ONLINE APPENDIX FOR EJIS-ER140052
APPENDIX A. DESCRIPTION OF MEET-U
Appendix A.1. Purpose of Meet-U
The aim of Meet-U is to support users in organizing and arranging meetings and events with their
friends. The goal is to effectively support users in every situation, but, at the same time, the users
should not feel disturbed by the IS.
The users are supported not only during the planning of an event, but also on the way to an event, as
well as at the event itself. They can provide private data such as their date of birth, and indicate
interests to improve recommendations for events or people with similar interests. Users can register
for public events or create private events where they can invite other people to join them. When
planning to visit a public event, the system creates recommendations of possibly interesting events,
based on private data of the users (such as their preferred leisure activities) and user-generated content
that is related to the available events (such as the viewers’ rating for a movie in IMDb). Assuming
users want to create a private event, the application provides recommendations on which of their
friends should be invited, based on the characteristics of the event and information about their friends.
When an event appointment approaches, Meet-U reminds its users and provides, e.g., navigation
information. At the event site, the system recognizes available third-party services and integrates these
services. Examples of such services are a ticketing service or an event map including the points of
interest.
1
Appendix A.2. Dashboard of the prototype that was used by the participants
Translation from German:
1
1
Name of the prototype
2
Upcoming Events
3
Name, location and date of the event
4
Active invitations
5
No active invitations
6
My Events
7
Contacts
8
My Profile
9
Search
10
History
11
Privacy Policy
12
Visibility of the current location (is on,
can be turned off) and address
2
3
4
5
6
9
7
10
8
11
12
2
Appendix A.3. Screenshot showing the simulated indoor navigation used in the free
simulation experiment
Translation from German:
1
1
Indoor navigation
2
Name, location and time left until event
begins
2
Simulation of indoor navigation during the
free simulation experiment.
The map is a real floor-plan of the location
of the evaluation. The big dot marks the start
of the navigation (the PC lab of the
university) and the small dot marks the event
location. Altogether, three different events
were recommended to users, based on their
preferences and user-generated content.
In the case of outdoor navigation,
GoogleMaps would be used.
3
APPENDIX B. COMMON METHOD BIAS
Appendix B.1. Addressing common method bias
Recently, a number of researchers have brought up the problem of common method bias in
behavioural research (Podsakoff et al. 2003; Sharma et al. 2009). These publications point out that a
significant amount of variance explained in a model is attributed to the measurement method rather
than to the constructs the measures represent (Podsakoff et al. 2003). This holds especially true if only
one data source is used and data for the independent and dependent constructs were gathered from the
same participants (Podsakoff et al. 2003). A major finding from Sharma et al. (2009) is that a metaanalysis of the TAM model showed a failure of perceived usefulness to predict adoption and usage
when controlling for common method bias. Aware of this fact, we used the Trust-TAM (Gefen et al.
2003) as theoretical foundation for our research model, since it was not part of their analysis (Sharma
et al. 2009) but was independently tested for common method bias by Malhotra et al. (2006), with the
result that common method variance does not affect the validity of Gefen et al.’s (2003) results. To
further ensure that common method variance is also no issue in our study, we followed the guidelines
of Podsakoff et al. (2003).
According to their first recommendation, we followed the suggested procedural remedies related to
questionnaire design. We used simple structured questions in simple language and avoided vague
concepts and ambiguous terms. For the extreme points and the midpoints of the scales we provided
verbal labels.
Further, we assured anonymity to the participants by explicitly stating in the introduction of the
questionnaire that all answers would be anonymous, and no relationship between any answers and a
participant would be established. Second, the introduction also stated that there were no ‘right or
wrong’ answers, emphasizing that we were interested in the participants' honest opinions. We
developed a cover story for the questionnaire in order to make it appear to the participants that the
independent and dependent constructs were unconnected.
4
Apart from the procedural remedies, we used statistical remedies afterwards. Therefore, we used
common tests in IS to control common method bias: Harman’s single factor test and the unmeasured
latent marker construct (ULMC) technique (Liang et al. 2007; Podsakoff et al. 2003). Regarding
Harman’s single factor test, we conducted an exploratory factor analysis and defined that only one
factor should be extracted, which accounted for 37.58% of the variance, and is below the threshold of
50%. Thus, according to this test, common method bias is not a serious problem in our study. The
results of the ULMC analysis (see Appendices B.2 and B.3) show that the average variance explained
by the respective construct is 0.8361, whereas the average variance explained by the method factor is 0.00255. Furthermore, all expected loadings are notably larger than the loadings with the method
factor (the lowest difference is 0.520 for the process indicator). Furthermore, only two method factor
loadings were found to be significant. Following Liang et al. (2007), this indicates that common
method bias is unlikely for the detailed results of our ULMC analysis.
