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This study aims to examine involvement and perceived usefulness (PU) as the drivers of loyalty for online booking websites, revealing findings of theoretical and practical relevance. The proposed conceptual model is drawn on elaboration likelihood model (ELM) along with the consideration of expectation confirmation model for loyalty development.

A two-stage structural equation modeling was applied to test the reliability and validity of the constructs and the strength of the hypothesized relationships.

Attitudinal loyalty (AL) is found to have a significant role, resulting in behavioral loyalty (BL) toward online booking. The results of this study supported the ELM framework, where involvement and PU resulted in BL through AL. PU and AL also served as mediating mechanisms underlying the impact of consumer involvement on BL.

This study applies the ELM to online ticketing mechanisms and nuances amid AL and BL with their drivers.

This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship.

A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP.

The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU.

The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers.

To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.

Technology triggers business collaboration and partnership as well as becomes an essential part for disruptive business strategies. The literature on this topic is dispersed, and the chapter addresses this gap. This chapter aims to present a systematic literature review of academic studies in disruptive collaborative innovation frameworks and provide guidance for future research by identifying current research gaps. The study uses the PRISMA framework and collects literature from 2015 to 2023 from highly ranked academic venues with the final sample of 112 papers. The section is structured around the PRISMA framework, and results are reported accordingly. The analysis was conducted using a range of methods, including bibliometric analysis, multiple correspondence analysis (MCA), and text network analysis.

The analysis of the literature is presented to identify the main areas of research as well as to gauge the emerging streams. The overall results suggest that this area is starting to develop with limited research available to date. The study identified four theoretical foundations for existing research, aligned with behavioral, network, resource-based (RBV), and knowledge-based views (KBV). The identified theoretical views and themes are discussed, and examples of the studies are provided.

This paper aims to investigate the impact of digital inclusive finance on entrepreneurial activities in rural areas, as well as the underlying mechanisms and the variations of this impact across regions and household characteristics.

Utilizing the China Household Finance Survey Data, this paper applies the Two-Stage Least Squares with instrumental variables to assess the effect of digital inclusive finance on rural entrepreneurship.

The empirical findings indicate that digital inclusive finance significantly promotes entrepreneurial activities in rural areas by alleviating credit constraints, reducing financial information barriers, and altering risk attitudes for rural households. Additionally, this effect is more pronounced in the eastern region of China and for the “opportunity entrepreneurial activities.” Furthermore, the impact varies across rural households' income and consumption status, as well as the personal characteristics of household heads.

Firstly, this study broadens our understanding of the mechanisms through which digital inclusive finance influences entrepreneurial activities, thereby filling a gap in existing entrepreneurship research. Secondly, the study's findings affirm the inclusive nature of digital finance, contributing significantly to the literature on regional equality and illuminating potential pathways toward achieving “common prosperity.”

This study aims to examine the ability of Generative Pre-trained Transformer 4 (GPT-4), one of the most powerful large language models, to generate a literature review for peer-reviewed journal publications. The objective is to determine whether business scholars can rely on GPT-4’s assistance with literature reviews and how the nature of human–artificial intelligence (AI) interaction may affect the quality of the reviews generated by GPT-4.

A survey of 30 experienced researchers was conducted to assess the quality of the literature reviews generated by GPT-4 in comparison with a human-authored literature review published in a Social Science Citation Index (SSCI) journal. The data collected were then analyzed with analysis of variance to ascertain whether we may trust GPT-4’s assistance in writing literature reviews.

The statistical analysis reveals that when a highly structured approach being used, GPT-4 can generate a high-quality review comparable to that found in an SSCI journal publication. However, when a less structured approach is used, the generated review lacks comprehensive understating and critical analysis, and is unable to identify literature gaps for future research, although it performed well in adequate synthesis and quality writing. The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction.

The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Nonetheless, cautions should be taken due to the limitations of this study discussed in the text.

By breaking down the specific tasks of a literature review and using a quantitative rather than qualitative assessment method, this study provides robust and more objective findings about the ability of GPT-4 to assist us with a very important research task. The findings of this study should enhance our understanding of how GPT-4 may change our research endeavor and how we may take a full advantage of the advancement in AI technology in the future research.

