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Environmental turbulence represents a double-edged sword, simultaneously fueling and hindering a firm’s entrepreneurial orientation (EO). Drawing on the theories of EO and network relationships, this study aims to develop and test a conceptual model that provides a nuanced account of the relationship between environmental turbulence and firm EO.

Data for this study were collected using a survey of high-technology firms in Hsinchu Science Park (HSP) in Taiwan. Questionnaires were mailed to 297 high-technology firms in the semiconductor, photoelectric and communication industries within HSP. Completed questionnaires were received from 94 firms, which included responses from 94 research and development managers and 462 employees.

The results reveal that the degree of environmental turbulence exhibits an inverted U-shaped relationship with a firm’s EO. Moreover, this relationship is positively moderated by network relationships between firms, but negatively moderated by organizational inertia.

The empirical and conceptual findings have important implications for understanding EO, because the findings explain causal relationships that transform a firm’s interactive and inner control capabilities into firm-level results.

Building upon the framework of glocalization, the purpose of this paper is to summarize the development of National Basketball Association (NBA) in Chinese market, explore its successful and unsuccessful places, and propose strategies of glocalization for the NBA as well as other overseas sport leagues.

The current case study was organized by summarizing the developmental history of NBA in China, analyzing its current promotional practices, investigating into its marketing strategies, and extrapolating practical references for other sport leagues aiming to penetrating into the Chinese marketplace.

The current case study concluded that when facing the current challenges, the NBA needs to bring authentic American cultural commodities while adding Chinese characteristics to accommodate local fans. Meanwhile, the NBA management needs to continue seeking ways to work out and through the differences in government models and cultural contexts between China and USA. In addition, this study suggested that the research framework of glocalization would be an ever intriguing inquiry needed for other sport organizations or leagues seeking expansion to overseas markets.

A thorough case study with the NBA that has achieved huge successes in Chinese markets will provide valuable implications for sport leagues to broaden their overseas markets.

Employees’ bootlegging innovation behavior is common and plays an important role in enterprise management. Based on the resource conservation theory and self-regulation theory, the purpose of this study is to explore the influence mechanism of leaders’ abusive supervision on employees’ bootlegging innovation behavior, with psychological safety as a mediator and mindfulness at workplace as a moderator.

Survey data were gathered from 591 employees’ self-assessment questionnaires in China. Hierarchical regression analysis was used to test the research model through SPSS and AMOS.

This study found that the leaders’ abusive supervision negatively affects employees’ bootlegging innovation behavior; employees’ psychological safety completely mediates the negative effect of leaders’ abusive supervision on employees’ bootlegging innovation behavior; and mindfulness at work moderates the influence of leaders’ abusive supervision on employee’ bootlegging innovation behavior, as well as the influence of leaders’ abusive supervision on employees’ psychological safety.

This study has significant implications in passive leadership that affect employees’ innovation. Authors found that leaders’ abusive supervise, mindfulness at workplace play a crucial role in employees’ bootleg innovation through psychological safety.

Theoretically, this study has enriched the antecedent research on employees’ bootlegging innovation behavior from the perspective of negative leadership behavior and employee psychology. And this study considered mindfulness at workplace as a boundary condition.

Firm size has been identified as one of the most important correlates with corporate social performance (CSP). Both conceptual and empirical research has been done to try to explicate and determine this relationship; however, the results from both theoretical and empirical research have indicated a mixed and sometimes inconsistent relationship because of endogeneity between firm size and CSP. This paper aims to add to the body of knowledge by identifying and addressing some of the limitations in determining the relationship between firm size and CSP.

Using the Arellano–Bond method to control for the endogeneity, this study tests the relationship between CSP and firm size using a panel of 380 public companies of various sizes; in various industry types; and across 19 countries in North America, Europe and Asia over a six-year period.

The results of the study show that firm size positively influences CSP and its subcomponents when endogeneity has been controlled for.

This study lends support for the theory of the firm framework that CSP attributes are embedded in the production process that leads to higher economies of scale, and the resource-based view of firms where firms that possess valuable and inimitable resources in CSR can lead to a sustainable competitive advantage over competitors. This suggests that as firms grow in size, they can leverage their resources to achieve greater economies of scale that will lead to better CSP over time.

This study addresses the potential endogeneity problem between firm size and CSP and offers a broader testing context.

The purpose of this study aims to modify a self-mixing laser mouse as an extremely cost-effective displacement sensor to measure the mechanical oscillation of a commercial shaker and a nano-positioning stage.

