=Paper=
{{Paper
|id=Vol-3630/paper29
|storemode=property
|title=Bridging the Gap: Examining the trust dimensions of smart contracts using supply chain applications
|pdfUrl=https://ceur-ws.org/Vol-3630/LWDA2023-paper29.pdf
|volume=Vol-3630
|authors=Wieland Müller,Michael Leyer
|dblpUrl=https://dblp.org/rec/conf/lwa/MullerL23
}}
==Bridging the Gap: Examining the trust dimensions of smart contracts using supply chain applications==
https://ceur-ws.org/Vol-3630/LWDA2023-paper29.pdf
Bridging the Gap: Examining the trust dimensions of
smart contracts using supply chain applications
Wieland Müller1, Michael Leyer2,3
1 Rostock University, Ulmenstraße 69, 18057 Rostock, Germany
2 Marburg University, Universitätsstraße 25, 35037 Marburg, Germany
3 Queensland University of Technology, 4101 Brisbane, Australia
Abstract
This paper examines trust towards distributed ledger-based smart contracts in supply chain
management, aiming to develop a multidimensional model of trust and provide practical insights. A
quantitative survey was conducted among supply chain employees in the United States. Confirmatory
factor analysis validated the proposed model, revealing five trust dimensions: dispositional trust,
situational social trust, information-technology trust, interaction-informed trust, and learned data trust.
These dimensions capture the complexity of trust towards smart contract-enabled supply chains,
considering the interplay between social and technical factors. The findings contribute to theoretical
understanding and offer practical guidance for enhancing collaboration, minimizing risks, and
maximizing the benefits of trustless supply chain networks. Future research should incorporate
additional influencing factors to develop a more holistic trust model for smart contracts.
Keywords
smart contracts, distributed ledger technology, dimensions of trust
1. Introduction
Trust plays a pivotal role in the conventional management of supply chains, ensuring the
seamless flow of goods and services among various stakeholders. Suppliers, manufacturers,
distributors, and customers rely on mutual trust to fulfil their obligations, as any breach can result
in delays, disputes, and financial setbacks. However, the emergence of smart contracts and
distributed ledger technology holds the potential to revolutionize supply chain operations. By
eliminating the need for intermediaries or third-party authorities, these technologies encode
transaction rules and conditions into self-executing smart contracts. This not only mitigates the
risk of fraud and human error but also streamlines the entire process while enhancing
transparency. In the context of supply chains, trust manifest itself through various aspects.
Distributed ledger based smart contracts have emerged as a promising technology that aims
to enable trustless network and transaction processes within various domains. The concept of a
trustless network suggests that smart contracts have the potential to eliminate the reliance on
trust towards traditional network architectures (Christidis and Devetsikiotis 2016). However,
contrary to the belief that technology can entirely eradicate the need for trust, it actually presents
a novel approach to substitute information from alternative sources to establish trust and
subsequently act upon it (Lemieux et al. 2019). Consequently, a trustless system does not imply
a system devoid of trust, but rather a system where trust is redefined and placed upon verifiable
and automated mechanisms. Therefore, it is crucial to develop an understanding of trust and its
various facets in order to facilitate the development and successful deployment of efficient
trustless systems within supply chains.
Proceedings Acronym: Proceedings of the LWDA 2023 Workshops, October 09–11, 2023, Marburg, Germany
wieland.mueller@uni-rostock.de (W. Müller); michael.leyer@wiwi.uni-marburg.de (M. Leyer)
0000-0002-2172-8725 (W. Müller); 0000-0001-9429-7770 (M. Leyer)
© 2023 Copyright for this paper by its authors. Copying permitted only for private and academic purposes.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
To explain the dimensions of trust in organisations, different studies have explored trust from
various organisational perspectives (Yamagishi and Yamagishi 1994; Mayer, Davis, and
Schoorman 1995; Rousseau et al. 1998), including the trust in technologies (McKnight et al. 2011;
Pavlou 2003). Nonetheless, none of these models consider smart contracts or fully account for
the unique characteristics of this technology. Therefore, there is a research gap that needs to be
addressed by developing a multidimensional model of trust and empirically investigating its
implications in the field of supply chain management.
To close this gap, we want to answer following research question: How should a model be
designed to represent the dimensions of trust towards distributed ledger based smart contracts?
