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Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

Illegitimate tasks are pervasive in organizations, presenting a challenge for employees to mitigate their impact. However, despite their significance, there has been limited research on when and how employees can adapt to these tasks successfully. To address this gap, this study aimed to discover the relationship between illegitimate tasks and adaptive performance, drawing upon the job demands-resources theory. Specifically, this study aimed to examine the moderating role of general self-efficacy and the mediating roles of employee problem-focused and emotion-focused coping.

To examine our hypotheses, we conducted a time-lagged survey, comprising 3 waves and involving 313 employees from twenty enterprises situated in Guangzhou and Sichuan, China. Ordinary least squares (OLS) was adopted to examine our hypotheses.

Our results suggested that general self-efficacy played a crucial role in determining how employees would cope with illegitimate tasks and their subsequent adaptive performance when confronted with illegitimate tasks. Notably, employees who possess high levels of general self-efficacy utilize problem-focused coping, improving adaptive performance in handling illegitimate tasks. Conversely, employees who possess low levels of general self-efficacy rely on emotion-focused coping, leading to lower levels of adaptive performance.

This research contributes to the literature on illegitimate tasks by uncovering when and how such tasks can either promote or hinder adaptive performance. These findings offer practical insights into ways to assist employees in managing illegitimate tasks effectively.

Previous studies have mainly focused on the negative impact of illegitimate tasks on recipients but ignored its impact on observers. Drawing on deservingness theory, this research developed a moderated serial mediation model to examine the underlying mechanism in the relationship between observed illegitimate tasks and observers’ helping behaviour.

This research tested the model using a sample of 218 employees in China through a two-wave field study. A path analysis was conducted with the bootstrapping procedure to test the hypothesized model.

The results showed that the effect of observing illegitimate tasks was contingent upon the degree of the observer’s interpersonal liking towards the recipient. Specifically, when the observer had a high level of interpersonal liking for a coworker, the observer may perceive that the coworker was undeserving of being assigned illegitimate tasks, which increased sympathetic emotion and the sequent helping behaviour.

This study shifted the perspective of the illegitimate task literature from the recipient to the third parties. Additionally, it provided a finer-grained understanding of the mechanism between observed workplace disrespectful treatment and the observer’s helping behaviour.

This study proposes to conceptualize and explicate the progress of end-to-end data pipelines to enable seamless data flow, addressing the mounting demand for instantaneous information in the vibrant field of marketing analytics. Ensuring timely access to inclusive analytics, it empowers marketers to make conversant decisions promptly. The study espouses a conceptual approach, starting with the classification and identification of various data sources crucial for thorough marketing analytics. It probes into the complexities of designing adaptable and scalable architectures for extensive data pipelines, ensuring effective data collection, processing, and storage. Emphasizing case analyses of marketing analytics tools, the study delivers practical insights and exhaustive examinations of existing solutions. The findings offer a broad understanding of the key components and strategies necessary for realizing smooth data flow in real-time marketing analytics. This investigation contributes expressively to the field by outlining a robust framework for continuous data flow, identifying the benefits and challenges associated with data pipelines. From a hands-on standpoint, it provides organizations with appreciated insights for building effective end-to-end data pipelines, bestowing strategies for implementing real-time marketing analytics outlines that enhance decision-making processes. The originality and value of this research lie in its universal approach to addressing the challenges of real-time marketing analytics, offering a fresh viewpoint on creating seamless data flow frameworks. The gathered information has the latent to drive innovation in marketing analytics, serving as a crucial resource for companies aiming to gain a competitive edge in today’s data-driven market. This inclusive approach ensures that the study’s contributions are both theoretically significant and practically applicable.

The advent of digital technologies has ushered in a new era of quality management (QM) known as Quality 4.0 (Q4.0). The successful implementation of Q4.0 requires the involvement of both leaders and employees. Leadership plays a critical role in motivating employees involved in quality practices. However, the mechanisms by which leadership inspires quality professionals to engage in continuous learning and remain vigorous in their roles are not well understood. This study aims to determine the impact of Q4.0 leadership on thriving at work (TAW) among quality professionals and to identify the underlying mechanisms.

Utilising the identity theory and social identity theory, a multilevel TAW model was developed. This study surveyed 63 team leaders and 243 subordinates, who are quality professionals working for companies implementing Q4.0. Multilevel structural equation modelling (MSEM) was applied to assess the hypotheses.

The study finds that Q4.0 leadership enhances TAW among quality professionals. The linkage between Q4.0 leadership and TAW is mediated by work group identification (WGI) at the group level and job identification (JI) at the individual level.

Insights from this study will enable organisations to make informed decisions regarding the leadership styles that best support TAW among quality professionals. By understanding the mechanisms linking Q4.0 leadership to TAW, organisations can foster both WGI and JI, ultimately enhancing engagement and performance in quality initiatives.

This study offers a novel contribution to the QM field by examining the role of Q4.0 leadership in motivating and sustaining the engagement of quality professionals. Exploring the relationships between Q4.0 leadership, WGI, JI and TAW helps to deepen our understanding of how Q4.0 leadership can enhance TAW among quality professionals.

Based on social exchange theory (SET), the current research aims to investigate the relationship between illegitimate tasks (ITs) and employees’ knowledge-hiding behavior (KHB), incorporating the mediating role of psychological contract violation (PCV) and the moderating role of optimism.

A two-wave time-lagged survey was conducted to collect data from 196 adult Chinese full-time employees.

