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 \acs{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.

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.

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.

As of today, many people are aware of the negative impacts of eating farmed meat. The current farming system is a burden on the environment and animal welfare, as well as an potential threat to human health. Moreover, to secure food for our growing society, a change in the current food system is inevitable. Since people are still hesitant to cut meat out of their diets, this asks for a more sustainable production of meat. We can think of improving our current farming system. However, new technologies can offer more efficient and sustainable alternatives to the (improved) existing farming system. One of these alternatives is cultured meat (CM). The advantage of CM compared to other (plant-based) meat substitutes, is that it is exactly the same product as farmed meat. Many academic research has already been performed and published on CM over the past few years. These papers mainly describe either the technical aspects, consumer acceptance, ethics, and/or environmental impact. The focus has rarely been on describing the entire socio-technical context of CM, and the formation and subsequent functioning of a future CM industry. This, while deep changes of the socio-technical system are required to succeed in developing CM. Therefore, this research aims at mapping out the Dutch socio-technical system of cultured meat, in order to evaluate the development of the socio-technical system and investigate its potential to grow. Eventually aiming at answering: ‘How has the Dutch socio-technical system of cultured meat been developing and under which circumstances will it grow?’. In finding an answer to this question, a framework combining the functions of innovation system (FIS) approach and the multi-level perspective (MLP), is adopted. To complement for the lack of focus on actor values and strategies of an integrated FIS-MLP framework, analysis of actor strategies and actor values are separately integrated in this model. Information has been retrieved from a combination of desk research and expert interviews. In the first part of the research, the Dutch cultured meat innovation system has been analysed in terms of its structural and functional components. The Dutch CM scene is still in its infancy. The technology is still strongly evolving – mostly at the two Dutch CM start-ups Mosa Meat & Meatable – , the networks are still small, little amount of suppliers are in sphere, and no product is on the market yet. Looking into the functioning of the system, no central guidance is present in the Dutch CM system, while this is important to align the visions of key stakeholders. Moreover, lobby activities are limited, while these can be crucial in increasing the legitimacy of the technology. In the second research part, the external context of the innovation system has been explored. First, the recent developments on landscape and regime levels have been determined, followed by the identification of actor strategies and actor values. Partly following from the lack of central guidance, we can observe diverging actor values and expectations. While all actors value sustainability and environmental benefits, their view on how to achieve this differs. Ranging from improving current farming system to meat alternatives and CM. The CM sector is no longer government funded, but fully dependent on private investors. This caused the research to shift from universities to the two start-ups, who keep their research highly confidential. This also causes the CM research and values within this research of actors to diverge. By collecting and coupling the outcomes of the first two research parts, the elements blocking or facilitating the development of the Dutch cultured meat innovation system have been identified. Based on these analysed system problems, recommendations for strategic actions for specific actor groups with influencing power, have been established. Altogether, the development of the Dutch CM scene and subsequent industry, requires more time and more money to do research. Implementing the suggested recommendations could help in facilitating this.

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.

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.

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.

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.