J.R. Ortt
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37 records found
1
Modular Non-Linear Technology and Innovation Diffusion Model
Trajectory Model and Navigation Framework
The model consists of two tightly coupled components. The trajectory model is descriptive and answers how technology and innovation diffuse. It conceptualizes diffusion as an eight phase modular system consisting of Invention or Discovery, Innovation, Pilot, Adaptation, Acceleration, Stabilization, Decline, and Legacy. Phases are defined by functional purpose and empirically interpretable entry and exit conditions. By treating phases as modular states rather than as a fixed chronological sequence, diffusion histories are reconstructed as transition sequences instead of a single canonical path. This enables systematic representation of skipping, looping, regression, compression, overlap, and parallel progression within a bounded transition space. The navigation framework is explanatory and answers why diffusion unfolds along a specific pathway. It identifies and categorizes internal and external drivers and links them to transition sequences through a structured driver transition matrix. This framework clarifies how interacting technological, resource, market, institutional, organizational, and infrastructural conditions shape feasible pathways and constrain alternative trajectories.
To operationalize the model, a methodological framework was developed that integrates criteria driven phase reconstruction, transition space mapping, driver typology construction, and structured case validation. The architecture was subjected to breadth oriented micro validation across more than forty historical innovation and technology cases and depth validation through two longitudinal illustrative cases, namely mRNA COVID 19 vaccines and passenger airplanes. Across cases, diffusion histories mapped coherently onto the modular phase architecture without imposing artificial linearity. The bounded transition grammar proved sufficiently flexible to represent diverse diffusion patterns while preserving analytical comparability. Driver mappings provided systematic explanatory depth for pathway divergence, acceleration, regression, and stabilization.
The thesis contributes an integrated descriptive and explanatory language for analyzing innovation diffusion, enabling cross case comparison, cumulative theory building, and more structured reasoning for managerial and policy decision making. ...
The model consists of two tightly coupled components. The trajectory model is descriptive and answers how technology and innovation diffuse. It conceptualizes diffusion as an eight phase modular system consisting of Invention or Discovery, Innovation, Pilot, Adaptation, Acceleration, Stabilization, Decline, and Legacy. Phases are defined by functional purpose and empirically interpretable entry and exit conditions. By treating phases as modular states rather than as a fixed chronological sequence, diffusion histories are reconstructed as transition sequences instead of a single canonical path. This enables systematic representation of skipping, looping, regression, compression, overlap, and parallel progression within a bounded transition space. The navigation framework is explanatory and answers why diffusion unfolds along a specific pathway. It identifies and categorizes internal and external drivers and links them to transition sequences through a structured driver transition matrix. This framework clarifies how interacting technological, resource, market, institutional, organizational, and infrastructural conditions shape feasible pathways and constrain alternative trajectories.
To operationalize the model, a methodological framework was developed that integrates criteria driven phase reconstruction, transition space mapping, driver typology construction, and structured case validation. The architecture was subjected to breadth oriented micro validation across more than forty historical innovation and technology cases and depth validation through two longitudinal illustrative cases, namely mRNA COVID 19 vaccines and passenger airplanes. Across cases, diffusion histories mapped coherently onto the modular phase architecture without imposing artificial linearity. The bounded transition grammar proved sufficiently flexible to represent diverse diffusion patterns while preserving analytical comparability. Driver mappings provided systematic explanatory depth for pathway divergence, acceleration, regression, and stabilization.
The thesis contributes an integrated descriptive and explanatory language for analyzing innovation diffusion, enabling cross case comparison, cumulative theory building, and more structured reasoning for managerial and policy decision making.
Introduction Strategies for Vehicle to Grid Technology in the United Kingdom
A comprehensive approach to understand and introduce the technology prior to large-scale diffusion
An exploration of societal impacts of quantum technology
Towards responsible quantum innovation
A framework to identify applications for a technical innovation
Case study: a pressure-activated, colour-changing and flexible material
Towards Inclusivity In Entrepreneurship: Utilizing Highly Skilled Refugees
A Research on Supporting Syrian Refugee Entrepreneurs in Germany and the Netherlands through Collaboration between Business Incubators and Local Governments
To answer this, a multiple case study was conducted on the scale-up support ecosystems of Planet B.io - Biotech Campus Delft, Copenhagen, and Brightlands Chemelot. This case study was performed through desk research and semi-structured expert interviews with 3 different types of experts (ecosystem, technical scale-up and start-up expert) per ecosystem, resulting in 9 interviewees. This case study applies the Technological Innovation System (TIS) framework to a novel context and integrates it with the four identified scale-up support elements (technical facilities \& services, funding \& business services, network formation \& coordination, and knowledge \& talent) offering a framework to study scale-up support ecosystems. This study identified the scale-up support requirements for industrial biotechnology. These scale-up support requirements are, among others, a flexible and fully-serviced shared piloting facility up until TRL 6 ($\approx$ 2000 L bioreactor), a lab- to pilot- and industrial-scale technical support service, investment planning service and help with raising funding. These should be offered within a scale-up support ecosystem using milestone-based billing as a preferred revenue model, whereas a government voucher system should be set up to pay for the lab- to pilot- and industrial-scale technical support service. Also, the most important stakeholders for a scale-up support ecosystem were identified, including multiple large corporations, government institutions, universities (and other types of education), suppliers, and service providers. Based on the findings, a roadmap for the development of the Planet B.io - Biotech Campus Delft scale-up support ecosystem was proposed, focusing on strengthening the network, knowledge, talent, and funding before offering a piloting facility and business services. This study contributes to the field with a framework to study scale-up support ecosystems as well as with practical recommendations for scale-up support ecosystems in industrial biotechnology and similar industries, identifying the scale-up support requirements, its business models and required stakeholders. ...
