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Highlights in academic papers serve as condensed summaries of the author’s key work, allowing readers to quickly grasp the paper’s focus. However, many journals do not currently offer highlights for their articles. To address this gap, some scholars have explored using supervised learning methods to extract highlights from academic papers. A significant challenge in this approach is the need for substantial amounts of training data.

This study examines the effectiveness of prompt-based learning for generating highlights. We develop task-specific prompt templates, populate them with paper abstracts and use them as input for language models. We employ both locally inferable pre-trained models, such as GPT-2 and T5, and the ChatGPT model accessed via API.

By evaluating the model’s performance across three datasets, we find that the ChatGPT model performed comparably to traditional supervised learning methods, even in the absence of training samples. Introducing a small number of training samples further enhanced the model’s performance. We also investigate the impact of prompt template content on model performance, revealing that ChatGPT’s effectiveness on specific tasks is highly contingent on the information embedded in the prompts.

This study advances the field of automatic highlights generation by pioneering the application of prompt learning. We employ several mainstream pre-trained language models, including the widely used ChatGPT, to facilitate text generation. A key advantage of our method is its ability to generate highlights without the need for training on domain-specific corpora, thereby broadening its applicability.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

The construction industry is notorious for high risks and accident rates, prompting professionals to adopt emerging technologies for improved construction workers’ health and safety (CWHS). Despite the recognized benefits, the practical implementation of these technologies in safety management within the Construction 4.0 era remains nascent. This study aims to investigate the mechanisms influencing the implementation of Construction 4.0 technologies (C4.0TeIm) to enhance CWHS in construction organizations.

Drawing upon integrated institutional theory, the contingency resource-based view of firms and the theory of planned behavior, this study developed and tested an integrated C4.0TeIm-CWHS framework. The framework captures the interactions among key factors driving C4.0TeIm to enhance CWHS within construction organizations. Data were collected via a questionnaire survey among 91 construction organizations and analyzed using partial least squares structural equation modeling to test the hypothesized relationships.

The results reveal that: (1) key C4.0TeIm areas are integrative and centralized around four areas, such as artificial intelligence and 3D printing, Internet of Things and extended reality; and (2) external coercive and normative forces, internal resource and capability, business strategy, technology competency and management (BST), organizational culture and use intention (UI) of C4.0 technologies, collectively influence C4.0TeIm-CWHS. The findings confirm the pivotal roles of BST and UI as mediators fostering positive organizational behaviors related to C4.0TeIm-CWHS.

Practically, it offers actionable insights for policymakers to optimize technology integration in construction firms, promoting industrial advancement while enhancing workforce well-being.

The novel C4.0TeIm-CWHS framework contributes to the theoretical discourses on safety management within the C4.0 paradigm by offering insights into internal strategic deployment and compliance challenges in construction organizations.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

With the development of e-commerce network platforms, platform enterprises have mostly completed the first stage of user accumulation during the start-up period. How to enhance users’ stickiness and stimulate their continual participation in platform business activities through innovation and platform design has become a decisive factor for platform enterprises. To increase the motivation of e-commerce platform users, this paper explores the positive impact of gamified rewards on platform user stickiness by dividing the gamified rewards design into social and functional rewards, and studies the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure.

This study applies the “S-O-R” (stimulus–organism–response) model as the theoretical basis for constructing a model of user stickiness for e-commerce platforms and subdivides gamified reward design into social rewards and functional rewards to explore how they affect platform user stickiness and the boundaries of the influencing mechanism.

It turns out both types of gamified rewards promote users’ perception of self-identification, which in turn affects the intention to continue using the platform. In addition, platforms with designs about users’ quantified self-behavior – perceived goal progress in the gaming experience can effectively enhance the effectiveness of users’ gamification rewards. Information disclosure moderates the relationship between the two types of gamification design and self-identification. For functional reward designs and social reward designs, information disclosure can improve users’ self-identification and therefore enhance users’ stickiness.

This study verifies the impact of gamification design on platform user stickiness, confirming the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure, which has theoretical and practical implications for how platform enterprise can maintain user activity in the digital context.

This research focuses on exploring a psychological mechanism between abusive supervision and job engagement via job insecurity and job alternative to understand how frontline construction workers' perceived abusive supervision impacts on their work engagement.

This study draws on the conservation of resources theory (COR) to postulate that workers' job insecurity acts as a mediation between abusive supervision and work engagement, and the perception of job alternative moderates the impact of abusive supervision on job insecurity. This study empirically tests survey data from 357 frontline construction workers and adopts partial least square structural equation modeling (PLS-SEM) to test the proposed model.

This paper proved the dark-side of abusive supervision, dual character of job insecurity and moderating role of job alternative. The game-changing mediation effects of job insecurity were examined, which suggested that work engagement would be higher when abusive supervision stimulates stronger job insecurity. Furthermore, the result indicated that the relationship between abusive supervision and job insecurity should be weaker when job alternative is high.

This research suggests construction project managers (CPMs) must take steps such as enhancing supervisors' emotional management skills to control frequency of abuse and thus lessen compromising engagement. Moreover, overall understanding frontline workers' job insecurity is meaningful for better construction performance management.