By comprehensively studying and addressing trust dimensions, we can enhance the design,
implementation, and governance of smart contract-enabled supply chains. This, in turn, fosters
greater trust among participants, boosts collaboration, minimizes transactional risks, and
unlocks the full potential of trustless supply chain networks to enhance global trade and
commerce.
The theoretical foundation of the proposed multidimensional model of trust are the three trust
layers according to Hoff and Bashir (2014) and the three layers of distributed ledger technology
according to Lemieux et al. (2019). A quantitative survey for the empirical analysis was conducted
in the United States of America and allowed employees from companies involved in a supply
chain. To analyse our research model, we initially applied the procedure outlined by Cenfetelli
and Bassellier (2009) to interpret the established formative measurement model. Subsequently,
we conducted a factor analysis, utilizing the maximum likelihood method. This method was
chosen for factor analysis due to its robustness and suitability for capturing the complex
relationships among the observed variables and latent constructs in our research model.
The paper is organised as follows: The following section presents the fundamental background
and previous research about trust towards supply chain networks and distributed ledger based
smart contracts. In the third section, a multidimensional model of trust towards smart contracts
is established based on theoretical literature. Section four explains the method of the study,
conducting a quantitative survey, and performing statistical analyses of the empirical data.
Furthermore, information about the sample and results of statistical tests are given. Section five
describes the development of the model and the hypotheses for the statistical analysis. The
results of these analyses are presented in the following section, followed by a critical discussion.
In the conclusion, we draw the theoretical and practical implications of the study as well as its
limitations and directions for future research.
2. Fundamental Background
2.1. Trust in supply chain networks
Trust is a fundamental element in the realm of supply chains due to the intricate nature of
relationships among the various actors involved. The presence of trust significantly impacts
the willingness of these actors to share information, mitigate risks, and pursue innovation
opportunities, all of which contribute to enhancing the efficiency and effectiveness of the
supply chain (Gligor and Holcomb 2013). Notably, the establishment of trust is crucial for
fostering robust relationships among participants within the supply chain network, as
emphasized by Halldorsson et al. (2007).
In the context of client-contractor relationships, Treiblmaier (2018) posits that complete
transparency of information flow has the potential to eliminate the necessity of trust,
underscoring the pivotal role of transparency in cultivating trust within supply chains.
Furthermore, Treiblmaier (2018) asserts that transparency and non-repudiation of data could
render personal relationships obsolete in the trust-building process. Lastly, Treiblmaier
(2018) highlights the potential of technology in mitigating trust-related challenges in supply
chains.
2.2. Trust towards smart contracts
Smart contracts are automated executing digital contracts that can be written by an "if-this-
then-that" code of a distributed ledger network structure. Distributed ledgers utilize
encryption technology to securely store and transmit information. Besides of immutability of
data, a complete traceability and transparency of data are two other common characteristics
of distributed ledger technologies (El Ioini and Pahl 2018). Once established, smart contracts
do not require any further management (Vo, Nguyen-Thi, and Nguyen-Hoang 2021).
The automatic execution and assurance of smart contracts including automatic penalties
for non-fulfilment of contracts lead to no or less need for trust for transactions in networks
(Treiblmaier 2018; Grosse, Guerpinar, and Henke 2021). In addition, mutual trust is
nevertheless increased due to the complete transparency of the information flow for both the
client and the contractor (Wang et al. 2019). Looking at automation technologies, different
levels of trust have been identified by Hoff and Bashir (2014), dispositional, situational and
learned trust.
Trust towards a specific information technology plays a key role in the development of IT-
related beliefs and behaviour. It is defined by the ability to perform the required functions for
a given task, to provide effective assistance when needed, and to work reliably or consistently
without failure (McKnight et al. 2011). Different studies highlight the potential of smart
contracts to enable automation of various business processes (Zheng et al. 2020; Eggers et al.
2021; Li and Kassem 2021). Trust towards automation depends mostly on the performance,
process or purpose of an automated system (Hoff and Bashir 2014).
