Introduction The current process for generating evidence in pharmacovigilance has several limitations, which often lead to delays in the evaluation of drug-associated risks.

Objectives In this study, we proposed and tested a near real-time epidemiological surveillance system using sequential, cumulative analyses focusing on the detection and preliminary risk quantification of potential safety signals following initiation of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs).

Methods We emulated an active surveillance system in an historical setting by conducting repeated annual cohort studies using nationwide Danish healthcare data (1996–2016). Outcomes were selected from the European Medicines Agency's Designated Medical Event list, summaries of product characteristics, and the literature. We followed patients for a maximum of 6 months from treatment initiation to the event of interest or censoring. We performed Cox regression analyses adjusted for standard sets of covariates. Potential safety signals were visualized using heat maps and cumulative hazard ratio (HR) plots over time.

Results In the total study population, 969,667 new users were included and followed for 461,506 person-years. We detected potential safety signals with incidence rates as low as 0.9 per 10,000 person-years. Having eight different exposure drugs and 51 medical events, we identified 31 unique combinations of potential safety signals with a positive association to the event of interest in the exposed group. We proposed that these signals were designated for further evaluation once they appeared in a prospective setting. In total, 21 (67.7%) of these were not present in the current summaries of product characteristics.

Conclusion The study demonstrated the feasibility of performing epidemiological surveillance using sequential, cumulative analyses. Larger populations are needed to evaluate rare events and infrequently used antidepressants.

Little research has been done on female board representation in emerging market multinational enterprises (EMNEs). Our paper considers the role of female board representation and its impact on open innovation (OI) in the unique context of emerging markets. We draw on upper echelons and institutional theories to understand how female board representation and cross-country institutional contexts influence coupled OI. Combining a 10-year (2009–2019) dataset with a rich in-depth content analysis of 183 (EMNEs) engaged in OI, our results reveal a significant positive association between female board representation and a firm's commitment to coupled OI initiatives. We also find that country-level institutional factors affect and positively moderate the relationship between female board representation and coupled OI. In emerging market environments where managerial perception and cultural beliefs sometimes hinder the promotion of females into top positions, our work has implications for EMNEs regarding how they harness diversity. We contribute to the OI literature by showing that female board representation enhances corporate OI investment within EMNEs.

Swedish railway infrastructure maintenance has been outsourced through gradualexposure since 2002. The idea behind outsourcing was to reduce cost and improveefficiency. However, railway maintenance cost has increased faster than railwaymaintenance operations, resulting in neglected railway maintenance. Since railwayoperation is governed by the contract design the purpose of this paper was to explore therelationship between contract design and maintenance outcome within Swedish railwayinfrastructure maintenance. To explore this relationship linear regression analysis wasused. The result indicates that asset knowledge and accessibility to the track areimportant to consider for improving maintenance operations.

Globalization and the surge of competition across industries forced companies to improve their supply chain capabilities to serve their customers efficiently and effectively. Due to this fact, businesses are no longer capable of handling all supply chain operations without collaboration and coordination with other firms. One of the key obstacles to coordination is the lack of information sharing and trust between firms since they view information as a sensitive asset. Digital technologies like blockchain, with its inherited features, have the capability to facilitate real-time information sharing, solve trust issues, and improve end-to-end visibility across the supply chain. This licentiate thesis highlights the impact of multiple aspects of information sharing on the bullwhip effect mitigation and explores the potential of blockchain technology as a new coordination mechanism for reducing information distortions, enhancing trust, and orchestrating decision making. Three research papers have been produced within this context and are appended to the thesis. Paper A presents an information sharing-based blockchain architecture to mitigate the bullwhip effect in service supply chains. Paper B aims to explores the literature in terms of using multiple aspects of information sharing to lessen the bullwhip effect. Finally, Paper C introduces an agent-based modeling and simulation approach for two aspects of information sharing: “what to share” and “how to share.” The results show that blockchain technology does provide a significant solution to trust-based issues and information sharing visibility considering the bullwhip effect mitigation. The results also provide a guide for supply chain managers to achieve better coordination and serve as a roadmap for supply chain researchers.