To answer this, a multiple case study was conducted on the scale-up support ecosystems of Planet B.io - Biotech Campus Delft, Copenhagen, and Brightlands Chemelot. This case study was performed through desk research and semi-structured expert interviews with 3 different types of experts (ecosystem, technical scale-up and start-up expert) per ecosystem, resulting in 9 interviewees. This case study applies the Technological Innovation System (TIS) framework to a novel context and integrates it with the four identified scale-up support elements (technical facilities \& services, funding \& business services, network formation \& coordination, and knowledge \& talent) offering a framework to study scale-up support ecosystems. This study identified the scale-up support requirements for industrial biotechnology. These scale-up support requirements are, among others, a flexible and fully-serviced shared piloting facility up until TRL 6 ($\approx$ 2000 L bioreactor), a lab- to pilot- and industrial-scale technical support service, investment planning service and help with raising funding. These should be offered within a scale-up support ecosystem using milestone-based billing as a preferred revenue model, whereas a government voucher system should be set up to pay for the lab- to pilot- and industrial-scale technical support service. Also, the most important stakeholders for a scale-up support ecosystem were identified, including multiple large corporations, government institutions, universities (and other types of education), suppliers, and service providers. Based on the findings, a roadmap for the development of the Planet B.io - Biotech Campus Delft scale-up support ecosystem was proposed, focusing on strengthening the network, knowledge, talent, and funding before offering a piloting facility and business services. This study contributes to the field with a framework to study scale-up support ecosystems as well as with practical recommendations for scale-up support ecosystems in industrial biotechnology and similar industries, identifying the scale-up support requirements, its business models and required stakeholders.
A ten-step framework for finding applications for a breakthrough technology
Partially applied to the case of quantum dots
The Technological Innovation System (TIS) framework, viewed from a company perspective, serves as the basis for this research. The TIS framework can be defined as "the network of actors interacting within the industry, subject to certain institutions and involved in the generation, diffusion, and utilisation of a novel innovation." Through the examination of seven essential dimensions, referred to as TIS building blocks, the most significant bottlenecks impeding the implementation of hydrogen and ultra-efficient aircraft have been identified.
This knowledge has been utilised to create a three-phased deployment strategy for both sustainable aircraft technologies, considering timing and scale. The research scope has focused on the regional market segment, using a 70-90 passenger Embraer 175 aircraft design as reference aircraft. Key insights from the research have led to proposed recommendations to industry stakeholders, policy-makers and academics. ...
The Technological Innovation System (TIS) framework, viewed from a company perspective, serves as the basis for this research. The TIS framework can be defined as "the network of actors interacting within the industry, subject to certain institutions and involved in the generation, diffusion, and utilisation of a novel innovation." Through the examination of seven essential dimensions, referred to as TIS building blocks, the most significant bottlenecks impeding the implementation of hydrogen and ultra-efficient aircraft have been identified.
This knowledge has been utilised to create a three-phased deployment strategy for both sustainable aircraft technologies, considering timing and scale. The research scope has focused on the regional market segment, using a 70-90 passenger Embraer 175 aircraft design as reference aircraft. Key insights from the research have led to proposed recommendations to industry stakeholders, policy-makers and academics.
Barriers and Strategies Analysis on Mass Adoption of Solar Electric Vehicle in Indonesia
A Technological Innovation System Study
“From the technical innovation system perspective, how could solar electric vehicles reach mass adoption in Indonesia?”
This research is conducted in collaboration with Lightyear – a SEV producer. As Lightyear is aiming to mass produce its SEV, therefore, this research is conducted to explore and evaluate the Indonesian market, so that the best strategy recommendation could be provided to Lightyear so it could commercialize its SEV in a large scale in Indonesia. The qualitative research approach is implemented in this research by reviewing diverse online literature, including both scientific and grey literature.
The TIS Framework (Ortt & Kamp, 2022) and the Ten Niche Strategies framework (Ortt et al., 2013) are used as the starting point of the research. TIS is defined as innovation systems around a specific technology that consists of four main elements: the technology, a network of actors, the institutions, and the demand. Ortt & Kamp’s TIS framework is a tool to examine the TIS of an innovation that is in its adaptation phase to judge whether the innovation is ready for its mass uptake or whether a small-scale niche introduction strategy is needed prior to the large-scale diffusion. The framework consists of three major elements: TIS building blocks (most important aspects needed for large-scale diffusion), influencing factors, and strategies. When certain influencing factors negatively affect the completeness of the TIS building blocks, they pose as barriers to the mass adoption of the innovation. Certain strategies could be implemented to circumvent these barriers, such as the generic Ten Niche Strategies proposed by Ortt et al.
