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To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.

This research focuses on developing a dual-nozzle slurry-based extrusion 3D printer capable of fabricating intricate zirconia structures. The designed 3D printer combines material extrusion and photopolymerization technologies to improve material diversity, precision and cost-effectiveness.

The 3D printer design incorporates ultraviolet curing to instantly cure extruded zirconia slurry thereby, eliminating the need for a step-wise curing procedure. Printing parameters were optimized to achieve high-quality prints, and supports made of polyethylene terephthalate glycol were used for intricate geometries. The printability and mechanical properties were evaluated for two different zirconia slurry compositions: 70 / 30 and 80 / 20 powder-to-resin weight percentages. The printed green body was subjected to a two-phase sintering process.

The 3D printer fabricated structures with features subtending angles greater than 50 degrees and a filling density above 80% without any supports. Shrinkage analysis showed the 80 / 20 composition resulted in higher density parts, with shrinkage ratios of 25.23%, 26.23% and 27.26% along the X, Y and Z axes, respectively. The sintered objects displayed hardness (1525 HV) and flexural strength (117 MPa), with minimal porosity.

This study demonstrates the development of a cost-effective dual-nozzle 3D printer that can effectively fabricate functional parts with complex material compositions and geometries that can cater to the futuristic requirements of high-end industries.

The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.

Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.

The subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.

This paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

Purpose: Recent years have seen a tremendous surge in research into corporate social responsibility (CSR). One noticeable aspect is the increasing relevance of CSR in the tourist and hospitality industry, which reflects the industry's fast growth and emphasis on solving societal issues. The current study aimed to assess CSR in tourism and hospitality offering a thorough understanding of CSR in a wider context of sustainability, financial performance and ethical considerations.

Methodology: A qualitative desk research approach was undertaken to conduct this study. Thorough review of contemporary research literature, including content analysis, was done for data gathering.

Findings: Findings show that CSR is rising as a key trend in the worldwide tourism and hospitality business, with a significant impact on the industry's performance and development. Tourism and hospitality industry shows both positive and negative economic impacts on the environment and society. To offset negative impacts, this industry is progressively embracing CSR initiatives. An integration between CSR and sustainable tourism, highlights the stability of socioeconomic, environmental, and cultural growth while considering interests of all stakeholders. The effect of CSR on the financial performance of the tourist and hospitality industry reveals that CSR has a favorable influence on financial performance of hotels, but mixed outcomes are shown in restaurants, cruises, and airlines. Highlighting insights on tourism corporations incorporating strategic and ethical CSR ideals into their activities, this study concludes with practical implications.

Originality: This study creates a unified integrated framework based on intersection of strategic CSR, financial performance, sustainability, and ethical aspects in tourism and hospitality.

In today’s competitive era, it has become significant for companies to understand their end consumer and target customers effectively. One of the ways to accomplish this goal is data-driven marketing. The current study seeks to explore the differences between traditional marketing and digital marketing, the pros and cons of data-driven marketing and usage of artificial intelligence (AI) in data-driven marketing. The research objective was met by exploration of published papers in the past 10 years covering the evolution of data-driven marketing, functions of data engineering, application of technology like AI in data-driven marketing and opportunities and challenges. This study is significant as it provides the insight into the relationship between marketing and data engineering and thus helps marketers to frame strategies by leveraging data-driven marketing to improve consumer experience and gain a competitive edge. Moreover, this study is an interdisciplinary study including marketing, engineering and data science. This study focusses on use of innovative methods to improve profitability of business and consumer experience.

The deviant behavior (DB) of construction workers has always been a troubling event in project management. Although scholars continue to search for the main causes of this behavior to curb it at the source, the authors know less about the role and contribution of the team. This study aims to uncover the mechanisms and conditions under which collective moral judgment focus on self (CMJS) effectively enhances DB.

Adopting Chinese construction enterprises as samples, a hierarchical linear model (HLM) is used to test the results of the hypothesis. Moderated mediating effects are used to analyze the potential mechanisms and boundary conditions of DB.

The results of the HLM analysis show that CMJS could directly and significantly induce DB, and moral disengagement (MD) plays a mediator role in this association. In addition, the positive relationship between MD and DB is stronger when performance-avoidance goal orientation (PaGO) or overqualification (Overq) is higher.

The conditions and mechanisms that influence DB are not unique. Future study could examine the explanatory and weighting mechanisms of DB from other perspectives or to construct a framework and summarize the factors that may influence DB.