This kind of laser mouse, mostly consisting of a pair of vertical cavity surface emitting lasers, two photodiodes and an integrated signal processing unit, is capable of directly giving the x-axis and y-axis components of the measured vibrating displacement. Based on the laser self-mixing interference, the velocity of the object is coded into the Doppler frequency shift of the feedback light, which allows accurate determination of the vibration of the object.

A commercial shaker has been used to provide standard harmonic oscillation to test the displacement sensor. Within a vibrating frequency range of 110 Hz, the experimental results show that the micrometer scale resolution has been achieved at the velocity of up to 2 m/s, which is much improved compared with the image-based optical mouse. Furthermore, the measurements of the two dimensional displacement of a nano-positioning stage are performed as well. The minimum measurable velocity limit for this sensor has been discussed in detail, and the relative measurement error can be greatly reduced by appropriate selection of the modulation frequency of the triangular injection current.

These results demonstrate the feasibility of this device for the industrial vibration sensing applications.

The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem.

In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning.

The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow.

This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

Understanding farmers’ preferences for crop insurance attributes is crucial in designing better insurance products and guiding government policies but such research is lacking, particularly in developing countries. The paper aims to discuss these issues.

This study uses a survey featuring a discrete choice experiment and policy simulation.

Overall, crop insurance has positive values to farmers, although preference is heterogeneous based on socioeconomic characteristics and risk position. Policy simulation confirms the roles of liability in strengthening insurance participants’ welfare and premium subsidy in encouraging participation. Introducing one more product into the market can accommodate farmers’ diverse needs and lead to increases in both aggregated social welfare and participation while maintaining the current level of government expense in subsidy – a potential Pareto improvement.

Methodology employed is not the most novel in the choice experiment literature as many of the advances in choice experiment design could not be applied due to the actual condition in rural China and Chinese farmers’ capability in understanding the experiment.

The results indicate that the current single-product market structure using “low liability with high premium subsidies” cannot accommodate the diverse needs among farmers. Providing more varieties of liability-subsidy combinations, e.g. a high liability with low premium subsidy insurance product, can substantially improve participants’ welfare with little impact to the probability of participation.

The authors believe that this is one of the very few studies that that analyze farmers’ preferences and willingness to pay for the attributes of crop insurance products. It also shows how crop insurance product design can build upon farmers’ choices to achieve a potential Pareto improvement in aggregated social welfare in the context of a fast-developing crop insurance market.

Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning.

A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach.

The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes.

This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.

The purpose of this paper is to examine the dividend policy for firms listed on the Taiwan Stock Exchange. The results are consistent with the prediction of the catering theory in that managers choose a dividend policy to cater to the demand of investors.

Logistic regressions are used to test the catering theory hypothesis.

The results find that the firms distribute more stock dividends than other types of dividends when the dividend premium (DP) for stock dividends is positive. In contrast, firms shift from stock dividends to other types of dividends such as mixed dividends and cash dividends when the DP for stock dividends is negative.

The marginal contribution of this paper is that the firms change their dividend policy via DP to cater to the demand of investors.

This paper aims to understand empirically how shares of standard and premium private label (PL) products affect a retailer’s marketing mix decisions toward national brands (NBs).

Using a comprehensive store-level data set covering 52 categories and 130 stores of two retailer chains during 2003-2009, this paper examines how shares of standard and premium PLs affect retailer marketing strategies for NB retail prices, promotions and product assortments. The empirical analysis uses a simultaneous equations model estimated by the generalized method of moments approach and controls for endogeneity between PL shares and NB decisions and potential confounding variables including consumer, manufacturer and retailer factors.

Standard PL shares are associated positively with NB retail prices and negatively with NB promotions and assortments. In contrast, premium PL shares are associated positively with NB retail prices, promotions and assortments.

The results indicate that retailers make strategic NB decisions through multitier PLs. Specifically, the evidence suggests that retailers use standard and premium PLs differently in promotion and assortment decisions toward NBs. NB manufacturers need to be cognizant of the increasing marketing power of retailers through their multitier PLs.

Prior research has mainly focused on the role of PLs as a strategic weapon to gain power in the channel and its impact on NB pricing decisions in a single PL context. After accounting for potential confounding factors (retailer, consumer and manufacturer) and endogeneity, the authors find empirical evidence that retailers appear to leverage standard and premium PLs differently in some marketing mix decisions toward NB. In particular, the results reveal PL performance to be a determinant of retailer NB assortment decisions.