi-
tion, they should pursue individualization of CDR activities with regard to the vari-
ous psycho-demographic consumer segments to cover different preferences and at
the same time exploit (monetary) appreciation.
4.4 Consumers’ valuation ofactivities regardingfurther CDR dimensions
A more in-depth assessment of consumers’ preferences and latent value of different
CDR activities within the remaining dimensions advances the ranking of the most
important CDR dimensions (BWS 1). The set or pre-studies (Mihale-Wilson etal.
2021) suggested that enterprises should definitely act in the sense of data privacy
and security. Additionally, product safety and liability, a highly regulated field, is
of importance. Thus, companies should target it to the best of abilities maybe even
above and beyond regulatory standards. Still, it might be reasonable to diversify
CDR activities also considering the implementation of activities within additional
CDR dimensions as a differentiator. In contrast to the scholarly debate on the data
privacy and security topic, research on other CDR dimensions like transparency and
its benefits is scarce and dispersed across various disciplines (Granados etal. 2010).
Thus, the following evaluation of the further CDR dimensions should serve as a
guidance for firms on a more comprehensive CDR operationalization and associ-
ated consumer segmentation. The examination covers the remaining five dimensions
but merging access and education as they overlap in some activities not to overload
the participants. Each CDR dimension represents a broad field of application, thus
one chosen subordinate attribute (i.e., activity) limits the participants’ effort (see
Table8). Not to exceed the participants’ effort, we did not conduct upstream BWS
studies on all five remaining dimensions to filter out the highest valued sub-dimen-
sions in each case unlike the top-ranked dimension. Instead, we selected attributes
and their levels from literature with focus on tangibility.
Table 8 Attributes and attribute levels of the further CDR dimensions—DURE 2
CDR dimension Attribute Range Attribute levels
Information and transparency Transparency regarding data
protection (data protection
declaration)
3 Detailed; one pager; tabular
form
Education and awareness/
access
User support 3 Call center support; online;
roboadvisor
Economic interests Interoperability 3 No interoperability; semi-
interoperability; seamless
interoperability
Dispute resolution and aware-
ness
Dispute resolution 3 Manufacturer specific;
manufacturer network;
independent agency
Price Price per month 3 10€; 15€; 25€
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The corresponding attribute to the high ranked dimension information and trans-
parency is transparency regarding data protection. This attribute is partly overlap-
ping with the dimension data privacy and security contained in the previous DURE
experiment. Still, information and transparency is especially important for consum-
ers in regard to data protection as this concern is central for consumers of digital
technologies (e.g., Felzmann etal. 2019) enforcing consumers’ informed decision
making (Wieringa etal. 2021). The GDPR partly covers this item. However, compa-
nies can voluntarily establish even more transparency exceeding regulatory require-
ments. User support covers the two dimensions education and access as an overlap-
ping attribute. It is an implementation in regard to education but also to access when
it comes to the design of the interaction as a prerequisite for genuine informed con-
sent (e.g., Felzmann etal. 2019). Interoperability captures the dimension economic
interest in the DURE analysis. Especially the lack of interoperability is perceived by
the consumer in everyday use (e.g., Lewis 2013; Felzmann etal. 2019). While con-
sumers perceive possible alternative attributes like competition policy less. Finally,
the corresponding attribute to dispute resolution manifests in the design of the dis-
pute resolution center (e.g., Thorun etal. 2017). Additionally, the price per month
allows evaluating consumers’ perceived latent valuation of CDR activities. We
based our pricing on the monthly costs of entertainment subscriptions as in DURE
1.4 Accordingly, Table8 comprises the deployed attributes in the DURE study and
their levels.
4.4.1 Data
The second DURE study expands the results of the BWS studies and uses the same
sample as the BWS studies. A market research company provided a representative
sample with 415 German participants completing both BWS and the second DURE
experiment also passing the attention checks. The sample has an almost equal gen-
der split and is between 17 and 74years old and (see Table13 in the Appendix).
4.4.2 Results
The primary goal of the conducted second DURE analysis is the identification of
consumer preferences for broader CDR activities and an according consumer seg-
mentation. Again, we first present characteristics of those who do not value the
operationalizing of further CDR dimensions and thus are not yet in the market. We
then proceed with a consumer segmentation of those who are in the market to guide
corporate practice in individualizing further CDR activities to meet the different
preferences. Reasonable signs and magnitudes of the parameter values indicate face
validity. Internal validity is high with a hit rate of 91.66%, thus suggesting an ade-
quate sample quality and high validity of the results.