This study provides a rich theoretical basis for the prevention and correction of construction workers' DB in Chinese construction firms from the perspective of CMJS. In addition, objective moral judgments contribute to guiding employees' moral cognitive processes and positive work.

This study extends existing research on DB and advances the practical outcomes of construction project governance. It not only illustrates that CMJS has a direct impact on DB but also clarifies the mechanisms and conditions that predispose to the generation of DB, filling the research gap on construction workers' DB from cross-level mechanisms and also enriching the theoretical system for preventing this behavior.

This study aims to examine conditions in both inter- and intra-alliance contexts within an oligopolistic alliance industry operating across multiple markets. It focuses on how a focal firm’s optimal performance depends on nuanced evaluations of the trade-offs associated with coopetitive synergy, and on decisions about whether to collaborate or compete with its members.

The authors analyze the six leading global container shipping firms within two major alliances (The Grand Alliance and the New World Alliance) from 2003 to 2010, gathering 7,825 news articles from the Cyber Shipping Guide, a comprehensive global container shipping business database in Japan.

The findings reveal the following: (1) the focal firm cooperating with members of a rival alliance decreases the level of inter-alliance competition. (2) The focal firm cooperating with members of a rival alliance increases the level of intra-alliance competition. (3) Increased inter-alliance competition negatively impacts the performance of the focal firm. (4) Increased intra-alliance competition negatively impacts the performance of the focal firm.

Global container shipping firms should make optimal decisions about which firms to cooperate with, focusing on those that contribute to the focal firm’s overall synergies and thus performance.

This study contributes to the literature on coopetition in strategic alliances by extending the concept of dynamic coopetition to include strategic alliance groupings, and by examining how focal firm members cooperate in both inter- and intra-alliance contexts.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

Amid the increasing water risks faced by firms, external investors are becoming more interested in corporate water disclosure and research on its drivers has become prominent. This paper aims to investigate the impact of water resource tax (WRT) on water disclosure and other related drivers.

This study uses the WRT policy as a quasi-natural experiment and applies the difference-in-differences method.

The results indicate that WRT policy significantly stimulates water disclosure. Improving green innovation and strengthening internal control are potential channels through which WRT works. Moreover, WRT’s effect is more pronounced in firms that face high institutional pressures and have better internal resource support.

The findings suggest that water-sensitive firms should disclose water information to acquire resources from external stakeholders to support their green transition. It also provides implications for governments to incorporate other external forces in shaping the direction and intensity of WRT and consider the resource constraints of small and private firms in green transformation.

This study is of assistance in promoting water environmental protection in areas experiencing water stress and provides an opportunity for external stakeholders (external investors, nongovernmental organizations, governments, consumers, suppliers, communities and media) to advocate the water disclosure of firms with high water risks.

The attempt is novel in the context of considering the water regulation risks and the demands of external stakeholders. It provides new insights into the factors influencing water disclosure from the perspective of political stakeholders.

Existing research on innovation has mainly focused on how to promote technological innovation in megaprojects and management innovation (MI) in megaprojects is still an unknown research field. The purposes of this study are to examine the effect of MI on megaproject performance and how the top management team (TMT) regulatory focus affects the use of MI in projects. At the same time, the moderating effects of project uncertainties are also tested.

On the basis of an explorative/exploitative ambidextrous analysis framework, this study divides MI into two dimensions: explorative and exploitative MI, and integrates the theoretical perspectives of the TMT regulatory focus and project uncertainties into a research model. Taking 314 responses from megaprojects’ TMTs in China as research data, this study empirically tests the above model.

Results show that exploratory MI has a U-shaped relationship with megaproject performance; whereas exploitative MI has an inverted U-shaped relationship with megaproject performance. The TMT promotion focus has a positive effect on exploratory and exploitative MI; and the TMT prevention focus has a negative effect on exploratory MI but has a positive effect on exploitative MI. Project uncertainties have a positive moderating effect on the positive relationship between TMT promotion focus and exploratory MI, whereas it has a negative moderating effect on the negative relationship between the TMT prevention focus and exploratory MI.

By empirically measuring the relationship between two types of MIs and megaproject performance, this study clarifies the differential mechanism of the effect of different MIs on megaproject performance. This study also examines the MI of megaprojects from the perspective of the TMT regulatory focus and expounds how changes in uncertainties affect the relationship between the TMT regulatory focus and MI.