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Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.

The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.

The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.

This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

Online health question-and-answer (Q&A) forums have developed a new business model whereby listeners (peer patients) can pay to read health information derived from consultations between askers (focal patients) and answerers (physicians). However, research exploring the mechanism behind peer patients' purchase decisions and the specific nature of the information driving these decisions has remained limited. This study aims to develop a theoretical model for understanding how peer patients make such decisions based on limited information, i.e. the first question displayed in each focal patient-physician interaction record, considering argument quality (interrogative form and information details) and source credibility (patient experience of focal patients), including the contingent role of urgency.

The model was tested by text mining 1,960 consultation records from a popular Chinese online health Q&A forum on the Yilu App. These records involved interactions between focal patients and physicians and were purchased by 447,718 peer patients seeking health-related information until this research.

Patient experience embedded in focal patients' questions plays a significant role in inducing peer patients to purchase previous consultation records featuring exchanges between focal patients and physicians; in particular, increasingly detailed information is associated with a reduced probability of making a purchase. When focal patients demonstrate a high level of urgency, the effect of information details is weakened, while the interrogative form is strengthened.

The originality of this study lies in its exploration of the monetization mechanism forming the trilateral relationship between askers (focal patients), answerers (physicians) and listeners (peer patients) in the business model “paying to view others' answers” in the online health Q&A forum and the moderating role of urgency in explaining the mechanism of how first questions influence peer patients' purchasing behavior.

This study aims to investigate the integration of smart contracts into the legal framework of Saudi Arabia, spotlighting the pivotal role of blockchain technology in revolutionizing contractual processes. It evaluates the capacity of smart contracts to enhance the efficiency, security and transparency of legal transactions, while critically examining the legal challenges their adoption presents.

Through qualitative analysis, this research explores the operational dynamics of smart contracts, with a focus on their autonomous execution and the digital codification of contractual terms. It scrutinizes the alignment of smart contracts with the Saudi legal system, concentrating on pivotal issues such as the establishment of mutual consent, the verification of contracting parties’ capacity and adherence to conventional legal doctrines.

This study uncovers the transformative potential of smart contracts in redefining the execution of contracts, highlighting their advantages in streamlining transactions and enhancing contractual reliability. However, it also identifies significant obstacles in the path of their full integration into Saudi Arabia’s legal landscape, notably the challenge of reconciling smart contracts’ technology-driven operations with established legal norms and rectifying potential legal inconsistencies.

Offering fresh perspectives on the confluence of technology and law, this paper illuminates the complex task of implementing smart contracts within a legal framework that is in the process of adapting to digital innovation. It advocates for a sophisticated strategy of regulatory adjustment that promotes the legal system's evolution alongside technological progress, ensuring the effective and legally sound utilization of smart contracts.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

This study was intended to investigate a small-scale School-based Motivational Interviewing (SBMI) pilot with first-year college students. This approach honors student autonomy, supports self-determination and has the potential to impact educational outcomes in higher education. Motivational Interviewing (MI) is an evidence based conversational skill set, defined as “a collaborative conversational style for strengthening a person's own motivation and commitment to change” (Miller and Rollnick, 2013, p. 12). Student perceptions of satisfaction with the faculty-student mentoring intervention were sought. Relational aspects of MI (partnership, empathy and alliance) were also explored.

A mixed-method approach was used for the SBMI study, focused on college students with recent academic setbacks (N = 19).

The intervention was deployed with high levels of MI technical fidelity and relational quality. Participants reported high satisfaction with the intervention. The relational aspects and participant perceived alliance with their faculty were highly correlated across the intervention, adding to the discussion of the mechanisms of MI that contribute to its effectiveness.

This work is formative, yet at this point is not generalizable given the scope of the study.

Findings are encouraging for further development of this innovative pedagogical approach. Possible future applications of research are provided.

Discussed herein, SBMI has the potential to meet the needs of traditionally underrepresented student groups.

The reported study is the initial portion of a larger intervention development project.

The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

Grounded in the conservation of resources theory, this study proposes the mechanisms and conditions under which customer mistreatment affects employee proactive behavior. This study focuses on insomnia as a mediating mechanism and resilience as a boundary condition for the indirect effect of customer mistreatment on employee proactive behavior via insomnia.

We conducted a single survey in two phases, with a time lag of three weeks, among 302 frontline South Korean employees. The data collected from these two points were then analyzed using hierarchical regression analysis and the PROCESS macro.

The findings demonstrated that there was a significant negative relationship between customer mistreatment and employee proactive behavior, and this relationship was mediated by insomnia. Furthermore, the results revealed that resilience moderates both the effect of customer mistreatment on insomnia and the indirect effect of customer mistreatment on employee proactive behavior through insomnia.

This research primarily focuses on Korean frontline workers, potentially limiting cultural generalizability. The reliance on self-reported data may introduce common method bias. Future studies should diversify participant demographics and utilize multi-source feedback to validate findings. Grounded in the Conservation of Resource Theory, this study underscores the neglected linkage between customer mistreatment and proactive behavior, especially in frontline employees. We introduce insomnia as a pivotal mediator, deepening our understanding of why mistreatment dampens proactivity. Additionally, we spotlight the role of resilience, revealing its buffering effect against mistreatment’s adverse outcomes.

Organizations should be aware of the detrimental effects of customer mistreatment on frontline employees, as it can hamper proactive behavior, primarily through the exacerbation of insomnia. Implementing resilience-training programs can be a proactive step, offering frontline staff tools to buffer against such negative outcomes. Managers are encouraged to recognize and address instances of customer mistreatment and prioritize employee well-being, which in turn can foster a more proactive and resilient workforce, enhancing organizational performance and customer satisfaction.

This study underscores the broader societal challenge of customer mistreatment in the service sector, highlighting its ripple effects on employee well-being and proactive behavior. It sheds light on the importance of fostering respectful interactions in public and private spaces, emphasizing mutual respect between customers and service providers. Recognizing the adverse impacts of mistreatment can prompt societal discourse on workplace ethics and encourage organizations to advocate for their employees' rights. Ultimately, nurturing a culture that condemns customer mistreatment can lead to healthier work environments, benefitting both employees and society at large.

This study presents a novel approach by investigating the impact of customer mistreatment on employee proactive behavior, considering insomnia as a mediator, a perspective that has received limited attention in existing literature. Additionally, it introduces the concept of resilience as a moderator, offering fresh insights into how individual resilience levels can affect the relationship between customer mistreatment and proactive behavior. The research goes beyond traditional analyses of workplace dynamics to explore the broader implications of these interactions on personal well-being and sleep patterns. Through the application of a moderated-mediation framework, this study enhances understanding of complex organizational behavior dynamics, particularly in the service sector, and provides valuable implications for both theoretical understanding and practical application.