The research is conducted in four major steps. First, basic information about SEV technology is explored. Second, a new framework called “the Best Strategy Framework” is developed to extend and complete the original TIS Framework and Ten Niche Strategies Framework so that the users/readers could select the best strategy based on the combination of barriers that hinder mass adoption of the innovations. Third, the newly developed framework is used to evaluate the Indonesian market and to identify the barriers that might hamper Lightyear’s SEV mass adoption in Indonesia. Finally, by using the newly developed framework, the best strategy that could circumvent the identified barriers is selected and proposed to Lightyear. ...
“From the technical innovation system perspective, how could solar electric vehicles reach mass adoption in Indonesia?”
This research is conducted in collaboration with Lightyear – a SEV producer. As Lightyear is aiming to mass produce its SEV, therefore, this research is conducted to explore and evaluate the Indonesian market, so that the best strategy recommendation could be provided to Lightyear so it could commercialize its SEV in a large scale in Indonesia. The qualitative research approach is implemented in this research by reviewing diverse online literature, including both scientific and grey literature.
The TIS Framework (Ortt & Kamp, 2022) and the Ten Niche Strategies framework (Ortt et al., 2013) are used as the starting point of the research. TIS is defined as innovation systems around a specific technology that consists of four main elements: the technology, a network of actors, the institutions, and the demand. Ortt & Kamp’s TIS framework is a tool to examine the TIS of an innovation that is in its adaptation phase to judge whether the innovation is ready for its mass uptake or whether a small-scale niche introduction strategy is needed prior to the large-scale diffusion. The framework consists of three major elements: TIS building blocks (most important aspects needed for large-scale diffusion), influencing factors, and strategies. When certain influencing factors negatively affect the completeness of the TIS building blocks, they pose as barriers to the mass adoption of the innovation. Certain strategies could be implemented to circumvent these barriers, such as the generic Ten Niche Strategies proposed by Ortt et al.
The research is conducted in four major steps. First, basic information about SEV technology is explored. Second, a new framework called “the Best Strategy Framework” is developed to extend and complete the original TIS Framework and Ten Niche Strategies Framework so that the users/readers could select the best strategy based on the combination of barriers that hinder mass adoption of the innovations. Third, the newly developed framework is used to evaluate the Indonesian market and to identify the barriers that might hamper Lightyear’s SEV mass adoption in Indonesia. Finally, by using the newly developed framework, the best strategy that could circumvent the identified barriers is selected and proposed to Lightyear.
Becoming a quantum safe organization: Why it is important and how to get there
Exploring the need and readiness for quantum safe communication, and the strategies that can be formulated as a result
This thesis proposes a model to illustrate the factors that influence the decision to enter the market. Aspects that influence the formulation of a market entry strategy are included in the model. For the sake of simplicity, only the strategy to enter a market has been investigated. Next to the model, we created a flowchart that describes the steps required to develop a market entry strategy. Eventually the goal is to make a well-thought decision. The dialogue of the deaf on first, second or late being most favourable has been the cause of this thesis but the conclusion is none of those. The most important part of first-mover advantages and disadvantages is the effect they bring along with the entry into a market. This thesis claims that first-mover advantages are advantages and disadvantages that may be evaluated over the development of a product category, rather than belonging to any particular order. The weights of (dis)advantages differ over the course of the development so at different points in time, different advantages can be utilised. The first-mover (dis)advantages present aspects that can be utilised at a particular situation. The decision maker should foresee whether, given the current (dis)advantages, after its introduction the situation is most favourable or if the situation is most favourable when you wait, and competitors have entered the market. The combination of the pattern of development and first-mover advantages show this principle and show why the dialogue of the deaf on market order is not useful. The decision to enter a market has never been simple, and it likely never will be. This thesis offers direction for the decision-making procedure and explains how to comprehend it. The complexities of market entry decision making are not yet resolved, but a well-thought decision is getting closer. ...
This thesis proposes a model to illustrate the factors that influence the decision to enter the market. Aspects that influence the formulation of a market entry strategy are included in the model. For the sake of simplicity, only the strategy to enter a market has been investigated. Next to the model, we created a flowchart that describes the steps required to develop a market entry strategy. Eventually the goal is to make a well-thought decision. The dialogue of the deaf on first, second or late being most favourable has been the cause of this thesis but the conclusion is none of those. The most important part of first-mover advantages and disadvantages is the effect they bring along with the entry into a market. This thesis claims that first-mover advantages are advantages and disadvantages that may be evaluated over the development of a product category, rather than belonging to any particular order. The weights of (dis)advantages differ over the course of the development so at different points in time, different advantages can be utilised. The first-mover (dis)advantages present aspects that can be utilised at a particular situation. The decision maker should foresee whether, given the current (dis)advantages, after its introduction the situation is most favourable or if the situation is most favourable when you wait, and competitors have entered the market. The combination of the pattern of development and first-mover advantages show this principle and show why the dialogue of the deaf on market order is not useful. The decision to enter a market has never been simple, and it likely never will be. This thesis offers direction for the decision-making procedure and explains how to comprehend it. The complexities of market entry decision making are not yet resolved, but a well-thought decision is getting closer.
