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This study aims to investigate the effects of corporate social responsibility (CSR) on corporate performance in the global retail industry. We adopt the modified data envelopment analysis (DEA) game cross-efficiency approach in assessing the longitudinal efficiency of a sample of 414 listed retail firm-year observations given by Forbes 2000 between 2013 and 2018. The performance result indicates that the Americas retail industry is growing steadily and continues to dominate other regions. Not only the retail industry but all industries in Europe have severe environmental safety rules and management requirements in business operations, hence, Europe also scored high in both the overall environmental index in CSR and all their component indices. Additionally, panel regression revealed that the environmental dimension in CSR is significantly and directly correlated to firm performance. This study’s findings will provide practitioners and policymakers with managerial and strategic implications to enhance their efficiency by applying the CSR dimension in the retail industry.

This research employs the Meta-Malmquist productivity index Data Envelopment analysis and panel data regression to figure out the performances of the construction industry from Taiwan, Korea, and C...

In the port industry, productivity is one of the vital factors for ports to develop competitiveness and market potential. The paper integrates context-dependent data envelopment analysis and forecasting models to conduct an efficient measurement of 53 Vietnamese ports and predict future performance in the port industry. By using the technique to get the measure of performance, this study builds the ports’ benchmark-learning stepping tools for the inefficient ports to enhance increasingly. The attractiveness and progress scores are resulted to assist the ports in situating themselves. Moreover, forecasting port performance results help decision-makers having more detailed information to make better decisions in their strategies and investments.

PurposeThis study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.Design/methodology/approachIn the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.FindingsThe DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.Research limitations/implicationsThe results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.Originality/valueThis study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

This study aims to evaluate the cargo and eco‐efficiencies of international container shipping companies (CSCs) for the period 2013–2017 for benchmarking purposes. An integration of two‐stage network directional distance function and network‐based approach in data envelopment analysis is utilized to distinguish the efficient operators. Results show that most CSCs are highly ranked in the cargo efficiency stage but lowly ranked in the eco‐efficiency stage and vice versa. This study emphasizes the strengths and weaknesses of each factor of every CSC. Results can help the inefficient CSCs find the areas for improvement by learning from their peers.

Several studies employ data envelopment analysis (DEA) in the area of portfolio optimization or stock market efficiency evaluation, however, they have not extended two-stage network DEA to further ...

This paper applies dynamic network slacks‐based data envelopment analysis to measure financial performance based on the interrelationship among investment, financing, and dividend decisions. The empirical results show that financial performance is determined simultaneously by the efficiency of decisions, and sample firms have good performance in investment stage, but need to improve their financing and dividend policies. The proposal financial performance measure explains the multicriteria of decision‐making rather than the single financial ratios. Besides, the research contributes a novel on the significant relationship between firm's ownership structure in the form of managers, government and foreign shareholders, and the firm's financial performance.

Assessing university resource allocation or misallocation is necessary to understand its impact on research and teaching productivity. To achieve this insight, this study conducts a two-stage data envelopment analysis to judge the “teaching and research efficiency” and “revenue efficiency” of 61 universities in Vietnam. We analyze what variables have the most effect on the efficiencies of the universities through improving results. Moreover, this research applies scale elasticity to distinguish the benchmark performance leader among Vietnam universities. Results indicate that universities are more efficient in terms of “revenue efficiency” than “teaching and research efficiency.” The scale elasticity decomposition findings show that multidisciplinary universities perform better than those specializing in science and technology and social science and economics. Finally, the competitive map to performance advancement strategies is exhibited. This map helps the university management to enhance their efficiencies.

This paper researches a method of rating competitiveness involving the estimation of the performance of semiconductor firms through Malmquist productivity index (MPI) and metafrontier Malmquist productivity index (MMPI). Regressions are used to find the relationship between intellectual capital and performance. Overall, technological innovations contribute to the improvement in the integrated circuit (IC) design sub-industry while increases in efficient production allow the IC foundry sub-industry and the IC packaging and testing sub-industry to maintain position. The regression results show human capital was critical to technological innovation while relational capital was important to efficient production.

