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This chapter reviews the competition between full-service carrier (FSC) and low-cost carrier (LCC) in China. More importantly, we discuss the impacts of the COVID-19 pandemic on FSC–LCC competition. Specifically, the airlines' route choices and also the market contact between FSCs and LCCs in China are examined and discussed. Our review results suggest that, despite the rapid growth of the independent LCC Spring Airlines and the establishment of new subsidiary LCCs by FSCs, China's LCC sector still plays relatively minor roles compared with many fully deregulated markets. Subsidiary LCCs serve more as competitive tools for their parent FSCs, primarily deployed on their parent FSCs' routes to jointly compete against rival FSCs. This competition is primarily focused on niche regional markets rather than engaging in full-scale competition. Spring Airlines also strategically avoided direct head-to-head competition with FSCs before the pandemic by mainly connecting with the secondary cities. However, the pandemic has introduced significant changes, notably the network differentiation between FSCs and LCCs in mainland China. With the relaxation of government's regulations on airline route entries into hub airports during pandemic, Chinese LCCs have shifted their focus toward serving more dense routes, especially those connected to the top five cities. This shift has led to an intensified head-to-head competition between LCCs and FSCs following the outbreak of the pandemic. Such a process is likely to continue in the years to come. This chapter's discussions could also provide new insights into LCC development and the impact of the pandemic on FSC–LCC competition interactions to supplement existing literature studying other major airline markets.

Purpose: The primary purpose is to discuss the productivity and digitalisation interaction at the theoretical level, analyse the productivity and digitalisation differences between the European Union (EU)-14 and EU-13 countries, and evaluate the digitalisation impact on the manufacturing sector labour productivity of the EU countries.

Need for study: The average added value created per capita in new EU countries (EU-13) is one-third lower than in old EU countries (EU-14). To increase productivity, manufacturing companies must adapt to modern trends and take advantage of industrial digitisation opportunities. Digitisation can improve production efficiency, reduce costs, and improve product quality, allowing continuous monitoring and analysis of production data, enabling informed decisions and faster problem-solving.

Methodology: Analysis of scientific literature, comparing viewpoints, insights, and conclusions. The empirical study includes calculating rates of change of indicators, differences between EU-14 and EU-13, and structural analysis. The impact of digitisation on the productivity of EU countries is studied by creating a correlation matrix and using regression analysis: ordinary least square models.

Findings: EU-13 countries are behind EU-14 in labour productivity and manufacturing digitalisation. Digitalisation positively impacts productivity per employee. A faster increase in digitisation, industrial robot use, and e-commerce sales could significantly increase productivity in EU-13, reducing productivity differences between countries.

Practical implications: This study highlights the need for policy promoting digitisation innovation, particularly in EU-13 countries, to be implemented by both national and EU-based economic development and regional and cohesion institutions.

This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation.

This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process.

The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics.

Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms.

The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

It is unclear what constitutes the tourism live streaming (TLS) servicescape and how it affects users' travel intention (TI). The study aims to explore the composition of the TLS servicescape, the influence mechanism of the TLS servicescape on users' TI and the formation of users' TI.

Based on stimulus organism response theory (SOR), we develop a mediation model to explore the influence of TLS servicescape on users' TI. This study collected data from 432 Chinese TLS users through an online questionnaire, and we used the structural equation model and the SPSS PROCESS macro to test the proposed model. In addition, we tested the variable relationships using fuzzy-set qualitative comparative analysis (fsQCA).

TLS servicescape is a second-order variable that can be categorized into physical element (PE), social element (SOE), symbolic element (SYE) and natural element (NE). TLS servicescape influences TI by affecting social presence (SP) and customer engagement (CE). The fsQCA reveals seven combinations of PE, SOE, SYE, NE, SP and CE that form a high TI for TLS users.

Using multiple data analysis methods, the study emphasizes the significance of the TLS servicescape for TLS. It explores how to evoke users' TI in TLS and provides a reference for TLS marketing.

Past research is mixed on family small and medium-sized enterprises’ (SMEs) use of external advisors and the limited empirical evidence is confined to developed markets. Drawing on the knowledge-based view of the firm, this research focuses on the “familiness” characteristic of SMEs and their use of external accountants as advisors in an emerging marketplace. Using internal resources for basic tasks is proposed to strengthen this relationship from a managerial cognition lens. Focusing also on SME internalization, this research probes the performance ramifications of using external accountants as advisors.

Hierarchical regression is used to test the hypotheses. The mediation hypothesis is tested by bootstrapping the indirect effect. The interaction hypothesis is visualized with simple slope analysis.

The results indicate that the familiness of SMEs is positively associated with the use of external advisors, and thereby, with high performance. SMEs with higher international exposure also use these external advisors to a greater degree. Family SMEs that have a focused use of internal resources for basic tasks benefit more from the use of external accountants for advising tasks.

This research sheds light on how family involvement in management influences firm performance, showing the moderating role of the use of internal advisors for basic tasks and the mediating role of the use of external accountants for advising. We add to the knowledge-based view by describing how family SMEs can utilize internal and external knowledge resources simultaneously.