Despite a growing number of Corporate Innovation Hubs (CIHs) in recent years, limited attention has been paid to understanding the key problems that arise among organizations collaborating through CIHs. In particular, organizations often experience Not-Invented-Here (NIH) and Not-Sold-Here (NSH) problems, i.e. negative attitudes towards absorbing external knowledge and towards sharing internal knowledge externally. Consequently, many CIHs fail to deliver and are regarded as “innovation theatres” rather than engines of renewal. By drawing upon an inductive multiple case study of five CIHs, their parent companies and associated startups, located in Silicon Valley (USA) and the Gothenburg region (Sweden), the article sheds light on how CIHs can mitigate NIH and NSH problems in knowledge transfer. Specifically, we investigate the causes, consequences and mitigating mechanisms of NIH and NSH problems among the organizations collaborating through a CIH. These findings are presented in a framework that connects causes and consequences with the corresponding mitigating mechanisms. We also present new theoretical implications for the literatures on NIH and NSH.

Data-driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. As a result, the use of real plant and process data in developing and testing data-driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating, and interpreting the data. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time-dependent processes. An R-Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP. 

Employing a panel setting of 88 counties in the State of Ohio over the five-year period ending in 2006, this study aims to investigate the applicability of the knowledge spillover theory of entrepreneurship in explaining the relationships between flagship enterprises, entrepreneurial clusters, and business entry rates. The study confirms the overall positive relationship between flagship enterprises and startup rates, and the negative relationship between entrepreneurial clusters and startup rates. It further demonstrates that the effect of clusters is moderated by local unemployment rates so that higher rates of unemployment weaken the negative impact of entrepreneurial clusters on startup rates. Based on the evidence collected, policy makers should increase support for flagship enterprises in their regions, and would-be business owners should consider locating their ventures in proximity to flagship companies.

During the COVID-19 crisis, small and medium-sized enterprises (SMEs) in developing markets, marred by significant institutional voids, grappled with a perennial lack of resources. This article seeks to understand how these SMEs activated their dynamic capabilities to manage business relationships during different phases of the crisis. Relying on the social exchange theory and drawing on semi-structured interviews with 42 business-to-business (B2B) SME owners in Nigeria, we examine the relational governance mechanisms of dynamic capabilities for SMEs during the COVID-19 crisis. Our findings reveal 12 relational governance mechanisms of dynamic capabilities of B2B SMEs. Furthermore, we disaggregate these 12 mechanisms into 34 relational governance micro-foundational components and demonstrate their relevance for B2B SMEs during different stages of the COVID-19 crisis in Nigeria.

Using data from world-leading digital-driven/technology multinational enterprises (DTMNEs), we draw from the resource orchestration theory to investigate the associations between business model (BM) drivers and firm performance during crisis periods. Drawing on data from the COVID-19 pandemic period, we deploy diverse analytical approaches including multivariate linear regressions and aggregated composite index statistical methods in examining how the BMs of our sampled DTMNEs drive firm performance. Our study highlights six methodological approaches that can be utilised by decision-makers in examining which variables in their BM drive better firm performance. Our findings revealed that the principal component analysis and multicriteria decision analysis (PROMETHEE methods) that espouse the use of aggregate composite index can provide significant and consistent predictive results in comparison to the traditional linear methods when examining the association between BM and firm performance during crisis periods. The paper provides policy and managerial implications on how firms and decision-makers can bolster business continuity, resilience, and plasticity by using analytical lenses that identify optimum resource orchestration during crises.

As the unfolding climate crisis escalates, incumbent manufacturing companies are increasingly sensing and seizing sustainability opportunities—ideas that help to generate value in a more sustainable way than existing alternatives. Prior literature has underscored the importance of opportunity recognition and has theorized various types of circular business models to address sustainability in practice. However, there is a knowledge gap regarding the step in between: undertaking an assessment that provides a foundation for subsequently pursuing a circular business model. Based on a multiple case study of four innovation projects pursuing sustainability, this article identifies capability assessment, ecosystem alignment, and value-capture viability as key dimensions in evaluating sustainability opportunities prior to circular business model design and development. These insights are aggregated into a framework that allows companies to conduct a systematic assessment of sustainability opportunities in practice. The framework provides new theoretical implications for the literature on circular economy and business model innovation, and it offers hands-on advice for management practice. 

Many engineering courses include assignments where students work together in projects. The approach promotes students, among other things, to be educated and skilled in project management and managing system thinking within complex engineering environments. However, a problem with project-based learning is to accomplish a fair and valid assessment of individuals in a team setting. For example, in many project-based courses, students are only graded pass or fail, or graded collectively as a group. This paper presents results from a new course design based on CDIO-principles, with the aim to increase our understanding of individual assessment and grading in a project-based course. A preliminary conclusion is that it is possible to introduce individual assessment in a project team, assessing the learning outcomes and obtain a high level of student satisfaction. The course development process described in this study has implied a lot of struggling for the teachers involved. Hence, some general aspects of introducing CDIO-principles in a project-based course are also discussed. 

