Open Access

The global economy has gained momentum in recent years, with advances in technology and digitalization leading to shorter product life cycles, increased competition, and transformed industries. These circumstances call for the need for constant innovation. Organizations are required to act and adapt quickly to technological changes, dynamic markets, competitive threats, and rapidly altering customer needs, without losing focus of their established business. Two notions are important for organizations in this setting: (1) reaching ambidexterity and (2) structuring the front-end of innovation. Ambidextrous companies, which own the ability to balance between innovation activities that exploit current competencies (exploitation) and those that explore new competencies (exploration), are more successful than companies which concentrate on only one of these activities (Gibson & Birkinshaw, 2004; He & Wong, 2004; Jansen, Van Den Bosch, & Volberda, 2006; Katila & Ahuja, 2002; C. Kim, Song, & Nerkar, 2012). However, both exploration and exploitation require the allocation of resources, causing a trade-off, which makes it difficult to perform the combination of both (Greve, 2007; Levinthal & March, 1993). Previous research does not focus on how organizations can adapt their innovation activities in order to reach ambidexterity (Cantarello, Martini, & Nosella, 2012; Judge & Blocker, 2008; Z. Wei, Yi, & Guo, 2014). Managing innovations poses an increasingly daunting task for organizations, demanding different requirements regarding the innovation management process. Managing innovation through a structured innovation process facilitates the creation and planning of innovation to transform ideas into marketable products. The first stage of this process – the front-end of innovation – is of significant meaning, since activities in the front-end of innovation are strongly linked to innovation success (Dwyer & Mellor, 1991; Markham, 2013; Moenart, De Meyer, Souder, & Deschoolmeester, 1995; Reid & de Brentani, 2004). The creation of value and competitive advantage takes primarily place in the front-end of innovation, and the actual costs of mismanagement can only be discovered at later stages (Markham, 2013; Reid & de Brentani, 2004; P. Smith & Reinertsen, 1991). A concept to foster ambidexterity and structure the front-end of innovation described mainly by practitioners are so-called innovation fields (Cooper, Edgett, & Kleinschmidt, 2004; Crawford, 1980; Hambrick & Fredrickson, 2001; Khurana & Rosenthal, 1998; Reid & de Brentani, 2004; Talke, Salomo, & Rost, 2010). Innovation fields establish guidelines that determine search strategy, scope, depth, and locus of innovation search by setting search boundaries. Literature describes different types of applications for innovation fields such as strategic purposes, ideation, lifting synergies, technology intelligence and portfolio extension. With innovation fields, organizations (1) can structure the front-end of innovation and align corporate objectives to innovation activities and (2) have an instrument at hand to facilitate the shift of resources and to prioritize innovation activities according to the balance between exploitation and exploration, thereby fostering ambidexterity. However, research on innovation fields is scarce, thus, the objective of this dissertation is to examine how and why perceived contextual factors influence the intended application and perceived proficiency of innovation fields in the front-end of innovation. The theoretical foundation is based on the theory of organizational learning. A research framework is derived from acknowledged literature, focusing on (1) strategic orientation, (2) organizational context and (3) external environment as main contextual factors influencing the intended application of innovation fields. An explorative research design is followed, composed of an embedded single case study design using a mixed-methods approach. As a case, a corporate R&D division of a Germany-based company is selected. First, a qualitative study with semi-structured interviews is conducted, followed by a quantitative survey to get a more comprehensive picture of the role of perceived contextual factors influencing intended innovation field applications and proficiency. Based on the underlying empirical research, distinct differences regarding perceived contextual factors and their influence on intended innovation field applications and proficiency have been identified. Notably, the perceived contextual factors vary across the different types of applications for innovation fields. Overall, the strategic orientation and external environment have a strong influence on the intended innovation field applications and proficiency, while organizational context only play a minor role. Furthermore, the findings substantiate the use of different types of applications for innovation fields in the front-end of innovation. This study contributes to theory by creating a research framework linking perceived contextual factors to intended innovation field applications and proficiency. Finally, this dissertation delivers a comprehensive description of innovation field applications. The findings enhance the existing body of knowledge regarding innovation research, specifically regarding the front-end of innovation and innovation fields as well as organizational learning. Besides the advancement of scientific knowledge, managerial implications are drawn for the application of innovation fields in a corporate context.