Influences on Technology Entrepreneurship
A Comparative Analysis Between The Netherlands and Japan
This thesis shows that TE and ME are different, should be treated as such, and has identified several influencing variables that affect TE differently than ME. Thus, TE and ME can be independently stimulated by increasing the levels of the influencing variables. For governments seeking to increase their levels of TE through relevant policies, despite a traditionally non-entrepreneurial environment, it means that all is not lost, and that the levels of TE can be raised by focusing on increasing levels of education, economic environment, and technological environment. Managerial recommendations include the geographical placement of technology start-ups and the diversification of the team to improve success. ...
This thesis shows that TE and ME are different, should be treated as such, and has identified several influencing variables that affect TE differently than ME. Thus, TE and ME can be independently stimulated by increasing the levels of the influencing variables. For governments seeking to increase their levels of TE through relevant policies, despite a traditionally non-entrepreneurial environment, it means that all is not lost, and that the levels of TE can be raised by focusing on increasing levels of education, economic environment, and technological environment. Managerial recommendations include the geographical placement of technology start-ups and the diversification of the team to improve success.
Knowing the time point of large-scale diffusion of a radically new high-tech innovation is a highly relevant topic. Companies, researchers, and government institutions can plan their research and development efforts, production, as well as marketing plans according to the predicted time point of large-scale diffusion. The research is based upon the assumption that specific indicators can predict the start of large-scale diffusion. The scientific field of forecasting the start of large-scale diffusion is relatively new. Therefore, an explorative methodology was required for this research. During the explorative process, it was ensured that indicators reflect on the holistic environment of an innovation by minding the so-called data collection cube. A data selection funnel was created, narrowing scientific branches down to a list of indicators in three steps. Each of these steps has its own criteria designed to: • Select scientific branches with the highest potential to find results in the literature reviews • Derive indicators that can observe the diffusion • Select indicators that can predict the start of large-scale diffusion The last step of the data selection funnel, selecting the indicators which can actually predict, was carried out with the support of three researchers. Eight criteria were used to select the most potential indicators: (i) Prediction, (ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data, (v) Quantifiable& Objectivity, (vi) Empirical proof, (vii) Generalizability, (viii)Simplicity. The researchers were asked to evaluate the indicators according to the criteria (i) and (vi) as part of the scientific quality gate selecting the most potential indicators. After the indicators have been evaluated, a sensitivity analysis has been performed to improve the robustness of the selection mechanisms and to rule out an arbitrary selection of the indicators. Out of 50 indicators found in the literature or derived from the literature, 38 indicators were selected according to the selection mechanism. These 38 indicators have been split into two sets of judgemental and non-judgemental indicators to prepare the design of the forecasting approach. The forecasting approach aims to guide a user towards the correct forecasting technique given an innovation and situation. Five forecasting techniques were found to befitting the: (i) assumptions-based modelling, (ii) Delphi method, (iii)analogous forecasting, (iv) time series & regression models, and (v)artificial neural networks. However, each of the five forecasting techniques have disadvantages that can be overcome by one of the other methods. Hence, the forecasting approach has two stages. First, the user is guided towards the primary method and subsequently towards an additional method overcoming the disadvantages of the first method and improving the overall reliability of the forecast. For each method, a set of indicators is recommended. Once the forecasting approach has been developed, the completeness of indicators has been checked by using Ortt & Kamp’s 14 factors influencing the pre-diffusion phase. Additionally, four validation interviews applying the research on green hydrogen have been performed to let external actors reflect. These validation interviews formed the practical quality gate forging a bridge to the earlier mentioned scientific quality gate.
Knowing
the time point of large-scale diffusion of a radically new high-tech innovation
is a highly relevant topic. Companies, researchers, and government institutions
can plan their research and development efforts, production, as well as
marketing plans according to the predicted time point of large-scale diffusion.
The research is based upon the assumption that specific indicators can predict
the start of large-scale diffusion. The scientific field of forecasting the
start of large-scale diffusion is relatively new. Therefore, an explorative
methodology was required for this research. During the explorative process, it
was ensured that indicators reflect on the holistic environment of an
innovation by minding the so-called data collection cube.
A data selection funnel was created, narrowing scientific branches down to a
list of indicators in three steps. Each of these steps has its own criteria
designed to:
• Select scientific branches with the highest potential to find results in the
literature reviews
• Derive indicators that can observe the diffusion
• Select indicators that can predict the start of large-scale diffusion The
last step of the data selection funnel, selecting the indicators which can
actually predict, was carried out with the support of three researchers. Eight
criteria were used to select the most potential indicators: (i) Prediction,
(ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data,
(v) Quantifiable& Objectivity, (vi) Empirical proof, (vii)
Generalizability, (viii)Simplicity. The researchers were asked to evaluate the
indicators according to the criteria (i) and (vi) as part of the scientific
quality gate selecting the most potential indicators. After the indicators have
been evaluated, a sensitivity analysis has been performed to improve the
robustness of the selection mechanisms and to rule out an arbitrary selection
of the indicators. Out of 50 indicators found in the literature or derived from
the literature, 38 indicators were selected according to the selection
mechanism. These 38 indicators have been split into two sets of judgemental and
non-judgemental indicators to prepare the design of the forecasting approach.