While some works argues that the underlying distributed ledger technology enables
coordination without interpersonal trust, other recognises that trust plays a crucial role in
distributed ledger networks (Becker and Bodo 2020). Despite the notion that technology
eliminates the requirement for trust, in reality, it provides a new approach to substituting
information from other sources to establish trust towards something or someone, and to act
on that trust (Lemieux et al. 2019). According to Lemieux et al. (2019) distributed ledger
systems rely on three interdependent trust layers, the social, data and technical layer.
3. Models of trust dimensions and hypotheses
3.1. Model conceptualisation
In the literature, there are different models of trust that represent the construct with
influencing factors. The Model of Trust by Yamagishi and Yamagishi (1994) suggests that trust
is influenced by both structural and cognitive factors. Another fundamental model is the
integrative model of trust by Mayer, Davis, and Schoorman (1995) which identifies ability,
benevolence, and integrity as the basis of trust, along with the influence of the trustor's
propensity and perceived risks. The model of trust from Rousseau et al. (1998) proposes that
trust in organizations is comprised of cognitive trust and affective trust. The game-theoretic
model according to Das and Teng (2001) suggests that trust in organizations is determined
by rational decision-making, with individuals considering the potential benefits and risks of
trusting others. Pavlou's (2003) model of trust towards technology adoption includes
cognitive and affective trust and incorporates integrity as a factor representing the reliability
and honesty of the technology. However, in all these models, trust is seen as a one-dimensional
construct that is not subdivided into different levels. We want to close this gap by developing
a model that takes into account both the automation characteristics and the characteristics of
the underlying distributed ledger technology and thus reflects different dimensions of trust.
To incorporate the specific attributes of distributed ledger technology as the underlying
infrastructure for smart contracts, the model has been designed to encompass various levels
of this technology. According to Lemieux et al. (2019), the social trust layer of the distributed
ledger technology deals with how actors interact and determine the types and sources of
information needed to establish trust and take action. The data layer provides the information
that actors have deemed necessary to obtain from the distributed ledger system to give them
confidence to act.
Finally, the technical layer pertains to the means by which actors create, store, and retrieve
tamper-resistant and non-repudiable proof of facts about their interactions (Lemieux et al.
2019). Since dispositional trust refers to a person's general attitude and is not related to the
application of a specific technology, this type of trust must be considered on its own.
Situational trust and learned trust can be considered from the different perspectives of the
three distributed ledger layers (social, data and technical). As can be seen in Figure 1, this
results in a total of seven different dimensions of trust regarding smart contract use.
Since smart contracts are essentially automation processes, we have considered different
levels of trust towards automation technologies in the model. Hoff and Bashir (2014) were
able to empirically establish three different layers for trust towards automation: The first is
dispositional trust, which refers to a person's general inclination to trust automation,
regardless of the situation or the specific system. It is used to describe long-term tendencies.
The second layer is situational trust, which takes into account both the external factors and
the operator's own context-dependent characteristics. The third layer is learned trust, which
reflects an operator's assessments of a system based on prior experience or current
interaction. Since smart contracts are characterised by the automation of contracts, this
differentiated dimensions are also suitable for our model (Hoff and Bashir 2014).
Based on the different layers, we created a multidimensional model of trust with seven
dimensions, visualized in Figure 1.
Figure 1: Dimensions of trust towards smart contracts based on Hoff and Bashir (2014) and
Lemieux et al. (2019).
The seven dimensions can be defined following according Hoff and Bashir (2014) and
Lemieux et al. (2019): Dispositional trust refers to an individual's general tendency to trust
others over the long-term, irrespective of the context or a particular system. Situational trust
in social layer relates to how social actors interact based on their trust in the external
environment and the operator's internal context, such as required information amount and
form. Situational trust on the data layer refers to the trust social actors have in the distributed
ledger technology system's information for them to confidently act, based on the operator's
internal and external context.
Situational trust on the technical layer refers to social actors' trust towards the distributed
platform's internal and external characteristics for creating, storing, and exchanging
information. Learned trust on the social layer is social actors' developed trust based on past
experiences or current interactions, regarding how they interact with each other and required
information amount or form. Learned trust on the data layer is social actors' developed trust
based on past experiences or current interactions with the information provided by the
system, crucial for them to confidently act. Finally, learned trust on the technical layer is the
social actors' developed trust based on past experiences or current interactions at a technical
level with the platform used for creating, storing, and exchanging information. This leads to
the following hypotheses:
H1: Dispositional trust is an independent dimension of trust.