Recent years have seen much interest in business models as vehicles towards sustainability, cf. [1]. Conventional LCA, however, fails to properly capture the environmental impacts of a business model. Here, we introduce the background and the principles of Business Model LCA, a new LCA methodology for the assessment of environmental performance of business models. Methodological innovations are based on an understanding of the difference and relatedness between product and business. The key innovation is that BM-LCA centres its analysis on the ’business model’ instead of the ’product function’ as in conventional LCA. This requires the functional unit to represent the business (e.g., as ‘profit per time unit’) and the need to couple the monetary flows of the business to the material and energy flows of the product system via a set of ’coupling’ equations. BM-LCA contributes to environmental business analysis and could open up a new avenue of research where LCA and business analysts collaborate on business model innovation for sustainability.

The emerging research stream on digital sustainability examines how digital technologies enable the creation of environmental and social value. The need to finance the creation of such value calls for a business model perspective that combines value creation and value capture. To extend the digital sustainability literature in this regard, we adopt a microfoundations perspective and argue that the configuration of value creation influences the decision of users to pay for a value proposition or not, which in turn affects organizational value capture. Applying a crisp-set qualitative comparative analysis to 130 smart city initiatives in Switzerland, we develop a three-dimensional framework of business models for digital sustainability. The framework comprises 12 theoretically possible business model types, representing distinct business model configurations. We contribute to the digital sustainability and the microfoundations literatures by analyzing, explaining, and classifying the diversity of digitally enabled business models in the context of smart cities.

Overview: Few companies and organizations remain innovative for long periods of time, and despite high ambitions, many innovation initiatives fail to yield the desired results. Identifying how best to measure the effectiveness of innovation initiatives is key to ensuring that those efforts actually help an organization achieve its overall goals. This article offers organizations and their leaders practical advice for measuring innovation effectively. Specifically, it defines innovation measurement and its importance in precise terms and summarizes six years of our research on innovation measurement in multiple firms and industries. We articulate nine critical lessons for improving innovation measurement in real-world practice clustered into three overarching themes: Strategy, Organization, and Measurement Design. Each of the nine lessons elucidates a problem, identifies potential consequences, and proposes concrete solutions that organizations can implement as they strive to better measure and, ultimately, improve their innovation management initiatives.

The number of fighter pilots in Sweden retiring from service, many prematurely, currently exceeds the number of those being trained to replace them. This article examines the factors and circumstances related to the work motivation of pilots and what the Swedish Armed Forces (SAF) can do to retain them. It examines the perspectives of fighter pilots serving today and those who have chosen to leave the SAF since 2013, providing descriptions of different ideal professional types and what might provoke them to leave the profession, or motivate them to stay longer. Four ideal types of pilots are identified. These ideal types are (a) the extrinsically motivated; (b) the high-performing; (c) the family-oriented; and (d) the specialist. This article supports the idea that there is no single inducement for fighter pilots to leave the SAF. Our recommendations for retaining pilots differ depending on type and experience level. While increased salaries and better retirement agreements are essential, local career opportunities, less time away from family, more administrative support to squadrons, long-term career planning, rewarding challenges, and opportunities to study are also important. Some uniquely positive aspects, such as serving a higher purpose, flying experiences, and squadron community, motivate fighter pilots to remain.

Project management is known for its tools and techniques that are used to plan and deliver projects in a controlled context. Megaprojects don't always fit well into this paradigm due to their size, complexity and longevity. Megaprojects often start without precisely defined goals and without a detailed knowledge of how the project will progress or the outcomes will be delivered. We examine the requirements for governance of megaprojects by reviewing the literature and reflecting on practice. We use the analytical model of where, how and what to illustrate different units of analysis (i.e., context, governance and goals) in megaprojects in three countries and to illustrate how goals and governance move. Building upon the governance and performance management literature, the paper contributes to the understanding of moving goals and governance for ensuring performance. We propose a framework for diagnosing goals and we list six systemic errors that result in a misfit.

Manufacturing firms struggle to break away from their pre-existing business models, offerings, routines, and capabilities. The present study used path dependency as a theoretical lens to investigate a single longitudinal case study of a leading manufacturing company based on in-depth interviews with senior executives and managers. The analysis contributes to extending the digital servitization and path-dependence literature by proposing four path-breaking mechanisms: (1) organizational reconfiguration, (2) reconfiguration of value offerings, (3) opportunity exploration, and (4) knowledge reconfiguration. The framework developed based on these mechanisms generated valuable insights for manufacturing firms seaking to to break away from their dominant paths. 