4 We opted for a maximum price slightly higher than the average spent per month for one subscription
service type and correspondingly lower price levels since this study examines a broader integration of
CDR activities.
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4.4.2.1 Evaluation ofparticipants’ characteristics not(yet) inthemarket Aggregated
over all 12 choices, we observe 54.46% of the respondents never choosing any prod-
uct (i.e., non-purchasers). Only 12.53% of the consumers always choose their favored
alternative (i.e., always-purchasers). The high share of no choices again supports the
choice of DURE compared to other widely used methods like CBC. To provide more
information for companies which consumer are more difficult to acquire with addi-
tional CDR activities we again conducted a logistic regression to characterize the non-
purchasers. When evaluating the characteristics of non-purchasers we found that their
decision not to subscribe to any of the IoT solutions with different CDR attributes is
significantly correlated with higher privacy concerns but also with change seeking
behavior (see Table9).5 Again, high privacy concerns can limit the general adop-
tion of IoT solutions and similar products and services (e.g., Carl and Mihale-Wilson
2020) despite CDR activities of companies. This illustrates the extent to which a
lack of trust due to privacy concerns also has an halo effect on further activities of
companies, especially in the context of CDR. Hence, companies should not treat data
privacy and security in isolation, but should take a more holistic approach to their
responsibilities in the digital context, which is supported by the concept of CDR.
Still, non-purchasers reveal a comparatively high change seeking behavior. Hence,
companies should exploit this psychographic attribute when they want to convince
previous non-purchasers. To achieve this, firms have to make it credibly clear to the
Table 9 Characteristics of non-purchasers—DURE 2
***p < 0.001, **p< 0.01, *p < 0.05
Logistic regression DV: non-purchaser
(0/1)
Coefficient Standard error Significance
Gender 0.868 0.204 0.547
Age 1.013 0.009 0.169
Education
Secondary school certificate 0.760 0.269 0.438
Abitur 1.177 0.466 0.681
Bachelor 0.593 0.293 0.291
Master/diploma or higher 0.904 0.351 0.796
Privacy concerns*** 1.659 0.223 0.000
Technophobia 1.008 0.088 0.931
Change seeking behavior** 0.595 0.099 0.002
Innovativeness 0.976 0.142 0.869
Trust 0.871 0.090 0.178
Risk appetite 1.012 0.112 0.914
Online transaction perception 0.807 0.091 0.056
5 Change seeking behavior describes a consumer’s likeliness to engage in exploratory behaviors, thus
seeking for change and novelty in the private and professional context (Steenkamp and Baumgartner
1995).
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consumer how much the company is pursuing a change in taking responsibilities and
generating a new level of trust in conducted activities.
4.4.2.2 Consumer segmentation regardingfurther CDR activities Again, a first anal-
ysis of the data for the entire sample (i.e., non-, sometimes-, and always-purchasers)
provides us estimated parameter values and importance weights of the attributes
across the entire sample (see Table10). Weighting the four deployed attributes, price
per month (41.29%) plays a striking role for the success of CDR activities. The com-
paratively highest valued activity within CDR is interoperability (29.39%). Surpris-
ingly, economic interests and correspondingly interoperability outperforms in the in-
depth analysis of possible activities. The reason might lie in the understanding of this
dimension. While consumers imagine the influence of CDR activities on safety and
liability, their understanding of how CDR activities can protect their economic inter-
ests might be rather limited. Therefore, specific measures might broaden their aware-
ness and thus influence their evaluated importance, especially as interoperability is
an everyday problem. Utility substantially increases with seamless interoperability
instead of solely independent devices and systems (enhancement of 2.41). User sup-
port (11.45%), dispute resolution (10.38%), and transparency regarding data protect-
ing (7.49%) are also crucial but less important for the success of CDR. The highest
increase in utility regarding dispute resolution is observable between the manufac-
turer as a point of contact and an independent consumer protection agency (increase
of 0.43). Surprisingly, in the dimension user support, utility values decline between
a call center and a roboadvisor (decline of 0.60). Thus, consumers seem to prefer the
personal contact instead of a faster solution. The same phenomena is observable in
case of transparency regarding data protection with a decline of 0.29 in utility values
Table 10 Perceived value of the further CDR dimension activities—DURE 2
Attribute Attribute levels Aggregated parameter
values (SD)
Average impor-
tance weights
(SD)
Constant − 0.26 (7.30)
Transparency regarding
data protection
Detailed 0.13 (0.25) 7.49% (8.24%)
One pager 0.03 (0.31)
Tabular form − 0.16 (0.35)
User support Call center support 0.36 (0.51) 11.45% (12.34%)
Online − 0.12 (0.32)
Roboadvisor − 0.24 (0.47)
Interoperability No interoperability − 1.53 (1.31) 29.39% (22.52%)
Semi-interoperability 0.65 (0.91)
Seamless interoperability 0.88 (1.34)
Dispute resolution Manufacturer specific − 0.09 (0.31) 10.38% (12.26%)
Manufacturer network − 0.25 (0.33)
Independent agency 0.34 (0.50)
Price per month 27.71 (57.22) 41.29% (33.32%)
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K.V.Carl et al.