This study employs the two-stage dynamic data envelopment analysis model to examine sustainability and corporate performances of the 29 largest multinational enterprises in the transport manufacturing industry from 2015 to 2019. The results show that, on average, sustainability has a positive relation to corporate performance and sustainability performance is better than corporate performance for all multinational enterprises (MNEs). In addition, we also found that corporate social responsibility (CSR) acts as a moderator in the relationship between sustainability and corporate performances. Overall, the American group leads the others in sustainability performance, followed by Asia and Europe. On the other hand, Europe has the highest score in terms of corporate performance. In terms of size, the results show that it is positively related to corporate performance. In contrast, leverage has a negative relationship with corporate performance. The findings of this research can assist inefficient MNEs in identifying areas for advancement by learning from their peers and provide perspective and insights into the plans for MNEs’ strategies and decisions in the future.

The COVID-19 crisis has an unprecedented impact on travel industry. Although Taiwan’s borders are still remain closed to non-essential international travel, travel web sites have become more important media for Taiwan residents to search for domestic travel activities. With the prevalence of the Internet and e-commerce nowadays, they are the rapidest and most efficient channels to look for information on the latest activities and travel security information. Information quality and system quality of travel web sites are extremely important bases since they decide users’ behavior of information exchange on the platforms. Since the Internet can immediately share information, consumers’ various knowledge sharing behaviors on sites can enhance knowledge creation. This study explores the correlations among information quality, system quality, knowledge sharing, and knowledge creation of travel web sites and treats consumers who have used travel web site services in Taiwan as subjects. It collects the data and analyzes hypotheses by structural equation modeling. The findings show that information quality and system quality of travel web sites positively and significantly influence knowledge sharing. Knowledge sharing shows positive and significant predictive power on knowledge creation. Considering about the coronavirus outbreak which has a significant impact on tourism industry, the conclusion provides suggestions for travel web site management and practice, which also can further serve as important strategies for continuous updating of travel web sites.

Given the environmental problems, especially in developing countries, as well as new environmental laws limiting the use of fossil fuels, governments seek to replace fossil fuels with clean energies like renewable energies. The purpose of this study was to assess the development of renewable energy as well as reviewing the laws and policies of China and Taiwan in this regard. We also constructed different scenarios based on national sources, in consideration of a significant increase in the electrical power generation and a reduction in the CO2 emission. Focus is placed on the renewable energy development and a significant reduction in the CO2 emission. The results showed that the photovoltaic solar systems have the greatest benefits in energy production and CO2 emission reduction in China and Taiwan. However, due to economic consideration and technical problems, there is still a long way to go.

Taiwan is an island in East Asia that strongly depends on imported fossil fuels. To reduce emissions and pollutants, there is interest in wind and solar energy. The current solar energy systems con...

For at least the last two decades, diversification has been an important issue in the strategic management research. This paper explores how diversification affects the corporate performance of the top 100 manufacturing companies in Taiwan, which were selected based on a ranking survey of Taiwanese manufacturing firms conducted by Common Wealth Magazine in 2014. This paper adopts a dynamic data envelopment analysis model to estimate efficiency in the first stage, and adopts OLS regression analysis in the second stage from 2009 to 2013. Two proxies, namely the Entropy approach and the Herfindahl–Hirschman Index, are used to proxy for diversification. The results indicate that diversification has positive impacts on dynamic efficiency, after controlling firm-specific factors. Concisely, this study empirically proves that diversification is an important corporate strategy for improving corporate performance over long-term periods. Overall, “putting eggs in various baskets” enable companies to not only survive, but also sustain competitive advantage in today’s challenging business world.

Nowadays, insurance companies are operating in a challenging and borderless business world. They should focus on improving their competitive advantages to secure increased profits and reduced costs. As fundamental elements of total quality management, performance measurement and risk management play important roles in ensuring sustainable operation. This study investigates the relationship between risk management committee structure and the operating efficiency of general insurance companies operating in Malaysia for the period 2008–2013. First, this research applies the dynamic network data envelopment analysis methodology, decomposing the typical two-stage operating process of general insurance companies. That is, we estimate the marketing efficiency and profitability efficiency. We find that the sample companies have to first improve their marketing efficiency, and then proceed to improve their profitability efficiency. Second, this study applies truncated regression to test the impact of risk manageme...

