Paz Rico-Belda and Bernardí Cabrer-Borrás
This study uses an extensive sample of firms from Germany, France, UK, Portugal and Spain with the aim of obtaining conclusive results on the determinants that drive a firm to be…
Abstract
Purpose
This study uses an extensive sample of firms from Germany, France, UK, Portugal and Spain with the aim of obtaining conclusive results on the determinants that drive a firm to be high-growth firm (HGF). This sample includes micro firms, which are not generally considered in the literature. There are several reasons to take them into account: not excluding an important part of the business fabric, the results can be extrapolated, the study can show if micro firms also present high growth and if there are differences in the factors that determine the probability of being an HGF between both segments of firms.
Design/methodology/approach
A multivariate dynamic model of binary choice is used to analyse the probability of a company being classified as high growth. Then, with the Blinder and Oaxaca decomposition, the differences in the probability of being an HGF between micro firms and non-micro firms are studied.
Findings
The results show that HGFs demonstrate persistence, and younger firms are more likely to be HGFs. Micro firms also register high growth, although they are less dynamic and show a negative differential with respect to larger firms as highlighted by the characteristic component.
Originality/value
In some countries, such as Spain and Portugal, micro firms predominate, and these tend to be less dynamic, so to identify how to improve business dynamics, the factors that limit the growth of this type of company must first be determined. In this paper, in line with Acs and Mueller (2008), we therefore include firms with less than ten employees so as not to exclude an important part of the business fabric and to ascertain whether this type of firm also shows high growth.
研究目的
由於高速增長的公司被發現較其對手享有較大的競爭優勢,故它們成為促使就業機會會異常地淨增的推動器。這些公司在這方面的能力,成為廣泛研究的課題。本文擬就這課題的探討作出一點貢獻。
研究設計/方法/理念
研究的方法是透過把研究焦點集中於一個何謂高速增長公司的更廣泛的定義,而該定義之所以更廣泛,是因為它納入高速增長的微型公司。研究人員以 Birch-Schreyer 指數把公司分類為高速增長。本研究旨在評定高速增長的公司在廣義上是否受賦予可把其區別於低增長公司的獨特特點。為達到這個目標,研究人員使用二元選擇的多元動態模型,去分析一間公司會被分類為高速增長的機率,繼而以 Blinder-Oaxaca 分解方法,去探討微型公司會被分類為高速增長的機率,與非微型公司的機率兩者之間的差異。
研究結果
研究結果顯示、高速增長的公司展示了毅力; 而且,年輕的公司更有可能成為高速增長的公司。研究結果亦顯示、雖然根據特徵成份所強調,微型公司的活力不及較大的公司; 而且,微型公司展示負面的差分,唯它們也躋身高速增長公司的行列。
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Bernardí Cabrer-Borrás, Paz Rico Belda and Dolores Botella Carrubi
The purpose of this paper is to analyse the determinants of the survival of Spanish companies.
Abstract
Purpose
The purpose of this paper is to analyse the determinants of the survival of Spanish companies.
Design/methodology/approach
Two approaches are used and they are complementary. The first approach analyses the determinants of survival probability. For this purpose, a binary choice model is built and estimated using a sample of companies from the main economic sectors taken from the SABI database. Likewise, the Blinder–Oaxaca decomposition is applied to quantify the difference between companies with employees and without employees and the proportion of this difference that owes to observed factors or unobserved factors. Finally, the second approach is a survival analysis carried out through the Cox proportional hazard model that identifies the determinants of the duration of business activity.
Findings
The results of the empirical analysis show that companies without employees present less favourable conditions for survival at all stages of their evolution than companies with employees.
Originality/value
The contribution of this study to the empirical literature consists in analysing the difference between companies with and without employees. Due to the structure of Spanish companies, this aspect and the determinants of such difference are essential for policymakers to increase the survival for companies.
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Paz Rico and Bernardí Cabrer-Borrás
The purpose of this paper is to analyse the gender differences of self-employment in Spain.
Abstract
Purpose
The purpose of this paper is to analyse the gender differences of self-employment in Spain.
Design/methodology/approach
A binary choice model is specified and estimated, using information from the Continuous Working Life Sample drawn from the registers of the Spanish Social Security. Moreover, the differences in self-employment between men and women are also analysed, through the decomposition proposed by Yun (2004).
Findings
The results indicate that the differences between both groups in the probability of being entrepreneurs stem from unobservable factors. The difference explained by the unobservable component is 84.12 per cent, whereas the rest, 15.88 per cent, is explained by the characteristics component. The explanatory factors of being an entrepreneur affect men and women in the same way, but to a different extent, explained mainly by factors related to gender.