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between a detailed form and a more standardized overview. Thus, consumers seem
to prefer a more extensive data protection declaration instead of a simpler and more
transparent approach to this matter. In the DURE analysis, the dimension of informa-
tion and transparency underperforms compared to the overall CDR ranking (BWS 1,
Mihale-Wilson etal. 2021) providing evidence for the need of a more detailed insight
into consumer preferences for specific CDR activities. Altogether, these findings
go in line with previous technology adoption research. On the one hand consumers
highly appreciate an effortless usage of connected products and on the other hand the
price for digital goods plays a striking role (Mihale-Wilson etal. 2019).
The likewise overall high standard deviations especially of the two top-ranked
attributes price (SD = 33.32%), and interoperability (SD = 22.52%) again indicate
heterogeneous preferences (see Table 10) and support the applicability of market
segmentation not only in terms of data privacy and security but also CDR in general.
Thus, we again conduct a cluster analysis for market segmentation. Before perform-
ing the cluster analysis, we again employed PCA to generate aggregated principal
components summarizing the psychographic attributes. In this case, the PCA gener-
ated two principal components aggregating the in love with the new (i.e., techno-
phobia (reversed), change seeking behavior, innovativeness) and the trusting nature
(i.e., privacy concerns (reversed), trust). Afterwards, the cluster analysis employ-
ing K-Means again effectively divides participants into three segments according to
the generated principal components (i.e., in love with the new, trusting nature) and
demographic information (i.e., age, education, employment status). To determine
the optimal number of clusters K we applied the “Elbow criterion”. For the sake of
correct WTP calculation, the adjusted sample contains 137 respondents (i.e., again
only sometimes-purchasers) (see Table11).
We labeled the clusters according to their demographics and the derived prin-
cipal component scores to distinguish them. Accordingly, segment 2, the young
achievers, exhibits the lowest average score for age and the highest for education
across the various segments. Similarly, we label the other segments young expedi-
tives, and elderly traditionalists. Table12 presents the resulting preferred product
variations as well as their associated latent value (WTP). It shows the three most
preferred product variations per consumer segment and for the entire sample (i.e.,
Table 11 Consumer segments—DURE 2
Cluster analysis Young expeditives
N = 42
Young achievers
N= 56
Elderly tradi-
tionalists
N = 39
Entire sample
N = 137
Principal components (PCA)
In love with the new 1.02 − 0.41 − 0.51 0.00
Trusting nature 0.10 0.16 − 0.34 0.00
Demographics
Age (average) 40 40 60 46
Education (% university degree) 28.57% 48.22% 23.08% 35.04%
Employment (% employed) 90.48% 100.00% 30.77% 77.37%
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Table 12 WTP and preferred products—DURE 2
User support Dispute resolution Transparency regarding
data protection
Interoperability Price
Rank-
ing
Call
center
support
Online Roboadvi-
sor
Manufac-
turer specific
Manu-
facturer
network
Independ-
ent agency
Detailed One
pager
Tabular
form
No interoper-
ability
Semi-interop-
erability
Seamless
interoper-
ability
WTP
(per
month)
Young expe-
ditives
1st
2nd
3rd
x
x
x
x x
x
x
x
x x
x
x
1.75€
1.65€
1.40€
Young
achievers
1st
2nd
3rd
x
x
x x
x
x
x
x
x x
x
x
0.72€
0.50€
0.50€
Elderly
tradition-
alists
1st
2nd
3rd
x
x
x
x
x
x
x x x x
x
x
0.84€
0.62€
0.51€
Entire sample 1st
2nd
3rd
x
x
x
x x
x
x
x
x x
x
x
1.07€
0.81€
0.69€
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K.V.Carl et al.