The digital age we live in offers companies many opportunities to jointly advance sustainability and competitiveness. New digital technologies can, in fact, support the incorporation of circular economy principles into businesses, enabling new business models and facilitating the redesign of products and value chains. Despite this considerable potential, the convergence between the circular economy and these technologies is still underinvestigated. By reviewing the literature, this paper aims to provide a definition and a conceptual framework, which systematize the smart circular economy paradigm as an industrial system that uses digital technologies during the product life-cycle phases to implement circular strategies and practices aimed at value creation. Following this conceptualization, the classical, underlying circular economy principle, ‘waste equals food’, is reshaped into an equation more fitting for the digital age-that is to say, ‘waste + data = resource’. Lastly, this paper provides promising research directions to further develop this field. To advance knowledge on the smart circular economy paradigm, researchers and practitioners are advised to: (i) develop research from exploratory and descriptive to confirmatory and prescriptive purposes, relying on a wide spectrum of research methodologies; (ii) move the focus from single organizations to the entire ecosystem and value chain of stakeholders; (iii) combine different enabling digital technologies to leverage their synergistic potential; and (iv) assess the environmental impact of digital technologies to prevent potential rebound effects.

Business settings and ecosystems have in the past three decades been transformed by software utilization. It is being applied in several industries shaping innovation—platforms—and business characteristics, thus attracting ever more interest from both practitioners and researchers. During the last 10 years, research on software ecosystems (SECOs) has expanded, and is strongly related to the development of digital ecosystems. This expansion has led to the need to review the status of SECO research, and the present article provides a state-of-the-art literature review on the topic. We explain the connection between the relatively new research field of SECOs and the traditional streams of ecosystem research. This article contributes novel definitions of SECOs and SECO configuration, and proposes a theoretical model illustrating the relationship between vital contingency categories and processes. We identify significant research gaps and present a future research agenda.

The prior Venture Capital research (VC) has examined the micro processes of syndication and alliance formation. However, a macro and more systemic view is lacking, where past research has neglected the global VC-ecosystem. Using a qualitative method and an abductive approach, we combine and integrate two strands of research, on VC and ecosystems, to shed light on the crucial dynamics in the VC industry. We provide a VC-ecosystem definition and portray the ecosystem architecture in a segmentation matrix of investor types and roles, including Active Hubs and Complementors. Moreover, our findings identify and explain central Hub orchestration mechanisms: enablers, governance, partner management, co-specialization, and nurturing. The study concludes with a discussion on the theoretical and managerial implications, and suggestions for future research on a global ecosystem, which operates at a higher level than the traditional firm-level ecosystems on which the previous research generally focuses.

This paper introduces business model life cycle assessment (BM-LCA), a new method for quantifying the environmental impacts of business models. Such a method is needed to guide business decisions towards decoupling economic activity from environmental impact. BM-LCA takes the business model itself as the unit of analysis and its economic performance as the basis of comparison. It can be applied to any type of business model involving material or resource use. In BM-LCA, monetary flows are coupled to material and energy flows. The methodology expands on conventional life cycle assessment (LCA) by elaborating the goal and scope definition and dividing it into two phases. The first descriptive phase details the business models to be compared. It includes a mapping of product chain actors and identifying business operations and transactions related to the product. The second coupling phase defines a profit-based functional unit and sets up the coupling equations expressing the economic relations to the product. Thereafter, conventional LCA procedures are followed to assess environmental impacts. The key innovation on LCA methodology is the development of a functional unit that captures the economic performance of a business model and links it to a product system. BM-LCA provides thus an important link between LCA and business competitive advantage.

In recent years, there have been studies focusing on the use of different types of autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and chemical processes. However, in many cases the focus was placed on detection. As a result, practitioners are encountering problems in trying to interpret such complex models and obtaining candidate variables for root cause analysis once an alarm is raised. This paper proposes a novel statistical process control (SPC) framework based on orthogonal autoencoders (OAEs). OAEs regularize the loss function to ensure no correlation among the features of the latent variables. This is extremely beneficial in SPC tasks, as it allows for the invertibility of the covariance matrix when computing the Hotelling T2 statistic, significantly improving detection and diagnosis performance when the process variables are highly correlated. To support the fault diagnosis and identification analysis, we propose an adaptation of the integrated gradients (IG) method. Numerical simulations and the benchmark Tennessee Eastman Process are used to evaluate the performance of the proposed approach by comparing it to traditional approaches as principal component analysis (PCA) and kernel PCA (KPCA). In the analysis, we explore how the information useful for fault detection and diagnosis is stored in the intermediate layers of the encoder network. We also investigate how the correlation structure of the data affects the detection and diagnosis of faulty variables. The results show how the combination of OAEs and IG represents a compelling and ready-to-use solution, offering improved detection and diagnosis performances over the traditional methods.