Since only limited statistical remedies exist (see e.g., the discussion on the usefulness of the Harman’s
single factor test (Podsakoff et al. 2003), and of the ULMC technique (Chin et al. 2012)), it is hardly
possible to statistically ensure that common method bias is no problem at all in a study. Nevertheless,
given the rigorously used procedural remedies and the good results from the statistical remedies, we
expect that common method bias is not a serious problem in our study.
Appendix B.2. Model including only reflective indicators
75
0.1
0.538***
0.518***
00
0.5
3
0.323
2*
**
***
INT_USE
R² = 0.581
**
*
0 .3
.s.
1n 0.26
1
0
3*
.
-0
*
PU
R² = 0.352
0.173*
TRUST_IS
R² = 0.482
-0.012n.s.
*
TRUST_INET
2 ***
0.36
*
TRUST_COMM
R² = 0.268
TRUST_PROV
R² = 0.069
0 . 33
0
0.0
6***
5
PEOU
R² = 0.113
.s.
4n
*** = p < 0.001
** = p < 0.01
* = p < 0.05
n.s. = not significant
Since Liang et al. (2007) recommend remodelling all formative indicators to reflective indicators
before running the UMLC technique, we remodelled our original model. Consequently, similar to the
comparison with the saturated model (see Appendix D.6), we need to evaluate the effect of this change
in the significance of single paths and changes in the R² of the endogenous constructs measured by
effect sizes f².
The comparison of both models shows that changing the direction of the measurement models did not
have a worrying impact on the model. In fact, the only change in significance can be found for the
path from trust in the Internet to trust in the provider. Here, the path coefficient changes from 0.262**
to 0.263***. The f² effect sizes provide similar results:
 f² for TRUST_COMM = (R²reflective model – R²original model)/(1 – R²reflective model) = (0.268 – 0.307)/(1 –
0.268) = -0.0533
 f² for TRUST_IS = 0.0058
 f² for TRUST_PROV = 0.0011
 f² for PU = -0.0170
 f² for PEOU = 0
 f² for INT_USE = -0.0119
Since we observed only a single f² values which exceeds the limit of 0.02 (the change had a small
impact on the R² of the trust in the community of Internet users construct), we can conclude that using
only reflective indicators did not impact our model in a way that would make it inappropriate for
testing for common method bias. Taking all results together, we can conclude that the model using
only reflective indicator is suitable for being used in a UMLC analysis.
Appendix B.3. UMLC technique results
Following Gefen et al.’s (2011) recommendation, we used the UMLC technique to investigate whether
common method bias is a serious issue in our study (Liang et al. 2007). Following Liang et al. (2007),
we specified all constructs to be reflective for the UMLC analysis. This approach is suitable, since we
compared our original model to the model including only reflective indicators in Appendix B.2, and
6
there were hardly any effects in terms of changes in signs, significances or R² of endogenous
constructs. The results of the UMLC analysis show that the average variance explained by the
respective construct is 0.8361, whereas the average variance explained by the method factor is 0.00255. Furthermore, all expected loadings are notably larger than the loadings with the method
factor (the lowest difference is 0.520 for the process indicator). Moreover, only two method factor
loadings were found significant. Following Liang et al. (2007), we can thus conclude that common
method bias is unlikely to be a serious concern in our study.
Construct
Indicator
situational_
normality
TRUST_INT
structural_
assurance
comm_ability
comm_
TRUST_COMM benevolence
comm_
integrity
performance
TRUST_IS
process
purpose
prov_ability
prov_
TRUST_PROV
benevolence
prov_integrity
PU1
PU
PU2
PU3
PEOU1
PEOU
PEOU2
PEOU3
INT_USE1
INT_USE
INT_USE2
INT_USE3
Average
*: p < 0.05
Substantive Factor
Loading (R1)
0.790***
R1²
0.624
Method Factor
Loading (R2)
-0.001n.s.
R2²
0.000
0.887***
0.787
0.001n.s.
0.000
0.655***
0.783***
0.429
0.613
-0.073n.s.