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all sometimes-purchasers) alongside with their associated monthly WTP. For exam-
ple, for the young expeditives, the most preferred product variation features call
center support, an independent agency for dispute resolution, a detailed data protec-
tion declaration, and seamless interoperability, with an associated WTP of 1.75€ per
month.
Across the various consumer segments, Table 12 immediately supports the
importance of seamless interoperability unanimously. We found no interest for lower
levels of interoperability in the data. Besides, data suggests a strong preference for
an independent agency handling potential disputes. However, young expeditives
value manufacturer specific dispute resolution almost equal accounting for only a
slightly lower WTP per month. Hence, companies should assess their costs for an
independent agency handling their dispute resolution process compared to the young
expeditives’ appreciation of this CDR measure. It might be worth considering for
firms to individualize their dispute resolution settlement for the different consumer
groups to better account for the occurring costs. Besides, most consumer segments
value call center support over more automated approaches. Still, young achievers
show some appreciation for roboadvisors. Accordingly, similar to the information
strategy in the context of data privacy and security activities, firms should consider
either to offer various user support approaches simultaneously or to offer this (pos-
sibly for companies more cost-effective) access to user support on an individualized
basis for the particular consumer segment of young achievers. Similarly to DURE 1,
DURE 2 underlines the heterogeneous preferences for transparency related to data
privacy and security activities. Hence, firms should consider offering several trans-
parency approaches simultaneously or to customize their informational approach
according to different consumer segments. Hence, to appropriately address differ-
ent market segments, one size does not fit all when it comes to communicating data
protection practices transparently (exceeding legal requirements, e.g., imposed by
the GDPR). Rather companies could rely on different communication strategies
for data protection information, adapting them to different communication chan-
nels to address different consumer segments through their preferred communication
media. Otherwise, firms could offer several approaches to a transparent data pro-
tection communication in parallel to satisfy different preferences across the various
consumer segments. Firms should also evaluate whether a lower-cost version with
lower CDR engagement is worthwhile for the IoT solutions offered, to better appeal
to more price-conscious consumers because price played a central role in the evalu-
ation of the product across the whole sample (see Table10). Companies could vary
user support and dispute resolution in particular for this purpose.
This study aims not only at evaluating consumer preferences but also at the latent
value of a more comprehensive set of CDR activities (see Table 12). We found
lower latent values for further CDR activities when compared to DURE 1. This
again underlines the appreciation of data privacy and security activities of consum-
ers compared to other dimensions of CDR in line with the set of pre-studies. How-
ever, consumers still value further CDR activities of companies. The observed latent
values range from 0.72€ to 1.75€ for the most preferred bundle. Surprisingly, the
elderly generation reveals a slightly higher WTP compared to the young achievers,
deviating from DURE 1. However, the low observed latent values for implementing
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further CDR activities would not account for the company’s expenses when imple-
menting the preferred levels of further CDR activities, e.g., an independent agency
for dispute resolution or seamless interoperability. Again, this supports the hypoth-
esis that consumers expect high standards in this field but only punish the absence
instead of being excited by their implementation (Goel and Shawky 2009). Still,
young expeditives are willing to pay twice as much as elderly traditionalists. Again,
these results support the assumption that different consumer segments do not value
the implementation of additional CDR activities (monetarily) equally and, accord-
ingly, companies cannot define themselves equally strongly towards different con-
sumers. Hence, companies should pursue consumer segmentation in practice, as one
size does not fit all equally. For this purpose, firms could also implement different
versions with lower levels of CDR. Besides, firms should pursue individualization of
CDR activities with regard to the various psycho-demographic consumer segments
to cover different preferences and at the same time exploit (monetary) appreciation.
