Data has often been stated to be “the new oil”. This is said because both oil and data can be very valuable but only after they have been pre-processed. Among other things, a challenge for creating value from data lies in realizing data exchange. This is what data marketplaces aim to do. The number of shutdowns of data marketplaces during the past years implies that their business models are not viable and that there are obstacles that should be overcome to create a data marketplace that is robust in the long run. In this research, we explore a new research context: the relationships between business models of data marketplaces and data marketplace obstacles. To do this, by the means of explorative theorizing, we first provide an overview of the obstacle categories for data marketplaces: trust, lack of governance mechanisms, privacy and control, product quality and product description, matching data providers and data buyers, data pricing mechanism, and ‘other’ that do not fall within a specific category. We then followed a multiple-case studying approach in which we subject three different real-life data marketplaces to a Business Model (BM) Stress Test on the identified obstacles. The cases were selected by theoretical sampling of the types of data marketplaces: personal data marketplaces, Internet-Of-Things (IoT) data marketplaces, and Business-to-Business (B2B) data marketplaces. By reflecting upon the results, we conclude that the assessment of the true viability of a data marketplace business model goes way beyond just the relationships between business model choices and obstacles. It is concerned with the business model strategy and dynamic capabilities of a data marketplace. We add to the literature by presenting a first explanatory model on the relationships between data marketplace business models and data marketplace obstacles. This model leads us to the practical implications of our results. These include the considerations that a data marketplace owner can take into account when making business model choices. Based on the results, we are also able to give recommendations that go beyond certain business model choices.

The applications of artificial intelligence (AI) are significant on a social and economic scale, and they can offer businesses great value and opportunities. However, because of AI's varied application areas, its inherent complexity, and the new organizational requirements that result from AI adoption, companies encounter pitfalls when deploying the technology. Potential implementation scenarios are not always clear. Understanding the AI readiness on an organizational level, which shows “the extent to which an organization has the ability to reap the benefits of AI” can improve the chances of effective AI deployment. This study aims to bridge the gap between academia and practice as most attention to artificial intelligence was paid to modeling steps in academia. Research on applying AI models to real-life problems and realizing business value has received insufficient attention. The current frameworks do not incorporate context-specific considerations for organizational readiness. This research investigates the organizational readiness of AI in the semiconductor industry. It is a critical step in avoiding costly failures, considering the capital-intensive characteristic of the semiconductor industry. A literature study has been conducted at first to review the existing AI organizational readiness framework and typical readiness factors. Then eight industry expert interviews are conducted to give a more holistic view of AI use cases across the semiconductor value chain as identifying the opportunity is the first step to establish AI readiness. Three challenges of AI deployment are summarized and the potential AI organizational readiness factors are listed to guide the case study interviews. The case study is carried out in ASML, one of the leading producers of chip-making equipment in the world. Through fourteen case study interviews, this research proposes an AI organizational readiness framework with six dimensions and conceptualizes twenty readiness factors. In the strategic alignment dimension, there are needs and added-value assessment, bottom-up proposal/innovation lab, top management support, business model innovation. In the resource dimension, there are talents, financial budget, IT infrastructure, competence center. In the process dimension, there are multidisciplinary team/collaboration, agile way of working, employee training, business process standardization. Regarding data dimension, there are data availability, data governance, data platform. In the AI model cluster, there are explainable AI with domain experts, context-aware modeling, model operation. In the external business environment, there are peers/competitors/software vendors and customer demand. Furthermore, twenty propositions on AI readiness in semiconductor organizations are given indicating their positive or negative influence on AI organizational readiness. This study contributes to the emerging literature on AI organizational readiness with the developed multi-dimension framework. It identifies AI-specific readiness factors under the dimensions of "Data" and "AI model". Moreover, among 20 readiness factors, 10 readiness factors are newly identified such as the agile way of working, competence center, context-aware modeling. Companies that seek to implement AI can use the proposed framework with readiness factors as a tool for assessment to help decision-makers, managers, and project teams to develop and deploy AI faster and more effectively.

Costa Rica’s electric grid is dominated by renewable sources, mostly by hydropower 74% and with geothermal 12.4% and wind 10.6%. Hence, in dry season when the water levels drop significantly, there are difficulties to supply the national electricity demand (CENCE, 2016). Despite this and the added fact that the country’s has enormous solar energy potential, solar PV technology has not been diffused to act as an energy complement during dry season it merely represents 0.01% of the energy grid as outlined by Valverde, Lara, Lobo, & Rojas (2015). However, recent global and local developments have spurred the country’s demand for solar PV projects. Globally, there has been a dramatic decrease in solar PV panel’s prices causing significant cost reductions for solar PV installations (Feldman & Barbose, 2015). In Costa Rica a recent executive decree resulted in enabling and regulating the distributed energy generation model on renewable systems however only for self-sustaining purposes including that of solar PV.

