ArticlePDF Available

Abstract and Figures

We propose that digital technologies and related data become increasingly prevalent and that, consequently, ethical concerns arise. Looking at four principal stakeholders, we propose corporate digital responsibility (CDR) as a novel concept. Specifically, we define CDR as the set of shared values and norms guiding an organization's operations with respect to the four main processes related to digital technology and data. These processes are the 1) creation of technology and data capture, (2) operation and decision making, (3) inspection and impact assessment, and (4) refinement of technology and data. On this basis, we expand our discussion of CDR by highlighting how to managerially effectuate CDR compliant behavior based on an organizational culture perspective. Our proposed conceptualization of CDR unlocks future research opportunities related to refining and expanding the concept, especially regarding pertinent antecedents and consequences. Managerially, we shed first light on how an organization's shared values and norms regarding CDR can get translated into actionable guidelines for users. This provides grounds for future discussions related to CDR readiness, implementation, and success.
Content may be subject to copyright.
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage:
Corporate digital responsibility
Lara Lobschat
, Benjamin Mueller
, Felix Eggers
, Laura Brandimarte
, Sarah Diefenbach
Mirja Kroschke
, Jochen Wirtz
School of Business and Economics, University of Münster, Am Stadtgraben 13-15, 48143 Münster, Germany
Faculty of Business and Economics (HEC), University of Lausanne, Internef 134, 1015 Lausanne, Switzerland
Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Kaiserstrasse 89, 76133 Karlsruhe, Germany
Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE Groningen, the Netherlands
Eller College of Management, University of Arizona, 130 E Helen St, Tucson, AZ 85721, USA
Faculty of Psychology and Educational Science, Ludwig-Maximilians-University (LMU) Munich, Leopoldstrasse 13, 80802 Munich, Germany
Etribes Connect GmbH, Wendenstrasse 130, 20537 Hamburg, Germany
NUS Business School, National University of Singapore, 15 Kent Ridge Drive, Singapore 119245, Singapore
Corporate digital responsibility (CDR)
Digital technologies
Organizational culture
We propose that digital technologies and related data become increasingly prevalent and that, consequently,
ethical concerns arise. Looking at four principal stakeholders, we propose corporate digital responsibility (CDR)
as a novel concept. We define CDR as the set of shared values and norms guiding an organization's operations
with respect to four main processes related to digital technology and data. These processes are the creation of
technology and data capture, operation and decision making, inspection and impact assessment, and refinement
of technology and data. We expand our discussion by highlighting how to managerially effectuate CDR com-
pliant behavior based on an organizational culture perspective. Our conceptualization unlocks future research
opportunities, especially regarding pertinent antecedents and consequences. Managerially, we shed first light on
how an organization's shared values and norms regarding CDR can get translated into actionable guidelines for
users. This provides grounds for future discussions related to CDR readiness, implementation, and success.
1. Introduction
For the last couple of decades, digital advances have enabled a wide
variety of systems with vast capabilities. Specifically, the benefits of
automation, data analytics, artificial intelligence (AI), and machine
learning to society are increasingly evident in daily life (Brynjolfsson &
McAfee, 2017), and applications range from fulfilling consumer re-
quests, making lending decisions, providing health advice, taking on
high-risk jobs, protecting endangered species, to transporting people
and goods (Wirtz et al., 2018). Yet along with this unprecedented
power comes ethical dilemmas, in both consumer and business con-
texts, such as those associated with smart devices that constantly record
data, the actions of autonomous vehicles in dangerous situations, and
algorithms making recruitment decisions. Even if introduced with the
best of intentions, malleable AI systems can be at risk of exploitation for
unintended purposes (e.g., Nambisan, Lyytinen, Majchrzak, & Song,
2017; Richter & Riemer, 2013). It is the responsibility of system de-
signers and the organizations that use these systems to recognize that
their technologies may be used in ways other than they had anticipated
with unwanted consequences for different stakeholders and society at
large. However, existing research providing guidance for organizations
in the face of ethical dilemmas related to the digital is scant.
In this sense, digital technologies that assist in human decision
making or make decisions autonomously need to be subject to moral
norms and ethical considerations similar to those that apply to humans.
If we accept the premise that human behavior (individual and collec-
tive) should be governed by moral norms and ethical considerations,
then any creation, operation, impact assessment, and refinement of
Received 15 July 2018; Received in revised form 3 October 2019; Accepted 3 October 2019
This paper is based on discussions at the Thought leadership Conference on Digital Business Models and Analytics at the University of Groningen in April 2018. We
thank the organizers for their support.
Corresponding author.
E-mail addresses: (L. Lobschat), (B. Mueller), (F. Eggers), (L. Brandimarte), (S. Diefenbach), (M. Kroschke), (J. Wirtz).
Lead authors. The other authors are listed in alphabetical order and contributed equally to the paper.
Journal of Business Research 122 (2021) 875–888
Available online 28 November 2019
0148-2963/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
Click here to download
the full article.
This article is an open
access publication.
This means, anyone
may distribute hard
and soft copies of the
original version of this
digital technology and data should be assessed according to such rules.
This argument presupposes that ensuring the ethical design and uses of
digital technologies and related data is not solely a technological
challenge (e.g., developing algorithms for ethical reasoning). Rather, it
requires organizations to develop a comprehensive, coherent set of
norms, embedded in their organizational culture, to govern the devel-
opment and deployment of digital technology and data. We refer to this
idea as corporate digital responsibility (CDR), defined as the set of
shared values and norms guiding an organization’s operations with
respect to the creation and operation of digital technology and data. It
requires tech companies, individual developers and designers, and any
corporate actor employing digital technologies or data processing to be
aware that the code they produce or deploy, as well as data they collect
and process, inherently create an ethical responsibility for them.
Consequently, organizations must determine how to operate re-
sponsibly in the digital age, while also complying with legal require-
ments and considering economic impacts on the organization (Schwartz
& Carroll, 2003).
Our work in this article is pursuant of two complementary research
objectives. First, we introduce the new concept of CDR, ask what the
specific nature of CDR is, and how to conceptualize it. In our con-
ceptualization and definition of CDR, we focus on the ethical issues that
are unique to the digital context. Furthermore, we differentiate corpo-
rate digital responsibility (CDR) from corporate social responsibility
(CSR) to highlight its distinctiveness while also drawing important links
between the two. We identify key related stakeholders and key stages
that CDR must address, namely, the creation, operation, impact as-
sessment, and refinement of technology and data.
Next, second, we raise the question of how CDR can manifest in
specific norms that effectuate CDR compliant behavior across levels. We
approach this objective by employing an organizational culture per-
spective. This allows us to discuss the role of specific CDR norms in
relation to artifact and behaviors. In the same vein, we sensitize deci-
sion makers to influences on and outcomes of CDR.
At the end, we synthesize our discussions by introducing a com-
prehensive framework that helps academics and managers build a CDR
culture. Combined, our contributions support organizations in trans-
lating their mission and values regarding digital responsibility into
actionable guidelines for users (i.e., managers, technology designers,
and other employees).
2. Making a case for CDR
In the face of ethical challenges arising from the development and
deployment of technology and data, organizations need to develop a
better understanding of how to manage ethical dillemas and overall act
digitally responsible. For this purpose, we turn to the concept of busi-
ness ethics, broadly defined as the norms and standards that govern
judgment and choices in business-related matters (Moriarty, 2016;
Treviño, Weaver, & Reynolds, 2006). Based on the broad idea of busi-
ness ethics, we define CDR as the set of values and specific norms that
govern an organization’s judgments and choices in matters that relate
specifically to digital issues. Such CDR-related values and norms share
some principles and goals with CSR, or an organization’s commitment
(and accountability) toward social and ecological causes in general.
Accordingly, CSR encompasses the economic, legal, and ethical ex-
pectations that society has of organizations at a given point in time
(Schwartz & Carroll, 2003), and we propose that a similar perspective is
inherent to any considerations of CDR as well. Notwithstanding this
similarity, we argue that CDR should be considered explicitly and se-
parately from CSR, because of the particularities of digital technologies.
To account explicitly for this difference, we highlight three character-
istics that justify the explicit consideration of the digital, beyond an
organization’s wider social responsibility.
First, technological developments exhibit exponential growth
(Moore, 1965). Building on the accelerated technological progress to
date, the coming decades appear likely to produce even more disruptive
innovations. According to Brynjolfsson and McAfee (2014), it is parti-
cularly recombinant growth among such innovations that requires
corporations to face what the digital means. For example, big data and
analytics are being combined with advances in machine learning and
AI, allowing for the vast amounts of data already being collected to be
put to even more efficient use.
Second, ethical and social concerns need to reflect the malleability
of digital technologies (Richter & Riemer, 2013; Soltani, 2019). Social
media were not created intentionally to spread fake news, but their
algorithms, designed to maximize engagement, have contributed to this
growing trend (Vosoughi, Roy, & Aral, 2018). From a corporate per-
spective (spanning from corporations that initially design and develop
new digital systems to those that deploy them), digital responsibility
thus entails a wide, complex, and highly dynamic set of moral chal-
lenges that cannot be exhaustively foreseen when a technology is de-
signed or data initially captured, but that will only unfold in use over
Third, arguments that specific corporate norms are to deal with
digital responsibility also derive from the pervasiveness of digital
technologies. It has become nearly impossible to perform daily activ-
ities without the use of digital technologies, whether directly (using an
app) or indirectly (an offline request gets processed by a digital tech-
nology in the background). Both corporations and consumers increas-
ingly lack realistic options to lead their daily lives without digital
technologies or avoid the potential effects of interrelated devices that
track their behaviors.
Combined, these three aspects—exponential growth in technolo-
gical development, malleability of technologies and data in use, and
pervasiveness of technology and data—suggest that the digital is not just
a linear development of previous technological advances but instead
represents a quantum leap in digital technology that involves novel and
specific challenges to corporations’ ethical behavior that go beyond
CSR. Nevertheless, CDR and CSR will likely prove complementary and
overlapping (e.g., environmental impacts of digital technologies). This
interplay is an important avenue for research; in this initial study, we
focus on introducing and conceptualizing CDR as a foundation.
3. Basic framework of CDR, stakeholders, and stages
An ad hoc literature review reveals that in information systems re-
search, eight leading journals have published only about 50 articles that
broadly deal with ethical issues, following the first influential con-
tribution in this vein in 1986 by Mason.
These articles cover hetero-
geneous topics, though without offering any concrete advice for specific
CDR norms. Other academic disciplines touch on elements that are
relevant to CDR (e.g., consumer privacy concerns, effects of hu-
man–computer interactions), and inside and outside business research
domains (see Table 1).
While a full analysis and integration of these perspectives is beyond
the scope of our efforts here, Table 1 illustrates that these isolated
discussions have not produced a specific conceptualization of CDR in a
business context yet. To address this conceptual gap, our engagement
with these various domains and their relations to our own backgrounds
and experiences leads us to propose a foundational framework of what
CDR is and its role in organizations (Fig. 1). This framework includes
four stakeholders that corporations must account for in their CDR ef-
forts (Section 3.1), as well as four key stages linked to digital technol-
ogies and data, which mirror their lifecycles (Section 3.2).
For our analysis, we used the Senior Scholars’ Basket of Journals re-
commended by the Association for Information Systems. Details can be found
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
3.1. Stakeholders
3.1.1. Organizations
Because organizations are the principal bearers of CDR, we expect
specific CDR norms to develop at this level. Similar to other corporate-
level frameworks (e.g., CSR), CDR provides organizations with a set of
shared values and norms to guide their operations with respect to the
creation and use of technology and data. In turn, other corporate actors,
such as suppliers and partners and their digital technologies and data,
must be considered. Various companies along the value chain develop or
deploy digital technologies, and we explicitly note the importance of
actors involved in software development or electrical engineering (e.g.,
semiconductors, communication networks, consumer devices, and apps),
as well as settings that feature digital technology embedded into more
traditional products or services (e.g., onboard computers in cars). The
proposed conceptualization of CDR suggests a focus on a focal corpora-
tion, but we acknowledge the complex network of interdependent actors,
beyond corporate boundaries, that are relevant ethical agents and critical
stakeholders for digital technologies and data.
3.1.2. Individual actors
Even if organizations are the most direct addressees and bearers of
CDR, the moral guidance offered by specific CDR norms also must ef-
fectuate CDR-compliant behavior across levels. To do so, the organi-
zation’s mission and values must be translated into actionable guide-
lines for users (managers, technology designers, other employees), and
Table 1
Role and Relevance of CDR in Key Disciplines.
Discipline Role and Relevance of CDR
Marketing management Marketing scholars focus on balancing organizational data needs and customer responses (Lwin et al., 2007) and on
organizational privacy failures, such as reputation risks related to hacking, data leaks, surveillance, profiling, and
micro-targeting (e.g., Martin et al., 2017).