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

This research studies the causal relationship between energy consumption, gross domestic product (GDP), and foreign direct investment (FDI) in Germany for a period of 1971-2010. The empirical results reveal that there is a unidirectional causality running from GDP to energy consumption and from GDP running to FDI in Germany. This is due to the highly rising trends of economic activities in the country which can lead to the expansion in energy consumption. As there is an increase in economic activities within the country, then the growth rate will be in the rising path. As a result, the foreign investors will see the promising future and then invest in the host country. The conservative energy policy is recommended to support the energy saving because it will have little or no adverse effect on GDP. The energy efficiency should be applied by encouraging the use of renewable energy sources in economic activities as an alternative to stimulate the economic growth of the country. Also, the public expenditure should be expanded to increase the country’s economy and attract foreign investors. In addition, the government should support for the service industry such as insurance, finance and banking, and tourism because this type of industry does not consume as much energy as the manufacturing industry does in the overall manufacturing processes. Besides, the government should provide tax credit for the manufacturers who can fulfill the energy efficiency for their operation.

With the aim of predicting Taiwan’s energy consumption for the short term (1 year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years), this study applies autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANNs) models and the mean absolute percentage error (MAPE) approach is employed to measure prediction accuracy. Based on data extracted from over the period 1965–2010, the results indicate that the single variable ARIMA models illustrate superior performance than that of ANNs1. As to multivariable models, ANNs8 model including variables of energy consumption and exports show the most accurate prediction in short term and medium-long term, while ANNs6 model comprising energy consumption, GDP, and exports has the highest accuracy for medium term prediction. Meanwhile, ANNs5 model consisting of energy consumption and population shows the best accuracy for the long term prediction. Overall, it may conclude that exports and population are two essential variables to predict Taiwan’s energy consumption for the short, medium, medium-long, and long term periods. The results support the assumption that parsimonious set of variables incorporated in research models may not sacrifice prediction accuracy. This concludes the contributions of this study.

This study examines the causal relationship among economic growth, energy consumption and tourism development in Taiwan over the period from 1965 to 2010. Three Principle test results emerge from this study. First, test results indicate a long-run equilibrium relationship and a bi-direction of causality between energy consumption and tourism development with one proxy, number of visitors, being more significant than the other, visitor expenditures. Second, a bi-directional causality between energy consumption and economic growth is observed. Third, test results indicate no reciprocal causal relationship between tourism development and economic growth. From an energy conservation and sustainable tourism point of view, it is suggested policy makers and industry leaders develop high value, high profit tourism products that aim on attracting more visitor expenditures rather than numbers of visitor.

This study examines the effect of tourism development on energy consumption, CO2 and economic growth in China over the period from 1981 to 2010. An extension of ARIMA model was performed to investigate the relationship between variables. Two principle test results emerge from this study. First, increases on visitors may largely give rise to GDP. On the other hand, increase on tourism receipts may result in greater energy consumption and CO2 emission to some extent as compared to number of visitors. However, the amount of effects from either tourism receipts or number of visitors to energy consumption and CO2 emission are limited. From an energy conservation and economic growth point of view, the results support the hypothesis of tourism-led economic growth in the China economy with relatively limited increase of energy consumption and CO2 emission.

The balance between economic growth and environmental protection has been the core concern of policy makers in developing countries for the past two decades. This study is one of the few studies to empirically inspect the relationship between economic growth, FDI, and energy consumption over the period 1978-2010 in China. The results reveal that there is a unidirectional Granger causality running from GDP to energy consumption. This suggests that increase of GDP will consume more energy and implementing of the energy conservation policies and energy demand management policies in China may not have negative impact on economic growth. Besides, a bi-directional Granger causality has been found between energy consumption and FDI. This implies that Chinese government should cautiously evaluate the positive and negative effects of FDI inflows and put efforts into making more effective control policies on environmental protection.