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This paper aims to identify the influence of innovation network and technological learning on innovation performance of high-tech cluster enterprises.

Using a questionnaire, data are collected from Dalian High-tech Industrial park in China. In addition, structural equation model is used to identify the influence of innovation network and technological learning on the innovation performance of high-tech cluster enterprise.

The findings of this study show that the centrality of network location and the strength of the network relationship have a direct positive effect on technology acquisition, technology digestion and technology exploit of high-tech cluster enterprises. Meanwhile, technology acquisition has a direct positive effect on technology digestion, technology digestion has a direct positive effect technology exploit, and technology exploit has a direct positive effect innovation performance of high-tech cluster enterprises.

To improve innovation performance, high-tech cluster enterprises should not only nurture and optimize innovation networks but also improve technological learning ability.

This paper empirically supports the significant influence of innovation network and technological learning on innovation performance. While the results provide guidance for researchers and practitioners, it also adds value to innovation-related research.

This paper aims to estimate the values of environmental efficiency of each province in China. Subsequently, it analyzes the changes of total factor productivity (TFP) before and after taking the environmental factors into account. Finally, the paper measures the effect of the components of environmental TFP on the convergence of economic growth.

The environmental control variables are taken as output variables and combined with a traditional data envelopment analysis model to build an environmental TFP Index. The growth of environmental TFP is compared among regions and the differences are analyzed. Furthermore, the decomposed components (advances in environmental technology and environmental technical efficiency) are used for regression analysis with labor productivity.

The environmental efficiency of most provinces in China has improved although some regions' efficiency remains low in Northeastern China. The value of environmental TFP is higher and more fluctuant during the period of 1997-2009 than that of traditional TFP. Technical efficiency has a convergence effect on economic growth of regions/provinces, but, after adding environmental factors, it turns into a divergence effect.

Because of data limitations, this paper does not consider the impacts of human capital and other factors on in the convergence analysis on economic. Neither does it consider the use of panel data to analyze the convergence of economic growth and validate the conclusions. These are potential further research directions.

The study confirms that improvements in environmental technologies play a dominant role in enhancing China's environmental TFP. Furthermore, it demonstrates that China's economic growth largely depends on technological progress. This finding, that China's economic growth depends on advances in environmental technology, implies that China should strive to improve its capacity for technological innovation.

The paper measures the value of environmental efficiency in the China's provinces and analyzes the relationship between pollution and economic development in each province. Panel data are used to compare the difference among environmental TFP, environmental factors and traditional TFP. The convergence of economic growth is analyzed in respect of environmental control variables.

Taking the lens of a cue diagnosticity framework and affective primacy theory, this study aims to examine the relative effects of cognitive and affective country image on consumer cognitive judgement, affective evaluation and behavioural tendency in one integrated model. It also explores how the direct effects may vary with the intra-valence nature (ambivalent vs. univalent) of cognition-affect.

The proposed research model was tested using data from a large Chinese sample and consumer responses to products from four countries − the USA, Japan, Brazil and India.

The results show that the relative effects of cognitive and affective country image are complex and differ by the intra-valence nature of cognition-affect. On a general level, cognitive and affective country image exert equal influence on affective evaluation and behavioural tendency. In contrast, cognitive country image demonstrates a more prominent effect than affective country image on cognitive judgement. Compared with univalent, ambivalent cognition-affect strengthens the positive impact of affective country image but does not significantly alter the positive impact of cognitive country image on consumer reactions.

This research contributes to the ongoing debate regarding implications of two focal aspects of macro country image by revealing their relative importance in an integrated framework and enriches country-of-origin research through unveiling the uni/ambivalent cognition-affect as a moderator of the relationship between cognitive/affective country image and consumer reactions. The research findings provide implications as to whether and when marketing strategies should focus on leveraging positive (negative) cognitive or affective country image.

Empirical evidence shows that banking development has a significant impact on macro-level economic growth through the finance-growth nexus and also highlights the prominent effect of development on local economy and household welfare, particularly in developing countries with restricted access to financial systems. The authors investigated the role of local banking development in affecting household welfare in Thailand which is a modest degree of financial access compare to other countries. The authors focus on the development of the banking sector in four dimensions, including financial depth, financial stability, financial efficiency and financial inclusion, and its impact on household welfare using the generalized method of moments approach to address the endogeneity problem. The authors employ biennial household welfare data from the National Statistical Office survey from 2007 to 2019 which covers all provinces in Thailand. The findings suggest that each type of banking development significantly affects household income and consumption in Thailand, although in different ways. Financial depth decreases income and consumption expenditure, while financial inclusion increases income and consumption expenditure (level effect). However, there are insignificant impacts on volatility of household income and consumption (volatility effect). Our findings prove that the implementation of policies to promote banking development either promote or decrease household welfare. This study can provide insight on policy impact and assist policymakers in considering the adoption of banking development policies to promote growth of the local economy, while at the same time aiming to reduce welfare inequality.