5 Discussion
This work advances the existing research base on the subject of CDR by empiri-
cally assessing CDR operationalization on measure-level (DURE 1, DURE 2) based
on prior research (Mihale-Wilson etal. 2021) that ranked possible CDR dimensions
(BWS 1) and its sub-dimensions (BWS 2). Hence, this work’s goal is to provide
concrete guidance for implementing CDR activities and a feasible consumer seg-
mentation in practice (DURE 1, DURE 2). Both DURE studies suggest that market
segmentation is sensible to cover the rather heterogeneous preferences of the various
segments in the best possible way.
With this study, we pursue a symbiotic approach to business ethics, thus
“envision[ing] a pragmatic, collaborate relationship between normative and empiri-
cal inquiry” (Weaver and Trevino 1994, p. 132). This approach enables guidance
by relying simultaneously on both types of inquiries. Nevertheless, the understand-
ing and operationalization of CDR highly depends on the individual perception of
organizations, their employees, and stakeholders (van Marrewijk 2003). Accord-
ingly, these organizations and stakeholders must evaluate the derived empirical find-
ings for applicability rather than understanding the results as what they should do
regarding CDR operationalization.
5.1 Theoretical contributions
This work enhances the existing research base on the evolving concept of CDR
by providing an in-depth empirical assessment of the operationalization of CDR
dimensions stemming from the current practice-driven debate. Thus, this study
supplements and extends initial empirical findings (Mihale-Wilson etal. 2021) on
basic preferences for CDR dimensions and their sub-dimensions (BWS 1, BWS 2)
by evaluating the concrete operationalization of CDR and an according consumer
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segmentation. In this way, we sharpen the understanding of consumer preferences
for the different CDR dimensions, especially of the data privacy and security dimen-
sion by providing an empirical evaluation of the appreciation of concrete activities
and a consumer segmentation (DURE 1). DURE 2 complements these findings with
an evaluation of activities within further dimensions and a consumer segmentation
approach. Besides, the findings of both DURE studies also provide insight into the
specific characteristics of participants not valuing CDR activities and thus not (yet)
being in the market (i.e., non-purchasers). Altogether, this research serves as a start-
ing point to make the CDR literature more comprehensive by adding the empirical
perspective to the current discussion. Hence, this work comprises several theoretical
contributions.
First, our results highlight the urgency to make consumers more aware of and
understand the concept of CDR. The gap between preferences regarding the CDR
dimensions in general (set of pre-studies, Mihale-Wilson et al. 2021) and actual
CDR activity preferences (set of main-studies) illustrates this need. The results sug-
gest that many consumers are not yet able to envision the concrete implementation
of CDR in practice and the influence on their own rights and concerns. Accordingly,
in the future, research should place emphasis on further educating consumers on this
point.
Second, the evaluation of characteristics of non-purchasers reveals for both
DURE studies the significant influence of privacy concerns on the appreciation of
CDR activities in general. This finding emphasizes the need not to consider respon-
sibilities in the digital context, such as data privacy and security, in isolation, but
to take a more holistic approach to digital responsibility. This underlines the rel-
evance of establishing a concept like CDR because concerns related to privacy have
a potential halo effect on other CDR activities of firms and consumers’ appreciation
of them. This demonstrates further that digital responsibility does not occur in isola-
tion in practice.
Third, we also show how companies can pursue individualization in operational-
izing the concept of CDR in practice. We were able to demonstrate the benefits of
consumer segmentation due to the very heterogeneous consumer preferences. Con-
sumer segmentation offers an opportunity for practice to target different consumer
groups. Besides, the set of main studies was able to demonstrate how important a
high level of CDR commitment is to consumers. In each of the main studies, the
most preferred bundles were characterized by extensive additional activities in the
CDR context.
Because CDR is very much dependent on the industry applied, this study employs
the example of IoT due to its omnipresence in professional and private everyday life
and thus its tangibility for the respondents. Accordingly, one goal of this publication
is to motivate future research to investigate other industries analogously and thus to
be able to develop a cross-industry understanding. Besides, we add to the literature
basis of hybrid stated preference methods in adoption research (Hinz etal. 2015).