This study was designed to answer the question of whether resource performance depends more on good governance or rather on effective institutional structures. The specific aim is to make clear the extent to which good governance and institutions promote small scale gold mining businesses, to explain empirically the nature of human rights challenges in the small-scale mining (SSM) industry from the perspective of mining mangers, to investigate the nature, determinants, and frequency of conflicts associated with SSM, and to discuss the challenges facing SSM operations and ways to confront them. The findings show that, in the context of efforts to spur economic development, the exploitation of mineral resources has the potential to bring about far-reaching environmental and social changes. These changes can create opportunities, but they also represent a business risk for corporations and a social risk for communities. There is as a consequence a pressing need to investigate recent threats to mineral resource exploration relating to economic development, peace and stability, and the survival of private businesses. These threats are particularly serious for less-developed countries that are net exporters of natural resources. Such countries could use these resources to drive economic development and decrease their dependence on aid from developed countries. In most of them, however, owing to a lack of strong institutions, mismanagement of mineral and other natural resources has fueled social conflict without producing meaningful development. In addition, there is often the perception in countries such as Ghana, which is the subject of this study, that mining, whatever its benefits, is responsible for significant environmental damage and for Human Rights Adverse Impacts (HRAI), including child labor and exploitation, displacement of rural households, and violence. For these reasons, investment in the mining sector and associated businesses has often faced stiff resistance. Given the right governmental institutions, small-scale gold mining and associated activities can prove beneficial to and be accepted by a society and can attract further investment; under the wrong circumstances, this type of mining can impact society negatively. At the very least, when SSM is poorly managed, the anticipated benefits to the business community and the broader society are unlikely to materialize. The evidence from large-scale mining, particularly in the wake of Ghana’s civil war, indicates a correlation between mineral resources and conflict. Less is known about the nature, frequency, and causes of conflicts that afflict households in Ghana’s artisanal mining communities. There is accordingly a need for research into ways to prevent human rights violations and to create share value in the SSM sector through social development and renewed incentives for investment in it. This thesis represents an attempt to fill this need by exploring whether the capacity of resources—in this case, gold mining—to spur economic development—here, by creating competitive SSM businesses, improving livelihoods, or reducing poverty—depends on governance structures and whether there is a correlation between SSM and conflict outside the context of civil war. This thesis is informed by three broad insights. The first concerns the challenges facing the SSM activities that play a vital role in the Ghanaian economy. Second, there is the importance of the role played by institutions in the development of SSM amid renewed attraction of investment in the sector. Third, changing social expectations are a crucial aspect sustainable mining and the protection of human rights.

This dissertation is dedicated to extending scholarly understanding of organizational transformation in the context of disruptive change. For this purpose, three independent studies explore both organizational- and individual-level aspects of organizational transformation. In doing so, this dissertation integrates two literature streams – disruptive innovation theory and organizational identity. Study 1 lays the ground by providing a descriptive, thematic analysis of organizational transformation induced by digital innovations and technologies. The paper systematically reviews 58 articles to critically assesses where, how and by whom research on digital transformation is conducted and how it unfolds at the organizational level. Studies 2 and 3 are located at the intersection of disruptive innovation adoption and organizational identity in the context of incumbent firms. Both studies apply an inductive, field-based single case design and primarily build on qualitative data gathered from 39 (Study 2) and 35 (Study 3) semistructured personal interviews at a major German car manufacturer. Study 2 examines how organizational identity change unfolds in an incumbent attempting to adopt multiple different disruptions at the same time, while Study 3 moves more towards the individual-level and attempts to understand how and why organizational members respond heterogeneously to disruption. Overall, this dissertation contributes in the following ways: (1) Studies 1 and 2 extend the conceptual- and organizational-level knowledge of disruptive innovation adoption during organizational transformation. In particular, Study 2 shows that different drivers of identity-induced organizational transformation become observable, dependent on the nature of a disruption, (2) Studies 2 and 3 extend the individual-level knowledge of organizational member’s attitudes and behavior during identity-threatening organizational transformation. For this purpose, Study 3 develops a typology which gives evidence for the existence of three types of member’s sensitivities and shows that identity and knowledge function as cognitive frames of reference to interpret change, whereas culture is seen as a contextual factor to support the transformation of identity and knowledge.