Service literature addresses the effects of service robots and privacy issues related to facial recognition, constant
monitoring of consumers, decision making by AI (Van Doorn et al., 2017; Wirtz et al., 2018), and the vast amount of
personal and transaction data captured by platform businesses such as Airbnb and Uber (Wirtz et al., 2019).
The Marketing Science Institute has made consumer privacy and the ethical discussions surrounding it a key research
priority for 2018–2020.
Consumer behavior, consumer psychology, behavioral
economics A focus on psychological privacy includes consumer privacy concerns and their antecedents, such as personality
traits, knowledge, and experience (Malhotra et al., 2004).
Consumer privacy concerns evoke responses (e.g., Inman & Nikolova, 2017; Martin & Murphy, 2017; Wirtz & Lwin,
2009); studies also consider the relationship between privacy attitudes and privacy-related behaviors such as the
privacy paradox and its mechanisms (e.g., cognitive biases; John et al., 2010; Kehr et al., 2015).
Privacy-related consequences include information disclosure and privacy protection behaviors (Son & Kim, 2008)
Key influencing contextual factors are the social context, control, and firm reputation (Steenkamp & Geyskens, 2006;
Xie et al., 2006).
There is a trade-off of data provision and privacy risk with convenience, speed, personalization, and customization
(Culnan & Bies, 2003; Smith et al., 2011; Wirtz & Lwin, 2009). Potential strategies to overcome consumers’ privacy
concerns include reducing privacy risk perceptions and increasing trust in the privacy policies and practices of an
organization (Holtrop et al., 2017; Lwin et al., 2016; Schumann et al., 2014).
Human–computer interaction Investigations of the perceived role, influence, and responsibility attributions in human–computer interaction reveal
the circumstances in which users blame computers for failed outcomes and unwanted effects (Hinds et al., 2004;
Moon & Nass 1998)
Whereas the computer-as-tool paradigm sees technology as a tool extending human capabilities, the computer-as-
partner paradigm sees technology as taking tasks delegated by the user through anthropomorphic means of
communication (Beaudouin-Lafon, 2004), and the computers-as-social-actors paradigm indicates that people’s
responses to computers are fundamentally “social,” such that they apply social rules, norms, and expectations that
mimic those in interpersonal relationships (Lee & Nass, 2010)
Social robots, perceived character, and capability attributions lead to trust, over-trust, and under-trust (Ullrich &
Diefenbach, 2017; Wirtz et al., 2018), such that people follow a robot’s instructions in emergencies, though it actually
performs poorly in navigation guidance (Robinette et al., 2016). In specific settings, people more readily follow a
social robot’s judgment than that of other humans (Ullrich et al., 2018).
Ethics, computer ethics Discussion of governmental privacy regulation and consumer data protection laws (Lwin et al., 2007; Sarathy &
Robertson, 2003).
Analysis of privacy policies, transparency and fair data practices (Milne & Culnan, 2004) as well as cultural values and
norms (Milberg et al., 2000).
Approaches to formalize ethical reasoning to enable computer-supported ethical decision making (Van den Hoven &
Lokhorst, 2002).
Proposing models and approaches to moral or responsible design of digital innovation (Van den Hoven et al., 2014).
Development of alternative moral regimes for the information society (Floridi, 2010).
Discussion of specific ethical norms for autonomous systems (e.g., robots; Moor, 2006).
MIS, systems science, system design The focus of this literature is on ethical aspects of technology development and organizations’ use of technology.
The impact of computer ethics of business-related IS research (Stahl et al., 2014) specifies impacts of technology on:
o Organizational ethical behavior (Chatterjee et al., 2015).
o Society at large (Avgerou & Madon, 2005; Lameijer et al., 2017).
Studies of the role of ethics in systems development and design cite:
o Specific norms to guide system design (Chatterjee et al., 2009).
o Abstract procedural guidance for developing and updating relevant ethical norms (Mingers & Walsham, 2010).
Digital ethics guide and define anticipation of (non)acceptable effects for users (Brey, 2012; Wright, 2011).
Special emphasis centers on the role of IT professionals (Oz, 1992; Walsham 1996).
Approaches ensure user and other stakeholder contributions in technology design processes (Olerup, 1989)
Design research, sustainability research Highlighting technology’s influence on users in design, such as a moral gamification design framework (Versteeg,
Sustainable interaction design implies that design is “an act of choosing among or informing choices of future ways of
being” (Blevis, 2007, p. 503), which relies on interactive technologies to promote more sustainable behaviors.
Recognition and discussion of unintended side effects of the digital transition – as well as rebound effects related to big
data, AI, conversational software, digital biotechnology, and other technological advancements – as prerequisites for
sustainable digital societies and environments (Montag & Diefenbach, 2018; Scholz et al., 2018).
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
those guidelines should be manifest in technological artifacts and be-
haviors. In this sense, institutions alone may be too abstract and col-
lective to be effective, reliable bearers of ethical responsibility. We thus
account for individual actors.
In a corporate context, managers are the principals that steer and
account for their organizations, so they likely represent primary sub-
jects of CDR on an individual level, followed by other employees. Thus,
CDR also pertains to individual users (i.e., as agents of the corporation).
Consequently, and beyond the immediate setting of a user employing a
digital technology to do a task (which could be seen to rather suggest a
concept like personal digital responsibility), we incorporate an inter-
personal perspective into CDR. For instance, a user might interact with
other members of the organization or beyond in technologically
mediated ways (e.g., interactions through social media or input–output
relations as in enterprise resource planning systems). Importantly, and
though it may seem counterintuitive, we suggest that CDR also needs to
take (potential) non-users of the technology into account to avoid lock-
out effects. For example, the creation and operation of digital tech-
nology and data can create a significant risk of exclusion (cf. discussions
of the digital divide). In such settings, we see CDR as a principal vehicle
to mitigate emergent “new forms of discrimination between those who
can be denizens of [digital society] and those who cannot” (Floridi,
2010, p. 9).
3.1.3. Artificial and technological actors
We propose that CDR must acknowledge the existence of artificial
actors. Despite their increasing relevance, such actors have not received
much attention yet. The field of research into machine or robot ethics is
relatively nascent (Crawford & Calo, 2016; Moor, 2006; cf. Van
Wynsberghe & Robbins, 2018), referring mainly to actors’ autonomous
ethical reasoning. We suggest that CDR goes beyond that point, to in-
clude guidelines for the development and deployment of artificial and
technological actors in an organization. Algorithmic decision-making,
machine learning, and AI involve non-human and non-social entities, so
a key question is whether and how we can delegate digital responsi-
bility to artificial actors and take responsibility for their actions. Recent
developments such as the recognition of socially biased algorithms
(e.g., Wolfangel, 2017) and AI that can learn to write software code
itself (Murali, Chaudhuri, & Jermaine 2018) highlight the need for
ethical norms applied to artificial actors, such that CDR should be re-
flected in code and provide decision-making guidance to developers
and algorithms.
3.1.4. Institutional, governmental, and legal actors
This category includes governmental or judicial entities (e.g., reg-
ulators and law enforcement) to which corporations are accountable in
their approach to CDR. For example, the European Union’s General
Data Protection Regulation (GDPR) is an important legal framework for
designing corporation-specific norms for CDR. Non-governmental or-
ganizations such as consumer and trade associations also can affect CDR
(e.g., professional associations like the Institute of Electrical and
Electronic Engineers (IEEE) with its code of conduct for software en-
3.2. Key lifecycle stages of digital technologies and data
In Fig. 1, we introduce four generic stages of the lifecycle of digital
technologies and data, each of which is affiliated with key sources of
ethical responsibility. In the interest of generality, Fig. 1 does not reflect
any company-specific operations or particular processes per se. Rather,
building on research into organizational knowledge processes (Intezari,
Taskin, & Pauleen, 2017), we identify the (1) creation of technology and
data capture, (2) operation and decision making, (3) inspection and impact
assessment, and (4) refinement of technology and data as key stages that
provide a better understanding of critical CDR-related issues related to
digital technology and data. These main stages build on each other in a
circular relationship.
Creation of technology and data capture refers to the initial stage in
which new technologies are developed and data are collected. In the
operation and decision making stage, the new technologies get applied
and the data are put to work, such as to create customer profiles, to
ultimately support decision making – whether by human or artificial
actors, or a mixture thereof. The inspection and impact assessment stage
features assessments of the resulting outcomes and captures how and to
Fig. 1. Basic Conceptual Constituents of CDR. *The outer layer of the graph (in grey) contains CDR-relevant stakeholders.
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
what extent an organization relies on those outcomes in future in-
stances of decision making. Finally, the refinement of technology and data
stage relates to potential revisions of technologies and data, as well as
the possibility of terminating an application or deleting data. In digital
contexts though, a clear distinction of these main stages is difficult,
because they tend to overlap (Nambisan et al., 2017). Nonetheless, we
use these stages to analytically structure our discussion and identify
potential ethical dilemmas and the related need for CDR when they
appear most pertinent.
3.2.1. Creation of technologies and data capture
When creating any digital asset (development of technology, cap-
turing data, training an AI), it is the responsibility of those designing
and implementing the asset to ensure that this design and im-
plementation embody ethical values. Consider software for example.
Ethical norms need to inform its implementation, so the resultant sys-
tems behave in accordance with these norms subsequently. For in-
stance, the design of a new machine learning algorithm must ensure the
presence of transparency and accountability characteristics (which are
particularly prominent in current debates). Similarly, designing data
models for consumer data and models to analyze and predict should be
guided by CDR norms, which then can help data scientists determine
which data they can collect ethically and the conditions in which they
can process that data.
This perspective on creation and capture does not apply solely to
corporations that design and implement digital technologies and data
models; it has powerful ethical implications for corporations that de-
ploy and employ the digital assets, too. Organizations should include
ethical considerations as criteria for their software selection and per-
form corresponding due diligence. Similarly, work with secondary data
must consider the source of the data and the conditions in which it was
generated (e.g., did users have a fair chance to offer informed consent
for the collection and use of their data?). Legal frameworks provide
important guidelines (e.g., GDPR), but a corporation needs specific
engagement with CDR to develop a culture and norms that guide cor-
porate behavior across levels. Thus, whether a corporation produces a
digital asset or simply acquires one for deployment, this stage covers all
corporate operations, from the initial ideation and design to the release
of the digital asset for usage by others, internal (e.g., employees) or
external (e.g., customers) to the corporation.
3.2.2. Operation and decision making
This stage covers all aspects related to the actual use of digital assets
after their deployment. In this phase, digital technology and data are
tightly intertwined in that the former is used to process the latter; while
the latter shapes the processing of the former (e.g., machine-learning-
based algorithms being trained on past data). Ultimately, we think of
this stage as leveraging digital assets to inform or conduct decision
The stage of operation and decision making constitutes a multilevel
phenomenon, spanning from corporate guidelines for how to use par-
ticular technologies and data to specific individual decisions related to
their day-to-day use. An explicit emancipation of this stage is important
from a CDR perspective because ethical responsibility cannot be as-
signed exclusively to those responsible for the creation of digital tech-
nologies and data.
This is particularly true because, as highlighted earlier, many digital
technologies are not closed; they permit more than one form of usage,
so corporations must recognize that technologies are malleable in use
(Richter & Riemer, 2013). In particular, IT-based solutions are akin to
universally reprogrammable machines (Moor, 1985), in constant states
of flux, even after their release (Nambisan et al., 2017). Corporations
thus cannot leave ethical responsibility only to those actors that create
the digital assets, particularly when technology and data interplay
tightly. Again, machine learning algorithms provide an example: results
produced by the algorithm and decisions based on these results depend
on the algorithm but also on the data used initially to train that algo-
rithm. For instance, recent evidence indicates that using machine
learning algorithms to support judicial processes or hiring practices can
lead to the unintended projection of past race or gender biases onto
future decisions if the algorithms are trained on historical data alone.
Current data fed into such a system similarly would shape the future
behavior of the system. Accordingly, CDR must sensitize corporations to
the potential impacts and longer-term mutability of their digital assets
in the operations and decision making stage.
3.2.3. Inspection and impact assessment
Organizations should critically assess the results operating digital
assets and the decision making that occurs on that bases. This must
include a broad perspective on the effects on all stakeholders, which
involves both intended and unintended consequences of the decision
taken. Generally, we assert that CDR norms must account for three
perspectives. First, an assessment perspective should consider the
beneficence of employing a corporation’s digital assets. When gen-
erating and collecting user data, for example, an assessment of bene-
ficence would require the corporation to determine whether the costs
and benefits for users are balanced (Lwin et al., 2007). In turn, this
requires that users learn about how the corporation is working with
their data and are adequately compensated for allowing the corporation
to do so (monetary or otherwise, such as enhanced convenience and
customization). The multisided natures of many markets for digital
products and services makes the assessment of beneficence for all in-
volved stakeholders complex (Lwin et al., 2016), but specific engage-
ment with CDR offers organizations an opportunity to adopt a clear
approach to this challenge and involve relevant parties along their
value chain.