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5.2 Managerial implications
From a managerial perspective, this work seeks to support the implementation and
operationalization of concrete CDR activities as these additional corporate respon-
sibilities can be costly (Lobschat etal. 2021). This work aims at providing concrete
guidance for implementing CDR and a consumer segmentation approach in prac-
tice directed to managers operating in the digital economy on the example of IoT
(DURE 1, DURE 2). In this vein, we may support a broader adoption of the concept
in practice. This work’s intention is not a cost–benefit analysis of conducting CDR
activities in a company but it can serve as a preliminary basis for future research
on this essential managerial aspect. Companies should not understand the empiri-
cal recommendations as what they should do, but rather evaluate to what extent the
results obtained are applicable within the company and match the understanding of
the CDR concept.
Both DURE studies reveal the potential halo effect of data privacy and security
activities on the perception of the CDR engagement. Participants not (yet) valuing
additional CDR activities reveal high privacy concerns, thus distrusting the CDR
engagement at large. Accordingly, companies should be aware that their activities
in one field of CDR can also have an impact on the external perception of other
CDR activities and that digital responsibility does not occur in isolation in practice.
Accordingly, a concept like CDR can support the implementation of a more holistic
approach to digital responsibility.
Our results indicate that for the most valued dimension of data privacy and secu-
rity, organizations should focus on state-of-the-art solutions to ensure secure storage
and processing of personal information (for companies operating in the EU exceed-
ing the requirements of the GDPR). However, consumer preferences within this
dimension are rather heterogeneous. Hence, companies should consider targeting
particular psycho-demographic consumer segments in terms of an individualized
information strategy employing, e.g., different communication media. Concerning
notifications of data breaches, consumers mostly prefer more proactive communica-
tion. The GDPR partly requires such direct communication depending on the sever-
ity of the incident and requirements of the supervisory authority. Still, companies
can exceed these legal minimum requirements and assume more responsibility vol-
untarily, also satisfying consumers’ expectations. However, it is very difficult to esti-
mate the negative impact of privacy and security breaches (Nofer etal. 2014), or the
effect of proactively communicating such breaches. Yet, proactive information on
data security breaches is not observable. Organizations integrating advanced data
privacy and security activities should include into their consideration that the costs
arising may not correspond to the appreciation by the consumers and their according
WTP (Mihale-Wilson etal. 2019), albeit, we have only examined an adequate (i.e.,
still exceeding legal minimum requirements, for instance, by the GDPR), but not an
exceptionally high level of security. Our findings reinforce that consumers also see
it as the responsibility of organizations to limit their activities to necessary ones.
Accordingly, companies should include in their implementation strategy which
activities are particularly suitable, taking into account the associated expenses, to
address the targeted consumer segments as effectively as possible.
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For the sake of differentiation, companies should consider addressing further
CDR dimensions once they established a sound set of activities within the data pri-
vacy and security dimension. This aligns with the activities of corporations that can
be observed in the market (Cabinakova etal. 2016). For instance, some organiza-
tions (e.g., Google, German Telekom) already moved down to address the informa-
tion and transparency dimension, albeit companies should incorporate the effects of
being more transparent by providing additional information (e.g., business model,
security breaches). The same applies to activities regarding a seamless interoper-
ability with devices and services from other manufacturers, which consumers highly
value, or the employment of an independent consumer protection agency for dis-
pute resolution. In case industry-wide initiatives enabling seamless interoperability
emerge, managers should consider joining them, at least when they have gained sig-
nificant tractions, as the high consumer valuation of seamless interoperability could
increase the overall size of the market, in addition to the company following its digi-
tal responsibilities. Again, consumer preferences are rather heterogeneous demon-
strating the value of consumer segmentation for firms especially for informational
and transparency approaches.
Summing up, the findings reveal that in case of CDR one size does not fit all. It
might be worth it to design digital products and services that are easy to adapt to
the needs of different consumer segments. Market segmentation is a quite common
business practice for digital products and services, e.g., in pricing and scope (e.g.,
Naous and Legner 2017; Mihale-Wilson etal. 2019). Results suggest that there is a
need for consumer segmentation according to the targeted CDR dimension(s). Both
studies show that it is advisable to adapt the communication strategy to the targeted
consumer segment and thus to use the preferred communication channels to address
this specific segment. In addition, the high importance of price for the evaluation
of such a solutions shows that it can be useful to offer a slimmed-down version in
terms of CDR activities, such as dispute resolution, for the more price-conscious
consumers.