0.051n.s.
0.005
0.003
0.873***
0.762
0.003n.s.
0.000
0.695***
0.516**
0.964***
0.871***
0.763***
0.483
0.266
0.929
0.759
0.582
0.174*
-0.004n.s.
-0.191**
-0.121n.s.
0.085n.s.
0.030
0.000
0.036
0.015
0.007
0.759***
0.888***
0.964***
0.869***
0.920***
0.879***
0.803***
0.982***
0.972***
0.889***
0.836
0.576
0.789
0.929
0.755
0.846
0.773
0.645
0.964
0.945
0.790
0.699
0.029n.s.
0.001
0.007n.s.
0.000
-0.026n.s.
0.001
0.020n.s.
0.000
-0.035n.s.
0.001
-0.065n.s.
0.004
0.096n.s.
0.009
-0.042n.s.
0.002
-0.022n.s.
0.000
0.063n.s.
0.004
-0.003
0.006
n.s.: not
significant
**: p < 0.01
***: p < 0.001
7
APPENDIX C. INDICATORS USED IN THE STUDY
Indicator
Statement
Mean
SD
Source
TRUST_PROV - Trust in the Provider (formative)
prov_ability
The provider does a good job.
7.51
1.563
prov_benevolence
It is important for the provider that ____
supports me in achieving my goals.
7.10
1.700
prov_integrity
I can count on the statements of the
provider.
5.79
1.997
Developed using theoretical
foundations provided by Mayer et
al. (1995).
TRUST_INET - Trust in the Internet (formative)
situational_
normality
I feel good about how thing go when do
activities on the Internet.
3.37
2.127
structural_
assurance
I feel assured that legal and technological
structures adequately protect me from
problems on the Internet.
3.70
1.924
Developed using theoretical
foundations provided by McKnight
et al. (2002).
TRUST_IS - Trust in the IS (formative)
performance
____ performs well.
6.43
1.860
process
I understand the inner processes ____ uses
to support me.
4.11
2.174
purpose
I understand why ____ was developed.
7.31
1.626
Developed using theoretical
foundations provided by Lee and
See (2004).
TRUST_COMM - Trust in the Community of Internet Users (formative)
comm_ability
Information provided by other users of the
Internet is valuable.
5.74
1.843
comm_benevolence
Other users of the Internet offer me help
when I have questions.
3.66
1.897
comm_integrity
In general, I can count on the information
provided by other internet users.
3.97
1.929
Developed using theoretical
foundations provided by Mayer et
al. (1995).
PEOU - Perceived Ease of Use (reflective)
PEOU1
Learning to use ____ would be easy for
me.
7.15
1.687
PEOU2
It would be easy for me to become skillful
at using ____.
6.92
1.710
PEOU3
____ is ease to use.
6.34
2.162
Adapted from Kamis et al. (2008).
PU - Perceived Usefulness (reflective)
PU1
Using ____ improves my performance in
organizing and managing events.
5.20
2.184
PU2
Using ____ improves my effectiveness in
organizing and managing events.
5.47
2.181
PU3
____ is a useful tool to support me in
organizing and managing events.
6.18
1.905
Adapted from Kamis et al. (2008).
INT_USE - Intention to Use (reflective)
INT_USE1
Assuming I had access to ____, I intend to
use it.
5.53
2.113
INT_USE2
Assuming I had access to ____, I plan to
use the system.
5.59
2.223
INT_USE3
Assuming I had access to ____, I would
use it to organize and manage my next
event.
5.48
2.256
8
Adapted fromWang and Benbasat
(2009) and Bhattacherjee and
Sanford (2006).
APPENDIX D. FURTHER TABLES UNDERLINING THE RESULTS
Appendix D.1. Cross-loadings and composite reliability for the reflective measurement
models
INT_USE
ρc = 0.963
AVE = 0.900
0.945
0.952
0.946
0.311
0.254
0.393
0.661
0.666
0.614
INT_USE1
INT_USE2
INT_USE3
PEOU1
PEOU2
PEOU3
PU1
PU2
PU3
PEOU
ρc = 0.900
AVE = 0.750
0.356
0.381
0.331
0.894
0.821
0.880
0.328
0.394
0.383
PU
ρc = 0.933
AVE = 0.824
0.665
0.624
0.733
0.329
0.285
0.424
0.894
0.942
0.886
Appendix D.2. AVE and correlation among construct scores (square root of the AVE bold
in diagonals).
