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University of Hohenheim | Digital Management30
Research Question
Baumbach et al. (2018)
What factors influence individuals to
behave in an environmentally sustainable
manner across the different life cycle
stages of information technology (IT)?
University of Hohenheim | Digital Management31
Baumbach et al. (2018)
Concerns an individual’s behavioral intention to use IT with the aim of increasing sustainability.
An increased sustainability can
either be due to adjusting energy-
saving settings of IT or to buying
"Green-IT".
Focuses on the way IT is disposed.
Intention is described as the
behavioral intention to dispose IT
sustainably.
A consumer’s attention to the production of IT, which can be considered within the IT purchase process.
The stage captures an individual’s
behavioral intention to buy
sustainably manufactured IT.
Manufacturing / Buy of IT Use of IT Disposal of IT
Life Cycle of IT
University of Hohenheim | Digital Management32
Ajzen (1985); Ajzen and Fishbein (1980); Baumbach et al. (2018)
EA/EC
Attitude
Social
Norms
Perceived
Behavioral
Control
Behavioral
Intention
GEK PEK
Manufacturing
/Buy
sustainable IT
Use IT
sustainable
Dispose IT
sustainably
Traditional theoretical constructs (Theory of Planned Behavior)
Newly developed constructs
Life cycle stages of IT
EA/EC
ENVIRONMENTAL AWARENESS / ENVIRONMENTAL CONCERN: Concern about the environment.
When I think of the consequences of IT on the
climate, I am very worried.
GENERAL ENVIRONMENTAL KNOWLEDGE: Common
understanding of environmental related issues.
Fossil fuels produce carbon dioxide in the
atmosphere when burned.
PERSONAL ENVIRONMENTAL KNOWLEDGE: Specific personal environmental knowledge and
understanding
I know the meaning of the labels affixed on the
sustainable technologies (e.g., energy-efficient
devices).
General EK
Personal
EK
Analysis
• Development of questionnaire
• Conduction of Online Survey >300 participants
• Application of Structural Equation Modeling
Where in this lifecycle does sustainability play a role from a customer’s perspective?
University of Hohenheim | Digital Management33
Managerial Implications
1. Individuals prefer to buy IT which is
sustainably produced sustainable
manufacturing and marketing campaigns
2. Individual’s use IT to behave sustainable IT
may be designed to offer sustainability
attributes during usage (e.g., improving
carbon footprint)
3. Individuals pay attention to the disposal of IT
IT should be designed to offer simple and
sustainable way of recycling
Baumbach et al. (2018)
Results Managerial Implications
“Environmental
Factors are positively
related to the
intention of
environmentally
sustainable behavior
across the life cycle
of IT”
Results and Implications
University of Hohenheim | Digital Management35
Research Article: Supporting Citizens’ Political Decision-Making Using Information Visualisation
University of Hohenheim | Digital Management36
Please prepare the following questions for the Live Session on June, 15
What are the paper‘s key thoughts? (~5 sentences)
Which (self-drawn) figure represents the paper?
What are the most interesting direct quotes? (~3 quotes)
Which references seem to be worth reading next? (~2)
What is most objectionable? (1-2 thoughts)
When / for what will I cite the paper? (1-2 thoughts)
Questions
1
2
3
4
5
6
University of Hohenheim | Digital Management37
Motivation| Living up to your Ideals may be challenging!
Everyday life Society at large
At the end of a cold winter day, there is hardly
anything more pleasant than a long hot shower…
…but wasn't I trying to limit my resource
consumption?
Damn, these pandemic measures have left me
isolated for weeks now and I really want to hang
out with my friends again…
…but how would relaxing social distancing
measures affect the overall spread of the virus?
University of Hohenheim | Digital Management38
Background | Some Theory on Human Decision-Making
Bordalo et al. (2012, 2013); Kluger and DeNisi (1996); Knobloch-Westerwick et al., (2020); Westerwick et al., (2017) ;
Can an Information-Systems (IS-) based tool influence individuals’ decision-making by
providing immediate feedback on decision consequences?
Salience Theory
Selective Information Search
FeedbackInterventionTheory
• Decision-makers are often found to make sub-optimal and irrational
decisions resulting from limited cognitive resources
• A bias in favor of the salient aspects of a decision leads to an attitude-
behavior gap
• People tend to seek information in ways that are partial towards their
existing beliefs
• This can determine the selective perception of salient decision aspects
• An effective way to overcome salience bias and selective information search
is by making the implications of one’s behavior salient in real time
• Individuals compare elements of a feedback intervention with (internal or
external) standards and adjust their behavior to attain the standard
University of Hohenheim | Digital Management39
Application Context | Citizens’ Decisions on Renewable Energy
• Public support for sustainability runs high
in all European countries – see
#FridaysForFuture
• Common mistake to expect citizens to
welcome developments
they claim to support
Acceptance of Renewable EnergyDecision-making in a citizen context
• Serious consequences at all levels of a
society
• Overwhelming complexity of decisions
often involves a multitude of outcomes
• Unlike in organizational and consumer
contexts, little attempts to make a
broad set of information available for
citizens to reflect on decision
implications
University of Hohenheim | Digital Management40
Approach | Research Hypothesis & Data Collection
ResearchHypothesis
ResearchModel
Data Collection
Citizens’ decisions on renewable energy change when respective consequences become clear.