Second, digital assets can have impacts beyond the stakeholders
immediately concerned with their development and usage, especially
platform and infrastructural technologies. For example, the emergence
of wearable technologies and health apps likely will have implications
for how health insurers calculate premiums, such that non-users might
have to pay higher premiums (or be refused coverage) simply as a result
of their unwillingness to share intimate health data, rather than any
specific evidence that they live healthy or unhealthy lives. Specific
norms for CDR thus need to account for such impacts which might
extend beyond those in immediate contact with a corporation’s digital
Third, an impact perspective needs to account for the indirect and
unintended effects of the creation and use of digital technologies and
data. Many corporations are exploring whether blockchain technology
offers opportunities for business model innovations, but few discussions
reflect on the environmental impact of this new technology. Bitcoin
alone, just one current blockchain application, requires electricity
equivalent to that of 4.3 million average U.S. households annually to
support its mining and trading systems, with corresponding environ-
mental impacts. Such externalities call for a sense of ethical responsi-
bility, which CDR’s impact perspective must address.
Taken together, these perspective must inform a careful inspection
of digial assets in terms of a critical review of their performance and
impact. While this stage can also involve utilitarian goals (e.g., profit-
ability, market share, etc.), we urge corporations that a true CDR per-
spective will require a careful and critical assessment of wider impacts
as well.
3.2.4. Refinement of technology and data
Based on the insights that result from the inspection and impact
assessment stage, and returning to the mutability of digital technologies
and data, CDR norms should provide guidance for dealing with the
inevitable changes to digital assets that are open and malleable in use.
Continued engagement with digital technologies and data appears cri-
tical in this sense. For example, designers of machine learning algo-
rithms should realize that the ethical responsibility for their creation
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
does not cease when the implementation stage is complete, and the
algorithm has been shipped. Instead, CDR must involve ongoing en-
gagement and monitoring; the corresponding norms might recommend
transparency and accountability in all algorithms, to enable people who
rely on them to understand how and why certain results arose. An
ability to intercede into the decision-making process and correct un-
wanted outcomes should also be specified in CDR norms for review
cycles and procedures or that define clear ownership and governance
rules. Pragmatically, continued engagement also compels corporations
to make sure digital technologies are patched and kept up to date,
which can help mitigate the impact of emerging security threats.
As a special form of refinement, CDR norms must also cover the
retirement of digital assets. Notably, CDR norms should specify how
long collected customer data are kept on file, as highlighted in current
discussions about the “right to be forgotten” on the Internet. Retirement
considerations also apply to digital technologies per se, especially when
these have become systemic or infrastructural, in that they set out ways
to avoid being locked into a system as well as fail-safe conditions and
Table 2 provides an example that illustrate the four stages and
discusses the potentially relevant ethical considerations that emerge in
the respective context. In turn, these considerations need to be reflected
in specific CDR norms that seek to guide and inform behaviors across
Synthesizing these discussions, Fig. 1 presents basic conceptual
elements of a corporation’s digital responsibility. The stages also con-
stitute sources of digital responsibility relative to the digital technolo-
gies and data that a corporation’s specific CDR norms should address.
The answers need to reflect the context, expectations, and specific re-
quirements that the four main stakeholders impose on a corporation’s
CDR norms. Only then can a set of specific norms guide the corpora-
tion’s operations with respect to digital technology and data across all
of the four stages we identify.
4. Toward a conceptual framework of CDR
With these four general stages of the lifecycle of digital technologies
and data as conceptual building blocks of CDR, we seek to embed the
concept in a corporate context to help decision makers better under-
stand how CDR emerges and appreciate its potential effects. Beyond
making the concept more accessible and concrete, this contextualiza-
tion also should facilitate further research. Accordingly, we borrow
from similar approaches (e.g., Homburg & Pflesser, 2000) to con-
textualize the influences on and effects of CDR according to organiza-
tional culture concepts. Specifically, we argue that culture provides a
conceptual rationale for how CDR is shaped by and is able to shape
corporate behavior. In line with Deshpandé and Webster (1989), we
define organizational culture as “the pattern of shared values and be-
liefs that help individuals understand organizational functioning and
thus provide them norms for behavior in the organization” (p. 4). In our
research context, a CDR-related organizational culture, or CDR culture,
describes the ways CDR is executed by an organization, which helps
organizations become more knowledgeable about what CDR entails.
4.1. Three layers of a CDR culture
Following Schein (2004), we posit that CDR culture exists at three
fundamental layers (Fig. 2) that differ in their degree of accessibility
and visibility to the observer but that also are strongly interrelated.
These layers are shared values, specific norms, and artifacts and behaviors.
The specific form of CDR culture relates to digital responsibility aspects
of an organization and embodies assumptions and shared values (layer
1) from which specific CDR norms are derived (layer 2), which then
result in specific artifacts and behaviors related to CDR (layer 3). Ac-
cordingly, we regard CDR norms as a form of applied ethics that in-
fluence employees’ ethical behavior through formal and informal
structures (Moriarty, 2016). The corporation’s CDR culture must enable
evaluations of alternative behavioral options and choices of the “right”
way forward, on both individual and organizational levels.
4.1.1. Shared values supporting CDR within an organization
At the highest level of abstraction (layer 1 in Fig. 2), values re-
present what is considered desirable in an organization, manifested in
its philosophies, strategies, and goals, which in turn are shared by all of
its members (Schein, 2004). Unlike specific norms, shared values are
not designed to guide behaviors in a specific CDR context; instead, they
provide general guidelines for the development of specific CDR-related
norms. For example, many organizations proclaim “respect for others”
as a core value, which forms a basis for specific norms and informs
context-specific behaviors.
To find general guidance regarding how to behave digitally re-
sponsibly, a long-standing discussion in computer ethics highlights the
difficulties (Bynum, 2001), exacerbated because the fundamental shifts
associated with the digital mean that “either no [moral or ethical]
policies for conduct in these situations exist or existing policies seem
inadequate” (Moor, 1985, p. 266). Rather than updating existing
norms, CDR appears to need an entirely new underpinning. The un-
certainty about which ethical norms apply (Rainie & Zickuhr, 2015)
and the parallel existence of different views on adequate behavior, or
norm fragmentation, likely induces conflict (Diefenbach & Ullrich,
2018). Corporations, therefore, are confronted with questions of whe-
ther established moral norms apply to their digital activities or if they
need new norms, and if so, from whom, where, and how. In any case,
their specific CDR norms differ with the moral grounds that provide
their basis (e.g., deontological moral reasoning likely yields different
CDR norms than utilitarian reasoning).
We do not seek to promote any specific set of values, but a few
sources of inspiration might be helpful. On a general level, moral
standards and responsibilities that might provide a foundation for a
corporation’s specific CDR norms could come from normative general
human rights, as in the Declaration of Human Duties and
Responsibilities (DHDR) or the Universal Declaration of Human Rights
. For example, Ashrafian (2015) proposes that if AI agents and
robots receive human-based rights, they also must receive equivalent
levels of duties and responsibilities. Beyond approaches based on nor-
mative general (human) rights, collections of specific ethical principles
and values appear in studies of technological developments that iden-
tify key dimensions of moral guidelines. For example, Brey (2012) and
Wright (2011) list some values that might guide a corporation’s de-
velopment of CDR norms.
4.1.2. Specific norms for CDR
With a higher degree of specificity than shared values, specific
norms (layer 2 in Fig. 2) provide expression to an organization’s shared
values in a particular context (Feldman 1984). Using our previous ex-
ample, the commonly shared value “respect for others” could translate
into a specific norm such as “safeguard consumers’ personal data” in a
CDR context. Such specific CDR norms then should guide all activities
by the organization in terms of what is right and wrong for the creation
and use of digital technology and data (Maignan & Ferrell, 2004). For a
business to be digitally responsible, its managers and employees must
align their behaviors with specific norms established by the organiza-
tion to achieve CDR. That is, specific norms make shared values explicit
and tangible to effectuate CDR-compliant behavior across
We thank the anonymous reviewer for this suggestion.
Other ways to determine the moral conformity of ideals and actions exist,
beyond such deontological approaches. However, for brevity, we limit our-
selves to these illustrative examples.
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
Table 2
Activities and Potential Concerns in the Example of Wearable Personal Health Devices.
Creation Operation and Decision-making Inspection and Impact Assessment Refinement
Activities Designing the wearable device (e.g.,
wristband) and deciding which sensors to
Ongoing and continued collection of user data Analyzing users’ progress vis-à-vis their goals/
Refining analytics/algorithms (even re-training if need
Building software and deciding which
sensors of the host device (e.g., smartphone)
to use
Analyzing user preferences and routines (e.g., exercise
Assessing health advice Refining decision rules; deciding when what
recommendation is given to users
Deciding which existing personal health data
on the host device to use
Compiling data into comprehensive user profile Analyzing customer complaints (e.g., on social
Implementing additional safety/security measures for
users’ data
Building a data and prediction model for
data analysis
Matching of user characteristics to other users based on
selected variables
Inspecting algorithms and analytics to
understand decision models
Deciding which new data need to be collected and which
data need to be discontinued/deleted
Capturing of data from the user Probabilistically extrapolating unobservable data (e.g.,
future development of health) and future events (e.g.,
health incidents)
Benchmarking with technological progress and
Improving algorithms and predictive models
Inferring health data from other sources
(e.g., social media)
Making recommendations to the user on health-related
Assessing wider network of stakeholders (e.g.,
third-parties interested in data)
Deciding on storage location of data and
data ownership
Analyzing of unintended effects (and ability to
discover these)
Potential Concerns Users are not given fair chance at informed
consent to collect data
Lack of transparency of data collection (e.g., which sensors)
and analytics (e.g., why specific recommendations were
Lack of transparency in algorithms leaves
systemic biases undetected
Right to “forget” not followed; data are kept without
clear retirement dates
Privacy concerns not considered Lack of transparency and control to potential and current
users about how data are used
Security concerns due to outdated technology or
algorithms (e.g., data not stored securely)
Technology embodies sustained biases, discrimination,
Data accuracy not considered Algorithmic decisions/recommendation lack validity
(accurately reflecting underlying truth)
Lack of contingency governance (e.g., who owns
data in case company is being bought)
Technology cannot be retired because data has become
systemic (e.g., health devices required for insurance
Over-collection of unrelated data (e.g., by
using additional sensors in host device)
“Hidden agenda”/unrelated variables infused into analytics Decisions/recommendations based on outdated
medical advice
Data ownership changes without clear information to
users (e.g., data acquisition)
“Coerced” data provision by customer to
gain customization, convenience,
Unintended discrimination, biases (e.g., gender,
Safekeeping of data (data breach; hacking;
identity theft)
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
organizational levels, so they help ensure the organization’s mission
and values get translated into actionable guidelines, which are espe-
cially important if conflicting interests and needs arise among different
stakeholders (Maignan, Ferrell, & Ferrell, 2005). For example, custo-
mers might demand that their data are protected and stored in secure
places without access to third parties, but the organization and its in-
vestors might prefer to share customer data with other firms to achieve
strategic advantages or for profit reasons as exemplified by Facebook
(Dance, LaForgia, & Confessore, 2018). Specific CDR norms, often
manifest in a firm’s mission or vision statement, can provide guidance
for determining which stakeholder demands should take precedence
(Maignan et al., 2005).
Clearly formulating and communicating specific CDR norms to all
stakeholders of an organization (e.g., through official communication,
websites, and annual reports) serves as a success factor for the im-
plementation of CDR. As prior research shows, executives’ activities and
values exert strong influences on organizational consequences due to
their high status in the organization (Finkelstein & Hambrick, 1990). It
follows that top management commitment to CDR is important.
4.1.3. Artifacts of CDR
Values and norms are abstract structures; the elements of layer 3 in
our framework are specific, concrete instances that embody commit-
ment. The built technology itself is an artifact that must reflect and
incorporate the corporation’s CDR norms. Any digital artifact (e.g.,
technology, product, or service) becomes an instantiation of CDR. That
is, acting with digital responsibility requires that the organization is not
only aware of the various potential effects of the digital on consumers
and society but also concerned for how its own actions may prompt
such effects. Accordingly, CDR culture must establish that designers and
creators of the technology bear responsibility for consequences that
arise from its creation, operation, impact assessment, and refinement.
However, to transfer good intentions into action (i.e., ensure the
corporation’s CDR norms are manifest in the artifact), designers also
have to consider the general claim of digital responsibility in their
concrete design decisions and be equipped with strategies to do so.