This research contributes to the disputed leadership style (abusive supervision) and positive psychology knowledge (work engagement) in construction projects by unveiling the psychosocial mechanism (game-changing effects of job insecurity) of construction frontline workers positive engaged feeling. This study highlights the function of personal and job resources in process of resource conservation.

Drawn from the push–pull perspective, this research aims to identify the determinants of Chinese technology's outward foreign direct investments (OFDI) into the Eurasian region.

The authors argue that contrary to the extant literature, technology-driven OFDI from emerging-market multinationals (EMNEs) do not always seek developed countries, and EMNEs' technology investments in emerging economies are rising indicating that there are factors in these economies that can prove attractive. The authors recognize the influence of the macroeconomic environment and the interaction of home and host-country institutional contexts that influence the location choice of EMNEs technology-driven OFDI into other emerging economies, mediated by the industry sector and firm's ownership structure. The authors test our hypotheses using a sample of 1,656 observations of Chinese MNEs' tech-investments in the Eurasian region from 2005 to 2019.

The study results indicate that bilateral diplomatic relations pave the way of the host-country institutional environment for Chinese MNEs uncovering the role of the Chinese government as an OFDI facilitator. This study also unveils a lower technology level of the Chinese MNEs' investments in the Eurasian region connoting an interest in market opportunities exploitation through their existing technologies – through its comparative advantage in the global markets – rather than strategic assets acquisition aiming at augmenting their technological capabilities. This trend is similar to that of other major foreign direct investment (FDI) source countries.

This research contributes to a better understanding of the characteristics and the location choice of technology investments from EMNEs into other emerging economies that have received scant attention in the literature. In addition, it extends the institutional theory by analyzing how home-country institutions, through bilateral diplomatic relations, may smooth the host country institutional environment for home-country MNEs' foreign investments and contributes as well to the debate on the applicability of the existing theoretical framework in the case of emerging-market MNEs.

This study aims to identify the factors and strategies that motivate Thai construction professionals to adopt Blockchain Technology (BT). Previous research highlights BT’s characteristics but lacks focus on the features most persuasive for Thai construction professionals.

Using Q methodology with 28 participants from the academic, construction and advanced technology sectors, this study explores their perceptions on BT adoption and addresses the gap in identifying persuasive features for Thai construction professionals.

The analysis identified eight distinct professional groups, each with unique perceptions of BT’s motivating factors. Based on these insights, seven strategies were proposed to promote BT adoption. A key finding is that BT adoption is influenced not only by professionals’ roles, positions and accountability but also by their existing technological competencies.

The use of Q methodology, while insightful, may not capture the full complexity of attitudes toward BT adoption. Additionally, the focus on the Thai construction industry and the small sample size may limit its generalizability to other cultural and economic contexts.

Identifying professional categories based on BT preferences and implementing strategies – such as automated systems, smart contracts, education centers and pilot projects – can enhance productivity in the Thai construction industry, drawing on global practices to address local challenges.

– With Thailand’s extensive infrastructure projects supporting ASEAN’s transportation hub vision, integrating blockchain is expected to enhance productivity and project outcomes, contributing to Thailand’s national infrastructure development goals.

A 3D finite element (FE) model based on the double ellipsoidal heat source was developed to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process. This paper aims to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process by developing a 3D finite element (FE) model based on the double ellipsoidal heat source.

Experimental thermal cycle curves and residual stresses were obtained by thermocouples and X-ray diffraction, respectively. The validity of the model was verified by the corresponding experimental results.

The deposition process of the upper pass led to the partial remelting of the lower deposited pass. The thermal process of the current-deposited pass alleviated the stress concentration in the previous-formed passes. A more uniform temperature distribution could be obtained by using the reciprocating deposition path. Compared to the reciprocating deposition path, the peak values of the transverse and longitudinal tensile residual stresses of the deposited sample under the unidirectional deposition path were reduced by 15 MPa and increased by 13 MPa, respectively. The heat conduction in the deposited passes could be improved by extending the inter-pass cooling time appropriately. With an increase in the inter-pass cooling time, the longitudinal residual stress in the middle region of sample along longitudinal and transverse directions showed increase and decrease–increase trends, respectively, while the transverse residual stress exhibited decrease trend.

This study enhances the understanding of temperature and stress fields evolution during the multilayer and multi-pass cold metal transfer-WAAM processes of magnesium alloy and provides the reference for parameter optimization.

The purpose of the paper is to understand whether, why and when role overload predicts an organization-directed CWB: cyberloafing. To do so, we utilize the Stressor-Emotion–CWB theory and social exchange theory.

The authors collected data from 506 employees working in five Information Technology (IT) firms in India using a multiwave survey design. Results revealed support for all the hypotheses.

This study found that negative affect partly carries the influence of role overload to cyberloafing. Secondly, the results indicate that role overload has a direct and positive relationship with cyberloafing. Third and finally, we found that narcissism moderates the positive relationship between role overload and negative affect, followed by cyberloafing.

This paper showcases that role overload can evoke cyberloafing both as a coping mechanism and a retaliatory response directed at the organization. This paper further demonstrates that employees high in narcissism are vulnerable to stressors like role overload, and as a result, they experience more negative emotions and engage in more cyberloafing.