5.3 Limitations andavenues forfuture research
Despite our best efforts, this study is not without limitations. Firstly, our sample
comprises individuals living in Germany only. Hence, the low valuation of CDR
dimensions like access is less surprising as most Germans have access to the Inter-
net and digital products in general, and the design of the study as an online experi-
ment even reinforces that. Accordingly, the study participants already had to have
access to the Internet for participation. Besides, the state of digital skills in a country
obviously influences such an evaluation of the operationalization of CDR in prac-
tice. Hence, consumers’ valuation of CDR operationalization might differ in other
countries or focus groups. To advance research on CDR, future research should also
address potential regional biases due to media visibility of certain CDR aspects or
previous experience with digital products and therefore consumers’ valuation. For
instance, data privacy and security is one of the more present topics in the media
especially in Germany or the US (Lobschat etal. 2021). The GDPR already enforces
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organizations operating in the EU to fulfill high data privacy and security standards
also triggering high consumer awareness for data privacy and security. On individ-
ual level, several factors might have an influence on the consumer’s evaluation and
economic valuation of CDR and its components. For instance, individual-level influ-
ences could be media exposure and personal experience concerning ethical incidents
arising in the context of digital products and services. Hence, several individual- and
macro-level factors might have an influence on the evaluation of a CDR operation-
alization. Accordingly, a comparison across different countries would be interesting
to assess the influence of several individual- and macro-level factors.
Secondly, this study emphasizes one key stakeholder group—consumers—and an
external motivational base. Albeit, other stakeholder groups (e.g., at the organiza-
tional or individual level) could also favor the implementation of CDR with diverg-
ing demands (e.g., Trittin-Ulbrich and Böckel 2022) and the motivation to operation-
alize CDR could also be intrinsically driven (i.e., by change agents). Hence, future
research should assess other stakeholder groups’ valuation of CDR, e.g., employ-
ees in their working environment or on company level in the business-to-business
context (Lobschat etal. 2021). Besides, future research should assess possible dif-
ferences in internally or externally motivated CDR commitment. However, also the
context influences the measurement of consumer preferences. CDR and consumer
preferences can differ between different industries (Mihale-Wilson etal. 2022), e.g.,
due to necessary data collection or sensitivity of data. In this case, we evaluated the
context of IoT with access to very comprehensive and often very personal data, and
an omnipresent role in private and professional everyday life. Following, we encour-
age future research to assess consumer preferences concerning different industries
and thus provide further perspectives and possible across-industry comparisons.
Finally, the evaluation of the CDR dimensions and its sub-dimensions stems from
a (hypothetical) consumer perspective due to the study design and research goal. It
is beyond the scope of the study to assess possible consequences or consumers’ val-
uation of additional corporate engagement regarding CDR in practice, especially for
groups who are dependent on in general less valued dimensions (i.e., access, educa-
tion). Hence, we call for future research to assess the final value of CDR activities
in real-world experiments. Because companies can shape consumer perceptions of a
firm through their commitment (Hann etal. 2007), outstanding CDR activities can
become a differentiator. Besides, our evaluation relies on one theoretical approach
to and understanding of the CDR concept. Therefore, the consumers’ evaluation of
the operationalization of CDR is highly dependent on these dimensions, sub-dimen-
sions, and scope of the concept. Yet, first consensus regarding the scope of CDR
is developing (e.g., Mihale-Wilson etal. 2022; Mueller 2022) which the selected
approach covers. Nevertheless, a different nomenclature with a different focus may
lead to different results. Hence, future research should assess whether other concep-
tual approaches lead to a diverging evaluation by consumers. In addition, the opera-
tionalization of CDR highly depends on the norms and values of the organization
and its stakeholders. Consequently, the understanding and thus the operationaliza-
tion of CDR may differ between companies and must be evaluated individually by
the company.
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Nevertheless, this work provides first guidance on the operationalization of the
CDR concept and therefore supplements the current research base by a quantitative
in-depth evaluation of consumer preferences for the concrete implementation of CDR
and a consumer segmentation. Thus, this study may foster a broader adoption and
operationalization of CDR in practice. Results suggest that companies should pursue
a more holistic approach to digital responsibilities and should employ consumer seg-
mentation strategies because of the rather heterogeneous consumer preferences.