INT_USE
PEOU
PU
TRUST_IS
TRUST_INET
TRUST_PROV
TRUST_COMM
INT_
USE
0.948
0.375
0.713
0.632
0.172
0.455
0.064
PEOU
PU
TRUST_
IS
TRUST_
INET
TRUST_
PROV
TRUST_
COMM
0.866
0.406
0.484
0.100
0.337
0.032
0.908
0.565
0.208
0.472
0.135
formative
0.157
0.629
0.085
formative
0.262
0.157
formative
0.137
formative
Appendix D.3. Evaluation of the formative measurement models
Construct
TRUST_IS
TRUST_INET
TRUST_PROV
TRUST_COMM
Indicator
VIF
Factor Weights
performance
1.704
0.713
< 0.001
process
1.301
0.266
< 0.01
purpose
1.363
0.310
< 0.001
situational_normality
2.023
0.640
< 0.001
structural_assurance
1.909
0.483
< 0.001
prov_ability
1.527
0.305
< 0.01
prov_benevolence
1.705
0.570
< 0.001
prov_integrity
1.821
0.365
< 0.01
comm_ability
1.775
-0.179
n.s.
comm_benevolence
1.198
0.887
< 0.001
comm_integrity
1.783
0.238
n.s.
9
p-value
Factor Loadings
0.115
0.643
We relied on the guidelines of Cenfetelli and Bassellier (2009) to evaluate our formative measurement
models. According to the first guideline, we checked for multicollinearity by computing the Variance
Inflation Factor (VIF). The results indicate that multicollinearity is not a problem in our study because
the highest VIF value (2.023) is below the limit of 3.33 (Diamantopoulos and Siguaw 2006).
In their second guideline, Cenfetelli and Bassellier (2009) state that a large number of indicators will
cause many non-significant weights. Since we observed only two non-significant weights (at the level
of 0.05, marked with “n.s.” in the table above) and their inclusion is based upon theory, we decided
not to drop any indicators. This decision is based on the argument that this is the first study of its kind,
and it should be checked whether this lack of significance could be observed in different studies before
questioning the relevance of these indicators (Cenfetelli and Bassellier 2009).
The third guideline deals with the co-occurrence of positive and negative weights. Due to the fact that
the only indicator with a negative weight was not found to be significant, there was no need to worry
about this point in our study (Cenfetelli and Bassellier 2009).
Guideline four suggests that researchers should check the indicator loadings when observing indicators
that have a low indicator weight. As a reason, Cenfetelli and Bassellier (2009) point out that the
indicator could have only a small formative impact on the construct (shown by a low weight), but it
still could be an important part of the construct (shown by a high loading). If this is the case, the
indicator is important and should be included in the measurement model (Cenfetelli and Bassellier
2009). Chin (1998) stipulates that a loading of 0.5 is weak but still acceptable. Checking the results
presented in the table above, we can see that the loadings of the two indicators with non-significant
weights vary highly. Whereas the indicator comm_integrity shows a loading above the threshold
(0.643 > 0.5), the indicator comm_ability shows a loading below the threshold (0.115 < 0.5). As a
result, the indicator comm_ability has a non-significant weight and a low loading. Nevertheless, since
this is the first study of this kind and the inclusion of the indicator is based on a solid theoretical basis,
we follow Cenfetelli and Bassellier’s (2009) advice and do not drop the indicator. However, the
observation that the indicator comm_ability shows a non-significant, negative weight and a low
10
loading challenges the theoretical basis. If similar results can be observed in future studies, the
indicator should be dropped, and the suitability of the theoretical basis suggesting this particular
relationship should be investigated.
In the fifth guideline, Cenfetelli and Bassellier (2009) recommend testing for nomological network
effects and construct portability. They suggest comparing the factor weights of the indicators across
different studies. Due to the fact that, to the best of our knowledge, ours is the first study investigating
different targets of trust in IS use and additionally following a formative measurement for each of
these constructs, a comparison of factor weights across different studies is not possible.
The sixth guideline cautions that the indicator weights can be slightly inflated when using the PLS
technique (Cenfetelli and Bassellier 2009). As we used the PLS technique, this is a limitation of our
study.
In summary, the evaluation of our formative measurement models shows that the models fulfil the
requirements posed by the guidelines of Cenfetelli and Bassellier (2009).