H2: Situational trust on the social layer is an independent dimension of trust.
H3: Situational trust on the data layer is an independent dimension of trust.
H4: Situational trust on the technical layer is an independent dimension of trust.
H5: Learned trust on the social layer is an independent dimension of trust.
H6: Learned trust on the data layer is an independent dimension of trust.
H7: Learned trust on the technical layer is an independent dimension of trust.
3.2. Model operationalisation
To test hypotheses, the model first needs to be conceptualised. For this, the following
constructs are necessary: Dispositional Trust, situational trust on the three layers, learned
trust on the three layers. To measure dispositional trust we use the five item scale by
MacCarthy (1983). For measuring situational trust, the six item scale of Holthausen et al.
(2020) was adapted and applied to each of the three distributed ledger trust layers
characteristics according to Lemieux et al. (2019). Since the original scale referred partly to
decision-making by automated systems, we removed two unsuitable items, resulting in a four-
item scale.
For the measurement of learned trust, there is no scale in current literature that could be
adopted. However, the literature shows that learned trust is based on previous experiences,
similar to the trustor’s propensity (Hoff and Bashir 2014). If no direct experience has been
made, there is a recourse to similar situations (Gonzalez, Lerch, and Lebiere 2003). However,
the propensity refers to general tendencies, the learned trust is based on learned experiences
regarding an specific context (Mayer and Davis 1999). Therefore, we have adopted the
propensity scale from general Mayer and Davis (1999) in a revised form. The items no longer
refer to trust tendencies of general, but refer to previous experienced situations with
automation technologies. In addition, we have assigned the items of the scale to the matching
layers of trust towards distributed ledger technologies to differentiate between them.
4. Research Method
4.1. Questionnaire and sample
The questionnaire (found in the appendix) incorporated several measures to ensure high data
quality. Initially, an attention test was administered to participants. Following that, the survey
included questions on dispositional trust, which assessed general trust tendencies according
to MacCarthy (1983). The subsequent section focused on the specific context of utilizing smart
contracts in supply chains. Within this section, participants were queried about situational
trust and adopted the scale from Holthausen et al. (2020) to the tree layer of trust towards
distributed ledgers: social trust, data trust, and technical trust Lemieux et al. (2019).
Moreover, learned trust was examined by an adoption from Mayer and Davis (1999) trustor’s
propensity items and also related to the tree layer of trust towards distributed ledgers.
The questionnaire was administered to participants via the online platform Clickworker to
supply chain employees in the United States of America. The US is a suitable location for a
survey on technologies in supply chain management due to the presence of many companies
with key supplier relationships and the diversity of industries and supply chains in the
country.
The questionnaire was answered completely by 193 persons. The minimum sample size
according to (Tinsley and Kass 1979) of 5-10 cases per item is thus achieved, as is the total
minimum of 100 cases for factor analysis according to Gorsuch (1990). 34.2% were women,
65.2% were men, and 0.6% were diverse. On average, participants were 36.6 years old.
4.2. Statistical analysis
By conducting a detailed assessment of the measurement model as a first part of our analysis,
we can ensure that the requirements for a confirmatory factor analysis are met and that the
results of the analysis are reliable and interpretable. We followed a five-step process for
validating a formative measurement construct in structural equation models according to
Cenfetelli and Bassellier (2009). In our study, however, we have only conducted the first four
steps, as the fifth step concerns the creation of a structural equation model, which is not the
focus of this study. In the first step, we looked at a possible multicollinearity among the items
using the variance inflation factor (VIF), aiming for values below 5 (Henseler, Ringle, and
Sinkovics 2009). Our analysis showed that none of the VIF value exceeded the limit. We also
tested for a bivariate correlation between indicators and construct. Since none of the
indicators exceeded the value of 0.9, it was not necessary to make adjustments here (Cenfetelli
and Bassellier 2009).
The second step was to reduce the number of indicators if their weights were found not to
be significant. Our results showed that all items have significant bivariate correlations with
the parent construct. In the third step, we examined the presence of suppression effects. These
occur when an indicator shares more variance with another indicator than with the
formatively measured construct. We found that suppression effects were present for the items
DT4, SS2, SD2 and ST2. These four items were eliminated. The fourth step entailed assessing
the importance of individual questions for the construct to ensure that their impact was not
negligible. Since all bivariate correlations between the questions and the construct were high
and significant, all items could be retained for further analysis.