Returning to our example, the specific norm to “safeguard consumers’
personal data” would require designers and programmers to implement
encryption technology into products, even if it requires more
computing power to deliver such products and services. Similarly, a
prudent designer would instantiate this norm by designing models that
only collect the amount of data necessary for the transaction in ques-
tion. Beyond the creation step, corporations that adopt this norm would
implement and enforce governance schemata that clearly define data
ownership and responsibility, so the activity of safeguarding becomes
more than a generic commitment.
In our framework, CDR artifacts have an important role and the
power to shape existence and experiences (Ihde, 1990). Referring to
technological artifacts, Verbeek and Kockelkoren (1998, p. 36) show
that “technologies invite certain ways of dealing with them.” Beyond
the technological, artifacts such as corporate guidelines, documenta-
tion, process models, standard operating procedures, and handbooks
provide instantiation to abstract values and norms (Pentland &
Feldman, 2005). Defined process models prescribe a certain sequence of
doing things that users draw on to plan their actions; software designs
impose certain procedures and sequences to follow in order, to be able
to transform inputs into desired outputs. Less manifest artifacts also can
give substance to specific norms, such as stories, arrangements, rituals,
and language (Homburg & Pflesser, 2000) or worldviews, goals, visions,
expectations, plans, and myths (Astley, 1984). Overall, any such re-
presentation (D’Adderio, 2011) can help document, codify, and make
explicit the corporation’s CDR norms and the shared values on which
they are based. Artifacts differ in their degree of prescriptive impact
(e.g., corporate myths have less immediate impact on behaviors than a
sequence of required data fields in a system interface), but they all
shape (and are shaped by) individual CDR-related behaviors.
4.1.4. CDR-related behaviors
A corporation’s specific CDR norms constitute applied ethics, in the
sense that they inform action and support judgments and choices.
Similar to artifacts, behaviors should instantiate a company’s specific
CDR norms and the shared values on which they are based. Layer 3 of
the CDR culture framework thus comprises immediate, concrete out-
comes in which CDR culture actually is manifested. For our exemplary
“safeguarding consumers’ personal data” norm, corporate and in-
dividual choices and judgments would need to reflect this norm. For
example, the CDR norm would dictate that a client has the right to keep
personal information private, and if the data is willingly shared, it
Fig. 2. Conceptual Framework of CDR. * Numbers 1–3 represent the layers of an organization’s CDR culture, which differ in their specificity from a low degree of
specificity (layer 1) to a high degree of specificity (layer 3).
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
should be accurate and up to date. Business decisions would prioritize
the primacy of this CDR norm over other motives (e.g., purely economic
ones). Privacy is a critical trade-off that corporations face already, be-
tween benefiting from the increasing value of data and protecting in-
dividual privacy and data security (Tucker, 2012). Behaviors that
pursue safeguarding consumer data would be reflected in corporate
practices related to ownership and access to these data. This example
also highlights the interplay of artifacts and behaviors: “Safeguarding
consumers’ personal data” requires a set of governance rules, roles, and
responsibilities, which guide specific behaviors, which enact the em-
bodied norms.
Therefore, CDR must determine which types of data should be
captured or provided, under which conditions, how to collaborate with
the data subject in updating or deleting them, and whether and how to
share these data with third parties. In a data use context, CDR can
define the purposes for which the data were originally collected and
enforce policies to avoid unintended and unauthorized uses. Ensuring
fair data uses and exchanges is a core challenge for the evaluation of the
impacts of data creation and use policies. For example, when defining
such policies, corporations might consider whether to use purchasing or
behavioral data to target advertisements or price discriminate and
whether to use photos posted on social networks to train facial re-
cognition algorithms. Have any issues emerged, with customers or
regulatory institutions? What costs did or will the corporation incur?
Answering such questions can support policy refinement and creative
approaches, such as considering the possibility of storing data in ag-
gregate form only for a limited amount of time.
4.2. Influences on a CDR culture
To provide insights into the constituents that influence an organi-
zation’s CDR-related decision making and CDR culture, we take a sta-
keholder approach (Yang & Rivers, 2009). Consistent with Yang and
Rivers (2009), we differentiate stakeholders from social contexts, in-
cluding public opinion, legal requirements, technological progress, and
industry factors, and those from organizational contexts, such as cus-
tomer and firm factors (which include employees).
4.2.1. Public opinion
Social pressure can vary in its time perspective (short- vs. long-term)
and channels, such as social media or the international press (Hoppner
& Vadakkepatt, 2019). Social media offer vast platforms for sharing
ethical dilemmas pertaining to CDR-related practices with large audi-
ences of consumers within seconds, which can exert immense pressure
on organizations to accede to stakeholder demands. Furthermore, data
privacy is a great risk in digitized settings (Solove, 2005); recent data
breaches involving large corporations (e.g., Equifax, Target, the U.S.
Office of Personnel Management) and exposures of controversial data-
sharing practices (e.g., Facebook data acquired by Cambridge Analytica
in breach of terms and contracts) have sensitized the public to the
importance of proper data management and its consequences (CNN,
2013; Granville, 2018; Koerner, 2016; The Economist, 2017).
In this sense, companies should realize that their key long-term asset
is not customers’ data alone but in combination with customers’ good
will and social capital. If they wish to avoid boycotts like the
#deleteFacebook debacles or costly litigation, they must consider the
serious responsibilities associated with receiving people’s personal data.
Furthermore, if social networks provide platforms for users to share
user-generated content, public debates (and media coverage) will focus
on their responsibility to control and (if applicable) proactively filter
inappropriate content, such as racist language or live broadcasts of
violent crimes (Isaac & Mele, 2017). An ongoing debate also questions
whether violent video games represent a potential catalyst of mass
shootings, increasing the social pressure on software firms to account
for such ethical considerations when designing video games (Salam &
Stack, 2018). In an AI context, public discussions about racial
discrimination prompt calls for “algorithmic accountability” in appli-
cations such as facial recognition, health care decision making, and
identifying reoffenders in judicial systems (Lohr, 2018). Growing sal-
ience of ethical issues in society at large will increase the social pressure
on organizations to engage in CDR.
4.2.2. Legal requirements
Of the many dimensions of CDR, data management actually features
some well-defined guidelines, reflecting existing laws and regulations.
However, because these regulations are country-specific, they pose
challenges to multinational corporations. Even with universally ac-
cepted guidelines for data security (ISO/IEC, 2013), data privacy suffers
from less standardized practices, largely because it is hard to define;
what should be kept private varies across cultures, individuals, and
times (Acquisti, Brandimarte, & Loewenstein, 2015; Moore, 1984). As a
consequence, countries have enacted vastly different legal frameworks
for data privacy. On one end of the spectrum, the European Union’s
centralized approach is characterized by strong regulations that treat
any personally identifiable data as a valuable asset, under the control of
the individual (Council of the European Union, 2016). The recently
released GDPR aims to harmonize data privacy laws across Europe and
reshape organizations’ approach to data management, by prioritizing
individual protections. At the other extreme, the decentralized, de-
regulated U.S. approach to privacy protection treats different data dif-
ferently and mostly allows corporations to self-regulate. This latter
approach reflects fair information practice principles and general
guidelines (FTC 2012). Harmonizing the legal practices surrounding
data creation, usage, assessment, and refinement thus is challenging,
especially internationally, with notable implications for the develop-
ment of an organizational CDR culture. For example, substantial legal
distance between countries in which an international organization op-
erates might force it to adopt local artifacts and CDR-related behaviors,
while maintaining its overall shared values and CDR-specific norms.
4.2.3. Technological progress
Much of our earlier discussion on the characteristics of the ‘digital’
highlights that CDR culture is also influenced by technological progress.
In particular, the three characteristics we discussed earlier – ex-
ponential growth, malleability in use, and pervasiveness – highlight
why contemporary technologies and their progress constitutes a special
influence on corporate efforts to ethically govern their engagement
with digital technologies and data. It will be difficult to spell out any
functional or even deterministic impacts of levels or kind of technolo-
gical progress on CDR. However, technologies such as machine-learning
algorithms with large amounts of digital data at their disposal that
require little human supervision or intervention make ethical concerns
more pressing and of a different nature than the use of more traditional
corporate computing (e.g., ERP or CRM system).
4.2.4. Industry factors
The industry in which an organization operates and the products it
markets influence the importance of CDR and the extent to which that
organization responds to CDR expectations with relevant organizational
practices (Hoppner & Vadakkeepatt, 2019). For example, if the orga-
nization’s business model already depends on digital technology and
data usage (e.g., AirBnB, Google; Wirtz et al., 2019), it will likely
confront substantial CDR-related expectations immediately. This holds
especially true for industries like the medical industry where very
sensitive patient data is collected and processed via digital technology
hence increasing the likelihood of ethical dilemmas. For these organi-
zations, establishing a CDR culture is instrumental.
In contrast, other industries which still await larger impacts of di-
gitalization, CDR may be less of a pressing issue (Wade 2017). For this
latter group, coping with ethical issues and engaging in CDR practices
may appear less urgent, even though prudent foresight would en-
courage such corporations to get ahead of the curve. Such CDR-related
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
expectations are fueled by public opinion, and competitive behavior,
beyond cross-industry differences, might also play a crucial role. For
example, if selected industry players engage in CDR (first-mover ad-
vantage), it might become a benchmark that forces others to live up to
these “newly established” CDR industry standards.
4.2.5. Customer factors
In its data management, a digitally responsible corporation ad-
dresses customer concerns about security and privacy (Lwin et al.,
2016). As we discuss subsequently in the legal requirement section,
well-established standards exist for security policies. However, defining
privacy policies is more challenging because of the inherent tension
between profit maximization through data use and protecting custo-
mers’ privacy.
Information systems research provides some guidelines by identi-
fying factors that affect people’s privacy concerns (Smith, Dinev, & Xu,
2011), including those that affect the rational evaluation of risks and
benefits associated with sharing personal data (i.e., the privacy cal-
culus; Klopfer & Rubenstein, 1977; Stone & Stone, 1990), emotions, and
psychological and behavioral biases that go beyond an economically
rational process of utility maximization (Dinev, McConnell, & Smith,
2015). In detail, such factors might reflect personal experiences of
privacy incidents (e.g., identity theft, discrimination based on personal
data), general awareness of privacy risks, personality/demographic
differences, and trust in the corporation, as well as cognitive biases,
heuristics, affect, and time constraints.
Each organization should consider which benefits (and risks) its
customers perceive when they provide personal data to evaluate the
significance of data privacy for them. For example, consumers who
strongly value their personal privacy and perceive an organization’s
data collection or use as invasive might not agree and even could voice
their concerns openly to other potential and existing customers.
Consequently, customers’ position in the power balance with the or-
ganization also should be taken into account (Greenaway, Chan, &
Crossler, 2015). From a more positive perspective, organizations could
construct a strong CDR culture by emphasizing its digitally responsible
organizational behavior, as a source of competitive advantage (Porter &
Kramer 2006). Using customer information to set up an appropriate
CDR culture thus can create win–win situations for customers and the
4.2.6. Firm factors
Corporations face an important trade-off when defining their CDR
strategies: By engaging with customers to provide a product or a ser-
vice, they gain access to valuable (more or less sensitive) data about
customers’ demographics, habits, interests, likes, financial and health
situation, and so on. If shared with or sold to third parties, such a
treasure trove of data could easily be turned into profit. Yet, a digitally
responsible corporation would recognize its customers’ privacy rights
(consumer factors), which limit the uses of those data. Examples of this
trade-off are relevant to targeted advertising (Tucker, 2012), product
customization (Lee, Ahn, & Bang, 2011), and enhanced service con-
venience (Lwin et al., 2007).
In theory, targeting, customization, and enhanced convenience
benefit the organization and its customers. For example, the corpora-
tion increases the chances that its promotion will prompt a purchase,
because the advertised offer matches the needs of the customer better,
and the customer receives information about an appealing product or
service that is aligned with her or his interests. In practice though,
organizations often fail to be transparent about how they use the data
that customers share with them. Advanced data gathering and tracking
technologies, and the lack of clear or well-enforced regulations (legal
requirements) also allow data to be collected without customers’
knowledge or explicit and informed consent. In other situations, they
obtain consent simply by imposing practices to customers without
making the option to refuse those practices clear. In such contexts, the
organization’s reputation and customer trust will strongly influence its
CDR strategy.
In particular, organizations that suffer from a low level of trust and
reputation are likely to experience more external pressure to establish a
strong CDR culture than organizations with high levels of trust. The
strong CDR culture then could issue a (positive) signal that the orga-
nization has taken responsibility for its technology and data-related
actions, which may improve its trust and reputation. From a more
strategic perspective, organizations with high reputation levels poten-
tially might leverage opportunities to develop a CDR culture to provide
(social) welfare and gain additional competitive advantages. Moreover,
its competitive positioning will determine the extent to which ethical
dilemmas related to technology and data will be salient and influence
the firm’s CDR-related decision making (Porter & Kramer, 2006).
