Recent Submissions

  • Forward-looking distribution network charges considering lumpy investments

    Govaerts, Niels; Bruninx, Kenneth; Le Cadre, Helene; Meeus, Leonardo; Delarue, Erik (Journal of Regulatory Economics, 2021)
    Many regulators are pushing for more cost-reflective distribution network charges to inform end users of the grid infrastructure costs their behavior causes. Since future investment costs can be avoided by reducing simultaneous peak loads, forward-looking, coincident peak charges are often proposed. Under the assumption of convex network costs, it has been shown that optimal charges signal long-run marginal network costs, triggering an optimal trade-off between network expansion and peak load reduction. In practice, however, network investments are lumpy, requiring engineering methods to estimate ill-defined marginal costs based on long-term peak demand forecasts. In this paper, we derive the optimal forward-looking network charge set by a social welfare maximizing regulator, endogenously considering investment lumpiness and uncertain consumer demand. While the optimal tariff still equals marginal network costs in essence, it now depends on a multitude of network- and demand-related parameters. Our results demonstrate that forward-looking network charges require accurate information on willingness to pay for peak demand, which currently is typically unknown to regulators.
  • Innovation, policy, and regulation in electricity markets

    Kiesling, Lynne; Meeus, Leonardo; Pollitt, Michael (Energies, 2021)
    The rise of intermittent renewable energy generation, the coming mass penetration of electric vehicles and moves to decarbonise the gas grid are leading to widespread innovation experiments within electricity systems and their associated markets. These innovative experiments give rise to policy and regulatory questions, which must be ad dressed if innovations are to become business as usual within the lower voltage electricity distribution grid
  • Structural sampling: A technique for exposing social structure

    Shah, Sonali; Gorbatai, Andreea (2015)
    Qualitative research has been heralded for contributing novel insights and theoretical perspectives to the management and organization literature (Eisenhardt, 1989; Pratt, 2009; Van Maanen, 1979, 1998; Whetten, 1989). The processes used by qualitative researchers to achieve these outcomes are often invisible to the reader, yet a set of principled, systematic approaches underlies the practices followed by qualitative researchers. In this chapter we illuminate a sampling technique that has been employed in recent works but has yet to be delineated as a methodology: structural sampling.
  • Testing Coleman’s Social Norm Enforcement Mechanism: Evidence from Wikipedia

    Piskorski, Mikołaj Jan; Gorbatai, Andreea (American Journal of Sociology, 2017)
    Since Durkheim, sociologists have believed that actors in dense network structures experience fewer norm violations. Coleman proposed one explanatory mechanism, arguing that dense networks provide an opportunity structure to reward those who punish norm violators, leading to more frequent punishment and in turn fewer norm violations. Despite ubiquitous scholarly references to Coleman’s theory, little empirical work has directly tested it in large-scale natural settings with longitudinal data. The authors undertake such a test using records of norm violations during the editing process on Wikipedia, the largest user-generated online encyclopedia. These data allow them to track all three elements required to test Coleman’s mechanism: norm violations, punishments for such violations, and rewards for those who punish violations. The results support Coleman’s mechanism.
  • Guest Editorial for ‘Management and the Future of Open Collaboration’

    Gorbatai, Andreea; Jemielniak, Dariusz; O'Neil, Mathieu (Journal of Organizational Change Management, 2016)
    Open collaboration gained prominence as a practice with the advent of Free and Open Source Software (FOSS) communities in the 1980s. Since then, technological advances have enabled individuals, firms and communities to implement applications relying on large-scale, open collaboration. Open collaboration research is a field of rapid growth in organizational theory and innovation. Initial work in this area has focused on the management and governance of FOSS projects as well as on a wide range of user communities in fields as different as sports, scientific equipment users and manufacturers, library information systems, computer games, and medical equipment. Another research stream has focused on open innovation from a corporate perspective, studying the ways in which traditional organizations can harness the power of communities to innovate, or on the creation of 'boundary' or 'hybrid' organizations that facilitate collaboration between open-source communities and firms. Yet another stream has examined open collaboration platforms, particularly the online encyclopedia Wikipedia, assessing participation processes and collaboration outcomes in this particular setting. Finally a more critical stream of research has characterized open collaboration both negatively, as ‘prosumption’ in which labor is transferred from workers to consumers, thereby generating new means of exploitation; or positively, as the ‘germ form’ of a post-capitalist society where exchange value will disappear altogether. T
  • The influence of private equity and venture capital on the post-IPO performance of newly-public acquirers