The increasing use of robots in the workplace presents a valuable opportunity to diversify the workforce by including underrepresented groups such as ethnic minorities and the elder population. However, high unemployment among these social groups shows that the opportunity to boost inclusivity and diversity is not grasped. Seizing this opportunity presents several challenges, especially since diversity can complicate the innovation process. Innovation is a complex multifaceted process influenced by numerous factors, among which formal and informal institutions. Formal institutions are written rules such as laws, standards and obligations, while informal institutions are unwritten rules, such as social and cultural norms, that shape people's perceptions and behaviors. Diversity can be related to social groups holding peculiar norms/beliefs that can conflict with other social norms or regulations. The role of formal and informal institutions in diversity and inclusion initiatives has been largely overlooked in the HRI literature, but existing studies suggest that institutions play a crucial role in enabling/hampering inclusion and diversity through technology. In this research, I make use of qualitative research and the RRI approach to analyze the complex socio-technical system in which robots are implemented and used in the real-world context of the KLM baggage handling facilities at Amsterdam Airport. The aim of this study is to build grounded theory through the analysis of the impact that institutions have on the implementation and utilization of robotic technology in the employment of a workforce that is diverse in terms of age, ethnicity, and race. The research question guiding the research is: “How do formal and informal institutions shape the implementation and use of robots at work aimed at including a diverse workforce?” This research question aims at tackling two different aspects of deploying robots at work: implementation and utilization. The focus on implementation pertains to the analysis of the process by which a diverse workforce is included or not in the deployment of robots in the workplace, and the role that institutions play in shaping this process. The focus on utilization pertains to the analysis of the role that institutions play in shaping the ability and willingness of a diverse workforce to use robots in the workplace. When it comes to the implementation of robots at work, results show that if there are no formal institutions in place to include a diverse workforce in the implementation process, then informal institutions, such as a willingness to use technology, play a prominent role in determining the likelihood that diverse workforce will be included in the process or not. In the Dutch context, factors like age or ethnic diversity do not affect the positive perception of robots at work and willingness to use technology, thus these diverse identities do not influence the participation of the diverse workforce in the implementation of robots at work. When it comes to the utilization of robots, diversity plays both a direct and indirect role. The direct role is related to formal institutions, for instance standard requirements to operate the robot, that may exclude certain ethnic/race groups with different physical characteristics from using the robot. An example of physical characteristics that play a role in the Dutch context are the average height and left-handedness. The indirect effect pertains to the influence of age diversity within the workforce on the formation of informal institutions, such as social norms. Members of the age diverse workforce may hold positions of opinion leadership within the workplace, enabling them to shape the opinions of their colleagues with regard to the use of robots. This, in turn, can influence the utilization of robotic technology by the workforce as a whole. This study has both strong practical and theoretical implications. From a practical standpoint, this study provides valuable insights for organizations seeking to employ a diverse workforce through the use of robotics. The findings highlight the importance of carefully considering both the practical and social implications of this approach, including the need to take into account the physical requirements of a diverse workforce and the potential influence of diversity on the formation of social norms within the workplace. From a research perspective, this study highlights the suitability of the cs{RRI} approach as a conceptual tool for HRI research to assess the socio-technical systems in robotics is used for diversity and inclusivity purposes. Future research could focus on analyzing the interactions between formal and informal institutions and other diverse identities, such as gender, in the context of implementing robots in the workplace. The main limitations of this study pertain to the limited number of participants and the lack of generalizability to SMME. These firms have different organizational structures and limited financial and intellectual resources, thus, when employing a diverse workforce, they may face considerably different challenges when implementing robotic technology in the workplace.