According to dual-process models, human behavior is the result of an interaction between automatic and controlled processes. Although automatic processes often lead to positive results, they can also lead to severely negative consequences. The current dissertation investigated via 4 studies the effect of self-control depletion, mindsets, framing, and preference for consistency on the usage of automatic processes in decision tasks where heuristics (e.g., reinforcement heuristic) can either conflict or be aligned with Bayesian updating. In particular, Study 1 hypothesized that when a reinforcement heuristic opposed Bayesian updating, ego-depletion would influence the reliance on automatic processes. Three sub-studies (1a, 1b and 1c) were conducted using different depletion manipulations plus controls. Although the manipulation checks indicated successful ego-depletion induction, only Study 1a found the predicted effect. It seems that the ego depletion effects in complex decision-making tasks are less robust than previously reported in the literature.

This doctoral thesis is concerned with two separate but intertwined topics in the field of financial econometrics: (i) the measurement and relevance of new sources of information on financial markets in the form of online investor sentiment and attention and (ii) nonlinearities in financial time series in the form of structural breaks. According to classical finance theory, competition among rational investors, often called arbitrageurs, leads to an equilibrium in which prices on capital markets equal the present value of expected future cash flows. Under this theoretical lens, the trading decisions of irrational investors have no significant impact on prices since their demands are offset by rational investors. However, the classical finance theory fails to fit the extreme levels of and changes in stock prices corresponding to events such as the Great Crash of 1929 or the Dot.com bubble of the 1990s, which are difficult to align with any rational explanation. Akin to the notion of "animal spirit" first coined by Keynes (1936), behavioral finance theory sets out to augment the classical model by explicitly taking into account two assumptions: Firstly, trading activities of investors are thought of to be partially influenced by subjective beliefs about investment risks and future cash flows, generally referred to as investor sentiment. Secondly, there are limits to arbitrage in the sense that betting against sentiment-driven investors is associated with higher risks and costs. Thus, inconsistent with predictions of the classical finance theory, arbitrageurs do not aggressively force prices to fundamentals. On this basis, irrational (collective) investor behavior has moved into the focus of modern finance theory and corresponding empirical applications. The widespread internet access and usage of social media platforms in recent years have led to new sources of information - and with them new sources and types of data that can be used by researchers and practitioners alike - pertaining to this collective investor behavior and corresponding financial market outcome: Short messages published on social media platforms such as Twitter or StockTwits on the one hand and online search queries on the other. The first part of this thesis makes use of such data in empirical financial applications, also from a high-frequency intraday perspective, in order to assess its impact on predictions of financial variables and to unravel new relationships. In general, it is reasonable to assume that many relationships in economics and finance are nonlinear. Thus, several kinds of nonlinearities can arise when considering financial markets and time series of financial variables that are not necessarily approximated well by simple linear models. Relating to the behavioral finance literature, the model of De Long et al. (1990) proposes that in the presence of sentiment-prone noise traders the price of a risky asset evolves as a nonlinear function of these noise traders' average bullishness (i.e., their mean misperception of the expected price) and its variance. Though being of a different philosophical nature than sentiment-induced noise trader theories, some other models of trade based on noninformational reasons, such as changes in risk aversion or liquidity needs, also involve nonlinear relations. The second part of the thesis focuses on one often overlooked kind of nonlinearity that entails potentially more severe implications, namely structural breaks in financial time series. Structural breaks, also referred to as change-points, in the data generating process underlying a given univariate time series do not only constitute a source of nonlinearity that can be modeled but also a more subtle source of nonstationarity. Given that endeavors of time series model building and prediction usually demand some stationarity assumption to be made, the latter poses a common problem in the analysis of univariate economic and financial time series. Matters are complicated by the fact that the exact number and timing of structural breaks are usually unknown ex-ante. Therefore, the consistent estimation of structural breaks, or change-points, has been studied extensively in the related literature. This thesis adds to the ongoing discussion by proposing a two-step model selection procedure for the detection and timing of change-points in structural break autoregressive models. A similar methodology is then used to investigate the effect of Box-Cox transforms on the estimation of structural breaks in realized volatility time series.