Baseline Decision
(Limited) Information Decision
Information Visualization
IS-tool supported Decision
• Participants are requested to decide on
the proportion of coal-fired plants they
would replace with renewable wind
energy – assuming they had free choice
• IS-Tool provides immediate feedback in
terms of visualizing the location of newly
required wind turbines on a map
• Participants can reevaluate their
decision until the decision outcome is in
line with their preference
University of Hohenheim | Digital Management41
Results of a Data Collection
• Young, urban, and environmentally aware
citizens are willing to accept a high percentage
of renewable wind energy. This result reflects
trends and socio-economic developments at
the time when the survey was conducted.
• The tool influences citizens’ decision-making.
In particular, we find that all analyzed cross-
sections of citizens (e.g., different age,
different political affinity, different levels of
education) within sample changes the amount
of renewable energy initially desired, after
interacting with our tool.
• Citizens update, however not completely turn
over their preferred level of renewable wind
energy after interaction with the tool.
Results
1
2
3
University of Hohenheim | Digital Management43
Motivation
• Food system is a major driver of global environmental challenges1
• Everyone can contribute by making sustainable food choices2
• These decisions about food consumption are increasingly made online3
The Need for Ecologically Sustainable Food Consumption
• e-commerce continuously grows4
• Grocery purchases made online as well as different kinds of online food
services are increasing3
• Many advantages, e.g., time savings, convenience, and flexibility,
especially in times of uncertainty like COVID-195
Rising Relevance of Online Food Shopping
Online grocery stores, delivery services, and subscription services represent choice environments in which consumers
decide between different food products. These choice environments can be modified using Digital Nudging Elements
(DNEs).
References: 1) Noleppa (2012); 2) Ferrari et al. (2019) and Mont et al. (2014); 3) Centraal Bureau voor de Statistiek (2019); 4) Wigand (1997); 5) Gassmann (2020); 6) PWC (2018)
University of Hohenheim | Digital Management44
Research Gap & Research Questions
• Modifying the choice environment to influence choices1
• Goal: Help making better choices without limiting freedom of
choice or manipulating incentives2
• Especially intuitive decisions are prone to heuristics, leading
to faster, but potentially undesirable decisions starting point
for nudging3
1. Which of the DNEs default rules, simplification, and social norms are effective in online food shopping
contexts regarding the promotion of ecologically sustainable food choices?
2. Do the DNEs differ in their influence on different consumer groups?
Research Questions
Nudging
• Only Demarque et al. (2015) focus on the design possibilities of social norms in online food contexts to promote ecologically
sustainable food choices
• Default rules and simplification are not evaluated in online food shopping contexts yet
• Hence, no comparison of the effects of these DNEs exists so far
Gap
Nudging to Promote Ecologically Sustainable Choices
• Food behavior is highly habitual prone to nudging4
• Lehner et al. (2016) and Ferrari et al. (2019) reviewed
prior research on the effect of nudging to leverage
healthier and ecologically sustainable food choices
= Default rules, changes to physical environment, simplification, and social norms
References: 1) Münscher et al. (2016); 2) Thaler and Sunstein (2009); 3) Kahneman (2011) and Tversky and Kahneman (1974); 4) van’t Riet et al. (2011)
University of Hohenheim | Digital Management45
Theoretical Background and Prior Research on Default Rules, Simplification, and Social Norms
Describes a setting in which the
preferred option is pre-selected
and will be maintained if the
person does nothing1
Campbell-Arvai et al. (2014):
default meat-free options
promote vegetarian meals
when eating out
Kallbekken and Sælen (2013)
and Vandenbroele et al.
(2018): default reduced plate
size leads to less food waste
Default Rules
Utilizes the effect of social
pressure & conformity by giving
information about appropriate
behavior within a group3
“70% bought at least one
ecological product”
(Demarque et al. 2015, p.