Companies are increasingly reliant on internal and external data sources to innovate their products and services. Currently, companies produce data for their own usage and store it in data silos afterwards. This hinders secondary data usage, when companies reuse external data. Data marketplace owners enable data trade across companies, but companies rarely trade industrial datasets on multilateral data marketplaces. Data marketplace owners apply business models to transform technical ideas into functioning value propositions. Research about business models of data marketplaces is required to further advance the development of data marketplaces. Literature mainly focuses on one type of data marketplace with a market orientation and independent ownership, whereas those data marketplaces do not move past the conceptual stage. To find viable business models of data marketplaces, we researched various types of data marketplaces that range from hierarchical to market orientation and private to independent ownership in the B2B automotive industry. The research question states "What business model archetypes are applied by data marketplace owners from different types of data marketplaces in the B2B automotive industry?" To answer this question, we created a taxonomy in which six data marketplaces are classified. Based on business model patterns in our taxonomy, we derived four business model archetypes. These are (i) the aggregating data marketplace archetype, (ii) the aggregating data marketplace with additional brokering service archetype, (iii) consulting data marketplace and (iv) the facilitating data marketplace archetype. Owners of data marketplaces with a market orientation and independent ownership, the data marketplace type that remains conceptual, apply the facilitating data marketplace archetype. They perform a data brokering service, but fail to attract customers. We advise them to complement their data brokering service with value-adding services such as a customized map service, data quality reviews or personal consults about data sale and purchase at their platform to attract more customers. Future researchers are recommended to extend our taxonomy by classifying additional data marketplaces from various orientation and ownership types. This may validate our results or generate new business model dimensions and characteristics.

With growing tensions in today's world, new powers rising and contesting old powers, two nations risk ending up in a so called security dilemma: One nation's increase in security threatens its opposing nation. The consequence could be the escalation of military buildups into arms races. With ongoing technological advancements, militaries around the world are expanding on autonomous capabilities that need less and less human control. In this research, I investigate under which conditions arms races for these systems between two fictitious nations may occur and how the nations can influence these. For this, I construct a system dynamics model based on literature and expert consultations and conduct experiments to generate a wide range of possible scenarios. The model results suggest that different types of arms races for autonomous military capabilities occur under specific conditions: the inferior nation lacks behind the superior nation and fills this initial gap not with a greater number of autonomous systems, but with higher quality autonomous systems, leading to an arms race in quality. Bilateral disarmament in the form of regulations on the level of autonomy seem not to be the answer for the superior nation: some of the arms racing is reduced, but the superior nation now loses both racing and non-racing scenarios. Restrict an opponent in their technological development is more effective for the superior nation. Still, the inferior nation is able to win in the specific case, where the superior nation requires "too much" human control. Generally, arms races can further be reduced when both nations avoid to be too "reactive" to each other's arming actions. This suggests, that a third party could step in and aim at relaxing the tensions between the two nations. Further, this third party could also aim at encouraging the inferior nation to raise its ethical standards. To conclude, the model can give insight into which decisions a nation or a third party can take with different goals in mind. These include, for the nations involved in a competition as a result from tensions, to either win the race towards autonomy or to prevent its opponent from winning it. A third party, interested in a peaceful world without having nations piling up autonomous military capabilities, might aim at preventing arms races whatsoever.

Dynamic capabilities theory is an explanatory framework that has gained academic and management attention for its ambition to explain sources of competitive advantage and profits in highly competitive environments. For this reason, I conducted an extensive literature review exploring where the dynamic capabilities theory and digital transformation phenomenon intersect. The review resulted in the notion that digital transformation is a source of constant change that necessitates dynamic capabilities. However, the dynamic capabilities of the firm are not well understood. A stream of research has been flourishing to illuminate the origins of dynamic capabilities, known as the microfoundations of capabilities. Reviewing research in dynamic capabilities revealed a framework that allows firms to respond to digital disruption through constant evolution in capabilities. My desk research uncovered the management research community’s ongoing investigation of dynamic capabilities’ underlying mechanisms known as microfoundations of dynamic capabilities. Examining these capability sub-dimensions showed that aggregated actions of individuals form routines that evolve into the firm’s capabilities. Developing capabilities of any kind starts from the learning and collaboration of individuals with different skill sets, beliefs, and values that make up the organization. I approached three legacy firms in the low-tech sector of fashion and retail as a case study that exemplifies the problem of digital transformation. They were chosen based on profiles typical of a digitally-transforming firm, such as investments in digital technologies, the appointment of chief digital/information officers, size, and age. In addition to content, the firm’s headquarter operations reside in the Netherlands, and their digital transformation initiatives occurred in the last five years. I consulted annual reports, financial filings, and company press releases to trace their digital transformation journey and validated through individual interviews from digital actors, people involved in the company’s digitalization. The empirical observations confirmed that the microfoundations perspective fits the research question due to the digital transformation’s multi-pronged approach. However, I did not observe all sub-dimensions of the dynamic capability in the case study companies. Nevertheless, I found at least one of each factor relating to individuals, processes, and structures. For example, the individual dimension emerged in the study due to the central roles of leadership and the workforce’s digital savviness in the transformation process. Furthermore, digital intensity (processual dimension) is a must in digital transformation since substantial digital technology investments are a core requirement. Their interaction and collaboration across different levels of the organization through proximity help build capabilities for digital transformation. The structural dimension also shows that a centrally organized digital initiative can move slower than a decentralized one. The framework suggests that dynamic building capabilities are rooted in individuals learning and collaborating. Additionally, proximity and the concepts of multi-dimensional relationships (geography, cognition, social, and organizational) facilitate the learning process. My study also suggests that individuals working at different levels of the organizations build firm-level capabilities, hinting at the multi-level characteristics of capabilities of the firm. The research in microfoundations of capabilities has only recently caught traction. Thus, the elements of my thesis form a foundation for future research.