    Matanova, Natalia; Steigner, Tanja; Sutton, Ninon; Thompson, Linh (The North American Journal of Economics and Finance, 2022)
    This paper examines the influence of private equity (PE) and venture capital (VC) ownership on the post-initial public offering (IPO) performance of newly-public acquirers. Our results show that acquirers with PE- or VC-backing at the time of the IPO perform better long-term than acquirers without such backing. More importantly, while acquirers without financial backing experience negative long-run returns from first-year acquisitions, acquirers with continued PE- and VC-backing perform significantly better when making acquisitions within the first year after going public. However, acquiring firms and investors should be aware that for mergers in the second and third year post-IPO, continued VC ownership has a detrimental long-term impact. In contrast, higher levels of continued PE ownership tend to have a positive relationship with long-run performance.
  • There’s many a slip ‘twixt the cup and the lip”: HR management practices and firm performance

    Vossaert, Lien; Anseel, Frederik; Collewaert, Veroniek; Foss, Nicolai J. (Journal of Management Studies, 2021)
    Divergent but complementary perspectives have been articulated regarding how management practices and their implementation influence firm performance. Integrating such perspectives in the human resource (HR) management literature, we examine how HR management practices formulated at firm level interact with HR decisions at lower levels, and how this affects firm performance. HR implementation models have proposed that consistency in HR practices across organizational levels and units is key; conversely, idiosyncratic deals (i-deals) theory advances individualization as a central principle, suggesting that lower-level initiative in making decisions that reflect local circumstances should have beneficial effects. Addressing the interplay between the consistency and individualization perspectives in a sample of 870 employees nested in 36 firms, we present evidence suggesting that individualized HR decisions positively affect firm performance only in the presence of strong firm-level HR practices. This interplay occurs through two mediating social exchange processes: perceived organizational support and perceived distributive justice.
  • Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management

    De Moor, Bram J.; Gijsbrechts, Joren; Boute, Robert (European Journal of Operational Research, 2021)
    Deep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop ‘good’ replenishment policies in inventory management. We show how transfer learning from existing, well-performing heuristics may stabilize the training process and improve the performance of DRL in inventory control. While the idea is general, we specifically implement potential-based reward shaping to a deep Q-network algorithm to manage inventory of perishable goods that, cursed by dimensionality, has proven to be notoriously complex. The application of our approach may not only improve inventory cost performance and reduce computational effort, the increased training stability may also help to gain trust in the policies obtained by black box DRL algorithms.
  • Digitization in the market for entrepreneurial finance: Innovative business models and new financing channels

    Bertoni, Fabio; Bonini, Stefano; Capizzi, Vincenzo; Colombo, Massimo; Manigart, Sophie (Entrepreneurship Theory and Practice, 2021)
    Digitization creates new financial channels that complement traditional intermediaries, but may raise concerns over fraud, cybersecurity, or bubbles. Artificial intelligence and machine learning change the way in which traditional investors work. This special issue focuses on economic, cultural, and regulatory determinants of fintech development, and on the new forms of information production and processing engendered by digital entrepreneurial finance. We provide a general overview of digitization in the market for entrepreneurial finance, illustrate how the different articles in the special issue contribute to advance our knowledge, and identify promising avenues for research.
  • Does the sector matter? An analysis of high-growth firms and industry growth rates