The forecasting approach aims to guide a user towards the correct forecasting
technique given an innovation and situation. Five forecasting techniques were
found to befitting the: (i) assumptions-based modelling, (ii) Delphi method,
(iii)analogous forecasting, (iv) time series & regression models, and
(v)artificial neural networks. However, each of the five forecasting techniques
have disadvantages that can be overcome by one of the other methods. Hence, the
forecasting approach has two stages. First, the user is guided towards the
primary method and subsequently towards an additional method overcoming the
disadvantages of the first method and improving the overall reliability of the
forecast. For each method, a set of indicators is recommended. Once the
forecasting approach has been developed, the completeness of indicators has
been checked by using Ortt & Kamp’s 14 factors influencing the
pre-diffusion phase. Additionally, four validation interviews applying the
research on green hydrogen have been performed to let external actors reflect.
These validation interviews formed the practical quality gate forging a bridge
to the earlier mentioned scientific quality gate.
...
Knowing the time point of large-scale diffusion of a radically new high-tech innovation is a highly relevant topic. Companies, researchers, and government institutions can plan their research and development efforts, production, as well as marketing plans according to the predicted time point of large-scale diffusion. The research is based upon the assumption that specific indicators can predict the start of large-scale diffusion. The scientific field of forecasting the start of large-scale diffusion is relatively new. Therefore, an explorative methodology was required for this research. During the explorative process, it was ensured that indicators reflect on the holistic environment of an innovation by minding the so-called data collection cube. A data selection funnel was created, narrowing scientific branches down to a list of indicators in three steps. Each of these steps has its own criteria designed to: • Select scientific branches with the highest potential to find results in the literature reviews • Derive indicators that can observe the diffusion • Select indicators that can predict the start of large-scale diffusion The last step of the data selection funnel, selecting the indicators which can actually predict, was carried out with the support of three researchers. Eight criteria were used to select the most potential indicators: (i) Prediction, (ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data, (v) Quantifiable& Objectivity, (vi) Empirical proof, (vii) Generalizability, (viii)Simplicity. The researchers were asked to evaluate the indicators according to the criteria (i) and (vi) as part of the scientific quality gate selecting the most potential indicators. After the indicators have been evaluated, a sensitivity analysis has been performed to improve the robustness of the selection mechanisms and to rule out an arbitrary selection of the indicators. Out of 50 indicators found in the literature or derived from the literature, 38 indicators were selected according to the selection mechanism. These 38 indicators have been split into two sets of judgemental and non-judgemental indicators to prepare the design of the forecasting approach. The forecasting approach aims to guide a user towards the correct forecasting technique given an innovation and situation. Five forecasting techniques were found to befitting the: (i) assumptions-based modelling, (ii) Delphi method, (iii)analogous forecasting, (iv) time series & regression models, and (v)artificial neural networks. However, each of the five forecasting techniques have disadvantages that can be overcome by one of the other methods. Hence, the forecasting approach has two stages. First, the user is guided towards the primary method and subsequently towards an additional method overcoming the disadvantages of the first method and improving the overall reliability of the forecast. For each method, a set of indicators is recommended. Once the forecasting approach has been developed, the completeness of indicators has been checked by using Ortt & Kamp’s 14 factors influencing the pre-diffusion phase. Additionally, four validation interviews applying the research on green hydrogen have been performed to let external actors reflect. These validation interviews formed the practical quality gate forging a bridge to the earlier mentioned scientific quality gate.
Knowing
the time point of large-scale diffusion of a radically new high-tech innovation
is a highly relevant topic. Companies, researchers, and government institutions
can plan their research and development efforts, production, as well as
marketing plans according to the predicted time point of large-scale diffusion.
The research is based upon the assumption that specific indicators can predict
the start of large-scale diffusion. The scientific field of forecasting the
start of large-scale diffusion is relatively new. Therefore, an explorative
methodology was required for this research. During the explorative process, it
was ensured that indicators reflect on the holistic environment of an
innovation by minding the so-called data collection cube.
A data selection funnel was created, narrowing scientific branches down to a
list of indicators in three steps. Each of these steps has its own criteria
designed to:
• Select scientific branches with the highest potential to find results in the
literature reviews
• Derive indicators that can observe the diffusion
• Select indicators that can predict the start of large-scale diffusion The
last step of the data selection funnel, selecting the indicators which can
actually predict, was carried out with the support of three researchers. Eight
criteria were used to select the most potential indicators: (i) Prediction,
(ii) Timeliness of prediction, (iii) Availability of data, (iv) Cost of data,
(v) Quantifiable& Objectivity, (vi) Empirical proof, (vii)
Generalizability, (viii)Simplicity. The researchers were asked to evaluate the
indicators according to the criteria (i) and (vi) as part of the scientific
quality gate selecting the most potential indicators. After the indicators have
been evaluated, a sensitivity analysis has been performed to improve the
robustness of the selection mechanisms and to rule out an arbitrary selection
of the indicators. Out of 50 indicators found in the literature or derived from
the literature, 38 indicators were selected according to the selection
mechanism. These 38 indicators have been split into two sets of judgemental and
non-judgemental indicators to prepare the design of the forecasting approach.