As a second part of the analysis, we conducted a Confirmatory factor analysis (CFA). This is
used when the researcher has a pre-specified hypothesis about the underlying factor structure
of the data. CFA is used to test whether the data fit the hypothesized factor structure (Graf,
Nagler, and Jacobs 2005). Confirmatory factor analysis (CFA) is a statistical technique
employed to assess the fit between observed data and a theoretical model. When conducting
a CFA, there are several methods available, including maximum likelihood (ML), principal
component analysis (PCA), and principal factor analysis (PFA). Among these methods,
maximum likelihood is the most widely utilized for CFA due to its flexibility and robustness in
handling various data types and model specifications. Conversely, PCA and PFA are
exploratory factor analysis techniques primarily employed for uncovering the underlying
structure of a dataset and are generally not employed for CFA purposes (Wood 2008). In
confirmatory factor analysis (CFA), orthogonal rotation methods such as Varimax are usually
used to simplify the factor structure and make it easier to interpret (Flora and Curran 2004).
The evaluation of the factor analysis reveals a very good adequacy of the sample for
conducting the factor analysis, as indicated by a Kaiser-Meyer-Olkin measure of sample
adequacy of 0.869 (Kaiser 1974). Furthermore, the significance value of the Bartlett's test of
sphericity, which is < 0.001, indicates that the sample data are suitable for factor analysis
(Bartlett 1954). The rotated sum of squared loadings shows that factor 1 accounts for the
highest share of the total variance with 20.39%. The other factors shares are distributed
between 3.33% and 8.38%.
5. Results
5.1. Descriptive results
Table 1 below shows the descriptive results of the items examined in the factor analysis.
Table 1
Descriptive results
Item Minimum Maximum Mean Standard deviation Variance
DT1 1 7 3,66 1,52 2,30
DT2 1 7 4,04 1,41 1,98
DT3 1 7 4,24 1,35 1,83
DT5 1 7 3,90 1,45 2,11
SS1 1 7 4,32 1,47 2,17
SS3 1 7 4,10 1,49 2,23
SS4 1 7 4,17 1,42 2,03
SD1 1 7 4,62 1,47 2,15
SD3 1 7 4,56 1,61 2,58
SD4 1 7 4,64 1,35 1,82
ST1 1 7 4,74 1,31 1,71
ST3 1 7 4,40 1,55 2,39
ST4 1 7 4,91 1,31 1,72
LS1 1 7 4,10 1,60 2,55
LS2 1 7 4,69 1,29 1,67
LD1 1 7 4,53 1,41 2,00
LD2 1 7 4,58 1,42 2,03
LT1 2 7 5,24 1,40 1,97
LT2 1 7 4,90 1,21 1,45
DT 1 7 3,96 1,00 1,00
SS 1 7 4,20 1,19 1,43
SD 1 7 4,60 1,27 1,61
ST 1 7 4,68 1,19 1,41
LS 2 7 4,40 0,88 0,78
LD 1 7 4,55 1,19 1,42
LT 2 7 5,07 0,89 0,78
5.2. Hypotheses results
The following table 2 shows the rotated factor loadings, which indicate the strength and
direction of the correlation between the variables and the factors. All values show a positive
direction of the correlation. We followed the Budaev (2010) procedure for rather small
sample sizes and set the minimum for interpretable loadings to higher than 0.4.
Table 2
Rotated factor matrix: Confirmatory factor analysis using maximum likelihood (loadings below
0.4 hidden if higher available).
