Rethinking New Venture Growth: A Time Series Cluster Analysis of Biotech Startups’ Heterogeneous Growth Trajectories

Startups are crucial job creators and drivers of economic growth. Research on startups has predominantly targeted high-growth startups, while a comprehensive understanding of alternative growth journeys remains limited. Addressing this gap, we employ the theory of early firm growth and the time-calibrated theory of entrepreneurial action to examine 416 biotech startups. We use time series cluster analysis to unveil four heterogeneous new venture growth trajectories. These are characterized by unique timings, paces, and sequences of financial, human, and innovative resource-related activities. This study contributes to new venture growth research, particularly in science-based high-tech startups, with its nuanced understanding of diverse growth pathways, including intriguing notions of early failure, growth reversal, and high and moderate steady growth

Human Capital in Corporate Venture Capital Units and Its Relation to Parent Firms’ Innovative Performance

Incumbent firms utilize corporate venture capital (CVC) as a vehicle to enhance their innovative performance. Still, little is known about the central individual in this context: the CVC unit head, who acts as a knowledge broker between portfolio ventures and the parent organization. We combine human capital theory with the attention-based view to investigate the effects of various facets of CVC unit heads' experience on parent firms' innovative inputs in the form of explorative and exploitative patenting and innovative outputs, specifically market and technological breakthrough innovation. Drawing on a dataset of U.S.-listed firms with CVC units, our findings contribute to the CVC literature in three ways. First, we introduce CVC unit heads' career experiences as new individual-level antecedents of parent firms' innovative performance. Second, we enhance the understanding of the CVC-core paradox, which is the tension between exploration and exploitation in the parent firm. Finally, by employing a combination of patents and new product introductions as metrics for innovative performance, we bridge the gap between learning and innovation in extant CVC research, demonstrating that the effects of CVC unit heads include customer-facing outcomes.</p

Business Model Innovation: Development, Concept and Future Research Directions

Purpose: Although business model innovation (BMI) has gained substantial importance in recent years, there is still a limited understanding of this phenomenon. Yet, the corresponding scholarly literature has previously been characterized by a heterogeneous comprehension of the concept. This situation demands an analysis that synthesizes current scienti c knowledge, uncovers research gaps and underdeveloped areas, and estab- lishes a solid foundation for future research. Design: The study applies an extensive quantitative and qualitative analysis of extant BMI literature, making the concept more transparent and manageable for science and management. Findings: The study presents a set of yielding de nitions of the extant BMI literature and an integrated de ni- tion to promote a common understanding of BMI. In addition, it classi es the eld into six particular research areas. Given the identi ed dominance of exploratory research designs, future research should put more em- phasis on well-founded conceptual articles that stabilize and consolidate basic research as well as con rma- tory quantitative empirical investigations. Research limitations / Implictions: Given the database-centered, eclectic nature of the analytical approach, it is unlikely that every available and applicable scienti c publication is included in the analysis. Furthermore, the classi cation of the studies according to certain criteria leads to a loss of information and sometimes can- not be conducted free of doubt since studies occasionally touch multiple criteria. Originality / Value: Against the background of the study’s focus on BMI, its comparably broad literature basis, and its quantitative and qualitative analysis approach, which provides straightforward recommendations for future research, the study caters an original contribution to the eld.

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Observation of gravitational waves from the coalescence of a 2.5–4.5 M ⊙ compact object and a neutron star

We report the observation of a coalescing compact binary with component masses 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M ⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of 55−47+127Gpc−3yr−1 for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap

Sharing Algorithmic Management Information in the Sharing Economy – Effects on Workers’ Intention to Stay with the Organization

While digital labor platforms market entrepreneurship to workers in the sharing economy, their algorithmic management (AM) also has negative consequences. Earlier studies suggest that workers’ perceived lack of information about the AM practices negatively affects their control over their work and their feelings towards the organizations. Yet, besides first evidence that an increased information transparency leads to higher worker cooperation, there is a dearth of research on the effects of providing high levels of information about the applied AM criteria and the assigned jobs on workers’ intention to stay with the organizations. Drawing on principal-agent theory, organizational algorithmic transparency and work autonomy, I perform an experimental ridehailing study addressing this gap. Providing high levels of the two information types separately leads to higher staying intentions, whereas combining high levels of both information types shows no significant effect. The study contributes to the research stream about digital labor platforms using AM