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2023 | OriginalPaper | Buchkapitel

2. The User Perspective

verfasst von : Leander Kauschke

Erschienen in: The Transition to Smart Mobility

Verlag: Springer Fachmedien Wiesbaden

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Abstract

This section provides an overview of acceptance theory, from the basic concept of acceptance to the latest revelations in research. Generally, the results of the below theory recap indicate that research on acceptance of new technology is a heterogeneous field depending on whether the focus is conceptual, methodological or technological. I conclude that for the analysis of technology systems such as smart mobility, comprehensive behavioral modeling is the most promising approach.

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Fußnoten
1
See Figure 2.2, Section 2.1.1.3 for a modeling attempt.
 
2
See Section 2.1.2  for details on the different approaches.
 
3
See operationalization of variables in Section 2.2.
 
4
Commonly referred to as information system (IS) research.
 
5
See Figures 2.42.6, Sections 2.1.2.2 ff. for examples.
 
6
Moderation refers to a linear effect of a third variable on the relation between two variables. Moderation may be so substantial that an increase in the third variable significantly strengthens or weakens the effect between the other two.
 
7
A key construct from consumer psychology.
 
8
A key construct from economics.
 
9
A key construct from sociology.
 
10
See Figure 1.​5, Section 1.​4.​3 for a selection.
 
11
Following van Noorden et al. (2014), more than ten citations indicate that a paper has reached the top ten percentile of papers worldwide.
 
12
E.g., the acceptance of eBikes is the topic of nine articles only, whereas AV acceptance is dealt with in 72 sources.
 
13
‘Not in my backyard’ is the socio-economic branding of growing hostile tendencies of residents towards projects in the vicinity (like wind power plants, highways, airports).
 
14
See Section 2.1. for subject, object and context of acceptance.
 
15
See Figure 2.2, Section 2.1.1.3 for the proposed taxonomy of acceptance.
 
16
See Figure 2.9, Section 2.1.3 for a quantitative classification.
 
17
Please refer to Table 2.3, 2.7 and 2.8, Sections 2.1.3.1 ff., which have been merged by forming average values.
 
18
Referred to the identified 139 most important articles, only 23 applied UTAUT and another six used an exploratory holistic approach. It is possible that factors which are actually significant lose their predictive power when the full range of predictors is considered.
 
19
See Figure 2.11, Section 2.1.3.5  for the MaaS acceptance environment.
 
20
See Table 2.7, Section 2.1.3.5 for details.
 
21
Compare i.a. Table 2.6, Section 2.1.3.4 for conflicting evidence on AVs.
 
22
See the level of acceptance in Figure 2.2, Section 2.1.1.3.
 
23
E.g., in AV-based transport, no driver would have to be paid.
 
24
E.g., from the system to the service to the technology.
 
25
E.g., between different services or technologies.
 
26
Please see Table 2.11, Section 2.3.3.1 for the list of items.
 
27
Respectively the sub-use cases Level 5 automated vehicles, mobility-as-a-service and e-Bikes.
 
28
Mediation refers to indirect effects exerted by one variable on another through a third variable, then called a mediator.
 
29
See Figure 2.7, Section 2.1.2.7 for the UTAUT2 model.
 
30
See Table 2.1, Section 2.1.1.4 for key methodologies.
 
31
See Section 2.3.2.1 for construct operationalizations.
 
32
A reminder: Following Ajzen (2002) this is the origin of strong acceptance predictors from perceived behavioral control to facilitating conditions.
 
33
See Table 2.10, Section 2.2.2.2 for alternative conceptualizations.
 
34
These are often based on sociodemographic characteristics.
 
35
These reach from general examination of ‘the Internet’ Gupta et al. (2008) to specific applications such as in ‘SMS- based eGovernment’ in Susanto and Goodwin (2010).
 
36
Things that are enabled by the technology like communication, banking or here: mobility.
 
37
E.g., pre- or post-adoption.
 
38
In keeping the research efficient, other conceptualizations of sociodemographic values like lifestyles (Schlüter and Weyer, 2019) or customer groups (Mohamed et al., 2018) will not be considered in present work.
 
39
Please see Section 2.4.3.3The Thomas Principle’.
 
40
Please see Section 2.3.2.1 for the different measurement models.
 
41
See Figure 2.2, Section 2.1.1.3 for the proposed levels of acceptance.
 
42
Derived from Latin: lateo (‘lie hidden’).
 
43
Figure 2.13, Section 2.3.1.1 displays a single-item measurement for the latent variable Y4. This must not be confused with a non-directional relationship but a special case, in which the construct and the item are simply the same. No measurement model is necessary.
 
44
See assessment of the measurement models in Section 2.3.4.2.
 
45
For details on software settings please refer to Section 2.3.4.1.
 
46
Since Mobility-as-a-Service in general, and fully automated vehicles in particular, are expected to be not accessible to the vast majority of survey participants, the use of digitally supported mobility services (as a substitute for MaaS) and the use of automated driving functions (as a substitute for Level 5 vehicles) are deployed as fallback operationalizations.
 
47
See Section 2.2.2.1, p. 64 for risk operationalization.
 
48
See Section 2.2.3.3, p. 71 for further information.
 
50
See Table 2.16, Section 2.3.2.4 for sample characteristics.
 
52
From a total of 2143 filled surveys valid after data cleaning.
 
53
See upcoming paragraph for the applied sampling procedure.
 
54
Too many people agreed on 3_EE1: ‘Learning how to use an eBike is easy for me.’
 
55
For reasons of clarity, the illustration is limited to the evaluation of the data of the main use case smart mobility.
 
