Choosing Between Growth and Glory

2020 ◽  
Vol 66 (5) ◽  
pp. 2050-2074 ◽  
Author(s):  
Sharon Belenzon ◽  
Aaron K. Chatterji ◽  
Brendan Daley
Keyword(s):  
Business Strategy ◽  
Formal Model ◽  
Firm Strategy ◽  
Empirical Support ◽  
High Growth ◽  
Large Data ◽  
New Venture ◽  
Strategic Choices ◽  
Data Set ◽  
Financing Environment

Prior work has established that the financing environment can impact firm strategy. We argue that this influence can shape the earliest strategic choices of a new venture by creating a potential trade-off between two objectives: rapid growth and reaping the benefits of a positive reputation (glory). We leverage a simple reputation-building strategic choice—naming the firm after the founder (eponymy)—that is associated with superior profitability. Next, we argue via a formal model that the availability of/dependence on external financing can explain why high-growth firms are rarely eponymous. We find empirical support for the model’s predictions using a large data set of 1 million European firms. Eponymous firms grow considerably more slowly than similarly profitable firms. Moreover, eponymy varies in accordance with the firm’s financing environment in a pattern consistent with our model. We discuss implications for the literature on new-venture strategy. This paper was accepted by Bruno Cassiman, business strategy.

scholarly journals ICT-based innovation and its competitive outcome: the role of information intensity

2019 ◽  
Vol 22 (2) ◽  
pp. 383-404 ◽  
Author(s):  
Paolo Neirotti ◽  
Danilo Pesce
Keyword(s):  
Information And Communication Technologies ◽  
Fixed Effects ◽  
Innovation Process ◽  
High Growth ◽  
Large Data ◽  
Competitive Dynamics ◽  
Data Set ◽  
Content Type ◽  
Role Of Information

Purpose Prior research highlights the vital role of Information and Communication Technologies (ICT) for innovation in response to environmental conditions. However, there is a lack of studies that analyse the determinants of ICT investments on the innovation activities of firms in relation with their impacts on the industrial and competitive dynamics using large data sets. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors investigate the effects of ICT investments on the industrial and competitive dynamics for a large and representative panel data set. All the industries are included, and lagged effects of ICT investments are studied. The model is tested on a seven-year panel (2008–2014) of 231 Italian industries using two-stage least squares instrumental-variables estimators with industry time and fixed effects. Findings The results indicate that munificent industries and higher ICT spending are interrelated facts, showing that in sectors with more growth opportunities firms invest more in ICT and this leads to higher industry concentration, greater profit dispersion and higher competitive turbulence in the sector. Also, the paper shows that SMEs can rarely take advantage of their ICT-based innovation to start high-growth phenomena. Practical implications The results suggest that ICT-based innovation may create competitive advantages that are hard to sustain over the long-term raising important implications for managers involved in ICT-enabled innovations and policy-makers involved in building programs to foster innovation. Originality/value Against the backdrop of today’s digital transformation, the paper enriches our understanding on the disruptive effects exerted by the digitalization of the innovation process and provides a base to continue the investigation of industrial changes and competitive dynamics.

The U-Shaped Investment Curve: Theory and Evidence

2007 ◽  
Vol 42 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Sean Cleary ◽  
Paul Povel ◽  
Michael Raith
Keyword(s):  
Financial Constraints ◽  
Empirical Support ◽  
Large Data ◽  
Conventional Wisdom ◽  
Empirical Literature ◽  
Investment Behavior ◽  
Data Set ◽  
Large Data Set ◽  
Internal Funds ◽  
Curve Theory

AbstractWe analyze how the availability of internal funds affects a firm's investment. We show that under fairly standard assumptions, the relation is U-shaped: investment increases monotonically with internal funds if they are large but decreases if they are very low. We discuss the tradeoff that generates the U-shape, and argue that models predicting an always increasing relation are based on restrictive assumptions. Using a large data set, we find strong empirical support for our predictions. Our results qualify conventional wisdom about the effects of financial constraints on investment behavior, and help to explain seemingly conflicting findings in the empirical literature.