This paper presents a new approach combining dynamic capabilities (DCs) theory and stages of growth model theory determining that growth leaders in the insurance sector surpass pre-digital organisations operating in traditional markets due to high embedded learning capacity. I conducted nine in-depth structured interviews with senior and director-level experts working in the Dutch insurance sector. This allows for generalisability within the Financial Services sector, being very diverse: varying between pre-digital organisations and digital enterprises. This setting provides a useful context to explore how Dutch insurance businesses individually build DCs that bring about change by exploring past, current, future transformations, and reflect on what is desired from a normative perspective. Moreover, by focusing on the underlying processes and routines, I further investigated questions related to the methods, successes, risks, and failures identified at Dutch insurance companies that trigger or hinder the development of DCs for digital transformation.

Today market success and competitive advantage depends on information, and the proper flow of information using information technologies is critical to maintain competitive advantage. To improve their business processes and networks, organizations need to leverage new information systems/information technologies (IS/IT). These new IT systems, which see a constant flux of innovations in software, computational and automation capabilities, include technologies such as robotic process automation (RPA), chatbots, analytics using machine learning and artificial intelligence to name a few. These technologies are currently gaining a lot of traction and popularity as solutions to bolstering an organization’s competitiveness. Organizations are struggling with managing these technologies to maintain competitive advantage. A challenge for organizations is to judge the effectiveness and the impact the new technology would have on their respective business processes before implementing the IT system. Organizations lack a framework that encompasses both the short-term and the long-term view of implementing these technologies as well as the challenges they would face because of these technologies. Through this research, we hope to allay this issue by asking and answering: How can an organization effectively evaluate the impact of new IT systems to improve its business processes? There exist various frameworks and measures in management literature that help organizations choose between multiple IT investments and there exists frameworks such as the balanced scorecard that help organization as performance measuring tools. The IT scorecard, a subset of the balance scorecard, was developed to measure performance of existing IT products and services in use. However the use of existing frameworks to evaluate new IT systems has not been empirically recorded. Existing frameworks also do not take into consideration the uniqueness of individual IT systems and the challenges that are associated with these systems. Other challenges include a greater focus on short- term monetary gains rather than long-term benefits, a lack of standardised parameters for accurate measurements of performance and the ethical challenges that come with using these new controversial technologies. The proposed evaluation framework aims to accommodate the weaknesses of the earlier frameworks. This is why an integrated approach has been taken in the framework. The framework consists of three levels with the evaluation of the IT system conducted at the final level. The top most strategic level offers the organization a perspective on the needs and goals of the business. This allows them to see if the IT system truly aligns with the organization’s strategic objectives. The next level is the business process level. The reason IT systems are introduced into organizational business processes is to improve efficiency of the business process by automating certain activities or reducing certain steps in the process, improve performance of employees or enhance customer experience thereby boosting overall productivity. However, if the IT systems are introduced into the existing business process without correcting for inefficiencies, these inefficiencies are carried into the new business process (the existing business process with the IT system introduced) which is detrimental to the organization in the long run. Therefore analysing the business process helps us identify these gaps existing in the process, solve for them and redesign the business process with the implementation of the IT system. This is achieved at the business process level of the framework. Finally the level of the IT system where its impact on the business process is evaluated. The IT system is evaluated from different perspectives which include the customer, the business value the system provides, the internal processes that are affected by the IT system and the future readiness of the organization to the IT system. This integrated approach, similar to the Balanced and IT scorecards, is augmented and made more dynamic and robust enough to evaluate new IT systems by using KPIs derived by the system developers and engineers as metrics of measurement. The framework also brings to light the ethical values and challenges that come with using new IT systems. To the initial four perspectives, the two new perspectives will provide a more complete view of the impact the IT system has on the business process. The better the evaluation, the better the decision making of the organization. To demonstrate the utility of the evaluation framework, it is applied to the incident management process which is a common business process of the service based enterprises. This business process is analysed and the common challenges found in this process are discussed. In order to improve the business process, two new IT systems are introduced separately; the robotic process automation (RPA) and the chatbot. The proposed evaluation framework is applied to both the situations and the results are compared to see which IT system better improves the business process. This work has shown that including the distinctive nature of the new IT systems such as RPA and chatbots improves the results of the impact evaluation. In case of RPA, aside from the obvious benefits of the technology organizations might not be aware of the improvement in compliance the system provides or the increased quality of data being processed due to fewer errors or the heightened security risks that increased automation can cause. Organizations must also be wary of any underlying biases and stereotyping when implementing chatbots into their business processes. The idiosyncratic properties of each technology can influence the evaluation results and hence the decision on whether the IT system will be implemented. Therefore, by including the indicators specific to these systems, organizations can be aware of the potential impact these systems have on a particular business process. In this way undesired effects such as the inherent challenges of the IT system as well as the consequences of ethical issues are captured. Besides the impact evaluation of the IT system itself, the analysis at the strategic level ensures that there is a strategic alignment with the objectives of the business and the IT system and the modelling of the business process ensures that a detailed analysis can be done to find the inefficiencies and challenges in the business process. Through the proposed framework, this study focuses largely on the IT system and the impact it has on a business process of an organization. Once these IT systems are more prolific in their applications and use, higher levels of evaluation such as at the strategic level will be possible. This will also be very useful for organizations interested in choosing the right IT system for their organizational structure, culture and business processes and therefore be an interesting field for future research. A limitation of this research is that while this paper explains the working of the evaluation framework, a detailed analysis of the application of the framework has not been made. This can be captured by future research through in depth empirical studies with organizations implementing these new IT systems and using this framework to conduct the impact evaluation on the business process. Comparative studies can also be conducted such as comparing this framework with other integrative evaluation approaches such as the multi-attribute utility theory, the voting analytic hierarchy process and information economics.