ITs were positively related to KHB, and this relationship was mediated by PCV. Furthermore, optimism buffered the connection between ITs and PCV as well as the mediating effect of PCV on the relationship between ITs and KHB.

For managers, this study made them aware of the hidden costs of ITs; that is, employees may respond by concealing knowledge in the organization.

This study provides a more nuanced understanding of the relationship between ITs and employees’ subtle deviant behavior, KHB and hitherto unspecified mediating and moderating factors that explain this process.

This research examines the impact of the National Rural E-commerce Comprehensive Demonstration Project (NRECDP) on poverty reduction and income growth in rural China.

The study develops a theoretical framework, which considers the role of geographical, technological, institutional and cultural factors for the e-commerce poverty alleviation (e-CPA) model. Empirically, this study applies the difference-in-differences (DID) model and the event study approach to evaluate the effectiveness of NRECDP on the basis of large-scale county-level and household-level panel data spanning 2010 to 2020.

The study found that the NRECDP, as a government-led, information and communication technology (ICT)-enabled, market-based program, has led to a significant increase in per capita output of primary industry employees, as well as in the disposable income of rural residents, especially those in national-level poverty-stricken (NP) counties. The interventions of the NRECDP achieved these positive outcomes through transportation and Internet infrastructure improvement, ICT adoption and human capital accumulation in impoverished towns and villages in remote rural areas. These effects are larger in the eastern region of China, followed by the central region, whereas the weakest effects were found in the western region. However, we found little evidence of the NRECDP increasing household developmental expenditure.

The study findings have important practical and policy implications for rural e-commerce development and self-sustained poverty alleviation solutions. The research revealed the significance of government NRECDP interventions for increasing rural income, reducing living costs, and empowering the rural population in its multiple social roles, namely, as consumers, producers, employees and microentrepreneurs. The local cultural context may also play a role in ICT adoption and entrepreneurship cultivation with a downstream effect on the effectiveness of e-CPA practices. Policymakers would need to ensure a supportive entrepreneur-friendly environment for rural e-commerce development and continue implementing progressive policies for poverty alleviation.

This study explores poverty alleviation issues in China by developing for the first time a multi-faceted framework that is subsequently tested by both county-level and household-level large-scale observations. Also, it is the first study to provide nationwide empirical evidence on the effectiveness of e-CPA in narrowing down the spatial and digital divides in China. In addition to the impact of geography, technology and governmental support, this study also sheds light on the role of culture in the adoption and diffusion of digital technologies and as a source of local entrepreneurial opportunities.

The purpose of our research is to explore the role of employee AI identity in influencing employee proactive behavior and its boundary conditions in AI workplace.

Based on the IT identity theory and motivation theory, our research discusses the effects of employee AI identity on employee proactive behavior and regarded the proactive work intention as a mediating variable. Meanwhile, we considered organization inducement as a boundary condition and discussed the moderating effects of it and its two sub-dimensions (development rewards and material rewards). Data were collected from 326 employees and partial least squares structural equation modeling was used to analyzed and draw the conclusions.

Findings showed that employee AI identity significantly affects employee proactive behavior, in which the proactive work intention play a mediating role. Moreover, three subdimensions (relatedness, emotional energy, dependence) of employee AI identity have different effects on formation of employee AI identity. And organization inducement acts as a positive moderating role, development rewards and material rewards play different roles in the formation of organization inducements.

Our research explores the different paths that influence employee proactive behavior and their boundary moderation, while analyzing the results of these influences in different subdimensions, deepening the research on employee AI identity and organization inducement. Our research is conducive to the development of the identity theory and organizational behavior research and provide suggestions for managers to improve their organizational management level.

This chapter explores the intersection of cutting-edge algorithms in the financial technology (FinTech) sector, with a particular focus on the application of machine learning (ML) and artificial intelligence (AI) to enhance sustainability and ethical finance practices. The transformative capabilities of ML and AI are examined through their roles in revolutionising financial services by increasing operational efficiency, improving decision-making processes and enhancing risk management strategies. Specific applications discussed include fraud detection, anomaly detection in financial statements, robo-advisory services, prediction of customer deposits, combating money laundering, sentiment analysis, loan eligibility prediction, stock price prediction and the automation of routine tasks through chatbots. Additionally, the chapter addresses the crucial role of AI and ML in supporting green finance initiatives, reducing carbon footprints and promoting financial inclusion, thereby contributing to sustainable economic development. The detailed exploration provides insights into how these advanced technologies are key in shaping the future of the financial industry towards more sustainable outcomes.

Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features for multimodal information effectively. To address these limitations, this study proposes a novel multimodal sarcasm detection model based on the directed graph isomorphism network with sentiment enhancement and multimodal fusion (DGIN-SE-MF).

The approach extracts image and text features through vision transformer and BERT, respectively. To deeply integrate the extracted features, the author develops a text-guided multi-head attention fusion mechanism module. Subsequently, a directed graph is constructed through SE and the multimodal factorized bilinear pooling method to integrate image features into the graph. The DGIN then fuses the image and text features, using a weighted attention mechanism to generate the final representation.

The model is validated on three datasets: English, Chinese and an Indonesian–English dataset. The results demonstrate that the proposed model consistently outperforms other baseline models, particularly on the Chinese and English sarcasm datasets, achieving F1 scores of 88.75 % and 83.10 %, respectively.

The proposed model addresses the inadequacies of previous methods by effectively integrating emotional cues and image features into sarcasm detection. To the best of the authors’ knowledge, this is the first work to leverage a DGIN-SE-MF for this task, leading to significant improvements in detection performance across different languages.