The purpose of this paper is to investigate the effects of hydrogen humidity on the performance of air-breathing proton exchange membrane (PEM) fuel cells.

An efficient mathematical model for air-breathing PEM fuel cells has been built in MATLAB. The sensitivity of the fuel cell performance to the heat transfer coefficient is investigated first. The effect of hydrogen humidity is also studied. In addition, under different hydrogen humidities, the most appropriate thickness of the gas diffusion layer (GDL) is investigated.

The heat transfer coefficient dictates the performance limiting mode of the air-breathing PEM fuel cell, the modelled air-breathing fuel cell is limited by the dry-out of the membrane at high current densities. The performance of the fuel cell is mainly influenced by the hydrogen humidity. Besides, an optimal cathode GDL and relatively thinner anode GDL are favoured to achieve a good performance of the fuel cell.

The current study improves the understanding of the effect of the hydrogen humidity in air-breathing fuel cells and this new model can be used to investigate different component properties in real designs.

The hydrogen relative humidity and the GDL thickness can be controlled to improve the performance of air-breathing fuel cells.

This paper aims to numerically investigate the impact of gas diffusion layer (GDL) anisotropic transport properties on the overall and local performance of polymer electrolyte fuel cells (PEFCs).

A three-dimensional numerical model of a polymer electrolyte fuel cell with a single straight channel has been developed to investigate the sensitivity of the fuel cell performance to the GDL anisotropic transport properties – gas permeability, diffusivity, thermal conductivity and electrical conductivity. Realistic experimentally estimated GDL transport properties were incorporated into the developed PEFC model, and a parametric study was performed to show the effect of these properties on fuel cell performance and the distribution of the key variables of current density and oxygen concentration within the cathode GDL.

The results showed that the anisotropy of the GDL must be captured to avoid overestimation/underestimation of the performance of the modelled fuel cell. The results also showed that the fuel cell performance and the distributions of current density and oxygen mass fraction within the cathode GDL are highly sensitive to the through-plane electrical conductivity of the GDL and, to a lesser extent, the through-plane diffusivity, and the thermal conductivity of the GDL. The fuel cell performance is almost insensitive to the gas permeability of the GDL.

This study improves the understanding of the importance of the GDL anisotropy in the modelling of fuel cells and provides useful insights on improving the efficiency of the fuel cells.

Realistic experimentally estimated GDL transport properties have been incorporated into the PEFC model for the first time, allowing for more accurate prediction of the PEFC performance.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Distancing people socially as a precautionary measure against the mushrooming of COVID-19’s health and economic crisis has deleteriously affected the performance of the eatery industry to a great extent. Many food outlets failed to cope up with crisis and opted to move out, and many still vie to survive through pandemic. It becomes vital for researchers to understand what factors influence the performance and survival of eateries during the pandemic? The study makes an attempt to fabricate new factors which affect the performance and contribute significantly towards the survival of eateries in this new COVID-19-prone world.

The present study is a cross-sectional analysis with the sample of 150 eateries from the walled city of Punjab (India), i.e. Amritsar. Factor analysis is employed to scrutinise the factors which influence the performance of eateries during the pandemic, and to analyse factors which contribute significantly for the survival of eateries, logistic regression is performed.

The empirical analysis reveals that at prior psychological factor, followed by turnover factor, external factor, financial factor and marketing factor influence the performance of eateries during the pandemic. Only three factors, namely turnover factor, external factor and financial factor, turned up to be significant towards the survival rate of an eatery. The marketing factor which is a crucial contributor for survival of business in literature has turned out to be insignificant during the times of pandemic.

With the arrival of pandemic, the preference of people has changed, and the business environment in which entities operate has turned more complex. The present study is one of the pioneer attempts to evaluate whether the factors responsible for performance or survival of an eatery during normal times is relevant during the pandemic as well. The study contributes to the literature of eatery industry by adding a new variable namely psychological factor, i.e. changes witnessed in customers’ preference to visit an eatery.

The purpose of this paper is to explore the dimensionality of intragroup conflict and to develop an instrument with acceptable psychometric properties for the comprehensive measurement of conflict.

This paper strictly follows the standard scale-developing method: first, establish theoretical dimensions of intragroup conflict; then, develop the initial scale through in-depth interviews and coding schemes; third, revise and verify the scale through exploratory factor analysis and confirmatory factor analysis; and, finally, examine the predictive validity of the new intragroup conflict scale.

This study identifies four dimensions of intragroup conflict – cognitive conflict, affective conflict, behavioral conflict, and interest-based conflict – and provides evidence of construct validity for a new measure. The results show that cognitive and interest-based conflict affect group innovation performance positively, whereas affective and behavioral conflict affects it negatively.

This study first detects interest-based conflict as a new dimension and explores a more comprehensive scale (ABCI) that reflects all the connotations of conflict, which deepens the understanding of intragroup conflict, laying a solid foundation for empirical studies of conflict.