1 2 3 4 5 6 7
DT1 .740
DT2 .693
DT3 .342 .357
DT5 .385
SS1 .432
SS3 .439 .471
SS4 .772
SD1 .770
SD3 .699
SD4 .772
ST1 .697
ST3 .689 .688
ST4 .672
LS1 .929
LS2 .442
LD1 .418
LD2 .881
LT1 .381
LT2 .564
DT3 was excluded due to its cross-loadings on factors 2 and 4, according to the criteria of
(Hair, 2009). Although LT1 and DT5 fell just below the threshold of 0.4, we decided to retain
them. This decision was based on our assessment that the content validity of the scale was not
violated (Fornell and Larcker 1981) and on the consistently positive results of all previous
statistical tests. ST3 also refers to cross-loadings, but since factor 7 would represent a single
dimension with a single factor, the item was retained and assigned to factor 1. Furthermore,
we also refer here to the content validity of the scale according to Fornell and Larcker (1981)
H1 stated, that dispositional trust is an independent dimension of trust. This hypothesis
could be confirmed, whereby only the items DT1 and DT2 can be assigned to this dimension.
H2 stated, that situational trust on the social layer is an independent dimension of trust. This
hypothesis could also be confirmed. All three items could be assigned to the dimension. H3
and H4 stated, that situational trust on the data layer and situational trust on the technical
layer are independent dimensions of trust. This hypothesis could not be confirmed. Contrary
to the assumption, however, the items of the two layers form a mutual dimension. H5 stated,
that learned trust on the social layer is an independent dimension of trust. This hypothesis
could not be confirmed either. However, it could be found that learned trust on social layer
together with learned trust on the technical layer represent a common dimension. H7
(Learned trust on the technical layer is an independent dimension of trust.) could therefore
not be confirmed either. H6 stated, that learned trust on the data layer is an independent
dimension of trust. This hypothesis could be confirmed, with both of the selected items
together forming the fifth trust dimension.
As can be seen in table 3, the results reveal the following dimensions with associated items
regarding trust towards smart contracts:
Table 3
Dimensions of trust towards smart contracts
Dimension Items
Dispositional trust DT1, DT2, DT5
Situation social trust SS1, SS2, SS4
Situational data and situational technical trust SD1, SD3, SD4, ST1, ST3, ST4
Learned social and learned technical trust LS1, LS2, LT1, LT2
Learned data trust LD1, LD2
The new dimensions result from the assignment of the items to the factors. If the items
correlated to different factors, the factor with the highest loading was selected. Since LT1 did
not have a value above the 0.4 limit, we checked for the next highest, which is 0.381. Since the
item proves to be conceptually important that it represents only one of the two items of
learned trust and it is only slightly below the limit, it may be justified to keep it (Mayer 2018).
Since the LD and LT load on a mutual factor with one item each, we have combined them into
one common dimension. This is possible due to the similarity in content of the items and
scales (Hair 2009), which all refer to learned trust, and thus facilitates interpretation of the
dimensions. The factor analysis results to an updated model of trust dimensions, seen in
Figure 2:
Figure 2: Dimensions of trust towards smart contracts.
6. Discussion
The findings of this study provide valuable insights into the dimensions of trust towards
distributed ledger-based smart contracts within supply chain management. The theoretical
foundation of the multidimensional model was based on previous literature from Lemieux et al.
(2019) and Hoff and Bashir (2014). While previous models focused on one-dimensional trust
constructs (Yamagishi and Yamagishi 1994; Mayer, Davis, and Schoorman 1995; Rousseau et al.
1998; Pavlou 2003; McKnight et al. 2011), this study aimed to develop a more comprehensive
model that considers the specific characteristics of smart contracts and distributed ledger
technology. The results of the confirmatory factor analysis (CFA) support the existence of
multiple dimensions of trust towards smart contracts. The analysis revealed the presence of five
distinct dimensions: dispositional trust, situational social trust, situational data and technical
trust, learned social and technical trust, and learned data trust. These dimensions capture
different aspects of trust in the context of smart contracts, demonstrating the complexity and
multidimensionality of trust in this technology-enabled setting.
One interesting finding from the factor analysis is the mixing of theoretical dimensions in
practice. Specifically, the original hypothesis that situational trust on the data layer and
situational trust on the technical layer would be independent dimensions of trust was not
supported. Instead, the items from these two layers formed a mutual dimension. Similarly, the
hypothesis that learned trust on the social layer and learned trust on the technical layer would
be independent dimensions was also not supported. These two dimensions have been merged
into a single dimension. The three trust layers of Hoff and Bashir (2014) could be confirmed,
while the three layers of Lemieux et al. (2019) were recognized in mixed forms.