Appendix
Figure 7 illustrates the deployed CDR dimensions, sub-dimensions, and CDR
activities in the two BWS (Mihale-Wilson etal. 2021) and two DURE experiments.
Table13 provides an overview of the demographic information per study.
Secure storage
and processing
of user data
CDR dimensions
Sub-dimensions
Activities
Information regarding
data collection (data
protection
declaration)
Access and
correction of
personal data
Notification of
incidents
BWS 1 (Mihale-Wilson et al. 2021)
DURE 1
Restricted data
collection
Access and
correction
Restricted data
use
Openness
about data
processing
practices
Data privacy
and security
Clear purpose
of data
collection
Data quality
Product safety
and liability
Information
and
transparency
Education and
awarenessAccess Economic
interests
Dispute
resolution and
awareness
BWS 2 (Mihale-Wilson et al. 2021)
(a) Consumers’ valuation of activities regarding the data privacy and security sub-dimension
Product safety
and liability CDR dimensions
Sub-dimensions
Activities
Transparency
regarding data
protection
User support Interoperability Dispute
resolution
BWS 1 (Mihale-Wilson et al. 2021)
DURE 2
Information
and
transparency
Education and
awareness Access Economic
interests
Data privacy
and security
Dispute
resolution and
awareness
(b) Consumers’ valuation of activities regarding further CDR dimensions
Fig. 7 Deployed CDR dimensions, sub-dimensions, and concrete CDR activities within the two BWS
and the two DURE experiments
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Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s11573- 023- 01142-y.
Acknowledgements This work has been supported by the Federal Ministry for Economic Affairs
and Energy under Grant Agreement number 01MD16009F (ENTOURAGE) and the Hessian State
Chancellery-Hessian Minister of Digital Strategy and Development under the promotional reference
6/493/71574093 (CDR-CAT).
Author contributions All authors contributed to the study conception and design, the material prepara-
tion, and the data collection. The analysis were performed by K. Valerie Carl. The first draft of the manu-
script was written by K. Valerie Carl and all authors commented on previous versions of the manuscript.
All authors read and approved the final manuscript.
Funding Open Access funding enabled and organized by Projekt DEAL. This work was supported by
the Federal Ministry for Economic Affairs and Energy under Grant Agreement number 01MD16009F
(ENTOURAGE) and the Hessian State Chancellery-Hessian Minister of Digital Strategy and Develop-
ment under Grant Agreement number 6/493/71574093 (CDR-CAT).
Data availability The data sets used in our publication are not publically available. Still, readers can
get access to the data sets used for calculation by contacting the authors in justified cases. The complete
questionnaire is part of the submission (Supplementary Information). All participants gave informed con-
sent to data collection and processing.
Table 13 Demographic characteristics per study
Demographics BWS 1 and 2
N = 663 (%)
DURE 1
N = 404 (%)
DURE 2
N = 415 (%)
Gender
Male 55.51 55.69 55.90
Female 44.49 44.31 44.10
Age
< 18 0.15 0.25 0.24
8–24 3.32 2.72 3.13
25–34 14.03 15.59 16.14
35–44 21.42 23.27 24.58
45–54 22.17 23.76 24.58
55–64 15.08 17.82 17.11
65–74 19.16 16.34 14.22
> 75 4.68 0.25 0.00
Education
Less than secondary school certificate 14.48 14.36 13.98
Secondary school certificate 34.69 35.64 34.70
Abitur 20.51 22.03 21.45
Bachelor 8.60 9.41 9.88
Master/diploma or higher 21.72 18.56 20.00
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Declarations
Conflict of interest The authors have no relevant financial or non-financial interests to disclose.
Consent All authors have approved the manuscript and agree with its submission to JBE.
Ethical approval One paper using a part of the same data set has been published (“Corporate Digital
Responsibility—Extended Conceptualization and Empirical Assessment”) before. This publication was
part of the ECIS 2021 and used only data from the Best–Worst Scaling part of the data set. Aim of this
paper was to provide an advanced conceptualization of CDR and an initial empirical assessment. Data
stemming from the Dual Response part of the data set has not been published yet. This paper has not been
published or accepted for publication. It is not under consideration at another journal or has been submit-
ted to JBE before.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
ses/ by/4. 0/.
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