    Dillen, Yannick; Vandekerkhof, Pieter (Journal of Small Business and Enterprise Development, 2021)
    Purpose This paper aims to analyze the effect of industry growth rates on the characteristics of high-growth firms (HGFs) that are active in a particular industry. By making a distinction between HGFs active in stable and declining industries and HGFs active in growing and high-growing industries, it is analyzed if the main dimensions of firm performance are significantly different for HGFs active in one of these different industry types. Gaining more insight into this industry aspect of high firm growth is important as governmental measures towards HGFs may be more effective if they have a specific sectoral focus. Design/methodology/approach A subset of 740 Belgian HGFs was analyzed. Data were gathered from the Belfirst database. HGFs were classified within their corresponding industry type: a declining industry (negative growth), a stable industry (0 −5% growth), a growing industry (5 −10% growth) and a high-growth industry (>10% growth). Four dimensions of structural firm performance that are expected to correlate with high growth were taken into consideration: productivity (value added per FTE), profitability (ROA), innovativeness (intangible assets) and financial health (solvency and liquidity).Tukey's range tests in conjunction with post-hoc analysis of variance (ANOVA) tests were carried out to test for significant differences in all the mentioned variables for the HGFs in the four different industry types. Findings Results show that HGFs active in a stable industry are not significantly more profitable or innovative than HGFs active in a growth industry. However, significant differences could be encountered when it comes to the other two dimensions of structural firm performance: productivity and financial health. It is shown that HGFs active in declining and stable industries are significantly more productive than HGFs active in growth industries and high-growth industries. Also, HGFs active in declining and stable industries have significantly higher liquidity ratios than firms active in growth industries, pointing towards a better financial health for HGFs in nongrowing industries. Research limitations/implications The results confirm the conceptual logic that the differences between resource-based view (RBV) and industrial organization (IO) propositions will have an impact on the drivers of firm performance and high business growth. Every future study that focuses on the growth determinants of HGFs should be aware that considering the subset of HGFs as one homogenous group may be suboptimal. It is likely that the growth determinants of both HGF types will indeed be fundamentally different. Originality/value Until now, all studies on HGFs have considered the subset of HGFs as a whole. This paper tried to disentangle the subset based on the growth rate of the industry in which HGFs are mainly active. In this proposition, a reason for the lack of knowledge about characteristics of HGFs may – at least partially – be found in the fact that industry membership plays an important role in determining the characteristics of a high-growth firm. Future studies focusing on high-growth determinants may benefit from systematically taking the industry growth rates into account, with the knowledge that the propositions of two different theories – IO and RBV – may be the fundamental drivers of a firm's high-growth rates.
  • Welcoming new entrants into European electricity markets

    Schittekatte, Tim; Reif, Valerie; Meeus, Leonardo (Energies, 2021)
    In this review paper, we select four important waves of new entrants that knocked on the door of European electricity markets to illustrate how market rules need to be continuously adapted to allow new entrants to come in and push innovation forward. The new entrants that we selected are utilities venturing into neighbouring markets after establishing a strong position in their home market, utility-scale renewables project developers, asset-light software companies aggregating smaller consumers and producers, and different types of communities. We show that well-intentioned rules designed for certain types of market participants can (unintentionally) become obstacles for new entrants. We conclude that the evolution of market rules illustrates the importance of dynamic regulation. At the start of the liberalisation process the view was that we would deregulate or re-regulate the sector after which the role of regulators could be reduced. However, their role has only increased. New players tend to improve the sustainability of the electricity sector in environmental, social, or economic terms but might also present new risks that require intervention by regulators.
  • The regulatory framework for independent aggregators

    Schittekatte, Tim; Deschamps, Vincent; Meeus, Leonardo (The Electricity Journal, 2021)
    The importance of independent aggregators has been acknowledged in the recently adopted EU Clean Energy Package (CEP). The CEP obliges all Member States to develop a regulatory framework to allow these players to enter the market, but it leaves many of the details of implementation to the national level. In this paper, we take stock of current practices in regulating the contractual relationship between the supplier and the independent aggregator. The actions of an independent aggregator can cause an imbalance in a supplier’s portfolio, and suppliers have also asked for a compensation payment for forgone revenues. We find that the first issue has been handled with a perimeter correction in most countries, while the second issue is more controversial. The need for a compensation payment has been challenged and many different compensation models are being tested. We distinguish between the regulated, the corrected, and the contracted model. We conclude that more guidance is needed at EU-level for convergence on a more harmonized approach.
  • Individual differences in the susceptibility to forecasting biases