In addition to contextual factors, leadership and staffing influence
CDR-related decision making. Consistent with CSR research (Godos-
Díez, Fernandez-Gago, & Martinez-Campillo, 2011), we predict a strong
impact of the CEO’s ethical engagement on the organization’s CDR
culture. An ethically involved CEO will sometimes sacrifice corporate
profit considerations for CDR matters, which can foster the develop-
ment of a strong CDR culture that comes to life across all departments
of the organization. Then, the organization’s employees determine the
CDR culture in that the more involved they are, the more likely the
organization will respond to internal pressures to address ethical di-
lemmas by establishing a CDR culture (Yang & Rivers, 2009). In terms
of privacy, employees might sense the need to protect their own per-
sonal information and demand that the organization take action by
establishing a CDR culture. Finally, employees’ positive attitudes to-
ward CDR should encourage a CDR culture that considers other stake-
holders’ positions.
4.3. Outcomes of a CDR culture
Corporate responsibility initiatives can be challenging to implement
because they require the coordination of various stakeholders, entail
high costs and complex implementation efforts across the corporation’s
various functions, require significant time to induce deep changes to
corporate and individual behaviors, and produce difficult-to-calculate
monetary returns. Similar, challenges apply to CDR initiatives such as
organizational privacy programs (Culnan & Williams, 2009). The re-
turns only arise in the long term, such that it may be impossible to
justify CDR activities simply on the basis of their financial returns. In-
stead, it is necessary to examine CDR benefits and costs for various
stakeholders, including individual actors (consumers), institutions,
governments, the legal system, and artificial and technological entities.
We tentatively review some of the outcomes of CDR relative to these
stakeholders from our framework (Fig. 1).
4.3.1. Organizations
Implementing a CDR culture can be costly for organizations, espe-
cially in the short term, as is illustrated by privacy protection projects
that demand security investments and reduce or at least limit financial
gains from selling data. Just as consumers face trade-offs (costs and
benefits) from their data disclosure, so do corporations (organizational
privacy calculus; Greenaway et al., 2015). Applying a digitally re-
sponsible approach to technology development and deployment re-
quires corporations to incorporate ethical questions into their invest-
ment decisions (Marshall, 1999), such as those pertaining to refinement
and retirement. Yet such questions may be hypothetical in nature, so
the organizational actors (e.g., corporation acquiring a new technology,
technology companies providing it) need to document their predictions
of future developments (e.g., complementary technologies, use-related
mutability of technology). Monitoring whether these assumptions hold
true and the potential implications of their violations represent ongoing
activities with far-reaching consequences for how corporations use di-
gital technology and data.
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
Corporations must determine their CDR-related obligations to their
customers and choose a CDR strategy that suits their business model by
balancing value creation and value appropriation. Moreover, they need
to note the potential negative consequences of neglecting their digital
responsibility relative to the investment costs they incur by establishing
and nurturing a CDR culture
. Prior research shows that privacy brea-
ches and security incidents result in significant losses in reputation and
firm value (Acquisti, Friedman, & Telang, 2006; Martin, Borah, &
Palmatier, 2017), as well as the risk of penalties enforced by govern-
mental authorities like the U.S. Federal Trade Commission. Organiza-
tions would be well-advised to consider these potential (extra) costs
when deciding if and to what extent they want to become digitally
responsible. In parallel, they might strategize to use their im-
plementation of CDR not solely as a loss prevention tool but as a
competitive advantage to achieve stronger financial performance in the
long term. Analytics advances provide some previously unavailable
solutions, such as supporting the derivation of useful managerial im-
plications, even without individual-level data (Holtrop, Wieringa,
Gijsenberg, & Verhoef, 2017; Wieringa et al., 2019).
Beyond these (short-term) financial considerations, we expect or-
ganizations implementing a CDR culture to experience (long-term)
positive financial impacts due to positive indirect performance effects
that move through upstream performance outcomes (Saeidi, Sofian,
Saeidi, Saeidi, & Saaeidi, 2015). Consistent with CSR research, we ex-
pect an organization’s CDR culture to increase customer satisfaction
and competitive advantages (Saeidi et al., 2015), customer trust and
loyalty, and firm reputation (Stanaland, Lwin, & Murphy, 2011).
Eventually, these effects should improve an organization’s financial
performance (Saeidi et al., 2015). In line with Chahal and Sharma
(2006), we also anticipate that a CDR culture will enhance the orga-
nization’s brand equity and competitive positioning in the market. As a
type of corporate social performance, CDR should reduce the cost of
capital, which can help justify CDR investments (Agarwal & Berens,
4.3.2. Individual actors
Not all consumers have the same privacy preferences. Some con-
sumers do not value their privacy highly and are willing to exchange
personal data for even small rewards (Athey, Catalini, & Tucker, 2017).
Others care deeply about privacy but still share their personal data with
companies because they value the services, enhanced convenience, and
customization they receive in return (Beke, Eggers, & Verhoef, 2018;
Ghose, 2017; Wirtz & Lwin, 2009). Perhaps the majority of users cannot
determine if a transaction is worthwhile, whether because they are not
aware of or do not understand the actual, practical costs and benefits
associated with a data transaction. The difficulty of reading and un-
derstanding privacy policies (McDonald & Cranor, 2008) and the
complexities of the data market—in which first, second, and third
parties, advertising networks, and data brokers buy and sell personal
data—suggest it may be impossible for individual laypeople to make
informed decisions about their true willingness to share their personal
data. Even if they devote greater effort to understand the costs and
benefits involved, they face inescapable limitations, ingrained in human
nature, such that behavioral and psychological biases make it im-
possible to achieve perfectly rational decisions when it comes to sharing
personal data (Adjerid, Acquisti, Brandimarte, & Loewenstein, 2013;
Brandimarte, Acquisti, & Loewenstein, 2013).
Transparent CDR guidelines might ease this decision-making pro-
cess, as well as reduce reactance (White et al., 2008) and ultimately
increase trust. For example, Aguirre et al. (2015) show that consumers
are less likely to click on online ads if corporations engage in covert
information collection and use that information for personalized tar-
geting without informing customers about the collection. If the
targeting reflects collected data that consumers were previously una-
ware of disclosing to the corporation, those consumers might feel un-
fairly treated and perceive a violation of normative standards of
openness and permission (Ashworth & Free, 2006).
Similar to CSR findings, we expect that an organization’s CDR cul-
ture will result in both functional and psychological benefits for cus-
tomers, especially if the ethical issues being addressed are relevant to
them (Bhattacharya, Korschun, & Sen, 2009). An organization’s CDR
culture can encourage customer–company identification (Homburg,
Stierl, & Bornemann, 2013) and positively affect customer outcome
measures such as satisfaction, trust, and loyalty toward the organization
(Saeidi et al., 2015).
Beyond this customer perspective, CDR norms and culture con-
stitute a form of applied ethics that guide individual behaviors within
the corporation. Individual users may find it alien to think of their uses
of digital technologies as having long-term impacts, even though each
usage instance has notable consequences. Accordingly, considering
CDR on the individual level may have strong implications for users’
reflexivity about their uses, according to the relationship that exists
between the user and the technologies s/he uses. Rather than simply
applying a machine learning algorithm to speed up decisions, in-
dividual users might be required to think about whether their decision
is in line with corporate goals and relevant ethical guidelines. Such CDR
norms and culture then should have wide-ranging consequences for
how people work with digital technologies and data, in a digitally re-
sponsible manner.
4.3.3. Institutional actors and society at large
Although abstract, this aspect likely accounts for the most wide-
ranging outcomes of CDR. Societal discourse on CDR likely will prompt
political actions to implement and frame high-level agreements about a
digitally responsible future. Along with the EU’s GDPR, such frame-
works promise notable consequences. Efforts to tax corporations ap-
propriately (whatever that means) when their business models rely on
data offer another example. Proposals to tax robotic work that sub-
stitutes human labor, based not on the capital employed but rather on
the work done, reveals a consequence of CDR that transcends the cor-
porate level.
The way that organizations approach CDR also may have implica-
tions for society. We suspect that current hot-button issues (e.g., fake
news, hate speech, and echo chambers) would be strongly influenced by
On a more general level, CDR can shape daily lives. Technological
advances already influence how languages change, how we interact,
and other social factors. Accordingly, CDR plays a key role in pursuing
what we discussed earlier: future developments that are not only guided
by the technologically feasible, but also by what is societally desirable
and sustainable. This is also a good example for the feedback effect (see
Fig. 2) we expect from CDR outcomes to influences on organizations’
(future) CDR-related decision making.
4.3.4. Artificial and technological actors
Exciting organizational and technical consequences of CDR are
likely to involve artificial actors and technology. If CDR is implemented
by a corporation, can its spirit consistently govern the behavior of ar-
tificial actors? Organizationally, updated governance schemes and new
roles will be required to ensure adherence to CDR norms. Ongoing
engagement with any digital technology, once created, requires the
instigation of corresponding roles, tasks, and processes, along with re-
cognition and rewards for the respective behaviors and sanctions for
negligence. Software development methodologies might need to be
updated to reflect consistent guidance by the ethical principles spelled
out in the corporation’s CDR norms. Technologically, the current debate
hints at increasing transparency and accountability in algorithmic de-
cision making. We expect CDR to be highly consequential in this do-
main. Beyond policing their behaviors, AI agents may be used
We thank the anonymous reviewer for this suggestion.
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
increasingly to ensure other individuals’ behaviors remain in line with
corporate policies, such as monitoring usage behaviors and preventing
unintentional data leakages (Hadasch, Li, & Mueller, 2013). Such op-
tions can improve data protection and privacy, as well as enforce spe-
cific CDR norms more directly.
5. Implications for academia, management practice, and society
With this conceptual article, we aim to set a cornerstone and sti-
mulate academic research about CDR as well as provide initial guidance
to practitioners interested with CDR. As outlined, CDR is hard to define;
our discussion does not claim to be comprehensive or definitive. Rather,
we aim to provide a catalyst for an integrative, interdisciplinary re-
search effort to develop useful insights pertaining to this multi-
dimensional, dynamic concept. As we have suggested, multiple research
avenues clearly arise, pertaining to understanding and analyzing the
nature, drivers, and outcomes of CDR for business scholars.
Perhaps the most critical question though is the impact of CDR on
the focal organization and its stakeholders. We anticipate a positive
relation of CDR with an organization’s competitiveness and financial
performance, but conceptual and analytical models should assess this
outcome explicitly. Such approaches would facilitate managerial deci-
sion making about CDR initiatives. Considering the different stages and
stakeholders involved in CDR, its measurement will be challenging and
likely require multidimensional scales, together with multiple monetary
and non-monetary performance measures. A (non-exhaustive) list of
research questions with the potential to extend thinking about oppor-
tunities and challenges related to CDR includes:
Which theoretical approaches and models can help build a robust
understanding of how individuals, organizations, and society be-
have digitally (ir)responsibly?
How can organizations balance the trade-off of ethical norms and
moral obligations with pressures to increase efficiency and profit by
creating new systems and data?
How should we capture and assess an organization’s CDR readiness
and degree of CDR implementation?
How can organizations successfully communicate a CDR culture to
its employees and incentivize them to also be committed towards
the CDR culture?
How does a CDR culture influence consumer reactions, including
perceptions (e.g., benefits, concerns), feelings (e.g., trust, commit-
ment), and behaviors (e.g., usage, loyalty, and engagement)?
How can organizations differentiate themselves and create a com-
petitive advantage by implementing a robust CDR culture?
When do changes in the internal and external environment posit the
need to update an organization’s CDR culture?
We call on executives to establish strong corporate CDR cultures and
develop corresponding norms and guidelines for their employees. Even
in settings in which implementing specific CDR norms may appear in-
effective, an increased emphasis on ethical reflection can influence
future behaviors positively, both with regard to technology develop-
ment and deployment, as well as data privacy. A similar logic applies to
corporate and organizational contexts, as well as in the domains of
public discourse and policy.
According to our conceptualization, corporations should not rely on
legal and regulatory guidelines alone to address emerging digital
challenges. Such guidelines typically cannot keep up with the speed of
technological progress. Rather, corporations should embrace a com-
prehensive set of CDR-related principles and processes to address sta-
keholder demands and secure their support, which requires both stra-
tegic and operational decisions and processes. On a strategic level,
organizations should develop and define their CDR-related mission and
vision. At the operational level, they need appropriate tools, techni-
ques, processes, and structures to implement their general strategic
view. The multidimensional role and diversity in the nature of CDR
likely requires changes through the entire organization, including or-
ganizational restructuring, training and development of employees, and
implementation of new processes (e.g., data management, commu-
In summary, this article introduces the important concept of CDR,
with the promise of opening a new research field. Contemporary CDR
questions are widespread, spanning consumer, organizational, and so-
cietal aspects; continued work in this field is important and urgently
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in
the age of information. Science, 347(6221), 509–514.