The forecasting approach aims to guide a user towards the correct forecasting
technique given an innovation and situation. Five forecasting techniques were
found to befitting the: (i) assumptions-based modelling, (ii) Delphi method,
(iii)analogous forecasting, (iv) time series & regression models, and
(v)artificial neural networks. However, each of the five forecasting techniques
have disadvantages that can be overcome by one of the other methods. Hence, the
forecasting approach has two stages. First, the user is guided towards the
primary method and subsequently towards an additional method overcoming the
disadvantages of the first method and improving the overall reliability of the
forecast. For each method, a set of indicators is recommended. Once the
forecasting approach has been developed, the completeness of indicators has
been checked by using Ortt & Kamp’s 14 factors influencing the
pre-diffusion phase. Additionally, four validation interviews applying the
research on green hydrogen have been performed to let external actors reflect.
These validation interviews formed the practical quality gate forging a bridge
to the earlier mentioned scientific quality gate.
A support system for strategic decision making in a Sea Rescue Institute
By applying a Multi-criteria Decision Making model and theory
Effects of a Digital Platform Within Container Shipping
Scenarios for the Reconfiguration of the Container Shipping Ecosystem
The introduction of digital platforms has greatly affected different industries, for example enabling direct booking within the air travel business. Replacing paper documentation with an electronic equivalent can enable an increase in efficiency, through reduced administration costs and improved planning and operational capabilities. Efforts in introducing a digital platform within the shipping industry have been taking place using different governmental research efforts. However, as a possible additional effect, the digital platform may put reconfiguration of the network in motion. This reconfiguration enables certain actors to partake a bigger role, whereas other actors might lose control of the supply chain process.
The TradeLens platform launched in 2018, is such a digital platform. This platform allows sharing both documentation (e.g. the commercial invoice, packing list, bill-of-lading) and supply chain events (e.g. lodging ENS, the actual time of arrival) with the other actors. The platform uses a blockchain infrastructure. This structure is used to increase the trustworthiness of the data. Firstly, the auditability hinders documentation fraud as the actors within the network can trace the exact moment and actor that placed uploaded a document. The immutability of the blockchain infrastructure allows the automation of information processes. When the information is uploaded it cannot be changed. It was developed by a co-operation between a shipping carrier and a technology developer. The platform was tasked with alleviating the pressure on the administrative systems of the different actors within the supply chain. This research investigated possible scenarios due to the introduction of a digital platform using the TradeLens platform as the main research case.
Research Question and Objectives
This research aims to address the different possible future configurations of the network and roles within the container supply chain. To address this, the following main research question was developed: What is the possible supply chain configurations that come with a digital information infrastructure?.
In addressing this research question a number of research steps and clarifications have to be answered. Firstly, the research has to determine what is considered an actor within the supply chain ecosystem and what are the key activities performed in that ecosystem. Secondly, the research aims to perform an analysis of each actor and thus explain the different roles to be able to perform this analysis a theoretical model has to be developed. Thirdly, the research will evaluate the ecosystem using this model. Fourthly, the different scenarios will be developed using the developed model.
Research Method
The research employed three main methods of data collection. Firstly, a literature review is performed to identify the important information and innovation concepts to be used throughout the research. Secondly, through analysing many different public sources, the research gains company insights and information for constructing different roles, activities, and resources. Thirdly, through interviews with experts on the TradeLens platform where careful attention is given to the shifting activities, resources and control within the configuration of the network.
The used approach can be defined in four steps. First, the researchers developed an initial meta-framework using the findings from the literature review. Secondly, the different concepts within the meta-framework were combined into constructing a model for the assessment of the ecosystem. Thirdly, a generic container shipping case is construed from the information gained from the different public and academic sources. Lastly, a comparison is performed between the construed case and a test case of a Dutch tyre importer. The main findings within these steps will be discussed in the following paragraph.
Main Findings
The research identified five key theories to be of importance within the model. These are 1. Ecosystem theory, 2. Stakeholder theory, 3. Diffusion theory, 4. Control Point theory and lastly, 5. Barriers and Stimulating Factors. Firstly, the concept of the business ecosystem. An ecosystem describes how different actors within a business domain influence and interact with the other actors outside and within the direct business network. This research investigates the effect of digital platforms on ecosystem reconfiguration. The chosen system of analysis for this ecosystem reconfiguration is the blockchain-enabled platform, TradeLens. This platform enables information sharing between the different actors using a trusted blockchain structure. The TradeLens ecosystem can be considered a service ecosystem, as the main value creation is intangible and the many different actors within the ecosystem co-produce the final value within the system. Secondly, stakeholder theory describes when someone can be considered a stakeholder and how to evaluate motivations and incentives. Thirdly, diffusion theory described how an innovation such as TradeLens goes through different phases before mass-market adoption. Fourth, the control points theory explain how different actors within a business process are able to exercise control on that process. Control points were used to describe how different actors are able to perform certain roles within the ecosystem. Lastly, barriers and stimulating factors describe how certain factors can enable or disable a certain development to progress further. In the case of TradeLens this was used to investigate further growth barriers and stimulating factors.