169)
Linder et al. (2018) and
Kameke and Fischer (2018)
used descriptive norms to
reduce food waste
Social Norm
Represents the transportation of
condensed information about a
complex construct/“Framing” of
information to activate values1,2
Van Gilder Cooke (2012) used
GHG emission labels to
promote environmentally-
friendly burgers
Redesign of menus in
restaurants (Bacon and Krpan,
2018; Kurz 2018)
Simplification
Definition
Prior
Research:
Food Context
Our Implementation
References: 1) Thaler and Sunstein (2009); 2) Sunstein (2014); 3) Aldrovandi et al. (2015) and Kormos et al. (2015)
University of Hohenheim | Digital Management46
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
University of Hohenheim | Digital Management47
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
University of Hohenheim | Digital Management48
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
• Structure: introduction, scenario description,
recipe, online shopping task, survey
• Run #1: random assignment to control group or
implementation of one of the three DNEs
• Run #2: repetition during revision for additional
DNE salience
Collect Empirical Data
References: 1) Onwezen et al. (2019) based on Steptoe et al. (1995); 2) Onwezen et al. (2014, 2019)
University of Hohenheim | Digital Management49
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
• Structure: introduction, scenario description,
recipe, online shopping task, survey
• Run #1: random assignment to control group or
implementation of one of the three DNEs
• Run #2: repetition during revision for additional
DNE salience
Collect Empirical Data
• Product analysis: identification of most and
least sustainable option for each ingredient
• Assignment: 0, 1, or 2 for least, second most,
and most sustainable ingredient option
• Calculation: sustainability score (SC) for each
participants shopping cart from 0-16
Determine Sustainability of Choices
References: 1) Onwezen et al. (2019) based on Steptoe et al. (1995); 2) Onwezen et al. (2014, 2019)
University of Hohenheim | Digital Management50
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
• Structure: introduction, scenario description,
recipe, online shopping task, survey
• Run #1: random assignment to control group or
implementation of one of the three DNEs
• Run #2: repetition during revision for additional
DNE salience
Collect Empirical Data
• Product analysis: identification of most and
least sustainable option for each ingredient
• Assignment: 0, 1, or 2 for least, second most,
and most sustainable ingredient option
• Calculation: sustainability score (SC) for each
participants shopping cart from 0-16
Determine Sustainability of Choices
• Comparison of SCs: ANOVA and Kruskal-Wallis
tests of SCs between control and DNE groups
• Multiple regression analysis: inclusion of
control variables regarding consumption
behaviour and motives (Food Choice Question-
naire FCQ1 and Self-reported Consumption SRC2)
Compare DNEs
References: 1) Onwezen et al. (2019) based on Steptoe et al. (1995); 2) Onwezen et al. (2014, 2019)
University of Hohenheim | Digital Management51
Research Process
Implement DNEsin online shop
Conduct field experiment with
shopping task
Calculate sustainability
score
RQ1:(non)parametric
tests and multiple regression
RQ2:cluster analysis and
(non)parametric tests
• Structure: introduction, scenario description,
recipe, online shopping task, survey
• Run #1: random assignment to control group or
implementation of one of the three DNEs
• Run #2: repetition during revision for additional
DNE salience
Collect Empirical Data
• Product analysis: identification of most and
least sustainable option for each ingredient
• Assignment: 0, 1, or 2 for least, second most,
and most sustainable ingredient option
• Calculation: sustainability score (SC) for each
participants shopping cart from 0-16
Determine Sustainability of Choices
• Comparison of SCs: ANOVA and Kruskal-Wallis
tests of SCs between control and DNE groups
• Multiple regression analysis: inclusion of
control variables regarding consumption
behaviour and motives (Food Choice Question-
naire FCQ1 and Self-reported Consumption SRC2)
Compare DNEs
• Clustering of participants: two-step cluster
analysis with hierarchical Ward’s and
partitioning k-means algorithms
• Comparison of SCs: ANOVA and Kruskal-Wallis
tests of SCs between control and DNE groups
within clusters
Compare DNEs within Participant Groups
References: 1) Onwezen et al. (2019) based on Steptoe et al. (1995); 2) Onwezen et al. (2014, 2019)
University of Hohenheim | Digital Management52
Results: Comparison of SCs between Control and DNE Groups
Total C DR S SN
N 291 73 74 68 76
Mean 9,35 9,29 9,55 9,53 9,04 C .
Standard deviation 2,92 2,71 3,16 2,97 2,86 S *
Median 10 9 10 10 10 SN .
Interquartile range 4 4 3 4 4
Pair
wis
e p
ost
-hoc
t-te
sts
Pair
wis
e p
ost
-hoc
Mann-W
hit
ney-
U t
est
s
p-value significance codes: *** for < 0.001, ** for < 0.01, * for < 0.05, + for < 0.1Sh
apir
o-W
ilk
norm
ali
ty t
est
Bart
lett
vari
ance
test
AN
OV
A
Kru
skal-
Wall
is t
est
SC
University of Hohenheim | Digital Management53
Results: Multiple Regression Analysis with DV SC Including Control Variables FCQ and SRC
Variable Description Estimate p-value
Intercept 6,05 0,000 ***
Group DR Default rules 0,80 0,094 .
