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This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

The paper examines the effect of language negativity of US financial analysts’ ancestral origins on their earnings forecast behavior.

The paper first developed a dictionary of the most emotionally negative words in 25 languages, based on the study by Dodds et al. (2015). The authors constructed firm-year analyst-level earnings forecast data and applied multivariate regression model along with a series of robustness tests to examine the research question.

The empirical results indicate that financial analysts with their ancestral countries characterized by a high level of language negativity tend to issue less optimistic earnings forecasts than other analysts. Additional evidence suggests that the effect of language negativity on analysts’ forecast is strengthened (1) during periods of financial crisis, (2) for firms with losses and a high level of earnings volatility and (3) for younger analysts and analysts working for small brokerage firms. Finally, we find evidence that higher levels of language negativity increase analysts’ forecast accuracy.

Collectively, the findings of this study support the conjecture that the level of negativity across languages can have a significant impact on capital market participants’ behavior. Thus, the study sheds light on how culturally inherited emotion can affect analysts’ earnings forecast properties.

The purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).

This study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.

The logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.

Previous researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

Linear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.

Determining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.

Arithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.

This study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.

Professional social networking sites (SNS) are widely employed by business individuals to build formal relationships, career opportunities, and professional development. While the characteristics of professional SNS are generally different from other SNS, there is limited understanding of the determinants of users’ continued usage on this platform. The study addresses this research gap by developing a conceptual framework that relates perceived values perspective (utilitarian value, hedonic value) and sociability dimensions (social presence, social benefit, social support, and self-presentation) to continuance intention to use professional SNS. Data were gathered from a questionnaire distributed on LinkedIn and analyzed using PLS-SEM. The findings contribute to the emerging literature on the IS continuance domain, particularly in the area of professional SNS. Furthermore, the study can help professional SNS providers properly manage to retain existing users for sustainable business performance.

Mobile banking (or m-banking) has become an inseparable part of the modern finance model. Its success relies on customers’ affective responses and behavioral decisions. This study aims to examine the important determinants of positive word-of-mouth (POW) toward m-banking among older consumers.

A quantitative approach was applied in examining a proposed model with data obtained from 358 respondents based on a Web-based survey from Vietnam using a questionnaire.

It was determined that attitude, usage intention and satisfaction are the fundamental facilitators of POW in m-banking. Furthermore, perceived usefulness, ease of use and trust are the main predictors of attitude and usage intention, and epistemic value, conditional value, social value and technological value are the primary motivators of usage intention. Ease of use and trust positively affect perceived usefulness. Usage intention fosters higher levels of satisfaction. This study affirms the insignificant effects of ease of use and hedonic value on usage intention as well as satisfaction on attitude.

The findings are insightful for developers to concentrate on how to promote cognitive, affective and behavioral responses among old consumers in m-banking. Marketers should boost value perceptions and trust as the prerequisite underlying judgment and behaviors toward m-banking.

This work validates the synergistic model of POW among older consumers in m-banking by combining the technology acceptance model (TAM) and theory of consumption values (TCV). Thus, it would increase the exploratory power of the theoretical base toward m-banking and in an emerging market.

Given the widespread adoption of empowerment in the workplace, increasing research has investigated the influences of empowering leadership. However, previous research has found confounding effects of it. This study aims to examine how and when empowering leadership exhibits “double-edged sword” effects on followers’ work outcomes.

The authors used a three-wave survey with a final sample of 215 full-time employees to test the research model.

The results indicate that followers’ role-breadth self-efficacy (RBSE) interacted with empowering leadership to predict their hindrance-related stress, subsequently influencing their turnover intention. Specifically, empowering leadership is found to elicit hindrance-related stress among followers with low RBSE. Furthermore, empowering leadership indirectly affects turnover intention by eliciting hindrance-related stress only among followers with low RBSE.

This study broadens the exploration of the “dark side” of empowering leadership, offering a more nuanced explanation of how it can lead to both beneficial and detrimental outcomes. It refines the understanding of empowering leadership’s effectiveness by highlighting the role of followers’ RBSE rather than focusing solely on the degree of empowerment. In addition, by contributing to the stress theory, this research demonstrates how individual differences influence followers’ cognitive appraisal of stress, shaping distinct stress experiences and driving the adoption of varying work-related coping strategies.

The study aims to explicate how Metaverse boosts learners’ cognition, decision confidence and active participation in Metaverse-based learning (MBL).

A survey is designed with 523 respondents. Structural equation modeling (SEM) is conducted using online data to verify a research model.

Results demonstrate that Metaverse-related characteristics, namely interactivity, corporeity, persistence, immersion and personalized experience, aid in strengthening learners’ cognitive processing and decision confidence, whilst escapism does not influence decision confidence in MBL. Furthermore, user-related dimensions, including personal innovativeness and perceived trendiness, are the underlying motivations for decision confidence. Additionally, cognitive processing is positively associated with decision confidence, which considerably fosters learners’ active participation in MBL.

Limited studies have been conducted to illuminate a mechanism of cognitive processing, decision confidence and active participation among learners toward MBL in light of the Stimulus-Organism-Response (S-O-R) paradigm. Therefore, a substantial amount of knowledge is supplemented to enlighten whether learners in a developing country may generate their engagement with MBL.

This study aims to gain a better understanding of artificial serendipity – pre-planned surprises intentionally crafted through deliberate designs – in online marketplaces. By exploring the key features of artificial serendipity, this study investigates whether serendipity can be intentionally designed, particularly with the use of artificial intelligence (AI). The findings from this research broaden the scope of serendipity studies, making them more relevant and applicable in the context of the AI era.

A narrative study was conducted, gathering insights from 32 Chinese online consumers through diaries and interviews. The data were analysed in close collaboration with participants, ensuring an authentic reflection of their perceptions regarding the features of artificial serendipity in online marketplaces.

Findings reveal that artificial serendipity, particularly when designed by AI, is still regarded by online consumers as genuine serendipity. It provides a sense of real surprise and encourages deeper reflection on personal knowledge, affording the two central qualities of genuine serendipity: unexpectedness and valuableness. However, since artificial serendipity is pre-planned through intentional design, consumers cannot have entire control over it. Therefore, compared to natural serendipity – fortune surprises arising from accidental correspondence between individuals and contexts – artificial serendipity tends to be more surprising yet less valuable.

For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity.

Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This study stands out as one of the few to provide a nuanced understanding of artificial serendipity, offering valuable insights for both research and practice. For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity. Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.