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.

In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.

The circular economy (CE) has the potential to capitalise upon emerging digital technologies, such as big data, artificial intelligence (AI), blockchain and the Internet of things (IoT), amongst others. These digital technologies combined with business model innovation are deemed to provide solutions to myriad problems in the world, including those related to circular economy transformation. Given the societal and practical importance of CE and digitalisation, last decade has witnessed a significant increase in academic publication on these topics. Therefore, this study aims to capture the essence of the scholarly work at the intersection of the CE and digital technologies. A detailed analysis of the literature based on emerging themes was conducted with a focus on illuminating the path of CE implementation. The results reveal that IoT and AI play a key role in the transition towards the CE. A multitude of studies focus on barriers to digitalisation-led CE transition and highlight policy-related issues, the lack of predictability, psychological issues and information vulnerability as some important barriers. In addition, product-service system (PSS) has been acknowledged as an important business model innovation for achieving the digitalisation enabled CE. Through a detailed assessment of the existing literature, a viable systems-based framework for digitalisation enabled CE has been developed which show the literature linkages amongst the emerging research streams and provide novel insights regarding the realisation of CE benefits.

Twenty-seven years have elapsed since Bergen, Dutta, and Walker Jr. (Citation1992) published their work on agency theory in marketing. Agency theory is still relevant in marketing today. However, since 1992, there has been no comprehensive update of the literature on agency theory in marketing-related contexts despite the various developments in marketing, such as the advent of the internet, that have occurred in the interim. This paper covers the application of agency theory to marketing-related topics between 1992 and 2018. It seeks to update the overall knowledge of this application, suggesting new areas of research and managerial implications.

The reliability of electronic devices depends on the environmental loads at which they are exposed. Climatic conditions vary greatly from one geographical location to another (from hot and humid to cold and dry areas), and the temperature and humidity vary from season to season and from day to day. High levels of temperature and relative humidity mean high water content in the air, but saturated conditions (i.e. 100 % RH) can also be reached at low temperatures. This paper analyses the relationship between temperature, dew point temperature, their difference (here called ΔT), and occurrence and time period of dew point closeness to temperature on transient condensation effects on electronics.

This paper has two parts: (i) Data analysis of typical climate profiles within the different zones of the Köppen -Geiger classification to pick up conditions where ΔT is very low (for example ≤0.4 °C). Various summary statistics of these events are calculated in order to assess the temperature at which these events happen, their durations and their frequency and (ii) Empirical investigation of the effect of ΔT ≤ 0.4 °C on the reliability of electronics by mimicking an electronic device, for which the time period of the ΔT is varied in one set of experiments, and the ambient temperature is varied in the other. The effect of the packaging of the electronics is also studied in this section.

The statistical study of the climate profiles shows that the transient events (ΔT ≤ 0.4 °C) occur in almost every location, at different temperature levels, with a duration of at least one observation (where observations were hourly in the database). The experimental results show that presence of the enclosure, cleanliness and bigger pitch size reduce the levels of leakage current, while similar high levels of leakage current are observed for the different durations of the transient events, indicating that these climatic transient conditions can have a big impact on the electronics reliability.

This commentary lays out four challenges that currently prevent capital markets from contributing to a socially and environmentally sustainable economy. It reflects on how these can be turned into opportunities and the role of transdisciplinary research and action in promoting such change.

Strategic talent management remains challenging for IT companies, especially since the COVID pandemic. This challenge is due mainly to the limited supply of highly skilled prospects. Add high workforce mobility to the mix, resulting in considerable hiring, training, and development costs. Employer Branding is an effective tool to help IT employers overcome these challenges and attract employees to these employers. Employer Branding is a strategy that firms can utilize to manage their reputation as a “great place to work.” With the proliferation of social media, review sites, and electronic word of mouth, this reputation has increasingly been influenced by the evaluations employees share publicly and access on those platforms, especially employer review sites. IT firms must understand what values matter most to employees to manage their brands effectively. Based on a content analysis of 94,365 employee reviews, this research evaluates eight values that IT professionals consider when evaluating IT employers. This article expands on previous research and looks at employees’ reactions to those values before and after COVID. By comparing the results of pre-COVID and after COVID, we can see which values are most important to employees and provide recommendations for IT firms on how they can use employer brand value propositions to attract and retain IT talent post-COVID.