56
In statistics, a Type I error signifies that the null hypothesis is rejected when it is actually true, while a Type II error signifies that the null hypothesis is not rejected when it is actually false Howell (2010).
 
57
Please see IPMA procedure in Section 2.3.4.4.
 
58
Adjusted to a six-point scale.
 
59
Within the cleaned data, we find a maximum of 3.5% missing data per indicator (for collective efficacy, which was located at the end of the survey and might thus be a victim of respondents’ fatigue).
 
60
See Section 2.3.2.2 for details on data collection.
 
61
See Figure 2.14, Section 2.3.1.1 for construct domains in reflective and formative measurements.
 
62
See Section 2.3.4.1 for further information.
 
63
No difference between actual distribution and random distribution is observable.
 
64
See Section 2.3.2.1 for a discussion on measurement issues.
 
65
Due to the linear appearance of the data, one can refrain from investigating non-linear effects (Hair et al., 2018).
 
66
See Table 2.26, Section 2.3.4.3 for a goodness criteria overview.
 
67
See Section 2.1.3 for literature review.
 
68
See the four areas of SEM research in Section 2.3.1.2.
 
69
Equals AVE in PLS.
 
70
Equals R2 in PLS.
 
71
In contrast, Becker et al. (2013) explain that unobservable heterogeneity refers to potential estimation distortions caused by unknown segmentation patterns in the data. These tend to occur if no observed heterogeneity is found or if little is known about a model’s theoretic background. For the analysis of unobservable heterogeneity, FIMIX (finite mixture PLS) or POS (prediction-orientated segmentation) can be applied. However, due to the relatively high number of observable control variables and given our knowledge about UTAUT, other differences in the data structure should be negligible. A review of the FIMIX segmentations and associated entropy values confirmed this assumption. This suggests that the issue of unobservable heterogeneity is not a major issue in present data.
 
72
Please see Section 2.3.4.1 for details.
 
73
These involved random items across variables. Hence, no pattern of group specific differences in item perception was unmasked.
 
74
In preparing for MGA, a series of moderation and multiple interaction tests was conducted as proposed by Venkatesh et al. (2012). However, no meaningful effects were revealed.
 
75
See literature review in Section 2.1.3.
 
76
M.(FaCon): 5.09; M.(Effort): 5.31; M.(Perform): 4.91.
 
77
M.(Price): 3.48; M.(Social): 3.79; M.(Habit): 3.51.
 
78
See Section 2.3.4.4 for IPMA results.
 
79
See Section 2.4.2.1, for details on intrinsic motivation.
 
80
This effect is even larger in rival models.
 
81
See Table 2.8, Section 2.1.5 for prior research findings.
 
82
See Section 2.2.1.2 for an explanation.
 
83
In the present study, collective efficacy served as a representative of the ecological component, which had been diversely conceptualized in past research (see Table 2.10, Section 2.2.2.2 for an overview).
 
84
See Figure 1.​5, Section 1.​4.​3 for additional information.
 
85
See Table 2.18, Section 2.3.3.2 for descriptive statistics.
 
86
See Table 2.30, Section 2.3.4.4 for IPMA comparison.
 
87
See Table 2.35, Section 2.3.4.4 for effect comparison; See Figure 2.25, Section 2.3.4.2 for levels of acceptance according to taxonomy.
 
88
See Table 2.32 and Table 2.33, Section 2.3.4.4 for moderation effects by age and gender.
 
89
The elaborated model was employed.
 
90
The interpretation was conducted analogously to the methodology in Section 2.3.4.4.
 
91
A reminder: on a seven-point Likert scale (with value 4 as its median).
 
92
These refer to what people relevant to an individual may think about their actions.
 
93
See Figure 2.23, Section 2.3.3.4 for gender effects across use cases.
 
94
See Table 2.6, Section 2.1.3.4 for UTAUT-based AV studies.
 
95
See Section 2.2.1.2, for a theoretic excurse on the issue.
 
96
See Section 2.2.3.2 for related theory.
 
97
See Figure 2.2, Section 2.1.1.3 for the taxonomy of acceptance.
 
98
See Figure 2.32, Section 2.3.4.4 for associated modeling.
 
99
E.g., gaia-x.eu for prospective service directories.
 
100
E.g., knowledge, seeing, feeling, time and intensity of exposure as well as related technology.
 
101
See Section 2.1.2.5 for an introduction.
 
102
See Figure 2.26, Section 2.3.3.5 for allocation of use cases within the taxonomy.
 
103
Due to many missing data, income was not considered in the initial evaluations.
 
104
I ran additional regressions with Thomas being a direct predictor of different kinds of norms (injunctive, subjective, etc.). Across all four use cases, no significant results were obtained.
 
105
Acceptance before first exposition to a technology (see Section 2.1.1.3 for a definition).
 
106
See Section 2.2.1.2 for related theory.
 
107
See Section 2.3.4.4 for IPMA terminology.
 
108
Please see Table 2.35, Section 2.3.4.4 for a structural comparison of the use cases.
 
110
In Germany, the National Platform for Electromobility has been further developed since 2019 into the National Platform for the Future of Mobility.
 
111
An interesting option for this purpose would be to employ target group information systems such as the SINUS milieus in Barth et al. (2018).
 
112
See Chapter 1.
 
Metadaten
Titel
The User Perspective
verfasst von
Leander Kauschke
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-658-43001-6_2

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