    De Baets, Shari; Vanderheyden, Karlien (Applied Cognitive Psychology, 2021)
    We set out to investigate whether interindividual differences in cognition affect the susceptibility to four forecasting biases: (a) optimism bias, (b) adding noise to forecasts, (c) presuming positive autocorrelation when series are independent, and (d) trend damping. All four biases were prevalent in the results, but we found no consistent relationships with cognition (cognitive style, cognitive reflection). Our sample included both novice and expert forecasters. They did not differ significantly in their susceptibility to biases. The lack of individual differences in bias susceptibility suggests that universal approaches to debiasing are possible.
  • Battery Energy Storage System (BESS) as a service in Finland: Business model and regulatory challenges

    Ramos, Ariana; Tuovinen, Markku; Ala-Juusela, Mia (Journal of Energy Storage, 2021)
    Battery Energy Storage Systems (BESS) can provide services to the final customer using electricity, to a microgrid, and/or to external actors such as the Distribution System Operator (DSO) and Transmission System Operator (TSO). In this paper, BESS as a service business model archetypes are drawn from case studies of 10 BESS as a service projects in Finland. It is found that, in addition to the service being provided by the BESS, the ownership of the system can vary: it can either be owned by the final consumer of electricity or by a third party who will provide the BESS as a service. The findings of the interviews are placed within the Finnish regulatory framework for storage and demand response services. It is concluded that the key enablers for the BESS as a service business model are a regulatory framework that allows stacked revenues and technological interoperability across a multi-customer business model.
  • The impact of the COVID-19 Crisis on growth-oriented SMEs: Building entrepreneurial resilience.

    Schepers, Jelle; Vandekerkhof, Pieter; Dillen, Yannick (Sustainability, 2021)
    This study explores how the COVID-19 pandemic has forced Flemish growth-oriented entrepreneurs to build entrepreneurial resilience. We rely on a research framework that consists of a "challenge-reaction-learning loop" to empirically investigate how entrepreneurial resilience is built in times of the COVID-19 crisis. To investigate this complex entrepreneurial learning process, we use data that have been collected during the first and second wave of the COVID-19 pandemic. By using several datapoints, we could identify (1) the specific challenges growth-oriented firms are facing as a result of the COVID-19 crisis; (2) how these entrepreneurs reacted to these challenges; and (3) what they learned during the first and second wave of the pandemic and how they perceive the future. By making this entrepreneurial learning process explicit and dividing it into an iterative "challenge-reaction-learning loop", this study is relevant for all entrepreneurs, as it contains several interesting lessons learned. We also contribute to academic literature as we provide future researchers a tangible framework to further elucidate how entrepreneurial resilience is built in times of crisis.
  • Making the TEN-E regulation compatible with the Green Deal: Eligibility, selection, and cost allocation for PCIs

    Schittekatte, Tim; Pototschnig, Alberto; Meeus, Leonardo; Jamasb, Tooraj; Llorca, Manuel
    The European Green Deal calls for a revision of the Regulation on guidelines for trans-European energy infrastructure (TEN-E Regulation). The focus of the TEN-E Regulation was on accelerating the development of strategically important projects linking energy networks across the EU, labelled as Projects of Common Interest (PCIs). We provide seven recommendations on how to revise the Regulation to align it with the new full decarbonisation objective. We split the analysis in three parts: the eligibility, selection and cost allocation of PCIs. Regarding eligibility, first, oil networks should be excluded, while the case of gas networks is debatable. Second, power-to-X technologies, electric charging infrastructure and (smart) gas distribution grids could be added to the scope. Regarding selection, first, the Ten-Year Network Development Plan (TYDNP) should be integrated over all energy vectors using an open-source model. Second and third, the scenarios used in the TYNDPs should be subject to the European Commission's approval, while the approval decision for cost-benefit analysis methodologies should be reallocated from the Commission to ACER. Finally, regarding cost allocation, first, cross-border cost allocation decisions should leave all involved jurisdictions with similar benefit-to-cost ratios to increase commitment. Second, affordability should be the only award criterion for European funding.
  • Regulatory experimentation in energy: Three pioneer countries and lessons for the green transition