Originality/value
This paper sets out to identify whether there are gender differences in the probability of becoming self-employed and, if there are, to quantify what part of the difference in entrepreneurship between men and women is explained by the characteristics of each gender group and what part is because of unobservable factors. From the perspective of the public authority, knowing the determinants that explain why the entrepreneurial activity is different depending on gender is fundamental in being able to reduce the entrepreneurial gap between men and women.
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Paz Rico and Bernardí Cabrer-Borrás
The purpose of this paper is to analyse if the divergences in the economic growth of the Spanish regions are a result of sectoral differences, company size or technological level…
Abstract
Purpose
The purpose of this paper is to analyse if the divergences in the economic growth of the Spanish regions are a result of sectoral differences, company size or technological level of the new firms that emerge in the market.
Design/methodology/approach
For this purpose, a model is specified and estimated in which the total factor productivity of Spanish regions is explained by business dynamics, innovation, human capital and the level of entrepreneurship in each region.
Findings
The results obtained lead the authors to conclude that entrepreneurship understood as both the creation of new firms and entrepreneurial activity, have a positive effect on productive efficiency and can explain the differences in the economic growth of the regions. In addition, the stock of human capital and the promotion of innovation act as catalysts for the productive efficiency of the regions. However, the results show that it is not enough to generate new firms to boost economic growth; these businesses must also be oriented towards sectors that promote technological innovation and with the objective to reach an adequate size.
Originality/value
Empirical studies use either the creation of new firms or the index of entrepreneurial activity as alternative measures of entrepreneurship. In this research, however, both variables are considered together. Specifically, the creation of new companies is used as a measure of regional business dynamics, and the entrepreneurial activity index, provided by the Global Entrepreneurship Monitor, as a measure of regional entrepreneurship. The main novelty of this paper’s approach is that it considers different types of entrepreneurial capital in considering productive sector, size and technological level of the new companies.
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The purpose of this paper is to explore the genesis of the first Macro‐Marketing Seminar and to review the institutionalization of macromarketing which resulted from it.
Abstract
Purpose
The purpose of this paper is to explore the genesis of the first Macro‐Marketing Seminar and to review the institutionalization of macromarketing which resulted from it.
Design/methodology/approach
The paper briefly reviews the history of macromarketing, the changes in society and marketing thought, and the seminal research which led to the first Macro‐Marketing Seminar.
Findings
Early macromarketing research was supplanted by a managerial marketing focus in mid‐twentieth century while at the same time society was awakening to the interconnections between human behavior and a broad range of societal problems. The early marketing theory seminars provided a template for the first Macro‐Marketing Seminar.
Originality/value
The paper explains the resurgence of macromarketing which from that first pivotal Macro‐Marketing Seminar has blossomed into a multifaceted and institutionalized area of study.
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Daniel Arturo Cernas Ortiz and Mark A. Davis
This paper aims to examine the influence of future and past negative time perspectives on job satisfaction and organizational commitment. The effect of national culture (Mexico…
Abstract
Purpose
This paper aims to examine the influence of future and past negative time perspectives on job satisfaction and organizational commitment. The effect of national culture (Mexico versus the USA) as a moderator of the above baseline relationships is also analyzed.
Design/methodology/approach
The research model is tested using survey data drawn from a sample of 287 Mexican and 274 US MBA students (N = 561). Regression analyses were used to test the hypotheses.
Findings
Future time perspective has a positive relationship with job satisfaction and organizational commitment. Past negative time perspective has a negative association with both job attitudes. The effect of future time perspective on job satisfaction was significantly stronger in Mexico than in the USA. No other significant differences between the countries were found in terms of the time perspective and job attitudes association.
Practical implications
The results have implications for managing dispositions that affect work-related attitudes and behaviors with consequences for organizational effectiveness.
Originality/value
The findings suggest that time perspective affects job attitudes. Further, they also suggest that the interplay between future time perspective and culture influences job satisfaction.
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Kerim Koc, Ömer Ekmekcioğlu and Asli Pelin Gurgun
Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management…
Abstract
Purpose
Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.
Design/methodology/approach
Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.
Findings
The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.
Practical implications
The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.
Social implications
Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.
Originality/value
The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.
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Xiaojie Xu and Yun Zhang
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important…
Abstract
Purpose
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.
Design/methodology/approach
The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.
Findings
The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.
Originality/value
The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.
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Bingzi Jin and Xiaojie Xu
The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both…
Abstract
Purpose
The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.
Design/methodology/approach
This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.
Findings
The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.
Originality/value
The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.
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Xiaojie Xu and Yun Zhang
The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention…
Abstract
Purpose
The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.
Design/methodology/approach
The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.
Findings
The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.
Originality/value
Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.