The mixing of theoretical dimensions in practice can be attributed to the interconnectedness
and interdependence of trust factors in smart contract-enabled supply chains. While theoretical
models often attempt to separate trust into distinct dimensions, the practical reality is that trust
is a complex construct influenced by various factors that interact and influence each other. In the
context of smart contracts, the distributed ledger technology and the social interactions among
actors create a dynamic environment where trust dimensions overlap and intertwine.
Considering the merging of theoretical dimensions in the empirical analysis, it is essential to
propose appropriate names for the new mixed dimensions based on their underlying
characteristics.
The dimension combining situational trust on the data layer and situational trust on the
technical layer reflects the intertwined trust towards the information provided by the distributed
ledger system and the characteristics of the distributed ledger platform. This dimension can be
labelled as "Information-Technology Trust" to capture the intertwined nature of trust towards
the technical aspects of the system and the reliability and accuracy of the data. The dimension
merging learned trust on the social layer and learned trust on the technical layer indicates the
importance of past experiences and interactions in shaping trust perceptions. This dimension can
be named "Interaction-Informed Trust" to highlight the role of previous interactions and learning
in establishing trust towards both the social dynamics and the technical aspects of smart contract-
enabled supply chains.
Understanding the influence of different dimensions on the variance provides insights into the
relative importance of each dimension in explaining trust towards smart contracts. By examining
the rotated sum of squared loadings, we can identify the dimensions that contribute the most to
the model.
Based on the results, it can be observed that the "Information-Technology Trust" dimension
(with situational trust on the data layer and situational trust on the technical layer) accounts for
the highest share of the total variance, contributing 20.39%. This suggests that trust in the
information provided by the distributed ledger system and the reliability of the blockchain
platform plays a significant role in shaping overall trust perceptions in smart contracts. The
combined "Interaction-Informed Trust" dimension (merging learned trust on the social layer and
learned trust on the technical layer) also contributes a substantial amount to the variance,
accounting for 11.55%. This indicates that past experiences and interactions, both social and
technical, have a significant impact on establishing trust towards smart contracts. The remaining
dimensions, contribute to the variance to a lesser extent. However, they still play important roles
in shaping trust perceptions in specific contexts within smart contract-enabled supply chains
with between 6.1% and 8.4% variance.
The transferability of the results of this study regarding trust dimensions to other industries
beyond supply chain management is an important consideration. While the specific context of
supply chains was examined in this research, the identified trust dimensions and their underlying
concepts have the potential to be applicable to various industries.
The multidimensional model of trust developed in this study provides a framework that can
be adapted and applied to other sectors. The dimensions of trust, such as dispositional trust,
situational trust, and learned trust, capture fundamental aspects of trust that are relevant across
different industries. The models' flexibility allows for customization and adaptation to specific
industry contexts, making it transferable to diverse sectors. Also, the technological advancements
and the concept of smart contracts explored in this study are not exclusive to supply chain
management. Many industries are adopting distributed ledger technology and exploring the
potential of smart contracts to streamline processes, enhance transparency, and reduce
transactional risks. The dimensions of trust identified in this study, which encompass both
technological and social aspects, can be relevant for understanding and managing trust in these
industries as well.
For example, in the financial sector, where blockchain technology is being explored for
applications such as digital currencies and smart contracts, the dimensions of trust identified in
this study can help financial institutions build trust with customers and counterparties. By
addressing the different aspects of trust, financial institutions can enhance security, reliability,
and transparency, which are critical for building trust in financial transactions. Similarly, in
healthcare, the use of smart contracts and distributed ledger technology has the potential to
transform patient care, medical record management, and supply chain processes. Applying the
trust dimensions identified in this study can help healthcare organizations establish trust in the
security, accuracy, and privacy of medical data and transactions, leading to improved patient
outcomes and efficiency.
7. Conclusion
7.1. Theoretical contribution
This study contributes to the theoretical understanding of trust towards distributed ledger-
based smart contracts within supply chain management in several ways. Firstly, it goes beyond
existing one-dimensional models by developing a multidimensional model of trust that
considers the unique characteristics of smart contracts and distributed ledger technology.
This multidimensional model captures the complexity and multidimensionality of trust in the
context of smart contracts, highlighting the interplay between social and technical factors.