    Schittekatte, Tim; Meeus, Leonardo; Jamasb, Tooraj; Llorca, Manuel (Energy Policy, 2021)
    Regulatory experimentation is a novel approach to enable innovation in the energy sector, while maintaining the protection of consumers. We define regulatory experimentation as a temporary removal of regulatory barriers. This can be in the form of a derogation from a rule, but it can also mean assigning responsibility to players to conduct activities that they are normally not allowed to engage in. The outcomes of regulatory experiments inform future regulation. In this paper, we discuss experiences with regulatory experimentation in the energy sector of three pioneering countries: the Netherlands, Great Britain, and Italy. We compare the implementations along six design dimensions: eligible project promoters, scope of the derogations, length of the derogations, administration of the experiments, the access to public funding, and transparency. We also discuss how the early approaches have evolved in these countries. Finally, we look ahead and discuss how regulatory experimentation can evolve in the future European context to contribute to the green transition.
  • Global market integration, efficiency orientation, and drivers of foreign subsidiary divestments

    Belderbos, René; De Michiel, Federico; Sleuwaegen, Leo; Wu, Shubin (Journal of World Business, 2021)
    Differences in global market integration across industries have important repercussions for MNC strategy and the drivers of manufacturing subsidiary divestment decisions. Global industry integration and the associated competitive pressures lead MNCs to adopt cost efficiency strategies for their subsidiary networks, and subsidiary divestment decisions are strongly driven by labor cost considerations. In non-integrated industries, host country demand conditions are the prime driver of divestments. These patterns are the most salient for MNCs that have aligned their strategy with the global industry environment. Analysis of the divestment hazards of 3827 Japanese manufacturing subsidiaries in 57 countries provides support for these conjectures.
  • The importance of board risk oversight in times of crisis

    Dupire, Marion; Haddad, Christian; Slagmulder, Regine (Journal of Financial Services Research, 2021)
    This study investigates the relationship between board risk oversight practices at financial institutions in the EU and systemic risk during the sovereign debt crisis. More specifically, we examine whether European banks and insurance companies that had strong board-level risk oversight in place before the onset of the sovereign debt crisis fared better during the crisis. We construct a risk oversight index based on publicly available, hand-collected data, which captures the strength of the institutions’ board-level risk governance practices. We find that financial institutions with stronger board risk oversight prior to the crisis were less vulnerable to the sovereign debt crisis, after controlling for other factors. The results are consistent and economically relevant for SRISK, probability of default, and Delta-CoVaR, three measures of systemic risk that are commonly used in the context of financial institutions.
  • Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning method

    Wellens, Arnoud P.; Udenio, Maxi; Boute, Robert (International Journal of Forecasting, 2021)
    The winning machine learning methods of the M5 Accuracy competition demonstrated high levels of forecast accuracy compared to the top-performing benchmarks in the history of the Mcompetitions. Yet, large-scale adoption is hampered due to the signi cant computational requirements to model, tune, and train these state-of-the-art algorithms. To overcome this major issue, we discuss the potential of transfer learning (TL) to reduce the computational e ort in hierarchical forecasting and provide proof of concept that TL can be applied on M5 top-performing methods. We demonstrate our easy-to-use TL framework on the recursive store level LightGBM models of the M5 winning method and attain similar levels of forecast accuracy with roughly 25% less training time. Our ndings provide evidence for a novel application of TL to facilitate practical applicability of the M5 winning methods in large-scale settings with hierarchically structured data.

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