Acquisti, A., Friedman, A., & Telang, R. (2006). Is there a cost to privacy breaches? An
event study. ICIS 2006 Proceedings, 94.
Adjerid, I., Acquisti, A., Brandimarte, L., & Loewenstein, G. (2013). Sleights of privacy:
Framing, disclosures, and the limits of transparency. Proceedings of the Ninth Symposium
on Usable Privacy and Security (Article No. 9). ACM.
Agarwal, M.K., & Berens, G. (2009). How corporate social performance influences fi-
nancial performance: Cash flow and cost of capital. MSI Working Paper, 09-001, 3-26.
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the
personalization paradox: The effect of information collection and trust-building
strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49.
Ashrafian, H. (2015). Intelligent robots must uphold human rights. Nature, Available at
17167 (Accessed: March 18, 2019).
Ashworth, L., & Free, C. (2006). Marketing dataveillance and digital privacy: Using
theories of justice to understand consumers’ online privacy concerns. Journal of
Business Ethics, 67(2), 107–123.
Astley, W. G. (1984). Subjectivity, sophistry and symbolism in management science.
Journal of Management Studies, 21(3), 259–272.
Athey, S., Catalini, C., & Tucker, C. (2017). The digital privacy paradox: Small money,
small costs, small talk. National Bureau of Economic Research Working Paper, No.
23488. Available at (Accessed: July 13, 2018).
Avgerou, C., & Madon, S. (2005). Information society and the digital divide problem in
developing countries. In Perspectives and Policies on ICT in Society: An IFIP Tc9
(Computers and Society) Handbook, J. Berleur and C. Avgerou (eds.). Boston, MA:
Springer US, 205-217.
Beaudouin-Lafon, M. (2004). Designing interaction, not interfaces. AVI ‘04.Proceedings of
the Working Conference on Advanced Visual Interfaces (pp. 15–22). .
Beke, F. T., Eggers, F., & Verhoef, P. C. (2018). Consumer informational privacy: Current
knowledge and research directions. Foundations and Trends in Marketing, 11(1), 1–71.
Bhattacharya, C. B., Korschun, D., & Sen, S. (2009). Strengthening stakeholder–company
relationships through mutually beneficial corporate social responsibility initiatives.
Journal of Business Ethics, 85(2), 257–272.
Blevis, E. (2007). Sustainable interaction design: Invention & disposal, renewal & reuse.
SIGCHI Conference on Human Factors in Computing Systems, 503–512.
Brandimarte, L., Acquisti, A., & Loewenstein, G. (2013). Misplaced confidences: Privacy
and the control paradox. Social Psychological and Personality Science, 4(3), 340–347.
Brey, P. A. E. (2012). Anticipating ethical issue in emerging IT. Ethics and Information
Technology, 14(4), 305–317.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and pros-
perity in a time of brilliant technologies (1st ed). New York: W.W. Norton & Company.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital
future. New York: W. W. Norton & Company, USA.
Bynum, T. W. (2001). Computer ethics: Its birth and its future. Ethics and Information
Technology, 3(2), 109–112.
Chahal, H., & Sharma, R. D. (2006). Implications of corporate social responsibility on
marketing performance: A conceptual framework. Journal of Services Research, 6(1),
Chatterjee, S., Moody, G., Lowry, P. B., Chakraborty, S., & Hardin, A. (2015). Strategic
relevance of organizational virtues enabled by information technology in organiza-
tional innovation. Journal of Management Information Systems, 32(3), 158–196.
Chatterjee, S., Sarker, S., & Fuller, M. A. (2009). A deontological approach to designing
ethical collaboration. Journal of the Association for Information Systems, 10(3),
CNN (2013). Target: 40 million credit cards compromised. Available at https://money. (Accessed:
July 8, 2019).
Council of the European Union (2016). General Data Protection Regulation. Available at
(Accessed: July 13, 2018).
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, October 13,
Available at
20805 (Accessed: July 14, 2018).
Culnan, M. J., & Bies, R. J. (2003). Consumer privacy: Balancing economic and justice
considerations. Journal of Social Issues, 59(2), 323–342.
Culnan, M. J., & Williams, C. C. (2009). How can ethics enhance organizational privacy:
Lessons learned from the ChoicePoint and TJX data breaches. MIS Quarterly, 33(4),
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
D’Adderio, L. (2011). Artifacts at the centre of routines: Performing the material turn in
routines theory. Journal of Institutional Economics, 7(2), 197–230.
Dance, G.J.X., LaForgia, M., & Confessore, N. (2018). As Facebook raised a privacy wall, it
carved an opening for tech giants. The New York Times, Available at https://www. (Accessed: March 18,
Deshpandé, R., & Webster, F. E. (1989). Organizational culture and marketing: Defining
the research agenda. Journal of Marketing, 53(1), 3–15.
Diefenbach, S., & Ullrich, D. (2018). Disrespectful technologies. Social norm conflicts in di-
gital worlds. 9th International Conference on Applied Human Factors and Ergonomics,
Orlando, Florida, USA.
Dinev, T., McConnell, A. R., & Smith, H. J. (2015). Research commentary—informing
privacy research through information systems, psychology, and behavioral eco-
nomics: Thinking outside the “APCO” box. Information Systems Research, 26(4),
Feldman, D. C. (1984). The development and enforcement of group norms. The Academy
of Management Review, 9(1), 47–53.
Finkelstein, S., & Hambrick, D. C. (1990). Top-management team tenure and organiza-
tional outcomes: The moderating role of managerial discretion. Administrative Science
Quarterly, 35(3), 484–503.
Floridi, L. (2010). Ethics after the information revolution. In L. Floridi (Ed.). Cambridge
Handbook of Information and Computer Ethics (pp. 3–19). Cambridge: Cambridge
University Press.
FTC (2012). Protecting consumer privacy in an era of rapid change: Recommendations for
businesses and policy makers. Available at
120326privacyreport.pdf (Accessed: July 13, 2018).
Ghose, A. (2017). Tap: Unlocking the mobile economy. Cambridge, MA: MIT Press.
Godos-Díez, J.-L., Fernández-Gago, R., & Martínez-Campillo, A. (2011). How important
are CEOs to CSR practices? An analysis of the mediating effect of the perceived role of
ethics and social responsibility. Journal of Business Ethics, 98(4), 531–548.
Granville, K. (2018). Facebook and Cambridge Analytica: What you need to know as
fallout widens. The New York Times, Available at
03/19/technology/facebook-cambridge-analytica-explained.html (Accessed: July 8,
Greenaway, K. E., Chan, Y. E., & Crossler, R. E. (2015). Firm information privacy or-
ientation: A conceptual framework. Information Systems Journal, 25(6), 579–606.
Hadasch, F., Li, Y., & Mueller, B. (2013). IS security policy enforcement with technolo-
gical agents: A field experiment. 21. European Conference on Information Systems
(ECIS 2013), Utrecht, The Netherlands.
Hinds, P. J., Roberts, T. L., & Jones, H. (2004). Whose job is it anyway? A study of human-
robot interaction in a collaborative task. Human-Computer Interaction, 19(1–2),
Holtrop, N., Wieringa, J. E., Gijsenberg, M. J., & Verhoef, P. C. (2017). No future without
the past? Predicting churn in the face of customer privacy. International Journal of
Research in Marketing, 34(1), 154–172.
Homburg, C., & Pflesser, C. (2000). A multiple-layer model of market-oriented organi-
zational culture: Measurement issues and performance outcomes. Journal of
Marketing Research, 37(4), 449–462.
Homburg, C., Stierl, M., & Bornemann, T. (2013). Corporate social responsibility in
business-to-business markets: How organizational customers account for supplier
corporate social responsibility engagement. Journal of Marketing, 77(6), 54–72.
Hoppner, J. J., & Vadakkepatt, G. G. (2019). Examining moral authority in the market-
place: A conceptualization and framework. Journal of Business Research, 95(2),
Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption
decision framework incorporating shopper attitudes and privacy concerns. Journal of
Retailing, 93(1), 7–28.
Ihde, D. (1990). Technology and the lifeworld: From garden to earth. Bloomington, In:
Indiana University Press.
Intezari, A., Taskin, N., & Pauleen, D. J. (2017). Looking beyond knowledge sharing: An
integrative approach to knowledge management culture. Journal of Knowledge
Management, 21(2), 492–515.
Isaac, M. & Mele, C. (2017). Video of killing casts Facebook in harsh light. The New York
Times, April 18, A1.
ISO/IEC (2013). Information technology – Security techniques – Information security
management systems – Requirements. American National Standards Institute.
John, L. K., Acquisti, A., & Loewenstein, G. (2010). Strangers on a plane: Context-de-
pendent willingness to divulge sensitive information. Journal of Consumer Research,
37(5), 858–873.
Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: The effects of
general privacy concerns, general institutional trust, and affect in the privacy cal-
culus. Information Systems Journal, 25(6), 607–635.
Klopfer, P. H., & Rubenstein, D. I. (1977). The concept privacy and its biological basis.
Journal of Social Issues, 33(3), 52–65.
Koerner, B.I. (2016). Inside the cyberattack that shocked the US government. Wired, Oct
23, 2016, Available at
shocked-us-government/ (Accessed: July 8, 2019).
Lameijer, C.S., Mueller, B., & Hage, M.L. (2017). Towards rethinking the digital divide –
Recognizing shades of grey in older adults’ digital inclusion. 38. International
Conference on Information Systems (ICIS 2017), Seoul, South Korea.
Lee, D.-J., Ahn, J.-H., & Bang, Y. (2011). Managing consumer privacy in personalization:
A strategic analysis of privacy protection. MIS Quarterly, 35(2), 423–444.
Lee, J. E. R., & Nass, C. I. (2010). Trust in computers: The computers-are-social-actors
(CASA) paradigm and trustworthiness perception in human-computer communica-
tion. In D. Latusek, & A. Gerbasi (Eds.). Trust and Technology in a Ubiquitous Modern
Environment: Theoretical and Methodological Perspectives (pp. 1–15). IGI Global:
Hershey, PA.
Lohr, S. (2018). Facial recognition works best if you’re a white guy. The New York Times,
February 12, B1.
Lwin, M., Wirtz, J., & Stanaland, A. J. S. (2016). The privacy dyad: Antecedents of pro-
motion- and prevention-focused online privacy behaviors and the mediating role of
trust and privacy concern. Internet Research, 26(4), 919–941.
Lwin, M., Wirtz, J., & Williams, J. D. (2007). Consumer online privacy concerns and
responses: A power-responsibility equilibrium perspective. Journal of the Academy of
Marketing Science, 35(4), 572–585.
Maignan, I., & Ferrell, O. C. (2004). Corporate social responsibility and marketing: An
integrative framework. Journal of the Academy of Marketing Science, 32(1), 3–19.
Maignan, I., Ferrell, O. C., & Ferrell, L. (2005). A stakeholder model for implementing
social responsibility in marketing. European Journal of Marketing, 39(9/10), 956–977.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy
concerns (IUIPC): The construct, the scale, and a causal model. Information Systems
Research, 15(4), 336–355.
Marshall, K. P. (1999). Has technology introduced new ethical problems? Journal of
Business Ethics, 19(1), 81–90.
Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and
firm performance. Journal of Marketing, 81(1), 36–58.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the
Academy of Marketing Science, 45(2), 135–155.
Mason, R. O. (1986). Four ethical issues of the information age. MIS Quarterly, 10(1),
McDonald, A. M., & Cranor, L. F. (2008). The cost of reading privacy policies. I/S: A
Journal of Law and Policy for the.Information Society, 4(3), 543–568.
Milberg, S. J., Smith, H. J., & Burke, S. J. (2000). Information privacy: Corporate man-
agement and national regulation. Organization Science, 11(1), 35–57.
Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why
consumers read (or don’t read) online privacy notices. Journal of Interactive Marketing,
18(3), 15–29.
Mingers, J., & Walsham, G. (2010). Toward ethical information systems: The contribution
of discourse ethics. MIS Quarterly, 34(4), 833–854.
Montag, C., & Diefenbach, S. (2018). Towards homo digitalis: Important research issues
for psychology and the neurosciences at the dawn of the internet of things and the
digital society. Sustainability, 10(2), 415.
Moon, Y., & Nass, C. (1998). Are computers scapegoats? Attributions of responsibility in
human–computer interaction. International Journal of Human-Computer Studies, 49(1),
Moor, J. H. (1985). What IS computer ethics? Metaphilosophy, 16(4), 266–275.
Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE
Intelligent Systems, 21(4), 18–21.
Moore, B. (1984). Privacy: Studies in social and cultural history. Armonk, NY: M.E. Sharpe.
Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics,
38(8), 114–117.
Moriarty, J. (2016). Business ethics. Stanford Encyclopedia of Philosophy, Available at (Accessed: July 15, 2017).
Murali, V., Qi, L., Chaudhuri, S., & Jermaine, C. (2018). Neural sketch learning for
conditional program generation. 6. International Conference on Learning
Representations (ICLR 2018), Vancouver, Canada.
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation man-
agement: Reinventing innovation management research in a digital world. MIS
Quarterly, 41(1), 223–238.
Olerup, A. (1989). Socio-technical design of computer-assisted work: A discussion of the
ethics and Tavistock approaches. Scandinavian Journal of Information Systems, 1,
Oz, E. (1992). Ethical standards for information systems professionals: A case for a unified
code. MIS Quarterly, 16(4), 423–433.
Pentland, B. T., & Feldman, M. S. (2005). Organizational routines as a unit of analysis.
Industrial and Corporate Change, 14(5), 793–815.
Porter, M. E., & Kramer, M. R. (2006). Strategy & society: The link between competitive
advantage and corporate social responsibility. Harvard Business Review, 84(12),
Rainie, L., & Zickuhr, K. (2015). Americans’ views on mobile etiquette. Pew Research
Center, Available at
mobile-etiquette/ (Accessed: July 14, 2018).
Richter, A., & Riemer, K. (2013). Malleable end-user software. Business & Information
Systems Engineering, 5(3), 195–197.
Robinette, P. Li, W., Allen, R. Howard, A.M., & Wagner, A.R. (2016). Overtrust of robots
in emergency evacuation scenarios. 2016 11th ACM/IEEE International Conference
on Human-Robot Interaction (HRI), 101–108.
Saeidi, S. P., Sofian, S., Saeidi, P., Saeidi, S. P., & Saaeidi, S. A. (2015). How does cor-
porate social responsibility contribute to firm financial performance? The mediating
role of competitive advantage, reputation, and customer satisfaction. Journal of
Business Research, 68(2), 341–350.
Salam, M. & Stack, L. (2018). Do video games lead to mass shootings? Researchers say no.
The New York Times, Available at
politics/trump-video-games-shootings.html (Accessed: March 19, 2019).
Sarathy, R., & Robertson, C. J. (2003). Strategic and ethical considerations in managing
digital privacy. Journal of Business Ethics, 46(2), 111–126.
Schein, E. H. (2004). Organizational culture and leadership (3d ed). San Francisco: Jossey-
Scholz, R. W., Bartelsman, E. J., Diefenbach, S., Franke, L., Grunwald, A., Helbing, D.,
et al. (2018). Unintended side effects of the digital transition: European scientists’
messages from a proposition-based expert round table. Sustainability, 10(6).
Schumann, J. H., Von Wangenheim, F., & Groene, N. (2014). Targeted online advertising:
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
Using reciprocity appeals to increase acceptance among users of free web services.
Journal of Marketing, 78(1), 59–75.
Schwartz, M., & Carroll, A. B. (2003). Corporate social responsibility: A three domain
approach. Business Ethics Quarterly, 13(4), 503–530.
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An inter-
disciplinary review. MIS Quarterly, 35(4), 989–1016.
Solove, D. J. (2005). A taxonomy of privacy. University of Pennsylvania Law Review,
154(3), 477–564.
Soltani, A. (2019). Abusability testing: Considering the ways your technology might be
used for harm. Enigma 2019. Burlingame, CA, USA.
Son, J. Y., & Kim, S. S. (2008). Internet users’ information privacy-protective responses: A
taxonomy and a nomological model. MIS Quarterly, 32(3), 503–529.
Stahl, B. C., Eden, G., Jirotka, M., & Coeckelbergh, M. (2014). From computer ethics to
responsible research and innovation in ICT: The transition of reference discourses
informing ethics-related research in information systems. Information & Management,
51(6), 810–818.
Stanaland, A. J. S., Lwin, M. O., & Murphy, P. E. (2011). Consumer perceptions of the
antecedents and consequences of corporate social responsibility. Journal of Business
Ethics, 102(1), 47–55.
Steenkamp, J.-B. E. M., & Geyskens, I. (2006). How country characteristics affect the
perceived value of web sites. Journal of Marketing, 70(3), 136–150.
Stone, E. F., & Stone, D. L. (1990). Privacy in organizations: Theoretical issues, research
findings, and protection mechanisms. Research in Personnel and Human Resources
Management, 8(3), 349–411.
The Economist (2017). The big data breach suffered by Equifax has alarming implica-
tions. September 16, Available at
implications (Accessed: July 9, 2019).
Treviño, L. K., Weaver, G. R., & Reynolds, S. J. (2006). Behavioral ethics in organizations:
A review. Journal of Management, 32(6), 951–990.
Tucker, C. E. (2012). The economics of advertising and privacy. International Journal of
Industrial organization, 30(3), 326–329.
Ullrich, D., & Diefenbach, S. (2017). Truly social robots. Understanding human-robot
interaction from the perspective of social psychology. Proceedings of the 12th
International Joint Conference on Computer Vision, Imaging and Computer Graphics
Theory and Applications (VISIGRAPP 2017), 39–45.
Ullrich, D., Butz, A., & Diefenbach, S. (2018). Who do you follow?: Social robots’ impact
on human judgment. HRI '18 Companion of the 2018 ACM/IEEE International
Conference on Human-Robot Interaction, 265–266.
Van den Hoven, J., Doorn, N., Swierstra, T., Koops, B.-J., & Romijn, H. (eds.). (2014).
Responsible Innovation 1 - Innovative Solutions for Global Issues. Dordrecht, the
Netherlands, et al.: Springer.
Van den Hoven, J., & Lokhorst, G.-J. (2002). Deontic logic and computer-supported
computer ethics. Metaphilosophy, 33(3), 376–386.
Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., et al.
(2017). Domo Arigato Mr. Roboto: Emergence of automated social presence in or-
ganizational frontlines and customers’ service experiences. Journal of Service
Research, 20(1), 43–58.
Van Wynsberghe, A., & Robbins, S. (2018). Critiquing the reasons for making artificial
moral agents. Science and Engineering Ethics, 25(3), 719–735.
Verbeek, P. P., & Kockelkoren, P. (1998). The things that matter. Design.Issues, 14(3),
Versteeg, M.J.J.M. (2013). Ethics & gamification design: a moral framework for taking
responsibility. Thesis, Faculty of Humanities.
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science,
359(6380), 1146–1151.
Wade, M.R. (2017). The Digital Vortex in 2017: It’s Not a Question of “When”. Available
(Accessed: May 17, 2019).
Walsham, G. (1996). Ethical theory, codes of ethics and IS practice. Information Systems
Journal, 6(1), 69–81.
White, T., Zahay, D., Thorbjornsen, H., & Shavitt, S. (2008). Getting too personal:
Reactance to highly personalized email solicitations. Marketing Letters, 19(1), 39–50.
Wieringa, J., Kannan, P. K., Ma, X., Reutterer, T., Risselada, H., Roodbergen, K. J., et al.
(2019). Data analytics in a privacy-concerned world. Journal of Business Research
Wirtz, J., & Lwin, M. O. (2009). Regulatory focus theory, trust and privacy concern.
Journal of Service Research, 12(2), 190–207.
Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V. N., Paluch, S., et al. (2018). Brave new
world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931.
Wirtz, J., So, K. K. F., Mody, M., Chun, E. H., Liu, S., & Chun, H. (2019). Platforms in the
peer-to-peer sharing economy. Journal of Service Management, 30(4), 452–483.
Wolfangel, E. (2017). Google und die Frau am Herd. die ZEIT, 35.
Wright, D. (2011). A framework for the ethical impact assessment of information tech-
nology. Ethics and Information Technology, 13(3), 199–226.
Xie, E., Teo, H.-H., & Wan, W. (2006). Volunteering personal information on the internet:
Effects of reputation, privacy notices, and rewards on online consumer behavior.
Marketing Letters, 17(1), 61–74.
Yang, Xiaohua, & Rivers, Cherly (2009). Antecedents of CSR practices in MNCs’ sub-
sidiaries: A stakeholder and institutional perspective. Journal of Business Ethics, 86(2),
L. Lobschat, et al. Journal of Business Research 122 (2021) 875–888
... Previous research shed light on defining CDR and its underlying responsibilities (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). ...
... 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). ...
... Hence, the conceptualization converges increasingly (Mihale-Wilson et al. 2022). Therefore, this publication rather moves in the encouraged direction of operationalization and empirical assessment of the CDR concept (Mueller 2022). To empirically assess the operationalization of CDR in practice, this work needs to choose one of the systematic approaches to the commonly agreed areas addressing consumers. ...
Full-text available
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.
... La era digital se caracteriza por la digitalización, el análisis de datos, la algoritmia y las inteligencias artificiales, los cuales otorgan un gran poder a las organizaciones y a las personas que las utilizan, tal como señala Londoño-Cardozo (2020). Sin embargo, estos avances tecnológicos pueden dar lugar a usos indebidos de los datos, aun cuando estos hayan sido recolectados o introducidos con buenas intenciones, lo que plantea preocupaciones éticas y legales (Lobschat et al., 2019). En este contexto, se hace relevante analizar el uso de la inteligencia artificial. ...
... Aquí, institucionalidad se entiende como todos los actores que hacen parte de la gobernabilidad aceptada por las personas. Es decir, que se pueden categorizar bajo la institucionalidad a i) las entidades gubernamentales, ii) poderes judiciales y fuerzas del orden y iii) así como el marco legal y constitucional nacional e internacional (Lobschat et al., 2019). Los actores institucionales son los encargados de la legalización y creación de marcos que permitan la creación, uso e interacción de las herramientas tecnológicas con base en la protección de los datos de los usuarios y la salvaguarda de lineamientos moralmente aceptados por la sociedad. ...
Resumen: Este documento discute sobre las regulaciones existentes para el uso de la tecnología, especialmente la digital, y la prevención del ciberacoso. Para ello se realizó una contextualización acerca del papel de la tecnología en la evolución humana. Aquí se discute sobre la postura transhumanista de la tecnología como complemento de las acciones humanas y no sobre la poshumanista de la tecnología como reemplazo del ser humano. Posteriormente se abordan temas de la cultura en contextos digitales y se argumenta que el comportamiento de las personas en estos espacios es un reflejo de la cultura análoga. Esto se relaciona con un análisis de las diferentes legislaciones sobre el tema existentes en algunos países enfocados en la prevención y sanción de acciones relacionadas con la ciber violencia, especialmente en las instituciones educativas. Finalmente, se propone la aplicación de los principios de la Responsabilidad Digital Organizacional para las organizaciones en las legislaciones y las personas. Esto cambiaria a un enfoque en el que se incorpora la tecnología como un complemento para las buenas acciones de las personas. Abstract This document discusses the existing regulations for the use of technology, especially digital technology, and the prevention of cyberbullying. To do so, a contextualization was made about the role of technology in human evolution. Here, the transhumanist stance of technology as a complement to human actions is discussed, rather than the posthumanist stance of technology as a replacement for human beings. Subsequently, issues of culture in digital contexts are addressed and it is argued that people's behavior in these spaces reflects analog culture. This is related to an analysis of the different legislations on the subject existing in some countries focused on the prevention and sanction of actions related to cyber violence, especially in educational institutions. Finally, the application of the principles of Organizational Digital Responsibility for organizations in legislation and individuals is proposed. This would change the focus to incorporate technology as a complement to people's good actions.
... The 2030 Agenda includes 17 goals. Goal No. 9 proposes the development of a resilient infrastructure to improve technological capabilities, increase internet access in less developed countries, and promote the integration of small industries and enterprises into their value chains [2]. ...
... Sustainability and digitalization are two megatrends which, according to the literature, are critical to shaping future economies and economic activities [9]. ...
Full-text available
COVID-19 has had a dramatic impact on the world. This study aims to investigate the possible implications of COVID-19 on sustainability and digitalization initiatives, by exploring a sample of 15 Italian coffee companies located in Northern, Central and Southern Italy, to highlight any differences and at the same time identify which are the main strands of their resilient behaviors. "Sustainab-lization" is our idea to define a business model in which sustainability and digitalization are closely related in companies' strategic initiatives. We have analyzed the various actions which have been undertaken to get out of the COVID-19 crisis, focusing on initiatives related to sustainable development and digitalization, critical also to fulfilling some of the 17 Sustainable Development Goals of the 2030 Agenda. Most of the companies have invested in sustainability and digitalization. The results show, for most of them, a resilient approach towards a sustainable business model, and also through increased digitalization.