This meta-framework was converted into a six-point assessment model. This model uses a comparison between different states of an ecosystem, to evaluate possible scenarios. The first case is that of the generic constructed benchmark. This benchmark has been developed from cross-referencing a selection of public and academic sources. The main task of the constructed case was to show a generic and common supply chain structure. To assess the enhanced version of the supply chain, a case study of a Dutch tyre importer was selected. This case was selected due to the extensive documentation around this case. Additionally, this case has ships of non-hazardous and non-perishable goods that do not require additional certificates and documentation that might be applicable for other goods. The constructed benchmark case and the tyre importer case were both evaluated using the six-point assessment. With regards to key activities, the main difference found was that the tyre importer self-organises its land transport as the organisation owns its own transportation vehicles. Secondly, the tyre importer case performed the import declaration itself. This in contrast with the benchmark case, where this task was delegated to a freight forwarder who organises both the land transport and the lodging of the import declaration. This main difference becomes more visible when assessing the second point, the key actors. Here it was observed that the freight forwarder was missing on the importing side within the tyre case. This was possible as the buyer/tyre importer performed the activities of the freight forwarder. Within the value exchanges and the key information, it was observed that the buyer was able to directly lodge the required data for the import declaration. This automated the customs lodging process and increased cost-effectiveness. Secondly, within the TI case, the buyer had its own land transport capabilities and did not rely on an intermodal operator to collect the goods from the port. This allowed the buyer to redevelop its strategy with regard to the supply chain. The effects of the digital platform allowed the process of lodging the customs declaration to be more efficient as the commercial invoice and HS codes could be directly gathered from this platform. This was made possible due to the API and blockchain data pipeline architecture of the digital platform. The API-structure allowed the data to be automatically collected, whereas the blockchain structure enhanced the trustworthiness of the submitted data. Regarding, intermodal transport. The digital platform allowed the buyer to have an accurate and actual time of release and arrival of the container. This allowed the buyer to improve the planning of the collection of the container. When observing the control points it was identified that the main control points of the freight forwarder are two-fold. First, it has the expertise and capabilities to be able to perform the customs lodging. Secondly, it has the capability of gathering and forwarding logistics data within the network. Within the tyre case, both of these control points were absorbed by the buyer.
Using the control point evaluation a set of four different scenarios were identified. These developed scenarios are not comprehensive, but a combination of these scenarios are likely to be observed in the near future. For every scenario, it is evaluated how the actor could use its current control points and the digital infrastructure to increase its control on the process and thus enable reconfiguration. Firstly, the status-quo scenario. In this scenario, there is not a clear actor who absorbs the activities of other actors. The main benefits of the digital infrastructure are experienced throughout the chain as the different actors increase their efficiency using automation and digitisation of the communication processes. In this case, no reconfiguration is thus observed. The second scenario is the development of capabilities to perform more logistical tasks within the supply chain by either the buyer, the seller or both. As observed within the tyre case, the buyer is able to more efficiently perform the customs lodging and the arrangement of land transport due to having access to the commercial invoice and the actual time of arrival and release of the container. An identified stimulating factor within the capability development of the buyer/seller is the standardisation of the data and the development of a market solution to booking and tracking logistical transport. The third scenario is where the carrier becomes a one-stop shop for logistics. Using their central position within the supply chain, they are able to redevelop their value offering. This offering is expanded with the logistical support of lodging customs data and providing intermodal transport. The fourth scenario is that of the freight forwarder expanding its value offering. Here the freight forwarder expands into managing the customer’s warehouse and perform a larger set of logistic services towards the customers.
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The introduction of digital platforms has greatly affected different industries, for example enabling direct booking within the air travel business. Replacing paper documentation with an electronic equivalent can enable an increase in efficiency, through reduced administration costs and improved planning and operational capabilities. Efforts in introducing a digital platform within the shipping industry have been taking place using different governmental research efforts. However, as a possible additional effect, the digital platform may put reconfiguration of the network in motion. This reconfiguration enables certain actors to partake a bigger role, whereas other actors might lose control of the supply chain process.
The TradeLens platform launched in 2018, is such a digital platform. This platform allows sharing both documentation (e.g. the commercial invoice, packing list, bill-of-lading) and supply chain events (e.g. lodging ENS, the actual time of arrival) with the other actors. The platform uses a blockchain infrastructure. This structure is used to increase the trustworthiness of the data. Firstly, the auditability hinders documentation fraud as the actors within the network can trace the exact moment and actor that placed uploaded a document. The immutability of the blockchain infrastructure allows the automation of information processes. When the information is uploaded it cannot be changed. It was developed by a co-operation between a shipping carrier and a technology developer. The platform was tasked with alleviating the pressure on the administrative systems of the different actors within the supply chain. This research investigated possible scenarios due to the introduction of a digital platform using the TradeLens platform as the main research case.
Research Question and Objectives
This research aims to address the different possible future configurations of the network and roles within the container supply chain. To address this, the following main research question was developed: What is the possible supply chain configurations that come with a digital information infrastructure?.
In addressing this research question a number of research steps and clarifications have to be answered. Firstly, the research has to determine what is considered an actor within the supply chain ecosystem and what are the key activities performed in that ecosystem. Secondly, the research aims to perform an analysis of each actor and thus explain the different roles to be able to perform this analysis a theoretical model has to be developed. Thirdly, the research will evaluate the ecosystem using this model. Fourthly, the different scenarios will be developed using the developed model.
Research Method
The research employed three main methods of data collection. Firstly, a literature review is performed to identify the important information and innovation concepts to be used throughout the research. Secondly, through analysing many different public sources, the research gains company insights and information for constructing different roles, activities, and resources. Thirdly, through interviews with experts on the TradeLens platform where careful attention is given to the shifting activities, resources and control within the configuration of the network.