In the context of green development in China, the circumstance in which Environmental, Social and Governance (ESG) ratings function has changed. As an important external governance mechanism of sustainable development, ESG ratings can also be a two-edged sword for the implementation of carbon emission reduction. This research examines the connection of ESG ratings and corporate carbon emission reduction in the context of green development. This present study postulates that the impact of ESG ratings on carbon emission reduction performance in the context of green development is inverted U-shaped.

To obtain empirical evidence for the hypotheses proposed, this study makes an empirical test based on the two-way fixed effects model. The data is taken from listed Chinese manufacturing firms between 2012 and 2021.

The study reveals that there is a significant inverted U-shape relationship between ESG ratings and carbon emission reduction performance in the context of green development. Managerial myopic behaviour plays a positive moderating role in the above relationship. In addition, it makes the inflection point of inverted U-shaped curve move to left. Heterogeneity analyses show that the above inverted U-shaped relationship is more significant for firms that don’t hire CEO with environmental protection background or big four accounting firms.

In the background of green development, this study helps to understand dual influence of ESG ratings on corporate carbon emission reduction deeply. It is beneficial to guide enterprises to utilize ESG ratings mechanism reasonably, thus enhancing the effectiveness of carbon emission reduction. This study provides decision-making reference for government to accelerate low-carbon transformation in microcosmic field.

Although Agtech firms have promoted digital platforms for retailing farm supplies (RFS), farmers are sceptical while purchasing them online. As a result, they struggle to generate a sustained demand. Among other approaches, these platforms onboard complementors to become full-stack farming solution providers. Whether platform complementarity can induce farmers' trust remains ambiguous. Literature on network externality theory highlights that complementarity positively affects the perceived value for buyers. The sociotechnical systems literature indicates that perceived value is an antecedent of user trust. In this vein, the authors ask: Does perceived complementarity affect farmers' trust in the RFS platform? Alternatively, the Agtech firms augment the platform's look and feel to make the digital retail setting appear “normal” to farmers. The extant research on the social cognitive theory indicates that a retail setting conforming with the generalised expectancy of buyers harbours their trust. Against this backdrop, the authors ask whether situational normality affects farmers' trust in the RFS platform.

The study is based on a questionnaire survey of 212 Indian farmers using RFS platforms. The data were analysed using structural equation modelling (SEM) analysis.

This study establishes that platforms' complementarity and situational normality ameliorate farmer trust. The authors also identify the socioeconomic factors shaping the farmers' trust in platforms.

The present study has taken all RFS together as a single umbrella category, which can be considered a limitation. Also, the study is based on the cross-sectional survey of RFS platform users; the farmers' attitudes are dynamic in nature and evolve over time; however, the temporal factors shaping the farmer attitudes have not been considered in this study.

The study establishes the epistemological relationship between complementarity, situational normality and farmers' trust in agricultural platforms.

In the context of growing efforts by online businesses to enhance consumer connections, understanding consumer engagement behaviors is imperative. This study explores consumer engagement within online shopping platforms, specifically introducing and examining the roles of crowdsourcing and crowdsending.

The study developed and validated measurement scales for crowdsourcing and crowdsending engagement across transactional, multi-sided and inspirational platforms.

Identifying five sub-dimensions within crowdsourcing and crowdsending, the results unveiled nuances in consumer–platform interactions, emphasizing the value of co-creation. Crowdsourcing entails transaction-oriented engagements such as knowledge gathering, utilitarian and hedonic browsing, interaction and co-shopping. The findings revealed that crowdsourcing significantly influenced platform commitment, surpassing the impact of crowdsending on transactional platforms. Conversely, crowdsending involves knowledge sharing, feedback, participation, advocacy and reciprocity, fostering active engagement and shared value within the platform ecosystem. Notably, the results showed that crowdsending strengthened commitment to inspirational platforms more than to conventional shopping platforms.

This study contributes to the theoretical understanding of a range of consumer engagement experiences in online shopping environments and presents practical applications, offering valuable insights for commerce businesses aiming to optimize their digital strategies.

Considering the bounded confidence of decision-makers (DMs), a new grey multi-criteria group consensus decision-making (GMCGCDM) model is established by using interval grey number (IGN), cobweb model, social network analysis (SNA) and consensus reaching process (CPR).

Firstly, the model analyzes the social relationship of DM under social networks and proposes a calculation method for DMs’ weights based on SNA. Secondly, the model defines a cobweb model to consider the preferences of decision-making alternatives in the decision-making process. The consensus degree is calculated by the area surrounded by the connections between each index value of DMs and the group. Then, the model coordinates the different opinions of various DMs to reduce the degree of bias of each DM and designs a consensus feedback mechanism based on bounded confidence to guide DMs to reach consensus.

The advantage of the proposed method is to highlight the practical application, taking the selection of low-carbon suppliers in the context of dual carbon as an example. Comparison analysis is performed to reveal the interpretability and applicability of the method.

The main contribution of this paper is to propose a new GMCGCDM model, which can not only expand the calculation method of DM’s weight and consensus degree but also reduce the time and cost of decision-making.