Mobile apps are used to support behavior change goals (e.g., stopping a lousy habit, increasing the physical activity frequency, or learning a new skill). Because these apps are pervasive, they are great tools to reach and help people in their self-improvement path towards better habits and well-being. However, users of these apps fail to reach their objectives because they lack the motivation to attain their goals. App creators might help to support motivation by integrating human motivation theories into their designs. Still, not many apps are theory-grounded. To address this issue, we organized our research around a comprehensive human motivation theory: The Self-Determination Theory (SDT). We explored three research streams: (1) developing an artifact that maps market app features to support the SDT Basic Psychological Needs (BPNs); (2) creating an SDT inspired physical activity app that provided empirical evidence that its design supported the BPNs; (3) providing empirical evidence of an SDT inspired appdesign that contributes to increasing the physical activity and motivation of individuals. The results of our studies show that the SDT can inform the design of behavior change app features. Moreover, these SDT inspired features can be used to create a physical activity app that improves individuals’ intrinsic motivation and physical activity level. This thesis results have practical implications for app designers, policymakers, and health practitioners whose interest lies in creating theory-informed and effective behavior change apps.
In view of the ever-changing organisational environment, it is essential to better understand the role that judgment plays in decision-making so that the organisation can function efficiently. This dissertation's overall research focus is on the role of human judgment in strategic, tactical, and operational decision-making inside organisations, as well as in diverse functional domains, specifically demand forecasting and strategic partner selection. Sustainable partner selection for collaborative networked organisations (CNO) is a strategic decision for long-term collaboration with a lasting impact on partners. The focus of this research includes the impact of human judgment for sustainable partner selection for CNO using multi-criteria decision-making methods. The role of human judgment in the demand planning and forecasting function is the focus at the tactical and operational decision-making levels in this research. This research demonstrates the use of human judgment at the operational decision-making level by modelling demand and in the selection of variables, at the tactical decision-making level in model selection in the case of several conflicting error measures, and in the selection of sustainable partners for collaboration at the strategic decision-making level of an organisation. It is found that the structured approaches proposed in the research articles on using human judgment in demand forecasting and partner selection support the decision-making processes at each respective level.
Empirical research on retail investors has attempted to explain online investor subpar performance by inferior decision-making and behavioural biases. The findings on human reasoning have mostly been based on quantitative transaction and account data provided by a single financial institution. The purpose of this study was to explore opportunities to improve investing processes co-created by investors and online investing platform service providers. Qualitative interviews and analysis of live platforms were used to conduct the research.
The analyses and evaluations show that investors have not fully adopted the online platform-provided decision support tools because of issues related to usability, user experience, incompatibility with current investing style and lack of transparency – leading to intuition-based decision-making. Investors requested less complexity and a reduction of information overflow by more meaningful data presentation and interpretation. A considerable proportion of investors had not implemented tenets of portfolio theory and asset allocation, preferring position-focused investing. Investors claimed having learned to mitigate the effect of behavioural biases through experience. A modified design science approach was applied to evaluate the potential for improved decision support adoption. The evaluations show that by alternative design, investors can be persuaded to implement portfolio-level management. Investors are a heterogeneous group of users and modularisation of investing services is suggested in order to match the diversity in investor needs and priorities.
This thesis consists of three scientific publications that use machine learning methods to understand industrial processes that were designed to improve environmental quality. The work contributes to efforts to make numerical models for simulation and process control that can be applied to improve equipment designs and process performance.
Chapter 1 reports the analysis of data obtained from continuous torrefaction trials conducted in an integrated pilot plant. The purpose of the work was to gain insights into the process behavior that might be useful in the conception of a process controller that aims to obtain a product having a high and constant calorific value from heterogeneous raw biomass having a low and fluctuating calorific value.
Chapter 2 reports the development of artificial neural network predictive models of data acquired during 12 months of operation of an anaerobic digester treating food wastes. The purpose of the work was to make a predictive model to map process performance, evaluated in terms of CH4 production, to measurable operational parameters. The models were then assessed for use as decision tools to control the digester feeding rate.
Chapter 3 reports the development of a problem-solving approach and a simulation tool for use in designing upflow anaerobic filters (UAF) for wastewater treatment. A multi-layer perceptron (MLP) artificial neural network model was used to run simulations of different design values of the UAF packing diameter, packing material type and the bioreactor height to diameter ratio. The ranked results of simulation were used to select the optimal design values.
Big old companies surprisingly often fail to seize the opportunities that come with new digital technologies. These well-established “incumbent” organisations do not generally lack financial resources for digital innovation. Nor do they lack ideas for new digital products, processes, and services. Rather, their challenge lies in successfully managing the development of digital ideas, especially the digital ideas that come from employees who have only limited IT skills – so-called “non-IT employees”.
This thesis describes our findings from studying incumbent organisations that support non-IT employees in creating digital innovation. We identify three types of calls for ideas—formal, informal, and mixed—that enable non-IT employees to contribute their knowledge, skills, and creativity to the initiation of digital innovation. We find that the success of these calls for ideas relies on the careful orchestration of organisational competences, managerial processes, and individual practices. We describe how a big old company can leverage these elements to better navigate the initiation of digital innovation.
Incumbent organisations face a challenge to simultaneously explore external technologies and knowledge to develop new revenue sources and exploit their existing assets and capabilities to secure existing revenue streams. Exploration and developing digital innovations call for new skills and flexible processes that allow for experimentation, iteration, and even failure, which can be stand in stark contrast to the accumulated skills and processes that have been refined for exploitation over the years. As a remedy, some organisations are choosing to move digital innovation activities into separate organisational units and toward the peripheries of the organisation. This can however hamper access to the existing knowledge base and capabilities in the rest of the organisation. To mitigate this, previous research has highlighted integration: a collaboration process to combine newly explored knowledge with existing knowledge and assets to develop digital innovation outputs. As yet, few research projects have focused specifically on understanding how distinct integration mechanisms can be used to join exploration and exploitation efforts to maximise the value of digital innovation outputs. Anchored in ambidexterity and digital innovation literature and based on qualitative data, we argue that incumbent organisations need to put in place formal integration mechanisms rather than relying on spontaneous knowledge exchange between explorative and exploitative activities. In terms of organisational units dedicated to digital innovation, we put forth the notion that solely setting up such a unit is unlikely to be sufficient as a performance boosting action but that the integration of such units must be carefully considered and managed at multiple levels, from senior management to individual employees’ behaviours.