The used approach can be defined in four steps. First, the researchers developed an initial meta-framework using the findings from the literature review. Secondly, the different concepts within the meta-framework were combined into constructing a model for the assessment of the ecosystem. Thirdly, a generic container shipping case is construed from the information gained from the different public and academic sources. Lastly, a comparison is performed between the construed case and a test case of a Dutch tyre importer. The main findings within these steps will be discussed in the following paragraph.
Main Findings
The research identified five key theories to be of importance within the model. These are 1. Ecosystem theory, 2. Stakeholder theory, 3. Diffusion theory, 4. Control Point theory and lastly, 5. Barriers and Stimulating Factors. Firstly, the concept of the business ecosystem. An ecosystem describes how different actors within a business domain influence and interact with the other actors outside and within the direct business network. This research investigates the effect of digital platforms on ecosystem reconfiguration. The chosen system of analysis for this ecosystem reconfiguration is the blockchain-enabled platform, TradeLens. This platform enables information sharing between the different actors using a trusted blockchain structure. The TradeLens ecosystem can be considered a service ecosystem, as the main value creation is intangible and the many different actors within the ecosystem co-produce the final value within the system. Secondly, stakeholder theory describes when someone can be considered a stakeholder and how to evaluate motivations and incentives. Thirdly, diffusion theory described how an innovation such as TradeLens goes through different phases before mass-market adoption. Fourth, the control points theory explain how different actors within a business process are able to exercise control on that process. Control points were used to describe how different actors are able to perform certain roles within the ecosystem. Lastly, barriers and stimulating factors describe how certain factors can enable or disable a certain development to progress further. In the case of TradeLens this was used to investigate further growth barriers and stimulating factors.
This meta-framework was converted into a six-point assessment model. This model uses a comparison between different states of an ecosystem, to evaluate possible scenarios. The first case is that of the generic constructed benchmark. This benchmark has been developed from cross-referencing a selection of public and academic sources. The main task of the constructed case was to show a generic and common supply chain structure. To assess the enhanced version of the supply chain, a case study of a Dutch tyre importer was selected. This case was selected due to the extensive documentation around this case. Additionally, this case has ships of non-hazardous and non-perishable goods that do not require additional certificates and documentation that might be applicable for other goods. The constructed benchmark case and the tyre importer case were both evaluated using the six-point assessment. With regards to key activities, the main difference found was that the tyre importer self-organises its land transport as the organisation owns its own transportation vehicles. Secondly, the tyre importer case performed the import declaration itself. This in contrast with the benchmark case, where this task was delegated to a freight forwarder who organises both the land transport and the lodging of the import declaration. This main difference becomes more visible when assessing the second point, the key actors. Here it was observed that the freight forwarder was missing on the importing side within the tyre case. This was possible as the buyer/tyre importer performed the activities of the freight forwarder. Within the value exchanges and the key information, it was observed that the buyer was able to directly lodge the required data for the import declaration. This automated the customs lodging process and increased cost-effectiveness. Secondly, within the TI case, the buyer had its own land transport capabilities and did not rely on an intermodal operator to collect the goods from the port. This allowed the buyer to redevelop its strategy with regard to the supply chain. The effects of the digital platform allowed the process of lodging the customs declaration to be more efficient as the commercial invoice and HS codes could be directly gathered from this platform. This was made possible due to the API and blockchain data pipeline architecture of the digital platform. The API-structure allowed the data to be automatically collected, whereas the blockchain structure enhanced the trustworthiness of the submitted data. Regarding, intermodal transport. The digital platform allowed the buyer to have an accurate and actual time of release and arrival of the container. This allowed the buyer to improve the planning of the collection of the container. When observing the control points it was identified that the main control points of the freight forwarder are two-fold. First, it has the expertise and capabilities to be able to perform the customs lodging. Secondly, it has the capability of gathering and forwarding logistics data within the network. Within the tyre case, both of these control points were absorbed by the buyer.
Using the control point evaluation a set of four different scenarios were identified. These developed scenarios are not comprehensive, but a combination of these scenarios are likely to be observed in the near future. For every scenario, it is evaluated how the actor could use its current control points and the digital infrastructure to increase its control on the process and thus enable reconfiguration. Firstly, the status-quo scenario. In this scenario, there is not a clear actor who absorbs the activities of other actors. The main benefits of the digital infrastructure are experienced throughout the chain as the different actors increase their efficiency using automation and digitisation of the communication processes. In this case, no reconfiguration is thus observed. The second scenario is the development of capabilities to perform more logistical tasks within the supply chain by either the buyer, the seller or both. As observed within the tyre case, the buyer is able to more efficiently perform the customs lodging and the arrangement of land transport due to having access to the commercial invoice and the actual time of arrival and release of the container. An identified stimulating factor within the capability development of the buyer/seller is the standardisation of the data and the development of a market solution to booking and tracking logistical transport. The third scenario is where the carrier becomes a one-stop shop for logistics. Using their central position within the supply chain, they are able to redevelop their value offering. This offering is expanded with the logistical support of lodging customs data and providing intermodal transport. The fourth scenario is that of the freight forwarder expanding its value offering. Here the freight forwarder expands into managing the customer’s warehouse and perform a larger set of logistic services towards the customers.