Yuping Yin, Frank Crowley, Justin Doran, Jun Du and Mari O'Connor
This paper examines the innovation behavior of family-owned firms versus non-family-owned firms. The role of internal family governance and the influence of external stimuli…
Abstract
Purpose
This paper examines the innovation behavior of family-owned firms versus non-family-owned firms. The role of internal family governance and the influence of external stimuli (competition) on innovation are also considered.
Design/methodology/approach
The data of 20,995 family and non-family firms across 38 countries are derived from the World Bank Enterprise Survey during the period 2019–2020. Probit models are used to examine the impact of family ownership, family governance, and competition on innovation outcomes.
Findings
Family firms are more likely to make R&D investments, acquire external knowledge, engage in product innovation (including innovations that are new to the market) and process innovation, relative to non-family firms. However, a high propensity of family member involvement in top management positions can reduce innovation. Competition has a negative impact on innovation outcomes for both family and non-family firms, but it has a positive moderating effect on the innovation activities of family firms where a higher level of family member involvement in management is present.
Originality/value
This paper provides novel insights into family firm innovation dynamics by identifying family firms as more innovative than non-family firms for all types of indicators, debunking the idea that family firms are conservative, reluctant to change, and averse to the risks in innovation activities. However, too much family involvement in decision making may stifle some innovation activities in family firms, except in cases where the operating environment is highly competitive; this provides new insights into the ownership-management dynamic of family firms.
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Yanwen Yang, Yuping Jiang, Qingqi Zhang, Fengyuan Zou and Lei Du
It is an important style classification way to sort out suits according to the button arrangement. However, since the different dressing ways of suit cause the buttons to be…
Abstract
Purpose
It is an important style classification way to sort out suits according to the button arrangement. However, since the different dressing ways of suit cause the buttons to be easily occluded, the traditional identification methods are difficult to identify the details of suits, and the recognition accuracy is not ideal. The purpose of this paper is to solve the problem of fine-grained classification of suit by button arrangement. Taking men's suits as an example, a method of coordinate position discrimination algorithm combined faster region-based convolutional neural network (R-CNN) algorithm is proposed to achieve accurate batch classification of suit styles under different dressing modes.
Design/methodology/approach
The detection algorithm of suit buttons proposed in this paper includes faster R-CNN algorithm and coordinate position discrimination algorithm. Firstly, a small sample base was established, which includes six suit styles in different dressing states. Secondly, buttons and buttonholes in the image were marked, and the image features were extracted by the residual network to identify the object. The anchors regression coordinates in the sample were obtained through convolution, pooling and other operations. Finally, the position coordinate relation of buttons and buttonholes was used to accurately judge and distinguish suit styles under different dressing ways, so as to eliminate the wrong results of direct classification by the network and achieve accurate classification.
Findings
The experimental results show that this method could be used to accurately classify suits based on small samples. The recognition accuracy rate reaches 95.42%. It can effectively solve the problem of machine misjudgment of suit style due to the cover of buttons, which provides an effective method for the fine-grained classification of suit style.
Originality/value
A method combining coordinate position discrimination algorithm with convolutional neural network was proposed for the first time to realize the fine-grained classification of suit style. It solves the problem of machine misreading, which is easily caused by buttons occluded in different suits.
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Sorphasith Xaisongkham and Xia Liu
The main purpose of this research is to examine the impact of institutional quality and sectoral employment on environmental degradation in developing countries. This paper also…
Abstract
Purpose
The main purpose of this research is to examine the impact of institutional quality and sectoral employment on environmental degradation in developing countries. This paper also re-examined the validity of the Environmental Kuznets Curve (EKC) hypothesis and estimated the long run impact of explanatory variables on CO2 emissions.
Design/methodology/approach
In this paper, the balanced panel data for the period 2002–2016 was used based on data availability and applied two-step SYS-GMM estimators.
Findings
The results showed that institutional quality such as government effectiveness (GE) and the rule of law (RL) reduce CO2 emissions and promote environmental quality in developing countries. Interestingly, the authors found new evidence that employment in agriculture and industry has a positive impact on pollution, while employment in the service sector was negatively associated with CO2 emissions, and the validity of the EKC hypothesis was confirmed. In addition, the research suggests that strong institutional frameworks and their effective implementation are the most important panacea and should be treated as a top priority to counteract environmental degradation and achieve the UN Sustainable Development Goals.
Originality/value
This is the first study to examine the short run and long run effects of institutional quality and sectoral employment on environmental degradation using the balanced panel data for a large sample of developing countries. This paper also used a special technique of Driscoll and Kraay standard error approach to confirm the robustness results and showed the different roles of sectoral employment on environmental quality.
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The purpose of this paper is to identify the causal effect of high-speed railways (HSRs) and investigate the affecting channels; the second purpose is to examine how HSRs change…
Abstract
Purpose
The purpose of this paper is to identify the causal effect of high-speed railways (HSRs) and investigate the affecting channels; the second purpose is to examine how HSRs change the distribution of economic activity across cities and sectors.
Design/methodology/approach
A difference-in-difference strategy is implemented to estimate the impact of recently built HSRs on local economic performance in China, exploiting the geography and time variations in HSR operations.
Findings
Using panel data from China’s City Statistical Yearbook 2001–2019, the authors find that HSRs lead to a significant increase in cities’ gross domestic product (GDP) and GDP per capita, but the authors do not find any significant change in GDP growth. This conclusion still holds true after the authors address the endogeneity problems. A mechanism analysis shows that HSRs improve local economic performance mainly by increasing fixed asset investment. The authors also find that the HSR investment is a policy that favors metropolitan areas due to the larger increase in the GDP for larger cities and with HSRs, the industrial and service sectors will further agglomerate in larger cities.
Originality/value
The authors contribute to the literature in several ways. First, this paper improves the estimation strategy in identifying the HSR impact on the local economic performance. Second, this paper investigates the affecting channels of HSRs. This paper proves that HSRs in China promote the cities’ economic performance mainly by increasing the fixed asset investment. Third, this study provides evidence for the new economic geography models pioneered by Krugman (1991).
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The purpose of this study is to exploring the factors influencing renewable energy consumption intentions and behaviors among eco-tourism visitors in Bangladesh, developing…
Abstract
Purpose
The purpose of this study is to exploring the factors influencing renewable energy consumption intentions and behaviors among eco-tourism visitors in Bangladesh, developing theory of sustainable consumption behaviors (TSCB).
Design/methodology/approach
Based on review of previous empirical studies and other literatures, and collection of 399 usable responses, the study is conducted through partial least squares structural equation modeling (PLS-SEM) by using Smart PLS3.3.3.
Findings
The study results divulge that renewable energy consumption intentions significantly influence renewable energy consumption behavior; and the carbon mitigation norms and energy saving norms significantly impact on renewable energy consumption intentions among eco-tourists in Bangladesh.
Practical implications
The findings imply that availability of renewable energy consumption options may attract tourists towards eco-tourism in Bangladesh.
Originality/value
This study is one of the first attempts to developing the theory of sustainable consumption, exploring the integrated impacts of carbon mitigation norms, energy saving norms and renewable energy consumption intentions on eco-tourists’ renewable energy consumption behaviors in Bangladesh.