Large infrastructure investments are expected to be of sustained value to society for a long time. Such investment projects include, for instance, hospitals, tunnels, sport arenas, power plants, roads, railways, and bridges. They involve a complex organization of contracts and agreements. The client is expected to plan, procure, and determine the critical steps of a project, while the contractor should solve issues raised by the client. Many of these agreements are path-dependent and reflect past routines, experiences, and contacts. As such, many investments tend to return to similar sources instead of replacing routines and collaborations that did not work. This can cause change orders that furthermore reflect consequences such as cost and time overruns. While much is known about these effects in construction projects, this paper sheds light on the drivers of change orders. We build upon a sample of 234 observations responding to a survey on investment planning. The results show that project assumptions are often wrong and inadequate in large investments. Such wrong assumptions are caused by interpersonal and leadership issues, poor planning, or sometimes even intentional profit-seeking. Our results show that clients and contractors have different perceptions and enter contractual obligations differently. The implication is, therefore, that better routines of documentation, more frequent feedback, and more accurate or precise standards may close the gap between planning and what is actually achieved. More precise contractual agreements may also create a better process to procure, manage projects, and allocate resources. 

Open innovation and innovation performance have been widely studied in the literature. However, few studies have examined the impact of openness to different types of external knowledge on start-ups’ innovation performance. Moreover, previous literature could be further complemented by additional investigation into how the coworking spaces provided by accelerator programs may engender informal sources of knowledge that enhance the innovation performance of accelerated start-ups. To address this research gap, we investigate whether start-ups participating in accelerator programs can enhance their innovation performance through information transfer from informal networks provided by business accelerator programs. In order to do so, we draw two-stage data collection data from 113 start-ups accelerated by Italian accelerators from 2013 to 2016 and the response data collected in 2018. Our results reveal that coopetitors, educators, and investors are beneficial for different innovation outcomes of accelerated start-ups. These findings contribute to the innovation management literature, the small business management literature, the literature on accelerators and the coworking spaces literature.

The traditional view that large industrial corporations are the primary engines of innovation and economic progress has gradually been replaced by the notion that place is the key organizing platform for innovative activity in the modern knowledge economy. The geography of innovation has shown that innovative activities tend to cluster in space, due to the advantages of certain cities and regions. Recent literature suggests that a regional diversity of economic activities that are related in a cognitive or technological sense is advantageous, as it facilitates knowledge spillovers that foster positive economic outcomes. This re-specifies the traditional dichotomy between economic specialization and diversity and opens up new lines of inquiry. This thesis aims to contribute to the literature on the geography of innovation and related variety with studies that treat the relationships between related industry variety, innovation and entrepreneurship in explicit fashions, thus addressing important research gaps. The overall purpose of the thesis is therefore to examine and explain the relationship between regional related variety and regional economic change, in terms of: (a) entrepreneurship, and; (b) innovation.  The thesis consists of an introductory text and five appended papers. Paper 1 provides a bibliometric study of the literature on related variety, while papers 2 to 5 provide empirical analyses that address the implications of related variety for regional economic outcomes in terms of growth, entrepreneurship and innovation. The main findings of the thesis work suggest that related variety facilitates knowledge spillovers that unlock entrepreneurial opportunities, and that knowledge spillover entrepreneurship underpins the external economies of scope that arise from related variety. In addition, the thesis finds that entrepreneurship is an important driver of economic diversification in related activities. Furthermore, findings presented in the thesis align with previous literature that has reported a positive influence of related variety on regional innovation, which indicates the presence of positive knowledge externalities that foster innovation. It is however argued that the thesis work extends on previous findings by emphasizing the function of entrepreneurship in realizing the knowledge spillover effects of related variety. The importance of entrepreneurship is further reinforced by the finding that unrelated variety in particular is associated with firm-level entrepreneurial innovation. Drawing on the notion of Knightian uncertainty, the thesis work concludes that the process of innovation may require business decisions under ‘true’ uncertainty about expected returns, particularly when combinations of unrelated knowledge are attempted as they are new and unprecedented, and such pursuits can be understood as acts of entrepreneurial innovation.