Group S Simplification 0,71 0,136
Group SN Social norms 0,07 0,876
FCQ1 Healthy 0,00 0,998
FCQ2 Enables mood monitoring 0,00 0,969
FCQ3 Convenient -0,07 0,568
FCQ4 Provides pleasurable sensations 0,06 0,669
FCQ5 Natural 0,17 0,379
FCQ6 Affordable -0,33 0,005 **
FCQ7 Helps control weight -0,06 0,483
FCQ8 Familiar 0,06 0,574
FCQ9 Environmentally friendly 0,25 0,195
FCQ10 Animal friendly 0,03 0,857
FCQ11 Fairly traded 0,27 0,193
SRC1 Vegetables 0,25 0,120
SRC2 Fruit -0,12 0,330
SRC3 Dairy 0,02 0,785
SRC4 Fish 0,05 0,736
SRC5 Meat -0,18 0,101
p-value significance codes:
*** for < 0.001, ** for < 0.01, * for < 0.05, + for < 0.1
University of Hohenheim | Digital Management54
Results: Comparison of SCs between Control and DNE Groups within Clusters of Participants
Total C DR S SN
C1 N 95 32 18 21 24
Mean 10,30 10,00 10,11 11,52 9,75
Standard deviation 2,60 2,89 2,70 1,97 2,42 . . C-S * C-S *
Median 10 10 10 11 10
Interquartile range 3 4 2 3 3
C2 N 90 16 31 23 20
Mean 8,36 8,50 9,90 8,13 7,65
Standard deviation 2,84 1,75 3,04 3,01 3,03 S *
Median 8 8 9 8 7
Interquartile range 3 1 4 4 3
C3 N 106 25 25 24 32
Mean 9,34 8,88 9,96 9,13 9,38
Standard deviation 3,00 2,83 3,57 2,80 2,84
Median 10 9 10 10 10
Interquartile range 4 3 4 4 3
Cluster codes: C1 - environmentally-conscious, C2 - environmentally-unconscious, C3 - pragmatic
p-value significance codes: *** for < 0.001, ** for < 0.01, * for < 0.05, + for < 0.1
Shapir
o-W
ilk
norm
ali
ty t
est
Bart
lett
vari
ance
test
AN
OV
A
Kru
skal-
Wall
is t
est
Pair
wis
e p
ost
-hoc
Mann-W
hit
ney-
U t
est
s
SC
SC
Pair
wis
e p
ost
-hoc
t-te
sts
SC
• Motives: high importance of naturalness,
environmental friendliness, fair trade, …
• Consumption: more plant-based or veggie
Environmentally-conscious
• Motives: low importance of naturalness,
environmental friendliness, fair trade, …
• Consumption: less plant-based or veggie
Environmentally-unconscious
• Motives: high importance of convenience,
price, familiarity, …
• Consumption: in-between
Pragmatic
University of Hohenheim | Digital Management55
Contribution
1. Default Rules can be implemented in online
food services that increasingly have the power to influence our food choices
2. Help environmentally-conscious customers
with simplification nudge to transfer their good intentions into concrete choices
3. Consumers could profit from time savings due
to reduced decision-making efforts as well as
support to act on their societal responsibility
4. Customers’ price sensitivity has a negative
influence on SCs; hence, this relationship
needs to be dissolved
Practical Contribution
1. Complementing the research by Demarque et
al. (2015) about the DNEs social norms, we
transferred two additional major NEs from the physical to the digital world
2. We compared different DNEs and shed new
light on possible differences in their impacts
3. We identified three typical consumer types,
which enabled us to examine the
effectiveness of the different DNEs in different consumer groups
4. We found that the DNE simplification proved
to be effective for environmentally-conscious
consumers
Theoretical Contribution
+
University of Hohenheim | Digital Management56
Limitations and Further Research
So far, only the three most common (D)NEs in
the food consumption domain have been
considered.
Only one implementation of each DNEs has been
considered yet.
Despite incentive to behave as usual, the
observations base on an artificial field
experiment.
The sample size is limited, especially regarding
within-cluster comparisons.
Limitation
Include further (D)NEs such as feedback,
reminders, and, also, salience.
Consider different implementations and levels
of DNEs.
Partner with online food services to collect
real-life data on consumer behavior.
Collect more data in collaboration with
partners to gain more reliable insights.
Implications for Further Research
DNE Number
DNE Design
Real-world
Observations
Sample Size
#