Rural development programs are widely used policy instruments mitigating rural-urban economic disparities. Yet, little research has examined their effect on rural labor. This study fills this knowledge gap by quantifying the causal impact of such programs on the labor allocation of farm households in Taiwan.

A theoretical framework based on the agricultural household model is constructed to guide the empirical specification. A unique dataset compiles administrative data on the program’s subsidies with farm household surveys across seven years. To cope with endogeneity bias, an instrumental variables model is applied. The eligibility rule for a township to participate in the program is used as the instrument.

We find that the program increases the labor supply of farm household members. These effects are more pronounced for off-farm work, particularly non-heads of farm households. The program’s subsidies supporting culture and promotion-related activities have larger effects. Finally, females benefited more from the program.

We focus on farm households since this group is the target of place-based rural development programs. In addition, we identify the causal impact of place-based development programs on rural labor. Finally, this study is relevant to the literature on intra-household models by demonstrating that place-based rural development programs can affect the labor supply of farm household members.

This research exclusively focuses on China’s elderly Internet users given how severe a threat disinformation has become for this particular population group as social media platforms thrive and the number of elderly netizens grows in China. The purpose of this study is to explore the mechanism of how elderly social media users’ intention to identify false information is influenced helps supplement the knowledge system of false information governance and provides a basis for correction practices.

This study focuses on the digital literacy of elderly social media users and builds a theoretical model of their intention to identify false information based on the theory of planned behaviour. It introduces two variables – namely, risk perception and self-efficacy – and clarifies the relationships between the variables. Questionnaires were distributed both online and offline, with a total of 468 collected. A structural equation model was built for empirical analysis.

The results show that digital literacy positively influences risk perception, self-efficacy, subjective norms and perceived behavioural control. Risk perception positively influences subjective norms, perceived behavioural control and the attitude towards the identification of false information. Self-efficacy positively influences perceived behavioural control but does not significantly impact the intention to identify. Subjective norms positively influence the attitude towards identification and the intention to identify. Perceived behavioural control positively influences the attitude towards identification but does not significantly impact the intention to identify. The attitude towards identification positively influences the intention to identify.

Based on relevant theories and the results of the empirical analysis, this study provides suggestions for false information governance from the perspectives of social media platform collaboration and elderly social media users.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.

The principle of infinite life design currently directs fatigue resistance strategies for metro car bodies. However, this principle might not fully account for the dynamic influence of operational loads and the inevitable presence of defects. This study aims to integrate methods of service life estimation and residual life assessment, which are based on operational loads, into the existing infinite life verification framework to further ensure the operational safety of subway trains.

Operational loads and fatigue loading spectra were determined through the field test. The material test was conducted to investigate characteristics of the fracture toughness and the crack growth rate. The fatigue strength of the metro car body was first verified using the finite element method and Moore–Kommers–Japer diagrams. The service life was then estimated by applying the Miner rule and high-cycle fatigue curves in a modified form of the Basquin equation. Finally, the residual life was assessed utilizing a fracture assessment diagram and a fitted curve of crack growth rate adhered to the Paris formula.

Neither the maximum utilization factor nor the cumulative damage exceeds the threshold value of 1.0, the metro car body could meet the design life requirement of 30 years or 6.6 million km. However, three out of five fatigue key points were significantly influenced by the operational loads, which indicates that a single fatigue strength verification cannot achieve the infinite life design objective of the metro car body. For a projected design life of 30 years, the tolerance depth is 12.2 mm, which can underscore a relatively robust damage tolerance capability.

The influence of operational loads on fatigue life was presented by the discrepancy analysis between fatigue strength verification results and service life estimation results. The fracture properties of butt-welded joints were tested and used for the damage tolerance assessment. The damage tolerance life can be effectively related by a newly developed equation in this study. It can be a valuable tool to provide the theoretical guidance and technical support for the structural improvements and maintenance decisions of the metro car body.