... Understanding how individuals use expertise and weigh personal norms against social norms to take a privacy and security decision, and emphasizing the strategies of different decision makers is a first step towards a responsible use and governance of digital platforms, where interests and values of a multitude of actors are considered and communicated effectively. Here, the frame of corporate digital responsibility (e.g., Lobschat et al., 2021) provides promising avenues for further investigation. Grasping the dynamics involved in individual decisions and motivations moreover lays the foundation to extend our understanding of the dynamics of platform use for remote working and situational privacy decisions. ...
During the onset of the COVID-19 pandemic and the subsequent lock-down, digital platforms like Zoom became essential for remote work. Yet at the same time, substantial security and privacy risks made the headlines. Using the lenses of Naturalistic Decision-making and the Theory of Multilevel Information Privacy, we find diverging responses to well-documented security risks of Zoom use in educational environments. We identify-three distinct response patterns, which we name the 'Agnostic', the 'Pragmatic' and the 'Sceptic', and show how the interplay of the salient social identity, personal privacy norms, and the privacy calculus guides the dynamic of privacy decision-making in light of experiential feedback, and the developing public discourse about security risks. We provide empirical evidence for multilevel decision-making and highlight the contextual and social nature of privacy decision-making about platform mode of use for remote work.
... As organizations progressively move forward with their digital HRM transformation, there is a need to focus on the ethical issues raised by digital technology. Lobschat et al. (2021) have developed the concept of Corporate Digital Responsibility (CDR), described as a group of accepted principles and values that direct a company's major information technology and data procedures. These processes are related to the creation of technology and access to data, operations and decision-making, inspection and impact assessment, and the refinement of technology and data. ...
Full-text available
The purpose of this study is to explore how human resource management can be digitally transformed in the context of the digital economy. The drivers, directions, and impacts of human resource management digital transformation constitute the major study content. The study proposes that five factors—internal customer digital needs, industry digital innovation, competitor challenges, digital innovation governance, and digital era needs—drive human resource management digital transformation. It analyzes the essence of the human resource management digital transformation, such as digital workplace, digital human resource management processes, and digital employee services. Particularly, the study points out that digital human resource management processes refer to the implementation of selection, training and development, and assessment functions leveraging state-of-the-art digital technologies. It is emphasized that although digital transformation brings benefits for business development, its potential impacts cannot be ignored, including how the old and new human resource management systems are converted and the negative effects of the new system.
... In den letzten Jahren tauchte der Begriff der Corporate Digital Responsibility nicht nur auf der Agenda einiger klassischer und digitaler Unternehmen auf, auch die Forschung hat sich mit dem Begriff (wenn auch bisher rudimentär) auseinandergesetzt. Auf die Diskussion der verschiedenen Inhalte und Ebenen einer unternehmerischen Digitalverantwortung wollen wir an dieser Stelle nicht eingehen, sondern lehnen uns an die Definition von Lobschat et al. (2021) an (für die verschiedenen Definitionen sei auf Pick 2022b verwiesen). Dort wird unter der Corporate Digital Responsibility "the set of shared values and norms guiding an organization's operations with respect to the creation and operation of digital technology and data" (Lobschat et al. 2021: 876) verstanden. ...
Auszug aus dem entsprechenden Buch Schlaile/Stöber (2022, S.13): Den Aufschlag machen in Kapitel 2 Sebastian Müller und Nils Christian Hoffmann, indem sie zunächst eine rollentheoretische Perspektive auf digitalen Konsum entwickeln und damit einen Beitrag zur konzeptionellen Begründung von Rollenverantwortung leisten. Gerade in Bezug auf nachhaltige, bewusstere Konsumentscheidungen stehen Konsumierende häufig vor einer Dilemmasituation, in der sie einem Rollenkonflikt ausgesetzt sind. Daran anknüpfend liefern Müller und Hoffmann methodische Einblicke in einen für die Konsumforschung adaptierten Forschungsansatz. Dieser moralpsychologische Ansatz verspricht, auch im digitalen Raum verschiedene rollenbedingte Konsummuster und normative Argumente zu erfassen, Rollenkonflikte aufzudecken und damit ggf. auch institutionelle (z.B. politische) Gestaltungsansätze zur Vereinfachung von Verantwortungsübernahme unterstützen zu können.
... We can look to the public and private sectors to consider how a framework of digital responsibility in this area could or should develop. Lobschat et al. (2021) focus on 'actionable guidelines' for corporations in relation to digital responsibility, while similar efforts to develop normative frameworks for responsible digital innovation emphasise responsibility within innovation process and outcome (van de Poel and Sand, 2018). ...
Full-text available
This op-ed outlines key issues humanitarians should consider when assessing their ‘digital responsibility’ to foster digital refugee livelihoods. This includes in particular the need to develop robust monitoring and evaluation frameworks of outcomes of digital livelihoods trainings for refugees – and spaces for critical engagement with the results of such evaluations, including stopping digital livelihoods programming when risks outweigh benefits. It argues that ethical humanitarian engagement in technology must include the development of coherent, contextualised sets of norms and frameworks for responsibility and protection in the digital sphere, including those that address humanitarian efforts to assist refugees to enter the digital economy.
... To ensure ethical use of citizen data, policy makers and legislators need to address population preferences and understand what factors should be considered when using data to promote social welfare-if data disclosure is voluntary, to create conditions that motivate individual data donation; if data disclosure is mandatory, to create conditions that reflect society's preferences to ensure ethical use (Ali and Bénabou, 2020). This connects well with emerging discussions on corporate digital responsibility (Lobschat et al., 2021) in that our insights provide guidance for decision makers on how to design data governance schemes that support individuals in their calculus on whether or not to donate data to a public good. According to our findings, legislators should particularly focus on the risk of a data leak, the organization that collects and manages the data, and the purpose for which the data will be used. ...
Full-text available
When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the perceived risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a human-supervised or a self-learning smart assistant.
Full-text available
Data is considered the new oil of the economy, but privacy concerns limit their use, leading to a widespread sense that data analytics and privacy are contradictory. Yet such a view is too narrow, because firms can implement a wide range of methods that satisfy different degrees of privacy and still enable them to leverage varied data analytics methods. Therefore, the current study specifies different functions related to data analytics and privacy (i.e., data collection, storage, verification, analytics, and dissemination of insights), compares how these functions might be performed at different levels (consumer, intermediary, and firm), outlines how well different analytics methods address consumer privacy, and draws several conclusions, along with future research directions.
Full-text available
Purpose The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems. Design/methodology/approach This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature. Findings First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced. Practical implications This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems. Originality/value This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.
Full-text available
>>> Purpose – The service sector is at an inflection point with regard to productivity gains and service industrialization similar to the industrial revolution in manufacturing that started in the 18th century. Robotics in combination with rapidly improving technologies like artificial intelligence (AI), mobile, cloud, big data and biometrics will bring opportunities for a wide range of innovations that have the potential to dramatically change service industries. This conceptual paper explores the potential role service robots will play in the future and advances a research agenda for service researchers. >>> Design/methodology/approach – This paper uses a conceptual approach that is rooted in the service, robotics, and AI literature. >>> Findings – The contribution of this article is threefold. First, it provides a definition of service robots, describes their key attributes, contrasts their features and capabilities with those of frontline employees, and provides an understanding for which types of service tasks robots will dominate and where humans will dominate. Second, this article examines consumer perceptions, beliefs and behaviors as related to service robots, and advances the service robot acceptance model (sRAM). Third, it provides an overview of the ethical questions surrounding robot-delivered services at the individual, market and societal level. >>> Practical implications – This article helps service organizations and their management, service robot innovators, programmers and developers, and policymakers better understand the implications of a ubiquitous deployment of service robots. >>> Originality/value – This is the first conceptual article that systematically examines key dimensions of robot-delivered frontline service and explores how these will differ in the future.
Full-text available
We present the main messages of a European Expert Round Table (ERT) on the unintended side effects (unseens) of the digital transition. Seventeen experts provided 42 propositions from ten different perspectives as input for the ERT. A full-day ERT deliberated communalities and relationships among these unseens and provided suggestions on (i) what the major unseens are; (ii) how rebound effects of digital transitioning may become the subject of overarching research; and (iii) what unseens should become subjects of transdisciplinary theory and practice processes for developing socially robust orientations. With respect to the latter, the experts suggested that the “ownership, economic value, use and access of data” and, related to this, algorithmic decision-making call for transdisciplinary processes that may provide guidelines for key stakeholder groups on how the responsible use of digital data can be developed. A cluster-based content analysis of the propositions, the discussion and inputs of the ERT, and a theoretical analysis of major changes to levels of human systems and the human–environment relationship resulted in the following greater picture: The digital transition calls for redefining economy, labor, democracy, and humanity. Artificial Intelligence (AI)-based machines may take over major domains of human labor, reorganize supply chains, induce platform economics, and reshape the participation of economic actors in the value chain. (Digital) Knowledge and data supplement capital, labor, and natural resources as major economic variables. Digital data and technologies lead to a post-fuel industry (post-) capitalism. Traditional democratic processes can be (intentionally or unintentionally) altered by digital technologies. The unseens in this field call for special attention, research and management. Related to the conditions of ontogenetic and phylogenetic development (humanity), the ubiquitous, global, increasingly AI-shaped interlinkage of almost every human personal, social, and economic activity and the exposure to indirect, digital, artificial, fragmented, electronically mediated data affect behavioral, cognitive, psycho-neuro-endocrinological processes on the level of the individual and thus social relations (of groups and families) and culture, and thereby, the essential quality and character of the human being (i.e., humanity). The findings suggest a need for a new field of research, i.e., focusing on sustainable digital societies and environments, in which the identification, analysis, and management of vulnerabilities and unseens emerging in the sociotechnical digital transition play an important role.
Consumers create a data trail by tapping their phones; businesses can tap into this trail to harness the power of the more than three trillion dollar mobile economy. According to this book's author, this two-way exchange can benefit both customers and businesses. Drawing on extensive research and on a variety of real-world examples from companies including Alibaba, China Mobile, Coke, Facebook, SK Telecom, Telefónica, and Travelocity, the book describes some intriguingly contradictory consumer behavior: people seek spontaneity, but they are predictable; they find advertising annoying, but they fear missing out; they value their privacy, but they increasingly use personal data as currency. When mobile advertising is done well, the book argues, the smartphone plays the role of a personal concierge. The book identifies nine forces that shape consumer behavior, including time, crowdedness, trajectory, and weather, and examines how these forces operate, separately and in combination. It highlights the true influence mobile wields over shoppers, the behavioral and economic motivations behind that influence, and the lucrative opportunities it represents. In a world of artificial intelligence, augmented and virtual reality, wearable technologies, smart homes, and the Internet of Things, the future of the mobile economy seems limitless.
Contemporary work on marketing management is grounded implicitly in a structural functionalist or contingency perspective of organizational functioning. However, the field of organizational behavior from which such a perspective derives has recently developed a major thrust into theoretical modeling and empirical research on organizational culture. The authors survey this emerging literature on organizational culture, integrate it in a conceptual framework, and then develop a research agenda in marketing grounded in the five cultural paradigms of comparative management, cohtingency management, organizational cognition, organizational symbolism, and structural/psychodynamism.
Business entities, such as CEOs, endorsers, brands and even firms, are more frequently being pressured to engage in social and political dialogue. Stakeholders want these entities to motivate change by taking a stance on various issues; i.e., to exert their moral authority. While mentioned in the popular press and consumer conversations, research has not yet examined the construct of moral authority. Drawing from current events and the extant business literature on morality, this article formally defines and conceptualizes the construct of moral authority and delineates a framework for examining if/how entities choose to exert their moral authority in the marketplace as well as identifying what the potential consequences could be. As a result, this article contributes to the literature by initiating a new research agenda into moral authority in the marketplace.
In the current age of information and big data, consumer informational privacy has become an important issue in marketing. Besides being worried about the growing collection, storage, and use of personal information, consumers are anxious about a lack of transparency or control over their personal data. Despite these growing concerns, understanding of how firms' privacy practices affect consumers remains limited. We review the relevant literature on consumer privacy from a marketing perspective and summarize current knowledge about how information collection, information storage, information use, transparency, and control influence consumers' behavior. In addition, we discuss to what extent the influence of firms' privacy practices differs between firms, consumers, and environments. On the basis of this knowledge, we formulate several hypotheses aimed at providing direction for future research regarding the role of consumer informational privacy in marketing.
Conference Paper
Social robots gain increasing relevance in many domains with high potential for social good (e.g., healthcare, care for the elderly, robots as social companion). However, their design bears high responsibility and asks for a profound understanding of to what degree psychological mechanisms of human-human interaction (HHI) apply to human-robot interaction (HRI). The present experiment (N=40) explores this issue by replicating the famous social influence experiments by Asch [1], which showed that human judgment can be influenced by confederates against better knowledge. Our study replaced one of the confederates by a robot and revealed that a robot's influence can be even higher than that of other humans. Implications for future research are discussed.