Secondly, the findings of the confirmatory factor analysis support the existence of five
distinct dimensions of trust: dispositional trust, situational social trust, information-
technology trust, experience-based trust, and learned data trust. This expands the
understanding of trust dimensions in smart contracts and underscores the intertwined
relationships among trust factors. The model and the identified dimensions contribute to the
advancement of smart contract-enabled supply chains.
Furthermore, the observation of mixed theoretical dimensions in the empirical analysis
suggests that trust towards smart contracts cannot be easily separated into discrete
dimensions. The interplay between social dynamics, technical characteristics, and past
experiences plays a crucial role in shaping trust perceptions in smart contract-enabled supply
chains. This highlights the need for a holistic approach that considers not only the trust
construct but also other influencing factors when examining trust towards smart contracts.
7.2. Practical contribution
The practical implications of this study have significant relevance for supply chain
practitioners and decision-makers. The identified trust dimensions and their implications
offer actionable insights that can be applied to enhance supply chain operations and foster
trust towards smart contract-enabled networks.
Firstly, understanding the different dimensions of trust and their relative importance
enables organizations to improve collaboration and mitigate transactional risks in supply
chain networks. By assessing the strengths and weaknesses of each dimension, supply chain
managers can identify areas that require attention and implement strategies to strengthen
trust. For instance, if the dimension of learned trust on the technical layer exhibits a low level
of trust among participants, efforts can be made to enhance technological literacy, provide
training programs, and improve user experience.
Secondly, the findings emphasize the importance of adopting a holistic approach to trust-
building in supply chains. While ensuring the reliability and security of smart contracts is
crucial, attention should also be given to fostering social aspects of trust. Supply chain
managers can cultivate a trust-oriented culture by promoting open communication,
transparency, and collaboration among participants. Establishing strong relationships and
maintaining effective communication channels can significantly contribute to building trust
and facilitating the successful implementation of smart contracts.
Furthermore, the results highlight the influence of both technological and social factors on
trust towards smart contracts. This implies that supply chain managers should consider not
only the technical aspects of implementing smart contracts but also the social dynamics and
human factors involved. Effective change management strategies, comprehensive training
programs, and targeted communication initiatives can help address potential resistance to
adopting smart contracts and build trust among supply chain participants.
7.3. Limitations and future research
This study has certain limitations that should be acknowledged. The sample used in the
empirical analysis was limited to supply chain employees in the United States, which may
restrict the generalizability of the findings to other industries and geographical regions. The
data collected in this study relied on self-report measures, which are subject to potential
biases such as social desirability and respondent interpretation.
Trust is a dynamic construct that can fluctuate over time due to changing circumstances
and experiences. This study captured trust perceptions at a specific point in time, but it is
essential to recognize that trust can evolve and be influenced by ongoing interactions, system
performance, and external events. Future research should consider longitudinal studies to
examine the temporal dynamics of trust towards smart contract-enabled supply chains.
To enhance the generalizability of the findings, future research should expand the study to
include participants from diverse industries beyond supply chain management. Different
industries may have unique characteristics and trust dynamics in the context of smart
contracts. Examining trust dimensions and their relative importance across various sectors
such as finance, healthcare, energy, and manufacturing would provide valuable insights and
enable the development of industry-specific frameworks for trust towards smart contract-
enabled environments.
Future research could explore the design and implementation of interventions aimed at
fostering trust towards smart contract-enabled supply chains. These interventions could
include strategies to improve transparency, establish reputation mechanisms, enhance
contractual governance, and address potential trust breaches. Evaluating the effectiveness of
such interventions in building and maintaining trust would provide practical guidance for
supply chain practitioners and policymakers.
While this study provides valuable insights into the multidimensional nature of trust
towards smart contracts, there is a need for a more holistic model that incorporates not only
the trust construct but also other influencing factors. Future research should explore
additional factors such as risk perception, transparency, reputation, contractual governance
mechanisms, and technological characteristics (e.g., scalability, interoperability) that can
influence trust towards smart contract-enabled supply chains. Developing a more
comprehensive model that considers these factors would provide a deeper understanding of
the complex dynamics of trust in this context.
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Appendix
The questionnaire, collected data, statistical tests and study results can be found online in an
open-access data repository:
https://osf.io/ns2yc/?view_only=f8816609bdcb4efd924ed78e441bc8f7