Appendix D.4. Multicollinearity among the predictors of the endogenous constructs
Predictors of TRUST_IS
Construct
VIF
TRUST_INET
1.522
TRUST_COMM
1.445
TRUST_PROV
1.199
PEOU
1.129
Predictors of PU
Construct
VIF
TRUST_IS
1.918
TRUST_PROV
1.657
PEOU
1.308
Predictors of NT_USE
Construct
VIF
TRUST_IS
1.658
PU
1.521
PEOU
1.351
Appendix D.5. f² and q² effect sizes for the structural model
Relationship
f² value
0.433
f² effect size
large
q² value
0.338
q² effect size
medium
PEOU  INT_USE
0.000
-
0.000
-
PEOU  PU
0.035
small
0.012
-
PEOU  TRUST_IS
0.157
medium
n/a
TRUST_IS  INT_USE
0.162
medium
0.128
small
TRUST_IS  PU
0.105
small
0.078
small
PU  INT_USE
11
TRUST_INET  TRUST_COMM
0.444
large
n/a
TRUST_INET  TRUST_IS
0.004
-
n/a
TRUST_INET  TRUST_PROV
0.074
small
n/a
TRUST_COMM  TRUST_IS
0.001
-
n/a
TRUST_PROV  TRUST_IS
0.444
large
n/a
TRUST_PROV  PU
0.031
small
0.020
small
TRUST_PROV  PEOU
0.128
small
0.091
small
Appendix D.6. Saturated Model
According to Gefen et al. (2011), the theorized model should be compared to a saturated model (see
the Figure below) regarding changes in the significance of single paths and changes in the R² of the
endogenous constructs measured by effect sizes f².
0.034n.s.
0.039n.s.
0.072n.s.
TRUST_IS
R² = 0.482
-0.019n.s.
0.5
2
0.332*
8n
0.1
5
**
TRUST_PROV
R² = 0.072
**
INT_USE
R² = 0.588
0.007n.s.
*
0.006n.s.
*
11
TRUST_COMM
R² = 0.308
3*
*
0.166*
*
0.531***
n.
s.
04
0.
0
5*
0 .3
0.555***
0.2
6
PU
R² = 0.371
*
.s.
TRUST_INET
1 **
0 . 37
0.32
00
-0.
5 ***
.s.
2n
PEOU
R² = 0.109
-0.037n.s.
-0.056n.s.
0.034n.s.
*** = p < 0.001
** = p < 0.01
* = p < 0.05
n.s. = not significant
The comparison of both models shows that our original model is sound. All added structural paths
were not found to be significant. Furthermore, we observed only three changes in terms of
12
significance. Two indicators went down one degree in significance, and the path between trust in the
provider and perceived usefulness became insignificant (see the Table below for a summary of the
results).
Relationship
structural_assurance 
TRUST_INET
prov_ability 
TRUST_PROV
TRUST_PROV  PU
Original Model
0.483***
t-value
3.360
Saturated Model
0.475**
t-value
2.824
0.305**
2.951
0.241*
2.280
0.185*
2.034
0.158n.s.
1.779
To assess the impact of the additional structural paths on the R² values, we need to compute the f²
comparing the original model and the saturated model:
 f² for TRUST_COMM = (R²saturated model – R²original model)/(1 – R²saturated model) = (0.308 – 0.307)/(1 –
0.308) = 0.0014
 f² for TRUST_IS = 0.0032
 f² for TRUST_PROV = 0.0043
 f² for PU = 0.0127
 f² for PEOU = -0.0045
 f² for INT_USE = 0.0049
Since we only observed f² values below the threshold of 0.02, we can conclude that the additional
paths did not have an alarming impact on our results (Gefen et al. 2011). Taking all results together,
we can conclude that we did not omit important structural relationships.
Appendix D.7 Comparison of the effects of trust in the IS and trust in the provider
Dependent
variable
PEOU
PU
INT_USE
Direct effects, f² effect sizes and q² effect sizes
TRUST_IS
TRUST_PROV
Direct
f²
q²
Direct
f²
q²
effect
effect
effect
effect
effect
effect
n/a
n/a
n/a
0.337*** 0.128
0.091
0.369***
0.105
0.078
0.185*
0.031
0.020
0.336***
0.162
0.128
n/a
n/a
n/a
13
Total effects
TRUST_
IS
n/a
0.369***
0.529***
TRUST_
PROV
0.337***
0.474***
0.460***
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