Innovazione e competitivitŕ nella trasformazione di prodotti da agricoltura biologica: il caso delle imprese artigiane agro-alimentari lombarde

2009 ◽  
pp. 77-96
Author(s):  
Anna Gaviglio ◽  
Alberto Pirani ◽  
Pedol Martina Licitra
Keyword(s):  
Business Strategy ◽  
Food Industry ◽  
Dynamic Capabilities ◽  
Firm Strategy ◽  
Value Added ◽  
Capabilities Approach ◽  
Global Market ◽  
The Other ◽  
Small Enterprise ◽  
Strategic Choices

- Innovation and competitiveness of organic food artisan enterprise in Lombardy The present work is inspired by the observation that today "innovation" is frequently a key word for Italian food industry. It seems that this is the only way to successfully face global market. We can see that the food industry is following two different strategies: on one hand there are enterprises, more and more specialized, industrial production oriented whereas on the other hand there are enterprises oriented towards high quality, value added and niche productions. The aim of this work is to investigate behaviour of organic artisan enterprises in Lombardy and to take it as an example of how much innovation helps to increase small enterprise's competitiveness. Particularly the study regards two of the main regional supply chains, dairying and cereal from organic agriculture. It tries to determinate new ways to get good economic performance and, consequently, new ways to take part in the strategic choices. The work tries to check also acquisitions of innovation and which role innovation plays in a business strategy based on an upgraded concept of differentiation. This means, therefore, to focus the attention on the strategic nature of innovative choices and to define what each artisan enterprise is able to do: in an exclusive way; distinguishing it from the contenders; showing it to costumers; maintaining a special position in the market. The method of survey to define resources and ability of each organic artisan firm of the sample, is the Dynamic capabilities approach. The study evidences the key-resource and the key-capabilities combination on which is based the introduced innovation and it describes the main course of this particular sector. The data reveal a prevailing asset: marketing ability is the opportunity that a lot of enterprises efficiently use to differentiate the own distributive system - export is an example - and also to satisfy different customers. This attitude shows a resources and capability configuration more complex than the other. A systematic approach toward innovation in small enterprise, involving all the actors of the supply chain, could carry best economic improvements. Organic market, despite of the conventional one, is a new sector and it offers several opportunities to successful marketing initiative. New products, new services, new communications can attract more customers at all levels.JEL Codes: Q13, L10Key words: innovation, competitiveness, firm strategy, agricultural and food-artisan Enterprises

scholarly journals Does distance from the equator predict self-control? Lessons from the Human Penguin Project

2017 ◽  
Vol 40 ◽  
Author(s):  
Hans IJzerman ◽  
Marija V. Čolić ◽  
Marie Hennecke ◽  
Youngki Hong ◽  
Chuan-Peng Hu ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Social Determinants ◽  
Empirical Support ◽  
Large Data ◽  
Self Control ◽  
Data Driven ◽  
Data Set ◽  
Large Data Set ◽  
The Social

AbstractWe comment on the proposition “that lower temperatures and especially greater seasonal variation in temperature call for individuals and societies to adopt … a greater degree of self-control” (Van Lange et al., sect. 3, para. 4) for which we cannot find empirical support in a large data set with data-driven analyses. After providing greater nuance in our theoretical review, we suggest that Van Lange et al. revisit their model with an eye toward the social determinants of self-control.

scholarly journals Strategic Ambiguity of Party Positions in Multi-Party Competition

2016 ◽  
Vol 6 (3) ◽  
pp. 527-548 ◽  
Author(s):  
Thomas Bräuninger ◽  
Nathalie Giger
Keyword(s):  
Formal Model ◽  
Winning Strategy ◽  
Party Competition ◽  
Policy Position ◽  
Strategic Choices ◽  
Data Set ◽  
Party Leaders ◽  
Clear Cut ◽  
Party Positions ◽  
Gathering Data

Party competition is largely about making policy promises to voters. We argue that the clarity of the expressed policy position may be equally important. If blurred messages toward different audiences and therefore ambiguous positions can attract votes from different groups, parties have incentives to present ambiguous rather than clear-cut policy platforms. We present a formal model of multi-party competition with stochastic voting where party leaders make strategic choices on both the position and the level of ambiguity of their platforms. Leaders respond to the demands of two principals, the general public and party core constituencies. We derive two hypothesis on the location and ambiguity of party platforms and provide initial tests of these hypotheses in a comparative setting in 14 Western European democracies gathering data on voter and party left-right positions from Eurobarometer surveys and electoral manifestos. Ambiguity of party profiles is estimated using a variant of Wordscores on a newly established data set of electoral manifestos. We find that platforms become more ambiguous as the preferences of the two principals diverge. Our findings imply that ambiguity can be a winning strategy for parties, especially in settings with strong partisan lines.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer
Keyword(s):  
Mitochondrial Genome ◽  
Tandem Repeats ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Molecular Data ◽  
Phylogenetic Position ◽  
Data Set ◽  
Living Fossil ◽  
Latimeria Chalumnae ◽  
Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

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