There is nowadays an increased use of data, reaching 2.5 exabytes globally, and is expected to double every two years (Morvan, 2016). An enormous amount of data can now be generated, ranging from smartphones to sensors in machineries. However, most organizations are reported to be able to only create and store data, and creating added value from data is a major challenge for many organizations (Akerkar et al., 2015). Only 4% of companies across industries have the capabilities to create value through Big Data (Bertocco & Padmanabhan, 2014). Organizations might want to look into their business model, as adjusting their business model can facilitate the implementation (Muhtaroğlu, Demir, Obalı, & Girgin, 2013), and can result in more efficient business operations (Loebbecke & Picot, 2015). Organizations also face different circumstances (e.g. problems, challenges, applications), since they are located in different sectors (Vega-Gorgojo, Fjellheim, Roman, Akerkar, & Waaler, 2016). Therefore, this study aims to see more into the relationship between sector characteristics and the impact on business model when implementing Big Data, by means of interviews in the setting of explorative & qualitative study. Oil & gas, manufacturing, and health sector are chosen for the purpose of this study, due to the potential of Big Data implementation in these three sectors. Canvas business model template is chosen to provide the clarity of the impact on business model. From this study, Big Data is seen as a systemic innovation, due to the necessary changes in technological, organizational, and institutional aspects of the existing innovation system in a sector. The study also shows that activities, resources, and value propositions are core in the implementation, where implementing Big Data into the activities will result in improvement of value proposition, and enabled by resources. The changes in those three aspects can lead to changes in other aspects of business model, such as partnership, customer relationship, and financial. Customer segments remain unaffected, due to the specificity of value perceived by a certain segment. However, it might change in the future in the case that a new value is offered. It is found that a certain sector characteristic or combination of sector characteristics influences how organization adjusts their business model, but more importantly, the causes that shapes such characteristics need to be considered well.

Truly new business models are enabled periodically by a socio-technological disruption and most of the times, firms have to select and refine the already existing models to foster growth and sustain competition. However, the selection process is challenging as there is no right or wrong business model and what works for one firm might not work for the other. The researchers in the field of strategic management advocate that the selection or design of a business model is dependent on the dynamic capabilities of a firm which is unique to it. Teece D.J. (2018) argued that the three clusters of dynamic capability namely sensing, seizing and transforming make a framework which can be used to select or design a business model and maintain a competitive advantage. However, there exists a shortage of empirical studies that furnish granular details on selecting a business model using dynamic capability framework as a tool. Secondly, the inclusion of a value network is important for the selection of a business model but the framework ignores the complexity and advantages of working in a network environment, a common trend in the present business ecosystem. Hence, with this research, we aim to generate empirical insights and details on the selection of a business model using dynamic capability framework as a took by doing a case study on a firm operating in video on demand [VOD] industry.